ctitwo routes to recognition), wh classifiers trained on spectral EEG features, we quantified neural evidence for recognition decisions as a function of time. Classifiers trained on a small portion of the decision period performed similarly to those also incorporating information from previous time points indicating that neural activity reflects an integrated evidence signal. We propose
A repeated exposure to people or objects sometimes evokes only a vague sense of familiarity; at others, it elicits vivid recollections of contextual details from previous encounters. This distinction is formalized in dual-process models of recognition memory that posit two independent types of evidence subserving recognition decisions (with recollection commonly, but not always, conceptualized as a threshold process; Diana, Reder, Arndt, & Park, 2006; Yonelinas, 2002; Yonelinas, Aly, Wang, & Koen, 2010; Malmberg, 2008). In apparent support of these models, neuroscientific studies of recognition memory have identified patterns of brain activity with distinct time courses thought to reflect an early familiarity signal (peaking around 400 ms after onset of a memory probe) and a later recollection signal (peaking around 600 ms after probe onset; Curran, 1999; Rugg & Curran, 2007).
Most dual-process models assume that familiarity and recollection signals can each separately lead to recognition (Reder et al., 2000). In some models, however, the memory system integrates evidence from different sources into a unitary evidence signal (Rotello, Macmillan, & Reeder, 2004; Wixted & Mickes, 2010). This results in a single route to recognition despite the contributions from different types of evidence. From this perspective, such models are conceptually similar to single-process models which assume only a single evidence source (Malmberg, 2008). One indication that two separate routes to recognition may not be neccesary to account for recognition performance is the fact that singleprocess models have been highly successful at accounting for intricate relationships between response time distributions, accuracy, and confidence ratings across a wide range of experimental manipulations (e.g., Ratcliff, 1978; Ratcliff & Starns, 2009; Wixted, 2007; Dunn, 2004, 2008; Cox & Shiffrin, 2012; Diller, Nobel, & Shiffrin, 2001; Starns, White, & Ratcliff, 2012; Starns & Ratcliff, 2014; Shiffrin & Steyvers, 1997). Additionally, recent work linking brain activity to parameters in a single-process model of recognition memory has cast doubt on the common interpretation of early and late electrophysiological signals indexing familiarity and recollection respectively (Ratcliff, Sederberg, Smith, & Childers, 2016, but see also Anderson, Zhang, Borst, & Walsh, 2016, and Borst & Anderson, 2015, for an alternative approach that preserves this interpretation—we will return to this issue in the discussion).
Because the single- vs. dual-process labels do not reliably differentiate between the number of routes to recognition, we will refer to models as single- or dual-route models to make this distinction explicit. Specifically, we label models that assume that different types of evidence can give rise to different kinds of recognition decisions (e.g., Yonelinas, 1994, 1997; Reder et al., 2000; Diana et al., 2006) as dual-route models. Single-route models are those that assume a single type of evidence source (e.g., Shiffrin & Steyvers, 1997) and those that assume that evidence from multiple sources/processes is integrated into a unitary evidence signal (e.g., Rotello et al., 2004; Wixted & Mickes, 2010). Within the framework of dual-route models, it makes sense to label individual recognition decisions with respect to the type of evidence (e.g., “familiarity” vs. “recollection”) that gave rise to them, whereas such a categorization of individual recognition memory decisions is not meaningful within the framework of single-route models, because information from all available sources contributes to recognition decisions. We propose that conflating the question about the number of recognition signals (i.e., the distinction between single- vs. dual-process models) with the question about the number of different routes to recognition may have contributed to the apparent disconnect between the evidence for separate familiarity and recollection signals and the success of single-process models.
Capitalizing on the presumed temporal separation of familiarity and recollection signals (Diana et al., 2006), we quantify the neural evidence distinguishing targets from lures in various partitions of the period leading up to the recognition decision. Specifically, we ask if combining neural evidence from multiple time bins during the recognition decision tells us more about whether an item has been studied than just the latest considered time bin by itself. If we are picking up on independent signals at different points in the recognition decision, then combining information from both should boost our ability to use neural activity to distinguish between old and new items. If, however, the neural signal corresponds to an integrated/unitary evidence signal, information from previous time points should not contain information that is not also present in the neural activity at later points.
Figure 1 illustrates our approach with the help of two toy models of evidence in recognition memory. Figure 1A shows activation for two sources of evidence containing information about the old/new status of an item as a function of time, and Figure 1B shows two alternative ways these sources could give rise to an evidence signal for the recognition decision (in this toy example we assume an “early” and a “late” source, analogous to the presumed dynamics of familiarity and recollection signals). The top panel of Figure 1B illustrates a dual-route model: the recognition decision is based exclusively on whichever source has accumulated more evidence at the time of response. Thus any information from the non-dominant evidence source is lost. Assuming sources with different temporal signatures, the evidence signal will initially be determined by activity from the early source, which sometimes will be exceeded by activity from the later source by the time the response is initiated. The bottom panel of Figure 1B illustrates a single route model: here the evidence for the recognition memory decision at any given time reflects the information accrued across all sources so far. Even whechange, no information is lost, because all relevant information contributes to the evidence signal.
(A) Probability density functions (PDFs) illustrating two sources of evidence for recognition memory decisions. (B) Cumulative density functions (CDFs) for the PDFs shown in (A) along with CDFs for the evidence on which the recognition memory decision is based. The top panel illustrates a case where the evidence is determined by a single source of evidence leading to different routes to recognition memory depending on which source determines the evidence signal at the time of response (in the case of two sources, we label this class of models “dual-route models”). The bottom panel illustrates a case where the evidence signal integrates information from all sources (we label this class of models “single-route models” regardless of the number of sources contributing to the evidence). (C) Expected patterns of performance for classifiers trained on features from individual (I) or cumulative (C) time bins partitioning the time between probe onset and recognition response (see text for details). Assuming the sources contribute independent information with distinct dynamics, dual-route models predict diverging performance for classifiers trained on individual and cumulative time bins (top panel), whereas single-route models predict identical performance (bottom panel; C and I lines are overlapping).  measure of classifier performance.
 on the basis of recognition decisions alone, because these presumably only reflect a snapshot of the evidence signal from around the time when the response was initiated. Recordings of brain activity, however, allow us to assess the evolution of a neural evidence signal in the lead-up to a recognition response. We used multivariate (“machine learning”) classifiers to quantify the neural evidence distinguishing between targets and lures during the processing of the probe (i.e., between probe onset and just prior to the execution of a response). By comparing performance for classifiers trained on neural features from various partitions of this time period, we can make inferences about whether relevant information is integrated into a single evidence signal or whether evidence from an earlier signal is sometimes lost.
Figure 1C illustrates the logic of the main analyses. As explained in the Methods section, we partition each recognition decision into time bins and train classifiers either on individual time bins or on a cumulatively increasing number of time bins. If brain activity reflects different evidence signals that contribute independent information at different time points, then performance of a classifier trained on neural features from multiple time bins should exceed that of a classifier trained on features from a single time point, since it is able to capitalize on the information from distinct evidence signals (top panel of Figure 1C). If, on the other hand, the neural, then the signals from previous time points do not contain additional information. Thus, we would expect no benefit for classifiersin that case (lower panel of Figure 1C).
The current data set of 132 participants is a subset of the data set for which we previously presented analyses of overt responses (Weidemann & Kahana, 2016; basic analyses of recognition accuracy and response times are repeated here for this subset). Each participant pWe selected those participants who completed 20 sessions of various free recall tasks. The availability of data from 20 sessions for each participant enabled us to train statistical classifiers on individual participants’ data from 19 sessions (holding out data from one session for cross-validation of classifier performance). As described below, we did this repeatedly such that data from each session was held out once () and all classifier performance measures are based on these held-out sessions only. This yielded enough data to train non-linear classifiers even in cases where not all trials contributed to the classification (as detailed below, some of our analyses placed restrictions on response times).
As part of a large-scale study of episodic memory, we asked participants at the end of each of 20 sessions to make recognition memory decisions and confidence ratings about words that had been presented earlier in the session for study in various free recall tasks. In each session, participants studied between 12 and 16 lists of 16 words that were each presented for 3 s followed by 0.8–1.2 s of interstimulus interval. In some sessions participants were asked to provide a size or animacy judgment for each word (see Lohnas & Kahana, 2013, for a detailed description of the methods for these sessions) and some sessions included distractor tasks between items and/or between lists. At the end of each list, participants were given 75 s to recall items in any order. A subset of 80 participants were additionally instructed to say aloud any words that were salient during the recall phases following study lists in 6 of the 20 sessions (i.e., an externalized free recall procedure). Some sessions additionally included a final free recall phase after the recall phase for the last list in the sessions. For this final free recall phase, participants were given 5 minutes to recall items from any of the previous lists (for analyses that condition on recall status, we considered recalls from both phases, but recalls during final free recall phases mostly repeated recalls from recall phases immediately following study lists). The recognition test always followed at the end of the session and consisted of 320 probes of which 80%, 75%, 62.5%, or 50% were targets (i.e., words that had been studied in any of the previous lists in the current session) and the rest previously unstudied lure words. The effect of the manipulation varying the proportion of targets vs. lures was small and is not further analyzed here (as detailed below, the training of classifiers took the baserates of the stimulus classes into account to avoid bias).
Throughout the experiment, we obtained high-density EEG recordings, allowing us to investigate brain activity as it unfolds during processing of a memory probe. Each recognition memory trial consisted of the presentation of a probe word, which required a verbal response to the question of whether the given item had been previously studied. We asked participants to substitute “pess” and “po” for “yes” and “no” when answering this question in order to facilitate determination of response times on the basis of the onset of the verbal response (we excluded trials with response times below 300 ms and above 3000 ms from further analyses). Following each binary recognition memory decision, we asked participants to indicate their confidence in the response on a scale from 1 to 5 with 5 indicating the highest level of confidence and 1 indicating low confidence. Most participants indicated confidence ratings verbally; any reference to response times in this manuscript is with respect to the binary recognition decision and not for the confidence ratings. After each classification and confidence rating response, participants pressed the space bar (the recognition response times were determined on the basis of the onset of the verbal recognition decision, however). Following a uniformly randomly jittered interval between 100 and 200 ms after the space-bar press following the confidence rating, participants received visual and auditory feedback on their recognition decision (automatically generated by custom speech recognition software). A 800–1200 ms (uniformly randomly jittered) blank screen separated the offset of the feedback from the presentation of the following recognition probe.
De-identified data and analysis code used in this study may be freely downloaded from the authors’ websites (http://cogsci.info and http://memory.psych.upenn.edu/Electrophysiological_Data).
EEG data were recorded with 129 channel Geodesic Sensor Nets using the Netstation acquisition environment (Electrical Geodesics, Inc.). Cz was used as a reference during recording, but all recordings were converted to an average reference offline. For most recording sessions, the EEG aqui-sition software logged periods where the signal changed by more than 250 μV within a short time period as artifactual. Channels where any such period exceeded 10 min. were excluded from the average reference. The multivariate classifiers (described below) weight neural features on the basis of their respective signal to noise ratios and we evaluated their performance by cross-validating on held-out data. Thus we did not attempt to further identify or remove any artifactual activity for our classifier analyses. However, we did exclude twenty-six electrodes that were placed on the face (rather than the scalp) from further analyses.
For event-related potential (ERP) analyses (Figure 2), we excluded channels with low absolute correlation with all other channels and those with extreme levels of raw or log-transformed variance both from the average reference and from the ERPs (we used z-scores of ±3 as exclusion thresholds). Additionally we excluded events with significant eye-movement artifacts from the ERP analyses (exclusion criteria were amplitudes in excess of 3 times the inter-quartile range on channels around the eyes any time from 100 ms prior to probe onset until 1000 ms after probe onset).
(A) Early (0.3–0.4 s) and late (0.5–0.6 s) ERP difference effects for the contrasts between recalled hits (R) and correct rejections (CR; top row), unrecalled hits (U) and CRs (middle row) and between R and U (bottom row). Positive t-values (red) correspond to more positive ERPs for R, U, and R trials, respectively. For each contrast we used a permutation test to limit the family-wise error rate across both time bins to 5% and set t-values for contrasts that did not exceed the critical t-values to 0. For electrodes where all participants contributed to the contrast the corresponding degrees of freedom were 131, but because we excluded data from some electrodes that appeared noisy in a given participant, this number represents an upper bound. (B) ERPs for R, U, and CR trials at electrode Cz (on the top of the head). Points below the ERPs indicate time points where the R vs. CR, the U vs. CR, and the R vs. U contrasts were significant which each contrast limiting the family-wise error rate across all electrodes and time points to 5% using a permutation test.
EEG data were partitioned into events starting 500 ms before the onset of a test item and ending 100 ms before the onset of the verbal recognition response. We applied a time-frequency decomposition using Morlet wavelets with 5 cycles for 15 log-spaced frequencies between 2 and 200 Hz, log-transformed the resulting power values, and z-transformed these within session. We chose this wide range of frequencies to ensure that we comprehensively capture signals associated with cognitive processes. By separately z-transforming power for each frequency within each session, we removed the variability due to baseline power-levels across frequencies and sessions. The multivariate classifier (described in detail below) could then weight power at each frequency, electrode, and time-point according to its predictive value. As is shown in Figure 3, power across all frequencies in the range that covaried with the status of the probe item.
Differences in power for previously recalled vs. not previously recalled hits (A) and for all targets vs. lures irrespective of response (B). Left and right panels show these differences for anterior and posterior ROIs (illustrated in the middle panels) respectively across frequencies and vincen-tized time bins (mean times associated with some of the time bins are indicated on the abscissas). We used t-values for the differences between trial categories for each participant to calculate t-values across participants. Red shades indicate higher power for previously recalled hits (A) or for all targets (B) and blue shades indicate higher power for not previously recalled hits (A) or for lures (B; within each panel, a false discovery rate of .05 are set to white).
We used a 1500 ms buffer at the beginning of the events and mirrored the last 1500 ms at the end of each event to avoid edge artifacts and to prevent EEG activity from periods during the verbal recognition memory response from bleeding into the analyzed time bins (Cohen, 2014). With the exception of 13 sessions across 5 participants (out of a total of 2640 sessions across all 132 participants) where data were accidentally recorded at 250 Hz, EEG was initially sampled at 500 Hz and down-sampled to 100 Hz after wavelet transformation. We then discarded samples before the onset of the test items, resampled power values for each event to 360 samples, and averaged these samples into 36 equal-time bins for the univariate analyses (Figure 3) and into six equal-time bins for the multivariate classifiers. The lengths of the individual time bins were identical within each trial, but, because response times varied across trials, so did the lengths of the (“vincentized”; Ratcliff, 1979) time bins. We chose to fix the number of time bins to allow us to compare the neural signals across trials as a function of the proportion of each trial’s response time, but we also present some complementary analyses using fixed-length (100 ms) time bins below. To aid with interpretation, whenever reasonable, figures show mean times associated with time bins rather than indicating the corresponding ordinal time bin numbers.
, where u and v are feature vectors and γ is a free parameter specifying the width of the Gaussian RBF kernel that determines the region of influence of any support vector. For very large values of γ (corresponding to a small variance of the Gaussian kernel) the support vectors are minimally affected by other training examples leading to a highly complex model that is prone to overfitting. Very low values of γ (corresponding to a large variance of the Gaussian kernel) make each support vector highly dependent on the entire training set and thus result in highly constrained models. The default setting for γ in scikit-learn’s svm.SVC class (using the “gamma=auto” option in its constructor) is to set γ to 1/nF, where nF is the number of features—we used this setting for all of our fits. Scaling γ by the number of features counteracts the increased danger of over-fitting with increasing numbers of features by adjusting each support vectors region of influence to account for larger expected distances between feature vectors as the number of dimensions increase.
The z-transformed log-power values across all frequencies and electrodes served as features for the multivariate classifiers. We performed the z-transformation separately for each frequency and within each session and thus this transformation was completely separate for training and testing data. These features came either from individual time bins or from varying ranges of time bins starting with the first time bin (bins 1–2, bins 1–3, bins 1–4, etc.). We refer to classifiers trained on features from individual time bins as “individual” classifiers and to classifiers trained on features from multiple consecutive time bins starting with the first time bin as “cumulative” classifiers.
In addtion to the γ parameter that specifies the width of the RBF kernel (discussed above), a separate regularization parameter (C in scikit-learn, in other contexts parameters with this function are often labeled α or λ) determines the width of the margin between the two classes which relates to the tolerance for miss-classifying training exemplars. We used the default setting for this parameter (C = 1.0) for all our fits. Because the number of exemplars in each class varied from session to session (as described in the methods the proportion of targets varied between 50% and 80%), we set up the classifiers such that the weights were adjusted inversely proportional to class frequencies (using “balanced” as input to the “class_weight” keyword for the constructor to scikit-learn’s svm.SVC class). This resulted in the regularization parameter (C) for each class, i, to be set to nS/(2ni), where nS is the number of samples and ni is the number of items in class i. This prevented the classifiers from being biased towards predicting the most frequenct class.
For the classification of features from 100 ms time bins, we only included trials where responses occurred 750 ms or more after probe onset and only included the 100 (out of 132) participants with at least 30 such trials in each session. Because we considered the time bin starting at probe onset, as well as 11 additional time bins that each had a 50 ms overlap with the previous time bin, this ensured that the last time bin (ending 650 ms after the probe onset) was separated from the response by at least 100 ms. These restrictions did not apply to classifiers using variable-length time bins which we applied to the entire recognition periods until 100 ms before response onset for all 132 participants.
Traditionally, researchers have averaged voltage time series from EEG recordings to obtain ERPs whose peaks and troughs can be compared across conditions (Luck, 2005). Figure 2A shows “early” (0.3–0.4 s) and “late” ERP (0.5–0.6 s) contrasts for comparisons between correctly recognized targets (“hits”) that were previously recalled (R) and correct rejections (CR), between previously unrecalled (U) hits and CRs, as well as between R and U trials. Even though there is considerable variability in the topography and timing of such ERP contrasts in the literature (Paller, Lucas, & Voss, 2012), the pattern of the contrasts between hits and CRs is broadly similar to that of comparable contrasts in other experiments (Burns, Tree, & Weidemann, 2014; Danker et al., 2008). Consistent with the traditional interpretation of recognition ERPs as reflecting an early familiarity signal and a late recollection signal (Wilding & Ranganath, 2012), ERPs at both time intervals distinguished hits from CRs and the topography of these contrasts differed across these intervals (Figure 2A). Studies of testing effects in recognition memory have suggested that previous recall of an item selectively enhances recollection in a recognition memory test (Chan & McDermott, 2007). To the extent that this conclusion holds, the fact that the difference in ERP contrasts for previously recalled vs. previously unrecalled hits was particularly pronounced for the late interval, further supports the interpretation of late ERP contrasts as reflecting a recollective signal. Figure 2B illustrates this pattern in in individual ERPs for R, U, and CR trials recorded at electrode Cz (at the top of the head): ERPs for hits and CRs separate early and remain separated for hundreds of ms, with ERPs for R and U trials only separating later.
ERPs mainly reflect phase-locked low-frequency power of the underlying EEG activity and are less sensitive to other spectral features that have been shown to reflect cognitive processes involved in episodic memory (Nyhus & Curran, 2010; Jacobs, Hwang, Curran, & Kahana, 2006). For all of our remaining analyses, we therefore decomposed the EEG signal into power across a wide range of frequencies. Additionally later time points in traditional ERP analyses frequently overlap with response periods. To avoid analyzing neural activity associated with executing the recognition response, we only analyzed brain activity up to 100 ms before the onset of this response and used a mirrored buffer that prevented any later brain activity from leaking into the analyzed time period (Cohen, 2014; see Methods for details).
Given that previous recall of an item has been linked to selectively enhanced recollection (Chan & McDermott, 2007), we aimed to identify any signals reflecting recollective processes by contrasting spectral power for previously recalled and previously unrecalled hits. Figure 3A shows the dynamic patterns of these contrasts for sensors in two regions of interest (ROIs) that have been frequently used in EEG investigations of familiarity and recollection (Schwikert & Curran, 2014) with both ROIs yielding broadly similar contrasts.
To directly track neural evidence distinguishing old from new items, we also calculated contrasts between spectral power for targets and lures irrespective of the subsequent response. Figure 3B shows that the pattern of these contrasts was remarkably similar to those for contrasts between previously recalled and not previously recalled hits (shown in Figure 3A). Under the assumption that memory is strongest for previously recalled targets, weaker for not previously recalled targets, and weakest/absent for lures, the patterns in Figure 3 could reflect a single memory strength signal that falls out of any contrast between two conditions that vary in memory strength (Squire, Wixted, & Clark, 2007). The fact that these patterns changed quite drastically in the lead-up to the memory decisions, however, might also reflect independent sources of evidence with distinct time courses. An assessment of the relative merits of these alternative accounts, therefore, requires us to quantify the neural evidence in the trial-by-trial variability of EEG activity that distinguishes between targets and lures in the lead-up to a recognition memory decision.
Despite previous efforts to relate the familiarity and recollection components of dual-process models to different (temporally distinct) neural signals (Curran, 1999; Rugg & Curran, 2007), little is known about the actual dynamics of information accumulation in recognition memory decisions and how they relate to accuracy, response times (RTs), and response confidence. Building on the success of machine learning techniques in neural data analyses that have provided unique(Polyn, Natu, Cohen, & Norman, 2005; Norman, Polyn, Detre, & Haxby, 2006; Ratcliff, Philiastides, & Sajda, 2009; Philiastides & Sajda, 2006; Ratcliff et al., 2016; Anderson et al., 2016), we trained statistical classifiers on spectral EEG features to track the neural dynamics of evidence accumulation during recognition memory decisions. A classifier’s ability to distinguish targets from lures can be directly compared to an individual’s recognition memory performance relating hits to “false alarms” (incorrect classifications of lures as “old”). The area under an ROC curve (AUC) serves as a convenient index of classification performance, with an AUC of .5 indicating chance performance and an AUC of 1.0 indicating perfect classification (Fawcett, 2006). We previously used confidence ratings and latencies for binary recognition memory decisions to generate ROC functions and showed a strong correspondence between the respective AUCs in the dataset from which the current dataset was derived (Weidemann & Kahana, 2016). Here we assess the evolution of a neural signal indexing evidence for the recognition memory decision by generating ROC functions from the outputs of classifiers that were trained to distinguish targets from lures using spectral EEG features from various intervals during the recognition period. To reduce computational complexity and generate more reliable features for the classifiers, we aggregated the time bins shown in Figure 3 by averaging them in groups of six, partitioning each recognition memory decision into six equaltime bins (see Methods for details).
For each participant, we trained a classifier on spectral EEG features from all six time bins to confirm (in held out sessions) that the neural signal in individual trials reliably distinguished between targets and lures (AUC = .71, t(131) = 34.59, SE = 0.021, p < .001; Figure 4A). Single-process models of recognition memory commonly assume that evidence for targets is more variable than that for lures (Wixted, 2007), with converging evidence for this assumption coming from fits of detailed models of evidence accumulation (Starns & Ratcliff, 2014; Starns, 2014; Ratcliff et al., 2016). Larger target variability can result in increased AUCs that are based only on “old” responses (or corresponding classifier output) compared to those reflecting overt responses or classifier output for “new” decisions only (Weidemann & Kahana, 2016), but this pattern is also compatible with dual-process models. Conditional AUCs indicate how much signal the measure of interest contains for each response class (beyond the signal contained in the binary classification of test items as “old” or “new”; Weidemann & Kahana, 2016) and were consistently larger for “old” classifications across all measures (t(131) = 8.22–33.22, SE = 0.005–0.008, ps < .001).
In principle, a classifier trained on neural data to distinguish targets from lures may use different signals than those which are most important for the individual’s recognition memory decision. Indeed, it is unlikely that the coarse measure of scalp EEG activity (compressed into power for a small number of frequencies) could reflect the neural signals leading to the recognition memory decision with high fidelity. In that light, it is of particular interest to what extent the qualitative pattern of (conditional) AUCs is similar across measures. Figure 4B illustrates similarly close relationships between AUCs based on overt responses (AUCC and AUCL, with the subscripts denoting confidence ratings and response latency, respectively) and AUCs based on EEG-classifier output (AUCEEG; r = .71 & .76, t(130) = 11.53 & 13.36, ps < .001) We also observed strong correlations between conditional AUCs based on overt responses and EEG activity (Figure 4C; r = .17–.81, t(130) = 1.97–15.58, p < .05).
(A) Areas under the ROC functions (AUCs) for confidence ratings, response latencies, and EEG activity. AUCs for conventional ROC functions are shown in gray and those for conditional ROC functions based on only “old” or “new” responses (or corresponding classifier output) are shown in red and blue respectively.(B) ScAUCs for either confidence ratings (AUCC; left panel) or response latencies (AUCL; right panel) and EEG activity (AUCEEG). (Cconditional AUCs for either confidence ratings (AUCC; top panels) or response latencies (AUCl; bottom panels) corresponding to “old” (left panels) or “new” (right panels) recognition decisions and EEG activity (AUCEEG) for “old” and “new” classifications. Corresponding correlation coefficients are indicated in the top left of each scatter plot and the main diagonals are shown for convenience.
Having established a neural signal that reliably distinguishes between targets and lures and that strongly correlates with recognition memory decisions, we next asked how this evidence accrues over time. If different types of evidence accrue with distinct time courses, this would lead to distinct relative contributions at different points in the decision process. In dual-route models, these signals are assumed to reflect independent sources of evidence and thus should give a distinct advantage to any classifier trained on features from multiple time bins (which would thus be posed to capitalize on both types of evidence to boost performance), relative to classifiers trained on features from a smaller portion of the recognition period. To test this prediction, we compareda cumulatively increasing number of time bins to performance fromonly. Figure 5A shows the corresponding AUCs for these two types of classifiers as a function of time, as well as respective differences (ΔAUC). The AUCs for both types of classifiers were very similar and increased gradually with time (regression slopes and intercepts for both were 0.02 and 0.59, respectively; corresponding r valuesfeatures were 0.52 and 0.53 respectively, SEs = 0.001, ps < .001). The two types of classifiers are identical for the first time bin and, in the absence of over-fitting, the additional features used by the cumulative classifier for later time bins cannot decrease classification performance relative to the classifiers using features from only one time bin. At the last time bin, the number of features differ by a factor of six for the two classifiers and, even though the features from earlier time bins clearly contained relevant signal (as shown by reliable classification performance for previous time bins) classifiers was minuscule (ΔAUC = .003 at the last time bin and < .01 through out). We observed the same pattern of results for fixed-lenght (100 ms) time bins (Figure 5B; regression slopes were 0.006 and intercepts were 0.5 for both classifiers, with r = 0.61 for the individual classifier and r = 0.60 for the cumulative classifier, SEs < 0.001, ps < .001; |ΔAUC| < .006). This pattern of results is what would be expected if the classifier output reflected an integrated evidence signal as it accumulates in the lead-up to a decision.
(A, B) AUCs for the EEG classifier as a function of vincentized time bin (A) or fixed-length (100 ms) time bin (B) for features from individual time bins (I) and for cumulatively adding features from each time bin (C; top panels). The bottom panels show the differences between AUCs (ΔAUCs). (C, DC) or fixed-length (100 ms) time bin (D) when tested on targets that were previously recalled (R) or previously unrecalled (U) and corresponding differences (ΔAUC). Mean times corresponding to vincentized time bins are indicated on the abscissa in (A); because RTs differed as a function of previous recall only ordinal time bin labels are indicated in (C). Shaded regions correspond to 95% confidence intervals.
To properly assess this evidence against dual-route accounts of recognition memory, it is important to link our neural classifiers to overt responses. Above we compared (conditional) AUCs for memory decisions to those from classifiers trained on neural features from all time bins (Figure 4). Here we track the correspondence between brain activity and overt responses across time by correlating the trial-by-trial output of classifiers trained on individual time bins with subsequent confidence ratings. If classifiers trained on features from different time bins were picking up on different types of relevant signals, we would expect the correlation between classifier output and confidence ratings to peak whenever each signal type provides maximal evidence (e.g., a peak in correlation reflecting an early familiarity signal, followed by another peak reflecting contributions from later recollective processes). From the perspective of a single, continuously accumulating, memory strength signal, however, we would expect gradually increasing correlations as a function of time. Figure 6 shows positive and increasing correlations (measured by Spearman’s ρ) for outputs from classifiers and confidence ratings as a function of time (regression slope and intercept were 0.43 and 0.22, respectively, r = .56, SE = 0.002, p < .001).
Mean Spearman’s ρ between confidence ratings and output of the EEG classifier using features from individual time bins as a function of time bin. Ts.
Given the converging evidence against dual-route accounts, we set out to maximize our ability to detect any differential contributions from different kinds of processes by assessing the classifiers’ performance conditional on previous recall of targets (Chan & McDermott, 2007). Figure 5C shows that AUCs from recalled trials exceeded those for unrecalled trials across all time bins (ts(131) = 21.08–31.19, SEs = 0.003, ps < .001) and that both types of AUCs increased with time (regression slopes for both were 0.02 with intercepts of 0.54 and 0.61 for AUCs based on unrecalled and recalled targets respectively). Furthermore, the differences between AUCs based on recalled and unrecalled targets also increased with time (regression slope and intercept for ΔAUC were 0.006 and 0.073, respectively, r = .27, SE < 0.001, p < .001). Regardless of previous recall status, classifier performance increased gradually with time as one would expect if evidence accumulated continuously (but at different rates) for both types of trials.
Because differential RTs for targets as a function of previous recall could have contributed to the differences shown in Figure 5C, we also trained classifiers on 100 ms bins of spectral EEG features that we moved from probe onset in steps of 50 ms until the time window that ended at 650 ms after probe onset. Again,99) = 3.6–19.76, SEs = 0.002–0.003, ps < .001) and both types of AUCs increased with time (with regression slopes of 0.004 and 0.009 and corresponding intercepts of 0.5 for the; corresponding rs = .45 & .64, SEs < 0.001, and ps < .001). Also as abov4 and 0.003, respectively, r = .53, SE < 0.001, p < .001). For both types of analyses, evidence accrual for recalled and unrecalled targets appears most consistent with the continuous accumulation of different amounts (rather than different kinds) of evidence.
Despite a long history of research on recognition memoryevidence that allows us to distinguish repeated encounters from novel experiences (Diana et al., 2006; Dunn, 2004, 2008; Wixted, 2007; Wixted & Mickes, 2010; Squire et al., 2007; Kirwan, Wixted, & Squire, 2010; Dede, Wixted, Hopkins, & Squire, 2013; Yonelinas, 2002; Yonelinas et al., 2010; Malmberg, 2008; Merkow, Burke, & Kahana, 2015; Ratcliff et al., 2016). By training multivariate classifiers to distinguish between previously studied and novel items based on spectral EEG features recorded prior to the execution of a recognition response, we were able to track the accrual of this evidence. We found performance of classifiers trained on a small fraction of the recognition period to be nearly identical to that of classifiers that were able to also capitalize on features from all previous time bins (Figure 5A–B). This suggests that the classifiers directly tracked an evidence signal, rather than a signal over which a decision process integrates to calculate the accumulated evidence—an interpretation also supported by the strong correspondence between classifier output and overt responses (Figures 4 & 6) and by the increasing classifier performance as a function of time (Figure 5). These findings, as well as the strong qualitative similarities between classifier performance as a function of timetargets (Figure 5C–D), are difficult to reconcile with dual-route models that posit different kinds of recognition decisions based on independent and temporally distinct familiarity and recollection signals (Reder et al., 2000; Yonelinas, 2002; Diana et al., 2006; Yonelinas et al., 2010).
Our work builds on andduring an associative recognition task to hypothesized processing stages (Anderson et al., 2016; Borst & Anderson, 2015) and a recent study relating brain activity during a single-item recognition task to parameters in a single-process recognition memory model (Ratcliff et al., 2016). Whereas the former set of studies associated different processing stages with separate familiarity and recollection signals, Ratcliff et al.’s conclusions are largely compatible with ours. Differences in tasks and analysis methods complicate direct comparisons between these studies and our results, but to the extent that the familiarity and recollection signals identified in the partitioning of EEG activity intore correlated, they would provide evidence for a unitary recognition signal in our approach which would complement our conclusions (more on this below).
An advantage of our approach is that it does not depend on the assumptions of any specific model of recognition memory, binary choice, or processing stages. By contrasting performance of multivariate classifiers trained on EEG activity during different partitions of the time between probe onset and just prior to the recognition response, we were able to show that “neural evidence” at any given time, incorporates information from previous time points rather than reflecting different kinds of independent evidence signals at different time points. We were also able to directly relate classifier performance and outputs to overt responses, suggesting a close link between neural activity and recognition decisions, as well as associated confidence ratings, that increased as classifiers were trained on later time bins (Figures 4 and 6). As we discuss next, to the extent that we are able to measure all relevant neural signals and to the extent that any signals reflecting independent evidence sources supporting multiple routes to recognition are temporally distinct, our results provide evidence for single-route models of recognition memory.
Does classifier output reflect recognition evidence?
We have used ouputs ofactivity to track evidence for recognition decisions as it evolved in the lead-up to a response. Our conclusions are conditional upon this approach’s ability to faithfully reflect information that is relevant for the recognition decision. Alternatively, our conclusion that a unitary evidence signal drives recognition decisions could also be due to our approach’s inability to detect neural activity associated with a separate evidence signal. The strong correlations of classifier output with overt responses offer some reassurance by limiting the variance that could be explained by unobserved evidence signals. Additionally, ERP analyses during recognition decisions are often interpreted as reflecting contributions of(Curran, 1999; Rugg & Curran, 2007), which should render these signals observable in our approach.
Might different evidence signals overlap?
Our analyses depend on different evidence signals exhibiting distinct temporal profiles. Whereas many dual-process models do not specify the dynamics of familiarity and recollection, as discussed above, the near universal assumption is that these signals are temporally distinct. It is generally assumed that an early familiarity signal precedes a later recognition signal, but the fact that responses that are thought to be driven by a recollection signal are made quicker than those thought to rely on familiarity has motivated proposals that recollection occurs before familiarity (Dewhurst, Holmes, Brandt, & Dean, 2006). This pattern of response times, however, is only at odds with an early familiarity and a late recollection signal within the framework of dual-route models. If evidence from all sources is integrated into a unitary evidence signal, as we propose, it is not meaningful to distinguish between “familiarity” and “recollection” responses and we would expect response times to be predominantly a function of overall evidence strength. If memory probes which elicit recollective experiencoverall evidence (e.g., because recollective evidence is considered particularly diagnostic), single-route models could thus predict faster response times in situations where specific episodic details were recollected, even if recollection signals occur later than familiarity signals.
Our approach is agnostic to the relative order of any familiarity and recollection signals, but to the extent that evidence signals for different routes to recognition overlap in time, we would not be able to distinguish them. Our results are thus compatible with dual-route models, but only under the assumption that recollection and familiarity signals are observable in the EEG activity at similar times or that at least one of these signals does not manifest in the EEG activity. Likewise, if familiarity and recollection signals are highly correlated, we would be unable to distinguish them in our approach. In this case, however, it is unclear to what extent this distinction is meaningful and one could conceptualize these signals as manifestations of a unitary evidence signal instead.
Whereas we have considered different ways in which a previously studied item might be recognized as “old”, some evidence suggests that the detection of novelty can also support recognition memory (Daselaar, Fleck, Prince,& Cabeza, 2006; Davelaar, Tian, Weidemann, & Huber, 2011; Kafkas & Montaldi, 2014; Bunzeck, Doeller, Fuentemilla, Dolan, & Duzel, 2009). Our approach quantifiesand thus is agnostic with respect to whether the relevant signals index familiarity or novelty. It is likely that any familiarity and novelty signals would be strongly (negatively) correlated in standard recognition memory tasks, and some evidence suggests similar temporal profiles for familiarity and novelty signals (Bunzeck et al., 2009). Our approach does not distinguish between strongly correlated and/or temporally overlapping signals and thus is unable to differentiate familiarity and novelty signals with these properties.
Debates about the relative merits of single- vs. dualprocess models often presuppose a false dichotomy between a single route to recognition and contributions from multiple sources of evidence (such as recollection and familiarity) to recognition decisions. Indeed, whereas single-process models assume a unitary “memory strength” signal, models that specify how this value is computed typically derive it from multiple features. For example, the Retrieving Effectively from Memory (REM; Shiffrin & Steyvers, 1997) model represents probes as vectors of features and the recognition decision is based on a computation of the probe’s match to stored memory traces. Thus, the memory strength signal in REM depends on a range of factors (such as the similarity between the probe and stored memory traces, the likelihood of correctly storing features during study, and how features might get confused), but it is considered a single-process model because the effects of all of these factors culminate in a unitary evidence signal. Models that assume separate familiarity and recollection processelonging to a different class of dual-process models, even when they explicitly allow for the associated memory signals to combine into a unitary evidence signal (Rotello et al., 2004; Wixted & Mickes, 2010). As we have argued above, we believe a more meaningful distinction is that between single and dual routes to recognition. Specifically this perspective could help resolve apparent conflicts between the success of single process models and evidence suggesting the contributions of distinct sources of evidence to recognition decisions.
There are inherent trade-offs between integrating all available evidence, and thus maximizing the ability to distinguish old from new items, and separately considering different sources of evidence, and thus maximizing the ability to qualify recognition decisions (e.g., with remember-know judgments). It is therefore possible thats is integrated into a unitary signal is sensitive to task demands (Rotello et al., 2004; Wixted & Mickes, 2010). At least for the standard old-new discrimination task considered here, however, our results indicate that the memory system integrates available evidence into a unitary evidence signal that drives recognition decisions. These findings thus firmly link recognition decisions to other types of decisions under uncertainty, which are commonly assumed to rely on a unitary evidence signal differentiating between the response options (Ratcliff et al., 2009; Nosofsky, Little, & James, 2012; Philiastides & Sajda, 2006).
Christoph T. Weidemann, D, UK andA; Michael J. Kah. The authors would like to thank Jesse Pazdera and other members of the Computational Memorg as well as Youssef Ezzyat, James Kragel, Nora Herweg, Ethan Solomon, and Rivka Cohen, grant NIMH RO1 55687 to MJK. This manuscript has been published as a preprint on BioRxiv (https://doi.org/10.1101/165225) and parts of this work have been presented at the 2017 Context and Episodic Memory Syposium and the 2017 T. Weidemann, ctw{at}cogsci.info. MIT University Press Group Ltd.
Curran, T. (1999). Thry. Neuropsychologia, 37, 771–785.
Luck, S.The MIT Press.EM—. doi: 10.3758/bf03209391.
Yonelinas, A.341–1354. doi: 10.1037/0278-73 |||IP_ADDRESS||| 1.


Barrier Pest Control is a local family-owned business founded in 2001. Situated at 20746 SW Olds Pl, Sherwood, OR 97140, the company serves cities in the Portland metro area, as well as Vancouver, Washington. Barrier Pest Control promises to eliminate wasps, spiders, mice, and rats as well as other common household pests.
The company advertises free estimates, advice, and inspections. After that, it charges a $65 fee for the first service and a yearly contract at $95, billed quarterly. Barrier Pest Control only provides services under yearly contracts, and if you need to break the contract for any reason, the company charges a $100 cancellation fee. The contract includes free reservice visits as needed to keep the pests away. Barrier Pest Control claims that they are able to treat homes from pests with family, pet, and environmentally safe products. This sets them apart from other companies that tend to use chemicals that are toxic to both pets and humans.
Satisfied customers praise the technicians who go above and beyond to serve them. The company responds quickly to requests and often makes same-day appointments, although several customers complained about their technicians arriving late. Many express that the technicians listen and explain the procedures, and one customer referenced a time when her technician even took the time to answer questions from her children. Other customers expressed gratitude for the company’s integrity. One individual described a time that a technician sprayed around her house while she wasn’t at home. He later called to inform her that he saw an envelope of cash under her doormat and wanted to make sure she picked it up. With a Google rating of 4.6, it’s clear that people are generally satisfied with the company’s service.
Customers also have a lot to say about Barrier Pest Control’s business practices. Most praise the technicians who come to their homes, but many express frustrations about how the company tries to sell you on its products. Several people wrote reviews about sales associates knocking on their doors and pushing them to sign contracts, claiming they were in the area. These residents felt uncomfortable in the aggressive way the sales associates spoke to them. A few customers felt as though they were roped into contracts that weren’t thoroughly explained, and others had trouble getting out of contracts they no longer wanted. With an A+ from the BBB, Barrier Pest Control seems to amend any problems that customers bring to its attention.
Overall, Barrier Pest Control is a family-owned business with caring and thorough technicians who provide family, pet, and environmentally friendly products, but may have some aggressive sales practices.


If you only have a few days, use this itinerary as a menu to select the activities that best suit your interests. Also, feel free to mix up the days – you don't have to follow the days in order – this is your vacation!
And the adventure doesn't stop when the sun goes down!
Looking to get your hands dirty?


by John C MaxwellJohn C Maxwell
By John C. Maxwell
Copyright © 2008 John C. Maxwell
I
My father's generation believed that leaders should never get too close to the people they lead. "Keep a distance" was a phrase I often heard. Good leaders were supposed to be a little above and apart from those they led. As a result, when I began my leadership journey, I made sure to keep some distance between me and my people. I tried to be close enough to lead them, but far enough away to not be influenced by them.
This balancing act immediately created a lot of inner conflict for me. Honestly, I liked being close to the people I led. Plus, I felt that one of my strengths was my ability to connect with people. Both of these factors caused me to fight the instruction I had received to keep a distance. And sure enough, within a few months of accepting my first leadership position, my wife, Margaret, and I began developing close friendships. We were enjoying our work and the people in the organization.
Like many leaders early in their career, I knew that I would not stay in this first job forever. It was a good experience, but I was soon ready for bigger challenges. After three years, I resigned to accept a position in Lancaster, Ohio. I'll never forget the response of most people when they realized we were leaving: "How could you do this after all we have done together?" Many people took my departure
In my second position, for the first time in my leadership journey, I could employ staff to help me. One young man showed great promise, so I hired him and began pouring my life into him. I soon discovered that training and developing people was both a strength and a joy.
This time I had learned my lesson. I once again determined to keep space between me and everyone around me. I would hire staff to do their jobs. And I would do my job. And we would only get together at the annual Christmas party!-and the risk-ooneliness Is Not a Leadership Issue
There's a cartoon in which an executive is shown sitting forlornly behind a huge, who says, "If it's any comfort to you, it's lonely at the bottom too." Being at the top doesn't mean you have to be lonely. Neither does being at the bottom. I've met lonely people at the bottom, on the top, and in the middle. I now realize that e.
e-ruths About the Top
Because this leadership issue has been so personal to me, I've given it a lot of thought over the years. Here are some things you need to know:
No One Ever Got to the Top Alone
p
1. Initiative: You have to get up to go up. 2. Sacrifice: You have to give up to go up. 3. Maturity: You have to grow up to go up.
"Go." A leader says, Much of the Time Leaders Are Not at the Top
Jules Ormont said, " Good leaders who remain connected with their people stoop-that's the only way to reach down and pull others up. If you want to be the best leader you can be, don't allow insecurity, pettiness, or jealousy to keep you from reaching out to others.
If you find yourself too far from your people-either by accident or by design-tYou may hurt others or be hurt yourself. B's how to get started:
1. Avoid Positional Thinking
ely. The time spent in building relationships creates friendships with others. Positional leaders, on the other hand,"persuade" their people to do something, they create distance between themselves and others. They are essentially saying, "I'm up here; you're down there. So do what I say." That makes people feel small, alienates them, and drives a wedge between them and the leader. Good leaders don't belittle people-they enlarge them.
2. Realize the Downsides of Success and Failure
Success can be dangerous-and so can failure. Anytime you think of yourself as "a success," y. You start to think, I don't need to see them, and you withdraw. Ironically, failure also leads to withdrawal, but for other reasons. If you think of yourself as "a failure," you avoid others, thinking, I don't want to see them. Both ex3. Understand That You Are in the People Businesss loving them! I've never met a good leader who didn't care about people. Ineffective leaders have the wrong attitude, saying, "I love mankind. It's the people I can't stand." But good leaders understalike people or you will never add value to them. And if you become indifferent to people, you may be only a few steps away from manipulating them. No leader should ever do that.
4. Buy Into the Law of Significance
The Law of Significance in The 17 Indispensible Laws of Teamwork statesI challenge you to think of one. (I've made this challenge at conferences for years and no one has succeeded in identifying one yet!) Honestly, if on your own you can fulfill the vision you have for your life and work, then you're aiming too low. Occasionally a person will introduce himself to me by saying, "I am a self-made man." I am often tempted to reply, "I'm so sorry. If you've made everything yourself, you haven't made much."
In my organizations I don't have employees; I have teammates. Yes, I do pay people and offer them benefits. But people don't work for me. They work with me. We are working together to fulfill the vision. Without them, I cannot succeed. Without me, they cannot succeed. We're a team. We reach our goals together. We need each other. If we didn't, then one of us is in the wrong place. * * *
People working together for a common vision can be an incredible experience. Years ago when operatic , a reporter tried to find out if there was a competitive spirit among them.
Each singer was a superstar, and the reporter was hoping to uncover a rivalry between them. Domingo dismissed it. "Yo" he said. "You can't be rivals when you're together making music."
"It's lonely at the top," to "If it's lonely at the top, I must be doing something wrong," to "Come up to the top and join me," to "Let's go to the top together," to "It's not lonely at the top."
Nowadays I never "climb the mountain" alone. My job is to make sure the team makes it to the top together. Some of the people I invite to go along pass me and climb higher than I do. That doesn't bother me. If I know I was able to give them a hand and pull them up along the way, then I feel very fulfilled. Sometimes they return the favor and pull me up to their level. I'm grateful for that too.
1. Are you better at the science or art of leadership? Some leaders are better at the technical side of leading: strategy, planning, finances, etc. Others are better at the people part: connecting, communicating, casting vision, motivating, etc. Which is your strength?
If you are more of a technical person, never lose sight of the fact that leadership is a people business. Take steps to improve your people skills. Try walking slowly through the halls so that you can talk to people and get to know them better. Read books or take courses. Ask a friend who is good with people to give you some tips. Seek counseling. Do whatever it takes to improve.
2. Why do you want to be at the top? For many, that means climbing the career ladder so that they can gain a higher position. If your only motivation for leading is career advancement and professional improvement, you are in danger of becoming the kind of positional leader who plays king of the hill with colleagues and employees. Spend some time soul searching to discover how your leadership can and should benefit others.
3. How big is your dream? What is your dream? What would you love to accomplish in your life and career? If it's something you can accomplish alone, you are missing your leadership potential. Anything worth doing is worth doing with others. Dream big. What can you imagine accomplishing that would require more than you can do on your own? What kinds of teammates would you need to accomplish it? How might the trip benefit them as well as you or the organization? Broaden your thinking and you will be more likely to think of climbing the summit with a team.
Excerpted from LEADERSHIP GOLD by John C. Maxwell Copyright © 2008 by John C. Maxwell. Excerpted by permission.
Searching for Gold vii 1
The Toughest Person to Lead Is Always Yourself 11
Defining Moments Define Your Leadership 20
When You Get Kicked in the Rear, You Know You're out in Front 32
Never Work a Day in Your Life 41
The Best Leaders Are Listeners 49
Get in the Zone and Stay There 57
A Leader's First Responsibility Is to Define Reality 66
To See How the Leader Is Doing, Look at the People 74
Don't Send Your Ducks to Eagle School 85
Keep Your Mind on the Main Thing 96 105
Don't Manage Your Time-Manage Your Life 114
Keep Learning to Keep Leading 124
Leaders Distinguish Themselves During Tough Times 134
People Quit People, Not Companies 143
Experience Is Not the Best Teacher 154
 164
Be a Connector, Not Just a Climber 174
The Choices You Make, Make You 184
Influence Should Be Loaned but Never Given 193
For Everything You Gain, You Give Up Something 203
Those Who Start the Journey with You Seldom Finish with You 213
Few Leaders Are Successful Unless a Lot of People Want Them to Be 223
You Only Get Answers to the Questions You Ask 232
People Will Summarize Your Life in One Sentence-Pick It Now 244
Maxwell (t) shares 26 nuggets of wisdom based on his nearly 40 years of leadership. A practical guide, complete with exercises and "mentoring moments," this collection offers a blend of advice, professional wisdom and personal recollection. Each chapter provides insight into a specific aspect of effective management. Some, such as "The Best Leaders Are Listeners" and "Keep Learning to Keep Leading," are hardly groundbreaking, but others such as "Don't Send Your Ducks to Eagle School" (a phrase borrowed from Jim Rohn) and "For Everything You Gain, You Give Up Something" provide perspective into less-explored facets of successful leadership. Maxwell also covers some of the more challenging aspects of his topic: defining personal success, guarding against unrealistic thinking and determining why people quit. Throughout, Maxwell includes call-out quotes from well-known leaders such as Jack Welch and Frances Hesselbein as well as from surprising voices like J.K. Rowling and Joyce Brothers. A solid addition to a crowded field, this book will be of value to seasoned leaders as well as those just starting out. (Apr.)4.3 out of 5 based on 0 ratings. 12 reviews.
It is amazing how God places people in our lives to impact the events that surround us. I had the priviledge to hear Dr. Maxwell teach at a conference several months ago and the topic was Leadership Gold. I have since read the book which is truly an important message for leaders. He caused me to rethink about where reality is in my own set of circumstances. His style of teaching and his own personal experiences helped me grasp the message and begin to refocus. By first defining reality we can develop a realistic starting point to lead from.
BelindaEllsworth More than 1 year ago
I recently read John C. Maxwell's boand can recommend it as a good reference piece for lessons in leadership. It's one that you can go to time and time again to look for some help with different aspects of leadership. It has some great nuggets of wisdom that can assist you when you come up against challenges, or you want to get a different perspective. A lot of his examples are a little dated and may be difficult for a younger reader to relate to, and sometimes he doesn't really provide adequate background for his examples, which can make it challenging to make connections to your own business. For instance, he refers to Winston Churchill as one of the greatest leaders and then references the "many challenges he faced" as part of one of the book's lessons. However, many of us would be hard-pressed to know exactly what challenges Maxwell means. This may not be a beach read, or even a leadership book that you'd become immersed in and finish quickly. What you'll find is that it's a great tool for finding pertinent information on a particular topic. You could just flip to a section and read a lesson. In fact, some valuable aspects of the book are the "application exercises" he includes at the end of each lesson. These exercises really help reinforce the message and give you specific action steps you can use toward applying that lesson and really learning it and benefiting from it. So often we read great tips and never implement them. With Maxwell's lesson and exercise structure you can avoid that trap and come away from each visit to the book with real benefits for your business.
StanR More than 1 year ago
Leadership Gold is another outstanding production by John Maxwell. Those of us who are familiar with John's past works know very well that he assembles material into a work that's captivating, compelling, and just plain GREAT! You laugh, you learn, and then you go out and lead better than before. The 26 topics in Leadership Gold all have relevance to just about anyone, and each is presented in the typical 'Maxwell' way, including John's own personal wisdom and wit, clips and quotes from numerous leadership pros who John acknowledges. I have made presentations that referred to John's material and they consistently go over as 'winners'!. I can't wait for John's next book!
book by john maxwell
book by rachel lynette:, of Leadership Gold with ...
Developing the Leaders Around You: How to Help... ...hip
If...


2013 Vw Jetta Tdi Fuse Box
2013 Vw Jetta Tdi Fuse BoxHowever,even the shop in your region. But before you do so, you have to be aware ofe biggest problem that you need to think about is whether the diagrams are accurate and current. You need to be certain thatay have been supplied with. Additionally, if the diagrams are no from ahe site which you will need to go to, you need to be sure thatneed to know how to use the feature should alsworld wide web to see in u make the he identical cable you had before or you can order different ones. There are different methods of getting a cable to work also. By way of ihave to purchase wires if you want to repair your scanner. Should you need to put something back , you can cut on the wire. Be careful when you are cutting the cable because you don't wisht at allould get or use to connect the wires. If you would likeso priceyareas, but you may wish to consider.
Vw 2014 Passat Fuse Box Questions U0026 Answers With Pictures
Which Fuse Is The Radio Fuse
Fuse Box Volkswagen Touareg 2002
2002 Jetta Tail Light Replacement
2014 Jsw Tdi 12 Volt Utility Plug Constant On


Is Recruiting Ready for A Credit Score?
The financial industry uses a credit score to predict an individual's credit risk. Could the recruiting industry be ready for a similar industry standard that consistently uses objective data to evaluate candidates?
If you look back at the credit industry, it has come a long way. According to the Fair Issac Corporation or FICO blog, the credit reporting practice began back in the 1800s' for commercial lending. At the time, most of the lending was determined by judgment of character and as such, was an inaccurate representation of the true ability to determine a borrower's lending risk. An alphanumeric system for credit evaluation was created shortly thereafter and in the 1900s', consumers soon began to borrow, too. After the creation of the Fair Credit Reporting Act or FCRA in the 1970s' and years of struggling to interpret credit data, the FICO score was established to serve as an "…
Will explains that 40 years later, the FICO score still "…
Will goes on to say: "The thought that the founders of our company had was, we should make this more objective, science-based, let's automate it, let's lower the cost of this evaluation." This makes complete sense. Why not take a more scientific approach and leverage data available, combined with expertise in evaluating certain consistent factors to determine the potential success rate of a candidate?
So what's holding the recruiting industry back from this approach?Well for one, Will says a big challenge will be getting other recruiters to buy in to a certain evaluation method, accepting and using it as the industry standard. It took a few years for the FICO score to get the proper recognition and it was mostly because its results spoke for itself. Institutions that adopted the FICO score could prove that it worked because they had more good credit scores than their competitors who didn't use FICO scores. Will points out that "…the most innovative companies jump on these kind of techniques and then the rest of the industry eventually follows."


L http://map.longdo.com/ an
Apart from viewing and searching the map, users can also contribute new locations by right-clicking on the map where you want to create a new location and choose Add a location. The contributed locations will be checked and approved and if successful, will be shown on the map.
Sat
Bom BMA.


Downloads Top 10; August 2006
On Computer Emu Zone a new MSX game called Infinity has been announced.
As in 2003 and 2004, Brain Games contributed once more to the yearly remakes competition organized by Retro Remakes.
Matra Computer Automations, well known for their releases such as Ark-A-Noah and Sex Bomb Bunny, have entirely updated their website.
We have just added a new entry to the MSXArt'06 challenge to our downloads database.


- MLS #: 387903
- 58 SqFt
Listings offered by Charter One Realty
Several boat slip options available with sizes ranging from 32 feet to 58 feethours.


The Secrets of Medicinal Marijuana
A Guide For Patients & Those Who Care For Them
by Barbara Harris, RT, CMT
However, the best part of the book is The Secret, as the title foretells. Many of us demand a greater life and health oriented approach to healthcare, and would like to move away from a symptomatic, pharmaceutical, corporate style of medicine. This lovely book is for us.
Book Review by: Doc Bud
Publisher: Muse House Press
Purchase The Secrets of Medical Marijuana


The practice of offshore software development is now as part of global trend adopted by some Western companies. The software development projects operating under geographically-separated settings present the tremendous challenges and require collaboration-intensive activities. This study examineswithin the context of offshoring software development projects. Furthermore, this study intends to develop a collaboration model for offshore software development that describes the key components operating under distributed development environment. The initial results and limitations of research are also discussed.
Chen, Hwang-Yeh, "Exploring the Collaboration Model of Offshore outsourcing: A Study of IT Software Development83.


Slovak Republic | EN
Axpo signs landmark PPA for Europe’s largest operational onshore wind farm in Romania
Eastern Europe Wind power
Radu Rat, Managing Director Axpo Energy Romania, added: “This  The PPA is a major milestone, both for the Romanian energy market and Axpo’s business here. With our long-standing PPA experience in many markets across Europe, we are well positioned to play a key role in Romania’s energy transition.”a.
About CEZ in Romania:
On 31 March 2021, Distributie Energie were acquired by.
Company 19.06.2019


E WITH THE SUBJECTIVE DVORAK SATELLITE INTENSITY ESTIMATES.
TROPOSPHERIC TROUGH JUST OFF THE WEST COAST OF THE UNITED STATES.
PROGRESSIVELY SHALLOWER CYCLONE IS STEERED BY THE LOW-LEVEL FLOW.
BETTER HANDLE ON THE RAPID WEAKENING. </s> I remember that other card you made some time ago using that same hearts die. I liked it then and I like this one now. I had to laugh at you having to put on your glasses. One day you’ll need glasses and even a magnifying glass sometimes to do detail work. Something to look forward to. Ha! Ha!
It’s funny – it started slow, but then it declined quickly! I can see myself in the next 10 years with headlamp with magnifying glass attached – lol.
I use the table model magnifier that you can move around. You adjust it close to your work and look over the magnifier to look into it. My mother bought it years ago but it is still available. I decided to use it out of curiosity one day and was amazed at how better I could see the details. So now I use it all the time with my watercoloring and even when using my Copic markers occasionally.
Love the way you inked the hearts. I have a hard time stamping with my ombré inks too. Looking forward to you showing us the trick to stamping with them.
Someone was supposed to send me their die cutter when they said they couldn’t use anymore because she had problems with her hands and she couldn’t use the hand crank feature. She said she was waiting till she could afford to mail it to me. So as you can see, without a die cutter I can’t make this card. I do love how you did it though. I love all the colors! Great card! </s> Specially formulated for ambient temperatures, GEARZ-EP-LT delivers high-quality cleaning and degreasing results even under heavy load. Fast working and easy to apply, it is tough on difficult-to-remove greases but protects gears by reducing the risk of galling. It’s also biodegradable and contains no chlorohydrocarbons, making it a greener and safer high-performance cleaning solution.
Sprayed directly onto gear mechanisms without pre-heating, GEARZ-EP-LT can quickly deliver excellent results. Its fortified formula removes difficult lubricants (containing lithium or asphlatines or molybdenum disulfide) that can build up in gear teeth over time. Rinsing with a high pressure hot water wash and a dilute solution of a water based degreaser like WONDERSOL or WHYNOT leaves gears thoroughly clean, ready to perform as new.
Is biodegradable and ozone-friendly, ensuring the minimal environmental impact and an overall greener cleaning solution.
Removes asphaltic, molybdenum disulfide, lithium and crater greases from the roots of gear teeth more effectively.
Keeps downtime to a minimum by cleaning mill gears while operating under load.
Vapour emissions, odour levels, and fire and handling hazards are all reduced making the working environment safer.
Helps lower maintenance costs by reducing likelihood of galling or scarring gears.
GEARZ-EP-LT  North America604 881 4700. </s> W’s orig’t work and she didn’tn’t’s name and realises she’s speaking to Abigail Van Helsing - her mother.
Again with the human testing. Can we call the ethics division on this? Or keep them away from sharp things? These guys will inject themselves with anything!
And he wants the elder not for the cure but to “perfect”Lady, she just buried an axe in someone’sn. </s> Work | Computer Enterprises, Inc.
For over 25 yearIT solutions to companies in the Mid-Market through the Fortune 1000. We are proud to share some of the stories of our work and the value we’ve delivered.
We understand your business – our team of experts helpWe’ve worked on customer projects within these industries for years, tailorithat companies face. </s> Windows and Doors Replacement is our specialty! Has been for over twenty years!
At Innovative Home Improvements we supply and install sliding patio doors with some very outstanding features. Our sliding doors come standard with super smoothe gliding nylon rollers which have an incredibly quiet effortless glide you have to see to believe! They also have a double bolt action metal lock for a much higher level of security. Combined with hidden bumpers incorporated into the doors results in very high quality sliding patio doors. All backed by a comprehensive LIFETIME WARRANTY. Be sure to check our SPECI


- SAIL: Stockport Advice and Information for Life
- Sally Ewbank
- Sandra-Ann Bahri
- Sarah Louise Blevins: Sarah's Childcare
- Sarah Louise Bulman
- Sarah McGrath
- SAYS: Stockport Action Youth Speakers
- Seashell Trust: Sleep Support workshops
- Seashell Trust: Stay and Play
- See, Make & Do: Autism Friendly Museum events/activities
- See, Make and Do:
- SEN Panel, tribunals and appealing decisions
- SEN School Travel Survey
- SENCO and support in school
- SEND Guidance for Settings, Schools, College and SEND practioners
- SEND Improvement Journey: Latest News & What's Going On
- SEND Reform Updates: Stockport
- Senploy
- SENSE Get Out There (GOT) Youth club
- Sensory Support Service: Diagnosis and Referral Pathways
- Sensory Support Service: Introducing our service
- Sensory support Service: Other support for you
- Shared Lives, Independent Options
- Sharon Ann Joinson
- Sharon Anne Frain
- Shaymah Ward
- Shayna Marie Bahri
- Short Breaks Service, Independent Options
- Short breaks Statement
- Signpost Stockport for Young Carers
- Signs and Symptoms of Autism in Children and Young People
- Sindy Jane Farrell
- Sinead Anne Lynch
- Siobhan Marie McNicholas
- SK1 Stockport Technical School
- SLT for Kids
- SLT UK
- Social Care: Further reading and useful document & websites
- Songlines Project: Music, art and Theatre for children with ASC.
- Songlines: Mixing music and theatre - SPACE: Stockport Parents of ADHD Children in Education
- - Special Provision Capital Fund
- Speech and Language Therapy Teams
- St Ambrose Breakfast Club
- St Ambrose RC Primary School Nursery Class
- St Anne's RC High School
- St Bernadette's Catholic Primary School
- St Bernadettes Catholic Primary School Nursery Class
- St Christopher's Before and After School Club
- St Christopher's Catholic Primary School
- St Cuthbert's Old School Nursery CIO
- St Elisabeth's C of E Primary School
- St Elisabeth's CE Primary School Nursery Class
- St George's C of E Primary School
- St Georges Out of School Clubs
- St Georges Pre-School
- St Johns CE Primary School After School Club
- St Joseph's RC Infant School Nursery Class
- St Joseph's RC Primary School (Reddish)
- St Mark's C of E Primary School
- St Marks C of E Primary School Breakfast Club
- St Marks CE Primary School Nursery Class
- St Mary's Breakfast Club


There was probably a link between the Times Square bomber and the Taliban. [NYT]
Where the alleged bomber was living. [NYDN]
Teachers still will. [WSJ]
The ’90s are done. Gen Art to close. [The Wrap]


In the same vein, several of the practitioners with whom we work closely are finding that essential oils can be used to rebalance the brain.
Essential Oils are Small and Lipid Soluble
This is due in part to the fact that esat. But one of the key reasons essential oils are so effective in their ability to access and heal the brain is the fact that the volatile compounds of essential oils are extraordinarily small making them uniquely suited to cross the blood brain barrier. Once crossedng.
Learn more about using Essential Oils to treat Candida
Categories Health Tips Tags blood brain barrier, essential oils
Ds
Is a career in the health sector right for you?


When Samsung took the wraps off the Gear Fit 2 Pro at the end of August, the fitness band was advertised as beingHowever, it looks like iPhone owners still can't use the device, despite it being commercially available for more than two weeks.
So, why is this happening? Well, Samsung is still waiting for Apple to approve the dedicated iOS app which pairs the Gear Fit 2 Pro to a given iPhone.
At this point, the reason for the hold-up is anyone's guess as Apple has not issued any statements on the matter. It could be related to Cupertino's rigorous policies for apps on the App Store, or the fact that the two tech giants are fierce rivals in both the smartphone and wearable markets and have recently released competing devices.
Older Samsung Gear wearables such as the Gear S2, S3, and Gear Fit 2 have been compatible with iOS since the beginning of 2017, but it took Samsung nearly a year to make it happen. However, it's important to note that the Korean firm did not promote these earlier devices as compatible with Apple's operating system when they launched. Meanwhile, Tim Cook & Co. don't appear to harbor any ambitions of introducing the Apple Watch to the Android space.


THE COALITION TO STOP DEMOLITION
link: The Coalition to Stop Demolition
Thank you for posting this. I've made 3 trips down
He's one of those people who put into the system hisplace:
And yes, they're a real cause.


- "UNOS "
- "Even after programs log high death rates, years sometimes pass before UNOS takes meaningful action."
- "UNOS often backs down after being challenged — or even defied — by medical centers it is supposed to regulate."
- "UNOS officials have missed obvious red flags, including troubling transplant center statistics available on its own website."
UNOS described its enforcement role thus,
Adherence to transplant policy is ensured through a comprehensive, systematic auditing and monitoring process. The policy compliance process is designed foster public trust in the transplant network. Any deviation from policy is corrected quickly and thoroughly to protect patients and ensure that their treatment is not interrupted.But according to the Times,
'UNOS is not the FBI,' said board member Dr. Gabriel Danovitch, medical director of UCLA Medical Center's kidney and pancreas transplant program. 'It's not a police force.'Although federal contracts give UNOS the role of maintaining "compliance," in fact UNOS is a membership organization. The largest number of its members are the transplant centers whose compliance UNOS is charged with enforcing. And, "it's a relatively small world in which colleagues - even friends - end up in judgment of one another."
Such collegiality is built into UNOS' very structure — and that's the problem, some critics say. UNOS isn't just a regulator; it is a membership organization, run mostly by transplant professionals. Centers, in effect, oversee one another.The Times interviewed Judith Braslow,
who oversaw the federal government's division of transplantation from 1990 to 1998, said that although UNOS generally does a good job, it is difficult for the group to be 'completely objective because they essentially wear two hats.'So this turns out in some way to be a familiar story. An organizational structure designed in a more genteel age, when hospitals were smaller, more community oriented organizations, threadbare in finances, and run by "superintendents" or "directors" (as per Kenneth Ludmerer's Time to Heal) may no longer be appropriate for an age when hospitals are multi-billion dollar organizations run like "businesses" by CEOs, and other professional (but usually not health professional) managers, to the latest tunes sung by management consultants. In the new bleeding edge world of health care, collegiality can turn into conflict of interest awfully quickly.
'In their capacity as the government contractor, they have responsibility to keep the public informed. In their capacity as a membership organization, they have responsibility and loyalty to their members,' she said.
LifeSharers is a grass-roots organ donation network. Members agree to donate their organs when they die. Furthermore, they agree to offer their organs first to fellowem to others. Anyone can join for free at or by calling 1-888-ORGAN88. LifeSharers has over 6,200.


"scam" given that the government was not sharing the price of the aircraft.
Watch this conversation with Business Standard’s Ajai Shukla and Institute Of Peace & Conflict Studies’ Abhijit Iyer Mitra. </s> Webinar: Instructional Designers, Protect Your Position – Advice From Dr. Baird
In an industry that abounds with stakeholders who don’t see the value in training, constant pressure to come up with a way to help salespeople… </s> Tooth Filling
Bacteria from food settles down on tooth surfaces and slowly starts penetrating the layers of the tooth forming black colored holes, causing cavities known as tooth decay.
The cavity starts spreading from the enamel to the dentin and then to the pulp
When the cavity is in the first two layers i.e. The enamel and the dentin, the tooth can be saved by a procedure known as tooth filling or restoration
Saving the tooth at the initial stage by getting a filling is less cumbersome, inexpensive and painless </s> Virtual Event
Why we built the First Response Mental Health App:
How does FRMH’s Proactive Peer Support App Work?
For more information, visit our website: firstresponsemh.com </s> A
Storvideo surveillance system. Looking back at old footage is the most common request we get for our camera support. If you go for the cheaps, it is likely that the storage capability of your system will be, well, garbage. Most commercial systems store anywhere between 30-40 days. This varies depending on camera count and hard drive size, but roughly a month is standard. Most online retailers do not actually offer a recording mechanism/device with their offerings – and thus leave it up to the buyer to figure out storage by themselves. If you are inexperienced with this, it can be an absolute nightmare.
So, shelling out for an NVR, with RAID storage, and a professional team just a phone call away, usually pays dividends down the line.
For more information, reach out directly to us or check out our website. </s> The BMW Z4 is one of the best handling cars the Bavarians are making today. It’s light, rear-wheel drive only and has great balance, all great attributes for a sports car. Some, however, are saying some more power could be useful, especially for the European model. I don’t agree but the guys from G-Power do, so they created an upgraded version of the Z4 M40i, with 500 HP and up to 700 Nm (516 lb-ft) of torque.
The 500 HP version might be a bit too much though as you need to remember, the Z4 is a RWD-only model. It’s light on its feet so with that kind of power, if kept under control, I can only imagine just how fast it could be. The tuner doesn’t provide any 0-60 mph estimates but claims the car will be capable of reaching 300 km/h (186 mph) which is quite a feat. </s> For a long time, the Audi A3 only had one major competitor in the US market, the Mercedes-Benz CLA-Class. The two went at it for a few years, both seeing surprising success on this side of the Atlantic. Now, though, a new German competitor is here to try and break up the party — the BMW 2 Series Gran Coupe.r


Welcome to, Dailyshams.com Proud to show you my Revenge of the Drones 2018 PLR Review hope you will enjoy it !
PLR Revenge of the Drones 2018 is completely loaded custom drone site filled with drone products. Gaz Cooper includes Custom Drone Graphics, Drone eBooks, Brand New Articles, Custom Drone Site loaded with the starter products, and secret list of new 2018 drone models. You will receive massive content for any niche. The users of this PLR Revenge of the Drones 2018 have more sales and treat their customer good. It includes everything that people get into this niche. PLR Revenge of the Drones 2018 helps you to get the easy commissions. Ready to get serious cash
Click Here at 9 AM EST on 2018-Feb-15 to get an early bird discount on “Revenge of the Drones 2018 PLR” along with my Exclusive Bonuses
Next articleClickbank Video Profit Pack [Vol. 1] Review
Daily Shams - August 31, 2017
Welcome to, Dailyshams.com Proud to show you my ConvertProof Review hope you will enjoy it ! In case you are looking for a detailed ConvertProof Review, Bonus and discount, keep...
Holiday Toy Blogging Profits PLR Review
You can really do this?
ProductDyno Review & Bonus
Vid Reaper Pro Review and Bonuses


, or to David Hume being in error on this or that particular point. To be truthful, I tended to set aside his comments, thinking that while they were made honestly, perhaps Tony still was not as open as he had thought.
In his introduction to this same text, Flew both raised at least a half-dozen new issues since his book had first appeared in 1966, as well as mentioning questions about each of these subjects. Included were discussions on contemporary cosmology, fine-tuning arguments, some thoughts regarding Darwin's work, reflections on Aristotle's view of God, as well as Richard Swinburne's many volumes on God and Christian theism. Hints of theism were interspersed alongside some tough questions.
Of course, book text must be completed well before the actual date of publication. But several news articles had appeared earlier, telling the story of what Flew referred to as his "conversion." Early in 2005, my lengthier interview with Flew was published in Philosophia Christi. Another excellent interview was conducted by Jim Beverly, in which Flew also evaluated the influence of several major Christian philosophers.
In many of these venues, Flew explained in his own words that he was chiefly persuaded to abandon atheism because of Aristotle's writings about God and due to a number of arguments that are often associated with Intelligent Design. But his brand of theism - or better yet, deism - was not a variety that admitted special revelation, including either miracles or an afterlife. While he acknowledged most of the traditional attributes for God, he stopped short of affirming any divine involvement with humans.
It 'Flew's  </s> An Unusual Chinese Cloisonne Stand or Arm Rest (FS27/52n unusual Chinese cloisonne stand or arm rest of scroll form, decorated with auspicious objects on a cracked ice turquoise ground and florette cell border, the underside with scrolling lotus, 42 x 25cm5th July 2015 "The Future of Offshore Wind" Fng.��HeThanks to a bill Governor Baker signed into law in 2016, Massachusetts is authorized to procure up to 1600MW of offshore wind energy through public solicitation.��The successful "83C" procurement held in 2017 resulted in the award of a long-term contract to our client Vineyard Wind for an 800MW project, now underway.��At the Forum, Governor Baker confirmed that the Administration intends to announce the second procurement for the remaining 800MW in June as anticipated.
With projects now under development, stakeholders are working together to solve industry problems and identify new opportunities. At a panel held earlier that morning, Rachel Pachter, the Vice President of Permitting Affairs at Vineyard Wind, discussed the complexities of the permitting process and praised local environmental organizations for coming to the table to develop a smart and practical plan to mitigate the impact of offshore wind development on marine mammals.��She also stressed that Vineyard Wind and others continue to commit time and resources to advancing similar collaborations with local fishermen.��et.��According to Governor Baker, such strategies together with the pursuit of new opportunities like energy storage and continued solicitation for capacity can ensure that Massachusetts in the United States. </s> Photo-realistic CGI is a cost effective way to create room sets at a fraction of the cost of change colours at the drop of a hat. For this client a full range of Victorian tiles in the full colour range were rendered to create swatches for their website. Pure have built a reputation for reliably creating high quality computer generated images and animations in short timescales for a number of high end bathroom companies.
Modern 3D rendering techniquesimages. At Pure we have the capability to fool the eye with amazing, highly detailed, photorealistic images. </s> Gallup within the margin of error..
A better question, who lacks it?
Obama may be buying up America and its media with his campaign war chest, but America can no longer afford to simply buy up the world, divided internally and somewhat cash strapped it seems rather obvious they are going to need a leader.. Not a big spender to overcome. In the end, they will inevitably get what they ordered either way.
Gallups for the Palestinian Authority – The Horse appears dead, won’t be galloping


bungy jumping in Queenstownildren.
Kapai nearly flips a feather when his friends Tamatea and Uncle Slim take him bungy jumping!
T.


We address the problem of dueling bandits defined on partially ordered sets, or posets. In this setting, arms may not be comparable, and there may be several (incomparable) optimal arms. We propose an algorithm, UnchainedBandits, that efficiently finds the set of optimal arms, or Pareto front, of any poset even when pairs of comparable arms cannot be a priori distinguished from pairs of incomparable arms, with a set of minimal assumptions. This means that UnchainedBandits does not require information about comparability and can be used with limited knowledge of the poset. To achieve this, the algorithm relies on the concept of decoys, which stems from social psychology. We also provide theoretical guarantees on both the regret incurred and the number of comparison required by UnchainedBandits, and we report compelling empirical results.


This seminar was hosted by CMIFL and AMTD Group was responsible for overall coordination. This is another important interaction between CMI group companies and the international capital markets in Hong Kong, following CMI’s 2016 Strategic Dialogue with Direct Investment and Investment Banking Leaders on 28 January. AMTD Group invited business leaders and senior management from world famousinstitutions, including: Mr. M.C. Hong, CEO and President of KEB Hana Global Finance, which is the international investment banking platform of Hana Financial Group; Mr. Li Ge, Assistant CEO of SPD Bank Hong Kong Branch; Mr. Brendan Tu, Co-head of Greater China Financial Institution Group of UBS AG; Mr. Zeng Xiangyun, Head of Standard Chartered PE China; Mr. Marc Syz, Managing Director of the world famous investment firm ACE and Company; Mr. Zhang Jianguang, Managing Director of RRJ Capital; Ms. Vivian Lam, Managing Director of RRJ Capital; Mr. Ye Yong, Assistant CEO, Head of Corporate Finance and Managing Director of GF Holdings Hong Kong; Ms. Ye Weilu, Managing Director, Corporate Finance of Haitong International; Mr. Zhuang Weiniaof Wing Lung Bank; Mr. Chen Ji, Director, China Investment Banking of Citigroup; Mr. He Xiang, Vice President, Financial Institution Group of JPMorgan; Ms. Zhao Ying, Executive Vice President of Keystone Group, etc. The guests had in-depth discussions and sharingglobal leasing industry, key execution considerations of cross-border M&As, capital markets financing proposals, etc. Senior management of AMTD Group, including Mr. Sidney Ku, Head of Insurance Brokerage and Client Coverage; Mr. Cliff Ip, Co-head of Capital Markets & Advisory Department; and Mr. William Fung, Head of DCM and Distributionguests. Guests attending the seminar had positive views on the future development of the global leasing industry and believed there would be huge growth potentials for the leasing market in China, especially in areas such as helicopter leasing, new energy equipment leasing and healthcare equipment leasing.
Ms. Wang, Chairman and President of CMIFL, expressed her warm welcome to all guests for their attendance in the seminar and support for CMIFL. According to Ms. Wang, CMIFL aims to become a sustainable large-scale leasing company with clear business focus and sound profitability, and be positioned as the leading player in China and a well-known name internationally. It also aims to position itself as the propeller of CMI’s strategy of industry consolidation with capital markets, and take part in the transformation and upgrade of China’s real economy, through its unique global perspectives and resources. CMIFL will take the opportunity of this seminar to establish close cooperation with more professionals from international investment institutions and investment banking sector, to source further investment opportunities, share M&A expertise and reach a win-win future together.


Consider the following regarding retirement: Most private sector workers never had a pension plan Of those that did, many never became vested, or work for
Pensions, saving and planning for retirement, pension laws
On average, workers with 401k plans are doing quite well. But are you saving and getting the investment returns you need? This year has been
Good news for seniors☹
In their infinite short-term wisdom politicians fudged the 2021 Medicare Part B premium. The premium increase was capped at 25% of the increase that normally


These large, spaciousthe rush over the weekend.
- Klipkamer –
- Houtkamer –
- Fonteinkamer –
- Akkerkamer –
- Trekkerkamer –
- Byekamer –
- Solderkamer – (
- Single suites 1-5 (