Elisabeth Gill Spellacy, 86
Was Prominent Civic Leader
April 23, 1997|By CONSTANCE NEYER; Courant Staff Writer
Elisabeth Gill Spellacy, who died recently of a heart ailment, was remembered Tuesday by friends and family for her strong civic leadership and role as head of a prominent political family. She was 86.
Barbara Bailey remembered that the first time she and her husband visited New York, they went with the Spellacys.
``Going to New York City in those days was quite a trip,'' she said. ``She was very bright, brilliant. She was well-read, educated and she loved music, and she was very independent.''
Days after the Japanese bombed Pearl Harbor, Mrs. Spellacy was named assistant chairwoman of the special Red Cross war fund campaign to raise about $350,000 in the Hartford area. In 1943, she was named chairwoman of the Hartford Junior Red Cross, where she helped get young people to make bandages and other items for wounded soldiers.
Susanne Spellacy said her grandmother ``ran the household, but she was not a cook.
``After Grandfather died, she traveled all over the world,'' she said. ``She was in Vietnam during the Vietnam War; Cambodia, Russia and throughout Europe and Africa.
Her son, Bourke G. Spellacy, said his mother was an avid reader. ``She was politically strong-minded and pretty firm in her opinions,'' he said.
Born in West Hartford
Thomas Spellacy was an especially popular mayor because he gave all of his salary to charity. Dikes to keep back the Connecticut River were built during his terms, when the mayor's position had considerably more power.Thomas Spellacy also served as U.S. district attorney, assistant to then-U.S. Naval Secretary Franklin Delano Roosevelt and assistant U.S. attorney general.
Mrs. Spellacy's son served as assistant corporation counsel for Hartford and associate counsel to the Metropolitan District Commission.
Besides her granddaughter and son, Mrs. Spellacy leaves a daughter-in-law, Linda L. Spellacy of Hartford; and two brothers, Judge Thomas D. Gill of West Hartford and author Brendan M. Gill of Yonkers, N.Y.
Burare Weiner. Solon L. Weiner
Delegation's Makeup Is Criticized
Radville. Isadore J. Radville
Cohen. Manuel ``manny'' Cohen


Opening Remarks: After #35, I discovered that its actually pretty fun if you let the characters write the story themselves. You put them in a situation and then you let their personality "react" to that situation and thus the story tells itself. With #36 and #37, I decided to try something different.
It becomes even more intense for me when a reader points me to a past cartoon page that they consider their favorite. It makes me feel like I slip in quality over the seasons all the time. In #38, I plan to stick real close to basics but I won't forget to use the lessons I've recently leraned. Its also a ghost story. Wooo.
I originally made its story for a flash game I intended to do a long time ago. It was suppose to be a choose your own adventure comic where the reader made choices for the characters and it influenced the flow and outcome of the story. Flash is perfect for it because people won't be able to cheat it by changing the urls of the htmls on the browser's address bar. But because of the amount of work in making that game, I've decided not to go through with it for now. And, yeah, thats why the first page has a lot of talk about karma.
Cartoon#1041 Apr 28, 2008 - Gah, so many words. It hurts to read.
Cartoon#1042 May 05, 2008 - The bunny queen?
Cartoon#1043 May 12, 2008 - The reason why the living room is on the 2nd floor is because...
Cartoon#1044 May 19, 2008 - Bunny rage!
Cartoon#1045 May 26, 2008 - Do you think she's making it up?
Cartoon#1046 Jun 02, 2008 - I contracted a severe case of cooties when I was young. It almost killed me.
Cartoon#1047 Jun 09, 2008 - What good is an ego for anyway? Sometimes I wish I could just turn it on or off with a switch.
Cartoon#1048 Jun 16, 2008 - She's always doing good just like any typical 12 year old girl would.
Cartoon#1049 Jun 22, 2008 - Its an opportunity to explain some stuff about Bikits.
Cartoon#1050 Jun 30, 2008 - I forgot my ruler for this sessions.
Cartoon#1051 Jul 07, 2008 - This was originally the first part of the story where the reader could make a decision to influence its outcome.
Cartoon#1053 Jul 21, 2008 - I've been eating too many garlic chips.
Cartoon#1054 Jul 28, 2008 - When you're young, all you need is a cardboard box and your imagination to have fun. Life is too long.
Cartoon#1055 Aug 04, 2008 - Oo. Tension.
Cartoon#1056 Aug 18, 2008 - Always with a story to tell.
Cartoon#1057 Aug 25, 2008 - The power of two!
Cartoon#1058 Sep 01, 2008 - Thats Rex's grandfather. His last appearance was a few years back.
Cartoon#1059 Sep 08, 2008 - Kita.
Cartoon#1060 Sep 15, 2008 - Who wants to be special when you can be part of a team, eh?
Cartoon#1061 Sep 22, 2008 - A time window... Like on that show about a family of... four teenage ninja turtles.
Cartoon#1062 Oct 06, 2008 - Ayakashi carrot.
Cartoon#1063 Oct 13, 2008 - Is that a Star Trek thing? Dunno. Who's a better captain, Kirk or Picard?
Cartoon#1064 Oct 20, 2008 - Has it been mentioned? That familliar's name is Tricky.
Cartoon#1065 Oct 27, 2008 - Car trouble.
Cartoon#1066 Nov 03, 2008 - Yes. Feelings make me puke too.
Cartoon#1067 Nov 10, 2008 - Feelings are for girls!
Cartoon#1068 Nov 17, 2008 - Demonic posession and a flying house.
Cartoon#1069 Nov 24, 2008 - Its always like that game.
Cartoon#1070 Dec 01, 2008 - Its always like that-... oh.
Cartoon#1071 Dec 08, 2008 - The other side.
Cartoon#1072 Dec 19, 2008 - Corporate friendliness.
Cartoon#1073 Dec 26, 2008 - Should I show the future?
Cartoon#1074 Jan 02, 2009 - The old year ended already? Freaky...
Cartoon#1075 Jan 09, 2009 - Story told in a future tense.
Cartoon#1076 Jan 16, 2009 - The smells like game.
Cartoon#1077 Jan 23, 2009 - He used to live on his own personal planet.
Cartoon#1078 Jan 30, 2009 - Bubble.
Cartoon#1079 Feb 06, 2009 - I forgot to put something on the blue guy.
Cartoon#1080 Feb 20, 2009 - Maegan returns.
Cartoon#1081 Feb 27, 2009 - Like Fox would say; Feelings are for girls.
Cartoon#1082 Mar 06, 2009 - Ah, summer time. Memmories come flooding back.
Cartoon#1083 Mar 13, 2009 - Evil duckling on a Friday the 13th.
Cartoon#1084 Mar 20, 2009 - Fox's prized yellow bean bag-like chair.
Cartoon#1085 Mar 27, 2009 - Long trips are longer than short trips.
Cartoon#1086 Apr 03, 2009 - That gem might be useful in the flash game.
Ending Remarks: Not my best story, I know. I didn't even get the chance to do all thos "Never Ending Story" parodies I had planned. Well, while this comic set was in the making, a bunch of stuff happened in my life. Changes that can't be avoided. They distracted me a lot. Anyway, life goes on. :) Theres always the next story.


Not to be outdone by the cast of former spandex-wearing lead singers, Alice Cooper got down to business himself with "No More Mr. Nice Guy." The recent Rock N' Roll Hall of Fame inductee blasted through "I'm Eighteen," and while Hairball served as a great backing band it still made no sense that Cooper didn't include his fantastic touring band. "Pretty soon it's gonna be 'I'm 80,'" Cooper joked after the song.
They dove back into their first record with "Deuce" but also visited their '80s catalog with "Lick it Up." They called the good doctor with the Simmon's staple "Dr. Love." The night came to an end with a finale of "Rock and Roll All Nite" with all of the evenings vocalist joining the band except for Joan Jett.
C.


Activities for the last week of term and over the summer break.
Skywriting is another great multisensory experience.
It is good if you can show them what the numbers look like on something – maybe a chalkboard, interactive board, or written on big pieces of paper.
Number lines are great for children to start to visualize what numbers look like in a sequence. They are also great for number recognition.
Refer to number lines as you sing songs, or do chants. The more they use them the better they will get.
Hav Gh’!
In this game you are going to draw three circles in a row on the ground. These could be planets, or they could be lily pads, or currant buns or something else if you prefer.
 Then they are going to jump across the planets, saying one sound each time they land on a planet – e.g. ‘C-u-p.’ Then they jump off the last planet into space and say the word, in this example ‘cup.
Having fun with bubbles!!!
The glycerine is very much an optional extra, but it does apparently help the bubbles to get a lot bigger.
Make it a couple of days before. This allows it to settle.
Use a washing up liquid that doesn’t smell too fruity, to discourage any children that may want to drink it. Some washing up liquid smells delicious, and this is best avoided.


Domingo Guccione, Untitled, ca. 1930-55. Colored pencil and graphite on paper 25 1/2 x 19 5/8 inches. Courtesy Ricco/Maresca Gallery.
Domingo Guccione, Untitled, ca. 1930–55. Colored pencil and graphite on paper 21 x 17 1/2 inches. Courtesy Ricco/Maresca Gallery.
,Another drawing, composed of yellow and red, looks like it depicts an interior space with yellow walls that angle up either to a stairway or to a mystical triangle rising upward. Windows surround the space, and a floor, embellished with black and white triangles, is found as well. Like all of Guccione’s drawings, the space is crowded with the shapes of his invention, and also as happens with his work, it is hard to separate individual structures from the overall design. This does not mean that the art is overly intricate—instead, it suggests a teeming creativity, in which the function of the shapes is meant to indicate spaces that could easily be termed sacred. The spiritual suggestiveness we experience in a drawing with a thin gray bar dividing it in half, exists in the top part as a complicated array of triangles with black and gray stripes, which are right side up and upside down. It is hard to see houses in the patterning. On the bottom half, we see what looks like a pair of walls offering geometric forms, leading to double doors, above which is an abstract pattern. The content is so intricate as to be mesmerizing, balanced as it is on the cusp of visionary realism and nonobjective design.
Guccione clearly deserves greater recognition. He felt that he was a medium translating the forms he imagined into something visible. The art cannot be seen as belonging to the geometric formalism that was quite popular when he made these drawings. But that is of small matter; instead, he devised a planar geometry capable of suggesting architecture and the spiritual imagination. His designs record a desire to animate forms with implications that are impossible to note by sight alone. Thus, his intricate patterns, realistic or not, became a way of transmitting an inner vision. It appears that he was not much influenced by the art trends; this made him original in ways that are memorable.


The H38i is an exciting addition to the proven Model H family of high production mowers. It is the most powerful Walker Mower featuring 38 horsepower, Delphi® fuel injection, and impressive torque. This machine is capable of operating all of Walker's biggest decks and attachments with ease. The H38i features a redesigned rear axle for superior handling at high speeds on rough ground. This, along with a 4-way adjustable Grammer suspension seat provides personalized comfort keeping you productive on long days.


indigenous people as well. These eco-systems are some of the last undisturbed places on earth that support habitat fornet.
It is important to be mindful that  Many products can boast using recycled content when they actually only contain a small percentage of post-consumer recycled content. Don’t worry though, finding 100% recycled toilet paper that is high quality and durable is not difficult. In fact, it’s likely already in stock at your favorite store.
I remember when I first discovered the dirty truth about paper and cleaning products. I was angry, upset and confused. Seventh Generation was one of the first brands I felt comfortable investing in. Additionally, I felt empowered knowing they give back to communities through The Seventh Generation Foundation. hey are held to a higher ethical and environmental standard. Plus, they have a whole list of other third party certifications too.
Recycle: If every household in the US replaced a 4 pack of 240 sheet virgin fiber toilet paper with recycled toilet paper we could save over 740,000 trees! Doing our best to purchase only 100% recycled paper products, like those from Seventh Generation is a perfect example. We can also compost used paper towels and napkins as long as they haven’t touched chemicals!
Thank you to Seventh Generation for generously sponsoring this post and raising awareness for the absurdity of using virgin paper for household paper products. As always, opinions are all my owfe Boxes Digital , Middle Safe Box Digital , Anti-drilling Safe Box Digitalho are now seeking a degree, diploma or certificate at Craven CCComplete an online admissions application, which can be found on the transfer institution’s website.
Review the various admissions and financial aid deadlines on the transfer institution’s website.
Applthe NC public universitieves or general education classes. </s> The fierce opener ‘Hardest’ is a bold statement that sees Podgy call out fake MC’s over a harsh, stripped back beat – its confidence only surpassed by his relentless delivery. The energy is knocked up a notch as the current single ‘Mad Ting’ bursts into the fray, full of rapid fire claps over a barrage of harsh 909 kicks, with a new-age bassline that provides the anvil for Podgy’s lyrical hammer-blows. The legendary Ghetts is the next grime heavyweight to be brought to the fold on ‘Fire Starter’. The production is reminiscent of a classic grime track, alongside ‘Like That‘, where Ellis flips Rebound X‘s ‘Rhythm & Gash‘ in another before heard way.
With acclaimed producers like Dot Rotten behind the buttons, ‘Gingerbread Season’ plays host to Podgy’s ever evolving diversity. High energy tracks such as ‘Gassed’ contrast perfectly with the downtempo singles ‘Anthem’ & ‘Turn Me Up’; the latter, a brooding departure from the hard, rave-ready records where emotion is further brought to life by Angel’s smooth R&B hook. Continuing Podgy’s high energy credentials, accompanied by an impeccably apt video, is ‘Gassed’, as gritty as ever, and flitting between the comforts of home and the barrenness of public transport woes of the London Underground station. Both humorous and light-hearted, ‘Wrap Up’ is a trademark single from the Acton native, where he and Ellis further show why theirs is a winning partnership. The jovial track is testament to who Podgy is an artist and the perfect way to close the project.
A multi-faced talent, Podgy Figures has come through poverty to start up his own fashion line Gingerbread Clothing, radio station Podgy FM 91.5, and has garnered acclaim from likes of The 405, Clash, Complex and GRM Daily. Four videos for ‘Anthem’, ‘Podgy Refix’, ‘Turn Me Up’ and ‘Wrap Up The Ting’, combined with his ability to craft an EP that brings dance-floor energy and self-reflection, whilst staying true to grime’s gritty London routes mirrors that of the greats and will no doubt cause heads to turn this summer. </s> Itravel to an upscale enclave of Los Angeles known as Bel Air — the most affluent neighborhood in the city, located about twelve miles west of downtown. The community was founded in 1923


Recently Pakistan Cricket Team Captain Shahid Afridi given a Interview to a Pakistani Tv Channel Samaa Tv in which he said that Allah has given Muslims Bigger hearts than others and Pakistanis are largehearted. After these comments of Shahid Afridi Indian Media started massive Propaganda against Shahid Afridi. Indian Media has old habit of lying, Misquoting and creating fake stories and this is what they done with Afridi's Comments.
Indian Media claimed that Shahid Afridi has said that Indians have small hearts while hearts of Pakistanis are bigger than Indians. One Indian News Channel also lied that Shahid Afridi has given this comment after he was threatened by Taliban. Pakistani Captain has now given Clarification about his comments that he is Misquoted and i not said anything wrong about Indians. Afridi also said that i got lot of Love in India.


People’s Fair Vendor Spotlight: Ashae SoapsPosted: May 26,Latasha Bell of Ashae Soaps, LLC will be exhibiting for the second time at the 2011 People’s Fair.
Though only in her second year, Latasha has already started gathering happy memories from her experience at the Fair.
“The People’s Fair is an excellent opportunity to expose I to this day have repeat business because of the exposure at the People’s Fair. I am looking forward to meeting exciting new people, and exposing my products again.”
Check out Ashae Soaps on Broadway at the People’s Fair June 4 & 5 (booth BW-02) download fair map here (PDF).


Obituary » Donald Earl Johnson
January 4, 1939 - April 1, 2021
Subscribe to updates for Donald Earl Johnson
Donald Earl Johnson passed Thursday, April 1st, 2021. Visitation Saturday, April 10, 2021 from 10:00 AM until 11:00 AM at J.C. Battle & Sons Funeral Home 543 Rockdale Ave Cincinnati, Ohio 45229. Traditional on Saturday, April 10, 2021 at 11:00 AM at J.C. Battle & Sons Funeral Home.
A virtual funeral service for Donald Earl Johnso.ly/Yy2750EkgHg:
Donald was joined in holy matrimony to Eddie Mae Johnson on November 12, 1960. To this union four children were born: William Earl, Torres Antoine (Schenelle), Kimberly Denise, and Bryon Edward.
He departed this life on Thursday April 1, 2021 at his home at 4:50pm Eddie Mae Johnson; his children, William Earl, Torres Antoine (Schenelle), and Kimberly Johnson. Bryon Edward proce, his sister, Rosetta Johnson. He is predeceased by his brother, William; and his sister, Ida Mae (Tutti). He also leaves to mourn his 10 grandchildren; Torres Johnson (Kimberly), Alisa Walters (Philip), Scierra Johnson, Tisha Johnson, William Johnson II, Daniel Johnson, Schenelle Johnson, Brandon Johnson (Shay), Chris Kinsey, and Bryon Johnson. He also leaves.


Sain their fourth and final film together.[3]"Top Grossers of 1948." Variety, January 5, 1949, p. 46.
"Box Office Figures for 1949." Box Office Story. Retrieved: August 20, 2016.
"Screen news: 'Saigon' planned by Metro on Japanese invasion." The New York Times, May 10,. 1943, p. 15.
Schallert, Edwin. "Dancing star heading toward drama future." Los Angeles Times, October 8, 1945, p. 9.
Schallert, Edwin. "'Saigon' new adventure subject for Alan Ladd." Los Angeles Times, October 24, 1946, p. A2.
"Special to The New York Times: Paramount names Lake, Ladd to film; Studio will co-star team in 'Saigon,' adventure story, Fenton to be Director." The New York Times, October 29, 1946, p. 42.
"Special to The New York Times: Nebenzal, film producer, pays $150,000 for world rights to 'Madame Butterfly'." The New York Times, October 24, 1946, p. 44.
Brady, Thomas F. "January 5, 1947, p. 26.
November 20, 1946, p. 51.
Schallert, Edwin. "McCracken deal hot; Medina goes to 20th." Los Angeles Times, November 21, 1946, p. A2.
Brady, Thomas F. "Bidding for 'Oscar': Hollywood producers race deadline for those Academy Awards; Other items." The New York Times, January 5, 1947, p. X5.
Scheuer, Philip K. "Review: 'Saigon' melodramatic fare." Los Angeles Times, March 5, 1948, p. 17.


Matching local features across images is a fundamental problem in computer vision. Targeting towards high accuracy and efficiency, we propose Seeded Graph Matching Network, a graph neural network with sparse structure to reduce redundant connectivity and learn compact representation. The network consists of 1) Seeding Module, which initializes the matching by generating a small set of reliable matches as seeds. 2) Seeded Graph Neural Network, which utilizes seed matches to pass messages within/across images and predicts assignment costs. Three novel operations are proposed as basic elements for message passing: 1) Attentional Pooling, which aggregates keypoint features within the image to seed matches. 2) Seed Filtering, which enhances seed features and exchanges messages across images. 3) Attentional Unpooling, which propagates seed features back to original keypoints. Experiments show that our method reduces computational and memory complexity significantly compared with typical attention-based networks while competitive or higher performance is achieved.
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DEMO-Net: Degree-specific Graph Neural Networks for Node and Graph Classification
Establishing reliable correspondences across images is an essential step to recover relative camera pose and scene structure in many computer vision tasks, sush as Structure-from-Motion (SfM) [38] , Multiview Stereo(MVS) [17] and Simultaneous Localization and Mapping (SLAM) [32] . In classical pipelines, correspondences are obtained by nearest neighbour search (NN
) of local feature descriptors and are usually further pruned by heuristic tricks, such as mutual nearest neighbour check (
MNN) and ratio test (RT) [25] .
In the past few years, great efforts have been made on designing learnable matching strategy. Early works [61, 44, 63] in this direction utilize PointNet [33]
-like networks to reject outliers of putative correspondences. In these works, the correspondence coordinates are fed into permutation-equivariant networks, then inlier likelihood scores are predicted for each correspondence. Despite showing exciting results, these methods are limited in two aspects: 1) They operate on pre-matched correspondences, whereas finding more matches than vanilla nearest-neighbour matching is impossible. 2) They only reason about the geometric distribution of putative correspondences, neglecting the critical information of original local visual descriptors.
Figure 1: The designs of message passing layer. (a) SuperGlue densely connects every node in the graph, resulting in O(N2) computational complexity. (b) Instead, the proposed network, SGMNet, adopts pooling/unpooling operations, reducing the complexity to O(NK), where K≪N in general.
Another thread of methods cast feature matching as a graph matching problem [36, 5, 50] , which mitigates the limit of vanilla nearest neighbour correspondences. The representative work, SuperGlue [36] , constructs densely-connected graphs between image keypoints to exchange messages about both visual and geometric context. However, the superior performance comes along with high computation and memory cost, especially when being applied to keypoints of larger number (e.g. up to 10k). As illustrated in Fig. 1(a), the message passing layer of SuperGlue first calculates similarity scores exhaustively between every two nodes, then gathers features to pass messages densely in the graph. This results in computational complexity of O(N2C) for matrix multiplications, and memory occupation of O(N2) to hold the attention matrix, supposing that the keypoint number is N and feature channel is C. The complexity increases even drastically for deeper graph networks. Given this, exploring more efficient and compact message passing operation is of practical significance.
Besides the major efficiency bottleneck, it is debatable if such densely-connected graph introduces too much redundancy or insignificant message exchange that may hinder the representation ability, especially in the context of feature matching where the match set is highly outlier-contaminated and a large portion of keypoints are unrepeatable. As a result, most graph edges from SuperGlue [36] tend to have zero strength, as reported in its original paper and also observed in our experiments. This phenomenon indicates that even a sparse graph is largely sufficient and less distracted from unnecessary message exchanges.
In this paper, we propose Seeded Graph Matching Network (SGMNet) to mitigate above limitations from two aspects. First, inspired by guided matching approaches [10, 43, 30] , we design a Seeding Module that initializes the matching from a small set of reliable matches so as to more effectively identify inlier compatibility. Second, we draw inspiration from graph pooling operations [62, 56] , and construct a Seeded Graph Neural Network whose graph structure is largely sparsified to lower the computation and reduce the redundancy. Specifically, three operations are proposed to construct our message passing blocks. As illustrated in Fig. 1(b), instead of densely attending to all features within/across images, original keypoint features are first pooled by 1) Attentional Pooling through a small set of seed nodes, of which the features will be further enhanced by 2) Seed Filtering, and finally recovered back to original keypoints through 3) Attentional Unpooling.
By using seeds as attention bottleneck between images, the computational complexity for attention is reduced from O(N2C) to O(NKC), where K is the number of seeds. When K≪N, for example, 8k features are pooled into 512 seeds, the actual computation will be significantly cut down. We evaluate SGMNet under different tasks to demonstrate both its efficiency and effectiveness, and summarize our contributions threefold:
A seeding mechanism is introduced in graph matching framework to effectively identify inlier compatibility.
A greatly sparsified graph neural network is designed that enables more efficient and clean message passing.
Competitive or higher accuracy is reported with remarkably improved efficiency over dense attentional GNN. As an example, when matching 10k features, SGMNet runs 7 times faster and consumes 50% less GPU memory than SuperGlue.
Learnable image matching.
Integrating deep learning techniques into geometry-based computer vision task, such as MVS
[58, 59, 64, 65] and Visual Localization [67, 46]
, has received inspiring success during the past few years. As the front-end component for geometry estimation, learnable image matching has also been proven effective, where works in this area can be roughly divided into two categories. The first one focuses on improving local descriptors
[60, 41, 27, 26, 31, 49] and keypoints [53, 12, 28, 35, 11]
with convolutional neural networks, while methods in the second category attempt to embed learning techniques into matching strategy, which involves learnable outlier rejection
[61, 44, 63] and robust estimator [4] .
Recently, a new framework, SuperGlue [36] , is proposed to integrate feature matching and outlier rejection into a single graph neural network (GNN). Though exhibiting promising results in different tasks, SuperGlue still suffers from the excessive computational cost of fully connected self/cross attention operation, especially when used to match features in high number.
Compared with SuperGlue, our method shares the same advantages, that is, feature matching and refinement are integrated into one single network and allows for end-to-end training. However, our network significantly reduces the computational and memory cost due to its efficient attention block, which is specially designed for image matching.
Figure 2: The network architecture of SGMNet, which takes the local features as input, then generates seed matches from seeding module, and finally extracts correspondence features from Seeded GNN with multiple attentional blocks. In practice, updated features will be fed into a reseeding module and a 3-layer seeded GNN for refinement, while we omit this procedure here for simplicity.
Efficient transformer architectures. Transformer [52] architectures have gained intensive interest during the past few years. Specially, in the contexts of graph convolution, the attention mechanism in transformer can be used to pass messages across nodes in graph structure [13, 54] . Despite its effectiveness in a wide range of tasks, one major concern about transformer is its quadratic complexity w.r.t. the input size, which hinders its application under large query/key elements number.
Recently, many efforts have been made to address the attention efficiency problem. In [24, 7] predefined sparse attention pattern are adopted to cut down memory/computation cost. In [47, 19] , attention span is pruned by using learnable partitioning or grouping on input elements. In [55, 21] , pooling operation is utilized to reduce elements number. Despite the inspiring progress, works in this area generally focus on self-attention, where keys and queries are derived from the same element set, while their effectiveness in cross-attention, where keys and queries come from two unaligned sets, remains unstudied.
We draw inspiration from induced set attention (ISA) [21] , where a set of learned but fixed nodes are utilized as bottleneck for efficient self attention. To be compatible with cross attention in graph matching, we establish attention between seed matches and original point sets. The selected reliable correspondences align features in both sides and pass message in a low cost way.
Graph matching. Graph matching, which aims to generating node correspondences across graphs, is a widely used model for feature matching in both 2D [50, 5] and 3D [22, 2] domain. Mathematically formulated as a quadratic assignment problem (QAP) [51] , graph matching is NP-hard in its most general form and requires infeasible expensive solver for precise solutions. Despite the intractable nature of general graph matching, some methods [14, 15, 29] leverage partially pre-matched correspondences, also called seeds, to help matching, which are referred to as Seeded Graph Matching(SGM). Inspired by SGM, our network integrate seeds into a GNN framework for compact message passing and robust matching.
We present Seeded Graph Matching Network, shorten as SGMNet, for learning correspondences between two sets of keypoints and their associated visual descriptors. As illustrated in Fig. 2, our network produces matches in two stages: 1) Seeding Module generates seeds to guide compact message passing, and 2) Seeded Graph Neural Network leverages seeds as message bottleneck to update per-node feature. In the following parts, an overview of our network architecture will be introduced first, followed by detailed description of each module.
Given a pair of images A and B, with n and m keypoints and associated visual descriptors respectively, indexed by α:={1,...,n},β:={1,...,m}, our objective is to establish reliable and robust keypoint matches across two images.
We formulate the keypoint matching task as a graph matching problem, where the nodes are the keypoints of each image. Instead of apply fully connected graph, we generate a set of keypoint correspondences, which we refer to as seed matches, to guide message pass across nodes in two graphs for subsequent matching. This critical difference allows for processing large number of keypoints with significantly lower memory and computation cost.
The input to our network are keypoints KA , KB in two images, KiI=(piI,diI), where I∈{A,B} and piI=(xiI,yiI) is the coordinate of keypoint i in image I. diI∈Rd is associated d dimensional visual descriptor.
Positions of keypoints are embedded into high dimensional feature space and combined with descriptors by element-wise summation for initial representation 0FA , 0FB.
0FiI= diI+MLP(piI),I∈{A,B},
A Seeding Module follows to construct a set of seed matches S. 0FA, 0FB and S are then fed into our Seeded Graph Neural Network which reasons about visual appearance similarity, neighbourhood consensus as well as the guidance provided by seed matches jointly to update keypoint features.
Inspired by the cascade refinement structure in OANet [63] , a second seeding module, or reseeding module, is introduced to generate more accurate seeds based on the updated features, which helps to further refine matches with another Seeded GNN. Final matches are then generated by formulating assignment matrix.
3.2 Seeding Module
Proposing a set of seed matches lays the foundation for subsequent matching. For initial seeding, we adopt a simple yet effective strategy: we generate putative matches by nearest neighbour matching and use the inverse of distance ratio, i.e., the ratio of distance to first and second nearest neighbours [25] , as reliability scores. We adopt Non-Maximum Suppression (NMS) for a better spatial coverage of seeds. More details of seeding module can be found in Appendix A.2. Despite potential noise in initial seeds, our network maintains robustness with proposed weighted unpooling operation and reseeding strategy, which will be discussed later.
The seeding module outputs S=(SA,SB), where SA, SB are index lists for seed matches in each image.
3.3 Seeded Graph Neural Network
Seeded GNN takes initial position-embedded features 0FA, 0FB and leverages seed matches S as attention bottlenecks for message passing. To this end, we adopt a pooling-processing-unpooling strategy in each processing unit: seed features first gather information from full point sets on each side through Attentional Pooling, then processed by Seed Filtering operation and finally be recoverd back to original size by Attentional Unpooling. Our Seeded GNN is constructed by stacking 6(3) such processing units for initial(refinement) stages.
Weighted attentional aggregation. We first introduce a weighted version of attentional aggregation, which allows for sharper and cleaner data-dependent message passing.
In a d-dimensional feature space, for mvectors to be updated: X∈Rm×d, n vectors to be attended to: Y∈Rn×d and a weight vector: w∈Rn, the weighted attentional aggregation Att is defined as,
Xr=\text{Att}(X,Y,w)=X+MLP(X||Δ),
θ(⋅) means row-wise softmax. Q is linear projection of X and K,V are linear projections of Y. Xr is renewed representation for X.
By attentional aggregation, elements in X retrieve and aggregate information from elements in Y. A weighting vector w is applied on V to adjust the importance for each element in Y.
Attentional pooling. As the first step in message passing, seed matches retrieves contexts from full keypoint set through attentional aggregration,
For input features tFA , tFB in layer t, features for seed matches are first retrieved by indices
tS1I=tFI[SI],I∈{A,B},
where [ ] is the indexing operation. Seed matches are then updated by retrieving context from nodes in each graph
where 1 is all one vector, which means no weights are applied.
A mutilayer perceptron follows to fuse the seed features,
MLP(tS2A||tS2B)
where || means concatenation along row dimension.
The outputs tS3A, tS3B, which encode both visual and position context for each graph and information from seed matches themselves, are fed into subsequent operations.
Figure 3: Visualization of attentional pooling/unpooling. In attentional pooling, one pair of seed match aggregates context from the other keypoints, while in attentional unpooling each original keypoint retrieves renewed message from seed matches.
Seed filtering. We propose seed filtering operation to (1) conduct intra/inter-graph communication between seed matches and (2) suppress the influence of outlier seed matches. More specifically, intra/inter-graph attentional aggregation is applied to the input seed correspondence features tS3I∈Rk×d, I∈{A,B}.
tS4I =Att(tS3I,tS3I,1),
tS4J,1), I,J∈{A,B},I≠J,
In addition, a context normalization [61] branch is used to predict inlier likelihood scores γ for each seed correspondence, which will be used as weighting score for seed features in later unpooling stage.
tγ=CN(tS5A||tS5B),
where CN is a lightweight stacked context normalization [61] blocks. Detailed structures of CN brach can be found in Appendix A.2.
The outputs of seed filtering are the filtered features tS5A , tS5B and inlier scores γ∈[0,1]k of seed matches.
Attentional unpooling. After the message exchange between seed matches and inlier score prediction, an inlier-score-weighted attentional aggregation is adopted to broadcast the pooled contexts to every keypoint in each graph, which we refer to as attentional unpooling.
Taking tS5A , tS5B , inlier score γ and tFA , tFB as input, attentional unpooling outputs the updated keypoint features t+1FA , t+1FB.
Applying inlier score to the aggregation process suppresses information broadcast from false seed matches and results in cleaner feature update, which contributes to the robustness of our network w.r.t. seeding noise (Appendix D.2, Appendix Fig 10).
Assignment matrix formulation. After all processing units, the updated features are used to construct the assignment matrix. Sinkhorn [8] algorithm is applied on the correlation matrix of features with a dustbin channel to produce final assignment matrix M.
Given the keypoint features NFA∈Rn×d, NFB∈Rm×d after N processing blocks, we compute the assignment matrix by
M=Sinkhorn(^C)
^Ci,j={Ci,j, for i⩽n,j⩽mz, otherwise,^C∈R(n+1)×(m+1)
where C=NFANFTB, z is a learnable parameter for dustbin. We derive final matches from the assignment matrix M with a confidence threshold to remove outliers.
Although Seeded GNN based on initial seeding exhibits strong capability to identify underlying matches, a second seeding module using updated feature provides even cleaner and richer seeds to further improve the performance. Thus, we adopt a reseeding module. Different from initial seeding where NN matches and ratio scores of raw descriptors are used, reseeding module employs assignment matrix M of updated features to regenerate seeds. More specifically, matches with highest score in both rows and columns are selected as candidates, where top-k matches are selected as new seeds and are fed into a second Seeded GNN for refinement. More details can be found in Appendix A.2.
3.5 Loss
Seeding module only outputs seed indices that requires no gradient back-propagation, thus our network is fully differentiable and can be trained end-to-end with supervision from indices of ground truth matches lm={(i,j)}∈α×β and unmatchable points luA,luB, where a point is regarded as unmatchable if there are no matchable points in the other image. From assignment matrix Mr for reseeding, final assignment matrix Mf, and inlier scores tγ,t∈{1,2,...,L} for L processing units, we formulate our loss as two parts,
L=Lassign+δΣt∈{1,2,...,L}Ltweight
Lassign=− ∑M∈{Mr,Mf}[∑(i,j)∈lmlog(Mi,j)+
∑i∈luAlog(Mi,m+1)+∑j∈luBlog(Mn+1,j)]
Ltweight is cross entropy loss for inlier/outlier binary classification in t-th processing unit, a seed correspondence is labeled as inlier if its epipolar distance is less than a threshold. δ is a weight to balance the two loss terms.
Feature Matcher CPC T&T TUM KITTI
%Recall #Corrs(-m) %Recall #Corrs(-m) %Recall #Corrs(-m) %Recall #Corrs
RootSIFT [1] NN+RT 52.9 92 (123) 82.1 208 (287) 61.9 365 (438) 90.6 847
OANet [63] 58.6 119 (167) 84.7 219 (306) 62.3 454 (396) 89.0 773
SuperGlue [36] 61.1 218 (466) 86.8 382 (767) 65.9 655 (1037) 91.0 1261
SGMNet 62.0 248 (524) 85.9 397 (789) 66.6 704 (1132) 91.2 1097
ContextDesc [26] NN+RT 62.4 169 (277) 85.5 222 (426) 58.7 456 (625) 90.6 1134
OANet 65.3 187 (288) 86.7 294 (425) 53.2 295 (327) 89.0 791
SuperGlue 67.0 260 (579) 89.1 491 (695) 60.1 408(690) 91.1 1401
SGMNet 70.8 370 (616) 89.9 514 (705) 61.6 423 (705) 90.3 1204
SuperPoint [11] MNN 34.5 152 (421) 72.8 287 (717) 56.7 280 (420) 88.6 848
SuperGlue 68.8 287 (719) 92.9 414 (987) 59.1 512 (1038) 88.7 957
SuperGlue∗ 76.7 302(712) 96.6 431(985) 59.0 121(177) 89.5 354
SGMNet 70.3 327 (829) 93.2 450 (1098) 65.8 666 (1315) 86.3 954
Table 1: Evaluation results on FM-Bench [3] , where %Recall denotes mean recall of all pairs, #Corrs(-m) denotes mean number of inlier correspondences after/before RANSAC. SuperGlue∗ indicates the results obtained from officially released model.
3.6 Implementation Details
We train our network on GL3D dataset [39] , which covers both indoor/outdoor scenes, to obtain general purpose model. We sample 1k keypoints and 128 seeds during training. We use Adam optimizer with learning rate of 10−4 in optimization and inlier score weight δ in loss is set to 250. We use 6/3 processing blocks for initial/refine stage and the gradients flow between the two stages are blocked in early iterations(140k iterations). We use 4-head attention in both attentional pooling/unpooling operations. For all experiments, we use a confidence threshold of 0.2 to retain matches and seeding number of 128#keypoint2000, where #keypoint is the number of keypoints. More details including training data generation and hyper-parameters can be found in Appendix A.
In the following sessions, we provide experiments results of our methods under a wide range of tasks, as well as further analysis of its computation and memory efficiency.
The performance of our method is first evaluated on image matching tasks and three benchmarks in two-view pose estimation, YFCC100M
[48] , FM-Bench [3] and ScanNet [9] dataset, are used for demonstration.
For YFCC100M [48] , we follow the setting in OANet [63] and choose 4 sequences for testing. FM-Bench [3] comprises four subsets in different scenarios: KITTI [16] for driving settings, TUM [42] for indoor SLAM settings, Tanks and Temples (T&T) [20] and CPC [57] for wide-baseline reconstruction tasks. ScanNet [9] is a widely used indoor reconstruction dataset. Following SuperGlue [36] , we use 1500 pairs in test set for evaluation.
Evaluation protocols. On YFCC100M and ScanNet dataset, pose estimation is performed on the correspondences after RANSAC post-processing. We report 1) AUC [36, 63, 61] under different thresholds, computed from the angular differences between ground truth and estimated vectors for both rotation and translation; 2) Mean matching score (M.S.) [36, 11] , the ratio of correct matches and total keypoint number; 3) Mean precision (Prec.) [36, 11] of the generated matches. We detect up to 2k keypoints for all features on YFCC100M, up to 1k keypoints for superpoint on ScanNet and up to 2k keypoints for other features .
On FM-Bench dataset, we estimate fundamental matrix for each evaluated pair with RANSAC post-processing, and use the normalized symmetric epipolar distance (SGD) [66, 3] as originally defined in FM-Bench paper, to measure the difference between the estimated fundamental matrix and the ground truth. An estimate is considered correct if its normalized SGD to ground truth is lower than a threshold (0.05 is used by default), and up to 4k keypoints are detected for each test pair. Following FM-Bench paper [3] , we report: 1) recall (%Recall) on fundamental matrix estimation; 2) mean number of correct correspondences (#Corrs(-m)) after/before RANSAC.
Feature Matcher AUC M.S. Prec.
@5° @10° @20°
RootSIFT NN + RT [25] 49.07 58.76 68.58 8.23 29.79
AdaLAM(4k) [6] 57.78 68.01 77.38 7.92 83.15
OANet [63] 58.00 67.80 77.46 5.84 81.80
SuperGlue∗ [36] 59.25 70.38 80.44 - -
SuperGlue 63.82 73.33 82.26 16.59 81.08
SGMNet 62.72 72.52 81.48 17.08 86.08
ContextDesc NN + RT 57.90 68.47 78.35 9.39 59.72
OANet 62.28 72.56 81.80 9.33 88.49
SuperPoint MNN 31.05 40.85 52.64 15.12 24.64
AdaLAM(2k) [6] 40.20 49.03 59.11 10.17 72.57
OANet 48.80 59.06 70.02 12.48 71.95
SuperGlue∗ 67.10 76.18 84.37 21.58 88.64
SIFT PointCN [61] 47.98 58.13 68.67 - -
ACNe [45] - - 78.00 - -
LGLFM [10] 49.60 60.36 71.37 - -
- RANSAC-Flow [40] 64.88 73.31 81.56 - -
Table 2: Results on YFCC100M [48] , where AUC evaluates the pose accuracy, M.S. denotes mean matching score and Prec. denotes mean matching precision. SuperGlue∗ indicates the results obtained from original paper or officially released model.
RootSIFT NN + RT [25] 9.08 19.75 32.66 2.28 28.83
AdaLAM [6] 8.24 18.57 31.01 3.10 47.59
SuperGlue 13.12 27.99 43.92 8.50 42.53
SGMNet 12.82 27.92 44.55 8.79 45.55
AdaLAM 8.45 19.81 33.11 6.58 44.08
SuperPoint MNN 9.44 21.57 36.41 13.27 30.17
AdaLAM 6.72 15.82 27.37 13.19 44.22
Table 3: Results on Scannet [9] . SuperGlue∗ indicates the results obtained from officially released model.
Figure 4: Correspondence visualizations. We showcase with SIFT features, and compare the results of traditional matching (MNN + RT), SuperGlue and our method (SGMNet). More visualizations available in Appendix.
Comparative methods. We compare our method with heuristic pruning strategy, ratio test [25] or MNN, and a various of learning-based matching methods [63, 36, 45, 10, 61, 40] . These methods are applied on both handcrafted descriptors [25, 1] and learning-based local features [26, 11] .
For a fair comparison, OANet, SuperGlue and SGMNet are all re-trained using the same sequences of GL3D [39] , where 1k keypoints are sampled per image. Noted that the official training code of SuperGlue is not available, and its public model (denoted as SuperGlue∗) is trained on MegaDepth [23] and Oxford and Paris dataset [34] . Instead, we retrain SuperGlue on GL3D [39] with similar data selection criteria described in orignal paper. This re-implementation achieves even better results on YFCC100M (Table 3) for RootSIFT than those reported in the original paper. However, there still remains some performance gap when using SuperPoint [11] , even though we have carefully tuned the training and enquired the authors about details. Nevertheless, we consider our re-implementation of SuperGlue [36] faithful and thus can be fairly compared. We report results of both official model and our re-implementation.
Results. For YFCC100M, ScanNet and two wide-baseline datasets of FM-Bench (CPC and T&T), our method mostly shows competitive results compared with the state-of-the-arts. For two small-baseline datasets in FM-Bench (TUM and KITTI), the advantages of all learnable methods tend to degenerate due to the reduced matching difficulty. Our method matches most inlier correspondences on almost all dataset regarding M.S. on YFCC100M/ScanNet and #Corrs(-m) on FM-Bench while maintaining a high matching precision, which contributes to the final pose accuracy. Though not specially trained in indoor scenarios, our method generalizes well on indoor setting.
4.2 Visual Localization
To evaluate how our method benefits real downstream applications, we integrate it in a visual localization pipeline and evaluate its performance.
Datasets. We resort to Aachen Day-Night dataset [37] to evaluate the effectiveness of our method on visual localization task. Aachen Day-Night consists of 4328 reference images and 922 (824 daytime, 98 nighttime) query images. All images are taken in urban scenes.
Evaluation protocols. We use the official pipeline of Aachen Day-Night benchmark. Correspondences between reference images are first used to triangulate a 3D reconstruction. Correspondences between each query and its retrieved reference images are then generated to recover the relative pose. Consistent with the official benchmark, we report the pose estimation accuracy under different thresholds. We extract 8k keypoints for RootSIFT, ContextDesc and 4k keypoints for SuperPoint.
Results. Compared with SuperGlue, our method exhibits better results when using RootSIFT and competitive results when using SuperPoint or ContextDesc. Our method consistently outperforms OANet using all three descriptors. The overall performance proves the generalization ability of our method on real challenging applications.
Feature Matcher 0.25m, 2° 0.5m, 5° 5m, 10°
RootSIFT [1] MNN 43.9 56.1 65.3
OANet [63] 69.4 83.7 94.9
SuperGlue [36] 63.3 80.6 98.0
SGMNet 70.4 85.7 98.0
ContextDesc [26] MNN 65.3 80.6 90.8
OANet 74.5 86.7 99.0
SuperGlue 77.6 86.7 99.0
SuperPoint [11] MNN 71.4 78.6 87.8
SuperGlue∗ 79.6 90.8 100.0
Table 4: Evaluation results on Aachen Day-Night dataset. We report pose accuracy under different thresholds for challenging night spilt. We include the results of official released model of SuperGlue with SuperPoint (denoted as SuperGlue∗).
Figure 5: The computation (a) and memory (b) efficiency compared the proposed method with SuperGlue. We report memory occupation averaged by batch size for training. The effect of keypoint number on Aachen Day-Night dataset is illustrated in (c).
In above experiments, the proposed method has shown competitive results against the state-of-the-arts. In this session, we demonstrate the major advantage of our method in time/memory efficiency compared with SuperGlue, a closely related method based on GNN.
Time/memory consumption. As shown in Fig. 5(a), the time cost of our method is remarkably lower than SuperGlue. Specifically, we report both run time with and without Sinkhorn iterations on a GTX 1080 GPU, in order to more precisely demonstrate the substantial improvements on GNN design itself. It is noteworthy that on 10k keypoints and without Sinkhorn iterations, the proposed method reduces the runtime by one magnitude. Besides, due to the reduction of redundancy, SGMNet also delivers better convergence during training (see Appendix C).
As shown in Fig. 5(b), during test phase, our method consumes half of memory than SuperGlue when keypoint number is larger than 2k, where the major memory peak of our method is seeding phase and sinkhorn iterations. The advantage becomes even more significant during training. With a batch size of 16 and keypoint number of 1k, SuperGlue occupies up to 23GB GPU memory for training while SGMNet only consumes less than 9GB memory.
Performance gain when using more keypoints. Within a reasonable range, larger keypoint number generally improves performance of downstream tasks, thus a manageable matching cost is of practical significance to extend the applicability. As a showcase, we vary the keypoint number of RootSIFT when evaluating on Aachen Day-Night Dataset. As can be seen from Fig. 5(c), the accuracy of SGMNet and SuperGlue increases as more keypoints are used. Considering the efficiency advantage of our method, SGMNet delivers better trade-off when increasing the keypoint number. We also provide an experiment of SfM, a typical keypoint-consuming application in Appendix D.3.
Matcher AUC@20° M.S. Prec.
NN + RT 68.58 10.05 56.38
SGMNet w Rand. Seed 71.25 12.94 55.57
SGMNet w/o W.U. 78.64 17.07 81.26
SGMNet w/o A.P. 79.35 17.11 82.15
SGMNet w/o Reseeding 80.41 17.12 84.47
SGMNet full 81.48 17.08 86.08
Table 5: Results of ablation study. w/o A.P. stands for w/o attentional pooling, where seed features are directly sent to seed filtering process without attending to original keypoints. w/o W.U. stands for w/o weighted unpooling, where vanilla attention are preformed in unpooling process. Rand. Seed means selecting seed correspondences randomly instead of picking the top-k scores. w/o Reseeding means only use initial seeding.
To evaluate the effectiveness of different components of our method, we conduct ablation study on YFCC100M dataset using RootSIFT. As shown in Table 5, all different component in our network contributes to the final performance notably. In particular, seeding reliable matches plays an important role, which further proves that seed matches is able to guide message across images for robust matching.
Type Matcher AUC Time
GNN SGMNet-10 66.62 76.33 84.71 114.81
SGMNet 67.42 76.63 84.66 284.66
SuperGlue-10 65.85 75.35 84.05 458.52
SuperGlue 66.65 75.41 84.12 604.11
Filter OANet 63.75 73.60 82.43 21.30
ACNe 63.37 73.67 82.74 18.26
Handcrafted AdaLAM 57.78 68.01 77.38 4.59
GMS 26.15 33.53 42.13 5.44
Table 6: Results on YFCC100M using 4k SIFT features. -10 means setting sinkhorn iterations as 10 instead of 100
5.2 Comparison with Filter-based Methods
For a well-around comparison, we provide in Table 6 more experiment results with filter-based methods (outlier rejection). When using 4k keypoints, SGMNet achieves best performance among all comparative methods while runs 4 times faster than SuperGlue when setting sinkhorn iterations as 10. Despite the fast inference speed of SOTA filter-based methods, a considerable performance gap still remains compared with GNN based methods.
5.3 Designs of SGMNet
We experiment on other designs for SGMNet, including other seeding strategy and pooling operations in GNN/Transformer architecture [52, 55, 62] , e.g. diffPool [63, 62] and set transformer [21] . We find 1) learnable seeding acheives limitied improvement over our simple heuristic seeding strategy 2) other general pooling operations, which are often verified on self-attention in GNN/Transformer architecture, are not effective alternatives of our seed-based pooling. Details of our experiments can be found in Appendix B. Hyper-parameter study on seeding number can be found in Appendix D.
In this paper, we propose SGMNet, a novel graph neural network for efficient image matching. The new operations we have developed enable message passing with a compact attention pattern. Experiments on different tasks and datasets prove that our method improves the accuracy of feature matching and downstream tasks to competitive or higher level against the state-of-the-arts with a modest computation/memory cost.
Acknowledgment. This work is supported by Hong Kong RGC GRF <DIGITS>, <DIGITS>, T22-603/15N and Guangzhou Okay Information Technology with the project GZETDZ18EG05.
We provide in this part details about our implementations as well as experiment settings.
A.1 Training Details
Training Data. We use GL3D dataset to generate training data. GL3D dataset is originally based on 3D reconstruction of 543 different scenes, including landmarks and small objects, while in its latest version additional 713 sequences of internet tourism photos are added.
We sample 1000 pairs for each sequence and filter out pairs that are either too hard or too easy for training. More specifically, we use the common track ratio provided by original dataset and rotation angles between cameras to determine pair difficulty, pairs with common track ratio in range [0.1,0.5] and rotation angle in range [6\textdegree,60\textdegree] are kept.
We reproject keypoints between images with depth maps and use reprojection distances to determine ground truth matches and unmatchable points. More specifically, a keypoint is labeled as unmatchable if its reprojection distances with all keypoints in the other image are larger than 10 pixels, while a pair of keypoints that are mutual nearest after reprojection and with a reprojection distance lower than 3 pixels are considered ground truth matches. We further filter out pairs with ground matches fewer than 50.
Our data generation protocol yields around 400k training pairs in total.
Figure 6: Visualizations of raw putative matches(left), seed correspondences(middle) and matches obtained by SGMNet(right). Note that even with heavily noisy seeds, SGMNet is capable of discovering underlying patterns, which are leveraged to guided message pass across keypoints for robust and accurate matching.
Training Parameters. We use Adam optimizer for training optimization with learning rate of 10−4. We apply learning rate decay after 300k iterations with decay rate of 0.999996 until 900k iterations. We block the gradient flow between initial/refinement stages for the first 140k iterations.
A.2 Network Details
As is done in SuperGlue [36] , we apply multi-head attention mechanism for all weighted attention operations in our network with head number of 4.
For initial Seeding Module, we apply additional mutual nearest neighbour check and ratio test to seed correspondences. NMS is also employed in seeding. The radius for NMS is θ|α|(|α|−1)Σ(i,j)∈α×α,i≠jdij, where α is index set for all keypoints, dij is distance between keypoint i,j, |.| denotes set size, θ is a hyper parameter, which we set as 10−2 for all experiment.
For Reseeding Module, we apply sinkhorn algorithm with 10 iterations on correlation matrix to obtain assignment matrix. Correspondences with top-k scores on both dimensions are sampled as seeds. NMS is also applied in reseeding module.
For the inlier likelihood predictor CN in Seeded GNN, we use the PointCN structure illustrated in Fig 7 (down).
We set iterations number of Sinkhorn algorithm to 100.
A.3 Experiment Settings
We use OpenCV implementation for SIFT and official implementation of SuperPoint and ContextDesc. For ContextDesc, we use the latest public model (denoted as ContextDesc++upright in the official GitHub repository).
YFCC100M. We extract SIFT and ContextDesc with images in original resolution and resize images so that the longest dimension is 1600 to extract SuperPoint. We use OpenCV findEssentialMat and recoverPose functions to recover relative poses with the embedded RANSAC, of which the threhold is set as 1 pixel under resolution of resized images. For matching score and precision, we use epipolar distance of 5×10−3 to determine inlier matches.
ScanNet. We resize images to [640,480] resolution to extract keypoints and use same protocol to recover relative poses, mathing scores and precision as in YFCC100M.
FM-Bench. The original evaluation pipeline of FM-Bench is based on Matlab and we reimplement it with Python. The parameter for evaluation is consistent with the original implementation. We use OpenCV findFundamental function for fundamental matrix estimation and set the threshold of embedded RANSAC to 1 pixel. Compared with original implementation, our evaluation pipeline tends to give out higher accuracy, especially for wide baseline datasets. We believe it is due to better performance of OpenCV findFundamental function and is beneficial for a more precise evaluation.
Aachen Day-Night. We use the official pipeline and default parameters for evaluation. We extract upright feature for both RootSIFT and ContextDesc.
B Designs of SGMNet
Despite the effectiveness of SGMNet, we provide our experiment results and analysis on some other potential designs in this part.
AUC M.S. Prec. Prec.
Designs @5° @10° @20°
Learned Seeding 62.80 72.55 81.09 17.25 85.15 51.22
DiffPool 50.85 60.50 69.68 10.18 61.52 -
ISA 52.11 61.44 70.03 10.65 63.24 -
SGMNet 62.72 72.52 81.48 17.08 86.08 39.24
NN+RT 49.07 58.76 68.58 10.05 56.38 -
Table 7: Evaluation Results on YFCC100M using RootSIFT with different designs. Prec.(S) denotes precision of seed correspondences.
Figure 7: PointCN structure(down) we use to construct learned seeding module(top). CN(a)/BN(a) denotes context normalization [61]
/batch normalization
[18] with input of a channels.
Learned Seeding. After obtaining initial nearest neighbour correspondences, we employ a light-weight permutation invariant Network (architecture illustrated in Fig 7) to determine each correspondence’s inlier likelihood score, as is done in previous works [63, 61] . We thus sample correspondences with top-k inlier-score instead of ratio scores as seeding correspondences.
Results. We report results in Table 7. Although applying light-weight pointCN block for inlier seeds prediction slightly increases seeding precision, it’s not enough to bring meaningful impacts on the level of pose estimation.
In general, we are open for the possibility to increase matching quality by introducing more complicated seeding strategies. However, targeting at efficient matching, our seeding method achieves good balance between performance and cost.
The critical component in our method for efficiency message passing is essentially pooling of original keypoints. In this part, we apply two well-studied pooling designs either in GNN or transformer architecture, namely DiffPool [62] and Induced Set Attention [21] , to image matching task and evaluate their performance.
DiffPool. As a pooling operation in GNN, DiffPool predicts assignment matrix based on each node’s embedding in graph, which is designed to build hierarchical and sparse graph representation [62] . In OANet [63] , DiffUnpool, the counter part of DiffPool, is proposed to recover node clusters to original size. As an experiment, we apply DiffPool/Unpool to keypoint graph in each image as a substitution for our proposed attentional pooling. Cross/self attention [36] will be performed on the pooled clusters for message exchange.
Induced Set Attention. Induced set attention(ISA) is first proposed in Set Transformer [21] . Different from using seed features as attention bottleneck, ISA adopts a set of learned fixed features(induced point) as attention pass between set elements and is only verified on self-attention for sparse input. We subsitute our seed-based attention with ISA. More specifically, we let the network learned induced points for both sides and let induced points attend to original keypoints(both cross/self) to perform message pass.
Results. We report evaluation result on YFCC100M. For all pooling method, we set pooling number to 128 and extract up to 2k keypoints. As illustrated in Table 7, both DiffPool and ISA shows only marginal improvements over baselines, which indicates that applying pooling methods designed for generic GNN/efficient transformer is not necessarily effective for image matching tasks, and further prove that our seed based attentional pooling/unpooling operation is critical for the success of our method.
C Fast Convergence
The compactness of SGMNet not only contributes to the cut-down on computation/memory complexity but also leads to a faster convergence for training. In Fig. 8 we plot the training curve for both SGMNet and SuperGlue. As illustrated, SGMNet takes fewer iterations to reach convergence.
Figure 8: Convergence curve.
D Additional Experiment Results
Figure 9: The effect of seed number when varing the keypoint number. Numbers in grids are Exact AUC@20° using RootSIFT.
Figure 10: Relationship between pose estimation accuracy and seed precision. Note SGMNet maintain a high matching quality even with seed precision of only 20%.
Figure 11: Reconstruction results of vanilla nearest neighbour matching(left) and SGMNet(right). The completeness of reconstruction is determined by matching quality between some critical frames. In this case, NN matching fails to generate descent correspondences between tall building(b)/statue(c) and tall building(b)/remains(a), which results in incomplete reconstruction, while SGMNet registered theses critical frames successfully.
D.1 Impact of Seeding Number
SGMNet requires seeding a set of seed correspondences, the number of which not only influences our method’s efficiency but also accuracy. Therefore, it is important to investigate the impact of seeding number. We carefully conduct grid search on YFCC100M with different keypoint and seeding number.
As is illustrated in Fig 9, an approximate proportional relationship between keypoint/seed numbers yields best performance, as too many seeds may deliver less reliable guidance while seeding too few correspondences results in severe information lost.
Methods #Registered Images #Sparse Points Mean Rro. Error Mean Track Len. Matching Time Total Time
NN+Ratio+Mutual Check <PHONE> 0.58px 11.27 1h 13min 2h 22min
SuperGlue <PHONE> 0.95px 10.93 42h 34min 44h 06min
SGMNet <PHONE> 1.10px 10.73 5h 37min 7h 56min
Table 8: SfM results for Alamo scene in 1DSFM dataset.
D.2 Robustness to Seed Noise
To evaluate the robustness of our method w.r.t. potential false seeds, we conduct experiment on YFCC100M. More specifically, for each pair we select a set of inlier matches, which is determined using ground truth, and pad them with random sampled noise to construct seed correspondences with different precision. We feed the pre-selected seed correspondences to Seeded GNN instead of applying Seeding Module.
As is shown in Fig 10, SGMNet maintains high matching quality even with heavily noisy seed correspondences. It’s noteworthy that for SGMNet without weighted unpooling, the pose estimation accuracy degenerate more rapidly as seed precision decreases, which indicates lower robustness to seed noise. More visualizations related to noisy seed and matching results can be seen in Fig 6.
D.3 SfM Experiment
Typical Structure from Motion(SfM) pipeline usually involves extracting keypoints in large number(e.g. 8k) and matching among hundreds/thousands of images to obtain ultra accurate poses and more complete reconstructions. In this section, we embed different matching methods into COLMAP SfM pipeline for comparison. We reconstruct challenging Alamo scene from 1DSFM [57] , which involves 2915 images taken under very different illumination conditions. 8k RootSIFT features are extracted for each image and we set sinkhorn iterations to 10 for both SGMNet and SuperGlue. We use a GTX 1080 GPU to preform matching sequentially. Apart from common statistics for reconstruction, we also report time consumption for matching and the whole SfM pipeline.
As shown in Tab 8, both SuperGlue and SGMNet produces much more complete reconstruction compared with vanilla NN matching and heuristic pruning. However, SuperGlue largely lengthen the time for whole SfM pipeline, while our method retains the matching time to a feasible level.
E More Visualizations
See Fig. 12 and Fig. 6.
Figure 12: More visualizations.
[1] Relja Arandjelović and Andrew Zisserman. Three things everyone should know to improve object retrieval. In CVPR, 2012.
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[26] Zixin Luo, Tianwei Shen, Lei Zhou, Jiahui Zhang, Yao Yao, Shiwei Li, Tian Fang, and Long Quan. Contextdesc: Local descriptor augmentation with cross-modality context. In CVPR, 2019.
[27] Zixin Luo, Tianwei Shen, Lei Zhou, Siyu Zhu, Runze Zhang, Yao Yao, Tian Fang, and Long Quan. Geodesc: Learning local descriptors by integrating geometry constraints. In ECCV, 2018.
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[30] Josef Maier, Martin Humenberger, Markus Murschitz, Oliver Zendel, and Markus Vincze. Guided matching based on statistical optical flow for fast and robust correspondence analysis. In ECCV, 2016.
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Revisiting oxford and paris: Large-scale image retrieval benchmarking.
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Guide to match: Multi-layer feature matching with a hybrid gaussian mixture model.
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[48] Bart Thomee, David A Shamma, Gerald Friedland, Benjamin Elizalde, Karl Ni, Douglas Poland, Damian Borth, and Li-Jia Li. Yfcc100m: The new data in multimedia research. Communications of the ACM, 2016.
[49] Yurun Tian, Bin Fan, and Fuchao Wu. L2-net: Deep learning of discriminative patch descriptor in euclidean space. In CVPR, 2017.
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Kfnet: Learning temporal camera relocalization using kalman filtering.


Reader's Question: "Take Her For A Drink?"
I w'd give her a "I w" My"
My Response: "No Contact Is Best"
"Hi Jason!It sounds like you're in a fairly good position with your ex right now…
Did you go through the 'no contact'
When you do decide to ask your ex girlfriend to meet for a drink — hopefully after you've left her alone for a while — I think the strategy you described in your email is a good one. I definitely like the ultra-casual "I was in the neighborhood…
Good luck, Jason!Hope it works out for you."
What are your thoughts on Jason's situation with his ex girlfriend?


Geostationary orbit - Simple English Wikipedia, the free ...
- GEO) is a type ofatitude).Like all geosynchronous orbits, it has a period (time for one orbit) that is 24 hours. This means it goes around the Earth as fast as the Earth spins, and so it appears to stay above the same spot all the time.
Geostationary satellite latency and time delay ms Most communications satellites are located in the Geostationary Orbit (GSO At this height the satellites go around the earth in a west to east direction at the same angular speed at the earth's rotation, so they appear to be almost ...


Aon, Minn.
The two
Evansville is about 20 miles northwest of Alexandria.


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I'm new to react js. I want to upload image asynchronously with react js Suppose I have this code
photo: []
data.append("photo", this.state.photo);
// is this the correct way to get file data?
getPhoto: function (e) {
photo: e.target.files[0]
<form action='.' enctype="multipart/form-data">
<input type='file'  onChange={this.getPhoto}/>
You can make use of FileReader
console.log('handle uploading-', this.state.file);
let {imagePreviewUrl} = this.state;
let imagePreview = null;
imagePreview = ();
imagePreview = (
Please select an Image for Preview
);
{imagePreview}
your answer is very smart. but i want the uploaded pdf to show with its thumb image when uploaded – steve Jul 21 '18 at 3:32
Use following module to select images.
You can then upload image to server using xhr request. Following is the sample code.
//your success code goes here
photo: [],
documents:[]
data.append("photo", component.state.photo, component.state.name);
var request = axios.post
request.then(function(response){
// you need to send data from server in response
console.log
component.state.documents.push
// pushed document data in documents array
getPhoto: function () {
var uploadfile = document.getElementById(upload_doc).files[0]
photo: uploadfile, name : uploadfile.name
var documents = this.state.documents.map((doc)=>{
{doc.name}

// you can show your documents uploaded this way using map function
<input type='file' id='upload_doc'  onChange={this.getPhoto}/>
{documents}
// this way you can see uploaded documents
You should consider adding an explanation of the answer you're proposing. – il_raffa Nov 22 '17 at 11:19
One more easier way, by using using axios node module axios-fileupload
npm install --save axios-fileupload
const axiosFileupload = require('axios-fileupload');
axiosFileupload(url,file);
You can use whatwg-fetch to make a post request. In your pressButton() function make the following changes -
pressButton: function (url) {
timeout: 3000 * 60 * 60
body : JSON.stringify
return fetch(url, callOptions).then(response => response.json());
devilsbanedevilsbane
You can use React Dropzone Uploader, which gives you a dropzone that shows file previews (including image thumbnails) for dropped or chosen files, and also handles uploads for you.
getUploadParams={() => ({ url: '<URL>' })} // specify upload params and url for your files
onChangeStatus={({ meta, file }, status) => { console.log(status, meta, file) }}
onSubmit={(files) => { console.log(files.map(f => f.meta)) }}
Uploads have progress indicators, and they can be cancelled or restarted. The UI is totally customizable, and the library has no dependencies.
Full disclosure: I wrote this library.


This Grocery Store Chain Has Already Done Over 250,000 COVID-19 Tests
In October, the chain started offering rapid antibody tests that are ready within 15 minutes and cost only $25. People who believe they may have had COVID-19 and are not currently experiencing any symptoms are eligible for the test.
Now, customers can go to TheLittleClinic.com to register and complete a screening prior to scheduling an appointment to get tested. Rlab receives the test. If the test is positive, a licensed healthcare professional will call to discuss the next steps. The Patient Portal keeps track of information and test results so it's easy to access.
CDC's Latest Grocery Shopping Rules
Follow these and you'll stay as safe as possible.
Grocery Shortages Are Forcing People To Do This
Some made this change—and they're never going back.
The Least Crowded Time to Grocery Shop
Going to the store at this time makes shopping easier.


If you are a business owner, hanything were to happen to you which may render you temporarily or permanently unavailable or unfit to proceed with your work?
All SME businesses have similar challenges in the event of the death of a key owner or manager. The standard concerns usually are: Could my spouse step in and run the show?Do any of our children have a stake or interest in running my business?Does a specific key manager or other employee have the best chances of successfully running or owning the company?
The last will and testament for your company, usually the, may in fact be a Buy-Sell Agreement amongst the owners that provide for ownership, management and transition upon death. Another consideration is that the costing of transferring ownership upon death of any of the shareholders, can also be financed through key-man life insurance that can be expensed out through your business. Does your business have a plan and policy in place?


In other words, implying people of color or their non-melanated allies aren’t pursuing rational ends when they bust up a police precinct serves to both patronize and stigmatize them.
Is looting ever defensible?
I’ve no doubt Osterweil isn’t one to mind about staid, liberal NPR throwing a fit about this.
Nearest I can tell from the two interviews I’ve read, Osterweil is human clickbait who just happened to have a book called “In Defense of Looting” ready to publish right as looting became a hot-button topic. Don’t loot it, though.


“We’re kind of behind the eight ball in Charlotte because the forces reducing our canopy are working faster than we can replace it or preserve it,” said Thrus Morton, chairman of the board of TreesCharlotte. “Our modest public-private nonprofit, working in partnership with the city of Charlotte, is not going to plant our way out of the problem. So what we have to do is basically educate the community so that the citizens all become tree canopy advocates.”
“Jane demonstrated her ability to develop a donor base and engage the community in the effort,” Morton said. “She’s very personable but very professional. She has corporate experience, is detail oriented and a doer. She’s the whole packageI”


Home WP Tutorialr Good!


The first thing that strikes anyone viewing the Gemballa Tornado 750 GTS is its body styling. Svelte and subtle it is not with bulges and air intakes all over the front, including the 70mm longer (than standard) forward opening bonnet. The bonnet isn't the only part of the Tornado 750 GTS that has been lengthened, the front and rear bumper/spoiler combinations have also been extended compared to the standard Porsche Cayenne. Whilst dimensions have increased weight has actually reduced, impressively the all carbon body styling is estimated to reduce the weight of the Tornado 750 GTS by 250kgs in comparison to a standard Cayenne.
The weight saving is doubly useful when one considers the power and torque of the tuned engine Gemballa {link} have produced for the Tornado 750 GTS. From its now 5 litre turbocharged engine the Gemballa Tornado 750 GTS produces a very impressive 750PS and 1050Nm of torque. Enough power to propel this über-Cayenne to 62mph from rest in 4.3 seconds and on to a top speed in excess of 185mph. The extreme front body styling draws air into the engine, larger intercooler, radiator and brakes as well as actually drawing air away from hot areas in the engine bay too. Exhaust gases exit the engine through a custom designed exhausts system with 4 tailpipes.
With such explosive performance available Gemballa have had to improve the suspension and brakes considerably. Behind the 12x22" wheels, covered by 335/25 tyres, sit huge brakes (415mm diameter front and 380mm diameter rear) which offer excellent and durable braking. The handling has been radically altered and improved, the Gemballa Tornado 750 GTS sits some 70mm lower on coilover suspension. Even so the 750 GTS is no out-and-out sports car and has a higher centre of gravity than would be considered perfect for a car with 750PS.
Gemballa have created a sumptuous, refined and yet sporty interior with 4 sports seats, full leather and carbon seating, dash and door panels.
Exclusivity is assured as Gemballa are limiting the production of the £370,000 Tornado 750 GTS to 50 worldwide. Interested parties should contact Gemballa's Leonberg offices quickly as the über-Cayenne offers such performance that Gemballa are unlikely to struggle selling out, even in these difficult economic times.
Labels: Gemballa, porsche
The 2008 Corvette C6 GT4 had been unveiled by General Motors. The 2008 Corvette C6 GT4 will compete in this year’s FIA GT4 Championship. Powered by a 6.2 V8 LS3 engine, the 2008 Corvette C6 GT4 developing 437 bhp. But for non-FIA race categories, Callaway can boost this to 500 bhp. The 2008 Corvette C6 GT4 engine transfers the power to a Tremec close-ratio 6-speed manual gearbox and a Tilton triple-plate race clutch. With option of an aerodynamic body kit that includes a large front spoiler with splitter and a rear wing for added downforce, the 2008 Corvette C6 GT4 price will be around 125,000 Euros mark. And for the DSC version it will costing around 150,000. Press Release Corvette C6 participates in FIA GT4 Championship Corvette GT4 built by Corvette specialist Callaway in Germany GT4 race car based on Corvette C6 GT4 version with minor changes also suited for Dutch Supercar Challenge This weekend Corvette will participate in the FIA GT4 European Cup at Silverstone. The English...
Labels: Corvette C6 GT4 Rally Car
Peugeot 505 coupe and sedan models cabrioSelain and station wagons, manufacturer Peugeot had made "prototype" Peugeot 505 coupe (2 door), Peugeot 505 cabriolet (convertible) and the Peugeot 505 pick-ups. Thus, in France many modifications change the form of a Peugeot 505 pick-up / trucks.
* "Stabilizer bars" is standard equipment on the front, while the rear was different depending on the model.
* In Peugeot 505 V6, power steeringnya been "the Speed Sensitive, central lock it using a remote infrared, heating and ventilation systems, including electronic climate control.
* The length and overall width of the saloon is a 4:58 505 meters and 1.73 meters, while for Family estate is 4.90 meters and 1.73 meters.
* Using the "Quad headlamp" different.
Antenna Position moved from the roof into the rear fenders, the model was replaced with a telescopic antenna.
* Types ditawarkanpun fewer machines, all had been adjusted to meet more stringent emission standards.
* Models sold in North America is the type: "GL", "S," "GLS", "STI", "DL", "Liberté", "STX", "TURBO", "GLX", "SW8" "V6", "TURBO S", and in 1983, offered a limited version of the Peugeot 505 "Anniversary Edition".


, The Fall of a Sparrow, and further fuel legendary artists: “ with orchestral arrangements and big builds…
The Canadian born Nick Nace is a “h” (-American Songwriter). Hede, The Concert Film!
Their debut album, In the Storm, was released in the fall of 2017, to exciting reviews:”exquisitely performed…an accomplished chamber folk-pop album of grace and elegance”
“diverse and multifaceted”p…makes for brilliant artistry.”
“They have music that engages all the senses and leaves listeners longing for more.”
“These guys are good”
“Unseen Strangers pushes the limits of stringed jam-band wildness to new heights. ”
“The expressiveness heard in their lovely ensemble brings a natural quality to their sound, forming a honest and genuine relationship with the listener.”
“At their most creative, the music is nearly irresistible, leaving you wondering what’s next and always coming through with a new turn to relish.”
“Interesting Music”
“Bluegrass Band Competition Winners 2013”
“Country/Bluegrass Song of the Year 2012”
We are having a Rummage Sale for TWO days!


CLEMMONS — Elon University golfer Jeff Krieger of Arden posted a 4-under 66 on Tanglewood Park’s Championship Course for the best score Thursday in North Carolina Amateur sectional qualifying.
Krieger, a transfer from Purdue, shot 31 on the front side on his way to a two-shot edge on Kenny Flynn of Winston-Salem.
Other scorers included Kevin Womack of Burlington with 75, Blair Bowland of Burlington with 79, and Elon golfer Dexter Blank with 84.


would like to recommend the following to the SteelSeries engineers in the next revision of these headphones:
2) Extend the length of the cord to 4 ft as opposed to 3 ft. I believe this would allow for greater mobility of the user without the need of the 6ft extension.
I did not mark these as cons because in the end, this is my personal preference andhe headphones or their construction. I actually applaud SteelSeries for bringing a product like this to the market. Thank you, SteelSeries.
- Able to upgrade the firmware in Windows 8.1
Other Thoughts: I'm running a A10-7700K at stock speeds/voltages and getting roughly 28-30 DegC. I have it set up with the two stock fans, so no push/pull (H100i). Mounting to the motherboard was about the easiest I have experienced with any cpu cooler.READ FULL REVIEW </s> With our flexible drive technology program, we can provide standardized drive and power transmission units for all kinds of construction. We have established Sfor producing flexible shaft for varied applications. We have a capacity to manufacture flexible shafts ranging 2 to 40 mm dia & for endless lengths up to 8 mm dia. These shafts are capable of transmitting high speeds up to 50,000 rpm & torques above 7000Ncm. In Speedo segment our cables are part of all major 2 & 4 wheelers.
Since flexible shafts lessen the need for exact alignment of motor and tool unit, simplification of the tool design is achieved. In the case of parallel misalignment of drive and tool unit the bending radius to be calculated. Athe flexible shaft will be used. For example, extremely high or low temperas etc. Such factors influence the choice of materials for core and casing and their fabrication. Our technical team will gladly offer their advice. The following clarifications of choice of core, casing end couplings should also be considered like longevity, flexibility, continuous or intermittent operation, fast tool holder change, shaft related to coupling or slide length and difference in length of core and casing. </s>  awesome! You guys deserve more recognition.
That was awesome. Mix of different songs.
In the first 10 seconds of the song, I knew it was going to be a good song. You know what? I was wrong. This song is A-MAZ-ING. I have never heard a song with such an awesome melody followed by a genius drum beat. Good work, I'm defiantly gonna check out your other songs. PLEASE, keep up the good work. 5/5, 11/10
Sex:mae(a mix of myself and male),IQ(109)Fav.Resterant:(Ruby Tuesday)Real Aura(an experimental druggie)pet peave(dying)fav insult(nothing you are!)fav ca(lambourghini gallardo)Fav. pop(pepsi)race:(who knows?)self esteem level(50/100)
fav.type of music(only the stupid music everyone hates.why do u get high ratings from me!kidding,)most notrious crime(staling at a cheapass dollar store)Fav.animals(cats!they are cool and dumb,like me) luck factor(0.1/100)Fav.t.v.show(jon dore.its funny. chec it out on youtube.)fav.clothing store(west 49)fav.rockband(A.C.D.C.certainly not you!)fav game(halo3).Fav rock(nal1200. See,ive got you wored up about the rockbands,but your not a band,you produce music)
This is amazing my son
Love this megamix its excellent my son perfect for newgrounds </s> Contra 3: Is too F** Hard
It's Payday Fellas !4.01 / 5.00 7,142 Views
GwainSaga - (Original Serie)3.94 / 5.00 1,824 ViewsJACKSONVILLE, Fla. - Old St. Augustine at Kori Road at about 1:30 p.m.

Tle. A passenger in the pickup was not injurume for a moment you wered fewer troop mortalities in Iraq this July than in the last eight months. Would you do your best to present this as good news?
I guess it's verboten at the AP to connect the president with good news in Iraq regardless of how much your article downplays it.
The hosts of MSNBC Live on Friday thought it was “crazy” that some young women aren’t supportingChris Jansing and Tamron Hall marveled at a New Hampshire focus group that included young, college-age females. Hall proclaimed, “” A shocked Jansing retorted, “That's crazy to me. It really is.” favorite in every state


I was around 40 and I used to get tired easily. A friend of mine is a doctor, who suggested me to have anabolic steroids (TRT) Testosterone Replacement Therapy. He further explained me the whole thing about the procedure of the TRT and causes of the tiredness a. I got to know this was because of the low level of testosterone.
For many, TRT act as a free ticket to massive “juicing.” Allowing athletes to take TRT as drugs enable them to have as many steroids as they want it is because drug tests can only test the elevated levels of testosterone but it cannot determine the dose intake.
It has been a big problem in the fighting industry as fighters take a high dose of such steroids to gain more strength. He further showed me some videos on YouTube that explained how having steroid is good for some and having an overdose or those who go to gym or fighters or athletes take steroids which is a matter of concern as these drugs are not allowed and dope test make things clear.
Guest post by Samuel Watson
[…] – Those who take steroids orally can find all the common products like Anavar, Anadrol, Turinabol, Methyl-1-testosterone and […]Reply


WATCH: Pelosi defends Swalwell amid controversy over suspected Chinese Spy connection
Asked about the Axios report connecting Democrat House member Eric Swalwell to a suspected Chinese Spy, House Speaker Nancy Pelosi deflected blame to House minority leader Kevin McCarthy.
Pelosi said McCarthy “has Qanon in his delegation over there. T”
WATCH: Eric Swalwell linked to Chinese spy