Introduction to Max-SAT and Max-SAT EvaluationMasahiro Sakai
The slides for my talk on Feb. 27 2014 at ZIB.
Abstract:
Maximum Satisfiability (Max-SAT) and its weighted variants are optimization extension of Boolean Satisfiability (SAT), and it is interesting that technologies from both AI/CP community and OR community are employed to solve Max-SAT problems.
In this talk, I present brief introduction of SAT/Max-SAT problems, some of solving approaches, and my experience of submitting SCIP and my own SAT-based solver "toysat" to the Max-SAT Evaluation 2013; the annual Max-SAT solver competition. After that, I would like to have a discussion on submitting SCIP to the upcoming Max-SAT Evaluation 2014.
Introduction to Max-SAT and Max-SAT EvaluationMasahiro Sakai
The slides for my talk on Feb. 27 2014 at ZIB.
Abstract:
Maximum Satisfiability (Max-SAT) and its weighted variants are optimization extension of Boolean Satisfiability (SAT), and it is interesting that technologies from both AI/CP community and OR community are employed to solve Max-SAT problems.
In this talk, I present brief introduction of SAT/Max-SAT problems, some of solving approaches, and my experience of submitting SCIP and my own SAT-based solver "toysat" to the Max-SAT Evaluation 2013; the annual Max-SAT solver competition. After that, I would like to have a discussion on submitting SCIP to the upcoming Max-SAT Evaluation 2014.
“Adoption and Focus: Practical Linear Types for Imperative Programming”他の紹介@P...Masahiro Sakai
PLDIr#6 (2010-02-11) での Adoption and Focus: Practical Linear Types for Imperative Programming と MaJIC: Compiling MATLAB for Speed and Responsivenes の紹介。
【DLゼミ】XFeat: Accelerated Features for Lightweight Image Matchingharmonylab
公開URL:https://arxiv.org/pdf/2404.19174
出典:Guilherme Potje, Felipe Cadar, Andre Araujo, Renato Martins, Erickson R. ascimento: XFeat: Accelerated Features for Lightweight Image Matching, Proceedings of the 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023)
概要:リソース効率に優れた特徴点マッチングのための軽量なアーキテクチャ「XFeat(Accelerated Features)」を提案します。手法は、局所的な特徴点の検出、抽出、マッチングのための畳み込みニューラルネットワークの基本的な設計を再検討します。特に、リソースが限られたデバイス向けに迅速かつ堅牢なアルゴリズムが必要とされるため、解像度を可能な限り高く保ちながら、ネットワークのチャネル数を制限します。さらに、スパース下でのマッチングを選択できる設計となっており、ナビゲーションやARなどのアプリケーションに適しています。XFeatは、高速かつ同等以上の精度を実現し、一般的なラップトップのCPU上でリアルタイムで動作します。
セル生産方式におけるロボットの活用には様々な問題があるが,その一つとして 3 体以上の物体の組み立てが挙げられる.一般に,複数物体を同時に組み立てる際は,対象の部品をそれぞれロボットアームまたは治具でそれぞれ独立に保持することで組み立てを遂行すると考えられる.ただし,この方法ではロボットアームや治具を部品数と同じ数だけ必要とし,部品数が多いほどコスト面や設置スペースの関係で無駄が多くなる.この課題に対して音𣷓らは組み立て対象物に働く接触力等の解析により,治具等で固定されていない対象物が組み立て作業中に運動しにくい状態となる条件を求めた.すなわち,環境中の非把持対象物のロバスト性を考慮して,組み立て作業条件を検討している.本研究ではこの方策に基づいて,複数物体の組み立て作業を単腕マニピュレータで実行することを目的とする.このとき,対象物のロバスト性を考慮することで,仮組状態の複数物体を同時に扱う手法を提案する.作業対象としてパイプジョイントの組み立てを挙げ,簡易な道具を用いることで単腕マニピュレータで複数物体を同時に把持できることを示す.さらに,作業成功率の向上のために RGB-D カメラを用いた物体の位置検出に基づくロボット制御及び動作計画を実装する.
This paper discusses assembly operations using a single manipulator and a parallel gripper to simultaneously
grasp multiple objects and hold the group of temporarily assembled objects. Multiple robots and jigs generally operate
assembly tasks by constraining the target objects mechanically or geometrically to prevent them from moving. It is
necessary to analyze the physical interaction between the objects for such constraints to achieve the tasks with a single
gripper. In this paper, we focus on assembling pipe joints as an example and discuss constraining the motion of the
objects. Our demonstration shows that a simple tool can facilitate holding multiple objects with a single gripper.
RClassify: Classifying Race Conditions in Web Applications via Deterministic Replay
1. RClassify: Classifying Race
Conditions in Web
Applications via Deterministic
Replay
著者: Lu Zhang and Chao Wang
(Virginia Tech and University of Southern California)
紹介者: 酒井 政裕 (Preferred Networks, Inc.)
@ ICSE2017勉強会 2017-08-24
要約:JavaScriptのデータ競合検査の偽陽性を、
スケジューリングしたリプレイでフィルタリング
9-2
2. 課題
JavaScript においては処理はアトミックに実行されるので
普通の意味でのデータ競合(data race)は存在しない
が、イベントの発生順によって、意図しない結果になる不
具合はありえる
– 右図で <script> 要素の
パース前に画像がロード
されたら?
既存のデータ競合の検出
ツール(例えばEventRacer)
は誤検出が非常に多い
7
9-2
o the application itself (see Section V). This is better
sting approaches because technologies are changing
and tools implemented using a particular version of
wser will quickly become obsolete. In contrast, our
m-agnostic approach will be more robust against these
and updates.
we concretely execute the application using deter-
replay, as opposed to heuristically filtering the warn-
1], [22] or applying conservative static analysis [18],
can robustly decide if a race condition is real (i.e., if
ecution orders are feasible). The reason why existing
port many bogus race conditions in the first place is
some hidden happens-before relations between events
accounted for, and precisely capturing all happens-
elations would have been prohibitively expensive.
econd challenge is to decide, during state recording
mparison, which fields of the program state are impor-
thus should be compared. For a typical client-side web
1 <html>
2 <head> ... </head>
3 <body>
4 <img src="image1.jpg" onload="image1Loaded()"
id="image1">
5 <!-- omitted elements... -->
6 <script id="script1">
7 function image1Loaded() {
8 document.getElementById("button1")
.addEventListener("click", func);
9 }
10 function func() {
11 document.getElementById("outputField").innerHTML
= "Well done!";
12 }
13 </script>
14 <!-- omitted elements... -->
15 <button id="button1"> button1 </button>
16 <!-- omitted elements... -->
17 <div id="outputField"> </div>
18 </body>
19 </html>
Fig. 2. Example: A client-side web application with race conditions.※ Zhang et al. RClassify: Classifying Race Conditions in Web Applications
via Deterministic Replay. In Proceedings of ICSE 2017 Fig. 2より引用
3. 提案手法: RCLASSIFY
1. サイトと既存検査ツールのwarningが入力
2. instrumentaionを施してイベント列を記録
3. 競合イベント(e1,e2)の実行順序が e1→e2 と e2→e1 とな
るようスケジュールしてリプレイ
(それ以外のイベント順序は出来るだけ保つ)
– 一方が実現不能であればデータ競合ではない (bogus)
4. 実行後に両者で状態
を比較
– 両者に本質的な違いが
あれば harmful な
データ競合
– 本質的に同じであれば
harmless なデータ競合
8
y: Classifying Race Conditions in Web
plications via Deterministic Replay
Lu Zhang
Virginia Tech
ksburg, VA, USA
Chao Wang
University of Southern California
Los Angeles, CA, USA
are common in web applications
and repair. Although there exist
web applications, they all report a
s. That is, the races they report are
n never occur in practice, or benign,
rroneous behaviors. Since manually
and error prone, reporting these
would be counter-productive. We
deterministic replay-based method
eal but also the truly harmful race
ng each pair of racing events in two
their impact on the program state:
Instrumented
Web Application
Compare the
Program States
Execution 1
URL of Web
Application
Race-condition
Warnings
Static Analysis
of HTML files
Replay the Racing
Event Pair
Execution 2
Harmful or
Harmless
Fig. 1. RCLASSIFY: Our evidence-based race-condition classification metho
017 IEEE/ACM 39th International Conference on Software Engineering017 IEEE/ACM 39th International Conference on Software Engineering
※ Zhang et al. RClassify: Classifying Race Conditions in Web Applications
via Deterministic Replay. In Proceedings of ICSE 2017 Fig. 1より引用
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