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קולוקוויום וסמינרים

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Academic Calendar at Technion site.

קולוקוויום וסמינרים בקרוב

event head separator מה ניתן ללמוד מערבוב דפי זכרון בעלי גדלים שונים
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מוחמד אגבאריה (הרצאה סמינריונית לדוקטורט)
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יום שני, 01.07.2024, 14:00
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הרצאת זום: 91705525071 וטאוב 601
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מנחה:  Prof. Dan Tsafrir

Cycle-accurate simulations, frequently used by computer architects, incur substantial overheads. To mitigate this, recent virtual memory studies have adopted a lighter-weight methodology that leverages partial simulations of only the memory subsystem. This approach feeds simulation outputs into a mathematical linear model to predict execution runtimes. While this methodology accelerates the simulation process, its accuracy has traditionally been assumed rather than rigorously validated.

In this study, we challenge this assumption with the development of Mosalloc, the Mosaic Memory Allocator. Mosalloc supports the virtual memory of applications with a variety of page sizes, specifically 4KiB, 2MiB, and 1GiB, creating diverse \\\"mosaic\\\" memory layouts. Contrary to previous approaches that utilized a singular page size to generate merely two execution samples for model development, Mosalloc is capable of producing a broad spectrum of samples, thereby allowing empirical validation of model accuracy. Our evaluations reveal that existing models exhibit prediction errors ranging from 25% to 192%. In response, we propose Mosmodel, a new runtime model that confines the maximal cross-validation error to below 6%, significantly enhancing reliability for experimental exploration.

The efficacy of these models hinges on the availability of diverse memory layouts. As the number of feasible layouts escalates exponentially with address space size, the selection process becomes increasingly complex. To address this, we introduce Moselect, an algorithm that autonomously identifies optimal memory layouts that ensure up to 4% gaps between consecutive data points, averaging 46 layouts. Moselect not only augments model accuracy with its diverse samples but also simplifies the modeling and simulation processes. This simplification trades a marginal increase in maximal error of up to 2.5% for significant reductions in budget and execution time. Moreover, it facilitates the use of a TLB-only simulator, eliminating the need for comprehensive cache hierarchy simulations and thus minimizing development efforts.

By amalgamating Mosalloc, Moselect, and Mosmodel, we present a comprehensive framework that automates memory layout selection, benchmark execution, and accurate runtime model construction. This framework also introduces techniques to streamline operations without materially compromising accuracy. Collectively, these innovations significantly advance the state-of-the-art in virtual memory research, providing a robust platform for future exploration and development.

event head separator Wrong Mathematical Proofs for Possibly Correct Claims: Implications and Applications in Cyber Security and Chip Design
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פרופ' עודד מרגלית (אוניברסיטת בן גוריון)
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יום שלישי, 02.07.2024, 14:30
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אודיטוריום 012
In this talk, we'll explore intriguing parallels between incorrect mathematical proofs and critical failures in technology, demonstrating their impact and lessons learned, like: 1. Mathematical Proofs and Social Engineering: How hand-waving a mathematical proof is similar to social engineering, and why larger hardware implementations might surprisingly be more efficient. 2. Cybersecurity and Mathematical Proofs: The surprising connection between a cyber attack that nearly caused an airplane crash and the proof that any finite set of horses has the same color, illustrating the importance of thorough proof validation in ensuring safety. 3. Hacking games and testing: Drawing a parallel between winning Super Mario in less than two minutes and the four-color theorem, highlighting similar challenges that cost Intel $475M. Bio: Oded Margalit is the Head Scientist at NextSilicon and an Adjunct Professor in the Computer Science department at Ben-Gurion University of the Negev. Previously, he was a researcher at the IBM Haifa Research Lab (HRL) and the CTO of Citi's Cyber Security Innovation Center (CSIC). Oded has an extensive background in machine learning, optimization, formal verification, and cybersecurity. He is also dedicated to educational outreach, having created mathematical and programming challenges for a wide range of learners, including elementary school students (CodeGuru), high school students (CodeGuru Xtreme), university students (IEEEXtreme), and other audiences (PonderThis and more). Technion Host: Erez Petrank
event head separator פונקציית אגירה עבור ערבול מידע ברשתות עצביות עבור גרפים
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אלמוג דוד (הרצאה סמינריונית למגיסטר)
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יום רביעי, 03.07.2024, 11:00
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חדר 014
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מנחה:  Dr. Chaim Baskin
Message Passing Graph Neural Networks (MPGNNs) have emerged as the standard method for modeling complex interactions across diverse graph entities. While the theory of such models is widely investigated, their aggregation module has not received sufficient attention. Sum-based aggregators have solid theoretical foundations regarding their separation capabilities. However, practitioners often prefer using more complex aggregations and mixtures of diverse aggregations. In this research, we unveil a possible explanation for this gap. We claim that sum-based aggregators fail to "mix" features belonging to distinct neighbors, preventing them from succeeding at downstream tasks. To this end, we introduce Sequential Signal Mixing Aggregation (SSMA), a novel plug-and-play aggregation for MPGNNs. SSMA treats the neighbor features as 2D discrete signals and sequentially convolves them, inherently enhancing the ability to mix features attributed to distinct neighbors. By performing extensive experiments, we show that combining SSMA with well-established MPGNN architectures achieves substantial performance gains across various benchmarks, achieving new state-of-the-art results in many settings.
event head separator מחקרים רחבי היקף של רשתות נוירונים עמוקות בהערכת אי ודאות וגילוי מחלקות מחוץ להתפלגות
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עידו גליל (הרצאה סמינריונית לדוקטורט)
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יום רביעי, 10.07.2024, 14:30
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הרצאת זום: 92810808120 וטאוב 9
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מנחה:  Prof. Ran El-Yaniv
In this seminar, I will discuss two of my papers (published in ICLR 2023) on uncertainty estimation and class-out-of-distribution detection. We present a novel framework to benchmark the ability of image classifiers to detect class-out-of-distribution (C-OOD) instances (i.e., instances whose true labels do not appear in the training distribution) at various levels of detection difficulty. We apply this technique to ImageNet, and benchmark 500+ pretrained, publicly available, ImageNet-1k classifiers. We then evaluate these classifiers both for their C-OOD detection performance (second paper) and for their uncertainty estimation performance (first paper), i.e., their ranking, calibration, and selective classification performance. This results in a large-scale study that reveals many factors (such as architecture types and training regimes) previously unknown to contribute to performance.
event head separator אינטראקציות בין רשתות בלוקצ'יין על בסיס ניתוח שוק
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אורי מזור (הרצאה סמינריונית למגיסטר)
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יום חמישי, 11.07.2024, 16:30
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הרצאת זום: 5701477766
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מנחה:  Prof. Ori Rottenstreich
Cryptocurrencies have gained popularity in recent years. They are often implemented on one or more of the over 1000 existing blockchain networks, including the popular Bitcoin and Ethereum networks. Recently, various technologies known as blockchain interoperability have been developed to connect these different blockchains, creating an interconnected blockchain ecosystem. Interoperability refers to the ability of blockchains to share information with each other. Decentralized Exchanges (DEXs), which are peer-to-peer marketplaces where traders can exchange cryptocurrencies, play a significant role in this interconnected ecosystem. We aim to understand the blockchain ecosystem and the connections between blockchains, we view that as the blockchain interoperability graph. Our approach is based on analyzing the correlation between cryptocurrency prices implemented over different blockchains. We examine over 4800 cryptocurrencies implemented on 76 blockchains based on their daily prices. This experimental study has potential implications for decentralized finance (DeFi), including portfolio investment strategies and risk management. Additionally, we present a framework to study cross-chain arbitrage between DEXs. We demonstrate the framework by analyzing two popular DEXs: QuickSwap which is implemented on the Polygon blockchain network and PancakeSwap that is implemented on the BNB Chain blockchain network. We study the potential revenue from conducting cross-chain arbitrage and lay the basis for understanding its potential impact on the future of blockchain technology. Overall, the analysis provides insight into the evolving blockchain ecosystem, highlighting opportunities for innovation and financial strategies in the decentralized market.
event head separator על היסודות האלגוריתמיים של תכנון סיוע במשימה
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איתן בלוך (הרצאה סמינריונית למגיסטר)
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יום רביעי, 17.07.2024, 09:30
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הרצאת זום: 98269146871 ומעבדת CRL, קומה ראשונה, בניין טאוב
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מנחה:  Dr. Oren Salzman
In this work we introduce the problem of task assistance planning where we are given two robots Rtask and Rassist. The first robot, Rtask, is in charge of performing a given task by executing a precomputed path. The second robot, Rassist, is in charge of assisting the task performed by Rtask using on-board sensors. The ability of Rassist to provide assistance to Rtask depends on the locations of both robots. Since Rtask is moving along its path, Rassist may also need to move to provide as much assistance as possible. The problem we study is how to compute a path for Rassist so as to maximize the portion of Rtask’s path for which assistance is provided. We limit the problem to the setting where Rassist moves on a roadmap which is a graph embedded in its configuration space and show that this problem is NP-hard. Fortunately, we show that when Rassist moves on a given path, and all we have to do is compute the times at which Rassist should move from one configuration to the following one, we can solve the problem optimally in polynomial time. Together with carefully crafted upper bounds, this polynomial-time algorithm is integrated into a Branch and Bound-based algorithm that can compute optimal solutions to the problem outperforming baselines by several orders of magnitude. We demonstrate our work empirically in simulated scenarios containing both planar manipulators and UR robots as well as in the lab on real robots.