אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

Quantitative computational models have become increasingly popular in the past few decades, since they can naturally model distributed systems and enable reasoning about quantitative properties and about infinite configuration spaces. Specific models include Petri nets, Vector Addition Systems (VAS\VASS), Weighted Automata and Counter Nets. Their usefulness lies in the fact that they retain decidability of some important decision problems.Nonetheless, the comp...
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A copy of a permutation pattern (say, 132) in a sequence of numbers is any subsequence whose values have the same relative order as in the pattern. (Say, for 132, the first element is smallest, the second is largest, and the third is in-between.). Counting permutation patterns has a surprisingly rich set of connections and applications in ranking, statistics, combinatorics, fine-grained complexity, and parametrized complexity, especially for fixed sma...
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DNA data storage presents an efficient solution for archiving, though synthesis time and cost pose challenges.This seminar focuses on cyclic synchronized synthesis technologies like photolithography, introducing performance metrics based on synthesis cycles.We extend prior work on channel capacity, achieving higher rates and capacities through improved encoding.Additionally, we analyze cost bounds and explore alphabet sizes larger than the standard four, inspired by recent advance...
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מוזנים לסדנה פרקטיתGen AI  של אינטל להרשמה:...
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Machine learning (ML) has achieved considerable successes in recent years and an ever-growing number of disciplines rely on it. However, this success crucially relies on human experts manually processing data to build, select and train models. In this work we focus on advancing two different aspects of Automated Machine Learning (AutoML) and provide automatic methods and processes to mitigate some of the reliance on ML experts.First, we introduce an interpreta...
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Flow matching models, ODE-based generative models, generate samples by gradually morphing a simple source distribution into a target distribution. In practice, these models still fall short of perfectly replicating the target distribution, mainly due to imperfections of the learned mapping. Previous work mainly focus on alleviating discretization error, which rises from sampling a continuous trajectory with a finite number of steps. In this work we focus on predic...
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Mechanistic interpretability research seeks to explain the internal mechanisms that operate within AI models as they perform different tasks. A widely used strategy to uncover and analyze those mechanisms is by identifying circuits within the model. A circuit is a sub-graph of the model’s computational graph, believed to be critical for executing a specific task. In recent years, several methods have been proposed for automatically identifying c...
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Machinery for data analysis often requires a numeric representation of the input. Towards that, a common practice is to embed components of structured data into a high-dimensional vector space. We study the embedding of the tuples of a relational database, where existing techniques are often based on optimization tasks over a collection of random walks from the database. The focus of this paper is on the recent FoRWaRD algorithm that is designed for dynamic databa...
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Suppose that n forecasting experts are providing daily rain/no-rain predictions, and the best among them is mistaken in at most k many days. For how many days will an optimal learner allowed to observe the predictions mis-predict? This is a fundamental problem in online learning, and other important classification problems can be reduced to it. It was studied in the 90’s by Cesa-Bianchi, Freund, Helmbold, and Warmuth who gave fine-grained bounds for the case where the learner may not us...
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Motion planning is a central challenge in robotics, with learning-based approaches gaining significant attention in recent years. This thesis focuses on a specific aspect of these approaches: using machine-learning techniques, particularly Support Vector Machines (SVM), to evaluate whether robot configurations are collision-free, an operation termed "collision detection". Despite the growing popularity of these methods, there is a lack of theoretical guarantees supporting their efficiency and...
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**טקס המצטיינים לא יתקיים היום 24.9.24 עקב המצב הבטחוני** לאור המצב הבטחוני ובהתאם להנחיות פיקוד העורף, אנו נאלצים לדחות את טקס...
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טקס בוגרי מחזור צ"ה יום שני, 23 בספטמבר 2024, כ' באלול, תשפ"ד רחבת לב הקמפוס  17:00 טקס טכניוני 19:00 התכנסות וכיבוד 20:00 תחילת הטקס החניה הינה על בסיס מקו...
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The algorithmic study of the principal-agent framework is an emerging frontier for algorithmic game theory. We extend this model by incorporating knapsack constraints to capture real-world resource limitations. To address the computational challenges arising from these constraints, we develop approximation algorithms that guarantee near-optimal outcomes for both the principal and agents. Our research contributes to the understanding of contract design in complex e...
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Hyperbolic Geometry of Google Maps:Navigation of the Google Maps (not to confuse with *driving with* Google Maps) on smartphones is perhaps the most intuitive and efficient UI in existence, – and the reason, as I will show, is the underlying structure of the 3D hyperbolic space.Professor Yuliy Baryshnikov’s short bio is here...
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How can two parties jointly sample from a source of correlated randomness (X,Y), say two hands of cards in a poker game, without leaking any extra information? This secure sampling question is strongly motivated by the design of efficient protocols for secure computation, allowing the two parties to compute a function of their secret inputs without revealing their inputs to each other.While there has been major progress on securely sampling some useful correlations, others seem much more ...
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Deep neural networks are successful in various tasks but are also susceptible to adversarial examples: malicious input perturbations designed to deceive the network. Many adversarial attacks on image classifiers involve making imperceptible changes bounded by a small ε with respect to an Lₚ norm (e.g., p = 0, 1, 2, ∞), by a small interval neighborhood, or by semantic feature perturbations, such as adjustments in brightness, translation, or rotation. To understand the robu...
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אירוע חשיפה למתעניינות בתארים מתקדמים בבינה מלאכותית ולמידת מכונה. בין אם את מתלבטת כיצד להיכנס לתחום ולא בטוחה איך להתחיל, או שאת כבר אחרי תואר שני וחושבת להתחיל דוקטורט, אנחנו מזמינים אותך להצטרף אלינו!   יום ב', 9 בספטמבר בשעה 18:00 בתוכנית: הרצאות מד�...
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The emerging paradigm of in-network computing leverages programmable network hardware, such as switches, to enhance the performance, reliability, and functionality of distributed systems. Traditionally, networks in distributed systems have been treated merely as conduits for data, with limited assumptions about their capabilities. However, by introducing higher-level abstractions that utilize the potential of programmable network devices, significant improvements in distributed applications c...
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Unsupervised latent variable models serve as highly effective tools for representing complex data such as images or videos, relevant for applications such as robotic manipulation, video generation, novelty detection, and many more. Variational Autoencoders (VAEs) provide compact latent representations with stability and efficiency. In this talk, we will explore modern VAEs that mitigate shortcomings of classical approaches such as blurry images, and can be used as a basis for strong world mod...
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Traditional discriminative Machine-Learning approaches often formulate problems as optimization ofa parameterized function mapping from a given input space to some desired output space.While this formulation is applicable to many theoretical and practical problems, it is inherently relianton the assumption that the dataset X is constant. The world around us is abundant with scenarioswhere this is either not the case, or where droppin...
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Metric theory offers powerful tools for analysing and processing shapes on curved manifolds, with Riemannian metrics being the most commonly used due to their simplicity and success in many applications. However, Riemannian metrics are limited by their symmetric nature, where lengths of paths are independent of traversal direction. Finsler metrics, a generalisation including asymmetric distances, provide broader tools but are rarely applied in practice, perhaps due to their theoretical comple...
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Automated deductive reasoning plays an important role in software verification, optimization, and mathematical theorem discovery. This talk explores novel applications and extensions of equality saturation, a technique for efficiently representing and manipulating large sets of equivalent expressions, to enhance automatic deductive reasoning across various domains.In this talk, we present a collection of three works: A symbolic theory exploration system, dubbed Thesy, based on equ...
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This talk will cover two topics:First, we suggest a new interpretation of Spearman correlation using k-subset permutations. We characterize the distribution of the Spearman correlation of a permutation that starts with the identity permutation over n elements and is perturbed by uniformly shuffling a random subset of k indices. We present some key aspects of this distribution, including its expected value and variance for every k between 1 and n. We then generalize it to any starting vect...
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In this talk, I will present topics from my PhD work. The talk consists of three topics:Feature selection is a core process in building machine learning models. It is often essential for optimizing performance and sometimes essential to support practical use, such as when the number of features to be measured in the execution stage is limited by hardware or other factors. We examined the procedure in challenging situations: feature selection on a very high-dim...
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Diagnosing Crohn's Disease (CD) typically involves examining 2D slices from magnetic resonance enterography (MRE). However, the anisotropic resolution of MRE complicates precise 3D measurements and visualization. The absence of automated 3D measurement systems further complicates assessment. Previous methods for generating isotropic volumes from anisotropic data often rely on extensive 3D data and focus solely on interslice resolution, lea...
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The rapid growth of data-intensive applications and high-speed I/O devices has led to increasing demands on I/O performance in both virtualized cloud environments and bare metal setups. But existing systems struggle to fully exploit the potential of modern hardware due to inefficiencies at various I/O stack layers. This thesis presents three novel techniques that optimize I/O performance across virtualized and bare metal environments: IOctopus, cinterrupts, and He...
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A q-locally correctable code (LCC) C:{0,1}^k->{0,1}^n  is a code in which it is possible to correct every bit of a (not too) corrupted codeword by making at most q queries to the word. The cases in which q is constant are of special interest, and so are the cases that C is linear. In a breakthrough result Kothari and Manohar (STOC 2024) showed that for linear 3-LCC n=2^Ω(k^1/8) . In this work we prove that n=2^Ω(k^1/4) . As Reed-Muller codes yield 3-LCC with n=2^O(...
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שלום לכולם,  שמחים להזמינכם ליריד הפרויקטים השנתי של הפקולטה למדעי המחשב ע"ש טאוב יחד עם תחרות הפרויקט המצטיין - יום ד', 21 באוגוסט, החל משעה 12:30 ב...
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Geometric Deep Learning attempts to apply deel learning methodlogies to domains where a grid structure doesn't exit. We advocate for using principled methods to define the primitives of these networks. As such we define networks that stem from the symmetries of geometric representations, And show how analyzing some of these primitives spectrally reveals that combining allows for SOTA performance. This is finally complemented by the introduction of a suite of identity losses that are customiza...
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Graph neural networks are popular architectures for graph machine learning, based on iterative computation of node representations of an input graph through a series of invariant transformations. A large class of graph neural networks follow a standard message-passing paradigm: at every layer, each node state is updated based on an aggregate of messages from its neighborhood. In this work, we propose a novel framework for training graph neural networks, where every node is viewed as a player ...
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ההרשמה לתחרות הפרויקט המצטיין 2024 בעיצומה! עשיתם פרויקט חדשני, מעניין, פורץ דרך? מוזמנים להגיש מועמדות בקישור: https://tinyurl.com/cs-projects24 הגשת מועמדות עד ה- 15 באוגוסט 2024 שלב הגמר יתקיים ב-21באוגוסט...
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הזמנה לערב להקות מוזמנים לערב להקות של הפקולטה למדעי המחשב!!סטודנטים וחברי סגל מוכשרים מהפקולטה שלנו במגוון הרכבים וסגנונות מוזיקליים. הצטרפו אלינו לערב בלתי נשכח של הופעות חיות, ואנרגיה מדהימה.  יום רביעי ה 14 באוגוסט בשעה 19:00 במרפסת טאוב.  �...
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The field of information design studies strategic information revelation by a certain sender to receivers. The Bayesian persuasion model, introduced by Kamenica and Gentzkow, assumes that the sender can trustworthily commit to a randomized information revelation policy, called a signaling scheme. In contrast, the cheap talk model assumes that the sender does not have such a commitment power. The research focuses on optimizing the sender’s utility.In this...
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A sequence of recent works, concluding with Mu et al. (Eurocrypt, 2024) has shown that every problem $\Pi$ admitting a non-interactive statistical zero-knowledge proof (NISZK) has an efficient zero-knowledge \emph{batch verification} protocol. Namely, an NISZK protocol for proving that $x_1, \dots, x_k \in \Pi$ with communication that only scales poly-logarithmically with $k$. A caveat of this line of work is that the prover runs in exponential-time, whereas for NP problems it is natural to h...
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Collecting network telemetry is essential for detecting problems in the network. In recent years we have seen an abundance of research on network telemetry in the data plane. Many of these solutions analyze traffic continuously over a long period of time, while resetting the structure from time to time. If shorter time intervals are needed, sliding windows are usually used, yet these incur significant resource and management overhead. However, often in order to understand what is ha...
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ועד הסטודנטים של מדעי המחשב מביאים לכם את פסטיבל "הטאוב, הרע והמכוער" יום שלישי, 6 באוגוסט18:30: מרצים מוזגים בירות, אוכל ושתיה חופשי, קריוקי והפתעות (כניסה לסטודנטים ממדמ"ח בלבד), במרפסת טאוב. 21:00 : מסיבה באולם השקוף (בהצגת כרטיס בלבד)  האירוע בחסות Plu...
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Statistical enrichment tools are highly useful in biological research. Current approaches to statistical enrichment in ranked or ordered lists such as, for example, GSEA and GOrilla, are limited to the suffix (prefix) of the list. These methods assess extreme density of 1s in binary vectors on either side. Statistical significance can be assigned using, e.g, Wilcoxon Rank Sum and mHG statistics.In this work we extend the mHG approach to also add...
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Modern programming languages rely on advanced and intricate type systems.Expressive type systems support more language features but often result in unexpected language capabilities. For example, we know that the Java type system is Turing complete, which means it is so complex that Java compilers cannot guarantee termination. But a powerful type system can also be a blessing in disguise. Over the years, crafty programmers found ways to coerce ty...
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Detecting unknown objects in noisy data is a key task in many problems. A natural model for the unknown objects is the linear subspace model, which assumes that the objects can be expanded in some known basis (such as the Fourier basis). In this talk, I will present an object detection algorithm that under the linear subspace model is asymptotically guaranteed to find all objects while making only a small percentage of false discoveries. We demonstrate our derivations for the problem of parti...
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מכירים כאלו שרוצים לעשות משהו גדול? היום הפתוח לתואר ראשון בפקולטה למדעי המחשב בטכניון מתקרב וכולכם...
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The computations required for deep learning research have been doubling every few months, resulting in an estimated 5,000x increase from 2018 to 2022. This trend has led to unprecedented success in a range of AI tasks. In this talk I will discuss a few troubling side-effects of this trend, touching on issues of lack of inclusiveness within the research community, and an increasingly large environmental footprint. I will then present Green AI – an alternative app...
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Famously, multiset neural networks based on sum-pooling can separate all distinct multisets, and as a result can be used by message passing neural networks (MPNNs) to separate all pairs of graphs that can be separated by the 1-WL graph isomorphism test. However, the quality of this separation may be very weak, to the extent that the embeddings of "separable" multisets and graphs might even be considered identical when using fixed finite precision. ...
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 קבוצת ה-CTF...
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This paper studies two problems that are motivated by the novel recent approach of composite DNA that takes advantage of the DNA synthesis property which generates a huge number of copies for every synthesized strand. Under this paradigm, every composite symbols does not store a single nucleotide but a mixture of the four DNA nucleotides. In the first problem, our goal is study how to carefully choose a fixed number of mixtures of the DNA nucleotides such that the decoding probability by the ...
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A partition mathcal{P} of a metric space (X,d_X) is (sigma,tau,Delta)-sparse if each cluster has a diameter at most Delta, and every ball of radius Delta/sigma intersects at most tau clusters. In this talk, we will explore the construction and different applications of sparse partitions in their various forms over the years. As time allows, we will discuss applications to: Universal TSP, Steiner point removal, universal Steiner tree, and facility location....
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In LLMs, the ability to update factual knowledge of the model is an important and attractive ability, due to the need to deal with everchanging nature of information in the world. However, predicting whether an edit applied to a LLM will be successful or not is difficult. In this work, we suggest two metrics that can predict the editing success: (1) where the knowledge is stored in the parameters as reflected by the logit-lens technique; (2) the probability that the...
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שלום לכולם, מוזמנים לערב  "מחקר על הבר" - שלושה חברי סגל בהרצאותTED   בגובה העיניים. ...
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"Noga Alon is being awarded the 2024 Wolf Prize for his profound impact on Discrete Mathematics and related areas." I will describe a recent joint work with Matia Bucic and Lisa Sauermann about the investigation of extremal problems in discrete geometry for typical norms. The results include surprisingly tight solutions of the unit and distinct distances problems for typical norms, as well as a determination of the chromatic number of the unit distance graph for a typical...
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גוגל מגיעים ליום זרקור בפקולטה ...
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The talk will focus on the d-dimensional vector bin packing problem. The input for the problem is a set of items, each associated with a d-dimensional weight vector. The objective is to partition the items into a minimal number of bins, such that the total weight of items in a bin is at most one in every dimension. The problem...
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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 muc...
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Hardware Reverse Engineering (HRE) involves gate-level Netlist extraction and specification discovery, wherein graph-based methods play a crucial role in identifying sub-circuits. We propose a novel approach for Subcircuit Recognition, essential for specification discovery in HRE, leveraging existing graph similarity kernels. Focusing on the reduced problem we formulate as Subgraph Localization, we delineate two key components: graph similarity metr...
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The rapid growth of generative models allows an ever-increasing variety of capabilities. Yet, these model...
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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), ...
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Characterizing learnability by a combinatorial dimension is a hallmark of machine learning theory. Starting from the fundamental characterization of binary classification PAC by the VC-dimension, through the characterization of online mistake bound learnability by the Littlestone dimension, all the way to recent papers characterizing multi-class learning and differential privacy. However, for some basic learning setup no such characterizations have been provided. I will focus ...
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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 classifi...
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יום זרקור ראשון הסמסטר!! חברת אנבידיה מגיעה לפגוש אתכם בטאוב יום ד׳, 10 ביולי, בין 12:30-14:30 בלובי טאוב בתוכנית: מפגש עם צוות הגיוס, המהנדסים והמהנדסות וכמובן, צ'ופר מתוק מחכים לכם!!...
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While language models (LMs) show impressive text manipulation capabilities, they also lack commonsense and reasoning abilities and are known to be brittle. In this talk, I will suggest a different LMs design paradigm, inspired by how humans understand it. I will present two papers, both shedding light on human-inspired NLP architectures aimed at delving deeper into the meaning beyond words. The first paper [1] accounts for the lack of commonsense and reasoning abilities by proposing a parad...
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Deep learning has revolutionized computer vision, achieving unprecedented performanc in tasks like classification and detection. However, these models are highly susceptible to adversarial attacks, prompting the development of robust training methods. A notable outcome of such training is the phenomenon of Perceptually Aligned Gradients (PAG), where input gradients align semantically with human perception. Our research explores both the practical and theoretical implications of PAG. We introd...
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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 ...
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קבוצת Technipwn מטיסה אתכם לוגאס!באוגוסט הקרוב קבוצת הCTF של הפקולטה משתתפת בכנס DEFCON שבלאס וגאס ומזמינה אתכם להצטרף!ביום שלישי הקרוב נערוך תחרות שמטרתה לבחור את הנבחרת שתטוס לייצג אותנו בכנס.נושאי התחרות הם הנושאים הרגילים של תחרויות ה־ctf כמו:&n...
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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 cras...
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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....
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בואו להיות חלק מקבוצת Capture The Flag - CTF של הפקולטה!! קבוצת הCTF הפקולטית, Technipwn, מקיימת מפגשים דו שבועיים בהם אנו פותרים אתגרים, מתאמנים לתחרויות ונהנים. המפגשים מתאימים למשתתפים מתחילים ומנוסים כאחד, ומתקיימים בימי ראשון בטאוב 9. מאורגן ומנוהל על ידי סטודנטים, ומיועד לכלל ה...
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As data storage challenges grow and existing technologies approach their limits, synthetic DNA emerges as a promising storage solution due to its remarkable density and durability advantages. While cost remains a concern, emerging sequencing and synthetic technologies aim to mitigate it, yet introduce challenges such as errors in the storage and retrieval process. One crucial task in a DNA storage system is clustering numerous DNA reads into groups that represent the original i...
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The memory wall bottleneck is throttling the performance of data-intensive applications as the data transfer between the processing units (e.g., CPU, GPU cores) and the memory is significantly slower than the compute itself. Therefore, emerging digital processing-in-memory systems overcome this bottleneck by performing parallel bitwise logic within the memory arrays themselves. This enables a drastic reduction in data transfer for vectored operations since the same instruction may be repeated ac...
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Computer systems that facilitate tight integration of data storage and processing can eliminate the "memory wall" and "power wall" bottlenecks. The memristive Memory Processing Unit (mMPU) architecture contains memory cells that can also execute logical functions, such as Memristor-Aided Logic (MAGIC) NOR gates. The mMPU supports both general-purpose computing and application-specific acceleration. This research contributes to both these aspects of the mMPU.First, we introduce frameworks...
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Deep learning has revolutionized drug development by enhancing various stages of the process, yet traditional optimization goals often lack the sophistication required for real-world applications. This work tackles more complex and nuanced problems beyond conventional property enhancement, focusing on optimizing under patentability constraints and translating preclinical success in animals to human clinical trials. In addressing patentability, we introduce a patent loss mechanism and the Molecul...
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We consider the problem of approximate counting of triangles and longer fixed length cycles in undirected and directed graphs. We provide an algorithm which is faster as t, the number of copies of the searched subgraph, increases. Our running time, which significantly improves upon the state of the art (Tětek [ICALP’22]), is the same as that of multiplying an n x (n/t) matrix by an (n/t) x n matrix, up to polylogarithmic factors. Finally, we show that under popular fine-grained hypotheses, t...
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The arrival of persistent memory devices to consumer market has revived the interest in transactional durable algorithms. Persistent memory is touted as having two attributes that distinguish it from other storage technologies: fine access granularity and fast persistece.In the first part of the talk we investigate how these attributes differentiate persistent memory from block storage in the context of buffered durability – a relaxed approach that allows some progress loss upon sys...
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Computed tomography (CT) aims to recover the volumetric three dimensional (3D) structure of heterogeneous objects. Traditionally, CT refers to a medical imaging modality. There the object, radiation source and detector array are fully controlled. This results in a linear image formation model. In contrast, we seek CT of natural objects, acquired outdoors, in an uncontrolled environment. We focus on underwater plankton and cloud droplets, which strongly affect climate. Cloud imagin...
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Multivariate time series forecasting is a pivotal task in several domains, including financial planning, medical diagnostics, and climate science. This paper presents the Neural Fourier Transform (NFT) algorithm, which combines multi-dimensional Fourier transforms with Temporal Convolutional Network layers to improve both the accuracy and interpretability of forecasts. The Neural Fourier Transform is empirically validated on fourteen diverse datasets, showing superior performance across multiple...
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ההאקתון השנתי של הפקולטה יוצא לדרך!  CS Hackathon - Doing Good שריינו את התאריכים: 20-21 ביוני, בטאוב. השנה, בהתאם לצו השעה, נתמקד בפיתוח פתרונות טכנולוגיים להגברת החוסן הנפשי בשיתוף אגף השיקום במשרד הביטחון, עמותות ומיזמים. פתיחת הרשמה, רעיונות לפיתוחים, וסדנאות הכנ...
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We develop a new framework, Polyform, for fast approximate matrix multiplication (AMM), through sums of sparse polynomial multiplications, using (variants of) FFT. Using this framework, we obtain new data-dependent speed-accuracy tradeoffs, which often improve on the worst-case accuracy of randomized sketching algorithms. Meanwhile, Polyform can be viewed as a cheap alternative bilinear operator to matrix multiplication in Deep Neural Networks, which is our motivating applicati...
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Jumping automata are finite automata that read their input in a non-sequential manner, by allowing a reading head to “jump” between positions on the input, consuming a permutation of the input word. We argue that allowing the head to jump should incur some cost. To this end, we propose three quantitative semantics for jumping automata, whereby the jumps of the head in an accepting run define the cost of the run. The three semantics correspond to different interpretations of jumps: the absolu...
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Neural network optimization is a challenging task, in large part because of the nonconvexity, in general, of the loss function. Various algorithms have been proposed to solve this task, but most of them focus on the convex subspaces of the loss function to avoid the challenge of finding the optimal step in concave subspaces. In addition to avoiding the concave spaces entirely (which can bear a significant computational burden!), they also simply take the minimizer of a second-degree Taylor ap...
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In this paper, we present a novel approach for test-time domain adaptation via online self-training, consisting of two components. First, we introduce a statistical framework that detects distribution shifts in the classifier's entropy values obtained on a stream of unlabeled samples. Second, we devise an online adaptation mechanism that utilizes the evidence of distribution shifts captured by the detection tool to dynamically update the classifier's parameters. The resulting adaptation process ...
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Atmospheric lidars are important remote sensing tools in aerosols and climate research. A pulsed time-of-flight lidar continuously samples vertical atmospheric profiles, through day and night, yielding a spatiotemporal atmospheric map. However, lidar analysis is challenged by low signal-to-noise ratios, sunlight interference, and need for frequent calibration. We advance lidar analysis. We develop a framework for simulating realistic spatiotemporal lidar data under diverse atmospheric and system...
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DNA emerges as a promising medium for the exponential growth of digital data due to its density and durability. This study extends recent research by addressing the coverage depth problem in practical scenarios, exploring optimal error-correcting code pairings with DNA storage systems to minimize coverage depth. Conducted within random access settings, the study provides theoretical analyses and experimental simulations to examine the expectation and probability distribution of samples needed fo...
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In the talk I will survey a research direction which combines coding-theory and computational complexity. This direction was introduced by Lipton, and refined by Guruswami and Smith. The goal is to construct error correcting codes which correct errors that are introduced by computationally bounded channels. This is in contrast to Hamming’s standard model which assumes a bound on the fraction of bits that a channel may flip, but allows the channel to be computationally unbounded.In rece...
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In recent years, machine learning (ML) has fueled extraordinary advances in fields such as natural language processing and computer vision. These results have encouraged researchers to utilize ML in additional application domains, such as computer and networked systems, with varying degrees of success. In this talk I will focus on the prominent challenge of rate control in computer networking, and devising a practical ML toolkit for the safe deployment of deep-learning-based solutions.No...
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קבוצת ה-CTF של הפקולטה - Technipwn מזמינה אתכם להרצאת פתיחת סמסטר- שנה של אבטחה התקפית: תקיפות על OpenAI, Atlassian, Apple ועוד! דובר>> רון מסאס, חוקר חולשות ב-Imperva במהלך עבודתו בחברת Imperva, חוקר האבטחה רון מסאס חשף מספר חולשות ופריצות מרתקות במספר חברות גדולות. בהרצאה הוא ייקח אותנו למסע �...
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In the last two decades, a main way to improve performance of computer processors has been producing processors with multiple cores, on which tasks may execute concurrently. Software is required to keep up with the hardware advances and supply concurrent algorithms for programs that run on multiple cores. This talk will focus on a fundamental building block of concurrent algorithms: concurrent data structures, designed with improved efficiency while also satisfying strong correctness and progres...
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יום פתוח ללימודי תואר ראשון בטכניון יתקיים ב-31/5 בין השעות 10:00-13:00 בשרונה, ת"א. באירוע תוכלו לפגוש את נציגי הפקולטה, להתייעץ, להתרשם ולקבל תשובות אודות הלימודים. הרשמה ליום הפתוח ...
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This week we plan to get acclimated to our seminar's new location (outside of Taub, due to construction work), in a more social atmosphere. This includes introductions of current and prospective members of the group,* and talk about plans for the semester, including theory courses scheduled. Come meet established theorists and folks interested in theory research! * if you know students doing or interested in theory who are not (or might not be) registered to this mailing list, please let them k...
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Multi-label neural networks are important in various tasks, including safety-critical tasks. Several works show that these networks are susceptible to adversarial attacks, which can remove a target label from the predicted label list or add a target label to this list. However, no verifier can deterministically determine the list of labels for which a multi-label neural network is locally robust. The main challenge is that the complexity of the analysis increases by a factor exponential in the n...
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Multi-label neural networks are important in various tasks, including safety-critical tasks. Several works show that these networks are susceptible to adversarial attacks, which can remove a target label from the predicted label list or add a target label to this list. However, no verifier can deterministically determine the list of labels for which a multi-label neural network is locally robust. The main challenge is that the complexity of the analysis increases by a factor exponential in the n...
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מפגש מתעניינים בלימודים לתארים מתקדמים בפקולטה למדעי המחשב ע"ש טאוב בטכניון יתקיים ביום רביעי 22.5.24, שעה 13:00, חדר 337 בוגרי תואר ראשון מצטיינים מכל האוניברסיטאות! זאת ההזדמנות שלכם להשתתף ולהתרשם מהפקולטה למדעי המחשב בטכניון, לפגוש חברי הסגל וסטודנטים לתארים מתקדמים ו...
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Payment Channel Networks (PCNs) are a leading method to scale the transaction throughput in cryptocurrencies. Two participants can use a bidirectional payment channel for making multiple mutual payments without committing them to the blockchain. Opening a payment channel is a slow operation that involves an on-chain transaction locking a certain amount of funds. These aspects limit the number of channels that can be opened or maintained. Users may route payments through a multi-hop path and thus...
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In the past few years, software has been at the heart of many applications ranging from appliances to virtual services. Helping software developers to write better code is a crucial task. In parallel, recent developments in machine learning and deep learning in particular have shown great promise in many fields, and code-related tasks in particular. The main challenge is how to represent code in a way that can be used by deep learning models effectively. While code can be treated as a sequence o...
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We introduce and study jumping automata over infinite words, a fascinating twist on traditional finite automata. These machines read their input in a non-consecutive manner, defying conventional word order. We explore three distinct semantics: one ensuring every letter is accounted for, another permitting word permutation within fixed windows, and a third allowing permutation within windows of an existentially-quantified bound. Our work covers expressiveness, closure properties, algorithmic char...
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Methods in question-answering (QA) that transform texts detailing processes into an intermediate code representation, subsequently executed to generate a response to the presented question, have demonstrated promising results in analyzing scientific texts that describe intricate processes. The limitations of these existing text-to-code models are evident when attempting to solve QA problems that require knowledge beyond what is presented in the input text. We propose a novel domain-agnostic mode...
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Distributed lending protocols are major financial mechanisms that match borrowers and lenders without the need for intermediaries. Smart contracts deployed on blockchains ensure the security of such loans. Borrowers mitigate the risk of default by providing collateral in the form of cryptocurrencies or by limiting the loan duration. In the first part of the talk, we describe various loan types, major platforms, and financial models that allow them. In the second part of the talk, we refer to a ...
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In biological imaging, fast acquisition of depth information is crucial e.g. for accurate 3D tracking of sub-cellular elements and for 3D super-resolution. In the first part of this talk, we present a series of works enhancing the success of snapshot depth sensing in the revolutionary field of single-molecule localization microscopy (Nobel Prize in Chemistry 2014). Specifically, we present an approach for jointly designing the “optics” of the microscope and the 3D reconstruction algorithm, b...
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The standard formulation of Markov decision processes (MDPs) assumes that the agent's decisions are executed immediately. However, in numerous realistic applications such as robotics or healthcare, actions are performed with a delay whose value can even be stochastic. In this work, we introduce stochastic delayed execution MDPs, a new formalism addressing random delays without resorting to state augmentation. We show that given observed delay values, it is sufficient to perform a policy search i...
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בוגרי הפקולטה מוזמנים עם ילדיהם ליום של הרצאות סדנאות ומידע לגבי רישום וקבלה - לילדים; וסיור נוסטלגי (להורים) יום שני, 15 באפריל, החל מ-10:00 בטכניון בתוכנית: 1. נפלאות האינסוף - הפקולטה למתמטיקה 2. מה זה האינטרנט של הדברים? - הפקולטה למדעי המחשב 3. ...
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Due to their increasing aggressiveness, recent congestion control algorithms (CCAs) can quickly starve standard TCP flows in their shared router queues. Existing solutions based on fair queueing are not scalable enough, and those based on admission control do not fit all CCAs. Independently, building on the popularization of machine learning, recent papers have designed several CCA classifiers.In this seminar, we introduce a buffer isolation mode where incoming flows first undergo CCA cl...
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Graduate Seminar Data augmentation has become an integral part of deep learning, as it is known to improve the generalization capabilities of neural networks.Since the most effective set of image transformations differs between tasks and domains, automatic data augmentation search aims to alleviate the extreme burden of manually finding the optimal image transformations. However, current methods are not able to jointly optimize all degrees of freedom: (1) the number of ...
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In the landscape of data stream processing, the challenges posed by online problems are of paramount importance. This paper introduces a pioneering method tailored to address these challenges within the context of data streams. While our initial focus centered on Complex Event Processing (CEP) problems, it is essential to underscore the versatility of our approach, as it is equally applicable to a diverse range of online problems. To the best of our knowledge, there exists no comprehensive metho...
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Assume that you have a huge graph, where edges and vertices may fail, and you want to answer questions about this graph quickly, without inspecting the whole graph. A fault-tolerant labeling scheme allows us to do just that. It is a distributed data structure, in which each vertex and edge is assigned a short label, such that given the labels of a pair of vertices s,t, and a set of failures F, you can answer questions about s and t in G\F. For example, determine if s and t are connected in G\F, ...
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Language Models (LMs) excel in many tasks, but understanding discourse – how sentences connect to form coherent text – remains a challenge. This is especially true for smaller models aiming to match the abilities of their larger counterparts in handling long and complex inputs. To address this, we introduce DEPTH, a new encoder-decoder model designed to foster robust discourse-level representations during the pre-training phase. DEPTH uniquely combines hierarchical sentence representations a...
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Between training and inference, lies a growing class of AI problems that involve fast optimization of a pre-trained model for a specific inference task. These are not pure “feed-forward” inference problems applied to a pre-trained model, because they involve some non-trivial inference-time optimization beyond what the model was trained for; neither are they training problems, because they focus on a specific input. These compute-heavy inference workflows raise new challenges in machine learn...
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יום המחקר העשירי לתארים מתקדמים בפקולטה למדעי המחשב יתקיים ביום רביעי, 03 באפריל 2024, בין השעות 12:30-14:30, בלובי של בניין טאוב למדעי המחשב. יום מחקר הוא הזדמנות עבור משתלמי הפקולטה להציג את מחקריהם באמצעות פוסטרים בפני חברי סגל, מנהלים מהתעשייה וסטודנטים בפקולטה ומכלל הט�...
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One of the major open problems in complexity theory is proving super-logarithmic lower bounds on the depth of circuits. Karchmer, Raz, and Wigderson (Computational Complexity 5(3/4), 1995) suggested approaching this problem by proving that depth complexity of a composition of two functions is roughly the sum of their individual depth complexities. They showed that the validity of this conjecture would imply the desired lower bounds.The intuition that underlies the KRW conjecture is that ...
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In the past year, numerous companies have incorporated Generative AI (GenAI) capabilities into new and existing applications, forming interconnected Generative AI (GenAI) ecosystems consisting of applications powered by GenAI services.While ongoing research highlighted risks associated with the GenAI layer of agents (e.g., dialog poisoning, membership inference, prompt leaking, jailbreaking), a critical question emerges: Can attackers develop malware to exploit the GenAI component of an ...
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Foundation models that connect vision and language have recently shown great promise for a wide array of tasks such as text-to-image generation. Significant attention has been devoted towards utilizing the visual representations learned from these powerful vision and language models. In this talk, I will present an ongoing line of research that focuses on the other direction, aiming at understanding what knowledge language models acquire through exposure to images during pretraining. We first co...
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In various applications, such as virtual reality and gaming, simulating the deformation of soft tissues in the human body during interactions with external objects is essential. Traditionally, Finite Element Methods (FEM) have been employed for this purpose, but they tend to be slow and resource-intensive. In this paper, we propose a unified representation of human body shape and soft tissue with a data-driven simulator of non-rigid deformations. This approach enables rapid simulation of realist...
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הנדסת העתיד של בלוקצ'יין 1.4.23 | 12:30-14:30 | מועדון משתלמים, טאוב, קומה 2 12:30 - מינגלינג וחטיפים 13:00 - עודד נאור, Ph.D., מנהל מוצר - Btockchain הומצא מחדש: צלילה עמוקה לתוך הפתרונות המשנים את המשחק של StarkWare 13:30 - נועה עובד, MSC, ראש צוות תוכנה - קידוד העתיד: הפ...
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יום פתוח ללימודים בטכניון יתקיים ביום ה' 28/3 החל מ-09:30 לפרטים והרשמה היכנסו לאתר רישום וקבלה בקישור ...
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In machine learning, there has been considerable interest over the past decade in understanding the ability of optimization algorithms to generalize—namely, to produce solutions (models) that extend well to unseen data—particularly in the context of overparameterized problems. I will survey several recent theoretical studies that explore generalization in classical convex optimization, that reveal intriguing behavior of common optimization methods and shed some light on the concept of gener...
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I will explain how successful innovations in software can usually be traced to just one or two "concepts" that offer new scenarios that, with seemingly small shifts, radically change how an application is used. I give examples from apps such as Zoom, WhatsApp and Calendly. I explain how concepts, each with their own characteristic scenarios, can be composed to form apps. I explain how this idea can be used to make software more usable, modular and consistent. More information at:...
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Age of Information (AoI) is a recently proposed performance metric that captures the freshness of the information from the perspective of the application. AoI measures the time that elapsed from the moment that the most recently received packet was generated to the present time. In this talk, we explore the AoI optimization problem in wireless networks.We start by considering a wireless network with a number of nodes transmitting information to a base station and develop low-complexity t...
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ה- Tech Experience של אינטל מגיע לקמפוס! מוכנים לפיקניק הטכנולוגי הכי חדשני שראיתם אי פעם? העמסנו כלים טכנולוגיים ואת המוחות הכי גדולים שלנו לטראק ואנחנו בדרך אליכם יום רביעי 27.3 | בניין מאייר קומה 3 10:00 - AR | VR | HR בואו להכיר אותנו ואת הטכנולוגיות שלנו 12:30 ...
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We propose a new approach for generative modeling based on training a neural network to be idempotent. An idempotent operator is one that can be applied sequentially without changing the result beyond the initial application, namely f(f(z))=f(z). The proposed model f is trained to map a source distribution (e.g, Gaussian noise) to a target distribution (e.g. realistic images) using the following objectives: (1) Instances from the target distribution should map to themselves, namely f(x)=x. We de...
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In this lecture we will explore collaborative caching algorithms in order to boost the effectiveness of caches in a distributed storage system. I'll introduce a scheme that partitions each node’s cache into two conceptual regions: an egoistic area whose goal is to contain the most valuable data for the node that owns the cache, and an altruistic area whose goal is to contain the most valuable data for the system as a whole. Each node’s division between these two regions is dynamically adjust...
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We propose a new way to explain and to visualize neural network classification through a decomposition-based explainable AI. Instead of providing an explanation heatmap, our method yields a decomposition of the image into class-agnostic and class-distinct parts, with respect to the data and chosen classifier. Following a fundamental signal processing paradigm of analysis and synthesis, the original image is the sum of the decomposed parts. We thus obtain a radically different way of explaining c...
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A finite metric space $(X,d)$ on a set of points $X$ is just the shortest path metric $d$ on a positively weighted graph $G=(X,E)$. In the online setting, the vertices of the input finite metric space $(X,d)$ are exposed one by one, together with their distances $d(*,*)$to the previously exposed vertices. The goal is to embed (map) $X$ into a given host metric space $(H,d_H)$ (finite or not) and so to distort the distances as little as possible (distortion is the worst case ratio of how ...
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מוזמנים ליום זרקור של חברת מובילאיי בטכניון יום רביעי 20.3 | 12:30 | בניין טאוב...
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The surveillance economy, where tracking and collecting data on uses for the purpose of advertising and other actions, is central to much of the money-making enterprises of the modern technology ecosystem. Due to regulations and other forces, some of the largest companies, such as Google and Apple, have prioritized mechanisms for users to better manage and receive information about the kinds of data that is being collected about them. In this talk, I will explore how effective these mechanisms r...
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יאהו מקיימים מפגש ייעודי עם סטודנטים לתארים מתקדמים יום שלישי, 19/3 ב-12:30 במועדון משתלמים בתוכנית: היכרות עם המחקר של יאהו בישראל ותוכנית התמחויות הקיץ. להרשמה לאירוע כאן מחכים לכם!! ...
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הצטרפו אלינו להרצאה ופיצה! אתגרי אבטחת סייבר בעידן המודרני מאת ירדן צוקרמן, מנהל אבטחת תוכנה, Nvidia יום שני | 18 במרץ, 2024 | 18:00 | אודיטוריום הפסנתר בניין טאוב הרשמה ...
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I'll present our paper about the role that null messages play in synchronous systems with and without failures. Our work provides necessary and sufficient conditions on the structure of protocols for information transfer and coordination there. We start by introducing a new and more refined definition of null messages. A generalization of message chains that allow these null messages is provided and is shown to be necessary and sufficient for information transfer in reliable systems. Coping with...
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Differential Privacy (DP) is a mathematical framewirk for ensuring the privacy of individuals in a dataset. Roughly speaking, it guarantees that privacy is protected in data analysis by ensuring that the output of an analysis does not reveal sensitive information about any specific individual, regardless of whether their data is included in the dataset or not.This talk presents a unified framework for characterizing both pure and approximate differentially private learnabiliity under the...
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To deploy and operate deep neural models in production, the quality of their predictions, which might be contaminated benignly or manipulated maliciously by input distributional deviations, must be monitored and assessed. Specifically, we study the case of monitoring the healthy operation of a deep neural network (DNN) receiving a stream of data, with the aim of detecting input distributional deviations over which the quality of the network’s predictions is potentially damaged. Using selective...
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Multi-rotor aerial autonomous vehicles (MAVs) primarily rely on vision for navigation purposes. However, visual localization and odometry techniques suffer from poor performance in low or direct sunlight, a limited field of view, and vulnerability to occlusions. Acoustic sensing can serve as a complementary or even alternative modality for vision in many situations, and it also has the added benefits of lower system cost and energy footprint, which is especially important for micro aircraft. Thi...
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We propose that spaceborne polarimetric imagers can be calibrated, or self-calibrated using zodiacal light (ZL). ZL is created by a cloud of interplanetary dust particles. It has a significant degree of polarization in a wide field of view. From space, ZL is unaffected by terrestrial disturbances. ZL is insensitive to the camera location, so it is suited for simultaneous cross-calibration of satellite constellations. ZL changes on a scale of months, thus being a quasi-constant target in realisti...
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I will share some of my learnings from working on problems on the intersection of Distributed Computing and Cryptography.On the one hand, I will show how some cryptographic protocols (MPC and DKG) can be improved by using distributed computing counterparts for notions such as zero knowledge proofs and proofs of knowledge. On the other hand, I will show how distributed computing protocols (ABA) can be improved by carefully using notions of binding from cryptography. One recurring theme is...
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Most works on modeling the conversation history in Conversational Question Answering (CQA) report a single main result on a common CQA benchmark. While existing models show impressive results on CQA leaderboards, it remains unclear whether they are robust to shifts in setting (sometimes to more realistic ones), training data size (e.g. from large to small sets) and domain. In this work, we design and conduct the first large-scale robustness study of history modeling approaches for CQA. We find t...
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אנבידיה מזמינה אתכם ליום זרקור וירטואלי בו מהנדסי ומהנדסות החברה יספרו על הקבוצות השונות והמשרות הפתוחות יום ראשון, 10 במרץ, בין השעות 18:00-19:30 להרשמה לאירוע כאן ...
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חברת Cadence מגיעה ליום זרקור בפקולטה יום ד' 6/3 | 12:30-14:30 | לובי טאוב בתוכנית: מפגש עם צוותי הגיוס, המהנדסים והמהנדסות לשיחה 1:1 על הזדמנויות תעסוקה וטיפים לכתיבת קו"ח. וכמובן מרצ' ומתוקים. מחכים לכם!! ...
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We design a new stochastic first-order algorithm for approximately solving matrix games as well as the more general problem of minimizing the maximum of smooth convex functions. Our central tool is ball oracle acceleration: a technique for minimizing any convex function with a small number of calls to a ball oracle that minimizes the same function restricted to a small ball around the query point. To design an efficient ball oracle for our problems of interest we leverage stochastic gradient des...
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Early time classification algorithms aim to label a stream of features without processing the full input stream, while maintaining accuracy comparable to that achieved by applying the classifier to the entire input. In this paper, we introduce a statistical framework that can be applied to any sequential classifier, formulating a calibrated stopping rule. This data-driven rule attains finite-sample, distribution-free control of the accuracy gap between full and early-time classification. We star...
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התקבלת למשרת סטודנט! מה עכשיו? קהילת הסטודנטיות בפקולטה SHE S מזמינה אותך למפגש בנושא: משרת סטודנט ראשונה: על ההזדמנויות, האתגרים והמיומנויות שרוכשים בעבודה הראשונה יום שני, 4/3 בשעה 18:30 באודיטוריום פסנתר נא הירשמי מראש: כאן ד�...
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We study the problem of computing an embedding of the tuples of a relational database in a manner that is extensible to dynamic changes of the database. In this problem, the embedding should be stable in the sense that it should not change on the existing tuples due to the embedding of newly inserted tuples (as database applications might already rely on existing embeddings); at the same time, the embedding of all tuples, old and new, should retain high quality. This task is challenging since in...
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יום זרקור לחברת אנבידיה בטכניון ב-28.2.24 אנבידיה מגיעה לפגוש את הסטודנטים והסטודנטיות של הפקולטה למדעי המחשב בטכניון! בואו לפגוש את מהנדסי ומהנדסות החברה פנים מול פנים יום רביעי 28.2.24 | 12:30-14:30 | לובי טאוב, קומה 0...
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Theory Seminar: An f-edge fault-tolerant distance sensitivity oracle (f-DSO) is a data-structure that, when queried with two vertices (s, t) and a set F of at most f edges of a graph G with n vertices, returns an estimate tilde{d}(s,t,F) of the distance d(s,t,F) from s to t in G – F. The oracle has stretch alpha if the estimate satisfies d(s,t,F) le tilde{d}(s,t,F) le alpha cdot d(s,t,F) . In the last two decades, extensive research has focused on developing efficient f-DSOs. This research aim...
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Locks are frequently used in concurrent systems to simplify code and ensure safe access to contended parts of memory. However, they are also known to cause bottlenecks in concurrent code, leading practitioners and theoreticians to sometimes opt for more intricate lock-free implementations. In this talk, I’ll show that, despite the seeming contradiction, it is possible to design practically and theoretically efficient lock-free locks; I'll present a lock-free lock algorithm with good bounds on ...
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In this talk, we delve into several fundamental questions in deep learning. We start by addressing the question, "What are good representations of data?" Recent studies have shown that the representations learned by a single classifier over multiple classes can be easily adapted to new classes with very few samples. We offer a compelling explanation for this behavior by drawing a relationship between transferability and an emergent property known as neural collapse. Later, we explore why certain...
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In the last decade, DNA-based storage systems emerged as a potential data archival solution due to their high data density and durability. This research delves into intrinsic error characteristics of DNA storage systems to devise robust coding strategies and innovative algorithms for enhanced reliability, efficiency, scalability, and cost-effectiveness. The research propels DNA storage feasibility while contributing to foundational theory.The work analyzes combinatorial structures tied t...
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High-dimensional data is increasingly available in diverse applications, ranging from images to shapes represented as point clouds. Such data raises novel questions and offers a unique opportunity to study them by developing new machine-learning tools. While the analysis of an individual sample may be challenging, leveraging the power of the data collection can be effective in tackling complex tasks. This talk delves into the challenges associated with studying such data, particularly focusing o...
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We show that every language in NP has an Interactive Oracle Proof (IOP) with inverse polynomial soundness error and small query complexity. This achieves parameters that surpass all previously known PCPs and IOPs. Specifically, we construct an IOP with perfect completeness, soundness error 1/n, round complexity O(loglog n), proof length poly(n) over an alphabet of size O(n), and query complexity O(loglog n). This is a step forward in the quest to establish the sliding-scale conjecture for IOPs (...
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This talk focuses on the distributed task of constructing an approximate \emph{maximum weight independent set (MWIS)}. Specifically, we are interested in deterministic CONGEST algorithms whose approximation guarantees are expressed as a function of the graph's \emph{arboricity} $\alpha$.Generally speaking, efficient deterministic non-trivial approximation algorithms for MWIS were not known until the recent breakthrough of Faour et al.~[SODA 2023] that obtained an $O(\Delta)$-approximatio...
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In clinical settings, a significant portion of ECG data is typically available in printed form, and the most convenient means of digitizing this information involves utilizing a mobile device. Despite notable progress in AI-based techniques for paper-based 12-lead ECG analysis, their adoption in clinical practice remains limited primarily due to challenges such as inadequate accuracy in clinical settings and a restricted ability to diagnose various cardiac conditions. Our objective was to tackle...
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Suppose that n forecasting experts (or functions) are providing daily rain/no-rain predictions, and the best among them is mistaken in at most k many days. For how many days will a person allowed to observe the predictions, but has no weather-forecasting prior knowledge, mispredict?In this talk, we will discuss how such classical problems can be reduced to calculating the (average) depth of binary trees, by using newly introduced complexity measures (aka dimensions) of the set of experts...
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With the rapid increase of storage demands and working sets of modern mobile apps, maintaining high I/O performance in mobile SSDs under strict resource constraints is challenging. The Flash Translation Layer (FTL) must increase the capacity of the Logical-To-Physical (L2P) address translation cache to keep up with the new workloads, but it comes at the cost of scaling the on-die SRAM, resulting in higher chip area, power consumption, and costs.In this talk, I will present RQFTL, a deman...
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בואו להיות חלק מקבוצת Capture The Flag-CTF חדשה בפקולטה המפגשים מתקיימים מדי יום ג' בשעה 18:30 בטאוב 9 וכוללים הרצאות אורח והתנסות מעשית בפתרון אתגרים. כולם מוזמנים! מתחילים ובעלי ניסיון לפרטים: Technionctf.com השבוע 13.2.24: 18:30 | טאוב 2 | עומר אטיאס - �...
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QUIC is an emerging transport protocol, offering multiple advantages over TCP. Yet, to fully unleash QUIC’s potential, a paradigm shift is needed in existing network infrastructure. We propose a novel 0-RTT-aware load balancing algorithm. 0-RTT is crucial for web performance, particularly on mobile networks. Our load balancing algorithm ensures 0-RTT while maintaining near-optimal load balancing performance.Through extensive simulations, using both synthetic and real-world traffic trac...
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מוזמנים ליום זרקור של חברת פיינל יום רביעי 07.02.2024 | 12:30-14:30 | אודיטוריום מרכז המבקרים 012, קומה 0 12:30 - בואו לפגוש מהנדסים, חוקרים ואת צוות הגיוס בפיינל, ולהכיר את היום יום בפיינל. 13:15 - מפגש על אופציות, הסתברויות ועולם האלגוטריידינג | נועם הורוביץ - חו�...
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בואו להיות חלק מקבוצת Capture The Flag-CTF חדשה בפקולטה המפגשים מתקיימים מדי יום ג' בשעה 18:30 בטאוב 9 וכוללים הרצאות אורח והתנסות מעשית בפתרון אתגרים. כולם מוזמנים! מתחילים ובעלי ניסיון לפרטים: Technionctf.com השבוע 6.2.24: מתחילים: פורנזיקה & רש�...
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בואו להיות חלק מקבוצת Capture The Flag-CTF חדשה בפקולטה המפגשים מתקיימים מדי יום ג' בשעה 18:30 בטאוב 9 וכוללים הרצאות אורח והתנסות מעשית בפתרון אתגרים. כולם מוזמנים! מתחילים ובעלי ניסיון לפרטים: Technionctf.com השבוע 6.2.24: מתחילים: פורנזיקה & רשת�...
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Many object-oriented applications in algorithm design rely on objects never changing during their lifetime. This is often tackled by marking object references as read-only, e.g., using the const keyword in C++. In other languages like Python or Java where such a concept does not exist, programmers rely on best practices that are entirely unenforced. While reliance on best practices is obviously too permissive, const-checking is too restrictive: it is possible for a method to mutate the internal ...
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The visual object category reports of artificial neural networks (ANNs) are notoriously sensitive to tiny, adversarial image perturbations. Because human category reports (aka human percepts) are thought to be insensitive to those same small-norm perturbations — and locally stable in general — this argues that ANNs are incomplete scientific models of human visual perception. Consistent with this, we show that when small-norm image perturbations are generated by standard ANN models, human obj...
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We study secure multiparty computation in the asynchronous setting with perfect security and optimal resilience (less than one-fourth of the participants are malicious). It has been shown that every function can be computed in this model [Ben-OR, Canetti, and Goldreich, STOC'1993]. Despite 30 years of research, all protocols in the asynchronous setting require $\Omega(n^2C)$ communication complexity for computing a circuit with $C$ multiplication gates. In contrast, for nearly 15 years, in the ...
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We study several graph layout problems with a min max objective. Here, given a graph we wish to find a linear ordering of the vertices that minimizes some worst case objective over the natural cuts in the ordering; which separate an initial segment of the vertices from the rest. A prototypical problem here is cutwidth, where we want to minimize the maximum number of edges crossing a cut. The only known algorithm here is by [Leighton-Rao J.ACM 99] based on recursively partitioning the graph using...
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We study several graph layout problems with a min max objective. Here, given a graph we wish to find a linear ordering of the vertices that minimizes some worst case objective over the natural cuts in the ordering; which separate an initial segment of the vertices from the rest. A prototypical problem here is cutwidth, where we want to minimize the maximum number of edges crossing a cut. The only known algorithm here is by [Leighton-Rao J.ACM 99] based on recursively partitioning the graph using...
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Racing to be first to market and deploy new features, developers rely on many external libraries to underpin their software. Each library uses more libraries, creating vast networks of dependencies that the developers know little about and have no control over, forming a knowledge gap that quickly turns into technical debt. Repaying this debt is difficult, as analyzing or examining all libraries is infeasible, and worse, the debt keeps growing due to frequent library updates. Attackers move quic...
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When training large neural networks, there are typically many solutions that perfectly fit the training data. Nevertheless, gradient-based methods often have a tendency to reach those which generalize well, namely, perform well also on test data. Thus, the training algorithm seems to be implicitly biased towards certain networks, which exhibit good generalization performance. Understanding this “implicit bias” has been a subject of extensive research recently. Moreover, in contradiction to c...
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Text-to-image diffusion models (T2I) use a latent representation of a text prompt to guide the image generation process. However, the encoder that produces the text representation is largely unexplored. We propose the Diffusion Lens, a method for analyzing the text encoder of T2I models by generating images from its intermediate representations. Using the Diffusion Lens, we perform an extensive analysis of two recent T2I models.We find that the text encoder gradually builds prompt repres...
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Hyperproperties are system properties that relate multiple execution traces to one another. Hyperproperties are essential to express a wide range of system requirements such as information flow and security policies; epistemic properties like knowledge in multi-agent systems; fairness; and robustness. With the aim of verifying program correctness, the two major challenges are (1) providing a specification language that can precisely express the desired properties; and (2) providing scalable veri...
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Stochastic convex optimization is one of the most well-studied models for learning in modern machine learning. Nevertheless, a central fundamental question in this setup remained unresolved: How many data points must be observed so that any empirical risk minimizer (ERM) shows good performance on the true population? This question was proposed by Feldman who proved that Ω(\frac{d}{ϵ} + \frac{1}{ϵ^2}) data points are necessary (where d is the dimension and ε > 0 is the accuracy parameter). P...
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קבוצת Technion CTF Team נפתחת בפקולטה אנו מזמינים אתכם להצטרף למפגשי Capture The Flag - CTF .CTF היא תחרות אתגרי סייבר ואבטחת מידע בנושאים: קריפטוגרפיה, הנדסה לאחור, פורנזיקה, ווב וכיוצ"ב. המפגשים יכללו הרצאות אורח והתנסות מעשית בפתרון אתגרים. מוזמנים להצטרף סטודנטים מתחילים וסטוד�...
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In this talk we consider the problem of manifold reconstruction from oriented point clouds for embedded manifolds of co-dimension larger than one. Using the framework of Poisson Surface Reconstruction, and formulating the problem in the language of alternating products, we show that the earlier approach for reconstructing hyper-surfaces extends to general manifolds, at the cost of replacing a quadratic energy with a multi-quadratic energy. We provide an efficient iterative hierarchical solver th...
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Vizing’s Theorem provides an algorithm that edge colors any graph of maximum degree Δ using Δ+1 colors, which is necessary for some graphs, and at most one higher than necessary for any graph. In online settings, the trivial greedy algorithm requires 2Δ-1 colors, and Bar-Noy, Motwani and Naor in the early 90s showed that this is best possible, at least in the low-degree regime. In contrast, they conjectured that for graphs of superlogarithmic-in-n maximum degree, much better can be done, an...
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Blockchains have ignited interest in Internet-scale consensus as a vital building block for decentralized applications and services that promise egalitarian access and robustness to faults and abuse. While the study of consensus has a 40+ year tradition, the new Internet-scale setting requires a fundamental rethinking of models, desiderata, and protocols. An emergent key challenge is to simultaneously serve clients with different requirements regarding the two fundamental aspects of security, li...
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12-lead electrocardiogram (ECG) recordings can be collected in any clinic and the interpretation is performed by a clinician. Modern machine learning tools may make them automatable. However, a large fraction of 12-lead ECG data is still available in printed paper or image only and comes in various formats. To digitize the data, smartphone cameras can be used. Nevertheless, this approach may introduce various artifacts and occlusions into the obtained images.Here, I will present 5 papers...
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I will present a theoretical framework for analyzing learning algorithms which rely on dependent, rather than independent, observations. While a common assumption is that the learning algorithm receives independent datapoints, such as unrelated images or texts, this assumption often does not hold. An example is data on opinions across a social network, where opinions of related people are often correlated, for example as a consequence of their interactions. I will present a line of work that mod...
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