Bar-Ilan Faculty of Engineering

We study the question of whether a sequence of positive integers is the degree sequence of some outerplanar (a.k.a. 1-page book embeddable) graph G. If so, G is an outerplanar realization of d and d is an outerplanaric sequence. The case where is easy, as d has a realization by a forest (which is trivially an outerplanar graph). In this paper, we consider the family of all sequences d of even sum , where is the number of x’s in d. (The second inequality is a necessary condition for a sequence d with to be outerplanaric.) We partition into two disjoint subfamilies, , such that every sequence in is provably non-outerplanaric, and every sequence in is given a realizing graph G enjoying a 2-page book embedding (and moreover, one of the pages is also bipartite).

The Ternary Content Addressable Memory (TCAM) is a crucial component of satellite communication systems. Space-oriented TCAMs face unique challenges, as they must operate within a very limited energy budget and are susceptible to high Soft Error Rates (SER) due to ionizing particle radiation. The Dual Interlocked Storage Cell (DICE) based memory is capable of withstanding soft errors. However, its reliability diminishes in presence of multiple node upsets. Moreover, recent studies indicate that DICE resilience to even single-node upsets degrades in advanced technology nodes. This issue is further exacerbated by the scaling of the supply voltage to reduce power consumption. In this paper, we propose SpaceCAM, a DICE-based TCAM that overcomes the above limitations and enables aggressive voltage scaling while withstanding multiple node upsets in each memory row. SpaceCAM enables soft error …

The time-multiplexing super-resolution concept requires post-processing for extracting the super-resolved image. Moreover, to perform the post-processing image restoration, one needs to know the exact high-resolution encoding pattern. Both of these limiting conditions are overcome by the method and experiment reported in this letter.

The time-multiplexing super-resolution concept requires post-processing for extracting the super-resolved image. Moreover, to perform the post-processing image restoration, one needs to know the exact high-resolution encoding pattern. Both of these limiting conditions are overcome by the method and experiment reported in this letter.

Acknowledging that current analytical models alone cannot provide the performance and sophistication that state-of-the-art systems should be endowed with, in the last decade, we have witnessed a rapid paradigm shift from model-based algorithms to data-driven ones, using primarily deep neural networks (DNNs), with many successful solutions in diverse application areas, such as speech and audio enhancement, source separation and localization, dereverberation, sparse representations of audio signals, audio rendering, acoustic event detection, music information retrieval, and more. However, learning-based methods, especially those based on DNNs, do not usually embrace the physical nature of the problem and rather optimize the nonlinear relationship between training data and expected results, relying on only computational power. Many problems call for more efficient solutions that minimize the …

Source-free domain adaptation (SFDA) aims to adapt a source-trained model to an unlabeled target domain without access to the source data. SFDA has attracted growing attention in recent years, where existing approaches focus on self-training that usually includes pseudo-labeling techniques. In this paper, we introduce a novel noise-learning approach tailored to address noise distribution in domain adaptation settings and learn to de-confuse the pseudo-labels. More specifically, we learn a noise transition matrix of the pseudo-labels to capture the label corruption of each class and learn the underlying true label distribution. Estimating the noise transition matrix enables a better true class-posterior estimation, resulting in better prediction accuracy. We demonstrate the effectiveness of our approach when combined with several SFDA methods: SHOT, SHOT++, and AaD. We obtain state-of-the-art results on three domain adaptation datasets: VisDA, DomainNet, and OfficeHome.

Interaction-free measurement (IFM) is a promising technique for low-dose detection and imaging, offering the unique advantage of probing an object with an overall reduced absorption of the interrogating photons. We propose an experiment to demonstrate IFM in the single x ray photon regime. The proposed scheme relies on the triple-Laue (LLL) symmetric x ray interferometer, where each Laue diffraction acts as a lossy beam splitter. In contrast to many quantum effects which are highly vulnerable to loss, we show that an experimental demonstration of this effect in the x ray regime is feasible and can achieve detection with reduced dose and high IFM efficiency even in the presence of substantial loss in the system. The latter aspect is claimed to be a general property of IFM based on our theoretical analysis. We scrutinize two suitable detection schemes that offer a dose reduction of up to half compared with direct …

An erratum to correct a mistake in the caption of Fig. 5. [Opt. Express 32, 12040 (2024)] 10.1364/OE.515419. The corrections have no influence on the results and conclusions of the original paper.

The Ternary Content Addressable Memory (TCAM) is a crucial component of satellite communication systems. Space-oriented TCAMs face unique challenges, as they must operate within a very limited energy budget and are susceptible to high Soft Error Rates (SER) due to ionizing particle radiation. The Dual Interlocked Storage Cell (DICE) based memory is capable of withstanding soft errors. However, its reliability diminishes in presence of multiple node upsets. Moreover, recent studies indicate that DICE resilience to even single-node upsets degrades in advanced technology nodes. This issue is further exacerbated by the scaling of the supply voltage to reduce power consumption. In this paper, we propose SpaceCAM, a DICE-based TCAM that overcomes the above limitations and enables aggressive voltage scaling while withstanding multiple node upsets in each memory row. SpaceCAM enables soft error …

The time-multiplexing super-resolution concept requires post-processing for extracting the super-resolved image. Moreover, to perform the post-processing image restoration, one needs to know the exact high-resolution encoding pattern. Both of these limiting conditions are overcome by the method and experiment reported in this letter.

Epidemic spreading over populations networks has been an important subject of research for several decades, and especially during the Covid-19 pandemic. Most epidemic outbreaks are likely to create multiple mutations during their spreading over the population. In this paper, we study the evolution of a pathogen which can mutate continuously during the epidemic spreading. We consider pathogens whose mutating parameter is the mortality mean-time, and study the evolution of this parameter over the spreading process. We use analytical methods to compute the dynamic equation of the epidemic and the conditions for it to spread. We also use numerical simulations to study the pathogen flow in this case, and to understand the mutation phenomena. We show that the natural selection leads to less violent pathogens becoming predominant in the population. We discuss a wide range of network structures and show how different effects are manifested in each case.

Semi Insulating GaAs alpha detectors with anode GaAs P+ contact layer were fabricated and characterized. The contact layer growth was carried out by Metal Organic Chemical Vapor Deposition (MOCVD) and the detector performances were compared to the performances of a front Schottky contact detector. The front side Schottky contact suffers from electron injection into the GaAs substrate. This injection is eliminated by using a P+ anode blocking layer with an ohmic contact, resulting in a reduction of leakage current at reverse bias values of up to 70 V. For example, at 30 V the leakage currents were 50 nA/cm2 and 150 nA/cm2 for the ohmic and the Schottky anode detectors, respectively. For both detectors, the charge collection efficiency was increased by a factor of ~2 after grinding the substrates from 650 μm to 310 μm thickness, with no leakage current degradation. In addition, rapid thermal process (RTP …

Recognizing the need for innovative therapeutic approaches in the management of autoimmune diseases , our current investigation explores the potential of autologous extracellular vesicles (EVs), derived from blood of rheumatoid arthritis (RA) patients, to serve as therapeutic vectors to improve drug delivery. We found that circulating EVs derived from arthritic mice (Collagen-induced arthritis model) express the joint/synovia homing receptor, αVβ3 integrin. Importantly, both autologous labelled EVs, derived from blood of arthritic mice (Collagen antibody-induced arthritis model) and healthy mice-derived EVs, exhibit targeted migration toward inflamed synovia without infiltrating healthy joints, as demonstrated by an in-vivo imaging system. Furthermore, EVs derived from plasma of RA patients show an overexpression of αV integrin and are effectively taken up by LPS/TNFα-induced activated human synovial cell …

The inference of gene regulatory networks (GRNs) from single-cell RNAseq data allows for mechanistic characterization of the different cell states and their dynamics in complex biological processes. While numerous algorithms have been proposed to infer GRNs from single-cell transcriptomic data, multiple network solutions may explain the same dataset, posing a challenge for biologically meaningful interpretation. Here, we use the Reasoning Engine for Interaction Networks (RE:IN), a computational tool based on formal reasoning, to characterize GRN ensembles in the context of acute kidney injury (AKI). To this end, we applied RE:IN to a single-cell RNAseq dataset from a mouse ischemia reperfusion injury (IRI) model, focusing on distinct proximal tubule cell states related to kidney injury and repair. We first created an Abstract Boolean Network (ABN) model for the kidney using RE:IN and synthesized an …

The inference of gene regulatory networks (GRNs) from single-cell RNAseq data allows for mechanistic characterization of the different cell states and their dynamics in complex biological processes. While numerous algorithms have been proposed to infer GRNs from single-cell transcriptomic data, multiple network solutions may explain the same dataset, posing a challenge for biologically meaningful interpretation. Here, we use the Reasoning Engine for Interaction Networks (RE:IN), a computational tool based on formal reasoning, to characterize GRN ensembles in the context of acute kidney injury (AKI). To this end, we applied RE:IN to a single-cell RNAseq dataset from a mouse ischemia reperfusion injury (IRI) model, focusing on distinct proximal tubule cell states related to kidney injury and repair. We first created an Abstract Boolean Network (ABN) model for the kidney using RE:IN and synthesized an …

Leakage-resilient cryptography aims to protect cryptographic primitives from so-called" side channel attacks" that exploit their physical implementation to learn their input or secret state. Starting from the works of Ishai, Sahai and Wagner (CRYPTO03) and Micali and Reyzin (TCC04), most works on leakage-resilient cryptography either focus on protecting general computations, such as circuits or multiparty computation protocols, or on specific non-interactive primitives such as storage, encryption and signatures. This work focuses on leakage-resilience for the middle ground, namely for distributed and interactive cryptographic primitives. Our main technical contribution is designing the first secret-sharing scheme that is equivocal, resists adaptive probing of a constant fraction of bits from each share, while incurring only a constant blowup in share size. Equivocation is a strong leakage-resilience guarantee, recently introduced by Hazay et al.(ITC21). Our construction is obtained via a general compiler which we introduce, that transforms any secret-sharing scheme into an equivocal scheme against adaptive leakage. An attractive feature of our compiler is that it respects additive reconstruction, namely, if the original scheme has additive reconstruction, then the transformed scheme has linear reconstruction. We extend our compiler to a general paradigm for protecting distributed primitives against leakage, and show its applicability to various primitives, including secret sharing, verifiable secret sharing, function secret sharing, distributed encryption and signatures, and distributed zero-knowledge proofs. For each of these primitives, our paradigm …

In the Zero-Knowledge Proof (ZKP) of a disjunctive statement, and agree on B fan-in 2 circuits over a field; each circuit has inputs, multiplications, and one output.’s goal is to demonstrate the knowledge of a witness,, st where neither nor is revealed. Disjunctive statements are effective, for example, in implementing ZKP based on sequential execution of CPU steps.

Rhesus macaques (RMs) are vital models for studying human disease, and are invaluable to pre-clinical pipelines for vaccine discovery and testing. Particularly in this regard, they are often used to study infection and vaccine-associated broadly neutralizing antibody responses. This has resulted in an increasing demand for improved genetic resources for the immunoglobulin (IG) loci, which harbor antibody-encoding genes. However, the highly polymorphic and structurally variable nature of these loci have them historically challenging to sequence and characterize at the level of both the genome and expressed repertoire. To address these challenges, we have developed a novel integrated analysis workflow for conducting the combined processing of B cell receptor repertoire sequencing data with matched whole-genome and targeted long-read genomic sequencing data. Using this novel approach, we have assembled the largest collection of IG germline alleles reported to date, amassed from 106 Indian origin RMs. Using a conservative annotation approach, requiring sample-level internal validation from both genomic and expressed datasets, we created a comprehensive resource that captures extensive diversity of IG heavy and light chain variable (V), diversity (D), and joining (J) alleles, as well as leader, intronic, and recombination signal sequences (RSSs). This publicly available, continually updated database will advance vaccine research for infectious disease, and provide a robust foundation for immunogenomics and future translational research.

We address the problem of Reliable Broadcast in asynchronous message-passing systems with n nodes, of which up to t are malicious (faulty), in addition to a message adversary that can drop some of the messages sent by correct (non-faulty) nodes. We present a Message-Adversary-Tolerant Byzantine Reliable Broadcast (MBRB) algorithm that communicates O (| m|+ nκ) bits per node, where| m| represents the length of the application message and κ= Ω (log n) is a security parameter. This communication complexity is optimal up to the parameter κ. This significantly improves upon the state-of-the-art MBRB solution (Albouy, Frey, Raynal, and Taïani, TCS 2023), which incurs communication of O (n| m|+ n²κ) bits per node. Our solution sends at most 4n² messages overall, which is asymptotically optimal. Reduced communication is achieved by employing coding techniques that replace the need for all nodes to (re-) broadcast the entire application message m. Instead, nodes forward authenticated fragments of the encoding of m using an erasure-correcting code. Under the cryptographic assumptions of threshold signatures and vector commitments, and assuming n> 3t+ 2d, where the adversary drops at most d messages per broadcast, our algorithm allows at least 𝓁= n-t-(1+ ε) d (for any arbitrarily low ε> 0) correct nodes to reconstruct m, despite missing fragments caused by the malicious nodes and the message adversary.

Quantum correlations, like entanglement, represent the characteristic trait of quantum mechanics, and pose essential issues and challenges to the interpretation of this pillar of modern physics. Although quantum correlations are largely acknowledged as a major resource to achieve quantum advantage in many tasks of quantum technologies, their full quantitative description and the axiomatic basis underlying them are still under investigation. Previous works suggested that the origin of nonlocal correlations is grounded in principles capturing (from outside the quantum formalism) the essence of quantum uncertainty. In particular, the recently-introduced principle of Relativistic Independence gave rise to a new bound intertwining local and nonlocal correlations. Here we test such a bound by realizing together sequential and joint weak measurements on entangled photon pairs, allowing to simultaneously quantify both local and nonlocal correlations by measuring incompatible observables on the same quantum system without collapsing its state, a task typically forbidden in the traditional (projective) quantum measurement framework. Our results demonstrate the existence of a fundamental limit on the extent of quantum correlations, shedding light on the profound role of uncertainty in both enabling and balancing them.

Deep learning in general focuses on training a neural network from large labeled datasets. Yet, in many cases there is value in training a network just from the input at hand. This may involve training a network from scratch using a single input or adapting an already trained network to a provided input example at inference time. This survey paper aims at covering deep internal-learning techniques that have been proposed in the past few years for these two important directions. While our main focus will be on image processing problems, most of the approaches that we survey are derived for general signals (vectors with recurring patterns that can be distinguished from noise) and are therefore applicable to other modalities. We believe that the topic of internal-learning is very important in many signal and image processing problems where training data is scarce and diversity is large on the one hand, and on the other, there is a lot of structure in the data that can be exploited.