Bar-Ilan Faculty of Engineering

Enhancing resilience in distributed networks in the face of malicious agents is an important problem for which many key theoretical results and applications require further development and characterization. This work focuses on the problem of distributed optimization in multi-agent cyberphysical systems, where a legitimate agent's dynamic is influenced both by the values it receives from potentially malicious neighboring agents, and by its own self-serving target function. We develop a new algorithmic and analytical framework to achieve resilience for the class of problems where stochastic values of trust between agents exist and can be exploited. In this case we show that convergence to the true global optimal point can be recovered, both in mean and almost surely, even in the presence of malicious agents. Furthermore, we provide expected convergence rate guarantees in the form of upper bounds on the expected squared distance to the optimal value. Finally, we present numerical results that validate the analytical convergence guarantees we present in this paper even when the malicious agents compose the majority of agents in the network.

Temporal alignment of sequences is a fundamental challenge in many applications, such as computer vision and bioinformatics, where local time shifting needs to be accounted for. Misalignment can lead to poor model generalization, especially in high-dimensional sequences. Existing methods often struggle with optimization when dealing with high-dimensional sparse data, falling into poor alignments. Feature selection is frequently used to enhance model performance for sparse data. However, a fixed set of selected features would not generally work for dynamically changing sequences and would need to be modified based on the state of the sequence. Therefore, modifying the selected feature based on contextual input would result in better alignment. Our suggested method, Conditional Deep Canonical Temporal Time Warping (CDCTW), is designed for temporal alignment in sparse temporal data to address these challenges. CDCTW enhances alignment accuracy for high dimensional time-dependent views be performing dynamic time warping on data embedded in maximally correlated subspace which handles sparsity with novel feature selection method. We validate the effectiveness of CDCTW through extensive experiments on various datasets, demonstrating superior performance over previous techniques.

In recent years, it has become popular to tackle image restoration tasks with a single pretrained diffusion model (DM) and data-fidelity guidance, instead of training a dedicated deep neural network per task. However, such "zero-shot" restoration schemes currently require many Neural Function Evaluations (NFEs) for performing well, which may be attributed to the many NFEs needed in the original generative functionality of the DMs. Recently, faster variants of DMs have been explored for image generation. These include Consistency Models (CMs), which can generate samples via a couple of NFEs. However, existing works that use guided CMs for restoration still require tens of NFEs or fine-tuning of the model per task that leads to performance drop if the assumptions during the fine-tuning are not accurate. In this paper, we propose a zero-shot restoration scheme that uses CMs and operates well with as little as 4 NFEs. It is based on a wise combination of several ingredients: better initialization, back-projection guidance, and above all a novel noise injection mechanism. We demonstrate the advantages of our approach for image super-resolution, deblurring and inpainting. Interestingly, we show that the usefulness of our noise injection technique goes beyond CMs: it can also mitigate the performance degradation of existing guided DM methods when reducing their NFE count.

Training and fine-tuning large language models (LLMs) come with challenges related to memory and computational requirements due to the increasing size of the model weights and the optimizer states. Various techniques have been developed to tackle these challenges, such as low-rank adaptation (LoRA), which involves introducing a parallel trainable low-rank matrix to the fixed pre-trained weights at each layer. However, these methods often fall short compared to the full-rank weight training approach, as they restrict the parameter search to a low-rank subspace. This limitation can disrupt training dynamics and require a full-rank warm start to mitigate the impact. In this paper, we introduce a new method inspired by a phenomenon we formally prove: as training progresses, the rank of the estimated layer gradients gradually decreases, and asymptotically approaches rank one. Leveraging this, our approach involves adaptively reducing the rank of the gradients during Adam optimization steps, using an efficient online-updating low-rank projections rule. We further present a randomized SVD scheme for efficiently finding the projection matrix. Our technique enables full-parameter fine-tuning with adaptive low-rank gradient updates, significantly reducing overall memory requirements during training compared to state-of-the-art methods while improving model performance in both pretraining and fine-tuning. Finally, we provide a convergence analysis of our method and demonstrate its merits for training and fine-tuning language and biological foundation models.

Temporal optics rises from the equivalence between light diffraction in free space and pulse dispersion in dispersive media, paving the way for the development of temporal devices and applications, such as time-lenses. A Four-wave mixing based time-lens allows single-shot measurements of ultra-short signals in high temporal resolution by imaging signals, and inducing temporal Fourier transform. We introduce a cascade time-lens by utilizing a cascade FWM process within the time-lens. We theoretically develop and experimentally demonstrate the cascade time-lens, and confirm that different cascade orders correspond to different effective temporal systems, leading to measuring in various temporal imaging configurations simultaneously with a single optical setup. This approach can simplify experiments and provide a more comprehensive view of a signal’s phase and temporal structure. Such capabilities are …

Accurate and reliable identification of the RTF between microphones with respect to a desired source is an essential component in the design of microphone array beamformers, specifically the MVDR criterion. Since an accurate estimation of the RTF in a noisy and reverberant environment is a cumbersome task, we aim at leveraging prior knowledge of the acoustic enclosure to robustify the RTF estimation by learning the RTF manifold. In this paper, we present a novel robust RTF identification method, tested and trained with real recordings, which relies on learning the RTF manifold using a GCN to infer a robust representation of the RTF in a confined area, and consequently enhance the beamformer's performance.

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).

Most emotion recognition systems fail in real-life situations (in the wild scenarios) where the audio is contaminated by reverberation. Our study explores new methods to alleviate the performance degradation of Speech Emotion Recognition (SER) algorithms and develop a more robust system for adverse conditions. We propose processing multi-microphone signals to address these challenges and improve emotion classification accuracy. We adopt a state-of-the-art transformer model, the Hierarchical Token-semantic Audio Transformer (HTS-AT), to handle multi-channel audio inputs. We evaluate two strategies: averaging mel-spectrograms across channels and summing patch-embedded representations. Our multimicrophone model achieves superior performance compared to single-channel baselines when tested on real-world reverberant environments.

Temporal alignment of sequences is a fundamental challenge in many applications, such as computer vision and bioinformatics, where local time shifting needs to be accounted for. Misalignment can lead to poor model generalization, especially in high-dimensional sequences. Existing methods often struggle with optimization when dealing with high-dimensional sparse data, falling into poor alignments. Feature selection is frequently used to enhance model performance for sparse data. However, a fixed set of selected features would not generally work for dynamically changing sequences and would need to be modified based on the state of the sequence. Therefore, modifying the selected feature based on contextual input would result in better alignment. Our suggested method, Conditional Deep Canonical Temporal Time Warping (CDCTW), is designed for temporal alignment in sparse temporal data to address …

In this paper, we study the problem of fair multi-agent multi-arm bandit learning when agents do not communicate with each other, except collision information, provided to agents accessing the same arm simultaneously. We provide an algorithm with regret O(N3f(log T)log T) (assuming bounded rewards, with unknown bound), where f(t) is any function diverging to infinity with t. In contrast to optimal algorithms which share the rewards with a selected leader, our algorithm does not require a centralized collection of the arm rewards, allowing each agent to keep its rewards private. We also significantly improved previous privacy-preserving algorithms with the same upper bound on the regret of order O(f(log T)log T) but an exponential dependence on the number of agents. Simulation results present the dependence of the regret on log T.

Introducing a domain shift, such as a change in language or environment, to a well-trained speech enhancement system can cause severe performance degradation. Most current research assumes that a domain shift has already been detected and focuses on either supervised or unsupervised domain adaptation techniques. Here, we address the problem of automatically detecting when a domain shift has occurred. We present a domain shift detection method based on monitoring the confidence of a network that predicts the quality of enhanced speech. The experimental results show that our method can effectively detect a domain mismatch between the training and test sets.

Accurate and continuous monitoring of blood pressure (BP) is essential for cardiovascular health assessment and early detection of potential health issues. Traditional BP measurement methods, such as the inflatable arm cuff, are often inconvenient and do not allow continuous tracking. This study introduces a new optical biosensor for non-invasive BP measurement based on the Iso-Pathlength (IPL) phenomenon, which isolates the scattering effects of light intensity to focus on absorption coefficients, providing a more accurate representation of blood volume changes. The biosensor consists of a single light source and five photodetectors, with one positioned at the IPL point. The sensor was tested on 44 subjects, with measurements taken on the upper arm and compared to reference BP readings from a traditional inflatable cuff monitor. The obtained light intensity was converted into absorption coefficients, from …

Biomedical optic systems provide rapid, non-invasive, and cost-effective solutions for diagnosing health conditions such as skin cancer. These conditions are influenced by optical properties, scattering and absorption, as well as by the different layers of the skin. The complexity of tissues makes it challenging to identify changes in specific layers and differentiate between their influences. Hence, we propose using the Q-sensing technique, which leverages polarized light to isolate superficial scattering from diffuse background signals, enabling the measurement of scattering coefficient and distinguishing layer contributions. By measuring co-polarized (I||) and cross-polarized (Isub>⊥) light, we utilize the Q parameter as it highlights superficial scattering. In our study, experiments were conducted with tissue-mimicking phantoms of varying thicknesses and scattering properties, validated through Monte Carlo simulations …

In this proceeding, we expand upon our recent work on the temporal Aharonov-Bohm (AB) effect, focusing on the joint temporal function (JSF) analysis to enhance the sensitivity and understanding of the system’s performance. Our original paper demonstrated the existence of a temporal analog of the Aharonov-Bohm effect using entangled photons and a temporal SU(1,1) interferometer. Here, we provide a more detailed exploration of the JSF, highlighting its role in improving signal-to-noise ratios (SNR) and its impact on measuring fast phase changes with enhanced temporal resolution. By leveraging the quantum properties of the system and utilizing the JSF between the signal and idler beams, we show that this approach allows for unprecedented sensitivity in detecting temporal phase shifts. This proceeding will also discuss how the JSF contributes to the interferometer’s ability to resolve femto-second dynamics …

Molecular characterization of brain tissues using optical methods present a problem of scale: brain tissues are intrinsically three-dimensional structures, with thickness of at least hundreds of micrometers; but nanoscale interrogation is needed to characterize molecules within neurites and synapses. Additionally, multiplexed interrogation of molecules is needed to characterize cell types and states inside brain tissues, and to detect deficiencies in neurological conditions. Currently, multiplexed imaging of molecules inside brain tissues is limited to thin sections, and almost impossible with super-resolution. Here we demonstrate multiplexed super-resolved characterization of thick brain tissues: complete fruit fly brain, human brain organoids, and mouse cortex.

Main challenge in designing nanoparticles (NPs) as nanocarriers for topical treatment drug is detecting NPs permeation through the skin. NPs are typically too small to be detected by regular noninvasive imaging techniques due to their size and depth. Thus, the research proposes the use of the iterative multi-plane optical properties extraction (IMOPE) technique. The technique is a non-invasive method for estimating optical properties of opaque media based on a phase image reconstruction and analysis, which enables the extraction of the sample’s optical properties. IMOPE technique has the capability to detect the presence of NPs in different skin layers as a change of the optical properties when tailored for the optical properties range of the biological sample and the NPs. In the research, an in vivo study was performed with mice treated with a gel formula of drug carried by NPs. After the mice were sacrificed the …

Photon-number splitting (PNS) attack poses a significant threat to the security of quantum key-distribution (QKD) systems that utilize weak coherent states (WCS). While this attack has been extensively explored in theory, its experimental realization remains elusive, raising questions about its practical implementation and impact. This study introduces a novel framework to experimentally demonstrate the PNS attack by leveraging single-photon Raman interaction (SPRINT), a well-developed technological capability. We analytically assess the feasibility and practical implications of this attack. Complementing our recently published analysis of the attack, in this work we calculate the detection statistics for phase-randomized WCS and analyze the purity of the quantum state post-attack. Our analysis reveals that indeed current technologies are sufficient to implement the PNS attack, but the eavesdropper’s information …

Communication of cancer cells with immune cells can inhibit or promote tumor proliferation. However, immune–tumor interactions in cancer tissues remain largely uncharacterized. A direct quantification of cell–cell interactions between individual immune and tumor cells can be obtained via in situ approaches, which use imaging to assess the identity and location of expressed genes. We recently developed a technology, termed expansion sequencing, which allows in situ sequencing of RNA molecules with super-resolution. Here, we show that super-resolved in situ sequencing can be used to quantify immune–tumor cell–cell interactions inside patients' biopsies, which might be utilized to predict response to immunotherapy drugs.

Objectives Dengue virus (DENV) infection is a significant global health concern, causing severe morbidity and mortality. While many cases present as a mild febrile illness, some progress to life-threatening severe dengue (SD). Early intervention is essential to improve outcomes, but current predictive methods lack specificity, burdening healthcare systems in endemic regions. Circulating long non-coding RNAs (lncRNAs) have emerged as stable and promising biomarkers. This study explored the use of lncRNAs as predictive markers for SD. Methods Differential expression and qPCR arrays were employed to identify lncRNAs associated with SD. Candidate lncRNAs were validated, and their plasma levels were measured in a cohort of Vietnamese dengue patients (n= 377) and healthy controls (n= 128) at admission. Machine learning algorithms were applied to predict the probability of SD progression. Results The …

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 (CRYPTO ‘03) and Micali and Reyzin (TCC ‘04), 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 incurs only a constant blowup in share size. Equivocation is a strong leakage-resilience guarantee, recently …

Using pluripotent stem cells from patients, it is now possible to create three-dimensional (3D) brain organoids that can be used in the study of neurological disorders. However, measuring the molecular content of 3D organoids is still a challenge, limiting the usability of organoids. Here we demonstrate the first multiplexed super-resolved characterization of intact brain organoids, using a combination of expansion microscopy, serial protein staining, expansion sequencing and enhanced super-resolution radial fluctuations. Overall, without special hardware or dyes, we obtain resolution improvement of ~10x, and perform highly multiplexed and super-resolved RNA and protein interrogation of intact brain organoids.