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Recent advances in Natural Language Processing (NLP) have ignited interest in developing effective methods for predicting protein-ligand interactions (PLIs) given their relevance to drug discovery and protein engineering efforts and the ever-growing volume of biochemical sequence and structural data available. The parallels between human languages and the "languages" used to represent proteins and ligands have enabled the use of NLP machine learning approaches to advance PLI studies. In this review, we explain where and how such approaches have been applied in the recent literature and discuss useful mechanisms such as long short-term memory, transformers, and attention. We conclude with a discussion of the current limitations of NLP methods for the study of PLIs as well as key challenges that need to be addressed in future work., Comment: 52 Pages and 3 Figures

Accurate and real-time monitoring of saturated oxygen level of the blood is an important clinical issue gaining great attention in recent years and during COVID 19 pandemic. Monitoring the patients` ventilation and respiration dynamic is widespread and has been adopted as a standard for anesthesia, neonatal care, and post-operative recovery as well. In this paper the fundamentals of current pulse oximeter devices are reviewed and development of a prototype is explained. Our system has two red and infrared light sources radiating to the tissue. The amount of absorbed and transmitted energy is measured by the photodetector, and finally the amount of oxygen content of the blood is estimated based on these values. We used MAX30100 module. Our system has two advantages relating to similar devices; robustness to errors due to power supply variations, ambient light and motion artifacts. Our system has also the ability to estimate heart rate and transmit the data to a smart phone. This makes our proposed system a potentially good hardware for home monitoring of blood oxygen and respiration efficiency if collaborated with a good mobile application for detecting blood de-saturations., Comment: 4 pages, 6 figures, 1 table

As autonomous vehicles become more prevalent, highly accurate and efficient systems are increasingly critical to improve safety, performance, and energy consumption. Efficient management of energy-reliability tradeoffs in these systems demands the ability to predict various conditions during vehicle operations. With the promising improvement of Large Language Models (LLMs) and the emergence of well-known models like ChatGPT, unique opportunities for autonomous vehicle-related predictions have been provided in recent years. This paper proposed MAPS using LLMs as map reader co-drivers to predict the vital parameters to set during the autonomous vehicle operation to balance the energy-reliability tradeoff. The MAPS method demonstrates a 20% improvement in navigation accuracy compared to the best baseline method. MAPS also shows 11% energy savings in computational units and up to 54% in both mechanical and computational units.

The weak quantum signal amplification is an essential task in quantum computing. In this study, a recently introduced structure of Josephson junctions array called Blochnium (N series Quarton structure) is utilized as a parametric amplifier. We begin by theoretical deriving the system's Lagrangian, quantum Hamiltonian, and then analyze the dynamics using the quantum Langevin equation. By transforming these equations into the Fourier domain and employing the input-output formalism, leading metric indicators of the parametric amplifier become calculated. The new proposed design offers significant advantages over traditional designs due to its ability to manipulate nonlinearity. This premier feature enhances the compression point (P1dB) of the amplifier dramatically, and also provides its tunability across a broad band. The enhanced linearity, essential for quantum applications, is achieved through effective nonlinearity management, which is theoretically derived. Also, the ability to sweep the C-band without significant spectral overlap is crucial for frequency multiplexing in scalable quantum systems. Simulation results show that Blochnium parametric amplifiers can reach to a signal gain around 25 dB with a compression point better than of -92 dBm. Therefore, our proposed parametric amplifier, with its superior degree of freedom, surpasses traditional designs like arrays of Josephson junctions, making it a highly promising candidate for advanced quantum computing applications.

We analyze the coherence dynamics of a central spin coupled to a spin chain with a time-dependent noisy magnetic field, focusing on how noise influences the system's decoherence. Our results show that decoherency due to the nonequilibrium critical dynamics of the environment is amplified in the presence of uncorrelated and correlated Gaussian noise. We demonstrate that decoherence factor consistently signals the critical points, and exhibits exponential scaling with the system size, the square of noise intensity, and the noise correlation time at the critical points. We find that strong coupling between the qubit and the environment allows partial revivals of coherence, which diminish with increasing noise intensity or decreasing noise correlation time. In contrast, weak coupling leads to monotonic enhanced decoherence. The numerical results illustrate that, the revivals decay and scale exponentially with noise intensity. Moreover, the revivals increase and indicate linear or power law scaling with noise correlation time depends on how the correlated noise is fast or slow. Additionally, we explore the non-Markovianity of the dynamics, finding that it decays in the presence of noise but increases as the noise correlation time grows. Our findings have potential applications in the noise spectroscopy of external signals.

3D models, particularly AI-generated ones, have witnessed a recent surge across various industries such as entertainment. Hence, there is an alarming need to protect the intellectual property and avoid the misuse of these valuable assets. As a viable solution to address these concerns, we rigorously define the novel task of automated 3D visible watermarking in terms of two competing aspects: watermark quality and asset utility. Moreover, we propose a method of embedding visible watermarks that automatically determines the right location, orientation, and number of watermarks to be placed on arbitrary 3D assets for high watermark quality and asset utility. Our method is based on a novel rigid-body optimization that uses back-propagation to automatically learn transforms for ideal watermark placement. In addition, we propose a novel curvature-matching method for fusing the watermark into the 3D model that further improves readability and security. Finally, we provide a detailed experimental analysis on two benchmark 3D datasets validating the superior performance of our approach in comparison to baselines. Code and demo are available., Comment: Accepted to WACV2025

The pervasive adoption of Internet of Things (IoT) has significantly advanced healthcare digitization and modernization. Nevertheless, the sensitive nature of medical data presents security and privacy challenges. On the other hand, resource constraints of IoT devices often necessitates cloud services for data handling, introducing single points of failure, processing delays, and security vulnerabilities. Meanwhile, the blockchain technology offers potential solutions for enhancing security, decentralization, and data ownership. An ideal solution should ensure confidentiality, access control, and data integrity while being scalable, cost-effective, and integrable with the existing systems. However, current blockchain-based studies only address some of these requirements. Accordingly, this paper proposes EdgeLinker; a comprehensive solution incorporating Proof-of-Authority consensus, integrating smart contracts on the Ethereum blockchain for access control, and advanced cryptographic algorithms for secure data communication between IoT edge devices and the fog layer in healthcare fog applications. This novel framework has been implemented in a real-world fog testbed, using COTS fog devices. Based on a comprehensive set of evaluations, EdgeLinker demonstrates significant improvements in security and privacy with reasonable costs, making it an affordable and practical system for healthcare fog applications. Compared with the state-of-the-art, without significant changes in the write-time to the blockchain, EdgeLinker achieves a 35% improvement in data read time. Additionally, it is able to provide better throughput in both reading and writing transactions compared to the existing studies. EdgeLinker has been also examined in terms of energy, resource consumption and channel latency in both secure and non-secure modes, which has shown remarkable improvements., Comment: This paper has been accepted in CPSAT 2024 and will be published in the IEEE Xplore Digital Library

Developmental dysgraphia is a neurological disorder that hinders children's writing skills. In recent years, researchers have increasingly explored machine learning methods to support the diagnosis of dysgraphia based on offline and online handwriting. In most previous studies, the two types of handwriting have been analysed separately, which does not necessarily lead to promising results. In this way, the relationship between online and offline data cannot be explored. To address this limitation, we propose a novel multimodal machine learning approach utilizing both online and offline handwriting data. We created a new dataset by transforming an existing online handwritten dataset, generating corresponding offline handwriting images. We considered only different types of word data (simple word, pseudoword & difficult word) in our multimodal analysis. We trained SVM and XGBoost classifiers separately on online and offline features as well as implemented multimodal feature fusion and soft-voted ensemble. Furthermore, we proposed a novel ensemble with conditional feature fusion method which intelligently combines predictions from online and offline classifiers, selectively incorporating feature fusion when confidence scores fall below a threshold. Our novel approach achieves an accuracy of 88.8%, outperforming SVMs for single modalities by 12-14%, existing methods by 8-9%, and traditional multimodal approaches (soft-vote ensemble and feature fusion) by 3% and 5%, respectively. Our methodology contributes to the development of accurate and efficient dysgraphia diagnosis tools, requiring only a single instance of multimodal word/pseudoword data to determine the handwriting impairment. This work highlights the potential of multimodal learning in enhancing dysgraphia diagnosis, paving the way for accessible and practical diagnostic tools.

Significance: Cerebral blood flow (CBF) imaging is crucial for diagnosing cerebrovascular diseases. However, existing large neuroimaging techniques with high cost, low sampling rate, and poor mobility make them unsuitable for continuous and longitudinal CBF monitoring at the bedside. Aim: This study aimed to develop a low-cost, portable, programmable scanning diffuse speckle contrast imaging (PS-DSCI) technology for fast, high-density, and depth-sensitive imaging of CBF in rodents. Approach: The PS-DSCI employed a programmable digital micromirror device (DMD) for remote line-shape laser (785 nm) scanning on tissue surface and synchronized a 2D camera for capturing boundary diffuse laser speckle contrasts. New algorithms were developed to address deformations of line-shape scanning, thus minimizing CBF reconstruction artifacts. The PS-DSCI was examined in head-simulating phantoms and adult mice. Results: The PS-DSCI enables resolving Intralipid particle flow contrasts at different tissue depths. In vivo experiments in adult mice demonstrated the capability of PS-DSCI to image global/regional CBF variations induced by 8% CO2 inhalation and transient carotid artery ligations. Conclusions: Compared to conventional point scanning, the line scanning in PS-DSCI significantly increases spatiotemporal resolution. The high sampling rate of PS-DSCI is crucial for capturing rapid CBF changes while high spatial resolution is important for visualizing brain vasculature., Comment: 23 Pages, 8 Figures, 4 Tables

With generative models producing high quality images that are indistinguishable from real ones, there is growing concern regarding the malicious usage of AI-generated images. Imperceptible image watermarking is one viable solution towards such concerns. Prior watermarking methods map the image to a latent space for adding the watermark. Moreover, Latent Diffusion Models (LDM) generate the image in the latent space of a pre-trained autoencoder. We argue that this latent space can be used to integrate watermarking into the generation process. To this end, we present LaWa, an in-generation image watermarking method designed for LDMs. By using coarse-to-fine watermark embedding modules, LaWa modifies the latent space of pre-trained autoencoders and achieves high robustness against a wide range of image transformations while preserving perceptual quality of the image. We show that LaWa can also be used as a general image watermarking method. Through extensive experiments, we demonstrate that LaWa outperforms previous works in perceptual quality, robustness against attacks, and computational complexity, while having very low false positive rate. Code is available here., Comment: Accepted to ECCV 2024

Community resilience refers to the ability to prepare for, absorb, recover from, and adapt to disruptive events, but specific definitions and measures for resilience can vary widely from researcher to researcher or from discipline to discipline. Community resilience is often measured using a set of indicators based on census, socioeconomic, and community organizational data, but these metrics and measures for community resilience provide little guidance for policymakers to determine how best to increase the community resilience. This article proposes to measure community resilience based on value focused thinking. We propose an objectives hierarchy that begins with a community decision makers' fundamental objective for resilience. Six high level objectives for community resilience, including social resilience, economic resilience, infrastructure resilience, environmental resilience, availability of resources, and functionality of critical services, are broken down into measurable attributes that focus on specific outcomes that a decision maker would like to achieve if a disruption occurs. This new way of assessing resilience is applied to measure the resilience of an illustrative community to an improvised explosive device, a cyberattack, a tornado, a flood, and a winter storm. Keywords: Community Resilience, Resiliency, Risk Analysis

This paper is about the recent notion of computably probably approximately correct learning, which lies between the statistical learning theory where there is no computational requirement on the learner and efficient PAC where the learner must be polynomially bounded. Examples have recently been given of hypothesis classes which are PAC learnable but not computably PAC learnable, but these hypothesis classes are unnatural or non-canonical in the sense that they depend on a numbering of proofs, formulas, or programs. We use the on-a-cone machinery from computability theory to prove that, under mild assumptions such as that the hypothesis class can be computably listable, any natural hypothesis class which is learnable must be computably learnable. Thus the counterexamples given previously are necessarily unnatural., Comment: This is a replacement of the earlier submission to just update the funding information

We study the quantum Rabi model for a two-level system coupled to a quantized cavity mode under periodic modulation of the cavity-dipole coupling in the ultrastrong coupling regime, leading to rich Floquet states. As an application of the theory, we show how purely mechanical driving can produce real photons, depending on the strength and frequency of the periodic coupling rate., Comment: 8 pages, 3 figures

Feature selection poses a challenge in small-sample high-dimensional datasets, where the number of features exceeds the number of observations, as seen in microarray, gene expression, and medical datasets. There isn't a universally optimal feature selection method applicable to any data distribution, and as a result, the literature consistently endeavors to address this issue. One recent approach in feature selection is termed frequency-based feature selection. However, existing methods in this domain tend to overlook feature values, focusing solely on the distribution in the response variable. In response, this paper introduces the Distance-based Mutual Congestion (DMC) as a filter method that considers both the feature values and the distribution of observations in the response variable. DMC sorts the features of datasets, and the top 5% are retained and clustered by KMeans to mitigate multicollinearity. This is achieved by randomly selecting one feature from each cluster. The selected features form the feature space, and the search space for the Genetic Algorithm with Adaptive Rates (GAwAR) will be approximated using this feature space. GAwAR approximates the combination of the top 10 features that maximizes prediction accuracy within a wrapper scheme. To prevent premature convergence, GAwAR adaptively updates the crossover and mutation rates. The hybrid DMC-GAwAR is applicable to binary classification datasets, and experimental results demonstrate its superiority over some recent works. The implementation and corresponding data are available at https://github.com/hnematzadeh/DMC-GAwAR

Identifying causal effects is a key problem of interest across many disciplines. The two long-standing approaches to estimate causal effects are observational and experimental (randomized) studies. Observational studies can suffer from unmeasured confounding, which may render the causal effects unidentifiable. On the other hand, direct experiments on the target variable may be too costly or even infeasible to conduct. A middle ground between these two approaches is to estimate the causal effect of interest through proxy experiments, which are conducted on variables with a lower cost to intervene on compared to the main target. Akbari et al. [2022] studied this setting and demonstrated that the problem of designing the optimal (minimum-cost) experiment for causal effect identification is NP-complete and provided a naive algorithm that may require solving exponentially many NP-hard problems as a sub-routine in the worst case. In this work, we provide a few reformulations of the problem that allow for designing significantly more efficient algorithms to solve it as witnessed by our extensive simulations. Additionally, we study the closely-related problem of designing experiments that enable us to identify a given effect through valid adjustments sets.

A graph $G$ is factored into graphs $H$ and $K$ via a matrix product if there exist adjacency matrices $A$, $B$, and $C$ of $G$, $H$, and $K$, respectively, such that $A = BC$. In this paper, we study the spectral aspects of the matrix product of graphs, including regularity, bipartiteness, and connectivity. We show that if a graph $G$ is factored into a connected graph $H$ and a graph $K$ with no isolated vertices, then certain properties hold. If $H$ is non-bipartite, then $G$ is connected. If $H$ is bipartite and $G$ is not connected, then $K$ is a regular bipartite graph, and consequently, $n$ is even. Furthermore, we show that trees are not factorizable, which answers a question posed by Maghsoudi et al., Comment: Comments are welcome

Deep neural networks (DNNs) are a type of artificial intelligence models that are inspired by the structure and function of the human brain, designed to process and learn from large amounts of data, making them particularly well-suited for tasks such as image and speech recognition. However, applications of DNNs are experiencing emerging growth due to the deployment of specialized accelerators such as the Google Tensor Processing Units (TPUs). In large-scale deployments, the energy efficiency of such accelerators may become a critical concern. In the voltage overscaling (VOS) technique, the operating voltage of the system is scaled down beyond the nominal operating voltage, which increases the energy efficiency and lifetime of digital circuits. The VOS technique is usually performed without changing the frequency resulting in timing errors. However, some applications such as multimedia processing, including DNNs, have intrinsic resilience against errors and noise. In this paper, we exploit the inherent resilience of DNNs to propose a quality-aware voltage overscaling framework for TPUs, named X-TPU, which offers higher energy efficiency and lifetime compared to conventional TPUs. The X-TPU framework is composed of two main parts, a modified TPU architecture that supports a runtime voltage overscaling, and a statistical error modeling-based algorithm to determine the voltage of neurons such that the output quality is retained above a given user-defined quality threshold. We synthesized a single-neuron architecture using a 15-nm FinFET technology under various operating voltage levels. Then, we extracted different statistical error models for a neuron corresponding to those voltage levels. Using these models and the proposed algorithm, we determined the appropriate voltage of each neuron. Results show that running a DNN on X-TPU can achieve 32% energy saving for only 0.6% accuracy loss.

Quantum computing is one of the most enticing computational paradigms with the potential to revolutionize diverse areas of future-generation computational systems. While quantum computing hardware has advanced rapidly, from tiny laboratory experiments to quantum chips that can outperform even the largest supercomputers on specialized computational tasks, these noisy-intermediate scale quantum (NISQ) processors are still too small and non-robust to be directly useful for any real-world applications. In this paper, we describe NASA's work in assessing and advancing the potential of quantum computing. We discuss advances in algorithms, both near- and longer-term, and the results of our explorations on current hardware as well as with simulations, including illustrating the benefits of algorithm-hardware co-design in the NISQ era. This work also includes physics-inspired classical algorithms that can be used at application scale today. We discuss innovative tools supporting the assessment and advancement of quantum computing and describe improved methods for simulating quantum systems of various types on high-performance computing systems that incorporate realistic error models. We provide an overview of recent methods for benchmarking, evaluating, and characterizing quantum hardware for error mitigation, as well as insights into fundamental quantum physics that can be harnessed for computational purposes., Comment: 27 pages, 0 figures

Recent Large Language Models (LLMs) have demonstrated impressive capabilities at tasks that require human intelligence and are a significant step towards human-like artificial intelligence (AI). Yet the performance of LLMs at reasoning tasks have been subpar and the reasoning capability of LLMs is a matter of significant debate. While it has been shown that the choice of the prompting technique to the LLM can alter its performance on a multitude of tasks, including reasoning, the best performing techniques require human-made prompts with the knowledge of the tasks at hand. We introduce a framework for what we call Combinatorial Reasoning (CR), a fully-automated prompting method, where reasons are sampled from an LLM pipeline and mapped into a Quadratic Unconstrained Binary Optimization (QUBO) problem. The framework investigates whether QUBO solutions can be profitably used to select a useful subset of the reasons to construct a Chain-of-Thought style prompt. We explore the acceleration of CR with specialized solvers. We also investigate the performance of simpler zero-shot strategies such as linear majority rule or random selection of reasons. Our preliminary study indicates that coupling a combinatorial solver to generative AI pipelines is an interesting avenue for AI reasoning and elucidates design principles for future CR methods., Comment: 13 pages, 3 figures

In this paper, we utilize an analog model of the general relativity and investigate the influence of spatial curvature on quantum properties of stimulated parametric down-conversion process. For this purpose, we use two-mode sphere coherent state as the input beams of the aforementioned process. These states are realization of coherent states of two-dimensional harmonic oscillator, which lies on a two-dimension sphere. We calculate the entanglement of output states of stimulated parametric down conversion process, measured by linear entropy, and show that it depends on the spatial curvature. So, by preparing the suitable two-mode sphere coherent states, it is possible to control the entanglement between the output states in the laboratory. In addition, we consider mean number and Mandel parameter of the output states of the process and also, their cross-correlation function, as the convince measures of non-classical behaviors., Comment: 18 pages, 8 figures

This paper introduces an innovative two-core fiber (TCF) optic sensor employing a Mach-Zehnder interferometer (MZI) to monitor the optogenetic response of light-sensitive human dental pulp stem cells (hDPSCs). The in-fiber MZI, formed using a segment of TCF optic, detects refractive index (RI) changes in the surrounding medium. The sensor utilizes the evanescent wave of one core as the sensing arm, necessitating a thin cladding achieved through one-sided chemical etching. This design allows the sensor to detect subtle alterations in the RI of the environment by observing displacements in the interference spectrum. The optogenetic stimulation of light-sensitive cells induces variations in ion concentrations, leading to a corresponding change in refractive index. The fabricated sensor, with a peak sensitivity of 675.74 nm/RIU within the RI range of 1.39-1.43, can detect these changes. A computer simulation validated the sensitivity and optimized fabrication parameters, exhibiting satisfactory agreement with experimental results. Spectrum displacements were recorded for both light-sensitive hDPSCs and regular hDPSCs (as a control test). Results from the experiment, analyzed and compared using data analysis software, revealed that 473 nm blue light effectively stimulated light-sensitive hDPSCs. Notably, the proposed sensor, a novel structure, demonstrated its capability to detect RI changes in the cell medium during optogenetic applications.

Considering the evolution of the food industry and its challenges, like high perishability, managing the food industry supply chain is a key focus for researchers and decision-makers. Uncertainty in decision-making has gained importance, particularly in the yogurt industry, known for its complexity. This study addresses production and distribution planning, scheduling, and routing in the yogurt supply chain. The problem is characterized by multiple products, a single plant, multiple distribution centers, multiple periods, and various transportation methods. A mixed-integer non-linear programming (MINLP) model is used to minimize total costs, including production, setup, overtime, unmet demand, and transportation. Additionally, a robust fuzzy programming approach is applied under uncertainty, with linearization procedures proposed to convert it into a linearized mixed-integer programming formulation. The problem is tested with two data types: a sample problem in three sizes (small, medium, and large) and real data from Kalle Dairy Company, Iran. A Genetic Algorithm (GA) is developed to solve the problem, with necessary modifications made for its application. The GA's performance is compared to an exact algorithm (Branch & Cut), showing that the company's production policy adapts daily to meet demand precisely. The shift to smaller batch production and longer shelf life allows better stock allocation and avoids shortages in uncertain conditions. The company's policies adapt to severe fluctuations in the business environment, though this requires high costs, such as inventory maintenance.

It is imperative that breast cancer is detected precisely and timely to improve patient outcomes. Diagnostic methodologies have traditionally relied on unimodal approaches; however, medical data analytics is integrating diverse data sources beyond conventional imaging. Using multi-modal techniques, integrating both image and non-image data, marks a transformative advancement in breast cancer diagnosis. The purpose of this review is to explore the burgeoning field of multimodal techniques, particularly the fusion of histopathology images with non-image data. Further, Explainable AI (XAI) will be used to elucidate the decision-making processes of complex algorithms, emphasizing the necessity of explainability in diagnostic processes. This review utilizes multi-modal data and emphasizes explainability to enhance diagnostic accuracy, clinician confidence, and patient engagement, ultimately fostering more personalized treatment strategies for breast cancer, while also identifying research gaps in multi-modality and explainability, guiding future studies, and contributing to the strategic direction of the field., Comment: 31 pages including references

In this paper, we experimentally report the saturated absorption spectroscopy (SAS) and frequency locking (FL) of a narrow-band DBR laser with 0.5MHz linewidth on the LD2-transition of Rb atoms., Comment: submitted to International Conference on Quantum Technologies & Industrial Applications at Shahid-Beheshti University, Tehran, Iran May 28-May 29, 2024

Let $G_S$ be the graph obtained by attaching a self-loop at every vertex in $S \subseteq V(G)$ of a simple graph $G$ of order $n.$ In this paper, we explore several new results related to the line graph $L(G_S)$ of $G_S.$ Particularly, we show that every eigenvalue of $L(G_S)$ must be at least $-2,$ and relate the characteristic polynomial of the line graph $L(G)$ of $G$ with the characteristic polynomial of the line graph $L(\widehat{G})$ of a self-loop graph $\widehat{G}$, which is obtained by attaching a self-loop at each vertex of $G$. Then, we provide some new bounds for the eigenvalues and energy of $G_S.$ As one of the consequences, we obtain that the energy of a connected regular complete multipartite graph is not greater than the energy of the corresponding self-loop graph. Lastly, we establish a lower bound of the spectral radius in terms of the first Zagreb index $M_1(G)$ and the minimum degree $\delta(G),$ as well as proving two Nordhaus-Gaddum-type bounds for the spectral radius and the energy of $G_S,$ respectively., Comment: 19 pages. To appear in Bulletin of the Malaysian Mathematical Sciences Society

We explore the dynamics of l_1-norm of steered quantum coherence (SQC), steered quantum relative entropy (SQRE), and magic resource quantifier (QRM) in the one-dimensional XY spin chain in the presence of time dependent transverse magnetic field. We find that the system's response is highly sensitive to the initial state and magnetic field strength. % We show that the dynamics of SQC, SQRE and MRQ revealing the critical point associated with equilibrium quantum phase transition (QPT) of the system. All quantities show maximum at QPT when the initial state is prepared in the ferromagnetic phase. Conversely, they undergo abrupt changes at quantum critical point if the initial state of the system is paramagnetic. Moreover, our results confirm that, when quench is done to the quantum critical point, the first suppression (revival) time scales linearly with the system size, and remarkably, its scaling ratio remains consistent for all quenches, irrespective of the initial phase of the system. % These results highlight the interplay between the quantum information resources and dynamics of quantum systems away from the equilibrium. Such insights could be vital for quantum information processing and understanding non-equilibrium phenomena in quantum many-body systems., Comment: 4 figures

We employ ab-initio electronic structure calculations to investigate the charge-density waves and periodic lattice distortions in bilayer 2H-NbSe$_2$. We demonstrate that the vertical stacking can give rise to a variety of patterns that may lower the symmetry of the CDW exhibited separately by the two composing 1H-NbSe$_2$ monolayers. The general tendency to a spontaneous symmetry breaking observed in the ground state and the first excited states is shown to originate from a non-negligible inter-layer coupling. Simulated images for scanning tunnelling microscopy (STM) as well as diffraction/scattering patterns show signatures of the different stacking orders. This may not only be useful to reinterpret past experiments on surfaces and thin films, but may also be exploited to devise ad-hoc experiments for the investigation of the stacking order in 2H-NbSe$_2$. We anticipate that our analysis does not only apply to the 2H-NbSe$_2$ bilayer, but is also relevant for thin films and bulk, whose smallest centro-symmetric component is indeed the bilayer. Finally, our results illustrate clearly that the vertical stacking is not only important for 1T structures, as exemplified by the metal-to-insulator transition observed in 1T-TaS$_2$, but seems to be a general feature of metallic layered transition metal dichalcogenides as well., Comment: 11 pages, 5 figures

Knowledge distillation from LLMs is essential for the efficient deployment of language models. Prior works have proposed data generation using LLMs for preparing distilled models. We argue that generating data with LLMs is prone to sampling mainly from the center of original content distribution. This limitation hinders the distilled model from learning the true underlying data distribution and to forget the tails of the distributions (samples with lower probability). To this end, we propose GOLD, a task-agnostic data generation and knowledge distillation framework, which employs an iterative out-of-distribution-guided feedback mechanism for the LLM. As a result, the generated data improves the generalizability of distilled models. An energy-based OOD evaluation approach is also introduced to deal with noisy generated data. Our extensive experiments on 10 different classification and sequence-to-sequence tasks in NLP show that GOLD respectively outperforms prior arts and the LLM with an average improvement of 5% and 14%. We will also show that the proposed method is applicable to less explored and novel tasks. The code is available.

Exoskeleton locomotion must be robust while being adaptive to different users with and without payloads. To address these challenges, this work introduces a data-driven predictive control (DDPC) framework to synthesize walking gaits for lower-body exoskeletons, employing Hankel matrices and a state transition matrix for its data-driven model. The proposed approach leverages DDPC through a multi-layer architecture. At the top layer, DDPC serves as a planner employing Hankel matrices and a state transition matrix to generate a data-driven model that can learn and adapt to varying users and payloads. At the lower layer, our method incorporates inverse kinematics and passivity-based control to map the planned trajectory from DDPC into the full-order states of the lower-body exoskeleton. We validate the effectiveness of this approach through numerical simulations and hardware experiments conducted on the Atalante lower-body exoskeleton with different payloads. Moreover, we conducted a comparative analysis against the model predictive control (MPC) framework based on the reduced-order linear inverted pendulum (LIP) model. Through this comparison, the paper demonstrates that DDPC enables robust bipedal walking at various velocities while accounting for model uncertainties and unknown perturbations.

Burning fossil fuels emits a significant amount of CO2, causing climate change concerns. CO2 Capture and Storage (CCS) aims to reduce emissions, with fullerenes showing promise as CO2 adsorbents. Recent research focuses on modifying fullerenes using an electric field. In light of this, we carried out DFT studies on some B, N, and P doped C20 (C20-nXn (n = 0, 1, 2, and 3; X = B, N, and P)) in the absence and presence of an electric field in the range of 0-0.02 a.u.. The cohesive energy was calculated to ensure their thermodynamic stability showing, that despite having lesser cohesive energies than C20, they appear in a favorable range. Moreover, the charge distribution for all structures was depicted using the ESP map. Most importantly, we evaluated the adsorption energy, height, and CO2 angle, demonstrating the B and N-doped fullerenes had the stronger interaction with CO2, which by far exceeded C20's, improving its physisorption to physicochemical adsorption. Although the adsorption energy of P-doped fullerenes was not as satisfactory, in most cases, increasing the electric field led to enhancing CO2 adsorption and incorporating chemical attributes to CO2-fullerene interaction. The HOMO--LUMO plots were obtained by which we discovered that unlike the P-doped C20, the surprising activity of B and N-doped C20s against CO2 originates from a high concentration of the HOMO-LUMO orbitals on B and N atoms. Additionally, the charge distribution for all structures was depicted using the ESP map. In the present article, we attempt to introduce more effective fullerene-based materials for CO2 capture as well as strategies to enhance their efficiency and revealing adsorption nature over B, N, and P-doped fullerenes.

The growing low-Earth orbit (LEO) satellite constellations have become an essential part of the fifth-generation (5G) non-terrestrial network (NTN) market. These satellites can enable direct-to-cell connectivity for mobile devices and support various applications with ubiquitous coverage for 5G and beyond networks. However, satellite-based NTNs bring several challenges to the 5G handover protocol design. The high mobility of satellites can lead to signaling storms and security compromises during handovers. This paper addresses these challenges by proposing a secure and efficient group handover protocol. The protocol's effectiveness is evaluated on a custom discrete-event simulator and compared against the baseline 5G handover scheme. The simulator is made publicly available., Comment: Accepted by the 2024 IEEE International Conference on Communications (ICC), 9-13 June 2024, Denver, CO, USA

We report on the quantum photon statistics of hexagonal boron nitride (h-BN) quantum emitters by analyzing the Mandel Q parameter. We have measured the Mandel Q parameter for h-BN quantum emitters under various temperatures and pump power excitation conditions. Under pulsed excitation we can achieve a Mandel Q of -0.002 and under continuous-wave (CW) excitation this parameter can reach -0.0025. We investigate the effect of cryogenic temperatures on Mandel Q and conclude that the photon statistics vary weakly with temperature. Through calculation of spontaneous emission from an excited two-level emitter model, we demonstrate good agreement between measured and calculated Mandel Q parameter when accounting for the experimental photon collection efficiency. Finally, we illustrate the usefulness of Mandel Q in quantum applications by the example of random number generation and analyze the effect of Mandel Q on the speed of generating random bits via this method., Comment: Main text: 22 pages, 8 figures, 1 table. Supplemental document: 2 pages, 1 figure. Submitted to APL Quantum

Causal discovery, i.e., learning the causal graph from data, is often the first step toward the identification and estimation of causal effects, a key requirement in numerous scientific domains. Causal discovery is hampered by two main challenges: limited data results in errors in statistical testing and the computational complexity of the learning task is daunting. This paper builds upon and extends four of our prior publications (Mokhtarian et al., 2021; Akbari et al., 2021; Mokhtarian et al., 2022, 2023a). These works introduced the concept of removable variables, which are the only variables that can be removed recursively for the purpose of causal discovery. Presence and identification of removable variables allow recursive approaches for causal discovery, a promising solution that helps to address the aforementioned challenges by reducing the problem size successively. This reduction not only minimizes conditioning sets in each conditional independence (CI) test, leading to fewer errors but also significantly decreases the number of required CI tests. The worst-case performances of these methods nearly match the lower bound. In this paper, we present a unified framework for the proposed algorithms, refined with additional details and enhancements for a coherent presentation. A comprehensive literature review is also included, comparing the computational complexity of our methods with existing approaches, showcasing their state-of-the-art efficiency. Another contribution of this paper is the release of RCD, a Python package that efficiently implements these algorithms. This package is designed for practitioners and researchers interested in applying these methods in practical scenarios. The package is available at github.com/ban-epfl/rcd, with comprehensive documentation provided at rcdpackage.com., Comment: 50 pages, 5 tables, 11 algorithms, 5 figures

The increasing use of artificial intelligence (AI) systems in our daily life through various applications, services, and products explains the significance of trust/distrust in AI from a user perspective. AI-driven systems (as opposed to other technologies) have ubiquitously diffused in our life not only as some beneficial tools to be used by human agents but also are going to be substitutive agents on our behalf, or manipulative minds that would influence human thought, decision, and agency. Trust/distrust in AI plays the role of a regulator and could significantly control the level of this diffusion, as trust can increase, and distrust may reduce the rate of adoption of AI. Recently, varieties of studies have paid attention to the variant dimension of trust/distrust in AI, and its relevant considerations. In this systematic literature review, after conceptualization of trust in the current AI literature review, we will investigate trust in different types of human-Machine interaction, and its impact on technology acceptance in different domains. In addition to that, we propose a taxonomy of technical (i.e., safety, accuracy, robustness) and non-technical axiological (i.e., ethical, legal, and mixed) trustworthiness metrics, and some trustworthy measurements. Moreover, we examine some major trust-breakers in AI (e.g., autonomy and dignity threat), and trust makers; and propose some future directions and probable solutions for the transition to a trustworthy AI.

Consider a nonsolvable finite group G, where R(G) represents the solvable radical of G. For any element x in G, the solvabilizer of x in G, denoted by Sol_G(x), is defined as the set of all elements y in G such that the subgroup generated by x and y is solvable. Notably, the entirety of G can be expressed as the union over all x in G\R(G) of their respective solvabilizers: $G = \cup_{x\in G\R(G)} Sol_G(x). A solvabilizer covering of G is characterized by a subset X of G\R(G) such that G= \cup_{x\in X} Sol_G(x). The solvabilizer number of G is then defined as the minimum cardinality among all solvabilizer coverings of G. This paper delves into the exploration of the solvabilizer number for diverse nonsolvable finite groups G, shedding light on the interplay between solvability and the structure of these groups.

Reliability management is one of the primary concerns in manycore systems design. Different aging mechanisms such as Negative-Bias Temperature Instability (NBTI), Electromigration (EM), and thermal cycling can reduce the reliability of these systems. However, state-of-the-art works mainly focused on NBTI and EM, whereas a few works have considered the thermal cycling effect. The thermal cycling effect can significantly aggravate the systems lifetime. Moreover, the thermal effects of cores on each other due to their adjacency may also influence the systems Mean Time to Failure (MTTF). This paper introduces a new technique to manage the reliability of manycore systems. The technique considers thermal cycling, adjacency of cores, and process variation-induced diversity of operating frequencies. It uses two levels of task mapping to improve system lifetime. At the first level, cores with close temperatures are packed into the same bin, and then, an arrived task is assigned to a bin with a similar temperature. Afterward in the second level, the task is assigned to a core inside the selected bin in the first level, based on performance requirements and the core frequency. Compared to the conventional TC-aware techniques, the proposed method is performed at a higher level (bins level) to reduce the thermal variations of cores inside a bin, and improves the system MTTFTC, making it a promising solution for manycore systems. The efficacy of our proposed technique is evaluated on 16, 32, 64, and 256 core systems using SPLASH2 and PARSEC benchmark suite applications. The results show up to 20% MTTFTC increment compared to the conventional thermal cycling-aware task mapping techniques.

Curating high quality datasets that play a key role in the emergence of new AI applications requires considerable time, money, and computational resources. So, effective ownership protection of datasets is becoming critical. Recently, to protect the ownership of an image dataset, imperceptible watermarking techniques are used to store ownership information (i.e., watermark) into the individual image samples. Embedding the entire watermark into all samples leads to significant redundancy in the embedded information which damages the watermarked dataset quality and extraction accuracy. In this paper, a multi-segment encoding-decoding method for dataset watermarking (called AMUSE) is proposed to adaptively map the original watermark into a set of shorter sub-messages and vice versa. Our message encoder is an adaptive method that adjusts the length of the sub-messages according to the protection requirements for the target dataset. Existing image watermarking methods are then employed to embed the sub-messages into the original images in the dataset and also to extract them from the watermarked images. Our decoder is then used to reconstruct the original message from the extracted sub-messages. The proposed encoder and decoder are plug-and-play modules that can easily be added to any watermarking method. To this end, extensive experiments are preformed with multiple watermarking solutions which show that applying AMUSE improves the overall message extraction accuracy upto 28% for the same given dataset quality. Furthermore, the image dataset quality is enhanced by a PSNR of $\approx$2 dB on average, while improving the extraction accuracy for one of the tested image watermarking methods.

The $\Delta$-edge stability number ${\rm es}_{\Delta}(G)$ of a graph $G$ is the minimum number of edges of $G$ whose removal results in a subgraph $H$ with $\Delta(H) = \Delta(G)-1$. Sets whose removal results in a subgraph with smaller maximum degree are called mitigating sets. It is proved that there always exists a mitigating set which induces a disjoint union of paths of order $2$ or $3$. Minimum mitigating sets which induce matchings are characterized. It is proved that to obtain an upper bound of the form ${\rm es}_{\Delta}(G) \leq c |V(G)|$ for an arbitrary graph $G$ of given maximum degree $\Delta$, where $c$ is a given constant, it suffices to prove the bound for $\Delta$-regular graphs. Sharp upper bounds of this form are derived for regular graphs. It is proved that if $\Delta(G) \geq\frac{|V(G)|-2}{3}$ or the induced subgraph on maximum degree vertices has a $\Delta(G)$-edge coloring, then ${\rm es}_{\Delta}(G) \le |V(G)|/2$.

We extend the theory of locally checkable labeling problems (LCLs) from the classical LOCAL model to a number of other models that have been studied recently, including the quantum-LOCAL model, finitely-dependent processes, non-signaling model, dynamic-LOCAL model, and online-LOCAL model [e.g. STOC 2024, ICALP 2023]. First, we demonstrate the advantage that finitely-dependent processes have over the classical LOCAL model. We show that all LCL problems solvable with locality $O(\log^\star n)$ in the LOCAL model admit a finitely-dependent distribution (with constant locality). In particular, this gives a finitely-dependent coloring for regular trees, answering an open question by Holroyd [2023]. This also introduces a new formal barrier for understanding the distributed quantum advantage: it is not possible to exclude quantum advantage for any LCL in the $\Theta(\log^\star n)$ complexity class by using non-signaling arguments. Second, we put limits on the capabilities of all of these models. To this end, we introduce a model called randomized online-LOCAL, which is strong enough to simulate e.g. SLOCAL and dynamic-LOCAL, and we show that it is also strong enough to simulate any non-signaling distribution and hence any quantum-LOCAL algorithm. We prove the following result for rooted trees: if we can solve an LCL problem with locality $o(\log \log n)$ in the randomized online-LOCAL model, we can solve it with locality $O(\log^\star n)$ in the classical deterministic LOCAL model. Put together, these results show that in rooted trees the set of LCLs that can be solved with locality $O(\log^\star n)$ is the same across all these models: classical deterministic and randomized LOCAL, quantum-LOCAL, non-signaling model, dynamic-LOCAL, and deterministic and randomized online-LOCAL., Comment: 60 pages, 10 figures. This version corrects a mistake in v1

In this work the multistream quasiparticle model of collective electron excitations is used to study the energy-density distribution of collective quantum excitations in an interacting electron gas with arbitrary degree of degeneracy. Generalized relations for the probability current and energy density distributions is obtained which reveals a new interesting quantum phenomenon of diffusive decay of pure quasiparticle states at microscopic level. The effects is studied for various cases of free quasiparticles, quasiparticle in an infinite square-well potential and half-space collective excitations. It is shown that plasmon excitations have the intrinsic tendency to decay into equilibrium state with uniform energy density spacial distribution. It is found that plasmon levels of quasipaticle in a square-well potential are unstable decaying into equilibrium state due to the fundamental property of collective excitations. The decay rates of pure plasmon states are determined analytically. Moreover, for damped quasiparticle excitations the non-vanishing probability current divergence leads to imaginary energy density resulting in damping instability of energy density dynamic. The pronounced energy density valley close to half-space boundary at low level excitations predicts attractive force close to the surface. Current research can have implications with applications in plasmonics and related fields. Current analysis can be readily generalized to include external potential and magnetic field effects.

Particle Image Velocimetry (PIV) data is a valuable asset in fluid mechanics. It is capable of visualizing flow structures even in complex physics scenarios, such as the flow at the exit of the rotor of a centrifugal fan. Machine learning is also a successful companion to PIV in order to increase data resolution or impute experimental gaps. While classical algorithms focus solely on replicating data using statistical metrics, the application of Physics Informed Neural Networks (PINN) contributes to both data reconstruction and adherence to governing equations. The present study utilizes a convolutional physics-informed auto-encoder to reproduce planar PIV fields in the gappy regions while also satisfying the mass conservation equation. It proposes a novel approach, which compromises experimental data reconstruction for compliance with physical restrictions. Simultaneously, it is aimed to ensure that the reconstruction error does not considerably deviate from the uncertainty band of the test data. Turbulence scale approximation is employed to set the relative weighting of the physical and non-physical terms in the loss function to ensure that both objectives are achieved. All steps are initially evaluated on a set of DNS data to demonstrate the general capability of the network. Finally, examination of the PIV data indicates that the proposed PINN auto-encoder can enhance reconstruction accuracy by about 28% and 29% in terms of mass conservation residual and velocity statistics, respectively, in expense of up to 5% increase in the number of vectors with reconstruction error higher than the uncertainty band of the PIV test data., Comment: 33 Pages; 13 Figures; 3 Tables

We investigate how small the Randi\'c index of a graph can be in terms of its matching number, and prove several results. We give best-possible linear bounds for graphs of small excess and for subcubic graphs; in the former case the size of excess we permit is qualitatively the best possible. We show that a linear bound holds for any sparse hereditary graph class (such as planar graphs). In general, however, we show that it can be much smaller than linear. We determine the asymptotic growth rate of the minimum Randi\'c index for graphs with a near perfect matching, and conjecture that the same bounds hold for all graphs., Comment: 11 pages, 2 figures. Added the observation that the assumptions in Corollary 6 are necessary, and other minor remarks

The renowned difference-in-differences (DiD) estimator relies on the assumption of 'parallel trends,' which does not hold in many practical applications. To address this issue, the econometrics literature has turned to the triple difference estimator. Both DiD and triple difference are limited to assessing average effects exclusively. An alternative avenue is offered by the changes-in-changes (CiC) estimator, which provides an estimate of the entire counterfactual distribution at the cost of relying on (stronger) distributional assumptions. In this work, we extend the triple difference estimator to accommodate the CiC framework, presenting the `triple changes estimator' and its identification assumptions, thereby expanding the scope of the CiC paradigm. Subsequently, we empirically evaluate the proposed framework and apply it to a study examining the impact of Medicaid expansion on children's preventive care., Comment: 39 pages, Difference in differences, triple difference, Changes in Changes, Triple Changes

Neglecting complex aerodynamic effects hinders high-speed yet high-precision multirotor autonomy. In this paper, we present a computationally efficient learning-based model predictive controller that simultaneously optimizes a trajectory that can be tracked within the physical limits (on thrust and orientation) of the multirotor system despite unknown aerodynamic forces and adapts the control input. To do this, we leverage the well-known differential flatness property of multirotors, which allows us to transform their nonlinear dynamics into a linear model. The main limitation of current flatness-based planning and control approaches is that they often neglect dynamic feasibility. This is because these constraints are nonlinear as a result of the mapping between the input, i.e., multirotor thrust, and the flat state. In our approach, we learn a novel representation of the drag forces by learning the mapping from the flat state to the multirotor thrust vector (in a world frame) as a Gaussian Process (GP). Our proposed approach leverages the properties of GPs to develop a convex optimal controller that can be iteratively solved as a second-order cone program (SOCP). In simulation experiments, our proposed approach outperforms related model predictive controllers that do not account for aerodynamic effects on trajectory feasibility, leading to a reduction of up to 55% in absolute tracking error.

We introduce a privacy auditing scheme for ML models that relies on membership inference attacks using generated data as "non-members". This scheme, which we call PANORAMIA, quantifies the privacy leakage for large-scale ML models without control of the training process or model re-training and only requires access to a subset of the training data. To demonstrate its applicability, we evaluate our auditing scheme across multiple ML domains, ranging from image and tabular data classification to large-scale language models., Comment: 19 pages

In the field of Multi-Agent Systems (MAS), known for their openness, dynamism, and cooperative nature, the ability to trust the resources and services of other agents is crucial. Trust, in this setting, is the reliance and confidence an agent has in the information, behaviors, intentions, truthfulness, and capabilities of others within the system. Our paper introduces a new graphical approach that utilizes factor graphs to represent the interdependent behaviors and trustworthiness among agents. This includes modeling the behavior of robots as a trajectory of actions using a Gaussian process factor graph, which accounts for smoothness, obstacle avoidance, and trust-related factors. Our method for evaluating trust is decentralized and considers key interdependent sub-factors such as proximity safety, consistency, and cooperation. The overall system comprises a network of factor graphs that interact through trust-related factors and employs a Bayesian inference method to dynamically assess trust-based decisions with informed consent. The effectiveness of this method is validated via simulations and empirical tests with autonomous robots navigating unsignalized intersections.

In this paper we document for the first time some of the effects of self-healing, a property of orbital-angular-momentum (OAM) or vortex beams, as observed on a millimeter-wave experimental communications link in an outdoors line-of-sight (LOS) scenario. The OAM beams have a helical phase and polarization structure and have conical amplitude shape in the far field. The Poynting vectors of the OAM beams also possess helical structures, orthogonal to the corresponding helical phase-fronts. Due to such non-planar structure in the direction orthogonal to the beam axis, OAM beams are a subset of structured light beams. Such structured beams are known to possess self-healing properties when partially obstructed along their propagation axis, especially in their near fields, resulting in partial reconstruction of their structures at larger distances along their beam axis. Various theoretical rationales have been proposed to explain, model and experimentally verify the self-healing physical effects in structured optical beams, using various types of obstructions and experimental techniques. Based on these models, we hypothesize that any self-healing observed will be greater as the OAM order increases. Here we observe the self-healing effects for the first time in structured OAM radio beams, in terms of communication signals and channel parameters rather than beam structures. We capture the effects of partial near-field obstructions of OAM beams of different orders on the communications signals and provide a physical rationale to substantiate that the self-healing effect was observed to increase with the order of OAM, agreeing with our hypothesis., Comment: 9 pages, 10 figures, pending submission to IEEE Access journal