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Machine learning models use high dimensional feature spaces to map their inputs to the corresponding class labels. However, these features often do not have a one-to-one correspondence with physical concepts understandable by humans, which hinders the ability to provide a meaningful explanation for the decisions made by these models. We propose a method for measuring the correlation between high-level concepts and the decisions made by a machine learning model. Our method can isolate the impact of a given high-level concept and accurately measure it quantitatively. Additionally, this study aims to determine the prevalence of frequent patterns in machine learning models, which often occur in imbalanced datasets. We have successfully applied the proposed method to fundus images and managed to quantitatively measure the impact of radiomic patterns on the model decisions., Comment: 11 pages, 8 figures, "to be published in the journal of Computer SCience"

Modeling the orbital dynamics of objects in galactic disks is crucial to understanding the stability and evolution of disk galaxies. While studies of galactic orbits are largely dominated by $N$-body simulations, perturbative analytical models offer a computationally inexpensive and conceptually insightful way of analyzing galactic dynamics. We utilize perturbation theory and the method of matched asymptotics to develop a technique by which the vertical motion of a point mass perpendicular to a thin axisymmetric disk galaxy can be computed analytically to high precision. The objective of this study is to provide an accurate model of non-planar dynamics that could be employed in galactic simulations to bypass computationally expensive integration. We construct a general solution for the dynamics at small $z$ displacements from the plane of the disk using perturbation theory. We solve for the dynamics at very large displacements from the plane of the disk and construct a function that interpolates the solutions at each length scale. To demonstrate the method's utility, we apply it to two commonly used models for the vertical structure of a galactic disk, the exponential model and the isothermal model. Finally, using the principle of adiabatic invariance, we analyze how to relate variations in radial motion to variations in the vertical motion and discuss possible applications of the model in numerical astrophysics.

Estimation of patient acuity in the Intensive Care Unit (ICU) is vital to ensure timely and appropriate interventions. Advances in artificial intelligence (AI) technologies have significantly improved the accuracy of acuity predictions. However, prior studies using machine learning for acuity prediction have predominantly relied on electronic health records (EHR) data, often overlooking other critical aspects of ICU stay, such as patient mobility, environmental factors, and facial cues indicating pain or agitation. To address this gap, we present MANGO: the Multimodal Acuity traNsformer for intelliGent ICU Outcomes, designed to enhance the prediction of patient acuity states, transitions, and the need for life-sustaining therapy. We collected a multimodal dataset ICU-Multimodal, incorporating four key modalities, EHR data, wearable sensor data, video of patient's facial cues, and ambient sensor data, which we utilized to train MANGO. The MANGO model employs a multimodal feature fusion network powered by Transformer masked self-attention method, enabling it to capture and learn complex interactions across these diverse data modalities even when some modalities are absent. Our results demonstrated that integrating multiple modalities significantly improved the model's ability to predict acuity status, transitions, and the need for life-sustaining therapy. The best-performing models achieved an area under the receiver operating characteristic curve (AUROC) of 0.76 (95% CI: 0.72-0.79) for predicting transitions in acuity status and the need for life-sustaining therapy, while 0.82 (95% CI: 0.69-0.89) for acuity status prediction...

Machine Learning (ML) models, such as deep neural networks, are widely applied in autonomous systems to perform complex perception tasks. New dependability challenges arise when ML predictions are used in safety-critical applications, like autonomous cars and surgical robots. Thus, the use of fault tolerance mechanisms, such as safety monitors, is essential to ensure the safe behavior of the system despite the occurrence of faults. This paper presents an extensive literature review on safety monitoring of perception functions using ML in a safety-critical context. In this review, we structure the existing literature to highlight key factors to consider when designing such monitors: threat identification, requirements elicitation, detection of failure, reaction, and evaluation. We also highlight the ongoing challenges associated with safety monitoring and suggest directions for future research., Comment: 25 pages, 2 figures

Modeling late reverberation at interactive speeds is a challenging task when multiple sound sources and listeners are present in the same environment. This is especially problematic when the environment is geometrically complex and/or features uneven energy absorption (e.g. coupled volumes), because in such cases the late reverberation is dependent on the sound sources' and listeners' positions, and therefore must be adapted to their movements in real time. We present a novel approach to the task, named modal decomposition of Acoustic Radiance Transfer (MoD-ART), which can handle highly complex scenarios with efficiency. The approach is based on the geometrical acoustics method of Acoustic Radiance Transfer, from which we extract a set of energy decay modes and their positional relationships with sources and listeners. In this paper, we describe the physical and mathematical meaningfulness of MoD-ART, highlighting its advantages and applicability to different scenarios. Through an analysis of the method's computational complexity, we show that it compares very favourably with ray-tracing. We also present simulation results showing that MoD-ART can capture multiple decay slopes and flutter echoes.

Novel reconfigurable intelligent surface (RIS) architectures, known as beyond diagonal RISs (BD-RISs), have been proposed to enhance reflection efficiency and expand RIS capabilities. However, their passive nature, non-diagonal reflection matrix, and the large number of coupled reflecting elements complicate the channel state information (CSI) estimation process. The challenge further escalates in scenarios with fast-varying channels. In this paper, we address this challenge by proposing novel joint channel estimation and prediction strategies with low overhead and high accuracy for two different RIS architectures in a BD-RIS-assisted multiple-input multiple-output system under correlated fast-fading environments with channel aging. The channel estimation procedure utilizes the Tucker2 decomposition with bilinear alternative least squares, which is exploited to decompose the cascade channels of the BD-RIS-assisted system into effective channels of reduced dimension. The channel prediction framework is based on a convolutional neural network combined with an autoregressive predictor. The estimated/predicted CSI is then utilized to optimize the RIS phase shifts aiming at the maximization of the downlink sum rate. Insightful simulation results demonstrate that our proposed approach is robust to channel aging, and exhibits a high estimation accuracy. Moreover, our scheme can deliver a high average downlink sum rate, outperforming other state-of-the-art channel estimation methods. The results also reveal a remarkable reduction in pilot overhead of up to 98\% compared to baseline schemes, all imposing low computational complexity., Comment: arXiv admin note: text overlap with arXiv:2406.07387

A common step at the core of many RNA transcript assembly tools is to find a set of weighted paths that best explain the weights of a DAG. While such problems easily become NP-hard, scalable solvers exist only for a basic error-free version of this problem, namely minimally decomposing a network flow into weighted paths. The main result of this paper is to show that we can achieve speedups of two orders of magnitude also for path-finding problems in the realistic setting (i.e., the weights do not induce a flow). We obtain these by employing the safety information that is encoded in the graph structure inside Integer Linear Programming (ILP) solvers for these problems. We first characterize the paths that appear in all path covers of the DAG, generalizing a graph reduction commonly used in the error-free setting (e.g. by Kloster et al. [ALENEX~2018]). Secondly, following the work of Ma, Zheng and Kingsford [RECOMB 2021], we characterize the \emph{sequences} of arcs that appear in all path covers of the DAG. We experiment with a path-finding ILP model (least squares) and with a more recent and accurate one. We use a variety of datasets originally created by Shao and Kingsford [TCBB, 2017], as well as graphs built from sequencing reads by the state-of-the-art tool for long-read transcript discovery, IsoQuant [Prjibelski et al., Nat.~Biotechnology~2023]. The ILPs armed with safe paths or sequences exhibit significant speed-ups over the original ones. On graphs with a large width, average speed-ups are in the range $50-160\times$ in the latter ILP model and in the range $100-1000\times$ in the least squares model. Our scaling techniques apply to any ILP whose solution paths are a path cover of the arcs of the DAG. As such, they can become a scalable building block of practical RNA transcript assembly tools, avoiding heuristic trade-offs currently needed on complex graphs.

From 2016-2023, College Futures Foundation supported several California State University (CSU) campuses to partner with California community colleges in their regions to increase the number of students transferring from community colleges and completing bachelor's degrees at the CSU. In an effort to learn more about this work, College Futures Foundation partnered with Education Insights Center (EdInsights) to engage key constituents in a retrospective learning engagement about these transfer partnership efforts. This report serves to both document and share these learnings, and to provide relevant information to those interested in forming intersegmental transfer partnerships, potential funders, law and policymakers, and researchers. College Futures Foundation identified five intersegmental partnerships for the authors to invite into this learning engagement. Through this process, the authors sought to answer the following questions: (1) What compelled partners to work together on transfer?; (2) What did partners undertake during their grant periods?; and (3) What attributes foster a successful intersegmental transfer partnership?

Superconductors are often discussed in the mean-field approximation that breaks $U(1)$ symmetry. Since the $U(1)$ symmetry underlies the charge conservation, naive application of response theory sometimes gives results that are not gauge-invariant. We study the effect of vertex corrections on the electromagnetic responses of superconductors by employing the gauge-invariant formulation, called consistent fluctuations of order parameters, that manifestly satisfy the Ward identity. For inversion broken two-band superconductors, we find that the linear and second-order optical responses are significantly affected by vertex corrections and the conductivities near the gap energy almost completely disappear. We also investigate the linear optical responses in $d$-wave superconductors and obtained qualitatively different results compared to the result using the solution to the Bethe-Salpeter equation. The full photon vertex used in our study, manifestly satisfies the Ward identity. Our results demonstrate the fundamental importance of vertex corrections in exploring optical responses in superconductors., Comment: 6 pages, 3 figures

Online platforms for social interactions are an essential part of modern society. With the advance of technology and the rise of algorithms and AI, content is now filtered systematically and facilitates the formation of filter bubbles. This work investigates the social consensus under limited visibility in a two-state majority-vote model on Barab\'asi-Albert scale-free networks. In the consensus evolution, each individual assimilates the opinion of the majority of their neighbors with probability $1-q$ and disagrees with chance $q$, known as the noise parameter. We define the visibility parameter $V$ as the probability of an individual considering the opinion of a neighbor at a given interaction. The parameter $V$ enables us to model the limited visibility phenomenon that produces synthetic neighborhoods in online interactions. We employ Monte Carlo simulations and finite-size scaling analysis to obtain the critical noise parameter as a function of the visibility $V$ and the growth parameter $z$. We find the critical exponents $\beta/\bar{\nu}$, $\gamma/\bar{\nu}$ and $1/\bar{\nu}$ of and validate their unitary relation for complex networks. Our analysis shows that installing and manipulating synthetic influence groups critically undermines consensus robustness., Comment: 10 pages, 9 figures, 1 table

Data literacy is essential in today's data-driven world, emphasizing individuals' abilities to effectively manage data and extract meaningful insights. However, traditional classroom-based educational approaches often struggle to fully address the multifaceted nature of data literacy. As education undergoes digital transformation, innovative technologies such as Virtual Reality (VR) offer promising avenues for immersive and engaging learning experiences. This paper introduces DataliVR, a pioneering VR application aimed at enhancing the data literacy skills of university students within a contextual and gamified virtual learning environment. By integrating Large Language Models (LLMs) like ChatGPT as a conversational artificial intelligence (AI) chatbot embodied within a virtual avatar, DataliVR provides personalized learning assistance, enriching user learning experiences. Our study employed an experimental approach, with chatbot availability as the independent variable, analyzing learning experiences and outcomes as dependent variables with a sample of thirty participants. Our approach underscores the effectiveness and user-friendliness of ChatGPT-powered DataliVR in fostering data literacy skills. Moreover, our study examines the impact of the ChatGPT-based AI chatbot on users' learning, revealing significant effects on both learning experiences and outcomes. Our study presents a robust tool for fostering data literacy skills, contributing significantly to the digital advancement of data literacy education through cutting-edge VR and AI technologies. Moreover, our research provides valuable insights and implications for future research endeavors aiming to integrate LLMs (e.g., ChatGPT) into educational VR platforms., Comment: 10 pages, this paper was accepted to ISMAR2024

Reconfigurable intelligent surfaces (RISs) have been introduced as arrays of nearly passive elements with software-tunable electromagnetic properties to dynamically manipulate the reflection/transmission of radio signals. Research works in this area are focused on two applications, namely {\it user-assist} RIS aiming at tuning the RIS to enhance the quality-of-service (QoS) of target users, and the {\it malicious} RIS aiming for an attacker to degrade the QoS at victim receivers through generating {\it intended} destructive interference. While both user-assist and malicious RIS applications have been explored extensively, the impact of RIS deployments on imposing {\it unintended} interference on various wireless user-equipments (EUs) remains underexplored. This paper investigates the challenges of integrating RISs into multi-carrier, multi-user, and multi-operator networks. We discuss how RIS deployments intended to benefit specific users can negatively impact other users served at various carrier frequencies through different network operators. While not an ideal solution, we discuss how ultra-narrowband metasurfaces can be incorporated into the manufacturing of RISs to mitigate some challenges of RIS deployment in wireless networks. We also present a simulation scenario to illuminate some practical challenges associated with the deployment of RISs in shared public environments.

We investigate the electronic properties of nanoribbons made out of monolayer Lieb, transition, and kagome lattices using the tight-binding model with a generic Hamiltonian. It allows us to map the evolutionary stages of the interconvertibility process between Lieb and kagome nanoribbons by means of only one control parameter. Results for the energy spectra, the density of states, and spatial probability density distributions are discussed for nanoribbons with three types of edges: straight, bearded, and asymmetric. We explore for different nanoribbon terminations: (i) the semiconductor-metallic transition due to the interconvertibility of the Lieb and kagome lattices, (ii) the effect of both nanoribbon width and inclusion of the next-nearest-neighbor hopping term on the degeneracy of the quasi-flat states, (iii) the behavior of the energy gap versus the nanoribbon width, (iv) the existence and evolution of edge states, and (v) the nodal spatial distributions of the total probability densities of the non-dispersive states., Comment: 21 pages, 12 figures

While the robustness of rate-splitting multiple access (RSMA) to imperfect channel state information (CSI) is well-documented, its susceptibility to attacks launched with malicious reconfigurable intelligent surfaces (RISs) remains unexplored. This paper fills this gap by investigating three potential RIS-induced attacks against RSMA in a multi-user multiple-input multiple-output (MIMO) network: random interference, aligned interference, and mitigation attack. The random interference attack employs random RIS coefficients to disrupt RSMA. The other two attacks are triggered by optimizing the RIS through weighted-sum strategies based on the projected gradient method. Simulation results reveal significant degradation caused by all the attacks under perfect CSI conditions. Remarkably, when imperfect CSI is considered, RSMA, owing to its flexible power allocation strategy designed to counter CSI-related interference, can be robust to the attacks even when the base station is blind to them. It is also shown that RSMA can significantly outperform conventional space-division multiple access (SDMA)., Comment: Accepted in IEEE Global Communications Conference (GLOBECOM), Dec. 2024, Cape Town, South Africa

We have performed a parametrized post-Newtonian (PPN) test of a black hole spacetime using observational data of the star S0-2/S2 orbiting the massive black hole at our galactic center Sgr A*. After introducing our PPN model of black hole spacetime, we report the result of $\chi^2$ fitting of the PPN model with the observational data. A new finding through our PPN model is a detectability of the gravitational lens effect on the null geodesics connecting S0-2 and observer under the present observational uncertainties, if a PPN parameter is about one order larger than the value for general relativity case. On the other hand, the effect of black hole spin on the S0-2's motion is not detectable. Thus our present PPN test is performed with spherically symmetric vacuum black hole spacetime. The resultant value of the PPN parameter, which corresponds to the minimum $\chi^2$, implies that the gravitational field of Sgr A* is not of Schwarzschild metric or that there exists a sufficient amount of dark matters around Sgr A* to be detected by present telescopes. However, the difference between the minimum $\chi^2$ and the $\chi^2$ of Schwarzschild case is not large enough to ensure a statistical significance of non-Schwarzschild result. A more precise statistical analysis than $\chi^2$ statistics is necessary to extract a statistically significant information of the gravitational field of Sgr A* from present observational data. We will report a result by a Bayesian analysis in next paper., Comment: 43 pages, 4 figures, Accepted for Publication in Prog.Theor.Exp.Phys / Typos are corrected

Biquard-Gauduchon have shown that conformally K\"ahler, Ricci-flat, ALF toric metrics on the complement of toric divisors are: the Taub-NUT metric with reversed orientation, in the Kerr-Taub-bolt family or in the Chen-Teo family. The same authors have also given a unified construction for the above families relying on an axi-symmetric harmonic function on $\mathbb{R}^3$. In this work, we reverse this construction and use methods from a paper of the second named author, "Uniqueness among scalar-flat K\"ahler metrics on non-compact toric 4-manifolds", to show that all conformally K\"ahler, Ricci-flat, toric metrics on the complement of toric divisors, under some mild assumptions on the associated moment polytope, are among the families above. In particular all such metrics are ALF., Comment: 23 pages, 1 figure

The attention mechanism forms the foundational blocks for transformer language models. Recent approaches show that scaling the model achieves human-level performance. However, with increasing demands for scaling and constraints on hardware memory, the inference costs of these models remain high. To reduce the inference time, Multi-Query Attention (MQA) and Grouped-Query Attention (GQA) were proposed in (Shazeer, 2019) and (Ainslieet al., 2023) respectively. In this paper, we propose a variation of Grouped-Query Attention, termed Weighted Grouped-Query Attention (WGQA). We introduced new learnable parameters for each key and value head in the T5 decoder attention blocks, enabling the model to take a weighted average during finetuning. Our model achieves an average of 0.53% improvement over GQA, and the performance converges to traditional Multi-head attention (MHA) with no additional overhead during inference. We evaluated the introduction of these parameters and subsequent finetuning informs the model about the grouping mechanism during training, thereby enhancing performance. Additionally, we demonstrate the scaling laws in our analysis by comparing the results between T5-small and T5-base architecture.

The vector cross product and pseudoscalar dot products of electric (E) and magnetic (H) fields are separately finite in vacuum transverse electric and magnetic (TEM) plane waves, and angular momentum structured light. Current theories of interactions beyond the standard model of particle physics invoke non-zero dot(E,H) as the source term in the axion law that describes interactions with the cosmological dark matter axion particles outside of the quartet of Maxwells equations. The non-zero dot(E,H) also drives relativistic spin-charge magnetoelectric excitations of axion quasiparticles at a distinctively higher condensed matter scale in magnetic and topological materials. Yet, how to drive coherent dot(E,H) responses is unknown, and provides motivation to examine the field polarizations in structured light on a deep sub-diffraction limited spatial scale and sub-optical cycle temporal scale by ultrafast nonlinear photoemission electron microscopy. By analytical theory and ultrafast coherent photoemission electron microscopy, we image dot(E,H) fields in surface plasmon polariton vortex cores at subwavelength scales, where we find that the magnetoelectric relative to the dipole density is intensified on a ~10 nm diameter scale as a universal property of plasmonic vortex fields. The generation and nanoscale localization of dot(E,H) fields introduces the magnetoelectric symmetry class, having the parity and time reversal broken, but the joint parity-time reversal symmetry preserved. The ability to image the optical fields of plasmonic vortex cores opens the research of ultrafast microscopy of magnetoelectric responses and interactions with axion quasiparticles in solid state materials., Comment: 5 Figures

This paper addresses the challenge of enhancing cybersecurity in Blockchain-based Internet of Things (BIoTs) systems, which are increasingly vulnerable to sophisticated cyberattacks. It introduces an AI-powered system model for the dynamic deployment of honeypots, utilizing an Intrusion Detection System (IDS) integrated with smart contract functionalities on IoT nodes. This model enables the transformation of regular nodes into decoys in response to suspicious activities, thereby strengthening the security of BIoT networks. The paper analyses strategic interactions between potential attackers and the AI-enhanced IDS through a game-theoretic model, specifically Bayesian games. The model focuses on understanding and predicting sophisticated attacks that may initially appear normal, emphasizing strategic decision-making, optimized honeypot deployment, and adaptive strategies in response to evolving attack patterns.

This paper presents a framework for securing blockchain-based IoT systems by integrating Physical Unclonable Functions (PUFs) and Zero-Knowledge Proofs (ZKPs) within a Hyperledger Fabric environment. The proposed framework leverages PUFs for unique device identification and ZKPs for privacy-preserving authentication and transaction processing. Experimental results demonstrate the framework's feasibility, performance, and security against various attacks. This framework provides a comprehensive solution for addressing the security challenges in blockchain-based IoT systems.

This study proposes a framework to enhance privacy in Blockchain-based Internet of Things (BIoT) systems used in the healthcare sector. The framework addresses the challenge of leveraging health data for analytics while protecting patient privacy. To achieve this, the study integrates Differential Privacy (DP) with Federated Learning (FL) to protect sensitive health data collected by IoT nodes. The proposed framework utilizes dynamic personalization and adaptive noise distribution strategies to balance privacy and data utility. Additionally, blockchain technology ensures secure and transparent aggregation and storage of model updates. Experimental results on the SVHN dataset demonstrate that the proposed framework achieves strong privacy guarantees against various attack scenarios while maintaining high accuracy in health analytics tasks. For 15 rounds of federated learning with an epsilon value of 8.0, the model obtains an accuracy of 64.50%. The blockchain integration, utilizing Ethereum, Ganache, Web3.py, and IPFS, exhibits an average transaction latency of around 6 seconds and consistent gas consumption across rounds, validating the practicality and feasibility of the proposed approach.

Reconfigurable intelligent surfaces (RISs) have emerged as a promising technology to enhance the performance of sixth-generation (6G) and beyond communication systems. The passive nature of RISs and their large number of reflecting elements pose challenges to the channel estimation process. The associated complexity further escalates when the channel coefficients are fast-varying as in scenarios with user mobility. In this paper, we propose an extended channel estimation framework for RIS-assisted multiple-input multiple-output (MIMO) systems based on a convolutional neural network (CNN) integrated with an autoregressive (AR) predictor. The implemented framework is designed for identifying the aging pattern and predicting enhanced estimates of the wireless channels in correlated fast-fading environments. Insightful simulation results demonstrate that our proposed CNN-AR approach is robust to channel aging, exhibiting a high-precision estimation accuracy. The results also show that our approach can achieve high spectral efficiency and low pilot overhead compared to traditional methods.