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Skin cancer is one of the most prevalent and potentially life-threatening diseases worldwide, necessitating early and accurate diagnosis to improve patient outcomes. Conventional diagnostic methods, reliant on clinical expertise and histopathological analysis, are often time-intensive, subjective, and prone to variability. To address these limitations, we propose a novel hybrid deep learning framework that integrates convolutional neural networks (CNNs) with Radial Basis Function (RBF) Networks to achieve high classification accuracy and enhanced interpretability. The motivation for incorporating RBF Networks lies in their intrinsic interpretability and localized response to input features, which make them well-suited for tasks requiring transparency and fine-grained decision-making. Unlike traditional deep learning models that rely on global feature representations, RBF Networks allow for mapping segments of images to chosen prototypes, exploiting salient features within a single image. This enables clinicians to trace predictions to specific, interpretable patterns. The framework incorporates segmentation-based feature extraction, active learning for prototype selection, and K-Medoids clustering to focus on these salient features. Evaluations on the ISIC 2016 and ISIC 2017 datasets demonstrate the model's effectiveness, achieving classification accuracies of 83.02\% and 72.15\% using ResNet50, respectively, and outperforming VGG16-based configurations. By generating interpretable explanations for predictions, the framework aligns with clinical workflows, bridging the gap between predictive performance and trustworthiness. This study highlights the potential of hybrid models to deliver actionable insights, advancing the development of reliable AI-assisted diagnostic tools for high-stakes medical applications., Comment: The paper has not been published by any journal/conference. It contains 14 pages, with six figures and five tables to demonstrate results

We consider a class of axially symmetric solutions to Einstein's equations incorporating a $\theta$-dependent scalar field and extend these solutions by introducing electric and magnetic charges via Harrison transformations. Subsequently, we enhance the charged metrics by incorporating the NUT parameter through Ehlers transformations, yielding a novel class of charged-Taub-NUT metrics that represent exact solutions to Einstein's equations. Finally, we investigate some of astrophysical aspects of the charged-Taub-NUT metrics, focusing on phenomena such as gravitational lensing and quasi-normal modes (QNMs)., Comment: 17 pages

The recent unveiling of the images of Sgr A* and M87* has significantly advanced our understanding of gravitational physics. In this study, we derive a class of Kerr-Taub-NUT metrics in the presence of a scalar field (KTNS). Treating these metrics as models for supermassive objects, we constrain the parameters using shadow size estimates done by observations of M87* and Sgr A* from the Event Horizon Telescope (EHT). Comparing the obtained results with M87* data, we show an upper limit on the NUT charge $n$ such that the constraint on the shadow deviation from circularity ($ \Delta C $) will be fulfilled for $ n<0.5 $, and this allowed range changes with a variation in other parameters. Additionally, our findings reveal that fast-rotating KTNS metrics are better candidates for supermassive M87* than slowly rotating ones. We continue our study by estimating parameters using Keck and VLTI observations of Sgr A* and find that the constraint on the fraction deviation $ \delta $ is maintained within a certain range of the NUT charge such that the Keck bound is satisfied for $ n<0.41 $. In contrast, the VLTI bound can be fulfilled for $ n>0.34 $. Finally, we investigate weak gravitational lensing using the Gauss-Bonnet theorem and illustrate that all model parameters increase the deflection angle, causing light rays to deviate more significantly near fast-rotating KTNS objects.

Visual impairment affects the ability of people to live a life like normal people. Such people face challenges in performing activities of daily living, such as reading, writing, traveling and participating in social gatherings. Many traditional approaches are available to help visually impaired people; however, these are limited in obtaining contextually rich environmental information necessary for independent living. In order to overcome this limitation, this paper introduces a novel wearable vision assistance system that has a hat-mounted camera connected to a Raspberry Pi 4 Model B (8GB RAM) with artificial intelligence (AI) technology to deliver real-time feedback to a user through a sound beep mechanism. The key features of this system include a user-friendly procedure for the recognition of new people or objects through a one-click process that allows users to add data on new individuals and objects for later detection, enhancing the accuracy of the recognition over time. The system provides detailed descriptions of objects in the user's environment using a large vision language model (LVLM). In addition, it incorporates a distance sensor that activates a beeping sound using a buzzer as soon as the user is about to collide with an object, helping to ensure safety while navigating their environment. A comprehensive evaluation is carried out to evaluate the proposed AI-based solution against traditional support techniques. Comparative analysis shows that the proposed solution with its innovative combination of hardware and AI (including LVLMs with IoT), is a significant advancement in assistive technology that aims to solve the major issues faced by the community of visually impaired people, Comment: N-A

The two-dimensional nanomaterial, hexagonal boron nitride (hBN) was cleanly transferred via a blister-based laser-induced forward-transfer method. The transfer was performed utilizing femtosecond and nanosecond laser pulses for separation distances of ~16 and ~200 micrometers between a titanium donor film deposited on a glass substrate and a silicon/silicon dioxide receiver. Transfer efficiency was examined for isolated laser pulses as well as for series of overlapping pulses and single layer transfer was confirmed. It was found that hBN is transferable for all tested combinations of pulse duration and transfer distances. The results indicate that transfer proceeds via direct stamping for short donor-to-receiver distances while, for the larger distance, the material is ejected from the donor and lands on the receiver. Furthermore, with overlapping pulses, nanosecond laser pulses enable a successful printing of hBN lines while, for fs laser pulses, the Ti film can be locally disrupted by multiple pulses and molten titanium may be transferred along with the hBN flakes. For reproducibility, and to avoid contamination with metal deposits, low laser fluence transfer with ns pulses and transfer distances smaller than the blister height provide the most favourable and reproducible condition., Comment: 13 pages, 5 pages Supplemental Material, 69 references

We present a JWST MIRI/MRS spectrum of the inner disk of WISE J044634.16$-$262756.1B (hereafter J0446B), an old ($\sim$34 Myr) M4.5 star but with hints of ongoing accretion. The spectrum is molecule-rich and dominated by hydrocarbons. We detect 14 molecular species (H$_2$, CH$_3$, CH$_4$, C$_2$H$_2$, $^{13}$CCH$_2$, C$_2$H$_4$, C$_2$H$_6$, C$_3$H$_4$, C$_4$H$_2$, C$_6$H$_6$, HCN, HC$_3$N, CO$_2$ and $^{13}$CO$_2$) and 2 atomic lines ([Ne II] and [Ar II]), all observed for the first time in a disk at this age. The detection of spatially unresolved H$_2$ and Ne gas strongly supports that J0446B hosts a long-lived primordial disk, rather than a debris disk. The marginal H$_2$O detection and the high C$_2$H$_2$/CO$_2$ column density ratio indicate that the inner disk of J0446B has a very carbon-rich chemistry, with a gas-phase C/O ratio $\gtrsim$2, consistent with what have been found in most primordial disks around similarly low-mass stars. In the absence of significant outer disk dust substructures, inner disks are expected to first become water-rich due to the rapid inward drift of icy pebbles, and evolve into carbon-rich as outer disk gas flows inward on longer timescales. The faint millimeter emission in such low-mass star disks implies that they may have depleted their outer icy pebble reservoir early and already passed the water-rich phase. Models with pebble drift and volatile transport suggest that maintaining a carbon-rich chemistry for tens of Myr likely requires a slowly evolving disk with $\alpha-$viscosity $\lesssim10^{-4}$. This study represents the first detailed characterization of disk gas at $\sim$30 Myr, strongly motivating further studies into the final stages of disk evolution., Comment: Accepted by ApJL. 8 figures in the main text

Performance optimization associated with optical modulators requires reasonably accurate predictive models for key figures of merit. Interleaved PN-junction topology offers the maximum mode/junction overlap and is the most efficient modulator in depletion-mode of operation. Due to its structure, the accurate modelling process must be fully three-dimensional, which is a nontrivial computational problem. This paper presents a rigorous 3D model for the modulation efficiency of silicon-on-insulator interleaved junction optical phase modulators with submicron dimensions. Solution of Drift-Diffusion and Poisson equations were carried out on 3D finite-element-mesh and Maxwell equations were solved using Finite-Difference-Time-Domain (FDTD) method on 3D Yee-cells. Whole of the modelling process has been detailed and all the coefficients required in the model are presented. Model validation suggests < 10% RMS error.

Full-vectorial finite difference method with perfectly matched layers boundaries is used to identify the single mode operation region of submicron rib waveguides fabricated using sili-con-on-insulator material system. Achieving high mode power confinement factors is emphasized while maintaining the single mode operation. As opposed to the case of large cross-section rib waveguides, theoretical single mode conditions have been demonstrated to hold for sub-micron waveguides with accuracy approaching 100%. Both, the deeply and the shallowly etched rib waveguides have been considered and the single mode condition for entire sub-micrometer range is presented while adhering to design specific mode confinement requirements.

In the field of Material Science, effective information retrieval systems are essential for facilitating research. Traditional Retrieval-Augmented Generation (RAG) approaches in Large Language Models (LLMs) often encounter challenges such as outdated information, hallucinations, limited interpretability due to context constraints, and inaccurate retrieval. To address these issues, Graph RAG integrates graph databases to enhance the retrieval process. Our proposed method processes Material Science documents by extracting key entities (referred to as MatIDs) from sentences, which are then utilized to query external Wikipedia knowledge bases (KBs) for additional relevant information. We implement an agent-based parsing technique to achieve a more detailed representation of the documents. Our improved version of Graph RAG called G-RAG further leverages a graph database to capture relationships between these entities, improving both retrieval accuracy and contextual understanding. This enhanced approach demonstrates significant improvements in performance for domains that require precise information retrieval, such as Material Science.

Vision-Language Models (VLMs) have shown remarkable capabilities across diverse visual tasks, including image recognition, video understanding, and Visual Question Answering (VQA) when explicitly trained for these tasks. Despite these advances, we find that current VLMs lack a fundamental cognitive ability: learning to localize objects in a scene by taking into account the context. In this work, we focus on the task of few-shot personalized localization, where a model is given a small set of annotated images (in-context examples) -- each with a category label and bounding box -- and is tasked with localizing the same object type in a query image. To provoke personalized localization abilities in models, we present a data-centric solution that fine-tunes them using carefully curated data from video object tracking datasets. By leveraging sequences of frames tracking the same object across multiple shots, we simulate instruction-tuning dialogues that promote context awareness. To reinforce this, we introduce a novel regularization technique that replaces object labels with pseudo-names, ensuring the model relies on visual context rather than prior knowledge. Our method significantly enhances few-shot localization performance without sacrificing generalization, as demonstrated on several benchmarks tailored to personalized localization. This work is the first to explore and benchmark personalized few-shot localization for VLMs, laying a foundation for future research in context-driven vision-language applications. The code for our project is available at https://github.com/SivanDoveh/IPLoc

Using different laser irradiation patterns to modify of silicon surface, it has been demonstrated that, at rather small overlapping between irradiation spots, highly regular laser-induced periodic surface structures (LIPSS) can be produced already starting from the second laser pulse, provided that polarization direction coincides with the scanning direction. If the laser irradiation spot is shifted from the previous one perpendicular to light polarization, LIPSS are not formed even after many pulses. This coherence effect is explained by a three-wave interference, - surface electromagnetic waves (SEWs) generated within the irradiated spot, SEWs scattered from the crater edge formed by the previous laser pulse, and the incoming laser pulse, - providing conditions for amplification of the periodic light-absorption pattern. To study possible consequences of SEW scattering from the laser-modified regions, where the refractive index can change due to material melting, amorphization, and the residual stress formed by previous laser pulses, hydrodynamic modelling and simulations have been performed within the melting regime. The simulations show that stress and vertical displacement could be amplified upon laser scanning. Both mechanisms, three-wave interference and stress accumulation, could enable an additional degree of controlling surface structuring., Comment: 12 pages + 4 pages of Supplementary Information, 9 figures

A computational study of I-BODIPY (2-ethyl-4,4-difluoro-6,7-diiodo-1,3-dimethyl-4-bora-3a,4a-diaza-s-indacene) was conducted to investigate its photophysical properties as a potential triplet photosensitizer for singlet oxygen generation. Multireference CASPT2 and CASSCF methods were used to calculate vertical excitation energies and spin-orbit couplings (SOCs) in a model monoiodinated BODIPY molecule to assess the applicability of the single-reference ADC(2) method. Time-dependent density functional theory (TD-DFT) with the Tamm-Dancoff approximation (TDA) was tested against ADC(2) using different exchange-correlation functionals, employing a two-component pseudopotential basis set for iodine. SOC magnitudes between excited states were discussed using the Slater-Condon rules. The geometry dependence of SOCs for the lowest states was also examined. TD-DFT/B3LYP and TD-DFT(TDA)/BHLYP were selected for subsequent absorption spectra and trajectory surface hopping (TSH) molecular dynamics (MD) simulations. Two bright states were identified in I-BODIPY's visible spectrum, showing a red shift due to iodine substitution. Excited-state MD simulations, including nonadiabatic effects and SOCs, were performed to investigate relaxation after photoexcitation to the S1 state. TSH MD simulations revealed that intersystem crossings occur on a similar timescale to internal conversions. After triplet population growth, a "saturation" phase was reached with a triplet-to-singlet ratio of about 4:1. The calculated triplet quantum yield of 0.85 agrees qualitatively with the experimental singlet oxygen generation yield of 0.99.

When training data are distributed across{ time or space,} covariate shift across fragments of training data biases cross-validation, compromising model selection and assessment. We present \textit{Fragmentation-Induced covariate-shift Remediation} ($FIcsR$), which minimizes an $f$-divergence between a fragment's covariate distribution and that of the standard cross-validation baseline. We s{how} an equivalence with popular importance-weighting methods. {The method}'s numerical solution poses a computational challenge owing to the overparametrized nature of a neural network, and we derive a Fisher Information approximation. When accumulated over fragments, this provides a global estimate of the amount of shift remediation thus far needed, and we incorporate that as a prior via the minimization objective. In the paper, we run extensive classification experiments on multiple data classes, over $40$ datasets, and with data batched over multiple sequence lengths. We extend the study to the $k$-fold cross-validation setting through a similar set of experiments. An ablation study exposes the method to varying amounts of shift and demonstrates slower degradation with $FIcsR$ in place. The results are promising under all these conditions; with improved accuracy against batch and fold state-of-the-art by more than $5\%$ and $10\%$, respectively.

The large-scale training of multi-modal models on data scraped from the web has shown outstanding utility in infusing these models with the required world knowledge to perform effectively on multiple downstream tasks. However, one downside of scraping data from the web can be the potential sacrifice of the benchmarks on which the abilities of these models are often evaluated. To safeguard against test data contamination and to truly test the abilities of these foundation models we propose LiveXiv: A scalable evolving live benchmark based on scientific ArXiv papers. LiveXiv accesses domain-specific manuscripts at any given timestamp and proposes to automatically generate visual question-answer pairs (VQA). This is done without any human-in-the-loop, using the multi-modal content in the manuscripts, like graphs, charts, and tables. Moreover, we introduce an efficient evaluation approach that estimates the performance of all models on the evolving benchmark using evaluations of only a subset of models. This significantly reduces the overall evaluation cost. We benchmark multiple open and proprietary Large Multi-modal Models (LMMs) on the first version of our benchmark, showing its challenging nature and exposing the models true abilities, avoiding contamination. Lastly, in our commitment to high quality, we have collected and evaluated a manually verified subset. By comparing its overall results to our automatic annotations, we have found that the performance variance is indeed minimal (<2.5%). Our dataset is available online on HuggingFace, and our code will be available here.

In this work, we propose GLOV, which enables Large Language Models (LLMs) to act as implicit optimizers for Vision-Language Models (VLMs) to enhance downstream vision tasks. GLOV prompts an LLM with the downstream task description, querying it for suitable VLM prompts (e.g., for zero-shot classification with CLIP). These prompts are ranked according to their fitness for the downstream vision task. In each respective optimization step, the ranked prompts are fed as in-context examples (with their accuracies) to equip the LLM with the knowledge of the type of prompts preferred by the downstream VLM. Furthermore, we explicitly guide the LLM's generation at each optimization step by adding an offset vector -- calculated from the embedding differences between previous positive and negative solutions -- to the intermediate layer of the network for the next generation. This offset vector biases the LLM generation toward the type of language the downstream VLM prefers, resulting in enhanced performance on the downstream vision tasks. We comprehensively evaluate our GLOV on two tasks: object recognition and the critical task of enhancing VLM safety. Our GLOV shows performance improvement by up to 15.0% and 57.5% for dual-encoder (e.g., CLIP) and encoder-decoder (e.g., LlaVA) models for object recognition and reduces the attack success rate (ASR) on state-of-the-art VLMs by up to $60.7\%$., Comment: Code: https://github.com/jmiemirza/GLOV

Personalized text-to-image diffusion models have grown popular for their ability to efficiently acquire a new concept from user-defined text descriptions and a few images. However, in the real world, a user may wish to personalize a model on multiple concepts but one at a time, with no access to the data from previous concepts due to storage/privacy concerns. When faced with this continual learning (CL) setup, most personalization methods fail to find a balance between acquiring new concepts and retaining previous ones -- a challenge that continual personalization (CP) aims to solve. Inspired by the successful CL methods that rely on class-specific information for regularization, we resort to the inherent class-conditioned density estimates, also known as diffusion classifier (DC) scores, for continual personalization of text-to-image diffusion models. Namely, we propose using DC scores for regularizing the parameter-space and function-space of text-to-image diffusion models, to achieve continual personalization. Using several diverse evaluation setups, datasets, and metrics, we show that our proposed regularization-based CP methods outperform the state-of-the-art C-LoRA, and other baselines. Finally, by operating in the replay-free CL setup and on low-rank adapters, our method incurs zero storage and parameter overhead, respectively, over the state-of-the-art. Our project page: https://srvcodes.github.io/continual_personalization/, Comment: Accepted to ICLR 2025

For high-stakes online exams, it is important to detect potential rule violations to ensure the security of the test. In this study, we investigate the task of detecting whether test takers are looking away from the screen, as such behavior could be an indication that the test taker is consulting external resources. For asynchronous proctoring, the exam videos are recorded and reviewed by the proctors. However, when the length of the exam is long, it could be tedious for proctors to watch entire exam videos to determine the exact moments when test takers look away. We present an AI-assisted gaze detection system, which allows proctors to navigate between different video frames and discover video frames where the test taker is looking in similar directions. The system enables proctors to work more effectively to identify suspicious moments in videos. An evaluation framework is proposed to evaluate the system against human-only and ML-only proctoring, and a user study is conducted to gather feedback from proctors, aiming to demonstrate the effectiveness of the system., Comment: Accepted to HCOMP-24 Works-in-Progress and Demonstration track

This work aims at providing fundamental general tools for the analysis of water spectra as observed in protoplanetary disks with JWST-MIRI. We analyze 25 high-quality spectra from the JDISC Survey reduced with asteroid calibrators as presented in Pontoppidan et al. (2024). First, we present a spectral atlas to illustrate the clustering of H$_2$O transitions from different upper level energies ($E_u$) and identify single (un-blended) transitions that provide the most reliable measurements. With that, we demonstrate two important excitation effects: the opacity saturation of ortho-para line pairs that overlap, and the non-LTE excitation of $v=1-1$ lines scattered across the $v=0-0$ rotational band. Second, we define a shorter list of fundamental lines spanning $E_u=$ 1500-6000 K to develop simple line-ratio diagnostic diagrams for the radial temperature distribution of water in inner disks, which can be interpreted using discrete temperature components or a radial gradient. Third, we report the detection of disk-rotation Doppler broadening of molecular lines, which confirms the radial distribution of water emission including, for the first time, the radially-extended $\approx$ 170-220 K reservoir close to the snowline. The combination of measured line ratios and broadening suggests that drift-dominated disks have shallower temperature gradients with an extended cooler disk surface enriched by ice sublimation. We also report the first detection of a H$_2$O-rich inner disk wind from narrow blue-shifted absorption in the ro-vibrational lines. We summarize these findings and tools into a general recipe to make the study of water in planet-forming regions reliable, effective, and sustainable for samples of $> 100$ disks., Comment: Accepted for publication in The Astronomical Journal

This study investigates the use of Large Language Models (LLMs) for improved depression detection from users social media data. Through the use of fine-tuned GPT 3.5 Turbo 1106 and LLaMA2-7B models and a sizable dataset from earlier studies, we were able to identify depressed content in social media posts with a high accuracy of nearly 96.0 percent. The comparative analysis of the obtained results with the relevant studies in the literature shows that the proposed fine-tuned LLMs achieved enhanced performance compared to existing state of the-art systems. This demonstrates the robustness of LLM-based fine-tuned systems to be used as potential depression detection systems. The study describes the approach in depth, including the parameters used and the fine-tuning procedure, and it addresses the important implications of our results for the early diagnosis of depression on several social media platforms., Comment: 16 pages

Recent advancements in Large Language Models(LLMs) have been notable, yet widespread enterprise adoption remains limited due to various constraints. This paper examines bias in LLMs-a crucial issue affecting their usability, reliability, and fairness. Researchers are developing strategies to mitigate bias, including debiasing layers, specialized reference datasets like Winogender and Winobias, and reinforcement learning with human feedback (RLHF). These techniques have been integrated into the latest LLMs. Our study evaluates gender bias in occupational scenarios and gender, age, and racial bias in crime scenarios across four leading LLMs released in 2024: Gemini 1.5 Pro, Llama 3 70B, Claude 3 Opus, and GPT-4o. Findings reveal that LLMs often depict female characters more frequently than male ones in various occupations, showing a 37% deviation from US BLS data. In crime scenarios, deviations from US FBI data are 54% for gender, 28% for race, and 17% for age. We observe that efforts to reduce gender and racial bias often lead to outcomes that may over-index one sub-class, potentially exacerbating the issue. These results highlight the limitations of current bias mitigation techniques and underscore the need for more effective approaches., Comment: 11 pages, 17 figures

We investigate the role of non-selective measurement on the estimation of system-environment parameters. Projective measurement is the popular method of initial state preparation which always prepares a pure state. However, in various physical situations of physical interest, this selective measurement becomes unrealistic. In this paper, we compare the estimation results obtained via projective measurement with the results obtained via unitary operation. We argue that in typical situations, parameters can be estimated with higher accuracy if the initial state is prepared with the unitary operator (a pulse). We consider the spin-spin model where a central two-level system (probe) interacts with the collections of two-level systems (bath). A probe interacts with a bath and attains a thermal equilibrium state, then via unitary operation, the initial state is prepared which evolves unitarily. The properties of the bath are imprinted on the reduced dynamics. Due to the initial probe-bath correlations present in the thermal equilibrium state, an additional factor arises in the dynamics which has a phenomenal role in the parameter estimation. In this paper, we study the estimation of bath temperature and probe-bath coupling strength which is quantified by the quantum Fisher information. Our results are promising as one can improve the precision of the estimates by orders of magnitude via non-selective measurement and by incorporating the effect of initial correlations., Comment: 10 Pages, 7 figures

The study of domination in graphs has led to a variety of domination problems studied in the literature. Most of these follow the following general framework: Given a graph $G$ and an integer $k$, decide if there is a set $S$ of $k$ vertices such that (1) some inner property $\phi(S)$ (e.g., connectedness) is satisfied, and (2) each vertex $v$ satisfies some domination property $\rho(S, v)$ (e.g., there is an $s\in S$ that is adjacent to $v$). Since many real-world graphs are sparse, we seek to determine the optimal running time of such problems in both the number $n$ of vertices and the number $m$ of edges in $G$. While the classic dominating set problem admits a rather limited improvement in sparse graphs (Fischer, K\"unnemann, Redzic SODA'24), we show that natural variants studied in the literature admit much larger speed-ups, with a diverse set of possible running times. Specifically, we obtain conditionally optimal algorithms for: 1) $r$-Multiple $k$-Dominating Set (each vertex must be adjacent to at least $r$ vertices in $S$): If $r\le k-2$, we obtain a running time of $(m/n)^{r} n^{k-r+o(1)}$ that is conditionally optimal assuming the 3-uniform hyperclique hypothesis. In sparse graphs, this fully interpolates between $n^{k-1\pm o(1)}$ and $n^{2\pm o(1)}$, depending on $r$. Curiously, when $r=k-1$, we obtain a randomized algorithm beating $(m/n)^{k-1} n^{1+o(1)}$ and we show that this algorithm is close to optimal under the $k$-clique hypothesis. 2) $H$-Dominating Set ($S$ must induce a pattern $H$). We conditionally settle the complexity of three such problems: (a) Dominating Clique ($H$ is a $k$-clique), (b) Maximal Independent Set of size $k$ ($H$ is an independent set on $k$ vertices), (c) Dominating Induced Matching ($H$ is a perfect matching on $k$ vertices).

Body composition analysis is vital in assessing health conditions such as obesity, sarcopenia, and metabolic syndromes. MRI provides detailed images of skeletal muscle (SKM), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT), but their manual segmentation is labor-intensive and limits clinical applicability. This study validates an automated tool for MRI-based 2D body composition analysis- (Data Analysis Facilitation Suite (DAFS) Express), comparing its automated measurements with expert manual segmentations using UK Biobank data. A cohort of 399 participants from the UK Biobank dataset was selected, yielding 423 single L3 slices for analysis. DAFS Express performed automated segmentations of SKM, VAT, and SAT, which were then manually corrected by expert raters for validation. Evaluation metrics included Jaccard coefficients, Dice scores, Intraclass Correlation Coefficients (ICCs), and Bland-Altman Plots to assess segmentation agreement and reliability. High agreements were observed between automated and manual segmentations with mean Jaccard scores: SKM 99.03%, VAT 95.25%, and SAT 99.57%; and mean Dice scores: SKM 99.51%, VAT 97.41%, and SAT 99.78%. Cross-sectional area comparisons showed consistent measurements with automated methods closely matching manual measurements for SKM and SAT, and slightly higher values for VAT (SKM: Auto 132.51 cm^2, Manual 132.36 cm^2; VAT: Auto 137.07 cm^2, Manual 134.46 cm^2; SAT: Auto 203.39 cm^2, Manual 202.85 cm^2). ICCs confirmed strong reliability (SKM: 0.998, VAT: 0.994, SAT: 0.994). Bland-Altman plots revealed minimal biases, and boxplots illustrated distribution similarities across SKM, VAT, and SAT areas. On average DAFS Express took 18 seconds per DICOM. This underscores its potential to streamline image analysis processes in research and clinical settings, enhancing diagnostic accuracy and efficiency.

Sparse connectivity is a hallmark of the brain and a desired property of artificial neural networks. It promotes energy efficiency, simplifies training, and enhances the robustness of network function. Thus, a detailed understanding of how to achieve sparsity without jeopardizing network performance is beneficial for neuroscience, deep learning, and neuromorphic computing applications. We used an exactly solvable model of associative learning to evaluate the effects of various sparsity-inducing constraints on connectivity and function. We determine the optimal level of sparsity achieved by the $l_0$ norm constraint and find that nearly the same efficiency can be obtained by eliminating weak connections. We show that this method of achieving sparsity can be implemented online, making it compatible with neuroscience and machine learning applications.

The mid-infrared water vapor emission spectrum provides a novel way to characterize the delivery of icy pebbles towards the innermost ($<5$ au) regions of planet-forming disks. Recently, JWST MIRI-MRS showed that compact disks exhibit an excess of low-energy water vapor emission relative to extended multi-gapped disks, suggesting that icy pebble drift is more efficient in the former. We carry out detailed emission line modeling to retrieve the excitation conditions of rotational water vapor emission in a sample of four compact and three extended disks within the JDISC Survey. We present two-temperature H$_2$O slab model retrievals and, for the first time, constrain the spatial distribution of water vapor by fitting parametric radial temperature and column density profiles. Such models statistically outperform the two-temperature slab fits. We find a correlation between the observable hot water vapor mass and stellar mass accretion rate, as well as an anti-correlation between cold water vapor mass and sub-mm dust disk radius, confirming previously reported water line flux trends. We find that the mid-IR spectrum traces H$_2$O with temperatures down to 180-300 K, but the coldest 150-170 K gas remains undetected. Furthermore the H$_2$O temperature profiles are generally steeper and cooler than the expected `super-heated' dust temperature in passive irradiated disks. The column density profiles are used to estimate icy pebble mass fluxes, which suggest that compact and extended disks may produce markedly distinct inner-disk exoplanet populations if local feeding mechanisms dominate their assembly., Comment: Accepted for publication in ApJ

This research develops a new method to detect anomalies in time series data using Convolutional Neural Networks (CNNs) in healthcare-IoT. The proposed method creates a Distributed Denial of Service (DDoS) attack using an IoT network simulator, Cooja, which emulates environmental sensors such as temperature and humidity. CNNs detect anomalies in time series data, resulting in a 92\% accuracy in identifying possible attacks.

The center-of-mass energy of two colliding particles could be arbitrarily high in the vicinity of event horizons of the extremal Myers-Perry black holes if the angular momentum of colliding particles is fine-tuned to the critical values. We investigate the maximum efficiency of two colliding particles in four and six dimensions. The efficiency of collision for two particles near the four-dimensional Kerr black holes is 130 %. We show that the efficiency increases to 145 % for collision in six dimensions. We also show that the region for the polar angle in which the particle can reach the high energy is larger when the dimension of space-time increases.

We present ALLaM: Arabic Large Language Model, a series of large language models to support the ecosystem of Arabic Language Technologies (ALT). ALLaM is carefully trained considering the values of language alignment and knowledge transfer at scale. Our autoregressive decoder-only architecture models demonstrate how second-language acquisition via vocabulary expansion and pretraining on a mixture of Arabic and English text can steer a model towards a new language (Arabic) without any catastrophic forgetting in the original language (English). Furthermore, we highlight the effectiveness of using parallel/translated data to aid the process of knowledge alignment between languages. Finally, we show that extensive alignment with human preferences can significantly enhance the performance of a language model compared to models of a larger scale with lower quality alignment. ALLaM achieves state-of-the-art performance in various Arabic benchmarks, including MMLU Arabic, ACVA, and Arabic Exams. Our aligned models improve both in Arabic and English from their base aligned models.

In recent years, there has been a growing focus on scrutinizing the security of cellular networks, often attributing security vulnerabilities to issues in the underlying protocol design descriptions. These protocol design specifications, typically extensive documents that are thousands of pages long, can harbor inaccuracies, underspecifications, implicit assumptions, and internal inconsistencies. In light of the evolving landscape, we introduce CellularLint--a semi-automatic framework for inconsistency detection within the standards of 4G and 5G, capitalizing on a suite of natural language processing techniques. Our proposed method uses a revamped few-shot learning mechanism on domain-adapted large language models. Pre-trained on a vast corpus of cellular network protocols, this method enables CellularLint to simultaneously detect inconsistencies at various levels of semantics and practical use cases. In doing so, CellularLint significantly advances the automated analysis of protocol specifications in a scalable fashion. In our investigation, we focused on the Non-Access Stratum (NAS) and the security specifications of 4G and 5G networks, ultimately uncovering 157 inconsistencies with 82.67% accuracy. After verification of these inconsistencies on open-source implementations and 17 commercial devices, we confirm that they indeed have a substantial impact on design decisions, potentially leading to concerns related to privacy, integrity, availability, and interoperability., Comment: Accepted at USENIX Security 24

In this paper we are interested in the inverse problem of recovering a compact supported function from its truncated Fourier transform. We derive new Lipschitz stability estimates for the inversion in terms of the truncation parameter. The obtained results show that the Lipschitz constant is of order one when the truncation parameter is larger than the spatial frequency of the function, and it grows exponentially when the truncation parameter tends to zero. Finally, we present some numerical examples of reconstruction of a compactly supported function from its noisy truncated Fourier transform. The numerical illustrations validate our theoretical results.

Eye movements provide a window into human behaviour, attention, and interaction dynamics. Challenges in real-world, multi-person environments have, however, restrained eye-tracking research predominantly to single-person, in-lab settings. We developed a system to stream, record, and analyse synchronised data from multiple mobile eye-tracking devices during collective viewing experiences (e.g., concerts, films, lectures). We implemented lightweight operator interfaces for real-time-monitoring, remote-troubleshooting, and gaze-projection from individual egocentric perspectives to a common coordinate space for shared gaze analysis. We tested the system in a live concert and a film screening with 30 simultaneous viewers during each of two public events (N=60). We observe precise time-synchronisation between devices measured through recorded clock-offsets, and accurate gaze-projection in challenging dynamic scenes. Our novel analysis metrics and visualizations illustrate the potential of collective eye-tracking data for understanding collaborative behaviour and social interaction. This advancement promotes ecological validity in eye-tracking research and paves the way for innovative interactive tools., Comment: Please refer to the supplementary video illustrating the proposed approach in this paper here: https://tinyurl.com/multipersonET

By reinterpreting a robust discriminative classifier as Energy-based Model (EBM), we offer a new take on the dynamics of adversarial training (AT). Our analysis of the energy landscape during AT reveals that untargeted attacks generate adversarial images much more in-distribution (lower energy) than the original data from the point of view of the model. Conversely, we observe the opposite for targeted attacks. On the ground of our thorough analysis, we present new theoretical and practical results that show how interpreting AT energy dynamics unlocks a better understanding: (1) AT dynamic is governed by three phases and robust overfitting occurs in the third phase with a drastic divergence between natural and adversarial energies (2) by rewriting the loss of TRadeoff-inspired Adversarial DEfense via Surrogate-loss minimization (TRADES) in terms of energies, we show that TRADES implicitly alleviates overfitting by means of aligning the natural energy with the adversarial one (3) we empirically show that all recent state-of-the-art robust classifiers are smoothing the energy landscape and we reconcile a variety of studies about understanding AT and weighting the loss function under the umbrella of EBMs. Motivated by rigorous evidence, we propose Weighted Energy Adversarial Training (WEAT), a novel sample weighting scheme that yields robust accuracy matching the state-of-the-art on multiple benchmarks such as CIFAR-10 and SVHN and going beyond in CIFAR-100 and Tiny-ImageNet. We further show that robust classifiers vary in the intensity and quality of their generative capabilities, and offer a simple method to push this capability, reaching a remarkable Inception Score (IS) and FID using a robust classifier without training for generative modeling. The code to reproduce our results is available at http://github.com/OmnAI-Lab/Robust-Classifiers-under-the-lens-of-EBM/ ., Comment: Accepted at European Conference on Computer Vision (ECCV) 2024

Generative models such as large language models are extensively used as code copilots and for whole program generation. However, the programs they generate often have questionable correctness, authenticity and reliability in terms of integration as they might not follow the user requirements, provide incorrect and/or nonsensical outputs, or even contain semantic/syntactic errors - overall known as LLM hallucination. In this work, we present several types of code hallucination. We have generated such hallucinated code manually using large language models. We also present a technique - HallTrigger, in order to demonstrate efficient ways of generating arbitrary code hallucination. Our method leverages 3 different dynamic attributes of LLMs to craft prompts that can successfully trigger hallucinations from models without the need to access model architecture or parameters. Results from popular blackbox models suggest that HallTrigger is indeed effective and the pervasive LLM hallucination have sheer impact on software development.

We investigate the effect of initial system-environment correlations to improve the estimation of environment parameters. By employing various physical situations of interest, we present results for the environment temperature and system-environment coupling strength. We consider the spin-spin model whereby a probe (a small controllable quantum system) interacts with a bath of quantum spins and attains a thermal equilibrium state. A projective measurement is then performed to prepare the initial state and allow it to evolve unitarily. The properties of the environment are imprinted upon the dynamics of the probe. The reduced density matrix of the probe state contains a modified decoherence factor and dissipation. This additional factor acts in such a way to improve the estimation of the environment parameters, as quantified by the quantum Fisher information (QFI). In the temperature estimation case, our results are promising as one can improve the precision of the estimates by orders of magnitude by incorporating the effect of initial correlations. The precision increases in the strong coupling regime even if the nearest neighbours' interaction is taken into account. In the case of coupling strength, interestingly the accuracy was found to be continuously increasing in both with and without correlations cases. More importantly, one can see the noticeable role of correlations in improving precision, especially at low temperatures., Comment: Comments Welcome. arXiv admin note: text overlap with arXiv:1808.04988 by other authors

Sample Return Capsules (SRCs) entering Earth's atmosphere at hypervelocity from interplanetary space are a valuable resource for studying meteor phenomena. The 24 September 2023 arrival of the OSIRIS-REx (Origins, Spectral Interpretation, Resource Identification, and Security-Regolith Explorer) SRC provided an unprecedented chance for geophysical observations of a well-characterized source with known parameters, including timing and trajectory. A collaborative effort involving researchers from 16 institutions executed a carefully planned geophysical observational campaign at strategically chosen locations, deploying over 400 ground-based sensors encompassing infrasound, seismic, distributed acoustic sensing (DAS), and GPS technologies. Additionally, balloons equipped with infrasound sensors were launched to capture signals at higher altitudes. This campaign (the largest of its kind so far) yielded a wealth of invaluable data anticipated to fuel scientific inquiry for years to come. The success of the observational campaign is evidenced by the near-universal detection of signals across instruments, both proximal and distal. This paper presents a comprehensive overview of the collective scientific effort, field deployment, and preliminary findings. The early findings have the potential to inform future space missions and terrestrial campaigns, contributing to our understanding of meteoroid interactions with planetary atmospheres. Furthermore, the dataset collected during this campaign will improve entry and propagation models as well as augment the study of atmospheric dynamics and shock phenomena generated by meteoroids and similar sources., Comment: 87 pages, 14 figures

In recent years, photon routing has garnered considerable research activity due to its key applications in quantum networking and optical communications. This paper studies the single photon routing scheme in many-emitter disordered chiral waveguide quantum electrodynamics (wQED) ladders. The wQED ladder consists of two one-dimensional lossless waveguides simultaneously and chirally coupled with a chain of dipole-dipole interacting two-level quantum emitters (QEs) or atoms. In particular, we analyze how a departure from the periodic placement of the QEs due to temperature-induced position disorder can impact the routing probability. This involves analyzing how the interplay between the collective atomic effects originating from the dipole-dipole interaction and disorder in the atomic location leading to single-photon localization can change the routing probabilities. As for some key results, we find that the routing probability exhibits a considerable improvement (more than $90\%$ value) for periodic and disordered wQED ladders when considering lattices consisting of twenty QEs. This robustness of collective effects against spontaneous emission loss and weak disorders is further confirmed by examining the routing efficiency and localization length for up to twenty QE chains. These results may find applications in quantum networking and distributed quantum computing under the realistic conditions of imperfect emitter trappings., Comment: 10 pages, 6 figures

A novel method to reconstruct the energy of hadronic showers in the CMS High Granularity Calorimeter (HGCAL) is presented. The HGCAL is a sampling calorimeter with very fine transverse and longitudinal granularity. The active media are silicon sensors and scintillator tiles readout by SiPMs and the absorbers are a combination of lead and Cu/CuW in the electromagnetic section, and steel in the hadronic section. The shower reconstruction method is based on graph neural networks and it makes use of a dynamic reduction network architecture. It is shown that the algorithm is able to capture and mitigate the main effects that normally hinder the reconstruction of hadronic showers using classical reconstruction methods, by compensating for fluctuations in the multiplicity, energy, and spatial distributions of the shower's constituents. The performance of the algorithm is evaluated using test beam data collected in 2018 prototype of the CMS HGCAL accompanied by a section of the CALICE AHCAL prototype. The capability of the method to mitigate the impact of energy leakage from the calorimeter is also demonstrated.

Comparing two images in terms of Commonalities and Differences (CaD) is a fundamental human capability that forms the basis of advanced visual reasoning and interpretation. It is essential for the generation of detailed and contextually relevant descriptions, performing comparative analysis, novelty detection, and making informed decisions based on visual data. However, surprisingly, little attention has been given to these fundamental concepts in the best current mimic of human visual intelligence - Large Multimodal Models (LMMs). We develop and contribute a new two-phase approach CaD-VI for collecting synthetic visual instructions, together with an instruction-following dataset CaD-Inst containing 349K image pairs with CaD instructions collected using CaD-VI. Our approach significantly improves the CaD spotting capabilities in LMMs, advancing the SOTA on a diverse set of related tasks by up to 17.5%. It is also complementary to existing difference-only instruction datasets, allowing automatic targeted refinement of those resources increasing their effectiveness for CaD tuning by up to 10%. Additionally, we propose an evaluation benchmark with 7.5K open-ended QAs to assess the CaD understanding abilities of LMMs., Comment: Project page: https://wlin-at.github.io/cad_vi ; Huggingface dataset repo: https://huggingface.co/datasets/wlin21at/CaD-Inst

This paper proposes a data-driven supervised machine learning (ML) for online thermal modeling of electrically excited synchronous motors (EESMs). EESMs are desired for EVs due to their high performance, efficiency, and durability at a relatively low cost. Therefore, obtaining precise EESM temperature estimations are significantly important, because online accurate temperature estimation can lead to EESM performance improvement and guaranteeing its safety and reliability. In this study, in addition to the default inputs' data, EESM losses data is leveraged to improve the performance of the proposed ML approach for thermal modeling. Exponentially weighted moving averages and standard deviations of the inputs are also incorporated in the learning process to consider the memory effect for modeling a dynamical thermal model. Using the experimental data of an EESM prototype, the performance of ordinary least squares (OLS) method is evaluated through a complete training, testing and cross-validation process. Finally, simulation results will provide the key performance metrics of OLS for EESM thermal modeling.

Compositional Reasoning (CR) entails grasping the significance of attributes, relations, and word order. Recent Vision-Language Models (VLMs), comprising a visual encoder and a Large Language Model (LLM) decoder, have demonstrated remarkable proficiency in such reasoning tasks. This prompts a crucial question: have VLMs effectively tackled the CR challenge? We conjecture that existing CR benchmarks may not adequately push the boundaries of modern VLMs due to the reliance on an LLM-only negative text generation pipeline. Consequently, the negatives produced either appear as outliers from the natural language distribution learned by VLMs' LLM decoders or as improbable within the corresponding image context. To address these limitations, we introduce ConMe -- a compositional reasoning benchmark and a novel data generation pipeline leveraging VLMs to produce `hard CR Q&A'. Through a new concept of VLMs conversing with each other to collaboratively expose their weaknesses, our pipeline autonomously generates, evaluates, and selects challenging compositional reasoning questions, establishing a robust CR benchmark, also subsequently validated manually. Our benchmark provokes a noteworthy, up to 33%, decrease in CR performance compared to preceding benchmarks, reinstating the CR challenge even for state-of-the-art VLMs., Comment: NeurIPS 2024 Camera Ready

Jet tagging is an essential categorization problem in high energy physics. In recent times, Deep Learning has not only risen to the challenge of jet tagging but also significantly improved its performance. In this article, we proposed an idea of a new architecture, Particle Multi-Axis transformer (ParMAT) which is a modified version of Particle transformer (ParT). ParMAT contains local and global spatial interactions within a single unit which improves its ability to handle various input lengths. We trained our model on JETCLASS, a publicly available large dataset that contains 100M jets of 10 different classes of particles. By integrating a parallel attention mechanism and pairwise interactions of particles in the attention mechanism, ParMAT achieves robustness and higher accuracy over the ParT and ParticleNet. The scalability of the model to huge datasets and its ability to automatically extract essential features demonstrate its potential for enhancing jet tagging.

We derive a class of Taub-NUT metrics in the presence of a scalar field (TNS) by using Ernst equations and potential, as well as using Ehlers transformations on the exact solutions that was recently introduced in Azizallahi et al. (Nucl Phys B 998:116414, https://doi.org/10.1016/j.nuclphysb.2023.116414, arXiv:2307.09328 [gr-qc], 2023) and Mirza et al. (Eur Phys J C 83:1161, https://doi.org/10.1140/epjc/s10052-023-12255-7, arXiv:2307.13588 [gr-qc], 2023). Furthermore, we investigate the effective potential, geodesics, topological charge, quasinormal modes (QNMs) and the deflection angle of light in a gravitational lensing for the obtained class of TNS metrics. We also use conformal transformations to generate a new class of exact solutions of the Einstein-conformal-scalar theory by using the obtained TNS solutions as seed metrics. Finally we compare QNMs of the class of exact solutions.

Among various aspects of ensuring the responsible design of AI tools for healthcare applications, addressing fairness concerns has been a key focus area. Specifically, given the wide spread of electronic health record (EHR) data and their huge potential to inform a wide range of clinical decision support tasks, improving fairness in this category of health AI tools is of key importance. While such a broad problem (mitigating fairness in EHR-based AI models) has been tackled using various methods, task- and model-agnostic methods are noticeably rare. In this study, we aimed to target this gap by presenting a new pipeline that generates synthetic EHR data, which is not only consistent with (faithful to) the real EHR data but also can reduce the fairness concerns (defined by the end-user) in the downstream tasks, when combined with the real data. We demonstrate the effectiveness of our proposed pipeline across various downstream tasks and two different EHR datasets. Our proposed pipeline can add a widely applicable and complementary tool to the existing toolbox of methods to address fairness in health AI applications, such as those modifying the design of a downstream model. The codebase for our project is available at https://github.com/healthylaife/FairSynth