November 2024 arXiv papers — page 48

Han Xu, Jiaqi Li, Jixiang Zhang, Tiecheng Song

In this work, we generalize the Stochastic Hybrid Systems (SHSs) analysis of traditional AoI to the AoII metric. Hierarchical ageing processes are adopted using the continuous AoII for the first time, where two different hierarchy schemes, i.e., a hybrid of linear ageing processes with different slopes and a hybrid of linear and quadratic ageing processes, a

Bin Chen, Wenbo Yu, Qinshan Zhang, Tianqu Zhuang

Interactive computer vision (CV) plays a crucial role in various real-world applications, whose performance is highly dependent on communication networks. Nonetheless, the data-oriented characteristics of conventional communications often do not align with the special needs of interactive CV tasks. To alleviate this issue, the recently emerged semantic commu

Kewei Bian, Yushuai Liu, Weilin Rong, Yuan Dong

Acoustic-optic (AO) modulation is critical for microwave and optical signal processing, computing and networking. Challenges remain to integrate AO devices on-chip using fabrication process compatible with complementary metal-oxide-semiconductor (CMOS) technology. This work presents the demonstration of an AO modulator exploiting a microring resonator (MRR)

Zaifu Zhan, Shuang Zhou, Mingchen Li, Rui Zhang

\textbf{Objective:} We aimed to develop an advanced multi-task large language model (LLM) framework to extract multiple types of information about dietary supplements (DS) from clinical records. \textbf{Methods:} We used four core DS information extraction tasks - namely, named entity recognition (NER: 2,949 clinical sentences), relation extraction (RE: 4,89

Yu-Xiang Peng, Biao Wang, Peng-Nan Sun, A-Man Zhang

Based on the Jacobi polynomial expansion, an arbitrary high-order Discontinuous Galerkin solver for compressible flows on unstructured meshes is proposed in the present work. First, we construct orthogonal polynomials for 2D and 3D isoparametric elements using the 1D Jacobi polynomials. We perform modal expansions of the state variables using the orthogonal

Shuli Chen, Junyong Eom, Gen Nakamura, Goro Nishimura

This paper concerns an inverse problem for fluorescence diffuse optical tomography (FDOT) reconstructing locations of multiple point targets from the measured temporal response functions. The targets are multiple fluorescent point objects with a nonzero fluorescence lifetime at unknown locations. Peak time, when the temporal response function of the fluoresc

Zi-Yue Zheng, Jin-Biao Wei, Huan Chen, Xiao-Ping Zheng

We investigate nonradial $f$-mode oscillations of hybrid neutron stars in full general relativity, employing hybrid equations of state describing a nuclear outer core and a pasta-phase transition to a quark-matter core. The validity of various universal relations is confirmed for those stars. Prospects of observations are also discussed.

Seyedkhashayar Hashemi, Masoud Ardakani, Hai Jiang

A reconfigurable intelligent surface (RIS) is a planar surface that can enhance the quality of communication by providing control over the communication environment. Reflection optimization is one of the pivotal challenges in RIS setups. While there has been lots of research regarding the reflection optimization of RIS, most works consider the independence o

Luis Vilaca, Yi Yu, Paula Vinan

Audio-visual correlation learning aims to capture and understand natural phenomena between audio and visual data. The rapid growth of Deep Learning propelled the development of proposals that process audio-visual data and can be observed in the number of proposals in the past years. Thus encouraging the development of a comprehensive survey. Besides analyzin

Alejandro Nieto Ramos, Elizabeth M. Cherry

It is generally assumed that all cells in models of the electrical behavior of cardiac tissue have the same properties. However, there are differences in cardiac cells that are not well characterized but cause spatial heterogeneity of the electrical properties in tissue. Optical mapping can be used to obtain experimental data from cardiac surfaces at high sp

Zhilong Liu, Run-Qiu Yang, Heng Fan, Jieci Wang

Simulating the nature of quantum fields in diverse spacetime backgrounds offers valuable insights for the fundamental comprehension of quantum mechanics and general relativity. Here we introduce a novel method for mapping the massless Dirac equation in 1+1D curved spacetime to a controllable quantum simulation model, applicable to various observers' perspect

Haotian Li, Rui Zhang, Lingzhi Wang, Bin Yu

Recent advances in knowledge graph completion (KGC) have emphasized text-based approaches to navigate the inherent complexities of large-scale knowledge graphs (KGs). While these methods have achieved notable progress, they frequently struggle to fully incorporate the global structural properties of the graph. Stochastic blockmodels (SBMs), especially the la

Shupei Yu, Qiaolin He, Shiquan Zhang, Qihong Yang

In the midst of the neural network's success in solving partial differential equations, tackling eigenvalue problems using neural networks remains a challenging task. However, the Physics Constrained-General Inverse Power Method Neural Network (PC-GIPMNN) approach was proposed and successfully applied to solve the single-group critical problems in reactor ph

Sizhe Liu, Yizhou Lu, Siyu Chen, Xiyang Hu

Recent progress in Large Language Models (LLMs) has drawn attention to their potential for accelerating drug discovery. However, a central problem remains: translating theoretical ideas into robust implementations in the highly specialized context of pharmaceutical research. This limitation prevents practitioners from making full use of the latest AI develop

Michael Simon, Salwa M. Din, Raja Jamal Chib

With advancements in microelectromechanical systems, low-power integrated circuits, and wireless communications, wireless sensor networks (WSNs) have become increasingly significant [1][2]. These distributed networks enable efficient resource utilization and open doors to numerous applications, including personal healthcare, home automation, environmental mo

Cheng-Xi Liu, Zi-Long Man, Tian-Le Gao, Xiang Liu

Our understanding of high-lying states within the charmonium family remains incomplete, particularly in light of recent observations of charmonium states at energies around 4 GeV. In this study, we investigate the spectroscopic properties of several high-lying charmonia, focusing on the $2D$ and $1F$ states. A mass spectrum analysis is conducted, incorporati

Fujie Tang, Diana Y. Qiu, Xifan Wu

Water wires, quasi-one-dimensional chains composed of hydrogen-bonded (H-bonded) water molecules, play a fundamental role in numerous chemical, physical, and physiological processes. Yet direct experimental detection of water wires has been elusive so far. Based on advanced $ab$ $initio$ many-body theory that includes electron-hole interactions, we report th

Longkun Guo, Jiawei Lin, Xuanming Xu, Peng Li

Emerging applications such as autonomous driving pose the challenge of efficient cost-driven offloading in edge-cloud environments. This involves assigning tasks to edge and cloud servers for separate execution, with the goal of minimizing the total service cost including communication and computation costs. In this paper, observing that the intra-cloud comm

Zhikun Xi, Minghui Liu, Jürgen Ulbricht

We used data mainly collected by OPAL experiment to test the signal significance of a parameter $\varepsilon$, which is equal to zero in SM. For total cross sections, we obtained no derivation of enough significance. But for differential cross sections, some derivation over 3$\sigma$ are observed(statistical uncertainty only), indicating that this contact in

Saurabhchand Bhati, Yuan Gong, Leonid Karlinsky, Hilde Kuehne

Large Audio Language Models (LALM) combine the audio perception models and the Large Language Models (LLM) and show a remarkable ability to reason about the input audio, infer the meaning, and understand the intent. However, these systems rely on Transformers which scale quadratically with the input sequence lengths which poses computational challenges in de

Zheng Ma, Zeping Mao, Ruixue Zhang, Jiazhen Chen

Data-Independent Acquisition (DIA) was introduced to improve sensitivity to cover all peptides in a range rather than only sampling high-intensity peaks as in Data-Dependent Acquisition (DDA) mass spectrometry. However, it is not very clear how useful DIA data is for de novo peptide sequencing as the DIA data are marred with coeluted peptides, high noises, a

Jing Luo, Qun Wei, Xiaofei Jia, Meiguang Zhang

Research on high-temperature superconductors has primarily focused on hydrogen-rich compounds, however, the need for extreme pressures limits their practical applications. The X2MH6-type structure Mg2IrH6 stands out because it exhibits superconductivity at 160 K under ambient pressure. This study investigates methods to increase the superconducting transitio

Julia Fernandez, Emanuel Sillero, Sol Alonso, Patricia Tissera

The catalog of ringed galaxies was compiled through visual classification of synthetic images from the TNG50 simulation. Galaxies were selected based on specific criteria: a redshift range of $0.01 < z < 0.1$, stellar mass $M_\star >10^9 M_\odot$, stellar half-mass radius $r_{50} > 1$ kpc, and specific star formation rate (sSFR), $\rm{log(sSFR/yr}^{-1}) > -1

Grace S. Garden, Benjamin Martin, Stephan Tillmann

Infinite families of 3-dimensional closed graph manifolds and closed Seifert fibered spaces are exhibited, each member of which contains an essential torus not detected by ideal points of the variety of $\text{SL}_2(\mathbb{F})$-characters over any algebraically closed field $\mathbb{F}$.

Yeonghoon Jin, Teng Qu, Siddharth Kumar, Nicola Kubzdela

We synthesized crystalline films of neodymium nickel oxide (NdNiO3), a perovskite quantum material, switched the films from a metal phase (intrinsic) into an insulator phase (electron-doped) by field-driven lithium-ion intercalation, and characterized their structural and optical properties. Time-of-flight secondary-ion mass spectrometry (ToF-SIMS) showed th

Zhong-Yu Li, Xin Jin, Boyuan Sun, Chun-Le Guo

Existing object detection methods often consider sRGB input, which was compressed from RAW data using ISP originally designed for visualization. However, such compression might lose crucial information for detection, especially under complex light and weather conditions. We introduce the AODRaw dataset, which offers 7,785 high-resolution real RAW images with

Arash Amini, Yigit Ege Bayiz, Eun-Ju Lee, Zeynep Somer-Topcu

Competition among news sources may encourage some sources to share fake news and misinformation to influence the public. While sharing misinformation may lead to a short-term gain in audience engagement, it may damage the reputation of these sources, resulting in a loss of audience. To understand the rationale behind sharing misinformation, we model the comp

Yarui Liu, Zhao Wang, Zixuan Xiang, Qikun Wang

With the increasing number of ion qubits and improving performance of sophisticated quantum algorithms, more and more scalable complex ion trap electrodes have been developed and integrated. Nonlinear ion shuttling operations at the junction are more frequently used, such as in the areas of separation, merging, and exchanging. Several studies have been condu

Paimon Goulart, Evangelos E. Papalexakis

Large Language Models (LLMs) have demonstrated the ability to solve complex tasks through In-Context Learning (ICL), where models learn from a few input-output pairs without explicit fine-tuning. In this paper, we explore the capacity of LLMs to solve non-linear numerical computations, with specific emphasis on functions of the Singular Value Decomposition.

Jimmy Cheung, Smruthi Rangarajan, Amelia Maddocks, Xizhe Chen

Uncertainty quantification is crucial in time series prediction, and quantile regression offers a valuable mechanism for uncertainty quantification which is useful for extreme value forecasting. Although deep learning models have been prominent in multi-step ahead prediction, the development and evaluation of quantile deep learning models have been limited.

Anand Roy, Anna Eyal, Roni Majlin Skiff, Barun Barick

Nanowires (NWs) of topological materials are emerging as an exciting platform to probe and engineer new quantum phenomena that are hard to access in bulk phase. Their quasi-one-dimensional geometry and large surface-to-bulk ratio unlock new expressions of topology and highlight surface states. TaAs2, a compensated semimetal, is a topologically rich material

Fengmiao Li, Oliver Dicks, Myung-Geun Han, Solveig Aamlid

Realizing experimental control of superconductivity is of paramount importance to advancing both basic research and technological applications. Disorder, generally existing in most superconductors, intricately interacts with Cooper pairs and also impacts the performance of quantum devices. In this paper, we report the study of a series of Ti(O,N) crystalline

Alvi Md Ishmam, Christopher Thomas

In recent years there has been enormous interest in vision-language models trained using self-supervised objectives. However, the use of large-scale datasets scraped from the web for training also makes these models vulnerable to potential security threats, such as backdooring and poisoning attacks. In this paper, we propose a method for mitigating such atta

John C. Baez

The hexagonal tiling honeycomb is a beautiful structure in 3-dimensional hyperbolic space. It is called {6,3,3} because each hexagon has 6 edges, 3 hexagons meet at each vertex in a Euclidean plane tiled by regular hexagons, and 3 such planes meet along each edge of this honeycomb. It also appears naturally in algebraic geometry. If $\mathbb{E}$ denotes the

Damodar Panigrahi, Shaswata Mitra, Subash Neupane, Sudip Mittal

Cyberattacks are becoming increasingly difficult to detect and prevent due to their sophistication. In response, Autonomous Intelligent Cyber-defense Agents (AICAs) are emerging as crucial solutions. One prominent AICA agent is the Intrusion Response System (IRS), which is critical for mitigating threats after detection. IRS uses several Tactics, Techniques,

Ali Behrouz, Ali Parviz, Mahdi Karami, Clayton Sanford

Modern sequence models (e.g., Transformers, linear RNNs, etc.) emerged as dominant backbones of recent deep learning frameworks, mainly due to their efficiency, representational power, and/or ability to capture long-range dependencies. Adopting these sequence models for graph-structured data has recently gained popularity as the alternative to Message Passin

Zhuo Liu, Xujun Zhang

We establish a Skoda-type $L^2$ division theorem for $L^2$-optimal pairs, using a technique that combines a new Bochner-type inequality derived from the $L^2$-optimal conditions and Skoda's basic inequality. As applications, we provide some new characterizations of domains of holomorphy.

Junwei You, Rui Gan, Weizhe Tang, Zilin Huang

Vehicle trajectory prediction is crucial for advancing autonomous driving and advanced driver assistance systems (ADAS). Although deep learning-based approaches - especially those utilizing transformer-based and generative models - have markedly improved prediction accuracy by capturing complex, non-linear patterns in vehicle dynamics and traffic interaction

Ilias Diakonikolas, Daniel M. Kane

We study the task of learning latent-variable models. A common algorithmic technique for this task is the method of moments. Unfortunately, moment-based approaches are hampered by the fact that the moment tensors of super-constant degree cannot even be written down in polynomial time. Motivated by such learning applications, we develop a general efficient al

Yinghe Qi, Wei Guo

Understanding air propagation and condensation following a catastrophic vacuum break in particle accelerator beamlines cooled by liquid helium is essential for ensuring operational safety. This review summarizes experimental and theoretical work conducted in our cryogenics lab to address this issue. Systematic measurements were performed to study nitrogen ga

Koen Mesman, Yinglu Tang, Matthias Moller, Boyang Chen

A longstanding computational challenge is the accurate simulation of many-body particle systems. Especially for deriving key characteristics of high-impact but complex systems such as battery materials and high entropy alloys (HEA). While simple models allow for simulations of the required scale, these methods often fail to capture the complex dynamics that

Gaya Mehenni, Amal Zouaq

Large Language Models (LLMs) offer promising solutions for text summarization. However, some domains require specific information to be available in the summaries. Generating these domain-adapted summaries is still an open challenge. Similarly, hallucinations in generated content is a major drawback of current approaches, preventing their deployment. This st

Shubhang Goswami, Scott Jensen, Yubo Yang, Markus Holzmann

We present results and discuss methods for computing the melting temperature of dense molecular hydrogen using a machine learned model trained on quantum Monte Carlo data. In this newly trained model, we emphasize the importance of accurate total energies in the training. We integrate a two phase method for estimating the melting temperature with estimates f

Giorgio Nordo, Saeid Jafari, Maikel Yelandi Leyva Vazquez

This paper introduces an extension to the Python Neutrosophic Sets (PYNS) framework, originally detailed in Nordo et al. 2024, with the addition of the NSfamily class for constructing and manipulating neutrosophic topologies. Building on existing classes like NSuniverse and NSset, the NSfamily class enables the definition and testing of neutrosophic families

Himel Ghosh

This review report discusses the cold start latency in serverless inference and existing solutions. It particularly reviews the ServerlessLLM method, a system designed to address the cold start problem in serverless inference for large language models. Traditional serverless approaches struggle with high latency due to the size of LLM checkpoints and the ove

Jamie Bell

We provide a formula for the order of the Tate--Shafarevich group of elliptic curves over dihedral extensions of number fields of order $2n$, up to $4^{th}$ powers and primes dividing $n$. Specifically, for odd $n$ it is equal to the order of the Tate--Shafarevich group over the quadratic subextension. A similar formula holds for even $n$.

Emma Harvey, Emily Sheng, Su Lin Blodgett, Alexandra Chouldechova

To facilitate the measurement of representational harms caused by large language model (LLM)-based systems, the NLP research community has produced and made publicly available numerous measurement instruments, including tools, datasets, metrics, benchmarks, annotation instructions, and other techniques. However, the research community lacks clarity about whe

Johannes Schneider

Autoregressive language models like GPT aim to predict next tokens, while autoencoding models such as BERT are trained on tasks such as predicting masked tokens. We train a decoder-only architecture for predicting the second to last token for a sequence of tokens. Our approach yields higher computational training efficiency than BERT-style models by employin

Jingyang Li, T. Tony Cai, Dong Xia, Anru R. Zhang

Federated Learning (FL) has gained significant recent attention in machine learning for its enhanced privacy and data security, making it indispensable in fields such as healthcare, finance, and personalized services. This paper investigates federated PCA and estimation for spiked covariance matrices under distributed differential privacy constraints. We est

Amir Ofir, Gil Ben-Artzi

We present a novel approach for accelerating convolutions during inference for CPU-based architectures. The most common method of computation involves packing the image into the columns of a matrix (im2col) and performing general matrix multiplication (GEMM) with a matrix of weights. This results in two main drawbacks: (a) im2col requires a large memory buff

Seyyid Ali Benabdallah, Messoud Souilah

In this paper, we use the theory of nonlinear semigroups to establish the existence and uniqueness of both local and global solutions for a partial differential-algebraic equation (PDAE) of index one. This method is applied to a reaction-diffusion system coupled with an elliptic equation in one dimension by transforming the PDAE into a system of linear evolu

Rui Huang, Henry Zheng, Yan Wang, Zhuofan Xia

Open-vocabulary 3D object detection has recently attracted considerable attention due to its broad applications in autonomous driving and robotics, which aims to effectively recognize novel classes in previously unseen domains. However, existing point cloud-based open-vocabulary 3D detection models are limited by their high deployment costs. In this work, we

Promit Basak, Rusab Sarmun, Saidul Kabir, Israa Al-Hashimi

Automated lumbar spine segmentation is very crucial for modern diagnosis systems. In this study, we introduce a novel machine-agnostic approach for segmenting lumbar vertebrae and intervertebral discs from MRI images, employing a cascaded model that synergizes an ROI detection and a Self-organized Operational Neural Network (Self-ONN)-based encoder-decoder n

Parshuram N. Aarotale, Ajita Rattani

EMG-based hand gesture recognition uses electromyographic~(EMG) signals to interpret and classify hand movements by analyzing electrical activity generated by muscle contractions. It has wide applications in prosthesis control, rehabilitation training, and human-computer interaction. Using electrodes placed on the skin, the EMG sensor captures muscle signals

Saikat Dutta, Prashanti Jami, Pinaki Chaudhuri, Chandan Dasgupta

We uncover the dynamics of particles with Gaussian core interactions across melting in pure and disordered two-dimensional (2D) systems. Intriguing signatures of cooperative motion of particles in string-like paths are found at low temperatures. Such a motion, while common to glasses and supercooled liquids, are realized here in traditional equilibrium phase

Chen Xin, Thomas Motz, Andreas Hartel, Enkelejda Kasneci

Real-time object localization on edge devices is fundamental for numerous applications, ranging from surveillance to industrial automation. Traditional frameworks, such as object detection, segmentation, and keypoint detection, struggle in resource-constrained environments, often resulting in substantial target omissions. To address these challenges, we intr

Lotem Unger, Aldana Grichener, Noam Soker

We conduct a population synthesis study using the binary population synthesis code compas to explore the formation of circumbinary disks (CBDs) following the common envelope evolution (CEE) phase of a giant star and a neutron star (NS) or black hole (BH). We focus on massive binary systems that evolve into double compact object (DCO) binaries after the expos

John Lathrop, Benjamin Rivi`ere, Jedidiah Alindogan, Soon-Jo Chung

We present Model Predictive Trees (MPT), a receding horizon tree search algorithm that improves its performance by reusing information efficiently. Whereas existing solvers reuse only the highest-quality trajectory from the previous iteration as a "hotstart", our method reuses the entire optimal subtree, enabling the search to be simultaneously guided away f

Anne Rathsam, Jorge Meléndez, Amanda I. Karakas

Context. The chemistry and Galactic velocity components of the star HD 65907 suggest that despite its young isochronal age of $\sim$5 Gyr, it is in fact a merger of two old Population II stars. Its low Li abundance is also consistent with a mass accretion episode. Aims. We determine Li and Be abundances for this star and evaluate its radial velocity time ser

Andrew Suk, Ji Zeng

The monotone path $P_{n+2}$ is an ordered 3-uniform hypergraph whose vertex set has size $n+2$ and edge set consists of all consecutive triples. In this note, we consider the collection $\mathcal{J}_n$ of ordered 3-uniform hypergraphs named monotone paths with $n$ jumps, and we prove the following relation \begin{equation*} r(3;n) \leq R(P_{n+2},\mathcal{J}_

Ming Yin, Jingyang Zhang, Jingwei Sun, Minghong Fang

Model merging is an emerging technique that integrates multiple models fine-tuned on different tasks to create a versatile model that excels in multiple domains. This scheme, in the meantime, may open up backdoor attack opportunities where one single malicious model can jeopardize the integrity of the merged model. Existing works try to demonstrate the risk

Elad Amrani, Leonid Karlinsky, Alex Bronstein

We introduce XTRA, a vision model pre-trained with a novel auto-regressive objective that significantly enhances both sample and parameter efficiency compared to previous auto-regressive image models. Unlike contrastive or masked image modeling methods, which have not been demonstrated as having consistent scaling behavior on unbalanced internet data, auto-r

Steven A. Frank

Anomaly detection is a well-established field in machine learning, identifying observations that deviate from typical patterns. The principles of anomaly detection could enhance our understanding of how biological systems recognize and respond to atypical environmental inputs. However, this approach has received limited attention in analyses of cellular and

Arpit Kumar Shrivastav, Vaibhav Pant, Rohan Kumar, David Berghmans

Decayless kink oscillations, characterized by their lack of decay in amplitude, have been detected in coronal loops of varying scales in active regions, quiet Sun and coronal holes. Short-period (< 50 s) decayless oscillations have been detected in short loops (< 50 Mm) within active regions. Nevertheless, long-period decayless oscillations in these loops re

Gollam Rabby, Farhana Keya, Sören Auer

Mathematical reasoning presents significant challenges for large language models (LLMs). To enhance their capabilities, we propose Monte Carlo Self-Refine Tree (MC-NEST), an extension of Monte Carlo Tree Search that integrates LLM-based self-refinement and self-evaluation for improved decision-making in complex reasoning tasks. MC-NEST balances exploration a

A. V. Gasnikov, M. S. Alkousa, A. V. Lobanov, Y. V. Dorn

Frequently, when dealing with many machine learning models, optimization problems appear to be challenging due to a limited understanding of the constructions and characterizations of the objective functions in these problems. Therefore, major complications arise when dealing with first-order algorithms, in which gradient computations are challenging or even

Cheyanne Shariat, Smadar Naoz, Kareem El-Badry, Kyle Akira Rocha

A recent discovery shows that V404 Cygni, a prototypical black hole low-mass X-ray binary (BH-LMXB) is a hierarchical triple: the BH and donor star are orbited by a $1.2$ M$_{\odot}$ tertiary at a distance of at least $3500$ au. Motivated by this system, we evolve a grid of $\sim50,000$ triple star systems, spanning a broad range of initial orbits. Our calcu

Hanjiang Hu, Changliu Liu

The physical world dynamics are generally governed by underlying partial differential equations (PDEs) with unknown analytical forms in science and engineering problems. Neural network based data-driven approaches have been heavily studied in simulating and solving PDE problems in recent years, but it is still challenging to move forward from understanding t

Basdouri Imed, Jean Lerbet, Bouzid Mosbahi

The study of central derivations in low-dimensional algebraic structures is a crucial area of research in mathematics, with applications in understanding the internal symmetries and deformations of these structures. In this article, we investigate the central derivations of complex Zinbiel algebras of dimension $\leq 4$. Key properties of the central derivat

Martin Mathew, Javier Noda

Counting distinct permutations with replacement, especially when involving multiple subwords, is a longstanding challenge in combinatorial analysis, with critical applications in cryptography, bioinformatics, and statistical modeling. This paper introduces a novel framework that presents closed-form formulas for calculating distinct permutations with replace

H. W. T. Morgan, H. B. Tran Tan, R. Elwell, A. N. Alexandrova

Laser excitation of thorium-229 nuclei in doped wide bandgap crystals has been demonstrated recently, opening the possibility of developing ultrastable solid-state clocks and sensitive searches for new physics. We develop a quantitative theory of the internal conversion of isomeric thorium-229 in solid-state hosts. The internal conversion of the isomer proce

Tobi Olatunji, Charles Nimo, Abraham Owodunni, Tassallah Abdullahi

Recent advancements in large language model(LLM) performance on medical multiple choice question (MCQ) benchmarks have stimulated interest from healthcare providers and patients globally. Particularly in low-and middle-income countries (LMICs) facing acute physician shortages and lack of specialists, LLMs offer a potentially scalable pathway to enhance healt

O. S. Savchuk, M. S. Alkousa, A. S. Shushko, A. A. Vyguzov

In this paper, we propose some accelerated methods for solving optimization problems under the condition of relatively smooth and relatively Lipschitz continuous functions with an inexact oracle. We consider the problem of minimizing the convex differentiable and relatively smooth function concerning a reference convex function. The first proposed method is

Venus Kakdarvishi, Bowen Yu, Yasha Yi

In this work, we present a multi-layer optical phased array (OPA) designed for dual-axis beam steering on a silicon (Si) platform, utilizing only wavelength tuning. Our design eliminates the need for grating couplers, commonly required for dual-axis beam steering, thereby reducing energy losses due to substrate leakage. It also features the unique capability

Corentin Bodart, Laura Ciobanu, George Metcalfe

In this paper, the Identity Problem for certain groups, which asks if the subsemigroup generated by a given finite set of elements contains the identity element, is related to problems regarding ordered groups. Notably, the Identity Problem for a torsion-free nilpotent group corresponds to the problem asking if a given finite set of elements extends to the p

Benjamin Cox, Santiago Segarra, Victor Elvira

State-space models are a popular statistical framework for analysing sequential data. Within this framework, particle filters are often used to perform inference on non-linear state-space models. We introduce a new method, StateMixNN, that uses a pair of neural networks to learn the proposal distribution and transition distribution of a particle filter. Both

Benjamin Cox, Emilie Chouzenoux, Victor Elvira

State-space models (SSMs) are a powerful statistical tool for modelling time-varying systems via a latent state. In these models, the latent state is never directly observed. Instead, a sequence of observations related to the state is available. The state-space model is defined by the state dynamics and the observation model, both of which are described by p

Sachin Manjunath Naik, P. Sam Johnson

Complementable operators extend classical matrix decompositions, such as the Schur complement, to the setting of infinite-dimensional Hilbert spaces, thereby broadening their applicability in various mathematical and physical contexts. This paper focuses on the convergence properties of complementable operators, investigating when the limit of sequence of co

Ashwin Ramachandran, Sunita Sarawagi

Calibration is crucial as large language models (LLMs) are increasingly deployed to convert natural language queries into SQL for commercial databases. In this work, we investigate calibration techniques for assigning confidence to generated SQL queries. We show that a straightforward baseline -- deriving confidence from the model's full-sequence probability

Peter J. Forrester, Santosh Kumar, Bo-Jian Shen

The Gaussian and Laguerre orthogonal ensembles are fundamental to random matrix theory, and the marginal eigenvalue distributions are basic observable quantities. Notwithstanding a long history, a formulation providing high precision numerical evaluations for $N$ large enough to probe asymptotic regimes, has not been provided. An exception is for the largest

Michael Hardy

"Gold" and "ground truth" human-mediated labels have error. The effects of this error can escape commonly reported metrics of label quality or obscure questions of accuracy, bias, fairness, and usefulness during model evaluation. This study demonstrates methods for answering such questions even in the context of very low reliabilities from expert humans. We

Zhiyuan Yan, Jiangming Wang, Peng Jin, Ke-Yue Zhang

AI-generated images (AIGIs), such as natural or face images, have become increasingly important yet challenging. In this paper, we start from a new perspective to excavate the reason behind the failure generalization in AIGI detection, named the \textit{asymmetry phenomenon}, where a naively trained detector tends to favor overfitting to the limited and mono

Preston Jones, Quentin G. Bailey, Andri Gretarsson, Edward Poon

Research on the projective measurement of gravitons increasingly supports Dysons conclusions that the detection of single gravitons is not physically possible. It is therefore prudent to consider alternative signatures of non-classicality in gravitational wave detections to determine if gravity is quantized. Coincident multiple detector operations make it po

Ning Ma, Heng Li

Due to the scarcity of quantum computing resources, researchers and developers have very limited access to real quantum computers. Therefore, judicious planning and utilization of quantum computer runtime are essential to ensure smooth execution and completion of projects. Accurate estimation of a quantum circuit's execution time is thus necessary to prevent

Wei Liu, Mitchell C. Burnett, Dan Werthimer, Jonathon Kocz

The increased bandwidth coupled with the large numbers of antennas of several new radio telescope arrays has resulted in an exponential increase in the amount of data that needs to be recorded and processed. In many cases, it is necessary to process this data in real time, as the raw data volumes are too high to be recorded and stored. Due to the ability of

Reza Aghazadeh Ayoubi, Eugenio Moro, Marouan Mizmizi, Dario Tagliaferri

Smart Radio Environment (SRE) is a central paradigms in 6G and beyond, where integrating SRE components into the network planning process enables optimized performance for high-frequency Radio Access Network (RAN). This paper presents a comprehensive planning framework utilizing realistic urban scenarios and precise channel models to analyze diverse SRE comp

Reza Ghoddoosian, Nakul Agarwal, Isht Dwivedi, Behzad Darisuh

Vision-language models (VLMs) are capable of recognizing unseen actions. However, existing VLMs lack intrinsic understanding of procedural action concepts. Hence, they overfit to fixed labels and are not invariant to unseen action synonyms. To address this, we propose a simple fine-tuning technique, Action Concept Enhancement (ACE), to improve the robustness

Julien Chevallier, Guilherme Ost

Let $N$ components be partitioned into two communities, denoted ${\cal P}_+$ and ${\cal P}_-$, possibly of different sizes. Assume that they are connected via a directed and weighted Erd\"os-R\'enyi (DWER) random graph with unknown parameter $ p \in (0, 1).$ The weights assigned to the existing connections are of mean-field-type, scaling as $N^{-1}$. At each

Filip Ilievski, Barbara Hammer, Frank van Harmelen, Benjamin Paassen

Recent advances in AI -- including generative approaches -- have resulted in technology that can support humans in scientific discovery and forming decisions, but may also disrupt democracies and target individuals. The responsible use of AI and its participation in human-AI teams increasingly shows the need for AI alignment, that is, to make AI systems act

Anna Bykhovskaya, Vadim Gorin

For over a century canonical correlations, variables, and related concepts have been studied across various fields, with contributions dating back to Jordan [1875] and Hotelling [1936]. This text surveys the evolution of canonical correlation analysis, a fundamental statistical tool, beginning with its foundational theorems and progressing to recent developm

Boxin Zhao, Cong Ma, Mladen Kolar

Precision matrix estimation is essential in various fields; yet it is challenging when samples for the target study are limited. Transfer learning can enhance estimation accuracy by leveraging data from related source studies. We propose Trans-Glasso, a two-step transfer learning method for precision matrix estimation. First, we obtain initial estimators usi

Adamantios Ntakaris, Gbenga Ibikunle

This study presents an autonomous experimental machine learning protocol for high-frequency trading (HFT) stock price forecasting that involves a dual competitive feature importance mechanism and clustering via shallow neural network topology for fast training. By incorporating the k-means algorithm into the radial basis function neural network (RBFNN), the

Mengfei Lan, Lecheng Zheng, Shufan Ming, Halil Kilicoglu

Sequential sentence classification (SSC) in scientific publications is crucial for supporting downstream tasks such as fine-grained information retrieval and extractive summarization. However, current SSC methods are constrained by model size, sequence length, and single-label setting. To address these limitations, this paper proposes LLM-SSC, a large langua

Stephen T. Fuller, J. -X. Kent Zheng

The Solid-Electrolyte Interphase, SEI, formed on a battery electrode has been a central area of research for decades. This thin, complex layer profoundly impacts the electrochemical deposition morphology and stability of the metal in battery anodes. Departing from conventional approaches, we investigate metal dissolution, the reverse reaction of deposition,

Abhijit Mazumdar, Yuting Hou, Rafal Wisniewski

In this paper, we propose a distributionally robust safety verification method for Markov decision processes where only an ambiguous transition kernel is available instead of the precise transition kernel. We define the ambiguity set around the nominal distribution by considering a Wasserstein distance. To this end, we introduce a robust safety function to c

Lisa Weijler, Michael Reiter, Pedro Hermosilla, Margarita Maurer-Granofszky

This paper evaluates various deep learning methods for measurable residual disease (MRD) detection in flow cytometry (FCM) data, addressing questions regarding the benefits of modeling long-range dependencies, methods of obtaining global information, and the importance of learning local features. Based on our findings, we propose two adaptations to the curre

Jun Chen, Dannong Xu, Junjie Fei, Chun-Mei Feng

Large multimodal models (LMMs) have achieved impressive progress in vision-language understanding, yet they face limitations in real-world applications requiring complex reasoning over a large number of images. Existing benchmarks for multi-image question-answering are limited in scope, each question is paired with only up to 30 images, which does not fully

Jinwoo Ahn, Hyeokjoon Kwon, Hwiyeon Yoo

Recent advent of vision-based foundation models has enabled efficient and high-quality object detection at ease. Despite the success of previous studies, object detection models face limitations on capturing small components from holistic objects and taking user intention into account. To address these challenges, we propose a novel foundation model-based de

Puja Mukherjee, Ujjal Debnath, Himanshu Chaudhary, G. Mustafa

Black holes, one of the greatest enigmas of our Universe, are challenging to decipher. This work is dedicated to observing the changes in the mass of a non-singular black hole with the evolution of the Universe in the generalized Rastall gravity framework, considering the effects of parameter constraining. We examine two recently developed dynamical dark-ene

Florian B. Hinz, Matthew R. Masters, Julia N. Kieu, Amr H. Mahmoud

Water plays a fundamental role in the structure and function of proteins and other biomolecules. The thermodynamic profile of water molecules surrounding a protein are critical for ligand binding and recognition. Therefore, identifying the location and thermodynamic behavior of relevant water molecules is important for generating and optimizing lead compound