February 2025 arXiv papers — page 37

Peng Zhang, Xin Li, Xin Lin, Liang He

Recent 3D multi-object tracking (3D MOT) methods mainly follow tracking-by-detection pipelines, but often suffer from high false positives, missed detections, and identity switches, especially in crowded and small-object scenarios. To address these challenges, we propose Easy-Poly, a filter-based 3D MOT framework with four key innovations: (1) CNMSMM, a nove

Rui Liu, Yu Shen, Peng Gao, Pratap Tokekar

Multi-modal learning has emerged as a key technique for improving performance across domains such as autonomous driving, robotics, and reasoning. However, in certain scenarios, particularly in resource-constrained environments, some modalities available during training may be absent during inference. While existing frameworks effectively utilize multiple dat

Hao-Xiang Xu, Jun-Yu Ma, Zhen-Hua Ling, Ningyu Zhang

Large language models (LLMs) struggle with hallucinations due to false or outdated knowledge. Given the high resource demands of retraining these models, there is an increasing focus on developing model editing. However, the general abilities of LLMs across downstream tasks are prone to significant degradation during sequential editing. This paper statistica

Nam P. Vu, Daniel Dong, Xiaohan Dan, Ningyi Lyu

We introduce a computational framework for simulating non-adiabatic vibronic dynamics on circuit quantum electrodynamics (cQED) platforms. Our approach leverages hybrid oscillator-qubit quantum hardware with mid-circuit measurements and resets, enabling the incorporation of environmental effects such as dissipation and dephasing. To demonstrate its capabilit

Thawatchai Mayteevarunyoo, Boris A. Malomed

Stability is an essential problem in theoretical and experimental studies of solitons in nonlinear media with fractional diffraction, which is represented by the Riesz derivative with Levy index (LI) taking values LI < 2. Fractional solitons are unstable at LI smaller or equal to 1, or LI smaller or equal to 2 in uniform one-dimensional media with the cubic

Meilin Li, Chong Han, Shi Jin

The Terahertz band holds a promise to enable both super-accurate sensing and ultra-fast communication. However, challenges arise that severe Doppler effects call for a waveform with high Doppler robustness while severe propagation path loss urges for an ultra-massive multiple-input multiple-output (UM-MIMO) structure. To tackle these challenges, hybrid beamf

Md Kowsher, Nusrat Jahan Prottasha, Prakash Bhat, Chun-Nam Yu

This paper argues that generating output tokens is more effective than using pooled representations for prediction tasks because token-level generation retains more mutual information. Since LLMs are trained on massive text corpora using next-token prediction, generation aligns naturally with their learned behavior. Using the Data Processing Inequality (DPI)

Ehsan Farahbakhsh, Dakshi Goel, Dhiraj Pimparkar, R. Dietmar Muller

Traditional geological mapping, based on field observations and rock sample analysis, is inefficient for continuous spatial mapping of features like alteration zones. Deep learning models, such as convolutional neural networks (CNNs), have revolutionised remote sensing data analysis by automatically extracting features for classification and regression tasks

Adam Bouyamourn, Alexander Williams Tolbert

Disparities in lending to minority applicants persist even as algorithmic lending finds widespread adoption. We study the role of risk-management constraints, specifically Value-at-Risk ($\VaR$) and Expected Shortfall (ES), in inducing inequality in loan approval decisions, even among applicants who are equally creditworthy. Empirical research finds that dis

Hongyi Cai, Yuqian Fu, Hongming Fu, Bo Zhao

Instruction tuning is crucial for optimizing Large Language Models (LLMs), yet mainstream data selection methods heavily rely on LLMs as instruction quality scorers, leading to high computational costs and reduced data diversity. To address these limitations, we propose MergeIT, a novel LLM-based Merging strategy for better Instruction Tuning that shifts the

Ershadul Haque, Manoranjan Paul

Quantum image computing has emerged as a groundbreaking field, revolutionizing how we store and process data at speeds incomparable to classical methods. Nevertheless, as image sizes expand, so does the complexity of qubit connections, posing significant challenges in the efficient representation and compression of quantum images. In response, we introduce a

Xiaoshuang Chen, Yibo Wang, Yao Wang, Husheng Liu

Users and creators are two crucial components of recommender systems. Typical recommender systems focus on the user side, providing the most suitable items based on each user's request. In such scenarios, a few items receive a majority of exposures, while many items receive very few. This imbalance leads to poorer experiences and decreased activity among the

Wenlong Ji, Weizhe Yuan, Emily Getzen, Kyunghyun Cho

Large Language Models (LLMs) have emerged as transformative tools in artificial intelligence (AI), exhibiting remarkable capabilities across diverse tasks such as text generation, reasoning, and decision-making. While their success has primarily been driven by advances in computational power and deep learning architectures, emerging problems -- in areas such

Meng Feng, Viraj Parimi, Brian Williams

Safe navigation is essential for autonomous systems operating in hazardous environments. Traditional planning methods excel at long-horizon tasks but rely on a predefined graph with fixed distance metrics. In contrast, safe Reinforcement Learning (RL) can learn complex behaviors without relying on manual heuristics but fails to solve long-horizon tasks, part

Xiongxiao Xu, Haoran Wang, Yueqing Liang, Philip S. Yu

Time series anomaly detection (TSAD) has been a long-standing pillar problem in Web-scale systems and online infrastructures, such as service reliability monitoring, system fault diagnosis, and performance optimization. Large language models (LLMs) have demonstrated unprecedented capabilities in time series analysis, the potential of multimodal LLMs (MLLMs),

Chong Han, Weijun Gao, Zhepu Yin, Chuang Yang

The transition from isolated systems to integrated solutions has driven the development of space-air-ground integrated networks (SAGIN) as well as the integration of communication and radar sensing functionalities. By leveraging the unique properties of the Terahertz (THz) band, THz joint communication and radar sensing (JCRS) supports high-speed communicati

Ruiqi Yan, Xiquan Li, Wenxi Chen, Zhikang Niu

Recent advances in large language models (LLMs) have driven significant progress in end-to-end spoken dialogue models (SDMs). In contrast to text-based LLMs, the evaluation framework for SDMs should encompass both cognitive dimensions (e.g., logical reasoning, knowledge) and speech-related aspects (e.g., paralinguistic cues, audio quality). However, there is

Yang Cai, Yingkai Li, Jinzhao Wu

We study multi-product monopoly pricing where the seller jointly designs the selling mechanism and the information structure for the buyer to learn his values. Unlike the case with exogenous information, we show that when the seller controls information, even uniform pricing guarantees at least half of the optimal revenue. Moreover, for negatively affiliated

Weijun Gao, Chong Han, Zhi Chen, Yong Chen

To achieve ubiquitous connectivity in next-generation networks through aerospace communications while maintaining high data rates, Terahertz (THz) band communications (0.1-10 THz) with large continuous bandwidths are considered a promising candidate technology. However, key enabling techniques and practical implementations of THz communications for aerospace

Tianyi Zhuang, Chuqiao Kuang, Xiaoguang Li, Yihua Teng

We present DocPuzzle, a rigorously constructed benchmark for evaluating long-context reasoning capabilities in large language models (LLMs). This benchmark comprises 100 expert-level QA problems requiring multi-step reasoning over long real-world documents. To ensure the task quality and complexity, we implement a human-AI collaborative annotation-validation

Mst Shapna Akter, Md. Shazzad Hossain Shaon, Tasmin Karim, Md. Fahim Sultan

Distributed quantum networks are not merely information conduits but intricate systems that embody the principles of quantum mechanics. In our study, we examine the underlying mechanisms of quantum connectivity within a distributed framework by exploring phenomena such as superposition and entanglement and their influence on information propagation. We inves

G. D. Muro, C. M. S Cohen, Z. Xu, R. A. Leske

In the latter moments of 17 July 2023, the solar active region 13363, near the southwestern face of the Sun, was undergoing considerable evolution, which resulted in a significant solar energetic particle (SEP) event measured by Parker Solar Probe's Integrated Science Investigation of the Sun (ISOIS) and near-Earth spacecraft. Remote observations from GOES a

Aidan Paoli, Chunlei Liang

The objective of this study is to develop a fully compressible magnetohydrodynamic solver for fast simulations of the global dynamo of the Sun using unstructured grids and GPUs. Accurate modeling of the Sun's convective layers is vital to predicting the Sun's behavior, including the solar dynamo and sunspot cycles. Currently, there are many efficient codes c

Yu Xie, Menghang Wang, Senja Ramakers, Frans Spaepen

Silicon carbide (SiC) is an important technological material, but its high-temperature phase diagram has remained unclear due to conflicting experimental results about congruent versus incongruent melting. Here, we employ large-scale machine learning molecular dynamics (MLMD) simulations to gain insights into SiC decomposition and phase transitions. Our appr

Benjamin Côté, Ruodu Wang

Many results on the convex order in the literature were stated for random variables with finite mean. For instance, a fundamental result in dependence modeling is that the sum of a pair of random random variables is upper bounded in convex order by that of its comonotonic version and lower bounded by that of its counter-monotonic version, and all existing pr

Huaxin Lin

Let $H$ be an infinite dimensional separable Hilbert space, $B(H)$ the $C^*$-algebra of all bounded linear operators on $H,$ $U(B(H))$ the unitary group of $B(H)$ and ${\cal K}\subset B(H)$ the ideal of compact operators. Let $G$ be a countable discrete amenable group. We prove the following: For any $\epsilon>0,$ any finite subset ${\cal F}\subset G,$ and $

Shuming Shi, Ruobing Zuo, Gaolei He, Jianlin Wang

Automated theorem proving (ATP) is one of the most challenging mathematical reasoning tasks for Large Language Models (LLMs). Most existing LLM-based ATP methods rely on supervised fine-tuning, which results in a limited alignment between the theorem proving process and human preferences. Direct Preference Optimization (DPO), which aligns LLMs with human pre

Yuqing Wang, Xiao Yang

Cloud computing environments are increasingly vulnerable to security threats such as distributed denial-of-service (DDoS) attacks and SQL injection. Traditional security mechanisms, based on rule matching and feature recognition, struggle to adapt to evolving attack strategies. This paper proposes an adaptive security protection framework leveraging deep lea

Yihang Yao, Zhepeng Cen, Miao Li, William Han

Large Language Models (LLMs) have demonstrated strong reasoning capabilities across various tasks. However, even minor variations in query phrasing, despite preserving the underlying semantic meaning, can significantly affect their performance. To address this, we focus on enhancing LLMs' awareness of symmetry in query variations and propose syMmetry-ENhance

Se H. Kim, Hanjoo Lee, Dong Gwan Kim, Donghan Kim

Non-conventional epitaxial techniques, such as van der Waals epitaxy (vdWE) and remote epitaxy, have attracted substantial attention in the semiconductor research community for their capability to repeatedly produce high-quality free-standing films from a single mother wafer. Successful implementation of these epitaxial techniques depends on creating a robus

Indranil Ghosh, Hammed Olawale Fatoyinbo

We set up a system of Caputo-type fractional differential equations for a reduced-order model known as the {\em denatured} Morris-Lecar (dML) neurons. This neuron model has a structural similarity to a FitzHugh-Nagumo type system. We explore both a single-cell isolated neuron and a two-coupled dimer that can have two different coupling strategies. The main p

Yixiao Song, Parker Riley, Daniel Deutsch, Markus Freitag

Human evaluation is crucial for assessing rapidly evolving language models but is influenced by annotator proficiency and task design. This study explores the integration of comparative judgment into human annotation for machine translation (MT) and evaluates three annotation setups-point-wise Multidimensional Quality Metrics (MQM), side-by-side (SxS) MQM, a

Yisheng He, Xiaodong Gu, Xiaodan Ye, Chao Xu

We present LAM, an innovative Large Avatar Model for animatable Gaussian head reconstruction from a single image. Unlike previous methods that require extensive training on captured video sequences or rely on auxiliary neural networks for animation and rendering during inference, our approach generates Gaussian heads that are immediately animatable and rende

Mohamed-Ali Belabbas, Xudong Chen

We address the infinite-horizon minimum energy control problem for linear time-invariant finite-dimensional systems $(A, B)$. We show that the problem admits a solution if and only if $(A, B)$ is stabilizable and $A$ does not have imaginary eigenvalues.

Zelin Tao, Hao Deng, Mingqing Liu, Lijun Zhang

Online continual learning (OCL), which enables AI systems to adaptively learn from non-stationary data streams, is commonly achieved using experience replay (ER)-based methods that retain knowledge by replaying stored past during training. However, these methods face challenges of prediction bias, stemming from deviations in parameter update directions durin

Hyeonjeong Ha, Xiaomeng Jin, Jeonghwan Kim, Jiateng Liu

Text-to-image (T2I) models enable rapid concept design, making them widely used in AI-driven design. While recent studies focus on generating semantic and stylistic variations of given design concepts, functional coherence--the integration of multiple affordances into a single coherent concept--remains largely overlooked. In this paper, we introduce SYNTHIA,

Yupeng Yang, Yicheng Wang, Xinyi Dai

We constrain two vacuum decay models ($\Lambda(t)$CDM, proposed by the authors of~\cite{Brito:2024bhh}) utilizing the baryon acoustic oscillations (BAO) data released by the Dark Energy Spectroscopic Instrument (DESI), distance prior from the cosmic microwave background (CMB) observed by the Planck satellite, Hubble rate data obtained via the cosmic chronome

Azumi Sakai, Koichi Murase, Hirotsugu Fujii, Tetsufumi Hirano

We develop a dynamical model to describe critical fluctuations in heavy-ion collisions, incorporating the baryon diffusion current and chiral condensate as dynamical degrees of freedom, to address their nontrivial scale separation. The model couples fluctuations of the chiral condensate $\sigma$ with baryon density fluctuations $n$ and the diffusion current

Xinliang Zhai, Tailong Xiao, Jingzheng Huang, Jianping Fan

Demonstrating the utility of quantum algorithms is a long-standing challenge, where quantum machine learning becomes one of the most promising candidate that can be resorted to. In this study, we investigate a quantum neural compressive sensing algorithm for ghost imaging to showcase its utility. The algorithm utilizes the variational quantum circuits to rep

Torrance Hodgson

Radio interferometers must grapple with apparent fields of view that distort the true radio sky. These so-called 'A-term' distortions may be direction-, time- and baseline-dependent, and include effects like the primary beam and the ionosphere. Traditionally, properly handling these effects has been computationally expensive and, instead, less accurate, ad-h

Afonso Lourenço, João Rodrigo, João Gama, Goreti Marreiros

This literature review explores continual learning methods for on-device training in the context of neural networks (NNs) and decision trees (DTs) for classification tasks on smart environments. We highlight key constraints, such as data architecture (batch vs. stream) and network capacity (cloud vs. edge), which impact TinyML algorithm design, due to the un

Wei Liu, Yancheng He, Hui Huang, Chengwei Hu

With the development of large language models, their ability to follow simple instructions has significantly improved. However, adhering to complex instructions remains a major challenge. Current approaches to generating complex instructions are often irrelevant to the current instruction requirements or suffer from limited scalability and diversity. Moreove

Akshay Sathiya, Rohit Pandey

Combinatorial optimization problems are prevalent across a wide variety of domains. These problems are often nuanced, their optimal solutions might not be efficiently obtainable, and they may require lots of time and compute resources to solve (they are NP-hard). It follows that the best course of action for solving these problems is to use general optimizat

J. A. López-Vázquez, M. Fernández-López, J. M. Girart, S. Curiel

We present Atacama Large Millimeter/submillimeter Array Band 3 observations of N$_2$H$^+$ (1-0) and CH$_3$CN (5-4), as well as Band 7 observations of the H$_2$CO molecular line emissions from the protostellar system GGD 27-MM2(E). Through position-velocity diagrams along and across the outflow axis, we study the kinematics and structure of the outflow. We al

Yoshee Jain, John Hollander, Amber He, Sunny Tang

Reading comprehension is a key for individual success, yet the assessment of question difficulty remains challenging due to the extensive human annotation and large-scale testing required by traditional methods such as linguistic analysis and Item Response Theory (IRT). While these robust approaches provide valuable insights, their scalability is limited. Th

Haji Gul, Abdul Gani Haji Naim, Ajaz A. Bhat

Drug-target interactions are critical for understanding biological processes and advancing drug discovery. However, traditional methods such as ComplEx-SE, TransE, and DistMult struggle with unseen relationships and negative triplets, which limits their effectiveness in drug-target prediction. To address these challenges, we propose Multi-Context-Aware Sampl

Julian Sutaria, Cristian Staii

We report combined scanning probe microscopy and electrical measurements to investigate local electronic transport in reduced graphene oxide (rGO) devices. We demonstrate that quantum transport in these materials can be significantly tuned by the electrostatic potential applied with a conducting atomic force microscope (AFM) tip. Scanning gate microscopy (SG

S. Jin, I. Y. Dodin

Presented here is a novel formulation of the mean-field dynamo as a modulational instability of magnetohydrodynamic (MHD) turbulence. This formulation, termed mean-field wave kinetics (MFWK), is based on the Weyl symbol calculus and allows describing the interaction between the mean fields (magnetic field and fluid velocity) and turbulence without requiring

Jingjing Zhao, Xidong Mu, Kaiquan Cai, Yanbo Zhu

A novel concept of waveguide division multiple access (WDMA) is proposed for multi-user pinching-antenna systems (PASS). The key principle of WDMA is to allocate each user with a dedicated waveguide, which is regarded as a new type of radio resources, so as to facilitate multi-user communications. By adjusting the activation positions of pinching antennas (P

Mohamed Tarek Ibn Ziad, Sana Damani, Mark Stephenson, Stephen W. Keckler

Memory safety errors continue to pose a significant threat to current computing systems, and graphics processing units (GPUs) are no exception. A prominent class of memory safety algorithms is allocation-based solutions. The key idea is to maintain each allocation's metadata (base address and size) in a disjoint table and retrieve it at runtime to verify mem

R. Ryan Williams

We show that for all functions $t(n) \geq n$, every multitape Turing machine running in time $t$ can be simulated in space only $O(\sqrt{t \log t})$. This is a substantial improvement over Hopcroft, Paul, and Valiant's simulation of time $t$ in $O(t/\log t)$ space from 50 years ago [FOCS 1975, JACM 1977]. Among other results, our simulation implies that boun

Anastashia Jebraeilli, Chenxu Liu, Keyi Yin, Erik W Lentz

Quantum sensing (QS) harnesses quantum phenomena to measure physical observables with extraordinary precision, sensitivity, and resolution. Despite significant advancements in quantum sensing, prevailing efforts have focused predominantly on refining the underlying sensor materials and hardware. Given the growing demands of increasingly complex application d

Lei Zhao, Lin Cai, Wu-Sheng Lu

In the field of financial derivatives trading, managing volatility risk is crucial for protecting investment portfolios from market changes. Traditional Vega hedging strategies, which often rely on basic and rule-based models, are hard to adapt well to rapidly changing market conditions. We introduce a new framework for dynamic Vega hedging, the Adaptive Nes

Yifan He, To Eun Kim, Fernando Diaz, Jaime Arguello

Tip-of-the-tongue (TOT) search occurs when a user struggles to recall a specific identifier, such as a document title. While common, existing search systems often fail to effectively support TOT scenarios. Research on TOT retrieval is further constrained by the challenge of collecting queries, as current approaches rely heavily on community question-answerin

Tanawan Premsri, Parisa Kordjamshidi

Spatial reasoning is a fundamental aspect of human intelligence. One key concept in spatial cognition is the Frame of Reference, which identifies the perspective of spatial expressions. Despite its significance, FoR has received limited attention in AI models that need spatial intelligence. There is a lack of dedicated benchmarks and in-depth evaluation of l

Hau Wen Chang, J-Anne Yow, Lek Syn Lim, Wei Tech Ang

Robot-assisted feeding systems enhance the independence of individuals with motor impairments and alleviate caregiver burden. While existing systems predominantly rely on software-based safety features to mitigate risks during unforeseen collisions, this study explores the use of a mechanical fail-safe to improve safety. We designed a breakaway utensil attac

Hao Liang, Wanrong Zhang, Xinlei He, Kaishun Wu

Differentially Private Stochastic Gradient Descent (DPSGD) is widely used to protect sensitive data during the training of machine learning models, but its privacy guarantee often comes at a large cost of model performance due to the lack of tight theoretical bounds quantifying privacy loss. While recent efforts have achieved more accurate privacy guarantees

Chris Dongjoo Kim, Sangwoo Moon, Jihwan Moon, Dongyeon Woo

As with many other problems, real-world regression is plagued by the presence of noisy labels, an inevitable issue that demands our attention. Fortunately, much real-world data often exhibits an intrinsic property of continuously ordered correlations between labels and features, where data points with similar labels are also represented with closely related

Wei Liu, Qihang Lin, Yangyang Xu

Many recent studies on first-order methods (FOMs) focus on \emph{composite non-convex non-smooth} optimization with linear and/or nonlinear function constraints. Upper (or worst-case) complexity bounds have been established for these methods. However, little can be claimed about their optimality as no lower bound is known, except for a few special \emph{smoo

Lutong Sheng, Anna Duvakina, Hanchen Wang, Kei Yamamoto

Controlling spin current lies at the heart of spintronics and its applications. The sign of spin currents is monotonous in ferromagnets once the current direction is determined. Spin currents in antiferromagnets can possess opposite polarization, but requires enormous magnetic fields to lift the degeneracy. Controlling spin currents with different polarizati

M. Eremenko, V. Krayzman, S. Gorfman, A. Bosak

A complete understanding of the mechanisms for dielectric relaxation in relaxor ferroelectrics remains elusive. We used a structural refinement framework that integrates several types of experimental data to identify the nanoscale correlations of polarization and their relationship to the underlying chemistry in the classic relaxor system PbMg1/3Nb2/3O3-PbTi

Ethan N. Epperly, Anne Greenbaum, Yuji Nakatsukasa

This paper studies the solution of nonsymmetric linear systems by preconditioned Krylov methods based on the normal equations, LSQR in particular. On some examples, preconditioned LSQR is seen to produce errors many orders of magnitude larger than classical direct methods; this paper demonstrates that the attainable accuracy of preconditioned LSQR can be gre

Xin Tong, Shi Peng, Baojie Tian, Yufei Guo

Classical Transformer-based line segment detection methods have delivered impressive results. However, we observe that some accurately detected line segments are assigned low confidence scores during prediction, causing them to be ranked lower and potentially suppressed. Additionally, these models often require prolonged training periods to achieve strong pe

Mónica Clapp, Víctor A. Vicente-Benítez

We establish the existence of a fully nontrivial solution with nonnegative components for a weakly coupled competitive system for the $p$-Laplacian in $\mathbb{R}^N$ whose nonlinear terms are purely critical. We also show that the purely critical equation for the $p$-Laplacian in $\mathbb{R}^N$ has infinitely many nodal solutions.

Eric Xue, Ke Chen, Zeyi Huang, Yuyang Ji

Large language model (LLM) agents have emerged as a promising solution to automate the workflow of machine learning, but most existing methods share a common limitation: they attempt to optimize entire pipelines in a single step before evaluation, making it difficult to attribute improvements to specific changes. This lack of granularity leads to unstable op

Qile Jiang, Zhiwei Gao, George Em Karniadakis

Large Language Models (LLMs) have emerged as powerful tools for tackling a wide range of problems, including those in scientific computing, particularly in solving partial differential equations (PDEs). However, different models exhibit distinct strengths and preferences, resulting in varying levels of performance. In this paper, we compare the capabilities

Yuqing Wang, Xiao Yang

Traditional security protection methods struggle to address sophisticated attack vectors in large-scale distributed systems, particularly when balancing detection accuracy with data privacy concerns. This paper presents a novel distributed security threat detection system that integrates federated learning with multimodal large language models (LLMs). Our sy

Juan Niño, Luis Guayacán, Santiago Gómez, Fabio Martínez

Oculomotor alterations constitute a promising biomarker to detect and characterize Parkinson's disease (PD), even in prodromal stages. Currently, only global and simplified eye movement trajectories are employed to approximate the complex and hidden kinematic relationships of the oculomotor function. Recent advances on machine learning and video analysis hav

Cristina Almagro-Pérez, Andrew H. Song, Luca Weishaupt, Ahrong Kim

A comprehensive three-dimensional (3D) map of tissue architecture and gene expression is crucial for illuminating the complexity and heterogeneity of tissues across diverse biomedical applications. However, most spatial transcriptomics (ST) approaches remain limited to two-dimensional (2D) sections of tissue. Although current 3D ST methods hold promise, they

Dongwei Chen

This paper studies probabilistic dual frames and the associated dual frame potentials from the perspective of optimal mass transport. The main contribution of this work shows that given a probabilistic frame, its associated dual frame potential is minimized if and only if the probabilistic frame is tight and the probabilistic dual frame is the canonical dual

Liang Xu, Pengwu Song, Shilu Zhu, Yang Zhang

Continuous monitoring and in-situ assessment of microvascular connectivity have significant implications for culturing vascularized organoids and optimizing the therapeutic strategies. However, commonly used methods for vascular connectivity assessment heavily rely on fluorescent labels that may either raise biocompatibility concerns or interrupt the normal

Andrew Holliday

This thesis concerns the use of reinforcement learning to train neural networks to aid in the design of public transit networks. The Transit Network Design Problem (TNDP) is an optimization problem of considerable practical importance. Given a city with an existing road network and travel demands, the goal is to find a set of transit routes - each of which i

Lei Zhao, Lin Cai

Deep hedging represents a cutting-edge approach to risk management for financial derivatives by leveraging the power of deep learning. However, existing methods often face challenges related to computational inefficiency, sensitivity to noisy data, and optimization complexity, limiting their practical applicability in dynamic and volatile markets. To address

Govind Nandakumar, Nils Ryde, Mathias Schultheis, R. Michael Rich

An important step in understanding the formation and evolution of the Nuclear Star Cluster (NSC) is to investigate its chemistry and chemical evolution. Additionally, exploring the relationship of the NSC to the other structures in the Galactic Center and the Milky Way disks is of great interest. Extreme optical extinction has previously prevented optical st

Nagaraj Vernekar, Sara Lucatello, Pete Kuzma, Lorenzo Spina

Context. Omega Cen is the largest known globular cluster in the Milky Way. It is also quite a complex object with a large metallicity spread and multiple stellar populations. Despite a number of studies over the past several decades, the series of events that led to the formation of this cluster is still poorly understood. One of its peculiarities is the pre

Nicola Romanazzi

We derive a system with one degree of freedom that models a class of dynamical systems with strange attractors in three dimensions. This system retains all the characteristics of chaotic attractors and is expressed by a second-order integro-differential equation which mimics a spring-like problem. We determine the potential energy, the rate of change of the

Luigi Seminara, Giovanni Maria Farinella, Antonino Furnari

We introduce a gradient-based approach for learning task graphs from procedural activities, improving over hand-crafted methods. Our method directly optimizes edge weights via maximum likelihood, enabling integration into neural architectures. We validate our approach on CaptainCook4D, EgoPER, and EgoProceL, achieving +14.5%, +10.2%, and +13.6% F1-score impr

Yuchen Zhang, Bo Chen, Zheming Wang, Wen-An Zhang

Fusion estimation is often used in multi-sensor systems to provide accurate state information which plays an important role in the design of efficient control and decision-making. This paper is concerned with the distributed zonotopic fusion estimation problem for multi-sensor systems. The objective is to propose a zonotopic fusion estimation approach using

Tony Shaska

This paper presents a novel framework for graded neural networks (GNNs) built over graded vector spaces $\V_\w^n$, extending classical neural architectures by incorporating algebraic grading. Leveraging a coordinate-wise grading structure with scalar action $\lambda \star \x = (\lambda^{q_i} x_i)$, defined by a tuple $\w = (q_0, \ldots, q_{n-1})$, we introdu

Jamie Holber, Krishna Garikipati

Accurate free energy representations are crucial for understanding phase dynamics in materials. We employ a scale-bridging approach to incorporate atomistic information into our free energy model by training a neural network on DFT-informed Monte Carlo data. To optimize sampling in the high-dimensional Monte Carlo space, we present an Active Learning framewo

Wenqing Wang, Punit Kumar, David H. Cook, Flynn Walsh

The striking variation in damage tolerance among refractory complex concentrated alloys is examined through the analysis of atomistic fracture simulations, contrasting behavior in elemental Nb with that in brittle NbMoTaW and ductile Nb45Ta25Ti15Hf15. We employ machine-learning interatomic potentials (MLIPs), including a new MLIP developed for NbTaTiHf, in a

Shinwoo Park, Shubin Kim, Do-Kyung Kim, Yo-Sub Han

The rapid advancement of large language models (LLMs) increases the difficulty of distinguishing between human-written and LLM-generated text. Detecting LLM-generated text is crucial for upholding academic integrity, preventing plagiarism, protecting copyrights, and ensuring ethical research practices. Most prior studies on detecting LLM-generated text focus

Shinwoo Park, Hyundong Jin, Jeong-won Cha, Yo-Sub Han

Recent progress in large language models (LLMs) for code generation has raised serious concerns about intellectual property protection. Malicious users can exploit LLMs to produce paraphrased versions of proprietary code that closely resemble the original. While the potential for LLM-assisted code paraphrasing continues to grow, research on detecting it rema

Madeline L. Cross-Parkin, Cullan Howlett, Tamara M. Davis, Nandita Khetan

With the growing number of gravitational wave detections, achieving a competitive measurement of $H_0$ with dark sirens is becoming increasingly feasible. The expansion of the Ligo-Virgo-KAGRA Collaboration into a four detector network will reduce both the localisation area and the luminosity distance uncertainty associated with each gravitational wave event

Sebastián Donoso, Alejandro Maass, Vicente Saavedra-Araya

We introduce a new class of sparse sequences that are ergodic and pointwise universally $L^2$-good for ergodic averages. That is, sequences along which the ergodic averages converge almost surely to the projection to invariant functions. These sequences are generated randomly as return or hitting times in systems exhibiting a rapid correlation decay. This ca

Azmain A. Hossain, Sela Murphy, David S. Catherall, Anthony J. Ardizzi

Niobium nitride (NbN) is a metallic superconductor that is widely used for superconducting electronics due to its high transition temperature ($T_c$) and kinetic inductance. Processing-induced damage negatively affects the performance of these devices by mechanisms such as microwave surface loss. Atomic layer etching (ALE), with its ability to etch with Angs

Baozhen Wang, Xingye Qiao

In many real applications of statistical learning, collecting sufficiently many training data is often expensive, time-consuming, or even unrealistic. In this case, a transfer learning approach, which aims to leverage knowledge from a related source domain to improve the learning performance in the target domain, is more beneficial. There have been many tran

Ya-Ping Xie, S. V. Goloskokov

Exclusive $J/\psi$ production is investigated in proton-proton collisions employing GPD approach with GK model. Three sets gluon density are used to calculate exclusive $J/\psi$ production. The survival factors and equivalent photon approximation are applied to predict the exclusive $J/\psi$ photoproduction in proton-proton collisions. The GPD method predict

Gustavo Boré, Vicente Sufán, Sebastián Rodríguez-Martínez, Giancarlo Troni

The use of autonomous underwater vehicles (AUVs) for surveying, mapping, and inspecting unexplored underwater areas plays a crucial role, where maneuverability and power efficiency are key factors for extending the use of these platforms, making six degrees of freedom (6-DOF) holonomic platforms essential tools. Although Proportional-Integral-Derivative (PID

Zichun Xu, Daniela Witten, Ali Shojaie

We consider statistical inference under a semi-supervised setting where we have access to both a labeled dataset consisting of pairs $\{X_i, Y_i \}_{i=1}^n$ and an unlabeled dataset $\{ X_i \}_{i=n+1}^{n+N}$. We ask the question: under what circumstances, and by how much, can incorporating the unlabeled dataset improve upon inference using the labeled data?

Hang Wang, Qiaoyi Fang, Junshan Zhang

The past few years have witnessed a rapid growth of the deployment of automated vehicles (AVs). Clearly, AVs and human-driven vehicles (HVs) will co-exist for many years, and AVs will have to operate around HVs, pedestrians, cyclists, and more, calling for fundamental breakthroughs in AI designed for mixed traffic to achieve mixed autonomy. Thus motivated, w

Junyuan Bai, Hao Xue, Jiaming Li, Xueyong Pang

Aluminum-copper-magnesium-sliver (Al-Cu-Mg-Ag) alloys are extensively utilized in aerospace industries due to the formation of Omega nano-plates.However, the rapid coarsening of these nano-plates above 475 K restricts their application at elevated temperatures.When introducing scandium (Sc) to these alloys, the service temperature of the resultant alloys can

Chengsong Huang, Langlin Huang, Jixuan Leng, Jiacheng Liu

Increasing test-time computation is a straightforward approach to enhancing the quality of responses in Large Language Models (LLMs). While Best-of-N sampling and Self-Consistency with majority voting are simple and effective, they require a fixed number of sampling responses for each query, regardless of its complexity. This could result in wasted computati

Amine Mohamed Aboussalah, Abdessalam Ed-dib

Graph Neural Networks (GNNs) have demonstrated remarkable success in learning from graph-structured data. However, the influence of the input graph's topology on GNN behavior remains poorly understood. In this work, we explore whether GNNs are inherently limited by the structure of their input graphs, focusing on how local topological features interact with

Rentian Yao, Atsushi Nitanda, Xiaohui Chen, Yun Yang

Motivated by learning dynamical structures from static snapshot data, this paper presents a distribution-on-scalar regression approach for estimating the density evolution of a stochastic process from its noisy temporal point clouds. We propose an entropy-regularized nonparametric maximum likelihood estimator (E-NPMLE), which leverages the entropic optimal t

Arne Bang Huseby

Domination theory has been studied extensively in the context of binary monotone systems, where the structure function is a sum of products of the component state variables, and with coefficients given by the signed domination function. Using e.g., matroid theory, many useful properties of the signed domination function has been derived. In this paper we sho

Thivan M. Gunawardana, Frank Schindler, Ari M. Turner, Ryan Barnett

The modern theory of polarization does not apply in its original form to systems with non-trivial band topology. Chern insulators are one such example. Defining polarization for them is complicated because they are insulating in the bulk but exhibit metallic edge states. Wannier functions formed a key ingredient of the original modern theory of polarization,

Antônio Oliveira-Filho, Wellington Silva-de-Souza, Carlos Alberto Valderrama Sakuyama, Samuel Xavier-de-Souza

This paper presents Phoeni6, a systematic approach for assessing the energy consumption of neural networks while upholding the principles of fair comparison and reproducibility. Phoeni6 offers a comprehensive solution for managing energy-related data and configurations, ensuring portability, transparency, and coordination during evaluations. The methodology

Guilherme Pumi, Danilo Hiroshi Matsuoka, Taiane Schaedler Prass, Bruna Gregory Palm

Time series in natural sciences, such as hydrology and climatology, and other environmental applications, often consist of continuous observations constrained to the unit interval (0,1). Traditional Gaussian-based models fail to capture these bounds, requiring more flexible approaches. This paper introduces the Matsuoka Autoregressive Moving Average (MARMA)

Akash Vartak, Khondoker Murad Hossain, Tim Oates

Deep neural networks (DNNs) are becoming commonplace in critical applications, making their susceptibility to backdoor (trojan) attacks a significant problem. In this paper, we introduce a novel backdoor attack detection pipeline, detecting attacked models using graph convolution networks (DeBUGCN). To the best of our knowledge, ours is the first use of GCNs