May 2025 arXiv papers — page 15

Isaac Aguirre, Ivan Sipiran

We present a simple yet effective training-free approach for zero-shot 3D symmetry detection that leverages visual features from foundation vision models such as DINOv2. Our method extracts features from rendered views of 3D objects and backprojects them onto the original geometry. We demonstrate the symmetric invariance of these features and use them to ide

Zijie Xu, Tong Bu, Zecheng Hao, Jianhao Ding

Spiking Neural Networks (SNNs) offer low-latency and energy-efficient decision making on neuromorphic hardware, making them attractive for Reinforcement Learning (RL) in resource-constrained edge devices. However, most RL algorithms for continuous control are designed for Artificial Neural Networks (ANNs), particularly the target network soft update mechanis

Luiza Ribeiro, Alexandre Street, Jose Manuel Arroyo, Rodrigo Moreno

The increasing vulnerability of power systems has heightened the need for operating reserves to manage contingencies such as generator outages, line failures, and sudden load variations. Unlike energy costs, driven by consumer demand, operating reserve costs arise from addressing the most critical credible contingencies - prompting the question: how should t

Bo Fang, Wenhao Wu, Qiangqiang Wu, Yuxin Song

Employing Multimodal Large Language Models (MLLMs) for long video understanding remains a challenging problem due to the dilemma between the substantial number of video frames (i.e., visual tokens) versus the limited context length of language models. Traditional uniform sampling often leads to selection of irrelevant content, while post-training MLLMs on th

Seungjoon Lee, Suhwan Kim, Minhyeon Oh, Youngsik Yoon

Large Language Model (LLM)-based planning has advanced embodied agents in long-horizon environments such as Minecraft, where acquiring latent knowledge of goal (or item) dependencies and feasible actions is critical. However, LLMs often begin with flawed priors and fail to correct them through prompting, even with feedback. We present XENON (eXpErience-based

Chenyou Fan, Fangzheng Yan, Chenjia Bai, Jiepeng Wang

Learning a generalizable bimanual manipulation policy is extremely challenging for embodied agents due to the large action space and the need for coordinated arm movements. Existing approaches rely on Vision-Language-Action (VLA) models to acquire bimanual policies. However, transferring knowledge from single-arm datasets or pre-trained VLA models often fail

Ehtesamul Azim, Dongjie Wang, Tae Hyun Hwang, Yanjie Fu

Gene selection in high-dimensional genomic data is essential for understanding disease mechanisms and improving therapeutic outcomes. Traditional feature selection methods effectively identify predictive genes but often ignore complex biological pathways and regulatory networks, leading to unstable and biologically irrelevant signatures. Prior approaches, su

Veronika Dornan, William E. Harris

In this work we determine the total globular cluster (GC) counts and globular cluster system (GCS) total mass estimates for 27 extremely massive elliptical galaxies. The GC 2D spatial distributions of these galaxies were created from photometry of HST images using DOLPHOT in the near-IR wavelength range. The projected radial density profiles of these GCSs we

Matthias R. Schreiber, Diogo Belloni

Assuming that white dwarf (WD) magnetic fields are generated by a crystallization- and rotation-driven dynamo, the impact of the late appearance of WD magnetic fields in cataclysmic variables (CVs) has been shown to potentially solve several long-standing problems of CV evolution. However, recent theoretical works show that the dynamo idea might not be viabl

Anirban Polley

1-alkanols are well known to have anesthetic and penetration properties, though the mode of operation remains enigmatic. We perform extensive atomistic molecular dynamics simulation to study the penetration of 1-alkanols of different chain lengths in the dioleoyl-phosphatidylcholine (DOPC) bilayer model membrane. Our simulations show that the depth of penetr

Xingchen Liu, Shu Sun, Meixia Tao, Aryan Kaushik

The advent of 6G wireless networks promises unprecedented connectivity, supporting ultra-high data rates, low latency, and massive device connectivity. However, these ambitious goals introduce significant challenges, particularly in channel estimation due to complex and dynamic propagation environments. This paper explores the concept of channel knowledge ma

Harley Scammell, Oleg P. Sushkov

Employing a semiclassical method based on analytic continuation, we compute the electron-hole pair production rate in biased bilayer graphene subject to an in-plane electric field. This approach, originally due to Zwaan, bypasses the need for exact solutions at turning points, which are generally unavailable beyond linear or quadratic band structures. Applyi

Adam Piaseczny, Md Kamran Chowdhury Shisher, Shiqiang Wang, Christopher G. Brinton

Machine learning (ML) algorithms deployed in real-world environments are often faced with the challenge of adapting models to concept drift, where the task data distributions are shifting over time. The problem becomes even more difficult when model performance must be maintained under adherence to strict resource constraints. Existing solutions often depend

Ran Li, Kun Zhang, Jiayue Yang, Robert B. Mann

Reissner-Nordstr\"{o}m-Anti-de Sitter (RNAdS) black holes in the extended phase space exhibit critical behavior analogous to the liquid-gas system, with critical exponents matching those of van der Waals-type phase transitions. However, the kinetics of these transitions near spinodal and critical points remain poorly understood. We demonstrate that both the

Chiwei Zhu, Benfeng Xu, An Yang, Junyang Lin

Training language models with rationales augmentation has been shown to be beneficial in many existing works. In this paper, we identify that such a prevailing view does not hold consistently. We conduct comprehensive investigations to thoroughly inspect the impact of rationales on model performance as well as a novel perspective of model reliability. The re

Sen Niu, Jason Alicea, D. N. Sheng, Yang Peng

Helical trilayer graphene realizes a versatile moir\'e system for exploring correlated topological states emerging from high Chern bands. Motivated by recent experimental observations of anomalous Hall effects at fractional fillings of magic-angle helical trilayers, we focus on the higher Chern number $|C_{band}|=2$ band and explore gapped many-body Hall sta

Wilfried Genuist, Éric Savin, Filippo Gatti, Didier Clouteau

Building on recent advances in scientific machine learning and generative modeling for computational fluid dynamics, we propose a conditional score-based diffusion model designed for multi-scenarios fluid flow prediction. Our model integrates an energy constraint rooted in the statistical properties of turbulent flows, improving prediction quality with minim

Aditya Naik, Jovi Thomas, Teja Sree Mandava, Himavanth Reddy Vemula

Many people suffer from mental health problems but not everyone seeks professional help or has access to mental health care. AI chatbots have increasingly become a go-to for individuals who either have mental disorders or simply want someone to talk to. This paper presents a study on participants who have previously used chatbots and a scenario-based testing

Feiyu Yao, Qian Wang

As large language models (LLMs) continue to support increasingly longer contexts, the memory demand for key-value (KV) caches during decoding grows rapidly, becoming a critical bottleneck in both GPU memory capacity and PCIe bandwidth. Sparse attention mechanisms alleviate this issue by computing attention weights only for selected key-value pairs. However,

Jiaheng Chen, Daniel Sanz-Alonso

This paper establishes sharp concentration inequalities for simple random tensors. Our theory unveils a phenomenon that arises only for asymmetric tensors of order $p \ge 3:$ when the effective ranks of the covariances of the component random variables lie on both sides of a critical threshold, an additional logarithmic factor emerges that is not present in

Yaşar Utku Alçalar, Yu Cao, Mehmet Akçakaya

Physics-driven artificial intelligence (PD-AI) reconstruction methods have emerged as the state-of-the-art for accelerating MRI scans, enabling higher spatial and temporal resolutions. However, the high resolution of these scans generates massive data volumes, leading to challenges in transmission, storage, and real-time processing. This is particularly pron

Jinglong Gao, Xiao Ding, Lingxiao Zou, Bing Qin

In-Context Learning (ICL) enhances the performance of large language models (LLMs) with demonstrations. However, obtaining these demonstrations primarily relies on manual effort. In most real-world scenarios, users are often unwilling or unable to provide such demonstrations. Inspired by the human analogy, we explore a new ICL paradigm CrossICL to study how

Ziliang Chu, Diru Wu, Miao He, Jilei Xu

A total of 25600 3-inch photomultiplier tubes (PMTs), along with their corresponding frontend electronics, have been installed at the Jiangmen Underground Neutrino Observatory (JUNO). These electronics are housed in 200 stainless steel boxes that operate underwater. To verify the sealing integrity of the underwater boxes following integration, we developed a

Jiashuai Liu, Yingjia Shang, Yingkang Zhan, Di Zhang

With the widespread adoption of pathology foundation models in both research and clinical decision support systems, exploring their security has become a critical concern. However, despite their growing impact, the vulnerability of these models to adversarial attacks remains largely unexplored. In this work, we present the first systematic investigation into

Yimin Zhao, Weibo Wang, Xiong Wang, Linghe Kong

With the rapid growth of Low Earth Orbit (LEO) satellite networks, satellite-IoT systems using the LoRa technique have been increasingly deployed to provide widespread Internet services to low-power and low-cost ground devices. However, the long transmission distance and adverse environments from IoT satellites to ground devices pose a huge challenge to link

Yichen Xie, Runsheng Xu, Tong He, Jyh-Jing Hwang

The latest advancements in multi-modal large language models (MLLMs) have spurred a strong renewed interest in end-to-end motion planning approaches for autonomous driving. Many end-to-end approaches rely on human annotations to learn intermediate perception and prediction tasks, while purely self-supervised approaches--which directly learn from sensor input

Guoqing Chao, Zhenghao Zhang, Lei Meng, Jie Wen

Federated multi-view clustering has been proposed to mine the valuable information within multi-view data distributed across different devices and has achieved impressive results while preserving the privacy. Despite great progress, most federated multi-view clustering methods only used global pseudo-labels to guide the downstream clustering process and fail

Yichen Shi, Ze Zhang, Hongyang Wang, Zhuofu Tao

Analog/Mixed-Signal (AMS) circuits play a critical role in the integrated circuit (IC) industry. However, automating Analog/Mixed-Signal (AMS) circuit design has remained a longstanding challenge due to its difficulty and complexity. Although recent advances in Multi-modal Large Language Models (MLLMs) offer promising potential for supporting AMS circuit ana

Yunhao Ma, Wanyi Jia, Yanyu Lin, Wenjie Lin

With the growing demand for intelligent computing, neuromorphic computing, a paradigm that mimics the structure and functionality of the human brain, offers a promising approach to developing new high-efficiency intelligent computing systems. Spiking Neural Networks (SNNs), the foundation of neuromorphic computing, have garnered significant attention due to

Yaşar Utku Alçalar, Mehmet Akçakaya

Physics-driven deep learning (PD-DL) models have proven to be a powerful approach for improved reconstruction of rapid MRI scans. In order to train these models in scenarios where fully-sampled reference data is unavailable, self-supervised learning has gained prominence. However, its application at high acceleration rates frequently introduces artifacts, co

Levi Lorenzo

We study index pairings for crossed-product $C^*$-algebras arising from minimal actions on the Cantor set. We utilize Putnam's orbit-breaking AF-subalgebras and embeddings to show we can compute any index pairing for Cantor minimal system crossed products using Connes' trace formulas. In the case of odometers, we show that the associated algebras have unifor

Ricardo Baptista, Andrew M. Stuart, Son Tran

Multimodal contrastive learning is a methodology for linking different data modalities; the canonical example is linking image and text data. The methodology is typically framed as the identification of a set of encoders, one for each modality, that align representations within a common latent space. In this work, we focus on the bimodal setting and interpre

Zefan Cai, Wen Xiao, Hanshi Sun, Cheng Luo

Reasoning models have demonstrated impressive performance in self-reflection and chain-of-thought reasoning. However, they often produce excessively long outputs, leading to prohibitively large key-value (KV) caches during inference. While chain-of-thought inference significantly improves performance on complex reasoning tasks, it can also lead to reasoning

Kai Fukami, Ryo Araki

We perform an information-theoretic mode decomposition for separated aerodynamic flows. The current data-driven approach based on a neural network referred to as deep sigmoidal flow enables the extraction of an informative component from a given flow field snapshot with respect to a target variable at a future time stamp, thereby capturing the causality as a

Kin Ming Hui

Let $n\ge 3$, $0<m<\frac{n-2}{n}$, $η>0$, $η_0>0$, $ρ_1>0$, $β_-(ρ_1)=-\frac{ρ_1}{2}$, $β_+(ρ_1)=\frac{mρ_1}{n-2-nm}$, $β_-(ρ_1)\leβ\leβ_+(ρ_1)$ and $α=\frac{2β+ρ_1}{1-m}$. We will prove the existence of radially symmetric solution of the equation $Δ(f^m/m)+αf+βx\cdot\nabla f=0$, $f>0$, in $\mathbb{R}^n$, which satisfies $f(0)=η_0$, $f_r(0)=0$. When $β\leβ_+

S. -R. Eric Yang, Hyun Cheol Lee, Hoang-Anh Le, In-Hwan Lee

Our numerical study of the disordered Hubbard model with nearest-neighbor hopping shows that a two-leg electron ladder has a finite topological entanglement entropy in the regime where the density of states exhibits an exponentially decaying gap. The value of the topological entanglement entropy suggests that two-leg ladders belong to the same universality c

E. Wisnioski, J. T. Mendel, R. Leaman, T. Tsukui

Together optical/near infrared integral field spectroscopy and resolved sub-millimetre interferometry data have mapped the ionised and molecular gas motions in nearly one thousand galaxies at redshifts $z>0.5$. While these measurements have revealed a number of key properties about the evolution of disc structure and kinematics, heterogenous techniques and s

Y. Meng, Q. -S. Zhang, J. Lin

In this work, the straight flux rope in the model of giant flares on magnetars (Meng et al. 2014) was replaced with a curved one and the equilibrium behavior of the flux rope was investigated. Two footpoints of the flux rope are anchored to the spherical surface of magnetar. The forces acting on the flux rope include magnetic tension, magnetic pressure, curv

Avery Drennan, Jeffrey Covington, Dan Han, Andrew Attilio

Compartmental models are effective in modeling the spread of infectious pathogens, but have remaining weaknesses in fitting to real datasets exhibiting stochastic effects. We propose a stochastic SIHR model with a dynamic transmission rate, where the rate is modeled by the Black-Karasinski (BK) process - a mean-reverting stochastic process with a stable equi

Peter Belcak, Greg Heinrich, Jan Kautz, Pavlo Molchanov

Finetuning language models for a new domain inevitably leads to the deterioration of their general performance. This becomes more pronounced the more limited the finetuning data resource. We introduce minifinetuning (MFT), a method for language model domain adaptation that considerably reduces the effects of overfitting-induced degeneralization in low-data s

Jiuyu Sun, Yongping Du, Erjun Kan

The recently discovered altermagnets (AMs), hosting momentum-dependent spin splitting and vanishing net magnetization, have attracted intensive attention for their promising application in novel spintronics. However, limited by facility and material constraints, experimentally distinguishing them from conventional antiferromagnets (AFMs) remains a challenge,

Xilong Qu, Wenbin Pei, Haifang Li, Qiang Zhang

In complex systems (e.g., communication, transportation, and biological networks), high robustness ensures sustained functionality and stability even when resisting attacks. However, the inherent structure complexity and the unpredictability of attacks make robustness optimization challenging. Hypergraphs provide a framework for modeling complicated higher-o

Sutanay Bhattacharya

Given the rank $n$ superspace $\Omega_n$, the ring of polynomial-valued differential forms on $\mathbb C^n$, one can define an action of hyperoctahedral group $\mathfrak B_n$ on it. This leads to a superspace coinvariant ideal $SR_n^B$, defined as the quotient of $\Omega_n$ by two-sided ideal generated by all $\mathfrak B_n$ invariants with vanishing constan

Y. L. Yang, P. W. Zhao

The neutrinoless double-beta decay ($0\nu\beta\beta$) of two neutrons$nn \rightarrow ppee$ is the elementary subprocess of $0\nu\beta\beta$ decay in nuclei. Accurate knowledge of the $nn \rightarrow ppee$ amplitude is required to pin down the short-range contributions in the nuclear matrix elements of the candidate nuclei for large-scale $0\nu\beta\beta$ sea

Ai Jian, Weijie Qiu, Xiaokun Wang, Peiyu Wang

Vision-Language Models (VLMs) have demonstrated remarkable progress in multimodal understanding, yet their capabilities for scientific reasoning remain inadequately assessed. Current multimodal benchmarks predominantly evaluate generic image comprehension or text-driven reasoning, lacking authentic scientific contexts that require domain-specific knowledge i

Stefan Pasch

With the growing importance of AI governance, numerous high-level frameworks and principles have been articulated by policymakers, institutions, and expert communities to guide the development and application of AI. While such frameworks offer valuable normative orientation, they may not fully capture the practical concerns of those who interact with AI syst

Zheng-Xin Yong, Beyza Ermis, Marzieh Fadaee, Stephen H. Bach

This paper presents a comprehensive analysis of the linguistic diversity of LLM safety research, highlighting the English-centric nature of the field. Through a systematic review of nearly 300 publications from 2020--2024 across major NLP conferences and workshops at *ACL, we identify a significant and growing language gap in LLM safety research, with even h

Junzhi Huang, Matthew Zevenbergen

We produce lattice extensions of a dense family of classical Schottky subgroups of the isometry group of $d$-dimensional hyperbolic space. The extensions produced are said to be systolic, since all loxodromic elements with short translation length are conjugate into the Schottky groups. Various corollaries are obtained, in particular showing that for all $d\

Ananya Omanwar, Fady Alajaji, Tamás Linder

Given finite-dimensional random vectors $Y$, $X$, and $Z$ that form a Markov chain in that order (i.e., $Y \to X \to Z$), we derive upper bounds on the excess minimum risk using generalized information divergence measures. Here, $Y$ is a target vector to be estimated from an observed feature vector $X$ or its stochastically degraded version $Z$. The excess m

Masaki Murooka, Kevin Chappellet, Arnaud Tanguy, Mehdi Benallegue

In order for a humanoid robot to perform loco-manipulation such as moving an object while walking, it is necessary to account for sustained or alternating external forces other than ground-feet reaction, resulting from humanoid-object contact interactions. In this letter, we propose a bipedal control strategy for humanoid loco-manipulation that can cope with

Bhawana Chhaglani, Sarmistha Sarna Gomasta, Yuvraj Agarwal, Jeremy Gummeson

Audio is a rich sensing modality that is useful for a variety of human activity recognition tasks. However, the ubiquitous nature of smartphones and smart speakers with always-on microphones has led to numerous privacy concerns and a lack of trust in deploying these audio-based sensing systems. This paper addresses this critical challenge of preserving user

Jack Luong, Sarah Cassie Burnett, Andrea L. Bertozzi

We study bidisperse suspensions -- suspensions where there are two particle species of the same density but different sizes -- of a viscous fluid on an incline. We use a lubrication theory/thin film model to form a hyperbolic system of three conservation laws for the height and particle volume fractions. The model predicts, over a range of parameters, that t

Pengcheng Dai, Yuanqiu Mo, Wenwu Yu, Wei Ren

This paper studies the networked multi-agent reinforcement learning (NMARL) problem, where the objective of agents is to collaboratively maximize the discounted average cumulative rewards. Different from the existing methods that suffer from poor expression due to linear function approximation, we propose a distributed neural policy gradient algorithm that f

John P. Dickerson, Hadi Hosseini, Samarth Khanna, Leona Pierce

The rapid integration of Large Language Models (LLMs) in high-stakes decision-making -- such as allocating scarce resources like donor organs -- raises critical questions about their alignment with human moral values. We systematically evaluate the behavior of several prominent LLMs against human preferences in kidney allocation scenarios and show that LLMs:

Michael Tang, Miroslav Krstic, Jorge Poveda

In the theory of multi-agent systems, deception refers to the strategic manipulation of information to influence the behavior of other agents, ultimately altering the long-term dynamics of the entire system. Recently, this concept has been examined in the context of model-free Nash equilibrium seeking (NES) algorithms for noncooperative games. Specifically,

Jiangyu Han, Federico Landini, Johan Rohdin, Anna Silnova

Self-supervised learning (SSL) models like WavLM can be effectively utilized when building speaker diarization systems but are often large and slow, limiting their use in resource constrained scenarios. Previous studies have explored compression techniques, but usually for the price of degraded performance at high pruning ratios. In this work, we propose to

Sahil Rajesh Dhayalkar

We present a formal and constructive simulation framework for nondeterministic finite automata (NFAs) using time-shared, depth-unrolled feedforward networks (TS-FFNs), i.e., acyclic unrolled computations with shared parameters that are functionally equivalent to unrolled recurrent or state-space models. Unlike prior approaches that rely on explicit recurrent

Alina Devkota, Annahita Amireskandari, Joel Palko, Shyam Thakkar

Gastrointestinal (GI) endoscopy is essential in identifying GI tract abnormalities in order to detect diseases in their early stages and improve patient outcomes. Although deep learning has shown success in supporting GI diagnostics and decision-making, these models require curated datasets with labels that are expensive to acquire. Foundation models offer a

Nora Graves, Vitus Larrieu, Yingyue Trace Zhang, Joanne Peng

With the growth of AI, researchers are studying how to mitigate its environmental impact, primarily by proposing policy changes and increasing awareness among developers. However, research on AI end users is limited. Therefore, we introduce GPTFootprint, a browser extension that aims to increase consumer awareness of the significant water and energy consumpt

Dhruv Shah, Jorge Cortés

This paper considers a class of bilinear systems with a neural network in the loop. These arise naturally when employing machine learning techniques to approximate general, non-affine in the input, control systems. We propose a controller design framework that combines linear fractional representations and tools from linear parameter varying control to guara

Jiacheng Lin, Zhenbang Wu, Jimeng Sun

We present EHRMIND, a practical recipe for adapting large language models (LLMs) to complex clinical reasoning tasks using reinforcement learning with verifiable rewards (RLVR). While RLVR has succeeded in mathematics and coding, its application to healthcare contexts presents unique challenges due to the specialized knowledge and reasoning required for elec

Prashant Thakur, Adamu Issifu, Ishfaq Ahmad Rather, Y. Lim

This paper explores radial and non-radial oscillations of protoneutron stars (PNSs) as they evolve from hot, neutrino-rich configurations through deleptonization to cold, catalyzed states. The equation of state (EoS) is modeled using a density-dependent relativistic mean-field framework, with stellar evolution characterized by changes in entropy and lepton f

Peiran Xu, Yadong Mu

In this work, we focus on the task of weakly supervised affordance grounding, where a model is trained to identify affordance regions on objects using human-object interaction images and egocentric object images without dense labels. Previous works are mostly built upon class activation maps, which are effective for semantic segmentation but may not be suita

Mahmood Jasim, Narges Mahyar

Despite the recognized benefits of visual analytics systems in supporting data-driven decision-making, their deployment in real-world civic contexts often faces significant barriers. Beyond technical challenges such as resource constraints and development complexity, sociotechnical factors, including organizational hierarchies, misalignment between designers

Zhenran Xu

Length of stay (LOS) prediction in acute stroke is critical for improving care planning. Existing machine learning models have shown suboptimal predictive performance, limited generalisability, and have overlooked system-level factors. We aimed to enhance model efficiency, performance, and interpretability by refining predictors and developing an interpretab

Xinyue Fan, Sahab Hajebi, Sepehr Hajebi, Sophie Spirkl

For graphs $G$ and $H$, we say that $G$ is $H$-free if no induced subgraph of $G$ is isomorphic to $H$, and that $G$ is $H$-induced-saturated if $G$ is $H$-free but removing or adding any edge in $G$ creates an induced copy of $H$. A full characterization of graphs $H$ for which $H$-induced-saturated graphs exist remains elusive. Even the case where $H$ is a

Mohammad Shah Alam, William Ott, Ilya Timofeyev

In this paper, we explore the predictive capabilities of echo state networks (ESNs) for the generalized Kuramoto-Sivashinsky (gKS) equation, an archetypal nonlinear PDE that exhibits spatiotemporal chaos. Our research focuses on predicting changes in long-term statistical patterns of the gKS model that result from varying the dispersion relation or the lengt

Zhongmou He, Yee Man Choi, Kexun Zhang, Jiabao Ji

Verifiers play a crucial role in large language model (LLM) reasoning, needed by post-training techniques such as reinforcement learning. However, reliable verifiers are hard to get for difficult coding problems, because a well-disguised wrong solution may only be detected by carefully human-written edge cases that are difficult to synthesize. To address thi

Victor Li, Baiting Chen, Yuzhen Mao, Qi Lei

Calibrating blackbox machine learning models to achieve risk control is crucial to ensure reliable decision-making. A rich line of literature has been studying how to calibrate a model so that its predictions satisfy explicit finite-sample statistical guarantees under a fixed, static, and unknown data-generating distribution. However, prediction-supported de

J. Gamboa

We revisit the origin of the vacuum angle $\theta$ in QCD using the adiabatic approximation combined with Fujikawa's method. By implementing a local chiral transformation and selecting a constant parameter $\alpha(x) = \theta$, we show that the QCD $\theta$-term emerges naturally in the effective action. This construction provides a non-perturbative interpre

David I. Gonzalez-Aguirre, Javier Felip Leon, Javier Felix-Rendon, Roderico Garcia-Leal

Sensor-based reactive and hybrid approaches have proven a promising line of study to address imperfect knowledge in grasping and manipulation. However the reactive approaches are usually tightly coupled to a particular embodiment making transfer of knowledge difficult. This paper proposes a paradigm for modeling and execution of reactive manipulation actions

Tian Xia, Ziming Mao, Jamison Kerney, Ethan J. Jackson

Serving Large Language Models (LLMs) efficiently in multi-region setups remains a challenge. Due to cost and GPU availability concerns, providers typically deploy LLMs in multiple regions using instance with long-term commitments, like reserved instances or on-premise clusters, which are often underutilized due to their region-local traffic handling and diur

Danish Ali

In oriented knot theory, verifying a quantity is an invariant involves checking its invariance under all oriented Reidemeister moves, a process that can be intricate and time-consuming. A generating set of oriented moves simplifies this by requiring verification for only a minimal subset from which all other moves can be derived. While generating sets for cl

André Izidoro, Sean N. Raymond, Nathan A. Kaib, Alessandro Morbidelli

Gas giant planets have been detected on eccentric orbits several hundreds of astronomical units in size around other stars. It has been proposed that even the Sun hosts a wide-orbit planet of 5-10 Earth masses, often called Planet Nine, which influences the dynamics of distant Trans-Neptunian objects. However, the formation mechanism of such planets remains

Ju Li, Yuri Mishin

Extended one- and two-dimensional defects in crystalline materials are usually metastable. The thermodynamic ground state of the material is presumed to be defect-free. Here, we investigate the conditions under which extended defects, such as grain boundaries, can exist in a multicomponent alloy when the latter reaches the thermodynamic ground state allowed

Lesley Frew, Michael L. Nelson, Michele C. Weigle

The Environmental Governance and Data Initiative (EDGI) regularly crawled US federal environmental websites between 2016 and 2020 to capture changes between two presidential administrations. However, because it does not include the previous administration ending in 2008, the collection is unsuitable for answering our research question, Were the website terms

Karan Hanswadkar, Anika Kanchi, Shivani Tripathi, Shi Qiao

Artificial Intelligence (AI) is making a major impact on healthcare, particularly through its application in natural language processing (NLP) and predictive analytics. The healthcare sector has increasingly adopted AI for tasks such as clinical data analysis and medical code assignment. However, searching for clinical information in large and often unorgani

Haolin Pan, Hongyu Lin, Haoran Luo, Yang Liu

Compiler auto-tuning optimizes pass sequences to improve performance metrics such as Intermediate Representation (IR) instruction count. Although recent advances leveraging Large Language Models (LLMs) have shown promise in automating compiler tuning, two significant challenges still remain: the absence of high-quality reasoning datasets for agents training,

Marcus Lassila, Johan Östman, Khac-Hoang Ngo, Alexandre Graell i Amat

We develop practical and theoretically grounded membership inference attacks (MIAs) against both independent and identically distributed (i.i.d.) data and graph-structured data. Building on the Bayesian decision-theoretic framework of Sablayrolles et al., we derive the Bayes-optimal membership inference rule for node-level MIAs against graph neural networks,

Chen Huang, Skyler Seto, Hadi Pouransari, Mehrdad Farajtabar

Vision foundation models pre-trained on massive data encode rich representations of real-world concepts, which can be adapted to downstream tasks by fine-tuning. However, fine-tuning foundation models on one task often leads to the issue of concept forgetting on other tasks. Recent methods of robust fine-tuning aim to mitigate forgetting of prior knowledge w

Tobi Ramella, Nicholas P. Warner

The special locus plays an important role in the construction of the non-BPS microstate geometries known as microstrata. These supergravity solutions are dual to combinations of left-moving and right-moving momentum states in the D1-D5 CFT and because supersymmetry is broken the anomalous dimensions of these states are not protected. This means even the simp

Zeeshan Khan, Shizhe Chen, Cordelia Schmid

Generating images from text involving complex and novel object arrangements remains a significant challenge for current text-to-image (T2I) models. Although prior layout-based methods improve object arrangements using spatial constraints with 2D layouts, they often struggle to capture 3D positioning and sacrifice quality and coherence. In this work, we intro

Alireza Jafari, Fereshteh Yousefirizi, Vahid Seydi

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia associated with elevated health risks, where timely detection is pivotal for mitigating stroke-related morbidity. This study introduces an innovative hybrid methodology integrating unsupervised deep learning and gradient boosting models to improve AF detection. A 19-layer deep convolutional autoencod

David de Hevia, Pedro Tradacete

We survey recent developments on the structure of complemented subspaces of Banach lattices, including in particular the construction of a complemented subspace of a $C(K)$-space which is not linearly isomorphic to any Banach lattice. Motivated by this, several natural questions and directions of future research are presented. We provide an approach to some

Lucas Mendicino, Franco Mayo, Christian Schmiegelow, Augusto Roncaglia

We investigate the improvement of anti-Stokes laser cooling of a Yb$^{3+}$:YLF nanocrystal in a Fabry-P\'erot microcavity via the Purcell effect. Our analysis accounts for both the enhancement of emission lines resonant with the cavity transmission and the suppression of off-resonance emissions. Using a quantum-mechanical framework, we modeled the Yb$^{3+}$

J. I. Katz

The recent report of a period in the active repeating Fast Radio Burster 20201124A and of its spindown rate place bounds on the solid angle of its emission on the basis of energetics. The bound depends on the (unknown) efficiency of conversion of rotational energy to coherent radio emission and implies a lower bound on the Lorentz factor of the radiating cha

Stewart Koppell, Otavio D. A. R. Bittencourt, Dip Joti Paul, Junwu Huang

Light dark matter candidates such as axions and dark photons generically couple to electromagnetism, yielding dark-matter-to-photon conversion as a key search strategy. In addition to resonant conversion in cavities and circuits, light dark matter bosons efficiently convert to photons on material interfaces, with a broadband power proportional to the total a

Haozhan Tang, Tianyi Zhang, Matthew Johnson-Roberson, Weiming Zhi

Robot manipulation, especially bimanual manipulation, often requires setting up multiple cameras on multiple robot manipulators. Before robot manipulators can generate motion or even build representations of their environments, the cameras rigidly mounted to the robot need to be calibrated. Camera calibration is a cumbersome process involving collecting a se

Gerard I. Gállego, Oriol Pareras, Martí Cortada Garcia, Lucas Takanori

We propose a Speech-to-Text Translation (S2TT) approach that integrates phoneme representations into a Chain-of-Thought (CoT) framework to improve translation in low-resource and zero-resource settings. By introducing phoneme recognition as an intermediate step, we enhance cross-lingual transfer, enabling translation even for languages with no labeled speech

Jinbao Wang, Shiliang Zhang, Jun Liu, Xuehui Ma

Data-enabled predictive control (DeePC) leverages system measurements in characterizing system dynamics for optimal control. The performance of DeePC relies on optimizing its hyperparameters, especially in noisy systems where the optimal hyperparameters adapt over time. Existing hyperparameter tuning approaches for DeePC are more than often computationally i

Daniel Orr, Mark Shimozono, Joshua Jeishing Wen

We construct a novel family of difference-permutation operators and prove that they are diagonalized by the wreath Macdonald $P$-polynomials; the eigenvalues are written in terms of elementary symmetric polynomials of arbitrary degree. Our operators arise from integral formulas for the action of the horizontal Heisenberg subalgebra in the vertex representati

Sayed T. Nowroz, Nermeen M. Saleh, Siam Shakur, Sean Banerjee

The ESP32-CAM is one of the most widely adopted open-source modules for prototyping embedded vision applications. Since its release in 2019, it has gained popularity among both hobbyists and professional developers due to its affordability, versatility, and integrated wireless capabilities. Despite its widespread use, comprehensive documentation of the perfo

Zuyi Chen, Daniel P. Stark, Charlotte A. Mason, Mengtao Tang

Ly$\alpha$ spectroscopy with JWST is opening a new window on the sizes of ionized bubbles through the reionization epoch. Theoretical expectations suggest typical bubble radii should be 0.6-1.5 pMpc at $z\simeq 7$, assuming neutral hydrogen fractions of the intergalactic medium in the range $\overline{x}_{\rm HI}$=0.5-0.7. Here we investigate this picture us

Shihao Fu, Yan Lei

Test cases are indispensable for conducting effective fault localization (FL). However, test cases in practice are severely class imbalanced, i.e. the number of failing test cases (i.e. minority class) is much less than that of passing ones (i.e. majority class). The severe class imbalance between failing and passing test cases have hindered the FL effective

Zihan Zhang, Jan Hannig

Observational studies often present challenges for causal inference due to confounding and heterogeneity. In this paper, we illustrate how modern causal inference methods can be applied to large-scale academic salary data. Using records from 12,039 tenure-track faculty in the University of North Carolina system, linked with bibliometric indicators and instit

Ettore Biondi, Gabrielle Tepp, Ellen Yu, Jessie K. Saunders

We introduce a modular software framework designed to integrate distributed acoustic sensing (DAS) data into operational earthquake monitoring systems. Building on the infrastructure of the Advanced National Seismic System (ANSS) and the Southern California Seismic Network (SCSN), which employs the ANSS Quake Monitoring Software (AQMS), our solution supports

Huan Ning, Zhenlong Li, Manzhu Yu, Wenpeng Yin

Built environment auditing refers to the systematic documentation and assessment of urban and rural spaces' physical, social, and environmental characteristics, such as walkability, road conditions, and traffic lights. It is used to collect data for the evaluation of how built environments impact human behavior, health, mobility, and overall urban functional

Alec J. Linot, Barbara Lopez-Doriga, Yonghong Zhong, Kunihiko Taira

A wide range of techniques exist for extracting the dominant flow dynamics and features about steady, or periodic base flows. However, there have been limited efforts in extracting the dominant dynamics about unsteady, aperiodic base flow. These flows appear in many applications such as when there is a sudden change in the flow rate through a pipe, when an a

Hsin-yi Hao, Wousik Kim, David S. Shelton, Benjamin Farr

It has been demonstrated that lunar dust simulant can be efficiently lofted and removed from various room temperature surfaces in vacuum when exposed to a low-energy electron beam. This provides a potential solution to the well-known dust risks associated with future lunar exploration. Considering its application in extremely cold regions on the Moon, we exp

Edward L. Wang, Mohammad Sharifi Kiasari, Tianyu Wang, Hayden Helm

Large language models (LLMs) are powerful tools that, in a number of settings, overlap with the results of human pattern recognition and reasoning. Retrieval-augmented generation (RAG) further allows LLMs to produce tailored output depending on the contents of their RAG databases. However, LLMs depend on complex, computationally expensive algorithms. In this