March 2025 arXiv papers — page 163

Takuya Inoue

In this paper, we consider plane partitions $\text{PP}(\lambda; m)$ of a given shape $\lambda$, with entries at most $m$. We prove that the distributions of two statistics on $\text{PP}(\lambda; m)$ coincide: one is the number of rows containing $0$ and the other is the number of rows containing $m$. We also provide a bijective proof.

Yun-Hao Cao, Yangsong Wang, Shuzheng Hao, Zhenxing Li

The emergence of large language models (LLMs) like GPT-4 has revolutionized natural language processing (NLP), enabling diverse, complex tasks. However, extensive token counts lead to high computational and financial burdens. To address this, we propose Efficient and Flexible Prompt Compression (EFPC), a novel method unifying task-aware and task-agnostic com

Qi Wu, Yingguang Yang, hao liu, Hao Peng

Social bot detection is crucial for mitigating misinformation, online manipulation, and coordinated inauthentic behavior. While existing neural network-based detectors perform well on benchmarks, they struggle with generalization due to distribution shifts across datasets and frequently produce overconfident predictions for out-of-distribution accounts beyon

Yanyu Zhang, Jie Xu, Wei Ren

Accurate LiDAR-Camera (LC) calibration is challenging but crucial for autonomous systems and robotics. In this paper, we propose two single-shot and target-less algorithms to estimate the calibration parameters between LiDAR and camera using line features. The first algorithm constructs line-to-line constraints by defining points-to-line projection errors an

Wei Zhang, Yunning Zhao, Lin Ma, Shiming Wen

We present spatially-resolved spectroscopic observations of 10 isolated Galactic HII regions using data from the LAMOST Medium-Resolution Spectroscopic Survey of Nebulae (LAMOST MRS-N). The high spatial resolution of the data allows us to investigate the 1D radial profiles of emission line fluxes (Ha, [S II] and [N II]), flux ratios ([N II]/Ha, [S II]/Ha and

Benjamin Wilfong, Henry A. Le Berre, Anand Radhakrishnan, Ansh Gupta

Many problems of interest in engineering, medicine, and the fundamental sciences rely on high-fidelity flow simulation, making performant computational fluid dynamics solvers a mainstay of the open-source software community. Previous work, MFC 3.0, was published, documented, and made open-source by Bryngelson et al. CPC (2021) features numerous physical feat

Ishaan Malhi, Praneet Dutta, Ellie Talius, Sally Ma

We present a framework for high-fidelity product image recontextualization using text-to-image diffusion models and a novel data augmentation pipeline. This pipeline leverages image-to-video diffusion, in/outpainting & negatives to create synthetic training data, addressing limitations of real-world data collection for this task. Our method improves the qual

Yanyu Zhang, Dongming Wang, Jie Xu, Mengyuan Liu

A prior map serves as a foundational reference for localization in context-aware applications such as augmented reality (AR). Providing valuable contextual information about the environment, the prior map is a vital tool for mitigating drift. In this paper, we propose a map-based visual-inertial localization algorithm (NeRF-VIO) with initialization using neu

Yihong Li, Chengwei Zhang, Furui Zhan, Wanting Liu

Multi-agent reinforcement learning (MARL) has shown significant potential in traffic signal control (TSC). However, current MARL-based methods often suffer from insufficient generalization due to the fixed traffic patterns and road network conditions used during training. This limitation results in poor adaptability to new traffic scenarios, leading to high

Koichiro Takahashi, Hong-Fei Huang, Jie-Xiang Yu, Jiadong Zang

$\alpha$-MnTe, an $A$-type collinear antiferromagnet, has recently attracted significant attention due to its pronounced spin splitting despite having net zero magnetization, a phenomenon unique for a new class of magnetism dubbed altermagnetism. In this work, we develop a minimal effective Hamiltonian for $\alpha$-MnTe based on realistic orbitals near the F

Yifei Deng, Chenglong Li, Zhenyu Chen, Zihen Xu

The performance of traditional text-image person retrieval task is easily affected by lighting variations due to imaging limitations of visible spectrum sensors. In recent years, cross-modal information fusion has emerged as an effective strategy to enhance retrieval robustness. By integrating complementary information from different spectral modalities, it

Chenyang Huang, Amal S. Sebastian, Venkatasubramanian Viswanathan

This paper presents a data-driven framework for learning optimal second-order total variation diminishing (TVD) flux limiters via differentiable simulations. In our fully differentiable finite volume solvers, the limiter functions are replaced by neural networks. By representing the limiter as a pointwise convex linear combination of the Minmod and Superbee

Boyang Lou, Shenghai Yuan, Jianfei Yang, Wenju Su

LiDAR-Inertial Odometry (LIO) is widely used for autonomous navigation, but its deployment on Size, Weight, and Power (SWaP)-constrained platforms remains challenging due to the computational cost of processing dense point clouds. Conventional LIO frameworks rely on a single onboard processor, leading to computational bottlenecks and high memory demands, mak

Keito Hara, Youichi Yanase

Chiral materials exhibit a spin filtering effect, so-called chirality-induced spin selectivity (CISS). A recent observation of spin accumulation at the ends of a chiral-structured superconductor has opened up a new pathway for studying the CISS effect in superconductors. In chiral-structured superconductors, the admixture of the spin-singlet and spin-triplet

Chunlong Li, Chao Chen, Xiao Yan Chew

Advancements of very long baseline interferometry (VLBI) have facilitated unprecedented probing of superradiant phenomena in the vicinities of supermassive black holes (SMBHs), establishing an ideal laboratory to detect ultralight bosons beyond the Standard Model. In this study, we delve into how ultralight dilaton clouds, formed via SMBH superradiance, impa

Zhongpai Gao, Benjamin Planche, Meng Zheng, Anwesa Choudhuri

Real-time rendering of dynamic scenes with view-dependent effects remains a fundamental challenge in computer graphics. While recent advances in Gaussian Splatting have shown promising results separately handling dynamic scenes (4DGS) and view-dependent effects (6DGS), no existing method unifies these capabilities while maintaining real-time performance. We

Qiwei Tian, Sahar Izadi Vishkayi, Chen Zhang, Jiang Zeng

The kagome lattice has emerged as a promising platform for investigating exotic quantum phases. However, achieving a single-atomic-layer kagome lattice in elemental materials remains a significant challenge. Here, we introduce line-graph epitaxy, a novel approach that enables the atomic-scale synthesis of goldene, a monolayer of elemental gold atoms arranged

Lucas Caccia, Alan Ansell, Edoardo Ponti, Ivan Vulić

Dynamically integrating new or rapidly evolving information after (Large) Language Model pre-training remains challenging, particularly in low-data scenarios or when dealing with private and specialized documents. In-context learning and retrieval-augmented generation (RAG) face limitations, including their high inference costs and their inability to capture

Yubo Peng, Luping Xiang, Kun Yang, Feibo Jiang

Traditional single-modality sensing faces limitations in accuracy and capability, and its decoupled implementation with communication systems increases latency in bandwidth-constrained environments. Additionally, single-task-oriented sensing systems fail to address users' diverse demands. To overcome these challenges, we propose a semantic-driven integrated

Federico Di Vruno, Gary Hovey

Modelling is essential for studies that quantify the impact from satellite downlinks on radio astronomy sites. To estimate this impact it is necessary to know not only the position and velocity of satellites but also their behaviour in the radio spectrum domain. As many large satellite constellations are using steerable beam antennas, deterministically predi

Taoxu Zhao, Meisi Li, Kehao Chen, Liye Wang

Multimodal sentiment analysis enhances conventional sentiment analysis, which traditionally relies solely on text, by incorporating information from different modalities such as images, text, and audio. This paper proposes a novel multimodal sentiment analysis architecture that integrates text and image data to provide a more comprehensive understanding of s

Andrew Gao, Jun Liu

This paper presents a new method for anomaly detection in automated systems with time and compute sensitive requirements, such as autonomous driving, with unparalleled efficiency. As systems like autonomous driving become increasingly popular, ensuring their safety has become more important than ever. Therefore, this paper focuses on how to quickly and effec

Mohamed Hijas Mohamed Farook, Gabriele Giacomini, Gabriele DAmen, Giovanni Pinaroli

Low Gain Avalanche Diodes, also known as LGADs, are widely considered for fast-timing applications in high energy physics, nuclear physics, space science, medical imaging, and precision measurements of rare processes. Such devices are silicon-based and feature an intrinsic gain due to a $p{^+}$-doped layer that allows the production of a controlled avalanche

Minkyun Seo, Hyungtae Lim, Kanghee Lee, Luca Carlone

Recent advances in deep learning-based point cloud registration have improved generalization, yet most methods still require retraining or manual parameter tuning for each new environment. In this paper, we identify three key factors limiting generalization: (a) reliance on environment-specific voxel size and search radius, (b) poor out-of-domain robustness

Hin Wai Lui, Jeffrey L. Krichmar

This paper explores vision-based localization through a biologically-inspired approach that mirrors how humans and animals link views or perspectives when navigating their world. We introduce two sequential generative models, VAE-RNN and VAE-Transformer, which transform first-person perspective (FPP) observations into global map perspective (GMP) representat

Chenrui Ma, Xi Xiao, Tianyang Wang, Xiao Wang

While deep generative models have significantly advanced representation learning, they may inherit or amplify biases and fairness issues by encoding sensitive attributes alongside predictive features. Enforcing strict independence in disentanglement is often unrealistic when target and sensitive factors are naturally correlated. To address this challenge, we

Siyuan Wang, James R. Foulds, Md Osman Gani, Shimei Pan

In this paper, we introduce CIBER (Claim Investigation Based on Evidence Retrieval), an extension of the Retrieval-Augmented Generation (RAG) framework designed to identify corroborating and refuting documents as evidence for scientific claim verification. CIBER addresses the inherent uncertainty in Large Language Models (LLMs) by evaluating response consist

Yuanming Wang, Pavan Uttarkar, Ryan Shannon, Yu Wing Joshua Lee

The emerging population of long-period radio transients (LPTs) show both similarities and differences with normal pulsars. A key difference is that their radio emission is too bright to be powered solely by rotational energy. Various models have been proposed (including both white-dwarf or neutron star origins), and their nature remains uncertain. Known LPTs

Md Sharif Hossen, Anil Gurses, Mihail Sichitiu, Ismail Guvenc

Unmanned aerial vehicles (UAVs) enhance coverage and provide flexible deployment in 5G and next-generation wireless networks. The performance of such wireless networks can be improved by developing new navigation and wireless adaptation approaches in digital twins (DTs). However, challenges such as complex propagation conditions and hardware complexities in

Erfaun Noorani, Pasan Dissanayake, Faisal Hamman, Sanghamitra Dutta

Counterfactual explanations indicate the smallest change in input that can translate to a different outcome for a machine learning model. Counterfactuals have generated immense interest in high-stakes applications such as finance, education, hiring, etc. In several use-cases, the decision-making process often relies on an ensemble of models rather than just

Qinji Yu, Yirui Wang, Ke Yan, Dandan Zheng

Lymph node (LN) assessment is an essential task in the routine radiology workflow, providing valuable insights for cancer staging, treatment planning and beyond. Identifying scatteredly-distributed and low-contrast LNs in 3D CT scans is highly challenging, even for experienced clinicians. Previous lesion and LN detection methods demonstrate effectiveness of

Nirmit Joshi, Gal Vardi, Adam Block, Surbhi Goel

For a given base class of sequence-to-next-token generators, we consider learning prompt-to-answer mappings obtained by iterating a fixed, time-invariant generator for multiple steps, thus generating a chain-of-thought, and then taking the final token as the answer. We formalize the learning problems both when the chain-of-thought is observed and when traini

Zeynep Engin, Jon Crowcroft, David Hand, Philip Treleaven

As artificial intelligence transforms public sector operations, governments struggle to integrate technological innovations into coherent systems for effective service delivery. This paper introduces the Algorithmic State Architecture (ASA), a novel four-layer framework conceptualising how Digital Public Infrastructure, Data-for-Policy, Algorithmic Governmen

Sudeshna Patra, Neal J. Evans, Kee-Tae Kim, Mark Heyer

HCN and HCO$^+$ are the most common dense gas tracers used both in the Milky Way and external galaxies. The luminosity of HCN and HCO$^+$ $J = 1-0$ lines are converted to a dense gas mass by the conversion factor, $\alpha_{Q}$. Traditionally, this $\alpha_{Q}$ has been considered constant throughout the Galaxy and in other galaxies, regardless of the environ

Kenneth W. Lin, Abby Bault, Armin Karcher, Julien Guy

Multiple-Amplifier Sensing (MAS) charge-coupled devices (CCDs) have recently been shown to be promising silicon detectors that meet noise sensitivity requirements for next generation Stage-5 spectroscopic surveys and potentially, future space-based imaging of extremely faint objects on missions such as the Habitable Worlds Observatory. Building upon the capa

Cong Zhu, Bin Li, Yuxiang Fan, Chuanhui Yin

Motivated by recent reports of pressure-induced superconductivity in bilayer nickelate La$_3$Ni$_2$O$_7$, we present a comprehensive investigation into the structural, electronic, magnetic, and phonon properties of this compound across a pressure range of 0 to 29.5 GPa. DFT+U calculations reveal that the A-type antiferromagnetic ground state of La$_3$Ni$_2$O

Hunter McNichols, Fareya Ikram, Andrew Lan

The widespread availability of large language models (LLMs), such as ChatGPT, has significantly impacted education, raising both opportunities and challenges. Students can frequently interact with LLM-powered, interactive learning tools, but their usage patterns need to be observed and understood. We introduce StudyChat, a publicly available dataset capturin

Xia Li, Allen Kim

Classifying Non-Functional Requirements (NFRs) in software development life cycle is critical. Inspired by the theory of transfer learning, researchers apply powerful pre-trained models for NFR classification. However, full fine-tuning by updating all parameters of the pre-trained models is often impractical due to the huge number of parameters involved (e.g

Ken Nakahara, Roberto Calandra

In our daily life, we often encounter objects that are fragile and can be damaged by excessive grasping force, such as fruits. For these objects, it is paramount to grasp gently -- not using the maximum amount of force possible, but rather the minimum amount of force necessary. This paper proposes using visual, tactile, and auditory signals to learn to grasp

Bertrand Guenin, Levent Tunçel

We design new tools to study variants of Total Dual Integrality. As an application, we obtain a geometric characterization of Total Dual Integrality for the case where the associated polyhedron is non-degenerate. We also give sufficient conditions for a system to be Totally Dual Dyadic, and prove new special cases of Seymour's Dyadic conjecture on ideal clut

Chenzhi Ma, Hongru Du, Shengzhi Luan, Ensheng Dong

Building fires pose a persistent threat to life, property, and infrastructure, emphasizing the need for advanced risk mitigation strategies. This study presents a data-driven framework analyzing U.S. fire risks by integrating over one million fire incident reports with diverse fire-relevant datasets, including social determinants, building inventories, weath

Lukas Mandok, Pascal Isenring, Heiko Augustin, Marius Köppel

Muon-spin spectroscopy at continuous sources has stagnated at a stopped muons rate of ~40 kHz for the last few decades. The major limiting factor is the requirement of a single muon in the sample during the typical 10 μs data gate window. To overcome this limit and to be able to perform muon-spin relaxation (μSR) measurements on millimeter-sized samples, one

Mauro Marchese, Henk Wymeersch, Paolo Spallaccini, Stefano Chinnici

In this work, we propose a deep learning (DL)-based approach that integrates a state-of-the-art algorithm with a time-frequency (TF) learning framework to minimize overall latency. Meeting the stringent latency requirements of 6G orthogonal time-frequency space (OTFS) systems necessitates low-latency designs. The performance of the proposed approach is evalu

Jin Wenzhe, Tang Haina, Zhang Xudong

Vessel trajectory prediction is a critical component for ensuring maritime traffic safety and avoiding collisions. Due to the inherent uncertainty in vessel behavior, trajectory prediction systems must adopt a multimodal approach to accurately model potential future motion states. However, existing vessel trajectory prediction methods lack the ability to com

Evgeny Feigin, Anton Khoroshkin, Ievgen Makedonskyi, Daniel Orr

We study the algebra of functions on the Iwahori group via the category of graded bounded representations of its Lie algebra. In particular, we identify the standard and costandard objects in this category with certain generalized Weyl modules. Using this identification we express the characters of the standard and costandard objects in terms of specialized

Lie Fu, Ben Moonen

We study algebraic cycles on complex Gushel-Mukai (GM) varieties. We prove the generalised Hodge conjecture, the (motivated) Mumford-Tate conjecture, and the generalised Tate conjecture for all GM varieties. We compute all integral Chow groups of GM varieties, except for the only two infinite-dimensional cases (1-cycles on GM fourfolds and 2-cycles on GM six

Yu-Xuan Lin, Chu-Yao Xu, Chuan Wang

Multi-objective combinatorial optimization in wireless communication networks is a challenging task, particularly for large-scale and diverse topologies. Recent advances in quantum computing offer promising solutions for such problems. Coherent Ising Machines (CIM), a quantum-inspired algorithm, leverages the quantum properties of coherent light, enabling fa

Robert Besuner, Arjun Dey, Alex Drlica-Wagner, Haruki Ebina

The existence, properties, and dynamics of the dark sectors of our universe pose fundamental challenges to our current model of physics, and large-scale astronomical surveys may be our only hope to unravel these long-standing mysteries. In this white paper, we describe the science motivation, instrumentation, and survey plan for the next-generation spectrosc

Jean-Christophe Wallet

The $\rho$-Minkowski space-time, a Lie-algebraic deformation of the usual Minkowski space-time is considered. A star-product realization of this quantum space-time together with the characterization of the deformed Poincar\'e symmetry acting on it are presented. It is shown that appearance of UV/IR mixing is expected already in scalar field theories on $\rho

Lei Xiang, Rouyan Chen, Joanne Tan, Victoria Nankivell

Miniaturized fiber-optic fluorescence endoscopes play a crucial role in medical diagnostics and research, but system-induced autofluorescence remains a significant challenge, particularly in single-fiber setups. While recent advances, such as double-clad fiber (DCF) and DCF couplers, have reduced background noise, complete elimination remains challenging. Re

Samuel Cahyawijaya, Holy Lovenia, Joel Ruben Antony Moniz, Tack Hwa Wong

Southeast Asia (SEA) is a region of extraordinary linguistic and cultural diversity, yet it remains significantly underrepresented in vision-language (VL) research. This often results in artificial intelligence (AI) models that fail to capture SEA cultural nuances. To fill this gap, we present SEA-VL, an open-source initiative dedicated to developing high-qu

Samundra Karki, Mehdi Shadkah, Cheng-Hau Yang, Aditya Balu

Implicit neural representations have emerged as a powerful approach for encoding complex geometries as continuous functions. These implicit models are widely used in computer vision and 3D content creation, but their integration into scientific computing workflows, such as finite element or finite volume simulations, remains limited. One reason is that conve

Yixiao Song, Katherine Thai, Chau Minh Pham, Yapei Chang

Modern web agents possess computer use abilities that allow them to interact with webpages by sending commands to a virtual keyboard and mouse. While such agents have considerable potential to assist human users with complex tasks, evaluating their capabilities in real-world settings poses a major challenge. To this end, we introduce BEARCUBS, a "smallbut mi

Cyen J. Peterkin, Lance A. Waller, Emily N. Peterson

COVID-19 has had a large scale negative impact on the health of opioid users exacerbating the health of an already vulnerable population. Critical information on the total impact of COVID-19 on opioid users is unknown due to a lack of comprehensive data on COVID-19 cases, inaccurate diagnostic coding, and lack of data coverage. To assess the impact of COVID-

Swati Rallapalli, Shannon Gallagher, Andrew O. Mellinger, Jasmine Ratchford

We study the efficacy of fine-tuning Large Language Models (LLMs) for the specific task of report (government archives, news, intelligence reports) summarization. While this topic is being very actively researched - our specific application set-up faces two challenges: (i) ground-truth summaries maybe unavailable (e.g., for government archives), and (ii) ava

Raphi Kang, Yue Song, Georgia Gkioxari, Pietro Perona

Contrastive Language-Image Pre-Training (CLIP) is a popular method for learning multimodal latent spaces with well-organized semantics. Despite its wide range of applications, CLIP's latent space is known to fail at handling complex visual-textual interactions. Recent works attempt to address its shortcomings with data-centric or algorithmic approaches. But

Mauricio Toledo-Acosta, Luis Ángel Ramos-García, Jorge Hermosillo-Valadez

Clustering of high-dimensional data sets is a growing need in artificial intelligence, machine learning and pattern recognition. In this paper, we propose a new clustering method based on a combinatorial-topological approach applied to regions of space defined by signs of coordinates (hyperoctants). In high-dimensional spaces, this approach often reduces the

Michael V. Klibanov, Jingzhi Li, Zhipeng Yang

A version of the globally convergent convexification numerical method is constructed for the problem of Electrical Impedance Tomography in the 2D case. An important element of this version is the presence of the viscosity term. Global convergence analysis is carried out. Results of numerical experiments are presented.

Felix Lazebnik, Ye Wang

The families of graphs defined by a certain type of system of equations over commutative rings have been studied and used since 1990s. This survey presents these families and their applications related to graphs, digraphs, and hypergraphs. Some open problems and conjectures are mentioned.

Pranjal Atrey, Michael P. Brundage, Min Wu, Sanghamitra Dutta

Interpretable machine learning models offer understandable reasoning behind their decision-making process, though they may not always match the performance of their black-box counterparts. This trade-off between interpretability and model performance has sparked discussions around the deployment of AI, particularly in critical applications where knowing the

Robert Leonard, Spencer E. Olson

A lattice beam configuration which results in an isotropic 3D trap near the surface of an atom chip is described. The lattice is formed near the surface of a reflectively coated atom chip, where three incident beams and three reflected beams intersect. The coherent interference of these six beams form a phase-stable optical lattice which extends to the surfa

Manel Bouguenna, Mohammed Elamine Sebih

In this paper, we consider the Cauchy problem for a non-homogeneous wave equation generated by the fractional Laplacian and involving different kinds of lower order terms. We allow the equation coefficients and data to be of distributional type or less regular, having in mind the Dirac delta function and its powers, and we prove that the problem is well-pose

Xing Zi, Kairui Jin, Xian Tao, Jun Li

Pixel-level segmentation is essential in remote sensing, where foundational vision models like CLIP and Segment Anything Model(SAM) have demonstrated significant capabilities in zero-shot segmentation tasks. Despite their advances, challenges specific to remote sensing remain substantial. Firstly, The SAM without clear prompt constraints, often generates red

Todd Hildebrant

This paper investigates the independence polynomials arising from iterated strong products of cycle graphs, examining their algebraic symmetries and combinatorial structures. Leveraging modular arithmetic and Galois theory, we establish precise conditions under which these polynomials factor over finite fields, highlighting modular collapses based on prime a

Dennis Rotondi, Fabio Scaparro, Hermann Blum, Kai O. Arras

The concept of 3D scene graphs is increasingly recognized as a powerful semantic and hierarchical representation of the environment. Current approaches often address this at a coarse, object-level resolution. In contrast, our goal is to develop a representation that enables robots to directly interact with their environment by identifying both the location o

Tongkeun Chang, Minsuk Yang

In this paper, we study the existence of solutions of stochastic incompressible non-Newtonian fluid models in $\mathbb{R}$. For the existence of solutions, we assume that the extra stress tensor $S$ is represented by $S({\mathbb A}) = {\mathbb F} ( {\mathbb A}) {\mathbb A}$ for $ n \times n$ matrix ${\mathbb G}$. We assume that ${\mathbb F}(0) $ is uniformly

Ruixiao Yang, Gulai Shen, Ahmed S. Alahmed, Chuchu Fan

We address the co-optimization of behind-the-meter (BTM) distributed energy resources (DER), including flexible demands, renewable distributed generation (DG), and battery energy storage systems (BESS) under net energy metering (NEM) frameworks with demand charges. We formulate the problem as a stochastic dynamic program that accounts for renewable generatio

Qinghao Ye, Xianhan Zeng, Fu Li, Chunyuan Li

Image captioning has long been a pivotal task in visual understanding, with recent advancements in vision-language models (VLMs) significantly enhancing the ability to generate detailed image captions. However, the evaluation of detailed image captioning remains underexplored due to outdated evaluation metrics and coarse annotations. In this paper, we introd

S. V. Heuer, K. J. Genestreti, Y. -H Liu, J. R. Shuster

We present the results of a multi-event study of the normalized reconnection rate integrating events spanning the three primary regimes of reconnection observed by the Magnetospheric Multiscale (MMS) mission. We utilize a new method for determining the normalized reconnection rate with fewer sources of uncertainty by estimating the current sheet aspect ratio

Enrico M. Zucchelli, Daniel Jang, Richard Linares

All-vs-all orbital evolutionary simulations for the low Earth orbit (LEO) simulate the long term evolution of the LEO environment. Although these simulations typically offer the highest fidelity, they are also highly computationally intensive. One factor that effectively reduces the efficiency of the approach is that all-vs-all approaches are stochastic and

Payel Das, Ching-Yun Ko, Sihui Dai, Georgios Kollias

Large language models often expose their brittleness in reasoning tasks, especially while executing long chains of reasoning over context. We propose MemReasoner, a new and simple memory-augmented LLM architecture, in which the memory learns the relative order of facts in context, and enables hopping over them, while the decoder selectively attends to the me

Behrad Rabiei, Mahesh Kumar A. R., Zhirui Dai, Surya L. S. R. Pilla

This paper focuses on planning robot navigation tasks from natural language specifications. We develop a modular approach, where a large language model (LLM) translates the natural language instructions into a linear temporal logic (LTL) formula with propositions defined by object classes in a semantic occupancy map. The LTL formula and the semantic occupanc

Francesco Iodice, Elena De Momi, Arash Ajoudani

The integration of collaborative robots into industrial environments has improved productivity, but has also highlighted significant challenges related to operator safety and ergonomics. This paper proposes an innovative framework that integrates advanced visual perception, continuous ergonomic monitoring, and adaptive Behaviour Tree decision-making to overc

Ellen Davenport, Khoa Nguyen, Junsu Jang, Clair Ma

Cost-effective localization methods for Autonomous Underwater Vehicle (AUV) navigation are key for ocean monitoring and data collection at high resolution in time and space. Algorithmic solutions suitable for real-time processing that handle nonlinear measurement models and different forms of measurement uncertainty will accelerate the development of field-r

Bence G. Márkus, Dávid Beke, Lili Vajtai, András Jánossy

A long spin-lifetime of electrons is the holy grail of spintronics, a field exploiting the electron angular momentum as information carrier and storage unit. Previous reports indicated a spin lifetime, $\tau_{\text{s}}$ near $10$ ns at best in graphene-based devices at low temperatures. We detail the observation of $\tau_{\text{s}}$ approaching the ultralong

Massimiliano Meneghin, Ahmed H. Mahmoud

Volumetric data structures typically prioritize data locality, focusing on efficient memory access patterns. This singular focus can neglect other critical performance factors, such as occupancy, communication, and kernel fusion. We introduce a novel \emph{disaggregated} design that rebalances trade-offs between locality and these objectives -- reducing comm

Nestor R. Barraza, Gabriel Pena, Verónica Moreno

This work introduces a new markovian stochastic model that can be described as a non-homogeneous Pure Birth process. We propose a functional form of birth rate that depends on the number of individuals in the population and on the elapsed time, allowing us to model a contagion effect. Thus, we model the early stages of an epidemic. The number of individuals

Bradlee J. McIntosh, Bence G. Márkus, Anna Nyáry, Ferenc Simon

Ti$_3$C$_2$T$_x$ is a leading compound within the MXenes family and can find host in widespread applications. It is synthesized by selectively etching layers from the Ti$_3$AlC$_2$ precursor, and this process typically introduces surface terminations, T$_x$, such as $-$OH, $=$O, or $-$F. However, the aggressive chemical conditions required for its preparatio

Rikard Bøgvad, Boris Shapiro, Guillaume Tahar, Sangsan Warakkagun

In his shire theorem, G. P\'olya proves that the zeros of iterated derivatives of a meromorphic function in the complex plane accumulate on the union of edges of the Voronoi diagram of the poles of this function. By recasting the local arguments of P\'olya into the language of translation surfaces, we prove its generalisation describing the asymptotic distri

Diego Saldivar

The nascent field of neurogames relies on active Brain-Computer Interface input to drive its game mechanics. Consequently, users expect their conscious will to be meaningfully reflected on the virtual environment they're engaging in. Additionally, the videogame industry considers it paramount to provide gamers with seamless experiences to avoid disrupting th

Yi-Chun Hung, Xiaoting Zhou, Arun Bansil

Moir\'e engineering offers new pathways for manipulating emergent states in twisted layered materials and lattice-mismatched heterostructures. With the key role of the geometry of the underlying lattice in mind, here we introduce the watermill lattice, a two-dimensional structure with low-energy states characterized by massless pseudospin-3/2 fermions with h

Rubi Debnath, Luxi Zhao, Sebastian Steinhorst

Time-Sensitive Networking (TSN) supports multiple traffic types with diverse timing requirements, such as hard real-time (HRT), soft real-time (SRT), and Best Effort (BE) within a single network. To provide varying Quality of Service (QoS) for these traffic types, TSN incorporates different scheduling and shaping mechanisms. However, assigning traffic types

Casey Randazzo, Minkyung Kim, Melanie Kwestel, Marya L Doerfel

Recovering from crises, such as hurricanes or wildfires, is a complex process that can take weeks, months, or even decades to overcome. Crises have both acute (immediate) and chronic (long-term) effects on communities. Crisis informatics research often focuses on the immediate response phase of disasters, thereby overlooking the long-term recovery phase, whi

Jinhyuk Lee, Feiyang Chen, Sahil Dua, Daniel Cer

In this report, we introduce Gemini Embedding, a state-of-the-art embedding model leveraging the power of Gemini, Google's most capable large language model. Capitalizing on Gemini's inherent multilingual and code understanding capabilities, Gemini Embedding produces highly generalizable embeddings for text spanning numerous languages and textual modalities.

Yuru Jia, Valerio Marsocci, Ziyang Gong, Xue Yang

Self-supervised learning (SSL) has revolutionized representation learning in Remote Sensing (RS), advancing Geospatial Foundation Models (GFMs) to leverage vast unlabeled satellite imagery for diverse downstream tasks. Currently, GFMs primarily employ objectives like contrastive learning or masked image modeling, owing to their proven success in learning tra

Piyush S. Agram

We present a simple method to enable processing of Spotlight Synthetic Aperture Radar (SAR) imagery distributed in Polar Format (PFA) using standard Range-Doppler (RDA) geometry algorithms. Our approach is applicable to PFA SAR images characterized by a constant value of the Center of Aperture (COA) time. We present simplified expressions for forward (image-

Gwan In Kim, Violet Workman, Oğulcan E. Örsel, Jieun Yim

Optical isolators are essential for laser protection and robust signal routing, but the incorporation of the necessary magneto-optic (MO) materials in foundries has remained a challenge. As an alternative, several integrated non-magnetic isolators based on acousto-optic (AO) and electro-optic (EO) spatio-temporal modulation have been proposed. Unlike MO isol

K. K. Dudek, J. A. Iglesias Martínez, L. Hirsinger, M. Kadic

In recent years, there has been a notable increase in the significance of active mechanical metamaterials capable of being remotely manipulated through changes in external stimuli. While research in this area has achieved considerable success in controlling reconfiguration to induce shape morphing or alter static mechanical properties, the active control of

Claudia Flores-Saviaga, Benjamin V. Hanrahan, Kashif Imteyaz, Steven Clarke

The rapid adoption of generative AI in software development has impacted the industry, yet its effects on developers with visual impairments remain largely unexplored. To address this gap, we used an Activity Theory framework to examine how developers with visual impairments interact with AI coding assistants. For this purpose, we conducted a study where dev

Yong Tang, Yi Zhong, Julian Tao

Soil-dwelling organisms have evolved diverse strategies for efficient subterranean movement. For example, the seeds of Erodium cicutarium and Pelargonium species employ continuous rotational motion for self-burial, while the angled worm lizard Agamodon angeliceps tunnels by oscillating its head around its trunk's axis. These rotational movements significantl

Zachary Ravichandran, Alexander Robey, Vijay Kumar, George J. Pappas

Although the integration of large language models (LLMs) into robotics has unlocked transformative capabilities, it has also introduced significant safety concerns, ranging from average-case LLM errors (e.g., hallucinations) to adversarial jailbreaking attacks, which can produce harmful robot behavior in real-world settings. Traditional robot safety approach

Xinxin Zhao, Xinmei Huang, Haoyang Li, Jing Zhang

Index recommendation is crucial for optimizing database performance. However, existing heuristic- and learning-based methods often rely on inefficient exhaustive search and estimated costs, leading to low efficiency (due to the vast search space) and unsatisfactory actual latency (due to inaccurate estimations). Inspired by the refinement strategies of exper

Soumyashree Sahoo, Chinmaey Shende, Md. Zakir Hossain, Parit Patel

Currently, depression treatment relies on closely monitoring patients response to treatment and adjusting the treatment as needed. Using self-reported or physician-administrated questionnaires to monitor treatment response is, however, burdensome, costly and suffers from recall bias. In this paper, we explore using location sensory data collected passively o

Cagla Ipek Kocal, Onat Gungor, Aaron Tartz, Tajana Rosing

Minimizing computational overhead in time-series classification, particularly in deep learning models, presents a significant challenge. This challenge is further compounded by adversarial attacks, emphasizing the need for resilient methods that ensure robust performance and efficient model selection. We introduce ReLATE, a framework that identifies robust l

Luc Passemard, Amazigh Amrane, Uli Fahrenberg

We introduce higher-dimensional automata for infinite interval ipomsets ($\omega$-HDAs). We define key concepts from different points of view, inspired from their finite counterparts. Then we explore languages recognized by $\omega$-HDAs under B\"uchi and Muller semantics. We show that Muller acceptance is more expressive than B\"uchi acceptance and, in cont

Alexander Kosovichev, Sarbani Basu, Yuto Bekki, Juan Camilo Buitrago-Casas

High-resolution helioseismology observations with the Helioseismic and Magnetic Imager (HMI) onboard Solar Dynamics Observatory (SDO) provide a unique three-dimensional view of the solar interior structure and dynamics, revealing a tremendous complexity of the physical processes inside the Sun. We present an overview of the results of the HMI helioseismology

Alex Fang, Hadi Pouransari, Matt Jordan, Alexander Toshev

Data filtering has become a powerful tool for improving model performance while reducing computational cost. However, as large language model compute budgets continue to grow, the limited data volume provided by heavily filtered and deduplicated datasets will become a practical constraint. In efforts to better understand how to proceed, we study model perfor

Rahul Nair, Bhanu Tokas, Hannah Kerner

When we train models on biased datasets, they not only reproduce data biases, but can worsen them at test time - a phenomenon called bias amplification. Many of the current bias amplification metrics (e.g., BA (MALS), DPA) measure bias amplification only in classification datasets. These metrics are ineffective for image captioning datasets, as they cannot c

Di Wu, Chengshuai Shi, Ruida Zhou, Cong Shen

Pure exploration is one of the fundamental problems in multi-armed bandits (MAB). However, existing works mostly focus on specific pure exploration tasks, without a holistic view of the general pure exploration problem. This work fills this gap by introducing a versatile framework to study pure exploration, with a focus on identifying the pairwise relationsh

João Victor Monteiros de Andrade, Leonardo Santos da Cruz

This study analyzes the impact of the COVID-19 pandemic on currency circulation in Brazil by comparing actual data from 2000 to 2023 with counterfactual projections using the \textbf{SARIMA(3,1,1)(3,1,4)\textsubscript{12}} model. The model was selected based on an extensive parameter search, balancing accuracy and simplicity, and validated through the metric