March 2025 arXiv papers — page 97

Julia Amoros-Binefa, Morgan W. Mitchell, Jan Kolodynski

Quantum entanglement, in the form of spin squeezing, is known to improve the sensitivity of atomic instruments to static or slowly-varying quantities. Sensing transient events presents a distinct challenge, requires different analysis methods, and has not been shown to benefit from entanglement in practically-important scenarios such as spin-precession magne

Luisana Claudio-Pachecano, Hernán Larralde, Carlos Mejía-Monasterio

We study particle transport in a class of open channels of finite length, made of identical cells of connected open polygonal billiards with parallel boundaries. In these systems the Mean Square Displacement (MSD) grows in time faster than linearly. We show that irrespective of the geometry of these channels, the distribution of the first return times decays

Akram Touil, Bin Yan, Wojciech H. Zurek

Quantum Darwinism recognizes that decoherence imprints redundant records of preferred quasi-classical pointer states on the environment. These redundant records are then accessed by observers. We show how redundancy enables and even implies consensus between observers who use fragments of that decohering environment to acquire information about systems of in

Nicholas B. Andrews, Kristi A. Morgansen

This research provides a theoretical foundation for modeling and real-time estimation of both the pose and inertial parameters of a free-floating multi-link system with link thrusters, which are essential for safe and effective controller design and performance. First, we adapt a planar nonlinear multi-link snake robot model to represent a planar chain of bi

Cheongho Han, Weicheng Zang, Andrzej Udalski, Chung-Uk Lee

The United Kingdom Infrared Telescope (UKIRT) microlensing survey was conducted over four years, from 2016 to 2019, with the goal of serving as a precursor to future near-infrared microlensing surveys (Shvartzvald et al. 2017). Focusing on stars in the Galactic center and utilizing near-infrared passbands, the survey identified approximately one thousand mic

Oliver Maupin, Ashlyn D. Burch, Christopher G. Yale, Matthew N. H. Chow

We analyze the use of the Solovay Kitaev (SK) algorithm to generate an ensemble of one qubit rotations over which to perform randomized compilation. We perform simulations to compare the trace distance between the quantum state resulting from an ideal one qubit $R_{Z}$ rotation and discrete SK decompositions. We find that this simple randomized gate synthesi

Adolfo Guillot, Luís Gustavo Mendes

The works of Brunella and Santos have singled out three special singular holomorphic foliations on projective surfaces having invariant rational nodal curves of positive self-intersection. These foliations can be described as quotients of foliations on some rational surfaces under cyclic groups of transformations of orders three, four, and six, respectively.

Haiyang Ying, Matthias Zwicker

Edges are one of the most basic parametric primitives to describe structural information in 3D. In this paper, we study parametric 3D edge reconstruction from calibrated multi-view images. Previous methods usually reconstruct a 3D edge point set from multi-view 2D edge images, and then fit 3D edges to the point set. However, noise in the point set may cause

Nima Negarandeh, Carlos Mora, Ramin Bostanabad

Gaussian processes (GPs) are powerful probabilistic models that define flexible priors over functions, offering strong interpretability and uncertainty quantification. However, GP models often rely on simple, stationary kernels which can lead to suboptimal predictions and miscalibrated uncertainty estimates, especially in nonstationary real-world application

Anurag Bhattacharyya

This article presents a depth-first search (DFS)-based algorithm for evaluating sensitivity gradients in the topology optimization of soft materials exhibiting complex deformation behavior. The algorithm is formulated using a time-dependent adjoint sensitivity approach and is implemented within a PETSc-based C++ MPI framework for efficient parallel computing

Partho Bhoumik, Christopher Bailey, Krishnendu Chakrabarty

Fan-out wafer-level packaging (FOWLP) addresses the demand for higher interconnect densities by offering reduced form factor, improved signal integrity, and enhanced performance. However, FOWLP faces manufacturing challenges such as coefficient of thermal expansion (CTE) mismatch, warpage, die shift, and post-molding protrusion, causing misalignment and bond

Ge Yan, Tsui-Wei Weng

As deep neural networks(DNN) become increasingly prevalent, particularly in high-stakes areas such as autonomous driving and healthcare, the ability to detect incorrect predictions of models and intervene accordingly becomes crucial for safety. In this work, we investigate the detection of misclassified inputs for image classification models from the lens of

Sarah Brauner, Sylvie Corteel, Zajj Daugherty, Anne Schilling

Crystal skeletons were introduced by Maas-Gari\'epy in 2023 by contracting quasi-crystal components in a crystal graph. On the representation theoretic level, crystal skeletons model the expansion of Schur functions into Gessel's quasisymmetric functions. Motivated by questions of Schur positivity, we provide a combinatorial description of crystal skeletons,

Ioannis Zarkadas, Amanda Tomlinson, Asaf Cidon, Baris Kasikci

As models become larger, ML accelerators are a scarce resource whose performance must be continually optimized to improve efficiency. Existing performance analysis tools are coarse grained, and fail to capture model performance at the machine-code level. In addition, these tools often do not provide specific recommendations for optimizations. We present xPU-

Bill Jackson, Shin-ichi Tanigawa

Inspired by a recent result of Brakensiek et al. that symmetric tensor matroids and rigidity matroids are linked by matroid duality, we define abstract symmetric tensor matroids as a dual concept to abstract rigidity matroids and establish their basic properties. We then exploit this duality to obtain an alternative characterisation of the generic $d$-dimens

Akram Khatami-Rizi, Ahmad Mahmoudi-Aznaveh

Single-image super-resolution (SISR) is a fundamental problem in computer vision that aims to reconstruct high-resolution (HR) images from low-resolution (LR) inputs. Although convolutional neural networks (CNNs) have achieved substantial advancements, deeper architectures often introduce excessive parameters, higher memory usage, and computational cost, lim

Yuan Gao, K. A. Muttalib

A nanowire with its two ends fixed at two different temperatures by external baths is the simplest example of a fermionic system with a temperature inhomogeneity, and could be an easy platform to study thermodynamic and transport properties of a boundary-driven open quantum system. Starting with a temperature-dependent pseudo free energy derived from an exac

Mimi Dai

The electron magnetohydrodynamics (MHD) contains a highly nonlinear Hall term with an interesting structure. Exploring the Hall nonlinear structure, we investigate possible phenomena of finite time blow up for the electron MHD with a (non-rough) forcing. When the magnetic field has zero horizontal components, the vertical component equation has a mixing feat

Parameshwar R. Pasnoori, Ari Mizel, Patrick Azaria

We solve the one-dimensional massive Thirring model, which is equivalent to the one-dimensional sine-Gordon model, with two types of Dirchlet boundary conditions: open boundary conditions (OBC) and twisted open boundary conditions ($\widehat{\text{OBC}}$). The system exhibits a duality symmetry which relates models with opposite bare mass parameters and boun

Yuan Gao, K. A. Muttalib

A perturbative framework is developed within the standard non-equilibrium field theory techniques to incorporate a temperature gradient across a thermoelectric device. The framework uses a temperature-dependent pseudo-Hamiltonian generated from the exact density matrix in the presence of non-uniform temperatures. We develop a perturbation theory for small te

David Serrano-Lozano, Aditya Arora, Luis Herranz, Konstantinos G. Derpanis

White balance (WB) correction in scenes with multiple illuminants remains a persistent challenge in computer vision. Recent methods explored fusion-based approaches, where a neural network linearly blends multiple sRGB versions of an input image, each processed with predefined WB presets. However, we demonstrate that these methods are suboptimal for common m

Amin Yousefpour, Shirin Hosseinmardi, Xiangyu Sun, Ramin Bostanabad

We introduce a simultaneous and meshfree topology optimization (TO) framework based on physics-informed Gaussian processes (GPs). Our framework endows all design and state variables via GP priors which have a shared, multi-output mean function that is parametrized via a customized deep neural network (DNN). The parameters of this mean function are estimated

Valentin Dichtl, Thorsten Schumacher, Markus Lippitz

Babinet's principle is a powerful tool for predicting the scattering behavior of planar structures where the solution for the complementary structure is already known. This makes it ubiquitous in the design of aperture antennas or metamaterials. Even for plasmonic nanostructures, a qualitative match of the behavior for complementary structures has been repor

Ben Treves, Emiliano De Cristofaro, Yue Dong, Michalis Faloutsos

What can we learn about online users by comparing their profiles across different platforms? We use the term profile to represent displayed personality traits, interests, and behavioral patterns (e.g., offensiveness). We also use the term {\it displayed personas} to refer to the personas that users manifest on a platform. Though individuals have a single rea

Kimo Pressler, Daniel R. Gamelin

Excitons in two-dimensional (2D) magnetic van der Waals (vdW) materials offer unique windows into the properties of strongly correlated electrons. Their generation can be used to drive magnetic phase transitions, manipulate spins coherently, or access novel non-equilibrium regimes. Despite extensive investigation into the spin physics of CrI3, exciton dynami

Jonathan Sturm, Adriana Pálffy

The role of polarization in the topology of quantum emitter chains is investigated theoretically, whereby "polarization" refers to the transition dipole moments of the emitters. We show that, if the chain is zigzag-shaped, different topological phases can be realized by adjusting the polarization direction. It turns out that long-range dipole-dipole coupling

Jonathan Beardsley

In this note I demonstrate that the collection of Dynkin systems on finite sets assembles into a Connes-Consani $\mathbb{F}_1$-module, with the collection of partitions of finite sets as a sub-module. The underlying simplicial set of this $\mathbb{F}_1$-module is shown to be isomorphic to the delooping of the Krasner hyperfield $\mathbb{K}$, where $1+1=\{0,1

Ryota Nakano, Rinsuke Yamada, Juba Bouaziz, Maurice Colling

A fundamental question concerns how topological electronic states are influenced by many-body correlations, and magnetic Weyl semimetals represent an important material platform to address this problem. However, the magnetic structures realized in these materials are limited, and in particular, no clear example of an undistorted helimagnetic state has been d

Negar Mehregan, Berk Bozkurt, Eric Granger, Mohammadjavad Hajikhani

Various deep learning models have been developed for indoor localization based on radio-frequency identification (RFID) tags. However, they often require adaptation to ensure accurate tracking in new target operational domains. To address this challenge, unsupervised domain adaptation (UDA) methods have been proposed to align pre-trained models with data fro

Karolina Gornicka, Michal J. Winiarski, Robert J. Cava, Michael A. McGuire

The synthesis, structural, magnetic, thermal and transport properties are reported for polycrystalline PrIr3. At room temperature PrIr3 displays the rhombohedral space group R-3m and a PuNi3- type structure. At around 70 K a phase transition to a monoclinic C2/m structure is observed and continued cooling reveals temperature independent behavior of the unit

Nilay Kushawaha, Radan Pathan, Niccolò Pagliarani, Matteo Cianchetti

Strain sensors are gaining popularity in soft robotics for acquiring tactile data due to their flexibility and ease of integration. Tactile sensing plays a critical role in soft grippers, enabling them to safely interact with unstructured environments and precisely detect object properties. However, a significant challenge with these systems is their high no

Nirmalya Thakur, Mingchen Shao, Victoria Knieling, Vanessa Su

This paper makes four scientific contributions to the area of misinformation detection and analysis on digital platforms, with a specific focus on investigating how conspiracy theories, fake remedies, and false reports emerge, propagate, and shape public perceptions in the context of COVID-19. A dataset of 5,614 posts on the internet that contained misinform

Sven Pappert

We view penalized risks through the lens of the calculus of variations. We consider risks comprised of a fitness-term (e.g. MSE) and a gradient-based penalty. After establishing the Euler-Lagrange field equations as a systematic approach to finding minimizers of risks involving only first derivatives, we proceed to exemplify this approach to the MSE penalize

Mateusz Dyksik, Michal Baranowski, Joshua J. P. Thompson, Zhuo Yang

A comprehensive study of excitonic properties of 2D layered perovskites is provided, with an emphasis on understanding and controlling the exciton fine structure. First, an overview of the optical properties is presented, discussing the challenges in determining the bandgap and exciton binding energies. Through magneto-optical spectroscopic measurements (up

Boaz Lubotzky, Hamza Abudayyeh, Niko Nikolay, Oliver Benson

Efficient readout of nitrogen-vacancy (NV) centers in diamond is crucial for various quantum information technologies. However, achieving high-fidelity, single-shot readout at room temperature remains challenging due to limited photon collection efficiency (CE) and background noise. In this work, we enhance the readout efficiency of NV centers by integrating

Kai Armstrong, Alexander Rodrigues, Alexander P. Willmott, Lei Zhang

This work aims to discuss the current landscape of kinematic analysis tools, ranging from the state-of-the-art in sports biomechanics such as inertial measurement units (IMUs) and retroreflective marker-based optical motion capture (MoCap) to more novel approaches from the field of computing such as human pose estimation and human mesh recovery. Primarily, t

Haoran Gao, Samuel D. Okegbile, Jun Cai

Split Federated Learning (SFL) offers a promising approach for distributed model training in wireless networks, combining the layer-partitioning advantages of split learning with the federated aggregation that ensures global convergence. However, in heterogeneous wireless environments, disparities in device capabilities and channel conditions make strict rou

Benjrada Mohammed Essalih

In reliability theory and survival analysis, observed data are often weakly dependent and subject to additive measurement errors. Such contamination arises when the underlying data are neither independent nor strongly mixed but instead exhibit association. This paper focuses on estimating the hazard rate by deconvolving the density function and constructing

NVIDIA, :, Alisson Azzolini, Junjie Bai

Physical AI systems need to perceive, understand, and perform complex actions in the physical world. In this paper, we present the Cosmos-Reason1 models that can understand the physical world and generate appropriate embodied decisions (e.g., next step action) in natural language through long chain-of-thought reasoning processes. We begin by defining key cap

Keyvan Malaie

The methods of Nicholson and Shain and Randles-Sevcik are the paradigms of voltammetry of redox species. However, as they were originally developed for aqueous redox couples, they cannot be directly applied to solid redox films such as those of battery materials. Herein, for the first time, we present a cyclic voltammetry model based on semi-infinite linear

Marcelo Sanchez, Gil Triginer, Ignacio Sarasua, Lara Raad

Existing image inpainting methods have shown impressive completion results for low-resolution images. However, most of these algorithms fail at high resolutions and require powerful hardware, limiting their deployment on edge devices. Motivated by this, we propose the first baseline for REal-Time High-resolution image INpainting on Edge Devices (RETHINED) th

Hou In Ivan Tam, Hou In Derek Pun, Austin T. Wang, Angel X. Chang

Despite recent advances in text-conditioned 3D indoor scene generation, there remain gaps in the evaluation of these methods. Existing metrics often measure realism by comparing generated scenes to a set of ground-truth scenes, but they overlook how well scenes follow the input text and capture implicit expectations of plausibility. We present SceneEval, an

Pinhao Song

Object detection aims to obtain the location and the category of specific objects in a given image, which includes two tasks: classification and location. In recent years, researchers tend to apply object detection to underwater robots equipped with vision systems to complete tasks including seafood fishing, fish farming, biodiversity monitoring and so on. H

Omar E. Rakha, Hazem M. Abbas

Word embeddings have been a key building block for NLP in which models relied heavily on word embeddings in many different tasks. In this paper, a model is proposed based on using Bidirectional LSTM/CRF with word embeddings to perform named entity recognition for any language. This is done by training a model on a source language (English) and transforming w

Matt Franchi, Nikhil Garg, Wendy Ju, Emma Pierson

Street scene datasets, collected from Street View or dashboard cameras, offer a promising means of detecting urban objects and incidents like street flooding. However, a major challenge in using these datasets is their lack of reliable labels: there are myriad types of incidents, many types occur rarely, and ground-truth measures of where incidents occur are

Peihan Zhang, Florian Richter, Ishan Duriseti, Albert Hsiao

Computed tomography (CT)-guided needle biopsies are critical for diagnosing a range of conditions, including lung cancer, but present challenges such as limited in-bore space, prolonged procedure times, and radiation exposure. Robotic assistance offers a promising solution by improving needle trajectory accuracy, reducing radiation exposure, and enabling rea

Máte Matolcsi, Ákos K. Matszangosz, Dániel Varga, Mihály Weiner

We initiate a systematic study of triplets of mutually unbiased bases (MUBs). We show that in $\mathbb{C}^d$ each MUB-triplet is characterized by a $d\times d\times d$ object that we call a Hadamard cube. We describe the basic properties of Hadamard cubes, and show how an MUB-triplet can be reconstructed from such a cube, up to unitary equivalence. We also p

Rohan Menon, Nicola Franco, Stephan Günnemann

Deriving tight Lipschitz bounds for transformer-based architectures presents a significant challenge. The large input sizes and high-dimensional attention modules typically prove to be crucial bottlenecks during the training process and leads to sub-optimal results. Our research highlights practical constraints of these methods in vision tasks. We find that

Sophia Hager, David Mueller, Kevin Duh, Nicholas Andrews

As large language models (LLMs) are increasingly used for factual question-answering, it becomes more important for LLMs to have the capability to communicate the likelihood that their answer is correct. For these verbalized expressions of uncertainty to be meaningful, they should reflect the error rates at the expressed level of confidence. However, when pr

Zsuzsanna Koczor-Benda, Shayantan Chaudhuri, Joe Gilkes, Francesco Bartucca

Plasmonic nanocavities are molecule-nanoparticle junctions that offer a promising approach to upconvert terahertz radiation into visible or near-infrared light, enabling nanoscale detection at room temperature. However, the identification of molecules with strong terahertz-to-visible frequency upconversion efficiency is limited by the availability of suitabl

Federico A. Bugni, Ivan A. Canay, Deborah Kim

This paper introduces a test for conditional stochastic dominance between two distributions at prespecified values of a conditioning covariate, referred to as target points. The test uses a one-sided Kolmogorov--Smirnov statistic computed from induced order statistics, the outcomes attached to the conditioning observations closest to the target point, and co

Inwoo Hwang, Jinseok Bae, Donggeun Lim, Young Min Kim

Creating expressive character animations is labor-intensive, requiring intricate manual adjustment of animators across space and time. Previous works on controllable motion generation often rely on a predefined set of dense spatio-temporal specifications (e.g., dense pelvis trajectories with exact per-frame timing), limiting practicality for animators. To pr

Mohamed Kahil, Nabil Joudieh, Nidal Chamoun

This srudy investigates the influence of relativistic effects on some properties of the halogen group and gold atoms, including their ions. The analysis covers radii, orbital's energy, first and second ionization energies, electron affinity, and polarizability. The study confirms that the p1/2 orbitals contract under relativistic effects, whereas for the p3/

DESI Collaboration, M. Abdul Karim, A. G. Adame, D. Aguado

In 2021 May the Dark Energy Spectroscopic Instrument (DESI) collaboration began a 5-year spectroscopic redshift survey to produce a detailed map of the evolving three-dimensional structure of the universe between $z=0$ and $z\approx4$. DESI's principle scientific objectives are to place precise constraints on the equation of state of dark energy, the gravita

W. Elbers, A. Aviles, H. E. Noriega, D. Chebat

The Dark Energy Spectroscopic Instrument (DESI) Collaboration has obtained robust measurements of baryon acoustic oscillations (BAO) in the redshift range, $0.1 < z < 4.2$, based on the Lyman-$\alpha$ forest and galaxies from Data Release 2 (DR2). We combine these measurements with external cosmic microwave background (CMB) data from Planck and ACT to place

K. Lodha, R. Calderon, W. L. Matthewson, A. Shafieloo

We conduct an extended analysis of dark energy constraints, in support of the findings of the DESI DR2 cosmology key paper, including DESI data, Planck CMB observations, and three different supernova compilations. Using a broad range of parametric and non-parametric methods, we explore the dark energy phenomenology and find consistent trends across all appro

U. Andrade, E. Paillas, J. Mena-Fernández, Q. Li

The Dark Energy Spectroscopic Instrument (DESI) data release 2 (DR2) galaxy and quasar clustering data represents a significant expansion of data from DR1, providing improved statistical precision in BAO constraints across multiple tracers, including bright galaxies (BGS), luminous red galaxies (LRGs), emission line galaxies (ELGs), and quasars (QSOs). In th

L. Casas, H. K. Herrera-Alcantar, J. Chaves-Montero, A. Cuceu

The second data release (DR2) of the Dark Energy Spectroscopic Instrument (DESI), containing data from the first three years of observations, doubles the number of Lyman-$\alpha$ (Ly$\alpha$) forest spectra in DR1 and it provides the largest dataset of its kind. To ensure a robust validation of the Baryonic Acoustic Oscillation (BAO) analysis using Ly$\alpha

A. Brodzeller, M. Wolfson, D. M. Santos, M. Ho

We present the Damped Ly$\alpha$ Toolkit for automated detection and characterization of Damped Ly$\alpha$ absorbers (DLA) in quasar spectra. Our method uses quasar spectral templates with and without absorption from intervening DLAs to reconstruct observed quasar forest regions. The best-fitting model determines whether a DLA is present while estimating the

DESI Collaboration, M. Abdul-Karim, J. Aguilar, S. Ahlen

We present the Baryon Acoustic Oscillation (BAO) measurements with the Lyman-alpha (LyA) forest from the second data release (DR2) of the Dark Energy Spectroscopic Instrument (DESI) survey. Our BAO measurements include both the auto-correlation of the LyA forest absorption observed in the spectra of high-redshift quasars and the cross-correlation of the abso

DESI Collaboration, M. Abdul-Karim, J. Aguilar, S. Ahlen

We present baryon acoustic oscillation (BAO) measurements from more than 14 million galaxies and quasars drawn from the Dark Energy Spectroscopic Instrument (DESI) Data Release 2 (DR2), based on three years of operation. For cosmology inference, these galaxy measurements are combined with DESI Lyman-$\alpha$ forest BAO results presented in a companion paper.

Futtaim Alhanzal, Nabil Joudieh, Khansaa Hussein, Nidal Chamoun

We perform a comprehensive theoretical analysis of C24 isomers in the gaseous phase using the PBE-D3-cc-pVTZ,B3LYP-cc-pVTZ,B3LYP-D3-cc-pVTZ , MP2-6-31G methods.We consider the basis set cc-pVTZ for the MP2 method , carry out optimized (single point) calculations in three isomers (remaining two isomers when convergence was too time consuming) in order to reve

Leonardo D. González, Joshua L. Pulsipher, Shengli Jiang, Tyler Soderstrom

We present a digital-twin simulator for a pastillation process. The simulation framework produces realistic thermal image data of the process that is used to train computer vision-based soft sensors based on convolutional neural networks (CNNs); the soft sensors produce output signals for temperature and product flow rate that enable real-time monitoring and

Songling Shan, Arthur Tanyel

Generalizing Chv\'atal's classic 1972 result, Ho\`ang proposed in 1995 the following conjecture, which strengthens Chv\'atal's result in terms of toughness: Let $t\ge 1$ be a positive integer and $G$ be a $t$-tough graph on $n \ge 3$ vertices with degree sequence $d_1, d_2, \dots, d_n$ in non-increasing order. Suppose for each $i\in [1, \lfloor\frac{n-1}{2}

NVIDIA, :, Johan Bjorck, Fernando Castañeda

General-purpose robots need a versatile body and an intelligent mind. Recent advancements in humanoid robots have shown great promise as a hardware platform for building generalist autonomy in the human world. A robot foundation model, trained on massive and diverse data sources, is essential for enabling the robots to reason about novel situations, robustly

Marco Biroli, Satya N. Majumdar, Gregory Schehr

In this paper, we introduce a new stochastic process of $N$ interacting particles on the line that evolve via Dyson Brownian motion (DBM) with Dyson's index $\beta > 0$ and undergo simultaneous resetting to their initial positions at a constant rate $r$. We call this process the resetting Dyson Brownian motion (RDBM) -- in short the $\beta$-RDBM. For $\beta

Catherine Gallagher, Tariq Yasin, Richard Stiskalek, Harry Desmond

The evolution of galaxies is known to be connected to their position within the large-scale structure and their local environmental density. We investigate the relative importance of these using the underlying dark matter density field extracted from the Constrained Simulations in BORG (CSiBORG) suite of constrained cosmological simulations. We define cosmic

Ben Chiaro, Yaxing Zhang

The ability to perform fast and accurate rotations between the computational basis states of quantum bits is one of the most fundamental requirements for building a quantum computer. Because physical qubits generally contain more than two levels, faster gates often result in a higher leakage rate outside of the computational space. In this letter, we enhance

Shiva Poudel, Poorva Sharma, Abhineet Parchure, Daniel Olsen

The uncertainty in distribution grid planning is driven by the unpredictable spatial and temporal patterns in adopting electric vehicles (EVs) and solar photovoltaic (PV) systems. This complexity, stemming from interactions among EVs, PV systems, customer behavior, and weather conditions, calls for a scalable framework to capture a full range of possible sce

Theodoros Constantinides, John Cartlidge

We introduce a zero-knowledge cryptocurrency mixer framework that allows groups of users to set up a mixing pool with configurable governance conditions, configurable deposit delays, and the ability to refund or confiscate deposits if it is suspected that funds originate from crime. Using a consensus process, group participants can monitor inputs to the mixe

Weijie Xu, Richard Futrell

How is the limited capacity of working memory efficiently used to support human linguistic behaviors? In this paper, we propose Strategic Resource Allocation (SRA) as an efficiency principle for memory encoding in sentence processing. The idea is that working memory resources are dynamically and strategically allocated to prioritize novel and unexpected info

Mehdi Delrobaei, Kenneth McIsaac

This paper presents an autonomous parking control strategy for an active-joint center-articulated mobile robot. We first derive a kinematic model of the robot, then propose a control law to stabilize the vehicle's configuration within a small neighborhood of the goal. The control law, designed using Lyapunov techniques, is based on the robot's polar coordina

Bo Huang, Josep M. Girart, Ian W. Stephens, Philip C. Myers

The {\em B}-field Orion Protostellar Survey (BOPS) recently obtained polarimetric observations at 870 ${\rm \mu m}$ towards 61 protostars in the Orion molecular clouds with $\sim 1^{\prime\prime}$ spatial resolution using the Atacama Large Millimeter/submillimeter Array. From the BOPS sample, we selected the 26 protostars with extended polarized emission wit

Krzysztof Zielinski, Slawomir Tadeja, Bruce Blumberg, Mikkel Baun Kjærgaard

Automating a production line with robotic arms is a complex, demanding task that requires not only substantial resources but also a deep understanding of the automated processes and available technologies and tools. Expert integrators must consider factors such as placement, payload, and robot reach requirements to determine the feasibility of automation. Id

Sebastian Zhao, Alan Zhu, Hussein Mozannar, David Sontag

While developers increasingly adopt tools powered by large language models (LLMs) in day-to-day workflows, these tools still require explicit user invocation. To seamlessly integrate LLM capabilities to a developer's workflow, we introduce CodingGenie, a proactive assistant integrated into the code editor. CodingGenie autonomously provides suggestions, rangi

Eman Abdullah AlOmar, Catherine DeMario, Roger Shagawat, Brandon Kreiser

Code quality is of paramount importance in all types of software development settings. Our work seeks to enable Machine Learning (ML) engineers to write better code by helping them find and fix instances of Data Leakage in their models. Data Leakage often results from bad practices in writing ML code. As a result, the model effectively ''memorizes'' the data

M. Mirzargar, S. Sorgun, M. J. Nadjafi Arani

The enhanced power graph of a group $G$ is the graph $P_e(G)$ whose vertex set is $G$, such that two distinct vertices $x$ and $y$, are adjacent if $\langle x, y\rangle$ is cyclic. In this paper, we analyze the structure of the enhanced power graph of a finite nilpotent group in terms of the enhanced power graphs of its Sylow subgroups. We establish that for

Vihaan Misra, Peter Schaldenbrand, Jean Oh

We present ShapeShift, a method for arranging rigid objects into configurations that visually convey semantic concepts specified by natural language. While pretrained diffusion models provide powerful semantic guidance, such as Score Distillation Sampling, enforcing physical validity poses a fundamental challenge. Naive overlap resolution disrupts semantic s

Miguel S. Soriano-García, Diego A. Mercado-Ravell

ViVa-SAFELAND is an open source software library, aimed to test and evaluate vision-based navigation strategies for aerial vehicles, with special interest in autonomous landing, while complying with legal regulations and people's safety. It consists of a collection of high definition aerial videos, focusing on real unstructured urban scenarios, recording mov

Hakyung Sung, Gyu-Ho Shin

We expand the second language (L2) Korean Universal Dependencies (UD) treebank with 5,454 manually annotated sentences. The annotation guidelines are also revised to better align with the UD framework. Using this enhanced treebank, we fine-tune three Korean language models and evaluate their performance on in-domain and out-of-domain L2-Korean datasets. The

Kenta Takatsu

In statistical inference, confidence set procedures are typically evaluated based on their validity and width properties. Even when procedures achieve rate-optimal widths, confidence sets can still be excessively wide in practice due to elusive constants, leading to extreme conservativeness, where the empirical coverage probability of nominal $1-\alpha$ leve

Omkar Gurjar, Kin Sum Liu, Praveen Kolli, Utsaw Kumar

Despite the success of vision-language models in various generative tasks, obtaining high-quality semantic representations for products and user intents is still challenging due to the inability of off-the-shelf models to capture nuanced relationships between the entities. In this paper, we introduce a joint training framework for product and user queries by

Pei-Hsin Lin, Jacob J. Lin, Shang-Hsien Hsieh

Construction site scaffolding is essential for many building projects, and ensuring its safety is crucial to prevent accidents. The safety inspector must check the scaffolding's completeness and integrity, where most violations occur. The inspection process includes ensuring all the components are in the right place since workers often compromise safety for

Paul Hriljac

This paper describes several new problems and ideas concerning algebraic geometry and complexity theory. It first uses the idea of coloring graphs with elements of finite fields. This procedure then shows that graph coloring problems can be converted into membership problems for a new family of algebraic varieties, coloring varieties, which are closely relat

Daniel Karapetyan

Over the last few decades, researchers have made considerable efforts to make decision support more accessible for small and medium enterprises by reducing the cost of designing, developing and maintaining automated decision support systems. However, due to the diversity of the underlying combinatorial optimisation problems, reusability of such systems has b

Jingrui An

This paper revises aesthetics theory through the lens of authenticity and investigates practical applications using a co-design approach. We encourage designers to include ordinary clients as co-creators in the co-design process, guiding them in expressing their aesthetics, values, and preferences while stimulating their creativity. This paper proposes a bes

Yixuan Wang, Paul Stynes, Pramod Pathak, Cristina Muntean

Ensuring the safety and well-being of elderly and vulnerable populations in assisted living environments is a critical concern. Computer vision presents an innovative and powerful approach to predicting health risks through video monitoring, employing human action recognition (HAR) technology. However, real-time prediction of human actions with high performa

Kush Jain, Claire Le Goues

Automated test generation holds great promise for alleviating the burdens of manual test creation. However, existing search-based techniques compromise on test readability, while LLM-based approaches are prohibitively expensive in practice. We present TestForge, an agentic unit testing framework designed to cost-effectively generate high-quality test suites

Xiaojie Fan, Yukun Yang, Himanshu Gupta, C. R. Ramakrishnan

We consider problems of distributing high-fidelity entangled states across nodes of a quantum network. We consider a repeater-based network architecture with entanglement swapping (fusion) operations for generating long-distance entanglements, and purification operations that produce high-fidelity states from several lower-fidelity states. The contributions

Valerio Guarrasi, Francesco Di Feola, Rebecca Restivo, Lorenzo Tronchin

Generating positron emission tomography (PET) images from computed tomography (CT) scans via deep learning offers a promising pathway to reduce radiation exposure and costs associated with PET imaging, improving patient care and accessibility to functional imaging. Whole-body image translation presents challenges due to anatomical heterogeneity, often limiti

Ricardo Moura, Mina Norouzirad, Vitor Augusto, Miguel Fonseca

The development and generation of synthetic data are becoming increasingly vital in the field of statistical disclosure control. The PSInference package provides tools to perform exact inferential analysis on singly imputed synthetic data generated through Plug-in Sampling assuming that the original dataset follows a multivariate normal distribution. Include

Zhenhua Wang, Paul A. Parker, Scott H. Holan

Small area estimation models are essential for estimating population characteristics in regions with limited sample sizes, thereby supporting policy decisions, demographic studies, and resource allocation, among other use cases. The spatial Fay-Herriot model is one such approach that incorporates spatial dependence to improve estimation by borrowing strength

Michal Prusik, Bartosz Frej, Michal W. Przewozniczek

Statistical Linkage Learning (SLL) is a part of many state-of-the-art optimizers. The purpose of SLL is to discover variable interdependencies. It has been shown that the effectiveness of SLL-using optimizers is highly dependent on the quality of SLL-based problem decomposition. Thus, understanding what kind of problems are hard or easy to decompose by SLL i

Bastian Pätzold, Jan Nogga, Sven Behnke

Vision-language models (VLMs) excel in visual understanding but often lack reliable grounding capabilities and actionable inference rates. Integrating them with open-vocabulary object detection (OVD), instance segmentation, and tracking leverages their strengths while mitigating these drawbacks. We utilize VLM-generated structured descriptions to identify vi

Jun-Yong Park, Tristan Phillips

As a consequence of their work on average Selmer ranks of elliptic curves with marked points, Bhargava and Ho proved that $100\%$ of elliptic curves over $\mathbb{Q}$ with an additional marked point have positive rank. In this note we provide an alternate proof which extends the result to global fields of characteristic not two or three.

Maryam Aliakbarpour, Arnav Burudgunte, Clément Cannone, Ronitt Rubinfeld

We extend the framework of augmented distribution testing (Aliakbarpour, Indyk, Rubinfeld, and Silwal, NeurIPS 2024) to the differentially private setting. This captures scenarios where a data analyst must perform hypothesis testing tasks on sensitive data, but is able to leverage prior knowledge (public, but possibly erroneous or untrusted) about the data d

Viansa Schmulbach, Jason Kim, Ethan Gao, Lucy Revina

This paper introduces NeCTAr (Near-Cache Transformer Accelerator), a 16nm heterogeneous multicore RISC-V SoC for sparse and dense machine learning kernels with both near-core and near-memory accelerators. A prototype chip runs at 400MHz at 0.85V and performs matrix-vector multiplications with 109 GOPs/W. The effectiveness of the design is demonstrated by run

Hodaya Barr, Yonatan Aumann, Sarit Kraus

We study the computational complexity of bribery in parliamentary voting, in settings where the briber is (also) interested in the success of an entire set of political parties - a ``coalition'' - rather than an individual party. We introduce two variants of the problem: the Coalition-Bribery Problem (CB) and the Coalition-Bribery-with-Preferred-party Proble

Shih-Chieh Dai, Jun Xu, Guanhong Tao

Large language models (LLMs) are widely used in software development. However, the code generated by LLMs often contains vulnerabilities. Several secure code generation methods have been proposed to address this issue, but their current evaluation schemes leave several concerns unaddressed. Specifically, most existing studies evaluate security and functional

Taishi Kotsuka, Enoch Yeung

Intracellular biomolecular systems exhibit intrinsic stochasticity due to low molecular copy numbers, leading to multimodal probability distributions that play a crucial role in probabilistic differentiation and cellular decision-making. Controlling the dispersion of multimodal probability distributions in biomolecular systems is critical for regulating stoc