December 2024 arXiv papers — page 178

Qiuye Jia, Mikhail Molodyk, Ethan Sussman

We prove the essential self-adjointness of the d'Alembertian $\square_g$, allowing a larger class of spacetimes than previously considered, including those that arise from perturbing Minkowski spacetime by gravitational radiation. We emphasize the fact, proven by Taira in closely related settings, that all tempered distributions $u$ satisfying $\square_g u =

Tim Morrison, Art B. Owen

Given covariates for $n$ units, each of which is to receive a treatment with probability $1/2$, we study the question of how best to correlate their treatment assignments to minimize the variance of the IPW estimator of the average treatment effect. Past work by \cite{bai2022} found that the optimal stratified experiment is a matched-pair design, where the m

Daniel Hathcock, Billy Jin, Kalen Patton, Sherry Sarkar

Online resource allocation is a rich and varied field. One of the most well-known problems in this area is online bipartite matching, introduced in 1990 by Karp, Vazirani, and Vazirani [KVV90]. Since then, many variants have been studied, including AdWords, the generalized assignment problem (GAP), and online submodular welfare maximization. In this paper, w

Yangkai Xue, Jindou Dai, Zhipeng Lu, Yuwei Wu

Hyperbolic graph convolutional networks (HGCNs) have demonstrated representational capabilities of modeling hierarchical-structured graphs. However, as in general GCNs, over-smoothing may occur as the number of model layers increases, limiting the representation capabilities of most current HGCN models. In this paper, we propose residual hyperbolic graph con

Jason Hausenloy, Duncan McClements, Madhavendra Thakur

Data is essential to train and fine-tune today's frontier artificial intelligence (AI) models and to develop future ones. To date, academic, legal, and regulatory work has primarily addressed how data can directly harm consumers and creators, such as through privacy breaches, copyright infringements, and bias and discrimination. Our work, instead, focuses on

Eric Kilgore

We show that Legendrian pre-quantization lifts of many non-exact Lagrangian submanifolds in $\mathbb{C}^n$ retain some quantitative rigidity from the symplectic base. In particular, they cannot be moved by Legendrian isotopy into an arbitrarily small pre-quantized cylinder. This is a high dimensional generalization of results of Dimitroglou Rizell--Sullivan

Vishakh Padmakumar, Chuanyang Jin, Hannah Rose Kirk, He He

Large language models (LLMs) are increasingly deployed via public-facing interfaces to interact with millions of users, each with diverse preferences. Despite this, preference tuning of LLMs predominantly relies on reward models trained using binary judgments where annotators select the preferred choice out of pairs of model outputs. In this work, we argue t

Xiang-dong Shi, Sheng-bang Qian, Li-ying Zhu, Liang Liu

The O- and B-type (OB-type) pulsating stars are important objects to study the structure and evolution of massive stars through asteroseismology. A large amount of data from various sky surveys provide an unprecedented opportunity to search for and study this kind of variable star. We identify 155 OB-type pulsating stars or candidates, including 38 Oe/Be sta

Yifan Li, Ryo Takahashi, Wakako Yukita, Kanata Matsutani

Prototyping electronic textile (e-textile) involves embedding electronic components into fabrics to develop smart clothing with specific functionalities. However, this process is still challenging since the complicated wiring setup is required during experimental phases. This paper presents plug-n-play e-knit, a large-scale, repositionable e-textile for prov

Haoyang Li, Marko Stamenkovic, Alexander Shmakov, Michael Fenton

The production of multiple Higgs bosons at the CERN LHC provides a direct way to measure the trilinear and quartic Higgs self-interaction strengths as well as potential access to beyond the standard model effects that can enhance production at large transverse momentum $p_{\mathrm{T}}$. The largest event fraction arises from the fully hadronic final state in

Joseph E. Dill, Chuan F. C. Chang, Debdeep Jena, Huili Grace Xing

We develop a two-carrier Hall effect model fitting algorithm to analyze temperature-dependent magnetotransport measurements of a high-density ($\sim4\times10^{13}$ cm$^2$/Vs) polarization-induced two-dimensional hole gas (2DHG) in a GaN/AlN heterostructure. Previous transport studies in GaN 2DHGs have reported a two-fold reduction in 2DHG carrier density fro

Sunghoon Kang, Hyeoneui Kim, Hyewon Park, Ricky Taira

The goal of this work was to compute the semantic similarity among publicly available health survey questions in order to facilitate the standardization of survey-based Person-Generated Health Data (PGHD). We compiled various health survey questions authored in both English and Korean from the NIH CDE Repository, PROMIS, Korean public health agencies, and ac

Quan Xie, Jiajia Liu, Chris J. Nelson, Robert Erdélyi

Swirl-shaped flow structures have been observed throughout the solar atmosphere, in both emission and absorption, at different altitudes and locations, and are believed to be associated with magnetic structures. However, the distribution patterns of such swirls, especially their spatial positions, remain unclear. Using the Automated Swirl Detection Algorithm

Yuzhen Du, Teng Hu, Jiangning Zhang, Ran Yi Chengming Xu

Image restoration (IR) aims to recover high-quality images from degraded inputs, with recent deep learning advancements significantly enhancing performance. However, existing methods lack a unified training benchmark for iterations and configurations. We also identify a bias in image complexity distributions between commonly used IR training and testing data

Gilles G. de Castro, Eun Ji Kang

We define the orbit morphism of partial dynamical systems and prove that an orbit morphism being an isomorphism in the category of partial dynamical systems and orbit morphisms is equivalent to the existence of a continuous orbit equivalence between the given partial dynamical systems that preserves the essential stabilisers. We show that this is equivalent

Guangben Lu, Yuzhen Du, Zhimin Sun, Ran Yi

Foreground-conditioned inpainting aims to seamlessly fill the background region of an image by utilizing the provided foreground subject and a text description. While existing T2I-based image inpainting methods can be applied to this task, they suffer from issues of subject shape expansion, distortion, or impaired ability to align with the text description,

Sanjoeng Wong, Yan Yan

Automatic detection of prohibited items in X-ray images plays a crucial role in public security. However, existing methods rely heavily on labor-intensive box annotations. To address this, we investigate X-ray prohibited item detection under labor-efficient point supervision and develop an intra-inter objectness learning network (I$^2$OL-Net). I$^2$OL-Net co

Samridh Dev Singh, Chris Byrohl, Dylan Nelson

votess is a library for computing parallel 3D Voronoi tessellations on heterogeneous platforms, from CPUs and GPUs, to future accelerator architectures. To do so, it leverages the SYCL abstraction layer to achieve portability and performance across these architectures. The core library is an implementation of a Voronoi cell-by-cell computation algorithm, pro

Riley M Whebell, Timothy J Moroney, Ian W Turner, Ravindra Pethiyagoda

The study of the shape of droplets on surfaces is an important problem in the physics of fluids and has applications in multiple industries, from agrichemical spraying to microfluidic devices. Motivated by these real-world applications, computational predictions for droplet shapes on complex substrates -- rough and chemically heterogeneous surfaces -- are de

Bryan S. Hernandez, Juan Paolo C. Santos, Patrick Vincent N. Lubenia, Eduardo R. Mendoza

Biochemical and environmental modeling typically relies on reaction networks to represent complex transformations. While the Linkage Class Decomposition (LCD) partitions networks based on visual standard connectivity, it often misaligns with the algebraic properties governing long-term dynamics. This work establishes the Finest Decompositions' Architecture (

Quang Nguyen, Truong Vu, Trong-Tung Nguyen, Yuxin Wen

Image editing technologies are tools used to transform, adjust, remove, or otherwise alter images. Recent research has significantly improved the capabilities of image editing tools, enabling the creation of photorealistic and semantically informed forged regions that are nearly indistinguishable from authentic imagery, presenting new challenges in digital f

Julie A. Campos, Kenneth R. Brown

We can design efficient quantum error-correcting (QEC) codes by tailoring them to our choice of quantum architecture. Useful tools for constructing such codes include Clifford deformations and appropriate gauge fixings of compass codes. In this work, we find Clifford deformations that can be applied to elongated compass codes resulting in QEC codes with impr

Yang Chen, Cheng Cheng

Conjugate phase retrieval considers the recovery of a function, up to a unimodular constant and conjugation, from its phaseless measurements. In this paper, we explore the conjugate phase retrieval in a shift-invariant space generated by a Gaussian funciton. First, we show that the modulus function in the Gaussian shift-invariant space can be determined from

Minghua Wang, Jinhui Xu

In this study, we introduce novel methodologies designed to adapt original data in response to the dynamics of persistence diagrams along Wasserstein gradient flows. Our research focuses on the development of algorithms that translate variations in persistence diagrams back into the data space. This advancement enables direct manipulation of the data, guided

Tianjun Ke, Zhiyu Xu

The stochastic block model (SBM) is a fundamental tool for community detection in networks, yet the finite-sample performance of inference methods remains underexplored. We evaluate key algorithms-spectral methods, variational inference, and Gibbs sampling-under varying conditions, including signal-to-noise ratios, heterogeneous community sizes, and multimod

Alexandra N. Busch, Roberto C. Budzinski, Federico W. Pasini, Ján Mináč

Recent advances in neural recording technology allow simultaneously recording action potentials from hundreds to thousands of neurons in awake, behaving animals. However, characterizing spike patterns in the resulting data, and linking these patterns to behaviour, remains a challenging task. The lack of a rigorous mathematical language for variable numbers o

John Subosits, Jenna Lee, Shawn Manuel, Paul Tylkin

Success in racing requires a unique combination of vehicle setup, understanding of the racetrack, and human expertise. Since building and testing many different vehicle configurations in the real world is prohibitively expensive, high-fidelity simulation is a critical part of racecar development. However, testing different vehicle configurations still requir

Yue-Wei Song, Heng Zhao, Li Chen, Yin-Hai Li

On-chip quantum sources based on nonlinear processes are pivotal components in integrated photonics, driving significant advancements in quantum information technologies over recent decades. Usually, the pump coherence has been considered to be crucial for ensuring the quality of generated states, therefore incoherent light is rarely used in quantum informat

Jialin Wang, Zhihua Duan

This paper explores the transformative role of Agent AI and LangGraph in advancing the automation and effectiveness of machine translation (MT). Agents are modular components designed to perform specific tasks, such as translating between particular languages, with specializations like TranslateEnAgent, TranslateFrenchAgent, and TranslateJpAgent for English,

Hongming Li, Shujian Yu, Bin Liu, Jose C. Principe

This paper proposes \emph{Episodic and Lifelong Exploration via Maximum ENTropy} (ELEMENT), a novel, multiscale, intrinsically motivated reinforcement learning (RL) framework that is able to explore environments without using any extrinsic reward and transfer effectively the learned skills to downstream tasks. We advance the state of the art in three ways. F

Kevin Wu, Rabab Haider, Pascal Van Hentenryck

Transmission Expansion Planning (TEP) is the process of optimizing the development and upgrade of the power grid to ensure reliable, efficient, and cost-effective electricity delivery while addressing grid constraints. To support growing demand and renewable energy integration, energy storage is emerging as a pivotal asset that provides temporal flexibility

Naafis Ahnaf Shahed, Kartik Samanta, Mohamed Elekhtiar, Kai Huang

The recent surge of interest in moir\'e superlattices of twisted van der Waals compounds has spotlighted the emergence of unconventional superconductivity and novel electronic phases. However, the range of moir\'e phenomena can be dramatically expanded by incorporating complex oxide materials into twisted heterostructures. In this study, motivated by the rec

Taban Baghfalaki, Reza Hashemi, Christophe Tzourio, Catherine Helmer

In many clinical and epidemiological studies, collecting longitudinal measurements together with time-to-event outcomes is essential. Accurately estimating the association between longitudinal markers and event risks, as well as identifying key markers for prediction, is especially important in the presence of competing risks. However, as the number of marke

Abdelrahaman A. Hassan, Radwa J. Hanafy, Mohammed E. Fouda

The growing prevalence and complexity of mental health disorders present significant challenges for accurate diagnosis and treatment, particularly in understanding the interplay between co-occurring conditions. Mental health disorders, such as depression and Anxiety, often co-occur, yet current datasets derived from social media posts typically focus on sing

Surya Dheeshjith, Adam Subel, Alistair Adcroft, Julius Busecke

AI emulators for forecasting have emerged as powerful tools that can outperform conventional numerical predictions. The next frontier is to build emulators for long climate simulations with skill across a range of spatiotemporal scales, a particularly important goal for the ocean. Our work builds a skillful global emulator of the ocean component of a state-o

Alex G. Kim

The Union3 "Spline-Interpolated Distance Moduli" model posterior has been distributed for third-party cosmology analysis. The posterior prefers a large value of $\Omega_M$, a small absolute value of $w_0$, and a negative $w_a$, but still accommodates $\Lambda$CDM; the supernova data alone are not strongly constraining. The posterior is built assuming an unde

Mohammad-Sadegh Vaezi, Davoud Nasr Esfahani

Several studies have emphasized the impact of long-range Coulomb interactions in lattice fermions, yet conventional Auxiliary Field Quantum Monte Carlo (QMC) methods face limitations due to their reliance on positive definite interaction matrices. We address this by decomposing the interaction matrix into positive- and negative-definite components, allowing

Mohammad Mahdi Nasiri Fatmehsari, Mohammad-Sadegh Vaezi

The nonuniform $\mathbb{Z}_2$ symmetric Kitaev chain, comprising alternating topological and normal regions, hosts localized states known as edge-zero modes (EZMs) at its interfaces. These EZMs can pair to form qubits that are resilient to quantum decoherence, a feature expected to extend to higher symmetric chains, i.e., parafermion chains. However, finite-

Xiao Li, Anouck Girard, Ilya Kolmanovsky

Autonomous driving heavily relies on perception systems to interpret the environment for decision-making. To enhance robustness in these safety critical applications, this paper considers a Deep Ensemble of Deep Neural Network regressors integrated with Conformal Prediction to predict and quantify uncertainties. In the Adaptive Cruise Control setting, the pr

Yuyang Wang, Anurag Ranjan, Josh Susskind, Miguel Angel Bautista

Flow matching models have emerged as a powerful method for generative modeling on domains like images or videos, and even on irregular or unstructured data like 3D point clouds or even protein structures. These models are commonly trained in two stages: first, a data compressor is trained, and in a subsequent training stage a flow matching generative model i

Chayce Hughes, Ryley Hill, Scott Chapman, Manuel Aravena

We present ALMA observations of the [CI] 492 and 806$\,$GHz fine-structure lines in 25 dusty star-forming galaxies (DSFGs) at $z\,{=}\,4.3$ in the core of the SPT2349$-$56 protocluster. The protocluster galaxies exhibit a median $L^\prime_{[\text{CI}](2-1)}/L^\prime_{[\text{CI}](1-0)}$ ratio of 0.94 with an interquartile range of 0.81-1.24. These ratios are

Nasit S Sony

Byzantine agreement protocols in asynchronous networks have gained renewed attention due to their independence from network timing assumptions to ensure termination. Traditional asynchronous Byzantine agreement protocols require every party to broadcast its requests (e.g., transactions), leading to high communication costs as parties ultimately agree on one

Jiaqi Zhao, Craig O. Heinke, Su Fu

Diffuse X-ray emission has been detected from a few Galactic globular clusters (GCs), whereas its nature still remains largely unclear. The GC Terzan 5 was previously found to show a significant diffuse thermal X-ray excess from its field, likely contributed by the Galactic background, and a non-thermal component described by a power-law model with photon in

Hailong Guo, Zhimin Zhang

Post-processing techniques are essential tools for enhancing the accuracy of finite element approximations and achieving superconvergence. Among these, recovery techniques stand out as vital methods, playing significant roles in both post-processing and pre-processing. This paper provides an overview of recent developments in recovery techniques and their ap

Randall Clark, Maxim Nurgaliev, Eduard Khayrutdinov, Georgy Subbotin

Plasma shape is a significant factor that must be considered for any Fusion Pilot Plant (FPP) as it has significant consequences for plasma stability and core confinement. A new simulator, NSFsim, has been developed based on a historically successful code, DINA, offering tools to simulate both transport and plasma shape. Specifically, NSFsim is a free bounda

A. M. Matthews, W. D. Cotton, W. M. Peters, L. Marchetti

Galaxy formation theory identifies superwinds as a key regulator of star formation rates, galaxy growth, and chemical enrichment. Thermal and radiation pressure are known to drive galactic-scale winds in dusty starbursting galaxies (e.g. M82), but modern numerical simulations have recently highlighted that cosmic-ray (CR) driven winds may be especially impor

Yerin Choi, Jeehyun Lee, Myoung-Wan Koo

Due to the subjective nature of current clinical evaluation, the need for automatic severity evaluation in dysarthric speech has emerged. DNN models outperform ML models but lack user-friendly explainability. ML models offer explainable results at a feature level, but their performance is comparatively lower. Current ML models extract various features from r

Sofiane Ennadir, Gabriela Zarzar Gandler, Filip Cornell, Lele Cao

Graphs are ubiquitous in real-world applications, ranging from social networks to biological systems, and have inspired the development of Graph Neural Networks (GNNs) for learning expressive representations. While most research has centered on static graphs, many real-world scenarios involve dynamic, temporally evolving graphs, motivating the need for Conti

Wan Chong Choi, Iek Chong Choi

This study examined the impact of Code.org's block-based coding curriculum on primary school students' computational thinking, motivation, attitudes, and academic performance. Twenty students participated, and a range of tools was used: the Programming Computational Thinking Scale (PCTS) to evaluate computational thinking, the Instructional Materials Motivat

Andrew Kyle Lampinen, Stephanie C. Y. Chan, Aaditya K. Singh, Murray Shanahan

The ability of language models to learn a task from a few examples in context has generated substantial interest. Here, we provide a perspective that situates this type of supervised few-shot learning within a much broader spectrum of meta-learned in-context learning. Indeed, we suggest that any distribution of sequences in which context non-trivially decrea

Holly C. M. Baldock, David M. Huang

Two-dimensional (2D) nanomaterials exhibit unique properties that are promising for diverse applications, including those relevant to concentration-gradient-driven transport of electrolyte solutions through porous membranes made from these materials, such as water desalination, osmotic power, and iontronics. Here we derive general equations, and determine sc

Aditya Narkhede, Shafquat Islam, Xingsheng Sun, Kevin Wang

Lightweight, single-use explosion containment structures provide an effective solution for neutralizing rogue explosives, combining affordability with ease of transport. This paper introduces a three-stage simulation framework that captures the distinct physical processes and time scales involved in detonation, shock propagation, and large, plastic structura

Tai Huy Ha, A. V. Jayanthan, Arvind Kumar, Thai Thanh Nguyen

We discuss how to understand the asymptotic resurgence number of a pair of graded families of ideals from combinatorial data of their associated convex bodies. When the families consist of monomial ideals, the convex bodies being considered are the Newton-Okounkov bodies of the families. When ideals in the second family are classical invariant ideals, for in

Sunkyu Yu, Xianji Piao, Namkyoo Park

Photonics has unlocked the potential for energy-efficient acceleration of deep learning. Most approaches toward photonic deep learning have diligently reproduced traditional deep learning architectures using photonic platforms, separately implementing linear-optical matrix calculations and nonlinear activations via electro-optical conversion, optical nonline

Y. Wang, S. Ku, A. Bravo-Doddoli

The Special Euclidean group on the plane $SE(2)$ has the left-invariant sub-Riemannian structure. Every sub-Riemannian manifold possesses a Hamiltonian function governing the sub-Riemannian geodesic flow. Two natural questions are: What are the necessary conditions for periodic sub-Riemannian geodesics? What geodesics are the metric lines in SE(2)? We answer

Xiang Li, Yunpeng Zhao, Qing Pan, Ning Hao

Community detection is the task of clustering objects based on their pairwise relationships. Most of the model-based community detection methods, such as the stochastic block model and its variants, are designed for networks with binary (yes/no) edges. In many practical scenarios, edges often possess continuous weights, spanning positive and negative values,

Halil Gevgilili, Dilhan M. Kalyon

This study explores the impact of wall slip on parameter estimation for integral-type non-linear viscoelastic models with time-strain separable memory functions, specifically examining high-density polyethylene (HDPE) and thermoplastic elastomer (TPE) melts. Significant wall slip was observed in HDPE under non-linear conditions, complicating the characteriza

I. Kolesnikov, V. M. Sampaio, R. R. de Carvalho, C. Conselice

Morphological classification of galaxies becomes increasingly challenging with redshift. We apply a hybrid supervised-unsupervised method to classify $\sim 14,000$ galaxies in the CANDELS fields at $0.2 \leq z \leq 2.4$ into spheroid, disk, and irregular systems. Unlike previous works, our method is applied to redshift bins of width 0.2. Comparison between m

Jungho Kim, Tae-Kyu Choi, Edward Mercer, Liam T. Schmidt

We present the first circularly polarized Floquet engineering time-resolved Resonant Inelastic X-ray Scattering (tr-RIXS) experiment in H$_3$LiIr$_2$O$_6$, an iridium-based Kitaev system. Our calculations and experimental results are consistent with the modification of the low energy magnetic excitations in H$_3$LiIr$_2$O$_6$ only during illumination by the

Stephen Lack, Shay Tobin

The categories of real and of complex Hilbert spaces with bounded linear maps have received purely categorical characterisations by Chris Heunen and Andre Kornell. These characterisations are achieved through Sol\`er's theorem, a result which shows that certain orthomodularity conditions on a Hermitian space over an involutive division ring result in a Hilbe

Delip Rao, Jonathan Young, Thomas Dietterich, Chris Callison-Burch

Retractions play a vital role in maintaining scientific integrity, yet systematic studies of retractions in computer science and other STEM fields remain scarce. We present WithdrarXiv, the first large-scale dataset of withdrawn papers from arXiv, containing over 14,000 papers and their associated retraction comments spanning the repository's entire history

Chun Hei Yip, Rajashree Agrawal, Lawrence Chan, Jason Gross

The goal of mechanistic interpretability is discovering simpler, low-rank algorithms implemented by models. While we can compress activations into features, compressing nonlinear feature-maps -- like MLP layers -- is an open problem. In this work, we present the first case study in rigorously compressing nonlinear feature-maps, which are the leading asymptot

Lianjun Liu, Hongli An, Pengxuan Chen, Longxiang Ye

With the rapid development of large language models (LLMs), which possess powerful natural language processing and generation capabilities, LLMs are poised to provide more natural and personalized user experiences. Their deployment on mobile devices is gradually becoming a significant trend in the field of intelligent devices. LLMs have demonstrated tremendo

Takashi Kumagai, Jian Wang, Meng-ge Zhang

In this paper, we study the local boundedness of local weak solutions to the following parabolic equation associated with fractional $p$-Laplacian type operators $$ \partial_t u(t,x)-\text{p.v.}\int_{\R^d}|u(t,y)-u(t,x)|^{p-2}(u(t,y)-u(t,x))J(t;x,y)\,dy=0,\quad (t,x)\in \R\times \R^d, $$ where $\text{p.v.}$ means the integral in the principal value sense, $p

Luigi Ferraro, Linoy Utkina

Let $\Bbbk$ be a field, and let $I$ be a monomial ideal in the polynomial ring $R=\Bbbk[x_1,\ldots,x_n]$. In her thesis, Taylor introduced a complex that provides a finite free resolution of $R/I$ as an $R$-module. Building on this, Ferraro, Martin and Moore extended this construction to monomial ideals in skew polynomial rings. Since the Taylor resolution i

Anirudh Rayas, Jiajun Cheng, Rajasekhar Anguluri, Deepjyoti Deka

Complex networked systems driven by latent inputs are common in fields like neuroscience, finance, and engineering. A key inference problem here is to learn edge connectivity from node outputs (potentials). We focus on systems governed by steady-state linear conservation laws: $X_t = {L^{\ast}}Y_{t}$, where $X_t, Y_t \in \mathbb{R}^p$ denote inputs and poten

Yiran Wang, Chenshu Liu, Yunfan Li, Sanae Amani

The exploration \& exploitation dilemma poses significant challenges in reinforcement learning (RL). Recently, curiosity-based exploration methods achieved great success in tackling hard-exploration problems. However, they necessitate extensive hyperparameter tuning on different environments, which heavily limits the applicability and accessibility of this l

Sikha Pentyala, Geetha Sitaraman, Trae Claar, Martine De Cock

The success of AI is based on the availability of data to train models. While in some cases a single data custodian may have sufficient data to enable AI, often multiple custodians need to collaborate to reach a cumulative size required for meaningful AI research. The latter is, for example, often the case for rare diseases, with each clinical site having da

Otero Nancy, Druga Stefania, Lan Andrew

This study introduces an evaluation benchmark for middle school algebra to be used in artificial intelligence(AI) based educational platforms. The goal is to support the design of AI systems that can enhance learner conceptual understanding of algebra by taking into account their current level of algebra comprehension. The data set comprises 55 misconception

Brian H. Lee, Kat Nykiel, Ava E. Hallberg, Brice Rider

Machine learning has become a central technique for modeling in science and engineering, either complementing or as surrogates to physics-based models. Significant efforts have recently been devoted to models capable of predicting field quantities but the limitations of current state-of-the-art models in describing complex physics are not well understood. We

Debadrita Saha, Philip Richerme, Srinivasan S. Iyengar

The accurate computational study of wavepacket nuclear dynamics is considered to be a classically intractable problem, particularly with increasing dimensions. Here we present two algorithms that, in conjunction with other methods developed by us, will form the basis for performing quantum nuclear dynamics in arbitrary dimensions. For one algorithm, we prese

Yu-Han Ni, Yi-Ning Wang, Chao Wu, Jiang-Hao Yu

We identify an extended Poincare symmetry $ISO(2) \times ISO(3,1)$ for on-shell massive scattering amplitudes, transforming under the $U(2)$ Little group symmetry. Thus the one-particle state involves in both spin and transversality $t$ (related to chirality), and the spin-spinors are extended to the spin-transverality spinors. The massive spin-$s$ spinors w

Ximing Wen

Pretrained transformer-based Language Models (LMs) are well-known for their ability to achieve significant improvement on NLP tasks, but their black-box nature, which leads to a lack of interpretability, has been a major concern. My dissertation focuses on developing intrinsically interpretable models when using LMs as encoders while maintaining their superi

John McConnell, Ivana Collado-Gonzalez, Paul Szenher, Brendan Englot

3D situational awareness is critical for any autonomous system. However, when operating underwater, environmental conditions often dictate the use of acoustic sensors. These acoustic sensors are plagued by high noise and a lack of 3D information in sonar imagery, motivating the use of an orthogonal pair of imaging sonars to recover 3D perceptual data. Thus f

Ruibo Ming, Jingwei Wu, Zhewei Huang, Zhuoxuan Ju

Recent advancements in auto-regressive large language models (LLMs) have led to their application in video generation. This paper explores the use of Large Vision Models (LVMs) for video continuation, a task essential for building world models and predicting future frames. We introduce ARCON, a scheme that alternates between generating semantic and RGB token

Alexey Vlaskin, Eduardo G. Altmann

Predicting missing links in complex networks requires algorithms that are able to explore statistical regularities in the existing data. Here we investigate the interplay between algorithm efficiency and network structures through the introduction of suitably-designed synthetic graphs. We propose a family of random graphs that incorporates both micro-scale m

Justin Theiss, Norman Müller, Daeil Kim, Aayush Prakash

Recently, text-to-image generation with diffusion models has made significant advancements in both higher fidelity and generalization capabilities compared to previous baselines. However, generating holistic multi-view consistent images from prompts still remains an important and challenging task. To address this challenge, we propose a diffusion process tha

Clement E. Bohr, Marti Mestieri, Frederic Robert-Nicoud

As countries develop, the relative importance of agriculture declines and economic activity becomes spatially concentrated. We develop a model integrating structural change and regional disparities to jointly capture these phenomena. A key modeling innovation ensuring analytical tractability is the introduction of non-homothetic Cobb-Douglas preferences, whi

Shuai Shao, Tingting Yu

Information Retrieval-based Fault Localization (IRFL) techniques aim to identify source files containing the root causes of reported failures. While existing techniques excel in ranking source files, challenges persist in bug report analysis and query construction, leading to potential information loss. Leveraging large language models like GPT-4, this paper

Adrián F. Hernández-Borda, María P. Rojas-Sepúlveda, Hanz Y. Ramírez-Gómez

One of the main requirements to achieve reliable quantum communications are on-demand sources of highly entangled photon pairs, and semiconductor quantum dots have emerged as prominent candidates to satisfy the necessary conditions of brightness and entanglement fidelity. However, in most cases the biexciton-exciton-vacuum cascade produces a pair of maximall

Debora Caldarola, Pietro Cagnasso, Barbara Caputo, Marco Ciccone

Federated learning (FL) enables collaborative model training with privacy preservation. Data heterogeneity across edge devices (clients) can cause models to converge to sharp minima, negatively impacting generalization and robustness. Recent approaches use client-side sharpness-aware minimization (SAM) to encourage flatter minima, but the discrepancy between

Hitesh Kishore Das, Max Gronke, Rainer Weinberger

Multiphase gas, with hot ($\sim10^6$K) and cold ($\sim10^4$K) gas, is ubiquitous in astrophysical media across a wide range of scales. However, simulating multiphase gas has been a long-standing challenge, due to the large separation between the size of cold gas structures and the scales at which such gas impacts the evolution of associated systems. In this

Matheus Brito, Vyjayanthi Chari

We introduce a family of modules for the quantum affine algebra which include as very special cases both the snake modules and the modules arising from a monoidal categorification of cluster algebras. We give necessary and sufficient conditions for these modules to be prime and prove a unique factorization result. We also give an explicit formula expressing

Dehui Zhang, Yucheng Zhang, Dong Xu, Shaolei Wang

Directly probing deep tissue activities from body surfaces offers a noninvasive approach to monitoring essential physiological processes1-3. However, this method is technically challenged by rapid signal attenuation toward the body surface and confounding motion artifacts4-6 primarily due to excessive contact impedance and mechanical mismatch with convention

Yuxuan Jiang, Ho Man Kwan, Tianhao Peng, Ge Gao

Recent advances in implicit neural representations (INRs) have shown significant promise in modeling visual signals for various low-vision tasks including image super-resolution (ISR). INR-based ISR methods typically learn continuous representations, providing flexibility for generating high-resolution images at any desired scale from their low-resolution co

CMS Collaboration

This paper presents a model-agnostic search for narrow resonances in the dijet final state in the mass range 1.8-6 TeV. The signal is assumed to produce jets with substructure atypical of jets initiated by light quarks or gluons, with minimal additional assumptions. Search regions are obtained by utilizing multivariate machine-learning methods to select jets

Jeong Min Kong, Ian P. Roberts

In this paper, we present a novel active beam learning method for in-band full-duplex wireless systems, that aims to design transmit and receive beams which suppress self-interference and maximize the sum spectral efficiency. Rather than rely on explicit estimation of the downlink, uplink, and/or self-interference channels like in most existing work, our met

Eun Woo Im, Junsung Shin, Sungyong Baik, Tae Hyun Kim

Relying on the representation power of neural networks, most recent works have often neglected several factors involved in haze degradation, such as transmission (the amount of light reaching an observer from a scene over distance) and atmospheric light. These factors are generally unknown, making dehazing problems ill-posed and creating inherent uncertainti

Nandini Gadhia, Michalis Smyrnakis, Po-Yu Liu, Damer Blake

Graph-based machine learning methods are useful tools in the identification and prediction of variation in genetic data. In particular, the comprehension of phenotypic effects at the cellular level is an accelerating research area in pharmacogenomics. In this article, a novel graph theoretic approach is proposed to infer a co-occurrence network from 16S micr

Jakob Schloer, Matthew Newman, Jannik Thuemmel, Antonietta Capotondi

While deep-learning models have demonstrated skillful El Ni\~no Southern Oscillation (ENSO) forecasts up to one year in advance, they are predominantly trained on climate model simulations that provide thousands of years of training data at the expense of introducing climate model biases. Simpler Linear Inverse Models (LIMs) trained on the much shorter obser

Dashen Yan

We carry out a gluing construction for collapsing warped-QAC (quasi-asymptotically-conical) Calabi-Yau manifolds in $\CC^{n+2}, n\geq 2$. This gluing theorem verifies a conjecture by Yang Li in \cite{li2019gluing} on the behavior of the warped QAC Calabi-Yau metrics on affine quadrics when two singular fibers of a holomorphic fibration go apart. We will also

Lucas P. Kreuzer, Fan Yang, Andreas Mayer, Noel Jakse

First-principle based molecular-dynamics simulations have been performed for binary Cu$_x$Ti$_{1-x}$ (x = 0.31, 0.50, and 0.76) alloys to investigate the relationship between local structure and dynamical properties in the liquid and undercooled melt. The undercooled melts show a pronounced short-range order, majorly a five-fold symmetry (FFS) around the Cu

Dilan Mian

The world can be a complex and difficult place to navigate. People with High-Functioning Autistic Spectrum Disorder as well as general social ineptitude often face navigation challenges that individuals of other demographics simply do not themselves. This can become even more pronounced with people of that specific group when they are in their teenage years

Abigail A. Tumborang, Jessica J. Spake, Heather A. Knutson, Megan Weiner Mansfield

We present a detailed reanalysis of the atmospheric properties of WASP-19b, an ultra-hot Jupiter (1.14 M Jup, 1.41 R Jup) orbiting an active Sun-like star every 0.79 day. We reanalyze a transit and secondary eclipse of WASP-19b observed by the Hubble Space Telescope's Wide Field Camera 3 spectrograph (1.1 - 1.7 microns). When combined with Spitzer photometry

Alessandro Marcomini, Guillermo Currás-Lorenzo, Davide Rusca, Angel Valle

Multi-photon emissions in laser sources represent a serious threat for the security of quantum key distribution (QKD). While the decoy-state technique allows to solve this problem, it requires uniform phase randomisation of the emitted pulses. However, gain-switched lasers operating at high repetition rates do not fully satisfy this requirement, as residual

Aleyeh Roknaldin, Zehao Zhang, Jiayuan Xu, Kamiar Alaei

Sepsis is a severe condition that causes the body to respond incorrectly to an infection. This reaction can subsequently cause organ failure, a major one being acute kidney injury (AKI). For septic patients, approximately 50% develop AKI, with a mortality rate above 40%. Creating models that can accurately predict AKI based on specific qualities of septic pa

Dewang Sultania, Zhaoyu Lu, Twisha Naik, Franck Dernoncourt

Domain specific question answering is an evolving field that requires specialized solutions to address unique challenges. In this paper, we show that a hybrid approach combining a fine-tuned dense retriever with keyword based sparse search methods significantly enhances performance. Our system leverages a linear combination of relevance signals, including co

Chaoyu Li, Eun Woo Im, Pooyan Fazli

Multimodal large language models (MLLMs) have recently shown significant advancements in video understanding, excelling in content reasoning and instruction-following tasks. However, hallucination, where models generate inaccurate or misleading content, remains underexplored in the video domain. Building on the observation that MLLM visual encoders often fai

Andre N. Souza, Simone Silvestri

We investigate the convergence behavior of the extended dynamic mode decomposition for constructing a discretization of the continuity equation associated with the Lorenz equations using a nonlinear dictionary of over 1,000,000 terms. The primary objective is to analyze the resulting operator by varying the number of terms in the dictionary and the timescale

Xu Feng, Loretta J. Mickley, Jed O. Kaplan, Makoto Kelp

Wildfire activity has increased dramatically in the western United States (US) over the last three decades, having a significant impact on air quality and human health. However, quantifying the drivers of trends in wildfires and subsequent smoke exposure is challenging, as both natural variability and anthropogenic climate change play important roles. Here w