November 2024 arXiv papers — page 105

Xiaoyi Liu, Hao Tang

We introduce DiffFNO, a novel diffusion framework for arbitrary-scale super-resolution strengthened by a Weighted Fourier Neural Operator (WFNO). Mode Rebalancing in WFNO effectively captures critical frequency components, significantly improving the reconstruction of high-frequency image details that are crucial for super-resolution tasks. Gated Fusion Mech

Aitor Iribar Lopez

The locus of non-simple abelian varieties in the moduli space of principally polarized abelian varieties gives rise to Noether-Lefschetz cycles. We study their intersection theoretic properties using the tautological projection constructed in [CMOP24], and show that projection defines a homomorphism when restricted to cycles supported on that locus. Using He

Janghwan Lee, Jiwoong Park, Jinseok Kim, Yongjik Kim

As large language models (LLMs) grow in parameter size and context length, computation precision has been reduced from 16-bit to 4-bit to improve inference efficiency. However, this reduction causes accuracy degradation due to activation outliers. Rotation-based INT4 methods address this via matrix calibration, but they introduce multi-hour overheads and lea

Hunter M. Campbell, Kalee E. Anderson, Nathan A. Kaib

Nearly one-third of objects occupying the most circular, coplanar Kuiper belt orbits (the cold classical belt) are binary, and several percent of them are "ultra-wide" binaries (UWBs): 100-km-sized companions spaced by tens of thousands of km. UWBs are dynamically fragile, and their existence is thought to constrain early Solar System processes and condition

Jinhua Wang, Shengtai Fan, Yiwen Li, Xiyu Zhu

Kagome materials with inherent geometric frustration can produce many interesting physical properties, such as flat bands, quantum spin liquid, chiral magnetism, superconductivity and density-wave orders. Sometimes, the localized 4$f$ electrons from Ce atoms coupled with other conduction electrons would also give rise to the flat bands near the Fermi level,

Rui Meng, Bingxuan Xu, Xiaodong Xu, Mengying Sun

To ensure secure and reliable communication in wireless systems, authenticating the identities of numerous nodes is imperative. Traditional cryptography-based authentication methods suffer from issues such as low compatibility, reliability, and high complexity. Physical-Layer Authentication (PLA) is emerging as a promising complement due to its exploitation

Yanqin Wu, Sam Hadden, Janosz Dewberry, Kareem El-Badry

Orbits of stellar binaries are in general eccentric. These eccentricities encode information about their early lives. Here, we use thousands of main-sequence binaries from the Gaia DR3 catalog to reveal that, binaries inwards of a few AU exhibit a simple Rayleigh distribution with a mode sigma_e ~ 0.3. We find the same distribution for binaries from M to A s

Takuya Kiyokawa, Eiki Nagata, Yoshihisa Tsurumine, Yuhwan Kwon

Mobile grasping enhances manipulation efficiency by utilizing robots' mobility. This study aims to enable a commercial off-the-shelf robot for mobile grasping, requiring precise timing and pose adjustments. Self-supervised learning can develop a generalizable policy to adjust the robot's velocity and determine grasp position and orientation based on the targ

Wenlin Ma, Hong Guo, Haojie Xu, Michael G. Jones

We present the first HI mass function (HIMF) measurement for the recent FAST All Sky HI (FASHI) survey and the most complete measurements of HIMF in the local universe thus far. We obtained these results by combining the HI catalogues from HI Parkes All Sky Survey (HIPASS), Arecibo Legacy Fast ALFA (ALFALFA) and FASHI surveys at redshift 0 < z < 0.05, coveri

Chanho Kim, Jaegeun Park, Hongjun An, Kaya Mori

We present a detailed X-ray investigation of a region (S1) exhibiting non-thermal X-ray emission within the supernova remnant (SNR) CTB 37B hosting the magnetar CXOU J171405.7$-$381031. Previous analyses modeled this emission with a power law (PL), inferring various values for the photon index ($\Gamma$) and absorbing column density ($N_{\rm H}$). Based on t

Xiao-Bin Chen, Xuan-Han Liang, Ruo-Yu Liu, Xiang-Yu Wang

The Boomerang nebula is a bright radio and X-ray pulsar wind nebula (PWN) powered by an energetic pulsar, PSR~J2229+6114. It is spatially coincident with one of the brightest ultrahigh-energy (UHE, $\ge 100$\,TeV) gamma-ray sources, LHAASO~J2226+6057. While X-ray observations have provided radial profiles for both the intensity and photon index of the nebula

Majid Molaei, Marcello Restelli, Alberto Maria Metelli, Matteo Papini

Policy search methods are crucial in reinforcement learning, offering a framework to address continuous state-action and partially observable problems. However, the complexity of exploring vast policy spaces can lead to significant inefficiencies. Reducing the policy space through policy compression emerges as a powerful, reward-free approach to accelerate t

Yaacov Kopeliovich, Michael Pokojovy

With the recent advancements in machine learning (ML), artificial neural networks (ANN) are starting to play an increasingly important role in quantitative finance. Dynamic portfolio optimization is among many problems that have significantly benefited from a wider adoption of deep learning (DL). While most existing research has primarily focused on how DL c

Haijun Yu, Shuo Zhang

Deep neural network approaches show promise in solving partial differential equations. However, unlike traditional numerical methods, they face challenges in enforcing essential boundary conditions. The widely adopted penalty-type methods, for example, offer a straightforward implementation but introduces additional complexity due to the need for hyper-param

Miriam Peña, Fracisco Ruiz-Escobedo, Brenda N. Juárez Torres

In this work we analyze the proposed relation between ADFs and ionized masses in planetary nebulae. For this, we have collected from the literature the ADFs and other parameters such as heliocentric distances, H$\beta$ luminosities, logarithmic reddening correction at H$\beta$, c(H$\beta$), electron densities and others and we calculated the ionized mass for

Arman Ter-Petrosyan, Michael Holden, Jenna A. Bilbrey, Sarah Akers

The development of high-performance materials for microelectronics, energy storage, and extreme environments depends on our ability to describe and direct property-defining microstructural order. Our present understanding is typically derived from laborious manual analysis of imaging and spectroscopy data, which is difficult to scale, challenging to reproduc

Xuewei Feng, Qi Li, Kun Sun, Ke Xu

After more than 40 years of development, the fundamental TCP/IP protocol suite, serving as the backbone of the Internet, is widely recognized for having achieved an elevated level of robustness and security. Distinctively, we take a new perspective to investigate the security implications of cross-layer interactions within the TCP/IP protocol suite caused by

Yanyan Huang, Weiqin Zhao, Yihang Chen, Yu Fu

Whole slide image (WSI) analysis is gaining prominence within the medical imaging field. Recent advances in pathology foundation models have shown the potential to extract powerful feature representations from WSIs for downstream tasks. However, these foundation models are usually designed for general-purpose pathology image analysis and may not be optimal f

Faith Johnson, Bryan Bo Cao, Ashwin Ashok, Shubham Jain

Visual navigation takes inspiration from humans, who navigate in previously unseen environments using vision without detailed environment maps. Inspired by this, we introduce a novel no-RL, no-graph, no-odometry approach to visual navigation using feudal learning to build a three tiered agent. Key to our approach is a memory proxy map (MPM), an intermediate

Alexander E. Siemenn, Basita Das, Kangyu Ji, Fang Sheng

Integrating robotically driven contact-based material characterization techniques into self-driving laboratories can enhance measurement quality, reliability, and throughput. While deep learning models support robust autonomy, current methods lack reliable pixel-precision positioning and require extensive labeled data. To overcome these challenges, we propos

Yihong Guo, Yixuan Wang, Yuanyuan Shi, Pan Xu

Training a policy in a source domain for deployment in the target domain under a dynamics shift can be challenging, often resulting in performance degradation. Previous work tackles this challenge by training on the source domain with modified rewards derived by matching distributions between the source and the target optimal trajectories. However, pure modi

J. Berra-Montiel, G. F. Torres del Castillo

In this paper, we investigate the transformation laws of the Wigner function under changes of reference frames. By employing the coordinate transformation of the wave functions, we derive an integral representation for the transformed Wigner function in both position and momentum representations. To illustrate our results, we include some basic examples.

Alexander Johnston, Ruth E. Baker, Matthew J. Simpson

Parameter inference is essential when interpreting observational data using mathematical models. Standard inference methods for differential equation models typically rely on obtaining repeated numerical solutions of the differential equation(s). Recent results have explored how numerical truncation error can have major, detrimental, and sometimes hidden imp

Longhui Yang, Dejian Liu, Chaojie Hao, Zehao Lin

Based on the Gaia Data Release 3 and APOGEE datasets, we investigate the kinematic differences between young stellar objects (YSOs) and their parent clouds in five nearby star-forming regions. Overall, the 1D velocity differences between Class II YSOs and their parent molecular cloud range from [0, 1.4] km/s. In feedback environments dominated by outflows, m

Rômulo Damasclin Chaves dos Santos, Jorge Henrique de Oliveira Sales

This paper introduces a novel mathematical framework for examining the regularity and energy dissipation properties of solutions to the stochastic Navier-Stokes equations. By integrating Sobolev-Besov hybrid spaces, fractional differential operators, and quantum-inspired modeling techniques, we provide a comprehensive analysis that captures the multiscale an

Tian Niu, Kaizhao Zhang, Zhongxue Gan, Wenchao Ding

Planning safe trajectories for autonomous vehicles is essential for operational safety but remains extremely challenging due to the complex interactions among traffic participants. Recent autonomous driving frameworks have focused on improving prediction accuracy to explicitly model these interactions. However, some methods overlook the significant influence

Chen-Te Ma, Hui Zhang

Our review of the lattice chiral fermion delves into some critical areas of lattice field theory. By abandoning Hermiticity, the non-Hermitian formulation circumvents the Nielsen-Ninomiya theorem while maintaining chiral symmetry, a novel approach. Comparing the Wilson and overlap fermions gives insight into how lattice formulations handle chiral symmetry. T

Buyi Zhang, Divya Chalise, Yuqiang Zeng, Sumanjeet Kaur

Solid-state batteries (SSBs) are promising next-generation batteries due to their high energy density and enhanced thermal stability and safety. However, their sluggish kinetics and transport at room temperature results in high internal impedance and critically reduces the attainable discharge energy density. Taking advantage of their strong temperature-depe

Xiaowu Zhang, Hongfei Zhao, Xuan Chang

This paper proposes a Chinese spelling correction method based on plugin extension modules, aimed at addressing the limitations of existing models in handling domain-specific texts. Traditional Chinese spelling correction models are typically trained on general-domain datasets, resulting in poor performance when encountering specialized terminology in domain

Lankun Han, Zhenyuan Zeng, Min Long, Menghan Song

A Dirac quantum spin liquid hosts Dirac spinons, which are low-energy fractionalized neutral quasiparticles with spin 1/2 that obey the Dirac equation. Recent inelastic neutron scattering studies have revealed a cone spin continuum in YCu$_3$(OD)$_6$Br$_2$[Br$_{x}$(OD)$_{1-x}$], consistent with the convolution of two Dirac spinons. In this work, we further s

Xin Wang, Tianze Sheng, Yuan Sun

The cold atom qubit platform emerges as an attractive choice for the next stage of quantum computation research, where a special family of synthetic analytical pulses has considerably improved the experimental performance of Controlled-PHASE Rydberg blockade gates in recent studies. The success of Controlled-PHASE Rydberg blockade gates triggers the intrigui

Emre Yildirim, Tansel Yucelen, John T. Hrynuk

Symbiotic control synergistically integrates fixed-gain control and adaptive learning architectures to mitigate system uncertainties more predictably than adaptive learning alone and without requiring prior knowledge of uncertainty bounds as compared to fixed-gain control alone. Specifically, increasing the fixed-gain control parameter achieves a desired lev

Olga Movilla Miangolarra, Asmaa Eldesoukey, Ander Movilla Miangolarra, Tryphon T. Georgiou

Reaction-diffusion equations are commonly used to model a diverse array of complex systems, including biological, chemical, and physical processes. Typically, these models are phenomenological, requiring the fitting of parameters to experimental data. In the present work, we introduce a novel formalism to construct reaction-diffusion models that is grounded

Jie Huang

Network data packet anomaly detection faces numerous challenges, including exploring new anomaly supervision signals, researching weakly supervised anomaly detection, and improving model interpretability. This paper proposes NIDS-GPT, a GPT-based causal language model for network intrusion detection. Unlike previous work, NIDS-GPT innovatively treats each nu

Huan Liu, Yuzhe Zhang, Guanjian Liu, Xinxin Du

Attention is a vital cognitive process in the learning and memory environment, particularly in the context of online learning. Traditional methods for classifying attention states of online learners based on behavioral signals are prone to distortion, leading to increased interest in using electroencephalography (EEG) signals for authentic and accurate asses

Hana Ševčíková, James Raymer, Adrian E. Raftery

Most population projection models require age-specific information on net migration totals as a key demographic component of population change. Existing methods for predicting future patterns of net migration by age have proven inadequate. The main reason is that methods applied to model net migration are unable to distinguish factors influencing the inflows

Shimul Akhanjee

A new quantum mechanical distribution function $n^I(\varepsilon)$, is derived for the condition $n \ge g$, where in contrast to the exclusion principle $n \le g$ for fermions, each energy state must be populated by at least one particle. Although the particles share many features with bosons, the anomalous behavior of $n^I(\varepsilon)$ precludes Bose-Einste

Jungho Mun, Sathwik Bharadwaj, Zubin Jacob

The refractive index of a matter is foundational to quantify the light-matter interaction of the medium. However, the classical description of refractive index is based on macroscopic homogenization and is limited to describing the local optical response of materials. A complete quantum description of light-matter interaction should consider nonlocality and

Ha-Na Jo, Young-Seok Kweon, Seo-Hyun Lee

This study investigates the sleep characteristics and brain activity of individuals in the gray zone of insomnia, a population that experiences sleep disturbances yet does not fully meet the clinical criteria for chronic insomnia. Thirteen healthy participants and thirteen individuals from the gray zone were assessed using polysomnography and electroencephal

Chin-Sung Tung, Sheng-Fu Liang, Shu-Feng Chang, Chung-Ping Young

Electroencephalography (EEG) plays a crucial role in the diagnosis of various neurological disorders. However, small hospitals and clinics often lack advanced EEG signal analysis systems and are prone to misinterpretation in manual EEG reading. This study proposes an innovative hybrid artificial intelligence (AI) system for automatic interpretation of EEG ba

Haocong Cheng, Si Chen, Christopher Perdriau, Shriya Mokkapati

Intelligent tutoring systems (ITS) using artificial intelligence (AI) technology have shown promise in supporting learners with diverse abilities. Large language models (LLMs) provide new opportunities to incorporate personas to AI-based tutors and support dynamic interactive dialogue. This paper explores how DHH learners interact with LLM-powered AI tutors

Changpeng Pan

This paper investigates the generalizations and applications of weakly $p$-K\"ahler hyperbolic manifolds.

Giang H. Le, Anh Q. Nguyen, Byeongkeun Kang, Yeejin Lee

Remarkable progress has been achieved in image generation with the introduction of generative models. However, precisely controlling the content in generated images remains a challenging task due to their fundamental training objective. This paper addresses this challenge by proposing a novel image generation framework explicitly designed to incorporate desi

Jari van Steen, Nathan van de Wouw, Alessandro Saccon

With the goal of increasing the speed and efficiency in robotic manipulation, a control approach is presented that aims to utilize intentional simultaneous impacts to its advantage. This approach exploits the concept of the time-invariant reference spreading framework, in which partly-overlapping ante- and post-impact reference vector fields are used. These

Wentao Wang, Guoping Li, Andreas Kontoleon, Yiming Ma

To achieve sustainable development, there is widespread of the need to protect natural resource and improve government oversight in achieving China's economic security and ecological civilization. Compilation of natural resources balance sheet (NRBS) and enhancement of resources management are becoming an important topic in China. How to compile NRBS to affi

Huiguang Zhang, Baoguo Liu

In the field of signal processing, phase transition phenomena have recently attracted great attention. Donoho's work established the signal recovery threshold using indicators such as restricted isotropy (RIP) and incoherence and proved that phase transition phenomena occur in compressed sampling. Nevertheless, the phase transition phenomenon of structured s

Hongbo Li, Lingjie Duan

Today navigation applications (e.g., Waze and Google Maps) enable human users to learn and share the latest traffic observations, yet such information sharing simply aids selfish users to predict and choose the shortest paths to jam each other. Prior routing game studies focus on myopic users in oversimplified one-shot scenarios to regulate selfish routing v

Lanwei Zhang, Jamie Evans, Jingge Zhu

Compute-and-forward (CF) is a relaying strategy which allows the relay to decode a linear combination of the transmitted messages. This work studies the optimal power allocation problem for the CF scheme in fast fading channels for maximizing the symmetric computation rate, which is a non-convex optimization problem with no simple analytical or numerical sol

Eric Kleinherbers, Shane P. Kelly, Yaroslav Tserkovnyak

We present how entanglement between a spatially separated pair of color centers can be created by letting them weakly interact with the quantum fluctuations of a nonequilibrium magnetic environment. To this end, we consider two coupled ferromagnets, one in the ground state and one in an inverted state with respect to an applied magnetic field. The resulting

Daniel Douglas, Aashwin Mishra, Daniel Ratner, Felix Petersen

Sequential or chained models are increasingly prevalent in machine learning for scientific applications, due to their flexibility and ease of development. Chained models are particularly useful when a task is separable into distinct steps with a hierarchy of meaningful intermediate representations. In reliability-critical tasks, it is important to quantify t

Jingyi Cao, Xiangyi Chen, Bo Liu, Ming Ding

The widespread use of image acquisition technologies, along with advances in facial recognition, has raised serious privacy concerns. Face de-identification usually refers to the process of concealing or replacing personal identifiers, which is regarded as an effective means to protect the privacy of facial images. A significant number of methods for face de

Doyon Kim

We give a new proof of the existence of Whittaker functionals for principal series representation of $\text{GL}(n,\mathbb{R})$, utilizing the analytic theory of distributions. We realize Whittaker functionals as equivariant distributions on $\text{GL}(n,\mathbb{R})$, whose restriction to the open Schubert cell is unique up to a constant. Using a birational m

Akhil Shajan, Danil Kaliakin, Abhishek Mitra, Javier Robledo Moreno

Computing ground-state properties of molecules is a promising application for quantum computers operating in concert with classical high-performance computing resources. Quantum embedding methods are a family of algorithms particularly suited to these computational platforms: they combine high-level calculations on active regions of a molecule with low-level

Mark L. Watson, Alexander B. Stilgoe, Itia A. Favre-Bulle, Halina Rubinsztein-Dunlop

Accessing the ballistic regime of single particles in liquids remains an experimental challenge that shrouds our understanding of the particle-liquid interactions on exceedingly short time scales. We demonstrate the ballistic measurements of rotational probes to observe these interactions in the rotational regime within microscopic systems. This study uses s

Ishna Satyarth, Chao Yin, Devin A. Matthews, Maggie Myers

The factorization of skew-symmetric matrices is a critically understudied area of dense linear algebra, particularly in comparison to that of general and symmetric matrices. While some algorithms can be adapted from the symmetric case, the cost of algorithms can be reduced by exploiting skew-symmetry. This work examines the factorization of a skew-symmetric

Xiaoyu Yang, Lijian Xu, Hongsheng Li, Shaoting Zhang

This paper proposes a scalable and straightforward pre-training paradigm for efficient visual conceptual representation called occluded image contrastive learning (OCL). Our OCL approach is simple: we randomly mask patches to generate different views within an image and contrast them among a mini-batch of images. The core idea behind OCL consists of two desi

Alexandru Chirvasitu

Consider a proper action of $\mathbb{Z}^d$ on a smooth (perhaps non-paracompact) manifold $M$. The $p^{th}$ cohomology $H^p(\mathbb{Z}^d,\ \Gamma_{\mathrm{c}}(\mathcal{F}))$ valued in the space of compactly-supported sections of a natural sheaf $\mathcal{F}$ on $M$ (such as those of smooth function germs, smooth $k$-form germs, etc.) vanishes for $p\ne d$ (t

Xiaoxuan Hou, Jiayi Yuan, Joel Z. Leibo, Natasha Jaques

InvestESG is a novel multi-agent reinforcement learning (MARL) benchmark designed to study the impact of Environmental, Social, and Governance (ESG) disclosure mandates on corporate climate investments. The benchmark models an intertemporal social dilemma where companies balance short-term profit losses from climate mitigation efforts and long-term benefits

John B. Bell, Andrew Nonaka, Alejandro L. Garcia

We simulate the mesoscopic dynamics of droplets formed by phase separated fluids at nanometer scales where thermal fluctuations are significant. Both spherical droplets fully immersed in a second fluid and sessile droplets which are also in contact with a solid surface are studied. Our model combines a Cahn-Hillard formulation with incompressible fluctuating

Eric Balkanski, Will Ma, Andreas Maggiori

Algorithms with predictions is a recent framework for decision-making under uncertainty that leverages the power of machine-learned predictions without making any assumption about their quality. The goal in this framework is for algorithms to achieve an improved performance when the predictions are accurate while maintaining acceptable guarantees when the pr

Jirawat Tangpanitanon

The pursuit of energy transition necessitates the coordination of several technologies, including more efficient and cost-effective distributed energy resources (DERs), smart grids, carbon capture, utilization, and storage (CCUS), energy-efficient technologies, Internet of Things (IoT), edge computing, artificial intellience (AI) and nuclear energy, among ot

Pranav Guruprasad, Negar Mokhberian, Nikhil Varghese, Chandra Khatri

Intent discovery is crucial for both building new conversational agents and improving existing ones. While several approaches have been proposed for intent discovery, most rely on clustering to group similar utterances together. Traditional evaluation of these utterance clusters requires intent labels for each utterance, limiting scalability. Although some c

Zhichen Zeng, Xiaolong Liu, Mengyue Hang, Xiaoyi Liu

Click-through rate (CTR) prediction, which predicts the probability of a user clicking an ad, is a fundamental task in recommender systems. The emergence of heterogeneous information, such as user profile and behavior sequences, depicts user interests from different aspects. A mutually beneficial integration of heterogeneous information is the cornerstone to

Saber Ahmed, Natasha Crepeau, Paul R. Dessauer, Alexis Edozie

A parameter of a mathematical model is structurally identifiable if it can be determined from noiseless experimental data. Here, we examine the identifiability properties of two important classes of linear compartmental models: directed-cycle models and catenary models (models for which the underlying graph is a directed cycle or a bidirected path, respectiv

Ho Fung Tsoi, Dylan Rankin, Cecile Caillol, Miles Cranmer

We introduce SymbolFit, a framework that automates parametric modeling by using symbolic regression to perform a machine-search for functions that fit the data while simultaneously providing uncertainty estimates in a single run. Traditionally, constructing a parametric model to accurately describe binned data has been a manual and iterative process, requiri

Shijie Zhou, Huaisheng Zhu, Rohan Sharma, Jiayi Chen

Diffusion models have emerged as a powerful foundation model for visual generations. With an appropriate sampling process, it can effectively serve as a generative prior for solving general inverse problems. Current posterior sampling-based methods take the measurement (i.e., degraded image sample) into the posterior sampling to infer the distribution of the

Amir Reza Asadi, Shiva Ghasemi

This systematic literature review paper explores perspectives on the ideal metaverse from user experience, business, and national levels, considering both academic and industry viewpoints. The study examines the metaverse as a sociotechnical imaginary, enabled collectively by virtual reality (VR), augmented reality (AR), and mixed reality (MR) technologies.

Serge Cantat, Romain Dujardin

We prove several new rigidity results for polynomial automorphisms of $\mathbb C^2$ with positive entropy. A first result is that a complex slice of the (forward or backward) Julia set is never a smooth, or even rectifiable, curve. We also show that such an automorphism cannot preserve a global holomorphic foliation, nor a real-analytic foliation with comple

Niccolò Sellati, Jacopo Fiore, Stefano Paolo Villani, Lara Benfatto

Hybrid lattice-light modes, known as phonon-polaritons, represent the backbone of advanced protocols based on THz pumping of infrared modes. Here we provide a theoretical framework able to capture the different roles played by phonon-polaritons in experimental protocols based either on Raman-like pump and probe schemes, typical of four-wave-mixing processes,

Benjamin Doerr, Dimitri Korkotashvili, Martin S. Krejca

The NSGA-II is the most prominent multi-objective evolutionary algorithm (cited more than 50,000 times). Very recently, a mathematical runtime analysis has proven that this algorithm can have enormous difficulties when the number of objectives is larger than two (Zheng, Doerr. IEEE Transactions on Evolutionary Computation (2024)). However, this result was sh

Ahmed Aboulfotouh, Ashkan Eshaghbeigi, Dimitrios Karslidis, Hatem Abou-Zeid

Foundational deep learning (DL) models are general models, trained on large, diverse, and unlabelled datasets, typically using self-supervised learning techniques have led to significant advancements especially in natural language processing. These pretrained models can be fine-tuned for related downstream tasks, offering faster development and reduced train

Richard Duong, Nicolaj Rux, Viktor Stein, Gabriele Steidl

We consider Wasserstein gradient flows of maximum mean discrepancy (MMD) functionals $\text{MMD}_K^2(\cdot, \nu)$ for positive and negative distance kernels $K(x,y) := \pm |x-y|$ and given target measures $\nu$ on $\mathbb{R}$. Since in one dimension the Wasserstein space can be isometrically embedded into the cone $\mathcal C(0,1) \subset L_2(0,1)$ of quant

Lara Kassab, Erin George, Deanna Needell, Haowen Geng

There has been a recent critical need to study fairness and bias in machine learning (ML) algorithms. Since there is clearly no one-size-fits-all solution to fairness, ML methods should be developed alongside bias mitigation strategies that are practical and approachable to the practitioner. Motivated by recent work on ``fair" PCA, here we consider the more

Hang Du, Vijay Krishna Palepu, James A. Jones

Mutation testing was proposed to identify weaknesses in test suites by repeatedly generating artificially faulty versions of the software (mutants) and determining if the test suite is sufficient to detect them (kill them). When the tests are insufficient, each surviving mutant provides an opportunity to improve the test suite. We conducted a study and found

Margaret Doig, Chase Gehringer

We study the booklink, a braid-like embedding with local maxima and minima, and the bridge-braid spectrum of a link, which captures the smallest number of braid-strands in a booklink with a prescribed number of critical points. This spectrum spans the gap between the classical bridge and braid indices. We apply a foliation theory argument to provide a formul

İrem Üstek, Miguel Arana-Catania, Alexander Farr, Ivan Petrunin

Wildfires pose a significantly increasing hazard to global ecosystems due to the climate crisis. Due to its complex nature, there is an urgent need for innovative approaches to wildfire prediction, such as machine learning. This research took a unique approach, differentiating from classical supervised learning, and addressed the gap in unsupervised wildfire

Martin Nava-Callejas, Yuri Cavecchi, Dany Page

One long standing tension between theory and observations of Type I X-ray burst is the accretion rate at which the burst disappear due to stabilization of the nuclear burning that powers them. This is observed to happen at roughly one third of the theoretical expectations. Various solutions have been proposed, the most notable of which is the addition of a y

Luca Palazzo, Matteo Pennisi, Federica Proietto Salanitri, Giovanni Bellitto

In this paper, we present FedRewind, a novel approach to decentralized federated learning that leverages model exchange among nodes to address the issue of data distribution shift. Drawing inspiration from continual learning (CL) principles and cognitive neuroscience theories for memory retention, FedRewind implements a decentralized routing mechanism where

Kevin Scheuer

Excited meson states can often lie hidden within mass spectra beneath more dominant resonances, making it difficult to extract their physical properties. We can unveil these states through amplitude analysis, which disentangles the overlapping states via fits to their unique production and decay angular distributions. Understanding the light-meson spectrum i

Peng Wang, Peter Luh

Brownian motion have long been studied on a diversity of fields, not only in physics of statistical mechanics, but also in biological models, finance and economic process, and social systems. In the past twenty years, there has been a growing interest in studying the model in self-propelled feature and interaction force such that the model also fits into stu

Roman Aranda, Fraser Binns, Margaret Doig

We introduce "book links" as a generalization of braids in open book decompositions; this new class of objects includes both braids and plats as special cases. We then prove a version of Markov's theorem in this general setting by extending the theory of open book foliations.

Sanghyun Byun, Kayvan Shah, Ayushi Gang, Christopher Apton

Many state-of-the-art computer vision architectures leverage U-Net for its adaptability and efficient feature extraction. However, the multi-resolution convolutional design often leads to significant computational demands, limiting deployment on edge devices. We present a streamlined alternative: a 1D convolutional encoder that retains accuracy while enhanci

Kirill Vasilevski, Dayi Lin, Ahmed E. Hassan

To balance the quality and inference cost of a Foundation Model (FM, such as large language models (LLMs)) powered software, people often opt to train a routing model that routes requests to FMs with different sizes and capabilities. Existing routing models rely on learning the optimal routing decision from carefully curated data, require complex computation

Raquel Díaz, Víctor González-Aguilera

Let ${\mathcal M}_g$ be the moduli space of compact connected Riemann surfaces of genus $g\geq 2$ and let $\widehat{{\mathcal M}_g}$ be its Deligne-Mumford compactification, which is stratified by the topological type of the stable Riemann surfaces. We consider the equisymmetric loci in $\mathcal M_g$ corresponding to Riemann surfaces whose automorphism grou

M. Spieker, D. Bazin, S. Biswas, P. D. Cottle

We report new experimental data for excited states of $^{72,74}$Se obtained from proton removal from $^{73,75}$Br secondary beams on a proton target. The experiments were performed with the Ursinus-NSCL Liquid Hydrogen Target and the combined GRETINA+S800 setup at the Coupled Cyclotron Facility of the National Superconducting Cyclotron Laboratory at Michigan

Pedram Hosseini, Jessica M. Sin, Bing Ren, Bryceton G. Thomas

There is a lack of benchmarks for evaluating large language models (LLMs) in long-form medical question answering (QA). Most existing medical QA evaluation benchmarks focus on automatic metrics and multiple-choice questions. While valuable, these benchmarks fail to fully capture or assess the complexities of real-world clinical applications where LLMs are be

Mikhail R. Guzman

Let $ M = G/K $ be a full flag manifold. In this work, we investigate the $ G$-stability of Einstein metrics on $M$ and analyze their stability types, including coindices, for several cases. We specifically focus on $F(n) = \mathrm{SU}(n)/T$, emphasizing $n = 5$, where we identify four new Einstein metrics in addition to known ones. Stability data, including

Nantel Bergeron, Vincent Pilaud

For a hypergraph $\mathbb{H}$ on $[n]$, the hypergraphic poset $P_\mathbb{H}$ is the transitive closure of the oriented skeleton of the hypergraphic polytope $\triangle_\mathbb{H}$ (the Minkowski sum of the standard simplices $\triangle_H$ for all $H \in \mathbb{H}$). Hypergraphic posets include the weak order for the permutahedron (when $\mathbb{H}$ is the

A. N. Kuchera, G. Ryan, G. Selby, D. Snider

The resonance region of $^{11}$B covering excitation energies from 8.4 MeV to 13.6 MeV was investigated with the $(d,p)$ reaction performed on an enriched $^{10}$B target at the Florida State University Super-Enge Split-Pole Spectrograph of the John D. Fox Superconducting Linear Accelerator Laboratory. Complementary measurements were performed with a target

Hesham Abdelfattah, Sameh A. Eisa, Peter Stechlinski

Optimal control theory extending from the calculus of variations has not been used to study the wind turbine power system (WTPS) control problem, which aims at achieving two targets: (i) maximizing power generation in lower wind speed conditions; and (ii) maintaining the output power at the rated level in high wind speed conditions. A lack of an optimal cont

Daniel A. Yahalomi, David Kipping

There are now thousands of single-planet systems observed to exhibit transit timing variations (TTVs), yet we largely lack any interpretation of the implied masses responsible for these perturbations. Even when assuming these TTVs are driven by perturbing planets, the solution space is notoriously multi-modal with respect to the perturber's orbital period an

Nabil Bennenni, André Leroy

In this article, we introduce new scalar products over finite rings via additive isomorphisms. This allows us to define new notions of right (respectively left) orthogonal codes, that are not necessarily linear. This leads to definitions of right (resp. left) dual codes and left LCD codes similar to the classical LCD codes. Furthermore, we provide necessary

Risto Uuk, Annemieke Brouwer, Tim Schreier, Noemi Dreksler

The systemic risks posed by general-purpose AI models are a growing concern, yet the effectiveness of mitigations remains underexplored. Previous research has proposed frameworks for risk mitigation, but has left gaps in our understanding of the perceived effectiveness of measures for mitigating systemic risks. Our study addresses this gap by evaluating how

S. Scott Collis, Matthias Heinkenschloss

We study the effect of the streamline upwind/Petrov Galerkin (SUPG) stabilized finite element method on the discretization of optimal control problems governed by linear advection-diffusion equations. We compare two approaches for the numerical solution of such optimal control problems. In the discretize-then-optimize approach, the optimal control problem is

David W. Romero

The emergence of Deep Learning has marked a profound shift in machine learning, driven by numerous breakthroughs achieved in recent years. However, as Deep Learning becomes increasingly present in everyday tools and applications, there is a growing need to address unresolved challenges related to its efficiency and sustainability. This dissertation delves in

Michael Döll, Markus Döhring, Andreas Müller

Gender bias in artificial intelligence has become an important issue, particularly in the context of language models used in communication-oriented applications. This study examines the extent to which Large Language Models (LLMs) exhibit gender bias in pronoun selection in occupational contexts. The analysis evaluates the models GPT-4, GPT-4o, PaLM 2 Text B

Ariel Norambuena, Diego Tancara, Vicente Chomalí-Castro, Daniel Castillo

The electron-phonon interaction is one of the most fundamental mechanisms in condensed matter physics. Phonons can induce memory effects in solid-state platforms when localized electronic states interact with lattice vibrations in non-unitary dynamical maps. In this work, we demonstrate how single-mode and structured phonon environments can give rise to non-

Jinge Ma, Xiaoyan Zhang, Gautham Vinod, Siddeshwar Raghavan

Food portion estimation is crucial for monitoring health and tracking dietary intake. Image-based dietary assessment, which involves analyzing eating occasion images using computer vision techniques, is increasingly replacing traditional methods such as 24-hour recalls. However, accurately estimating the nutritional content from images remains challenging du

Mikhailo Dokuchaev, Emmanuel Jerez

Given a group \( G \), a field \( \kappa \), and a factor set \( \sigma \) arising from a partial projective \( \kappa \)-representation of \( G \). This leads to the construction of a topological partial dynamical system \( (\Omega_\sigma, G, \hat{\theta}) \), where \( \Omega_\sigma \) is a compact, totally disconnected Hausdorff space, and \( \sigma \) act

Yian Wang, Xiaowen Qiu, Jiageng Liu, Zhehuan Chen

Creating large-scale interactive 3D environments is essential for the development of Robotics and Embodied AI research. Current methods, including manual design, procedural generation, diffusion-based scene generation, and large language model (LLM) guided scene design, are hindered by limitations such as excessive human effort, reliance on predefined rules