March 2025 arXiv papers — page 154

Meltem Tatlı, Arpan Mukherjee, Prashanth L. A., Karthikeyan Shanmugam

This paper introduces a general framework for risk-sensitive bandits that integrates the notions of risk-sensitive objectives by adopting a rich class of distortion riskmetrics. The introduced framework subsumes the various existing risk-sensitive models. An important and hitherto unknown observation is that for a wide range of riskmetrics, the optimal bandi

Hu Xu, Frederic Marazzato, Paul Plucinsky

Origami metamaterials made of repeating unit cells of parallelogram panels joined at folds dramatically change their shape through a collective motion of their cells. Here we develop an effective elastic model and numerical method to study the large deformation response of these metamaterials under a broad class of loads. The model builds on an effective pla

Zhiyuan Zeng, Yizhong Wang, Hannaneh Hajishirzi, Pang Wei Koh

An ideal model evaluation should achieve two goals: identifying where the model fails and providing actionable improvement guidance. Toward these goals for language model (LM) evaluations, we formulate the problem of generating a weakness profile, a set of weaknesses expressed in natural language, given an LM's performance on every individual instance in a b

Whit Lewis, Ryan G. McClarren

Methods for computing the integral of the Planck blackbody function over a finite spectral range, the so-called incomplete Planck integral, are necessary to perform multigroup radiative transfer calculations. We present a comparison, in terms of speed and accuracy, of a wide array of approaches to numerically evaluating these integrals. Our results indicate

Jorge de Heuvel, Daniel Marta, Simon Holk, Iolanda Leite

Aligning robot navigation with human preferences is essential for ensuring comfortable, and predictable robot movement in shared spaces. While preference-based learning methods, such as reinforcement learning from human feedback (RLHF), enable this alignment, the choice of the preference collection interface may influence the process. Traditional 2D interfac

Zhiwen You, Yue Guo

Hallucinated outputs from large language models (LLMs) pose risks in the medical domain, especially for lay audiences making health-related decisions. Existing automatic factual consistency evaluation methods, such as entailment- and question-answering (QA) -based, struggle with plain language summarization (PLS) due to elaborative explanation phenomenon, wh

Ethan Horstman, Adrian Woodley, Thomas C. Underwood

The presence of magnetohydrodynamic (MHD) acceleration modes in pulsed plasma thrusters has been verified using the magnetic extension of Rankine-Hugoniot theory. However, the impact of initial conditions within the accelerator volume on the formation and structure of these modes remains poorly understood. This work develops a regime map to clarify how key i

Ricardo Chávez, Rosa Amelia González-Lópezlira, Gustavo Bruzual

This study presents a comprehensive analysis of the youngest stellar clusters in the Large Magellanic Cloud (LMC), utilising a multi-wavelength approach. We analyse data spanning from infrared to ultraviolet wavelengths, with the goal of enhancing our understanding of these clusters' physical properties, such as age, mass, and size. Our methodology includes

Souvik Bose, Jayant Joshi, Paola Testa, Bart De Pontieu

Spicules have often been proposed as substantial contributors toward the mass and energy balance of the solar corona. While their transition region (TR) counterpart has unequivocally been established over the past decade, the observations concerning the coronal contribution of spicules have often been contested. This is mainly attributed to the lack of adequ

Kristian D. Barajas, Wesley C. Campbell

We present a quantum averaging theory (QAT) for analytically modeling unitary gate dynamics in driven quantum systems beyond the rotating-wave approximation. QAT addresses the simultaneous presence of distinct timescales by generating a rotating frame with a dynamical phase operator that toggles with the high-frequency dynamics and yields an effective Hamilt

Mehmet Onur Fen, Fatma Tokmak Fen

Quasilinear systems with piecewise constant arguments of generalized type are under investigation from the asymptotic point of view. The systems have discontinuous right-hand sides which are identified via a discrete-time map. It is rigorously proved that homoclinic and heteroclinic solutions are generated, and they are taken into account in the functional s

Kuang-Da Wang, Ping-Chun Hsieh, Wen-Chih Peng

Stackelberg games, widely applied in domains like economics and security, involve asymmetric interactions where a leader's strategy drives follower responses. Accurately modeling these dynamics allows domain experts to optimize strategies in interactive scenarios, such as turn-based sports like badminton. In multi-agent systems, agent behaviors are interdepe

Haoyang Wang, Haoran Guo, Lingfeng Tao, Zhengxiong Li

Dexterous telemanipulation critically relies on the continuous and stable tracking of the human operator's commands to ensure robust operation. Vison-based tracking methods are widely used but have low stability due to anomalies such as occlusions, inadequate lighting, and loss of sight. Traditional filtering, regression, and interpolation methods are common

Tetsuma Mandokoro, Yoichi Shiota, Tomoya Ito, Hiroki Matsumoto

Spin superfluidity, a phenomenon enabling low dissipative spin transport analogous to superfluidity in liquid helium and superconductivity in electronic systems, has remained a theoretical concept. To realize the spin superfluidity in an antiferromagnet, it is necessary to excite a N\'eel vector rotation within the magnetic easy-plane, which has been elusive

Giovanni Toto, Antonio Canale

We introduce a Bayesian framework for indirect local clustering of functional data, leveraging B-spline basis expansions and a novel dependent random partition model. By exploiting the local support properties of B-splines, our approach allows partially coincident functional behaviors, achieved when shared basis coefficients span sufficiently contiguous regi

Shuoxue Li, Chenghan Li, Huanchen Zhai, Garnet Kin-Lic Chan

Density matrix embedding theory (DMET) provides a framework to describe ground-state expectation values in strongly correlated systems, but its extension to dynamical quantities is still an open problem. We show one route to obtaining excitations and dynamical spectral functions by using the techniques of DMET to approximate the matrix elements that arise in

Guangtao Wang, Shubhangi Upasani, Chen Wu, Darshan Gandhi

Efficient long-context inference is critical as large language models (LLMs) adopt context windows of ranging from 128K to 1M tokens. However, the growing key-value (KV) cache and the high computational complexity of attention create significant bottlenecks in memory usage and latency. In this paper, we find that attention in diverse long-context tasks exhib

R. G. Gomez, S. A. Fuselier, S. K. Vines, J. Goldstein

Ion measurements made with the Hot Plasma Composition Analyzers of the Magnetospheric Multiscale Mission (MMS-HPCAs) during the Mother's Day Storm (Gannon Storm) of 10-13 May 2024 yield the first observations of atomic and molecular nitrogen ions in the Earth's dayside outer magnetosphere. A population of ions identified as doubly charged nitrogen and oxygen

Boris Perminov, Aram Mkrtchyan, Yuriy Gladush, Dmitry V. Krasnikov

Mid-infrared fibre lasers are crucial for applications in spectroscopy, medical diagnostics, and environmental sensing, owing to their ability to interact with fundamental molecular vibrational bands. However, achieving stable ultrafast pulse generation in this spectral range remains challenging due to the limited availability of robust saturable absorbers.

Vincent C. Müller

This paper investigates a problem about freedom of information. Although freedom of information is generally considered desirable, there are a number of areas where there is substantial agreement that freedom of information should be limited. After a certain ordering of the landscape, I argue that we need to add the category of 'dangerous' information and th

A. Mishra, P. Personnettaz, G. Mamatsashvili, V. Galindo

The effects of axial boundaries, or endcaps are of fundamental interest in many Taylor-Couette (TC) flow experiments. A main challenge in those experiments has been to minimize these effects, which can substantially alter the flow structure compared to the axially unbounded idealized case. Therefore, understanding and disentangling the influence of endcaps o

Wantong Li, Gregory Duveiller, Fabian Gans, Jeroen Smits

It is increasingly recognized that the multiple and systemic impacts of Earth system change threaten the prosperity of society through altered land carbon dynamics, freshwater variability, biodiversity loss, and climate extremes. For example, in 2022, there are about 400 climate extremes and natural hazards worldwide, resulting in significant losses of lives

Žan Grad

For a Lie groupoid $G$, the differential forms on its nerve comprise a double complex. A natural question is if this statement extends to forms with values in a representation $V$ of $G$. In this paper, we research two types of covariant derivatives which commute with the simplicial differential, yielding two types of "curved" double complexes of forms with

Cameron Redovian

We integrate a meta-reinforcement learning algorithm with the DreamerV3 architecture to improve load balancing in operating systems. This approach enables rapid adaptation to dynamic workloads with minimal retraining, outperforming the Advantage Actor-Critic (A2C) algorithm in standard and adaptive trials. It demonstrates robust resilience to catastrophic fo

Michaël Liefsoens

Hypersurfaces are studied and classified under multiple additional assumptions in any Riemannian homogeneous space $(\mathbb{C}P^3, g_a)$, including nearly K\"ahler $\mathbb{C}P^3$. Notably, all extrinsically homogeneous hypersurfaces are classified in all these spaces, with an explicit family of examples. Moreover, for nearly K\"ahler $\mathbb{C}P^3$, all H

Rafael R. Oexner, Robin Schmitt, Hyunchan Ahn, Ravi A. Shah

Predictive modelling is vital to guide preventive efforts. Whilst large-scale prospective cohort studies and a diverse toolkit of available machine learning (ML) algorithms have facilitated such survival task efforts, choosing the best-performing algorithm remains challenging. Benchmarking studies to date focus on relatively small-scale datasets and it is un

Reza Mirzaeifard, Stefan Werner

This paper presents a hierarchical federated learning (FL) framework that extends the alternating direction method of multipliers (ADMM) with smoothing techniques, tailored for non-convex and non-smooth objectives. Unlike traditional hierarchical FL methods, our approach supports asynchronous updates and multiple updates per iteration, enhancing adaptability

Araceli Bonifant, John Milnor

We study the parameter space ${\mathcal S}_p$ for cubic polynomial maps with a marked critical point of period $p$. We will outline a fairly complete theory as to how the dynamics of the map $F$ changes as we move around the parameter space ${\mathcal S}_p$. For every escape region ${\mathcal E}\subset {\mathcal S}_p$, every parameter ray in ${\mathcal E}$ w

Abdullah Alchihabi, Hanping Zhang, Yuhong Guo

Reinforcement Learning (RL) has demonstrated remarkable success in solving sequential decision-making problems. However, in real-world scenarios, RL agents often struggle to generalize when faced with unseen actions that were not encountered during training. Some previous works on zero-shot action generalization rely on large datasets of action observations

Praveen Balaji, Cianán Conefrey-Shinozaki, Patrick Draper, Jason K. Elhaderi

Lattice gauge theories in varying dimensions, lattice volumes, and truncations offer a rich family of targets for Hamiltonian simulation on quantum devices. In return, formulating quantum simulations can provide new ways of thinking about the quantum structure of gauge theories. In this work, we consider pure $SU(3)$ gauge theory in two and three spatial dim

Xin Fu, Bin Guo, Jian Song

Let X be a projective variety of complex dimension 3 with log terminal singularities. We prove that every singular Kahler metric on X with bounded Nash entropy and Ricci curvature bounded below induces a compact RCD space homeomorphic to the projective variety X itself. In particular, singular Kahler-Einstein spaces of complex dimension 3 with bounded Nash e

Dezhong Tong, Zhuonan Hao, Jiyu Li, Boxi Sun

Magnetic soft continuum robots (MSCRs) have emerged as a promising technology for minimally invasive interventions, offering enhanced dexterity and remote-controlled navigation in confined lumens. Unlike conventional guidewires with pre-shaped tips, MSCRs feature a magnetic tip that actively bends under applied magnetic fields. Despite extensive studies in m

Debajyoti Kar, Arindam Khan, Malin Rau

We study two fundamental three-dimensional (3D) geometric packing problems: 3D (Geometric) Bin Packing (3D-BP), and 3D Minimum Volume Bounding Box (3D-MVBB), where given a set of 3D (rectangular) cuboids, the goal is to find an axis-aligned nonoverlapping packing of all cuboids. In 3D-BP, we need to pack the given cuboids into the minimum number of unit cube

Henry Adams, Alex Elchesen, Sucharita Mallick, Michael Moy

For $X$ a metric space and $r\ge 0$, the anti-Vietoris-Rips metric thickening $\mathrm{AVR^m}(X;r)$ is the space of all finitely supported probability measures on $X$ whose support has spread at least $r$, equipped with an optimal transport topology. We study the anti-Vietoris-Rips metric thickenings of spheres. We have a homeomorphism $\mathrm{AVR^m}(S^n;r)

Nicolas Bridges, Shawn Cui

We give invariants of flat bundles over 4-manifolds generalizing a result by Chaidez, Cotler, and Cui (Alg. \& Geo. Topology '22). We utilize a structure called a Hopf $G$-triplet for $G$ a group, which generalizes the notion of a Hopf triplet by Chaidez, Cotler, and Cui. In our construction, we present flat bundles over 4-manifolds using colored trisection

Julien Deparday, Yuriy Marikovskiy, Imad Abdallah, Sarah Barber

The wind energy sector is growing rapidly with the installation of wind turbines with long, slender blades in a diverse range of locations. To enhance the operational performance under specific wind conditions and to validate the aerodynamic design of flexible blades, it is crucial to obtain comprehensive data on the aerodynamic behaviour of the blades in th

Brunó B. Englert, Gijs Dubbelman

Unsupervised Domain Adaptation (UDA) enables strong generalization from a labeled source domain to an unlabeled target domain, often with limited data. In parallel, Vision Foundation Models (VFMs) pretrained at scale without labels have also shown impressive downstream performance and generalization. This motivates us to explore how UDA can best leverage VFM

Alva Markelius, Julie Bailey, Jenny L. Gibson, Hatice Gunes

This paper presents an iterative, participatory, empirical study that examines the potential of using artificial intelligence, such as social robots and large language models, to support mediation and advocacy for students with disabilities in higher education. Drawing on qualitative data from interviews and focus groups conducted with various stakeholders,

Daniel Baxter, Rouven Essig, Yonit Hochberg, Margarita Kaznacheeva

Solid-state phonon and charge detectors probe the scattering of weakly interacting particles, such as dark matter and neutrinos, through their low recoil thresholds. Recent advancements have pushed sensitivity to eV-scale energy depositions, uncovering previously-unseen low-energy excess backgrounds. While some arise from known processes such as thermal radi

Sepehr Samavi, Anthony Lem, Fumiaki Sato, Sirui Chen

To navigate crowds without collisions, robots must interact with humans by forecasting their future motion and reacting accordingly. While learning-based prediction models have shown success in generating likely human trajectory predictions, integrating these stochastic models into a robot controller presents several challenges. The controller needs to accou

Rafael Carranza, Mateo Alejandro Rojas

This paper introduces a novel approach to Dialogue State Tracking (DST) that leverages Large Language Models (LLMs) to generate natural language descriptions of dialogue states, moving beyond traditional slot-value representations. Conventional DST methods struggle with open-domain dialogues and noisy inputs. Motivated by the generative capabilities of LLMs,

C. A. G Almeida, I. Andrade, M. A. Marques, R. Menezes

In this work, we investigate the presence of vortex configurations with logarithmic tails, which we call super long-range vortices, in Maxwell-Higgs models with gauge field dynamics modified by generalized magnetic permeability in the Lagrangian density. By taking advantage of a first-order formalism, we study which behavior the magnetic permeability must ha

S. G. Porsev, D. Filin, C. Cheung, M. S. Safronova

Assembly of ultracold polar molecules containing silver (Ag) from laser-cooled atoms requires knowledge of the dynamic polarizabilities of Ag at convenient laser wavelengths. We present calculations and analysis of the energies and electric-dipole dc and ac polarizabilities of the low-lying states of neutral Ag. Calculations of the properties of the 4d^{10}x

Nathan Golovich, Trevor Steil, Alex Geringer-Sameth, Keita Iwabuchi

Modern astronomical surveys detect asteroids by linking together their appearances across multiple images taken over time. This approach faces limitations in detecting faint asteroids and handling the computational complexity of trajectory linking. We present a novel method that adapts ``digital tracking" - traditionally used for short-term linear asteroid m

Sahil Dagli, Jiyong Shim, Hamish Carr Delgado, Halleh B. Balch

Electrically tunable metasurfaces that control the amplitude and phase of light through biasing of nanoscale antennas present a route to compact, sub-micron thick modulator devices. However, most platforms face limitations in bandwidth, absolute optical efficiency, and tuning response. Here, we present electro-optically tunable metasurfaces capable of both G

Rohan Vernekar, Faisal Ahmad, Martin Garic, Dácil Idaira Yánez Martín

Inspired by small intestine motility, we investigate the flow induced by a propagating pendular-wave along the walls of a channel lined with rigid, villi-like microstructures. The villi undergo harmonic axial oscillations with a phase lag relative to their neighbours, generating travelling patterns of intervillous contraction. Using two-dimensional lattice B

Wasif J. Hussain, Don-Roberts Emenonye, R. Michael Buehrer, Harpreet S. Dhillon

Age of Information (AoI) is a key metric used for evaluating data freshness in communication networks, particularly in systems requiring real-time updates. In positioning applications, maintaining low AoI is critical for ensuring timely and accurate position estimation. This paper introduces an age-informed metric, which we term as Age of Positioning (AoP),

Tobias Wegel, Filip Kovačević, Alexandru Ţifrea, Fanny Yang

Simultaneously addressing multiple objectives is becoming increasingly important in modern machine learning. At the same time, data is often high-dimensional and costly to label. For a single objective such as prediction risk, conventional regularization techniques are known to improve generalization when the data exhibits low-dimensional structure like spar

Zhengye Zhou

We consider a family of Pfaffian Schur processes whose first coordinate marginal relates to the half--space geometric last passage percolation. We show that the line ensembles corresponding to the Pfaffian Schur processes with geometric weights converge uniformly over compact sets to the Airy line ensemble. By detailed asymptotic analysis of the kernels, we

The MMGPDs Collaboration, Muhammad Goharipour, Fatemeh Irani, M. H. Amiri

The concept of nucleon radii plays a central role in our understanding of the internal structure of protons and neutrons, providing critical insights into the non-perturbative regime of quantum chromodynamics (QCD). While the charge radius is often interpreted as the ``size" of the nucleon, this interpretation is an oversimplification that overlooks the mult

Luis Damiano, Walter M. Hannah, Chih-Chieh Chen, James J. Benedict

Simulating the QBO remains a formidable challenge partly due to uncertainties in representing convectively generated gravity waves. We develop an end-to-end uncertainty quantification workflow that calibrates these gravity wave processes in E3SM to yield a more realistic QBO. Central to our approach is a domain knowledge-informed, compressed representation o

Dmitry Melnikov

These notes review a description of quantum mechanics in terms of the topology of spaces, basing on the axioms of Topological Quantum Field Theory and path integral formalism. In this description quantum states and operators are encoded by the topology of spaces that are used as modules to build the quantum mechanical model, while expectation values and prob

Darwish Ahmad Herati, Maria Clara Aderne, Fabio Kon

This manuscript focuses on the environmental, social, and individual sustainability dimensions within the modern software development lifecycle, aiming to establish a holistic approach termed Sustainable DevOps (SusDevOps). Moving beyond the already well-researched economic and technical aspects, our approach to SusDevOps emphasizes the importance of minimiz

Sarah Perez, Florian Doster, Julien Maes, Hannah Menke

Structural uncertainties and unresolved features in fault zones hinder the assessment of leakage risks in subsurface CO2 storage. Understanding multi-scale uncertainties in fracture network conductivity is crucial for mitigating risks and reliably modelling upscaled fault leakage rates. Conventional models, such as the Cubic Law, which is based on mechanical

Anna Madison, Kaleb McDowell, Vinicius G. Goecks, Jeff Hansberger

Future warfare will occur in more complex, fast-paced, ill-structured, and demanding conditions that will stress current Command and Control (C2) systems. Without modernization, these C2 systems may fail to maintain overmatch against adversaries. We previously proposed robust partnerships between humans and artificial intelligence systems, and directly focus

Rajitha de Silva, Jonathan Cox, Marija Popovic, Cesar Cadena

Robust robot navigation in outdoor environments requires accurate perception systems capable of handling visual challenges such as repetitive structures and changing appearances. Visual feature matching is crucial to vision-based pipelines but remains particularly challenging in natural outdoor settings due to perceptual aliasing. We address this issue in vi

Tianyu Sun, Kun Qian, Wenhong Wang

Multi-party dialogue generation presents significant challenges due to the complex interplay of multiple speakers and interwoven conversational threads. Traditional approaches often fall short in capturing these complexities, particularly when relying on manually annotated dialogue relations. This paper introduces Speaker-Attentive LLM (SA-LLM), a novel gene

Evan O'Leary, Zhuoqi Li, Lin-Lin Wang, Benjamin Schrunk

We use high resolution angle-resolved photoemission spectroscopy (ARPES) and density functional theory (DFT) to investigate the electronic structure of trigonal phase ${\rm PtBi_2}$ (t-${\rm PtBi_2}$), a proposed Weyl semimetal that is expected to exhibit topological Fermi Arcs. Our ARPES data elucidates the topography of these objects and confirms their Fer

C. J. Riseley, T. Vernstrom, L. Lovisari, E. O'Sullivan

In the context of the life cycle and evolution of active galactic nuclei (AGN), the environment plays an important role. In particular, the over-dense environments of galaxy groups, where dynamical interactions and bulk motions have significant impact, offer an excellent but under-explored window into the life cycles of AGN and the processes that shape the e

Jianqi Chen, Biao Zhang, Xiangjun Tang, Peter Wonka

We present V2M4, a novel 4D reconstruction method that directly generates a usable 4D mesh animation asset from a single monocular video. Unlike existing approaches that rely on priors from multi-view image and video generation models, our method is based on native 3D mesh generation models. Naively applying 3D mesh generation models to generate a mesh for e

Duncan Laurie

We introduce a new topological coproduct $\Delta^{\psi}_{u}$ for quantum toroidal algebras $U_{q}(\mathfrak{g}_{\mathrm{tor}})$ in all untwisted types, leading to a well-defined tensor product on the category $\widehat{\mathcal{O}}_{\mathrm{int}}$ of integrable representations. This is defined by twisting the Drinfeld coproduct $\Delta_{u}$ with an anti-invo

Burak Suyunu, Özdeniz Dolu, Ibukunoluwa Abigail Olaosebikan, Hacer Karatas Bristow

Proteins are the essential drivers of biological processes. At the molecular level, they are chains of amino acids that can be viewed through a linguistic lens where the twenty standard residues serve as an alphabet combining to form a complex language, referred to as the language of life. To understand this language, we must first identify its fundamental u

Graeme Baker, Ben Hambly, Philipp Jettkant

We study a system of reflected Brownian motions on the positive half-line in which each particle has a drift toward the origin determined by the local times at the origin of all the particles. If this local time drift is too strong, such systems exhibit a breakdown in their solutions in that there is a time beyond which the system cannot be extended. In the

Dylan Cashman, Mark Keller, Hyeon Jeon, Bum Chul Kwon

Dimensionality reduction is used as an important tool for unraveling the complexities of high-dimensional datasets in many fields of science, such as cell biology, chemical informatics, and physics. Visualizations of the dimensionally reduced data enable scientists to delve into the intrinsic structures of their datasets and align them with established hypot

Zifeng Wang, Xiaoning Jin

Roll-to-roll (R2R) printing technologies are promising for high-volume continuous production of substrate-based electronic products. One of the major challenges in R2R flexible electronics printing is achieving tight alignment tolerances, as specified by the device resolution (usually at the micro-meter level), for multi-layer printed electronics. The alignm

Ali Shamooni, Oliver T. Stein, Andreas Kronenburg

In recent years, sub-grid models for turbulent mixing have been developed by data-driven methods for large eddy simulation (LES). Super-resolution is a data-driven deconvolution technique in which deep convolutional neural networks are trained using direct numerical simulation (DNS) data to learn mappings between the input data from a low resolution domain t

Dimitri Ara, Léonard Guetta

Motivated by the Grothendieck construction, we study the functorialities of the comma construction for strict $\omega$-categories. To state the most general functorialities, we use the language of Gray $\omega$-categories, that is, categories enriched in the category of strict $\omega$-categories endowed with the oplax Gray tensor product. Our main result is

Niels Henrik Aase, Erik Wegner Hodt, Karl Bergson Hallberg, Asle Sudbø

The emergence of an orbital angular momentum (OAM) response to a charge current holds promise for technological applications, allowing electrical control of magnetization dynamics. Often, the OAM current is invoked in explaining experimental results for very large orbital transport effects, but this is conceptually challenging as the OAM current is not a con

Ivan Sabolić, Matej Grcić, Siniša Šegvić

We propose VIBE, a model-agnostic framework that trains classifiers resilient to backdoor attacks. The key concept behind our approach is to treat malicious inputs and corrupted labels from the training dataset as observed random variables, while the actual clean labels are latent. VIBE then recovers the corresponding latent clean label posterior through var

Karthekeyan Chandrasekaran, Chandra Chekuri, Shubhang Kulkarni

We consider deletion problems in graphs and supermodular functions where the goal is to reduce density. In Graph Density Deletion (GraphDD), we are given a graph $G=(V,E)$ with non-negative vertex costs and a non-negative parameter $\rho \ge 0$ and the goal is to remove a minimum cost subset $S$ of vertices such that the densest subgraph in $G-S$ has density

Donghee Lee, Hye-Sung Lee, Jaeok Yi

Theoretical understanding of deep learning remains elusive despite its empirical success. In this study, we propose a novel "synaptic field theory" that describes the training dynamics of synaptic weights and biases in the continuum limit. Unlike previous approaches, our framework treats synaptic weights and biases as fields and interprets their indices as s

Omar Maraqa, Majid H. Khoshafa, Olutayo O. Oyerinde, Telex M. N. Ngatched

Integrating BD-RIS into wireless communications systems has attracted significant interest due to its transformative potential in enhancing system performance. This survey provides a comprehensive analysis of BD-RIS technology, examining its modeling, structural characteristics, and network integration while highlighting its advantages over traditional diago

Wouter Buijsman, Masudul Haque, Ivan M. Khaymovich

We explore interpretations of the power-law banded random matrix (PLBRM) ensemble as Hamiltonians of one-dimensional quantum many-body systems. We introduce and compare a number of labeling schemes for assigning random matrix basis indices to many-body basis vectors. We compare the physical properties of the resulting Hamiltonians, focusing on the half-syste

Cristian Barbero, Íñigo J. Sola, Benjamín Alonso

Single-shot characterization techniques are crucial when dealing with shot-to-shot pulse-shape fluctuations (e.g., unstable laser systems, high-power, or with low repetition rate) since the scanning configurations cannot measure single pulses. The demand for simple setups that can be easily adapted to a wide variety of experimental conditions is continuously

Ce Guo, Tong Zhao

Field-Programmable Gate Arrays (FPGAs) are widely used in modern hardware design, yet writing Hardware Description Language (HDL) code for FPGA implementation remains a complex and time-consuming task. Large Language Models (LLMs) have emerged as a promising tool for HDL generation, but existing benchmarks for LLM-based code generation primarily focus on fun

P. C. Mahato, Suprotim Saha, Ritesh Kumar, D. Banik

Steel, traditionally valued for its structural strength, emerges in this study as a remarkable material for exploring novel magnetic phenomena. We investigate how common processing techniques-thermal treatments and mechanical strain-significantly affect the magnetic properties of low-carbon steels (0.05 percent by weight). Our findings show that slow anneali

Wejdene Ben Nasr, Hélène Halconruy, Stéphane Jaffard

The motivation of this article is to estimate multifractality classification and model selection parameters: the first-order scaling exponent $c_1$ and the second-order scaling exponent (or intermittency coefficient) $c_2$. These exponents are built on wavelet leaders, which therefore constitute fundamental tools in applied multifractal analysis. While most

Kelsey Hanser, Nicholas Mayers

Recently, Pan and Yu showed that Lascoux polynomials can be defined in terms of certain collections of diagrams consisting of unit cells arranged in the first quadrant. Starting from certain initial diagrams, one forms a finite set of diagrams by applying two types of moves: Kohnert and ghost moves. Both moves cause at most one cell to move to a lower row wi

Jingwen Deng, Zihao Wang, Shaofei Cai, Anji Liu

Learning skills in open-world environments is essential for developing agents capable of handling a variety of tasks by combining basic skills. Online demonstration videos are typically long but unsegmented, making them difficult to segment and label with skill identifiers. Unlike existing methods that rely on sequence sampling or human labeling, we have dev

Md baharul Islam, Afsana Ahsan Jeny

Existing learning-based video compression methods still face challenges related to inaccurate motion estimates and inadequate motion compensation structures. These issues result in compression errors and a suboptimal rate-distortion trade-off. To address these challenges, this work presents an end-to-end video compression method that incorporates several key

Adrián Nadal-Rosa, Gonzalo Manzano

Molecular motors are in charge of almost every process in the life cycle of cells, such as protein synthesis, DNA replication, and cell locomotion, hence being of crucial importance for understanding the cellular dynamics. However, given their size scales on the order of nanometers, direct measurements are rather challenging, and the information that can be

Yanhao Yang, Nina L. Hecht, Yousef Salaman-Maclara, Nathan Justus

Salps are marine animals consisting of chains of jellyfish-like units. Their efficient underwater locomotion by coordinating multi-jet propulsion has aroused great interest in robotics. This paper presents a geometric mechanics framework for salp-inspired robots. We study a new type of geometric mechanics models inspired by salps, in which control inputs are

Nate S. P. Bernstein, Daniel Drury, Cheng-Wei Lee, Tatau Shimada

Thin films of aluminum hafnium nitride (Al$_{1-x}$Hf$_{x}$N) were synthesized via reactive magnetron sputtering for Hf contents up to $x$ = 0.13. X-ray diffraction showed a single $c$-axis oriented wurtzite phase for all films. Hard X-ray photoelectron spectroscopy demonstrated homogeneous Al:Hf distribution through the thin films and confirmed their insulat

Danielle Villa, Maria Chang, Keerthiram Murugesan, Rosario Uceda-Sosa

Large Language Models (LLMs) are often asked to explain their outputs to enhance accuracy and transparency. However, evidence suggests that these explanations can misrepresent the models' true reasoning processes. One effective way to identify inaccuracies or omissions in these explanations is through consistency checking, which typically involves asking fol

Andrea E. Davidson, Jessica M. Ray, Ayush K. Patel, Yulia Strekalova Levites

This study reports the findings of qualitative interview sessions conducted with ICU clinicians for the co-design of a system user interface of an artificial intelligence (AI)-driven clinical decision support (CDS) system. This system integrates medical record data with wearable sensor, video, and environmental data into a real-time dynamic model that quanti

Tymoteusz Chmiel, Lorenzo Guerrieri, Xianglong Ni, Jerzy Weyman

In this article we study minimal free resolutions of Gorenstein ideals of codimension four, using methods coming from representation theory. We introduce families of higher structure maps associated with such resolution, defined similarly to the codimension three case. As our main application, we prove that every Gorenstein ideal of codimension four minimall

E. Hueichapán, J. L. Prieto, R. Cartier, C. Contreras

We present observations of ASASSN-13dn, one of the first supernovae discovered by ASAS-SN, and a new member of the rare group of Luminous Type II Supernovae (LSNe II). It was discovered near maximum light, reaching an absolute magnitude of M$_{v}$ $\sim$ -19 mag, placing this object between normal luminosity type II SNe and superluminous SNe A detailed analy

Somnath Jana, Ronny Knut, Puloma Singh, Kelvin Yao

The delayed demagnetization in Ni relative to Fe in the ultrafast demagnetization studies in FeNi alloy has led to two competing theoretical explanations: The Inhomogeneous Magnon Generation (IMG) and the Optically Induced Spin Transfer (OISTR) model. The IMG attributes the delay to the preferential magnon generation at the Fe sites and its subsequent propag

Daniel Abou-Ras, Matthias Maiberg

The present work revisits the recombination velocities ($s_{\mathrm{GB}}$) of minority-charge carriers determined at grain boundaries in polycrystalline absorber materials for solar cells. The equations describing $s_{\mathrm{GB}}$ as well as the barriers for electrons and holes were derived. It is shown that for given net-doping density and absolute tempera

András Bátkai, Marjeta Kramar Fijavž, Abdelaziz Rhandi

This paper investigates the well-posedness and positivity of solutions to a class of delayed transport equations on a network. The material flow is delayed at the vertices and along the edges. The problem is reformulated as an abstract boundary delay equation, and well-posedness is proved by using the Staffans-Weiss theory. We also establish spectral theory

Elise Colin, Razvigor Ossikovski

We consider two random variables $X$ and $Y$ following correlated Gamma distributions, characterized by identical scale and shape parameters and a linear correlation coefficient $\rho$. Our focus is on the parameter: \[ D(X,Y) = \frac{|X - Y|}{X + Y}, \] which appears in applied contexts such as dynamic speckle imaging, where it is known as the \textit{Fujii

A. Ashtari Esfahani, D. M. Asner, S. Böser, N. Buzinsky

Cyclotron Radiation Emission Spectroscopy (CRES) is a novel technique for the precise measurement of relativistic electron energy. This technique is being employed by the Project~8 collaboration for measuring a high-precision tritium beta decay spectrum to perform a frequency-based measurement of the neutrino mass. In this work, we describe the Project 8 Pha

Yisu Zong, Joshua Reiss

Sound effects model design commonly uses digital signal processing techniques with full control ability, but it is difficult to achieve realism within a limited number of parameters. Recently, neural sound effects synthesis methods have emerged as a promising approach for generating high-quality and realistic sounds, but the process of synthesizing the desir

Mikey Shechter, Yair Carmon

We introduce Filter Like You Test (FLYT), an algorithm for curating large-scale vision-language datasets that learns the usefulness of each data point as a pretraining example. FLYT trains a scoring model that learns to weigh each example's features using gradient signals from downstream tasks training sets. Based on FLYT, we implement Mixing-FLYT (M-FLYT),

Soumya Kanti Ganguly, Indrajit Mukherjee

The inadequacy of the classical viscous damping model in capturing dissipation across a wide range of applications has led to the development of non-viscous damping models. While non-viscous models describe damping force satisfactorily, they offer limited physical insight. Leveraging an existing framework, well known to the physics community, this article pr

Johan R. Portela, Nicolás Perez, Rubén Manrique

Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), serves as a crucial area within the domain of Natural Language Processing (NLP). This area fundamentally empowers machines to discern semantic relationships between assorted sections of text. Even though considerable work has been executed for the English language, it has b

Qingyun Yang, Fangjie Li, Jiayi Xu, Zixuan Liu

Head and neck squamous cell carcinoma (HNSCC) has one of the highest rates of recurrence cases among solid malignancies. Recurrence rates can be reduced by improving positive margins localization. Frozen section analysis (FSA) of resected specimens is the gold standard for intraoperative margin assessment. However, because of the complex 3D anatomy and the s

Zixuan Liang

This paper presents novel methods for estimating certified radii in randomized smoothing, a technique crucial for certifying the robustness of neural networks against adversarial perturbations. Our proposed techniques significantly improve the accuracy of certified test-set accuracy by providing tighter bounds on the certified radii. We introduce advanced al

Robin Zhang

We determine the number of positive integral points on $n$-dimensional affine varieties associated to arbitrary $n \times n$ generalized Cartan matrices. An application to the theory of cluster algebras and combinatorics is the resolution of the Fontaine-Plamondon conjecture, which says that there are exactly $4400$ and $26952$ positive integral friezes of t

Carlos M. Parra-Londoño, Andrés F. Uribe-Zapata

This article explores the concept of absoluteness in the context of mathematical analysis, focusing specifically on the Riemann integral on $\mathbb{R}^{n}$. In mathematical logic, "absoluteness" refers to the invariance of the truth value of certain statements in different mathematical universes. Leveraging this idea, we investigate the conditions under whi