December 2024 arXiv papers — page 121

Luke Bailey, Alex Serrano, Abhay Sheshadri, Mikhail Seleznyov

Recent latent-space monitoring techniques have shown promise as defenses against LLM attacks. These defenses act as scanners that seek to detect harmful activations before they lead to undesirable actions. This prompts the question: Can models execute harmful behavior via inconspicuous latent states? Here, we study such obfuscated activations. We show that s

Jiyao Hu, Zhenyu Zhou, Xiaowei Yang

Cable broadband networks are one of the few "last-mile" broadband technologies widely available in the U.S. Unfortunately, they have poor reliability after decades of deployment. The cable industry proposed a framework called Proactive Network Maintenance (PNM) to diagnose the cable networks. However, there is little public knowledge or systematic study on h

Oscar Skean, Md Rifat Arefin, Yann LeCun, Ravid Shwartz-Ziv

Understanding what defines a good representation in large language models (LLMs) is fundamental to both theoretical understanding and practical applications. In this paper, we investigate the quality of intermediate representations in various LLM architectures, including Transformers and State Space Models (SSMs). We find that intermediate layers often yield

Zhenyang Yuan, Simon Demange, Kilian Oberleithner, André V. G. Cavalieri

The aim of the present work is to investigate the mechanisms of broadband trailing-edge noise generation to improve prediction tools and control strategies. We focus on a NACA 0012 airfoil at 3 degrees angle of attack and chord Reynolds number Re = 200,000. A high-fidelity wall-resolved compressible implicit large eddy simulation (LES) is performed to collec

Olga Arroyo-Gascón, Sergio Bravo, Mónica Pacheco, Leonor Chico

Obstructed atomic phases, with their realizations in systems of diverse dimensionality, have recently arisen as one of the topological states with greatest potential to show higher-order phenomena. In this work we report a special type of obstruction, known as orbital-mediated atomic obstruction, in monolayers of materials with spatial symmetry described by

Vaibhav Mishra, Somaditya Singh, Dhruv Ahlawat, Mohd Zaki

Materials discovery and development are critical for addressing global challenges. Yet, the exponential growth in materials science literature comprising vast amounts of textual data has created significant bottlenecks in knowledge extraction, synthesis, and scientific reasoning. Large Language Models (LLMs) offer unprecedented opportunities to accelerate ma

Ivan Arzhantsev, Roman Avdeev, Yulia Zaitseva

We complete the classification of algebraic monoid structures on the affine 3-space. The result is based on a reduction of the general case to that of commutative monoids. We also study various algebraic properties of all monoids appearing in the classification.

Kostas Tzanavaris, Latham Boyle, Neil Turok

Einstein's equations imply that a gravitationally collapsed object forms an event horizon. But what lies on the other side of this horizon? In this paper, we question the reality of the conventional solution (the black hole), and point out another, topologically distinct solution: the black mirror. In the black hole solution, the horizon connects the exterio

Vivek Sabarad, Vishal Varma, T. S. Mahesh

Quantum kernel learning (QKL) promises efficient machine learning by encoding feature maps onto exponentially large Hilbert spaces inherent in quantum systems. Using the liquid-state nuclear magnetic resonance (NMR) platform, we implement and benchmark QKL for one-dimensional regression and two-dimensional classification tasks. We then classify entangling an

Xiaokai Chen, Tianyu Cao, Gesualdo Scutari

We study decentralized multiagent optimization over networks, modeled as undirected graphs. The optimization problem consists of minimizing a nonconvex smooth function plus a convex extended-value function, which enforces constraints or extra structure on the solution (e.g., sparsity, low-rank). We further assume that the objective function satisfies the Kur

Jean Gutt, Vinicius G. B. Ramos

In this paper, we prove that the Ekeland-Hofer capacities coincide on all star-shaped domain in $\mathbb{R}^{2n}$ with the equivariant symplectic homology capacities defined by the first author and Hutchings, answering a 35 years old question. Along the way, we prove that given a Hamiltonian $H$, the (equivariant) Floer homology of $H$ is chain-complex isomo

Sepehr Gourabi, Parinaz Khosravani, Shahrzad Nosrat, Roya Mohammadi

Understanding emotional processing in the human brain requires examining the complex interactions between different brain regions. While previous studies have identified specific regions involved in emotion processing, a holistic network approach may provide deeper insights. We use Structural Balance Theory to investigate the stability and triadic structures

Bijaya Basnet, Sathyanarayana Paladugu, Oleksandr Kurochkin, Oleksandr Buluy

Electric field-induced splay of molecular orientation, called the Fr\'eedericksz transition, is a fundamental electro-optic phenomenon in nonpolar nematic liquid crystals. In a ferroelectric nematic NF with a spontaneous electric polarization P, the splay is suppressed since it produces bound electric charges. Here, we demonstrate that an alternating current

Dirceu Bagio, Eliezer Batista, Hector Pinedo

Let $\Bbbk$ be a field, $H$ a Hopf algebra over $\Bbbk$, and $R = (_iM_j)_{1 \leq i,j \leq n}$ a generalized matrix algebra. In this work, we establish necessary and sufficient conditions for $H$ to act partially on $R$. To achieve this, we introduce the concept of an opposite covariant pair and demonstrate that it satisfies a universal property. In the spec

Yihong Sun, Hao Zhou, Liangzhe Yuan, Jennifer J. Sun

We explore a novel video creation experience, namely Video Creation by Demonstration. Given a demonstration video and a context image from a different scene, we generate a physically plausible video that continues naturally from the context image and carries out the action concepts from the demonstration. To enable this capability, we present $\delta$-Diffus

Quentin Simmons, Shi-Zeng Lin, Gia-Wei Chern

We present a comprehensive numerical study of a six-state clock model with a long-range dipolar type interaction. This model is motivated by the ferroelectric orders in the multiferroic hexagonal manganites. At low temperatures, trimerization of local atomic structures leads to six distinct but energetically degenerate structural distortion, which can be mod

Yi-Lun Lee, Chen-Yu Lee, Wei-Chen Chiu, Yi-Hsuan Tsai

Multimodal incremental learning needs to digest the information from multiple modalities while concurrently learning new knowledge without forgetting the previously learned information. There are numerous challenges for this task, mainly including the larger storage size of multimodal data in exemplar-based methods and the computational requirement of finetu

Zekun Hao, David W. Romero, Tsung-Yi Lin, Ming-Yu Liu

Meshes are fundamental representations of 3D surfaces. However, creating high-quality meshes is a labor-intensive task that requires significant time and expertise in 3D modeling. While a delicate object often requires over $10^4$ faces to be accurately modeled, recent attempts at generating artist-like meshes are limited to $1.6$K faces and heavy discretiza

Muhammad Arslan Manzoor, Ruihong Zeng, Dilshod Azizov, Preslav Nakov

In the current era of rapidly growing digital data, evaluating the political bias and factuality of news outlets has become more important for seeking reliable information online. In this work, we study the classification problem of profiling news media from the lens of political bias and factuality. Traditional profiling methods, such as Pre-trained Languag

Roberto Soler, Andrew Hillier

Magnetic flux tubes in the solar corona support a rich variety of transverse oscillations, which are theoretically interpreted as magnetohydrodynamic (MHD) modes with a fast and/or Alfv\'enic character. In the standard flux tube model made of a straight cylindrical tube, these modes can be classified according to their azimuthal wavenumber, $m$. Sausage $m=0

Joshua Evan Greene, Andrew Lobb

We prove that for every smooth Jordan curve $\gamma \subset \mathbb{C}$ and for every set $Q \subset \mathbb{C}$ of six concyclic points, there exists a non-constant quadratic polynomial $p \in \mathbb{C}[z]$ such that $p(Q) \subset \gamma$. The proof relies on a theorem of Fukaya and Irie. We also prove that if $Q$ is the union of the vertex sets of two con

Xueting Li, Ye Yuan, Shalini De Mello, Gilles Daviet

We introduce SimAvatar, a framework designed to generate simulation-ready clothed 3D human avatars from a text prompt. Current text-driven human avatar generation methods either model hair, clothing, and the human body using a unified geometry or produce hair and garments that are not easily adaptable for simulation within existing simulation pipelines. The

Paria Rashidinejad, Yuandong Tian

Aligning AI systems with human preferences typically suffers from the infamous reward hacking problem, where optimization of an imperfect reward model leads to undesired behaviors. In this paper, we investigate reward hacking in offline preference optimization, which aims to improve an initial model using a preference dataset. We identify two types of reward

Árpád Bényi, Tadahiro Oh, Rodolfo H. Torres

We prove a symbolic calculus for a class of pseudodifferential operators, and discuss its applications to $L^2$-compactness via a compact version of the $T(1)$ theorem.

Andre Luiz Cordeiro dos Santos, Felipe Marques dos Santos, Nelson Violante-Carvalho, Luiz Mariano Carvalho

Cross-assignment of directional wave spectra is a critical task in wave data assimilation. Traditionally, most methods rely on two-parameter spectral distances or energy ranking approaches, which often fail to account for the complexities of the wave field, leading to inaccuracies. To address these limitations, we propose the Controlled Four-Parameter Method

William Groger, Hayk Hakobyan, Lorenzo Sironi

Astrophysical relativistic outflows are launched as Poynting-flux-dominated, yet the mechanism governing efficient magnetic dissipation, which powers the observed emission, is still poorly understood. We study magnetic energy dissipation in relativistic "striped" jets, which host current sheets separating magnetically dominated regions with opposite field po

Serban Cicortas, Christoph Kehle

The problem of constructing naked singularities in general relativity can be naturally divided into two parts: (i) the construction of the region exterior to the past light cone of the singularity, extending all the way to (an incomplete) future null infinity and yielding the nakedness property (what we will call exterior-naked singularity regions); (ii) att

Ruyi Pan, Luis E. Nieto-Barajas, Radu V. Craiu

Copula-based dependence modeling often relies on parametric formulations. This is mathematically convenient, but can be statistically inefficient when the parametric families are not suitable for the data and model in focus. A Bayesian nonparametric mixture of Archimedean copulas is introduced to increase the flexibility of copula-based dependence modeling.

Jiachen T. Wang, Dawn Song, James Zou, Prateek Mittal

Traditional data influence estimation methods, like influence function, assume that learning algorithms are permutation-invariant with respect to training data. However, modern training paradigms, especially for foundation models using stochastic algorithms and multi-stage curricula, are sensitive to data ordering, thus violating this assumption. This mismat

Gustavo J. Turiaci

Lecture notes prepared for the Les Houches school "Quantum Geometry: Mathematical Methods for Gravity, Gauge Theories and Non-Perturbative Physics" that took place during the summer 2024. We cover the techniques to perform the exact gravitational path integral of two-dimensional dilaton-gravity, and supergravity, over spacetimes with arbitrary topology, with

Simon Demange, Zhenyang Yuan, Simon Jekosch, Ennes Sarradj

Trailing-edge (TE) noise is the main contributor to the acoustic signature of flows over airfoils. It originates from the interaction of turbulent structures in the airfoil boundary layer with the TE. This study experimentally identifies the flow structures responsible for TE noise by decomposing the data into spanwise modes and examining the impact of spanw

Ashwin Sah, Mehtaab Sawhney, Daniel G. Zhu

Extending a previous result of the first two authors, we prove a local limit theorem for the joint distribution of subgraph counts in the Erd\H{o}s-R\'{e}nyi random graph $G(n,p)$. This limit can be described as a nonlinear transformation of a multivariate normal distribution, where the components of the multivariate normal correspond to the graph factors of

Víctor Bresó, Gudrun Heinrich, Vitaly Magerya, Anton Olsson

The calculation of scattering amplitudes at higher orders in perturbation theory has reached a high degree of maturity. However, their usage to produce physical predictions within Monte Carlo programs is often precluded by the slow evaluation of two- and higher-loop virtual amplitudes, particularly those calculated numerically. As a remedy, interpolation fra

Måns J. Mattsson, Kham M. Niang, Jared Parker, David J. Meeth

The complete subgap defect density of states (DoS) is measured using the ultrabroadband (0.15 to 3.5 eV) photoconduction response from p-type thin-film transistors (TFTs) of tin oxide, SnO, and copper oxide, Cu$_2$O. The TFT photoconduction spectra clearly resolve all bandgaps that further show the presence of interfacial and oxidized minority phases. In tin

Edward Olex, Wojciech A. Hellwing, Alexander Knebe

The study of large-scale structure can benefit from accurate and robust identification of the cosmic web. Having such classification can facilitate a more complete extraction of cosmological information encoded therein. Classification methods like T-web and V-web, based on the Hessian matrix, are widely used to signal-out voids, sheets, filaments, and knots.

Han Wang, Yuxiang Nie, Yongjie Ye, Deng GuanYu

The application of Large Vision-Language Models (LVLMs) for analyzing images and videos is an exciting and rapidly evolving field. In recent years, we've seen significant growth in high-quality image-text datasets for fine-tuning image understanding, but there is still a lack of comparable datasets for videos. Additionally, many VideoLLMs are extensions of s

Qiaoyu Zheng, Chaoyi Wu, Pengcheng Qiu, Lisong Dai

We introduce RadA-BenchPlat, an evaluation platform that benchmarks the performance of large language models (LLMs) act as agent cores in radiology environments using 2,200 radiologist-verified synthetic patient records covering six anatomical regions, five imaging modalities, and 2,200 disease scenarios, resulting in 24,200 question-answer pairs that simula

Arjun Ghosh, Ritoban Basu Thakur

Quadrature Hybrid Couplers (QHDC) are critical components in RF, mm-wave, and sub-mm wave astronomical instrumentation, where wideband performance with minimal passband ripple is essential. Traditional designs have been limited to 5-sections at most due to computational limitations. In this work, we introduce a new analytical technique to design couplers wit

Haoming Li, Zhaoliang Chen, Songyuan Liu, Yiming Lu

In this work, we provide a systematic analysis of how large language models (LLMs) contribute to solving planning problems. In particular, we examine how LLMs perform when they are used as problem solver, solution verifier, and heuristic guidance to improve intermediate solutions. Our analysis reveals that although it is difficult for LLMs to generate correc

Ajith Sampath, Devin Crichton, Kavilan Moodley, H. Cynthia Chiang

The Hydrogen Intensity and Real-time Analysis eXperiment (HIRAX) is an upcoming radio interferometric telescope designed to constrain dark energy through the 21cm intensity mapping of Baryon Acoustic Oscillations (BAO). Instrumental systematics must be controlled and carefully characterized to measure the 21cm power spectrum with fidelity and achieve high-pr

Lurdes Ondaro-Mallea, Raul E. Angulo, Giovanni Aricò, Joop Schaye

Most of the visible matter in the Universe is in a gaseous state, subject to hydrodynamic forces and galaxy formation processes that are much more complex to model than gravity. These baryonic effects can potentially bias the analyses of several cosmological probes, such as weak gravitational lensing. In this work, we study the gas density and velocity field

Florian Eisele, Leo Margolis

We obtain restrictions on units of even order in the integral group ring $\mathbb{Z}G$ of a finite group $G$ by studying their actions on the reductions modulo $4$ of lattices over the $2$-adic group ring $\mathbb{Z}_2G$. This improves the "lattice method" which considers reductions modulo primes $p$, but is of limited use for $p=2$ essentially due to the fa

Luka Blagojević, Ivan Bonamassa, Márton Pósfai

We explore the robustness of complex networks against physical damage. We focus on spatially embedded network models and datasets where links are physical objects or physically transfer some quantity, which can be disrupted at any point along its trajectory. To simulate physical damage, we tile the networks with boxes of equal size and sequentially damage th

Lidia Fernández, Juan Antonio Villegas

Polynomials known as Multiple Orthogonal Polynomials in a single variable are polynomials that satisfy orthogonality conditions concerning multiple measures and play a significant role in several applications such as Hermite-Pad\'e approximation, random matrix theory or integrable systems. However, this theory has only been studied in the univariate case. We

Xishuo Wei, Javier H Nicolau, Gyungjin Choi, Zhihong Lin

Gyrokinetic simulations are utilized to study effects of magnetic islands on the ion temperature gradient (ITG) turbulence in the KSTAR tokamak with resonant magnetic perturbations. Simulations show that the transport is controlled by the nonlinear interactions between the ITG turbulence and self-generated vortex flows and zonal flows, leading to an anisotro

Shengxuming Zhang, Weihan Li, Tianhong Gao, Jiacong Hu

Pathological diagnosis is vital for determining disease characteristics, guiding treatment, and assessing prognosis, relying heavily on detailed, multi-scale analysis of high-resolution whole slide images (WSI). However, existing large vision-language models (LVLMs) are limited by input resolution constraints, hindering their efficiency and accuracy in patho

Mincheol Kim, Nahyun Kwon, Jung-Yup Kim

Deep reinforcement learning (DRL) has emerged as an innovative solution for controlling legged robots in challenging environments using minimalist architectures. Traditional control methods for legged robots, such as inverse dynamics, either directly manage joint torques or use proportional-derivative (PD) controllers to regulate joint positions at a higher

Pierre-Louis Montagard, Iván Pan, Alvaro Rittatore

Let $D$ be a simple derivation of the polynomial ring $\mathbb{k}[x_1,\dots,x_n]$, where $\mathbb{k}$ is an algebraically closed field of characteristic zero, and denote by $\operatorname{Aut}(D)\subset\operatorname{Aut}(\mathbb{k}[x_1,\dots,x_n])$ the subgroup of $\mathbb{k}$-automorphisms commuting with $D$. We show that the connected component of $\operat

Ruiqi Zhang, Yuguo Shao, Fuchuan Wei, Song Cheng

Quantum error mitigation (QEM) is critical for harnessing the potential of near-term quantum devices. Particularly, QEM protocols can be designed based on machine learning, where the mapping between noisy computational outputs and ideal ones can be learned on a training set consisting of Clifford circuits or near-Clifford circuits that contain only a limited

Andrea Bucci, Michele Palma, Chao Zhang

Traditional methods employed in matrix volatility forecasting often overlook the inherent Riemannian manifold structure of symmetric positive definite matrices, treating them as elements of Euclidean space, which can lead to suboptimal predictive performance. Moreover, they often struggle to handle high-dimensional matrices. In this paper, we propose a novel

H. Carranza, J. Yu, B. Brown, S. Blanchard

We present the result of a search for inelastic boosted dark matter using the data corresponding to an exposure of 0.13 kton$\cdot$year, collected by the ICARUS T-600 detector during its 2012--2013 operational period at the INFN Gran Sasso Underground National Laboratory. The benchmark boosted dark matter model features a multi-particle dark sector with a U(

Daniel Vitas

We show that the formal skew Laurent series ring $R = D(\! ( x; \sigma )\! )$ over a commutative Dedekind domain $D$ with an automorphism $\sigma$ is a noncommutative Dedekind domain. If $\sigma$ acts trivially on the ideal class group of $D$, then $K_0(R)$, the Grothendieck group of $R$, is isomorphic to $K_0(D)$. Furthermore, we determine the Krull dimensi

Nawaraj Paudel, Chiara Tarantini, Shreyas Balachandran, William L. Starch

Nb3Sn conductors are important candidates for high-field magnets for particle accelerators, and they continue to be widely used for many laboratory and NMR magnets. However, the critical current density, Jc, of present Nb3Sn conductors declines swiftly above 12-15T. State-of-the-art Ta- and Ti-doped strands exhibit upper critical field, Hc2, values of 24-26.

Lingfeng Yang, Zhenyuan Chen, Xiang Li, Peiyang Jia

As information becomes more accessible, user-generated videos are increasing in length, placing a burden on viewers to sift through vast content for valuable insights. This trend underscores the need for an algorithm to extract key video information efficiently. Despite significant advancements in highlight detection, moment retrieval, and video summarizatio

Alberto Saldaña, Delia Schiera, Hugo Tavares

We consider the following Lane-Emden system with Neumann boundary conditions \[ -\Delta u= |v|^{q-1}v \text{ in } \Omega,\qquad -\Delta v= |u|^{p-1}u \text{ in } \Omega,\qquad \partial_\nu u=\partial_\nu v=0 \text{ on } \partial \Omega, \] where $\Omega$ is a bounded smooth domain of $\mathbb{R}^N$ with $N \ge 1$. We study the multiplicity of solutions and t

Dongyue Lu, Lingdong Kong, Tianxin Huang, Gim Hee Lee

Identifying affordance regions on 3D objects from semantic cues is essential for robotics and human-machine interaction. However, existing 3D affordance learning methods struggle with generalization and robustness due to limited annotated data and a reliance on 3D backbones focused on geometric encoding, which often lack resilience to real-world noise and da

Christian Biello, Javier Mazzitelli, Aparna Sankar, Marius Wiesemann

We study the production of a Higgs boson in association with a bottom-quark pair ($b \bar b H$) at hadron colliders. Our calculation is performed in the four-flavour scheme with massive bottom quarks. This work presents the first computation of next-to-next-to-leading-order (NNLO) QCD corrections to this process, and we combine them with all-order radiative

Han-yuan Zuo, Zheng-xin Liu

The Sachdev-Ye-Kitaev (SYK) model is zero-dimensional model simulating quantum chaos using interacting Majorana fermions.Previously proposals have been made to realize the SYK model in fermionic systems that can support majorana zero modes. In this work, we simulate the SYK model in a Kitaev spin chain, where zero modes also exist in the ground state from th

Grigori Avramidi, Boris Okun, Kevin Schreve

We show that Gromov-Thurston branched covers satisfy the Singer conjecture whenever the degree of the cover is not divisible by a finite set of primes determined by the base manifold and the branch locus.

Rafayel Mkrtchyan, Edvard Ghukasyan, Khoren Petrosyan, Hrant Khachatrian

Indoor pathloss prediction is a fundamental task in wireless network planning, yet it remains challenging due to environmental complexity and data scarcity. In this work, we propose a deep learning-based approach utilizing a vision transformer (ViT) architecture with DINO-v2 pretrained weights to model indoor radio propagation. Our method processes a floor m

Khadiga H. A. Sayed, Maarten J. L. F. Cruyff, Andrea Petróczi, Peter G. M. van der Heijden

The Extended Crosswise Model is a popular randomized response design that employs a sensitive and a randomized innocuous statement, and asks respondents if one of these statements is true, or that none or both are true. The model has a degree of freedom to test for response biases, but is unable to detect random answering. In this paper, we propose two new m

Sandeep Banik, Jinrae Kim, Naira Hovakimyan, Luca Carlone

Vertical take-off and landing (VTOL) unmanned aerial vehicles (UAVs) are versatile platforms widely used in applications such as surveillance, search and rescue, and urban air mobility. Despite their potential, the critical phases of take-off and landing in uncertain and dynamic environments pose significant safety challenges due to environmental uncertainti

Ian Bentley, James Tedder, Marwan Gebran, Ayan Paul

Twelve physics-informed machine learning models have been trained to model binding energy residuals. Our approach begins with determining the difference between measured experimental binding energies and three different mass models. Then four machine learning approaches are used to train on each energy difference. The most successful ML technique, both in in

Maximo Bañados, Ernesto Bianchi, Ivan Muñoz, Kostas Skenderis

We show that AdS amplitudes are CFT correlators to all orders in the loop expansion by showing that they obey the conformal Ward identities. In particular, we provide explicit formulas for the constants and functions of cross-ratios that determine the CFT correlators in terms of bulk data.

Samuel Folorunsho, Maggie Ni, William Norris

This paper presents the development of a comprehensive dynamics and stabilizing control architecture for Tethered Unmanned Aerial Vehicle (TUAV) systems. The proposed architecture integrates both onboard and ground-based controllers, employing nonlinear backstepping control techniques to achieve asymptotic stability of the TUAV's equilibrium. The onboard con

Zhisheng Zhong, Chengyao Wang, Yuqi Liu, Senqiao Yang

As Multi-modal Large Language Models (MLLMs) evolve, expanding beyond single-domain capabilities is essential to meet the demands for more versatile and efficient AI. However, previous omni-models have insufficiently explored speech, neglecting its integration with multi-modality. We introduce Lyra, an efficient MLLM that enhances multimodal abilities, inclu

Gregory Kang Ruey Lau, Wenyang Hu, Diwen Liu, Jizhuo Chen

Large Language Models (LLMs), particularly smaller variants, still struggle with complex reasoning tasks. While inference-time prompting can guide reasoning, existing methods often rely on sequential queries. Ensemble approaches offer a promising path to performance gains, especially given recent batch inference speed-ups. This work introduces DIPPER, a nove

Jai Bardhan, Cyrin Neeraj, Subhadip Mitra, Tanumoy Mandal

We construct a surrogate loss to directly optimise the significance metric used in particle physics. We evaluate our loss function for a simple event classification task using a linear model and show that it produces decision boundaries that change according to the cross sections of the processes involved. We find that the models trained with the new loss ha

Mehrdad Raeesi, Saba Mansour, Sina Changizian

Aiming for a greener transportation future, this study introduces an innovative control system for plug-in hybrid electric vehicles (PHEVs) that utilizes machine learning (ML) techniques to forecast energy usage in the pure electric mode of the vehicle and optimize power allocation across different operational modes, including pure electric, series hybrid, p

Qiyang Han, Xiaocong Xu

Gradient descent is one of the most widely used iterative algorithms in modern statistical learning. However, its precise algorithmic dynamics in high-dimensional settings remain only partially understood, which has limited its broader potential for statistical inference applications. This paper provides a precise, non-asymptotic joint distributional charact

Andy A. Shen, Aidan McLoughlin, Zoe Vernon, Jonathan Lin

Multiple sclerosis is a chronic autoimmune disease that affects the central nervous system. Understanding multiple sclerosis progression and identifying the implicated brain structures is crucial for personalized treatment decisions. Deformation-based morphometry utilizes anatomical magnetic resonance imaging to quantitatively assess volumetric brain changes

Qihang Li, Zhuoqun Chen, Haoze Zheng, Haonan He

Trajectory planning in robotics aims to generate collision-free pose sequences that can be reliably executed. Recently, vision-to-planning systems have gained increasing attention for their efficiency and ability to interpret and adapt to surrounding environments. However, traditional modular systems suffer from increased latency and error propagation, while

Martin Lopez-Corredoira

Vertical stellar kinematics+density can be used to trace the dark matter distribution [or the equivalent phantom mass in a Modified Newtonian Dynamics (MOND) scenario] through Jeans equations. In this paper, we want to improve this type of analysis by making use of the recent data of the 6D information from the Gaia-DR3 survey in the anticenter and the Galac

Samir D. Mathur, Madhur Mehta

We describe the puzzles that arise in the quantum theory of black holes, and explain how they are resolved in string theory. We review how the Bekenstein entropy is obtained through the count of brane bound states. We describe the fuzzball construction of black hole microstates. These states have no horizon and radiate from their surface like a normal body,

Bidyut Hazarika, Mozib Bin Awal, Prabwal Phukon

This paper seeks to establish a connection between the cosmological constant and Renyi entropy within the framework of Kalb-Raymond(K-R) gravity. Our analysis is supported by evidence showing the equivalence of the thermodynamic topology of K-R AdS black holes in the Gibbs-Boltzmann (GB) statistical framework and K-R flat black holes in the Renyi statistical

Marco Inversi, Alessandro Violini

This paper is concerned with the inhomogeneous incompressible Euler system. We establish a Duchon--Robert type approximation theorem for the distribution describing the local energy flux of bounded solutions. The velocity field is assumed to have bounded variation or bounded deformation with respect to the spatial variable. The density satisfies no-vacuum co

Pierre Fernandez, Hady Elsahar, I. Zeki Yalniz, Alexandre Mourachko

The proliferation of AI-generated content and sophisticated video editing tools has made it both important and challenging to moderate digital platforms. Video watermarking addresses these challenges by embedding imperceptible signals into videos, allowing for identification. However, the rare open tools and methods often fall short on efficiency, robustness

Antonina Maj, V. P. Nair

We consider the vacuum wave function of a free scalar field theory in space partitioned into two regions, with the field obeying Robin conditions (of parameter $\kappa$) on the interface. A direct integration over fields in a subregion is carried out to obtain the reduced density matrix. This leads to a constructive proof of the Reeh-Schlieder theorem. We an

Azadeh Maleknejad

Partially chiral stochastic gravitational wave backgrounds can arise from various processes in the early Universe. The gravitational ABJ anomaly links the chirality of the gravitational field to the chiral fermions, resulting in the stochastic generation of fermionic matter in the radiation era. We show that this mechanism can account for the entire dark mat

Samuel J. R. Holt, Andrea Petrocchi, Martin Lang, Swapneel A. Pathak

Finite difference based micromagnetic simulations are a powerful tool for the computational investigation of magnetic structures. In this paper, we demonstrate how the discretization of continuous micromagnetic equations introduces a numerical 'discretization anisotropy'. We demonstrate that, in certain scenarios, this anisotropy operates on an energy scale

Fulvio Melia

A measurement of the redshift drift constitutes a model-independent probe of fundamental cosmology. Several approaches are being considered to make the necessary observations, using (i) the Extremely Large Telescope (ELT), (ii) the Cosmic Accelerometer, and (iii) the differential redshift drift methodology. Our focus in this {\it Letter} is to assess how the

Benjamin Liégeois, Ramasubramanian Chitra, Nicolò Defenu

In this work, we extend the study of the interplay between scaling symmetries and statistics to one-dimensional fluids by studying the Calogero model in a harmonic trap modulated through time. The latter harbors an interpretation in terms of free particles imbued with exclusion statistics and is an example of a scale invariant fluid in one-dimension displayi

Zachary J. Allamon, Quentin A. Hales, Andrew B. Royston, Douglas L. Rutledge

The forced soliton equation is the starting point for semiclassical computations with solitons away from the small momentum transfer regime. This paper develops necessary analytical and numerical tools for analyzing solutions to the forced soliton equation in the context of two-dimensional models with kinks. Results include a finite degree of freedom regular

Leandro C. Souza, Bruno C. Guingo, Gilson Giraldi, Renato Portugal

The literature reflects a mutually beneficial relationship between machine learning and quantum computing, where progress in one field frequently drives improvements in the other. Motivated by the rich connection between these areas, we use a resource-efficient and scalable Single-Qubit Quantum Neural Network (SQQNN) for both regression and classification ta

Federico Capone, Andy O'Bannon, Ronnie Rodgers, Somyadip Thakur

Celestial holography is the conjecture that scattering amplitudes in $(d+2)$-dimensional asymptotically Minkowski spacetimes are dual to correlators of a $d$-dimensional conformal field theory (CFT) on the celestial sphere, called the celestial CFT (CCFT). In a CFT, we can calculate sub-region entanglement R\'{e}nyi entropies (EREs), including entanglement e

Pia Stammer, Tiberiu Burlacu, Niklas Wahl, Danny Lathouwers

Deterministically solving charged particle transport problems at a sufficient spatial and angular resolution is often prohibitively expensive, especially due to their highly forward peaked scattering. We propose a model order reduction approach which evolves the solution on a low-rank manifold in time, making computations feasible at much higher resolutions

Azucena L. Jimenez Martinez, Kanika Sood, Rakeshkumar Mahto

This research presents preliminary work to address the challenge of identifying at-risk students using supervised machine learning and three unique data categories: engagement, demographics, and performance data collected from Fall 2023 using Canvas and the California State University, Fullerton dashboard. We aim to tackle the persistent challenges of higher

Eric Xia, Yuling Yan, Martin J. Wainwright

Panel data consists of a collection of $N$ units that are observed over $T$ units of time. A policy or treatment is subject to staggered adoption if different units take on treatment at different times and remains treated (or never at all). Assessing the effectiveness of such a policy requires estimating the treatment effect, corresponding to the difference

Alexander Impertro, SeungJung Huh, Simon Karch, Julian F. Wienand

Periodically driven quantum systems can realize novel phases of matter that are not present in time-independent Hamiltonians. One important application is the engineering of synthetic gauge fields, which opens the realm of topological many-body physics to neutral atom quantum simulators. In this work, we leverage a neutral atom quantum simulator to experimen

Leslie G. Valiant

The educability model is a computational model that has been recently proposed to describe the cognitive capability that makes humans unique among existing biological species on Earth in being able to create advanced civilizations. Educability is defined as a capability for acquiring and applying knowledge. It is intended both to describe human capabilities

Anaëlle Pfister, Anna-Laura Sattelberger

We investigate Mellin integrals of products of hyperplanes, raised to an individual power each. We refer to the resulting functions as combinatorial correlators. We investigate their behavior when moving the hyperplanes individually. To encode these functions as holonomic functions in the constant terms of the hyperplanes, we aim to construct a holonomic ann

Paul Brunzema, Mikkel Jordahn, John Willes, Sebastian Trimpe

Gaussian Processes (GPs) are widely seen as the state-of-the-art surrogate models for Bayesian optimization (BO) due to their ability to model uncertainty and their performance on tasks where correlations are easily captured (such as those defined by Euclidean metrics) and their ability to be efficiently updated online. However, the performance of GPs depend

Lee J. Mitchell, Justin D. Finke, Bernard Phlips, W. Neil Johnson

SIRI-2 is a collection of Strontium Iodide gamma-ray detectors sensitive at approximately 400 keV to 10 MeV, launched on the Department of Defense's STPSat-6 to geosynchronous orbit. SIRI-2 detected the gamma-ray burst (GRB) 221009A and, unlike most GRB detectors, was not saturated and did not require any pulse pile-up corrections. The energetics of this bur

Oishi Deb, KR Prajwal, Andrew Zisserman

In this paper, we present a novel keypoint-based classification model designed to recognise British Sign Language (BSL) words within continuous signing sequences. Our model's performance is assessed using the BOBSL dataset, revealing that the keypoint-based approach surpasses its RGB-based counterpart in computational efficiency and memory usage. Furthermore

Md Nurul Absur, Sourya Saha, Sifat Nawrin Nova, Kazi Fahim Ahmad Nasif

A Content Delivery Network (CDN) is a powerful system of distributed caching servers that aims to accelerate content delivery, like high-definition video, IoT applications, and ultra-low-latency services, efficiently and with fast velocity. This has become of paramount importance in the post-pandemic era. Challenges arise when exponential content volume grow

Michele Frigerio, Natascia Vignaroli

A future multi-TeV muon collider would provide an important probe for Majorana neutrinos. A muon collider with a collision energy of $\sim$30 TeV would be sensitive to $\nu_e-\nu_\mu$ transition dipole moments of the order of $\sim 10^{-12}\mu_B$ and would be thus competitive with the latest astrophysical observation and laboratory experiments. Contrary to t

Md. Arifuzzaman, Iftekhar Ahmed, Md. Jalal Uddin Chowdhury, Shadman Sakib

Chronic Kidney Disease (CKD) represents a significant global health challenge, characterized by the progressive decline in renal function, leading to the accumulation of waste products and disruptions in fluid balance within the body. Given its pervasive impact on public health, there is a pressing need for effective diagnostic tools to enable timely interve

Rui Yu, Wen Sun

This paper investigate the sparse multi-type Erd\H{o}s R\'enyi random graphs studied in S\"{o}derberg~\cite{soderberg2002general} and also Bollob\'as et al.~\cite{bollobas2007phase}. Although the corresponding central limit results are currently unknown, we establish moderate deviation principles for the size of the largest connected component, the number of

Matthew Houtput, Luigi Ranalli, Carla Verdi, Serghei Klimin

Describing electron-phonon interactions in a solid requires knowledge of the electron-phonon matrix elements in the Hamiltonian. State-of-the-art first-principles calculations for the electron-phonon interaction are limited to the 1-electron-1-phonon matrix element, which is suitable for harmonic materials. However, there is no first-principles theory for 1-

Rob Cornish

Cornish (2024) recently gave a general theory of neural network symmetrisation in the abstract context of Markov categories. We give a high-level overview of these results, and their concrete implications for the symmetrisation of deterministic functions and of Markov kernels.