November 2024 arXiv papers — page 52

Hanxiang Zhang, Saeed Zolfaghary Pour, Hao Yan, Powei Liu

In this paper, a low-cost monopulse receiver with an enhanced direction of arrival (DoA) estimation accuracy via deep neural network (DNN) is proposed. The entire system is composed of a 4-element patch array, a fully planar symmetrical monopulse comparator network, and a down conversion link. Unlike the conventional design topology, the proposed monopulse c

Nandita Saraf, Yogendra Shastri

Decarbonization of transport sector through adoption of cleaner vehicle options will depend on the environmental awareness of consumers and their priorities. This work develops and uses a system dynamics approach to understand possible adoption pathways of novel vehicle options, i.e., ethanol-blended fuel (E85), electric, and compressed natural gas (CNG) veh

Siya Zhu, Raymundo Arróyave, Doğuhan Sarıtürk

Accurate phase diagram prediction is crucial for understanding alloy thermodynamics and advancing materials design. While traditional CALPHAD methods are robust, they are resource-intensive and limited by experimentally assessed data. This work explores the use of machine learning interatomic potentials (MLIPs) such as M3GNet, CHGNet, MACE, SevenNet, and ORB

William D. Compton, Noel Csomay-Shanklin, Cole Johnson, Aaron D. Ames

Safe navigation of cluttered environments is a critical challenge in robotics. It is typically approached by separating the planning and tracking problems, with planning executed on a reduced order model to generate reference trajectories, and control techniques used to track these trajectories on the full order dynamics. Inevitable tracking error necessitat

Brent A. Griffin, Jacob Marks, Jason J. Corso

Deep learning increasingly relies on massive data with substantial storage, annotation, and training costs. To reduce costs, coreset selection finds a representative subset of data to train models while ideally performing on par with the full data training. To maximize performance, current state-of-the-art coreset methods select data using dataset-specific g

Leonidas Askianakis

Although vital for life on Earth, solar activity poses questions and increasing threats to humanity due to the Sun's unknown dynamics, intensified by our dependence on terrestrial and space-based infrastructure. This situation is compounded by significant gaps in our understanding of space weather phenomena, the Sun's magnetic field, and the need for rapid r

Magnus Lindgaard Nielsen, Jonas Skjold Raaschou-Pedersen, Emil Chrisander, David Dreyer Lassen

Student dropout is a significant concern for educational institutions due to its social and economic impact, driving the need for risk prediction systems to identify at-risk students before enrollment. We explore the accuracy of such systems in the context of higher education by predicting degree completion before admission, with potential applications for p

John Igieobo, Stephen McKean, Steven Sanchez, Dae'Shawn Taylor

For each configuration of rational points on the affine line, we define an operation on the group of unstable A1 motivic homotopy classes of endomorphisms of the projective line. We also derive an algebraic formula for the image of such an operation under Cazanave and Morel's unstable degree map, which is valued in an extension of the Grothendieck--Witt grou

Nuria Navarro Navarro, Tsvi Piran

Tidal disruption events (TDEs) of giant stars by supermassive black holes (SMBH) differ significantly from those of main sequence ones. Most (all for SMBH of more than a~ few times 10^5 m_\odot) giant-TDEs are partial: only a fraction of the envelope is torn apart. The dense stellar core and the rest of the envelope remain intact. In this work, we explore, u

Julien Paupert, Connor Sell

McReynolds showed that every compact Nil 3-manifold occurs as the cusp cross-section of some arithmetic complex hyperbolic 2-manifold. We classify which commensurability classes of cusped, arithmetic, complex hyperbolic 2-manifolds admit cusps with cross-section homeomorphic to a given compact Nil 3-manifold. In particular, there are some Nil 3-manifolds whi

Zhenbang Dai, Feliciano Giustino

Titanium dioxide (TiO2) is a wide-gap semiconductor with numerous applications in photocatalysis, photovoltaics, and neuromorphic computing. The unique functional properties of this material critically depend on its ability to transport charge in the form of polarons, namely narrow electron wavepackets accompanied by local distortions of the crystal lattice.

Brian K. Tran, Ben S. Southworth

We present a mathematical framework for Galerkin formulations of path integrals in lattice field theory. The framework is based on using the degrees of freedom associated to a Galerkin discretization as the fundamental lattice variables. We formulate standard concepts in lattice field theory, such as the partition function and correlation functions, in terms

Lucía Adame, Carlos Román-Zúñiga, Jesús Hernández, Ricardo López-Valdivia

We present tonalli, a spectroscopic analysis python code that efficiently predicts effective temperature, stellar surface gravity, metallicity, $\alpha$-element abundance, and rotational and radial velocities for stars with effective temperatures between 3200 and 6250 K, observed with the Apache Point Observatory Galactic Evolution Experiment 2 (APOGEE-2). t

Kendall I. Sippy, Jake K. Eager-Nash, Ryan C. Hickox, Nathan J. Mayne

We present a study of the effects of ultraviolet (UV) emission from active galactic nuclei (AGN) on the atmospheric composition of planets and potential impact on life. It is expected that all supermassive black holes, which reside at galactic centers, have gone through periods of high AGN activity in order to reach their current masses. We examine potential

Simone Devoto, Massimiliano Grazzini, Stefan Kallweit, Javier Mazzitelli

We present the first fully differential next-to-next-to-leading order (NNLO) QCD calculation for the production of a top-antitop quark pair in association with a Higgs boson ($t \bar t H$) at hadron colliders. The computation is exact, except for the finite part of the two-loop virtual contribution, which we estimate using two different methods that yield co

Ahmed Khaled, A. Aadhi, Chaoran Huang, Alexander N. Tait

High-speed signal processing is essential for maximizing data throughput in emerging communication applications, like multiple-input multiple-output (MIMO) systems and radio-frequency (RF) interference cancellation. However, as these technologies scale, they increase hardware complexity, computing power demands, and create significant digital signal processi

Henning Fernau, Lakshmanan Kuppusamy, Indhumathi Raman

Matrix grammars are one of the first approaches ever proposed in regulated rewriting, prescribing that rules have to be applied in a certain order. Originally, they have been introduced by \'Abrah\'am on linguistic grounds. In traditional regulated rewriting, the most interesting case shows up when all rules are context-free. Typical descriptional complexity

Beth Branman, George Domat, Hannah Hoganson, Robert Alonzo Lyman

By analogy with the Cayley graph of a group with respect to a finite generating set or the Cayley--Abels graph of a totally disconnected, locally compact group, we detail countable connected graphs associated to Polish groups that we term Cayley--Abels--Rosendal graphs. A group admitting a Cayley--Abels--Rosendal graph acts on it continuously, coarsely metri

Salma Afifi, Ishan Thakkar, Sudeep Pasricha

The rapid proliferation of deep learning has revolutionized computing hardware, driving innovations to improve computationally expensive multiply-and-accumulate operations in deep neural networks. Among these innovations are integrated silicon-photonic systems that have emerged as energy-efficient platforms capable of achieving light speed computation and co

James Anderson

Defective coloring (also known as relaxed or improper coloring) is a generalization of proper coloring defined as follows: for $d \in \mathbb{N}$, a coloring of a graph is $d$-defective if every vertex is colored the same as at most $d$ of its neighbors. We investigate defective coloring of planar graphs in the context of correspondence coloring, a generaliz

David Jesus, Edgard A. Pimentel, David Stolnicki

We examine boundary regularity for a fully nonlinear free transmission problem. We argue using approximation methods, comparing the operators driving the problem with a limiting profile. Working natural conditions on the data of the problem, we produce regularity estimates in Sobolev and $C^{1,{\rm Log-Lip}}$-spaces. Our findings extend recent developments i

Yuri Prokhorov

We study quotients of projective and affine spaces by various actions of the icosahedral group. Basically we concentrate on the rationality questions.

Md Shafayat Hossain, Qi Zhang, Eun Sang Choi, Danilo Ratkovski

Determining the types of superconducting order in quantum materials is a challenge, especially when multiple degrees of freedom, such as bands or orbitals, contribute to the fermiology and when superconductivity competes, intertwines, or coexists with other symmetry-breaking orders. Here, we study the Kagome-lattice superconductor CsV3Sb5, in which multiband

Serbülent Ünsal, Sinem Özdemir, Bünyamin Kasap, M. Erşan Kalaycı

In this study, we propose HOPER (HOlistic ProtEin Representation), a novel multimodal learning framework designed to enhance protein function prediction (PFP) in low-data settings. The challenge of predicting protein functions is compounded by the limited availability of labeled data. Traditional machine learning models already struggle in such cases, and wh

Roman Voliansky

Due to the processes that occur during the functioning of modern electromechanical systems, these systems can be considered complex nonlinear dynamic systems from the point of view of the theory of dynamic systems. The movement of such systems is completely determined by external influences acting on the EMS, their parameters, and initial operating condition

Yuhong Song, Edwin Hsing-Mean Sha, Longshan Xu, Qingfeng Zhuge

With the development of quantum computing, quantum processor demonstrates the potential supremacy in specific applications, such as Grovers database search and popular quantum neural networks (QNNs). For better calibrating the quantum algorithms and machines, quantum circuit simulation on classical computers becomes crucial. However, as the number of quantum

Abdeljalil Zoubir, Badr Missaoui

This paper tackles the pressing challenge of mutagenicity prediction by introducing three ground-breaking approaches. First, it showcases the superior performance of 2D scattering coefficients extracted from molecular images, compared to traditional molecular descriptors. Second, it presents a hybrid approach that combines geometric graph scattering (GGS), G

Vladimir Mikhailets, Olena Atlasiuk

The paper contains a review of results on linear systems of ordinary differential equations of an arbitrary order on a finite interval with the most general inhomogeneous boundary conditions in Sobolev spaces. The character of the solvability of such problems is investigated, their Fredholm properties are established, and their indexes and the dimensions of

Francis Halzen, John Kelley

In this chapter, we describe how the IceCube Neutrino Observatory transformed a cubic kilometer of natural ice at the geographic South Pole into a neutrino telescope. The concept of using the neutrino as an astronomical messenger is as old as the neutrino itself, and the challenge to open this new window on the high-energy universe was technological in natur

Xiangxiang Xu, Lizhong Zheng

We study the problem of learning feature representations from a pair of random variables, where we focus on the representations that are induced by their dependence. We provide sufficient and necessary conditions for such dependence induced representations, and illustrate their connections to Hirschfeld--Gebelein--R\'{e}nyi (HGR) maximal correlation function

A. F. S. Cardoso, O. Cavichia, M. Mollá, L. Sánchez-Menguiano

The study of chemical evolution is of paramount importance for understanding the galaxies evolution. Models and observations propose an inside-out mechanism in the formation of spiral galaxy disks implying a negative radial gradient of elemental abundances when represented in logarithmic scale. However, observed chemical abundance gradients, in some instance

Sayan Bhadra, Anuj Srivastava

Functional data contains two components: shape (or amplitude) and phase. This paper focuses on a branch of functional data analysis (FDA), namely Shape-Based FDA, that isolates and focuses on shapes of functions. Specifically, this paper focuses on Scalar-on-Shape (ScoSh) regression models that incorporate the shapes of predictor functions and discard their

Morgan Ohana, Yan-Fei Jiang, Omer Blaes, Bryance Oyang

We present the results of four magnetohydrodynamic simulations and one alpha-disk simulation of accretion disks in a compact binary system, neglecting vertical stratification and assuming a locally isothermal equation of state. We demonstrate that in the presence of net vertical field, disks that extend out to the 3:1 mean motion resonance grow eccentricity

Alexander Lytchak, Burkhard Wilking

We prove that a Riemannian submersion between smooth, compact, non-negatively curved Riemannian manifolds has to be smooth, resolving a conjecture by Berestovskii--Guijarro. We show that without any curvature assumption, the smoothness of the base is implied by the smoothness of the total space. Results are proven in the much more general setting of submetri

Aurys Silinga, András Kovács, Stephen McVitie, Rafal E. Dunin-Borkowski

Methods for characterisation of 3D magnetic spin structures are necessary to advance the performance of 3D magnetic nanoscale technologies. However, as the component dimensions approach the nanometre range, it becomes more challenging to analyse 3D magnetic configurations with the appropriate spatial resolution. In this paper, we present a method based on Lo

Sugandima Weragoda, Ping Xia, Kevin Stephans, Neil Woody

Stereotactic Body Radiation Therapy (SBRT) can be a precise, minimally invasive treatment method for liver cancer and liver metastases. However, the effectiveness of SBRT relies on the accurate delivery of the dose to the tumor while sparing healthy tissue. Challenges persist in ensuring breath-hold reproducibility, with current methods often requiring manua

Max Lahn

We give a characterization of the Anosov condition for reducible representations in terms of the eigenvalue magnitudes of the irreducible block factors of its block diagonalization. As in previous work, these Anosov representations comprise a collection of bounded convex domains in a finite-dimensional vector space, and this perspective allows us to conclude

Gerald Friedland, Xin Huang, Yueying Cui, Vishaal Kapoor

We propose PPLqa, an easy to compute, language independent, information-theoretic metric to measure the quality of responses of generative Large Language Models (LLMs) in an unsupervised way, without requiring ground truth annotations or human supervision. The method and metric enables users to rank generative language models for quality of responses, so as

Aislan Leal Fontes

The goal of this paper is to classify all minimal monads whose cohomology is a stable rank 2 bundle on $\mathbb{P}^3$ with Chern classes $c_1=0$ and $c_2=9$, with possible exception of two non-negative minimal monads, and thus we extend the classification of the minimal monads made by Hartshorne and Rao in \cite[Section 5.3]{HR91} when $c_2\leq8$. We also pr

Anh Tung Nguyen, Sribalaji C. Anand, André M. H. Teixeira

This paper addresses the security allocation problem in a networked control system under stealthy injection attacks. The networked system is comprised of interconnected subsystems which are represented by nodes in a digraph. An adversary compromises the system by injecting false data into several nodes with the aim of maximally disrupting the performance of

Roman Voliansky

Mobile robots are widely used to perform various technological operations in several sectors of the national economy. These operations are related to transporting goods and equipment, performing work to determine the condition of a technical object or structure, their construction or repair, performing work to study a specific territory and compile relevant

Daniel Luo, Alexander Wolitzky

We study reputation formation where a long-run player repeatedly observes private signals and takes actions. Short-run players observe the long-run player's past actions but not her past signals. The long-run player can thus develop a reputation for playing a distribution over actions, but not necessarily for playing a particular mapping from signals to acti

Dipul Chawla, Eric Kazyak, Melih Eriten, Corinne R. Henak

Mechanical stress-induced damage to the articular cartilage can result in fracture imitation or significant tissue degeneration leading to osteoarthritis (OA) compromising joint mobility. Despite the clinical significance, a comprehensive understanding of the crack progression in cartilage damage remains elusive due to complex mechanical responses and variab

Jogi Suda Neto, Roy T. Forestano, Sergei Gleyzer, Kyoungchul Kong

Discovering new phenomena at the Large Hadron Collider (LHC) involves the identification of rare signals over conventional backgrounds. Thus binary classification tasks are ubiquitous in analyses of the vast amounts of LHC data. We develop a Lie-Equivariant Quantum Graph Neural Network (Lie-EQGNN), a quantum model that is not only data efficient, but also ha

Bojun Zhang, Fan Zou, W. N. Brandt, Shifu Zhu

The coevolution of supermassive black holes and their host galaxies represents a fundamental question in astrophysics. One approach to investigating this question involves comparing the star-formation rates (SFRs) of active galactic nuclei (AGNs) with those of typical star-forming galaxies. At relatively low redshifts ($z\lesssim 1$), radio AGNs manifest dim

Kirtan P. Dixit, Don A. Gregory

Subwavelength-scale light absorbers and reflectors have gained significant attention for their potential in photonic applications. These structures often utilize a metal-insulator-metal (MIM) architecture, similar to a Fabry-Perot nanocavity, using noble metals and dielectric or semiconductor spacers for narrow-band light absorption. In reflection mode, they

Csaba Csáki, Rotem Ovadia, Maximilian Ruhdorfer, Ofri Telem

We present a fully calculable UV complete toy model of a Peccei-Quinn (PQ) axion coupled to magnetic monopoles as well as electric charges. The theory has manifest electric-magnetic duality built in. We find that the axion-photon coupling contains the usual anomaly term, plus periodic corrections which can also become large if the monopole is light, without

Claudemir Alcantara, Makson Santos

We investigate fractional regularity estimates up to the boundary for solutions to fully nonlinear elliptic equations with measurable ingredients. Specifically, under the assumption of uniform ellipticity of the operator, we demonstrate that viscosity solutions to a second-order operator satisfy a fractional Laplacian equation. This result implies that the s

Kiana Salehi, Rahul Kumar Walia, Dominic Chang, Prashant Kocherlakota

Recent observations of the near-horizon regions of BHs, particularly the images captured by the Event Horizon Telescope (EHT) collaboration, have greatly advanced our understanding of gravity in extreme conditions. These images reveal a bright, ring-like structure surrounding the central dark area of supermassive BHs, created by the images of unstable photon

Clifford Rostomily, Heidi Lee, Amy Tresenrider, Riza Daza

Zebrafish are an ideal system to study the effect(s) of chemical, genetic, and environmental perturbations on development due to their high fecundity and fast growth. Recently, single cell sequencing has emerged as a powerful tool to measure the effect of these perturbations at a whole embryo scale. These types of experiments rely on the ability to isolate n

Gyula Csató, Albert Mas

This paper deals with the behavior of the periodic Gagliardo seminorm under two types of rearrangements, namely under a periodic, and respectively a cylindrical, symmetric decreasing rearrangement. Our two main results are P\'olya-Szeg\H{o} type inequalities for these rearrangements. We also deal with the cases of equality. Our method uses, among others, som

Frode K. Hansen, Diego Garcia Lambas, Heliana E. Luparello, Facundo Toscano

We confirm at the $5.7\sigma$ level previous studies reporting Cosmic Microwave Background (CMB) temperatures being significantly lower around nearby spiral galaxies than expected in the $\Lambda$CDM model. Results from our earlier work was disputed in a recent paper, but in that paper, areas far beyond the galactic halos were included in the analysis while

Yeshwanth Cherapanamjeri, Daniel Lee

A large body of work in the statistics and computer science communities dating back to Huber (Huber, 1960) has led to statistically and computationally efficient outlier-robust estimators. Two particular outlier models have received significant attention: the adversarial and heavy-tailed models. While the former models outliers as the result of a malicious a

Fatemeh Lotfi, Stefan Roth, Anas Chaaban, Aydin Sezgin

Covert communication in wireless networks ensures that transmissions remain undetectable to adversaries, making it a potential enabler for privacy and security in sensitive applications. However, to meet the high performance and connectivity demands of sixth-generation (6G) networks, future wireless systems will require larger antenna arrays, higher operatin

Avijit Maity, Haoyu Guo, Subir Sachdev, Vikram Tripathi

Thermal Hall transport has emerged as a valuable tool for probing the fractionalized excitations in chiral quantum spin liquids. Observing quantized thermal Hall response, expected at temperatures below the spectral gap, has been challenging and controversial. The finite temperature behavior, especially in the quantum critical regime above the spectral gap,

Carlos Henrique de Lima, David McKeen, John N. Ng, Michael Shamma

Same-sign lepton colliders offer a promising environment to probe lepton number violation. We study processes that change lepton number by two units in the context of Majorana heavy neutral leptons and neutrinophilic scalars at $\mu$TRISTAN, a proposed same-sign muon collider. Our work shows that such colliders, with modest energy and luminosity requirements

Steven Weilong Hsia, Ahmed Rakin Kamal, Linus Wulff

When reduced from $10$ to $10-d$ dimensions tree-level string theory exhibits an $O(d,d)$ symmetry. This symmetry, which is closely related to T-duality, appears only after certain field redefinitions. We find a simple form for a subset of these redefinitions at order $\alpha'^3$ and show that they cannot be lifted to ten dimensions. This is inconsistent wit

Indranil Banik, Harry Desmond, Nick Samaras

It has been proposed that the gravitational constant $G$ abruptly decreased around 130 Myr ago, making Type Ia supernovae (SNe) in the Hubble flow intrinsically brighter than those in host galaxies with Cepheid distances. This would make Hubble flow SNe more distant, causing redshifts to rise slower with distance, potentially solving the Hubble tension. We e

Cyuan-Han Chang, Vasiliy Dommes, Rajeev S. Erramilli, Alexandre Homrich

We compute observables of the critical 3d Ising model to high precision by applying the numerical conformal bootstrap to mixed correlators of the leading scalar operators $\sigma$ and $\epsilon$, and the stress tensor $T_{\mu\nu}$. We obtain new precise determinations of scaling dimensions $(\Delta_\sigma, \Delta_\epsilon) = (0.518148806(24), 1.41262528(29))

Cristian Voinea, Ruihua Fan, Nicolas Regnault, Zlatko Papić

Recently introduced ''fuzzy sphere'' method has enabled accurate numerical regularizations of certain three-dimensional (3D) conformal field theories (CFTs). The regularization is provided by the non-commutative geometry of the lowest Landau level filled by electrons, such that the charge is trivially gapped due to the Pauli exclusion principle at filling fa

Carlota Andres, Fabio Dominguez, Jack Holguin, Cyrille Marquet

Collider experiments involving nuclei provide a direct means of studying exotic states of nuclear matter. Recent measurements of energy correlators in both proton-nucleus (p-A) and nucleus-nucleus (A-A) collisions reveal sizable modifications, attributable to nuclear effects, compared to proton-proton (p-p) collisions. Energy correlators, and their associate

Clara Puerto-Sánchez, Melanie Habouzit, Marta Volonteri, Yueying Ni

Detecting dual active galactic nuclei (DAGN) in observations and understanding theoretically which massive black holes (MBHs) compose them and in which galactic and large-scale environment they reside are becoming increasingly important questions as we enter the multi-messenger era of MBH astronomy. This paper presents the abundance and properties of DAGN pr

Chaoyou Fu, Yi-Fan Zhang, Shukang Yin, Bo Li

As a prominent direction of Artificial General Intelligence (AGI), Multimodal Large Language Models (MLLMs) have garnered increased attention from both industry and academia. Building upon pre-trained LLMs, this family of models further develops multimodal perception and reasoning capabilities that are impressive, such as writing code given a flow chart or c

Ben Pennell, Zack Li, James M. Sullivan

With an aim towards modeling cosmologies beyond the $\Lambda$CDM paradigm, we demonstrate the automatic construction of recombination history emulators while enforcing a prior of causal dynamics. These methods are particularly useful in the current era of precision cosmology, where extremely constraining datasets provide insights into a cosmological model do

Bencheng Liao, Shaoyu Chen, Haoran Yin, Bo Jiang

Recently, the diffusion model has emerged as a powerful generative technique for robotic policy learning, capable of modeling multi-mode action distributions. Leveraging its capability for end-to-end autonomous driving is a promising direction. However, the numerous denoising steps in the robotic diffusion policy and the more dynamic, open-world nature of tr

Xin Huang, Tengfei Wang, Ziwei Liu, Qing Wang

We present Material Anything, a fully-automated, unified diffusion framework designed to generate physically-based materials for 3D objects. Unlike existing methods that rely on complex pipelines or case-specific optimizations, Material Anything offers a robust, end-to-end solution adaptable to objects under diverse lighting conditions. Our approach leverage

Amey Bhangale, Subhash Khot, Yang P. Liu, Dor Minzer

We prove that any subset $A \subseteq [3]^n$ with $3^{-n}|A| \ge (\log\log\log\log n)^{-c}$ contains a combinatorial line of length $3$, i.e., $x, y, z \in A$, not all equal, with $x_i=y_i=z_i$ or $(x_i,y_i,z_i)=(0,1,2)$ for all $i = 1, 2, \dots, n$. This improves on the previous best bound of $3^{-n}|A| \ge \Omega((\log^* n)^{-1/2})$ of [D.H.J. Polymath, An

Prajna G. Malettira, Shubham Negi, Wachirawit Ponghiran, Kaushik Roy

Spiking Neural Networks (SNNs) with their bio-inspired Leaky Integrate-and-Fire (LIF) neurons inherently capture temporal information. This makes them well-suited for sequential tasks like processing event-based data from Dynamic Vision Sensors (DVS) and event-based speech tasks. Harnessing the temporal capabilities of SNNs requires mitigating vanishing spik

Amey Bhangale, Subhash Khot, Yang P. Liu, Dor Minzer

Let $\Sigma_1,\ldots,\Sigma_k$ be finite alphabets, and let $\mu$ be a distribution over $\Sigma_1 \times \dots \times \Sigma_k$ in which the probability of each atom is at least $\alpha$. We prove that if $\mu$ does not admit Abelian embeddings, and $f_i: \Sigma_i \to \mathbb{C}$ are $1$-bounded functions (for $i=1,\ldots,k$) such that \[ \left|\mathbb{E}_{

Joseph C. Chapman, Muneer Alshowkan, Kazi Reaz, Tian Li

Quantum networking continues to encode information in polarization states due to ease and precision. The variable environmental polarization transformations induced by deployed fiber need correction for deployed quantum networking. Here we present a new method for automatic polarization compensation (APC) and demonstrate its performance on a metropolitan qua

Amey Bhangale, Subhash Khot, Yang P. Liu, Dor Minzer

We prove local and global inverse theorems for general $3$-wise correlations over pairwise-connected distributions. Let $\mu$ be a distribution over $\Sigma \times \Gamma \times \Phi$ such that the supports of $\mu_{xy}$, $\mu_{xz}$, and $\mu_{yz}$ are all connected, and let $f: \Sigma^n \to \mathbb{C}$, $g: \Gamma^n \to \mathbb{C}$, $h: \Phi^n \to \mathbb{C

Roberto Ruiz, Alejandro Sopena, Balázs Pozsgay, Esperanza López

We consider the preparation of all the eigenstates of spin chains using quantum circuits. It is known that generic eigenstates of free-fermionic spin chains can be prepared with circuits whose depth grows only polynomially with the length of the chain and the number of particles. We show that the polynomial growth is also achievable for selected interacting

Ri-Zhao Qiu, Yuchen Song, Xuanbin Peng, Sai Aneesh Suryadevara

'In-the-wild' mobile manipulation aims to deploy robots in diverse real-world environments, which requires the robot to (1) have skills that generalize across object configurations; (2) be capable of long-horizon task execution in diverse environments; and (3) perform complex manipulation beyond pick-and-place. Quadruped robots with manipulators hold promise

Jiaze Cai, Vishnu Sangli, Mintae Kim, Koushil Sreenath

Bird-sized flapping-wing robots offer significant potential for agile flight in complex environments, but achieving agile and robust trajectory tracking remains a challenge due to the complex aerodynamics and highly nonlinear dynamics inherent in flapping-wing flight. In this work, a learning-based control approach is introduced to unlock the versatility and

Alessandro Trevisan, Harry Giddens, Sarah Dillon, Alan F. Blackwell

What can we learn about language from studying how it is used by ChatGPT and other large language model (LLM)-based chatbots? In this paper, we analyse the distinctive character of language generated by ChatGPT, in relation to questions raised by natural language processing pioneer, and student of Wittgenstein, Margaret Masterman. Following frequent complain

Atilla P. Kiraly, Sebastien Baur, Kenneth Philbrick, Fereshteh Mahvar

Robust medical Machine Learning (ML) models have the potential to revolutionize healthcare by accelerating clinical research, improving workflows and outcomes, and producing novel insights or capabilities. Developing such ML models from scratch is cost prohibitive and requires substantial compute, data, and time (e.g., expert labeling). To address these chal

Arnav M. Das, Chi Ian Tang, Fahim Kawsar, Mohammad Malekzadeh

Sensing human motions through Inertial Measurement Units (IMUs) embedded in personal devices has enabled significant applications in health and wellness. Labeled IMU data is scarce, however, unlabeled or weakly labeled IMU data can be used to model human motions. For video or text modalities, the "pretrain and adapt" approach utilizes large volumes of unlabe

Finn Gagliano, Iñaki García Etxebarria

Recently, the notion of symmetry descent has been introduced in order to obtain the $(d+1)$-dimensional Symmetry TFT (SymTFT) of a $d$-dimensional QFT from the edge mode behaviour of a theory in $(d+2)$-dimensions. This method has so far been used to obtain SymTFTs for discrete higher-form symmetries of geometrically engineered QFTs. In this note, we extend

Gianni Petrella

We embed several copies of the derived category of a quiver and certain line bundles in the derived category of an associated moduli space of representations, giving the start of a semiorthogonal decomposition. This mirrors the semiorthogonal decompositions of moduli of vector bundles on curves. Our results are obtained with QuiverTools, an open-source packa

Nathan Lambert, Jacob Morrison, Valentina Pyatkin, Shengyi Huang

Language model post-training is applied to refine behaviors and unlock new skills across a wide range of recent language models, but open recipes for applying these techniques lag behind proprietary ones. The underlying training data and recipes for post-training are simultaneously the most important pieces of the puzzle and the portion with the least transp

Georg Stettinger

We consider Witten's open string field theory in the presence of a non-trivial boundary of spacetime. For the kinetic term, we derive a Gibbons-Hawking-type contribution that has to be added to the action to guarantee a well-defined variational principle. The derivation is done first in a heuristic way and then confirmed by a path integral based approach usi

Xiaoman Zhang, Hong-Yu Zhou, Xiaoli Yang, Oishi Banerjee

AI-driven models have demonstrated significant potential in automating radiology report generation for chest X-rays. However, there is no standardized benchmark for objectively evaluating their performance. To address this, we present ReXrank, https://rexrank.ai, a public leaderboard and challenge for assessing AI-powered radiology report generation. Our fra

J. Racker

We study leptogenesis from the decay of the lightest sterile neutrino in the scotogenic model with a scalar dark matter candidate. Our analysis focuses on the possible exponential suppression of washouts for sizable values of the inert Higgs mass and the crucial role of some spectator processes for this to happen. We show that leptogenesis can be successful

Rahul Kumar Walia, Prashant Kocherlakota, Dominic O. Chang, Kiana Salehi

We explore the universal symmetries of the black hole photon ring in a wide range of non-Kerr spacetimes, including the Kerr-Newman, Kerr-Sen, Kerr-Bardeen, and Kerr-Hayward metrics. The demagnification exponent ($\gamma$) controls the size and flux scaling of higher-order images, which appear in the photon ring, the time delay ($\tau$) determines the timing

Katarzyna Macieszczak

We introduce occupation uncertainty relations (OURs) for dynamics of a Markov process over discrete configurations. Those are lower bounds on uncertainties of system observables that are time-integrated along stochastic trajectories. The uncertainty is defined as the ratio of the variance to the square of the average, with the latter necessarily shifted by t

Chih-Chun Hsu, Jason J. Wang, Geoffrey A. Blake, Jerry W. Xuan

The $\sim$5 Myr PDS 70 is the only known system with protoplanets residing in the cavity of the circumstellar disk from which they formed, ideal for studying exoplanet formation and evolution within its natal environment. Here we report the first spin constraint and C/O measurement of PDS 70b from Keck/KPIC high-resolution spectroscopy. We detected CO (3.8 $

Michael Allen, Brian Grove, Ling Long, Fang-Ting Tu

In the first paper of this sequence, we provided an explicit hypergeometric modularity method by combining different techniques from the classical, $p$-adic, and finite field settings. In this article, we explore an application of this method from a motivic viewpoint through some known hypergeometric well-poised formulae of Whipple and McCarthy. We first use

Daeun Lee, Jaehong Yoon, Jaemin Cho, Mohit Bansal

Recent text-to-video (T2V) diffusion models have made remarkable progress in generating high-quality videos. However, they often struggle to align with complex text prompts, particularly when multiple objects, attributes, or spatial relations are specified. We introduce VideoRepair, the first self-correcting, training-free, and model-agnostic video refinemen

Darshan Thaker, Abhishek Goyal, René Vidal

Image restoration aims to recover high-quality images from degraded observations. When the degradation process is known, the recovery problem can be formulated as an inverse problem, and in a Bayesian context, the goal is to sample a clean reconstruction given the degraded observation. Recently, modern pretrained diffusion models have been used for image res

Hjalmar Wijk, Tao Lin, Joel Becker, Sami Jawhar

Frontier AI safety policies highlight automation of AI research and development (R&D) by AI agents as an important capability to anticipate. However, there exist few evaluations for AI R&D capabilities, and none that are highly realistic and have a direct comparison to human performance. We introduce RE-Bench (Research Engineering Benchmark, v1), which consi

Samarth N Ramesh, Zhixue Zhao

As text-to-image models grow increasingly powerful and complex, their burgeoning size presents a significant obstacle to widespread adoption, especially on resource-constrained devices. This paper presents a pioneering study on post-training pruning of Stable Diffusion 2, addressing the critical need for model compression in text-to-image domain. Our study t

Afrah Farea, Mustafa Serdar Celebi

Physics-Informed Neural Networks (PINNs) have emerged as a promising approach for solving Partial Differential Equations (PDEs). However, they face challenges related to spectral bias (the tendency to learn low-frequency components while struggling with high-frequency features) and unstable convergence dynamics (mainly stemming from the multi-objective natur

Irfan Nafiz Shahan, Arban Hossain, Saadman Sakib, Al-Mubin Nabil

In the recent years, we have witnessed a paradigm shift in the field of Computer Vision, with the forthcoming of the transformer architecture. Detection Transformers has become a state of the art solution to object detection and is a potential candidate for Road Object Detection in Autonomous Vehicles. Despite the abundance of object detection schemes, real-

Robert Jencks

Zebrafish have been used as a model organism in many areas of biology, including the study of pattern formation. The mean-field survival model is a coupled ODE system describing the expected evolution of chromatophores coordinating to form stripes in zebrafish. This paper presents analysis of the model focusing on parameters for the number of cells, length o

Frederik Eaton

In this paper we describe an efficient method for providing a regression model with a sense of curiosity about its data. In the field of machine learning, our framework for representing curiosity is called Active Learning, which concerns the problem of automatically choosing data points for which to query labels in the semi-supervised setting. The methods we

Valentino Delle Rose, Alexander Kozachinskiy, Tomasz Steifer

Delle Rose et al.~(COLT'23) introduced an effective version of the Vapnik-Chervonenkis dimension, and showed that it characterizes improper PAC learning with total computable learners. In this paper, we introduce and study a similar effectivization of the notion of Littlestone dimension. Finite effective Littlestone dimension is a necessary condition for com

Subhaditya Bhattacharya, Dipankar Pradhan, Jahaan Thakkar

Pseudo-feebly Interacting Massive Particle (pFIMP) has been postulated in two component dark matter (DM) scenarios, where it has feeble interaction with the visible sector, but sizeable one with a thermal bath partner. In this work, we study the possibility and dynamics of pFIMP in presence of a Strongly Interacting Massive Particle (SIMP), which is well kno

Ramón J. Aliaga, Eva Pernecká, Alicia Quero

We prove that Pelczy\'nski's property (V$^*$) is locally determined for Lipschitz-free spaces, and obtain several sufficient conditions for it to hold. We deduce that $\mathcal{F}(M)$ has property (V$^*$) when the complete metric space $M$ is locally compact and purely 1-unrectifiable, a Hilbert space, or belongs to a class of Carnot-Carath\'eodory spaces sa

Alexandros Stergiou, Ronald Poppe

We have witnessed impressive advances in video action understanding. Increased dataset sizes, variability, and computation availability have enabled leaps in performance and task diversification. Current systems can provide coarse- and fine-grained descriptions of video scenes, extract segments corresponding to queries, synthesize unobserved parts of videos,

Maximilian Pichler, Florian Hartig

In island biogeography, it is widely accepted that species richness on island depends on the area and isolation of the island as well as the species pool on the mainland. Delavaux et al. (2024) suggest that species richness on oceanic islands also depends on the proportion of mutualists on the mainland, based on the idea that mutualists require specific inte