December 2024 arXiv papers — page 40

Antony Seabra, Claudio Cavalcante, Joao Nepomuceno, Lucas Lago

We propose a methodology that combines several advanced techniques in Large Language Model (LLM) retrieval to support the development of robust, multi-source question-answer systems. This methodology is designed to integrate information from diverse data sources, including unstructured documents (PDFs) and structured databases, through a coordinated multi-ag

Ge Zhang, Mohammad Ali Alomrani, Hongjian Gu, Jiaming Zhou

Large language models (LLMs) possess vast semantic knowledge but often struggle with complex reasoning tasks, particularly in relational reasoning problems such as kinship or spatial reasoning. In this paper, we present Path-of-Thoughts (PoT), a novel framework for solving relation reasoning that decomposes the task into three key stages: graph extraction, p

Yuying Li, Kexiang Xu, Dániel Gerbner, Wenzhong Liu

A graph $G$ is uniquely $H$-saturated if it contains no copy of a graph $H$ as a subgraph, but adding any new edge into $G$ creates exactly one copy of $H$. Let $C_{4}^{+}$ be the diamond graph consisting of a $4$-cycle $C_{4}$ with one chord and $C_{3}^{*}$ be the graph consisting of a triangle with a pendant edge. In this paper we prove that a nontrivial u

Liangliang Zhang, Haoran Bao, Yao Ma

As graph data grows increasingly complicate, training graph neural networks (GNNs) on large-scale datasets presents significant challenges, including computational resource constraints, data redundancy, and transmission inefficiencies. While existing graph condensation techniques have shown promise in addressing these issues, they are predominantly designed

Larissa F. Rodrigues Moreira, Liziane S. Soares, Adriana Z. Martinhago

This paper presents initiatives aimed at fostering female involvement in the realm of computing and endeavoring to inspire more women to pursue careers in these fields. The Meninas++ Project coordinates activities at both the high school and higher education levels, facilitating dialogue between young women and computing professionals, and promoting female r

Qusay Shihab Hamad, Hussein Samma, Shahrel Azmin Suandi

Breast cancer detection based on pre-trained convolution neural network (CNN) has gained much interest among other conventional computer-based systems. In the past few years, CNN technology has been the most promising way to find cancer in mammogram scans. In this paper, the effect of layer freezing in a pre-trained CNN is investigated for breast cancer dete

Andreas Brandhuber, Graham R. Brown, Gabriele Travaglini, Pablo Vives Matasan

We study the effect of the two independent parity-even cubic interactions $I_1 = {R^{\alpha \beta}}_{\mu \nu} {R^{\mu \nu}}_{\rho \sigma} {R^{\rho \sigma}}_{\alpha \beta}$ and $ G_3 = I_1 -2 {R^{\mu \nu \alpha}}_\beta {R^{\beta \gamma}}_{\nu \sigma} {R^\sigma}_{\mu \gamma \alpha}$ on the spectrum of gravitational waves emitted in the quasi-circular inspiral

S. Rasoulzadeh, M. Bank, I. Kovacic, K. Schinegger

Recent advances in 3D generative models have shown promising results but often fall short in capturing the complexity of architectural geometries and topologies and fine geometric details at high resolutions. To tackle this, we present ArchComplete, a two-stage voxel-based 3D generative pipeline consisting of a vector-quantised model, whose composition is mo

Qusay Shihab Hamad, Hussein Samma, Shahrel Azmin Suandi

Convolutional Neural Networks (CNNs) have been successfully utilized in the medical diagnosis of many illnesses. Nevertheless, identifying the optimal architecture and hyperparameters among the available possibilities might be a substantial challenge. Typically, CNN hyperparameter selection is performed manually. Nonetheless, this is a computationally costly

Prabhu Vellaisamy, Harideep Nair, Joseph Finn, Manav Trivedi

General Matrix Multiplication (GEMM) is a ubiquitous compute kernel in deep learning (DL). To support energy-efficient edge-native processing, new GEMM hardware units have been proposed that operate on unary encoded bitstreams using much simpler hardware. Most unary approaches thus far focus on rate-based unary encoding of values and perform stochastic appro

Kimberlee Chestnut Chang, Reed Jensen, Rohan Paleja, Sam L. Polk

In cooperative training, humans within a team coordinate on complex tasks, building mental models of their teammates and learning to adapt to teammates' actions in real-time. To reduce the often prohibitive scheduling constraints associated with cooperative training, this article introduces a paradigm for cooperative asynchronous training of human teams in w

Deepthi Pavurala, Duoduo Liao, Chaithra Reddy Pasunuru

This pilot study presents a novel, automated, and scalable methodology for detecting and evaluating subsurface defect-prone regions in concrete slabs using Impact Echo (IE) signal analysis. The approach integrates advanced signal processing, clustering, and visual analytics to identify subsurface anomalies. A unique adaptive thresholding method tailors frequ

Parker B. Edwards, Jonathan D. Hauenstein

Similarly to the global case, the local structure of a holomorphic subvariety at a given point is described by its local irreducible decomposition. Following the paradigm of numerical algebraic geometry, an algebraic subvariety at a point is represented by a numerical local irreducible decomposition comprised of a local witness set for each local irreducible

Fangzhou Lin, Songlin Hou, Haotian Liu, Shang Gao

Chamfer Distance (CD) is widely used as a metric to quantify difference between two point clouds. In point cloud completion, Chamfer Distance (CD) is typically used as a loss function in deep learning frameworks. However, it is generally acknowledged within the field that Chamfer Distance (CD) is vulnerable to the presence of outliers, which can consequently

Hazem Abdelsalam, Domenico Corona, Renebeth B. Payod, Mahmoud A. S. Sakr

Two-dimensional topological insulators with propagating topological edge states are promising for dissipationless transport, while their one-dimensional analogs are capable of hosting localized topological junction states that are mainly envisaged for quantum computing and spintronics. Here, in contrast, we propose to use the localized nature of topological

Daniel Tan, Neftali Watkinson Medina

NNUE (Efficiently Updatable Neural Networks) has revolutionized chess engine development, with nearly all top engines adopting NNUE models to maintain competitive performance. A key challenge in NNUE training is the creation of high-quality datasets, particularly in complex domains like chess, where tactical and strategic evaluations are essential. However,

Siddhant Gupta, Siddh Singhal, Azmine Toushik Wasi

This work focuses on two subtasks related to hate speech detection and target identification in Devanagari-scripted languages, specifically Hindi, Marathi, Nepali, Bhojpuri, and Sanskrit. Subtask B involves detecting hate speech in online text, while Subtask C requires identifying the specific targets of hate speech, such as individuals, organizations, or co

Adamu Issifu, Prashant Thakur, Franciele M. da Silva, Kau D. Marquez

For the first time, we use relativistic mean-field (RMF) approximation with density-dependent couplings, adjusted by the DDME2 parameterization, to investigate the effects of dark matter on supernova remnants. We calculate the nuclear equation of state for nuclear and dark matter separately, under the thermodynamic conditions related to the evolution of supe

Artyom Aguichine, Natalie Batalha, Jonathan J. Fortney, Nadine Nettelmann

Sub-Neptunes occupy an intriguing region of planetary mass-radius space, where theoretical models of interior structure predict that they could be water-rich, where water is in steam and supercritical state. Such planets are expected to evolve according to the same principles as canonical H$_2$-He rich planets, but models that assume a water-dominated atmosp

Antony Seabra de Medeiros, Luiz Afonso Glatzl Junior, Sergio Lifschitz

This study investigates the mechanisms of Surveillance Capitalism, focusing on personal data transfer during web navigation and searching. Analyzing network traffic reveals how various entities track and harvest digital footprints. The research reveals specific data types exchanged between users and web services, emphasizing the sophisticated algorithms invo

Yuli Wang, Victoria Shi, Wen-Chi Hsu, Yuwei Dai

Purpose: To evaluate various Segmental Anything Model (SAM) prompt strategies across four lesions datasets and to subsequently develop a reinforcement learning (RL) agent to optimize SAM prompt placement. Materials and Methods: This retrospective study included patients with four independent ovarian, lung, renal, and breast tumor datasets. Manual segmentatio

Antony Seabra, Claudio Cavalcante, Joao Nepomuceno, Lucas Lago

We present a question-and-answer (Q\&A) application designed to support the contract management process by leveraging combined information from contract documents (PDFs) and data retrieved from contract management systems (database). This data is processed by a large language model (LLM) to provide precise and relevant answers. The accuracy of these response

Yiyu Zhou, Mohan Shen, Chunzhen Li, Jiacheng Xie

Sapphire is a commonly used substrate for wide-bandgap III-nitride photonic materials. However, its relatively high refractive index results in low transmission efficiency in grating couplers. Here, we propose and demonstrate that the transmission efficiency can be significantly enhanced by bottom-side coupling. A metal reflector is deposited on the top side

Pablo Vázquez-Montejo, Bojan Božič, Jemal Guven

We examine the deformation of homogeneous spherical fluid vesicles along their equator by a circular rigid ring. We consider deformations preserving the axial and equatorial mirror symmetries of the vesicles. The configurations of the vesicle are determined employing the spontaneous curvature model subject to the constraints imposed by the ring as well as of

Vladimir Sayevich, Whi Dong Kim, Zachary L. Robinson, Oleg V. Kozlov

Heterostructured quantum dots (QDs) based on narrow-gap PbSe and wide-gap CdSe have been studied with an eye on their prospective applications in near-infrared (NIR) light sources, photodetectors, and solar cells. The most common structural motif is a spherical QD comprising a PbSe core enclosed into a CdSe shell. However, the potential barrier created by th

Philippe Brax, Danièle A. Steer

A scalar field coupled conformally and disformally to matter affects both the linear memory effect for binary systems on hyperbolic orbits, as well as the kick velocity for binaries on bound or unbound orbits. We study these corrections in detail, their order of magnitude, and discuss their detectability. In particular, we find that the disformal interaction

Anirban Bhaduri, Yael Davidov, Eleonore Faber, Katrina Honigs

In this paper, we explore the derived McKay correspondence for several reflection groups, namely reflection groups of rank two generated by reflections of order two. We prove that for each of the reflection groups $G=G(2m,m,2)$, $G_{12}$, $G_{13}$, or $G_{22}$, there is a semiorthogonal decomposition of the following form, where $B_1,\ldots,B_r$ are the norm

Danial Kazemikia

Electric motors are crucial in many applications, but traditional control methods struggle with nonlinearities, parameter uncertainties, and external disturbances. Reinforcement Learning (RL) offers a promising solution as a data-driven approach that can learn optimal control strategies without an explicit model. This review paper examines the current state

Hans Christianson, John A. Toth

Let $\Omega$ be an $n$-dimensional compact Riemannian manifold $(n \geq 3)$ with $C^\infty$ boundary, and consider $L^2$-normalized eigenfunctions $ - \Delta \phi_{\lambda} = \lambda^2 \phi_\lambda$ with Dirichlet or Neumann boundary conditions . In this note, we extend well-known interior nonconcentration bounds up to the boundary. Specifically, in Theorem

Kamal Shayegan

In recent years, Unmanned Aerial Vehicles (UAVs) have been utilized as effective platforms for carrying Wi-Fi Access Points (APs) and cellular Base Stations (BSs), enabling low-cost, agile, and flexible wireless networks with high Quality of Service (QoS). The next generation of wireless communications will rely on increasingly higher frequencies, which are

Martin Fajcik, Martin Docekal, Jan Dolezal, Karel Ondrej

We present BenCzechMark (BCM), the first comprehensive Czech language benchmark designed for large language models, offering diverse tasks, multiple task formats, and multiple evaluation metrics. Its duel scoring system is grounded in statistical significance theory and uses aggregation across tasks inspired by social preference theory. Our benchmark encompa

P. Rabán, J. R. Peláez, J. Ruiz de Elvira

We present preliminary results of an improved pion-pion scattering dispersive analysis that includes: a refined treatment of inelasticities, the introduction of G-waves, the extension of Forward Dispersion Relations as constraints up to 1.6 GeV, and data description up to roughly 1.8 GeV. Additionally, we impose Roy-like dispersion relations. As a result, we

Anatoly Kulikov, Simon Storz, Josua D. Schär, Martin Sandfuchs

Successful realization of Bell tests has settled an 80-year-long debate, proving the existence of correlations which cannot be explained by a local realistic model. Recent experimental progress allowed to rule out any possible loopholes in these tests, and opened up the possibility of applications in cryptography envisaged more than three decades ago. A prom

Jeffrey Shallit

The paperfolding sequences form an uncountable class of infinite sequences over the alphabet $\{ -1, 1 \}$ that describe the sequence of folds arising from iterated folding of a piece of paper, followed by unfolding. In this note we observe that the sequence of run lengths in such a sequence, as well as the starting and ending positions of the $n$'th run, is

Zirui Li, Minghao Guo, Mayank Barad, Wei Tang

We make the case that variational algorithm ansatzes for near-term quantum computing are well-suited for the quantum circuit cutting strategy. Previous demonstrations of circuit cutting focused on the exponential execution and postprocessing costs due to the cuts needed to partition a circuit topology, leading to overly pessimistic evaluations of the approac

Daniel E. Reichart

Here we briefly describe each of the modules that constitute Skynet's new "Astrophotography of the Multi-Wavelength Universe!", or MWU!, curriculum.

Onyx Gautam

We establish a decay result in the black hole exterior region of spherically symmetric solutions to the Einstein-Maxwell-scalar field system arising from compactly supported admissible data. Our result allows for large initial data, and it is the first decay statement for higher order derivatives of the scalar field. Solutions to this model generically devel

Andrey E. Schegolev, Marina V. Bastrakova, Michael A. Sergeev, Anastasia A. Maksimovskaya

The extensive development of the field of spiking neural networks has led to many areas of research that have a direct impact on people's lives. As the most bio-similar of all neural networks, spiking neural networks not only allow the solution of recognition and clustering problems (including dynamics), but also contribute to the growing knowledge of the hu

Michał Fiedorowicz

It is conjectured by Chen and Raspaud that for each integer $k \ge 2$, any graph $G$ with \[ \mathrm{mad}(G) < \frac{2k+1}{k} \quad\text{and}\quad \mathrm{odd\text{-}girth}(G) \ge 2k+1 \] admits a homomorphism into the Kneser graph $K(2k+1,k)$. The base cases $k=2$ and $k=3$ are known from earlier work. A modular inductive proof is provided here, in which gr

Bartłomiej Marek, Piotr Kawa, Piotr Syga

Since the majority of audio DeepFake (DF) detection methods are trained on English-centric datasets, their applicability to non-English languages remains largely unexplored. In this work, we present a benchmark for the multilingual audio DF detection challenge by evaluating various adaptation strategies. Our experiments focus on analyzing models trained on E

Mohammad M. Amirian, Zoe V. Finkel, Emmanuel Devred, Andrew J. Irwin

Mathematical models of photosynthesis-irradiance relationships in phytoplankton are used to compute integrated water-column photosynthetic rates and predict primary production. Models typically ignore an important phenomenon observed in most experiments: photosynthetic rate remains constant over a range of irradiances before declining due to photoinhibition.

Francesca Cappelli, Giovanni Colavizza, Silvio Peroni

Author Name Disambiguation (AND) is a critical task for digital libraries aiming to link existing authors with their respective publications. Due to the lack of persistent identifiers used by researchers and the presence of intrinsic linguistic challenges, such as homonymy, the development of Deep Learning algorithms to address this issue has become widespre

Anna Plaksienko, Magne Thoresen, Vera Djordjilović

We provide a review and a comparison of methods for differential network estimation in Gaussian graphical models with focus on structure learning. We consider the case of two datasets from distributions associated with two graphical models. In our simulations, we use five different methods to estimate differential networks. We vary graph structure and sparsi

Filippos Bellos, Nam H. Nguyen, Jason J. Corso

Although LLMs have demonstrated remarkable capabilities in processing and generating textual data, their pre-trained vocabularies are ill-suited for capturing the nuanced temporal dynamics and patterns inherent in time series. The discrete, symbolic nature of natural language tokens, which these vocabularies are designed to represent, does not align well wit

A. Farhan, M. Saccone, B. F. L. Ward

In Refs.[1-4] Dirac and Schwinger showed the existence of a magnetic monopole required a charge quantization condition which we write following Dirac as $\frac{eg}{4\pi\hbar}=\frac{n}{2},\; n=0,\pm 1,\; \pm 2, \ldots$. Here, $g$ is the magnetic monopole charge and $e$ is the electric charge of the positron. Recently, in Refs. [5,6], it has been shown experim

Haohong Lin, Xin Huang, Tung Phan-Minh, David S. Hayden

Simulation is critical for safety evaluation in autonomous driving, particularly in capturing complex interactive behaviors. However, generating realistic and controllable traffic scenarios in long-tail situations remains a significant challenge. Existing generative models suffer from the conflicting objective between user-defined controllability and realism

Valentina Crespi, Carlos R. Argüelles, Jorge A. Rueda

We characterize the overdensity (spike) of fermionic dark matter (DM) particles around a supermassive black hole (SMBH) within a general relativistic analysis. The initial DM halo distribution is obtained by solving the equilibrium equations of a self-gravitating system of massive fermions at a finite temperature, according to the Ruffini-Arg\"uelles-Rueda (

Xiaopo Cheng, Christina Caruso, Wilbur A. Lam, Michael D. Graham

The impact of cell segregation and margination in blood disorders on microcirculatory hemodynamics within bifurcated vessels are physiologically significant, yet poorly understood. This study presents a comprehensive computational investigation of red blood cell (RBC) suspension dynamics, with a focus on a model of sickle cell disease (SCD) as an example of

Yukiya Saito, Iris Dillmann, Reiner Krücken, Matthew R. Mumpower

This work explores the construction of a fast emulator for the calculation of the final pattern of nucleosynthesis in the rapid neutron capture process (the $r$-process). An emulator is built using a feed-forward artificial neural network (ANN). We train the ANN with nuclear data and relative abundance patterns. We take as input the $\beta$-decay half-lives

Pierre-Antoine Bernard, Luc Vinet

Quantum $n$-qubit states that are totally symmetric under the permutation of qubits are essential ingredients of important algorithms and applications in quantum information. Consequently, there is significant interest in developing methods to prepare and manipulate Dicke states, which form a basis for the subspace of fully symmetric states. Two simple proto

Matthew King-Roskamp, Rustum Choksi, Tim Hoheisel

We establish the theoretical framework for implementing the maximumn entropy on the mean (MEM) method for linear inverse problems in the setting of approximate (data-driven) priors. We prove a.s. convergence for empirical means and further develop general estimates for the difference between the MEM solutions with different priors $\mu$ and $\nu$ based upon

Anton I. Sergeev, Alexander S. Frolov, Nadezhda V. Vladimirova, Arseny A. Naumov

Magnetic topological materials (MTIs) including MnBi$_2$Te$_4$ are of scientific interest due to the possibility to reveal the interplay between topological and magnetic orders. For these materials preparation issues are still challenging. In our paper we report new synthetic protocol using two-phase source in Bridgman-like growth procedure. We applied it to

Maria Alejandra Alvarez, Salim Rivière, Nadina Rojas, Sonia Vera

We exhibit in this article a contraction of the direct product Lie algebra $g\oplus g$ of a finite-dimensional complex Lie algebra $g$ onto the semi-direct product Lie algebra $g\rtimes g$, where the first factor $g$ is viewed as a trivial Lie algebra and as the adjoint $g$-module. This contraction gives rise to a non-zero cohomology class in the second coho

Keisuke Inomata, Marc Kamionkowski, Kentaro Kasai, Bibhushan Shakya

We discuss a new source of gravitational waves (GWs) from first-order phase transitions. The collisions of bubbles of the new phase can efficiently produce particles that couple to the background field undergoing the transition, thereby transferring a significant fraction of the released vacuum energy into a distribution of inhomogeneous and dynamic particle

Bo Han, David F. Mross

We introduce a spin ladder with discrete symmetries designed to emulate a two-dimensional spin-1/2 boson system at half-filling. Using global properties, such as the structure of topological defects, we establish a correspondence between the two systems and construct a dictionary of symmetries and operators. In particular, translation invariance leads to Lie

Vansh Nagpal, Siva Likitha Valluru, Kausik Lakkaraju, Nitin Gupta

A common decision made by people, whether healthy or with health conditions, is choosing meals like breakfast, lunch, and dinner, comprising combinations of foods for appetizer, main course, side dishes, desserts, and beverages. Often, this decision involves tradeoffs between nutritious choices (e.g., salt and sugar levels, nutrition content) and convenience

Jonathan Conrad, Joseph T. Iosue, Ansgar G. Burchards, Victor V. Albert

We investigate state designs for continuous-variable quantum systems using the aid of lattice-like quantum states. These are code states of Gottesman-Kitaev-Preskill (GKP) codes. We show that for an n-mode system, the set of all GKP states forms a rigged continuous-variable state 2-design. We use these lattice state designs to construct a continuous variable

Orson Mengara

With the rapid development of generative artificial intelligence, particularly large language models a number of sub-fields of deep learning have made significant progress and are now very useful in everyday applications. For example,financial institutions simulate a wide range of scenarios for various models created by their research teams using reinforceme

Kris Kraack

Traditional psychological evaluations rely heavily on human observation and interpretation, which are prone to subjectivity, bias, fatigue, and inconsistency. To address these limitations, this work presents a multimodal emotion recognition system that provides a standardised, objective, and data-driven tool to support evaluators, such as psychologists, psyc

Spencer Tamagni

We introduce nonabelian analogs of shift operators in the enumerative theory of quasimaps. We apply them on the one hand to strengthen the emerging analogy between enumerative geometry and the geometric theory of automorphic forms, and on the other hand to obtain results about quantized Coulomb branch algebras. In particular, we find a short and direct proof

Andrew C. Yuan, Nick Crawford

In quenched disordered systems, the existence of ordering is generally believed to be only possible in the weak disorder regime (disregarding models of spin-glass type). In particular, sufficiently large random field is expected to prohibit any finite temperature ordering. Here, we show that this is not necessarily true. We provide physically motivated examp

Spencer Tamagni

Using ideas from the gauge theory approach to the geometric Langlands program, we revisit supersymmetric localization with monopole operators in 3d $\mathcal{N} = 4$ supersymmetric gauge theories subject to $\Omega$-deformation. The key novel feature of our setup is a pair of dual boundary conditions, which drastically simplify the dynamics of the theory and

Anthony J. Brady, Yu-Xin Wang, Victor V. Albert, Alexey V. Gorshkov

We address the metrological problem of estimating collective stochastic properties imprinted on a network of quantum sensors. Canonical examples include center-of-mass quadrature fluctuations in a system of bosonic modes and correlated dephasing in an ensemble of qubits (e.g., spins), bosons, or fermions. We develop a theoretical framework to determine the l

Justin Berman, Nicholas Geiser

We derive analytic constraints on the weakly-coupled spectrum of theories with a massless scalar under the standard assumptions of the S-matrix bootstrap program. These bootstrap bounds apply to any theory (with or without gravity) with fully crossing symmetric (i.e. $stu$-symmetric) four-point amplitudes and generalize results for color- or flavor-ordered (

Kaho Morii, Patricio Sanhueza, Timea Csengeri, Fumitaka Nakamura

Recent high-angular-resolution observations indicate the need for core growth to form high-mass stars. To understand the gas dynamics at the core scale in the very early evolutionary stages before being severely affected by feedback, we have conducted Atacama Large Millimeter/submillimeter Array (ALMA) observations toward a 70 $\mu$m dark massive clump, G337

Achilleas Gitsis, Falk Hassler

We revisit duality-covariant higher-derivative corrections which arise from the generalized Bergshoeff-de Roo (gBdR) identification, a prescription that gives rise to a two parameter family of $\alpha'$-corrections to the low-energy effective action of the bosonic and the heterotic string. Although it is able to reproduce all corrections at the leading and s

Neha S. Wadia

The Gibbs sampler, also known as the coordinate hit-and-run algorithm, is a Markov chain that is widely used to draw samples from probability distributions in arbitrary dimensions. At each iteration of the algorithm, a randomly selected coordinate is resampled from the distribution that results from conditioning on all the other coordinates. We study the beh

Lei Zhang, Han Yan, Xian Chen, Jinhai Zhang

Previous studies of the response of the Moon to gravitational waves have been carried out using analytical or semi-analytical models assuming ideal lunar structures. Such models are advantageous for their high-speed calculation but fail to account for the extremely heterogeneous subsurface and/or interior structures of the Moon. Numerical calculations are ne

Raffaele Tito D'Agnolo, Sebastian A. R. Ellis

We derive a lower bound on the sensitivity of generic mechanical and electromagnetic gravitational wave detectors. We consider both classical and quantum detection schemes, although we focus on the former. Our results allow for a simple reproduction of the sensitivities of a variety of experiments, including optical interferometers, resonant bars, optomechan

Luca Di Luzio, Sebastian Hoof, Coenraad Marinissen, Vaisakh Plakkot

We extend the catalogue of "phenomenologically preferred" hadronic axion models to include heavy fermion representations associated with higher-dimensional decay operators. The latter have recently been shown to self-consistently trigger a period of early matter domination, making the underlying axion models cosmologically viable. After identifying all possi

Monica Jinwoo Kang, Craig Lawrie, Ki-Hong Lee, Jaewon Song

We study a landscape of four-dimensional $\mathcal{N}=1$ superconformal field theories (SCFTs) with identical central charges. These theories are obtained by renormalization group flows triggered by supersymmetry-preserving superpotential deformations of the $\mathcal{N}=1$ gauging of the flavor symmetry of a collection of $\mathcal{N}=2$ $\mathcal{D}_p(G)$

Falk Hassler, David Osten, Yuho Sakatani

Duality covariant curvature and torsion tensors in double field theory/generalized geometry are central in analyzing consistent truncations, generalized dualities, and related integrable $\sigma$-models. They are constructed systematically with the help of a larger, auxiliary space in a procedure inspired by Cartan geometry originally proposed by Pol\'a\v{c}

Weijie Lyu, Yi Zhou, Ming-Hsuan Yang, Zhixin Shu

We present FaceLift, a novel feed-forward approach for generalizable high-quality 360-degree 3D head reconstruction from a single image. Our pipeline first employs a multi-view latent diffusion model to generate consistent side and back views from a single facial input, which then feeds into a transformer-based reconstructor that produces a comprehensive 3D

Siyuan Bian, Chenghao Xu, Yuliang Xiu, Artur Grigorev

We introduce ChatGarment, a novel approach that leverages large vision-language models (VLMs) to automate the estimation, generation, and editing of 3D garments from images or text descriptions. Unlike previous methods that struggle in real-world scenarios or lack interactive editing capabilities, ChatGarment can estimate sewing patterns from in-the-wild ima

Ziyang Wu, Tianjiao Ding, Yifu Lu, Druv Pai

The attention operator is arguably the key distinguishing factor of transformer architectures, which have demonstrated state-of-the-art performance on a variety of tasks. However, transformer attention operators often impose a significant computational burden, with the computational complexity scaling quadratically with the number of tokens. In this work, we

Mingu Kang, Sunje Kim, Yuting Qian, Paul M. Neves

Understanding the geometric properties of quantum states and their implications in fundamental physical phenomena is at the core of modern physics. The Quantum Geometric Tensor (QGT) is a central physical object in this regard, encoding complete information about the geometry of the quantum state. The imaginary part of the QGT is the well-known Berry curvatu

Rui Chen, Jianfeng Zhang, Yixun Liang, Guan Luo

Recent 3D content generation pipelines commonly employ Variational Autoencoders (VAEs) to encode shapes into compact latent representations for diffusion-based generation. However, the widely adopted uniform point sampling strategy in Shape VAE training often leads to a significant loss of geometric details, limiting the quality of shape reconstruction and d

Sijia Chen, En Yu, Wenbing Tao

Referring Multi-Object Tracking (RMOT) is an important topic in the current tracking field. Its task form is to guide the tracker to track objects that match the language description. Current research mainly focuses on referring multi-object tracking under single-view, which refers to a view sequence or multiple unrelated view sequences. However, in the sing

Lea Müller, Hongsuk Choi, Anthony Zhang, Brent Yi

We present "Humans and Structure from Motion" (HSfM), a method for jointly reconstructing multiple human meshes, scene point clouds, and camera parameters in a metric world coordinate system from a sparse set of uncalibrated multi-view images featuring people. Our approach combines data-driven scene reconstruction with the traditional Structure-from-Motion (

Yazhou Xing, Yang Fei, Yingqing He, Jingye Chen

Learning a robust video Variational Autoencoder (VAE) is essential for reducing video redundancy and facilitating efficient video generation. Directly applying image VAEs to individual frames in isolation can result in temporal inconsistencies and suboptimal compression rates due to a lack of temporal compression. Existing Video VAEs have begun to address te

Yidi Shao, Mu Huang, Chen Change Loy, Bo Dai

We introduce GausSim, a novel neural network-based simulator designed to capture the dynamic behaviors of real-world elastic objects represented through Gaussian kernels. We leverage continuum mechanics and treat each kernel as a Center of Mass System (CMS) that represents continuous piece of matter, accounting for realistic deformations without idealized as

Precious Jones, Weisi Liu, I-Chan Huang, Xiaolei Huang

Data imbalance is a fundamental challenge in applying language models to biomedical applications, particularly in ICD code prediction tasks where label and demographic distributions are uneven. While state-of-the-art language models have been increasingly adopted in biomedical tasks, few studies have systematically examined how data imbalance affects model p

Wen Yin

The QCD axion is widely studied as a dark matter (DM) candidate and as a solution to the strong CP problem of the Standard Model. In conventional field-theoretic models, a much larger mass scale than the electroweak (EW) scale is typically introduced to spontaneously break Peccei-Quinn (PQ) symmetry with a large enough axion decay constant, $f_a$, thereby av

Thomas Chalopin, Petar Bojović, Si Wang, Titus Franz

In strongly correlated materials, interacting electrons are entangled and form collective quantum states, resulting in rich low-temperature phase diagrams. Notable examples include cuprate superconductors, in which superconductivity emerges at low doping out of an unusual "pseudogap" metallic state above the critical temperature. The Fermi-Hubbard model, des

Yitong Chen, Wenhao Yao, Lingchen Meng, Sihong Wu

Enabling models to recognize vast open-world categories has been a longstanding pursuit in object detection. By leveraging the generalization capabilities of vision-language models, current open-world detectors can recognize a broader range of vocabularies, despite being trained on limited categories. However, when the scale of the category vocabularies duri

Akarsh Kumar, Chris Lu, Louis Kirsch, Yujin Tang

With the recent Nobel Prize awarded for radical advances in protein discovery, foundation models (FMs) for exploring large combinatorial spaces promise to revolutionize many scientific fields. Artificial Life (ALife) has not yet integrated FMs, thus presenting a major opportunity for the field to alleviate the historical burden of relying chiefly on manual d

Charles Murphy, Ariane Lizotte, François Thibault, Vincent Thibeault

Network reconstruction consists in retrieving the hidden interaction structure of a system from observations. Many reconstruction algorithms have been proposed, although less research has been devoted to describe their theoretical limitations. In this work, we take a first-principles approach and build on our earlier definition of reconstructability-the frac

Scott Emmons, Caspar Oesterheld, Vincent Conitzer, Stuart Russell

We study partially observable assistance games (POAGs), a model of the human-AI value alignment problem which allows the human and the AI assistant to have partial observations. Motivated by concerns of AI deception, we study a qualitatively new phenomenon made possible by partial observability: would an AI assistant ever have an incentive to interfere with

Orchid Chetia Phukan, Drishti Singh, Swarup Ranjan Behera, Arun Balaji Buduru

In this work, we investigate various state-of-the-art (SOTA) speech pre-trained models (PTMs) for their capability to capture prosodic signatures of the generative sources for audio deepfake source attribution (ADSD). These prosodic characteristics can be considered one of major signatures for ADSD, which is unique to each source. So better is the PTM at cap

A. Levin, N. Sakharova

The modular curves serve as excellent objects for testing conjectures in arithmetic geometry. They possess a natural geometric definition in contrast with rather nontrivial structure. On the other hand, they are well-studied from the perspective of number theory. Furthermore, there is a well-developed and powerful analytic technique available. We will use th

Erik Garrison

Recent breakthroughs in AI capability have been attributed to increasingly sophisticated architectures and alignment techniques, but a simpler principle may explain these advances: memory makes computation universal. Memory enables universal computation through two fundamental capabilities: recursive state maintenance and reliable history access. We formally

Fernando Lopes, Dominique Gibert, Vincent Courtillot, Jean-Louis Le Mouël

Singular Spectrum Analysis (SSA) occupies a prominent place in the real signal analysis toolkit alongside Fourier and Wavelet analysis. In addition to the two aforementioned analyses, SSA allows the separation of patterns directly from the data space into the data space, with data that need not be strictly stationary, continuous, or even normally sampled. In

Canyi Chen, Liping Zhu

Massive data analysis calls for distributed algorithms and theories. We design a multi-round distributed algorithm for canonical correlation analysis. We construct principal directions through the convex formulation of canonical correlation analysis and use the shift-and-invert preconditioning iteration to expedite the convergence rate. This distributed algo

Sampurna Kundu, Jayant Jha, Subir Kumar Bhandari

We consider the procedure proposed by Bhandari et al. (2009) in the context of two-treatment clinical trials, with the objective of minimizing the applications of the less effective drug to the least number of patients. Our focus is on an adaptive sequential procedure that is both simple and intuitive. Through a refined theoretical analysis, we establish tha

Fan Lu, Zhengwei Liu

We suggest a classification scheme for subfactorizable fusion bialgebras, particularly for exchange relation planar algebras. This scheme begins by transforming infinite diagrammatic consistency equations of exchange relations into a finite set of algebraic equations of degree at most 3. We then introduce a key concept, the fusion graph of a fusion bialgebra

Luke Ellert-Beck, Wenchao Ge

Trapped-ion systems are a promising route toward the realization of both near-term and universal quantum computers. However, one of the pressing challenges is improving the fidelity of two-qubit entangling gates. These operations are often implemented by addressing individual ions with laser pulses using the Molmer-Sorensen (MS) protocol. Amplitude modulatio

Kateřina Charvátová, Pavel Malý

Multidimensional optical spectroscopy observes transient excitation dynamics through the time evolution of spectral correlations. Its action-detected variants offer several advantages over the coherent detection and are thus becoming increasingly widespread. Nevertheless, a drawback of action-detected spectra is the presence of a large stationary background

Xinmiao Yu, Xiaocheng Feng, Yun Li, Minghui Liao

Recent Large Vision-Language Models (LVLMs) have shown promising reasoning capabilities on text-rich images from charts, tables, and documents. However, the abundant text within such images may increase the model's sensitivity to language. This raises the need to evaluate LVLM performance on cross-lingual text-rich visual inputs, where the language in the im

Michal P. Heller, Jacopo Papalini, Tim Schuhmann

One of the important open problems in quantum black hole physics is a dual interpretation of holographic complexity proposals. To date the only quantitative match is the equality between the Krylov spread complexity in triple-scaled SYK at infinite temperature and the complexity = volume proposal in classical JT gravity. Our work utilizes the recent connecti

Adi Armoni, Bartosz Pyszkowski, Shigeki Sugimoto, Dorin Weissman

We investigate the high-energy fixed-angle scattering of pions and $\rho$-mesons in a bottom-up holographic QCD model. To this end, we generalise the approach of Polchinski and Strassler arXiv:hep-th/0109174 to write an ansatz for meson scattering amplitudes based on superstring scattering amplitudes in asymptotically AdS space. We demonstrate that our gener