March 2024 arXiv papers — page 16

Shuai Zhang, Huangxuan Zhao, Zhenghong Zhou, Guanjun Wu

Four-dimensional Digital Subtraction Angiography (4D DSA) is a medical imaging technique that provides a series of 2D images captured at different stages and angles during the process of contrast agent filling blood vessels. It plays a significant role in the diagnosis of cerebrovascular diseases. Improving the rendering quality and speed under sparse sampli

Sandra Albrechtsen

Carmesin and Gollin proved that every finite graph has a canonical tree-decomposition $(T, \mathcal{V})$ of adhesion less than $k$ that efficiently distinguishes every two distinct $k$-profiles, and which has the further property that every separable $k$-block is equal to the unique part of $(T, \mathcal{V})$ in which it is contained. We give a shorter proof

Seonho Kim, Kiryung Lee

We consider a least absolute deviation (LAD) approach to the robust phase retrieval problem that aims to recover a signal from its absolute measurements corrupted with sparse noise. To solve the resulting non-convex optimization problem, we propose a robust alternating minimization (Robust-AM) derived as an unconstrained Gauss-Newton method. To solve the inn

Zhongliang Zhou, Jielu Zhang, Zihan Guan, Mengxuan Hu

Geolocating precise locations from images presents a challenging problem in computer vision and information retrieval.Traditional methods typically employ either classification, which dividing the Earth surface into grid cells and classifying images accordingly, or retrieval, which identifying locations by matching images with a database of image-location pa

Alexander J. Izzo

It is shown that there exists a nontrivial uniform algebra that is Dirichlet on its maximal ideal space and has a dense set of elements that are exponentials. This answers a 65-year-old question of John Wermer and a 17-year-old question of Garth Dales and Joel Feinstein. Our example is P(X) for a certain compact set X in complex Euclidean 2-space ($\mathbb{C

Péter Bálint, Dalia Terhesiu

We obtain a generalized law of the iterated logarithm for a class of dependent processes with superdiffusive behaviour. Our results apply in particular to the Lorentz gas with infinite horizon.

Mahima Singh, Jyotirmoy Sau, Banik Rai, Arunanshu Panda

The intrinsic anomalous Hall conductivity (AHC) in a ferromagnetic metal is completely determined by its band structure. Since the spin orientation direction is an important band-structure tuning parameter, it is highly desirable to study the anomalous Hall effect in a system with multiple spin reorientation transitions. We study a layered tetragonal room te

Zhenyu Wang, Yali Li, Taichi Liu, Hengshuang Zhao

In the current state of 3D object detection research, the severe scarcity of annotated 3D data, substantial disparities across different data modalities, and the absence of a unified architecture, have impeded the progress towards the goal of universality. In this paper, we propose \textbf{OV-Uni3DETR}, a unified open-vocabulary 3D detector via cycle-modalit

Ozgu Goksu, Nicolas Pugeault

The pursuit of learning robust representations without human supervision is a longstanding challenge. The recent advancements in self-supervised contrastive learning approaches have demonstrated high performance across various representation learning challenges. However, current methods depend on the random transformation of training examples, resulting in s

Norman Di Palo, Edward Johns

We show that off-the-shelf text-based Transformers, with no additional training, can perform few-shot in-context visual imitation learning, mapping visual observations to action sequences that emulate the demonstrator's behaviour. We achieve this by transforming visual observations (inputs) and trajectories of actions (outputs) into sequences of tokens that

Yohan Beugin, Patrick McDaniel

The Topics API for the web is Google's privacy-enhancing alternative to replace third-party cookies. Results of prior work have led to an ongoing discussion between Google and research communities about the capability of Topics to trade off both utility and privacy. The central point of contention is largely around the realism of the datasets used in these a

Yuki Tsutsui

L\'opez de Medrano, Rinc\'on and Shaw defined the Chern classes on tropical manifolds as an extension of their theory of the Chern-Schwartz-MacPherson cycles on matroids. This makes it possible to define the Riemann-Roch number of tropical Cartier divisors on compact tropical manifolds. In this paper, we introduce the notion of a moderate position, and discu

Akhil Antony, Fabio Finelli, Dhiraj Kumar Hazra, Daniela Paoletti

We search for super-imposed oscillations linearly or logarithmically spaced in Fourier wavenumbers $k$ in Planck and South Pole Telescope (SPT-3G) 2018 temperature and polarization data. The SPT-3G temperature and polarization data provide a new window to test these oscillations at high multipoles beyond the Planck angular resolution and sensitivity. We cons

MicroBooNE collaboration, P. Abratenko, O. Alterkait, D. Andrade Aldana

Charged-current neutrino interactions with final states containing zero mesons and at least one proton are of high interest for current and future accelerator-based neutrino oscillation experiments. Using the Booster Neutrino Beam and the MicroBooNE detector at Fermi National Accelerator Laboratory, we have obtained the first double-differential cross sectio

Esme Bajo, Matthias Beck, Andrés R. Vindas-Meléndez

We study a $q$-version of the chromatic polynomial of a given graph $G=(V,E)$, namely, \[ \chi_G^\lambda(q,n) \ := \sum_{\substack{\text{proper colorings}\\ c\,:\,V\to[n]}} q^{ \sum_{ v \in V } \lambda_v c(v) }, \] where $\lambda \in \mathbb{Z}_{>0}^V$ is a fixed linear form. Via work of Chapoton (2016) on $q$-Ehrhart polynomials, $\chi_G^\lambda(q,n)$ turns

Donald W. Peltier, Isaac Kaminer, Abram Clark, Marko Orescanin

Understanding the characteristics of swarming autonomous agents is critical for defense and security applications. This article presents a study on using supervised neural network time series classification (NN TSC) to predict key attributes and tactics of swarming autonomous agents for military contexts. Specifically, NN TSC is applied to infer two binary a

M. M. Glazov, R. A. Suris

Excitons in van der Waals heterostructures based on atomically thin transition metal dichalcogenides are considered as potential candidates for the formation of a superfluid state in two-dimensional systems. A number of studies reported observations of ultrafast nondiffusive propagation of excitons in van der Waals heterostructures, which was considered by t

Edafetanure-Ibeh Faith, Evah Patrick Tamarauefiye, Mark Uwuoruya Uyi

The aim of attending an educational institution is learning, which in turn is sought after for the reason of independence of thoughts, ideologies as well as physical and material independence. This physical and material independence is gotten from working in the industry, that is, being a part of the independent working population of the country. There needs

Andrzej Dulny, Paul Heinisch, Andreas Hotho, Anna Krause

Predicting the evolution of spatiotemporal physical systems from sparse and scattered observational data poses a significant challenge in various scientific domains. Traditional methods rely on dense grid-structured data, limiting their applicability in scenarios with sparse observations. To address this challenge, we introduce GrINd (Grid Interpolation Netw

Robert Cori, Gábor Hetyei

The problem of counting unicellular hypermonopoles by the number of their hyperedges is equivalent to describing the cycle length distribution of a product of two circular permutations, first solved by Zagier. The solution of this problem has also been used in the study of the cycle graph model of Bafna and Pevzner and of related models in mathematical biolo

Mattia Cavicchi

We construct a motivic lift of the action of the Hecke algebra on the cohomology of PEL Shimura varieties $S_K$. To do so, when $S_K$ is associated with a reductive algebraic group $G$ and $V$ is a local system on $S_K$ coming from a $G$-representation, we define a motivic Hecke algebra $\mathcal{H}^M(G,K)$ as a natural sub-algebra of the endomorphism algebr

Nachi Avraham-Re'em, Emmanuel Roy

We define and study Poissonian actions of Polish groups as a framework to Poisson suspensions, characterize them spectrally, and provide a complete characterization of their ergodicity. We further construct 'spatial' Poissonian actions, answering partially a question of Glasner, Tsirelson & Weiss about L\'evy groups. We also construct for every diffeomorphis

A. O. Lopes, E. R. Oliveira

We will denote by $\mathcal{M}$ the space of Borel probabilities on the symbolic space $\Omega=\{1,2...,m\}^\mathbb{N}$. $\mathcal{M}$ is equipped Monge-Kantorovich metric. We consider here the push-forward map $\mathfrak{T}:\mathcal{M} \to \mathcal{M}$ as a dynamical system. The space of Borel probabilities on $\mathcal{M}$ is denoted by $\mathfrak{M}$. Giv

Christian Johansson, James Newton, Carl Wang-Erickson

We give a categorical formulation of the $p$-adic local Langlands correspondence for $\mathrm{GL}_2(\mathbb{Q}_p)$,as an embedding of the derived category of locally admissible representations into the category of Ind-coherent sheaves on the moduli stack of two-dimensional representations of $\mathrm{Gal}(\overline{\mathbb{Q}}_p/\mathbb{Q}_p)$. Moreover, we

Souvik Dey, Pat Lank

This work is concerned with a relationship regarding the closedness of the singular locus of a Noetherian scheme and existence of classical generators in its category of coherent sheaves, associated bounded derived category, and singularity category. Particularly, we extend an observation initially made by Iyengar and Takahashi in the affine context to the g

Dmitry Arkhangelsky, Kazuharu Yanagimoto, Tom Zohar

We study the estimation of causal effects on group-level parameters identified from microdata (e.g., child penalties). We demonstrate that standard one-step methods (such as pooled OLS and IV regressions) are generally inconsistent due to an endogenous weighting bias, where the policy affects the implicit weights (e.g., altering fertility rates). In contrast

Ronak M Soni

In JT gravity coupled to a CFT, I argue without using the path integral that the entanglement wedge of a boundary region is bounded by a quantum extremal surface (QES). For any candidate not bounded by a QES, a unitary in the complement can make reconstruction within the candidate inconsistent with boundary causality. The case without islands is a direct con

Fu Luo, Xi Lin, Zhenkun Wang, Xialiang Tong

The end-to-end neural combinatorial optimization (NCO) method shows promising performance in solving complex combinatorial optimization problems without the need for expert design. However, existing methods struggle with large-scale problems, hindering their practical applicability. To overcome this limitation, this work proposes a novel Self-Improved Learni

Wunmin Jang, Hong Gao, Tilman Michaeli, Enkelejda Kasneci

Pair programming is widely recognized as an effective educational tool in computer science that promotes collaborative learning and mirrors real-world work dynamics. However, communication breakdowns within pairs significantly challenge this learning process. In this study, we use eye-tracking data recorded during pair programming sessions to study communica

Janis Goldzycher, Paul Röttger, Gerold Schneider

Hate speech detection models are only as good as the data they are trained on. Datasets sourced from social media suffer from systematic gaps and biases, leading to unreliable models with simplistic decision boundaries. Adversarial datasets, collected by exploiting model weaknesses, promise to fix this problem. However, adversarial data collection can be slo

Ernesto Pini, Peter Naglič, Miran Bürmen, Alexander Gatto

Structurally anisotropic materials are ubiquitous in several application fields, yet their accurate optical characterization remains challenging due to the lack of general models linking their scattering coefficients to the macroscopic transport observables, and the need to combine multiple measurements to retrieve their direction-dependent values. Here, we

Pawel Matras, Leon van Wyk, Michal Ziembowski

A subalgebra of the full matrix algebra Mn(K), K a field, satisfying the identity [x1, y1][x2, y2]...[xq, yq] = 0 is called a Dq subalgebra of Mn(K). In the paper we deal with the structure, conjugation and isomorphism problems of maximal Dq subalgebras of Mn(K). We show that a maximal Dq subalgebra A of Mn(K) is conjugated with a block triangular subalgebra

Mohammed Rashid, Jeffrey A. Nanzer

Energy detector (ED) is a popular choice for distributed cooperative spectrum sensing because it does not need to be cognizant of the primary user (PU) signal characteristics. However, the conventional ED-based sensing usually requires large number of observed samples per energy statistic, particularly at low signal-to-noise ratios (SNRs), for improved detec

Sergey Savchenko

Let $T$ be a tournament of odd order $n\ge 5,$ $c_{m}(T)$ be the number of its $m$-cycles, and $s_{m}(T)$ be the number of its strongly connected $m$-subtournaments. Due to work of L.W. Beineke and F. Harary, it is well known that $s_{m}(T)\le s_{m}(RLT_{n}),$ where $RLT_{n}$ is the regular locally transitive tournament of order $n.$ For $m=3$ and $m=4,$ $c_

R. Gnana Praveen, Jahangir Alam

In video-based emotion recognition, audio and visual modalities are often expected to have a complementary relationship, which is widely explored using cross-attention. However, they may also exhibit weak complementary relationships, resulting in poor representations of audio-visual features, thus degrading the performance of the system. To address this issu

Takahiro Aoi

In this paper, we prove the Skoda-Zeriahi type integrability theorem with respect to some measure with $L^1$-density. In addition, we introduce the log-log threshold in order to detect singularities of K\"{a}hler potentials. We prove the positivity of the integrability threshold for such a measure and K\"{a}hler potentials with uniform log-log threshold. As

Antonio Enea Romano

Gravitational waves (GW) are expected to interact with dark energy and dark matter, affecting their propagation on cosmological scales. In order to model this interaction, we derive a gauge invariant effective equation and action valid for all GWs polarizations, based on encoding the effects of the interaction of GWs at different order in perturbations, in a

Junghee Ryu, Hoon Ryu

With recent experimental advancements demonstrating high-fidelity universal logic gates and basic programmability, Silicon-based spin quantum bit (qubit) have emerged as promising candidates for scalable quantum computing. However, implementation of more complex quantum information protocols with many qubits still remains a critical challenge for realization

Zhongkai Tao

We prove that the imaginary parts of scattering resonances for negatively curved asymptotically hyperbolic surfaces are uniformly bounded away from zero and provide a resolvent bound in the resulting resonance-free strip. This provides an essential spectral gap without the pressure condition. This is done by adapting the methods of [arXiv:1004.3361], [arXiv:

Raquel Lazcano, Daniel Madroñal, Giordana Florimbi, Jaime Sancho

Hyperspectral (HS) imaging presents itself as a non-contact, non-ionizing and non-invasive technique, proven to be suitable for medical diagnosis. However, the volume of information contained in these images makes difficult providing the surgeon with information about the boundaries in real-time. To that end, High-Performance-Computing (HPC) platforms become

Ganlin Zhang, Erik Sandström, Youmin Zhang, Manthan Patel

Recent advancements in RGB-only dense Simultaneous Localization and Mapping (SLAM) have predominantly utilized grid-based neural implicit encodings and/or struggle to efficiently realize global map and pose consistency. To this end, we propose an efficient RGB-only dense SLAM system using a flexible neural point cloud scene representation that adapts to keyf

Piotr Molenda, Adian Liusie, Mark J. F. Gales

Watermarking generative-AI systems, such as LLMs, has gained considerable interest, driven by their enhanced capabilities across a wide range of tasks. Although current approaches have demonstrated that small, context-dependent shifts in the word distributions can be used to apply and detect watermarks, there has been little work in analyzing the impact that

Laurent Freidel, Seyed Faroogh Moosavian, Daniele Pranzetti

We propose a solution to a classic problem in gravitational physics consisting of defining the spin associated with asymptotically-flat spacetimes. We advocate that the correct asymptotic symmetry algebra to approach this problem is the generalized-BMS algebra $\textsf{gbms}$ instead of the BMS algebra used hitherto in the literature for which a notion of sp

Mubashara Akhtar, Omar Benjelloun, Costanza Conforti, Luca Foschini

Data is a critical resource for machine learning (ML), yet working with data remains a key friction point. This paper introduces Croissant, a metadata format for datasets that creates a shared representation across ML tools, frameworks, and platforms. Croissant makes datasets more discoverable, portable, and interoperable, thereby addressing significant chal

Jie Luo, Karine Miras, Carlo Longhi, Oliver Weissl

This study explores the integration of Lamarckian system into evolutionary robotics (ER), comparing it with the traditional Darwinian model across various environments. By adopting Lamarckian principles, where robots inherit learned traits, alongside Darwinian learning without inheritance, we investigate adaptation in dynamic settings. Our research, conducte

Marola W. Issa, Steve V. Barancyk, Reza M. Rock, James F. Gilchrist

The work herein investigates the impact of resin molecular weight and solvent choice on drying kinetics and sag velocity in polymer films. These films, ranging in thickness from ~60 micrometer to ~120 micrometer were formulated with 45% by weight polymer resin in one of two solvent packages with different relative evaporation rates (RER). Gravimetry was init

Luka Leskovec, Stefan Meinel, Marcus Petschlies, John Negele

Lattice Quantum Chromodynamics (QCD) has significantly contributed to our understanding of the CKM matrix through precise determinations of hadronic matrix elements. With advancements in theoretical methodologies and computational resources, investigations can now extend to processes involving QCD-unstable hadrons such as the $\rho$ and $K^\star(892)$. These

Eunseong Lee, Richard Battye, Boris Bolliet

We have adapted the Planck cluster likelihood in such a way that it can be applied to the sample of clusters detected by the Atacama Cosmology Telescope (ACT). Applying it to the 2016 sample from Planck and the 2018 sample from ACT we find, by fixing the cosmology using CMB observations and the cluster model adopted by Planck, that the mass bias required by

George Rupp

I comment on a paper by Christoph Hanhart and Alexey Nefediev, published in Phys. Rev. D 106, 114003 (2022). The authors discuss the interpretation of mesons close to their lowest decay threshold and present a mechanism for the formation of molecular states. The proposed formalism is then applied to the axial-vector mesons $D_{s1}(2536)$ and $D_{s1}(2460)$,

Bin Wang, Yaolin Jiang

In this paper, we propose and analyse a novel third-order low-regularity trigonometric integrator for the semilinear Klein-Gordon equation with non-smooth solution in the $d$-dimensional space, where $d=1,2,3$. The integrator is constructed based on the full use of Duhamel's formula and the employment of a twisted function tailored for trigonometric integral

Yuzheng Wang, Dingkang Yang, Zhaoyu Chen, Yang Liu

Data-Free Knowledge Distillation (DFKD) is a promising task to train high-performance small models to enhance actual deployment without relying on the original training data. Existing methods commonly avoid relying on private data by utilizing synthetic or sampled data. However, a long-overlooked issue is that the severe distribution shifts between their sub

V. Mishnyakov, I. Myakutin

In this paper we propose a resolution to the problem of $\beta$-deforming the non-Gaussian monomial matrix models. The naive guess of substituting Schur polynomials with Jack polynomials does not work in that case, therefore, we are forced to look for another basis for superintegrability. We find that the relevant symmetric functions are given by Uglov polyn

Marta Baldomero-Naranjo, Luisa I. Martínez-Merino, Antonio M. Rodríguez-Chía

This paper introduces a new hierarchical facility location model with three levels: first-level facilities which manufacture different products, second-level facilities which act as warehouses and a third-level consisting of the clients who demand the products that have been manufactured in the first level and stored in the second level. In this model, calle

Eldad Bettelheim, Baruch Meerson

The Simple Inclusion Process (SIP) interpolates between two well-known lattice gas models: the independent random walkers and the Kipnis-Marchioro-Presutti model. Here we study large deviations of nonstationary mass transfer in the SIP at long times in one dimension. We suppose that $N\gg 1$ particles start from a single lattice site at the origin, and we ar

Tenio Popmintchev, Ming-Chang Chen, Dimitar Popmintchev, Paul Arpin

High harmonic generation traditionally combines ~100 near-infrared laser photons, to generate bright, phase matched, extreme ultraviolet beams when the emission from many atoms adds constructively. Here we show that by guiding a mid-infrared femtosecond laser in a high pressure gas, ultrahigh harmonics can be generated up to orders > 5000, that emerge as a b

Yulin Pan, Chaojie Mao, Zeyinzi Jiang, Zhen Han

Prior studies have made significant progress in image inpainting guided by either text description or subject image. However, the research on inpainting with flexible guidance or control, i.e., text-only, image-only, and their combination, is still in the early stage. Therefore, in this paper, we introduce the multimodal promptable image inpainting project:

Michelle C. Anderson, Amro Dodin, Thomas P. Fay, David T. Limmer

We introduce a general definition of a quantum committor in order to clarify reaction mechanisms and facilitate control in processes where coherent effects are important. With a quantum committor, we generalize the notion of a transition state to quantum superpositions and quantify the effect of interference on the progress of the reaction. The formalism is

Daniel Cookman

The SNO+ experiment is a large multi-purpose neutrino detector, currently filled with liquid scintillator. For the first time in a single experiment, SNO+ is able to measure the neutrino oscillation parameters $\theta_{12}$ and $\Delta m^{2}_{21}$ simultaneously through both reactor anti-neutrinos and $^{8}B$ solar neutrinos. The latter approach is demonstra

Qiuhao Wang, Xu Yang, Saiyu Qi, Yong Qi

Graphs have more expressive power and are widely researched in various search demand scenarios, compared with traditional relational and XML models. Today, many graph search services have been deployed on a third-party server, which can alleviate users from the burdens of maintaining large-scale graphs and huge computation costs. Nevertheless, outsourcing gr

Thomas Niedermayer, Pietro Saggese, Bernhard Haslhofer

The integration of bots in Distributed Ledger Technologies (DLTs) fosters efficiency and automation. However, their use is also associated with predatory trading and market manipulation, and can pose threats to system integrity. It is therefore essential to understand the extent of bot deployment in DLTs; despite this, current detection systems are predomina

Sara Heydari, Gerardo Iñiguez, János Kertész, Jari Saramäki

The structure of personal networks reflects how we organise and maintain social relationships. The distribution of tie strengths in personal networks is heterogeneous, with a few close, emotionally intense relationships and a larger number of weaker ties. Recent results indicate this feature is universal across communication channels. Within this general pat

S. G. Rajeev

We study the coupling constant renormalization of gauge theories with an infinite multiplet of fermions, using the zeta function method to make sense of the infinite sums over fermions. If the gauge group K is the maximal compact subgroup of a simple non-compact group G, such infinite multiplets can arise naturally, as reductions of discrete series unitary r

Xiao Lin, Wenfei Yang, Yuan Gao, Tianzhu Zhang

Category-level 6D object pose estimation aims to estimate the rotation, translation and size of unseen instances within specific categories. In this area, dense correspondence-based methods have achieved leading performance. However, they do not explicitly consider the local and global geometric information of different instances, resulting in poor generaliz

Amazigh Amrane, Hugo Bazille, Uli Fahrenberg, Marie Fortin

In this paper we study finite higher-dimensional automata (HDAs) from the logical point of view. Languages of HDAs are sets of finite bounded-width interval pomsets with interfaces (iiPoms<=k) closed under order extension. We prove that languages of HDAs are MSO-definable. For the converse, we show that the order extensions of MSO-definable sets of iiPoms<=k

Mojtaba Mojtahedi, Konstantinos Papafilippou

We examine the interplay between projectivity (in the sense that was introduced by S.~Ghilardi) and uniform post-interpolant for the classical and intuitionistic propositional logic. More precisely, we explore whether a projective substitution of a formula is equivalent to its uniform post-interpolant, assuming the substitution leaves the variables of the in

E. Shablovinskaya, C. Ricci, C-S. Chang, A. Tortosa

The origin of a compact millimeter (mm, 100-250 GHz) emission in radio-quiet active galactic nuclei (RQ AGN) remains debated. Recent studies propose a connection with self-absorbed synchrotron emission from the accretion disk X-ray corona. We present the first joint ALMA ($\sim$100 GHz) and X-ray (NICER/XMM-Newton/Swift; 2-10 keV) observations of the unobscu

Moritz Schwarzmeier, Suraj Raju, Željko Tuković, Mathis Fricke

We provide an implementation of the unstructured Finite-Volume Arbitrary Lagrangian / Eulerian (ALE) Interface-Tracking method for simulating incompressible, immiscible two-phase flows as an OpenFOAM module. In addition to interface-tracking capabilities that include tracking of two fluid phases, an implementation of a Subgrid-Scale (SGS) modeling framework

Dong-Hwan Jang, Sangdoo Yun, Dongyoon Han

This paper introduces an efficient fine-tuning method for large pre-trained models, offering strong in-distribution (ID) and out-of-distribution (OOD) performance. Breaking away from traditional practices that need a multitude of fine-tuned models for averaging, our approach employs significantly fewer models to achieve final weights yet yield superior accur

Ismaïl Razack

When $\mathcal{M}$ is a smooth, oriented, compact and simply connected manifold, Luc Menichi has shown that $HH^\ast(C^\ast(\mathcal{M}; \mathbb{F}))$, the Hochschild cohomology of the singular cochain complex of $\mathcal{M}$ is a Batalin-Vilkovisky algebra. Using the properties of algebras over the Barratt-Eccles operad, we show that this results holds eve

Ang Lv, Yuhan Chen, Kaiyi Zhang, Yulong Wang

In this paper, we delve into several mechanisms employed by Transformer-based language models (LLMs) for factual recall tasks. We outline a pipeline consisting of three major steps: (1) Given a prompt ``The capital of France is,'' task-specific attention heads extract the topic token, such as ``France,'' from the context and pass it to subsequent MLPs. (2) A

Kilian Carolan, Laura Fennelly, Alan F. Smeaton

Large Language Models (LLMs) have recently emerged as a focal point of research and application, driven by their unprecedented ability to understand and generate text with human-like quality. Even more recently, LLMs have been extended into multi-modal large language models (MM-LLMs) which extends their capabilities to deal with image, video and audio inform

J. J. Relancio, L. Santamaría-Sanz

In this work, we explore the quantum theories of the free massive scalar, the massive fermionic, and the electromagnetic fields, in a doubly special relativity scenario. This construction is based on a geometrical interpretation of the kinematics of this kind of theory. In order to describe the modified actions, we find that a higher (indeed infinite) deriva

Philip Wykeham Bradford, Valeria Ospina-Bohorquez, Michael Ehret, Jose-Luis Henares

Understanding the physics of electromagnetic pulse emission and nozzle damage is critical for the long-term operation of laser experiments with gas targets, particularly at facilities looking to produce stable sources of radiation at high repetition rate. We present a theoretical model of plasma formation and electrostatic charging when high-power lasers are

Daichi Hayakawa, Thomas E. Videbæk, Gregory M. Grason, W. Benjamin Rogers

Recent advances enable the creation of nanoscale building blocks with complex geometries and interaction specificities for self-assembly. This nearly boundless design space necessitates design principles for defining the mutual interactions between multiple particle species to target a user-specified complex structure or pattern. In this article, we develop

Guangyu Wang, Jinzhi Zhang, Fan Wang, Ruqi Huang

We propose XScale-NVS for high-fidelity cross-scale novel view synthesis of real-world large-scale scenes. Existing representations based on explicit surface suffer from discretization resolution or UV distortion, while implicit volumetric representations lack scalability for large scenes due to the dispersed weight distribution and surface ambiguity. In lig

Ning Zhang, Xiaowen Dong, Mihai Cucuringu

Graph clustering is a fundamental task in unsupervised learning with broad real-world applications. While spectral clustering methods for undirected graphs are well-established and guided by a minimum cut optimization consensus, their extension to directed graphs remains relatively underexplored due to the additional complexity introduced by edge directions.

Mayukh Choudhury, Debraj Das

Generalized linear model or GLM constitutes a large class of models and essentially extends the ordinary linear regression by connecting the mean of the response variable with the covariate through appropriate link functions. On the other hand, Lasso is a popular and easy-to-implement penalization method in regression when not all covariates are relevant. Ho

Jie Wen, Zheng Zhang, Yong Xu, Bob Zhang

In recent years, incomplete multi-view clustering, which studies the challenging multi-view clustering problem on missing views, has received growing research interests. Although a series of methods have been proposed to address this issue, the following problems still exist: 1) Almost all of the existing methods are based on shallow models, which is difficu

Brenda Cobeña, Ivan Contreras, Luisa I. Martínez-Merino, Antonio M. Rodríguez-Chía

This paper deals with an extension of the hub line location problem considering demand elasticity with respect to travel times. The proposed model aims to capture the impact the hub network topology has on demand. The objective is to maximize the total revenue generated by each unit of demand using the hub line. We propose mixed-integer nonlinear formulation

Matthias Deiml, Daniel Peterseim

This paper presents a quantum algorithm for the solution of prototypical second-order linear elliptic partial differential equations discretized by $d$-linear finite elements on Cartesian grids of a bounded $d$-dimensional domain. An essential step in the construction is a BPX preconditioner, which transforms the linear system into a sufficiently well-condit

Shan Chen, Jack Gallifant, Marco Guevara, Yanjun Gao

Generative models have been showing potential for producing data in mass. This study explores the enhancement of clinical natural language processing performance by utilizing synthetic data generated from advanced language models. Promising results show feasible applications in such a high-stakes domain.

Xiaoguang Li, Zitao Li, Ninghui Li, Wenhai Sun

Recent studies reveal that local differential privacy (LDP) protocols are vulnerable to data poisoning attacks where an attacker can manipulate the final estimate on the server by leveraging the characteristics of LDP and sending carefully crafted data from a small fraction of controlled local clients. This vulnerability raises concerns regarding the robustn

Eleanor Chodroff, Blaž Pažon, Annie Baker, Steven Moran

Research in speech technologies and comparative linguistics depends on access to diverse and accessible speech data. The UCLA Phonetics Lab Archive is one of the earliest multilingual speech corpora, with long-form audio recordings and phonetic transcriptions for 314 languages (Ladefoged et al., 2009). Recently, 95 of these languages were time-aligned with w

Grzegorz Skorupko, Richard Osuala, Zuzanna Szafranowska, Kaisar Kushibar

While deep learning holds great promise for disease diagnosis and prognosis in cardiac magnetic resonance imaging, its progress is often constrained by highly imbalanced and biased training datasets. To address this issue, we propose a method to alleviate imbalances inherent in datasets through the generation of synthetic data based on sensitive attributes s

Xuan Zhang, Jacob Helwig, Yuchao Lin, Yaochen Xie

We consider using deep neural networks to solve time-dependent partial differential equations (PDEs), where multi-scale processing is crucial for modeling complex, time-evolving dynamics. While the U-Net architecture with skip connections is commonly used by prior studies to enable multi-scale processing, our analysis shows that the need for features to evol

Celia Chen, Alex Leitch

This position paper argues that large language models (LLMs) constitute promising yet underutilized academic reading companions capable of enhancing learning. We detail an exploratory study examining Claude from Anthropic, an LLM-based interactive assistant that helps students comprehend complex qualitative literature content. The study compares quantitative

Ryosuke Shimada, Teppei Takamatsu

We study the supersingular locus of the Siegel modular variety of genus 3 or 4. More concretely, we decompose the supersingular locus into a disjoint union of the product of a Deligne-Lusztig variety of Coxeter type and a finite-dimensional affine space after taking perfection.

Melody Huang

Estimating externally valid causal effects is a foundational problem in the social and biomedical sciences. Generalizing or transporting causal estimates from an experimental sample to a target population of interest relies on an overlap assumption between the experimental sample and the target population--i.e., all units in the target population must have a

Ji-Cai Liu

We establish supercongruences for two kinds of Ap\'ery-like numbers, which involve Bernoulli numbers and Bernoulli polynomials. Conjectural supercongruences of the same type for another four kinds of Ap\'ery-like numbers are also proposed.

Jasmine Simms

The large depth of the SNO+ experiment (2070 m, 6010 m.w.e.) means that only a few muons per day pass through the detector. However, their high energy causes muon induced backgrounds which can affect multiple physics analyses. Reconstructing the muon track would allow for improved rejection for these induced backgrounds. Currently there is no muon tracker fo

Stijn De Backer, Luis E. C. Rocha, Jan Ryckebusch, Koen Schoors

Accurate modeling of the temporal evolution of asset prices is crucial for understanding financial markets. We explore the potential of discrete-time quantum walks to model the evolution of asset prices. Return distributions obtained from a model based on the quantum walk algorithm are compared with those obtained from classical methodologies. We focus on sp

Ming Yan, Yan Zhang, Shuqiang Cai, Shuqi Fan

Comprehensive capturing of human motions requires both accurate captures of complex poses and precise localization of the human within scenes. Most of the HPE datasets and methods primarily rely on RGB, LiDAR, or IMU data. However, solely using these modalities or a combination of them may not be adequate for HPE, particularly for complex and fast movements.

Frederiek Wesel, Kim Batselier

Tensor Networks (TNs) have recently been used to speed up kernel machines by constraining the model weights, yielding exponential computational and storage savings. In this paper we prove that the outputs of Canonical Polyadic Decomposition (CPD) and Tensor Train (TT)-constrained kernel machines recover a Gaussian Process (GP), which we fully characterize, w

Peng Yan, Guodong Long

Personalized Federated Learning (PerFL) is a new machine learning paradigm that delivers personalized models for diverse clients under federated learning settings. Most PerFL methods require extra learning processes on a client to adapt a globally shared model to the client-specific personalized model using its own local data. However, the model adaptation p

Yuelin Shao, Xi Dai

We investigate the electrical transport properties of the mini-valley polarized state proposed recently in slightly doped Bernal Bilayer Graphene (BLG) in large electric displacement fields. By minimizing the Hartree-Fock energy functional, we first confirm the appearance of mini-valley polarized phase. At the low carrier doping regime, the 1-pocket state wi

Pulkit Khandelwal, Michael Tran Duong, Lisa Levorse, Constanza Fuentes

Magnetic resonance imaging (MRI) is the standard modality to understand human brain structure and function in vivo (antemortem). Decades of research in human neuroimaging has led to the widespread development of methods and tools to provide automated volume-based segmentations and surface-based parcellations which help localize brain functions to specialized

Rafael Hunt-Stokes

The SNO+ Collaboration has recently concluded loading its liquid scintillator with PPO, the primary fluor, and the loading of the wavelength shifter, bisMSB, is ongoing. For each stage of the experiment, reliable position and energy reconstruction is essential, and in the face of a changing scintillator cocktail, methods have been developed to rapidly calibr

Avinash Paliwal, Wei Ye, Jinhui Xiong, Dmytro Kotovenko

The field of 3D reconstruction from images has rapidly evolved in the past few years, first with the introduction of Neural Radiance Field (NeRF) and more recently with 3D Gaussian Splatting (3DGS). The latter provides a significant edge over NeRF in terms of the training and inference speed, as well as the reconstruction quality. Although 3DGS works well fo

Anqi Mao, Mehryar Mohri, Yutao Zhong

Learning to defer with multiple experts is a framework where the learner can choose to defer the prediction to several experts. While this problem has received significant attention in classification contexts, it presents unique challenges in regression due to the infinite and continuous nature of the label space. In this work, we introduce a novel framework

Zheng Zheng, Laurette S. Tuckerman, Tobias M. Schneider

In a vertical channel driven by an imposed horizontal temperature gradient, numerical simulations have previously shown steady, time-periodic and chaotic dynamics. We explore the observed dynamics by constructing invariant solutions of the three-dimensional Oberbeck-Boussinesq equations, characterizing the stability of these equilibria and periodic orbits, a