March 2024 arXiv papers — page 111

Bo Song, Yuanhao Xu, Yichao Wu

Machine learning models have achieved significant milestones in various domains, for example, computer vision models have an exceptional result in object recognition, and in natural language processing, where Large Language Models (LLM) like GPT can start a conversation with human-like proficiency. However, abstract reasoning remains a challenge for these mo

Mohammad J. Aljubran, Roland N. Horne

This study presents a data-driven spatial interpolation algorithm based on physics-informed graph neural networks used to develop national temperature-at-depth maps for the conterminous United States. The model was trained to approximately satisfy the three-dimensional heat conduction law by simultaneously predicting subsurface temperature, surface heat flow

Zhaoyang Shi, Chinmoy Bhattacharjee, Krishnakumar Balasubramanian, Wolfgang Polonik

We derive Gaussian approximation bounds for $k$-Potential Nearest Neighbor ($k$-PNN) based random forest predictions based on a set of training points given by a Poisson process under fairly mild regularity assumptions on the data generating process. Our approach is based on the key observation that $k$-PNN based random forest predictions satisfy a certain g

Ievgen Makedonskyi, Igor Makhlin

We demonstrate how pipe dreams can be applied to the theory of poset polytopes to produce toric degenerations of flag varieties. Specifically, we present such constructions for marked chain-order polytopes of Dynkin types A and C. These toric degenerations also give rise to further algebraic and geometric objects such as PBW-monomial bases and Newton--Okounk

Yuhao Liu, Xinyu Bian, Yizhou Xu, Tianqi Hou

In order to control the inter-cell interference for a multi-cell multi-user multiple-input multiple-output network, we consider the precoder design for coordinated multi-point with downlink coherent joint transmission. To avoid costly information exchange among the cooperating base stations in a centralized precoding scheme, we propose a decentralized one by

Shuai Feng, Shi-Yin Shen, Fang-Ting Yuan, Wen-Xin Zhong

We investigate the suppression of star formation in galaxy pairs based on the isolated galaxy pair sample derived from the SDSS survey. By comparing the star formation rate between late-type galaxies in galaxy pairs and those in the isolated environment, we detect the signal of star formation suppression in galaxy pairs at $d_p < 100$kpc and $200$kpc$ < d_p

Noora Kartiosuo, Joni Virta, Jaakko Nevalainen, Olli Raitakari

Compositional data arise when count observations are normalised into proportions adding up to unity. To allow use of standard statistical methods, compositional proportions can be mapped from the simplex into the Euclidean space through the isometric log-ratio (ilr) transformation. When the counts follow a multinomial distribution with fixed class-specific p

Mikhail Tokman, Jitendra Verma, Alexey Belyanin

We present a general formalism and specific implementation of quantum gates based on interaction of single photons with open dissipative nanocavities containing ensembles of quantum emitters. Rich dynamics of entangled bright and dark states of quantum emitters coupled to a nanocavity field gives rise to efficient manipulation of the quantum state of an inci

Min Jin, Junbin Ye, Rongxuan Shen, Huaxing Lu

Passwords are the most widely used method of authentication and password guessing is the essential part of password cracking and password security research. The progress of deep learning technology provides a promising way to improve the efficiency of password guessing. However, current research on neural network password guessing methods mostly focuses on m

Xin Zheng, Dongjin Song, Qingsong Wen, Bo Du

Evaluating the performance of a well-trained GNN model on real-world graphs is a pivotal step for reliable GNN online deployment and serving. Due to a lack of test node labels and unknown potential training-test graph data distribution shifts, conventional model evaluation encounters limitations in calculating performance metrics (e.g., test error) and measu

Rui Li

We theoretically investigate the dephasing of a central spin-1 model. An interesting mechanism of spin decoherence is found with this model, namely {\em hyperfine mediated spectral diffusion}. This mechanism contains both the features of dipolar interactions induced spectral diffusion and hyperfine mediated interactions. We also find an anomalous magnetic fi

Michael Ragone

This thesis studies a pair of symmetry protected topological (SPT) phases which arise when considering one-dimensional quantum spin systems possessing a natural orthogonal group symmetry. Particular attention is given to a family of exactly solvable models whose ground states admit a matrix product state description and generalize the AKLT chain. We call the

Shaohui Yi, Zhiyu Liao, Qi Wang, Haiyang Ma

We report on the charge dynamics of kagome FeGe, an antiferromagnet with a charge density wave (CDW) transition at $T_{\mathrm{CDW}} \simeq 105$ K, using polarized infrared spectroscopy and band structure calculations. We reveal pronounced optical anisotropy along the $a$- and $c$-axis, as well as an unusual response associated with three-dimensional CDW ord

Lia Bronsard, Andrew Colinet, Dominik Stantejsky

We consider minimizers $u_\varepsilon$ of the Ginzburg-Landau energy with quadratic divergence penalization on a simply-connected two-dimensional domain $\Omega$. On the boundary, strong tangential anchoring is imposed. We prove that minimizers satisfy a $L^\infty$-bound uniform in $\varepsilon$ when $\Omega$ has $C^{2,1}-$boundary and that the Lipschitz con

Zhixiu Lu, Hailong Li, Nehal A. Parikh, Jonathan R. Dillman

The integration of artificial intelligence (AI) with radiology marks a transformative era in medicine. Vision foundation models have been adopted to enhance radiologic imaging analysis. However, the distinct complexities of radiologic 2D and 3D radiologic data pose unique challenges that existing models, pre-trained on general non-medical images, fail to add

Aymen Sekhri, Marouane Tliba, Mohamed Amine Kerkouri, Yassine Nasser

Conventional imaging diagnostics frequently encounter bottlenecks due to manual inspection, which can lead to delays and inconsistencies. Although deep learning offers a pathway to automation and enhanced accuracy, foundational models in computer vision often emphasize global context at the expense of local details, which are vital for medical imaging diagno

Andrew Sullivan, Chris Power, Connor Bottrell, Aaron Robotham

We investigate the connection between a cluster's structural configuration and observable measures of its gas emission that can be obtained in X-ray and Sunyaev-Zeldovich (SZ) surveys. We present an analytic model for the intracluster gas density profile: parameterised by the dark matter halo's inner logarithmic density slope, $\alpha$, the concentration, $c

Michela Artebani, Sofía Pérez Garbayo

This paper deals with the problem of computing a generating set for the Cox ring $R(X)$ of a smooth projective rational surface $X$ with nef anticanonical class. In case $R(X)$ is finitely generated, we show that the degrees of its generators are either classes of negative curves, elements of the Hilbert basis of the nef cone or certain ample classes of anti

Zihan Xia, Juan Garcia-Nila, Daniel Lidar

Quantum master equations are commonly used to model the dynamics of open quantum systems, but their accuracy is rarely compared with the analytical solution of exactly solvable models. In this work, we perform such a comparison for the damped Jaynes-Cummings model of a qubit in a leaky cavity, for which an analytical solution is available in the one-excitati

Kada Williams

Various authors have calculated how many pairwise incomparable points can be selected from a partially ordered set. We tackle this question for the family of subsets of a finite set obtained by removing or adding a bounded number of elements from a given subset. Our versatile approach is proven valid under the condition of the set size exceeding the cube of

Ziya Ata Yazıcı, İlkay Öksüz, Hazım Kemal Ekenel

Glioblastoma is a highly aggressive and malignant brain tumor type that requires early diagnosis and prompt intervention. Due to its heterogeneity in appearance, developing automated detection approaches is challenging. To address this challenge, Artificial Intelligence (AI)-driven approaches in healthcare have generated interest in efficiently diagnosing an

Swetha Ganesh, Jiayu Chen, Gugan Thoppe, Vaneet Aggarwal

Federated Reinforcement Learning (FRL) allows multiple agents to collaboratively build a decision making policy without sharing raw trajectories. However, if a small fraction of these agents are adversarial, it can lead to catastrophic results. We propose a policy gradient based approach that is robust to adversarial agents which can send arbitrary values to

Mohamed Amine Kerkouri, Marouane Tliba, Aladine Chetouani, Alessandro Bruno

Neural network quantization is a critical technique for deploying models on resource-limited devices. Despite its widespread use, the impact of quantization on model perceptual fields, particularly in relation to class activation maps (CAMs), remains underexplored. This study investigates how quantization influences the spatial recognition abilities of visio

Abigail Price, Ada Stelzer, Alexander Yong

Matrix Schubert varieties (Fulton '92) carry natural actions of Levi groups. Their coordinate rings are thereby Levi-representations; what is a combinatorial counting rule for the multiplicities of their irreducibles? When the Levi group is a torus, (Knutson-Miller '04) answers the question. We present a general solution, a common refinement of the multigrad

Mohammad Jabed Morshed Chowdhury, Naveed Ul Hassan, Wayes Tushar, Dustin Niyato

The adoption of renewable energy resources, such as solar power, is on the rise. However, the excessive installation and lack of recycling facilities pose environmental risks. This paper suggests a circular economy approach to address the issue. By implementing blockchain technology, the end-of-life (EOL) of solar panels can be tracked, and responsibilities

Wuxiao Han, Tiansong Zhang, Pengcheng Zhao, Longfei Yang

Two-dimensional (2D) Fe-chalcogenides have emerged with rich structures, magnetisms and superconductivities, which sparked the growing research interests in the torturous transition mechanism and tunable properties for their potential applications in nanoelectronics. Uniaxial strain can produce a lattice distortion to study symmetry breaking induced exotic p

Étienne Lantagne-Hurtubise, Iliya Esin, Gil Refael, Frederik Nathan

We show that rhombohedral multilayer graphene supports topological frequency conversion, whereby a fraction of electrons transfer energy between two monochromatic light sources at a quantized rate. The pristine nature and gate tunability of these materials, along with a Berry curvature that directly couples to electric fields, make them ideal platforms for t

Javier Orts

The main result of this paper is the proof that all the symmetric products of a (finite) Galois-Maximal space are also Galois-Maximal spaces. This applies to the special case of real algebraic varieties, solving the problem first stated by Biswas and D'Mello in \cite{biswas&d'mello:symmetric_products_M-curves} about symmetric products of Maximal curves, and

Pragna Mannam, Xingyu Liu, Ding Zhao, Jean Oh

We automate soft robotic hand design iteration by co-optimizing design and control policy for dexterous manipulation skills in simulation. Our design iteration pipeline combines genetic algorithms and policy transfer to learn control policies for nearly 400 hand designs, testing grasp quality under external force disturbances. We validate the optimized desig

Cullen Haselby, Mark Iwen, Santhosh Karnik, Rongrong Wang

We propose two provably accurate methods for low CP-rank tensor completion - one using adaptive sampling and one using nonadaptive sampling. Both of our algorithms combine matrix completion techniques for a small number of slices along with Jennrich's algorithm to learn the factors corresponding to the first two modes, and then solve systems of linear equati

Jack M. Jenkins, Christopher M. J. Osborne, Ye Qiu, Rony Keppens

Solar prominences observed close to the limb commonly include a bright feature that, from the perspective of the observer, runs along the interface between itself and the underlying chromosphere. Despite several idealised models being proposed to explain the underlying physics, a more general approach remains outstanding. In this manuscript we demonstrate as

Luca Grillotti, Maxence Faldor, Borja G. León, Antoine Cully

A key aspect of intelligence is the ability to demonstrate a broad spectrum of behaviors for adapting to unexpected situations. Over the past decade, advancements in deep reinforcement learning have led to groundbreaking achievements to solve complex continuous control tasks. However, most approaches return only one solution specialized for a specific proble

Chihiro Kurihara, Akihito Kiyama, Yoshiyuki Tagawa

This study experimentally investigates the pressure fluctuations of liquids in a column under short-time acceleration and demonstrates that the Strouhal number $St$ [$=L/(c\Delta t)$, where $L$, $c$, and $\Delta t$ are the liquid column length, speed of sound, and acceleration duration, respectively] provides a measure of the pressure fluctuations both for l

Alexei M. Frolov

Bound state properties of the ground (bound) ${}^{1}S(L = 0)-$state(s) in the four-body positronium hydrides ${}^{1}$HPs, ${}^{2}$HPs (DPs), ${}^{3}$HPs (TPs) and MuPs are determined and investigated. By using numerical data from our computations of these four-body systems we have determined a number of different annihilation rates for each of these positron

Brian Gilbert, Katherine L. Hoffman, Nicholas Williams, Kara E. Rudolph

We demonstrate a comprehensive semiparametric approach to causal mediation analysis, addressing the complexities inherent in settings with longitudinal and continuous treatments, confounders, and mediators. Our methodology utilizes a nonparametric structural equation model and a cross-fitted sequential regression technique based on doubly robust pseudo-outco

Ryley G Hill, Matthew Weingarten, Cornelius Langenbruch, Yuri Fialko

Fluid injection can induce seismicity by altering stresses on pre-existing faults. Here, we investigate minimizing induced seismic hazard by optimizing injection operations in a physics-based forecasting framework. We built a 3D finite element model of the poroelastic crust for the Raton Basin, Central US, and used it to estimate time dependent Coulomb stres

Ludovick Bouthat, Ángel Chávez, Stephan Ramon Garcia

A theorem of Hunter ensures that the complete homogeneous symmetric polynomials of even degree are positive definite functions. A probabilistic interpretation of Hunter&#39;s theorem suggests a broad generalization: the construction of so-called random vector norms on square complex matrices. This paper surveys these ideas, starting from the fundamental noti

Francesco Vona, Miladin Ćeranić, Irma Rybnikova, Jan-Niklas Voigt-Antons

Given that experience is a pivotal dimension of learning processes in the field of leadership, the ongoing and unresolved issue is how such experiential moments could be provided when developing leadership skills and competencies. Role-plays and business simulations are widely used in this context as they are said to teach relevant social leadership skills,

Merlin Carl

Combining the approaches made in works with Galeotti and Passmann, we define and study a notion of &#34;almost sure&#34; realizability with parameter-free ordinal Turing machines (OTMs). In particular, we show that, in contrast to the classical case, almost sure realizability differs from plain realizability, while closure under intuitionistic predicate logi

Yarema A. Prykarpatskyy, Petro Ya. Pukach, Myroslava I. Vovk, Michal Greguš

We analyze smooth nonlinear mappings for Hilbert and Banach spaces that carry small balls to convex sets, provided that the radius of the balls is small enough. Being focused on the study of new and mild sufficient conditions for a nonlinear mapping of Hilbert and Banach spaces to be locally convex, we address a suitably reformulated local convexity problem

Jesús A. De Loera, Brittney Marsters, Luze Xu, Shixuan Zhang

We investigate the semigroup of integer points inside a convex cone. We extend classical results in integer linear programming to integer conic programming. We show that the semigroup associated with nonpolyhedral cones can sometimes have a notion of finite generating set. We show this is true for the cone of positive semidefinite matrices (PSD) and the seco

Sungsoon Lim, Eric W. Peng, Patrick Côté, Laura Ferrarese

We study the size and structure of globular clusters (GC) systems of 118 early-type galaxies from the NGVS, MATLAS, and ACSVCS surveys. Fitting S\'ersic profiles, we investigate the relationship between effective radii of GC systems ($R_{e, \rm gc}$) and galaxy properties. GC systems are 2--4 times more extended than host galaxies across the entire stellar m

Saeid Amiri, Parisa Zehtabi, Danial Dervovic, Michael Cashmore

Industries frequently adjust their facilities network by opening new branches in promising areas and closing branches in areas where they expect low profits. In this paper, we examine a particular class of facility location problems. Our objective is to minimize the loss of sales resulting from the removal of several retail stores. However, estimating sales

Ayano Nakamura, Shinichi Nishihaya, Hiroaki Ishizuka, Markus Kriener

Here we study nonmonotonic features which appear both in magnetoresistivity and anomalous Hall resistivity during the simple magnetization process, by systematically measuring type-II magnetic Weyl semimetal EuCd$_2$Sb$_2$ films over a wide carrier density range. We find that a positive magnetoresistivity hump can be explained as manifestation of a field-lin

Yingqing Chen, Christos G. Cassandras, Kaiyuan Xu

This paper develops a controller for Connected and Automated Vehicles (CAVs) traversing a single-lane roundabout. The controller simultaneously determines the optimal sequence and associated optimal motion control jointly minimizing travel time and energy consumption while providing speed-dependent safety guarantees, as well as satisfying velocity and accele

G. C. Bento, J. X. Cruz Neto, J. O. Lopes, B. S. Mordukhovich

This paper is devoted to general nonconvex problems of multiobjective optimization in Hilbert spaces. Based on Mordukhovich's limiting subgradients, we define a new notion of Pareto critical points for such problems, establish necessary optimality conditions for them, and then employ these conditions to develop a refined version of the vectorial proximal poi

Eric Easthope

I humbly introduce a concept I call "Fregean flows," a graph theoretic representation of classical logic, to show how higher-dimensional graph characteristics might be useful to prove or perhaps at best show the provability of simple deductive statements typically represented as one-dimensional strings of characters. I apply these to a very simple proof, nam

Joel Shor, Carson McNeil, Yotam Intrator, Joseph R Ledsam

$\textbf{Background}$: Generalizability of AI colonoscopy algorithms is important for wider adoption in clinical practice. However, current techniques for evaluating performance on unseen data require expensive and time-intensive labels. $\textbf{Methods}$: We use a "Masked Siamese Network" (MSN) to identify novel phenomena in unseen data and predict polyp d

Yunfei Cheng, Aonan Zhang, Xuanyu Zhang, Chong Wang

We present Recurrent Drafter (ReDrafter), an advanced speculative decoding approach that achieves state-of-the-art speedup for large language models (LLMs) inference. The performance gains are driven by three key aspects: (1) leveraging a recurrent neural network (RNN) as the draft model conditioning on LLM's hidden states, (2) applying a dynamic tree attent

Atif Belal, Akhil Meethal, Francisco Perdigon Romero, Marco Pedersoli

Domain adaptation methods for object detection (OD) strive to mitigate the impact of distribution shifts by promoting feature alignment across source and target domains. Multi-source domain adaptation (MSDA) allows leveraging multiple annotated source datasets and unlabeled target data to improve the accuracy and robustness of the detection model. Most state

B. P. Blakley, Will M. Grundy, Jordan K. Steckloff, Sugata P. Tan

Few laboratory studies have investigated the vapor pressures of the volatiles that may be present as ices in the outer solar system; even fewer studies have investigated these species at the temperatures and pressures suitable to the surfaces of icy bodies in the Saturnian and Uranian systems ($\lt$100 K, $\lt10^{-9}$ bar). This study adds to the work of Gru

Alnadhief H. A. Alfedeel, M. Koussour, N. Myrzakulov

In this paper, we investigate the cosmological implications and constraints of Weyl-type $f(Q, T)$ gravity. This theory introduces a coupling between the non-metricity $Q$ and the trace $T$ of the energy-momentum tensor, using the principles of proper Weyl geometry. In this geometry, the scalar non-metricity $Q$, which characterizes the deviations from Riema

Li Lin, Sarah Papabathini, Xin Wang, Shu Hu

Human affective behavior analysis aims to delve into human expressions and behaviors to deepen our understanding of human emotions. Basic expression categories (EXPR) and Action Units (AUs) are two essential components in this analysis, which categorize emotions and break down facial movements into elemental units, respectively. Despite advancements, existin

Vishal Asnani, John Collomosse, Tu Bui, Xiaoming Liu

Generative AI (GenAI) is transforming creative workflows through the capability to synthesize and manipulate images via high-level prompts. Yet creatives are not well supported to receive recognition or reward for the use of their content in GenAI training. To this end, we propose ProMark, a causal attribution technique to attribute a synthetically generated

Yangyang Cheng, Katherine Staden

Given graphs $G_1,\ldots,G_s$ all on a common vertex set and a graph $H$ with $e(H) = s$, a copy of $H$ is \emph{transversal} or \emph{rainbow} if it contains one edge from each $G_i$. We establish a stability result for transversal Hamilton cycles: the minimum degree required to guarantee a transversal Hamilton cycle can be lowered as long as the graph coll

Marisa Hocking, Christina E. Henzinger, Steven Tran, Mihir Pendharkar

New properties can arise at van der Waals (vdW) interfaces hosting a moir\'e pattern generated by interlayer twist and strain. However, achieving precise control of interlayer twist/strain remains an ongoing challenge in vdW heterostructure assembly, and even subtle variation in these structural parameters can create significant changes in the moir\'e period

Özgür D. Gürcan

General problem of plasma turbulence can be formulated as advection of potential vorticity (PV), which handles flow self-organization, coupled to a number of other fields, whose gradients provide free energy sources. Therefore, focusing on PV evolution separates the underlying linear instability from the flow self-organization, and clarifies key spatial scal

Mario H. Amante, Andrés Lizardo, Javier Chagoya, C. Ortiz

We analyze cosmography as a tool to constrain modified gravity theories. We take four distinct models and obtain their parameters in terms of the cosmographic parameters favored by observational data of strong gravitational lensing. We contrast with the values obtained by direct comparison between each model and the observational data. In general, we find co

Yifeng Huang

In [NVP22], Nguyen and Van Peski raised the question of whether the surjective flag of $\mathbb Z_p$-modules modeled by $\mathrm{cok}(M_1\cdots M_k)\twoheadrightarrow \dots\twoheadrightarrow \mathrm{cok}(M_1)$ for independent random matrices $M_1,\dots,M_k\in \mathrm{Mat}_n(\mathbb Z_p)$ satisfies the Cohen--Lenstra heuristic. We answer the question affirmat

Farshad Bagherpour, Saeed Mahdavifar, Elham Hosseini Lapasar, Hanif Hadipour

Experimental observations of anisotropic tightly bound excitons in black phosphorene, and correlated phenomena such as room temperature magnetically active edges in phosphorene nanoribbons (PNRs), sparked discussions on the controversial screening of the Coulomb interaction in phosphorene-based materials. In this way, we investigate the first-principles elec

Xiaowu Dai, Huiying Zhong

Kernel ridge regression (KRR) is widely used for nonparametric regression over reproducing kernel Hilbert spaces. It offers powerful modeling capabilities at the cost of significant computational costs, which typically require $O(n^3)$ computational time and $O(n^2)$ storage space, with the sample size n. We introduce a novel framework of multi-layer kernel

Jiahui Wu, Chengjie Lu, Aitor Arrieta, Tao Yue

Large Language Models (LLMs) are demonstrating outstanding potential for tasks such as text generation, summarization, and classification. Given that such models are trained on a humongous amount of online knowledge, we hypothesize that LLMs can assess whether driving scenarios generated by autonomous driving testing techniques are realistic, i.e., being ali

Vishnu Sashank Dorbala, Bhrij Patel, Amrit Singh Bedi, Dinesh Manocha

Embodied navigation methods commonly operate in static environments with stationary objects. In this work, we present approaches for tackling navigation in dynamic scenarios with non-stationary targets. In an indoor environment, we assume that these objects are everyday portable items moved by human intervention. We therefore formalize the problem as a perso

Kai Yi, Georg Meinhardt, Laurent Condat, Peter Richtárik

Federated Learning (FL) has garnered increasing attention due to its unique characteristic of allowing heterogeneous clients to process their private data locally and interact with a central server, while being respectful of privacy. A critical bottleneck in FL is the communication cost. A pivotal strategy to mitigate this burden is Local Training, which inv

Ryan Greenough, Kohei Murakami, Michael Davidson, Jan Kleissl

Public safety power shutoffs (PSPS) are a common pre-emptive measure to reduce wildfire risk due to power system equipment. System operators use PSPS to de-energize electric grid elements that are either prone to failure or located in regions at a high risk of experiencing a wildfire. Successful power system operation during PSPS involves coordination across

Shokhrukh Yu. Kholmatov

We study forced anisotropic curvature flow of droplets on an inhomogeneous horizontal hyperplane. As in [Bellettini, Kholmatov: J. Math. Pures Appl. (2018)] we establish the existence of smooth flow, starting from a regular droplet and satisfying the prescribed anisotropic Young's law, and also the existence of a $1/2$-H\"older continuous in time minimizing

Sepideh Neshatfar, Salimeh Yasaei Sekeh

Graph neural networks (GNNs) have attracted significant attention for their outstanding performance in graph learning and node classification tasks. However, their vulnerability to adversarial attacks, particularly through susceptible nodes, poses a challenge in decision-making. The need for robust graph summarization is evident in adversarial challenges res

Jing Liang, Amirreza Payandeh, Daeun Song, Xuesu Xiao

We present a novel end-to-end diffusion-based trajectory generation method, DTG, for mapless global navigation in challenging outdoor scenarios with occlusions and unstructured off-road features like grass, buildings, bushes, etc. Given a distant goal, our approach computes a trajectory that satisfies the following goals: (1) minimize the travel distance to

Shawn Berry

Over the last several years, several well-established and prominent brick-and-mortar retail chains have ceased operations, raising concerns for something that some have referred to as a retail apocalypse. While the demise of brick-and-mortar is far from certain, scholars have attempted to model the likelihood that a retailer is about to fail using different

Md Atik Ahamed, Qiang Cheng

Long-term time-series forecasting remains challenging due to the difficulty in capturing long-term dependencies, achieving linear scalability, and maintaining computational efficiency. We introduce TimeMachine, an innovative model that leverages Mamba, a state-space model, to capture long-term dependencies in multivariate time series data while maintaining l

Csaba Fábri, András Csehi, Gábor J. Halász, Lorenz S. Cederbaum

The exchange of energy between electronic and nuclear motion is the origin of non-adiabaticity and plays an important role in many molecular phenomena and processes. Conical intersections (CIs) of different electronic potential energy surfaces lead to the most singular non-adiabaticity and have been intensely investigated. The coupling of light and matter in

Adamantios P. Synanidis, P. A. D. Gonçalves, Claus Ropers, F. Javier García de Abajo

The interaction between free electrons and nanoscale optical fields has emerged as a unique platform to investigate ultrafast processes in matter and explore fundamental quantum phenomena. In particular, optically modulated electrons are employed in ultrafast electron microscopy as noninvasive probes that push the limits of spatiotemporal and spectral resolu

Giorgio Tosti Balducci, Boyang Chen

The wide adoption of composite structures in the aerospace industry requires reliable numerical methods to account for the effects of various damage mechanisms, including delamination. Cohesive elements are a versatile and physically representative way of modelling delamination. However, using their standard form which conforms to solid substrate elements, m

Zsuzsanna Lipták, Francesco Masillo, Gonzalo Navarro

Despite consistently yielding the best compression on repetitive text collections, the Lempel-Ziv parsing has resisted all attempts at offering relevant guarantees on the cost to access an arbitrary symbol. This makes it less attractive for use on compressed self-indexes and other compressed data structures. In this paper we introduce a variant we call BAT-L

Jonathan Dunn, Lane Edwards-Brown

This paper develops an approach to language identification in which the set of languages considered by the model depends on the geographic origin of the text in question. Given that many digital corpora can be geo-referenced at the country level, this paper formulates 16 region-specific models, each of which contains the languages expected to appear in count

Thennal D K, Ganesh Nathan, Suchithra M S

Fine-tuning pre-trained models provides significant advantages in downstream performance. The ubiquitous nature of pre-trained models such as BERT and its derivatives in natural language processing has also led to a proliferation of task-specific fine-tuned models. As these models typically only perform one task well, additional training or ensembling is req

Sankar Das Sarma, Yi-Ting Tu

We theoretically consider the possibility that phonons may be playing a role in the observed linear-in-$T$ resistivity in cuprates by focusing on the obvious question: How can phonon scattering be consistent with a linear-in-$T$ resistivity with a constant slope given that cuprates have many phonon modes with different energies and electron-phonon couplings

Yihang Chen, Fanghui Liu, Yiping Lu, Grigorios G. Chrysos

Despite the widespread empirical success of ResNet, the generalization properties of deep ResNet are rarely explored beyond the lazy training regime. In this work, we investigate \emph{scaled} ResNet in the limit of infinitely deep and wide neural networks, of which the gradient flow is described by a partial differential equation in the large-neural network

Robert Law

Europa's surface exhibits many regions of complex topography termed 'chaos terrains'. One set of hypotheses for chaos terrain formation requires upward migration of liquid water from perched water bodies within the icy shell formed by convection and tidal heating. However, consideration of the behavior of terrestrial ice sheets suggests the upwards movement

Thales Sales Almeida, Hugo Abonizio, Rodrigo Nogueira, Ramon Pires

We introduce Sabi\'a-2, a family of large language models trained on Portuguese texts. The models are evaluated on a diverse range of exams, including entry-level tests for Brazilian universities, professional certification exams, and graduate-level exams for various disciplines such as accounting, economics, engineering, law and medicine. Our results reveal

Lucia Caporaso, Amos Turchet

We investigate plane curves intersecting in at most two unibranched points to study the algebraic exceptional set appearing in standard conjectures of diophantine and hyperbolic geometry. Our first result compares the local geometry of two hypertangent curves, i.e. curves having maximal contact at one unibranched point. This is applied to fully describe the

Zhiming Hu, Syn Schmitt, Daniel Haeufle, Andreas Bulling

We present GazeMotion, a novel method for human motion forecasting that combines information on past human poses with human eye gaze. Inspired by evidence from behavioural sciences showing that human eye and body movements are closely coordinated, GazeMotion first predicts future eye gaze from past gaze, then fuses predicted future gaze and past poses into a

Ibrahim Patrick, Sondipon Adhikari, Mahmoud I. Hussein

Vibration energy harvesting is a technology that enables electric power generation by augmenting vibrating materials or structures with piezoelectric elements. In a recent work, we quantified the intrinsic energy-harvesting availability of a piezoelectric phononic crystal (Piezo-PnC) by calculating its damping ratio across the Brillouin zone and subtracting

Matthew Lisondra, Junseo Kim, Riku Murai, Kourosh Zareinia

Focal-Plane Sensor-Processor Arrays (FPSP)s are an emerging technology that can execute vision algorithms directly on the image sensor. Unlike conventional cameras, FPSPs perform computation on the image plane -- at individual pixels -- enabling high frame rate image processing while consuming low power, making them ideal for mobile robotics. FPSPs, such as

Lawrence Liu

Two novel frameworks for handling mathematical and physical problems are introduced. The first, the emerging Jordan form, generalizes the concept of the Jordan canonical form, a well-established tool of linear algebra. The second, dual Jordan quantum physics, generalizes the framework of quantum physics to one in which the hermiticity postulate is considerab

Dario Maddaloni, Riccardo Marchesin, Roberto Zunino

We consider the execution of smart contracts on Bitcoin. There, every contract step corresponds to appending to the blockchain a new transaction that spends the output representing the old contract state, creating a new one for the updated state. This standard procedure requires the contract participants to pay transaction fees for every execution step. In t

Sheila Sagear, Adrian M. Price-Whelan, Sarah Ballard, Yuxi

Stellar age measurements are fundamental to understanding a wide range of astronomical processes, including Galactic dynamics, stellar evolution, and planetary system formation. However, extracting age information from main-sequence stars is complicated, with techniques often relying on age proxies in the absence of direct measurements. The Gaia data release

Motong Chen, Henry Lam, Zhenyuan Liu

In stochastic simulation, input uncertainty refers to the propagation of the statistical noise in calibrating input models to impact output accuracy, in addition to the Monte Carlo simulation noise. The vast majority of the input uncertainty literature focuses on estimating target output quantities that are real-valued. However, outputs of simulation models

Sigurd Angenent, Evan Patrick Davis, Ellie DeCleene, Paige Ellingson

We study possible tangles that can occur in singularities of solutions to plane Curve Shortening Flow. We exhibit solutions in which more complicated tangles with more than one self-intersection disappear into a singular point. It seems that there are many examples of this kind and that a complete classification presents a problem similar to the problem of c

Jonito Aerts Arguëlles

We show that colors are light quanta for human visual perception in a similar way as photons are light quanta for physical measurements of light waves. Our result relies on the identification in the quantum measurement process itself of the warping mechanism which is characteristic of human perception. This warping mechanism makes stimuli classified into the

Aiden Swann, Matthew Strong, Won Kyung Do, Gadiel Sznaier Camps

In this work, we propose a novel method to supervise 3D Gaussian Splatting (3DGS) scenes using optical tactile sensors. Optical tactile sensors have become widespread in their use in robotics for manipulation and object representation; however, raw optical tactile sensor data is unsuitable to directly supervise a 3DGS scene. Our representation leverages a Ga

M. N. Najafi, A. Ramezanpour, M. A. Rajabpour

We introduce a novel field theory representation for the Sum of Powers of Principal Minors (SPPM), a mathematical construct with profound implications in quantum mechanics and statistical physics. We begin by establishing a Berezin integral formulation of the SPPM problem, showcasing its versatility through various symmetries including $SU(n)$, its subgroups

Jacob Bardzell, Kevin Federico, Danielle Smith, Timm Wrase

For decades intersecting D-branes and O-planes have been playing a very important role in string phenomenology in the context of particle physics model building and in the context of flux compactifications. The corresponding supergravity equations are hard to solve so generically solutions only exist in a so-called smeared limit where the delta function sour

Gilberto Campaña, Pablo Muñoz, Enrique Otarola

We investigate a convective Brinkman--Forchheimer problem coupled with a heat equation. The investigated model considers thermal diffusion and viscosity depending on the temperature. We prove the existence of a solution without restriction on the data and uniqueness when the solution is slightly smoother and the data is suitably restricted. We also propose a

Fangqiang Ding, Yunzhou Zhu, Xiangyu Wen, Gaowen Liu

Designing egocentric 3D hand pose estimation systems that can perform reliably in complex, real-world scenarios is crucial for downstream applications. Previous approaches using RGB or NIR imagery struggle in challenging conditions: RGB methods are susceptible to lighting variations and obstructions like handwear, while NIR techniques can be disrupted by sun

Marc Chardin, S. Hamid Hassanzadeh, Claudia Polini, Aron Simis

In this article, we study the generalized Poincare problem from the opposite perspective, by establishing lower bounds on the degree of the vector field in terms of invariants of the variety.

Tim G. J. Rudner, Ya Shi Zhang, Andrew Gordon Wilson, Julia Kempe

Machine learning models often perform poorly under subpopulation shifts in the data distribution. Developing methods that allow machine learning models to better generalize to such shifts is crucial for safe deployment in real-world settings. In this paper, we develop a family of group-aware prior (GAP) distributions over neural network parameters that expli

Riley B. Dawkins, Mingyuan Hong, Chenglong You, Omar S. Magana-Loaiza

The quantum theory of the electromagnetic field uncovered that classical forms of light were indeed produced by distinct superpositions of nonclassical multiphoton wavepackets. Specifically, partially coherent light represents the most common kind of classical light. Here, for the first time, we demonstrate the extraction of the constituent multiphoton quant

Asif Newaz, Md. Salman Mohosheu, MD. Abdullah al Noman, Taskeed Jabid

Class imbalance poses a major challenge in different classification tasks, which is a frequently occurring scenario in many real-world applications. Data resampling is considered to be the standard approach to address this issue. The goal of the technique is to balance the class distribution by generating new samples or eliminating samples from the data. A w

Mona Asadinamin, Aleksandar Živkovic, Nora H. De Leeuw, Steven P. Lewis

Photocatalysis, a promising solution for environmental challenges, relies on the generation and utilization of photogenerated charge carriers within photocatalysts. However, recombination of these carriers often limits efficiency. Heterostructures, especially Cu2O/TiO2, have emerged as effective solutions to enhance charge separation. This study systematical