February 2024 arXiv papers — page 171

Narayan Tondapu

Using Betfair's time series data, an analysis of the United Kingdom (UK) horse racing market reveals an interesting paradox: a market with short tails, rapidly decaying autocorrelations, and no long-term memory. There seems to be a remarkably high level of informational efficiency in betting exchange returns, in contrast to financial assets that are characte

Matteo Pagliardini, Amirkeivan Mohtashami, Francois Fleuret, Martin Jaggi

The transformer architecture by Vaswani et al. (2017) is now ubiquitous across application domains, from natural language processing to speech processing and image understanding. We propose DenseFormer, a simple modification to the standard architecture that improves the perplexity of the model without increasing its size -- adding a few thousand parameters

Alessandro Betti, Marco Gori

The spectacular results achieved in machine learning, including the recent advances in generative AI, rely on large data collections. On the opposite, intelligent processes in nature arises without the need for such collections, but simply by online processing of the environmental information. In particular, natural learning processes rely on mechanisms wher

Ali Khalesi, Petros Elia

In this paper, we investigate the problem of multi-user linearly decomposable function computation, where $N$ servers help compute functions for $K$ users, and where each such function can be expressed as a linear combination of $L$ basis subfunctions. The process begins with each server computing some of the subfunctions, then broadcasting a linear combinat

Piotr Kalaczyński

Atmospheric muons are the most frequently observed form of cosmic radiation. Despite this, the existence of the muon flux component produced in decays of short-lived parent particles, called prompt muon flux still awaits experimental confirmation. This contribution to the muon flux is expected to start dominating at high energies, around PeV, since many of t

James Tagg, William Reid, Daniel Carlin

For nearly 100 years, the paradox of Schr\"odinger's Cat has remained unresolved. Why does the world we live in appear classical despite being composed of quantum particles governed by the Schr\"odinger wave equation? Lajos Di\'osi and Roger Penrose propose the wavefunction collapses because it describes two incompatible space-times, demonstrating an inconsi

Alexandra Saliba, Yuanchao Li, Ramon Sanabria, Catherine Lai

The efficacy of self-supervised speech models has been validated, yet the optimal utilization of their representations remains challenging across diverse tasks. In this study, we delve into Acoustic Word Embeddings (AWEs), a fixed-length feature derived from continuous representations, to explore their advantages in specific tasks. AWEs have previously shown

David Wu, Gokul Swamy, J. Andrew Bagnell, Zhiwei Steven Wu

Inverse Reinforcement Learning (IRL) is a powerful framework for learning complex behaviors from expert demonstrations. However, it traditionally requires repeatedly solving a computationally expensive reinforcement learning (RL) problem in its inner loop. It is desirable to reduce the exploration burden by leveraging expert demonstrations in the inner-loop

Qidong He, Ian Jauslin

In this paper, we prove the existence of a crystallization transition for a family of hard-core particle models on periodic graphs in arbitrary dimensions. We establish a criterion under which crystallization occurs at sufficiently high densities. The criterion is more general than that in [Jauslin, Lebowitz, Comm. Math. Phys. 364:2, 2018], as it allows mode

Jakub Ciazela, Jaroslaw Bakala, Miroslaw Kowalinski, Bartosz Pieterek

Lunar sulfides and oxides are a significant source of noble and base metals and will be vital for future human colonies' self-sustainability. Sulfide detection (pyrite and troilite) applies to many technological fields and use cases, for example, as a raw material source (available in situ on the Lunar surface) for new solar panel production methods. Ilmenit

F. Espinosa, J. J. García, A. Hernández, M. Mazo

This work describes a classifier designed to identify rail breakages in double-track railway lines, completing the electronic equipment carried out by authors. The main objective of this proposal is to guarantee the integrity of tracks before the railway traffic starts working. In addition, it facilitates maintenance tasks providing information about possibl

Nick Walker, Xuning Yang, Animesh Garg, Maya Cakmak

The performance of prediction-based assistance for robot teleoperation degrades in unseen or goal-rich environments due to incorrect or quickly-changing intent inferences. Poor predictions can confuse operators or cause them to change their control input to implicitly signal their goal. We present a new assistance interface for robotic manipulation where an

Chinmay Mittal, Krishna Kartik, Mausam, Parag Singla

Can the large language models (LLMs) solve challenging first-order combinatorial reasoning problems such as graph coloring, knapsack, and cryptarithmetic? By first-order, we mean these problems can be instantiated with potentially an infinite number of problem instances of varying sizes. They are also challenging being NP-hard and requiring several reasoning

A. García-Yeguas, J. Ledo, P. Piña-Varas, J. Prudencio

In this work we have done a 3D joint interpretation of magnetotelluric and seismic tomography models. Previously we have described different techniques to infer the inner structure of the Earth. We have focused on volcanic regions, specifically on Tenerife Island volcano (Canary Islands, Spain). In this area, magnetotelluric and seismic tomography studies ha

I. I. Tupitsyn, I. M. Savelyev, Y. S. Kozhedub, D. A. Telnov

The problem of orbital collapse of the $5g$ and $6f$ electrons in atoms of superheavy elements (SHE) is considered. Previously, the presence of the orbital collapse was established for the $4f$ and $5f$ elements of the periodic table. Because of the large centrifugal term for the $f$ and $g$ electrons, the effective radial potential has two wells, one narrow

David Wu, Sanjiban Choudhury

Existing inverse reinforcement learning methods (e.g. MaxEntIRL, $f$-IRL) search over candidate reward functions and solve a reinforcement learning problem in the inner loop. This creates a rather strange inversion where a harder problem, reinforcement learning, is in the inner loop of a presumably easier problem, imitation learning. In this work, we show th

Zifei FeiFei Han, Jionghao Lin, Ashish Gurung, Danielle R. Thomas

One-on-one tutoring is an effective instructional method for enhancing learning, yet its efficacy hinges on tutor competencies. Novice math tutors often prioritize content-specific guidance, neglecting aspects such as social-emotional learning. Social-emotional learning promotes equity and inclusion and nurturing relationships with students, which is crucial

Rui Liu, Yun Lu, Jianping Pan

A leading industry standard for secure and trusted communication in vehicular ad-hoc networks (VANETs) is the Security Credential Management System (SCMS). It uses anonymous certificates, functioning as pseudonyms, to preserve the privacy of vehicles. With the rapid development of advanced applications in VANETs, such as crowdsensing and federated learning,

Jouni Järvinen, Sándor Radeleczki, Umberto Rivieccio

Over the past 50 years, Nelson algebras have been extensively studied by distinguished scholars as the algebraic counterpart of Nelson's constructive logic with strong negation. Despite these studies, a comprehensive survey of the topic is currently lacking, and the theory of Nelson algebras remains largely unknown to most logicians. This paper aims to fill

Tiberiu Coconet, Virgilius-Aurelian Minuta, Constantin-Cosmin Todea

We show that a skew category algebra can be embedded into a twisted tensor product algebra. We investigate the extension of some concepts of Puig and Turull from group algebras to category algebras and their behavior with respect to skew category algebras.

Fangzhou Guo, Jie Lin, Xiaofeng Wang, Xiaodian Chen

Periodic variables are always of great scientific interest in astrophysics. Thanks to the rapid advancement of modern large-scale time-domain surveys, the number of reported variable stars has experienced substantial growth for several decades, which significantly deepened our comprehension of stellar structure and binary evolution. The Tsinghua University-M

Manuel Mar, Vishnu Chellapandi, Liangqi Yuan, Ziran Wang

In the landscape of technological innovation, autonomous racing is a dynamic and challenging domain that not only pushes the limits of technology, but also plays a crucial role in advancing and fostering a greater acceptance of autonomous systems. This paper thoroughly explores challenges and advances in autonomous racing vehicle design and performance, focu

Damian Arellanes

Classical models of computation have been successful in capturing the very essence of individual computing devices. Although they are useful to understand computability power and limitations in the small, such models are not suitable to study large-scale complex computations. Accordingly, plenty of formalisms have been proposed in the last half century as an

Rafael de la Rosa, María Luz Gandarias, María de los Santos Bruzón

In this paper we study the generalized variable-coefficient Gardner equations of the form $u_t + A(t)u^n\,u_x+ C(t)\,u^{2n}u_x + B(t)\,u_{xxx} + Q(t)\,u =0$. This class broadens out many other equations previously considered: Johnpillai and Khalique (2010), Molati and Ramollo (2012) and Vaneeva, Kuriksha and Sophocleous (2015). Equivalence group of the class

Brian Etter, James Lee Hu, Mohammedreza Ebrahimi, Weifeng Li

Adversarial Malware Generation (AMG), the generation of adversarial malware variants to strengthen Deep Learning (DL)-based malware detectors has emerged as a crucial tool in the development of proactive cyberdefense. However, the majority of extant works offer subtle perturbations or additions to executable files and do not explore full-file obfuscation. In

D. Rodríguez, G. Bejarano, M. Vargas, J. M. Lemos

This work addresses the modelling, power control, and optimization of a thermal energy storage (TES) system combined with a vapour-compression refrigeration facility based on phase change materials (PCM). Given a novel design of a PCM-based TES tank and its interconnection with an existing refrigeration system, the joint dynamic modelling is first studied, e

Nico Schick, Franjo Čičak

This study underscores the vital importance of intelligent driving functions in enhancing road safety and driving comfort. Central to our research is the challenge of obtaining sufficient test data for evaluating these functions, especially in high-risk, safety-critical driving scenarios. Such scenarios often suffer from a dearth of available data, primarily

G. Bejarano, M. Vargas, M. G. Ortega, F. Castaño

This paper proposes a computationally efficient simulation strategy for cold thermal energy storage (TES) systems based on phase change material (PCM). Taking as a starting point the recent design of a TES system based on PCM, designed to complement a vapour-compression refrigeration plant, the new highly efficient modelling strategy is described and its per

Michael Klamkin, Mathieu Tanneau, Pascal Van Hentenryck

In many practical applications of constrained optimization, scale and solving time limits make traditional optimization solvers prohibitively slow. Thus, the research question of how to design optimization proxies -- machine learning models that produce high-quality solutions -- has recently received significant attention. Orthogonal to this research thread

Jafar Aljasem, Vladimir V. Kisil

We introduce a concept of the operator (non-commutative) projective line PH defined by a Hilbert space H and a symplectic structure on it. Points of PH are Lagrangian subspaces of H. If a particular Lagrangian subspace is fixed then we can define SL(2,R)-action on PH. This gives a consistent framework for linear fractional transformations of operators. Some

Lydia Bieri, Alexander Polnarev

In this article, we compare in detail the linear and nonlinear approach to the Gravitational Waves Displacement and Velocity Memory (GWDM and GWVM) effects. We consider astrophysical situations that give rise to gravitational waves with GWVM effect, i.e. with a residual velocity (the so-called "velocity-coded memory") and discuss the possibility of future de

G. Bejarano, J. J. Suffo, M. Vargas, M. G Ortega

This paper studies the design and dynamic modelling of a novel thermal energy storage (TES) system combined with a refrigeration system based on phase change materials (PCM). Cold-energy production supported by TES systems is a very appealing field of research, since it allows flexible cold-energy management, combining demand fulfilment with cost reduction s

Vivswan Shah, Nathan Youngblood

Real-world analog systems intrinsically suffer from noise that can impede model convergence and accuracy on a variety of deep learning models. We demonstrate that differentiable activations like GELU and SiLU enable robust propagation of gradients which help to mitigate analog quantization error that is ubiquitous to all analog systems. We perform analysis a

Gerald Woo, Chenghao Liu, Akshat Kumar, Caiming Xiong

Deep learning for time series forecasting has traditionally operated within a one-model-per-dataset framework, limiting its potential to leverage the game-changing impact of large pre-trained models. The concept of universal forecasting, emerging from pre-training on a vast collection of time series datasets, envisions a single Large Time Series Model capabl

Xin Wang, Mingcheng Hu, Connie W. Tsao, Hongtu Zhu

Recent advancements in Artificial Intelligence (AI) have significantly influenced the field of Cardiovascular Disease (CVD) analysis, particularly in image-based diagnostics. Our paper presents an extensive review of AI applications in image-based CVD analysis, offering insights into its current state and future potential. We systematically categorize the li

Yerai Doval, Manuel Vilares, Jesús Vilares

User-generated content published on microblogging social networks constitutes a priceless source of information. However, microtexts usually deviate from the standard lexical and grammatical rules of the language, thus making its processing by traditional intelligent systems very difficult. As an answer, microtext normalization consists in transforming those

Mario Martínez-Zarzuela, David González-Ortega, Míriam Antón-Rodríguez, Francisco Javier Díaz-Pernas

The use of a wide range of computer vision solutions, and more recently high-end Inertial Measurement Units (IMU) have become increasingly popular for assessing human physical activity in clinical and research settings. Nevertheless, to increase the feasibility of patient tracking in out-of-the-lab settings, it is necessary to use a reduced number of devices

David James, Elisha Kahan, Erik Rauer

We settle the Ramsey problem $R(K_6 - e, K_4)$, also known as $R(J_6, K_4)$ and $R(K_6^-, K_4)$. Previously, the best bounds were $30 \leq R(K_6 - e, K4) \leq 32$. We prove that $R(K_6 - e, K_4) = 30$. Our technique is based on the recent approach of Angeltveit and McKay and on older algorithms of McKay and Radziszowski.

Arthur Leroy, Varsha Gupta, Mya Thway Tint, Delicia Ooi Shu Qin

Clinicians often investigate the body mass index (BMI) trajectories of children to assess their growth with respect to their peers, as well as to anticipate future growth and disease risk. While retrospective modelling of BMI trajectories has been an active area of research, prospective prediction of continuous BMI trajectories from historical growth data ha

Yuning You, Ruida Zhou, Yang Shen

Accurate modeling of system dynamics holds intriguing potential in broad scientific fields including cytodynamics and fluid mechanics. This task often presents significant challenges when (i) observations are limited to cross-sectional samples (where individual trajectories are inaccessible for learning), and moreover, (ii) the behaviors of individual partic

María Ángeles Pérez-Juárez, David González-Ortega, Javier Manuel Aguiar-Pérez

Technology enables a more sustainable and universally accessible educational model. However, technology has brought a paradox into students' lives: it helps them engage in learning activities, but it is also a source of distraction. During the academic year 2021-2022, the authors conducted a study focusing on classroom distractions. One of the objectives was

Lidong Li, Andrea Bisoffi, Claudio De Persis, Nima Monshizadeh

We consider the problem of synthesizing a dynamic output-feedback controller for a linear system, using solely input-output data corrupted by measurement noise. To handle input-output data, an auxiliary representation of the original system is introduced. By exploiting the structure of the auxiliary system, we design a controller that robustly stabilizes all

Rodolfo Gambini, Jorge Pullin

We will argue that a phenomenological analysis of consciousness, similar to that of Husserl, shows that the effects of phenomenal qualities shape our perception of the world. It also shows the way the physical and mathematical sciences operate, allowing us to accurately describe the observed regularities in terms of communicable mathematical laws. The latter

Rodolfo Gambini, Jorge Pullin

We continue our work on the study of spherically symmetric loop quantum gravity coupled to two spherically symmetric scalar fields, one that acts as a clock. As a consequence of the presence of the latter, we can define a true Hamiltonian for the theory. In previous papers we have studied the theory for large values of the radial coordinate, that is, far awa

Javier González-Alonso, David Oviedo-Pastor, Héctor J. Aguado, Francisco J. Díaz-Pernas

Recent studies confirm the applicability of Inertial Measurement Unit (IMU)-based systems for human motion analysis. Notwithstanding, high-end IMU-based commercial solutions are yet too expensive and complex to democratize their use among a wide range of potential users. Less featured entry-level commercial solutions are being introduced in the market, tryin

Abhiroop Bhattacharjee, Abhishek Moitra, Priyadarshini Panda

Transformers have revolutionized various real-world applications from natural language processing to computer vision. However, traditional von-Neumann computing paradigm faces memory and bandwidth limitations in accelerating transformers owing to their massive model sizes. To this end, In-memory Computing (IMC) crossbars based on Non-volatile Memories (NVMs)

Zewen Zhang, Roger Paredes, Bhuvanesh Sundar, David Quiroga

The SAT problem is a prototypical NP-complete problem of fundamental importance in computational complexity theory with many applications in science and engineering; as such, it has long served as an essential benchmark for classical and quantum algorithms. This study shows numerical evidence for a quadratic speedup of the Grover Quantum Approximate Optimiza

David González-Ortega, Francisco Javier Díaz-Pernas, Mario Martínez-Zarzuela, Míriam Antón-Rodríguez

In this paper, we present an Android application to control and monitor the physiological sensors from the Shimmer platform and its synchronized working with a driving simulator. The Android app can monitor drivers and their parameters can be used to analyze the relation between their physiological states and driving performance. The app can configure, selec

Herman Verlinde, Mengyang Zhang

We introduce and study a candidate gravity dual to the double scaled SYK model in the form of an exactly soluble 2D de Sitter gravity model consisting of two spacelike Liouville CFTs with complex central charge adding up to $c_+ + c_- = 26$. In [1] it was shown that the two-point function of physical operators in a doubled SYK model matches in the semi-class

Chong Mou, Xintao Wang, Jiechong Song, Ying Shan

Large-scale Text-to-Image (T2I) diffusion models have revolutionized image generation over the last few years. Although owning diverse and high-quality generation capabilities, translating these abilities to fine-grained image editing remains challenging. In this paper, we propose DiffEditor to rectify two weaknesses in existing diffusion-based image editing

Mihir Odhavji, Maria Alexandra Oliveira, João Nuno Silva

There is a lot of data about mean sea level variation from studies conducted around the globe. This data is dispersed, lacks organization along with standardization, and in most cases, it is not available online. In some instances, when it is available, it is often in unpractical ways and different formats. Analyzing it would be inefficient and very time-con

Jing-Xuan Wang, Zhenfeng Ouyang, Rong-Qiang He, Zhong-Yi Lu

High temperature superconductivity was recently found in the bilayer nickelate $\rm{La}_3 \rm{Ni}_2 \rm{O}_7$ (La327), followed by the discovery of superconductivity in the trilayer $\rm{La}_4 \rm{Ni}_3 \rm{O}_{10}$ (La4310), under high pressure. Through studying the electronic correlation of La4310 with DFT+DMFT, and further comparing it with that of La327,

Belle Collaboration, M. Nayak, S. Dey, A. Soffer

We report a search for a heavy neutral lepton (HNL) that mixes predominantly with $\nu_\tau$. The search utilizes data collected with the Belle detector at the KEKB asymmetric energy $e^+ e^-$ collider. The data sample was collected at and just below the center-of-mass energies of the $\Upsilon(4S)$ and $\Upsilon(5S)$ resonances and has an integrated luminos

Nicolas Lanchier, Max Mercer

This paper is concerned with a stochastic model for the spread of kindness across a social network. Individuals are located on the vertices of a general finite connected graph, and are characterized by their kindness belief. Each individual, say $x$, interacts with each of its neighbors, say $y$, at rate one. The interactions can be kind or unkind, with kind

Lisa Voigt

Weak gravitational lensing of distant galaxies provides a powerful probe of dark energy. The aim of this study is to investigate the application of convolutional neural networks (CNNs) to precision shear estimation. In particular, using a shallow CNN, we explore the impact of point spread function (PSF) misestimation and `galaxy population bias' (including `

Pascal Anastasopoulos, Ignatios Antoniadis, Karim Benakli, François Rondeau

We present a set of example models in which the Standard Model (SM) symmetry group is extended by a new abelian symmetry. This additional symmetry appears anomalous in the effective low-energy theory; however, the anomalies cancel out when massive chiral fermions not present in the effective low-energy theory are taken into account. These chiral fermions und

Bibrak Qamar Chandio, Maciej Brodowicz, Thomas Sterling

Computer systems that have been successfully deployed for dense regular workloads fall short of achieving scalability and efficiency when applied to irregular and dynamic graph applications. Conventional computing systems rely heavily on static, regular, numeric intensive computations while High Performance Computing systems executing parallel graph applicat

David González-Ortega, Francisco Javier Díaz-Perna, Mario Martínez-Zarzuela, Míriam Antón-Rodríguez

Driver's gaze information can be crucial in driving research because of its relation to driver attention. Particularly, the inclusion of gaze data in driving simulators broadens the scope of research studies as they can relate drivers' gaze patterns to their features and performance. In this paper, we present two gaze region estimation modules integrated in

Riley Thornton

We show that, for every $\epsilon>0$, the 4-regular tree has an fiid 4-coloring where a given vertex is assigned the 4th color with probability at most $\epsilon$. We also construct 5-colorings of $T_6$ improving known bounds on the measurable and approximate chromatic number of $F_3$.

Guanxiong Sun, Chi Wang, Zhaoyu Zhang, Jiankang Deng

Frame quality deterioration is one of the main challenges in the field of video understanding. To compensate for the information loss caused by deteriorated frames, recent approaches exploit transformer-based integration modules to obtain spatio-temporal information. However, these integration modules are heavy and complex. Furthermore, each integration modu

Francisco Javier Díaz-Pernas, Mario Martínez-Zarzuela, Míriam Antón-Rodríguez, David González-Ortega

In this paper, we present a fully automatic brain tumor segmentation and classification model using a Deep Convolutional Neural Network that includes a multiscale approach. One of the differences of our proposal with respect to previous works is that input images are processed in three spatial scales along different processing pathways. This mechanism is ins

Jaihyun Park, Ryan Cordell

Warning: This paper contains examples of offensive language targetting marginalized population. The digitization of historical texts invites researchers to explore the large-scale corpus of historical texts with computational methods. In this study, we present computational text analysis on a relatively understudied topic of how Asian workers are represented

Avi Rudich, Isaac Rudich, Rachel Rue

Simple Stochastic Games (SSGs) were introduced by Anne Condon in 1990, as the simplest version of Stochastic Games for which there is no known polynomial-time algorithm. Condon showed that Stochastic Games are polynomial-time reducible to SSGs, which in turn are polynomial-time reducible to Stopping Games. SSGs are games where all decisions are binary and ev

Huanshuo Dong, Hong Wang, Haoyang Liu, Jian Luo

Recent advancements in data-driven approaches, such as Neural Operator (NO), have demonstrated their effectiveness in reducing the solving time of Partial Differential Equations (PDEs). However, one major challenge faced by these approaches is the requirement for a large amount of high-precision training data, which needs significant computational costs duri

Junchen Deng, Samhita Marri, Jonathan Klein, Wojtek Pałubicki

Robotic harvesting has the potential to positively impact agricultural productivity, reduce costs, improve food quality, enhance sustainability, and to address labor shortage. In the rapidly advancing field of agricultural robotics, the necessity of training robots in a virtual environment has become essential. Generating training data to automatize the unde

G. Bejarano, M. G. Ortega, J. E. Normey-Rico, F. R Rubio

This work is focused on optimal control of mechanical compression refrigeration systems. A reduced-order state-space model based on the moving boundary approach is proposed for the canonical cycle, which eases the controller design. The optimal cycle (that satisfying the cooling demand while maximizing efficiency) is defined by three variables, but only two

Yunyan Bai, Yuxing Liu, Luo Luo

This paper considers the optimization problem of the form $\min_{{\bf x}\in{\mathbb R}^d} f({\bf x})\triangleq \frac{1}{n}\sum_{i=1}^n f_i({\bf x})$, where $f(\cdot)$ satisfies the Polyak--{\L}ojasiewicz (PL) condition with parameter $\mu$ and $\{f_i(\cdot)\}_{i=1}^n$ is $L$-mean-squared smooth. We show that any gradient method requires at least $\Omega(n+\k

Oren Elishav, Ofir Blumer, T. Kyle Vanderlick, Barak Hirshberg

Controlling the size distribution in the nucleation of copper particles is crucial for achieving nanocrystals with desired physical and chemical properties. However, their synthesis involves a complex system of solvents, ligands, and copper precursors with intertwining effects on the size of the nanoclusters. We combine molecular dynamics simulations and DFT

Danila Demin, Maksim Zhukovskii

For a sequence of random structures with $n$-element domains over a relational signature, we define its first order (FO) complexity as a certain subset in the Banach space $\ell^{\infty}/c_0$. The well-known FO zero-one law and FO convergence law correspond to FO complexities equal to $\{0,1\}$ and a subset of $\mathbb{R}$, respectively. We present a hierarc

Akash Patel, Mario A V Saucedo, Christoforos Kanellakis, George Nikolakopoulos

In this article, we propose a novel navigation framework that leverages a two layered graph representation of the environment for efficient large-scale exploration, while it integrates a novel uncertainty awareness scheme to handle dynamic scene changes in previously explored areas. The framework is structured around a novel goal oriented graph representatio

G. Bejarano, J. M. Manzano, J. R. Salvador, D. Limon

This work proposes a nonlinear model predictive control-based guidance strategy for unmanned surface vehicles, focused on path following. The application of this strategy, in addition to overcome drawbacks of previous line-of-sight-based guidance laws, intends to enable the application of predictive strategies also to the low-level control, responsible for t

Yang Yu, Zheng Chen, Xian Gao

Scalar fields play an important role in constructing modified gravity theories. In the case of a single scalar field with timelike gradient, the corresponding Lagrangian in the unitary gauge takes the form of spatially covariant gravity (SCG), which is proved useful in analyzing and extending the generally covariant theories. In this work, we apply the SCG m

Danit Yshaayahu Levi, Reut Tsarfaty

Contemporary multilingual dependency parsers can parse a diverse set of languages, but for Morphologically Rich Languages (MRLs), performance is attested to be lower than other languages. The key challenge is that, due to high morphological complexity and ambiguity of the space-delimited input tokens, the linguistic units that act as nodes in the tree are no

Yu Shang, Yu Li, Fengli Xu, Yong Li

Large language models (LLMs) have shown impressive emergent abilities in a wide range of tasks, but the associated expensive API cost greatly limits the real application. Previous works like chain-of-thought (CoT) and tree-of-thoughts (ToT) have predominately focused on enhancing accuracy, but overlook the rapidly increasing API cost, which could be particul

Stefan Junk, Hubert Lacoin

We show that if the normalized partition function $W^{\beta}_n$ of the directed polymer model on $\mathbb Z^d$ converges to zero, then it does so exponentially fast. This implies that there exists a critical value $\beta_c$ for the inverse temperature such that the normalized partition function has a non-degenerate limit for all $\beta\in [0,\beta_c]$ -- wea

Han Yuan, Chuan Hong

Active learning selects the most informative samples from the unlabelled dataset to annotate in the context of a limited annotation budget. While numerous methods have been proposed for subsequent sample selection based on an initialized model, scant attention has been paid to the indispensable phase of active learning: selecting samples for model cold-start

Larry Bates, Martin Bendersky, Richard Churchill

We apply the method of spectral sequences to study classical problems in analysis. We illustrate the method by finding polynomial vector fields that commute with a given polynomial vector field and finding integrals of polynomial Hamiltonian systems. For the later we describe the integrals for the Henon-Heiles Hamiltonian which arises in celestial mechanics.

Yue Zhang, Quan Guo, Parisa Kordjamshidi

Existing work on vision and language navigation mainly relies on navigation-related losses to establish the connection between vision and language modalities, neglecting aspects of helping the navigation agent build a deep understanding of the visual environment. In our work, we provide indirect supervision to the navigation agent through a hint generator th

Ata Mustafa

Large Language Models have revolutionized various fields and industries, such as Conversational AI, Content Generation, Information Retrieval, Business Intelligence, and Medical, to name a few. One major application in the field of medical is to analyze and investigate clinical trials for entailment tasks.However, It has been observed that Large Language Mod

Wei-Can Syu, Da-Shin Lee

We consider two-component Bose-Einstein condensates (BECs) and introduce the BEC vortex in $1+2$ dimensions. We focus on two types of gapped excitations induced by the modes of two-component BECs with relative phases of $0$ and $\pi$, analogous to the massive scalar field with positive and negative mass squared, respectively. The inclusion of space-dependent

Marcello Basili, Luca Pratelli

This paper introduces a novel concept of interval probability measures that enables the representation of imprecise probabilities, or uncertainty, in a natural and coherent manner. Within an algebra of sets, we introduce a notion of weak complementation denoted as $\psi$. The interval probability measure of an event $H$ is defined with respect to the set of

Oryan Yehezkel, Alon Zolfi, Amit Baras, Yuval Elovici

Vision transformers have contributed greatly to advancements in the computer vision domain, demonstrating state-of-the-art performance in diverse tasks (e.g., image classification, object detection). However, their high computational requirements grow quadratically with the number of tokens used. Token sparsification mechanisms have been proposed to address

Dmitriy Korzun, Elena Lanina, Alexey Sleptsov

The Jones problem is a question whether there is a non-trivial knot with the trivial Jones polynomial in one variable $q$. The answer to this fundamental question is still unknown despite numerous attempts to explore it. In braid presentation the case of 4-strand braids is already open. S. Bigelow showed in 2000 that if the Burau representation for four-stra

Justin Dumouchelle, Esther Julien, Jannis Kurtz, Elias B. Khalil

Bilevel optimization deals with nested problems in which a leader takes the first decision to minimize their objective function while accounting for a follower's best-response reaction. Constrained bilevel problems with integer variables are particularly notorious for their hardness. While exact solvers have been proposed for mixed-integer linear bilevel opt

Sochima Biereagu

This paper explores an innovative aspect of the Set Shaping Theory, the use of a negative shaping order K. Traditionally, the theory utilizes a positive K to extend the length of data strings, enhancing their testability and compressibility. We propose a paradigm shift by employing a negative K, which shortens data strings and potentially improves compressio

Mehdi Heydari Shahna, Seyed Adel Alizadeh Kolagar, Jouni Mattila

In robotics, contemporary strategies are learning-based, characterized by a complex black-box nature and a lack of interpretability, which may pose challenges in ensuring stability and safety. To address these issues, we propose integrating a collision-free trajectory planner based on deep reinforcement learning (DRL) with a novel auto-tuning low-level contr

Artem Golovizin

In neutral atom optical clocks, the higher-order atomic polarizability terms lead to the clock transition frequency shift which is motion-state dependent and nonlinear with the optical lattice depth. We propose to use an auxiliary optical lattice to compensate the influence of the E2-M1 differential polarizability or tune the associated coefficient to a favo

Hanwen Zhang, Qingyi Si, Peng Fu, Zheng Lin

Table-based Fact Verification (TFV) aims to extract the entailment relation between statements and structured tables. Existing TFV methods based on small-scaled models suffer from insufficient labeled data and weak zero-shot ability. Recently, the appearance of Large Language Models (LLMs) has gained lots of attraction in research fields. They have shown pow

Ronen Tamari

In contrast to classical cognitive science which studied brains in isolation, ecological approaches focused on the role of the body and environment in shaping cognition. Similarly, in this thesis we adopt an ecological approach to grounded natural language understanding (NLU) research. Grounded language understanding studies language understanding systems si

Ramez Yousri, Zeyad Essam, Yehia Kareem, Youstina Sherief

Mixed Reality (MR) and Artificial Intelligence (AI) are increasingly becoming integral parts of our daily lives. Their applications range in fields from healthcare to education to entertainment. MR has opened a new frontier for such fields as well as new methods of enhancing user engagement. In this paper, We propose a new system one that combines the power

Youzhi Qu, Chen Wei, Penghui Du, Wenxin Che

During the evolution of large models, performance evaluation is necessarily performed to assess their capabilities and ensure safety before practical application. However, current model evaluations mainly rely on specific tasks and datasets, lacking a united framework for assessing the multidimensional intelligence of large models. In this perspective, we ad

John M. Campbell

Let $R(q)$ denote the Rogers-Ramanujan continued fraction for $|q| < 1$. By applying the RootApproximant command in the Wolfram language to expressions involving the theta function $f(-q) := (q;q)_{\infty}$ given in modular relations due to Yi, this provides a systematic way of obtaining experimentally discovered evaluations for $R\big(e^{-\pi\sqrt{r}}\big)$

Emil Hovad, Therese Hougaard-Jensen, Line Katrine Harder Clemmensen

Recent advances of deep learning makes it possible to identify specific events in videos with greater precision. This has great relevance in sports like tennis in order to e.g., automatically collect game statistics, or replay actions of specific interest for game strategy or player improvements. In this paper, we investigate the potential and the challenges

Dilxat Muhtar, Zhenshi Li, Feng Gu, Xueliang Zhang

The revolutionary capabilities of large language models (LLMs) have paved the way for multimodal large language models (MLLMs) and fostered diverse applications across various specialized domains. In the remote sensing (RS) field, however, the diverse geographical landscapes and varied objects in RS imagery are not adequately considered in recent MLLM endeav

Youquan Xian, Peng Liu, Dongcheng Li, Xueying Zeng

In recent years, the Decentralized Finance (DeFi) market has witnessed numerous attacks on the price oracle, leading to substantial economic losses. Despite the advent of truth discovery methods opening up new avenues for oracle development, it falls short in addressing high-value attacks on price oracle tasks. Consequently, this paper introduces a dynamical

Nasrin Sultana, Md. Abubakar Siddik

In the optical (380-700 nm) region, a simulation study was conducted to assess the low-signal gain, power conversion efficiency (PCE), and optical output power of a praseodymium-doped fiber optic amplifier (PDFA). The PDFA performance was assessed using the optimized Pr3+ fiber length, Pr3+ ion concentration, and pump power. Additionally, the effects of inpu

Rui Cao, Jing Jiang

Previous solutions to knowledge-based visual question answering~(K-VQA) retrieve knowledge from external knowledge bases and use supervised learning to train the K-VQA model. Recently pre-trained LLMs have been used as both a knowledge source and a zero-shot QA model for K-VQA and demonstrated promising results. However, these recent methods do not explicitl

Dmytro Zakharov, Oleksandr Kuznetsov, Emanuele Frontoni, Natalia Kryvinska

Biometric authentication systems are crucial for security, but developing them involves various complexities, including privacy, security, and achieving high accuracy without directly storing pure biometric data in storage. We introduce an innovative image distortion technique that makes facial images unrecognizable to the eye but still identifiable by any c

Yu-Wen Peng, Wei Liang, Xiaonu Xiong, Chu-Wen Xiao

With the measurements of the decay $D^+_s \rightarrow K^0_S K^0_S \pi^+$ by the BESIII Collaboration, we investigate this three-body weak decay via the chiral unitary approach for the final state interaction, where the resonances $S(980)$ and $S(1710)$ are dynamically reproduced with the interaction of eleven coupled channels, and the $W$-external and -inter

Peng Chen, Yingying Zhang, Yunyao Cheng, Yang Shu

Transformers for time series forecasting mainly model time series from limited or fixed scales, making it challenging to capture different characteristics spanning various scales. We propose Pathformer, a multi-scale Transformer with adaptive pathways. It integrates both temporal resolution and temporal distance for multi-scale modeling. Multi-scale division