February 2024 arXiv papers — page 127

Satoshi Ogawa

We give a sufficient condition for the existence of a holomorphic tubular neighborhood of a compact Riemann surface holomorphically embedded in a non-singular complex surface. Our sufficient condition is described by an arithmetical condition of unitary flat line bundles which can be regarded as an analogue of the Brjuno condition for irrational numbers whic

Behzad Akbari, Mingfeng Yuan, Hao Wang, Haibin Zhu

In the field of Multi-Agent Systems (MAS), known for their openness, dynamism, and cooperative nature, the ability to trust the resources and services of other agents is crucial. Trust, in this setting, is the reliance and confidence an agent has in the information, behaviors, intentions, truthfulness, and capabilities of others within the system. Our paper

Changwoo J. Lee, Alessandro Zito, Huiyan Sang, David B. Dunson

The beta distribution serves as a canonical tool for modeling probabilities in statistics and machine learning. However, there is limited work on flexible and computationally convenient stochastic process extensions for modeling dependent random probabilities. We propose a novel stochastic process called the logistic-beta process, whose logistic transformati

Arjun B. Savel, Megan Bedell, Eliza M. -R. Kempton

The absorption and emission of light by exoplanet atmospheres encode details of atmospheric composition, temperature, and dynamics. Fundamentally, simulating these processes requires detailed knowledge of the opacity of gases within an atmosphere. When modeling broad wavelength ranges at high resolution, such opacity data, for even a single gas, can take up

Mohammad K. Mardini, Anna Frebel, Anirudh Chiti

We report on the discovery of the first ultra metal-poor (UMP) star 2MASS~J20500194$-$6613298 (J2050$-$6613; \mbox{[Fe/H] = $-4.05$}) selected from the Gaia BP/RP spectral catalog that belongs to the ancient Atari disk component. We obtained a high-resolution spectrum for the star with the MIKE spectrograph on the Magellan-Clay telescope. J2050$-$6613 displa

Kathleen Salazar-Serna, Jesus D. Diaz, Isabel C. Garcia

Urban mobility in developing countries, particularly in cities like Cali, Colombia, faces multifaceted challenges influenced by socioeconomic factors and the distinct characteristics of transport users Despite motorcycles emerging as a prevalent mode, their impact remains underexplored in the literature. This study employs extensive survey data collection, d

Maria Rah, Manolya Yatman, Ali Taani, Ahmad A. Abushattal

The Milky Way is a spiral galaxy comprising three main components: the Bulge, the Disk, and the Halo. Of particular interest is the Galactic disk, which holds a significant portion of the baryonic matter angular momentum and harbors at least two primary stellar populations: the thin and thick disks. Understanding the formation and evolution of the Galactic d

Jingnan Cai, Robin Cantor, Johanne Hizanidis, Nikos Lazarides

We consider, for the first time, the effects of strong capacitive and inductive coupling between radio frequency Superconducting Quantum Interference Devices (rf SQUIDs) in an overlapping metamaterial geometry when driven by rf flux at and near their self-resonant frequencies. The equations of motion for the gauge-invariant phases on the Josephson junctions

Elvis Dohmatob, Yunzhen Feng, Pu Yang, Francois Charton

As AI model size grows, neural scaling laws have become a crucial tool to predict the improvements of large models when increasing capacity and the size of original (human or natural) training data. Yet, the widespread use of popular models means that the ecosystem of online data and text will co-evolve to progressively contain increased amounts of synthesiz

Daniel Lehmann

P-algebras are a non-commutative, non-associative generalization of Boolean algebras that are for quantum logic what Boolean algebras are for classical logic. P-algebras have type <X, 0, ', .> where 0 is a constant, ' is unary and . is binary. Elements of X are called features. A partial order is defined on the set X of features by x <= y iff x.y = x. Featur

Riku Kikuta, Daniel Carruth, John Ball, Reuben Burch

Currently, human drivers outperform self-driving vehicles in many conditions such as collision avoidance. Therefore, understanding human driver behaviour in these conditions will provide insight for future autonomous vehicles. For understanding driver behaviour, risk assessment is applied so far as one of the approaches by using both subjective and objective

Sven Cattell, Avijit Ghosh, Lucie-Aimée Kaffee

Harm reporting in Artificial Intelligence (AI) currently lacks a structured process for disclosing and addressing algorithmic flaws, relying largely on an ad-hoc approach. This contrasts sharply with the well-established Coordinated Vulnerability Disclosure (CVD) ecosystem in software security. While global efforts to establish frameworks for AI transparency

Pietro Pustina, Davide Calzolari, Alin Albu-Schäffer, Alessandro De Luca

Continuum soft robots are nonlinear mechanical systems with theoretically infinite degrees of freedom (DoFs) that exhibit complex behaviors. Achieving motor intelligence under dynamic conditions necessitates the development of control-oriented reduced-order models (ROMs), which employ as few DoFs as possible while still accurately capturing the core characte

Pietro Pustina, Cosimo Della Santina, Alessandro De Luca

Robotics is shifting from rigid, articulated systems to more sophisticated and heterogeneous mechanical structures. Soft robots, for example, have continuously deformable elements capable of large deformations. The flourishing of control techniques developed for this class of systems is fueling the need of efficient procedures for evaluating their inverse dy

Jonathan Holland, George Sparling

This article, the first in a series, analyzes the general theory of plane wave spacetimes. Following Dmitri Aleekseevsky, these are defined as spacetimes admitting a group of dilations leaving invariant a smooth curve. If this curve is specified as part of the structure, the spacetime is termed a Penrose limit, whose theory was developed first by Roger Penro

Ioana Marinescu, R. Thomas McCoy, Thomas L. Griffiths

Humans can learn new concepts from a small number of examples by drawing on their inductive biases. These inductive biases have previously been captured by using Bayesian models defined over symbolic hypothesis spaces. Is it possible to create a neural network that displays the same inductive biases? We show that inductive biases that enable rapid concept le

Srijeet Halder, Kereshmeh Afsari, Abiola Akanmu

Effective progress monitoring is crucial for the successful delivery of the construction project within the stipulated time and budget. Construction projects are often monitored irregularly through time-consuming physical site visits by multiple project stakeholders. Remote monitoring using robotic cyber-physical systems (CPS) can make the process more effic

Keisuke Kamahori, Tian Tang, Yile Gu, Kan Zhu

Large Language Models (LLMs) with the Mixture-of-Experts (MoE) architectures have shown promising performance on various tasks. However, due to the huge model sizes, running them in resource-constrained environments where the GPU memory is not abundant is challenging. Some existing systems propose to use CPU resources to solve that, but they either suffer fr

Elias N. Pergantis, Priyadarshan, Nadah Al Theeb, Parveen Dhillon

Efficient electric heat pumps that replace fossil-fueled heating systems could significantly reduce greenhouse gas emissions. However, electric heat pumps can sharply increase electricity demand, causing high utility bills and stressing the power grid. Residential neighborhoods could see particularly high electricity demand during cold weather, when heat dem

Chih-Hong Cheng, Paul Stöckel, Xingyu Zhao

Modeling and calibrating the fidelity of synthetic data is paramount in shaping the future of safe and reliable self-driving technology by offering a cost-effective and scalable alternative to real-world data collection. We focus on its role in safety-critical applications, introducing four types of instance-level fidelity that go beyond mere visual input ch

Takao Inoué, Tadayoshi Miwa

On March 8, 1995, was found the following \it nontrivial \rm single axiom-schema characteristic of Le\'{s}niewski-Ishimoto's propositional ontology $\bf L_1$ (Inou\'{e}, 1995b \cite{inoue16}). $$(\mathrm{A_{M8})} \enspace \epsilon ab \wedge \epsilon cd . \supset . \epsilon aa \wedge \epsilon cc \wedge (\epsilon bc \supset . \epsilon ad \wedge \epsilon ba).$$

Lucy D'Agostino McGowan

This paper explores an innovative approach to teaching data wrangling skills to students through hands-on activities before transitioning to coding. Data wrangling, a critical aspect of data analysis, involves cleaning, transforming, and restructuring data. We introduce the use of a physical tool, mathlink cubes, to facilitate a tangible understanding of dat

Prabir Rudra

In this paper, we explore a collapsing scenario in the background of energy-momentum squared gravity (EMSG). EMSG claims to have terms that originate from the quantum gravity effects mimicking loop quantum gravity. As a result, the framework admits a bounce at a finite time thus avoiding a singularity. So the question that naturally arises: Is there any real

Paul Garnier, Gauthier Guinet

We consider the problem of aligning two sets of continuous word representations, corresponding to languages, to a common space in order to infer a bilingual lexicon. It was recently shown that it is possible to infer such lexicon, without using any parallel data, by aligning word embeddings trained on monolingual data. Such line of work is called unsupervise

Yeqi Gao, Zhao Song, Ruizhe Zhang

Given its widespread application in machine learning and optimization, the Kronecker product emerges as a pivotal linear algebra operator. However, its computational demands render it an expensive operation, leading to heightened costs in spectral approximation of it through traditional computation algorithms. Existing classical methods for spectral approxim

Lucas Queiroz

We investigate, by means of the scattering approach, the Casimir-Polder interaction between a neutral anisotropic polarizable particle and a corrugated surface made of a realistic material. By focusing on the lateral force (arising from the presence of corrugation on the surface), we investigate the conditions for the particle to be attracted to the nearest

Gholamali Aminian, Yixuan He, Gesine Reinert, Łukasz Szpruch

This work provides a theoretical framework for assessing the generalization error of graph neural networks in the over-parameterized regime, where the number of parameters surpasses the quantity of data points. We explore two widely utilized types of graph neural networks: graph convolutional neural networks and message passing graph neural networks. Prior t

Joao Castanheira, Pedro Vicente, Ruben Martinez-Cantin, Lorenzo Jamone

Robust grasping is a major, and still unsolved, problem in robotics. Information about the 3D shape of an object can be obtained either from prior knowledge (e.g., accurate models of known objects or approximate models of familiar objects) or real-time sensing (e.g., partial point clouds of unknown objects) and can be used to identify good potential grasps.

Ankit Pal, Malaikannan Sankarasubbu

Large language models have the potential to be valuable in the healthcare industry, but it's crucial to verify their safety and effectiveness through rigorous evaluation. For this purpose, we comprehensively evaluated both open-source LLMs and Google's new multimodal LLM called Gemini across Medical reasoning, hallucination detection, and Medical Visual Ques

R. Maqsood, P. Ceravolo, C. Romero, S. Ventura

Students' engagements reflect their level of involvement in an ongoing learning process which can be estimated through their interactions with a computer-based learning or assessment system. A pre-requirement for stimulating student engagement lies in the capability to have an approximate representation model for comprehending students' varied (dis)engagemen

Wende C. Safari, Ignacio López-de-Ullibarri, M. Amalia Jácome

We introduce a nonparametric estimator of the conditional survival function in the mixture cure model for right censored data when cure status is partially known. The estimator is developed for the setting of a single continuous covariate but it can be extended to multiple covariates. It extends the estimator of Beran (1981), which ignores cure status inform

Ruben Martinez-Cantin

Active policy search combines the trial-and-error methodology from policy search with Bayesian optimization to actively find the optimal policy. First, policy search is a type of reinforcement learning which has become very popular for robot control, for its ability to deal with complex continuous state and action spaces. Second, Bayesian optimization is a s

C. Romero, S. Ventura

This survey is an updated and improved version of the previous one published in 2013 in this journal with the title data mining in education. It reviews in a comprehensible and very general way how Educational Data Mining and Learning Analytics have been applied over educational data. In the last decade, this research area has evolved enormously and a wide r

Burak Dagli, Umit Kaya, Arif Ozturk, Saleh Sultansoy

We discuss a possibility to construct multi-TeV scale {\mu}p collider at FNAL. Main advantage of this project is existence of two ring tunnels tangential to each other. There are two possible options, namely, muons in main injector with protons in Tevatron ring and vice versa. Two choices are considered for center-of-mass energy values: 2.57 TeV using 8 T be

H. Abdolahzadeh Ahangar, M. Chellali, S. M. Sheikholeslami, J. C. Valenzuela-Tripodoro

Let $\{0,1,\dots, t\}$ be abbreviated by $[t].$ A double Roman dominating function (DRDF) on a graph $\Gamma=(V,E)$ is a map $l:V\rightarrow [3]$ satisfying \textrm{(i)} if $l(r)=0$ then there must be at least two neighbors labeled 2 under $l$ or a neighbor $r'$ with $l(r')=3$; and \textrm{(ii)} if $l(r)=1$ then $r$ must be adjacent to a vertex $r'$ such tha

Rati Devidze, Parameswaran Kamalaruban, Adish Singla

Reward functions are central in specifying the task we want a reinforcement learning agent to perform. Given a task and desired optimal behavior, we study the problem of designing informative reward functions so that the designed rewards speed up the agent's convergence. In particular, we consider expert-driven reward design settings where an expert or teach

M. Marongiu, A. Pellizzoni, S. Righini, S. Mulas

One of the most important objectives of solar physics is the physical understanding of the solar atmosphere, the structure of which is also described in terms of the density (N) and temperature (T) distributions of the atmospheric matter. Several multi-frequency analyses show that the characteristics of these distributions are still debated, especially for t

Alessio Cesarano, Charles Dapogny, Peter Gangl

This article is devoted to the shape optimization of the internal structure of an electric motor, and more precisely of the arrangement of air and ferromagnetic material inside the rotor part with the aim to increase the torque of the machine. The governing physical problem is the time-dependent, non linear magneto-quasi-static version of Maxwell's equations

Yinghao Zhu, Changyu Ren, Shiyun Xie, Shukai Liu

The integration of multimodal Electronic Health Records (EHR) data has significantly improved clinical predictive capabilities. Leveraging clinical notes and multivariate time-series EHR, existing models often lack the medical context relevent to clinical tasks, prompting the incorporation of external knowledge, particularly from the knowledge graph (KG). Pr

Diyath Pannipitiya

The celebrated Thue-Morse sequence, or the Prouhet-Thue-Morse sequence (A010060 in the OEIS), has a number of interesting properties and is a rich source to many (counter)examples. We introduce two different square-free sequences on three letters with one of them is equivalent (up-to permutations of letters) to Thue's original square-free sequence on three l

Mohammed El Azar, Ahmed Bouhlal, Ahmed Jellal

We study the effects of a magnetic flux and an inhomogeneous gap on the energy spectrum of graphene magnetic quantum dots (GMQDs). By considering the Dirac equation in the infinite mass framework, we can analytically obtain eigenspinor expressions. By applying boundary conditions, we obtain an energy spectrum equation in terms of system parameters such as ra

H. Abdollahzadeh Ahangar, M. Chellali, S. M. Sheikholeslami, J. C. Valenzuela-Tripodoro

A maximal double Roman dominating function (MDRDF) on a graph $G=(V,E)$ is a function $f:V(G)\rightarrow \{0,1,2,3\}$ such that \textrm{(i) }every vertex $v$ with $f(v)=0$ is adjacent to least two vertices { assigned $2$ or to at least one vertex assigned $3,$} \textrm{(ii) }every vertex $v$ with $f(v)=1$ is adjacent to at least one { vertex assigned $2$ or

Gabriel F. Calvo, Belén Cortés-Llanos, Juan Belmonte-Beitia, Gorka Salas

Nanoparticle (NP)-based applications are becoming increasingly important in the biomedical field. However, understanding the interactions of NPs with biofluids and cells is a major issue in order to develop novel approaches aimed at boosting their internalization and, therefore, their translation into the clinic. To this end, we put forward a transport mathe

Zhenheng Tang, Yonggang Zhang, Shaohuai Shi, Xinmei Tian

Federated Learning (FL) models often experience client drift caused by heterogeneous data, where the distribution of data differs across clients. To address this issue, advanced research primarily focuses on manipulating the existing gradients to achieve more consistent client models. In this paper, we present an alternative perspective on client drift and a

Mateusz Dubiel, Anastasia Sergeeva, Luis A. Leiva

Manipulative design in user interfaces (conceptualized as dark patterns) has emerged as a significant impediment to the ethical design of technology and a threat to user agency and freedom of choice. While previous research focused on exploring these patterns in the context of graphical user interfaces, the impact of speech has largely been overlooked. We co

Hossein Abdollahzadeh Ahangar, M. Pilar Alvarez, Mustapha Chellali, Seyed Mahmoud Sheikholeslami

The Roman domination in graphs is well-studied in graph theory. The topic is related to a defensive strategy problem in which the Roman legions are settled in some secure cities of the Roman Empire. The deployment of the legions around the Empire is designed in such a way that a sudden attack to any undefended city could be quelled by a legion from a strong

Leo Egghe, Ronald Rousseau

We introduce and define three types of small worlds: small worlds based on the diameter of the network (SWD), those based on the average geodesic distance between nodes (SWA), and those based on the median geodesic distance (SWMd). These types of networks are defined as limiting properties of sequences of sets. We show the exact relation between these three

Tianyi Ren, Ethan Honey, Harshitha Rebala, Abhishek Sharma

Tumor segmentation from multi-modal brain MRI images is a challenging task due to the limited samples, high variance in shapes and uneven distribution of tumor morphology. The performance of automated medical image segmentation has been significant improvement by the recent advances in deep learning. However, the model predictions have not yet reached the de

Dominik K. Klein, Rogelio Ortigosa, Jesús Martínez-Frutos, Oliver Weeger

In the present work, the applicability of physics-augmented neural network (PANN) constitutive models for complex electro-elastic finite element analysis is demonstrated. For the investigations, PANN models for electro-elastic material behavior at finite deformations are calibrated to different synthetically generated datasets, including an analytical isotro

Kamil Bader El Dine, Noujoud Nader, Mohamad Khalil, Catherine Marque

Preterm labor (PL) has globally become the leading cause of death in children under the age of 5 years. To address this problem, this paper will provide a new approach by analyzing the EHG signals, which are recorded on the abdomen of the mother during labor and pregnancy. The EHG signal reflects the electrical activity that induces the mechanical contractio

David R. Smith, Jesse W. Wilson, Siddarth Shivkumar, Herve Rigneault

We demonstrate low-frequency interferometric impulsive stimulated Raman scattering (ISRS) imaging with high robustness to distortions by optical scattering. ISRS is a pump-probe coherent Raman spectroscopy that can capture Raman vibrational spectra. Recording of ISRS spectra requires isolation of a probe pulse from the pump pulse. While this separation is si

Yucong Huang, Aram Karakhanyan

The aim of this paper is to analyse the Dirichlet-Neumann operator in axially symmetric conical domains. We provide a constructive treatment of the generic singularity at the vertex by using a new coordinate system that maps the conical domain to a strip. Building upon the paradifferential theory, we then establish our main Sobolev estimates. We also find th

Román Salmerón

The objective of this work is to analyze the usefulness to transform the information to measure sports performance. This analysis is carried out within the field of basketball due to the existing tradition in this sport in data collection, although it is easily adaptable to any other sport. As a result, a modification of the Performance Index Rating (PIR) is

Yuecheng Li, Lele Fu, Tong Wang, Jian Lou

To defend against privacy leakage of user data, differential privacy is widely used in federated learning, but it is not free. The addition of noise randomly disrupts the semantic integrity of the model and this disturbance accumulates with increased communication rounds. In this paper, we introduce a novel federated learning framework with rigorous privacy

Snir Ben Ovadia, Yan Mary He

For certain families of complex maps, we give a formula for the Hausdorff dimension of the equilibrium measure. In particular, given an endomorphism $f$ of $\mathbb C\mathbb P^k$ of algebraic degree $d \ge2$, and given the equilibrium measure $\mu$ with Lyapunov exponents $\chi_1\geq \ldots\geq \chi_k$, we show $\dim_\mathrm{H}(\mu) = \log d\sum_{i\leq k}\fr

S. Shayan Mousavi Masouleh, Corey A. Sanz, Ryan P. Jansonius, Samuel Shi

By 2035, the need for battery-grade lithium is expected to quadruple. About half of this lithium is currently sourced from brines and must be converted from a chloride into lithium carbonate (Li2CO3) through a process called softening. Conventional softening methods using sodium or potassium salts contribute to carbon emissions during reagent mining and batt

Théo Durandard, Matteo Camboni

How do decisions change with the economic environment and with time? This paper studies general nonstationary stopping problems and provides the methodological tools to answer these questions. First, we identify conditions that ensure a monotone relation between decisions' timing and outcomes. These conditions apply to a prevalent class of economic environme

C. B. Caporusso, L. F. Cugliandolo, P. Digregorio, G. Gonnella

Molecular dynamics simulations were employed to investigate the phase separation process of a two-dimensional active Brownian dumbbell model. We evaluated the time dependence of the typical size of the dense component using the scaling properties of the structure factor, along with the averaged number of clusters and their radii of gyration. The growth obser

James M. Nemec, Amanda F. Linnell Nemec, Pawel Moskalik, László Molnár

The results of a Fourier analysis of high-precision Kepler photometry of 75 double-mode RR~Lyrae (RRd) stars observed during NASA's K2 Mission (2014-18) are presented. Seventy-two of the stars are `classical' RRd (cRRd) stars lying along a well-defined curve in the Petersen diagram and showing no evidence of Blazhko modulations. The remaining three stars are

Gabriel S. Rocha, Gabriel S. Denicol

We calculate all transport coefficients of second order transient hydrodynamics in two effective kinetic theory models: a hadron-resonance gas and a quasiparticle model with thermal masses tuned to reproduce QCD thermodynamics. We compare the corresponding results with calculations for an ultrarelativistic single-component gas, that are widely employed in hy

Zuobai Zhang, Jiarui Lu, Vijil Chenthamarakshan, Aurélie Lozano

Protein function annotation is an important yet challenging task in biology. Recent deep learning advancements show significant potential for accurate function prediction by learning from protein sequences and structures. Nevertheless, these predictor-based methods often overlook the modeling of protein similarity, an idea commonly employed in traditional ap

Alex. Sheremet, Victor I. Shrira

Dispersive shock waves (DSW) are a salient feature of long water waves often observed in tidal bores and tsunami/meteotsunami contexts. Their interaction with bathymetry is poorly understood. The shoreline hazard from tsunamis and meteotsunamis critically depends on the fraction of incoming energy flux transmitted across the shallow nearshore shelf. Here, by

Bernhard A. Moser, Michael Lunglmayr

Leaky-integrate-and-fire (LIF) is studied as a non-linear operator that maps an integrable signal $f$ to a sequence $\eta_f$ of discrete events, the spikes. In the case without any Dirac pulses in the input, it makes no difference whether to set the neuron's potential to zero or to subtract the threshold $\vartheta$ immediately after a spike triggering event

Isaac Corley, Caleb Robinson, Anthony Ortiz

In recent years, there has been an explosion of proposed change detection deep learning architectures in the remote sensing literature. These approaches claim to offer state-of-the-art performance on different standard benchmark datasets. However, has the field truly made significant progress? In this paper we perform experiments which conclude a simple U-Ne

Sebastián Franco

We discuss the realization of $2d$ $(0,2)$ gauge theories in terms of branes focusing on Brane Brick Models, which are T-dual to D1-branes probing toric Calabi-Yau 4-folds. These brane setups fully encode the infinite class of $2d$ $(0,2)$ quiver gauge theories on the worldvolume of the D1-branes and substantially streamline their connection to the probed ge

Raja Marjieh, Pol van Rijn, Ilia Sucholutsky, Harin Lee

The speech-to-song illusion is a robust psychological phenomenon whereby a spoken sentence sounds increasingly more musical as it is repeated. Despite decades of research, a complete formal account of this transformation is still lacking, and some of its nuanced characteristics, namely, that certain phrases appear to transform while others do not, is not wel

Rakesh John Amala Arokia Nathan, Sigrid Strand, Dmitriy Shutin, Oliver Bimber

We propose a guidance strategy to optimize real-time synthetic aperture sampling for occlusion removal with drones by pre-scanned point-cloud data. Depth information can be used to compute visibility of points on the ground for individual drone positions in the air. Inspired by Helmholtz reciprocity, we introduce reciprocal visibility to determine the dual s

Chris von Csefalvay

Over the recent years, the emergence of large language models (LLMs) has given rise to a proliferation of domain-specific models that are intended to reflect the particularities of linguistic context and content as a correlate of the originating domain. This paper details the conception, design, training and evaluation of DAEDRA, a LLM designed to detect reg

Ahmad Ayaz Amin

Many tasks can be easily solved using machine learning techniques. However, some tasks cannot readily be solved using statistical models, requiring a symbolic approach instead. Program induction is one of the ways that such tasks can be solved by means of capturing an interpretable and generalizable algorithm through training. However, contemporary approache

James E. McCarthy

As the power of Artificial Intelligence (AI) continues to advance, there is increased interest in how best to combine AI-based agents with humans to achieve mission effectiveness. Three perspectives have emerged. The first stems from more conventional human factors traditions and views these entities as highly capable tools that humans can use to accomplish

Thien Udomsrirungruang, Nobuko Yoshida

Session types are a type discipline for describing and specifying communication behaviours of concurrent processes. Session subtyping, firstly introduced by Gay and Hole, is widely used for enlarging typability of session programs. This paper gives the complexity analysis of three algorithms for subtyping of synchronous binary session types. First, we analys

Ayşe Berkman, Alexandre Borovik

In this work, we complete the classification of generically multiply transitive actions of groups on solvable groups in the finite Morley rank setting. We prove that if $G$ is a connected group of finite Morley rank acting definably, faithfully and generically $m$-transitively on a connected solvable group $V$ of finite Morley rank where $\operatorname{rk}(V

Ge Zhu, Jordan Darefsky, Zhiyao Duan

Despite recent advancements, audio-text models still lag behind their image-text counterparts in scale and performance. In this paper, we propose to improve both the data scale and the training procedure of audio-text contrastive models. Specifically, we craft a large-scale audio-text dataset containing 13,000 hours of text-labeled audio, using pretrained la

Z. E. Musielak

A theory of quantum jumps is developed by using a new asymmetric equation, which is complementary to the Schr\"odinger equation. The new equation displays Bohr's rules for quantum jumps, and its solutions demonstrate that once a quantum jump takes place at a random time, then its evolution is continuous and coherent. The temporal solutions are used to determ

Long Bai, Guankun Wang, Jie Wang, Xiaoxiao Yang

In the realm of automated robotic surgery and computer-assisted interventions, understanding robotic surgical activities stands paramount. Existing algorithms dedicated to surgical activity recognition predominantly cater to pre-defined closed-set paradigms, ignoring the challenges of real-world open-set scenarios. Such algorithms often falter in the presenc

Xiaofeng Liu, Fangxu Xing, Jiachen Zhuo, Maureen Stone

Understanding the relationship between tongue motion patterns during speech and their resulting speech acoustic outcomes -- i.e., articulatory-acoustic relation -- is of great importance in assessing speech quality and developing innovative treatment and rehabilitative strategies. This is especially important when evaluating and detecting abnormal articulato

Alejandro Laso Garcia, Long Yang, Victorien Bouffetier, Karen Apple

Equation of state measurements at Jovian or stellar conditions are currently conducted by dynamic shock compression driven by multi-kilojoule multi-beam nanosecond-duration lasers. These experiments require precise design of the target and specific tailoring of the spatial and temporal laser profiles to reach the highest pressures. At the same time, the stud

Xiaofeng Liu, Nadya Shusharina, Helen A Shih, C. -C. Jay Kuo

In this work, we aim to predict the survival time (ST) of glioblastoma (GBM) patients undergoing different treatments based on preoperative magnetic resonance (MR) scans. The personalized and precise treatment planning can be achieved by comparing the ST of different treatments. It is well established that both the current status of the patient (as represent

S Salcedo-Sanz, C Camacho-Gómez, A Magdaleno, E Pereira

In this paper we tackle a problem of optimal design and location of Tuned Mass Dampers (TMDs) for structures subjected to earthquake ground motions, using a novel meta-heuristic algorithm. Specifically, the Coral Reefs Optimization (CRO) with Substrate Layer (CRO-SL) is proposed as a competitive co-evolution algorithm with different exploration procedures wi

J. M. Luna, H. M. Fardoun, F. Padillo, C. Romero

The aim of this paper is to categorize and describe different types of learners in massive open online courses (MOOCs) by means of a subgroup discovery approach based on MapReduce. The final objective is to discover IF-THEN rules that appear in different MOOCs. The proposed subgroup discovery approach, which is an extension of the well-known FP-Growth algori

Sergei Chmutov

This is an expository paper extending the tutorial talk at the MATRIX Workshop on Uniqueness and Discernment in Graph Polynomials in October 2023. The explanation is mainly based on the paper "Partial Duality of Hypermaps" by S.Chmutov and F.Vignes-Tourneret with maximal possible simplifications.

Rogério Jorge de Assis, Ciro Micheletti Diniz, Norton Gomes de Almeida, Celso Jorge Villas-Bôas

We present a scheme that utilizes an ion confined within a bi-dimensional trap to simulate a quantum Otto heat engine whose working substance is a two-level system. In this scheme, the electronic component of the ion (the two-level system) can interact with effective heat reservoirs of different types. We specifically focus on effective thermal reservoirs (t

Manuel Alberto M. Ferreira

A method to study and evaluate the occupation of a Hospital Surgery Service, with some specificity in its activity, is outlined in this work. Its application is exemplified with real data, and it is shown that it is simple, practical, and useful and allows a practical management of the service occupation.

Taotao Zhou, Teng Xu, Dong Zhang, Yuyang Jiao

We present Sophia-in-Audition (SiA), a new frontier in virtual production, by employing the humanoid robot Sophia within an UltraStage environment composed of a controllable lighting dome coupled with multiple cameras. We demonstrate Sophia's capability to replicate iconic film segments, follow real performers, and perform a variety of motions and expression

V. Y. Pinchenkova, S. I. Matveenko, G. V. Shlyapnikov

We consider a superfluid transition in two-component dipolar Fermi gases in a two-dimensional lattice with a weak on-site disorder. The momentum dependent dipole-dipole interaction amplitude violates the Anderson theorem and in the weakly interacting regime this leads to an increase of the superfluid transition temperature. We find that in a sufficiently dee

Hanwen Ren, Ahmed H. Qureshi

Rearrangement planning for object retrieval tasks from confined spaces is a challenging problem, primarily due to the lack of open space for robot motion and limited perception. Several traditional methods exist to solve object retrieval tasks, but they require overhead cameras for perception and a time-consuming exhaustive search to find a solution and ofte

A. Ayuela, E. Ogando, N. Zabala

We have studied Pb thin films as a function of the thickness up to 60 monolayers (MLs) using ab initio first principles and model calculations. Magic heights corresponding to a modulated oscillatory pattern of the energy of Pb(111) films have been measured up to about 30 MLs. We demonstrate that this behaviour continues even for higher thickness due to an ex

Marc Bartholet, Taehyeon Kim, Ami Beuret, Se-Young Yun

Federated Learning (FL) faces significant challenges with domain shifts in heterogeneous data, degrading performance. Traditional domain generalization aims to learn domain-invariant features, but the federated nature of model averaging often limits this due to its linear aggregation of local learning. To address this, we propose a robust framework, coined a

Yingru Li

We present a simplified and unified analysis of the Johnson-Lindenstrauss (JL) lemma, a cornerstone of dimensionality reduction for managing high-dimensional data. Our approach simplifies understanding and unifies various constructions under the JL framework, including spherical, binary-coin, sparse JL, Gaussian, and sub-Gaussian models. This unification pre

William Gantt, Alexander Martin, Pavlo Kuchmiichuk, Aaron Steven White

We introduce event-keyed summarization (EKS), a novel task that marries traditional summarization and document-level event extraction, with the goal of generating a contextualized summary for a specific event, given a document and an extracted event structure. We introduce a dataset for this task, MUCSUM, consisting of summaries of all events in the classic

N. Khusnutdinov, D. Vassilevich

We study the influence of impurities in graphene described by a scattering rate $\Gamma$ on the Casimir interaction between graphene and an ideal conductor or between two identical sheets of graphene at zero temperature and chemical potential. To this end, we compute the polarization tensor of quasiparticles in graphene and corresponding conductivities for T

Junwei Ma, Valentin Thomas, Guangwei Yu, Anthony Caterini

Foundation models have revolutionized tasks in computer vision and natural language processing. However, in the realm of tabular data, tree-based models like XGBoost continue to dominate. TabPFN, a transformer model tailored for tabular data, mirrors recent foundation models in its exceptional in-context learning capability, being competitive with XGBoost's

Minjie Tian

This research comprehensively describes the basic theory of transversally Heisenberg elliptic operators, and investigates the index theory of Heisenberg elliptic and transversally Heisenberg elliptic operators from the perspective of $KK$-theory, applying Kasparov's methodology. Moreover, the analysis methodically examines specific conditions, with a focus o

Ayman Abaid, Muhammad Ali Farooq, Niamh Hynes, Peter Corcoran

Stable Diffusion (SD) has gained a lot of attention in recent years in the field of Generative AI thus helping in synthesizing medical imaging data with distinct features. The aim is to contribute to the ongoing effort focused on overcoming the limitations of data scarcity and improving the capabilities of ML algorithms for cardiovascular image processing. T

Breno Serrano, Alexandre M. Florio, Stefan Minner, Maximilian Schiffer

We study the vehicle routing problem with time windows (VRPTW) and stochastic travel times, in which the decision-maker observes related contextual information, represented as feature variables, before making routing decisions. Despite the extensive literature on stochastic VRPs, the integration of feature variables has received limited attention in this con

Jian Wang, Chak Tou Leong, Jiashuo Wang, Dongding Lin

Tuning language models for dialogue generation has been a prevalent paradigm for building capable dialogue agents. Yet, traditional tuning narrowly views dialogue generation as resembling other language generation tasks, ignoring the role disparities between two speakers and the multi-round interactive process that dialogues ought to be. Such a manner often

Pouria Golshanrad, Fathiyeh Faghih

Recurrent neural networks (RNNs) have emerged as powerful tools for processing sequential data in various fields, including natural language processing and speech recognition. However, the lack of explainability in RNN models has limited their interpretability, posing challenges in understanding their internal workings. To address this issue, this paper prop

Jan Scherz, Anja Schlömerkemper

We derive a mathematical model for the motion of several insulating rigid bodies through an electrically conducting fluid. Starting from a universal model describing this phenomenon in generality, we elaborate (simplifying) physical assumptions under which a mathematical analysis of the model becomes feasible. Our main focus lies on the derivation of the bou

Francis G. VanGessel, Efrem Perry, Salil Mohan, Oliver M. Barham

We present a demonstration of the utility of NLP for aiding research into energetic materials and associated systems. The NLP method enables machine understanding of textual data, offering an automated route to knowledge discovery and information extraction from energetics text. We apply three established unsupervised NLP models: Latent Dirichlet Allocation,

Hannes Nilsson, Rikard Johansson, Niklas Åkerblom, Morteza Haghir Chehreghani

We propose a new framework for contextual multi-armed bandits based on tree ensembles. Our framework adapts two widely used bandit methods, Upper Confidence Bound and Thompson Sampling, for both standard and combinatorial settings. As part of this framework, we propose a novel method of estimating the uncertainty in tree ensemble predictions. We further demo