March 2024 arXiv papers — page 149

David Fong, Tianshu Chu, Matthew Heflin, Xiaosi Gu

We introduce a multi-layer perceptron (MLP) called the COVID-19 Depression and Anxiety Predictor (CoDAP) to predict mental health trends, particularly anxiety and depression, during the COVID-19 pandemic. Our method utilizes a comprehensive dataset, which tracked mental health symptoms weekly over ten weeks during the initial COVID-19 wave (April to June 202

Christopher I. Calle, Shaunak D. Bopardikar

In this work, we address the problem of sensor selection for state estimation via Kalman filtering. We consider a linear time-invariant (LTI) dynamical system subject to process and measurement noise, where the sensors we use to perform state estimation are randomly drawn according to a sampling with replacement policy. Since our selection of sensors is rand

Dalia Gala, Milo Phillips-Brown, Naman Goel, Carinal Prunkl

Machine learning requires defining one's target variable for predictions or decisions, a process that can have profound implications for fairness, since biases are often encoded in target variable definition itself, before any data collection or training. The downstream impacts of target variable definition must be taken into account in order to responsibly

Mee Seong Im, Mikhail Khovanov

This is the first in a series of papers where scissor congruence and K-theoretical invariants are related to cobordism groups of foams in various dimensions. A model example is provided where the cobordism group of weighted one-foams is identified, via the Sah-Arnoux-Fathi invariant, with the first homology of the group of interval exchange automorphisms and

Alexander Zuyev, Lihong Feng, Peter Benner

This paper aims at characterizing the approximability of bounded sets in the range of nonlinear operators in Banach spaces by finite-dimensional linear varieties. In particular, the class of operators we consider includes the endpoint maps of nonlinear distributed-parameter control systems. We describe the relationship between the Kolmogorov n-width of a bou

Ziang Chen, Jianfeng Lu, Yulong Lu, Xiangxiong Zhang

This paper studies the numerical approximation of the ground state of the Gross-Pitaevskii (GP) eigenvalue problem with a fully discretized Sobolev gradient flow induced by the $H^1$ norm. For the spatial discretization, we consider the finite element method with quadrature using $P^k$ basis on a simplicial mesh and $Q^k$ basis on a rectangular mesh. We prov

Felix H. Krones, Ben Walker, Guy Parsons, Terry Lyons

This work showcases our team's (The BEEGees) contributions to the 2023 George B. Moody PhysioNet Challenge. The aim was to predict neurological recovery from coma following cardiac arrest using clinical data and time-series such as multi-channel EEG and ECG signals. Our modelling approach is multimodal, based on two-dimensional spectrogram representations de

Nahid Binandeh Dehaghani, A. Pedro Aguiar, Rafal Wisniewski

This paper introduces a Lyapunov-based control approach with homodyne measurement. We study two filtering approaches: (i) the traditional quantum filtering and (ii) a modified version of the extended Kalman filtering. We examine both methods in order to directly estimate the evolution of the coherence vector elements, using sequential homodyne current measur

Léo Boisvert, Hélène Verhaeghe, Quentin Cappart

In recent years, there has been a growing interest in using learning-based approaches for solving combinatorial problems, either in an end-to-end manner or in conjunction with traditional optimization algorithms. In both scenarios, the challenge lies in encoding the targeted combinatorial problems into a structure compatible with the learning algorithm. Many

Zhe Huang, Xiaowei Yu, Benjamin S. Wessler, Michael C. Hughes

Automated interpretation of ultrasound imaging of the heart (echocardiograms) could improve the detection and treatment of aortic stenosis (AS), a deadly heart disease. However, existing deep learning pipelines for assessing AS from echocardiograms have two key limitations. First, most methods rely on limited 2D cineloops, thereby ignoring widely available D

Mohsen Khazeni, Mohammad Heydari, Amir Albadvi

The lack of a suitable tool for the analysis of conversational texts in the Persian language has made various analyses of these texts, including Sentiment Analysis, difficult. In this research, we tried to make the understanding of these texts easier for the machine by providing PSC, Persian Slang Converter, a tool for converting conversational texts into fo

Patrick W. Krantz, Alexander Tyner, Pallab Goswami, Venkat Chandrasekhar

There has been intense recent interest in the two-dimensional electron gases (2DEGs) that form at the surfaces and interfaces of KTaO$_3$ (KTO), with the discovery of superconductivity at temperatures significantly higher than those of similar 2DEGs based on SrTiO$_3$ (STO). Like STO heterostructures, these KTO 2DEGs are formed by depositing an overlayer on

Bing He, Sreyashi Nag, Limeng Cui, Suhang Wang

E-commerce platforms typically store and structure product information and search data in a hierarchy. Efficiently categorizing user search queries into a similar hierarchical structure is paramount in enhancing user experience on e-commerce platforms as well as news curation and academic research. The significance of this task is amplified when dealing with

Rohan Asthana, Joschua Conrad, Youssef Dawoud, Maurits Ortmanns

Neural architecture search automates the design of neural network architectures usually by exploring a large and thus complex architecture search space. To advance the architecture search, we present a graph diffusion-based NAS approach that uses discrete conditional graph diffusion processes to generate high-performing neural network architectures. We then

Tariq Nadhim Jassim, Rasha Tahseen Ibrahim, Mohsen Hamoud Jasim, Nahd Jabbar Dalfi

Background, For the purpose of determining the appropriate course of therapy to maintain fertility, a correct diagnosis of ovarian cysts is crucial. Objective, To contrast the results of magnetic resonance imaging and ultrasonography in individuals with ovarian cysts . Methods: research was carried out in the radiology division of Al-Hilla General Teaching h

Christopher Toukmaji

Large pre-trained language models (PLMs) are at the forefront of advances in Natural Language Processing. One widespread use case of PLMs is "prompting" - or in-context learning - where a user provides a description of a task and some completed examples of the task to a PLM as context before prompting the PLM to perform the task on a new example. Only the la

Xiaowei Qian, Zhimeng Guo, Jialiang Li, Haitao Mao

Fair graph learning plays a pivotal role in numerous practical applications. Recently, many fair graph learning methods have been proposed; however, their evaluation often relies on poorly constructed semi-synthetic datasets or substandard real-world datasets. In such cases, even a basic Multilayer Perceptron (MLP) can outperform Graph Neural Networks (GNNs)

Dihia Lanasri

Important advances in pillar domains are derived from exploiting query-logs which represents users interest and preferences. Deep understanding of users provides useful knowledge which can influence strongly decision-making. In this work, we want to extract valuable information from Linked Open Data (LOD) query-logs. LOD logs have experienced significant gro

Nicholas Waltz

Despite their performance and widespread use, little is known about the theory of Random Forests. A major unanswered question is whether, or when, the Random Forest algorithm is consistent. The literature explores various variants of the classic Random Forest algorithm to address this question and known short-comings of the method. This paper is a contributi

Jeonghwan Park, Paul Miller, Niall McLaughlin

We consider the hard label based black box adversarial attack setting which solely observes predicted classes from the target model. Most of the attack methods in this setting suffer from impractical number of queries required to achieve a successful attack. One approach to tackle this drawback is utilising the adversarial transferability between white box s

Ferhat Erata, Arda Goknil, Bedir Tekinerdogan, Geylani Kardas

We present Tarski, a tool for specifying configurable trace semantics to facilitate automated reasoning about traces. Software development projects require that various types of traces be modeled between and within development artifacts. For any given artifact (e.g., requirements, architecture models and source code), Tarski allows the user to specify new tr

Melda Alaluf, Giulia Crippa, Sinong Geng, Zijian Jing

We study paycheck optimization, which examines how to allocate income in order to achieve several competing financial goals. For paycheck optimization, a quantitative methodology is missing, due to a lack of a suitable problem formulation. To deal with this issue, we formulate the problem as a utility maximization problem. The proposed formulation is able to

Auwais Ahmed, Peter A. Kottke, Andrei G. Fedorov

The advancement of liquid phase electron beam induced deposition has enabled an effective direct-write approach for functional nanostructure synthesis with the possibility of three-dimensional control of morphology. For formation of a metallic solid phase, the process employs ambient temperature, beam-guided, electrochemical reduction of precursor cations re

Swapnaja Achintalwar, Adriana Alvarado Garcia, Ateret Anaby-Tavor, Ioana Baldini

Large language models (LLMs) are susceptible to a variety of risks, from non-faithful output to biased and toxic generations. Due to several limiting factors surrounding LLMs (training cost, API access, data availability, etc.), it may not always be feasible to impose direct safety constraints on a deployed model. Therefore, an efficient and reliable alterna

Chirag C. Palekar, Barbara Rosa, Niels Heermeier, Ching-Wen Shih

We report on multi-resonance chirped distributed Bragg reflector (DBR) microcavities. These systems are employed to investigate the light-mater interaction with both intra- and inter-layer excitons of transition metal dichalcogenide (TMDC) bilayer heterostructures. The chirped DBRs consisting of SiO2 and Si3N4 layers with gradually changing thickness exhibit

Christoforos N. Hadjicostis, Alejandro D. Dominguez-Garcia

We consider the problem of average consensus in a distributed system comprising a set of nodes that can exchange information among themselves. We focus on a class of algorithms for solving such a problem whereby each node maintains a state and updates it iteratively as a linear combination of the states maintained by its in-neighbors, i.e., nodes from which

Manvir Grewal, Y. T. Albert Law

We study real-time finite-temperature correlators for free scalars of any mass in a $dS_{d+1}$ static patch in any dimension. We show that whenever the inverse temperature is a rational multiple of the inverse de Sitter temperature, certain Matsubara poles of the symmetric Wightman function disappear. At the de Sitter temperature, we explicitly show how the

Jaemin Kim, Keon Ho Kim, Nikolaos Bouklas

We present a comprehensive theoretical and computational model that explores the behavior of a thin hydrated film bonded to a non-hydrated / impermeable soft substrate in the context of surface and bulk elasticity coupled with surface diffusion kinetics. This type of coupling can manifests as an integral aspect in diverse engineering processes encountered in

Ariestha Widyastuty Bustan, ANM Salman, Pritta Etriana Putri

The locating rainbow connection number of a graph is defined as the minimum number of colors required to color vertices such that every two vertices there exists a rainbow vertex path and every vertex has a distinct rainbow code. This rainbow code signifies a distance between vertices within a given set of colors in a graph. This paper aims to determine the

Yiting Yao

In this work, we study the integrability, as well as the dynamics of the Lorenz System. This include a very useful identity:\[ \beta z^2(\sigma t)+y^2(\beta\sigma t)=\rho x^2(\beta t)+\nu e^{-2\beta\sigma t}, \]where $\nu\in\mathbb{R}$ is a constant. And we will see some applications of this identity.

Evan Ellis, Gaurav R. Ghosal, Stuart J. Russell, Anca Dragan

Preference-based reward learning is a popular technique for teaching robots and autonomous systems how a human user wants them to perform a task. Previous works have shown that actively synthesizing preference queries to maximize information gain about the reward function parameters improves data efficiency. The information gain criterion focuses on precisel

Ilya Barmak, Alexander Gelfgat, Neima Brauner

This work deals with stability of two-phase stratified air-water flows in horizontal circular pipes. For this purpose, we performed a linear stability analysis, which considers all possible three-dimensional infinitesimal disturbances and takes into account deformations of the air-water interface. The main results are presented in form of stability maps, whi

Tal Schwartz, James A. Hutchison

Recently, an article by the Barnes group reported on the experimental study of a photoisomerization reaction inside an optical cavity, claiming to reproduce previous results by Hutchison et al. and making the point that in such setups, changes in the absorption of ultraviolet radiation by the molecules in the cavity can lead to modifications in the photochem

Z. Ahmed, R. S. Evans, I. Goel, G. M. Huber

There is increasing interest in deep exclusive meson production (DEMP) reactions, as they provide access to Generalized Parton Distributions over a broad kinematic range, and are the only means of measuring pion and kaon charged electric form factors at high $Q^2$. Such investigations are a particularly useful tool in the study of hadronic structure in QCD's

Adam Lee

This paper considers hypothesis testing in semiparametric models which may be non-regular. I show that C($\alpha$) style tests are locally regular under mild conditions, including in cases where locally regular estimators do not exist, such as models which are (semiparametrically) weakly identified. I characterise the appropriate limit experiment in which to

Xuan Kien Phung

We establish generalizations of the well-known surjunctivity theorem of Gromov and Weiss as well as the dual-surjunctivity theorem of Capobianco, Kari and Taati for cellular automata (CA) to local perturbations of CA over sofic group universes. We also extend the results to a class of non-uniform cellular automata (NUCA) consisting of global perturbations wi

Arijit Saha, Arkadeep Datta, Arani Mukhopadhyay, Amitava Datta

Water collection from natural and industrial fogs has recently been viewed as a viable freshwater source. An interesting outgrowth of the relevant research as focused on arresting of the drift losses (un-evaporated and re-condensed water droplets present in the exhaust plume from industrial cooling towers. Such exploits in fog collection have implemented met

Marcel Hussing, Claas Voelcker, Igor Gilitschenski, Amir-massoud Farahmand

We show that deep reinforcement learning algorithms can retain their ability to learn without resetting network parameters in settings where the number of gradient updates greatly exceeds the number of environment samples by combatting value function divergence. Under large update-to-data ratios, a recent study by Nikishin et al. (2022) suggested the emergen

Victor Sam Moses Babu K., Sidharthenee Nayak, Divyanshi Dwivedi, Pratyush Chakraborty

Faults on electrical power lines could severely compromise both the reliability and safety of power systems, leading to unstable power delivery and increased outage risks. They pose significant safety hazards, necessitating swift detection and mitigation to maintain electrical infrastructure integrity and ensure continuous power supply. Hence, accurate detec

Oscar Amador, Manuel Urueña, Maria Calderon, Ignacio Soto

This paper evaluates the performance of the ETSI Contention-Based Forwarding (CBF) GeoNetworking protocol for distributing warning messages in highway scenarios, including its interaction with the Decentralized Congestion Control (DCC) mechanism. Several shortcomings of the standard ETSI CBF algorithm are identified, and we propose different solutions to the

Stephanie Diem, Laila El-Guebaly, Aditi Verma

Fusion energy, the process that uses the same reaction that powers the sun and the stars, offers the promise of virtually unlimited, carbon-free energy and is approaching reality. Recently, there's been a dramatic global increase in the investment and research focused on addressing the hurdles to commercialize fusion energy. While a majority of the effort ha

Anant Telikicherla, Thomas N. Woods, Bennet D. Schwab

In this study we present the analysis of six solar flare events that occurred in 2022, using new data from the third-generation Miniature X-Ray Solar Spectrometer (MinXSS), also known as the Dual-zone Aperture X-ray Solar Spectrometer (DAXSS). The primary focus of this study is on the flare's "onset phase", which is characterized by elevated soft X-ray emiss

Victor Sam Moses Babu K., Sidharthenee Nayak, Divyanshi Dwivedi, Pratyush Chakraborty

Electrical fault classification is vital for ensuring the reliability and safety of power systems. Accurate and efficient fault classification methods are essential for timely and effective maintenance. In this paper, we propose a novel approach for effective fault classification through Grassmann manifolds, which is a non-Euclidean space that captures the i

Michael Shapiro, Herut Dor, Anna Gurevich-Shapiro, Tal Etan

Background: Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment but can result in severe immune-related adverse events (IrAEs). Monitoring IrAEs on a large scale is essential for personalized risk profiling and assisting in treatment decisions. Methods: In this study, we conducted an analysis of clinical notes from patients who received

Heinrich M. Jaeger, Arvind Murugan, Sidney R. Nagel

Biological systems offer a great many examples of how sophisticated, highly adapted behavior can emerge from training. Here we discuss how training might be used to impart similarly adaptive properties in physical matter. As a special form of materials processing, training differs in important ways from standard approaches of obtaining sought after material

Md. Galib Ishraq Emran, Rhidi Barma, Akram Hussain Khan, Mrinmoy Roy

Globally, the water crisis has become a significant problem that affects developing and industrialized nations. Water shortage can harm public health by increasing the chance of contracting water-borne diseases, dehydration, and malnutrition. This study aims to examine the causes of the water problem and its likely effects on human health. The study scrutini

Chunhui Wang, Chang Zeng, Bowen Zhang, Ziyang Ma

Token-based text-to-speech (TTS) models have emerged as a promising avenue for generating natural and realistic speech, yet they grapple with low pronunciation accuracy, speaking style and timbre inconsistency, and a substantial need for diverse training data. In response, we introduce a novel hierarchical acoustic modeling approach complemented by a tailore

Galina L. Klimchitskaya, Vladimir M. Mostepanenko

This review considers the theoretical approaches to the understanding of dark energy which comprises approximately 68\% of the energy of our Universe and explains an acceleration in its expansion. Following a discussion of the main approach based on Einstein's equations with the cosmological term, the explanations of dark energy using the concept of some kin

R. A. Street, E. Bachelet, Y. Tsapras, M. P. G. Hundertmark

The ROME/REA (Robotic Observations of Microlensing Events/Reactive Event Assessment) Survey was a Key Project at Las Cumbres Observatory (hereafter LCO) which continuously monitored 20 selected fields (3.76 sq.deg.) in the Galactic Bulge throughout their seasonal visibility window over a three-year period, between March 2017 and March 2020. Observations were

Matthias Kern, Ferhat Erata, Markus Iser, Carsten Sinz

This paper proposes an approach for a tool-agnostic and heterogeneous static code analysis toolchain in combination with an exchange format. This approach enhances both traceability and comparability of analysis results. State of the art toolchains support features for either test execution and build automation or traceability between tests, requirements and

Jan Bohr, François Monard, Gabriel P. Paternain

Transport twistor spaces are degenerate complex $2$-dimensional manifolds $Z$ that complexify transport problems on Riemannian surfaces, appearing, e.g., in geometric inverse problems. This article considers maps $\beta\colon Z\to \mathbb{C}^2$ with a holomorphic blow-down structure that resolve the degeneracy of the complex structure and allow to gain insig

Forrest Mozer, Oleksiy Agapitov, Stuart Bale, Keith Goetz

$\textit{AIMS.}$ To investigate processes associated with generation of type-III radiation. $\textit{METHODS.}$ Measure the amplitudes and phase velocities of Parker Solar Probe observed electric fields, magnetic fields, and plasma density fluctuations. $\textit{RESULTS.}$ (1) There are slow electrostatic waves near the Langmuir frequency and at as many as s

Galina L. Klimchitskaya, Constantine C. Korikov, Vladimir M. Mostepanenko

The Casimir-Polder force between spherical nanoparticles and a graphene-coated silica plate is investigated in situations out of thermal equilibrium, i.e., with broken time-reversal symmetry. The response of graphene coating to the electromagnetic field is described on the basis of first principles of quantum electrodynamics at nonzero temperature using the

Pinni Venkata Abhiram, Ananya Rathore, Abhir Mirikar, Hari Krishna S

The paper presents a novel Auto Language Prediction Dictionary Capsule (ALPDC) framework for language prediction and machine translation. The model uses a combination of neural networks and symbolic representations to predict the language of a given input text and then translate it to a target language using pre-built dictionaries. This research work also ai

Niroop Sugunaraj

Designing secure information infrastructure is a function of design and usability. However, security is seldom given priority when systems are being developed. Secure design practices should balance between functionality (i.e., proper design) to meet minimum requirements and user-friendliness. Design recommendations such as those with a user-centric approach

S. K. Rajput, M. K. Panda

The linear stability of a suspension of phototactic algae is investigated numerically with particular emphasis on the effects of the angle of incidence of the illuminating oblique collimated irradiation with thermal effects. The suspension is illuminated by the oblique collimated irradiation from the top and heated/cooled from the bottom. The linear stabilit

Micha Berkooz, Nadav Brukner, Yiyang Jia, Ohad Mamroud

We study transitions from chaotic to integrable Hamiltonians in the double scaled SYK and $p$-spin systems. The dynamics of our models is described by chord diagrams with two species. We begin by developing a path integral formalism of coarse graining chord diagrams with a single species of chords, which has the same equations of motion as the bi-local ($G\S

Younes Ghazagh Jahed, Seyyed Ali Sadat Tavana

Feature selection plays a crucial role in improving predictive accuracy by identifying relevant features while filtering out irrelevant ones. This study investigates the importance of effective feature selection in enhancing the performance of classification models. By employing reinforcement learning (RL) algorithms, specifically Q-learning (QL) and SARSA l

S. K. Rajput, M. K. Panda

In this study, our focus is mainly to check the effect of light scattering on the onset of thermal-phototactic-bioconvection in an algal suspension where the suspension is illuminated by the collimated oblique irradiation from above while simultaneously applying heating or cooling from below. We conduct a numerical investigation into the linear stability of

Christopher Funk, Benjamin Noack

This work introduces a flexible and versatile method for the data-efficient yet conservative transmission of covariance matrices, where a matrix element is only transmitted if a so-called triggering condition is satisfied for the element. Here, triggering conditions can be parametrized on a per-element basis, applied simultaneously to yield combined triggeri

Andrew Wu, Javier Cerrillo, Jianshu Cao

The emerging field of molecular cavity polaritons has stimulated a surge of experimental and theoretical activities and presents a unique opportunity to develop the many-body simulation methodology. This paper presents a numerical scheme for the extraction of key kinetic information of lossy cavity polaritons based on the transfer tensor method (TTM). Steady

Amin Abolghasemi, Leif Azzopardi, Arian Askari, Maarten de Rijke

In most recent studies, gender bias in document ranking is evaluated with the NFaiRR metric, which measures bias in a ranked list based on an aggregation over the unbiasedness scores of each ranked document. This perspective in measuring the bias of a ranked list has a key limitation: individual documents of a ranked list might be biased while the ranked lis

Hua Cheng

Concurrency of transcranial magnetic stimulation with electroencephalography (TMS-EEG) technique is a powerful and challenging methodology for basic research and clinical applications. Aspects considered in experiments for effective TMS-EEG recordings and analysis, including artifact management, data analysis and interpretation and protocols. mini review off

Osman Nuri Irkicatal, Elif Tugce Ceran, Melda Yuksel

This study explores the application of the rate-splitting multiple access (RSMA) technique, vital for interference mitigation in modern communication systems. It investigates the use of precoding methods in RSMA, especially in complex multiple-antenna interference channels, employing deep reinforcement learning. The aim is to optimize precoders and power all

Dennis Ulmer, Martin Gubri, Hwaran Lee, Sangdoo Yun

As large language models (LLMs) are increasingly deployed in user-facing applications, building trust and maintaining safety by accurately quantifying a model's confidence in its prediction becomes even more important. However, finding effective ways to calibrate LLMs - especially when the only interface to the models is their generated text - remains a chal

Jianwen Li, Hyunsang Park, Wenjian Hao, Lei Xin

In this paper, we discuss the development and deployment of a robust autonomous system capable of performing various tasks in the maritime domain under unknown dynamic conditions. We investigate a data-driven approach based on modular design for ease of transfer of autonomy across different maritime surface vessel platforms. The data-driven approach alleviat

Long Wang, Feng Fu, Xingru Chen

Evolutionary Game Theory (EGT) and Artificial Intelligence (AI) are two fields that, at first glance, might seem distinct, but they have notable connections and intersections. The former focuses on the evolution of behaviors (or strategies) in a population, where individuals interact with others and update their strategies based on imitation (or social learn

Shivangi Mittal, Yogesh M. Joshi, Sachin Shanbhag

Harmonic balance (HB) is a popular Fourier-Galerkin method used in the analysis of nonlinear vibration problems where dynamical systems are subjected to periodic forcing. We adapt HB to find the periodic steady-state response of nonlinear differential constitutive models subjected to large amplitude oscillatory shear flow. By incorporating the alternating-fr

Ran Ji, Chongwen Huang, Xiaoming Chen, Wei E. I. Sha

It is well known that there is inherent radiation pattern distortion for the commercial base station antenna array, which usually needs three antenna sectors to cover the whole space. To eliminate pattern distortion and further enhance beamforming performance, we propose an electromagnetic hybrid beamforming (EHB) scheme based on a three-dimensional (3D) sup

Cory W. Natoli, Edward D. White, Beau A. Nunnally, Alex J. Gutman

Experimental studies often fail to appropriately account for the number of collected samples within a fixed time interval for functional responses. Data of this nature appropriately falls under an Infill Asymptotic domain that is constrained by time and not considered infinite. Therefore, the sample size should account for this infill asymptotic domain. This

Keenan Burnett, Angela P. Schoellig, Timothy D. Barfoot

Treating IMU measurements as inputs to a motion model and then preintegrating these measurements has almost become a de-facto standard in many robotics applications. However, this approach has a few shortcomings. First, it conflates the IMU measurement noise with the underlying process noise. Second, it is unclear how the state will be propagated in the case

Ved Prakash Gupta, Sumit Kumar

Given any irreducible inclusion $\mB \subset \mA$ of unital $C^*$-algebras with a finite-index conditional expectation $E: \mA \to \mB$, we show that the set of $E$-compatible intermediate $C^*$-subalgebras is finite, thereby generalizing a finiteness result of Ino and Watatani (from \cite{IW}). A finiteness result for a certain collection of intermediate $C

Sana Ayromlou, Vahid Reza Khazaie, Fereshteh Forghani, Arash Afkanpour

The rapid advancement in self-supervised representation learning has highlighted its potential to leverage unlabeled data for learning rich visual representations. However, the existing techniques, particularly those employing different augmentations of the same image, often rely on a limited set of simple transformations that cannot fully capture variations

Shane Gardner, Edward White, Brent Langhals, Todd McNamara

The Lightning Launch Commit Criteria (LLCC) are a set of complex rules to avoid natural and rocket-triggered lightning strikes to in-flight space launch vehicles. The LLCC are the leading source of scrubs and delays to space launches from Cape Canaveral Air Force Station (CCAFS) and NASA Kennedy Space Center (KSC). An LLCC climatology would be useful for des

Kostas Sozos, Francesco Da Ros, Metodi P. Yankov, George Sarantoglou

In this paper, we experimentally validate our previous numerical works in recurrent optical spectrum slicing (ROSS) accelerators for dispersion compensation in high-speed IM/DD links. For this, we utilize recurrent filters implemented both through a waveshaper and by exploiting novel integrated programmable photonic platforms. Different recurrent configurati

A. Mironov, A. Oreshina, A. Popolitov

We consider a two $\beta$-ensemble realization of the series of $\beta$-deformed WLZZ matrix models. We demonstrate that such a realization involves $\beta$-deformed Harish-Chandra-Itzykson-Zuber integrals, one of them providing a coupling to the external field. We also construct Ward identities in the corresponding two $\beta$-ensemble model, which requires

David Hunt, Shaocheng Luo, Amir Khazraei, Xiao Zhang

In this work, we present RadCloud, a novel real time framework for directly obtaining higher-resolution lidar-like 2D point clouds from low-resolution radar frames on resource-constrained platforms commonly used in unmanned aerial and ground vehicles (UAVs and UGVs, respectively); such point clouds can then be used for accurate environmental mapping, navigat

Dingkang Yang, Kun Yang, Mingcheng Li, Shunli Wang

Context-aware emotion recognition (CAER) has recently boosted the practical applications of affective computing techniques in unconstrained environments. Mainstream CAER methods invariably extract ensemble representations from diverse contexts and subject-centred characteristics to perceive the target person's emotional state. Despite advancements, the bigge

Tanmoy Kundu, Moshe Rafaeli, Anton Gulyaev, Vadim Indelman

In multi-robot systems, ensuring safe and reliable decision making under uncertain conditions demands robust multi-robot belief space planning (MR-BSP) algorithms. While planning with multiple robots, each robot maintains a belief over the state of the environment and reasons how the belief would evolve in the future for different possible actions. However,

Komeil Moghaddasi, Shakiba Rajabi

With the rapid growth of Internet of Things (IoT) applications, there's a big demand for more processing power and resources in devices. Mobile Edge Computing (MEC) looks promising for enhancing performance and reducing costs by offloading the computing work of IoT to MEC servers. However, the current methods for offloading have issues with privacy and secur

Andrew Graham

We extend the construction of the $p$-adic $L$-function interpolating unitary Friedberg--Jacquet periods in previous work of the author to include the $p$-adic variation of Maass--Shimura differential operators. In particular, we develop a theory of nearly overconvergent automorphic forms in higher degrees of coherent cohomology for unitary Shimura varieties

Michael Nayak

Based on technical work and development conducted under the LunA-10 study, I have identified six hypotheses where, if revolutionary improvements in technology can be made, I assess that a direct acceleration to the fielding of a lunar economy is likely to occur. In this short paper, I explain these six hypotheses, and recommend that these topics be focused o

Rudy Semola, Julio Hurtado, Vincenzo Lomonaco, Davide Bacciu

Hyperparameter selection in continual learning scenarios is a challenging and underexplored aspect, especially in practical non-stationary environments. Traditional approaches, such as grid searches with held-out validation data from all tasks, are unrealistic for building accurate lifelong learning systems. This paper aims to explore the role of hyperparame

Ramesh Dhakal, Samuel Griffith, Stephen M. Winter

FePS3 is a layered van der Waals (vdW) Ising antiferromagnet that has recently been studied in the context of true 2D magnetism, and emerged as an ideal material platform for investigating strong spin-phonon coupling, and non-linear magneto-optical phenomena. In this work, we demonstrate an important unresolved role of spin-orbit coupling (SOC) in the ground

Nanna Inie

Companies, organizations, and governments across the world are eager to employ so-called 'AI' (artificial intelligence) technology in a broad range of different products and systems. The promise of this cause c\'el\`ebre is that the technologies offer increased automation, efficiency, and productivity - meanwhile, critics sound warnings of illusions of objec

Pritam Halder, Srijon Ghosh, Saptarshi Roy, Tamal Guha

We put forth a notion of optimality for extracting ergotropic work, derived from an energy constraint governing the necessary dynamics for work extraction in a quantum system. Within the traditional ergotropy framework, which predicts an infinite set of equivalent pacifying unitaries, we demonstrate that the optimal choice lies in driving along the geodesic

Ekaterina Shumitskaya, Anastasia Antsiferova, Dmitriy Vatolin

No-reference image- and video-quality metrics are widely used in video processing benchmarks. The robustness of learning-based metrics under video attacks has not been widely studied. In addition to having success, attacks that can be employed in video processing benchmarks must be fast and imperceptible. This paper introduces an Invisible One-Iteration (IOI

T. B. Lantaño, Dayou Yang, K. M. R. Audenaert, S. F. Huelga

We propose a state preparation protocol based on sequential measurements of a central spin coupled with a spin ensemble, and investigate the usefulness of the generated multi-spin states for quantum enhanced metrology. Our protocol is shown to generate highly entangled spin states, devoid of the necessity for non-linear spin interactions. The metrological se

Chuandong Lin, Kai H. Luo, Huilin Lai

A multi-relaxation-time discrete Boltzmann model (DBM) with split collision is proposed for both subsonic and supersonic compressible reacting flows, where chemical reactions take place among various components. The physical model is based on a unified set of discrete Boltzmann equations that describes the evolution of each chemical species with adjustable a

Kaidong Song, A. N. M. Tanvir, Md Omarsany Bappy, Yanliang Zhang

Thermoelectric materials, which can convert waste heat into electricity or act as solid-state Peltier coolers, are emerging as key technologies to address global energy shortages and environmental sustainability. However, discovering materials with high thermoelectric conversion efficiency is a complex and slow process. The emerging field of high-throughput

Yuxuan Xu, Guo Chen, Fei Du, Ming Li

Cubic gauche nitrogen (cg-N) has received wide attention due to its high energy density and environmental friendliness. However, existing synthesis methods for cg-N predominantly rely on the high-pressure techniques, or the utilization of nanoconfined effects using highly toxic and sensitive sodium azide as precursor, which significantly restrict the practic

Ramin Mousa, Mitra Khezli, Mohamadreza Azadi, Vahid Nikoofard

Accurate classification of objects in 3D point clouds is a significant problem in several applications, such as autonomous navigation and augmented/virtual reality scenarios, which has become a research hot spot. In this paper, we presented a deep learning strategy for 3D object classification in augmented reality. The proposed approach is a combination of t

Samuel Schmidgall, Ji Woong Kim, Jeffrey Jopling, Axel Krieger

The absence of openly accessible data and specialized foundation models is a major barrier for computational research in surgery. Toward this, (i) we open-source the largest dataset of general surgery videos to-date, consisting of 680 hours of surgical videos, including data from robotic and laparoscopic techniques across 28 procedures; (ii) we propose a tec

Tattwamasi Amrutam, Jacopo Bassi

Let $\Gamma$ be a discrete group acting on a compact Hausdorff space $X$. Given $x\in X$, and $\mu\in\text{Prob}(X)$, we introduce the notion of contraction of $\mu$ towards $x$ with respect to unitary elements of a group von Neumann algebra not necessarily coming from group elements. Using this notion, we study relative commutants of subalgebras in tracial

Marco Cicalese, Andrea Kubin

We investigate the area-preserving mean-curvature-type motion of a two-dimensional lattice crystal obtained by coupling constrained minimizing movements scheme introduced by Almgren, Taylor and Wang with a discrete-to-continuous analysis. We first examine the continuum counterpart of the model and establish the existence and uniqueness of the flat flow, orig

Wei-be He, Guo-yun Shao, Chong-long Xie, Ren-xin Xu

In the Beam Energy Scan phase II (BES-II) experiments at RHIC STAR, the quark-gluon plasma (QGP) produced with changing collision energies may probe different regions of the QCD phase diagram. Correspondingly, studying the transport coefficients of quark matter in these regions will contribute to extracting the QCD phase structure through hydrodynamic approa

Sourav Pal, Parna Roy, Abhik Basu

We propose and study a conceptual one-dimensional model to explore how the combined interplay between fixed resources and particle exchanges between different parts of an extended system can affect the stationary densities in a current carrying channel connecting different parts of the system. To this end, we consider a model composed of a totally asymmetric

Hugo Matias, Daniel Silvestre

This paper introduces a trajectory planning algorithm for search and coverage missions with an Unmanned Aerial Vehicle (UAV) based on an uncertainty map that represents prior knowledge of the target region, modeled by a Gaussian Mixture Model (GMM). The trajectory planning problem is formulated as an Optimal Control Problem (OCP), which aims to maximize the

Fedor V. Fomin, Petr A. Golovach, Nikola Jedličková, Jan Kratochvíl

The classic theorem of Gallai and Milgram (1960) generalizes several fundamental results in Graph Theory, such as Dilworth's theorem on posets and K\H{o}nig's theorem on matchings in bipartite graphs. The theorem asserts that for every graph G, the vertex set of G can be partitioned into at most \alpha(G) vertex-disjoint paths, where \alpha(G) is the maximum

Ho Man Cheung, Hoi Ping Luk

We classify the dihedral edge-to-edge tilings of the sphere by regular polygons with gonality at least 5 and rhombi.