April 2023 arXiv papers — page 112

Zongyuan Li

In this work, we study the unique continuation properties of Robin boundary value problems with Robin potentials $\eta \in L_{d-1+\varepsilon}$. Our results generalize earlier ones in which $\eta$ was assumed to be either zero (Neumann problem) or differentiable.

David Simchi-Levi, Zeyu Zheng, Feng Zhu

We study the optimal trade-off between expectation and tail risk for regret distribution in the stochastic multi-armed bandit model. We fully characterize the interplay among three desired properties for policy design: worst-case optimality, instance-dependent consistency, and light-tailed risk. New policies are proposed to characterize the optimal regret ta

Yoshikata Kida

For certain HNN extensions including Baumslag-Solitar groups, a treeing is constructed from their certain probability-measure-preserving actions. This is a treeing of a quotient groupoid of the translation groupoid associated with their actions. As its application, for some of those HNN extensions, we show that the kernel of the modular homomorphism is measu

Zengzhi Wang, Qiming Xie, Yi Feng, Zixiang Ding

Recently, ChatGPT has drawn great attention from both the research community and the public. We are particularly interested in whether it can serve as a universal sentiment analyzer. To this end, in this work, we provide a preliminary evaluation of ChatGPT on the understanding of \emph{opinions}, \emph{sentiments}, and \emph{emotions} contained in the text.

Tenglong Lu, Sheng Meng, Miao Liu

This paper presents the calculation results of electron-phonon interactions within the LuH$_2$, LuH$_3$, and LuN systems under 0 GPa and 10 GPa via density functional theory at the GGA-PBE level. The purpose of this work is to provide useful data that may be of the interests of the superconducting community as it was reported that the Lu-H-N compound is like

Viktor V. Dodonov, Alexandre V. Dodonov

We study the evolution of the energy and magnetic moment of a quantum charged particle placed in a homogeneous magnetic field, when this field changes adiabatically its sign. We show that after a single magnetic field passage through zero value, the famous adiabatic invariant ratio of energy to frequency is reestablished again, but with the proportionality c

Takuto Ichikawa, Aizitiaili Abulikemu, Muneaki Hase

In this work, we systematically investigated the ultrafast optical properties of aluminum (Al) thin films on silicon cantilevers using a microscopic femtosecond optical pump-probe technique to explore the effect of light irradiation upon cantilevers while considering radiation pressure and photothermal effects. The ultrafast laser pulses used for the study w

Chanhyeok Park, Minhyuk Sung

Achieving tight bounding boxes of a shape while guaranteeing complete boundness is an essential task for efficient geometric operations and unsupervised semantic part detection. But previous methods fail to achieve both full coverage and tightness. Neural-network-based methods are not suitable for these goals due to the non-differentiability of the objective

Hong-Quan Li, Jie-Xiang Zhu

In this paper we are concerned with the Riesz transform on the direct product manifold ${\mathbb{H}}^n \times M$, where ${\mathbb{H}}^n$ is the $n$-dimensional real hyperbolic space and $M$ is a connected complete non-compact Riemannian manifold satisfying the volume doubling property and generalized Gaussian or sub-Gaussian upper estimates for the heat kern

Kai Jiang, Shifeng Li, Pingwen Zhang

We present an analysis of the approximation error for a $d$-dimensional quasiperiodic function $f$ with Diophantine frequencies, approximated by a periodic function with the fundamental domain $[0,L_1)\times [0,L_2)\times \cdots \times[0,L_d)$. When $f$ has a certain regularity, its global behavior can be described by a finite number of Fourier components an

Shivam K Panda, Yongkyu Lee, M. Khalid Jawed

The successful implementation of vision-based navigation in agricultural fields hinges upon two critical components: 1) the accurate identification of key components within the scene, and 2) the identification of lanes through the detection of boundary lines that separate the crops from the traversable ground. We propose Agronav, an end-to-end vision-based a

Yihong Zhang, Yisu Remy Wang, Oliver Flatt, David Cao

We present egglog, a fixpoint reasoning system that unifies Datalog and equality saturation (EqSat). Like Datalog, it supports efficient incremental execution, cooperating analyses, and lattice-based reasoning. Like EqSat, it supports term rewriting, efficient congruence closure, and extraction of optimized terms. We identify two recent applications--a unifi

Gregory Berkolaiko, Igor Zelenko

In many applied problems one seeks to identify and count the critical points of a particular eigenvalue of a smooth parametric family of self-adjoint matrices, with the parameter space often being known and simple, such as a torus. Among particular settings where such a question arises are the Floquet--Bloch decomposition of periodic Schr\"odinger operators,

Anqi Gong, Joseph M. Renes

The successive cancellation list decoder (SCL) is an efficient decoder for classical polar codes with low decoding error, approximating the maximum likelihood decoder (MLD) for small list sizes. Here we adapt the SCL to the task of decoding quantum polar codes and show that it inherits the high performance and low complexity of the classical case, and can ap

Da Xu, Bo Yang

The use of pretrained embeddings has become widespread in modern e-commerce machine learning (ML) systems. In practice, however, we have encountered several key issues when using pretrained embedding in a real-world production system, many of which cannot be fully explained by current knowledge. Unfortunately, we find that there is a lack of a thorough under

Xiangsheng Xu

We study an initial boundary value problem for a cross-diffusion system in population dynamics. The mathematical challenge is due to the fact that the determinant of the coefficient matrix of the system changes signs. As a result, the system is only partially parabolic. We design an approximation scheme. The sequence of approximate solutions generated by our

Igor Baskov

We consider the algebra $A^0 (X)$ of polynomial functions on a simplicial complex $X$. The algebra $A^0 (X)$ is the $0$th component of Sullivan's dg-algebra $A^\bullet (X)$ of polynomial forms on $X$. Our main interest lies in computing the de Rham cohomology of the algebra $A^0(X)$, that is, the cohomology of the universal dg-algebra $\Omega ^\bullet _{A^0(

Jinyi Ye, Nikhil Jindal, Francesco Pierri, Luca Luceri

Despite their drawbacks and unintended consequences, social media networks have recently emerged as a crucial resource for individuals in distress, particularly during times of crisis. These platforms serve as a means to seek assistance and support, share reliable information, and appeal for action and solidarity. In this paper, we examine the online network

Deepak Ravikumar, Gobinda Saha, Sai Aparna Aketi, Kaushik Roy

Decentralized learning enables serverless training of deep neural networks (DNNs) in a distributed manner on multiple nodes. This allows for the use of large datasets, as well as the ability to train with a wide variety of data sources. However, one of the key challenges with decentralized learning is heterogeneity in the data distribution across the nodes.

Shiyang Lu, Yunfu Deng, Abdeslam Boularias, Kostas Bekris

This work proposes a self-supervised learning system for segmenting rigid objects in RGB images. The proposed pipeline is trained on unlabeled RGB-D videos of static objects, which can be captured with a camera carried by a mobile robot. A key feature of the self-supervised training process is a graph-matching algorithm that operates on the over-segmentation

Sean F. DePalma, Omkar H. Ramachandran, Leo C. Kempel, B. Shanker

Optimization of strongly non-linear tightly coupled feeds attached to antennas is a challenging problem from a purely computational perspective. One can imagine that an optimization would (a) need to be in the time domain, and (b) has to be self-consistently coupled with the linear antenna (or electromagnetic) system. These two imply that the cost of optimiz

Max A. Alekseyev

The desire for privacy significantly impacts various aspects of social behavior as illustrated by people's tendency to seek out the most secluded spot when multiple options are available. In particular, this can be seen at rows of payphones, where people tend to occupy an available payphone that is most distant from those already occupied. Assuming that ther

Gunnar Birke, Christian Engwer, Sandra May, Florian Streitbürger

Cut-cell meshes are an attractive alternative to avoid common mesh generation problems. For hyperbolic problems they pose additional challenges, as elements can become arbitrarily small, leading to prohibitive time step restrictions for explicit time stepping methods. To alleviate this small cell problem we consider a particular stabilization method, the Dom

Victor Guba

A (discrete) group is called amenable whenever there exists a finitely additive right invariant probablity measure on it. For Thompson's group $F$ the problem whether it is amenable is a long-standing open question. We consider presentation of $F$ in terms of non-spherical semigroup diagrams. There is a natural partition of $F$ into 7 parts in terms of these

Ran Gong, Jiangyong Huang, Yizhou Zhao, Haoran Geng

Understanding the continuous states of objects is essential for task learning and planning in the real world. However, most existing task learning benchmarks assume discrete (e.g., binary) object goal states, which poses challenges for the learning of complex tasks and transferring learned policy from simulated environments to the real world. Furthermore, st

Rafael Cerna Loli, Onur Dizdar, Bruno Clerckx, Petar Popovski

This work investigates the design of Hybrid Automatic Repeat Request (HARQ) strategies for downlink Rate-Splitting Multiple Access (RSMA). The existence of private and common stream as well as their conditioning for Successive Interference Cancellation (SIC), gives rise to an expanded set of opportunities for retransmission of failed packets. Specifically, w

Hoel Kervadec, Marleen de Bruijne

In the past few years, in the context of fully-supervised semantic segmentation, several losses -- such as cross-entropy and dice -- have emerged as de facto standards to supervise neural networks. The Dice loss is an interesting case, as it comes from the relaxation of the popular Dice coefficient; one of the main evaluation metric in medical imaging applic

Florian Jacob, Hannes Hartenstein

We uncover the extend-only directed posets (EDP) structure as a unification of recently discussed DAG-based Byzantine-tolerant conflict-free replicated data types (CRDT). We also show how a key-value map model can be derived from the EDP formulation, and give an outlook on an EDP-based systemic access control CRDT as a formalization of the CRDT used in the M

Jawahar Sivabharathy Samuthira Pandi, Ahmet Gungor, Chandan Bose, Antonio Attili

The effects of transverse gusts on free-falling plates are investigated using two-way coupled fluid-structure interaction simulations for a Galilei number (Ga) between 10 and 50 and a density ratio (rho) between 5 and 50. We consider gust ratios (GR) of up to 5, where GR is the ratio of the free-stream velocity change to an estimate of the terminal velocity.

Dany Nina Huaman, Miguel R. Nuñez-Chávez

In this paper, a quasi-linear parabolic equation with a diffusion term dependent on the gradient to the state with Dirichlet boundary conditions is considered. The goal of this paper is to prove the existence of control that insensitizes the system under study which is the case that Xu Liu left open in 2012. It is well known that the insensitizing control pr

Xinquan Huang, Tariq Alkhalifah

Microseismic source imaging plays a significant role in passive seismic monitoring. However, such a process is prone to failure due to aliasing when dealing with sparsely measured data. Thus, we propose a direct microseismic imaging framework based on physics-informed neural networks (PINNs), which can generate focused source images, even with very sparse re

Kai Vetter, Donald Gunter, Paul Luke, Victor Negut

The prompt and in-situ assessment of non-gamma ray emitting radionuclides such as Sr-90 remains an outstanding challenge, particularly in radiological emergency response and consequence management situations. We have developed a new concept to quantitatively assess a wide range of radionuclides, including beta-only emitters, using coplanar-grid CdZnTe detect

Md. Mijanur Rahman, A. S. M. Badrudduza, Noor Ahmad Sarker, Md. Ibrahim

The appearance of sixth-generation networks has resulted in the proposal of several solutions to tackle signal loss. One of these solutions is the utilization of reconfigurable intelligent surfaces (RIS), which can reflect or refract signals as required. This integration offers significant potential to improve the coverage area from the sender to the receive

Ruchi Mishra, Ronaldo S. S. Vieira, Włodek Kluźniak

In general relativity, the gravitational field of an electrically charged, non-rotating, spherically symmetric body is described by the Reissner-Nordstr\"om (RN) metric. Depending on the charge to mass ratio, the solution describes a black hole or a naked singularity. In the naked-singularity regime, a general property of this metric is the existence of a ra

Peizhong Ju, Yingbin Liang, Ness B. Shroff

Meta-learning has arisen as a successful method for improving training performance by training over many similar tasks, especially with deep neural networks (DNNs). However, the theoretical understanding of when and why overparameterized models such as DNNs can generalize well in meta-learning is still limited. As an initial step towards addressing this chal

Johno van IJsseldijk, Hadi Hajibeygi, Kees Wapenaar

Reservoir simulations for subsurface processes play an important role in successful deployment of geoscience applications such as geothermal energy extraction and geo-storage of fluids. These simulators provide time-laps dynamics of the coupled poro-mechanical processes within the reservoir and its over-, under-, and side-burden environments. For more reliab

Owen Moulding, Samuel Gallego-Parra, Yingzheng Gao, Pierre Toulemonde

In recent years, there has been a fervent search for room-temperature superconductivity within the binary hydrides. However, as the number of untested compounds dwindled, it became natural to begin searching within the ternary hydrides. This led to the controversial discovery of room-temperature superconductivity at only 1GPa in nitrogen-doped lutetium hydri

Maxim Vidgof, Stefan Bachhofner, Jan Mendling

Large language models are deep learning models with a large number of parameters. The models made noticeable progress on a large number of tasks, and as a consequence allowing them to serve as valuable and versatile tools for a diverse range of applications. Their capabilities also offer opportunities for business process management, however, these opportuni

Dimitris Bertsimas, Leonard Boussioux

Accurate time series forecasting is critical for a wide range of problems with temporal data. Ensemble modeling is a well-established technique for leveraging multiple predictive models to increase accuracy and robustness, as the performance of a single predictor can be highly variable due to shifts in the underlying data distribution. This paper proposes a

Elizaveta Semenova, Prakhar Verma, Max Cairney-Leeming, Arno Solin

Recent advances have shown that GP priors, or their finite realisations, can be encoded using deep generative models such as variational autoencoders (VAEs). These learned generators can serve as drop-in replacements for the original priors during MCMC inference. While this approach enables efficient inference, it loses information about the hyperparameters

Tae Wook Kim, Quan Tan

Text-generating AI technology has the potential to revolutionize writing education. However, current AI writing-support tools are limited to providing linear feedback to users. In this work, we demonstrate how text-generating AI can be repurposed into a thought-provoking writing tutor with the addition of recursive feedback mechanisms. Concretely, we develop

Josh Buckley, Yasemin Ozkan-Aydin

A distinctive feature of quadrupeds that is integral to their locomotion is the tail. Tails serve many purposes in biological systems including propulsion, counterbalance, and stabilization while walking, running, climbing, or jumping. Similarly, tails in legged robots may augment the stability and maneuverability of legged robots by providing an additional

E. Aydi, L. Chomiuk, J. Mikołajewska, J. Brink

We present early spectral observations of the very slow Galactic nova Gaia22alz, over its gradual rise to peak brightness that lasted 180 days. During the first 50 days, when the nova was only 3--4 magnitudes above its normal brightness, the spectra showed narrow (FWHM $\approx$ 400 km s$^{-1}$) emission lines of H Balmer, He I, He II, and C IV, but no P Cyg

Daniil Nozdriukhin, Sandeep Kumar Kalva, Cagla Özsoy, Michael Reiss

Major biological discoveries have been made by interrogating living organisms with light. However, the limited penetration of unscattered photons within biological tissues severely limits the depth range covered by optical methods. Deep-tissue imaging has been achieved by combining light and ultrasound. Optoacoustic imaging uniquely exploits optical generati

Sean Even, Tongjia Zheng, Hai Lin, Yasemin Ozkan-Aydin

Soft actuators offer compliant and safe interaction with an unstructured environment compared to their rigid counterparts. However, control of these systems is often challenging because they are inherently under-actuated, have infinite degrees of freedom (DoF), and their mechanical properties can change by unknown external loads. Existing works mainly relied

Alexander B. Watson, Dionisios Margetis, Mitchell Luskin

In this expository article, we present a systematic formal derivation of the Kubo formula for the linear-response current due to a time-harmonic electric field applied to non-interacting, spinless charged particles in a finite volume in the quantum setting. We model dissipation in a transparent way by assuming a sequence of scattering events occurring at ran

Xiao Xiong, Xinyu Zhang, Huanhao Huang, Kangyao Huang

The concept of aerial-aquatic robots has emerged as an innovative solution that can operate both in the air and underwater. Previous research on the design of such robots has been mainly focused on mature technologies such as fixed-wing and multi-rotor aircraft. Flying fish, a unique aerial-aquatic animal that can both swim in water and glide over the sea su

Sean Even, Yasemin Ozkan-Aydin

This paper presents a soft earthworm robot that is capable of both efficient locomotion and obstacle avoidance. The robot is designed to replicate the unique locomotion mechanisms of earthworms, which enable them to move through narrow and complex environments with ease. The robot consists of multiple segments, each with its own set of actuators, that are co

Yihong Ma, Yijun Tian, Nuno Moniz, Nitesh V. Chawla

The rapid advancement in data-driven research has increased the demand for effective graph data analysis. However, real-world data often exhibits class imbalance, leading to poor performance of machine learning models. To overcome this challenge, class-imbalanced learning on graphs (CILG) has emerged as a promising solution that combines the strengths of gra

Nnamdi Chikere, Yasemin Ozkan-Aydin

Microorganisms such as algae and bacteria move in a viscous environment with extremely low Reynolds ($Re$), where the viscous drag dominates the inertial forces. They have adapted to this environment by developing specialized features such as whole-body deformations and flexible structures such as flagella (with various shapes, sizes, and numbers) that break

Guangyi Chen, Zhenhao Chen, Shunxing Fan, Kun Zhang

The indeterminate nature of human motion requires trajectory prediction systems to use a probabilistic model to formulate the multi-modality phenomenon and infer a finite set of future trajectories. However, the inference processes of most existing methods rely on Monte Carlo random sampling, which is insufficient to cover the realistic paths with finite sam

Anakha V Babu, Tekin Bicer, Saugat Kandel, Tao Zhou

We present an end-to-end automated workflow that uses large-scale remote compute resources and an embedded GPU platform at the edge to enable AI/ML-accelerated real-time analysis of data collected for x-ray ptychography. Ptychography is a lensless method that is being used to image samples through a simultaneous numerical inversion of a large number of diffr

Édouard Bonnet, Romain Bourneuf, Julien Duron, Colin Geniet

We construct a hereditary class of triangle-free graphs with unbounded chromatic number, in which every non-trivial graph either contains a pair of non-adjacent twins or has an edgeless vertex cutset of size at most two. This answers in the negative a question of Chudnovsky, Penev, Scott, and Trotignon. The class is the hereditary closure of a family of (tri

Yi-Han Luo, Baoqi Shi, Wei Sun, Ruiyang Chen

The analysis of optical spectra - emission or absorption -- has been arguably the most powerful approach for discovering and understanding matters. The invention and development of many kinds of spectrometers have equipped us with versatile yet ultra-sensitive diagnostic tools for trace gas detection, isotope analysis, and resolving hyperfine structures of a

Venkatraman Gopalan

Pulli Kolam is an ancient mathematical artform that is still practiced today in south India by over a quarter million people. "Pulli" in the Tamil language means dots. A specific type of pulli kolam is "sikku" kolam where a series of dots are placed using rice flour and lines are drawn around them with three simple rules: all dots are individually encircled,

T. Yontan, S. Bilir, H. Cakmak, M. Raul

This paper presents photometric, astrometric, and kinematic analyses of the open clusters NGC 189, NGC 1758 and NGC 7762 based on CCD UBV photometric and Gaia Data Release 3 (DR3) data. According to membership analyses, we identified 32, 57 and 106 most probable member stars with membership probabilities $P\geq 0.5$ in NGC 189, NGC 1758 and NGC 7762, respect

Abu Nayem Md. Asraf Siddiquee, Benjamin Colfer, Yasemin Ozkan-Aydin

Soft robots have the ability to adapt to their environment, which makes them suitable for use in disaster areas and agricultural fields, where their mobility is constrained by complex terrain. One of the main challenges in developing soft terrestrial robots is that the robot must be soft enough to adapt to its environment, but also rigid enough to exert the

Mate Soos, Randal E. Bryant

Traditional Boolean satisfiability (SAT) solvers based on the conflict-driven clause-learning (CDCL) framework fare poorly on formulas involving large numbers of parity constraints. The CryptoMiniSat solver augments CDCL with Gauss-Jordan elimination to greatly improve performance on these formulas. Integrating the TBUDDY proof-generating BDD library into Cr

Ivan Ferreira-Chacua, Ardiansyah Koeshidayatullah

Micropaleontology in geosciences focuses on studying the evolution of microfossils (e.g., foraminifera) through geological records to reconstruct past environmental and climatic conditions. This field heavily relies on visual recognition of microfossil features, making it suitable for computer vision technology, specifically deep convolutional neural network

David Fuentes, Diana McSpadden, Sodiq Adewole

In this paper, we propose a distributed Generative Adversarial Networks (discGANs) to generate synthetic tabular data specific to the healthcare domain. While using GANs to generate images has been well studied, little to no attention has been given to generation of tabular data. Modeling distributions of discrete and continuous tabular data is a non-trivial

Andrea Ottolini, Stefan Steinerberger

We consider Erd\H{o}s-R\'enyi graphs $G(n,p)$ for $0 < p < 1$ fixed and $n \rightarrow \infty$ and study the expected number of steps, $H_{wv}$, that a random walk started in $w$ needs to first arrive in $v$. A natural guess is that an Erd\H{o}s-R\'enyi random graph is so homogeneous that it does not really distinguish between vertices and $H_{wv} = (1+o(1))

Anita Arora, Hiranya Kishore Dey, Shivani Goel

The enhanced power graph of a group $G$ is the graph $\mathcal{G}_E(G)$ with vertex set $G$ and edge set $ \{(u,v): u, v \in \langle w \rangle,~\mbox{for some}~ w \in G\}$. In this paper, we compute the spectrum of the distance matrix of the enhanced power graph of non-abelian groups of order $pq$, dihedral groups, dicyclic groups, elementary abelian groups

Yilber Fabian Bautista

Using the recently derived higher spin gravitational Compton amplitude from low-energy analytically continued ($a/Gm\gg1$) solutions of the Teukolsky equation for the scattering of a gravitational wave off the Kerr black hole, observables for non-radiating super-extremal Kerr binary systems at second post-Minkowskian (PM) order and up to sixth order in spin

Padmalochan Panda, Soumyadip Chatterjee, Siddharth Tallur, Apurba Laha

This work reports on the fabrication and characterization of an Au/ZnO/Pt-based high-overtone bulk acoustic resonator (HBAR) on SiC substrates. We evaluate its microwave characteristics comparing with Si substrates for micro-electromechanical applications. Dielectric magnetron sputtering and an electron beam evaporator are employed to develop highly c-axis-o

N. W. Borsato, H. J. Hoeijmakers, B. Prinoth, B. Thorsbro

Cross-correlation spectroscopy is an invaluable tool in the study of exoplanets. However, aliasing between spectral lines makes it vulnerable to systematic biases. This work strives to constrain the aliases of the cross-correlation function to provide increased confidence in the detections of elements in the atmospheres of ultra-hot Jupiters (UHJs) observed

Andrew Clarke, Viviana del Barco, Andrés J. Moreno

We study the G$_2$-instanton condition for a family of metric connections arisen from the characteristic connection, on $7$-dimensional $2$-step nilpotent Lie groups with left-invariant coclosed G$_2$-structures. According to the dimension of the commutator subgroup, we establish necessary and sufficient conditions for the connection to be an instanton, in t

Anna Puecher, Anuradha Samajdar, Tim Dietrich

Over the last few years, there has been a large momentum to ensure that the third-generation era of gravitational wave detectors will find its realisation in the next decades, and numerous design studies have been ongoing for some time. Some of the main factors determining the cost of the Einstein Telescope lie in the length of the interferometer arms and it

Chaitanya Afle, Suman Kumar Kundu, Jenna Cammerino, Eric R Coughlin

Core-collapse supernovae are among the astrophysical sources of gravitational waves that could be detected by third-generation gravitational-wave detectors. Here, we analyze the gravitational-wave strain signals from two- and three-dimensional simulations of core-collapse supernovae generated using the code F{\sc{ornax}}. A subset of the two-dimensional simu

Sarah A. Toonsi, Jeff S. Shamma

The framework of multi-agent learning explores the dynamics of how individual agent strategies evolve in response to the evolving strategies of other agents. Of particular interest is whether or not agent strategies converge to well known solution concepts such as Nash Equilibrium (NE). Most "fixed order" learning dynamics restrict an agent's underlying stat

Tommaso Favalli, Augusto Smerzi

We study the dynamical evolution of two quantum clocks interacting with a relativistic gravitational potential. We find a time dilation effect for the clocks in agreement with the gravitational time dilation as obtained from the Schwarzschild solution in General Relativity. We perform our investigation via the Page and Wootters quantum time formalism, explor

Hicham Mangach, Younes Achaoui, Muamer Kadic, Abdenbi Bouzid

The recent emergence of chirality in mechanical metamaterials has revolutionized the field, enabling achievements in wave propagation and polarization control. Despite being an intrinsic feature of some molecules and ubiquitous in our surroundings, the incorporation of chirality into mechanical systems has only gained widespread recognition in the last few y

Yanis Labrak, Adrien Bazoge, Richard Dufour, Mickael Rouvier

This paper introduces FrenchMedMCQA, the first publicly available Multiple-Choice Question Answering (MCQA) dataset in French for medical domain. It is composed of 3,105 questions taken from real exams of the French medical specialization diploma in pharmacy, mixing single and multiple answers. Each instance of the dataset contains an identifier, a question,

Nicolas Lenner, Matthias Häring, Stephan Eule, Jörg Großhans

The dynamics of living systems often serves the purpose of reaching functionally important target states. We previously proposed a theory to analyze stochastic biological dynamics evolving towards target states in reverse time. However, a large class of systems in biology can only be adequately described using state-dependent noise, which had not been discus

Erik Sandström, Yue Li, Luc Van Gool, Martin R. Oswald

We propose a dense neural simultaneous localization and mapping (SLAM) approach for monocular RGBD input which anchors the features of a neural scene representation in a point cloud that is iteratively generated in an input-dependent data-driven manner. We demonstrate that both tracking and mapping can be performed with the same point-based neural scene repr

Joshua Fagin, Ji Won Park, Henry Best, James Hung-Hsu Chan

Quasars are bright and unobscured active galactic nuclei (AGN) thought to be powered by the accretion of matter around supermassive black holes at the centers of galaxies. The temporal variability of a quasar's brightness contains valuable information about its physical properties. The UV/optical variability is thought to be a stochastic process, often repre

Yehonatan Fridman, Guy Tamir, Gal Oren

Over the last decade, most of the increase in computing power has been gained by advances in accelerated many-core architectures, mainly in the form of GPGPUs. While accelerators achieve phenomenal performances in various computing tasks, their utilization requires code adaptations and transformations. Thus, OpenMP, the most common standard for multi-threadi

Karan Aggarwal, Jaideep Srivastava

Missing data in time series is a challenging issue affecting time series analysis. Missing data occurs due to problems like data drops or sensor malfunctioning. Imputation methods are used to fill in these values, with quality of imputation having a significant impact on downstream tasks like classification. In this work, we propose a semi-supervised imputat

Simon Morelli, Marcus Huber, Armin Tavakoli

We introduce two families of criteria for detecting and quantifying the entanglement of a bipartite quantum state of arbitrary local dimension. The first is based on measurements in mutually unbiased bases and the second is based on equiangular measurements. Both criteria give a qualitative result in terms of the state's entanglement dimension and a quantita

Prithila Angkan, Behnam Behinaein, Zunayed Mahmud, Anubhav Bhatti

Through this paper, we introduce a novel driver cognitive load assessment dataset, CL-Drive, which contains Electroencephalogram (EEG) signals along with other physiological signals such as Electrocardiography (ECG) and Electrodermal Activity (EDA) as well as eye tracking data. The data was collected from 21 subjects while driving in an immersive vehicle sim

M. Yu. Melnikov, A. A. Shakirov, A. A. Shashkin, S. -H. Huang

The effective mass at the Fermi level is measured in the strongly interacting two-dimensional (2D) electron system in ultra-clean SiGe/Si/SiGe quantum wells in the low-temperature limit in tilted magnetic fields. At low electron densities, the effective mass is found to be strongly enhanced and independent of the degree of spin polarization, which indicates

Karan Aggarwal, Jaideep Srivastava

Labeling time series data is an expensive task because of domain expertise and dynamic nature of the data. Hence, we often have to deal with limited labeled data settings. Data augmentation techniques have been successfully deployed in domains like computer vision to exploit the use of existing labeled data. We adapt one of the most commonly used technique c

Yuqing Wang, Yun Zhao, Linda Petzold

Large language models (LLMs) have made significant progress in various domains, including healthcare. However, the specialized nature of clinical language understanding tasks presents unique challenges and limitations that warrant further investigation. In this study, we conduct a comprehensive evaluation of state-of-the-art LLMs, namely GPT-3.5, GPT-4, and

Luting Xu, Jing Bai, Wei Feng, Xin-Qi Li

In this work we perform real time simulations for the dynamics of braiding a pair of Majorana zero modes (MZMs) through a quantum dot in a minimal setup of pure 1D realization. We reveal the strong nonadiabatic effect when the dot energy level approaches to zero in order to achieve a geometric phase $\pi/4$ which is required for a full exchange between the M

Xuan Ju, Ailing Zeng, Chenchen Zhao, Jianan Wang

Controllable human image generation (HIG) has numerous real-life applications. State-of-the-art solutions, such as ControlNet and T2I-Adapter, introduce an additional learnable branch on top of the frozen pre-trained stable diffusion (SD) model, which can enforce various conditions, including skeleton guidance of HIG. While such a plug-and-play approach is a

Megha H. Tippur, Edward H. Adelson

Camera-based tactile sensors have shown great promise in enhancing a robot's ability to perform a variety of dexterous manipulation tasks. Advantages of their use can be attributed to the high resolution tactile data and 3D depth map reconstructions they can provide. Unfortunately, many of these tactile sensors use either a flat sensing surface, sense on onl

Zhuoran Xiao, Zhaoyang Zhang, Zirui Chen, Zhaohui Yang

In this paper, we propose an innovative learning-based channel prediction scheme so as to achieve higher prediction accuracy and reduce the requirements of huge amounts and strict sequential format of channel data. Inspired by the idea of the neural ordinary differential equation (Neural ODE), we first prove that the channel prediction problem can be modeled

Mohammad Sadeghi, Karam Bahari, Kayoomars Karami

Observations show that there are twisted magnetic flux tubes and plasma flow throughout the solar atmosphere. The main purpose of this work is to obtain the damping rate of sausage modes in the presence of magnetic twist and plasma flow. We obtain the dispersion relation for sausage modes in slow continuity in an inhomogeneous layer under the conditions of m

John Wrenn, Anjali Pal, Alexa VanHattum, Shriram Krishnamurthi

The R programming language is widely used in large-scale data analyses. It contains especially rich built-in support for dealing with vectors, arrays, and matrices. These operations feature prominently in the applications that form R's raison d'\^etre, making their behavior worth understanding. Furthermore, ostensibly for programmer convenience, their behavi

Yang Luo, Xiqing Guo, Mingtao Dong, Jin Yu

RGB-T tracking involves the use of images from both visible and thermal modalities. The primary objective is to adaptively leverage the relatively dominant modality in varying conditions to achieve more robust tracking compared to single-modality tracking. An RGB-T tracker based on mixed attention mechanism to achieve complementary fusion of modalities (refe

Shangxiong Huangfu, Zurab Guguchia, Tian Shang, Hai Lin

Neodymium nickelates have attracted research interest due to their strongly correlated behaviour and remarkable magnetic properties. More importantly, superconductivity has recently been confirmed in thin-film samples of Sr-doped NdNiO2, bringing the layered rare earth nickel oxides into the research spotlight. In this report, we present results on a series

Weijian Luo

Diffusion Models (DMs), also referred to as score-based diffusion models, utilize neural networks to specify score functions. Unlike most other probabilistic models, DMs directly model the score functions, which makes them more flexible to parametrize and potentially highly expressive for probabilistic modeling. DMs can learn fine-grained knowledge, i.e., ma

Sudip Sinha, Sayak Biswas, L. Santos, S. Sinha

Recently created self-bound quantum droplets of binary Bose mixtures open intriguing possibilities for the study of impurity physics. We show that the properties of impurities embedded in quasi-one-dimensional droplets are determined by the interplay between back-action and quantum fluctuations. Due to such back-action, repulsive impurities may form a metast

Dexuan Zhou, Huajie Chen, Cheuk Hin Ho, Christoph Ortner

The atomic cluster expansion (ACE) (Drautz, 2019) yields a highly efficient and intepretable parameterisation of symmetric polynomials that has achieved great success in modelling properties of many-particle systems. In the present work we extend the practical applicability of the ACE framework to the computation of many-electron wave functions. To that end,

Amir Nazemi, Zeyad Moustafa, Paul Fieguth

Continual learning in real-world scenarios is a major challenge. A general continual learning model should have a constant memory size and no predefined task boundaries, as is the case in semi-supervised Video Object Segmentation (VOS), where continual learning challenges particularly present themselves in working on long video sequences. In this article, we

Jiachen T. Wang, Ruoxi Jia

Data valuation is a growing research field that studies the influence of individual data points for machine learning (ML) models. Data Shapley, inspired by cooperative game theory and economics, is an effective method for data valuation. However, it is well-known that the Shapley value (SV) can be computationally expensive. Fortunately, Jia et al. (2019) sho

Qiangqiang Gu, Shishir Kumar Pandey, Yihao Lin

In this computational study, we explore a viable route to access the Kitaev-Quantum Spin Liquid (QSL) state in recently synthesized monolayer of a so-called spin-orbit assisted Mott insulator OsCl$_3$. In addition to other magnetic ground states in different regions, the small $J_\text{H}$/$U$ region of our Hubbard $U$--Hund's $J_\text{H}$ quantum phase diag

Wenbo Pan, Qiguang Chen, Xiao Xu, Wanxiang Che

Zero-shot dialogue understanding aims to enable dialogue to track the user's needs without any training data, which has gained increasing attention. In this work, we investigate the understanding ability of ChatGPT for zero-shot dialogue understanding tasks including spoken language understanding (SLU) and dialogue state tracking (DST). Experimental results

Kamran Karimi, Stefanos Papanikolaou

Complex, concentrated, multi-component alloys have been shown to display outstanding thermo-mechanical properties, that have been typically attributed to sluggish diffusion, entropic, and lattice distortion effects. Here, we investigate two metal alloys with such exemplary properties, the equiatomic, single-phase, face-centered-cubic (FCC) alloys NiCoCr and

Nitesh Ghodichor, Raj Thaneeghavl., Dinesh Sahu, Gautam Borkar

MANET is a collection of mobile nodes that communicate through wireless networks as they move from one point to another. MANET is an infrastructure-less network with a changeable topology; as a result, it is very susceptible to attacks. MANET attack prevention represents a serious difficulty. Malicious network nodes are the source of network-based attacks. I

Robert Blaga, Delia Calinoiu, Marius Paulescu

The atmospheric aerosol loading may significantly influence the performance in solar power production. The impact can be very different both in space (even in short distance) and time (shortterm fluctuations as well as long-term trend). Aiming to ensure a high degree of generality, this study is focused on the aerosol impact on the collectable solar energy.