May 2023 arXiv papers — page 124

Ivan Butakov, Alexander Tolmachev, Sofia Malanchuk, Anna Neopryatnaya

The Information Bottleneck (IB) principle offers an information-theoretic framework for analyzing the training process of deep neural networks (DNNs). Its essence lies in tracking the dynamics of two mutual information (MI) values: between the hidden layer output and the DNN input/target. According to the hypothesis put forth by Shwartz-Ziv & Tishby (2017),

Bernhard A. Moser, Michael Lunglmayr

In spiking neural networks (SNN), at each node, an incoming sequence of weighted Dirac pulses is converted into an output sequence of weighted Dirac pulses by a leaky-integrate-and-fire (LIF) neuron model based on spike aggregation and thresholding. We show that this mapping can be understood as a quantization operator and state a corresponding formula for t

Kaushik Roy, Manas Gaur, Misagh Soltani, Vipula Rawte

Current Virtual Mental Health Assistants (VMHAs) provide counseling and suggestive care. They refrain from patient diagnostic assistance because they lack training in safety-constrained and specialized clinical process knowledge. In this work, we define Proknow as an ordered set of information that maps to evidence-based guidelines or categories of conceptua

Opal Issan, Pete Riley, Enrico Camporeale, Boris Kramer

The ambient solar wind plays a significant role in propagating interplanetary coronal mass ejections and is an important driver of space weather geomagnetic storms. A computationally efficient and widely used method to predict the ambient solar wind radial velocity near Earth involves coupling three models: Potential Field Source Surface, Wang-Sheeley-Arge (

Wen-Liang Li, D. L. Zhou

A nanodiamond with an embedded nitrogen-vacancy (NV) center is one of the experimental systems that can be coherently manipulated within current technologies. Entanglement between NV center electron spin and mechanical rotation of the nanodiamond plays a fundamental role in building a quantum network connecting these microscopic and mesoscopic degrees of mot

D. Osin

Let $\mathcal G$ denote the space of finitely generated marked groups. For any finitely generated group $G$, we construct a continuous, injective map $f$ from the space of subgroups $Sub(G)$ to $\mathcal G$ that sends conjugate subgroups to isomorphic marked groups; in addition, if $G$ is finitely presented and $H\le G$ is finitely generated, then $f(H)$ is

Tsung-Han Wu, Luis Ledezma, Connor Fredrick, Pooja Sekhar

The introduction of nonlinear nanophotonic devices to the field of optical frequency comb metrology has enabled new opportunities for low-power and chip-integrated clocks, high-precision frequency synthesis, and broad bandwidth spectroscopy. However, most of these advances remain constrained to the near-infrared region of the spectrum, which has restricted t

Erik Derner, Kristina Batistič

The increasing popularity of large language models (LLMs) such as ChatGPT has led to growing concerns about their safety, security risks, and ethical implications. This paper aims to provide an overview of the different types of security risks associated with ChatGPT, including malicious text and code generation, private data disclosure, fraudulent services,

Ashraf Naderzadeh-ostad, Seyed Javad Akhtarshenas

The question of how fast a quantum state can evolve is considered. Using the definition of squared speed based on the Euclidean distance given in [Phys. Rev. Research, {\bf 2}, 033127 (2019)], we present a systematic framework to obtain the optimal speed of a $d$-dimensional system evolved unitarily under a time-independent Hamiltonian. Among the set of mixe

Byungkyun Kang, Yongbin Lee, Liqin Ke, Hyunsoo Kim

Intricate nature of magnetism in uranium-based Kondo lattices is a consequence of correlations between U-5$f$ and conduction electrons. Using linearized quasiparticle self-consistent GW plus dynamical mean-field theory, we demonstrate a crossover from incoherent to coherent $f$-$d$ Kondo cloud in the paramagnetic phase of UTe$_2$ with reduced volumes, USbTe

Syed Tariq Shah, Jaheon Gu, Syed Faraz Hasan, Min Young Chung

The resource allocation in SC-FDMA is constrained by the condition that multiple subchannels should be allocated to a single user only if they are adjacent. Therefore, the scheduling scheme of a D2D-cellular system that uses SC-FDMA must also conform to the so-called adjacency constraint. This paper proposes a heuristic algorithm with low computational compl

Zhao Song, Song Yue

Stochastic gradient descent (SGD) now acts as a fundamental part of optimization in current machine learning. Meanwhile, deep learning architectures have shown outstanding performance in a wide range of fields, such as natural language processing, bioinformatics, and computer vision. Nevertheless, as the parameter size $d$ increases, these models encounter s

Yingpeng Deng, Lina J. Karam

Learning-based image compression was shown to achieve a competitive performance with state-of-the-art transform-based codecs. This motivated the development of new learning-based visual compression standards such as JPEG-AI. Of particular interest to these emerging standards is the development of learning-based image compression systems targeting both humans

A. R. Olamaei, S. Rostami, K. Azizi

According to general understanding, the proton as one of the main ingredients of the nucleus is composed of one down and two up quarks bound together by gluons, described by Quantum Chromodynamics (QCD). In this view, heavy quarks do not contribute to the primary wave function of the proton. Heavy quarks arise in the proton perturbatively by gluon splitting

CMS Collaboration

A search for resonances in events with the $\gamma$+jet final state has been performed using proton-proton collision data collected at $\sqrt{s}$ = 13 TeV by the CMS experiment at the LHC. The total data analyzed correspond to an integrated luminosity of 138 fb$^{-1}$. Models of excited quarks and quantum black holes are considered. Using a wide-jet reconstr

Morgan Sandler, Arun Ross

The accuracy of automated speaker recognition is negatively impacted by change in emotions in a person's speech. In this paper, we hypothesize that speaker identity is composed of various vocal style factors that may be learned from unlabeled data and re-combined using a neural network to generate a holistic speaker identity representation for affective scen

Yuesheng Xu

This paper introduces a successive affine learning (SAL) model for constructing deep neural networks (DNNs). Traditionally, a DNN is built by solving a non-convex optimization problem. It is often challenging to solve such a problem numerically due to its non-convexity and having a large number of layers. To address this challenge, inspired by the human educ

Anqiao Li, Chenyu Yang, Jonas Frey, Joonho Lee

Mobile ground robots require perceiving and understanding their surrounding support surface to move around autonomously and safely. The support surface is commonly estimated based on exteroceptive depth measurements, e.g., from LiDARs. However, the measured depth fails to align with the true support surface in the presence of high grass or other penetrable v

Robert Rust

The Whitehead Model of free groups can be used to measure the complexity, or degree, of automorphisms of free groups. The bound for the degree of the $f \circ g$ for deg$(f) =$ deg($g) = 0$ had previously been discovered. We extend this result to the case where at least one of our automorphisms has degree 0.

Lin An, Andrew A. Li, Benjamin Moseley, R. Ravi

The classic newsvendor model yields an optimal decision for a ``newsvendor'' selecting a quantity of inventory, under the assumption that the demand is drawn from a known distribution. Motivated by applications such as cloud provisioning and staffing, we consider a setting in which newsvendor-type decisions must be made sequentially, in the face of demand dr

Dganit Hanania, Daniella Bar-Lev, Yevgeni Nogin, Yoav Shechtman

DNA labeling is a powerful tool in molecular biology and biotechnology that allows for the visualization, detection, and study of DNA at the molecular level. Under this paradigm, a DNA molecule is being labeled by specific k patterns and is then imaged. Then, the resulted image is modeled as a (k + 1)- ary sequence in which any non-zero symbol indicates on t

Abhinav Sagar, Sai Teja Gilukara

Sampling-based path planning algorithms play an important role in autonomous robotics. However, a common problem among these algorithms is that the initial path generated is not optimal, and the convergence is too slow for real-world applications. In this paper, we propose a novel image-based learning algorithm using a Convolutional Block Attention Generativ

Matúš Pikuliak, Ivan Srba, Robert Moro, Timo Hromadka

Fact-checkers are often hampered by the sheer amount of online content that needs to be fact-checked. NLP can help them by retrieving already existing fact-checks relevant to the content being investigated. This paper introduces a new multilingual dataset -- MultiClaim -- for previously fact-checked claim retrieval. We collected 28k posts in 27 languages fro

G. O. Heymans, N. F. Svaiter, G. Krein

Using results of statistical field theory for systems with an anisotropic disorder, we present an analog model for Euclidean wormholes and topological fluctuation effects in a Riemannian space $\mathcal{M}^\mathrm{d}$. The contribution of wormholes and topological fluctuations to the Euclidean gravitational functional integral is modeled by quenched randomne

Rolf Bergs

The study addresses the patterns of ground sealing for two different types of German cities during 2006 to 2018. By using urban scaling law and corresponding panel regressions, it can be shown that sealing of ground in the distribution of cities with own district administration is significantly stronger and more dynamic than in a corresponding distribution w

Haochen Tan, Han Wu, Wei Shao, Xinyun Zhang

Meetings typically involve multiple participants and lengthy conversations, resulting in redundant and trivial content. To overcome these challenges, we propose a two-step framework, Reconstruct before Summarize (RbS), for effective and efficient meeting summarization. RbS first leverages a self-supervised paradigm to annotate essential contents by reconstru

Ken Dykema, Amudhan Krishnaswamy-Usha

Results about angles between Haagerup--Schultz projections for DT-operators whose measures have atoms are proved, which in some cases imply that such operators are non-spectral. Several examples are considered.

Jinho Lim, Anupam Garg, John B. Ketterson

We simulate the magnetization dynamics of a permalloy spheroid of nanoscopic size in zero external field, such that both dipolar and exchange interactions are important. Low excitation power is used to obtain the frequencies and mode patterns of many normal modes. At higher power, non-linear three and four mode couplings between magnons carrying orbital angu

Cheng Wang, Linfeng Jiang, Chao Sun

Turbulent plane-Couette flow suspended with finite-size spheroidal particles is studied using fully particle-resolved direct numerical simulations. The effects of particle aspect ratio on turbulent arguments and particle statistics are explored, leading to the same conclusions as the previous experimental findings \citep{wang2022finite}. By performing stress

Deqing Fu, Ameya Godbole, Robin Jia

Detecting negatives (such as non-entailment relationships, unanswerable questions, and false claims) is an important and challenging aspect of many natural language understanding tasks. Though manually collecting challenging negative examples can help models detect them, it is both costly and domain-specific. In this work, we propose Self-labeled Counterfact

Lev Tauz, Lara Dolecek

In this paper, we present a novel variation of the coded matrix multiplication problem which we refer to as fully private grouped matrix multiplication (FPGMM). In FPGMM, a master wants to compute a group of matrix products between two matrix libraries that can be accessed by all workers while ensuring that any number of prescribed colluding workers learn no

Kung-Hsiang Huang, Hou Pong Chan, Heng Ji

Faithfully correcting factual errors is critical for maintaining the integrity of textual knowledge bases and preventing hallucinations in sequence-to-sequence models. Drawing on humans' ability to identify and correct factual errors, we present a zero-shot framework that formulates questions about input claims, looks for correct answers in the given evidenc

Blake McGrane-Corrigan, Oliver Mason, Rafael de Andrade Moral

The internal behaviour of a population is an important feature to take account of when modelling their dynamics. In line with kin selection theory, many social species tend to cluster into distinct groups in order to enhance their overall population fitness. Temporal interactions between populations are often modelled using classical mathematical models, but

Amin Akhavan

We define the state of minimum energy while the expectation values of the field operators and their time derivatives in a determined moment in such a state are constrained. As an axiom, we consider such a state as the background of the quantum field theory. As an example, we consider the scalar field with {\lambda}/4!{\Phi}4 interaction. To the third order o

Yun Guo, Xueyao Xiao, Xiaoxiong Wang, Yi Li

In this technical report, we briefly introduce the solution of our team HUST\li VIE for GT-Rain Challenge in CVPR 2023 UG$^{2}$+ Track 3. In this task, we propose an efficient two-stage framework to reconstruct a clear image from rainy frames. Firstly, a low-rank based video deraining method is utilized to generate pseudo GT, which fully takes the advantage

Silvia Noschese, Lothar Reichel

Let a network be represented by a simple graph $\mathcal{G}$ with $n$ vertices. A common approach to investigate properties of a network is to use the adjacency matrix $A=[a_{ij}]_{i,j=1}^n\in\R^{n\times n}$ associated with the graph $\mathcal{G}$, where $a_{ij}>0$ if there is an edge pointing from vertex $v_i$ to vertex $v_j$, and $a_{ij}=0$ otherwise. Both

Jarosław Wątróbski, Aleksandra Bączkiewicz, Iga Rudawska

The recent studies signify the growing concern of researchers towards monitoring and measuring sustainability performance at various levels and in many fields, including healthcare. However, there is no agreed approach to assessing the sustainability of health systems. Moreover, social indicators are less developed and less succinct. Therefore, the authors s

Mikhail M. Ivanov, Oliver H. E. Philcox

Galaxy surveys map the three-dimensional distribution of matter in the Universe, encoding information about both the primordial cosmos and its subsequent evolution. By comparing the angular and physical scales of features in the galaxy distribution, we can compute the physical distance to the sample, and thus extract the Hubble parameter, $H_0$. In this chap

Tanmay Kumar Sinha, Jayadev Naram, Pawan Kumar

Recent approaches to the tensor completion problem have often overlooked the nonnegative structure of the data. We consider the problem of learning a nonnegative low-rank tensor, and using duality theory, we propose a novel factorization of such tensors. The factorization decouples the nonnegative constraints from the low-rank constraints. The resulting prob

Nicusor Minculete, Diana Savin

The aim of this paper is to study certain properties of the Kullback-Leibler distance between two positive integer numbers or between two ideals. We present some results related the entropy of a positive integer number and the divergence of two numbers. We also study the entropy of some types of ideals and the divergence of two ideals. Finally, we find some

Ho Yiu Chung, Cihan Okay, Igor Sikora

A linear constraint system is specified by linear equations over the group $\ZZ_d$ of integers modulo $d$. Their operator solutions play an important role in the study of quantum contextuality and non-local games. In this paper, we use the theory of simplicial sets to develop a framework for studying operator solutions of linear systems. Our approach refines

Noah A. Crum, Leanto Sunny, Pooya Ronagh, Raymond Laflamme

Motivated by applications of quantum computers in Gibbs sampling from continuous real-valued functions, we ask whether such algorithms can provide practical advantages for machine learning models trained on classical data and seek measures for quantifying such impacts. In this study, we focus on deep energy-based models (EBM), as they require continuous-doma

Agam Shah, Suvan Paturi, Sudheer Chava

Monetary policy pronouncements by Federal Open Market Committee (FOMC) are a major driver of financial market returns. We construct the largest tokenized and annotated dataset of FOMC speeches, meeting minutes, and press conference transcripts in order to understand how monetary policy influences financial markets. In this study, we develop a novel task of h

Atsushi Suzuki, Atsushi Nitanda, Taiji Suzuki, Jing Wang

Recent studies have experimentally shown that we can achieve in non-Euclidean metric space effective and efficient graph embedding, which aims to obtain the vertices' representations reflecting the graph's structure in the metric space. Specifically, graph embedding in hyperbolic space has experimentally succeeded in embedding graphs with hierarchical-tree s

Steve Phelps, Yvan I. Russell

We investigated the capability of the GPT-3.5 large language model (LLM) to operationalize natural language descriptions of cooperative, competitive, altruistic, and self-interested behavior in two social dilemmas: the repeated Prisoners Dilemma and the one-shot Dictator Game. Using a within-subject experimental design, we used a prompt to describe the task

Yutian Chen, Hao Kang, Vivian Zhai, Liangze Li

This paper presents a novel approach for detecting ChatGPT-generated vs. human-written text using language models. To this end, we first collected and released a pre-processed dataset named OpenGPTText, which consists of rephrased content generated using ChatGPT. We then designed, implemented, and trained two different models for text classification, using R

Miguel A. Porras, Miguel Casado-Álvaro, Isabel Gonzalo

We report on the possibility of teleportation of a quantum particle, a distinctly different phenomenon from the teleportation of a quantum state through entanglement. With the first meaning, teleportation is theoretically possible by placing the particle initially at rest (with a certain uncertainty) out of any equilibrium point of a potential well or barrie

Jayadev Naram, Tanmay Kumar Sinha, Pawan Kumar

We consider the problem of learning low-rank tensors from partial observations with structural constraints, and propose a novel factorization of such tensors, which leads to a simpler optimization problem. The resulting problem is an optimization problem on manifolds. We develop first-order and second-order Riemannian optimization algorithms to solve it. The

Houcine Ben Dali, Maciej Dołęga

We give an explicit formula for the power-sum expansion of Jack polynomials. We deduce it from a more general formula, which we provide here, that interprets Jack characters in terms of bipartite maps. We prove Lassalle's conjecture from 2008 on integrality and positivity of Jack characters in Stanley's coordinates and give a new formula for Jack polynomials

Xinyi Luo, Yuyang Wang, Lik-Hang Lee, Zihan Xing

Virtual reality interview simulator (VRIS) provides an effective and manageable approach for candidates prone to being very nervous during interviews, yet, the major anxiety-inducing elements remain unknown. During an interview, the anxiety levels, overall experience, and performance of interviewees might be affected by various circumstances. By analyzing el

Diego Berti, Luca Bisconti, Davide Catania

We consider a 3D Tropical Climate Model with damping terms in the equation of the barotropic mode $u$ and in the equation of the first baroclinic mode $v$ of the velocity. The equation for the temperature $\theta$ is free from dampings. We prove global existence in time for this system assuming the initial data $(u_0, v_0,\theta_0)$ small, in terms of the ho

Eduardo Barredo-Alamilla, Luis F. Urrutia, Manoel M. Ferreira

Starting from the modified Maxwell equations in Carroll-Field-Jackiw electrodynamics we study the electromagnetic radiation in a chiral medium characterized by an axion coupling $\theta(x)=b_\mu x^\mu$, with $b_\mu= (0,\mathbf{b})$, which gives rise to the magnetoelectric effect. Employing the stationary phase approximation we construct the Green's matrix in

Constantin Runge, Thomas Wiegart, Diego Lentner

A modified successive cancellation list (SCL) decoder is proposed for polar-coded probabilistic shaping. The decoder exploits the deterministic encoding rule for shaping bits to rule out candidate code words that the encoder would not generate. This provides error detection and decreases error rates compared to standard SCL decoding while at the same time re

Luke Friedman, Sameer Ahuja, David Allen, Zhenning Tan

A Conversational Recommender System (CRS) offers increased transparency and control to users by enabling them to engage with the system through a real-time multi-turn dialogue. Recently, Large Language Models (LLMs) have exhibited an unprecedented ability to converse naturally and incorporate world knowledge and common-sense reasoning into language understan

Ozer Can Devecioglu, Serkan Kiranyaz, Amer Elhmes, Sadok Sassi

Automatic sensor-based detection of motor failures such as bearing faults is crucial for predictive maintenance in various industries. Numerous methodologies have been developed over the years to detect bearing faults. Despite the appearance of numerous different approaches for diagnosing faults in motors have been proposed, vibration-based methods have beco

Julien Bahimuzi

In this thesis, we investigated some properties of (left)near fields and derived some results. We are focusing on $D(\alpha, \beta)$ which is the generalized set of distributive elements of a nearfield. In particular, we investigated some conditions on $\alpha,\beta, \alpha+\beta$ for $D(\alpha, \beta)$ to be a subfield of $\mathbb{F}_{q^{n}}$. In nearfield

Tommaso Aldinucci

Given the increasing interest in interpretable machine learning, classification trees have again attracted the attention of the scientific community because of their glass-box structure. These models are usually built using greedy procedures, solving subproblems to find cuts in the feature space that minimize some impurity measures. In contrast to this stand

Patrick Wienhöft, Marnix Suilen, Thiago D. Simão, Clemens Dubslaff

In an offline reinforcement learning setting, the safe policy improvement (SPI) problem aims to improve the performance of a behavior policy according to which sample data has been generated. State-of-the-art approaches to SPI require a high number of samples to provide practical probabilistic guarantees on the improved policy's performance. We present a nov

Gabriel T. Landi

A continuously measured quantum system with multiple jump channels gives rise to a stochastic process described by random jump times and random emitted symbols, representing each jump channel. While much is known about the waiting time distributions, very little is known about the statistics of the emitted symbols. In this letter we fill in this gap. First,

Mark Sellke

We study the Langevin dynamics for spherical $p$-spin models, focusing on the short time regime described by the Cugliandolo-Kurchan equations. Confirming a prediction of [Cugliandolo-Kurchan, Phys. Rev. Lett. 1993], we show the asymptotic energy achieved is exactly $E_{\infty}(p)=2\sqrt{\frac{p-1}{p}}$ in the low temperature limit. The upper bound uses hard

H. S. Melihcan Erol, Erixhen Sula, Lizhong Zheng

We study the problem of estimating the joint probability mass function (pmf) over two random variables. In particular, the estimation is based on the observation of $m$ samples containing both variables and $n$ samples missing one fixed variable. We adopt the minimax framework with $l^p_p$ loss functions, and we show that the composition of uni-variate minim

Ayelet Heimowitz, Yosi Keller

This work presents an unsupervised and semi-automatic image segmentation approach where we formulate the segmentation as a inference problem based on unary and pairwise assignment probabilities computed using low-level image cues. The inference is solved via a probabilistic graph matching scheme, which allows rigorous incorporation of low level image cues an

Jitendra Kumar

The Ehrenfest paradox for a rotating ring is examined and a kinematic resolution, within the framework of the special theory of relativity, is presented. Two different ways by which a ring can be brought from rest to rotational motion, whether by keeping the rest lengths of the blocks constituting the ring constant or by keeping their lengths in the inertial

Yang Ai, Zhen-Hua Ling

This paper presents a novel neural vocoder named APNet which reconstructs speech waveforms from acoustic features by predicting amplitude and phase spectra directly. The APNet vocoder is composed of an amplitude spectrum predictor (ASP) and a phase spectrum predictor (PSP). The ASP is a residual convolution network which predicts frame-level log amplitude sp

Bo Zhou

This paper aims to address the issue of semiparametric efficiency for cointegration rank testing in finite-order vector autoregressive models, where the innovation distribution is considered an infinite-dimensional nuisance parameter. Our asymptotic analysis relies on Le Cam's theory of limit experiment, which in this context takes the form of Locally Asympt

Miles Everett, Mingjun Zhong, Georgios Leontidis

Capsule Networks, an extension to Neural Networks utilizing vector or matrix representations instead of scalars, were initially developed to create a dynamic parse tree where visual concepts evolve from parts to complete objects. Early implementations of Capsule Networks achieved and maintain state-of-the-art results on various datasets. However, recent stud

Daniel D. Spiegel

This thesis investigates parametrized quantum spin systems in the thermodynamic limit from a $C^*$-algebraic point of view. Our main physical result is the construction of a phase invariant for one-dimensional quantum spin chains parametrized by a topological space $X$. This invariant is constructed using $C^*$-algebraic techniques and takes values in degree

Sayantani Lahiri, Luciano Rezzolla

We here investigate bulk-viscosity driven quasi de-Sitter inflation, that is, the period of accelerated expansion in the early universe during which $-\dot{H}\ll H^2$, with $H(t)$ being the Hubble expansion rate. We do so in the framework of a causal theory of relativistic hydrodynamics that takes into account non-equilibrium effects associated to bulk visco

Mochama Victor Samuel, Calford Odhiambo Otieno

We report results on the ab-initio study of the mechanical, electronic and structural properties of the iron Pnictide compound CaFe_2 As_2 at zero pressure. Ground State energy computation was done within the Density Functional Theory (DFT) using the Projector Augmented Wave (PAW) Pseudo Potentials and the Plane Wave (PW) basis set. The Generalized Gradient

Jacob Beal

Flow cytometry is a powerful quantitative assay supporting high-throughput collection of single-cell data with a high dynamic range. For flow cytometry to yield reproducible data with a quantitative relationship to the underlying biology, however, requires that 1) appropriate process controls are collected along with experimental samples, 2) these process co

Richmond Opoku-Sarkodie, Ferenc A. Bartha, Mónika Polner, Gergely Röst

We consider the SIRWJS epidemiological model that includes the waning and boosting of immunity via secondary infections. We carry out combined analytical and numerical investigations of the dynamics. The formulae describing the existence and stability of equilibria are derived. Combining this analysis with numerical continuation techniques, we construct glob

Irina N. Gracheva, Margarita A. Sadovnikova, Fadis F. Murzakhanov, Georgy V. Mamin

Optically active point defects in semiconductors have received great attention in the field of solid-state quantum technologies. Hexagonal boron nitride, with an ultra-wide band gap E_g = 6 eV, containing a negatively charged boron vacancy (V_B^-) with unique spin, optical, and coherent properties presents a new two-dimensional platform for the implementatio

Fangxun Zhong, Yun-Hui Liu

Agile maneuvers are essential for robot-enabled complex tasks such as surgical procedures. Prior explorations on surgery autonomy are limited to feasibility study of completing a single task without systematically addressing generic manipulation safety across different tasks. We present an integrated planning and control framework for 6-DoF robotic instrumen

Minsig Han, Ameha T. Abebe, Chung G. Kang

This letter proposes a deep learning-based data-aided active user detection network (D-AUDN) for grant-free sparse code multiple access (SCMA) systems that leverages both SCMA codebook and Zadoff-Chu preamble for activity detection. Due to disparate data and preamble distribution as well as codebook collision, existing D-AUDNs experience performance degradat

Jericho McLeod, Unchitta Kan, Eduardo López

People interact face-to-face on a frequent basis if (i) they live nearby and (ii) make the choice to meet. The first constitutes an availability of social ties; the second a propensity to interact with those ties. Despite being distinct social processes, most large-scale human interaction studies overlook these separate influences. Here, we study trends of i

Xinyu Lin, Yingjie Zhou, Xun Zhang, Yipeng Liu

Binary feature descriptors have been widely used in various visual measurement tasks, particularly those with limited computing resources and storage capacities. Existing binary descriptors may not perform well for long-term visual measurement tasks due to their sensitivity to illumination variations. It can be observed that when image illumination changes d

Xinxu Zhang, Jiahao Wei, Hui Jia, Jiamin Liu

In the ball-stick model, interatomic cohesions are considered "sticks". But enormous details and features of the "sticks" are usually oversimplified as indexed quantities or equivocated as geometry characteristics. These indexed quantities or geometry characteristics not only limit the explanatory capability to a few chemical/physical aspects but also elimin

A. Deur

We comment on the methods and the conclusion of Ref. [1], "Does gravitational confinement sustain flat galactic rotation curves without dark matter?" The article employs two methods to investigate whether non-perturbative corrections from General Relativity are important for galactic rotation curves, and concludes that they are not. This contradicts a series

Ling Liu1, Shaofang Zhang, Xinzhu Chen, Guo Li

Although developing artificial enzymes has made great progress, there is still a gap between artificial enzymes and natural enzymes in catalytic performance. Designing and constructing efficient artificial biocatalysts is extremely desirable because of their high stability, low cost and easy storage. Here, we report a synthesized amino-functionalized graphen

Xiaofei Guan, Boya Hu, Shipeng Mao, Xintong Wang

Designing efficient and high-accuracy numerical methods for complex dynamic incompressible magnetohydrodynamics (MHD) equations remains a challenging problem in various analysis and design tasks. This is mainly due to the nonlinear coupling of the magnetic and velocity fields occurring with convection and Lorentz forces, and multiple physical constraints, wh

Julio Arrechea, Carlos Barceló

We show that the repulsive effects associated to the zero-point energies of quantum fields are capable of supporting ultracompact stars that overcome the compactness limits present in general relativity for any object in hydrostatic equilibrium. These objects are exact self-consistent solutions in semiclassical gravity that incorporate the backreaction of th

Wenbo Li, Shiping Liu

We develop a systematical way of constructing S-Ricci flat graphs which are not Abelian Cayley via graph bundle with explicit examples. For this purpose, we prove that, with some natural constrains, a non-trivial graph bundle can not be isomorphic (as graphs) to the product of the base graph and fiber graph. It stands in clear contrast to the continuous case

Lu Yin, Joby Kochappan, Tuhin Ghosh, Bum-Hoon Lee

Exciting clues to isotropic cosmic birefringence have recently been detected in the $EB$ cross-power spectra of the polarization data of the cosmic microwave background (CMB). Early Dark Energy (EDE) models with a pseudoscalar field coupled to photons via a Chern-Simons term can be used to explain this phenomenon, and can also potentially be used to simultan

Chao-Jiang Xu, Yan Xu

We study the Cauchy problem of the fractional Kramers-Fokker- Planck equation and show that the solution to the Cauchy problem enjoys an analytic Gelfand-Shilov regularizing effect for positive time.

Yuang Chen, Hancheng Lu, Langtian Qin, Chang Wu

Millimeter-wave(mmWave) technology has emerged as a promising enabler for unleashing the full potential of 360-degree virtual reality (VR). However, the explosive growth of VR services, coupled with the reliability issues of mmWave communications, poses enormous challenges in terms of wireless resource and quality-of-service (QoS) provisioning for mmWave-ena

Shreyansh Padarha

This article discusses the risks and complexities associated with the exponential rise in data and the misuse of data by large corporations. The article presents instances of data breaches and data harvesting practices that violate user privacy. It also explores the concept of "Weapons Of Math Destruction" (WMDs), which refers to big data models that perpetu

E. Schilirò, S. E. Panasci, A. M. Mio, G. Nicotra

In this paper, the atomic layer deposition (ALD) of ultra-thin films (<4 nm) of $Al_{2}O_{3}$ and $HfO_{2}$ on Au-supported monolayer (1L) $MoS_{2}$ is investigated, providing an insight on the nucleation mechanisms in the early stages of the ALD process. A preliminary multiscale characterization of large area 1L-$MoS_{2}$ exfoliated on sputter-grown Au/Ni f

Simone Bacchio

We show that an infinitesimal step of gradient flow can be used for defining a novel approach for computing gradients of physical observables with respect to action parameters. Compared to the commonly used perturbative expansion, this approach does not require calculating any disconnected contribution or vacuum expectation value and can provide results up t

Tian Gao, Cheng-Zhong Xu, Le Zhang, Hui Kong

Vision Transformer (ViT) has performed remarkably in various computer vision tasks. Nonetheless, affected by the massive amount of parameters, ViT usually suffers from serious overfitting problems with a relatively limited number of training samples. In addition, ViT generally demands heavy computing resources, which limit its deployment on resource-constrai

Haekyu Park, Gonzalo Ramos, Jina Suh, Christopher Meek

Re-finding information is an essential activity, however, it can be difficult when people struggle to express what they are looking for. Through a need-finding survey, we first seek opportunities for improving re-finding experiences, and explore one of these opportunities by implementing the FoundWright system. The system leverages recent advances in languag

Min Fang, Lile Wang, Gregory J. Herczeg, Jun Hashimoto

Disk winds are thought to play a critical role in the evolution and dispersal of protoplanetary disks. A primary diagnostic of this physics is emission from the wind, especially in the low-velocity component of the [O I] $\lambda6300$ line. However, the interpretation of the line is usually based on spectroscopy alone, which leads to confusion between magnet

Qianglong Chen, Feng Ji, Feng-Lin Li, Guohai Xu

Knowledge distillation is of key importance to launching multilingual pre-trained language models for real applications. To support cost-effective language inference in multilingual settings, we propose AMTSS, an adaptive multi-teacher single-student distillation framework, which allows distilling knowledge from multiple teachers to a single student. We firs

Chulun Zhou, Yunlong Liang, Fandong Meng, Jinan Xu

Multilingual vision-language (V&L) pre-training has achieved remarkable progress in learning universal representations across different modalities and languages. In spite of recent success, there still remain challenges limiting further improvements of V&L pre-trained models in multilingual settings. Particularly, current V&L pre-training methods rely heavil

Zhaojian Liang, Jingyi Wang, Liang An, Yang Wang

The intermittency of solar and wind power can be addressed by integrating them with Solid Oxide Cells (SOCs). This study delves into the transient characteristics of SOCs and their dependence on dynamic heat and mass transfer processes. Non-dimensional analysis was used to identify influential parameters, followed by a 3-D numerical simulation-based parametr

A. Blokh, L. Oversteegen, N. Selinger, V. Timorin

We describe a model $\mathcal{M}_3^{comb}$ for the boundary of the connectedness locus $\mathcal{M}^{sy}_3$ of the parameter space of cubic symmetric polynomials $p_c(z)=z^3-3c^2z$. We show that there exists a monotone continuous function $\pi:\partial \mathcal{M}_c^{sy}\to \mathcal{M}_3^{comb}$ which is a homeomorphism if $\mathcal{M}^{sy}_3$ is locally con

Basanta R. Pahari, Sagar Bhat, Siri Davidi, William Oates

The discovery of derivatives and integrals was a tremendous leap in scientific knowledge and completely revolutionized many fields, including mathematics, physics, and engineering. The existence of higher-order derivatives means better approximation and, thus, more accurate modeling of any physical phenomenon. Here we use smooth operators that are infinitely

Sadashige Matsuo, Takaya Imoto, Tomohiro Yokoyama, Yosuke Sato

Superconducting devices with broken time-reversal and spatial-inversion symmetries can exhibit novel superconducting phenomena. The observation of superconducting diode effects, which is applicable for dissipationless rectification, provides information on the breaking of such symmetries. We experimentally study a Josephson junction (JJ) coupled to another a

Yue Wang, Hung Le, Akhilesh Deepak Gotmare, Nghi D. Q. Bui

Large language models (LLMs) pretrained on vast source code have achieved prominent progress in code intelligence. However, existing code LLMs have two main limitations in terms of architecture and pretraining tasks. First, they often adopt a specific architecture (encoder-only or decoder-only) or rely on a unified encoder-decoder network for different downs

Muhammad Izzatullah, Matteo Ravasi, Tariq Alkhalifah

Full waveform inversion (FWI) enables us to obtain high-resolution velocity models of the subsurface. However, estimating the associated uncertainties in the process is not trivial. Commonly, uncertainty estimation is performed within the Bayesian framework through sampling algorithms to estimate the posterior distribution and identify the associated uncerta

Ke Zhang, Yan Yang, Jun Yu, Hanliang Jiang

In recent years, the growing demand for medical imaging diagnosis has placed a significant burden on radiologists. As a solution, Medical Vision-Language Pre-training (Med-VLP) methods have been proposed to learn universal representations from medical images and reports, benefiting downstream tasks without requiring fine-grained annotations. However, existin