April 2023 arXiv papers — page 151

Arindam Chowdhury, Gunjan Verma, Ananthram Swami, Santiago Segarra

We develop an efficient and near-optimal solution for beamforming in multi-user multiple-input-multiple-output single-hop wireless ad-hoc interference networks. Inspired by the weighted minimum mean squared error (WMMSE) method, a classical approach to solving this problem, and the principle of algorithm unfolding, we present unfolded WMMSE (UWMMSE) for MU-M

Jiawei Zhang, Tiantian Wang, Zhixi Feng, Shuyuan Yang

Automatic modulation classification (AMC) is a crucial stage in the spectrum management, signal monitoring, and control of wireless communication systems. The accurate classification of the modulation format plays a vital role in the subsequent decoding of the transmitted data. End-to-end deep learning methods have been recently applied to AMC, outperforming

Yassine Laguel, Necdet Serhat Aybat, Mert Gürbüzbalaban

We consider stochastic strongly-convex-strongly-concave (SCSC) saddle point (SP) problems which frequently arise in applications ranging from distributionally robust learning to game theory and fairness in machine learning. We focus on the recently developed stochastic accelerated primal-dual algorithm (SAPD), which admits optimal complexity in several setti

Takao Komatsu, Shanta Laishram, Pooja Punyani

For a nonnegative integer $p$, we give explicit formulas for the $p$-Frobenius number and the $p$-genus of generalized Fibonacci numerical semigroups. Here, the $p$-numerical semigroup $S_p$ is defined as the set of integers whose nonnegative integral linear combinations of given positive integers $a_1,a_2,\dots,a_k$ are expressed more than $p$ ways. When $p

Chunli Jiang, Abdullah Nazir, Ghasem Abbasnejad, Jungwon Seo

This paper presents the technique of flex-and-flip manipulation. It is suitable for grasping thin, flexible linear objects lying on a flat surface. During the manipulation process, the object is first flexed by a robotic gripper whose fingers are placed on top of it, and later the increased internal energy of the object helps the gripper obtain a stable pinc

Shuheng Zhou, Kristjan Greenewald

Many modern datasets exhibit dependencies among observations as well as variables. A decade ago, Kalaitzis et. al. (2013) proposed the Bigraphical Lasso, an estimator for precision matrices of matrix-normals based on the Cartesian product of graphs; they observed that the associativity of the Kronecker sum yields an approach to the modeling of datasets organ

Songjie Yang, Chenfei Xie, Wanting Lyu, Boyu Ning

Near-field communications present new opportunities over near-field channels, however, the spherical wavefront propagation makes near-field signal processing challenging. In this context, this paper proposes efficient near-field channel estimation methods for wideband MIMO mmWave systems with the aid of extremely large-scale reconfigurable intelligent surfac

Rebecca Salles, Janio Lima, Michel Reis, Rafaelli Coutinho

Time series event detection methods are evaluated mainly by standard classification metrics that focus solely on detection accuracy. However, inaccuracy in detecting an event can often result from its preceding or delayed effects reflected in neighboring detections. These detections are valuable to trigger necessary actions or help mitigate unwelcome consequ

Jingcheng Lu, Eitan Tadmor

Slope limiters play an essential role in maintaining the non-oscillatory behavior of high-resolution methods for nonlinear conservation laws. The family of minmod limiters serves as the prototype example. Here, we revisit the question of non-oscillatory behavior of high-resolution central schemes in terms of the slope limiter proposed by van Albada et. al. 1

Yuwei Sun, Hideya Ochiai, Jun Sakuma

Trojan attacks embed perturbations in input data leading to malicious behavior in neural network models. A combination of various Trojans in different modalities enables an adversary to mount a sophisticated attack on multimodal learning such as Visual Question Answering (VQA). However, multimodal Trojans in conventional methods are susceptible to parameter

Haitian Liu, Ye Guo, Hao Liu

Improving renewable energy resource utilization efficiency is crucial to reducing carbon emissions, and multi-parametric programming has provided a systematic perspective in conducting analysis and optimization toward this goal in smart grid operations. This paper focuses on two aspects of interest related to multi-parametric linear/quadratic programming (mp

Jun Song, Hai-hong Li, Feng-lan Shao

We apply an equal-velocity quark combination model to study the $\Lambda_{c}^{+}/D^{0}$ ratio in the range $p_{T}\lesssim10$ GeV/c in $pp$ collisions at $\sqrt{s}=13$ TeV. We decompose the ratio into four parts which are related to quark numbers, light-flavor quark $p_{T}$ spectrum, charm quark $p_{T}$ spectrum, momentum correlation between light and charm q

Weimin Zhou, Umberto Villa, Mark A. Anastasio

Medical imaging systems are often evaluated and optimized via objective, or task-specific, measures of image quality (IQ) that quantify the performance of an observer on a specific clinically-relevant task. The performance of the Bayesian Ideal Observer (IO) sets an upper limit among all observers, numerical or human, and has been advocated for use as a figu

Anish Muthali, Haotian Shen, Sampada Deglurkar, Michael H. Lim

We investigate methods to provide safety assurances for autonomous agents that incorporate predictions of other, uncontrolled agents' behavior into their own trajectory planning. Given a learning-based forecasting model that predicts agents' trajectories, we introduce a method for providing probabilistic assurances on the model's prediction error with calibr

G. Wang, P. S. Barry, T. Cecil, C. L. Chang

The complex conductivity of a superconducting thin film is related to the quasiparticle density, which depends on the physical temperature and can also be modified by external pair breaking with photons and phonons. This relationship forms the underlying operating principle of Kinetic Inductance Detectors (KIDs), where the detection threshold is governed by

Jing Huang

Strong cocomparability graphs are the reflexive graphs whose adjacency matrix can be rearranged by a simultaneous row and column permutation to avoid the submatrix with rows $01, 10$. Strong cocomparability graphs form a subclass of cocomparability graphs (i.e., the complements of comparability graphs) and can be recognized in polynomial time. In his seminal

Chengliang Liu, Jie Wen, Zhihao Wu, Xiaoling Luo

Incomplete multi-view clustering is a hot and emerging topic. It is well known that unavoidable data incompleteness greatly weakens the effective information of multi-view data. To date, existing incomplete multi-view clustering methods usually bypass unavailable views according to prior missing information, which is considered as a second-best scheme based

Sixiang Wen, Peter G. Jonker, Nicholas C. Stone, Sjoert Van Velzen

We predict late-time optical/UV emission from tidal disruption events (TDEs) from our slim accretion disc model \citep{Wen20} and explore the impact of the black hole mass $M_\bullet$, black hole spin $a_\bullet$, and accretion disc size. We use these synthetic spectra to successfully fit the multi-band \emph{Swift} observations of ASASSN-14li at >350 days,

Rohitash Chandra, Joshua Simmons

Bayesian inference provides a methodology for parameter estimation and uncertainty quantification in machine learning and deep learning methods. Variational inference and Markov Chain Monte-Carlo (MCMC) sampling methods are used to implement Bayesian inference. In the past three decades, MCMC sampling methods have faced some challenges in being adapted to la

Cory McCartan

Redistricting practitioners must balance many competing constraints and criteria when drawing district boundaries. To aid in this process, researchers have developed many methods for optimizing districting plans according to one or more criteria. This research note extends a recently-proposed single-criterion optimization method, short bursts (Cannon et al.,

Zeyin Song, Yifan Zhao, Yujun Shi, Peixi Peng

Few-shot class-incremental learning (FSCIL) aims at learning to classify new classes continually from limited samples without forgetting the old classes. The mainstream framework tackling FSCIL is first to adopt the cross-entropy (CE) loss for training at the base session, then freeze the feature extractor to adapt to new classes. However, in this work, we f

Yichen Zhang, Ruixiang Zhou, Hanlin Wu, Ji Seop Oh

The interplay between charge order (CO) and nontrivial band topology has spurred tremendous interest in understanding topological excitations beyond the single-particle description. In a quasi-one-dimensional nonsymmorphic crystal TaTe$_4$, the (2a$\times$2b$\times$3c) charge ordered ground state drives the system into a space group where the symmetry indica

Seokeon Choi, Debasmit Das, Sungha Choi, Seunghan Yang

Single domain generalization aims to train a generalizable model with only one source domain to perform well on arbitrary unseen target domains. Image augmentation based on Random Convolutions (RandConv), consisting of one convolution layer randomly initialized for each mini-batch, enables the model to learn generalizable visual representations by distorting

Dimitra Maoutsa

The dynamics of systems of many degrees of freedom evolving on multiple scales are often modeled in terms of stochastic differential equations. Usually the structural form of these equations is unknown and the only manifestation of the system's dynamics are observations at discrete points in time. Despite their widespread use, accurately inferring these syst

Daiki Yamasaki, Yu Wei Huang, Yuki Hashimoto, Denis P. Cabezas

Solar filaments are dense and cool plasma clouds in the solar corona. They are supposed to be supported in a dip of coronal magnetic field. However, the models are still under argument between two types of the field configuration; one is the normal polarity model proposed by Kippenhahn & Schlueter (1957), and the other is the reverse polarity model proposed

Joshua Foo, Robert B. Mann, Magdalena Zych

The quantum superposition principle states that quantum-mechanical systems such as atoms can be placed in a superposition of mass-energy eigenstates. Inspired by this idea and the seminal conjecture of Bekenstein, who proposed that black holes in quantum gravity must possess a discrete mass eigenspectrum, here we analyze the effects produced by a black hole

Runzhe Wan, Yu Liu, James McQueen, Doug Hains

With the growing needs of online A/B testing to support the innovation in industry, the opportunity cost of running an experiment becomes non-negligible. Therefore, there is an increasing demand for an efficient continuous monitoring service that allows early stopping when appropriate. Classic statistical methods focus on hypothesis testing and are mostly de

Gregory Schwartzman

We answer the question: "Does local progress (on batches) imply global progress (on the entire dataset) for mini-batch $k$-means?". Specifically, we consider mini-batch $k$-means which terminates only when the improvement in the quality of the clustering on the sampled batch is below some threshold. Although at first glance it appears that this algorithm mig

Liliana Camargo, Sergio Rojas, Patrick Vega

We propose and analyze two a posteriori error indicators for hybridizable discontinuous Galerkin (HDG) discretizations of the Helmholtz equation. These indicators are built to minimize the residual associated with a local superconvergent postprocessing scheme for the primal variable, measured in a dual norm of an enlarged discrete test space. The residual mi

Gennadiy Feldman

Let X be a compact Abelian group. In the article we obtain a characterization of shifts of Haar distributions on compact open subgroups of the group X by the symmetry of the conditional distribution of one linear form of independent random variables taking values in X given another. Coefficients of the linear forms are topological automorphisms of the group

Baihan Lin, Djallel Bouneffouf, Guillermo Cecchi, Kush R. Varshney

Recent advances in large language models (LLMs) have led to the development of powerful AI chatbots capable of engaging in natural and human-like conversations. However, these chatbots can be potentially harmful, exhibiting manipulative, gaslighting, and narcissistic behaviors. We define Healthy AI to be safe, trustworthy and ethical. To create healthy AI sy

Wenju Xu, Chengjiang Long, Yongwei Nie

Arbitrary style transfer has been demonstrated to be efficient in artistic image generation. Previous methods either globally modulate the content feature ignoring local details, or overly focus on the local structure details leading to style leakage. In contrast to the literature, we propose a new scheme \textit{``style kernel"} that learns {\em spatially a

Jan Heinrich Reimer, Sebastian Schmidt, Maik Fröbe, Lukas Gienapp

The Archive Query Log (AQL) is a previously unused, comprehensive query log collected at the Internet Archive over the last 25 years. Its first version includes 356 million queries, 166 million search result pages, and 1.7 billion search results across 550 search providers. Although many query logs have been studied in the literature, the search providers th

Teresa Esteban-Casanelles, Duarte Gonçalves

How do incentive levels affect strategic behaviour? We address this with an experiment that separately identifies own- and opponent-incentive effects in two dominance-solvable games that differ in strategic complexity. Higher own incentives favour more strategically sophisticated actions and increase best responding to stated beliefs. Beliefs shift in a para

Suman Kunwar

Computer vision methods have shown to be effective in classifying garbage into recycling categories for waste processing, existing methods are costly, imprecise, and unclear. To tackle this issue, we introduce MWaste, a mobile application that uses computer vision and deep learning techniques to classify waste materials as trash, plastic, paper, metal, glass

Tomoya Sasaki, Narin Okazaki, Takatoshi Yoshida, Alfonso Balandra

We propose SolefulTap for a novel tap dancing experience. It allows users to feel as if they are tap dancing or appreciate a tap dancing performance using the sensations of their own feet. SolefulTap uses a method called Step Augmentation that provides audio-haptic feedback to users, generating impacts in response to users' simple step motions. Our prototype

Naoki Yokoyama, Alex Clegg, Joanne Truong, Eric Undersander

We present Adaptive Skill Coordination (ASC) -- an approach for accomplishing long-horizon tasks like mobile pick-and-place (i.e., navigating to an object, picking it, navigating to another location, and placing it). ASC consists of three components -- (1) a library of basic visuomotor skills (navigation, pick, place), (2) a skill coordination policy that ch

Yizheng Chen, Zhoujie Ding, Lamya Alowain, Xinyun Chen

We propose and release a new vulnerable source code dataset. We curate the dataset by crawling security issue websites, extracting vulnerability-fixing commits and source codes from the corresponding projects. Our new dataset contains 18,945 vulnerable functions spanning 150 CWEs and 330,492 non-vulnerable functions extracted from 7,514 commits. Our dataset

Simon Diemert, Jens H. Weber

Self-adaptive systems are able to change their behaviour at run-time in response to changes. Self-adaptation is an important strategy for managing uncertainty that is present during the design of modern systems, such as autonomous vehicles. However, assuring the safety of self-adaptive systems remains a challenge, particularly when the adaptations have an im

Marek Karliner, Jonathan L. Rosner

The LHCb experiment has recently reported two excited $\Omega_c$ resonances decaying to $\Xi_c^+ K^-$, with masses about 3185 and 3327 MeV. We discuss their assignment to $2S_{1/2}$ and $2S_{3/2}$ states, which can be compared with masses based on extrapolation from the observed 1S states. The agreement is not perfect, but weighs against an earlier alternati

Mehmet Demirci, Ramazan Sever

We report bound state solutions of the Klein Gordon equation with a novel combined potential, the Eckart plus a class of Yukawa potential, by means of the parametric Nikiforov-Uvarov method. To deal the centrifugal and the coulombic behavior terms, we apply the Greene-Aldrich approximation scheme. We present any $\ell$-state energy eigenvalues and the corres

Owen Chase, Robin Ciardullo, Martin Roth, George Jacoby

Planetary nebula (PN) surveys in systems beyond ~10 Mpc often find high-excitation, point-like sources with [O III] $\lambda 5007$ fluxes greater than the apparent bright-end cutoff of the planetary nebula luminosity function (PNLF). Here we identify PN superpositions as one likely cause for the phenomenon and describe the proper procedures for deriving PNLF

Young Geun Kim, Udit Gupta, Andrew McCrabb, Yonglak Son

To improve the environmental implications of the growing demand of computing, future applications need to improve the carbon-efficiency of computing infrastructures. State-of-the-art approaches, however, do not consider the intermittent nature of renewable energy. The time and location-based carbon intensity of energy fueling computing has been ignored when

Wei-Liang Qian, Kai Lin, Chong Ye, Jin Li

In relativistic heavy-ion collisions, event-by-event fluctuations are known to have non-trivial implications. Even though the probability distribution is geometrically isotropic for the initial conditions, the anisotropic $\varepsilon_n$ still differs from zero owing to the statistical fluctuations in the energy profile. On the other hand, the flow harmonics

Sareh Seyedi, Valerie K. Harris, Stefania E. Kapsetaki, Daniel Saha

One of the main reasons we have not been able to cure cancers is that drugs select for drug-resistant cancer cells. Pest managers face similar challenges with pesticides selecting for pesticide-resistant organisms. Lessons in pest management have led to four heuristics that can be translated to controlling cancers 1. limit use (of chemical controls or modes

Henryk Gzyl

It is proved that a classical (respec. quantum) system consisting of a particle in a constant magnetic field is canonically (respec. unitarily) equivalent to a 2-dimensional harmonic oscillator plus a free particle. It is also shown that the eigenvectors of the discrete spectrum are entangled states of the 2-dimensional harmonic oscillator.

Jiankai Gao, Yang Li, Bin Wang, Haibo Wu

The implementation of a multi-microgrid (MMG) system with multiple renewable energy sources enables the facilitation of electricity trading. To tackle the energy management problem of a MMG system, which consists of multiple renewable energy microgrids belonging to different operating entities, this paper proposes a MMG collaborative optimization scheduling

Juanjo Rué, Christoph Spiegel

We study an analogue of the Ramsey multiplicity problem for additive structures, in particular establishing the minimum number of monochromatic 3-APs in 3-colorings of $\mathbb{F}_3^n$ as well as obtaining the first non-trivial lower bound for the minimum number of monochromatic 4-APs in 2-colorings of $\mathbb{F}_5^n$. The former parallels results by Cuming

Maximilian Weiherer, Bernhard Egger

Becoming a (super) hero is almost every kid's dream. During their sheltered childhood, they do whatever it takes to grow up to be one. Work hard, play hard -- all day long. But as they're getting older, distractions are more and more likely to occur. They're getting off track. They start discovering what is feared as simple math. Finally, they end up as a re

El Mahdi Mouloua, Mustapha Najmeddine, Maria Isabel Garcia-Planas, Hassan Ouazzou

The monomial codes over a Galois field F_q that can be thought invariant subspaces are essential to us in this study. More specifically, we look into the link between monomial codes and characteristic subspaces and the decomposition of monomial codes into minimal invariant subspaces. Additionally, we study some of the characteristics of monomial codes and ge

Nishanth Venkatesh, Viet-Anh Le, Aditya Dave, Andreas A. Malikopoulos

Highway merging scenarios featuring mixed traffic conditions pose significant modeling and control challenges for connected and automated vehicles (CAVs) interacting with incoming on-ramp human-driven vehicles (HDVs). In this paper, we present an approach to learn an approximate information state model of CAV-HDV interactions for a CAV to maneuver safely dur

Tam N. Nguyen

Serverless cloud is an innovative cloud service model that frees customers from most cloud management duties. It also offers the same advantages as other cloud models but at much lower costs. As a result, the serverless cloud has been increasingly employed in high-impact areas such as system security, banking, and health care. A big threat to the serverless

Hiroki Waida, Yuichiro Wada, Léo Andéol, Takumi Nakagawa

Contrastive learning is an efficient approach to self-supervised representation learning. Although recent studies have made progress in the theoretical understanding of contrastive learning, the investigation of how to characterize the clusters of the learned representations is still limited. In this paper, we aim to elucidate the characterization from theor

Donald Pinckney, Federico Cassano, Arjun Guha, Jonathan Bell

The NPM package repository contains over two million packages and serves tens of billions of downloads per-week. Nearly every single JavaScript application uses the NPM package manager to install packages from the NPM repository. NPM relies on a "semantic versioning" ('semver') scheme to maintain a healthy ecosystem, where bug-fixes are reliably delivered to

Tomasz Klimsiak, Andrzej Rozkosz

We study the Dirichlet problem for semilinear equations on general open sets with measure data on the right-hand side and irregular boundary data. For this purpose we develop the classical method of orthogonal projection. We treat in a unified form equations with operators belonging to the broad class of integro-differential operators associated with symmetr

Chayan Maitra, Dibyendu B. Seal, Rajat K. De

The task of dimensionality reduction and visualization of high-dimensional datasets remains a challenging problem since long. Modern high-throughput technologies produce newer high-dimensional datasets having multiple views with relatively new data types. Visualization of these datasets require proper methodology that can uncover hidden patterns in the data

Mohammad Al-Jarrah, Bamdad Hosseini, Amirhossein Taghvaei

This paper is concerned with the theoretical and computational development of a new class of nonlinear filtering algorithms called the optimal transport particle filters (OTPF). The algorithm is based on a recently introduced variational formulation of the Bayes' rule, which aims to find the Brenier optimal transport map between the prior and the posterior d

Lila Fontes, Sophie Laplante, Mathieu Lauriere, Alexandre Nolin

We study the two-party communication complexity of functions with large outputs, and show that the communication complexity can greatly vary depending on what output model is considered. We study a variety of output models, ranging from the open model, in which an external observer can compute the outcome, to the XOR model, in which the outcome of the protoc

Esteban Rojas-Gatjens, Kaila Yallum, Yangwei Shi, Yulong Zheng

We explore the application of excitation correlation spectroscopy to detect nonlinear photophysical dynamics in two distinct semiconductor classes through time-integrated photoluminescence and photocurrent measurements. In this experiment, two variably delayed femtosecond pulses excite the semiconductor, and the time-integrated photoluminescence or photocurr

Thomas Schlögl, Ulrich Schmid

Existing protocols for byzantine fault tolerant distributed systems usually rely on the correct agents' ability to detect faulty agents and/or to detect the occurrence of some event or action on some correct agent. In this paper, we provide sufficient conditions that allow an agent to infer the appropriate beliefs from its history, and a procedure that allow

Cosmas Heiß, Ingo Gühring, Martin Eigel

We combine concepts from multilevel solvers for partial differential equations (PDEs) with neural network based deep learning and propose a new methodology for the efficient numerical solution of high-dimensional parametric PDEs. An in-depth theoretical analysis shows that the proposed architecture is able to approximate multigrid V-cycles to arbitrary preci

Anshul Shah, Aniket Roy, Ketul Shah, Shlok Kumar Mishra

Supervised learning of skeleton sequence encoders for action recognition has received significant attention in recent times. However, learning such encoders without labels continues to be a challenging problem. While prior works have shown promising results by applying contrastive learning to pose sequences, the quality of the learned representations is ofte

C. Reichhardt, C. J. O. Reichhardt

We numerically examine the transport of skyrmions driven over weak random quenched disorder using a modified Thiele approach that includes the thermal softening of skyrmion pairwise interactions introduced by Wang et al., Phys. Rev. Appl. 18, 044024 (2022). The depinning transition is elastic at low temperatures but becomes plastic with a reduced threshold a

Chunqiu Steven Xia, Lingming Zhang

Automated Program Repair (APR) aims to automatically generate patches for buggy programs. Recent APR work has been focused on leveraging modern Large Language Models (LLMs) to directly generate patches for APR. Such LLM-based APR tools work by first constructing an input prompt built using the original buggy code and then queries the LLM to generate patches.

Jack Morava

We interpret the moment generating function ${\bf E}(e^{tX}):= {\rm exp}_F(t) \in {\bf R}[[t]]$ of a random variable $X$ as the exponential of an associated one-dimensional formal group law $F$ defined over ${\bf R}$.

Kh. V. Navoyan

Let $X$ be a Banach space with a basis $(e_k)_k$ and biorthogonals $(e^\ast_k)_k$. An operator on $X$ is said to have a $\textit {large diagonal}$ if $\inf\limits_{k} |e_k^\ast(T(e_k))| > 0$. The basis $(e_k)_k$ is said to have the $\textit {factorization property}$ if the identity factors through any operator with a large diagonal. Under the assumption that

Reiner Czerwinski

Due to Savitch's theorem we know $NL\subseteq DSPACE(\log^2(n))$. To show this upper bound, Savitch constructed an algorithm with $O(\log^2(n))$ space on the working tape. We will show that Savitch's algorithm also described a lower bound under the Strong Exponential Time Hypothesis. Every algorithm for the Connectivity Problem needs $O(\log^2(n))$ space in

Timothy M. Craig, Ajinkya A Kadu, Kees Joost Batenburg, Sara Bals

Electron tomography is a widely used technique for 3D structural analysis of nanomaterials, but it can cause damage to samples due to high electron doses and long exposure times. To minimize such damage, researchers often reduce beam exposure by acquiring fewer projections through tilt undersampling. However, this approach can also introduce reconstruction a

Wojciech Ozga, Patricia Sagmeister, Tamás Visegrády, Silvio Dragone

Existing attestation mechanisms lack scalability and support for heterogeneous virtual execution environments (VEEs), such as virtual machines and containers executed inside or outside hardware isolation on different vendors' hardware in clouds managed by various organizations. To overcome these limitations, hardware vendors and cloud providers implement pro

Vedat Tanriverdi

In some cases, it is possible to show the conservation of energy by using equations of motion in mechanics. By considering these results, some people can think that the conservation of energy is the result of equations of motion or Newton's second law. If we consider the conservation of energy by itself, it is valid for nearly all natural sciences and more g

Abed AlRahman Al Makdah, Fabio Pasqualetti

In this paper we provide direct data-driven expressions for the Linear Quadratic Regulator (LQR), the Kalman filter, and the Linear Quadratic Gaussian (LQG) controller using a finite dataset of noisy input, state, and output trajectories. We show that our data-driven expressions are consistent, since they converge as the number of experimental trajectories i

Manish Kumar Sharma, Saumyen Kundu, Prasanta Kumar Das

The light braneworld radion, stabilized via the Goldberger-Wise mechanism in the Randall-Sundrum model, can be produced copiously inside the supernova core due to plasmon-plasmon annihilations. The radion, thus produced, subsequently decays to a neutrino-antineutrino pair and takes away the energy released in the SN1987A explosion. Assuming that the supernov

Gregory Holste, Douwe van der Wal, Hans Pinckaers, Rikiya Yamashita

Prostate cancer is one of the leading causes of cancer-related death in men worldwide. Like many cancers, diagnosis involves expert integration of heterogeneous patient information such as imaging, clinical risk factors, and more. For this reason, there have been many recent efforts toward deep multimodal fusion of image and non-image data for clinical decis

Xiou Ge, Yun-Cheng Wang, Bin Wang, C. -C. Jay Kuo

The cascade of 2D geometric transformations were exploited to model relations between entities in a knowledge graph (KG), leading to an effective KG embedding (KGE) model, CompoundE. Furthermore, the rotation in the 3D space was proposed as a new KGE model, Rotate3D, by leveraging its non-commutative property. Inspired by CompoundE and Rotate3D, we leverage

Reiner Czerwinski

The $P$ versus $NP$ problem is still unsolved. But there are several oracles with $P$ unequal $NP$ relative to them. Here we will prove, that $P\not=NP$ relative to a $P$-complete oracle. In this paper, we use padding arguments as the proof method. The padding arguments are not bounded by a computable function. Such as we can use methods from computability t

Jialin Li, Maha Elgarf, Alia Waleed, Hanan Salam

In personalized machine learning, the aim of personalization is to train a model that caters to a specific individual or group of individuals by optimizing one or more performance metrics and adhering to specific constraints. In this paper, we discuss the need for personalization in affective computing and present the first survey of existing approaches for

Sebastiano Cominelli, Carlo Sinigaglia, Davide Enrico Quadrelli, Francesco Braghin

In this paper, we propose a novel approach for controlling surface water waves and their interaction with floating bodies. We consider a floating target rigid body surrounded by a control region where we design three control strategies of increasing complexity: an active strategy based on controlling the pressure at the air-water interface and two passive st

Mohamed Naveed Gul Mohamed, Raman Goyal, Suman Chakravorty

In this paper, we consider the infinite horizon optimal control problem for nonlinear systems. Under the conditions of controllability of the linearized system around the origin, and nonlinear controllability of the system to a terminal set containing the origin, we establish an approximate regularized solution approach consisting of a ``finite free final ti

Chong Yang Du, André Guerra, Adam McElligott, Milan Marić

Reversible addition-fragmentation chain-transfer polymerization with a switchable chain-transfer agent was employed to synthesize amphiphilic block copolymers poly(styrene-b-vinylpyrrolidone) and poly(pentafluorostyrene-b-vinylpyrrolidone) at 10 wt.% hydrophobic content as kinetic hydrate inhibitors for methane hydrates. The dynamic viscosity of methane hydr

Jingxuan Zhu, Alvaro Velasquez, Ji Liu

This paper presents a resilient distributed algorithm for solving a system of linear algebraic equations over a multi-agent network in the presence of Byzantine agents capable of arbitrarily introducing untrustworthy information in communication. It is shown that the algorithm causes all non-Byzantine agents' states to converge to the same least squares solu

Hieu H. Pham, Ha Q. Nguyen, Hieu T. Nguyen, Linh T. Le

We conducted a prospective study to measure the clinical impact of an explainable machine learning system on interobserver agreement in chest radiograph interpretation. The AI system, which we call as it VinDr-CXR when used as a diagnosis-supporting tool, significantly improved the agreement between six radiologists with an increase of 1.5% in mean Fleiss' K

Shuo Liu, Wei Xiao, Calin A. Belta

This paper studies safety guarantees for systems with time-varying control bounds. It has been shown that optimizing quadratic costs subject to state and control constraints can be reduced to a sequence of Quadratic Programs (QPs) using Control Barrier Functions (CBFs). One of the main challenges in this method is that the CBF-based QP could easily become in

Michael Baddeley, Carlo Alberto Boano, Antonio Escobar-Molero, Ye Liu

The popularity of concurrent transmissions (CT) has soared after recent studies have shown their feasibility on the four physical layers specified by BLE 5, hence providing an alternative to the use of IEEE 802.15.4 for the design of reliable and efficient low-power wireless protocols. However, to date, the extent to which physical layer properties affect th

Mateusz Łełyk, Bartosz Wcisło

We investigate abstract model theoretic properties which holds for models in which a truth or satisfaction predicate for a sublanguage of the signature is definable. We analyse in which cases those properties in fact ensure the definability of the respective truth predicate. In some cases, we formulate different axiomatic theories which are indispensable for

Taniya Kapoor, Hongrui Wang, Alfredo Núñez, Rolf Dollevoet

This paper presents a new approach to simulate forward and inverse problems of moving loads using physics-informed machine learning (PIML). Physics-informed neural networks (PINNs) utilize the underlying physics of moving load problems and aim to predict the deflection of beams and the magnitude of the loads. The mathematical representation of the moving loa

H. Avetisyan, V. Mkrtchian, A. E. Allahverdyan

We study an inverse scattering problem in which the far-field spectral cross-correlation functions of scattered fields are used to determine the unknown dielectric susceptibility of the scattering object. One-photon states for the incident field can resolve (at $100\%$ visibility) twice more Fourier components of the susceptibility compared to the (naive) Ra

Peter Du, Surya Murthy, Katherine Driggs-Campbell

As advances in artificial intelligence enable increasingly capable learning-based autonomous agents, it becomes more challenging for human observers to efficiently construct a mental model of the agent's behaviour. In order to successfully deploy autonomous agents, humans should not only be able to understand the individual limitations of the agents but also

Maud Szusterman

In [SZ], Soprunov and Zvavitch have translated the Bezout inequalities (from Algebraic Geometry) into inequalities of mixed volumes satisfied by the simplex. They conjecture this set of inequalities characterizes the simplex, among all convex bodies in R^n. Together with Saroglou, they proved the characterization among all polytopes [SSZ1] and, for a larger

Peter Du, Katherine Driggs-Campbell

Uncovering potential failure cases is a crucial step in the validation of safety critical systems such as autonomous vehicles. Failure search may be done through logging substantial vehicle miles in either simulation or real world testing. Due to the sparsity of failure events, naive random search approaches require significant amounts of vehicle operation h

Weiguang Han, Jimin Huang, Qianqian Xie, Boyi Zhang

Although pair trading is the simplest hedging strategy for an investor to eliminate market risk, it is still a great challenge for reinforcement learning (RL) methods to perform pair trading as human expertise. It requires RL methods to make thousands of correct actions that nevertheless have no obvious relations to the overall trading profit, and to reason

Miguel Cavadas, Pablo Gamallo

Automatic Authorship Attribution (AAA) is the result of applying tools and techniques from Digital Humanities to authorship attribution studies. Through a quantitative and statistical approach this discipline can draw further conclusions about renowned authorship issues which traditional critics have been dealing with for centuries, opening a new door to sty

Stefano Cristiani, Matteo Porru, Francesco Guarneri, Giorgio Calderone

The QUBRICS (QUasars as BRIght beacons for Cosmology in the Southern hemisphere) survey aims at constructing a sample of the brightest quasars with z>~2.5, observable with facilities in the Southern Hemisphere. QUBRICS makes use of the available optical and IR wide-field surveys in the South and of Machine Learning techniques to produce thousands of bright q

A. Acus, A. Dargys

Closed form expressions for a logarithm of general multivector (MV) in base-free form in real geometric algebras (GAs) Cl(p,q) are presented for all n=p+q=3. In contrast to logarithm of complex numbers (isomorphic to Cl(0,1), 3D logarithmic functions, due to appearance of two double angle arc tangent functions, allow to include two sets of sheets characteriz

Cuihong Li, Yuanyuan Ma, Jinchuan Wang, Shaochong Zhu

Optically levitated nanomechanical resonators in vacuum perform ultrahigh sensitivity for mechanical quantities by overcoming the limitations of clamped resonators. However, the generally levitated silica nanoparticles (NPs) with low absorption and high transparence still face difficulties surviving in high vacuum with unclear reason. By monitoring the physi

John M. Campbell

Sun, in 2022, introduced a conjectured evaluation for a series of convergence rate $\frac{1}{2}$ involving harmonic numbers. We prove both this conjecture and a stronger version of this conjecture, using a summation technique based on a beta-type integral we had previously introduced. Our full proof also requires applications of Bailey's ${}_{2}F_{1}\left( \

Yukang Cao, Kai Han, Kwan-Yee K. Wong

We address the problem of clothed human reconstruction from a single image or uncalibrated multi-view images. Existing methods struggle with reconstructing detailed geometry of a clothed human and often require a calibrated setting for multi-view reconstruction. We propose a flexible framework which, by leveraging the parametric SMPL-X model, can take an arb

Saadat Ullah Khan, Muhammad Majid, Syed Muhammad Anwar

An accurate classification of upper limb movements using electroencephalography (EEG) signals is gaining significant importance in recent years due to the prevalence of brain-computer interfaces. The upper limbs in the human body are crucial since different skeletal segments combine to make a range of motion that helps us in our trivial daily tasks. Decoding

Steven Obua

Logic really is just algebra, given one uses the right kind of algebra, and the right kind of logic. The right kind of algebra is abstraction algebra, and the right kind of logic is abstraction logic.

H. Yan, G. J. Omar, Z. T. Zhao, Lim Zhi Shiuh

Soft ferromagnetic NiFe thin films are promising for applications in spintronic devices because of their constituent electrical and magnetic properties. Electron beam evaporation and sputtering techniques have been used to deposit NiFe thin films. For in-situ stacking of NiFe with functional complex oxides, the pulsed laser deposition (PLD) method is highly

Junxiong Hua, Jiangbo Luo, Yuntian Zheng, Jiayu Chen

Two-dimensional (2D) materials provide a platform for developing novel spintronic devices and circuits for low-power electronics. In particular, inducing magnetism and injecting spins in graphene have promised the emerging field of graphene spintronics. This review focuses on the magnetic proximity effect at the interface of 2D materials and magnetic oxide i