April 2023 arXiv papers — page 114

Abdolvahab Khademi

ChatGPT and Bard are AI chatbots based on Large Language Models (LLM) that are slated to promise different applications in diverse areas. In education, these AI technologies have been tested for applications in assessment and teaching. In assessment, AI has long been used in automated essay scoring and automated item generation. One psychometric property tha

T. Ssettumba, L. T. N. Landau, R. C. de Lamare

This paper proposes a cell-free massive multiple-input multiple-output (CF-mMIMO) architecture with joint list-based detection with soft interference cancelation (soft-IC) and access points (APs) selection. In particular, we derive a new closed-form expression for the minimum mean-square error receive filter while taking the uplink transmit powers and APs se

Haiyan Zhao, Tianyi Zhou, Guodong Long, Jing Jiang

Distribution shift (e.g., task or domain shift) in continual learning (CL) usually results in catastrophic forgetting of neural networks. Although it can be alleviated by repeatedly replaying buffered data, the every-step replay is time-consuming. In this paper, we study which modules in neural networks are more prone to forgetting by investigating their tra

Anandaswarup Vadapalli

Purpose: This work explores the use of external phrase break prediction models to enhance listener comprehension in End-to-End Text-to-Speech (TTS) systems. Methods: The effectiveness of these models is evaluated based on listener preferences in subjective tests. Two approaches are explored: (1) a bidirectional LSTM model with task-specific embeddings traine

Zhongqi Wang, Jie Zhang, Zhilong Ji, Jinfeng Bai

With the development of deep generative models, recent years have seen great success of Chinese landscape painting generation. However, few works focus on controllable Chinese landscape painting generation due to the lack of data and limited modeling capabilities. In this work, we propose a controllable Chinese landscape painting generation method named CCLA

Ruining Deng, Can Cui, Quan Liu, Tianyuan Yao

The segment anything model (SAM) was released as a foundation model for image segmentation. The promptable segmentation model was trained by over 1 billion masks on 11M licensed and privacy-respecting images. The model supports zero-shot image segmentation with various segmentation prompts (e.g., points, boxes, masks). It makes the SAM attractive for medical

Yidi Wang, Wei Zheng, Shuangnan Zhang, Minyu Ge

This article provides a review on X-ray pulsar-based navigation (XNAV). The review starts with the basic concept of XNAV, and briefly introduces the past, present and future projects concerning XNAV. This paper focuses on the advances of the key techniques supporting XNAV, including the navigation pulsar database, the X-ray detection system, and the pulse ti

Kevin Huang, Shuzhong Zhang

In this paper, we discuss variational inequality (VI) problems without monotonicity from the perspective of convergence of projection-type algorithms. In particular, we identify existing conditions as well as present new conditions that are sufficient to guarantee convergence. The first half of the paper focuses on the case where a Minty solution exists (als

Tiandeng Wu, Qijiong Liu, Yi Cao, Yao Huang

Graph convolutional network (GCN) has been successfully applied to capture global non-consecutive and long-distance semantic information for text classification. However, while GCN-based methods have shown promising results in offline evaluations, they commonly follow a seen-token-seen-document paradigm by constructing a fixed document-token graph and cannot

Yan Luo, Haoyi Duan, Ye Liu, Fu-lai Chung

Location recommendation plays a vital role in improving users' travel experience. The timestamp of the POI to be predicted is of great significance, since a user will go to different places at different times. However, most existing methods either do not use this kind of temporal information, or just implicitly fuse it with other contextual information. In t

Kevin Zakka, Philipp Wu, Laura Smith, Nimrod Gileadi

Replicating human-like dexterity in robot hands represents one of the largest open problems in robotics. Reinforcement learning is a promising approach that has achieved impressive progress in the last few years; however, the class of problems it has typically addressed corresponds to a rather narrow definition of dexterity as compared to human capabilities.

Sofia Sheikh, Brent Vela, Pejman Honarmandi, Peter Morcos

Designing alloys for additive manufacturing (AM) presents significant opportunities. Still, the chemical composition and processing conditions required for printability (ie., their suitability for fabrication via AM) are challenging to explore using solely experimental means. In this work, we develop a high-throughput (HTP) computational framework to guide t

Zongbo Han, Zhipeng Liang, Fan Yang, Liu Liu

Subpopulation shift exists widely in many real-world applications, which refers to the training and test distributions that contain the same subpopulation groups but with different subpopulation proportions. Ignoring subpopulation shifts may lead to significant performance degradation and fairness concerns. Importance reweighting is a classical and effective

Bambang L. Widjiantoro, Moh Kamalul Wafi, Katherin Indriawati

The concerns to autonomous vehicles have been becoming more intriguing in coping with the more environmentally dynamics non-linear systems under some constraints and disturbances. These vehicles connect not only to the self-instruments yet to the neighborhoods components, making the diverse interconnected communications which should be handled locally to eas

Ajai Choudhry

In this paper we present a new method of solving the classical diophantine equation $A^4+B^4=C^4+D^4$. Two methods of solving this equation, given by Euler, yield parametric solutions given by polynomials of degrees 7 and 13. Several other parametric solutions are now known, and with the exception of one solution of degree 11, all the published solutions are

Polaki Durga Prasad, Yelleti Vivek, Vadlamani Ravi

Protecting data privacy is paramount in the fields such as finance, banking, and healthcare. Federated Learning (FL) has attracted widespread attention due to its decentralized, distributed training and the ability to protect the privacy while obtaining a global shared model. However, FL presents challenges such as communication overhead, and limited resourc

Keiichi Maeda, Akihiro Suzuki, Luca Izzo

Engine-driven explosions with continuous energy input from the central system have been suggested for supernovae (SNe) associated with a Gamma-Ray Burst (GRB), super-luminous SNe (SLSNe), and at least a fraction of broad-lined SNe Ic (SNe Ic-BL) even without an associated GRB. In the present work, we investigate observational consequences in this scenario, f

Moh Kamalul Wafi, Katherin Indriawati

The concept of fault-tolerant control has extensively been explored with various mapping of development. It starts from the system characteristic, the robustness of the controller, estimation methods and optimization, to the combination of the faults such that it can touch the true observed system. The mathematical concepts of the scrubber plant taking into

Sanaz Sadat Hosseini, Mona Azarbayjani, Jason Lawrence, Hamed Tabkhi

Access to reliable public transportation is essential for addressing socio-economic disparities, particularly in low-income communities that rely heavily on transit for accessing jobs, healthcare, and essential services. This study investigates the challenges faced by transit-dependent populations in Charlotte, NC, focusing on the spatial and service-related

Liang Fang, Jian Wang

Optical spin and chirality play key roles in engineering photonic emission and light-matter interactions. Here we show that 3D evanescent coupling of guided modes by strongly confined waveguides can extrinsically produce optical spin and chirality. These emerging optical quantities are essentially derived from an intrinsic phase retardation of {\pi}/2 betwee

Christian Baer, Simon Brendle, Tsz-Kiu Aaron Chow, Bernhard Hanke

Our work proves rigidity theorems for initial data sets associated with compact smooth spin manifolds with boundary and with compact convex polytopes, subject to the dominant energy condition. For manifolds with smooth boundary, this is based on the solution of a boundary value problem for Dirac operators. For convex polytopes we use approximations by manifo

Moh Kamalul Wafi, Bambang L. Widjiantoro

This paper presents a unified modeling, control, and estimation framework for the quadruple-tank process, a benchmark multivariable system that exhibits either minimum phase or nonminimum phase behavior depending on valve flow ratios. A decentralized PI control strategy is employed to regulate water levels, while a distributed state estimation scheme is deve

Natalie Klco, D. H. Beck

When observing a quantum field via detectors with access to only the mixed states of spatially separated, local regions -- a ubiquitous experimental design -- the capacity to access the full extent of distributed entanglement can be limited, shrouded by classical correlations. By performing projective measurements of the field external to two detection patch

James M. Dolezal, Sara Kochanny, Emma Dyer, Andrew Srisuwananukorn

Deep learning methods have emerged as powerful tools for analyzing histopathological images, but current methods are often specialized for specific domains and software environments, and few open-source options exist for deploying models in an interactive interface. Experimenting with different deep learning approaches typically requires switching software l

Alessio Corti

I state a conjecture asserting that for all generic klt Fano varieties X, there exists a generalised cluster variety U and a surjection from the set of torus charts on U to the set of toric specializations of X. I prove the conjecture in dimension 2 after work of Kasprzyk-Nill-Prince, Lutz, Hacking and Lai-Zhou. This confirms a deep and surprising structure

Jie Yang, Chaoqun Wang, Zhen Li, Junle Wang

This paper presents Scalable Semantic Transfer (SST), a novel training paradigm, to explore how to leverage the mutual benefits of the data from different label domains (i.e. various levels of label granularity) to train a powerful human parsing network. In practice, two common application scenarios are addressed, termed universal parsing and dedicated parsi

Xuemei Li, Chenxi Liu, Xingdong Tang, Guixiang Xu

In this paper, we show the nondegeneracy of positive bubble solutions for generalized energy-critical Hartree equations (NLH) \begin{equation*} -{\Delta u}\sts{x} -{\bm\alpha}\sts{N,\lambda} \int_{\R^N} { \frac{ u^{p}\sts{y}}{\pabs{\,x-y\,}{\lambda}} }\diff{y}\, u^{p-1}\sts{x} =0,\quad x\in \R^N \end{equation*} where $N\geq 3$, $0<\lambda<N$, $p=\frac{2N-\la

Yoshiyuki Inoue, Dmitry Khangulyan

NGC 4151, a nearby Seyfert galaxy, has recently been reported to emit gamma rays in the GeV range, posing an intriguing astrophysical mystery. The star formation rate of NGC 4151 is too low to explain the observed GeV flux, but the galaxy is known for its coronal activity in X-ray and jet activity in radio. We propose that either the combination of these two

Xiwen Chen, Huayu Li, Rahul Amin, Abolfazl Razi

In some practical learning tasks, such as traffic video analysis, the number of available training samples is restricted by different factors, such as limited communication bandwidth and computation power. Determinantal Point Process (DPP) is a common method for selecting the most diverse samples to enhance learning quality. However, the number of selected s

Jingtao Lin, Jingtao Shi

This paper is concerned with a kind of risk-sensitive optimal control problem for fully coupled forward-backward stochastic systems. The control variable enters the diffusion term of the state equation and the control domain is not necessarily convex. A new global maximum principle is obtained without assuming that the value function is smooth. The maximum c

Wenxiang Xu, Yongcheng Jing, Linyun Zhou, Wenqi Huang

The problem of deep long-tailed learning, a prevalent challenge in the realm of generic visual recognition, persists in a multitude of real-world applications. To tackle the heavily-skewed dataset issue in long-tailed classification, prior efforts have sought to augment existing deep models with the elaborate class-balancing strategies, such as class rebalan

Ryusei Watanabe, Daiki Yamamoto, Mark Sadgrove

We investigate numerically and experimentally the properties of a two color optical fiber taper trap, for which the evanescent field of the modes in the fiber taper give rise to a three-dimensional trapping potential. Experimentally, we use the technique to confine colloidal nanoparticles near the surface of an optical fiber taper, and show that the trapping

Federico Semeraro, Yi Zhang, Wenying Wu, Patrick Carroll

We present Surf-NeRF, a modified implementation of the recently introduced Shadow Neural Radiance Field (S-NeRF) model. This method is able to synthesize novel views from a sparse set of satellite images of a scene, while accounting for the variation in lighting present in the pictures. The trained model can also be used to accurately estimate the surface el

Boris Shoikhet

There are known two different constructions of contractible dg 2-operads, providing a weak 2-category structure on the following dg 2-quiver of small dg 2-categories. Its vertices are small dg 2-categories over a given field, arrows are dg functors, and the 2-arrows $F\Rightarrow G$ are defined as the Hochschild cochains of $C$ with coefficients in $C$-bimod

Jezdimir Milosevic, Mathieu Dahan, Saurabh Amin, Henrik Sandberg

We consider a strategic network monitoring problem involving the operator of a networked system and an attacker. The operator aims to randomize the placement of multiple protected sensors to monitor and protect components that are vulnerable to attacks. We account for the heterogeneity in the components' security levels and formulate a large-scale maximin op

David J. Moss

Photonic RF transversal signal processors, which are equivalent to reconfigurable electrical digital signal processors but implemented with photonic technologies, have been widely used for modern high-speed information processing. With the capability of generating large numbers of wavelength channels with compact micro-resonators, optical microcombs bring ne

Daniel Hickey, Matheus Schmitz, Daniel Fessler, Paul Smaldino

On October 27th, 2022, Elon Musk purchased Twitter, becoming its new CEO and firing many top executives in the process. Musk listed fewer restrictions on content moderation and removal of spam bots among his goals for the platform. Given findings of prior research on moderation and hate speech in online communities, the promise of less strict content moderat

Meidai Xuanyuan, Yuwang Wang, Honglei Guo, Xiao Ma

Imagine an interesting multimodal interactive scenario that you can see, hear, and chat with an AI-generated digital character, who is capable of behaving like Sheldon from The Big Bang Theory, as a DEEP copy from appearance to personality. Towards this fantastic multimodal chatting scenario, we propose a novel task, named Deep Personalized Character Creatio

Davide Scaramuzza, Elia Kaufmann

We present our latest research in learning deep sensorimotor policies for agile, vision-based quadrotor flight. We show methodologies for the successful transfer of such policies from simulation to the real world. In addition, we discuss the open research questions that still need to be answered to improve the agility and robustness of autonomous drones towa

Yang Chen, Shulin Lyu

We study the Hankel determinant generated by the Gaussian weight with jump discontinuities at $t_1,\cdots,t_m$. By making use of a pair of ladder operators satisfied by the associated monic orthogonal polynomials and three supplementary conditions, we show that the logarithmic derivative of the Hankel determinant satisfies a second order partial differential

Abiola Akinnubi, Nitin Agarwal

Deliberation and communication within the national space have had numerous implications on how citizens online and offline perceive government. It has also impacted the relationship between opposition and incumbent governments in the Indo-Pacific region. Authoritarian regimes have historically had control over the dissemination of information, thereby contro

Tianmu Li, Shurui Li, Puneet Gupta

Approximate computing methods have shown great potential for deep learning. Due to the reduced hardware costs, these methods are especially suitable for inference tasks on battery-operated devices that are constrained by their power budget. However, approximate computing hasn't reached its full potential due to the lack of work on training methods. In this w

Suthakaran Ratnasingam, Spencer Wallace, Imran Amani, Jade Romero

The Lorenz curve portrays the inequality of income distribution. In this article, we develop three modified empirical likelihood (EL) approaches including adjusted empirical likelihood, transformed empirical likelihood, and transformed adjusted empirical likelihood to construct confidence intervals for the generalized Lorenz ordinate. We have shown that the

Mauricio Baker

Security risks from AI have motivated calls for international agreements that guardrail the technology. However, even if states could agree on what rules to set on AI, the problem of verifying compliance might make these agreements infeasible. To help clarify the difficulty of verifying agreements on AI$\unicode{x2013}$and identify actions that might reduce

Moh Kamalul Wafi

The area of fault detection is becoming more interesting since there have been many unique designs to detect or even compensate the faults, either from sensor or actuator. This paper applies the hydraulic system with interconnected tanks by implementing a leakage on one of the three tanks. The mathematical model along with the details of stability properties

E. M. Chudnovsky, D. A. Garanin

We study analytically and numerically on lattices containing $10^5$ spins, the integral absorption of microwaves by a random-anisotropy magnet, $\int d\omega P(\omega)$. It scales as $D^2_R/J$ on the random-anisotropy strength $D_R$ and the strength of the ferromagnetic exchange $J$ in low-anisotropy amorphous magnetic materials. At high anisotropy and in lo

Shanglin Zhou, Mikhail A. Bragin, Lynn Pepin, Deniz Gurevin

Network pruning is a widely used technique to reduce computation cost and model size for deep neural networks. However, the typical three-stage pipeline significantly increases the overall training time. In this paper, we develop a systematic weight-pruning optimization approach based on Surrogate Lagrangian relaxation, which is tailored to overcome difficul

Valentin V. Andreev, Miron B. Bekker, Joseph A. Cima

A function which is analytic and bounded in the Unit disk is called a generator for the Hardy space or the Bergman space if polynomials in that function are dense in the corresponding space. We characterize generators in terms of sub-spaces which are invariant under multiplication by the generator and also invariant under multiplication by z, and study wande

Muskan Garg

Motivated with recent advances in inferring users' mental state in social media posts, we identify and formulate the problem of finding causal indicators behind mental illness in self-reported text. In the past, we witness the presence of rule-based studies for causal explanation analysis on curated Facebook data. The investigation on transformer-based model

Oluwatosin Ogundare, Gustavo Quiros Araya, Yassine Qamsane

Industrial process engineering and PLC program development have traditionally favored Function Block Diagram (FBD) programming over classical imperative style programming like the object oriented and functional programming paradigms. The increasing momentum in the adoption and trial of ideas now classified as 'No Code' or 'Low Code' alongside the mainstream

Marcus Nordström, Henrik Hult, Atsuto Maki

This work presents a study on label noise in medical image segmentation by considering a noise model based on Gaussian field deformations. Such noise is of interest because it yields realistic looking segmentations and because it is unbiased in the sense that the expected deformation is the identity mapping. Efficient methods for sampling and closed form sol

Joyce Reimer, Sebastián A. Domínguez-Rivera, Joakim Sundnes, Raymond J. Spiteri

The refractory period of cardiac tissue can be quantitatively described using strength-interval (SI) curves. The information captured in SI curves is pertinent to the design of anti-arrhythmic devices including pacemakers and implantable cardioverter defibrillators. As computational cardiac modelling becomes more prevalent, it is feasible to consider the gen

Carsten Dietzel

Despite being a vast generalization of Garside groups, right $\ell$-groups with noetherian lattice structure and strong order unit share a lot of the properties of Garside groups. In the present work, we prove that every modular noetherian right $\ell$-group with strong order unit decomposes as a direct product of beams, which are sublattices that correspond

Sofia Sheikh, Meelad Ranaiefar, Pejman Honarmandi, Brent Vela

In additive manufacturing, the optimal processing conditions need to be determined to fabricate porosity-free parts. For this purpose, the design space for an arbitrary alloy needs to be scoped and analyzed to identify the areas of defects for different laser power-scan speed combinations and can be visualized using a printability map. Constructing printabil

Hamed Amini, Zhongyuan Cao, Agnès Sulem

We study continuous stochastic games with heterogeneous mean field interactions and jumps on large networks and explore their limit counterparts. We introduce the graphon game model based on a controlled graphon mean field stochastic differential equation system with jumps, which can be regarded as the limiting case of a finite game dynamic system as the num

Moh Kamalul Wafi

The theories of system identification have been highly elaborated so as to achieve the true system. This paper much discuses regarding the stochastic processes along with the divergent of whether or not the system has zero-mean under scenario of either white and coloured noise. The mathematical foundations, including mean, variance, covariance, optimal param

Luis H. Gallardo, Olivier. Rahavandrainy

We give necessary conditions for perfection of some families of odd numbers with special multiplicative forms. Extending earlier work of Steuerwald, Kanold, McDaniel et al.

Mohammed Salah, Abdulla Ayyad, Mohammed Ramadan, Yusra Abdulrahman

Countersink inspection is crucial in various automated assembly lines, especially in the aerospace and automotive sectors. Advancements in machine vision introduced automated robotic inspection of countersinks using laser scanners and monocular cameras. Nevertheless, the aforementioned sensing pipelines require the robot to pause on each hole for inspection

Mohammed Barkatou

The aim of this paper is first to give necessary and sufficient condition of existence (of free boundaries) for both Laplacian and bi-Laplacian operators in the case where the overdetermined condition is not constant. second, by using some classical ineqhalities, we get existence for several (other) overdetermined free boundary problems.

Kun Han, Yifeng Xiong, Chenyu You, Pooya Khosravi

Acquiring and annotating sufficient labeled data is crucial in developing accurate and robust learning-based models, but obtaining such data can be challenging in many medical image segmentation tasks. One promising solution is to synthesize realistic data with ground-truth mask annotations. However, no prior studies have explored generating complete 3D volu

Jared McDonald, Michael R. von Spakovsky, William T. Reynolds

The steepest-entropy-ascent quantum thermodynamic (SEAQT) framework is utilized to study the effects of temperature on polymer brushes. The brushes are represented by a discrete energy spectrum and energy degeneracies obtained through the Replica-Exchange Wang-Landau algorithm. The SEAQT equation of motion is applied to the density of states to establish a u

Irfan Glogić, Sarah Kistner, Birgit Schörkhuber

We study singularity formation for the heat flow of harmonic maps from $\R^d$. For each $d \geq 4$, we construct a compact, $d$-dimensional, rotationally symmetric target manifold that allows for the existence of a corotational self-similar shrinking solution (shortly \emph{shrinker}) that represents a stable blowup mechanism for the corresponding Cauchy pro

Cristian Meo, Anirudh Goyal, Justin Dauwels

Uncovering data generative factors is the ultimate goal of disentanglement learning. Although many works proposed disentangling generative models able to uncover the underlying generative factors of a dataset, so far no one was able to uncover OOD generative factors (i.e., factors of variations that are not explicitly shown on the dataset). Moreover, the dat

Asena Çetinkaya, Dmitrii Karp

Series containing the digamma function arise when calculating the parametric derivatives of the hypergeometric functions and play a role in evaluation of Feynman diagrams. As these series are typically non-hypergeometric, a few instances when they are summable in terms of hypergeometric functions are of importance. In this paper, we convert multi-term identi

Andrew M. Nguyen

Digital health, an emerging field integrating digital technologies into healthcare, is rapidly evolving and holds the potential to transform medical practices. Blockchain technology has garnered significant attention as a potential solution to various issues within digital health, including data security, automation, interoperability, and patient data owners

Zhanpeng He, Nikhil Chavan-Dafle, Jinwook Huh, Shuran Song

The choice of a grasp plays a critical role in the success of downstream manipulation tasks. Consider a task of placing an object in a cluttered scene; the majority of possible grasps may not be suitable for the desired placement. In this paper, we study the synergy between the picking and placing of an object in a cluttered scene to develop an algorithm for

Susik Yoon, Dongha Lee, Yunyi Zhang, Jiawei Han

Unsupervised discovery of stories with correlated news articles in real-time helps people digest massive news streams without expensive human annotations. A common approach of the existing studies for unsupervised online story discovery is to represent news articles with symbolic- or graph-based embedding and incrementally cluster them into stories. Recent l

Alejandra Manjarres-Triana, Juan Acevedo-Serna, Andrés A. Ramírez-Duque, Mario F. Jiménez

Surface electromyography (sEMG) is a technology to assess muscle activation, which is an important component in applications related to diagnosis, treatment, progression assessment, and rehabilitation of specific individuals' conditions. Recently, sEMG potential has been shown, since it can be used in a non-invasive manner; nevertheless, it requires careful

Anna N. Morozovska, Eugene A. Eliseev, Ayana Ghosh, Mykola E. Yelisieiev

Strain-induced transitions of polarization reversal in thin films of a ferrielectric CuInP$_2$S$_6$ (CIPS) with ideally-conductive electrodes is explored using the Landau-Ginzburg-Devonshire (LGD) approach with an eighth-order free energy expansion in polarization powers. Due to multiple potential wells, the height and position of which are temperature- and

Andrew M. Nguyen

This paper explores the information governance (IG) maturity of Amazon's One Medical (AOM), a digital health and telehealth primary care organization. Combining Amazon's technology expertise with One Medical's healthcare services, AOM aims to transform healthcare through a human-centered, technology-powered model. However, successfully integrating Amazon's d

Yuansi Chen, Khashayar Gatmiry

We study the mixing time of Metropolis-Adjusted Langevin algorithm (MALA) for sampling a target density on $\mathbb{R}^d$. We assume that the target density satisfies $\psi_\mu$-isoperimetry and that the operator norm and trace of its Hessian are bounded by $L$ and $\Upsilon$ respectively. Our main result establishes that, from a warm start, to achieve $\eps

Omar Maraqa, Saad Al-Ahmadi, Aditya Rajasekaran, Hamza Sokun

The various requirements in terms of data rates and latency in beyond 5G and 6G networks have motivated the integration of a variety of communications schemes and technologies to meet these requirements in such networks. Among these schemes are Terahertz (THz) communications, cooperative non-orthogonal multiple-access (NOMA)-enabled schemes, and mobile edge

Daniel T. Chen, Ethan H. Hansen, Xinpeng Li, Vinooth Kulkarni

Quantum circuit cutting has been proposed to help execute large quantum circuits using only small and noisy machines. Intuitively, cutting a qubit wire can be thought of as classically passing information of a quantum state along each element in a basis set. As the number of cuts increase, the number of quantum degrees of freedom needed to be passed through

R. Idema, C. Vuik

It is well known that for general linear systems, only optimal Krylov methods with long recurrences exist. For special classes of linear systems it is possible to find optimal Krylov methods with short recurrences. In this paper we consider the important class of linear systems with a shifted skew-symmetric coefficient matrix. We present the MRS3 solver, a m

Yuhang Wu, Zeyu Zheng, Tingyu Zhu

We formulate, analyze and solve the problem of best arm identification with fairness constraints on subpopulations (BAICS). Standard best arm identification problems aim at selecting an arm that has the largest expected reward where the expectation is taken over the entire population. The BAICS problem requires that an selected arm must be fair to all subpop

Dongyun Han, Abdullah-Al-Raihan Nayeem, Jason Windett, Isaac Cho

Sub-national governments across the United States implement a variety of policies to address large societal problems and needs. Many policies are picked up or adopted in other states. This process is called policy diffusion and allows researchers to analyze and compare social, political, and contextual characteristics that lead to adopting certain policies,

Cesar Cuenca, Maciej Dołęga, Alexander Moll

This paper establishes universal formulas describing the global asymptotics of two distinct discrete versions of $\beta$-ensembles in the high, low and fixed temperature regimes. Our results affirmatively answer a question posed by the second author and \'Sniady. We first introduce a special class of Jack measures on Young diagrams of arbitrary size, called

Wilson de Souza Junior, Taufik Abrao

In this work, two machine learning (ML)-based structures for joint detection-channel estimation in OFDM systems are proposed and extensively characterized. Both ML architectures, namely Deep Neural Network (DNN) and Extreme Learning Machine (ELM), are developed {to provide improved data detection performance} and compared with the conventional matched filter

Yash Belhe, Bing Xu, Sai Praveen Bangaru, Ravi Ramamoorthi

We propose a set of techniques to efficiently importance sample the derivatives of several BRDF models. In differentiable rendering, BRDFs are replaced by their differential BRDF counterparts which are real-valued and can have negative values. This leads to a new source of variance arising from their change in sign. Real-valued functions cannot be perfectly

Tanveer Ahmed Belal, G. M. Shahariar, Md. Hasanul Kabir

This paper presents a deep learning-based pipeline for categorizing Bengali toxic comments, in which at first a binary classification model is used to determine whether a comment is toxic or not, and then a multi-label classifier is employed to determine which toxicity type the comment belongs to. For this purpose, we have prepared a manually labeled dataset

Jasper van Tilburg, Luciano C. Siebert, Jochen L. Cremer

This paper presents a decentralized Multi-Agent Reinforcement Learning (MARL) approach to an incentive-based Demand Response (DR) program, which aims to maintain the capacity limits of the electricity grid and prevent grid congestion by financially incentivizing residential consumers to reduce their energy consumption. The proposed approach addresses the key

Tri Nguyen, Elia Merzari

The design of advanced nuclear reactors (Gen IV) involves an array of challenging fluid-flow issues that affect its safety and performance. The calculated DNS database will be instrumental in understanding the flow behavior in the downcomer. Ultimately, we aim to evaluate existing heat transfer correlations and some modifications are proposed.

Ashutosh Kumar, David Bérardan, Francois Brisset, Diana Dragoe

Development of new high-entropy oxides having configurational entropy dominating the phase stability has become a hot topic since the discovery of rock salt structure entropy-stabilized (ES)(MgCoNiCuZn)O in 2015. Herein, we report a set of novel entropy-stabilized fluorite oxides: Zr0.2Hf0.2Ce0.2Sn0.2Mn0.2O2-{\delta}, Zr0.2Hf0.2Ti0.2Mn0.2Ce0.2O2-{\delta}, Zr

S. P. Maydanyuk, V. S. Vasilevsky

A new model of bremsstrahlung emission in the scattering of light nuclei is constructed with main focus on strict cluster formulation of nuclear processes. Analysis is performed in frameworks of the folding approximation of the formalism with participation of $s$-nuclei. Reactions $p$ +$^4$He, $^2$D + $^4$He, $^3$H + $^4$He, $^3$He + $^4$He are included to a

Victor S. Monakhov, Irina L. Sokhor

We prove that if every Schmidt subgroup of a group $G$ is subnormal or modular, then $G/F(G)$ is cyclic

Amir Rajabpoor Alisepahi, Siddhartha Sarkar, Kai Sun, Jihong Ma

Topological insulators hold promises to realize exotic quantum phenomena in electronic, photonic, and phononic systems. Conventionally, topological indices, such as winding numbers, have been used to predict the number of topologically protected domain-wall states (TPDWSs) in topological insulators, a signature of the topological phenomenon called bulk-edge

Ben Kenwright

Writing an uncomplicated, robust, and scalable three-dimensional convex hull algorithm is challenging and problematic. This includes, coplanar and collinear issues, numerical accuracy, performance, and complexity trade-offs. While there are a number of methods available for finding the convex hull based on geometric calculations, such as, the distance betwee

R. A. Dantas, H. F. Santana Mota, E. R. Bezerra de Mello

In this paper we investigate the bosonic Casimir effect in a Lorentz-violating symmetry scenario. The theoretical model adopted consists of a real massive scalar quantum field confined in a region between two large parallel plates, having its dynamics governed by a modified Klein-Gordon equation that presents a Lorentz symmetry breaking term. In this context

Daniel Flores-Araiza, Francisco Lopez-Tiro, Jonathan El-Beze, Jacques Hubert

Identifying the type of kidney stones can allow urologists to determine their cause of formation, improving the prescription of appropriate treatments to diminish future relapses. Currently, the associated ex-vivo diagnosis (known as Morpho-constitutional Analysis, MCA) is time-consuming, expensive and requires a great deal of experience, as it requires a vi

Minjae Jeon, Lang Tong, Qing Zhao

We consider the problem of behind-the-meter EV charging by a prosumer, co-optimized with rooftop solar, electric battery, and flexible consumptions such as water heaters and HVAC. Under the time-of-use net energy metering tariff with the stochastic solar production and random EV charging demand, a finite-horizon surplus-maximization problem is formulated. We

Edmund Dable-Heath, Laura Casas, Victor Hertz, Christian Porter

Quantum computing poses a threat to contemporary cryptosystems, with advances to a state in which it will cause problems predicted for the next few decades. Many of the proposed cryptosystems designed to be quantum-secure are based on the Shortest Vector Problem and related problems. In this paper we use the Quadratic Unconstrained Binary Optimisation formul

Sumit Mukherjee, Daiki Tagami

In this paper we study a class of exponential family on permutations, which includes some of the commonly studied Mallows models. We show that the pseudo-likelihood estimator for the natural parameter in the exponential family is asymptotically normal, with an explicit variance. Using this, we are able to construct asymptotically valid confidence intervals.

Moumita Das, Biswajit Sen, Kishore Thapliyal, Anirban Pathak

The effect of the presence of two probe waveguides on the dynamics of hyper-Raman processes is studied in terms of quantum Zeno and anti-Zeno effects. Specifically, the enhancement (diminution) of the evolution of the hyper-Raman processes due to interaction with the probe waveguides via evanescent waves is viewed as quantum Zeno (anti-Zeno) effect. We consi

Y. Mishin

In Part I of this work, we proposed a stochastic model describing solute interactions with stationary and moving grain boundaries (GBs) and applied it to planar GBs in 1D systems. The model reproduces nonlinear GB dynamics, solute saturation in the segregation atmosphere, and all basic features of the solute drag effect. Part II of this work extends the mode

Haokai Hong, Min Jiang, Gary G. Yen

The large-scale multiobjective optimization problem (LSMOP) is characterized by simultaneously optimizing multiple conflicting objectives and involving hundreds of decision variables. Many real-world applications in engineering fields can be modeled as LSMOPs; simultaneously, engineering applications require insensitivity in performance. This requirement usu

Y. Mishin

A simple stochastic model of solute drag by moving grain boundaries (GBs) is presented. Using a small number of parameters, the model describes solute interactions with GBs and captures nonlinear GB dynamics, solute saturation in the segregation atmosphere, and the breakaway from the atmosphere. The model is solved by kinetic Monte-Carlo (KMC) simulations wi

Corrie Green, Dr Yang Jiang, Dr John Isaacs

Designed with an accessible first design approach, the presented paper describes how exploiting humans proprioception ability in 3D space can result in a more natural interaction experience when using a 3D graphical user interface in a virtual environment. The modularity of the designed interface empowers the user to decide where they want to place interface

Francesco Mauro, Luigi Russo, Fjoralba Janku, Alessandro Sebastianelli

This study aims to analyse the Nitrogen Dioxide (NO2) pollution in the Emilia Romagna Region (Northern Italy) during 2019, with the help of satellite retrievals from the Sentinel-5P mission of the European Copernicus Programme and ground-based measurements, obtained from the ARPA site (Regional Agency for the Protection of the Environment). The final goal is

Javad Peymanfard, Ali Lashini, Samin Heydarian, Hossein Zeinali

Lip-reading has made impressive progress in recent years, driven by advances in deep learning. Nonetheless, the prerequisite such advances is a suitable dataset. This paper provides a new in-the-wild dataset for Persian word-level lipreading containing 244,000 videos from approximately 1,800 speakers. We evaluated the state-of-the-art method in this field an

Haokai Hong, Min Jiang, Qiuzhen Lin, Kay Chen Tan

We define very large-scale multiobjective optimization problems as optimizing multiple objectives (VLSMOPs) with more than 100,000 decision variables. These problems hold substantial significance, given the ubiquity of real-world scenarios necessitating the optimization of hundreds of thousands, if not millions, of variables. However, the larger dimension in