October 2022 arXiv papers — page 13

Ebbie Awino, Lilian Wanzare, Lawrence Muchemi, Barack Wanjawa

Building automatic speech recognition (ASR) systems is a challenging task, especially for under-resourced languages that need to construct corpora nearly from scratch and lack sufficient training data. It has emerged that several African indigenous languages, including Kiswahili, are technologically under-resourced. ASR systems are crucial, particularly for

Tianduo Wang, Wei Lu

Fine-tuning a pre-trained language model via the contrastive learning framework with a large amount of unlabeled sentences or labeled sentence pairs is a common way to obtain high-quality sentence representations. Although the contrastive learning framework has shown its superiority on sentence representation learning over previous methods, the potential of

Luca Castri, Sariah Mghames, Marc Hanheide, Nicola Bellotto

Exploiting robots for activities in human-shared environments, whether warehouses, shopping centres or hospitals, calls for such robots to understand the underlying physical interactions between nearby agents and objects. In particular, modelling cause-and-effect relations between the latter can help to predict unobserved human behaviours and anticipate the

Jingyang Zhao, Mingyu Xiao

The Capacitated Vehicle Routing Problem (CVRP) is one of the most extensively studied problems in combinatorial optimization. Based on customer demand, we distinguish three variants of CVRP: unit-demand, splittable, and unsplittable. In this paper, we consider $k$-CVRP in general metrics and on general graphs, where $k$ is the vehicle capacity. All three ver

Erik Duse

We show that the energy-momentum equations arising from inner variations whose Lagrangian satisfies a generic symmetry condition are generically ill-posed. This is done by proving that there exists a subclass of Lipschitz solutions that are also solutions to a differential inclusion. In particular these solutions can be nowhere C1. We prove that these soluti

Xiupeng Xie, Zhun Lu

We present a model calculation of transverse momentum dependent (TMD) gluon fragmentation functions for the spin-1/2 and spin-0 hadrons. The model is based on the assumption that a time-like off-shell gluon can fragments into a hadron and a single spectator particle. So far such spectator models have only been used to calculate the TMD distribution functions

H. Chen, K. T. Gkikas, P. T. Nguyen

In this paper, we study the Poisson problem involving a fractional Hardy operator and a measure source. The complex interplay between the nonlocal nature of the operator, the peculiar effect of the singular potential and the measure source induces several new fundamental difficulties in comparison with the local case. To overcome these difficulties, we perfo

Seyed Roozbeh Razavi Rohani, Saeed Hedayatian, Mahdieh Soleymani Baghshah

Sample efficiency has been a key issue in reinforcement learning (RL). An efficient agent must be able to leverage its prior experiences to quickly adapt to similar, but new tasks and situations. Meta-RL is one attempt at formalizing and addressing this issue. Inspired by recent progress in meta-RL, we introduce BIMRL, a novel multi-layer architecture along

C. Rorai, F. Toschi, I. Pagonabarraga

In active nematic liquid crystals activity is able to drive chaotic spatiotemporal flows referred to as active turbulence. Active turbulence has been characterized through theoretical and experimental work as a low Reynolds number phenomenon. We show that, in two-dimensions, the active forcing alone is able to trigger hydrodynamic turbulence leading to the c

Tanoy Kanti Konar, Ayan Patra, Rivu Gupta, Srijon Ghosh

We provide an architecture for a multimode quantum battery (QB) based on the framework of continuous variable (CV) systems. We examine the performance of the battery by using a generic class of multimode initial states whose parameters can be tuned to produce separable as well as entangled states and that can be charged locally as well as globally by Gaussia

Sambhab Dan, Kuldeep Kargeti, R. C. Sahoo, Shovan Dan

In the present study, we have discussed the up-turn behavior in the resistivity pattern of the topological nodal line semimetal InBi. We argued that such nature could be generalized with a mathematical model, that can be applied to any compounds exhibiting similar behavior. The extremely high magnetoresistance (XMR) has also been explained by the carrier com

Aliakbar Alijani

Let $\ell$ be the category of all locally compact abelian (LCA) groups. Let $G\in\ell$ and $H\subseteq G$. The first Ulm subgroup of $G$ is denoted by $G^{(1)}$ and the closure of $H$ by $\overline{H}$. A proper short exact sequence $0\to A\stackrel{\phi}{\to} B\stackrel{\psi}{\to} C\to 0$ in $\ell$ is said to be a $TFU$ extension if $0\to \overline{A^{(1)}}

Ziyu Wang, Yucen Luo, Yueru Li, Jun Zhu

Many problems in causal inference and economics can be formulated in the framework of conditional moment models, which characterize the target function through a collection of conditional moment restrictions. For nonparametric conditional moment models, efficient estimation often relies on preimposed conditions on various measures of ill-posedness of the hyp

Jie Yan, Jing Liu, Ji Qi, Zhong-Yuan Zhang

Federated clustering (FC) is an extension of centralized clustering in federated settings. The key here is how to construct a global similarity measure without sharing private data, since the local similarity may be insufficient to group local data correctly and the similarity of samples across clients cannot be directly measured due to privacy constraints.

Subaveerapandiyan A, Priyanka Sinha

Digital literacy is the skill of finding, evaluating, consuming, and generating information using digital technologies. The study attempted to comprehend university students' digital reading habits and skills. It also provides a glimpse of the pupils' favorite reading materials, including physical and digital sources. We examined BSc and BE Computer Science

Jarah Evslin, Andrew B. Royston, Baiyang Zhang

We answer the question: If a vacuum sector Hamiltonian is regularized by an energy cutoff, how is the one-kink sector Hamiltonian regularized? We find that it is not regularized by an energy cutoff, indeed normal modes of all energies are present in the kink Hamiltonian, but rather the decomposition of the field into normal mode operators yields coefficients

Alexander Filusch, Holger Fehske

Mechanical deformations in $\alpha$-$\mathcal{T}_3$ lattices induce local pseudomagnetic fields of opposite directionality for different valleys. When this strain is equipped with a dynamical drive, it generates a complementary valley-asymmetric pseudoelectric field which is expected to accelerate electrons. We propose that by combining these effects by a ti

Hongguang Sun, Zelun Zhao, Hu Cheng, Jiangbin Lyu

Emerged as a promising solution for future wireless communication systems, intelligent reflecting surface (IRS) is capable of reconfiguring the wireless propagation environment by adjusting the phase-shift of a large number of reflecting elements. To quantify the gain achieved by IRSs in the radio frequency (RF) powered Internet of Things (IoT) networks, in

Youngjoon Lee, Sangwoo Park, Joonhyuk Kang

Federated learning (FL) aims at optimizing a shared global model over multiple edge devices without transmitting (private) data to the central server. While it is theoretically well-known that FL yields an optimal model -- centrally trained model assuming availability of all the edge device data at the central server -- under mild condition, in practice, it

Youngjoon Lee, Sangwoo Park, Joonhyuk Kang

While being an effective framework of learning a shared model across multiple edge devices, federated learning (FL) is generally vulnerable to Byzantine attacks from adversarial edge devices. While existing works on FL mitigate such compromised devices by only aggregating a subset of the local models at the server side, they still cannot successfully ignore

Yusong Wang, Shaoning Li, Xinheng He, Mingyu Li

Geometric deep learning has been revolutionizing the molecular modeling field. Despite the state-of-the-art neural network models are approaching ab initio accuracy for molecular property prediction, their applications, such as drug discovery and molecular dynamics (MD) simulation, have been hindered by insufficient utilization of geometric information and h

Malvika Garikapati, Santosh Kumar, He Zhang, Yong Meng Sua

We experimentally demonstrate a programmable parametric mode sorter of high-dimensional signals in a composite spatiotemporal Hilbert space through mode-selective quantum frequency up-conversion. As a concrete example and with quantum communication applications in mind, we consider the Laguerre-Gaussian and Hermite-Gaussian modes as the spatial and temporal

ALICE Collaboration, Joyful Elma Mdhluli

Heavy-flavour (HF) quarks, i.e. charm and beauty, are produced in the early stages of ultra-relativistic collisions via hard scattering processes. The measurement of heavy flavour as a function of charged-particle multiplicity not only provides information on how the production mechanisms are influenced by the event activity, but can also be considered as a

Fu-Bo Li, Kun Xu, Ze-Chun Hu

Let $B(n,p)$ denote a binomial random variable with parameters $n$ and $p$. Vas\v{e}k Chv\'{a}tal conjectured that for any fixed $n\geq 2$, as $m$ ranges over $\{0,\ldots,n\}$, the probability $q_m:=P(B(n,m/n)\leq m)$ is the smallest when $m$ is closest to $\frac{2n}{3}$. This conjecture has been solved recently. Motivated by this conjecture, in this paper,

Yu-Hua Huang, Xinzhi Teng, Jiang Zhang, Zhi Chen

The inherent characteristics of lung tissues, which are independent of breathing manoeuvre, may provide fundamental information on lung function. This paper attempted to study function-correlated lung textures and their spatial distribution from CT. 21 lung cancer patients with thoracic 4DCT scans, DTPA-SPECT ventilation images (V), and available pulmonary f

Elijah Pelofske

Quantum annealing is a novel type of analog computation that aims to use quantum mechanical fluctuations to search for optimal solutions of Ising problems. Quantum annealing in the transverse field Ising model, implemented on D-Wave devices, works by applying a time dependent transverse field, which puts all qubits into a uniform state of superposition, and

Kohei Honda, Hiroyuki Okuda, Tatsuya Suzuki, Akira Ito

This study presents a new framework for vehicle motion planning and control based on the automatic generation of model predictive controllers (MPCs) named MPC Builder. In this framework, several components necessary for MPC, such as prediction models, constraints, and cost functions, are prepared in advance. The MPC Builder then generates various MPCs online

K. Ritsuka, Karen Rudie

Solutions to decentralized discrete-event systems problems are characterized by the way local decisions are fused to yield a global decision. A fusion rule is colloquially called an architecture. Current approaches do not provide a direct way to compare existing architectures. Determining whether an architecture is more permissive than another architecture h

Kohei Honda, Kenji Koide, Masashi Yokozuka, Shuji Oishi

This paper presents a LiDAR odometry estimation framework called Generalized LOAM. Our proposed method is generalized in that it can seamlessly fuse various local geometric shapes around points to improve the position estimation accuracy compared to the conventional LiDAR odometry and mapping (LOAM) method. To utilize continuous geometric features for LiDAR

Huiying Pan, Shusen Zhao, Weibin Zhang, Huitao Zhang

Multi-spectral CT (MSCT) is increasingly used in industrial non-destructive testing and medical diagnosis because of its outstanding performance like material distinguishability. The process of obtaining MSCT data can be modeled as nonlinear equations and the basis material decomposition comes down to the inverse problem of the nonlinear equations. For diffe

Yuhe Guo, Zhewei Wei

Graph Convolutional Networks (GCNs), which use a message-passing paradigm with stacked convolution layers, are foundational methods for learning graph representations. Recent GCN models use various residual connection techniques to alleviate the model degradation problem such as over-smoothing and gradient vanishing. Existing residual connection techniques,

Xiangrui Yang

Predicting material properties base on micro structure of materials has long been a challenging problem. Recently many deep learning methods have been developed for material property prediction. In this study, we propose a crystal representation learning framework, Orbital CrystalNet, OCrystalNet, which consists of two parts: atomic descriptor generation and

Yuntian Liu, Jiayu Li, Qihang Liu

The long-sought Chern insulators that manifest quantum anomalous Hall effect are typically considered to occur solely in ferromagnets. Here, we theoretically predict the realizability of Chern insulators in antiferromagnets, of which the magnetic sublattices are connected by symmetry operators enforcing zero net moment. Our symmetry analysis provides compreh

Xiangyan Ding, Jing Ma, Liying Tan, Amr S. Helmy

Photon pairs generated by spontaneous parametric down-conversion are essential for optical quantum information processing, in which the quality of biphoton states is crucial for the performance. To engineer the biphoton wavefunction (BWF) on-chip, the pump envelope function and the phase matching function are commonly adjusted, while the modal field overlap

Jiayi Yao, Ping Li, Xiatao Kang, Yuzhe Wang

Convolutional Neural Network (CNN) is more and more widely used in various fileds, and its computation and memory-demand are also increasing significantly. In order to make it applicable to limited conditions such as embedded application, network compression comes out. Among them, researchers pay more attention to network pruning. In this paper, we encode th

Yao Chen, Zilong Zhang, Sulan Ni, Hao Ning

According to the standard scenario of plasma emission, escaping radiations are generated by the nonlinear development of the kinetic bump-on-tail instability driven by a single beam of energetic electrons interacting with plasmas. Here we conduct fully-kinetic electromagnetic particle-in-cell simulations to investigate plasma emission induced by the ring-dis

Meng Li, Xue-Ping Wang

This paper deals with the resolutions of fuzzy relation equations with addition-min composition. When the fuzzy relation equations have a solution, we first propose an algorithm to find all minimal solutions of the fuzzy relation equations and also supply an algorithm to find all maximal solutions of the fuzzy relation equations, which will be illustrated, r

Hajime Koba

We study the governing equations for the motion of the fluid particles near air-water interface from an energetic point of view. Since evaporation and condensation phenomena occur at the interface, we have to consider phase transition. This paper applies an energetic variational approach to derive multiphase flow systems with phase transition, where a multip

Freiman Triana-Arango, Gabriel Ramos-Ortiz, Roberto Ramírez-Alarcón

Recently, different experimental methods intended to detect the entangled two-photon absorption (ETPA) phenomenon in a variety of materials have been reported. The present work explores a different approach on which the ETPA process is studied based on the changes induced in the visibility of a Hong-Ou-Mandel (HOM) interferogram. By using an organic solution

Ruiming Zhang

Abstract. In this work we use an elementary method to derive an upper bound on the right half-plane for genus 0 entire functions if it has only negative zeros. The bound only uses information of the function on the positive real axis. Applications to the modified Bessel functions and the Airy function are also provided.

Jubee Sohn, Margaret J. Geller, Ho Seong Hwang, Daniel G. Fabricant

HectoMAP is a dense redshift survey of 95,403 galaxies based primarily on MMT spectroscopy with a median redshift $z = 0.345$. The survey covers 54.64 square degrees in a 1.5$^\circ$ wide strip across the northern sky centered at a declination of 43.25$^\circ$. We report the redshift, the spectral indicator D$_{n}$4000, and the stellar mass. The red selected

Jiachen Lian, Alan W Black, Yijing Lu, Louis Goldstein

Articulatory representation learning is the fundamental research in modeling neural speech production system. Our previous work has established a deep paradigm to decompose the articulatory kinematics data into gestures, which explicitly model the phonological and linguistic structure encoded with human speech production mechanism, and corresponding gestural

Jiyue He, Arkady M. Pertsov, Elizabeth M. Cherry, Flavio H. Fenton

Over the past two decades there has been a steady trend towards the development of realistic models of cardiac conduction with increasing levels of detail. However, making models more realistic complicates their personalization and use in clinical practice due to limited availability of tissue and cellular scale data. One such limitation is obtaining informa

Asma Elmaizi, Maria Merzouqi, Elkebir Sarhrouni, Ahmed hammouch

The high dimensionality of hyperspectral images often imposes a heavy computational burden for image processing. Therefore, dimensionality reduction is often an essential step in order to remove the irrelevant, noisy and redundant bands. And consequently, increase the classification accuracy. However, identification of useful bands from hundreds or even thou

Deepanway Ghosal, Navonil Majumder, Rada Mihalcea, Soujanya Poria

We propose a simple refactoring of multi-choice question answering (MCQA) tasks as a series of binary classifications. The MCQA task is generally performed by scoring each (question, answer) pair normalized over all the pairs, and then selecting the answer from the pair that yield the highest score. For n answer choices, this is equivalent to an n-class clas

Victor Dibia, Adam Fourney, Gagan Bansal, Forough Poursabzi-Sangdeh

Large language models have demonstrated great potential to assist programmers in generating code. For such human-AI pair programming scenarios, we empirically demonstrate that while generated code is most often evaluated in terms of their functional correctness (i.e., whether generations pass available unit tests), correctness does not fully capture (e.g., m

Feng Chen, Ming Xue

Neural network quantum states (NQS), incorporating with variational Monte Carlo (VMC) method, are shown to be a promising way to investigate quantum many-body physics. Whereas vanilla VMC methods perform one gradient update per sample, we introduce a novel objective function with proximal optimization (PO) that enables multiple updates via reusing the mismat

Shibin Dai, Joseph Renzi, Steven M. Wise

The degenerate de Gennes-Cahn-Hilliard (dGCH) equation is a model for phase separation which may more closely approximate surface diffusion than others in the limit when the thickness of the transition layer approaches zero. As a first step to understand the limiting behavior, in this paper we study the $\Gamma$--limit of the dGCH energy. We find that its $\

Chunlin Liu, Xue Liu

Let $(X, d)$ be a compact metric space, $f: X \to X$ be a continuous transformation with the almost weak specification property and $\varphi: X \to \mathbb{R}$ be a continuous function. We consider the set (called the irregular set for $\varphi$) of points for which the Birkhoff average of $\varphi$ does not exist and show that this set is either empty or ca

Thomas Britz, Himadri Shekhar Chakraborty, Reina Ishikawa, Tsuyoshi Miezaki

In this work, we introduce the harmonic generalization of the $m$-tuple weight enumerators of codes over finite Frobenius rings. A harmonic version of the MacWilliams-type identity for $m$-tuple weight enumerators of codes over finite Frobenius ring is also given. Moreover, we define the demi-matroid analogue of well-known polynomials from matroid theory, na

Jinta Weng, Yue Hu, Jing Qiu, Heyan Huan

The effectiveness of prompt learning has been demonstrated in different pre-trained language models. By formulating suitable template and choosing representative label mapping, prompt learning can be used as an efficient knowledge probe. However, finding suitable prompt in existing methods requires multiple experimental attempts or appropriate vector initial

Nathan Ma

Many real world person-person or person-product relationships can be modeled graphically. More specifically, bipartite graphs can be especially useful when modeling scenarios that involve two disjoint groups. As a result, many existing papers have utilized bipartite graphs for the classical link recommendation problem. In this paper, using the principle of b

Andrew Aberdein

Disagreements that resist rational resolution, often termed ``deep disagreements'', have been the focus of much work in epistemology and informal logic. In this paper, I argue that they also deserve the attention of philosophers of mathematics. I link the question of whether there can be deep disagreements in mathematics to a more familiar debate over whethe

Cheng Cheng, Lin Fu

In this work, we study the scale characteristics of the log- and outer-region motions and structures in subsonic and supersonic turbulence. To this end, a series of direct numerical simulations of the compressible turbulent channel flow at medium Reynolds numbers are performed. Based on this database, the streamwise and spanwise length scales of the outer-re

Mitch Hill, Erik Nijkamp, Jonathan Mitchell, Bo Pang

This work proposes a method for using any generator network as the foundation of an Energy-Based Model (EBM). Our formulation posits that observed images are the sum of unobserved latent variables passed through the generator network and a residual random variable that spans the gap between the generator output and the image manifold. One can then define an

Allison Irvine, Tan Dang, M. Murat Dundar, Bartek Rajwa

Moment invariants are well-established and effective shape descriptors for image classification. In this report, we introduce a package for R-language, named IM, that implements the calculation of moments for images and allows the reconstruction of images from moments within an object-oriented framework. Several types of moments may be computed using the IM

Jun Xia, Yanqiao Zhu, Yuanqi Du, Stan Z. Li

Deep learning has achieved remarkable success in learning representations for molecules, which is crucial for various biochemical applications, ranging from property prediction to drug design. However, training Deep Neural Networks (DNNs) from scratch often requires abundant labeled molecules, which are expensive to acquire in the real world. To alleviate th

Vikram Mahamiya, Juhee Dewangan, Alok Shukla, Brahmananda Chakraborty

Motivated by the recent successful synthesis of biphenylene structure [Science 372, (2021), 852], we have explored the sensing properties of this material towards the catechol biomolecule by performing the first-principles density functional theory and molecular dynamics simulations. Pristine biphenylene sheet adsorbs catechol molecule with a binding energy

Zichu Liu, Lacra Pavel

Despite the success of generative adversarial networks (GANs) in generating visually appealing images, they are notoriously challenging to train. In order to stabilize the learning dynamics in minimax games, we propose a novel recursive reasoning algorithm: Level $k$ Gradient Play (Lv.$k$ GP) algorithm. In contrast to many existing algorithms, our algorithm

Yongqiang Wang, Zhehuai Chen, Chengjian Zheng, Yu Zhang

We propose a novel method to accelerate training and inference process of recurrent neural network transducer (RNN-T) based on the guidance from a co-trained connectionist temporal classification (CTC) model. We made a key assumption that if an encoder embedding frame is classified as a blank frame by the CTC model, it is likely that this frame will be align

Xichan Liu, Ligong Wang

Let $G$ be a graph with adjacency matrix $A(G)$ and degree diagonal matrix $D (G)$. In 2017, Nikiforov defined the matrix $A_\alpha(G) = \alpha D(G) + (1-\alpha)A(G)$ for any real $\alpha\in[0,1]$. The largest eigenvalue of $A_\alpha(G)$ is called the $A_\alpha$ spectral radius or the $A_\alpha$-index of $G$. Let $\mathcal{G}_{n,k}^d$ be the set of $k$-conne

Xiao-Ming Bi, Lu Chen, Ke Wang

Primordial black holes (PBHs) are mainly characterized by their mass function, in which there may be some huge suppression for certain mass spans. If this is the case, the absence of these PBHs will form mass gaps. In this paper, we investigate the PBH mass function with mass gap. Firstly, to obtain a data-supported PBH mass function with mass gap for subsol

Ziyu Shan, Qi Yang, Rui Ye, Yujie Zhang

With the rapid development of 3D vision, point cloud has become an increasingly popular 3D visual media content. Due to the irregular structure, point cloud has posed novel challenges to the related research, such as compression, transmission, rendering and quality assessment. In these latest researches, point cloud quality assessment (PCQA) has attracted wi

Bing Mao, Xiaoqun Wu, Hui Liu, Yuhua Xu

Adaptive fuzzy control strategies are established to achieve global prescribed performance with prescribed-time convergence for strict-feedback systems with mismatched uncertainties and unknown nonlinearities. Firstly, to quantify the transient and steady performance constraints of the tracking error, a class of prescribed-time prescribed performance functio

Seyed Mehdi Iranmanesh, Xiaotong Chen, Kuo-Chin Lien

The DETR object detection approach applies the transformer encoder and decoder architecture to detect objects and achieves promising performance. In this paper, we present a simple approach to address the main problem of DETR, the slow convergence, by using representation learning technique. In this approach, we detect an object bounding box as a pair of key

Zhenghuan Gao, Bendong Lou, Jinju Xu

In this paper we consider a mean curvature flow $V=H+A$ in a high dimensional cylinder $\Omega\times \R$, where, $A$ is a constant, $\Omega$ is a bounded domain in $\R^n$, and, for a hypersurface $y=u(x,t)$ over $\Omega$, $V$ and $H$ denote its normal velocity and mean curvature, respectively. Assume the hypersurface contacts the cylinder boundary $\partial

Catherine Pfaff, Emily Hunter, Haozhi Hong, Daniel Forestell

In this paper, we examine how soccer players can use their spatial relationships to control parts of the field and safely move play up the field via chains of ``safe configurations,'' i.e. configurations of players on a team ensuring the possessor of the ball has a collection of open passing options all connected by open passing lanes. An underlying philosop

Chang Wang, Jinzhu Jia

The Behrens-Fisher Problem is a classical statistical problem. It is to test the equality of the means of two normal populations using two independent samples, when the equality of the population variances is unknown. Linnik (1968) has shown that this problem has no exact fixed-level tests based on the complete sufficient statistics. However, exact conventio

Moitreya Chatterjee, Narendra Ahuja, Anoop Cherian

There exists an unequivocal distinction between the sound produced by a static source and that produced by a moving one, especially when the source moves towards or away from the microphone. In this paper, we propose to use this connection between audio and visual dynamics for solving two challenging tasks simultaneously, namely: (i) separating audio sources

Ben Kenwright

In this paper, we examine a ready-to-use, robust, and computationally fast fixed-size memory pool manager with no-loops and no-memory overhead that is highly suited towards time-critical systems such as games. The algorithm achieves this by exploiting the unused memory slots for bookkeeping in combination with a trouble-free indexing scheme. We explain how i

Rehana Mahfuz, Yinyi Guo, Erik Visser

Audio captioning quality metrics which are typically borrowed from the machine translation and image captioning areas measure the degree of overlap between predicted tokens and gold reference tokens. In this work, we consider a metric measuring semantic similarities between predicted and reference captions instead of measuring exact word overlap. We first ev

Bendong Lou, Maolin Zhou

Consider the Cauchy problem of one dimensional porous medium equation (PME) with reactions. We first prove a general convergence result, that is, any bounded global solution starting at a nonnegative compactly supported initial data converges as $t\to \infty$ to a nonnegative zero of the reaction term or a ground state stationary solution. Based on it, we gi

A. J. Healey, S. C. Scholten, A. Nadarajah, P. Singh

Dense, near-surface (within 10 nm) ensembles of nitrogen-vacancy (NV) centres in diamond are rapidly moving into prominence as the workhorse of a variety of envisaged applications, ranging from the imaging of fast-fluctuating magnetic signals to the facilitation of nuclear hyperpolarisation. Unlike their bulk counterparts, near-surface ensembles suffer from

Roben Delos Reyes, Kyunghwan Son, Jinhwan Jung, Wan Ju Kang

Intrinsic rewards have been increasingly used to mitigate the sparse reward problem in single-agent reinforcement learning. These intrinsic rewards encourage the agent to look for novel experiences, guiding the agent to explore the environment sufficiently despite the lack of extrinsic rewards. Curiosity-driven exploration is a simple yet efficient approach

Xinquan Yang, Xuguang Li, Xuechen Li, Peixi Wu

Implant prosthesis is the most appropriate treatment for dentition defect or dentition loss, which usually involves a surgical guide design process to decide the implant position. However, such design heavily relies on the subjective experiences of dentists. In this paper, a transformer-based Implant Position Regression Network, ImplantFormer, is proposed to

Xichan Liu, Ligong Wang

Let $G$ be a graph with adjacency matrix $A(G)$ and degree diagonal matrix $D (G)$. In 2017, Nikiforov [Appl. Anal. Discrete Math., 11 (2017) 81--107] defined the matrix $A_\alpha(G) = \alpha D(G) + (1-\alpha)A(G)$ for any real $\alpha\in[0,1]$. The largest eigenvalue of $A(G)$ is called the spectral radius of $G$, while the largest eigenvalue of $A_\alpha(G

Marco Bernardi

Exact simulations of quantum circuits (QCs) are currently limited to $\sim$50 qubits because the memory and computational cost required to store the QC wave function scale exponentially with qubit number. Therefore, developing efficient schemes for approximate QC simulations is a current research focus. Here we show simulations of QCs with a method inspired

Roy H. Goodman, Brandon M. Behring

The point vortex system is a system of longstanding interest in nonlinear dynamics, describing the motion of a two-dimensional inviscid fluid that is irrotational except at a discrete set of moving point vortices, at which the vorticity diverges. The leapfrogging orbit consists of two rotating pairs of like-signed vortices which, taken as a quartet, propagat

Hung-Jui Guo, Jonathan Z. Bakdash, Laura R. Marusich, Balakrishnan Prabhakaran

Augmented Reality (AR) and Mixed Reality (MR) have been two of the most explosive research topics in the last few years. Head-Mounted Devices (HMDs) are essential intermediums for using AR and MR technology, playing an important role in the research progress in these two areas. Behavioral research with users is one way of evaluating the technical progress an

Philip Lu, Kiyoharu Kawana, Alexander Kusenko

The intermediate mass black hole range, $10\lesssim M_{\rm BH}^{}/M_\odot^{}\lesssim 10^{5} $, has long offered enticing possibilities for primordial black holes (PBH), with populations in this range postulated to be responsible for some of the black hole binary merger detected events as well as the existence of supermassive black holes embedded at galactic

Saurav K. Aryal, Howard Prioleau, Gloria Washington

With increasing globalization and immigration, various studies have estimated that about half of the world population is bilingual. Consequently, individuals concurrently use two or more languages or dialects in casual conversational settings. However, most research is natural language processing is focused on monolingual text. To further the work in code-sw

Yuntao Wang, Zhou Su, Abderrahim Benslimane, Qichao Xu

The proliferation of unmanned aerial vehicles (UAVs) opens up new opportunities for on-demand service provisioning anywhere and anytime, but also exposes UAVs to a variety of cyber threats. Low/medium interaction honeypots offer a promising lightweight defense for actively protecting mobile Internet of things, particularly UAV networks. While previous resear

Byunghyun Ban

An easier and intuitive interface architecture is necessary for digital twin of plant factory. I suggest an immersive and interactive mixed reality interface for digital twin models of smart farming, for remote work rather than simulation of components. The environment is constructed with UI display and a streaming background scene, which is a real time scen

Laurel Heck, Victor Reis, Thomas Rothvoss

The vector balancing constant $\mathrm{vb}(K,Q)$ of two symmetric convex bodies $K,Q$ is the minimum $r \geq 0$ so that any number of vectors from $K$ can be balanced into an $r$-scaling of $Q$. A question raised by Schechtman is whether for any zonotope $K \subseteq \mathbb{R}^d$ one has $\mathrm{vb}(K,K) \lesssim \sqrt{d}$. Intuitively, this asks whether a

Alexey S. Koshelev, K. Sravan Kumar, Alexei A. Starobinsky

In [1], a most general higher curvature non-local gravity action was derived that admits a particular $R^2$-like inflationary solution predicting the spectral index of primordial scalar perturbations $n_s(N)\approx 1-\frac{2}{N}$, where $N$ is the number of e-folds before the end of inflation, $N\gg 1$, any value of the tensor-to-scalar ratio $r(N)<0.036$ an

Satesh Ramdhani

The separation of performance metrics from gradient based loss functions may not always give optimal results and may miss vital aggregate information. This paper investigates incorporating a performance metric alongside differentiable loss functions to inform training outcomes. The goal is to guide model performance and interpretation by assuming statistical

Yi Cui, Yao Li, Jayson R. Miedema, Sherif Farag

Automated region of interest detection in histopathological image analysis is a challenging and important topic with tremendous potential impact on clinical practice. The deep-learning methods used in computational pathology help us to reduce costs and increase the speed and accuracy of regions of interest detection and cancer diagnosis. In this work, we pro

Jason M. Altschuler

In this short expository note, we describe a unified algorithmic perspective on several classical problems which have traditionally been studied in different communities. This perspective views the main characters -- the problems of Optimal Transport, Minimum Mean Cycle, Matrix Scaling, and Matrix Balancing -- through the same lens of optimization problems o

Vishal Srikanth, Dylan Peverall, Andrey V. Kuznetsov

The focus of this paper is to systematically study the influence of solid obstacle surface roughness in porous media on the microscale flow physics and report its effect on macroscale drag and Nusselt number. The Reynolds averaged flow field is numerically simulated for a flow through a periodic porous medium consisting of an in-line arrangement of square cy

Yashish M. Siriwardena, Carol Espy-Wilson, Shihab Shamma

Most organisms including humans function by coordinating and integrating sensory signals with motor actions to survive and accomplish desired tasks. Learning these complex sensorimotor mappings proceeds simultaneously and often in an unsupervised or semi-supervised fashion. An autoencoder architecture (MirrorNet) inspired by this sensorimotor learning paradi

Neelanjan Bhowmik, Toby P. Breckon

X-ray baggage security screening is in widespread use and crucial to maintaining transport security for threat/anomaly detection tasks. The automatic detection of anomaly, which is concealed within cluttered and complex electronics/electrical items, using 2D X-ray imagery is of primary interest in recent years. We address this task by introducing joint objec

David Boozer

We describe a strategy for constructing reduced Khovanov homology for links in lens spaces by generalizing a symplectic interpretation of reduced Khovanov homology for links in $S^3$ due to Hedden, Herald, Hogancamp, and Kirk. The strategy relies on a partly conjectural description of the Fukaya category of the traceless $SU(2)$ character variety of the 2-to

Jianqiao Wangni

We propose a robust variant of boosting forest to the various adversarial defense methods, and apply it to enhance the robustness of the deep neural network. We retain the deep network architecture, weights, and middle layer features, then install gradient boosting forest to select the features from each layer of the deep network, and predict the target. For

Yashish M. Siriwardena, Carol Espy-Wilson

In this work, we incorporated acoustically derived source features, aperiodicity, periodicity and pitch as additional targets to an acoustic-to-articulatory speech inversion (SI) system. We also propose a Temporal Convolution based SI system, which uses auditory spectrograms as the input speech representation, to learn long-range dependencies and complex int

Thomas J. Misa

This paper examines the historical dimension of gender bias in the US computing workforce. It offers new quantitative data on the computing workforce prior to the availability of US Census data in the 1970s. Computer user groups (including SHARE, Inc., and the Mark IV software user group) are taken as a cross-section of the computing workforce. A novel metho

Chanyoung Sung

A smooth closed manifold $M$ is called almost Ricci-flat if $$\inf_g||\textrm{Ric}_g||_\infty\cdot \textrm{diam}_g(M)^2=0$$ where $\textrm{Ric}_g$ and $\textrm{diam}_g$ denote the Ricci tensor and the diameter of $g$ respectively and $g$ runs over all Riemannian metrics on $M$. By using Kummer-type method we construct a smooth closed almost Ricci-flat nonspi

Yu Liu, Milinda Abeykoon, Niraj Aryal, David Graf

We report a thermal transport study of IrSbSe, which crystallizes in a noncentrosymmetric cubic structure with the $P2_13$ space group and shows a narrow-gap semiconducting behavior. The large discrepancy between the activation energy for conductivity [$E_\rho$ = 128(2) meV] and for thermopower [$E_S$ = 17.7(9) meV] from 200 to 300 K indicates the polaronic

Özkan Demir

We study Measurable Imbeddability between groups, which is an order-like generalization of Measure Equivalence that allows the imbedded group to have an infinite measure fundamental domain. We prove if $\Lambda_1$ measurably imbeds into $\Gamma_1$, and $\Lambda_2$ measurably imbeds into $\Gamma_2$ under an additional assumption that lets the corresponding fu

Sitaram Ramakrishnan, Jin-Ke Bao, Claudio Eisele, Bikash Patra

Ho2Ir3Si5 belongs to the family of three-dimensional (3D) R2Ir3Si5 (R = Lu, Er and Ho) compounds that exhibit a colossal first-order charge density wave (CDW) transition where there is a strong orthorhombic-to-triclinic distortion of the lattice accompanied by superlattice reflections. The analysis by single-crystal X-ray diffraction (SXRD) has revealed that

Di Wen, Athol J. Kemball

We examine the flux density ratio anomaly in the quadruply-imaged strong gravitational lens, B1422+231, and consider the contribution of $10-10^3M_{\odot}$ primordial black holes (PBHs) as a potential dark matter constituent. We describe the first flux density ratio measurement of B1422+231 in the millimeter-wave band using the Atacama Large Millimeter Array