August 2022 arXiv papers — page 71

Adriano Tiribocchi, Mihir Durve, Marco Lauricella, Andrea Montessori

Active droplets are artificial microswimmers built from a liquid dispersion by microfluidic tools and showing self-propelled motion. These systems hold particular interest for mimicking biological phenomena, such as some aspects of cell locomotion and collective behaviors of bacterial colonies, as well as for the design of droplet-based biologically inspired

Harukuni Ikeda

Here we investigate the single-layer linearized perceptron near the SAT-UNSAT transition point as a prototypical model of the convex continuous satisfaction problems. The simplicity of the model allows us to take into account the effects of the quantum fluctuation, which have not been fully investigated before. We found that the classical and quantum models

Dongyang Kuang, Craig Michoski

In this work, a kernel attention module is presented for the task of EEG-based emotion classification with neural networks. The proposed module utilizes a self-attention mechanism by performing a kernel trick, demanding significantly fewer trainable parameters and computations than standard attention modules. The design also provides a scalar for quantitativ

Luke Thorburn, Maria Polukarov, Carmine Ventre

Spatial models of preference, in the form of vector embeddings, are learned by many deep learning and multiagent systems, including recommender systems. Often these models are assumed to approximate a Euclidean structure, where an individual prefers alternatives positioned closer to their "ideal point", as measured by the Euclidean metric. However, Bogomolna

Yonghwan Kim, Masahiro Shibata, Yuichi Sudo, Junya Nakamura

An autonomous mobile robot system consisting of many mobile computational entities (called robots) attracts much attention of researchers, and to clarify the relation between the capabilities of robots and solvability of the problems is an emerging issue for a recent couple of decades. Generally, each robot can observe all other robots as long as there are n

Sambuddha Majumder, Sooraj Ravindran

In this paper, we have designed and investigated the performance of radial GaAs/AlGaAs pin junction nanocone array solar cells by performing coupled optoelectronic simulations to obtain the most optimal design configuration based on its photovoltaic properties. Each model has been compared with its GaAs shell counterparts for different levels of surface pass

Andre Thevapalan, Gabriele Kern-Isberner

Answer set programs used in real-world applications often require that the program is usable with different input data. This, however, can often lead to contradictory statements and consequently to an inconsistent program. Causes for potential contradictions in a program are conflicting rules. In this paper, we show how to ensure that a program $\mathcal{P}$

Hanif Heidari, Gerhard Hellstern, Murugappan Murugappan

Heart disease morbidity and mortality rates are increasing, which has a negative impact on public health and the global economy. Early detection of heart disease reduces the incidence of heart mortality and morbidity. Recent research has utilized quantum computing methods to predict heart disease with more than 5 qubits and are computationally intensive. Des

Huai-Yu Wang

It was long believed that there is a zero-point energy in the form of h\omega/2 for massive particles, which is obtained from Schr\"odinger equation for the harmonic oscillator model. In this paper, it is shown, by the Dirac oscillator, that there is no such a zero-point energy. It is argued that when a particle's wave function can spread in the whole space,

Josh Williams, Uwe Wolfram, Ali Ozel

In dilute turbulent particle-laden flows, such as atmospheric dispersion of pollutants or virus particles, the dynamics of tracer-like to low inertial particles are significantly altered by the fluctuating motion of the carrier fluid phase. Neglecting the effects of fluid velocity fluctuations on particle dynamics causes poor prediction of particle transport

Dongyang Kuang, Craig Michoski, Wenting Li, Rui Guo

In this work, a parameter-efficient attention module is presented for emotion classification using a limited, or relatively small, number of electroencephalogram (EEG) signals. This module is called the Monotonicity Constrained Attention Module (MCAM) due to its capability of incorporating priors on the monotonicity when converting features' Gram matrices in

Jeremy Belhassen, Simha Glass, George A. Stanciu, Denis E. Tranca

A thorough understanding of biological species and of emerging nanomaterials requires, among others, their in-depth characterization with optical techniques capable of nano-resolution. Nanoscopy techniques based on tip-enhanced optical effects have gained over the past years tremendous interest given their potential to probe various optical properties with r

Torsten Linß, Goran Radojev

A class of linear parabolic equations are considered. We give a posteriori error estimates in the maximum norm for a method that comprises extrapolation applied to the backward Euler method in time and finite element discretisations in space. We use the idea of elliptic reconstructions and certain bounds for the Green's function of the parabolic operator.

Andrea Cianchi, Mikhail V. Korobkov, Jan Kristensen

A comprehensive theory of the effect of Orlicz-Sobolev maps, between Euclidean spaces, on subsets with zero or finite Hausdorff measure is offered. Arbitrary Orlicz-Sobolev spaces embedded into the space of continuous function and Hausdorff measures built upon general gauge functions are included in our discussion. An explicit formula for the distortion of t

T. Mithun, A. R. Fritsch, I. B. Spielman, P. G. Kevrekidis

Here we revisit the topic of stationary and propagating solitonic excitations in self-repulsive three-dimensional Bose-Einstein condensates by quantitatively comparing theoretical analysis and associated numerical computations with our experimental results. Using fully 3d numerical simulations, we explore the existence, stability, and evolution dynamics of p

Yunjin Choi, Haeran Cho, Hyelim Son

Data collected from a bike-sharing system exhibit complex temporal and spatial features. We analyze shared-bike usage data collected in three large cities at the level of individual stations, accounting for station-specific behavior and covariate effects. For this, we adopt a penalized regression approach with a multilayer network fused Lasso penalty. These

Haixiao Chi, Dawei Wang, Gaojie Cui, Feng Mao

Interpretability has become an essential topic for artificial intelligence in some high-risk domains such as healthcare, bank and security. For commonly-used tabular data, traditional methods trained end-to-end machine learning models with numerical and categorical data only, and did not leverage human understandable knowledge such as data descriptions. Yet

Banoj Kumar Nayak, Eyal Buks

We study magneto-optical coupling in a ferrimagnetic sphere resonator made of Yttrium iron garnet. We find that the resonator can be operated in the telecom band as a polarization-selective optical modulator. Intermodulation gain can be employed in the nonlinear regime for amplification.

Matthias Fuegger, Christoph Lenzen, Ulrich Schmid

Given a discrete-state continuous-time reactive system, like a digital circuit, the classical approach is to first model it as a state transition system and then prove its properties. Our contribution advocates a different approach: to directly operate on the input-output behavior of such systems, without identifying states and their transitions in the first

Martin Schalk, Riccardo Silvioli, Karina Houska, Niels van Venrooy

Magnetic resonance imaging of spin systems combines scientific applications in medicine, chemistry and physics. Here, we investigate the pixel-wise coherent quantum dynamics of spins consisting of a 40 by 40 micron sized region of interest implanted with nitrogen vacancy centers (NV) coupled to a nano-magnetic flake of $\mathrm{CrTe_2}$. $\mathrm{CrTe_2}$ is

Hua Li, Junyan Liang, Ruiqi Wu, Runmin Cong

Stereo superpixel segmentation aims at grouping the discretizing pixels into perceptual regions through left and right views more collaboratively and efficiently. Existing superpixel segmentation algorithms mostly utilize color and spatial features as input, which may impose strong constraints on spatial information while utilizing the disparity information

Natsuya Takahashi

In this paper, we prove that the trisection genus of the Akbulut cork is $3$ and construct infinitely many corks with trisection genus $3$. These results give the first examples of contractible $4$-manifolds whose trisection genera are determined except for the $4$-ball. We also give a lower bound for the trisection genus of a $4$-manifold with boundary. In

W. -R. Hannes, M. F. Ciappina

The simplest approach to deal with light excitations in direct-gap semiconductors is to model them as a two-band system: one conduction and one valence band. For such models, particularly simple analytical expressions are known to exist for the optical response such as multi-photon absorption coefficients. Here we show that generic multi-band models do not r

Yu Zhang, Mao Song, Liangwen Chen

Electromagnetically neutral dark sector particles may feebly interact with photons through higher dimensional effective operators, such as mass-dimension 5 magnetic and electric dipole moment, and a mass-dimension 6 anapole moment and charge radius operators. In this work, we use hypercharge gauge field form factors to treat dark states, which will induce no

Wen-Long Ma, Shu-Shen Li, Ren-Bao Liu

The relation between projective measurements and generalized quantum measurements is a fundamental problem in quantum physics, and clarifying this issue is also important to quantum technologies. While it has been intuitively known that projective measurements can be constructed from sequential generalized or weak measurements, there is still lack of a proof

Oleksandra Klymenko, Stephen Meisenbacher, Florian Matthes

As the tide of Big Data continues to influence the landscape of Natural Language Processing (NLP), the utilization of modern NLP methods has grounded itself in this data, in order to tackle a variety of text-based tasks. These methods without a doubt can include private or otherwise personally identifiable information. As such, the question of privacy in NLP

Amlan Chakraborty, Giulio Gambuti

We present novel techniques for the computation of three-loop four-parton scattering amplitudes in full color, non-planar gauge theories. We elaborate on how the analytic results for these amplitudes can be used to confirm the conjectured infrared structure of QCD up to three loops and how all-orders data can be extracted from their high-energy limit.

Aleksandr Beknazaryan

We show that $d$-variate polynomials of degree $R$ can be represented on $[0,1]^d$ as shallow neural networks of width $2(R+d)^d$. Also, by SNN representation of localized Taylor polynomials of univariate $C^\beta$-smooth functions, we derive for shallow networks the minimax optimal rate of convergence, up to a logarithmic factor, to unknown univariate regre

Rinku Maji, Qaisar Shafi

We discuss how in $SO(10)$ grand unification an observable number density of topologically stable intermediate mass ( $\sim 10^{14}$ GeV) monopoles survive inflation driven by a Coleman-Weinberg potential and non-minimal coupling of the inflaton field to gravity. The scalar spectral index $n_s$ is in excellent agreement with the current observations, and the

Junsong Sun, Tianyu Liu, Yi Du, Huaiming Guo

We investigate the effects of a nonuniform uniaxial strain and a triaxial strain on the $\alpha-{\cal T}_3$ lattice. The analytical expressions of the pseudo-Landau levels (pLLs) are derived based on low-energy Hamiltonians, and are verified by tight-binding calculations. We find the pseudo-magnetic field leads to the oscillating density of states, and the f

Lin Ding, Peng Liu, Wenfeng Shen, Weijia Lu

Model-Agnostic Meta-Learning (MAML) is one of the most successful meta-learning techniques for few-shot learning. It uses gradient descent to learn commonalities between various tasks, enabling the model to learn the meta-initialization of its own parameters to quickly adapt to new tasks using a small amount of labeled training data. A key challenge to few-s

David Jones, Petr Kabath, Jorge Garcia-Rojas, Josef Hanus

In this article, we detail the strategic partnerships "Per Aspera Ad Astra Simul" and "European Collaborating Astronomers Project: Espa\~na-Czechia-Slovakia". These strategic partnerships were conceived to foment international collaboration for educational activities (aimed at all levels) as well as to support the development and growth of early career resea

Bo Liu, Yihao Feng, Qiang Liu, Peter Stone

Goal-conditioned reinforcement learning (GCRL) has a wide range of potential real-world applications, including manipulation and navigation problems in robotics. Especially in such robotics tasks, sample efficiency is of the utmost importance for GCRL since, by default, the agent is only rewarded when it reaches its goal. While several methods have been prop

Dung Anh Hoang, Cuong Nguyen, Belagiannis Vasileios, Thanh-Toan Do

Meta-learning is an effective method to handle imbalanced and noisy-label learning, but it depends on a validation set containing randomly selected, manually labelled and balanced distributed samples. The random selection and manual labelling and balancing of this validation set is not only sub-optimal for meta-learning, but it also scales poorly with the nu

Fang-Ching Chen, Kuan-Dar Chen, Yi-Wen Liu

Semi-supervised learning and domain adaptation techniques have drawn increasing attention in the field of domestic sound event detection thanks to the availability of large amounts of unlabeled data and the relative ease to generate synthetic strongly-labeled data. In a previous work, several semi-supervised learning strategies were designed to boost the per

Elwin Huaman, Dieter Fensel

Knowledge Graphs (KGs) have shown to be very important for applications such as personal assistants, question-answering systems, and search engines. Therefore, it is crucial to ensure their high quality. However, KGs inevitably contain errors, duplicates, and missing values, which may hinder their adoption and utility in business applications, as they are no

Pablo G. Tello, Donato Bini, Stuart Kauffman, Sauro Succi

This Letter proposes a solution of the Vacuum Energy and the Cosmological Constant (CC) paradox based on the Zel'dovich's ansatz, which states that the observable contribution to the vacuum energy density is given by the gravitational energy of virtual particle-antiparticle pairs, continually generated and annihilated in the vacuum state. The novelty of this

Minsig Han, Ameha Tsegaye Abebe, Chung G. Kang

The recent works on a deep learning (DL)-based joint design of preamble set for the transmitters and data-aided active user detection (AUD) in the receiver has demonstrated a significant performance improvement for grant-free sparse code multiple access (GF-SCMA) system. The autoencoder for the joint design can be trained only in a given environment, but in

Shobhan Dev Mandal, Sakuntala Chatterjee

In the chemotactic motion of Escherichia coli, the switching of transmembrane chemoreceptors between active and inactive states is one of the most important steps of the signaling pathway. We study the effect of this switching time-scale on the chemotactic performance of the cell. We quantify performance by the chemotactic drift velocity of the cell. Our ext

L. T. Dickson, C. I. D. Underwood, F. Filippi, R. J. Shalloo

Experimental results, supported by precise modelling, demonstrate optimisation of a plasma-based injector with intermediate laser pulse energy ($<1$ J), corresponding to a normalised vector potential $a_0 = 2.15$, using ionisation injection in a tailored plasma density profile. An increase in electron bunch quality and energy is achieved experimentally with

Yang Li, Kee Siong Ng, Michael Purcell

Why study Lattice-based Cryptography? There are a few ways to answer this question. 1. It is useful to have cryptosystems that are based on a variety of hard computational problems so the different cryptosystems are not all vulnerable in the same way. 2. The computational aspects of lattice-based cryptosystem are usually simple to understand and fairly easy

Jinle Zeng, Min Li, Zhihua Wu, Jiaqi Liu

Pre-training models are an important tool in Natural Language Processing (NLP), while the BERT model is a classic pre-training model whose structure has been widely adopted by followers. It was even chosen as the reference model for the MLPerf training benchmark. The distributed training performance optimization of BERT models plays an important role in acce

Wataru Komatsubara, Kuniaki Konishi, Junji Yumoto, Makoto Kuwata-Gonokami

The self-trapping critical power of light propagation is one of the key physical quantities characterizing nonlinear-beam propagation. Above the critical power, the spatial and temporal profiles of the beam deviate from its original shapes. Therefore, the critical power is considered an important indicator in nonlinear optical phenomena, such as filamentatio

M. Abu-Shady, Etido P. Inyang

Trigonometric Rosen-Morse Potential is employed as a mesonic potential interaction. The extended Nikiforov-Uvarov method is used to solve the N-radial Fractional Schrodinger equation analytically. Using the generalized fractional derivative, the energy eigenvalues are obtained in the fractional form. The current findings are used to calculate the masses of m

Artem Lopatin, Alexandr N. Zubkov

Over an algebraically closed field, we described a minimal set of representatives for G_2-orbits on the set of pairs of octonions.

R. González-Albaladejo, A. Carpio, L. L. Bonilla

The Vicsek model encompasses the paradigm of active dry matter. Motivated by collective behavior of insects in swarms, we have studied finite size effects and criticality in the three dimensional, harmonically confined Vicsek model. We have discovered a phase transition that exists for appropriate noise and small confinement strength. On the critical line of

Weijie Wang, Song Liu, Qinfeng Shan, Lihao Jia

Swing arms have an irreplaceable role in promoting highly dynamic locomotion on bipedal robots by a larger angular momentum control space from the viewpoint of biomechanics. Few bipedal robots utilize swing arms and its redundancy characteristic of multiple degrees of freedom due to the lack of appropriate locomotion control strategies to perfectly integrate

Magnús M. Halldórsson, Yannic Maus, Alexandre Nolin

We present ${\rm poly\log\log n}$-round randomized distributed algorithms to compute vertex splittings, a partition of the vertices of a graph into $k$ parts such that a node of degree $d(u)$ has $\approx d(u)/k$ neighbors in each part. Our techniques can be seen as the first progress towards general ${\rm poly\log\log n}$-round algorithms for the Lov\'asz L

Sindhu B Hegde, Rudrabha Mukhopadhyay, Vinay P Namboodiri, C. V. Jawahar

In this paper, we explore an interesting question of what can be obtained from an $8\times8$ pixel video sequence. Surprisingly, it turns out to be quite a lot. We show that when we process this $8\times8$ video with the right set of audio and image priors, we can obtain a full-length, $256\times256$ video. We achieve this $32\times$ scaling of an extremely

Tomás E. Müller-Bravo, Lluís Galbany

Type Ia supernovae (SNe Ia) have assumed a fundamental role as cosmological distance indicators since the discovery of the accelerating expansion rate of the universe. Correlations between their optical peak luminosity, the decline rate of their light curves and their optical colours allow them to be standardised, reducing their observed r.m.s scatter. Over

Zican Hu, Wurui Shi, Hongkun Liu, Xueyun Chen

Road detection based on remote sensing images is of great significance to intelligent traffic management. The performances of the mainstream road detection methods are mainly determined by their extracted features, whose richness and robustness can be enhanced by fusing features of different types and cross-layer connections. However, the features in the exi

Christian Gerloff, Kerstin Konrad, Jana Kruppa, Martin Schulte-Rüther

Research in machine learning for autism spectrum disorder (ASD) classification bears the promise to improve clinical diagnoses. However, recent studies in clinical imaging have shown the limited generalization of biomarkers across and beyond benchmark datasets. Despite increasing model complexity and sample size in neuroimaging, the classification performanc

T. Machida, Y. Nagai, T. Hanaguri

When the exchange interaction between the impurity spin and the spins of itinerant quasiparticles are strong or weak enough, the ground states for a magnetic impurity in a superconductor are the screened or free spins, respectively. In both cases, the lowest excited state is a bound state within the superconducting gap, known as the Yu-Shiba-Rusinov (YSR) st

Jihyeon Hyeong, Jayoung Kim, Noseong Park, Sushil Jajodia

Tabular data typically contains private and important information; thus, precautions must be taken before they are shared with others. Although several methods (e.g., differential privacy and k-anonymity) have been proposed to prevent information leakage, in recent years, tabular data synthesis models have become popular because they can well trade-off betwe

Wolfgang Lucha

The Poincar\'e-covariant quantum-field-theoretic description of bound states by the homogeneous Bethe-Salpeter equation usually exhibits an intrinsic complexity that can be attenuated by allowing this formalism to undergo various simplifications. The resulting approximate outcome's reliability can be assessed by applying several rigorous constraints on the n

Hyounguk Shon, Janghyeon Lee, Seung Hwan Kim, Junmo Kim

Pre-trained representation is one of the key elements in the success of modern deep learning. However, existing works on continual learning methods have mostly focused on learning models incrementally from scratch. In this paper, we explore an alternative framework to incremental learning where we continually fine-tune the model from a pre-trained representa

Aleksandar Bulj, Vjekoslav Kovač

The main result of this note is the strengthening of a quite arbitrary a priori Fourier restriction estimate to a multi-parameter maximal estimate of the same type. This allows us to discuss a certain multi-parameter Lebesgue point property of Fourier transforms, which replaces Euclidean balls by ellipsoids. Along the lines of the same proof, we also establi

Hongyuan Lu, Wai Lam

Curriculum Data Augmentation (CDA) improves neural models by presenting synthetic data with increasing difficulties from easy to hard. However, traditional CDA simply treats the ratio of word perturbation as the difficulty measure and goes through the curriculums only once. This paper presents \textbf{PCC}: \textbf{P}araphrasing with Bottom-k Sampling and \t

Silvia Cascianelli, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara

Handwritten Text Recognition (HTR) in free-layout pages is a challenging image understanding task that can provide a relevant boost to the digitization of handwritten documents and reuse of their content. The task becomes even more challenging when dealing with historical documents due to the variability of the writing style and degradation of the page quali

Timo Dimitriadis, Tobias Fissler, Johanna Ziegel

We characterize the full classes of M-estimators for semiparametric models of general functionals by formally connecting the theory of consistent loss functions from forecast evaluation with the theory of M-estimation. This novel characterization result opens up the possibility for theoretical research on efficient and equivariant M-estimation and, more gene

Masato Minamitsuji, Shinji Tsujikawa

We propose a new model of scalarized neutron stars (NSs) realized by a self-interacting scalar field $\phi$ nonminimally coupled to the Ricci scalar $R$ of the form $F(\phi)R$. The scalar field has a self-interacting potential and sits at its vacuum expectation value $\phi_v$ far away from the source. Inside the NS, the dominance of a positive nonminimal cou

Jing Jiang, Weihong Deng

Facial expression recognition (FER) is a challenging problem because the expression component is always entangled with other irrelevant factors, such as identity and head pose. In this work, we propose an identity and pose disentangled facial expression recognition (IPD-FER) model to learn more discriminative feature representation. We regard the holistic fa

Bai Xue, Naijun Zhan, Martin Fränzle, Ji Wang

In this paper we propose novel optimization-based methods for verifying reach-avoid (or, eventuality) properties of continuous-time systems modelled by ordinary differential equations. Given a system, an initial set, a safe set and a target set of states, we say that the reach-avoid property holds if for all initial conditions in the initial set, any traject

Feiqi Cao, Soyeon Caren Han, Siqu Long, Changwei Xu

Attention mechanism has been used as an important component across Vision-and-Language(VL) tasks in order to bridge the semantic gap between visual and textual features. While attention has been widely used in VL tasks, it has not been examined the capability of different attention alignment calculation in bridging the semantic gap between visual and textual

Daniel Sinambela

In this paper, we consider capillary-gravity waves propagating on the interface separating two fluids of finite depth and constant density. The flow in each layer is assumed to be incompressible and of constant vorticity. We prove the existence of small-amplitude solitary wave solutions to this system in the strong surface tension regime via a spatial dynami

David Otto Tiede, Nihit Saigal, Hossein Ostovar, Vera Döring

The disentanglement of single and many particle properties in 2D semiconductors and their dependencies on high carrier concentration is challenging to experimentally study by pure optical means. We establish an electrolyte gated WS2 monolayer field-effect structure capable to shift the Fermi level from the valence into the conduction band suitable to optical

Tomohiro Fujita, Yuto Minami, Maresuke Shiraishi, Shuichiro Yokoyama

Recently, the cross-correlation between $E$- and $B$-mode polarization of the cosmic microwave background (CMB), which is well explained by cosmic birefringence with rotation angle $\beta\approx 0.3$ deg, has been found in CMB polarization data. We carefully investigate the possibility of explaining the observed $EB$ correlation by the primordial chiral grav

Shangqing Liu, Yanzhou Li, Xiaofei Xie, Yang Liu

GitHub commits, which record the code changes with natural language messages for description, play a critical role for software developers to comprehend the software evolution. To promote the development of the open-source software community, we collect a commit benchmark including over 7.99 million commits across 7 programming languages. Based on this bench

Hanqing Zhu, Keren Zhu, Jiaqi Gu, Harrison Jin

Analog computing has been recognized as a promising low-power alternative to digital counterparts for neural network acceleration. However, conventional analog computing is mainly in a mixed-signal manner. Tedious analog/digital (A/D) conversion cost significantly limits the overall system's energy efficiency. In this work, we devise an efficient analog acti

M. V. Takook

The appearance of infinity together with collapsing quantum state due to the observation or interaction, which are two challenging features of quantum field theory, become very serious problems in quantum gravity as well as in quantum geometry of space-time. These problems can be divided into two categories: technical and conceptual parts. In the technical a

Ziyi Ye, Xiaohui Xie, Yiqun Liu, Zhihong Wang

With the growth of information on the Web, most users heavily rely on information access systems (e.g., search engines, recommender systems, etc.) in their daily lives. During this procedure, modeling users' satisfaction status plays an essential part in improving their experiences with the systems. In this paper, we aim to explore the benefits of using Elec

Takuto Ishikawa, Mamoru Matsuo, Takeo Kato

We study the temperature dependence of spin Hall magnetoresistance (SMR) in antiferromagnetic insulator (AFI)/metal bilayer systems. We calculate the amplitude of the SMR signal by using a quantum Monte Carlo simulation and examine how the SMR depends on the amplitude of the spin, thickness of the AFI layer, and randomness of the exchange interactions. Our r

R. Shahriyari, R. Nikandish, A. Tehranian, H. Rasouli

Let $R$ be a commutative ring with identity. The co-maximal ideal graph of $R$, denoted by $\Gamma(R)$, is a simple graph whose vertices are proper ideals of $R$ which are not contained in the Jacobson radical of $R$ and two distinct vertices $I, J$ are adjacent if and only if $I+J=R$. In this paper, we use Gallai$^{^,}$s Theorem and the concept of strong re

Longxuan Ma, Ziyu Zhuang, Weinan Zhang, Mingda Li

This paper introduces a novel Self-supervised Fine-grained Dialogue Evaluation framework (SelF-Eval). The core idea is to model the correlation between turn quality and the entire dialogue quality. We first propose a novel automatic data construction method that can automatically assign fine-grained scores for arbitrarily dialogue data. Then we train \textbf

Paresh Baidya, Swagata Mondal, Rourab Paul

Ring Learning With Error (RLWE) algorithm is used in Post Quantum Cryptography (PQC) and Homomorphic Encryption (HE) algorithm. The existing classical crypto algorithms may be broken in quantum computers. The adversaries can store all encrypted data. While the quantum computer will be available, these encrypted data can be exposed by the quantum computer. Th

Jaskirat Singh, Liang Zheng, Cameron Smith, Jose Echevarria

Controllable image synthesis with user scribbles is a topic of keen interest in the computer vision community. In this paper, for the first time we study the problem of photorealistic image synthesis from incomplete and primitive human paintings. In particular, we propose a novel approach paint2pix, which learns to predict (and adapt) "what a user wants to d

Heng Yao, Sanaz Motamedi, Wayne C. W. Giang, Alexandra Kondyli

Alertness monitoring in the context of driving improves safety and saves lives. Computer vision based alertness monitoring is an active area of research. However, the algorithms and datasets that exist for alertness monitoring are primarily aimed at younger adults (18-50 years old). We present a system for in-vehicle alertness monitoring for older adults. Th

Jianyuan Ni, Anne H. H. Ngu, Yan Yan

Wearable sensor-based Human Action Recognition (HAR) has achieved remarkable success recently. However, the accuracy performance of wearable sensor-based HAR is still far behind the ones from the visual modalities-based system (i.e., RGB video, skeleton, and depth). Diverse input modalities can provide complementary cues and thus improve the accuracy perform

Jaron Mar, Jiamou Liu

Few-shot learning (FSL) is an emergent paradigm of learning that attempts to learn to reason with low sample complexity to mimic the way humans learn, generalise and extrapolate from only a few seen examples. While FSL attempts to mimic these human characteristics, fundamentally, the task of FSL as conventionally formulated using meta-learning with episodic-

Chendi Li, Haipeng Jia, Hang Cao, Jianyu Yao

In recent years, general matrix-matrix multiplication with non-regular-shaped input matrices has been widely used in many applications like deep learning and has drawn more and more attention. However, conventional implementations are not suited for non-regular-shaped matrix-matrix multiplications, and few works focus on optimizing tall-and-skinny matrix-mat

Maryam Soleymani, Mahdi Bonyani, Meghdad Attarzadeh

Resource allocation is one of the most critical issues in planning construction projects, due to its direct impact on cost, time, and quality. There are usually specific allocation methods for autonomous resource management according to the projects objectives. However, integrated planning and optimization of utilizing resources in an entire construction org

Zhengding Luo, Dongyuan Shi, Junwei Ji, Woon-seng Gan

Active noise control (ANC) systems can efficiently attenuate low-frequency noises by introducing anti-noises to combine with the unwanted noises. In ANC systems, the filtered-x least mean square (FxLMS) and filtered-X normalized least-mean-square (FxNLMS) algorithm are well-known algorithms for adaptively adjusting control filters. Multi-channel ANC systems

Konstantinos Konstantinidis, Namrata Vaswani, Aditya Ramamoorthy

A plethora of modern machine learning tasks require the utilization of large-scale distributed clusters as a critical component of the training pipeline. However, abnormal Byzantine behavior of the worker nodes can derail the training and compromise the quality of the inference. Such behavior can be attributed to unintentional system malfunctions or orchestr

Zhengding Luo, Dongyuan Shi, Woon-Seng Gan

Due to its rapid response time and a high degree of robustness, the selective fixed-filter active noise control (SFANC) method appears to be a viable candidate for widespread use in a variety of practical active noise control (ANC) systems. In comparison to conventional fixed-filter ANC methods, SFANC can select the pre-trained control filters for different

Zhijun Tu, Xinghao Chen, Pengju Ren, Yunhe Wang

This paper studies the Binary Neural Networks (BNNs) in which weights and activations are both binarized into 1-bit values, thus greatly reducing the memory usage and computational complexity. Since the modern deep neural networks are of sophisticated design with complex architecture for the accuracy reason, the diversity on distributions of weights and acti

Zongyuan Zhang, Qingwen Bu, Tianyang Duan, Zheng Lin

Deep neural networks (DNNs) are vulnerable to adversarial examples, in which DNNs are misled to false outputs due to inputs containing imperceptible perturbations. Adversarial training, a reliable and effective method of defense, may significantly reduce the vulnerability of neural networks and becomes the de facto standard for robust learning. While many re

Zhengding Luo, Dongyuan Shi, Woon-Seng Gan

The selective fixed-filter active noise control (SFANC) method selecting the best pre-trained control filters for various types of noise can achieve a fast response time. However, it may lead to large steady-state errors due to inaccurate filter selection and the lack of adaptability. In comparison, the filtered-X normalized least-mean-square (FxNLMS) algori

Yat Fan Lau, Tai Kai Ng

We study in this paper the effect of weak, short-ranged interaction on disordered metals. Through analysing the interaction matrix elements between different eigenstates of the non-interacting and corresponding Hartree-Fock single-particle Hamiltonian, we argue that as a result of localized single-particle eigenstates around the Fermi surface, the quasi-part

Monther Aldwairi

Distance education had existed for a long time, then it has undergone a renaissance with the advent of computers and the Internet. Distance education relied on physically delivered material and assessments to students, who work offline at home. More recently, online learning or e-learning introduced virtual classrooms, assessments, online tests and transform

Dong Won Lee, Chaitanya Ahuja, Paul Pu Liang, Sanika Natu

Lecture slide presentations, a sequence of pages that contain text and figures accompanied by speech, are constructed and presented carefully in order to optimally transfer knowledge to students. Previous studies in multimedia and psychology attribute the effectiveness of lecture presentations to their multimodal nature. As a step toward developing AI to aid

Shreyansh S. Dave, Oindrila Ganguly, Saumia P. S., Ajit M. Srivastava

Acoustic black holes are formed when a fluid flowing with subsonic velocities, accelerates and becomes supersonic. When the flow is directed from the subsonic to supersonic region, the surface on which the normal component of fluid velocity equals the local speed of sound acts as an acoustic horizon. This is because no acoustic perturbation from the superson

Menghao Li, Wenquan Feng, Shuchang Lyu, Lijiang Chen

Cell instance segmentation is a new and challenging task aiming at joint detection and segmentation of every cell in an image. Recently, many instance segmentation methods have applied in this task. Despite their great success, there still exists two main weaknesses caused by uncertainty of localizing cell center points. First, densely packed cells can easil

M. C. Donohue, O. Langford, P. Insel, C. H. van Dyck

Mixed model repeated measures (MMRM) is the most common analysis approach used in clinical trials for Alzheimer's disease and other progressive diseases measured with continuous outcomes measured over time. The model treats time as a categorical variable, which allows an unconstrained estimate of the mean for each study visit in each randomized group. Catego

Debapriya Roy, Sanchayan Santra, Diganta Mukherjee, Bhabatosh Chanda

The idea of \textit{Virtual Try-ON} (VTON) benefits e-retailing by giving an user the convenience of trying a clothing at the comfort of their home. In general, most of the existing VTON methods produce inconsistent results when a person posing with his arms folded i.e., bent or crossed, wants to try an outfit. The problem becomes severe in the case of long-

Fabio Isa, Matthew Joliffe, Brendan Wouterlood, Naomi He Ho

Isolated, micro-meter sized diamonds are grown by micro-wave plasma chemical vapour deposition technique on Si(001) substrates. Each diamond is uniquely identified by markers milled in the Si substrate by Ga+ focused ion beam. The morphology and micrograin structure analysis indicates that the diamonds are icosahedral or bi-crystals. Icosahedral diamonds hav

Shuai Zhang, Shihao Li, Zhe Guo, Yan Xu

Detection of vector magnetic fields at nanoscale dimensions is critical in applications ranging from basic material science, to medical diagnostic. Meanwhile, an all-electric operation is of great significance for achieving a simple and compact sensing system. Here, we propose and experimentally demonstrate a simple approach to sensing a vector magnetic fiel

Zhan Li, Zhiwei Wang

We show the invariance of plurigenera for generalized polarized pairs with abundant nef parts and generalized canonical singularities. This is obtained by investigating a type of newly introduced multiplier ideal sheaf which is of bimeromorphic nature.

Eleazar Cuautle, Edgar Domínguez Rosas, Mario Rodríguez-Cahuantzi

The multiplicity and average transverse momentum of the charged and identified particles produced in different kinds of colliding systems are an example of global observables used to characterize events over a wide range of energy. Studying these observables provides insights into the collective phenomena and the geometric scaling properties of the systems c

Jong-Wouk Kim, Yang-Sae Moon, Mi-Jung Choi

Malware developers use combinations of techniques such as compression, encryption, and obfuscation to bypass anti-virus software. Malware with anti-analysis technologies can bypass AI-based anti-virus software and malware analysis tools. Therefore, classifying pack files is one of the big challenges. Problems arise if the malware classifiers learn packers' f

Christa Jenkins, Mark Moir, Harold Carr

We present a lightweight, open source Agda framework for manually verifying effectful programs using predicate transformer semantics. We represent the abstract syntax trees (AST) of effectful programs with a generalized algebraic datatype (GADT) AST, whose generality enables even complex operations to be primitive AST nodes. Users can then assign bespoke pre