November 2022 arXiv papers — page 67

Qingyang Liu, Xianzheng Huang, Rai Bai

Compared to mean regression and quantile regression, the literature on modal regression is very sparse. A unifying framework for Bayesian modal regression is proposed, based on a family of unimodal distributions indexed by the mode, along with other parameters that allow for flexible shapes and tail behaviors. Sufficient conditions for posterior propriety un

Sergio A. Hojman, Eduardo Nahmad-Achar, Adolfo Sánchez-Valenzuela

Harmonic wave functions for integer and half-integer angular momentum are given in terms of the Euler angles $(\theta,\phi,\psi)$ that define a rotation in $SO(3)$, and the Euclidean norm in ${\mathbb R}^3$. Following a classical work by Schwinger, $2$-dimensional harmonic oscillators are used to produce raising and lowering operators that change the total a

Noah D. Brenowitz, W. Andre Perkins, Jacqueline M. Nugent, Oliver Watt-Meyer

Cloud microphysical parameterizations in atmospheric models describe the formation and evolution of clouds and precipitation, a central weather and climate process. Cloud-associated latent heating is a primary driver of large and small-scale circulations throughout the global atmosphere, and clouds have important interactions with atmospheric radiation. Clou

Nick Rittler, Kamalika Chaudhuri

$k$-nearest neighbor classification is a popular non-parametric method because of desirable properties like automatic adaption to distributional scale changes. Unfortunately, it has thus far proved difficult to design active learning strategies for the training of local voting-based classifiers that naturally retain these desirable properties, and hence acti

Maoyuan Ye, Jing Zhang, Shanshan Zhao, Juhua Liu

End-to-end text spotting aims to integrate scene text detection and recognition into a unified framework. Dealing with the relationship between the two sub-tasks plays a pivotal role in designing effective spotters. Although Transformer-based methods eliminate the heuristic post-processing, they still suffer from the synergy issue between the sub-tasks and l

Yash Patel, Ambuj Tewari

The generation of conformers has been a long-standing interest to structural chemists and biologists alike. A subset of proteins known as intrinsically disordered proteins (IDPs) fail to exhibit a fixed structure and, therefore, must also be studied in this light of conformer generation. Unlike in the small molecule setting, ground truth data are sparse in t

Claude Pruneau, Victor Gonzalez, Brian Hanley, Ana Marin

We investigate the impact of non-vanishing net-charge in collision systems on measurements of balance functions and their integrals. We show that the nominal balance function definition yields integrals that deviate from unity because of the non-vanishing net-charge. However, the integral of unified balance functions is shown to appropriately converge to uni

Manuel Costanzo, Enzo Rucci, Carlos García Sánchez, Marcelo Naiouf

Bioinformatics and Computational Biology are two fields that have been exploiting GPUs for more than two decades, being CUDA the most used programming language for them. However, as CUDA is an NVIDIA proprietary language, it implies a strong portability restriction to a wide range of heterogeneous architectures, like AMD or Intel GPUs. To face this issue, th

Vinodkumar Prabhakaran, Rida Qadri, Ben Hutchinson

Artificial intelligence (AI) systems attempt to imitate human behavior. How well they do this imitation is often used to assess their utility and to attribute human-like (or artificial) intelligence to them. However, most work on AI refers to and relies on human intelligence without accounting for the fact that human behavior is inherently shaped by the cult

Siyang Song, Yuxin Song, Cheng Luo, Zhiyuan Song

Graph is powerful for representing various types of real-world data. The topology (edges' presence) and edges' features of a graph decides the message passing mechanism among vertices within the graph. While most existing approaches only manually define a single-value edge to describe the connectivity or strength of association between a pair of vertices, ta

Jiakai Li

We define a "real" version of Kronheimer-Mrowka's monopole Floer homology for a 3-manifold equipped with an involution. As a special case, we obtain invariants for links via their double branched covers. The new input is the notion of a real spin-c structure, which consists of a spin-c structure along with a compatible anti-linear involution on the spinor bu

Robert D. Polak, Michael R. Harris, Kiet A. Nguyen, Anthony Kearns

Engaging with physical and material properties through empirical observation is a fundamental part of undergraduate physics and engineering education. Several works have proposed experiments to determine thermal physical constants of materials such as finding the coefficient of linear expansion. As Dajbych and Polak et al. have shown, methods for experimenta

Arnob Ray, Timo Bröhl, Arindam Mishra, Subrata Ghosh

The role of topological heterogeneity in the origin of extreme events in a network is investigated here. The dynamics of the oscillators associated with the nodes are assumed to be identical and influenced by mean-field repulsive interactions. An interplay of topological heterogeneity and the repulsive interaction between the dynamical units of the network t

Hanuma Teja Maddali, Amanda Lazar

Gardening is an activity that involves a number of dimensions of increasing interest to HCI and CSCW researchers, including recreation, sustainability, and engagement with nature. This paper considers the garden setting in order to understand the role that collaborative and social computing technologies might play for practitioners engaging in outdoor skille

Alexander V. Osipov

A topological space $X$ is Baire if the intersection of any sequence of open dense subsets of $X$ is dense in $X$. One of the interesting problems for the space of Baire functions is the Banakh-Gabriyelyan problem: Let $\alpha$ be a countable ordinal. Characterize topological spaces $X$ and $Y$ for which the function space $B_{\alpha}(X,Y)$ is Baire. In this

Ana Aleksandric, Mohit Singhal, Anne Groggel, Shirin Nilizadeh

Online harassment and abusive language continue to be a growing concern on social media platforms. In this study, we explore the power of group dynamics to shape the toxicity of Twitter conversations. First, we examine how the presence of others in a conversation can potentially diffuse Twitter users' responsibility to address a toxic reply. Second, we exami

Libo Zhang, Lutao Jiang, Ruyi Ji, Heng Fan

Automatic security inspection relying on computer vision technology is a challenging task in real-world scenarios due to many factors, such as intra-class variance, class imbalance, and occlusion. Most previous methods rarely touch the cases where the prohibited items are deliberately hidden in messy objects because of the scarcity of large-scale datasets, h

Komla Domelevo, Stefanie Petermichl, Kristina Ana Škreb

We present a fundamentally new proof of the dimensionless Lp boundedness of the Bakry Riesz vector on manifolds with bounded geometry. Our proof has the significant advantage that it allows for a much stronger conclusion than previous arguments, namely that of some new dimensionless weighted estimates with optimal exponent. Part of the importance of this tas

Mykhailo Zarichnyi

Answering a question of Ma, Siegert, and Dydak we show that there is no universal proper metric space for the asymptotic dimension $n\ge1$.

Javier Marin

This work proposes a method to evaluate synthetic tabular data generated to augment small sample datasets. While data augmentation techniques can increase sample counts for machine learning applications, traditional validation approaches fail when applied to extremely limited sample sizes. Our experiments across four datasets reveal significant inconsistenci

Durvudkhan Suragan, Bharat Talwar

We prove that $\frac{Q}{Q-2}$ is the Fujita exponent for a semilinear heat equation on an arbitrary stratified Lie group with homogeneous dimension $Q$. This covers the Euclidean case and gives new insight into proof techniques on nilpotent Lie groups. The equation we study has a forcing term which depends only upon a group element and has positive integral.

Huipeng Gu, Mingchao Cai, Jingzhi Li, Guoliang Ju

This paper concentrates on a priori error estimates of two fully discrete coupled schemes for Biot's consolidation model based on the three-field formulation introduced by Oyarzua et al. (SIAM Journal on Numerical Analysis, 2016). The spatial discretizations are based on the Taylor-Hood finite elements combined with Lagrange elements for the three primary va

Naoki Sato, Michio Yamada

This paper studies the problem of finding a three-dimensional solenoidal vector field such that both the vector field and its curl are tangential to a given family of toroidal surfaces. We show that this question can be translated into the problem of determining a periodic solution with periodic derivatives of a two-dimensional linear elliptic second-order p

Hanuma Teja Maddali, Emma Dixon, Alisha Pradhan, Amanda Lazar

There has been a growing interest in HCI to understand the specific technological needs of people with dementia and supporting them in self-managing daily activities. One of the most difficult challenges to address is supporting the fluctuating accessibility needs of people with dementia, which vary with the specific type of dementia and the progression of t

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

Using data samples with a total integrated luminosity of approximately 18 fb$^{-1}$ collected by the BESIII detector operating at the BEPCII, the process $e^+e^-\rightarrow\Lambda\bar{\Lambda} \eta$ is studied at center-of-mass energies between 3.5106 and 4.6988 GeV. The Born cross section for the process $e^+e^-\rightarrow\Lambda\bar{\Lambda}\eta$ is measur

Shristi Das Biswas, Adarsh Kosta, Chamika Liyanagedera, Marco Apolinario

Event cameras detect changes in per-pixel intensity to generate asynchronous `event streams'. They offer great potential for accurate semantic map retrieval in real-time autonomous systems owing to their much higher temporal resolution and high dynamic range (HDR) compared to conventional cameras. However, existing implementations for event-based segmentatio

Mohamed Akrout, Amal Feriani, Faouzi Bellili, Amine Mezghani

With the proliferation of deep learning techniques for wireless communication, several works have adopted learning-based approaches to solve the channel estimation problem. While these methods are usually promoted for their computational efficiency at inference time, their use is restricted to specific stationary training settings in terms of communication s

Yihan Wu, Xinda Li, Florian Kerschbaum, Heng Huang

Adversarial training has been actively studied in recent computer vision research to improve the robustness of models. However, due to the huge computational cost of generating adversarial samples, adversarial training methods are often slow. In this paper, we study the problem of learning a robust dataset such that any classifier naturally trained on the da

Sinan Deveci

We present many novel results in number theory, including a double series formula for the natural logarithm and a proof concerning the H\"older mean based on the functional equation for the Riemann zeta function. We find a harmonic mean analogue of Chebyshev's inequality for the prime counting function involving the Euler-Mascheroni constant. Furthermore, we

Alex McDonald, Krystal Taylor

We show that products of sufficiently thick Cantor sets generate trees in the plane with constant distance between adjacent vertices. Moreover, we prove that the set of choices for this distance has non-empty interior. We allow our trees to be countably infinite, which further distinguishes this work from previous results on patterns in fractal sets. This bu

Subhaditya Bhattacharya, Purusottam Ghosh, Jayita Lahiri, Biswarup Mukhopadhyaya

The identification and isolation of two WIMP dark matter (DM) components at colliders is of wide interest on the one hand but extremely challenging on the other, especially when the dominant signal of both DM components is of the mono-X type ($X=\gamma, Z, H$). After emphasizing that an $e^+e^-$ collider is more suitable for this goal, we first identify the

Zhongyuan Zhao, Bojan Radojicic, Gunjan Verma, Ananthram Swami

We propose a throughput-optimal biased backpressure (BP) algorithm for routing, where the bias is learned through a graph neural network that seeks to minimize end-to-end delay. Classical BP routing provides a simple yet powerful distributed solution for resource allocation in wireless multi-hop networks but has poor delay performance. A low-cost approach to

Christopher Beckham, Alexandre Piche, David Vazquez, Christopher Pal

In model-based optimisation (MBO) we are interested in using machine learning to design candidates that maximise some measure of reward with respect to a black box function called the (ground truth) oracle, which is expensive to compute since it involves executing a real world process. In offline MBO we wish to do so without assuming access to such an oracle

M. Alexander R. Kaltenborn, Michael J. Falato, Oleg Korobkin, Irina Sagert

Studying compact star binaries and their mergers is integral to determining progenitors for observable transients. Today, compact-star mergers are typically studied via state-of-the-art computational fluid dynamics codes. One such numerical technique, Smoothed Particle Hydrodynamics (SPH), is frequently chosen for its excellent mass, energy, and momentum con

Maneesh Kumar Singh

This research article discusses a numerical solution of the radiative transfer equation based on the weak Galerkin finite element method. We discretize the angular variable by means of the discrete-ordinate method. Then the resulting semi-discrete hyperbolic system is approximated using the weak Galerkin method. The stability result for the proposed numerica

Bogdan Toader, Fred J. Sigworth, Roy R. Lederman

Macromolecules change their shape (conformation) in the process of carrying out their functions. The imaging by cryo-electron microscopy of rapidly-frozen, individual copies of macromolecules (single particles) is a powerful and general approach to understanding the motions and energy landscapes of macromolecules. Widely-used computational methods already al

Wen Li, Ralf Klasing, Yaping Mao, Bo Ning

Foucaud {\it et al.} recently introduced and initiated the study of a new graph-theoretic concept in the area of network monitoring. Let $G$ be a graph with vertex set $V(G)$, $M$ a subset of $V(G)$, and $e$ be an edge in $E(G)$, and let $P(M, e)$ be the set of pairs $(x,y)$ such that $d_G(x, y)\neq d_{G-e}(x, y)$ where $x\in M$ and $y\in V(G)$. $M$ is calle

Olga Mula, Anthony Nouy

Most common Optimal Transport (OT) solvers are currently based on an approximation of underlying measures by discrete measures. However, it is sometimes relevant to work only with moments of measures instead of the measure itself, and many common OT problems can be formulated as moment problems (the most relevant examples being $L^p$-Wasserstein distances, b

Artyom Radomskii

We generalize Romanoff's theorem. Also, we obtain a result on sums related to Euler's totient function.

Gui-Bin Liu, Zeying Zhang, Zhi-Ming Yu, Yugui Yao

Motivated by easy access to complete corepresentation (corep) data of all the 1651 magnetic space groups (MSGs) in three-dimensional space, we have developed a Mathematica package MSGCorep to provide an offline database of coreps and various functions to manipulate them, based on our previous package SpaceGroupIrep. One can use the package MSGCorep to obtain

Chang Liu, Yuwen Yang, Yue Ding, Hongtao Lu

The message-passing scheme is the core of graph representation learning. While most existing message-passing graph neural networks (MPNNs) are permutation-invariant in graph-level representation learning and permutation-equivariant in node- and edge-level representation learning, their expressive power is commonly limited by the 1-Weisfeiler-Lehman (1-WL) gr

Paul Topf Aguiar de Medeiros, Paul Njayou, Flavie Rometsch, Tommy Nilsson

The European Space Agency (ESA) has a clear mission to go forward to the Moon in preparation of human presence on Mars. One of the technologies looked at to increase safety and efficiency of astronauts in this context is Augmented Reality (AR). This technology allows digital visual information to be overlaid onto the user's environment through some type of d

Ke Liang, Yue Liu, Sihang Zhou, Wenxuan Tu

Knowledge graph embedding (KGE) aims at learning powerful representations to benefit various artificial intelligence applications. Meanwhile, contrastive learning has been widely leveraged in graph learning as an effective mechanism to enhance the discriminative capacity of the learned representations. However, the complex structures of KG make it hard to co

Simla Burcu Harma, Ayan Chakraborty, Nicholas Sperry, Babak Falsafi

The unprecedented demand for computing resources to train DNN models has led to a search for minimal numerical encoding. Recent state-of-the-art (SOTA) proposals advocate for multi-level scaled narrow bitwidth numerical formats. In this paper, we show that single-level scaling is sufficient to maintain training accuracy while maximizing arithmetic density. W

Diego Santos de Jesus, Hermano Velten, Federico Piazza

We revisit the decay of the orbital period in binary systems that occurs due to the emission of gravitational waves in the context of modified gravity models where the coupling $G_{gw}$ between matter and on-shell gravitons is allowed to differ from the Newton constant $G_N$. Using the most precise orbital parameters of binary pulsars, those of the Double Pu

Hrvoje Belani, Petar Solic, Toni Perkovic

Ontologies serve as a one of the formal means to represent and model knowledge in computer science, electrical engineering, system engineering and other related disciplines. Ontologies within requirements engineering may be used for formal representation of system requirements. In the Internet of Things, ontologies may be used to represent sensor knowledge a

Thomas Fiedler

This paper contains the first knot polynomials which can distinguish the orientations of classical knots and which make no excplicit use of the knot group. But they make extensive use of the meridian and of the longitude in a geometric way. Let $M$ be the topological moduli space of long knots up to regular isotopy, and for any natural number $n > 1$ let $M_

R. V. Ilin, S. A. Paston

We propose an approach that allows one to reformulate $n$-dimensional $p$-form mimetic gravity (including usual mimetic gravity as particular case $p = 0$) as nonlinear $(n-p-1)$-form electrodynamics via electric-magnetic duality. The resulting dual Lagrangian density is just the square root of the ordinary quadratic Lagrangian density of $(n-p-1)$-form elec

Alankar Kotwal, Anat Levin, Ioannis Gkioulekas

We introduce an interferometric technique for passive time-of-flight imaging and depth sensing at micrometer axial resolutions. Our technique uses a full-field Michelson interferometer, modified to use sunlight as the only light source. The large spectral bandwidth of sunlight makes it possible to acquire micrometer-resolution time-resolved scene responses,

S. Berti, G. Boffetta, S. Musacchio

We study, by means of extensive direct numerical simulations, the turbulent flow produced by a two-dimensional cellular forcing in a cubic box with periodic boundary conditions. In spite of the strong anisotropy of the forcing, we find that turbulence recovers almost complete isotropy at small scales. Nonetheless, the signature of the forcing remains in the

Anna S. Bondarenko, Roel Tempelaar

Fewest-switches surface hopping (FSSH) has emerged as one of the leading methods for modeling the quantum dynamics of molecular systems. While its original formulation was limited to adiabatic populations, the growing interest in the application of FSSH to coherent phenomena prompts the question how one should construct a complete density matrix based on FSS

Tao Feng, Panshuo Wang, Zhijia Han, Liang Zhou

To achieve thermoelectric energy conversion, a large transverse thermoelectric effect in topological materials is crucial. However, the general relationship between topological electronic structures and transverse thermoelectric effect remains unclear, restricting the rational design of novel transverse thermoelectric materials. Herein, we demonstrate a topo

Daniel Alvestad, Rasmus Larsen, Alexander Rothkopf

This study explores the utility of a kernel in complex Langevin simulations of quantum real-time dynamics on the Schwinger-Keldysh contour. We give several examples where we use a systematic scheme to find kernels that restore correct convergence of complex Langevin. The schemes combine prior information we know about the system and the correctness of conver

Tao Han, Adam K. Leibovich, Yang Ma, Xiao-Ze Tan

After the great triumph of the Higgs discovery in 2012, the next target at the energy frontier will be to study the Higgs properties and to search for the next scale beyond the SM. Experimentally, the $H\to c \bar{c}$ channel would be extremely difficult to dig out because of both the weak Yukawa coupling and the daunting SM di-jet background. We propose to

M. Szydagis, J. Balajthy, G. A. Block, J. P. Brodsky

This paper will discuss the microphysical simulation of interactions in liquid xenon, the active detector medium in many leading rare-event searches for new physics, and describe experimental observables useful for understanding detector performance. The scintillation and ionization yield distributions for signal and background will be presented using the No

Pao-Sheng Vincent Sun, Alexander Titterton, Anjlee Gopiani, Tim Santos

Spiking neural networks (SNNs) have achieved orders of magnitude improvement in terms of energy consumption and latency when performing inference with deep learning workloads. Error backpropagation is presently regarded as the most effective method for training SNNs, but in a twist of irony, when training on modern graphics processing units (GPUs) this becom

Youwei Huang, Sen Fang, Jianwen Li, Jiachun Tao

In recent years, research in software security has concentrated on identifying vulnerabilities in smart contracts to prevent significant losses of crypto assets on blockchains. Despite early successes in this area, detecting developers' intents in smart contracts has become a more pressing issue, as malicious intents have caused substantial financial losses.

Gerard t Hooft

Black holes are more than just odd-looking curiosities in gravity theory. They uniquely intertwine the basic principles of General Relativity with those of Quantum Theory. Just by demanding that they nevertheless obey acceptable laws of dynamics, just like stars and planets, we hit upon strange structures that must play key roles in the quantum effects that

Shengyao Li, Lijuan Zhang, Ke Huang, Chen Ye

Ratchet motion of superconducting vortices, which is a directional flow of vortices in superconductors, is highly useful for exploring quantum phenomena and developing superconducting devices, such as superconducting diode and microwave antenna. However, because of the challenges in the quantitative characterization of the dynamic motion of vortices, a phase

Bo Liu, Jianfeng Zhang, Wenpeng Luan, Zishuai Liu

Load event detection is the fundamental step for the event-based non-intrusive load monitoring (NILM). However, existing event detection methods with fixed parameters may fail in coping with the inherent multi-timescale characteristics of events and their event detection accuracy is easily affected by the load fluctuation. In this regard, this paper extends

Benjamin Aymard

In this article, we carry out a study of long-term behavior of reaction-diffusion systems augmented with self- and cross-diffusion, using an augmented Gray-Scott system as a general example. The methodology remains generic, and is therefore applicable to other systems. Simulations of the temporal model (nonlinear parabolic system) reveal the presence of stea

Chenkang Zhang, Lei Luo, Bin Gu

Deep Metric Learning (DML) is a group of techniques that aim to measure the similarity between objects through the neural network. Although the number of DML methods has rapidly increased in recent years, most previous studies cannot effectively handle noisy data, which commonly exists in practical applications and often leads to serious performance deterior

DES Collobration, :, Jiaming Pan, Hsing Wen Lin

The Jupiter Trojans are a large group of asteroids that are co-orbiting with Jupiter near its L4 and L5 Lagrange points. The study of Jupiter Trojans is crucial for testing different models of planet formation that are directly related to our understanding of solar system evolution. In this work, we select known Jupiter Trojans listed by the Minor Planet Cen

Anastasia Kurmukova, Fedor Ivanov, Victor Zyablov

In this paper, we provide a new approach to the analytical estimation of the bit-error rate (BER) for convolutional codes for Viterbi decoding in the binary symmetric channel (BSC). The expressions we obtained for lower and upper BER bounds are based on the active distances of the code and their distance spectrum. The estimates are derived for convolutional

Gabriel Stoltz

Equilibrium properties in statistical physics are obtained by computing averages with respect to Boltzmann-Gibbs measures, sampled in practice using ergodic dynamics such as the Langevin dynamics. Some quantities however cannot be computed by simply sampling the Boltzmann-Gibbs measure, in particular transport coefficients, which relate the current of some p

Fanze Kong, Xiyuan Liu, Benxu Tang, Jiarong Lin

The emergence of low-cost, small form factor and light-weight solid-state LiDAR sensors have brought new opportunities for autonomous unmanned aerial vehicles (UAVs) by advancing navigation safety and computation efficiency. Yet the successful developments of LiDAR-based UAVs must rely on extensive simulations. Existing simulators can hardly perform simulati

Sen He, Yi-Zhe Song, Tao Xiang

Multi-pose virtual try-on (MPVTON) aims to fit a target garment onto a person at a target pose. Compared to traditional virtual try-on (VTON) that fits the garment but keeps the pose unchanged, MPVTON provides a better try-on experience, but is also more challenging due to the dual garment and pose editing objectives. Existing MPVTON methods adopt a pipeline

Mauro Martini, Andrea Eirale, Simone Cerrato, Marcello Chiaberge

Learning agents can optimize standard autonomous navigation improving flexibility, efficiency, and computational cost of the system by adopting a wide variety of approaches. This work introduces the \textit{PIC4rl-gym}, a fundamental modular framework to enhance navigation and learning research by mixing ROS2 and Gazebo, the standard tools of the robotics co

Xiaoshan Zhou, Pin-Chao Liao

Classifying brain signals collected by wearable Internet of Things (IoT) sensors, especially brain-computer interfaces (BCIs), is one of the fastest-growing areas of research. However, research has mostly ignored the secure storage and privacy protection issues of collected personal neurophysiological data. Therefore, in this article, we try to bridge this g

Fedor Ivanov, Dmitry Osipov

This paper devotes to comparison of different coding schemes (various constructions of Polar and LDPC codes, Product codes and BCH codes) for the case when information is transmitted over AWGN channel with quantization with lowest possible complexity and resolution: 1-bit. We examine performance (in terms of Frame-error-rate -- FER) for schemes mentioned abo

Ivan A. Strelnikov, Natalya A. Kovaleva, Artem P. Klinov, Elena A. Zubova

The DNA duplex may be locally strongly bent in complexes with proteins, for example, with polymerases or in a nucleosome. At such bends, the DNA helix is locally in the non-canonical forms A (with a narrow major groove and a large amount of north sugars) or C (with a narrow minor groove and a large share of BII phosphates). To model the formation of such com

Hai Yang, Feng Yuan, Tom Kwan, Lixin Dai

Wind and jet are important medium of AGN feedback thus it is crucial to obtain their properties for the feedback study. In this paper we investigate the properties of wind and jet launched from a magnetized super-Eddington accretion flow around a supermassive black hole. For this aim, we have performed radiation magnetohydrodynamical simulation of a magnetic

Xuri Tang, Huifang Ye

One way to resolve the actuation problem of metaphorical language change is to provide a statistical profile of metaphorical constructions and generative rules with antecedent conditions. Based on arguments from the view of language as complex systems and the dynamic view of metaphor, this paper argues that metaphorical language change qualifies as a self-or

Samah Baraheem, Zhongmei Yao

Nowadays, machine learning models and applications have become increasingly pervasive. With this rapid increase in the development and employment of machine learning models, a concern regarding privacy has risen. Thus, there is a legitimate need to protect the data from leaking and from any attacks. One of the strongest and most prevalent privacy models that

TaeYoung Kang, Hanbin Lee

Vaccine adverse events have been presumed to be a relatively objective measure that is immune to political polarization. The real-world data, however, shows the correlation between presidential disapproval ratings and the subjective severity of adverse events. This paper investigates the partisan bias in COVID vaccine adverse events coverage with language mo

Erik Schnaubelt, Nicolas Marsic, Herbert De Gersem

In many time-harmonic electromagnetic wave problems, the considered geometry exhibits an axial symmetry. In this case, by exploiting a Fourier expansion along the azimuthal direction, fully three-dimensional (3D) calculations can be carried out on a two-dimensional (2D) angular cross section of the problem, thus significantly reducing the computational effor

Zhiyang Dou, Qingxuan Wu, Cheng Lin, Zeyu Cao

In this paper, we introduce a set of simple yet effective TOken REduction (TORE) strategies for Transformer-based Human Mesh Recovery from monocular images. Current SOTA performance is achieved by Transformer-based structures. However, they suffer from high model complexity and computation cost caused by redundant tokens. We propose token reduction strategie

Maria Lymperaiou, Giorgos Stamou

Multimodal learning has been a field of increasing interest, aiming to combine various modalities in a single joint representation. Especially in the area of visiolinguistic (VL) learning multiple models and techniques have been developed, targeting a variety of tasks that involve images and text. VL models have reached unprecedented performances by extendin

Vikash Kumar, A. Swaminathan

In this work, the concept of quasi-type Kernel polynomials with respect to a moment functional is introduced. Difference equation satisfied by these polynomials along with the criterion for orthogonality conditions are discussed. The process of recovering orthogonality for the linear combination of a quasi-type kernel polynomial with another orthogonal polyn

Maurice H. P. M. van Putten, Massimo Della Valle

The central engine of GRB170817A post-merger to GW170817 is probed by GW-calorimetry and event timing, applied to a post-merger descending chirp which can potentially break the degeneracy in spin-down of a neutron star or black hole remnant by the relatively large energy reservoir in the angular momentum, $E_J$, of the latter according to the Kerr metric. Th

Jiaming Sui, Junxiong Jia

This paper proposes a non-centered parameterization based infinite-dimensional mean-field variational inference (NCP-iMFVI) approach for solving the hierarchical Bayesian inverse problems. This method can generate available estimates from the approximated posterior distribution efficiently. To avoid the mutually singular obstacle that occurred in the infinit

Zhongnian Li, Jian Zhang, Mengting Xu, Xinzheng Xu

Complementary Labels Learning (CLL) arises in many real-world tasks such as private questions classification and online learning, which aims to alleviate the annotation cost compared with standard supervised learning. Unfortunately, most previous CLL algorithms were in a stable environment rather than an open and dynamic scenarios, where data collected from

Chandan Kumar Sheemar, Stefano Tomasin, Dirk Slock, Symeon Chatzinotas

Full duplex (FD) systems suffer from very high hardware cost and high power consumption to mitigate the self-interference (SI) in the analog domain. Moreover, in millimeter wave (mmWave) they rely on hybrid beamforming (HYBF) as a signal processing tool to partially deal with the SI, which presents many drawbacks such as high insertion loss and high power co

Clara R. P. Baldansa, Roberto C. G. Porto, Bruno José Olivieri de Souza, Vítor G. Andrezo Carneiro

This paper shows that, when considering outdoor scenarios and wireless communications using the IEEE 802.11 protocol with dipole antennas, the ground reflection is a significant propagation mechanism. This way, the Two-Ray model for this environment allows predicting, with some accuracy, the received signal power. This study is relevant for the application i

Alejandro Quirós Rodríguez, Tomas Fullana, Vincent Le Chenadec, Taraneh Sayadi

The treatment of complex geometries in Computational Fluid Dynamics applications is a challenging endeavor, which immersed boundary and cut-cell techniques can significantly simplify by alleviating the meshing process required by body-fitted meshes. These methods however introduce new challenges, as the formulation of accurate and well-posed discrete operato

Mykola Yelisieiev, Vyacheslav Kochelap

We present the theoretical analysis of steady state regimes and low-frequency noises in quasi-ballistic FETs. The noise analysis is based on the Langevin approach, which accounts for the microscopic sources of fluctuations originated from intrachannel electron scattering. The general formulas for local fluctuations of the carrier concentration, velocity and

Marcelo Paulon J. V., Bruno José Olivieri de Souza, Thiago de Souza Lamenza, Markus Endler

Testing network algorithms in physical environments using real hardware is an important step to reduce the gap between theory and practice in the field, and an interesting way to explore technologies such as Bluetooth Mesh. We implemented a Bluetooth Mesh data collection strategy and deployed it in indoor and outdoor settings, using ESP-32 microcontrollers.

Muqian Wen, John Houlihan

Resampling by interpolation is the traditional method to process interferograms from non-uniformly sampled Fourier transform spectrometers. The non-uniform fast Fourier transform (NUFFT) is an alternative approach that has been mostly overlooked. With the aid of experiments on a high-resolution interferometer with a variety of optical sources, these two meth

Tahere Parto, Shahrzad Dehghani, Atefeh Javadi, Elham Saremi

We conducted an optical monitoring survey of the Sagittarius dwarf irregular galaxy (SagDIG) during the period of June 2016 -- October 2017, using the 2.5-m Isaac Newton Telescope (INT) at La Palama. Our goal was to identify Long Period Variable stars (LPVs), namely asymptotic giant branch stars (AGBs) and red supergiant stars (RSGs), to obtain the Star Form

M. Arivazhagan, P. Muruganandam, N. Athavan

We study the creation of vortices by triggering the rotating Bose-Einstein condensates in a toroidal trap with trap parameters such as laser beam waist and Gaussian potential depth. By numerically solving the time-dependent Gross-Pitaevskii equation in two dimensions, we observe a change in vortex structure and a considerable increase in the number of vortic

Daisuke Murakami, Mami Kajita, Seiji Kajita

Although spatial prediction is widely used for urban and environmental monitoring, its accuracy is often unsatisfactory if only a small number of samples are available in the study area. The objective of this study was to improve the prediction accuracy in such a case through transfer learning using larger samples obtained outside the study area. Our proposa

Motomi Aoki, Ei Shigematsu, Ryo Ohshima, Teruya Shinjo

Self-induced spin-orbit torques (SI-SOTs) in ferromagnetic (FM) layers have been overlooked when estimating the spin Hall angle (SHA) of adjacent nonmagnetic (NM) layers. In this work, we observe anomalous sign inversion of the total SOT in the spin-torque ferromagnetic resonance due to the enhanced SI-SOT, and successfully rationalize the sign inversion thr

Ziqiao Wang, Yongyi Mao

Stochastic differential equations (SDEs) have been shown recently to characterize well the dynamics of training machine learning models with SGD. When the generalization error of the SDE approximation closely aligns with that of SGD in expectation, it provides two opportunities for understanding better the generalization behaviour of SGD through its SDE appr

M. F. Mridha, Md. Kishor Morol, Md. Asraf Ali, Md Sakib Hossain Shovon

Generally, human epidermal growth factor 2 (HER2) breast cancer is more aggressive than other kinds of breast cancer. Currently, HER2 breast cancer is detected using expensive medical tests are most expensive. Therefore, the aim of this study was to develop a computational model named convoHER2 for detecting HER2 breast cancer with image data using convoluti

Jorge G. Russo, Paul K. Townsend

All solutions of the no-birefringence conditions for nonlinear electrodynamics are found. In addition to the known Born-Infeld and Plebanski cases, we find a ``reverse Born-Infeld'' case, which has a limit to Plebanski, and an ``extreme-Born-Infeld'' case, which arises as a Lagrangian constraint. Only Born-Infeld has a weak-field limit, and only Born-Infeld

Marina Speranskaya, Sameh Methias, Benjamin Roth

We propose to use reinforcement learning to inform transformer-based contextualized link prediction models by providing paths that are most useful for predicting the correct answer. This is in contrast to previous approaches, that either used reinforcement learning (RL) to directly search for the answer, or based their prediction on limited or randomly selec

Claudio Grimaldi

For about the last 60 years the search for extraterrestrial intelligence has been monitoring the sky for evidence of remotely detectable technological life beyond Earth, with no positive results to date. While the lack of detection can be attributed to the highly incomplete sampling of the search space, technological emissions may be actually rare enough tha

Tobias Breiten, Dorothea Hinsen, Benjamin Unger

The framework of port-Hamiltonian (pH) systems is a powerful and broadly applicable modeling paradigm. In this paper, we extend the scope of pH systems to time-delay systems. Our definition of a delay pH system is motivated by investigating the Kalman-Yakubovich-Popov inequality on the corresponding infinite-dimensional operator equation. Moreover, we show t

Yudong Li, Yunlin Lei, Xu Yang

Spiking neural networks (SNNs) have made great progress on both performance and efficiency over the last few years,but their unique working pattern makes it hard to train a high-performance low-latency SNN.Thus the development of SNNs still lags behind traditional artificial neural networks (ANNs).To compensate this gap,many extraordinary works have been pro

Lin Xiao, Pengyu Xu, Liping Jing, Xiangliang Zhang

Multi-label text classification (MLTC) is one of the key tasks in natural language processing. It aims to assign multiple target labels to one document. Due to the uneven popularity of labels, the number of documents per label follows a long-tailed distribution in most cases. It is much more challenging to learn classifiers for data-scarce tail labels than f