July 2022 arXiv papers — page 72

Peter Nicholas Hansen, Thomas T. Enevoldsen, Dimitrios Papageorgiou, Mogens Blanke

Fully or partial autonomous marine vessels are actively being developed by many industry actors. In many cases, the autonomous vessels will be operating close to shore, and within range of a Remote Control Center (RCC). Close to shore operation requires that the autonomous vessel is able to navigate in close proximity to other autonomous or manned vessels, a

Yujin WU, Mohamed Daoudi, Ali Amad, Laurent Sparrow

Existing multimodal stress/pain recognition approaches generally extract features from different modalities independently and thus ignore cross-modality correlations. This paper proposes a novel geometric framework for multimodal stress/pain detection utilizing Symmetric Positive Definite (SPD) matrices as a representation that incorporates the correlation r

Yinzhi Lu, Liu Yang, Simon X. Yang, Qiaozhi Hua

Edge enabled Industrial Internet of Things (IIoT) platform is of great significance to accelerate the development of smart industry. However, with the dramatic increase in real-time IIoT applications, it is a great challenge to support fast response time, low latency, and efficient bandwidth utilization. To address this issue, Time Sensitive Network (TSN) is

Eduardo Reck Miranda, Brian N. Siegelwax

This paper introduces a system that learns to sing new tunes by listening to examples. It extracts sequencing rules from input music and uses these rules to generate new tunes, which are sung by a vocal synthesiser. We developed a method to represent rules for musical composition as quantum circuits. We claim that such musical rules are quantum native: they

Jingwen Ye, Yifang Fu, Jie Song, Xingyi Yang

Life-long learning aims at learning a sequence of tasks without forgetting the previously acquired knowledge. However, the involved training data may not be life-long legitimate due to privacy or copyright reasons. In practical scenarios, for instance, the model owner may wish to enable or disable the knowledge of specific tasks or specific samples from time

Giseon Baek, Jeong-Eun Lee, Tomoya Hirota, Kee-Tae Kim

Recent astrochemical models and experiments have explained that complex organic molecules (COMs; molecules composed of six or more atoms) are produced on the dust grain mantles in cold and dense gas in prestellar cores. However, the detailed chemical processes and the roles of physical conditions on chemistry are still far from understood. To address these q

F. De Zela

We present a local-realistic description of both wave-particle duality and Bohmian trajectories. Our approach is relativistic and based on Hamilton's principle of classical mechanics, but departs from its standard setting in two respects. First, we address an ensemble of extremal curves, the so-called Mayer field, instead of focusing on a single extremal cur

Dung M. Ha, Hieu T. Ngo

In this work and its sequel, we study the expanding phenomenon of matrices over a finite chain ring of large residue field. A sum-product estimate is proved. It is showed that $x+yz$ is a moderate expander on $n\times n$ matrices with exponent $\frac{n+1}{6}$. These results generalise the main theorems in a recent work of Xie and Ge. The proofs use spectral

Yuanzheng Ci, Chen Lin, Lei Bai, Wanli Ouyang

Contrastive-based self-supervised learning methods achieved great success in recent years. However, self-supervision requires extremely long training epochs (e.g., 800 epochs for MoCo v3) to achieve promising results, which is unacceptable for the general academic community and hinders the development of this topic. This work revisits the momentum-based cont

Lorenz Vaitl, Kim A. Nicoli, Shinichi Nakajima, Pan Kessel

We propose an algorithm to estimate the path-gradient of both the reverse and forward Kullback-Leibler divergence for an arbitrary manifestly invertible normalizing flow. The resulting path-gradient estimators are straightforward to implement, have lower variance, and lead not only to faster convergence of training but also to better overall approximation re

Andrea Plati, Andrea Puglisi, Alessandro Sarracino

We derive a Thermodynamic Uncertainty Relation bounding the mean squared displacement of a Gaussian process with memory, driven out of equilibrium by unbalanced thermal baths and/or by external forces. Our bound is tighter with respect to previous results and also holds at finite time. We apply our findings to experimental and numerical data for a many-body

Devin Coughlin, Maylee Gagnon, Victoria Grasso, Guanyi Mou

Experts combating wildlife trafficking manually sift through articles about seizures and arrests, which is time consuming and make identifying trends difficult. We apply natural language processing techniques to automatically extract data from reports published by the Eco Activists for Governance and Law Enforcement (EAGLE). We expanded Python spaCy's pre-tr

Nicholas Lozano, Vinh Tran, Prashanth Jaikumar

Neutron stars provide a unique physical laboratory to study the properties of matter at high density. We study a diagnostic of the composition of high-density matter, namely, g-mode oscillations, which are driven by buoyancy forces. These oscillations can be excited by tidal forces and couple to gravitational waves. We extend prior results for the g-mode spe

Matthias Chung, Rosemary Renaut

Inference by means of mathematical modeling from a collection of observations remains a crucial tool for scientific discovery and is ubiquitous in application areas such as signal compression, imaging restoration, and supervised machine learning. The inference problems may be solved using variational formulations that provide theoretically proven methods and

Renbo Su, Yingjun Tian, Mingwei Du, Charlie C. L. Wang

In this paper, we presented a data-driven framework to optimize the out-of-plane stiffness for soft grippers to achieve mechanical properties as hard-to-twist and easy-to-bend. The effectiveness of this method is demonstrated in the design of a soft pneumatic bending actuator (SPBA). First, a new objective function is defined to quantitatively evaluate the o

Shenhan Jia, Yanmei Jiao, Zhuqing Zhang, Rong Xiong

In recent years, Visual-Inertial Odometry (VIO) has achieved many significant progresses. However, VIO methods suffer from localization drift over long trajectories. In this paper, we propose a First-Estimates Jacobian Visual-Inertial-Ranging Odometry (FEJ-VIRO) to reduce the localization drifts of VIO by incorporating ultra-wideband (UWB) ranging measuremen

Alberto Tarable, Francisco J. Escribano

Phase noise (PN) is a major disturbance in MIMO systems, where the contribution of different oscillators at the transmitter and the receiver side may degrade the overall performance and offset the gains offered by MIMO techniques. This is even more crucial in the case of massive MIMO, since the number of PN sources may increase considerably. In this work, we

Kexin Wang, Zhixu Li, Jiaan Wang, Jianfeng Qu

Grounding dialogue system with external knowledge is a promising way to improve the quality of responses. Most existing works adopt knowledge graphs (KGs) as the external resources, paying attention to the contribution of entities in the last utterance of the dialogue for context understanding and response generation. Nevertheless, the correlations between k

Peijun Sang, Bing Li

We propose a nonlinear function-on-function regression model where both the covariate and the response are random functions. The nonlinear regression is carried out in two steps: we first construct Hilbert spaces to accommodate the functional covariate and the functional response, and then build a second-layer Hilbert space for the covariate to capture nonli

Ke Fan, Yikai Wang, Qian Yu, Da Li

Neural networks are known to produce over-confident predictions on input images, even when these images are out-of-distribution (OOD) samples. This limits the applications of neural network models in real-world scenarios, where OOD samples exist. Many existing approaches identify the OOD instances via exploiting various cues, such as finding irregular patter

Michel Brion

Every action of a finite group scheme $G$ on a variety admits a projective equivariant model, but not necessarily a normal one. As a remedy, we introduce and explore the notion of $G$-normalization. In particular, every curve equipped with a $G$-action has a unique projective $G$-normal model, characterized by the invertibility of ideal sheaves of all orbits

Muzaffer Özbey, Onat Dalmaz, Salman UH Dar, Hasan A Bedel

Imputation of missing images via source-to-target modality translation can improve diversity in medical imaging protocols. A pervasive approach for synthesizing target images involves one-shot mapping through generative adversarial networks (GAN). Yet, GAN models that implicitly characterize the image distribution can suffer from limited sample fidelity. Her

Gavin Niendorf, Tres Reid, Peter Wittich, Peter Elmer

The major challenge posed by the high instantaneous luminosity in the High Luminosity LHC (HL-LHC) motivates efficient and fast reconstruction of charged particle tracks in a high pile-up environment. While there have been efforts to use modern techniques like vectorization to improve the existing classic Kalman Filter based reconstruction algorithms, Line S

Andrej Dujella, Juan Carlos Peral

In this note we present the main details of the construction of an elliptic curve over $\mathbb{Q}(u)$ with torsion $\mathbb{Z}/4\mathbb{Z}$ and rank 6. Previously only rank 5 examples for such curves were known.

Yanjun Ji, Sebastian Brandhofer, Ilia Polian

Today's Noisy Intermediate-Scale Quantum (NISQ) computers support only limited sets of available quantum gates and restricted connectivity. Therefore, quantum algorithms must be transpiled in order to become executable on a given NISQ computer; transpilation is a complex and computationally heavy process. Moreover, NISQ computers are affected by noise that c

Yajie Bao, Michael Crawshaw, Shan Luo, Mingrui Liu

As a prevalent distributed learning paradigm, Federated Learning (FL) trains a global model on a massive amount of devices with infrequent communication. This paper investigates a class of composite optimization and statistical recovery problems in the FL setting, whose loss function consists of a data-dependent smooth loss and a non-smooth regularizer. Exam

Albert Bruch

Cataclysmic variables (CVs) exhibit a plethora of variable phenomena many of which require long, uninterrupted light curves to reveal themselves in detail. The month long datasets provided by TESS are well suited for this purpose. TESS has the additional advantage to have observed a huge number of stars, among them many CVs. Here, a search for periodic varia

Koki Kakiichi, Tobias Schmidt, Joseph Hennawi

In the standard picture, episodes of luminous quasar activity are directly related to supermassive black hole (SMBH) growth. The ionising radiation emitted over a quasar's lifetime alters the ionisation state of the surrounding intergalactic medium (IGM), enhancing the Ly$\alpha$ forest transmission -- so-called proximity effect -- which can be observed in a

Zhihong Zhang, Yuxiao Cheng, Jinli Suo, Liheng Bian

Under low-light environment, handheld photography suffers from severe camera shake under long exposure settings. Although existing deblurring algorithms have shown promising performance on well-exposed blurry images, they still cannot cope with low-light snapshots. Sophisticated noise and saturation regions are two dominating challenges in practical low-ligh

Sadeed Bin Sayed, Rui Chen, Huseyin Arda Ulku, Hakan Bagci

A time domain electric field volume integral equation (TD-EFVIE) solver is proposed for analyzing electromagnetic scattering from dielectric objects with Kerr nonlinearity. The nonlinear constitutive relation that relates electric flux and electric field induced in the scatterer is used as an auxiliary equation that complements TD-EFVIE. The ordinary differe

Khoh Choon Hwa, Selvakumar Manickam, Mahmood A. Al-Shareeda

Cybercrimes are becoming a bigger menace to both people and corporations. It poses a serious challenge to the modern digital world. According to a press release from 2019 Cisco and Cybersecurity Ventures, Cisco stopped seven trillion threats in 2018, or 20 billion threats every day, on behalf of its clients. According to Cybersecurity Ventures, the global co

Gianluca Detommaso, Alberto Gasparin, Andrew Wilson, Cedric Archambeau

As we move away from the data, the predictive uncertainty should increase, since a great variety of explanations are consistent with the little available information. We introduce Distance-Aware Prior (DAP) calibration, a method to correct overconfidence of Bayesian deep learning models outside of the training domain. We define DAPs as prior distributions ov

Krishnakanta Bhattacharya

The Einstein-Hilbert (EH) action is peculiar in many ways. Some of the Peculiar features have already been highlighted in literature. In the present article, we have discussed some peculiar features of EH action which has not been discussed earlier. It is well-known that there are several ways of decomposing the EH action into the bulk and the surface part w

Jules P. Halpern, Karen I. Perez, Slavko Bogdanov

Several redback and black widow millisecond pulsar binaries have episodes of flaring in X-rays and optical. We initially detected such behavior from the Fermi selected redback candidate 1FGL J0523.5$-$2529 during optical time-series monitoring. Triggered observations with the Neil Gehrels Swift Observatory over the next $\approx100$ days showed episodic flar

Christoph Tietz

We present an order-theoretical fixed point theorem for increasing multivalued operators suitable for the method of sub-supersolutions and its application to the following multivalued quasi-variational inclusion: Let $\Omega \subset \mathbb R^N$ be a bounded Lipschitz domain and $W = W_0^{1,p}(\Omega)$. Find $u\in W$ such that for some measurable selection $

Vaclav Vavrycuk

Modified Newtonian equations for gravitational orbits in the expanding universe indicate that local gravitationally bounded systems like galaxies and planetary systems are unaffected by the expansion of the Universe. This result is derived under the assumption of the space expansion described by the standard FLRW metric. In this paper, an alternative metric

Pourya Behmandpoor, Puya Latafat, Andreas Themelis, Marc Moonen

We introduce SPIRAL, a SuPerlinearly convergent Incremental pRoximal ALgorithm, for solving nonconvex regularized finite sum problems under a relative smoothness assumption. Each iteration of SPIRAL consists of an inner and an outer loop. It combines incremental gradient updates with a linesearch that has the remarkable property of never being triggered asym

Rafael Accácio Nogueira, Romain Bourdais, Simon Leglaive, Hervé Guéguen

Controlling large-scale systems sometimes requires decentralized computation. Communication among agents is crucial to achieving consensus and optimal global behavior. These negotiation mechanisms are sensitive to attacks on those exchanges. This paper proposes an algorithm based on Expectation Maximization to mitigate the effects of attacks in a resource al

Wenlong Wang

We present a large-scale simulation of the ground state interface properties of the diluted Sherrington-Kirkpatrick spin glass of Gaussian disorder for a broad range of the bond occupation probability $p$ using the strong disorder renormalization group and the population annealing Monte Carlo methods. We find that the interface is space-filling independent o

Zeyi Liu, Zhenjia Xu, Shuran Song

We introduce BusyBoard, a toy-inspired robot learning environment that leverages a diverse set of articulated objects and inter-object functional relations to provide rich visual feedback for robot interactions. Based on this environment, we introduce a learning framework, BusyBot, which allows an agent to jointly acquire three fundamental capabilities (inte

Zongze Chen, Wenxia Yang, Xin Li

Chinese characters carry a wealth of morphological and semantic information; therefore, the semantic enhancement of the morphology of Chinese characters has drawn significant attention. The previous methods were intended to directly extract information from a whole Chinese character image, which usually cannot capture both global and local information simult

Jinlu Li, Yanghai Yu, Weipeng Zhu

In the paper, we gave a strengthening of our previous work in [32] (J. Differ. Equ. 269 (2020)) and proved that the data-to-solution map for the Camassa-Holm equation is nowhere uniformly continuous in $B^s_{p,r}(\R)$ with $s>\max\{1+1/{p},3/2\}$ and $(p,r)\in [1,\infty]\times[1,\infty)$. The method applies also to the b-family of equations which contain the

Shoummo Ahsan Khandoker, Jawaril Munshad Abedin, Mohamed Hibat-Allah

Combinatorial optimization problems can be solved by heuristic algorithms such as simulated annealing (SA) which aims to find the optimal solution within a large search space through thermal fluctuations. The algorithm generates new solutions through Markov-chain Monte Carlo techniques. This sampling scheme can result in severe limitations, such as slow conv

Awnon Bhowmik, Emon Hossain, Mahmudul Hasan

In recent years, a lot of research has gone into creating multi-layer chaotic mapping-based cryptosystems. Random-like behavior, a continuous broadband power spectrum, and a weak baseline condition dependency are all characteristics of chaotic systems. Chaos could be helpful in the three functional components of compression, encryption, and modulation in a d

Georgios Misyris, Deepak Ramasubramanian, Parag Mitra, Vikas Singhvi

This paper evaluates the frequency performance of an AC system when primary frequency response is provided by inverter-based resources located at remote-areas. Due to potentially larger wave propagation constants over longer lines, fast active power response from inverter based resources may have a negative impact on the system frequency response. Within thi

Sannara Ek, Romain Rombourg, François Portet, Philippe Lalanda

Federated Learning is a new machine learning paradigm dealing with distributed model learning on independent devices. One of the many advantages of federated learning is that training data stay on devices (such as smartphones), and only learned models are shared with a centralized server. In the case of supervised learning, labeling is entrusted to the clien

Na Qin, Cheng Chen, Shiqiao Du, Xian Du

Using high-resolution angle-resolved photoemission spectroscopy (ARPES) and ab-initio calculation, we systematically investigate the electronic structure of the chiral helimagnet Cr1/3NbS2 and its temperature evolution. The comparison with NbS2 suggests that the electronic structure of Cr1/3NbS2 is strongly modified by the intercalation of Cr atoms. Our ab-i

Lei Zhang, Yuxuan Sun, Wei Wei

Exploiting pseudo labels (e.g., categories and bounding boxes) of unannotated objects produced by a teacher detector have underpinned much of recent progress in semi-supervised object detection (SSOD). However, due to the limited generalization capacity of the teacher detector caused by the scarce annotations, the produced pseudo labels often deviate from gr

Sauradip Nag, Xiatian Zhu, Yi-Zhe Song, Tao Xiang

Existing temporal action detection (TAD) methods rely on large training data including segment-level annotations, limited to recognizing previously seen classes alone during inference. Collecting and annotating a large training set for each class of interest is costly and hence unscalable. Zero-shot TAD (ZS-TAD) resolves this obstacle by enabling a pre-train

Samir Panja

In this article, by considering $T=(T_1,\dots, T_d)$, an $d$-tuple of commuting contractions on a Hilbert space $\mathcal{H}$, we study $T$-Toeplitz operators which consists of bounded operators $X$ on $\mathcal{H}$ such that \[ T_i^*XT_i=X \] for all $i=1,\dots,d$. We show that any positive $T$-Toeplitz operator can be factorized in terms of an isometric ps

Dawei Dai, Donggen Li, Zhiguo Zhuang

In supervised learning, the presence of noise can have a significant impact on decision making. Since many classifiers do not take label noise into account in the derivation of the loss function, including the loss functions of logistic regression, SVM, and AdaBoost, especially the AdaBoost iterative algorithm, whose core idea is to continuously increase the

Davide Sclosa

We prove that the Kuramoto model on a graph can contain infinitely many non-equivalent stable equilibria. More precisely, we prove that for every positive integer d there is a connected graph such that the set of stable equilibria contains a manifold of dimension d. In particular, we solve a conjecture of R. Delabays, T. Coletta and P. Jacquod about the numb

Anastasiia Usmanova, François Portet, Philippe Lalanda, German Vega

Federated Learning has been introduced as a new machine learning paradigm enhancing the use of local devices. At a server level, FL regularly aggregates models learned locally on distributed clients to obtain a more general model. Current solutions rely on the availability of large amounts of stored data at the client side in order to fine-tune the models se

Anastasiia Usmanova, François Portet, Philippe Lalanda, German Vega

Federated Learning has been introduced as a new machine learning paradigm enhancing the use of local devices. At a server level, FL regularly aggregates models learned locally on distributed clients to obtain a more general model. In this way, no private data is sent over the network, and the communication cost is reduced. However, current solutions rely on

Thierry Desot, François Portet, Michel Vacher

Spoken Language Understanding (SLU) is a core task in most human-machine interaction systems. With the emergence of smart homes, smart phones and smart speakers, SLU has become a key technology for the industry. In a classical SLU approach, an Automatic Speech Recognition (ASR) module transcribes the speech signal into a textual representation from which a N

Xinwei Liu, Jian Liu, Yang Bai, Jindong Gu

As a common security tool, visible watermarking has been widely applied to protect copyrights of digital images. However, recent works have shown that visible watermarks can be removed by DNNs without damaging their host images. Such watermark-removal techniques pose a great threat to the ownership of images. Inspired by the vulnerability of DNNs on adversar

M. A. Ali, M. M. Hossain, M. M. Uddin, A. K. M. A. Islam

An interesting class of ternary metallic borides, known as the 212 MAX phase borides, is the recent advancement of the MAX phase family. In this article, results from ab-initio calculations on unexplored Ti$_2$PB$_2$, Zr$_2$PbB$_2$, and Nb$_2$AB$_2$ [A = P, S] are reported wherein Ti$_2$PB$_2$ along with its 211 boride phase Ti$_2$PB are predicted for the fi

Hidetaka Houtani, Hiroshi Sawada, Takuji Waseda

The Akhmediev breather (AB) solution of the nonlinear Schr$\"{o}$dinger equation (NLSE) shows that the maximum crest height of modulated wave trains reaches triple the initial amplitude as a consequence of nonlinear long-term evolution. Several fully nonlinear numerical studies have indicated that the amplification can exceed 3, but its physical mechanism ha

Xuanzhe Liu, Jinfeng Wen, Zhenpeng Chen, Ding Li

Serverless computing is a popular cloud computing paradigm that frees developers from server management. Function-as-a-Service (FaaS) is the most popular implementation of serverless computing, representing applications as event-driven and stateless functions. However, existing studies report that functions of FaaS applications severely suffer from cold-star

Fedor Pakhomov, Juvenal Murwanashyaka, Albert Visser

We show that there is no theory that is minimal with respect to interpretability among recursively enumerable essentially undecidable theories.

David Blanc, Nir Shvalb

A $configuration$ of a linkage $\Gamma$ is a possible positioning of $\Gamma$ in $\mathbb{R}^d$ and the collection of all such forms the configuration space $\mathcal{C}(\Gamma)$ of $\Gamma$. We here introduce the notion of the $symmetric configuration space$ of a linkage, in which we identify configurations which are geometrically indistinguishable. We show

Jan Wiegerinck

Examples by Poletsky and the author and by Zwonek show the existence nowhere extendable holomorphic functions with the property that the pluripolar hull of their graphs is much larger than the graph of the respective functions and contains multiple sheets. We will explain this phenomenon by fine analytic continuation of the function over part of a Cantor-typ

Banafshe Felfeliyan, Abhilash Hareendranathan, Gregor Kuntze, David Cornell

Many successful methods developed for medical image analysis that are based on machine learning use supervised learning approaches, which often require large datasets annotated by experts to achieve high accuracy. However, medical data annotation is time-consuming and expensive, especially for segmentation tasks. To solve the problem of learning with limited

Shuang Li, William J. Swartworth, Martin Takáč, Deanna Needell

Recently the "SP" (Stochastic Polyak step size) method has emerged as a competitive adaptive method for setting the step sizes of SGD. SP can be interpreted as a method specialized to interpolated models, since it solves the interpolation equations. SP solves these equation by using local linearizations of the model. We take a step further and develop a meth

D. Wang, B. P. Gong

The post-Keplerian(PK) parameters inferred from pulsar timing provide a convenient way to test Einstein's general theory of relativity. However, before obtaining a pure orbital decay $\dot{P}_b$ induced by gravitational wave radiation, which is one of the PK parameters, a number of factors need to be accounted for carefully. The effect of tidal dissipation o

Dima Kagan, Mor Levy, Michael Fire, Galit Fuhrmann Alpert

In the last decades, global awareness towards the importance of diverse representation has been increasing. Lack of diversity and discrimination toward minorities did not skip the film industry. Here, we examine ethnic bias in the film industry through commercial posters, the industry's primary advertisement medium for decades. Movie posters are designed to

Athmakoori Prashant, S. Francis Raj

The class of $2K_2$-free graphs has been well studied in various contexts in the past. In this paper, we study the chromatic number of $\{butterfly, hammer\}$-free graphs, a superclass of $2K_2$-free graphs and show that a connected $\{butterfly, hammer\}$-free graph $G$ with $\omega(G)\neq 2$ admits $\binom{\omega+1}{2}$ as a $\chi$-binding function which i

Xinfu Li, Li Xu, Meiling Zhu

This paper studies the multiplicity of normalized solutions to the Schr\"{o}dinger equation with mixed nonlinearities \begin{equation*} \begin{cases} -\Delta u=\lambda u+h(\epsilon x)|u|^{q-2}u+\eta |u|^{p-2}u,\quad x\in \mathbb{R}^N, \\ \int_{\mathbb{R}^N}|u|^2dx=a^2, \end{cases} \end{equation*} where $a, \epsilon, \eta>0$, $q$ is $L^2$-subcritical, $p$ is

Hamidreza Daniali, Davoud Kamani

In the context of the bosonic string theory, we shall extract the general radiation amplitude of a massless closed string from the interaction of two parallel unstable D$p$-branes. The branes are non-stationary and have been dressed by background fields. The foregoing amplitude will be rewritten for the massless state radiation from the branes with the large

Nikita Markov, Ilya Kotov, Konstantin Ushenin, Yakov Bozhko

Heart rate variability (HRV) indices describe properties of interbeat intervals in electrocardiogram (ECG). Usually HRV is measured exclusively in normal sinus rhythm (NSR) excluding any form of paroxysmal rhythm. Atrial fibrillation (AF) is the most widespread cardiac arrhythmia in human population. Usually such abnormal rhythm is not analyzed and assumed t

Qiujing Lu, Yipeng Zhang, Mingjian Lu, Vwani Roychowdhury

We propose a novel framework, On-Demand MOtion Generation (ODMO), for generating realistic and diverse long-term 3D human motion sequences conditioned only on action types with an additional capability of customization. ODMO shows improvements over SOTA approaches on all traditional motion evaluation metrics when evaluated on three public datasets (HumanAct1

Yunhan Ma, Yong Niu, Zhu Han, Bo Ai

With the explosive growth of mobile data, the demand of high-speed railway (HSR) passengers for broadband wireless access services urgently needs the support of ultra-highspeed scenario broadband wireless communication. Millimeterwave (mmWave) can achieve high data transmission rates, but it is accompanied by high propagation loss and vulnerability to blocka

Vladislav Dordiuk, Ekaterina Demicheva, Fernando Polanco Espino, Konstantin Ushenin

There are hundreds of methods for analysis of data obtained in mRNA-sequencing. The most of them are focused on small number of genes. In this study, we propose an approach that reduces the analysis of several thousand genes to analysis of several clusters. The list of genes is enriched with information from open databases. Then, the descriptions are encoded

Xiaokang Li, Jahyun Koo, Zengwei Zhu, Kamran Behnia

During the past two decades, it has been established that a non-trivial electron wave-function topology generates an anomalous Hall effect (AHE), which shows itself as a Hall conductivity non-linear in magnetic field. Here, we report on an unprecedented case of field-linear AHE. In Mn$_3$Sn, a kagome magnet, the out-of-plane Hall response, which shows an abr

Tomáš Kepka, Miroslav Korbelář, Günter Landsmann

Let $S$ be a multiplicatively idempotent congruence-simple semiring. We show that $|S|=2$ if $S$ has a multiplicatively absorbing element. We also prove that if $S$ is finite then either $|S|=2$ or $S\cong End(L)$ or $S^{op}\cong End(L)$ where $L$ is a 2-element semilattice. It seems to be an open question, whether $S$ can be infinite at all.

Shaoyu Dou, Kai Yang, Yang Jiao, Chengbo Qiu

Time series analysis has achieved great success in diverse applications such as network security, environmental monitoring, and medical informatics. Learning similarities among different time series is a crucial problem since it serves as the foundation for downstream analysis such as clustering and anomaly detection. It often remains unclear what kind of di

Erhan Bayraktar, Zhenhua Wang, Zhou Zhou

We investigate the stability of the equilibrium-induced optimal value in one-dimensional diffusion setting for a time-inconsistent stopping problem under non-exponential discounting. We show that the optimal value is semi-continuous with respect to the drift, volatility, and reward function. An example is provided showing that the exact continuity may fail.

Yu Mingcan, Wang Junying

Although principles of neuroscience like reinforcement learning, visual perception and attention have been applied in machine learning models, there is a huge gap between machine learning and mammalian learning. Based on the advances in neuroscience, we propose the context sequence theory to give a common explanation for multiple types of learning in mammals

Dor Cohen, Ofer Strichman

Over the last decade, Neural Networks (NNs) have been widely used in numerous applications including safety-critical ones such as autonomous systems. Despite their emerging adoption, it is well known that NNs are susceptible to Adversarial Attacks. Hence, it is highly important to provide guarantees that such systems work correctly. To remedy these issues we

Paolo A. Sossi, Ingo L. Stotz, Seth A. Jacobson, Alessandro Morbidelli

Earth is depleted in volatile elements relative to chondritic meteorites, its possible building blocks. The extent of this depletion increases with decreasing condensation temperature, and is approximated by a cumulative normal distribution, unlike that in any chondrite. However, moderately volatile elements, occupying the mid-range of the distribution, have

Luca Gamberi, Pierpaolo Vivo, Yanik-Pascal Förster, Evan Tzanis

The Bradley effect concerns the discrepancy between opinion polls and actual election outcomes that emerges when candidates do not exhibit ideological, sexual or racial mainstream features. This effect was first observed during the 1982 election for the Governor of California that resulted in a significant loss for the black Democratic candidate, Tom Bradley

Se-Ho Kim, Kang Dong, Huan Zhao, Ayman A. El-Zoka

Si-anodes have long been candidates thanks to an expected ten-fold increase in capacity compared to graphite. However, details of the mechanisms governing their degradation remain elusive, hindering science-guided development of long-lived Si-based anodes. Here we demonstrate how the latest developments in cryo-atom probe tomography enable the in-depth analy

Anshu, Suvrajit Bhattacharjee, Atibur Rahaman, Sutanu Roy

We construct a braided analogue of the quantum permutation group and show that it is the universal braided compact quantum group acting on a finite space in the category of $\mathbb{Z}/N\mathbb{Z}$-$\textrm{C}^*$-algebras with a twisted monoidal structure. As an application, we prove the existence of braided quantum symmetries of finite, simple, undirected,

Qiang Liu, Jie Zhang, Jian Wu

In the present study, two schemes named face discernment and flux correction are proposed to achieve single-phase transportation of free charge in multiphase electrohydrodynamic(EHD) problems. Many EHD phenomena occur between air and another liquid while the free charge can only be transported in the liquid phase through ohmic conduction and convection due t

Edith Elkind, Abheek Ghosh, Paul W. Goldberg

We study a general scenario of simultaneous contests that allocate prizes based on equal sharing: each contest awards its prize to all players who satisfy some contest-specific criterion, and the value of this prize to a winner decreases as the number of winners increases. The players produce outputs for a set of activities, and the winning criteria of the c

Wong Yan Shen, Selvakumar Manickam, Mahmood A. Al-Shareeda

Internet security is getting less secure because of the existing of botnet threats. An attack plan can only be planned out to take down the botnet after the monitoring activities to understand the behaviour of a botnet. Nowadays, the architecture of the botnet is developed using Peer-to-Peer (P2P) connection causing it to be harder to be monitored and track

Xiao Han, Licheng Yu, Xiatian Zhu, Li Zhang

Large-scale Vision-and-Language (V+L) pre-training for representation learning has proven to be effective in boosting various downstream V+L tasks. However, when it comes to the fashion domain, existing V+L methods are inadequate as they overlook the unique characteristics of both the fashion V+L data and downstream tasks. In this work, we propose a novel fa

Ying Xu, You-Bo Ma, Feng Gu, Shan-Shan Yang

Graphene serves as an ideal platform to investigate the microscopic structure and reaction kinetics at the graphitic electrode interfaces. However, graphene is susceptible to various extrinsic factors, e.g. substrate, causing much confusion and controversy. Hereby we have obtained cm-sized substrate-free monolayer graphene suspended on electrolyte surface wi

Leonardo Scabini, Bernard De Baets, Odemir M. Bruno

The deep learning literature is continuously updated with new architectures and training techniques. However, weight initialization is overlooked by most recent research, despite some intriguing findings regarding random weights. On the other hand, recent works have been approaching Network Science to understand the structure and dynamics of Artificial Neura

Cihat Keçeci, Mohammad Shaqfeh, Hayat Mbayed, Erchin Serpedin

Federated learning enables many applications benefiting distributed and private datasets of a large number of potential data-holding clients. However, different clients usually have their own particular objectives in terms of the tasks to be learned from the data. So, supporting federated learning with meta-learning tools such as multi-task learning and tran

Maureen S. Golan, Javad Mohammadi

Developing models and metrics that can address resilience against disruptions is vital to ensure power grid reliability and that adequate recovery and adaptation mechanisms are in place. In this paper, we propose a novel disruption mapping approach and apply it to the publicly available U.S. Department of Energy DOE-417 Electric Emergency and Disturbance Rep

Sandipan Choudhuri, Hemanth Venkateswara, Arunabha Sen

In contrast to a standard closed-set domain adaptation task, partial domain adaptation setup caters to a realistic scenario by relaxing the identical label set assumption. The fact of source label set subsuming the target label set, however, introduces few additional obstacles as training on private source category samples thwart relevant knowledge transfer

Stefan Schoder

This paper is a short guideline to the decomposition of a compressible velocity into vortical and compressible structures using standard flow solvers. In particular, this is a fast solution to get an idea of the compressible fields inside your simulation, respectively acoustics for low Mach number isothermal flows. The details of the implementation are prese

Valentin Liévin, Christoffer Egeberg Hother, Andreas Geert Motzfeldt, Ole Winther

Although large language models (LLMs) often produce impressive outputs, it remains unclear how they perform in real-world scenarios requiring strong reasoning skills and expert domain knowledge. We set out to investigate whether close- and open-source models (GPT-3.5, LLama-2, etc.) can be applied to answer and reason about difficult real-world-based questio

Maximilian Berbig

Inspired by the S.M.A.S.H. framework we construct a model that addresses the strong CP problem, axion dark matter, inflation and Dirac neutrino masses as well as leptogenesis. The model possesses only two dynamical scales, namely the SM breaking scale $v_H$ and the Peccei Quinn (PQ) breaking scale $v_\sigma$. We introduce heavy vector-like quarks in the usua

Shiwen Ni, Hung-Yu Kao

Recently, for few-shot or even zero-shot learning, the new paradigm "pre-train, prompt, and predict" has achieved remarkable achievements compared with the "pre-train, fine-tune" paradigm. After the success of prompt-based GPT-3, a series of masked language model (MLM)-based (e.g., BERT, RoBERTa) prompt learning methods became popular and widely used. Howeve

Siang-Yun Lee, Heinz Riener, Giovanni De Micheli

Correctness and robustness are essential for logic synthesis applications, but they are often only tested with a limited set of benchmarks. Moreover, when the application fails on a large benchmark, the debugging process may be tedious and time-consuming. In some fields such as compiler construction, automatic testing and debugging tools are well-developed t

T. A. Zheng, Y. A. Yang, S. -Z. Wang, J. T. Singh

The permanent electric dipole moment (EDM) of the $^{171}$Yb $(I=1/2)$ atom is measured with atoms held in an optical dipole trap (ODT). By enabling a cycling transition that is simultaneously spin-selective and spin-preserving, a quantum non-demolition measurement with a spin-detection efficiency of 50$\%$ is realized. A systematic effect due to parity mixi

Hanadi Hassen Mohammed, Junaid Malik, Somaya Al-Madeed, Serkan Kiranyaz

Deep Convolutional Neural Networks (CNNs) have recently reached state-of-the-art Handwritten Text Recognition (HTR) performance. However, recent research has shown that typical CNNs' learning performance is limited since they are homogeneous networks with a simple (linear) neuron model. With their heterogeneous network structure incorporating non-linear neur

Ruofan Chen, Xiansong Xu

Stochastic resonance is a phenomenon where the response signal to external driving is enhanced by environment noise. In quantum regime, the effect of environment is often intrinsically non-Markovian. Due to the combination of such non-Markovian quantum noise and external driving force, it is difficult to evaluate the correlation function and hence the power