November 2022 arXiv papers — page 29

Matthew J. Smith, Matteo Quartagno, Edmund Njeru Njagi

Background: Multiple imputation is often used to reduce bias and gain efficiency when there is missing data. The most appropriate imputation method depends on the model the analyst is interested in fitting. Several imputation approaches have been proposed for when this model is a logistic regression model with an interaction term that contains a binary parti

Jase Clarkson

Graph Neural Networks (GNNs) are able to achieve high classification accuracy on many important real world datasets, but provide no rigorous notion of predictive uncertainty. Quantifying the confidence of GNN models is difficult due to the dependence between datapoints induced by the graph structure. We leverage recent advances in conformal prediction to con

Seongtae Kim, Kyoungkook Kang, Geonung Kim, Seung-Hwan Baek

Few-shot domain adaptation to multiple domains aims to learn a complex image distribution across multiple domains from a few training images. A na\"ive solution here is to train a separate model for each domain using few-shot domain adaptation methods. Unfortunately, this approach mandates linearly-scaled computational resources both in memory and computatio

Ruven A/L Sundarajoo, Gwo Chin Chung, Wai Leong Pang, Soo Fun Tan

In the 21st century, sending babies or children to daycare centres has become more and more common among young guardians. The balance between full-time work and child care is increasingly challenging nowadays. In Malaysia, thousands of child abuse cases have been reported from babysitting centres every year, which indeed triggers the anxiety and stress of th

Junlin Hou, Jilan Xu, Fan Xiao, Rui-Wei Zhao

Automatic diabetic retinopathy (DR) grading based on fundus photography has been widely explored to benefit the routine screening and early treatment. Existing researches generally focus on single-field fundus images, which have limited field of view for precise eye examinations. In clinical applications, ophthalmologists adopt two-field fundus photography a

Yunfeng Liu, Xin Han, Zijian Zhang

The oblique detonation engine (ODE) has established a clear superiority for hypersonic flight because of its high thermal efficiency and compact structure. It has become the research hot spot all over the world in recent years. The aim of this study is to derive a criterion on the propulsive balance of ODE, from which we can find the key parameters governing

Arata Yamamoto, Takumi Doi

One of the ultimate missions of lattice QCD is to simulate atomic nuclei from the first principle of the strong interaction. This is an extremely hard task for the current computational technology, but might be reachable in coming quantum computing era. In this paper, we discuss the computational complexities of classical and quantum simulations of lattice Q

Yuna Baba, Maki Nakasuji, Mika Sakata

As properties of poly-Bernoulli numbers, a number of formulas such as the duality formula, explicit formula using the Stirling numbers of the second kind and periodicity for negative upper-index have been established. For the multi-indexed poly-Bernoulli numbers generalized by Kaneko-Tsumura, among such properties only the duality formula was obtained. In th

Jianhong Tu, Zeyu Cui, Xiaohuan Zhou, Siqi Zheng

The goal of expressive Text-to-speech (TTS) is to synthesize natural speech with desired content, prosody, emotion, or timbre, in high expressiveness. Most of previous studies attempt to generate speech from given labels of styles and emotions, which over-simplifies the problem by classifying styles and emotions into a fixed number of pre-defined categories.

Liangliang Chang, Joshua Mack, Benjamin Willis, Xing Chen

In this study, we introduce a methodology for automatically transforming user applications in the radar and communication domain written in C/C++ based on dynamic profiling to a parallel representation targeted for a heterogeneous SoC. We present our approach for instrumenting the user application binary during the compilation process with barrier synchroniz

Suzanne Schlich

We prove that the set of Bowditch representations (introduced by Bowditch in 1998, then generalized by Tan, Wong and Zhang in 2008) and the set of primitive-stable representations (introduced by Minsky in 2013) of the free group of rank two in the isometry group of a Gromov-hyperbolic space are equal. The case of $\mathrm{PSL}(2,\mathbb{C})$-representations

Xing Yan, Yonghua Su, Wenxuan Ma

We propose a novel, succinct, and effective approach for distribution prediction to quantify uncertainty in machine learning. It incorporates adaptively flexible distribution prediction of $\mathbb{P}(\mathbf{y}|\mathbf{X}=x)$ in regression tasks. This conditional distribution's quantiles of probability levels spreading the interval $(0,1)$ are boosted by ad

Wan-Cyuan Fan, Cheng-Fu Yang, Chiao-An Yang, Yu-Chiang Frank Wang

We tackle the problem of target-free text-guided image manipulation, which requires one to modify the input reference image based on the given text instruction, while no ground truth target image is observed during training. To address this challenging task, we propose a Cyclic-Manipulation GAN (cManiGAN) in this paper, which is able to realize where and how

Irham Taufik Andika, Knud Jahnke, Arjen van der Wel, Eduardo Bañados

Over the last two decades, around 300 quasars have been discovered at $z\gtrsim6$, yet only one has identified as being strongly gravitationally lensed. We explore a new approach -- enlarging the permitted spectral parameter space, while introducing a new spatial geometry veto criterion -- which is implemented via image-based deep learning. We first apply th

Soojong Kim, Jisu Kim

There has been concern about the proliferation of the "QAnon" conspiracy theory on Facebook, but little is known about how its misleading narrative propagated on the world's largest social media platform. Thus, the present research analyzed content generated by 2,813 Facebook pages and groups that contributed to promoting the conspiracy narrative between 201

Vladimir Poliakov, Kenan Niu, Emmanuel Vander Poorten, Dzmitry Tsetserukou

This work presents an RL-based agent for outpatient hysteroscopy training. Hysteroscopy is a gynecological procedure for examination of the uterine cavity. Recent advancements enabled performing this type of intervention in the outpatient setup without anaesthesia. While being beneficial to the patient, this approach introduces new challenges for clinicians,

Jinran Nie, Liner Yang, Yun Chen, Cunliang Kong

Text generation rarely considers the control of lexical complexity, which limits its more comprehensive practical application. We introduce a novel task of lexical complexity controlled sentence generation, which aims at keywords to sentence generation with desired complexity levels. It has enormous potential in domains such as grade reading, language teachi

Sunjae Kwon, Zhichao Yang, Hong Yu

In this paper, we introduce a comprehensive framework for developing a machine learning-based SOAP (Subjective, Objective, Assessment, and Plan) classification system without manually SOAP annotated training data or with less manually SOAP annotated training data. The system is composed of the following two parts: 1) Data construction, 2) A neural network-ba

Fatih Cogen, Burak Ahmet Ozden, Erdogan Aydin, Nihat Kabaoglu

In this study, a novel index modulation based communication system is proposed by combining the recently popular code index modulation-spread spectrum (CIM-SS) and reconfigurable intelligent surface (RIS) techniques. This technique is called CIM-RIS in short. In this proposed system, in addition to the traditional modulated symbols, the spreading code indice

Fredrik Fryklund, Leslie Greengard

We describe a new, adaptive solver for the two-dimensional Poisson equation in complicated geometries. Using classical potential theory, we represent the solution as the sum of a volume potential and a double layer potential. Rather than evaluating the volume potential over the given domain, we first extend the source data to a geometrically simpler region w

Stephan Narison

We present a compact review of the status of QCD spectral sum rules until 2022. We emphasize the recent progresses for determining the QCD input parameters ($\alpha_s$, running quark masses, quark and gluon condensates) where their correlations have been taken into account. Some selected phenomenological uses of the sum rules in different channels (light and

Victor Chulaevsky

This paper is a follow-up of our earlier work [11] where a uniform exponential Anderson localization was proved for a class of deterministic (including quasi-periodic) Hamiltonians with the help of a variant of the KAM (Kolmogorov--Arnold--Moser) approach. Building on [11], we prove for the same class of operators a non-local variant of the Minami eigenvalue

Valentin Debarnot, Sidharth Gupta, Konik Kothari, Ivan Dokmanic

We propose a framework to jointly determine the deformation parameters and reconstruct the unknown volume in electron cryotomography (CryoET). CryoET aims to reconstruct three-dimensional biological samples from two-dimensional projections. A major challenge is that we can only acquire projections for a limited range of tilts, and that each projection underg

Olivier Gilles, Franck Viguier, Nikolai Kosmatov, Daniel Gracia Pérez

RISC-V is an open instruction set architecture recently developed for embedded real-time systems. To achieve a lasting security on these systems and design efficient countermeasures, a better understanding of vulnerabilities to novel and potential future attacks is mandatory. This paper demonstrates that RISC-V is sensible to Jump-Oriented Programming, a cla

Bailu Guo, Boyu Zhao, Zishun Zhou

Visual position recognition affects the safety and accuracy of automatic driving. To accurately identify the location, this paper studies a visual place recognition algorithm based on HMM filter and HMM smoother. Firstly, we constructed the traffic situations in Canberra city. Then the mathematical models of the HMM filter and HMM smoother were performed. Fi

Surya Giri, S. Sivaprasad Kumar

In this paper, we derive the sharp bounds of Toeplitz determinants for a class of holomorphic mappings on the bounded starlike circular domain $\Omega$ in $\mathbb{C}^n$, which extend certain known bounds for various subclasses of normalized analytic univalent functions in the unit disk to higher dimensions.

Anik Pramanik, Pan Xu, Yifan Xu

There are many news articles reporting the obstacles confronting poverty-stricken households in access to public transits. These barriers create a great deal of inconveniences for these impoverished families and more importantly, they contribute a lot of social inequalities. A typical approach addressing the issue is to build more transport infrastructure to

Ahmed Atallah, Ahmad Bani Younes

We study the A-stability and accuracy characteristics of Clenshaw-Curtis collocation. We present closed-form expressions to evaluate the Runge-Kutta coefficients of these methods. From the A-stability study, Clenshaw-Curtis methods are A-stable up to a high number of nodes. High accuracy is another benefit of these methods; numerical experiments demonstrate

Diego Artacho, Marie-Amélie Lawn, Miguel Ortega

Translators can be regarded as submanifolds which satisfy the mean curvature flow equation when evolving by translations along a distinguished vector field of the ambient space. We study translators in Generalised Robertson-Walker spacetimes, due to their importance as Lorentzian manifolds, and because they admit a natural conformal Killing timelike vector f

Ivan Prusak, Monica Nonino, Davide Torlo, Francesco Ballarin

The aim of this work is to present a model reduction technique in the framework of optimal control problems for partial differential equations. We combine two approaches used for reducing the computational cost of the mathematical numerical models: domain-decomposition (DD) methods and reduced-order modelling (ROM). In particular, we consider an optimisation

Neha Verma, S. Sivaprasad Kumar

In this paper, we provide an estimation for the sharp bound of the third Hankel determinant of starlike functions of order $\alpha$, where $\alpha$ ranges in the interval $[0, 1/6]\cup \{1/2\}$ and thereby extending the result of Rath et al. (Complex Anal Oper Theory: No. 65, 16(5), 8 pp 2022).

Farhod Shokir

The results of numerical simulation of the interaction of topological solitons (2+1)-dimensional O(3) non-linear sigma model in reversed time are presented. At the first stage, models of interactions of topological vortices are developed, where, depending on the dynamic parameters, processes of their decay into localized perturbations and phased annihilation

Ewa Bednarczuk, Giovanni Bruccola, Gabriele Scrivanti, The Hung Tran

We investigate inexact proximity operators for weakly convex functions. To this aim, we derive sum rules for proximal {\epsilon}-subdifferentials, by incorporating the moduli of weak convexity of the functions into the respective formulas. This allows us to investigate inexact proximity operators for weakly convex functions in terms of proximal {\epsilon}-su

Fengzhao Shi, Ren Li, Yanan Cao, Yanmin Shang

Graph Neural Networks (GNNs) have achieved remarkable success in diverse real-world applications. Traditional GNNs are designed based on homophily, which leads to poor performance under heterophily scenarios. Current solutions deal with heterophily mainly by mixing high-order neighbors or passing signed messages. However, mixing high-order neighbors destroys

Yang Zhang, Yang Zhou, Huilin Pan, Bo Wu

Fault detection for key components in the braking system of freight trains is critical for ensuring railway transportation safety. Despite the frequently employed methods based on deep learning, these fault detectors are highly reliant on hardware resources and are complex to implement. In addition, no train fault detectors consider the drop in accuracy indu

Jinxin Lv, Xiaoyu Zeng, Sheng Wang, Ran Duan

One-shot segmentation of brain tissues is typically a dual-model iterative learning: a registration model (reg-model) warps a carefully-labeled atlas onto unlabeled images to initialize their pseudo masks for training a segmentation model (seg-model); the seg-model revises the pseudo masks to enhance the reg-model for a better warping in the next iteration.

Wei Jin

In this paper, we determine the class of finite 2-arc-transitive bicirculants. We show that a connected $2$-arc-transitive bicirculant is one of the following graphs: $C_{2n}$ where $n\geqslant 2$, $\K_{2n}$ where $n\geqslant 2$, $\K_{n,n}$ where $n\geqslant 3$, $ \K_{n,n}-n\K_2$ where $n\geqslant 4$, $B(\PG(d-1,q))$ and $B'(\PG(d-1,q))$ where $d\geq 3$ and

Anand Pal

The equation for the size strain plot methods reported by A. Khorsand Zak et al. (Solid State Sci. 13 (2011), 251) does not follow the dimensional homogeneity, consequently leading to an inaccurate estimation of the crystallite size and strain values of the materials under investigation and the dimensions of the obtained parameters. We also perceived an erro

Kaitao Tang, Thomas Adcock, Wouter Mostert

We present novel numerical simulations investigating the bag breakup of liquid droplets. We first examine the viscous effect on drop deformation before the onset of bag breakup, comparing with theory and experiment. Next, a bag film forms at late time and is susceptible to spurious mesh-induced breakup in numerical simulations, which has prevented previous s

Chandrajit Bajaj, Omatharv Bharat Vaidya, Yi Wang

Task learning in neural networks typically requires finding a globally optimal minimizer to a loss function objective. Conventional designs of swarm based optimization methods apply a fixed update rule, with possibly an adaptive step-size for gradient descent based optimization. While these methods gain huge success in solving different optimization problems

Wenbin Li, Meihao Kong, Xuesong Yang, Lei Wang

In recent years, a variety of contrastive learning based unsupervised visual representation learning methods have been designed and achieved great success in many visual tasks. Generally, these methods can be roughly classified into four categories: (1) standard contrastive methods with an InfoNCE like loss, such as MoCo and SimCLR; (2) non-contrastive metho

Fan Yang, Yang Wu, Zheng Wang, Xiang Li

Although sketch-to-photo retrieval has a wide range of applications, it is costly to obtain paired and rich-labeled ground truth. Differently, photo retrieval data is easier to acquire. Therefore, previous works pre-train their models on rich-labeled photo retrieval data (i.e., source domain) and then fine-tune them on the limited-labeled sketch-to-photo ret

Yuting Xiao, Yiqun Zhao, Yanyu Xu, Shenghua Gao

We represent the ResNeRF, a novel geometry-guided two-stage framework for indoor scene novel view synthesis. Be aware of that a good geometry would greatly boost the performance of novel view synthesis, and to avoid the geometry ambiguity issue, we propose to characterize the density distribution of the scene based on a base density estimated from scene geom

Karol Palczynski, Thorren Kirschbaum, Annika Bande, Joachim Dzubiella

Diamondoids are promising materials for applications in catalysis and nanotechnology. Since many of their applications are in aqueous environments, to understand their function it is essential to know the structure and dynamics of the water molecules in their first hydration shells. In this study, we develop an improved reactive force field (ReaxFF) paramete

Daoan Zhang, Chenming Li, Haoquan Li, Wenjian Huang

Unsupervised image semantic segmentation(UISS) aims to match low-level visual features with semantic-level representations without outer supervision. In this paper, we address the critical properties from the view of feature alignments and feature uniformity for UISS models. We also make a comparison between UISS and image-wise representation learning. Based

Zhong Ji, Junhua Hu, Deyin Liu, Lin Yuanbo Wu

Text-based person search (TBPS) is of significant importance in intelligent surveillance, which aims to retrieve pedestrian images with high semantic relevance to a given text description. This retrieval task is characterized with both modal heterogeneity and fine-grained matching. To implement this task, one needs to extract multi-scale features from both i

Yuyuan Liu, Choubo Ding, Yu Tian, Guansong Pang

Semantic segmentation models classify pixels into a set of known (``in-distribution'') visual classes. When deployed in an open world, the reliability of these models depends on their ability not only to classify in-distribution pixels but also to detect out-of-distribution (OoD) pixels. Historically, the poor OoD detection performance of these models has mo

B. F. Farrell, E. Kim, H. J. Bae, M. -A. Nikolaidis

Both linear growth processes associated with non-normality of the mean flow and nonlinear interaction transferring energy among fluctuations contribute to maintaining turbulence. However, a detailed understanding of the mechanism by which they cooperate in sustaining the turbulent state is lacking. In this report, we examine the role of fluctuation-fluctuati

Xiangsheng Xu

In this paper we study the modulus of continuity of weak solutions to a singular elliptic equation in the plane under very weak assumption on the integrability of the elliptic coefficients. Our investigation reveals that the modulus of continuity can be described by the reciprocal of the logarithmic function raised to a power. However, the power can be arbit

Jinyang He, Ziyang Cheng, Zishu He

The OFDM sequences with low correlation sidelobe level (SLCL) is desired in many 5G wireless systems. In this letter, the OFDM sequences and mismatch filter are jointly designed to achieve the SLCL under the constraints of spectra and peak to average power ratio (PAPR). Specifically, we formulate the optimization problem by maximizing the peak side lobe rati

Xiaojun Meng, Wenlin Dai, Yasheng Wang, Baojun Wang

Recently, semantic parsing using hierarchical representations for dialog systems has captured substantial attention. Task-Oriented Parse (TOP), a tree representation with intents and slots as labels of nested tree nodes, has been proposed for parsing user utterances. Previous TOP parsing methods are limited on tackling unseen dynamic slot values (e.g., new s

Yu. G. Ignat'ev

On the basis of the previously formulated mathematical model of a statistical system with scalar interaction of fermions and the theory of gravitational-scalar instability of a cosmological model based on a one-component statistical system of scalarly charged degenerate fermions (\MIO models), the possibility of the formation of black holes in the early Univ

Duomin Wang, Yu Deng, Zixin Yin, Heung-Yeung Shum

We present a novel one-shot talking head synthesis method that achieves disentangled and fine-grained control over lip motion, eye gaze&blink, head pose, and emotional expression. We represent different motions via disentangled latent representations and leverage an image generator to synthesize talking heads from them. To effectively disentangle each motion

Sandhya Aneja, Nagender Aneja, Ponnurangam Kumaraguru

Media news are making a large part of public opinion and, therefore, must not be fake. News on web sites, blogs, and social media must be analyzed before being published. In this paper, we present linguistic characteristics of media news items to differentiate between fake news and real news using machine learning algorithms. Neural fake news generation, hea

Prakash Pandey, Vivek Pandey, Sudhir K. Pandey

In the present work, we have tried to investigate the importance of electronic correlation on transport properties and phononic states of Vanadium (V). The temperature-dependent electrical resistivity ($\rho$) and electronic part of thermal conductivity ($\kappa_e$) due to electron-electron interactions (EEIs) and electron-phonon interactions (EPIs) are comp

Filipe de Avila Belbute-Peres, J. Zico Kolter

Neural networks with sinusoidal activations have been proposed as an alternative to networks with traditional activation functions. Despite their promise, particularly for learning implicit models, their training behavior is not yet fully understood, leading to a number of empirical design choices that are not well justified. In this work, we first propose a

Yu Li, Dongwei Ren, Xinya Shu, Wangmeng Zuo

By adopting popular pixel-wise loss, existing methods for defocus deblurring heavily rely on well aligned training image pairs. Although training pairs of ground-truth and blurry images are carefully collected, e.g., DPDD dataset, misalignment is inevitable between training pairs, making existing methods possibly suffer from deformation artifacts. In this pa

Adeel Afridi, Jan Gieseler, Nadine Meyer, Romain Quidant

Reconfigurable metasurfaces offer great promises to enhance photonics technology by combining integration with improved functionalities. Recently, reconfigurability in otherwise static metasurfaces has been achieved by modifying the electric permittivity of the meta-atoms themselves or their immediate surrounding. Yet, it remains challenging to achieve signi

Kazuhiro Agatsuma

The expansion of the universe on short distance scales is a new frontier to investigate the dark energy. The excess orbital decay in binary pulsars may be related to acceleration by the local cosmic expansion, called the cosmic drag. Modern observations of two independent binaries (PSR B1534+12 and PSR B1913+16) support this interpretation and result in a sc

Joseph Stember, Mehrnaz Jenabi, Luca Pasquini, Kyung Peck

Whereas MRI produces anatomic information about the brain, functional MRI (fMRI) tells us about neural activity within the brain, including how various regions communicate with each other. The full chorus of conversations within the brain is summarized elegantly in the adjacency matrix. Although information-rich, adjacency matrices typically provide little i

Aman Desai, Shyamal Gandhi, Sachin Gupta, Manan Shah

The continuous rise in CO2 emission into the environment is one of the most crucial issues facing the whole world. Many countries are making crucial decisions to control their carbon footprints to escape some of their catastrophic outcomes. There has been a lot of research going on to project the amount of carbon emissions in the future, which can help us to

Subhanik Purkayastha, Hrithwik Shalu, David Gutman, Shakeel Modak

Artificial intelligence (AI) in radiology has made great strides in recent years, but many hurdles remain. Overfitting and lack of generalizability represent important ongoing challenges hindering accurate and dependable clinical deployment. If AI algorithms can avoid overfitting and achieve true generalizability, they can go from the research realm to the f

Manideep Kolla, Aravinth Savadamuthu

Face recognition algorithms, when used in the real world, can be very useful, but they can also be dangerous when biased toward certain demographics. So, it is essential to understand how these algorithms are trained and what factors affect their accuracy and fairness to build better ones. In this study, we shed some light on the effect of racial distributio

Zeyu Guo, Ben Lee Volk, Akhil Jalan, David Zuckerman

We construct explicit deterministic extractors for polynomial images of varieties, that is, distributions sampled by applying a low-degree polynomial map $f : \mathbb{F}_q^r \to \mathbb{F}_q^n$ to an element sampled uniformly at random from a $k$-dimensional variety $V \subseteq \mathbb{F}_q^r$. This class of sources generalizes both polynomial sources, stud

Takaaki Aoki, Shota Fujishima, Naoya Fujiwara

Human flow data are rich behavioral data relevant to people's decision-making regarding where to live, work, go shopping, etc., and provide vital information for identifying city centers. However, it is not as easy to understand massive relational data, and datasets have often been reduced merely to the statistics of trip counts at destinations, discarding r

Milind Nakul, Anupama Rajoriya, Rohit Budhiraja

We consider the problem of estimating the channel in reconfigurable intelligent surface (RIS) assisted millimeter wave (mmWave) systems. We propose two variational expectation maximization (VEM) based algorithms for channel estimation in RIS-aided wireless systems. The first algorithm is a structured mean field-based sparse Bayesian learning (SM-SBL) algorit

Benoit Haut, Cyril Karamaoun, Benjamin Mauroy, Benjamin Sobac

A secondary function of the mammals' respiratory system is during inspiration to heat the air to body temperature and to saturate it with water before it reaches the alveoli. Relying on a mathematical model, we comprehensively analyze this function, considering all the terrestrial mammals (spanning six orders of magnitude of the body mass, $M$) and focusing

Yuan Sun, Winton Nathan-Roberts, Tien Dung Pham, Ellen Otte

In biomanufacturing, developing an accurate model to simulate the complex dynamics of bioprocesses is an important yet challenging task. This is partially due to the uncertainty associated with bioprocesses, high data acquisition cost, and lack of data availability to learn complex relations in bioprocesses. To deal with these challenges, we propose to use a

Yuan Sun, Su Nguyen, Dhananjay Thiruvady, Xiaodong Li

Constraint programming (CP) is a powerful technique for solving constraint satisfaction and optimization problems. In CP solvers, the variable ordering strategy used to select which variable to explore first in the solving process has a significant impact on solver effectiveness. To address this issue, we propose a novel variable ordering strategy based on s

Wentao Pan, Jiangpeng Yan, Hanbo Chen, Jiawei Yang

Recently, deep neural networks have greatly advanced histopathology image segmentation but usually require abundant annotated data. However, due to the gigapixel scale of whole slide images and pathologists' heavy daily workload, obtaining pixel-level labels for supervised learning in clinical practice is often infeasible. Alternatively, weakly-supervised se

Xintong Zhai, Zhonghao Xu

Identifying important nodes in complex networks is essential in theoretical and applied fields. A small number of such nodes have deterministic power to decide information spreading, so it is of importance to find a set of nodes that maximize the propagation in networks. Based on baseline ranking methods, various improved methods were proposed, but there doe

Yu-Neng Chuang, Kwei-Herng Lai, Ruixiang Tang, Mengnan Du

Knowledge graph data are prevalent in real-world applications, and knowledge graph neural networks (KGNNs) are essential techniques for knowledge graph representation learning. Although KGNN effectively models the structural information from knowledge graphs, these frameworks amplify the underlying data bias that leads to discrimination towards certain group

Xinping Zhou, Yuandeng Shen, Hongfei Liang, Zhining Qu

About the driven mechanisms of the quasi-periodic fast-propagating (QFP) wave trains, there exist two dominant competing physical explanations: associated with the flaring energy release or attributed to the waveguide dispersion. Employing Solar Dynamics Observatory (SDO) Atmospheric Imaging Assembly (AIA) 171 A images, we investigated a series of QFP wave t

Mats L. Richter, Christopher Pal

Minimal changes to neural architectures (e.g. changing a single hyperparameter in a key layer), can lead to significant gains in predictive performance in Convolutional Neural Networks (CNNs). In this work, we present a new approach to receptive field analysis that can yield these types of theoretical and empirical performance gains across twenty well-known

Ramkrishna Mandal, Apurba Das

The notion of matching Rota-Baxter algebras was recently introduced by Gao, Guo and Zhang [{\em J. Algebra} 552 (2020) 134-170] motivated by the study of algebraic renormalization of regularity structures. The concept of matching Rota-Baxter algebras generalizes multiple integral operators with kernels. The same authors also introduced matching dendriform al

Lixiang Lin, Songyou Peng, Qijun Gan, Jianke Zhu

We propose an approach for optimizing high-quality clothed human body shapes in minutes, using multi-view posed images. While traditional neural rendering methods struggle to disentangle geometry and appearance using only rendering loss, and are computationally intensive, our method uses a mesh-based patch warping technique to ensure multi-view photometric c

Arkajyoti Chakraborty, Inder Khatri, Arjun Choudhry, Pankaj Gupta

Recent works on fake news detection have shown the efficacy of using emotions as a feature for improved performance. However, the cross-domain impact of emotion-guided features for fake news detection still remains an open problem. In this work, we propose an emotion-guided, domain-adaptive, multi-task approach for cross-domain fake news detection, proving t

Chunna Zeng, Xu Dong, Yaling Wang, Lei Ma

In an earlier paper \cite{mazeng} the authors introduced the notion of curvature entropy, and proved the plane log-Minkowski inequality of curvature entropy under the symmetry assumption. In this paper we demonstrate the plane log-Minkowski inequality of curvature entropy for general convex bodies. The equivalence of the uniqueness of cone-volume measure, th

De-Zhang Li, Yu-Jun Zhao, Yao Yao, Xiao-Bao Yang

We study the residual entropy of a two-dimensional Ising model with crossing and four-spin interactions, both for the case that in zero magnetic field and that in an imaginary magnetic field i({\pi}/2)kT. The spin configurations of this Ising model can be mapped into the hydrogen configurations of square ice with the defined standard direction of the hydroge

Inder Khatri, Aaryan Gupta, Arjun Choudhry, Aryan Tyagi

Social networks have enabled user-specific advertisements and recommendations on their platforms, which puts a significant focus on Influence Maximisation (IM) for target advertising and related tasks. The aim is to identify nodes in the network which can maximize the spread of information through a diffusion cascade. We propose a community structures-based

Anthony J Guttmann, Iwan Jensen

Recently Kauers, Koutschan and Spahn announced a significant increase in the length of the so-called {\em gerrymander sequence}, given as A348456 in the OEIS, extending the sequence from 3 terms to 7 terms. We give a further extension to 11 terms, but more significantly prove that the coefficients grow as $\lambda^{4L^2},$ where $\lambda \approx 1.7445498, $

Muralikrishnan Srinivasan, Jinxiang Song, Alexander Grabowski, Krzysztof Szczerba

Optical interconnects (OIs) based on vertical-cavity surface-emitting lasers (VCSELs) are the main workhorse within data centers, supercomputers, and even vehicles, providing low-cost, high-rate connectivity. VCSELs must operate under extremely harsh and time-varying conditions, thus requiring adaptive and flexible designs of the communication chain. Such de

Ding Tao, Chen Liu, Shihan Wan

Acute myocardial infarction (AMI) is one of the most severe manifestation of coronary artery disease. ST-segment elevation myocardial infarction (STEMI) is the most serious type of AMI. We proposed to develop a machine learning algorithm based on the home page of electronic medical record (HPEMR) for predicting in-hospital mortality of patients with STEMI in

Nadhifa Zahira Ramadhani Mart, Terry Mart

A simple homemade greenhouse was constructed as a part of the high school project during the learning-from-home period. The greenhouse was used to show CO$_2$ absorption by Sansevieria trifasciata during the night, obeying Fick's law, while the CO$_2$ emission by the medium used to grow the plant does not. Although other plants can be used for this purpose,

Ronghe Jin, Yan Wang, Zhi Gao, Xiaoji Niu

Visual-Inertial Odometry (VIO) usually suffers from drifting over long-time runs, the accuracy is easily affected by dynamic objects. We propose DynaVIG, a navigation and object tracking system based on the integration of Monocular Vision, Inertial Navigation System (INS), and Global Navigation Satellite System (GNSS). Our system aims to provide an accurate

Yuquan Lan, Dongxu Li, Yunqi Zhang, Hui Zhao

Zero-shot relation triplet extraction (ZeroRTE) aims to extract relation triplets from unstructured texts under the zero-shot setting, where the relation sets at the training and testing stages are disjoint. Previous state-of-the-art method handles this challenging task by leveraging pretrained language models to generate data as additional training samples,

Matthew Ciesler, Damien West, Shengbai Zhang

A real-space method using generating integers is used to classify the possible moire patterns for two equal hexagonal lattices. The result is that the rotations that take (n,m) to (m,n) with n,m relatively prime form the fundamental moire transformations, and the number of lattice coincidence areas within each supercell is given by (n-m)^2. The scheme may be

Jie Zhou, Yefei Wang, Yiyang Yuan, Qing Huang

The automatic generation of Chinese fonts is an important problem involved in many applications. The predominated methods for the Chinese font generation are based on the deep generative models, especially the generative adversarial networks (GANs). However, existing GAN-based methods (say, CycleGAN) for the Chinese font generation usually suffer from the mo

Hua Y. Geng

The temperature-dependent effective potential (TDEP) method for anharmonic phonon dispersion is generalized to the full potential case by combining with path integral formalism. This extension naturally resolves the intrinsic difficulty in the original TDEP at low temperature. The new method is applied to solid metallic hydrogen at high pressure. A colossal

Yuchen Li, Zongxia Liang, Shunzhi Pang

This paper compares the optimal investment problems based on monotone mean-variance (MMV) and mean-variance (MV) preferences in the L\'{e}vy market with an untradable stochastic factor. It is an open question proposed by Trybu{\l}a and Zawisza. Using the dynamic programming and Lagrange multiplier methods, we get the HJBI and HJB equations corresponding to t

Satoshi Ishiwata, Hiroshi Kawabi

In the present paper, we prove that the $C_{0}$-semigroup generated by a Schr\"odinger operator with drift on a complete Riemannian manifold is approximated by the discrete semigroups associated with a family of discrete time random walks with killing in a flow on a sequence of proximity graphs, which are constructed by partitions of the manifold. Furthermor

Lu Ping, Gillian E. Minarik, Hongze Gao, Jun Cao

Layered transition metal hydroxides (LTMHs) with transition metal centers sandwiched between layers of coordinating hydroxide anions have attracted considerable interest for their potential in developing clean energy sources and storage technologies. However, two dimensional (2D) LTMHs remain largely unstudied in terms of their physical properties and the ap

Liang Zhang, Jinsong Su, Yidong Chen, Zhongjian Miao

Document-level relation extraction (RE) aims to extract the relations between entities from the input document that usually containing many difficultly-predicted entity pairs whose relations can only be predicted through relational inference. Existing methods usually directly predict the relations of all entity pairs of input document in a one-pass manner, i

Abhi Gupta, Ted Moskovitz, David Alvarez-Melis, Aldo Pacchiano

Transferring knowledge across domains is one of the most fundamental problems in machine learning, but doing so effectively in the context of reinforcement learning remains largely an open problem. Current methods make strong assumptions on the specifics of the task, often lack principled objectives, and -- crucially -- modify individual policies, which migh

Caspar Oesterheld, Johannes Treutlein, Roger Grosse, Vincent Conitzer

As machine learning agents act more autonomously in the world, they will increasingly interact with each other. Unfortunately, in many social dilemmas like the one-shot Prisoner's Dilemma, standard game theory predicts that ML agents will fail to cooperate with each other. Prior work has shown that one way to enable cooperative outcomes in the one-shot Priso

Ange Lou, Xing Yao, Ziteng Liu, Jintong Han

Surgical instrument tracking is an active research area that can provide surgeons feedback about the location of their tools relative to anatomy. Recent tracking methods are mainly divided into two parts: segmentation and object detection. However, both can only predict 2D information, which is limiting for application to real-world surgery. An accurate 3D s

Zixiang Ding, Guoqing Jiang, Shuai Zhang, Lin Guo

In this paper, we propose Stochastic Knowledge Distillation (SKD) to obtain compact BERT-style language model dubbed SKDBERT. In each iteration, SKD samples a teacher model from a pre-defined teacher ensemble, which consists of multiple teacher models with multi-level capacities, to transfer knowledge into student model in an one-to-one manner. Sampling dist

Naoki Yamamoto, Di-Lun Yang

We develop an approach to chiral kinetic theories for electrons close to equilibrium and neutrinos away from equilibrium based on a systematic power counting scheme for different timescales of electromagnetic and weak interactions. Under this framework, we derive electric and energy currents along magnetic fields induced by neutrino radiation in general none

Jinlei Zhang, Jiayong Zhang, Dapeng Cui, Li Ye

Electronic phase separation (EPS) originates from an incomplete transformation between electronic phases, causing the inhomogeneous spatial distribution of electronic properties. In the system of two-dimensional electron gas (2DEG), the EPS is usually identified based on a percolative metal-to-superconductor transition. Here, we report a metal-insulator tran

Yan Peng

The existence of null circular geodesics has been proved in the background of non-extremal spherically symmetric asymptotically flat black holes in previous works. Then it is an interesting question that whether extremal black holes possess null circular geodesics outside horizons. In the present paper, we pay attentions to the extremal spherically symmetric