November 2022 arXiv papers — page 65

Sheryl L. Chang, Quang Dang Nguyen, Alexandra Martiniuk, Vitali Sintchenko

We modelled emergence and spread of the Omicron variant of SARS-CoV-2 in Australia between December 2021 and June 2022. This pandemic stage exhibited a diverse epidemiological profile with emergence of co-circulating sub-lineages of Omicron, further complicated by differences in social distancing behaviour which varied over time. Our study delineated distinc

J. Lotz, M. ten Eikelder, I. Akkerman

In this paper we propose a space-time framework for the computation of periodic flows. We employ the isogeometric analysis framework to achieve higher-order smoothness in both space and time. The discretization is performed using residual-based variational multiscale modelling and weak boundary conditions are adopted to enhance the accuracy near the moving b

Praveen Kumar Rajendran, Quoc-Vinh Lai-Dang, Luiz Felipe Vecchietti, Dongsoo Har

Identifying the camera pose for a given image is a challenging problem with applications in robotics, autonomous vehicles, and augmented/virtual reality. Lately, learning-based methods have shown to be effective for absolute camera pose estimation. However, these methods are not accurate when generalizing to different domains. In this paper, a domain adaptiv

Renzo Angles, Angela Bonifati, Stefania Dumbrava, George Fletcher

Property graphs have reached a high level of maturity, witnessed by multiple robust graph database systems as well as the ongoing ISO standardization effort aiming at creating a new standard Graph Query Language (GQL). Yet, despite documented demand, schema support is limited both in existing systems and in the first version of the GQL Standard. It is antici

Yunhao Gou, Tom Ko, Hansi Yang, James Kwok

Most existing vision-language pre-training (VLP) approaches adopt cross-modal masked language modeling (CMLM) to learn vision-language associations. However, we find that CMLM is insufficient for this purpose according to our observations: (1) Modality bias: a considerable amount of masked tokens in CMLM can be recovered with only the language information, i

Omer Chor, Amir Sohachi, Rémi Goerlich, Eran Rosen

Szil\'ard's information engine is a canonical example in the field of thermodynamics of information. We realize experimentally a macroscopic many-particle Szil\'ard engine that consists of active particles and use it to lift a mass against gravity. We show that the extractable work per cycle increases when the raised weight is changed more gradually during t

Jinyuan Liu, Runjia Lin, Guanyao Wu, Risheng Liu

Infrared and visible image fusion targets to provide an informative image by combining complementary information from different sensors. Existing learning-based fusion approaches attempt to construct various loss functions to preserve complementary features, while neglecting to discover the inter-relationship between the two modalities, leading to redundant

Emanuel Ydrefors, Tobias Frederico

The projection onto the Light-Front of a Minkowski space Faddeev-Bethe-Salpeter equation model truncated at the valence level is applied to study the proton structure with constituent quarks. The dynamics of the model has built-in: (i) a bound diquark brought by a contact interaction, and (ii) the separation by \Lambda_QCD of the infrared and ultraviolet int

Keiichi Shigechi

We define and study noncommutative crossing partitions which are a generalization of non-crossing partitions. By introducing a new cover relation on binary trees, we show that the partially ordered set of noncommutative crossing partitions is a graded lattice. This new lattice contains the Kreweras lattice, the lattice of non-crossing partitions, as a sublat

Malte Mosbach, Sven Behnke

Grasping objects of different shapes and sizes - a foundational, effortless skill for humans - remains a challenging task in robotics. Although model-based approaches can predict stable grasp configurations for known object models, they struggle to generalize to novel objects and often operate in a non-interactive open-loop manner. In this work, we present a

Yunuo Xiong, Hongwei Xiong

In this work we study the recently developed parametrized partition function formulation and show how we can infer the thermodynamic properties of fermions based on numerical simulation of bosons and distinguishable particles at various temperatures. In particular, we show that in the three dimensional space defined by energy, temperature and the parameter c

Yibin Feng, Shengnan Hu, Lei Xu

Existence of symmetric (resp. asymmetric) solutions to the $L_p$ Gaussian Minkowski problem for $p\leq 0$ (resp. $p\geq 1$) will be provided. Moreover, existence and uniqueness of smooth solutions to the problem for $p>n$ will also be proved without the restriction that the Gaussian volumes of convex bodies are not less than one-second.

Manyi Zhang, Xuyang Zhao, Jun Yao, Chun Yuan

Real-world large-scale datasets are both noisily labeled and class-imbalanced. The issues seriously hurt the generalization of trained models. It is hence significant to address the simultaneous incorrect labeling and class-imbalance, i.e., the problem of learning with noisy labels on long-tailed data. Previous works develop several methods for the problem.

Thomas Stüber, Lukas Osswald, Steffen Lindner, Michael Menth

Time-Sensitive Networking (TSN) is an enhancement of Ethernet which provides various mechanisms for real-time communication. Time-triggered (TT) traffic represents periodic data streams with strict real-time requirements. Amongst others, TSN supports scheduled transmission of TT streams, i.e., the transmission of their frames by end stations is coordinated i

Kiseop Lee, Seongje Lim, Hyungbin Park

This paper studies how to price and hedge options under stock models given as a path-dependent SDE solution. When the path-dependent SDE coefficients have Fr\'{e}chet derivatives, an option price is differentiable with respect to time and the path, and is given as a solution to the path-dependent PDE. This can be regarded as a path-dependent version of the F

Marco Baldovin, Fabio Cecconi, Antonello Provenzale, Angelo Vulpiani

The detection of cause-effect relationships from the analysis of paleoclimatic records is a crucial step to disentangle the main mechanisms at work in the climate system. Here, we show that the approach based on the generalized Fluctuation-Dissipation Relation, complemented by the analysis of the Transfer Entropy, allows the causal links to be identified bet

Zirui Liu, Xiangyu Xie, Pingxin Zhang, Chunhao Tian

Thin-gap Resistive Plate Chambers (RPCs) with a 1 mm gap size are introduced in the Phase-II ATLAS upgrade. Smaller avalanche charge due to the reduced gap size raises concerns for signal integrity. This work focuses on the RPC signal propagation process in lossless conditions, and an analytical study is implemented for the ATLAS RPC. Detector modeling is pr

Xichen Pan, Pengda Qin, Yuhong Li, Hui Xue

Conditioned diffusion models have demonstrated state-of-the-art text-to-image synthesis capacity. Recently, most works focus on synthesizing independent images; While for real-world applications, it is common and necessary to generate a series of coherent images for story-stelling. In this work, we mainly focus on story visualization and continuation tasks a

Kang-Hyun Lee, Gun Jin Yun

Microstructure reconstruction has been an essential part of computational material engineering to reveal the relationship between microstructures and material properties. However, finding a general solution for microstructure characterization and reconstruction (MCR) tasks is still challenging, although there have been many attempts such as the descriptor-ba

Quan Nguyen, Hieu H. Pham, Kok-Seng Wong, Phi Le Nguyen

We introduce FedDCT, a novel distributed learning paradigm that enables the usage of large, high-performance CNNs on resource-limited edge devices. As opposed to traditional FL approaches, which require each client to train the full-size neural network independently during each training round, the proposed FedDCT allows a cluster of several clients to collab

Liwen Feng, Jiayuan Cao, Tim Priessnitz, Yunyun Dai

2H-NbSe2 is an archetypal system in which superconductivity and charge-density-wave (CDW) coexist and compete macroscopically with each other. In particular, this interplay also manifests in their dynamical fluctuations. As a result, the superconducting amplitude fluctuations (i.e. Higgs mode) is pushed below the quasiparticle continuum, allowing it to becom

Or Hirschorn, Shai Avidan

Video anomaly detection is an ill-posed problem because it relies on many parameters such as appearance, pose, camera angle, background, and more. We distill the problem to anomaly detection of human pose, thus decreasing the risk of nuisance parameters such as appearance affecting the result. Focusing on pose alone also has the side benefit of reducing bias

Zhenxing Di, Liping Li, Li Liang, Yajun Ma

We construct a flat model structure on the category $_{\mathcal{Q},R}{\mathsf{Mod}}$ of additive functors from a small preadditive category $\mathcal{Q}$ satisfying certain conditions to the module category $_{R}{\mathsf{Mod}}$ over an associative ring $R$, whose homotopy category is the $\mathcal{Q}$-shaped derived category introduced by Holm and Jorgensen.

Songhao Jiang, Yan Chu, Tianxing Ma, Tianning Zang

Deep learning usually relies on training large-scale data samples to achieve better performance. However, over-fitting based on training data always remains a problem. Scholars have proposed various strategies, such as feature dropping and feature mixing, to improve the generalization continuously. For the same purpose, we subversively propose a novel traini

Frédéric Berdoz, Abhishek Singh, Martin Jaggi, Ramesh Raskar

Privacy-preserving machine learning has become a key conundrum for multi-party artificial intelligence. Federated learning (FL) and Split Learning (SL) are two frameworks that enable collaborative learning while keeping the data private (on device). In FL, each data holder trains a model locally and releases it to a central server for aggregation. In SL, the

Milad Hajebrahimi, Hassan Manshouri, Mohammad Sharifian, Moslem Zarei

Quantum sensors based on the superposition of neutral atoms are promising for sensing the nature of dark matter (DM). In this study, we utilize the Stern-Gerlach (SG) interferometer configuration to seek a novel method for the detection of detect axion-like particles (ALPs). Using an SG interferometer, we create a spatial quantum superposition of neutral ato

Oscar Fuentealba, Marc Henneaux, Cédric Troessaert

We extend the BMS(4) group by adding logarithmic supertranslations. This is done by relaxing the boundary conditions on the metric and its conjugate momentum at spatial infinity in order to allow logarithmic terms of carefully designed form in the asymptotic expansion, while still preserving finiteness of the action. Standard theorems of the Hamiltonian form

Eliran Talker, Yefim Barash, Noa Mazurski, Uriel Levy

We study gain without inversion due to coherence effects in a Doppler-broadened degenerate three-level system of a rubidium-hydrogen mixture in a miniaturized micron scale custom vapor cell. The cell miniaturization gives rise to collisions of atoms with the walls of the cell. This, combined with the high collision rate with the hydrogen buffer gas allows us

Meysam Miralaei, Ali Mohammadian, Behruz Tayfeh-Rezaie

For two graphs $G$ and $F$, we say that $G$ is weakly $F$-saturated if $G$ contains no copy of $F$ as a subgraph and one could join all the nonadjacent pairs of vertices of $G$ in some order so that a new copy of $F$ is created at each step. The weak saturation number $\mathrm{wsat}(n, F)$ is the minimum number of edges of a weakly $F$-saturated graph on $n$

Hyungmin Kim, Sungho Suh, Sunghyun Baek, Daehwan Kim

We present a novel adversarial penalized self-knowledge distillation method, named adversarial learning and implicit regularization for self-knowledge distillation (AI-KD), which regularizes the training procedure by adversarial learning and implicit distillations. Our model not only distills the deterministic and progressive knowledge which are from the pre

Y. X. Ding, X. Y. Xie, J. X. Li, K. L. Han

The conventional readout method of the RPC detector uses two sets of orthogonal readout strips placed at the both sides of the gas gap to collect signals of opposite polarities to obtain space points. A new readout method utilizing the reflected signals is proposed which only requires one set of readout strips. The reflection readout method utilizes the diff

Cong Zhang, Zhiguang Cao, Wen Song, Yaoxin Wu

Recent studies in using deep reinforcement learning (DRL) to solve Job-shop scheduling problems (JSSP) focus on construction heuristics. However, their performance is still far from optimality, mainly because the underlying graph representation scheme is unsuitable for modelling partial solutions at each construction step. This paper proposes a novel DRL-gui

Qianqian Liu, Jingfeng Wang, Chunmei Li, Heping Zhang

In a region $R$ consisting of unit squares, a domino is the union of two adjacent squares and a (domino) tiling is a collection of dominoes with disjoint interior whose union is the region. The flip graph $\mathcal{T}(R)$ is defined on the set of all tilings of $R$ such that two tilings are adjacent if we change one to another by a flip (a $90^{\circ}$ rotat

Akira Taniguchi, Yoshiki Tabuchi, Tomochika Ishikawa, Lotfi El Hafi

Autonomous robots need to learn the categories of various places by exploring their environments and interacting with users. However, preparing training datasets with linguistic instructions from users is time-consuming and labor-intensive. Moreover, effective exploration is essential for appropriate concept formation and rapid environmental coverage. To add

Wenli Sun, Xinyang Jiang, Shuguang Dou, Dongsheng Li

In recent years, person Re-identification (ReID) has rapidly progressed with wide real-world applications, but also poses significant risks of adversarial attacks. In this paper, we focus on the backdoor attack on deep ReID models. Existing backdoor attack methods follow an all-to-one or all-to-all attack scenario, where all the target classes in the test se

Min Liu, Yulin Li

By introducing the concept of quantaloidal completions for an order-enriched category, relationships between the category of quantaloids and the category of order-enriched categories are studied. It is proved that quantaloidal completions for an order-enriched category can be fully characterized as compatible quotients of the power-set completion. As applica

Jianqiang Huang, Jian Wang, Qianru Sun, Hanwang Zhang

Extracting class activation maps (CAM) is a key step for weakly-supervised semantic segmentation (WSSS). The CAM of convolution neural networks fails to capture long-range feature dependency on the image and result in the coverage on only foreground object parts, i.e., a lot of false negatives. An intuitive solution is ``coupling'' the CAM with the long-rang

Takayuki Myo, Myagmarjav Odsuren, Kiyoshi Kato

In neutron-rich $^8$He, we study the soft dipole resonance, which is regarded as a dipole oscillation of four valence neutrons against the $^4$He core, and its effect on the low-energy electric dipole strength with a $^4$He+$n$+$n$+$n$+$n$ five-body cluster model. This work is an extended study of an earlier letter [T. Myo and K. Kat\=o, Phys. Rev. C106, L02

Yige Yuan, Bingbing Xu, Huawei Shen, Qi Cao

Graph contrastive learning (GCL) emerges as the most representative approach for graph representation learning, which leverages the principle of maximizing mutual information (InfoMax) to learn node representations applied in downstream tasks. To explore better generalization from GCL to downstream tasks, previous methods heuristically define data augmentati

S. I. Dimitrov

Let $\gamma<1<c$ and $19(c-1)+171(1-\gamma)<9$. In this paper, we establish an asymptotic formula for exponential sums over Piatetski-Shapiro primes $p=[n^{1/\gamma}]$ in arithmetic progressions.

Jiahao Nie, Zhiwei He, Yuxiang Yang, Mingyu Gao

Current 3D single object tracking methods are typically based on VoteNet, a 3D region proposal network. Despite the success, using a single seed point feature as the cue for offset learning in VoteNet prevents high-quality 3D proposals from being generated. Moreover, seed points with different importance are treated equally in the voting process, aggravating

Ting-Li Chen, Elizabeth P. Chou, Min-Yi Chen, Hsieh Fushing

We study Covid-19 spreading dynamics underlying 84 curves of daily Covid-19 infection rates pertaining to 84 districts belonging to the largest seven cities in Taiwan during her pristine surge period. Our computational developments begin with selecting and extracting 18 features from each smoothed district-specific curve. This step of computing effort allows

Ovidiu Preda, Miron Stanciu

In this article, we prove that the blow-up of a locally irreducible lcK space $X$ along a subspace $Z$ which verifies certain conditions is lcK if and only if $X$ is induced gcK, generalizing a theorem of Ornea-Verbitsky-Vuletescu to singular locally irreducible spaces. We also show that even if modifications of lcK spaces are not always of lcK type, they al

Jin Zhang, Wenchao Zheng

For singularly perturbed reaction-diffusion problems in 1D and 2D, we study a local discontinuous Galerkin (LDG) method on a Shishkin mesh. In these cases, the standard energy norm is too weak to capture adequately the behavior of the boundary layers that appear in the solutions. To deal with this deficiency, we introduce a balanced norm stronger than the en

Ruohan Meng, Zhili Zhou, Qi Cui, Kwok-Yan Lam

To prevent fake news images from misleading the public, it is desirable not only to verify the authenticity of news images but also to trace the source of fake news, so as to provide a complete forensic chain for reliable fake news detection. To simultaneously achieve the goals of authenticity verification and source tracing, we propose a traceable and authe

Wenyan Pan, Zhili Zhou, Guangcan Liu, Teng Huang

Generally, current image manipulation detection models are simply built on manipulation traces. However, we argue that those models achieve sub-optimal detection performance as it tends to: 1) distinguish the manipulation traces from a lot of noisy information within the entire image, and 2) ignore the trace relations among the pixels of each manipulated reg

Thanh Phuong Pham

Deep learning has been widely used in various applications from different fields such as computer vision, natural language processing, etc. However, the training models are often manually developed via many costly experiments. This manual work usually requires substantial computing resources, time, and experience. To simplify the use of deep learning and all

LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta

Charged-particle trajectories are usually reconstructed with the LHCb detector using combined information from the tracking devices placed upstream and downstream of the 4\,T\,m dipole magnet. Trajectories reconstructed using only information from the tracker downstream of the dipole magnet, which are referred to as T tracks, have not been used for physics a

Shivanshi Tiwari, Rajesh Kumar, Manisha Kumari, Arvind Kumar

The in-medium properties of $\eta$ mesons are studied in hot and dense isospin asymmetric strange magnetized matter using the chiral SU(3) hadronic mean-field model. The scalar and vector density of baryons are expressed in terms of thermal distribution functions at finite temperature and magnetic field and have dependence on the scalar fields $\sigma$, $\ze

Jolan Bestautte, Szilvia Kalácska, Denis Béchet, Zacharie Obadia

Slow strain rates tests (SSRT) were conducted on hydrogen-containing specimens of PH13-8Mo maraging stainless steel. Hydrogen-assisted subcritical quasi-cleavage cracking was shown to take place during SSRT, thus accelerating material failure. Fractographic analysis showed that quasi-cleavage is composed of flat brittle areas and rougher areas. Using cross-s

Sheng Lü, Mao-Zhi Yang

We study $B\to \pi\pi$ decay with a modified perturbative QCD approach. The branching ratios and $CP$ violation are calculated with the transverse momenta of partons considered. Sudakov factor associated with each meson is included to suppress soft contribution in QCD. With the wave function of $B$ meson obtained in QCD-inspired relativistic potential model

Yiqi Jin, Ziyu Zhu, Tongda Xu, Yuhuan Lin

Recently, deep learning methods have shown promising results in point cloud compression. For octree-based point cloud compression, previous works show that the information of ancestor nodes and sibling nodes are equally important for predicting current node. However, those works either adopt insufficient context or bring intolerable decoding complexity (e.g.

Félix Llorente de Andrés, Carmen Morales-Durán

We developed a mathematical model to derive time scales and the presence of BS stars. The model is based on the variation of mass through a circle into the cluster defined by a radius, and at a time; this mass cross is translated into a differential equation that it can be integrated for a given radius (r) and a determined time (t). From this equation we can

Jiale Sun, Xiaoshui Lin

The two-dimensional Dirac equation has been widely used in graphene physics, the surface of topological insulators, and especially quantum scarring. Although a numerical approach to tackling an arbitrary confining problem was proposed several years ago, several fundamental issues must be thoroughly understood and solved. In this work, we conceal and address

Hui Liu, Jian Wang, Jingzhi Yan

In this article, we give a growth rate about the number of periodic orbits in the Franks type theorem obtained by the authors \cite{LWY}. As applications, we prove the following two results: there exist infinitely many distinct non-contractible closed geodesics on $\mathbb{R}P^2$ endowed with a Riemannian metric such that its Gaussian curvature is positive,

Kazuo Murota, Akihisa Tamura

Integrally convex functions constitute a fundamental function class in discrete convex analysis, including M-convex functions, L-convex functions, and many others. This paper aims at a rather comprehensive survey of recent results on integrally convex functions with some new technical results. Topics covered in this paper include characterizations of integra

Alain de Cheveigné

Data are rapidly growing in size and importance for society, a trend motivated by their enabling power. The accumulation of new data, sustained by progress in technology, leads to a boundless expansion of stored data, in some cases with an exponential increase in the accrual rate itself. Massive data are hard to process, transmit, store, and exploit, and it

Chuang Yu

Depression is a common mental disorder that causes people to experience depressed mood, loss of interest or pleasure, feelings of guilt or low self-worth. Traditional clinical depression diagnosis methods are subjective and time consuming. Since depression can be reflected by human facial expressions, We propose a non-verbal facial behavior-based automatic d

Qiang Li

An invited research highlight article on Charged lepton flavor violation searches in the charmonium system.

Yiru Chen, Silu Huang

Aggregated time series are generated effortlessly everywhere, e.g., "total confirmed covid-19 cases since 2019" and "total liquor sales over time." Understanding "how" and "why" these key performance indicators (KPI) evolve over time is critical to making data-informed decisions. Existing explanation engines focus on explaining one aggregated value or the di

Jiaqi Xue, Qian Lou

Emerging self-supervised learning (SSL) has become a popular image representation encoding method to obviate the reliance on labeled data and learn rich representations from large-scale, ubiquitous unlabelled data. Then one can train a downstream classifier on top of the pre-trained SSL image encoder with few or no labeled downstream data. Although extensive

Daehwan Kim, Kwangrok Ryoo, Hansang Cho, Seungryong Kim

Annotating the dataset with high-quality labels is crucial for performance of deep network, but in real world scenarios, the labels are often contaminated by noise. To address this, some methods were proposed to automatically split clean and noisy labels, and learn a semi-supervised learner in a Learning with Noisy Labels (LNL) framework. However, they lever

Chen Chen, Zhiqian Lan, Guojian Zhan, Yao Lyu

There will be a long time when automated vehicles are mixed with human-driven vehicles. Understanding how drivers assess driving risks and modelling their individual differences are significant for automated vehicles to develop human-like and customized behaviors, so as to gain people's trust and acceptance. However, the reality is that existing driving risk

Lefan Zhang, Zhang-Hao Tian, Wujun Zhou, Wei Wang

The success of deep learning depends on large-scale and well-curated training data, while data in real-world applications are commonly long-tailed and noisy. Many methods have been proposed to deal with long-tailed data or noisy data, while a few methods are developed to tackle long-tailed noisy data. To solve this, we propose a robust method for learning fr

Di Zhu, Majid Kheirkhah, Zhongbo Yan

Bound states at sharp corners have been widely viewed as the hallmark of two-dimensional second-order topological insulators and superconductors. In this work, we show that the existence of sublattice degrees of freedom can enrich the tunability of bound states on the boundary and hence lift the constraint on their locations. We take the Kane-Mele model with

Yi Xu, Junjie Ou, Hui Xu, Luoyi Fu

Temporal knowledge graph, serving as an effective way to store and model dynamic relations, shows promising prospects in event forecasting. However, most temporal knowledge graph reasoning methods are highly dependent on the recurrence or periodicity of events, which brings challenges to inferring future events related to entities that lack historical intera

Daniel J. Scheeres

The dynamics and control of a satellite in proximity to the asteroid Apophis across its Earth close approach in 2029 is evaluated and investigated. First, the feasibility of carrying out close proximity operations about Apophis when in its heliocentric orbit phase is evaluated and shown to be feasible. Then three different types of close proximity motion rel

Andrew C. Li, Zizhao Chen, Pashootan Vaezipoor, Toryn Q. Klassen

Natural and formal languages provide an effective mechanism for humans to specify instructions and reward functions. We investigate how to generate policies via RL when reward functions are specified in a symbolic language captured by Reward Machines, an increasingly popular automaton-inspired structure. We are interested in the case where the mapping of env

Jiacheng Yao, Jindan Xu, Wei Xu, Chau Yuen

To fully exploit the additional dimension brought by reconfigurable intelligent surface (RIS), it is recently suggested by information theory that modulating information upon RIS phases is able to send extra information with increased communication rate. In this paper, we propose a novel superimposed RIS-phase modulation (SRPM) scheme to transfer extra messa

Youjun Hu, Xingyuan Xu, Yunchan Hu, Kaiyang He

Neutral beam current drive (NBCD) on the EAST tokamak is studied by using Monte-Carlo test particle code TGCO. Phase-space structure of the steady-state fast ion distribution is examined and visualized. We find that trapped ions carry co-current current near the edge and counter-current current near the core. However, the magnitude of the trapped ion current

X. Yang, W. Yang

The (anti)neutrino nucleus scattering plays a very important role in probing the hadronic structure as well as the electroweak phenomenologies. To this end, we calculate the jet production semi-inclusive deeply inelastic (anti)neutrino nucleus scattering process. The initial (anti)neutrino is assumed to be scattered off by a target particle with spin 1. Due

Peter Braunsteins, Sophie Hautphenne, Carmen Minuesa

We derive the first conditionally consistent estimators for a class of parametric Markov population models with logistic growth, which are suitable for modelling endangered populations in restricted habitats with a carrying capacity. We focus on discrete-time parametric population-size-dependent branching processes, for which we propose a new class of weight

Matthew Andres Moreno, Charles Ofria

Here, we test the performance and scalability of fully-asynchronous, best-effort communication on existing, commercially-available HPC hardware. A first set of experiments tested whether best-effort communication strategies can benefit performance compared to the traditional perfect communication model. At high CPU counts, best-effort communication improved

Jintang Li, Jiaying Peng, Liang Chen, Zibin Zheng

Recent studies demonstrate that Graph Neural Networks (GNNs) are vulnerable to slight but adversarially designed perturbations, known as adversarial examples. To address this issue, robust training methods against adversarial examples have received considerable attention in the literature. \emph{Adversarial Training (AT)} is a successful approach to learning

Tao Wang, Baoyindureng Wu

Let $f_{o}(G)$ be the maximum order of an odd induced subgraph of $G$. In 1992, Scott proposed a conjecture that $f_{o}(G)\geq \frac {n} {2\chi(G)}$ for a graph $G$ of order $n$ without isolated vertices, where $\chi(G)$ is the chromatic number of $G$. In this paper, we show that the conjecture is not true for bipartite graphs, but is true for all line graph

İsmail Emir Yüksel, Ataberk Olgun, Behzad Salami, F. Nisa Bostancı

Prior works propose SRAM-based TRNGs that extract entropy from SRAM arrays. SRAM arrays are widely used in a majority of specialized or general-purpose chips that perform the computation to store data inside the chip. Thus, SRAM-based TRNGs present a low-cost alternative to dedicated hardware TRNGs. However, existing SRAM-based TRNGs suffer from 1) low TRNG

Bin Han, Qiongqiong Pan

We introduce a kind of $(p, q, t)$-Catalan numbers of Type A by generalizing the Jacobian type continued fraction formula, we proved that the corresponding expansions could be expressed by the polynomials counting permutations on $\S_n(321)$ by various descent statistics. Moreover, we introduce a kind of $(p, q, t)$-Catalan numbers of Type B by generalizing

Vahid Reza Khazaie, Anthony Wong, Mohammad Sabokrou

This paper presents a novel evaluation framework for Out-of-Distribution (OOD) detection that aims to assess the performance of machine learning models in more realistic settings. We observed that the real-world requirements for testing OOD detection methods are not satisfied by the current testing protocols. They usually encourage methods to have a strong b

Florian Vogel, Matthias Fuchs

We investigate the vibrational properties of topologically disordered materials by analytically studying particles that harmonically oscillate around random positions. Exploiting classical field theory in the thermodynamic limit at $T=0$, we build up a self-consistent model by analyzing the Hessian utilizing Euclidean Random Matrix theory. In accordance with

Haonan Wang, Jieyu Zhang, Qi Zhu, Wei Huang

Existing graph contrastive learning (GCL) techniques typically require two forward passes for a single instance to construct the contrastive loss, which is effective for capturing the low-frequency signals of node features. Such a dual-pass design has shown empirical success on homophilic graphs, but its effectiveness on heterophilic graphs, where directly c

Beinan Wang, Chunxu Tang, Rongrong Zhong, Bin Fan

Presto is an open-source distributed SQL query engine for OLAP, aiming for "SQL on everything". Since open-sourced in 2013, Presto has been consistently gaining popularity in large-scale data analytics and attracting adoption from a wide range of enterprises. From the development and operation of Presto, we witnessed a significant amount of CPU consumption o

Shanshan Zhao, Mingming Gong, Xi Li, Dacheng Tao

Recent years have witnessed the great success of deep learning on various point cloud analysis tasks, e.g., classification and semantic segmentation. Since point cloud data is sparse and irregularly distributed, one key issue for point cloud data processing is extracting useful information from local regions. To achieve this, previous works mainly extract th

Laxman Dhulipala, Quanquan C. Liu, Sofya Raskhodnikova, Jessica Shi

Differentially private algorithms allow large-scale data analytics while preserving user privacy. Designing such algorithms for graph data is gaining importance with the growth of large networks that model various (sensitive) relationships between individuals. While there exists a rich history of important literature in this space, to the best of our knowled

Alexey Glazyrin, Roman Karasev, Alexandr Polyanskii

We prove a complex polynomial plank covering theorem for not necessarily homogeneous polynomials. As the consequence of this result, we extend the complex plank theorem of Ball to the case of planks that are not necessarily centrally symmetric and not necessarily round. We also prove a weaker version of the spherical polynomial plank covering conjecture for

Fan Qian, Jiqing Han

Speech emotion recognition is a challenge and an important step towards more natural human-computer interaction (HCI). The popular approach is multimodal emotion recognition based on model-level fusion, which means that the multimodal signals can be encoded to acquire embeddings, and then the embeddings are concatenated together for the final classification.

Lijing Wang, Takuya Kurihana, Aurelien Meray, Ilijana Mastilovic

Soil and groundwater contamination is a pervasive problem at thousands of locations across the world. Contaminated sites often require decades to remediate or to monitor natural attenuation. Climate change exacerbates the long-term site management problem because extreme precipitation and/or shifts in precipitation/evapotranspiration regimes could re-mobiliz

Shailza Sharma, Abhinav Dhall, Vinay Kumar, Vivek Singh Bawa

Recently, there has been numerous breakthroughs in face hallucination tasks. However, the task remains rather challenging in videos in comparison to the images due to inherent consistency issues. The presence of extra temporal dimension in video face hallucination makes it non-trivial to learn the facial motion through out the sequence. In order to learn the

Kun Fang, Qinghua Tao, Yingwen Wu, Tao Li

Randomized Smoothing (RS) is a promising technique for certified robustness, and recently in RS the ensemble of multiple Deep Neural Networks (DNNs) has shown state-of-the-art performances due to its variance reduction effect over Gaussian noises. However, such an ensemble brings heavy computation burdens in both training and certification, and yet under-exp

Tianyi Wang, Xin Liao, Kam Pui Chow, Xiaodong Lin

The mushroomed Deepfake synthetic materials circulated on the internet have raised a profound social impact on politicians, celebrities, and individuals worldwide. In this survey, we provide a thorough review of the existing Deepfake detection studies from the reliability perspective. We identify three reliability-oriented research challenges in the current

Weng Tingyu, Xiao Jun, Jiang Haiyong

3D recognition is the foundation of 3D deep learning in many emerging fields, such as autonomous driving and robotics.Existing 3D methods mainly focus on the recognition of a fixed set of known classes and neglect possible unknown classes during testing. These unknown classes may cause serious accidents in safety-critical applications, i.e. autonomous drivin

William Chang, Fariba Ariaei, Edmond Jonckheere

Bode integrals of sensitivity and sensitivity-like functions along with complementary sensitivity and complementary sensitivity-like functions are conventionally used for describing performance limitations of a feedback control system. In this paper, we investigate the Bode integral and evaluate what happens when a fractional order Proportional-Integral-Deri

Renjie Pi, Weizhong Zhang, Yueqi Xie, Jiahui Gao

The Federated Learning (FL) paradigm is known to face challenges under heterogeneous client data. Local training on non-iid distributed data results in deflected local optimum, which causes the client models drift further away from each other and degrades the aggregated global model's performance. A natural solution is to gather all client data onto the serv

M. Khorrami, A. H. Fatollahi, A. Shariati

A photon source is located in a rotating waveguide. An absorber with a sharp absorbing frequency absorbs some of the emitted photons. This decreases the number of photons which are detected by a detector. The frequency (energy) spectrum measured in the absorber depends on the setup (the positions of the source and the detector), and this spectrum determines

Farhan Dhanani, Muhammad Rafi

This paper presents solutions to the Machine Learning Model Attribution challenge (MLMAC) collectively organized by MITRE, Microsoft, Schmidt-Futures, Robust-Intelligence, Lincoln-Network, and Huggingface community. The challenge provides twelve open-sourced base versions of popular language models developed by well-known organizations and twelve fine-tuned

Omar Mustafa

We discuss KG-oscillators in the (1+2)-dimensional G\"{u}rses spacetime and under position-dependent mass (PDM) settings. We observe that the KG-G\"{u}rses oscillators are introduced as a byproduct of the very nature of the G\"{u}rses spacetime structure. We report that the energy levels of such KG-G\"{u}rses oscillators admit vorticity-energy correlations a

Sudipta Goswami, Shubhankar Mishra, Kausik Dana, Ashok Kumar Mandal

Room-temperature magnetoelectric multiferroicity has been observed in c-axis oriented GaFeO$_3$ thin films (space group $Pna2_1$), grown on economic and technologically important (100)Si substrates by pulsed laser deposition technique. Structural analysis and comprehensive mapping of Ga:Fe ratio across a length scale range of 10$^4$ reveal coexistence of epi

Nandor Simanyi

In this paper we present a conditional proof of Wojtkowski's Ergodicity Conjecture for the system of 1D perfectly elastic balls falling down in a half line under constant gravitational acceleration. Namely, we prove that almost every such system is (completely hyperbolic and) ergodic, by assuming the transversality between different singularities and between

Nour Makke, Sanjay Chawla

Symbolic regression is emerging as a promising machine learning method for learning succinct underlying interpretable mathematical expressions directly from data. Whereas it has been traditionally tackled with genetic programming, it has recently gained a growing interest in deep learning as a data-driven model discovery method, achieving significant advance

Wenlu Du, Junyi Ye, Jingyi Gu, Jing Li

Traffic signal control is safety-critical for our daily life. Roughly one-quarter of road accidents in the U.S. happen at intersections due to problematic signal timing, urging the development of safety-oriented intersection control. However, existing studies on adaptive traffic signal control using reinforcement learning technologies have focused mainly on

Nouman Butt, Patrick Draper, Jiayu Shen

We compute the low-lying spectrum of 4D SU(2) Yang-Mills in a finite volume using quantum simulations. In contrast to small-volume lattice truncations of the Hilbert space, we employ toroidal dimensional reduction to the ``femtouniverse" matrix quantum mechanics model. In this limit the theory is equivalent to the quantum mechanics of three interacting parti