March 2023 arXiv papers — page 147

Siqi Zhang, Lu Zhang, Zhiyong Liu

Domain adaptive object detection (DAOD) assumes that both labeled source data and unlabeled target data are available for training, but this assumption does not always hold in real-world scenarios. Thus, source-free DAOD is proposed to adapt the source-trained detectors to target domains with only unlabeled target data. Existing source-free DAOD methods typi

Y. Li, I. Brevik, O. I. Malyi, M. Boström

We explore the Casimir-Lifshitz free energy theory for surface freezing of methane gas hydrates near the freezing point of water. The theory enables us to explore different pathways, resulting in anomalous (stabilising) ice layers on methane hydrate surfaces via energy minimization. Notably, we will contrast the gas hydrate material properties, under which t

Yuhan Cao, Haoran Jiang, Zhenghong Yu, Qi Li

While Generative Adversarial Networks (GANs) have recently found applications in image editing, most previous GAN-based image editing methods require largescale datasets with semantic segmentation annotations for training, only provide high level control, or merely interpolate between different images. Previous researchers have proposed EditGAN for high-qual

Nirjhar Sarkar, Prabhakar R. Bandaru, Robert C. Dynes

Highly oriented pyrolytic graphite (HoPG) may be the only known monatomic crystal with the ability to host naturally formed moire patterns on its cleaved surfaces, which are coherent over micrometer scales and with discrete sets of twist angles of fixed periodicity. Such an aspect is in marked contrast to twisted bilayer graphene (TBG) and other multilayered

Rogier Brussee

Fuzzy logic is a way to argue with boolean predicates for which we only have a confidence value between 0 and 1 rather than a well defined truth value. It is tempting to interpret such a confidence as a probability. We use Markov kernels, parametrised probability distributions, to do just that. As a consequence we get general fuzzy logic connectives from pro

Rohan Pratap Singh, Zhaoming Xie, Pierre Gergondet, Fumio Kanehiro

Recent advances in deep reinforcement learning (RL) based techniques combined with training in simulation have offered a new approach to developing robust controllers for legged robots. However, the application of such approaches to real hardware has largely been limited to quadrupedal robots with direct-drive actuators and light-weight bipedal robots with l

Yubin Wang, Yulin Li, Zengqi Peng, Hakim Ghazzai

Lane change in dense traffic typically requires the recognition of an appropriate opportunity for maneuvers, which remains a challenging problem in self-driving. In this work, we propose a chance-aware lane-change strategy with high-level model predictive control (MPC) through curriculum reinforcement learning (CRL). In our proposed framework, full-state ref

H. J. Muhren, Paul van der Schoot

Recently, Maassen et al. measured an appreciable pH difference between the bulk solution and the solution in the lumen of virus-like particles, self-assembled in an aqueous buffer solution containing the coat proteins of a simple plant virus and polyanions. [Maassen, S. J.; et al. Small 2018, 14, 1802081] They attribute this to the Donnan effect, caused by a

H. B. Tran Tan, A. Derevianko

We compute the reduced electric-dipole matrix elements $\langle{nS_{1/2}}||D||{n'P_J}\rangle$ with $n=6,7$ and $n'=6,7,\ldots,12$ in cesium using the most complete to date ab initio relativistic coupled-cluster method which includes singles, doubles, perturbative core triples, and valence triples. Our results agree with previous calculations at the linearize

Zengyang Gong, Yuxiang Zeng, Lei Chen

Querying the shortest path between two vertexes is a fundamental operation in a variety of applications, which has been extensively studied over static road networks. However, in reality, the travel costs of road segments evolve over time, and hence the road network can be modeled as a time-dependent graph. In this paper, we study the shortest path query ove

Rong Zhou, Tailong Zhou

We prove two anisotropic type weighted geometric inequalities that hold for star-shaped and $F$-mean convex hypersurfaces in $\mathbb{R}^{n+1}$. These inequalities involve the anisotropic $p$-momentum, the anisotropic perimeter and the volume of the region enclosed by the hypersurface. We show that the Wulff shape of $F$ is the unique minimizer of the corres

E. Crosby, H. Jerjen, O. Müller, M. Pawlowski

We present initial results of a survey of host $L_{*}$ galaxies environments in the Local Volume ($D<10\,$Mpc) searching for satellite dwarf galaxy candidates using the wide-field Hyper Suprime-Cam imager on the 8m Subaru Telescope. The current paper presents complete results on NGC2683 ($M_{B_T,0}=-19.62$, $D=9.36\,Mpc$, $v_{\odot}=411\,km\,s^{-1}$), an iso

Bac Nguyen, Stefan Uhlich, Fabien Cardinaux

Self-supervised learning (SSL) has recently shown remarkable results in closing the gap between supervised and unsupervised learning. The idea is to learn robust features that are invariant to distortions of the input data. Despite its success, this idea can suffer from a collapsing issue where the network produces a constant representation. To this end, we

Fabian Sturm, Elke Hergenroether, Julian Reinhardt, Petar Smilevski Vojnovikj

This work presents the Industrial Hand Action Dataset V1, an industrial assembly dataset consisting of 12 classes with 459,180 images in the basic version and 2,295,900 images after spatial augmentation. Compared to other freely available datasets tested, it has an above-average duration and, in addition, meets the technical and legal requirements for indust

D. -S. Wang

Unravelling the source of quantum computing power has been a major goal in the field of quantum information science. In recent years, the quantum resource theory (QRT) has been established to characterize various quantum resources, yet their roles in quantum computing tasks still require investigation. The so-called universal quantum computing model (UQCM),

Alvin Heng, Abdul Fatir Ansari, Harold Soh

We present Flow-Guided Density Ratio Learning (FDRL), a simple and scalable approach to generative modeling which builds on the stale (time-independent) approximation of the gradient flow of entropy-regularized f-divergences introduced in recent work. Specifically, the intractable time-dependent density ratio is approximated by a stale estimator given by a G

Drazen Adamovic, Kazuya Kawasetsu, David Ridout

The Bershadsky--Polyakov algebras are the subregular quantum hamiltonian reductions of the affine vertex operator algebras associated with $\mathfrak{sl}_3$. In arXiv:2007.00396 [math.QA], we realised these algebras in terms of the regular reduction, Zamolodchikov's W$_3$-algebra, and an isotropic lattice vertex operator algebra. We also proved that a natura

Nirjhar Sarkar, Prabhakar R. Bandaru, Robert C. Dynes

A methodology to test the interlayer bonding strength of two-dimensional (2D) surfaces and associated one (1D)- and two (2D)- dimensional surface defects using scanning tunneling microscope tip-induced deformation, is demonstrated. Surface elastic deformation characteristics of soft 2D monatomic sheets of graphene and graphite in contrast to NbSe2 indicates

Pascal Nasahl, Stefan Mangard

Secure elements physically exposed to adversaries are frequently targeted by fault attacks. These attacks can be utilized to hijack the control-flow of software allowing the attacker to bypass security measures, extract sensitive data, or gain full code execution. In this paper, we systematically analyze the threat vector of fault-induced control-flow manipu

Athul P, D. Ramesh Kumar

In this paper, we obtain coupled fixed point theorem for (\psi, \phi)-contractions under some generalized conditions on the real valued functions \psi and \phi defined on (0,\infinity). Also, we present a generalized version of coupled fixed point theorem for the same (\psi, \phi)- contractions. A new approach to fractal generation using the relation between

Shuai Wang, Daoan Zhang, Jianguo Zhang, Weiwei Zhang

In this paper, considering the balance of data/model privacy of model owners and user needs, we propose a new setting called Back-Propagated Black-Box Adaptation (BPBA) for users to better train their private models via the guidance of the back-propagated results of a Black-box foundation/source model. Our setting can ease the usage of foundation/source mode

Karel Van Bockstal, Mahmoud A. Zaky, Ahmed S. Hendy

In this contribution, a wave equation with a time-dependent variable-order fractional damping term and a nonlinear source is considered. Avoiding the circumstances of expressing the nonlinear variable-order fractional wave equations via closed-form expressions in terms of special functions, we investigate the existence and uniqueness of this problem with Rot

Shangshang Shi, Zhimin Wang, Ruimin Shang, Yanan Li

The taxonomic composition and abundance of phytoplankton, having direct impact on marine ecosystem dynamic and global environment change, are listed as essential ocean variables. Phytoplankton classification is very crucial for Phytoplankton analysis, but it is very difficult because of the huge amount and tiny volume of Phytoplankton. Machine learning is th

Abdul Rehman, Rabeeh Ayaz Abbasi, Irfan ul Haq Qureshi, Akmal Saeed Khattak

The FakeNews task in MediaEval 2022 investigates the challenge of finding accurate and high-performance models for the classification of conspiracy tweets related to COVID-19. In this paper, we used BERT, ELMO, and their combination for feature extraction and RandomForest as classifier. The results show that ELMO performs slightly better than BERT, however t

Ziqi Yin, Qi Zhang, Wentao Zhang, Rong-Hua Li

Maximal biclique enumeration is a fundamental problem in bipartite graph data analysis. Existing biclique enumeration methods mainly focus on non-attributed bipartite graphs and also ignore the \emph{fairness} of graph attributes. In this paper, we introduce the concept of fairness into the biclique model for the first time and study the problem of fairness-

Atta Ullah, Rabeeh Ayaz Abbasi, Akmal Saeed Khattak, Anwar Said

In this paper we proposed a Graph-Based conspiracy source detection method for the MediaEval task 2022 FakeNews: Corona Virus and Conspiracies Multimedia Analysis Task. The goal of this study was to apply SOTA graph neural network methods to the problem of misinformation spreading in online social networks. We explore three different Graph Neural Network mod

Hongan Wei, Jiaqi Liu, Bo Chen, Liqun Lin

360$^\circ$ videos have received widespread attention due to its realistic and immersive experiences for users. To date, how to accurately model the user perceptions on 360$^\circ$ display is still a challenging issue. In this paper, we exploit the visual characteristics of 360$^\circ$ projection and display and extend the popular just noticeable difference

Anis Allagui, Ahmed Elwakil

The capacitance of capacitive energy storage devices can not be directly measured, but can be estimated from the input and output signals expressed in the time or frequency domains. Here the time-domain voltage-charge relationship in non-ideal electrochemical capacitors is treated as an ill-conditioned convolution integral equation where the unknown capacita

Chengkuan Hong, Christian R. Shelton

Neyman-Scott processes (NSPs) have been applied across a range of fields to model points or temporal events with a hierarchy of clusters. Markov chain Monte Carlo (MCMC) is typically used for posterior sampling in the model. However, MCMC's mixing time can cause the resulting inference to be slow, and thereby slow down model learning and prediction. We devel

Quancheng Wang, Ming Tang, Jianming Fu

As the Internet of Things (IoT) continues to evolve, smartphones have become essential components of IoT systems. However, with the increasing amount of personal information stored on smartphones, user privacy is at risk of being compromised by malicious attackers. Although malware detection engines are commonly installed on smartphones against these attacks

Amin Kargar-Barzi, Ebrahim Farahmand, Nooshin Taheri Chatrudi, Ali Mahani

With the ongoing development of Indoor Location-Based Services, the location information of users in indoor environments has been a challenging issue in recent years. Due to the widespread use of WiFi networks, WiFi fingerprinting has become one of the most practical methods of locating mobile users. In addition to localization accuracy, some other critical

Siqi Zhang, Lu Zhang, Zhiyong Liu, Hangtao Feng

Domain adaptive object detection (DAOD) aims to adapt the detector from a labelled source domain to an unlabelled target domain. In recent years, DAOD has attracted massive attention since it can alleviate performance degradation due to the large shift of data distributions in the wild. To align distributions between domains, adversarial learning is widely u

Tharindu Kumarage, Joshua Garland, Amrita Bhattacharjee, Kirill Trapeznikov

Recent advancements in pre-trained language models have enabled convenient methods for generating human-like text at a large scale. Though these generation capabilities hold great potential for breakthrough applications, it can also be a tool for an adversary to generate misinformation. In particular, social media platforms like Twitter are highly susceptibl

Chun-tai Wu, Zhi-Lei She, Xin-Ye Peng, Xiao-Lin Kang

A dynamically constrained phase-space coalescence (DCPC) model was introduced to study the exotic state $\chi_{c 1}(3872)$ yield for three possible structures: tetraquark state, nuclear-like state, and molecular state respectively, where the hadronic final states generated by the parton and hadron cascade model (PACIAE). The $\chi_{c 1}(3872)$/$\psi (2S)$ cr

Zhiwen Deng, Jing Wang, Hongsheng Liu, Hairun Xie

The Reynolds-averaged Navier-Stokes equation for compressible flow over supercritical airfoils under various flow conditions must be rapidly and accurately solved to shorten design cycles for such airfoils. Although deep-learning methods can effectively predict flow fields, the accuracy of these predictions near sensitive regions and their generalizability t

Arijit Kayal, Sraboni Dey, Harikrishnan G., Renjith Nadarajan

The extraordinary mechanical properties of 2D TMDCs make them ideal candidates for investigating strain-induced control of various physical properties. Here we explore the role of non-uniform strain in modulating optical, electronic and transport properties of semiconducting, chemical vapour deposited monolayer MoS2, on periodically nanostructured substrates

Parker R. Wray, Harry A. Atwater

We present a linear coordinate transform to expand the solution of scattering and emission problems into a basis of forward and backward directional vector harmonics. The transform provides intuitive algebraic and geometric interpretations of systems with directional scattering/emission across a broad range of wavelength-to-size ratios. The Kerker, generaliz

So Matsuura, Kazutoshi Ohta

In this paper, we examine a modification of the Kazakov-Migdal (KM) model with gauge group $U(N_c)$, where the adjoint scalar fields in the conventional KM model are replaced by $N_f$ fundamental scalar fields (FKM model). After tuning the coupling constants and eliminating the fundamental scalar fields, the partition function of this model is expressed as a

Yen-Chang Huang

The classical result of Cauchy's surface area formula states that the surface area of the boundary $\partial K=\Sigma$ of any $n$-dimensional convex body in the $n$-dimensional Euclidean space $\mathbb{R}^n$ can be obtained by the average of the projected areas of $\Sigma$ along all directions in $\mathbb{S}^{n-1}$. In this notes, we generalize the formula t

Xuan Zhao, Ran Yang, Ren-jun Qi, Hong Sun

A fully implicit numerical scheme is established for solving the time fractional Swift-Hohenberg (TFSH) equation with a Caputo time derivative of order $\alpha\in(0,1)$. The variable-step L1 formula and the finite difference method are employed for the time and the space discretizations, respectively. The unique solvability of the numerical scheme is proved

Kang Li, Yan Song, Li-Rong Dai, Ian McLoughlin

In this paper, we propose an effective sound event detection (SED) method based on the audio spectrogram transformer (AST) model, pretrained on the large-scale AudioSet for audio tagging (AT) task, termed AST-SED. Pretrained AST models have recently shown promise on DCASE2022 challenge task4 where they help mitigate a lack of sufficient real annotated data.

Erika Kawakami, Jiabao Chen, Mónica Benito, Denis Konstantinov

We present a blueprint for building a fault-tolerant quantum computer using the spin states of electrons on the surface of liquid helium. We propose to use ferromagnetic micropillars to trap single electrons on top of them and to generate a local magnetic field gradient. Introducing a local magnetic field gradient hybridizes charge and spin degrees of freedo

Wenting Wang, Heng Zhou, Xinghe Jiang, Tristan Melton

Dissipative Kerr soliton microcombs in microresonators has enabled fundamental advances in chip scale precision metrology, communication, spectroscopy, and parallel signal processing. Here we demonstrate polarization diverse soliton transitions and deterministic switching dynamics of a self stabilized microcomb in a strongly coupled dispersion-managed micror

Karan Muvvala, Morteza Lahijanian

This work introduces efficient symbolic algorithms for quantitative reactive synthesis. We consider resource-constrained robotic manipulators that need to interact with a human to achieve a complex task expressed in linear temporal logic. Our framework generates reactive strategies that not only guarantee task completion but also seek cooperation with the hu

M. A. Yurischev, Saeed Haddadi

A two-spin-1/2 Heisenberg XYZ model with Dzyaloshinsky--Moriya (DM) and Kaplan--Shekhtman--Entin-Wohlman--Aharony (KSEA) interactions in the presence of an inhomogeneous external magnetic field is considered at thermal equilibrium. Its density matrix has the general X form for which we derive explicit formulas for the local quantum Fisher information (LQFI)

Mingzhen Sun, Weining Wang, Xinxin Zhu, Jing Liu

Motion, scene and object are three primary visual components of a video. In particular, objects represent the foreground, scenes represent the background, and motion traces their dynamics. Based on this insight, we propose a two-stage MOtion, Scene and Object decomposition framework (MOSO) for video prediction, consisting of MOSO-VQVAE and MOSO-Transformer.

Sneha Pradhan, Sanjay Mandal, P. K. Sahoo

In the current research, we present a novel gravastar model based on the Mazur-Mottola (2004) method with an isotropic matter distribution in $f(Q)$ gravity. The gravastar, a hypothesized substitute for a black hole, is built using the Mazur-Mottola mechanism. This approach allows us to define gravastar as having three stages. The first one is an inner regio

J. C. Saywell, M. S. Carey, P. S. Light, S. S. Szigeti

Atom-interferometric quantum sensors could revolutionize navigation, civil engineering, and Earth observation. However, operation in real-world environments is challenging due to external interference, platform noise, and constraints on size, weight, and power. Here we experimentally demonstrate that tailored light pulses designed using robust control techni

Heda Zhang, Michael A McGuire, Andrew F May, Joy Chao

$\alpha$-RuCl$_3$, a well-known candidate material for Kitaev quantum spin liquid, is prone to stacking disorder due to the weak van der Waals bonding between the honeycomb layers. After a decade of intensive experimental and theoretical studies, the detailed correlation between stacking degree of freedom, structure transition, magnetic and thermal transport

Honghui Shang, Yi Fan, Li Shen, Chu Guo

Quantum computing is moving beyond its early stage and seeking for commercial applications in chemical and biomedical sciences. In the current noisy intermediate-scale quantum computing era, quantum resource is too scarce to support these explorations. Therefore, it is valuable to emulate quantum computing on classical computers for developing quantum algori

Juanjuan Weng, Zhiming Luo, Zhun Zhong, Shaozi Li

Previous works have extensively studied the transferability of adversarial samples in untargeted black-box scenarios. However, it still remains challenging to craft targeted adversarial examples with higher transferability than non-targeted ones. Recent studies reveal that the traditional Cross-Entropy (CE) loss function is insufficient to learn transferable

Chen Huang, Hanlin Goh, Jiatao Gu, Josh Susskind

Recent Self-Supervised Learning (SSL) methods are able to learn feature representations that are invariant to different data augmentations, which can then be transferred to downstream tasks of interest. However, different downstream tasks require different invariances for their best performance, so the optimal choice of augmentations for SSL depends on the t

Haocheng Ju, Haimiao Zhang, Lin Li, Xiao Li

Joint channel estimation and signal detection (JCESD) is crucial in orthogonal frequency division multiplexing (OFDM) systems, but traditional algorithms perform poorly in low signal-to-noise ratio (SNR) scenarios. Deep learning (DL) methods have been investigated, but concerns regarding computational expense and lack of validation in low-SNR settings remain

Milan Jain, Narmadha Meenu Mohankumar, Heng Wan, Sumitrra Ganguly

Disadvantaged communities (DAC), as defined by the Justice40 initiative of the Department of Energy (DOE), USA, identifies census tracts across the USA to determine where benefits of climate and energy investments are or are not currently accruing. The DAC status not only helps in determining the eligibility for future Justice40-related investments but is al

E. Ievlev, Michael R. R. Good

Thermal radiation from a moving point charge is found. The calculation is entirely from a classical point of view, but is shown to have an immediate connection to quantum field theory.

Hongbin Lin, Bin Li, Xiangyu Chu, Qi Dou

Needle picking is a challenging manipulation task in robot-assisted surgery due to the characteristics of small slender shapes of needles, needles' variations in shapes and sizes, and demands for millimeter-level control. Prior works, heavily relying on the prior of needles (e.g., geometric models), are hard to scale to unseen needles' variations. In this pa

Akihiro Sei

In theories with supersymmetry, we can calculate a special partition function, known as the superconformal index. In particular, for a gauge group of $\mathrm{U}(N)$ and particles belonging to the adjoint representation, there is a fast method known as the character expansion method, which uses the characters of the symmetric group. In this paper, we extend

Jiafei Duan, Samson Yu, Nicholas Tan, Yi Ru Wang

Understanding human intentions is key to enabling effective and efficient human-robot interaction (HRI) in collaborative settings. To enable developments and evaluation of the ability of artificial intelligence (AI) systems to infer human beliefs, we introduce a large-scale multi-modal video dataset for intent prediction based on object-context relations.

Tiangang Cui, Hans De Sterck, Alexander D. Gilbert, Stanislav Polishchuk

We develop new multilevel Monte Carlo (MLMC) methods to estimate the expectation of the smallest eigenvalue of a stochastic convection-diffusion operator with random coefficients. The MLMC method is based on a sequence of finite element (FE) discretizations of the eigenvalue problem on a hierarchy of increasingly finer meshes. For the discretized, algebraic

Md Awsafur Rahman, Bishmoy Paul, Tanvir Mahmud, Shaikh Anowarul Fattah

Melanoma is considered to be the deadliest variant of skin cancer causing around 75\% of total skin cancer deaths. To diagnose Melanoma, clinicians assess and compare multiple skin lesions of the same patient concurrently to gather contextual information regarding the patterns, and abnormality of the skin. So far this concurrent multi-image comparative metho

Yanqiao Ding, Kui Li, Huan Liu, Dongfeng Yan

This is the first of two papers on the uniform asymptotics for real double Hurwitz numbers with triple ramification. Real double Hurwitz numbers with triple ramification count the number of real ramified coverings of the complex projective line $\mathbb{C}\mathbb{P}^1$ by real Riemann surfaces of genus $g$, where the ramification profiles over $0$ and $\inft

Boxiao Yu, Reagan Tibbetts, Titon Barua, Ailani Morales

Underwater caves are challenging environments that are crucial for water resource management, and for our understanding of hydro-geology and history. Mapping underwater caves is a time-consuming, labor-intensive, and hazardous operation. For autonomous cave mapping by underwater robots, the major challenge lies in vision-based estimation in the complete abse

Xianyong Bai, Hui Tian, Yuanyong Deng, Zhanshan Wang

The Solar Upper Transition Region Imager (SUTRI) onboard the Space Advanced Technology demonstration satellite (SATech-01), which was launched to a sun-synchronous orbit at a height of 500 km in July 2022, aims to test the on-orbit performance of our newly developed Sc-Si multi-layer reflecting mirror and the 2kx2k EUV CMOS imaging camera and to take full-di

András M. Gunyhó, Suman Kundu, Jian Ma, Wei Liu

Measuring the state of qubits is one of the fundamental operations of a quantum computer. Currently, state-of-the-art high-fidelity single-shot readout of superconducting qubits relies on parametric amplifiers at the millikelvin stage. However, parametric amplifiers are challenging to scale beyond hundreds of qubits owing to practical size and power limitati

Jierun Chen, Shiu-hong Kao, Hao He, Weipeng Zhuo

To design fast neural networks, many works have been focusing on reducing the number of floating-point operations (FLOPs). We observe that such reduction in FLOPs, however, does not necessarily lead to a similar level of reduction in latency. This mainly stems from inefficiently low floating-point operations per second (FLOPS). To achieve faster networks, we

M. Mehrdad Morsali, Hoda Mohammadzade, Saeed Bagheri Shouraki

This paper presents a context-aware framework for feature selection and classification procedures to realize a fast and accurate audio event annotation and classification. The context-aware design starts with exploring feature extraction techniques to find an appropriate combination to select a set resulting in remarkable classification accuracy with minimal

Sarbeswar Pal

Let $X$ be a projective Fano manifold of Picard number one, different from the projective space. There is a folklore conjecture that any non-constant endomorphism of $X$ is an isomorphism. In the first half of this article, we will prove the folklore conjecture when the co-tangent bundle of $X$ is algebraically completely integrable system and the tangent bu

Piero Chiappina, Jash Banker, Srujan Meesala, David Lake

Coherent transduction of quantum states from the microwave to the optical domain can play a key role in quantum networking and distributed quantum computing. We present the design of a piezo-optomechanical device formed in a hybrid lithium niobate on silicon platform, that is suitable for microwave-to-optical quantum transduction. Our design is based on acou

Nadir Matringe, Omer Offen, Chang Yang

We prove the absolute convergence, functional equations and meromorphic continuation of local intertwining periods on parabolically induced representations of finite length for certain symmetric spaces over local fields of characteristic zero, including Galois pairs as well as pairs of Prasad and Takloo-Bighash type. Furthermore, for a general symmetric spac

Yihong Du, Wenjie Ni, Rong Wang

This paper is concerned with the long-time dynamics of an epidemic model whose diffusion and reaction terms involve nonlocal effects described by suitable convolution operators, and the epidemic region is represented by an evolving interval enclosed by the free boundaries in the model. In Wang and Du \cite{WangDu-JDE}, it was shown that the model is well-pos

Manuela M. Dantas, Kenneth J. Merkley, Felipe B. G. Silva

We propose four channels through which government guarantees affect banks' incentives to smooth income. Empirically, we exploit two complementary settings that represent plausible exogenous changes in government guarantees: the increase in implicit guarantees following the creation of the Eurozone and the removal of explicit guarantees granted to the Landesb

Aryan Odugoudar, Jaskaran Singh Walia

Arrhythmia is just one of the many cardiovascular illnesses that have been extensively studied throughout the years. Using multi-lead ECG data, this research describes a deep learning (DL) pipeline technique based on convolutional neural network (CNN) algorithms to detect cardiovascular lar arrhythmia in patients. The suggested model architecture has hidden

Xiaoqin Fu

More and more distributed software systems are being developed and deployed today. Like other software, distributed software systems also need very strong quality assurance support. Distributed software is often very large/complex, has distributed components, and does not have a global clock. All these characteristics make it very challenging to analyze the

Ersin Daş, Joel W. Burdick

Future NASA lander missions to icy moons will require completely automated, accurate, and data efficient calibration methods for the robot manipulator arms that sample icy terrains in the lander's vicinity. To support this need, this paper presents a Gaussian Process (GP) approach to the classical manipulator kinematic calibration process. Instead of identif

Saurabh Kataria, Jesús Villalba, Laureano Moro-Velázquez, Thomas Thebaud

Speech super-resolution/Bandwidth Extension (BWE) can improve downstream tasks like Automatic Speaker Verification (ASV). We introduce a simple novel technique called Self-FiLM to inject self-supervision into existing BWE models via Feature-wise Linear Modulation. We hypothesize that such information captures domain/environment information, which can give ze

Sumit Sarkar, Ram Janay Choudhary, Rajamani Raghunathan

Here we investigate the mechanism of charge-disproportionation (CD) in BaBiO3 (BBO) using density functional theory under different crystal symmetries and by employing strain as an external perturbation. The competition between Bi 6sp-O 2p (s-p) and O 2p-O 2p (p-p) charge-fluctuations decides the electronic ground state, charge-disproportionation and bond-di

Yu Zhang, Marc J. Cawkwell, Christian F. A. Negre, Oscar Grånäs

Extended Lagrangian Born-Oppenheimer molecular dynamics (XL-BOMD) [Phys. Rev. Lett. vol. 100, 123004 (2008)] is combined with Kohn-Sham density functional theory (DFT) using a DFT+U correction based on the Hubbard model. This combined XL-BOMD and DFT+U approach allows efficient Born-Oppenheimer molecular dynamics simulations with orbital-dependent correction

Muhammad Ifte Khairul Islam, Max Khanov, Esra Akbas

Graph Neural networks (GNNs) have recently become a powerful technique for many graph-related tasks including graph classification. Current GNN models apply different graph pooling methods that reduce the number of nodes and edges to learn the higher-order structure of the graph in a hierarchical way. All these methods primarily rely on the one-hop neighborh

Till Heine

The classical Dold-Kan correspondence is known to admit a categorification in the form of an equivalence between the $\infty$-categories of $2$-simplicial stable $\infty$-categories and connective chain complexes of stable $\infty$-categories. In this work, we extend these concepts to give a categorification of the classical Dwyer-Kan correspondence by showi

Manana Kachakhidze, Nino Kachakhidze-Murphy, Badri Khvitia

According to the presented work, VLF/LF electromagnetic emissions might be declared as the main precursor of earthquakes since based on these very emissions, it might predict ($M\ge 5$) inland earthquakes. As for ULF radiations, it governs some processes going on in the lithosphere-atmosphere-ionosphere coupling (LAIC) system. By these points, VLF/LF/ULF ele

Ekta U. Samani, Feng Tao, Harshavardhan R. Dasari, Sihao Ding

Bird's Eye View (BEV) representations are tremendously useful for perception-related automated driving tasks. However, generating BEVs from surround-view fisheye camera images is challenging due to the strong distortions introduced by such wide-angle lenses. We take the first step in addressing this challenge and introduce a baseline, F2BEV, to generate disc

Michael C. H. Choi, Geoffrey Wolfer

Given a target distribution $\pi$ and an arbitrary Markov infinitesimal generator $L$ on a finite state space $\mathcal{X}$, we develop three structured and inter-related approaches to generate new reversiblizations from $L$. The first approach hinges on a geometric perspective, in which we view reversiblizations as projections onto the space of $\pi$-revers

Hien Duy Nguyen

Model selection is a ubiquitous problem that arises in the application of many statistical and machine learning methods. In the likelihood and related settings, it is typical to use the method of information criteria (IC) to choose the most parsimonious among competing models by penalizing the likelihood-based objective function. Theorems guaranteeing the co

Zhifeng Kong, Amrita Roy Chowdhury, Kamalika Chaudhuri

Membership inference (MI) attack is currently the most popular test for measuring privacy leakage in machine learning models. Given a machine learning model, a data point and some auxiliary information, the goal of an MI attack is to determine whether the data point was used to train the model. In this work, we study the reliability of membership inference a

Mengxiao Zhang, Fernando Beltran, Jiamou Liu

A data marketplace is an online venue that brings data owners, data brokers, and data consumers together and facilitates commoditisation of data amongst them. Data pricing, as a key function of a data marketplace, demands quantifying the monetary value of data. A considerable number of studies on data pricing can be found in literature. This paper attempts t

Michael McGuigan

Quantum computing is a promising new area of computing with quantum algorithms offering a potential speedup over classical algorithms if fault tolerant quantum computers can be built. One of the first applications of the classical computer was to the study of the Riemann hypothesis and quantum computers may be applied to this problem as well. In this paper w

Subhrajyoti Bhattacharyya, Rupam Barman, Ajit Singh, Apu Kumar Saha

Andrews and Newman introduced the mex-function $\text{mex}_{A,a}(\lambda)$ for an integer partition $\lambda$ of a positive integer $n$ as the smallest positive integer congruent to $a$ modulo $A$ that is not a part of $\lambda$. They then defined $p_{A,a}(n)$ to be the number of partitions $\lambda$ of $n$ satisfying $\text{mex}_{A,a}(\lambda)\equiv a\pmod{

Jiazhen Liu, Tamang Kunal, Dashun Wang, Chaoming Song

Science progresses by building upon previous discoveries. It is commonly believed that the impact of scientific papers, as measured by citations, is positively correlated with the impact of past discoveries built upon. However, analyzing over 30 million papers and nearly a billion citations across multiple disciplines, we find that there is a long-term posit

Xiaolong Tang, Shuo Ye, Yufeng Shi, Tianheng Hu

Filter pruning has gained widespread adoption for the purpose of compressing and speeding up convolutional neural networks (CNNs). However, existing approaches are still far from practical applications due to biased filter selection and heavy computation cost. This paper introduces a new filter pruning method that selects filters in an interpretable, multi-p

Keino Brown, Olga Kharlampovich

We show that for any finitely generated subgroup $H$ of a limit group $L$ there exists a finite-index subgroup $K$ containing $H$, such that $K$ is a subgroup of a group obtained from $H$ by a series of extensions of centralizers and free products with $\mathbb Z$. If $H$ is non-abelian, the $K$ is fully residually $H$. We also show that for any finitely gen

Chien-Hua Chen

In this paper, we generalize Dorman's work to estimate singular moduli for higher rank Drinfeld modules. In particular, we give a lower bound on the valuation of singular moduli for Drinfeld modules with complex multiplication by an imaginary field extension over the rational function field. Furthermore, we compute several examples for rank-$3$ case.

Haris Aziz, Xinhang Lu, Mashbat Suzuki, Jeremy Vollen

The best-of-both-worlds paradigm advocates an approach that achieves desirable properties both ex-ante and ex-post. We launch a best-of-both-worlds fairness perspective for the important social choice setting of approval-based committee voting. To this end, we initiate work on ex-ante proportional representation properties in this domain and formalize a hier

Hisashi Kasuya, Jonas Stelzig

We give four constructions of non-$\partial\bar\partial$ (hence non-K\"ahler) manifolds: (1) A simply connected page-$1$-$\partial\bar\partial$-manifold (2) A simply connected $dd^c+3$-manifold (3) For any $r\geq 2$, a simply connected compact manifold with nonzero differential on the $r$-th page of the Fr\"olicher spectral sequence. (4) For any $r\geq 2$, a

Navid Hashemi, Bardh Hoxha, Tomoya Yamaguchi, Danil Prokhorov

Signal Temporal Logic (STL) has become a popular tool for expressing formal requirements of Cyber-Physical Systems (CPS). The problem of verifying STL properties of neural network-controlled CPS remains a largely unexplored problem. In this paper, we present a model for the verification of Neural Network (NN) controllers for general STL specifications using

Zhiqiang Zhou, Chaoli Zhang, Lingna Ma, Jing Gu

The existing resource allocation policy for application instances in Kubernetes cannot dynamically adjust according to the requirement of business, which would cause an enormous waste of resources during fluctuations. Moreover, the emergence of new cloud services puts higher resource management requirements. This paper discusses horizontal POD resources mana

Lei Du, Yanhong Bao, Dongxing Fu

Rota-Baxter operators and more generally $\mathcal{O}$-operators play a crucial role in broad areas of mathematics and physics, such as integrable systems, the Yang-Baxter equation and pre-Lie algebras. The main objects of study in the paper are certain $\mathcal{O}$-operator morphisms on associative algebras. The cohomology theory of an associative $\mathca

Emily Kuang, Ehsan Jahangirzadeh Soure, Mingming Fan, Jian Zhao

AI is promising in assisting UX evaluators with analyzing usability tests, but its judgments are typically presented as non-interactive visualizations. Evaluators may have questions about test recordings, but have no way of asking them. Interactive conversational assistants provide a Q&A dynamic that may improve analysis efficiency and evaluator autonomy. To

Lijuan Liu

Solar active regions (ARs) are the main sources of large solar flares and coronal mass ejections. It is found that the ARs producing large eruptions usually show compact, highly-sheared polarity inversion lines (PILs). A scenario named as collisional-shearing is proposed to explain the formation of this type of PILs and the subsequent eruptions, which stress

Bo Han, Xiao Wen

We introduce a new version of expansiveness similar to separating property for flows. Let $M$ be a compact Riemannian manifold without boundary and $X$ be a $C^1$ vector field on $M$ that generates a flow $\varphi_t$ on $M$. We call $X$ {\it rescaling separating} on a compact invariant set $\Lambda$ of $X$ if there is $\delta>0$ such that, for any $x,y\in \L

Yongpeng Jiang, Yongyi Jia, Xiang Li

Non-prehensile manipulation methods usually use a simple end effector, e.g., a single rod, to manipulate the object. Compared to the grasping method, such an end effector is compact and flexible, and hence it can perform tasks in a constrained workspace; As a trade-off, it has relatively few degrees of freedom (DoFs), resulting in an under-actuation problem