July 2022 arXiv papers — page 117

Meiirkhan B. Borikhanov, Michael Ruzhansky, Berikbol T. Torebek

In this paper, we study a critical exponent to the semilinear heat equation with forcing term on Heisenberg group. Our technique of proof is based on methods of nonlinear capacity estimates specifically adapted to the nature of the Heisenberg group. Surprisingly, the critical exponent will be bounded for all dimensions of the Heisenberg group, in contrast to

Mirco Piccinini

We study the obstacle problem related to a wide class of nonlinear integro-differential operators, whose model is the fractional subLaplacian in the Heisenberg group. We prove both the existence and uniqueness of the solution, and that solutions inherit regularity properties of the obstacle such as boundedness, continuity and H\"older continuity up to the bo

Abderrahmane Yakhelef, Serkan Akkoyun

One of the main ingredients in nuclear structure studies using shell model are the single-particle energy (spe). In order to obtain these values accurately, experimental data is needed. The region around the doubly magic nuclide 100Sn is very interesting for nuclear studies in terms of structure, reaction and nuclear astrophysics. Experimental spectrum data

Maria Manfredini, Giampiero Palatucci, Mirco Piccinini, Sergio Polidoro

We extend the celebrate De Giorgi-Nash-Moser theory to a wide class of nonlinear equations driven by nonlocal, possibly degenerate, integro-differential operators, whose model is the fractional $p$-Laplacian operator on the Heisenberg-Weyl group $\mathbb{H}^n$. Amongst other results, we prove that the weak solutions to such a class of problems are bounded an

Giampiero Palatucci, Mirco Piccinini

We deal with a wide class of nonlinear integro-differential problems in the Heisenberg-Weyl group $\mathbb{H}^n$, whose prototype is the Dirichlet problem for the $p$-fractional subLaplace equation. These problems arise in many different contexts in quantum mechanics, in ferromagnetic analysis, in phase transition problems, in image segmentations models, and

S. Sulis, D. Mary, L. Bigot, M. Deleuil

The detection of small exoplanets with the radial velocity (RV) technique is limited by various poorly known noise sources of instrumental and stellar origin. As a consequence, current detection techniques often fail to provide reliable estimates of the significance levels of detection tests (p-values). We designed an RV detection procedure that provides rel

Marta Lagomarsino, Marta Lorenzini, Elena De Momi, Arash Ajoudani

In hybrid industrial environments, workers' comfort and positive perception of safety are essential requirements for successful acceptance and usage of collaborative robots. This paper proposes a novel human-robot interaction framework in which the robot behaviour is adapted online according to the operator's cognitive workload and stress. The method exploit

Chao Yu, Yuhan Cheng

Insider information and model uncertainty are two unavoidable problems for the portfolio selection theory in reality. This paper studies the robust optimal portfolio strategy for an investor who owns general insider information under model uncertainty. On the aspect of the mathematical theory, we improve some properties of the forward integral and use Mallia

Shan Zou, Denis Konstantinov

The image-charge detection provides a new direct method for the detection of the Rydberg transition in electrons trapped on the surface of liquid helium. The interest in this method is motivated by the possibility to accomplish the spin state readout for a single trapped electron, thus opening a new pathway towards using electron spins on liquid helium for q

Zhuoran Xiao, Zhaoyang Zhang, Zirui Chen, Zhaohui Yang

Obtaining accurate channel state information (CSI) is crucial and challenging for multiple-input multiple-output (MIMO) wireless communication systems. Conventional channel estimation method cannot guarantee the accuracy of mobile CSI while requires high signaling overhead. Through exploring the intrinsic correlation among a set of historical CSI instances r

Yaryong Heo, Sunggeum Hong, Chan Woo Yang

We establish a H\"{o}rmander type theorem for the multilinear pseudo-differential operators, which is also a generalization of the results in \cite{MR4322619} to symbols depending on the spatial variable. Most known results for multilinear pseudo-differential operators were obtained by assuming their symbols satisfy pointwise derivative estimates(Mihlin-type

Marco Sobiech

Let $F$ be a field of characteristic $p>0$ and let $\Omega^n(F)$ be the $F$-vector space of $n$-differential forms over $F$. In this work we will study the behaviour of $\Omega^n(F)$ under iterated function field extensions of $p$-forms. We will use results from a previous work to rewrite the kernel of the restriction map $\Omega^n(F) \to \Omega^n(F(\varphi_

Céline Esser, Béatrice Vedel

We provide a multifractal analysis of lacunary wavelet series on Cantor sets. Byintroducing a desynchronization between the scales of the wavelets and the scales of the steps of the construction of the Cantor set, we obtain random processes that donot satisfy multifractal formalisms based on the Legendre transform and on the large deviation of wavelet leader

Magnus Carlson, Hee-Joong Chung, Dohyeong Kim, Minhyong Kim

We prove an arithmetic path integral formula for the inverse p-adic absolute values of the Kubota-Leopoldt p-adic L-functions at roots of unity.

Jin Takahashi, Hikaru Yamamoto

We study the solvability of the initial value problem for the semilinear heat equation $u_t-\Delta u=u^p$ in a Riemannian manifold $M$ with a nonnegative Radon measure $\mu$ on $M$ as initial data. We give sharp conditions on the local-in-time solvability of the problem for complete and connected $M$ with positive injectivity radius and bounded sectional cur

Yan Huang, Ying Sun, Zehan Zhu, Changzhi Yan

We develop a general framework unifying several gradient-based stochastic optimization methods for empirical risk minimization problems both in centralized and distributed scenarios. The framework hinges on the introduction of an augmented graph consisting of nodes modeling the samples and edges modeling both the inter-device communication and intra-device s

Rishi Agarwal, Tirupati Saketh Chandra, Vaidehi Patil, Aniruddha Mahapatra

Applications based on image retrieval require editing and associating in intermediate spaces that are representative of the high-level concepts like objects and their relationships rather than dense, pixel-level representations like RGB images or semantic-label maps. We focus on one such representation, scene graphs, and propose a novel scene expansion task

Yu Liu, He Wang

In this paper, we introduce the Laguerre bounded variation space and the Laguerre perimeter, thereby investigating their properties. Moreover, we prove the isoperimetric inequality and the Sobolev inequality in the Laguerre setting. As applications, we derive the mean curvature for the Laguerre perimeter.

Francesc Bars, Tarun Dalal

We determine all modular curves $X_0^+(N)$ that admit infinitely many cubic points over the rational field $\mathbb{Q}$.

Wen Wang, Shunda Hu, Shiqiang Zhu, Wei Song

A service robot serving safely and politely needs to track the surrounding people robustly, especially for Tour-Guide Robot (TGR). However, existing multi-object tracking (MOT) or multi-person tracking (MPT) methods are not applicable to TGR for the following reasons: 1. lacking relevant large-scale datasets; 2. lacking applicable metrics to evaluate tracker

B. Ansarinejad, D. N. A. Murphy, T. Shanks, N. Metcalfe

Taking advantage of $\sim4700$ deg$^2$ optical coverage of the Southern sky offered by the VST ATLAS survey, we construct a new catalogue of photometrically selected galaxy groups and clusters using the {\sc orca} cluster detection algorithm. The catalogue contains $\sim 22,000$ detections with $N_{200}>10$ and $\sim9,000$ with $N_{200}>20$. We estimate the

Elias Fekhari, Bertrand Iooss, Joseph Muré, Luc Pronzato

Unbiased assessment of the predictivity of models learnt by supervised machine-learning methods requires knowledge of the learned function over a reserved test set (not used by the learning algorithm). The quality of the assessment depends, naturally, on the properties of the test set and on the error statistic used to estimate the prediction error. In this

Liang Liao, Kangmin Xu, Haoning Wu, Chaofeng Chen

With the rapid growth of in-the-wild videos taken by non-specialists, blind video quality assessment (VQA) has become a challenging and demanding problem. Although lots of efforts have been made to solve this problem, it remains unclear how the human visual system (HVS) relates to the temporal quality of videos. Meanwhile, recent work has found that the fram

Fengmei Jin, Wen Hua, Boyu Ruan, Xiaofang Zhou

With the popularity of GPS-enabled devices, a huge amount of trajectory data has been continuously collected and a variety of location-based services have been developed that greatly benefit our daily life. However, the released trajectories also bring severe concern about personal privacy, and several recent studies have demonstrated the existence of person

Oktay K Pashaev

Geometric relations between separable and entangled two-qubit and two-qutrit quantum information states are studied. To characterize entanglement of two qubit states, we establish a relation between reduced density matrix and the concurrence. For the rebit states, the geometrical meaning of concurrence as double area of a parallelogram is found and for gener

Michael G. Adam, Martin Piccolrovazzi, Sebastian Eger, Eckehard Steinbach

The most popular evaluation metric for object detection in 2D images is Intersection over Union (IoU). Existing implementations of the IoU metric for 3D object detection usually neglect one or more degrees of freedom. In this paper, we first derive the analytic solution for three dimensional bounding boxes. As a second contribution, a closed-form solution of

Jian Wang, Jianliang Zhai, Jiahui Zhu

In this paper, we establish the existence and uniqueness of solutions of stochastic nonlinear Schr\"{o}dinger equations with additive jump noise in $L^2(\mathbb{R}^d)$. Our results cover all either focusing or defocusing nonlinearity in the full subcritical range of exponents as in the deterministic case.

Azusa Sawada, Taiki Miyagawa, Akinori F. Ebihara, Shoji Yachida

Time series data are often obtained only within a limited time range due to interruptions during observation process. To classify such partial time series, we need to account for 1) the variable-length data drawn from 2) different timestamps. To address the first problem, existing convolutional neural networks use global pooling after convolutional layers to

Yuto Yokoyama, Benjamin R. Mitchell, Ali Nassiri, Brad L. Kinsey

Integrated photoelasticity is investigated for a soft material subjected to a three-dimensional stress state. In the experiment, a solid sphere is pressed against a gelatin gel (Young's modulus is about 4.2 kPa) that deforms up to 4.5 mm depending on the loading forces. The resulting photoelastic parameters (phase retardation, azimuthal angle, and stress-opt

Tijs W. Alleman, Michiel Rollier, Jenna Vergeynst, Jan M. Baetens

In this work, we extend our previously developed compartmental SEIQRD model for SARS-CoV-2 in Belgium. We introduce SARS-CoV-2 variants of concern, vaccines, and seasonality in our model, as their addition has proven necessary for modelling SARS-CoV-2 transmission dynamics during the 2020-2021 COVID-19 pandemic in Belgium. The model is geographically stratif

Austin Costley, Greg Droge, Randall Christensen, Robert C. Leishman

Mission planners for aircraft operating under threat of detection by ground-based radar systems are concerned with the probability of detection. Current path planning methods for such scenarios consider the aircraft pose, radar position, and radar parameters to be deterministic and known. This paper presents a framework for incorporating uncertainty in these

Michael Kunzinger, Michael Oberguggenberger, James A. Vickers

We compare two standard approaches to defining lower Ricci curvature bounds for Riemannian metrics of regularity below $C^2$. These are, on the one hand, the synthetic definition via weak displacement convexity of entropy functionals in the framework of optimal transport, and the distributional one based on non-negativity of the Ricci-tensor in the sense of

Yucheng Suo, Zhedong Zheng, Xiaohan Wang, Bang Zhang

Sign language is the window for people differently-abled to express their feelings as well as emotions. However, it remains challenging for people to learn sign language in a short time. To address this real-world challenge, in this work, we study the motion transfer system, which can transfer the user photo to the sign language video of specific words. In p

Pavel Exner, Jiří Lipovský

Using a technique introduced by Sergei Naboko, we analyze a generalization of the parameter-controlled model of spectral transition, originally proposed by Smilansky and Solomyak, to the situation where the singular interaction responsible for the effect is characterized by the `full' family of four real numbers with the `diagonal' part of the coupling being

Heikki Mäntysaari, Farid Salazar, Björn Schenke

We show that when saturation effects are included one obtains a good description of the exclusive $\mathrm{J}/\psi$ production spectra in ultra peripheral lead-lead collisions as recently measured by the ALICE Collaboration at the LHC. As exclusive spectra are sensitive to the spatial distribution of nuclear matter at small Bjorken-$x$, this implies that glu

Honghui Shang, Li Shen, Yi Fan, Zhiqian Xu

Quantum computational chemistry (QCC) is the use of quantum computers to solve problems in computational quantum chemistry. We develop a high performance variational quantum eigensolver (VQE) simulator for simulating quantum computational chemistry problems on a new Sunway supercomputer. The major innovations include: (1) a Matrix Product State (MPS) based V

Francesca Biagini, Georg Bollweg, Katharina Oberpriller

We present a probabilistic construction of $\mathbb{R}^d$-valued non-linear affine processes with jumps. Given a set $\Theta$ of affine parameters, we define a family of sublinear expectations on the Skorokhod space under which the canonical process $X$ is a (sublinear) Markov process with a non-linear generator. This yields a tractable model for Knightian u

Oktay K Pashaev

A new relativistic integrable nonlinear model for real, Majorana type spinor fields in 1+1 dimensions, gauge equivalent to Papanicolau spin model, defined on the one sheet hyperboloid is introduced. By using the double numbers, the model is represented as hyperbolic complex valued relativistic massive Thirring type model. By Hirota's bilinear method, an exac

Xiaojiang Peng, Xiaomao Fan, Qingyang Wu, Jieyan Zhao

Automatic smoky vehicle detection in videos is a superior solution to the traditional expensive remote sensing one with ultraviolet-infrared light devices for environmental protection agencies. However, it is challenging to distinguish vehicle smoke from shadow and wet regions coming from rear vehicle or clutter roads, and could be worse due to limited annot

Ryan J. Turner, Chris Blake, Rossana Ruggeri

Galaxy peculiar velocities provide an integral source of cosmological information that can be harnessed to measure the growth rate of large scale structure and constrain possible extensions to General Relativity. In this work, we present a method for extracting the information contained within galaxy peculiar velocities through an ensemble of direct peculiar

Vittunyuta Maeprasart, Supatsara Wattanakriengkrai, Raula Gaikovina Kula, Christoph Treude

The risk to using third-party libraries in a software application is that much needed maintenance is solely carried out by library maintainers. These libraries may rely on a core team of maintainers (who might be a single maintainer that is unpaid and overworked) to serve a massive client user-base. On the other hand, being open source has the benefit of rec

Harald Garcke, Kei Fong Lam, Robert Nürnberg, Andrea Signori

This work concerns a structural topology optimisation problem for 4D printing based on the phase field approach. The concept of 4D printing as a targeted evolution of 3D printed structures can be realised in a two-step process. One first fabricates a 3D object with multi-material active composites and apply external loads in the programming stage. Then, a ch

Kentaro Nishida, Koki Sasai, Rongyang Xu, Te-Hsin Yen

In this paper, we theoretically and experimentally demonstrated photothermally-induced nonlinearities of both forward and backward scattering intensities from quasi-perfect absorbing silicon-based metasurface. The metasurface is efficiently heated up by photothermal effect under laser irradiation, which in turn modulates the scattering spectra via thermo-opt

Akio Kodaira, Yiyang Zhou, Pengwei Zang, Wei Zhan

With information from multiple input modalities, sensor fusion-based algorithms usually out-perform their single-modality counterparts in robotics. Camera and LIDAR, with complementary semantic and depth information, are the typical choices for detection tasks in complicated driving environments. For most camera-LIDAR fusion algorithms, however, the calibrat

Jingping Dong, Chuhan Wang, Xinlei Zhao, Miao Gao

We propose two novel two-dimensional topological Dirac materials, planar PtN4C2 and Pt2N8C6, which exhibit graphene-like electronic structures with linearly dispersive Dirac-cone states exactly at the Fermi level. Moreover, the Dirac cone is anisotropic, resulting in anisotropic Fermi velocities and making it possible to realize orientation-dependent quantum

Mashkoor Ali, Ankik Kumar Giri

This paper presents the existence of global solutions to the discrete Safronov-Dubvoski\v{i} coagulation equations for a large class of coagulation kernels satisfying $\Lambda_{i,j} = \theta_i \theta_j + \kappa_{i,j}$ with $\kappa_{i,j} \leq A\theta_i \theta_j, \ \ \forall \ \ i,j\ge 1$ where the sequence $(\theta_i)_{i\geq 1}$ grows linearly or superlinearl

Fei Qi

We study the extensions of two left modules $W_1, W_2$ for a meromorphic open-string vertex algebra $V$. We show that the extensions satisfying some technical but natural convergence conditions are in bijective correspondence to the first cohomology classes associated to the $V$-bimodule $\mathcal{H}_N(W_1, W_2)$ constructed in \cite{HQ-Red}. When $V$ is gra

Ren Guan

In this paper we consider the Vafa-Witten equations on closed, oriented and smooth 4-manifolds, and construct a set of perturbation terms to establish the transversality of the perturbed Vafa-Witten equations at the general part of the solutions. Then we show that for a generic choice of the perturbation terms, this part of the moduli space for the structure

Ran Liu, Zhongyuan Deng, Zhiqiang Cao, Muhammad Shalihan

To accomplish task efficiently in a multiple robots system, a problem that has to be addressed is Simultaneous Localization and Mapping (SLAM). LiDAR (Light Detection and Ranging) has been used for many SLAM solutions due to its superb accuracy, but its performance degrades in featureless environments, like tunnels or long corridors. Centralized SLAM solves

Minguk Jang, Sae-Young Chung

We propose Few-Example Clustering (FEC), a novel algorithm that performs contrastive learning to cluster few examples. Our method is composed of the following three steps: (1) generation of candidate cluster assignments, (2) contrastive learning for each cluster assignment, and (3) selection of the best candidate. Based on the hypothesis that the contrastive

Yifu Chen, Christos T. Maravelias, Xiaomin Zhang

Discretization-based methods have been proposed for solving nonconvex optimization problems with bilinear terms such as the pooling problem. These methods convert the original nonconvex optimization problems into mixed-integer linear programs (MILPs). In this paper we study tightening methods for these MILP models for the pooling problem, and derive valid co

Di Zhu, Ewan Pritchard, Sumanth Reddy Dadam, Vivek Kumar

Reducing energy consumption is a key focus for hybrid electric vehicle (HEV) development. The popular vehicle dynamic model used in many energy management optimization studies does not capture the vehicle dynamics that the in-vehicle measurement system does. However, feedback from the measurement system is what the vehicle controller actually uses to manage

William Murphy

Let $G$ be a finite simple group and $k$ be an algebraically closed field of prime characteristic dividing the order of $G$. We show that for all $2$-cocycles $\alpha \in Z^2(G;k^\times)$, the first Hochschild cohomology group of the twisted group algebra $HH^1(k_\alpha G)$ is nonzero.

Wen Chin Huang, Dejan Markovic, Alexander Richard, Israel Dejene Gebru

In this work, we present an end-to-end binaural speech synthesis system that combines a low-bitrate audio codec with a powerful binaural decoder that is capable of accurate speech binauralization while faithfully reconstructing environmental factors like ambient noise or reverb. The network is a modified vector-quantized variational autoencoder, trained with

M. H. A. Biswas, H. G. Feichtinger, R. Ramakrishnan

We study some fundamental properties of the special affine Fourier transform (SAFT) in connection with the Fourier analysis and time-frequency analysis. We introduce the modulation space $\boldsymbol {M}^{r,s}_A$ in connection with SAFT and prove that if a bounded linear operator between new modulation spaces commutes with $A$-translation, then it is a $A$-c

Zhiwei Chen, Ramotholo Sefako, Yang Yang, Zhibo Jiang

We report on the near-infrared polarimetric observations of G11.11-0.12 (hereafter G11) obtained with SIRPOL on the 1.4 m IRSF telescope. The starlight polarisation of the background stars reveals the on-sky component of magnetic fields in G11, and these are consistent with the field orientation observed from polarised dust emission at $850\,\mu$m. The magne

Kasun Weerakoon, Souradip Chakraborty, Nare Karapetyan, Adarsh Jagan Sathyamoorthy

We present a novel approach to improve the performance of deep reinforcement learning (DRL) based outdoor robot navigation systems. Most, existing DRL methods are based on carefully designed dense reward functions that learn the efficient behavior in an environment. We circumvent this issue by working only with sparse rewards (which are easy to design), and

Md. Sadman Sakib, David Paulius, Yu Sun

Flexible task planning continues to pose a difficult challenge for robots, where a robot is unable to creatively adapt their task plans to new or unseen problems, which is mainly due to the limited knowledge it has about its actions and world. Motivated by a human's ability to adapt, we explore how task plans from a knowledge graph, known as the Functional O

Jianing Qiu, Frank P. -W. Lo, Yingnan Sun, Siyao Wang

Automatic food recognition is the very first step towards passive dietary monitoring. In this paper, we address the problem of food recognition by mining discriminative food regions. Taking inspiration from Adversarial Erasing, a strategy that progressively discovers discriminative object regions for weakly supervised semantic segmentation, we propose a nove

Peihao Wang, Zhiwen Fan, Tianlong Chen, Zhangyang Wang

Representing visual signals by coordinate-based deep fully-connected networks has been shown advantageous in fitting complex details and solving inverse problems than discrete grid-based representation. However, acquiring such a continuous Implicit Neural Representation (INR) requires tedious per-scene training on tons of signal measurements, which limits it

Rakshit P. Vyas, Mihir J. Joshi

In this paper, we propose a new perspective of quantum spin (angular momentum) in which the Boltzmann constant \(k_{\beta}\), Planck temperature \(T_{P}\), Planck mass \(m_{P}\) and Planck area \(l_{P}^{2}\) are the integral part of the total angular momentum \(J\). With the aid of this new perspective, we modify the equation of the area and volume operator.

Minguk Jang, Sae-Young Chung, Hye Won Chung

Test-time adaptation (TTA) aims to adapt a trained classifier using online unlabeled test data only, without any information related to the training procedure. Most existing TTA methods adapt the trained classifier using the classifier's prediction on the test data as pseudo-label. However, under test-time domain shift, accuracy of the pseudo labels cannot b

Francisco Durán López, Silverio Martínez-Fernández, Michael Felderer, Xavier Franch

Background: When using deep learning models, there are many possible vulnerabilities and some of the most worrying are the adversarial inputs, which can cause wrong decisions with minor perturbations. Therefore, it becomes necessary to retrain these models against adversarial inputs, as part of the software testing process addressing the vulnerability to the

Maaz Bin Musa, Rishab Nithyanand

Data sharing between online trackers and advertisers is a key component in online behavioral advertising. This sharing can be facilitated through a variety of processes, including those not observable to the user's browser. The unobservability of these processes limits the ability of researchers and auditors seeking to verify compliance with regulations whic

Venus Haghighi, Behnaz Soltani, Adnan Mahmood, Quan Z. Sheng

Anomaly detection in attributed networks has received a considerable attention in recent years due to its applications in a wide range of domains such as finance, network security, and medicine. Traditional approaches cannot be adopted on attributed networks' settings to solve the problem of anomaly detection. The main limitation of such approaches is that t

Pengcheng Xu, Yunfeng Lu

Efficient and accurate remaining useful life prediction is a key factor for reliable and safe usage of lithium-ion batteries. This work trains a long short-term memory recurrent neural network model to learn from sequential data of discharge capacities at various cycles and voltages and to work as a cycle life predictor for battery cells cycled under differe

Xiemin Mo, Tao Liu, Xue Lyu

This paper focuses on the analytical target cascading (ATC) based scenario decomposition method which applies to the stochastic OPF problem of distribution networks with high photovoltaic penetration. The original two-stage stochastic OPF model is decomposed into a master problem in the upper level and multiple subproblems in the lower level. This decomposit

Shashank Kanade

Let $p<p'$ be a pair of coprime positive integers. In this note, generalizing Morton's work in the case of $\mathfrak{sl}_2$, we give a formula for the $\mathfrak{sl}_r$ Jones invariants of torus knots $T(p,p')$ coloured with the finite-dimensional irreducible representations $L_r(n\Lambda_1)$. When $r \leq p$, we show that appropriate limits of the shifted

Jun Lu, Minhui Wu

In this note, we introduce how to use Volatility Index (VIX) for postprocessing quantitative strategies so as to increase the Sharpe ratio and reduce trading risks. The signal from this procedure is an indicator of trading or not on a daily basis. Finally, we analyze this procedure on SH510300 and SH510050 assets. The strategies are evaluated by measurements

Wei Feng, Lin Wang, Lie Ju, Xin Zhao

Existing unsupervised domain adaptation methods based on adversarial learning have achieved good performance in several medical imaging tasks. However, these methods focus only on global distribution adaptation and ignore distribution constraints at the category level, which would lead to sub-optimal adaptation performance. This paper presents an unsupervise

Praise Adeyemo

A remarkable connection between the cohomology ring ${\rm H^{\ast}(Gr}(d, d+r),\Z)$ of the Grasssmannian ${\rm Gr}(d,d+r)$ and the lattice points of the dilation $r\Delta_{d}$ of the standard d-simplex is investigated. The natural grading on the cohomology induces different gradings of the lattice points of $r\Delta_{d}$. This leads to different refinements

Junfu Pu, Ying Shan

In this paper, we propose a novel framework for music-driven dance motion synthesis with controllable key pose constraint. In contrast to methods that generate dance motion sequences only based on music without any other controllable conditions, this work targets on synthesizing high-quality dance motion driven by music as well as customized poses performed

Behnaz Soltani, Venus Haghighi, Adnan Mahmood, Quan Z. Sheng

Federated Learning (FL) is an efficient distributed machine learning paradigm that employs private datasets in a privacy-preserving manner. The main challenges of FL is that end devices usually possess various computation and communication capabilities and their training data are not independent and identically distributed (non-IID). Due to limited communica

Minhao Zhang, Ruoyu Zhang, Yanzeng Li, Lei Zou

Semantic parsing solves knowledge base (KB) question answering (KBQA) by composing a KB query, which generally involves node extraction (NE) and graph composition (GC) to detect and connect related nodes in a query. Despite the strong causal effects between NE and GC, previous works fail to directly model such causalities in their pipeline, hindering the lea

Ziheng Zeng, Suma Bhat

Idiomatic expressions (IEs), characterized by their non-compositionality, are an important part of natural language. They have been a classical challenge to NLP, including pre-trained language models that drive today's state-of-the-art. Prior work has identified deficiencies in their contextualized representation stemming from the underlying compositional pa

Haoran Zhang, Junhui Wang

Signed networks are frequently observed in real life with additional sign information associated with each edge, yet such information has been largely ignored in existing network models. This paper develops a unified embedding model for signed networks to disentangle the intertwined balance structure and anomaly effect, which can greatly facilitate the downs

Alejandro Parada-Mayorga, Zhiyang Wang, Fernando Gama, Alejandro Ribeiro

In this paper we study the stability properties of aggregation graph neural networks (Agg-GNNs) considering perturbations of the underlying graph. An Agg-GNN is a hybrid architecture where information is defined on the nodes of a graph, but it is processed block-wise by Euclidean CNNs on the nodes after several diffusions on the graph shift operator. We deri

Haoran You, Baopu Li, Zhanyi Sun, Xu Ouyang

Neural architecture search (NAS) has demonstrated amazing success in searching for efficient deep neural networks (DNNs) from a given supernet. In parallel, the lottery ticket hypothesis has shown that DNNs contain small subnetworks that can be trained from scratch to achieve a comparable or higher accuracy than original DNNs. As such, it is currently a comm

Thomas H. Doherty, Axel Kuhn, Ezra Kassa

We report the realisation of a high-finesse open-access cavity array, tailored towards the creation of multiple coherent light-matter interfaces within a compact environment. We describe the key technical developments put in place to fabricate such a system, comprising the creation of tapered pyramidal substrates and an in-house laser machining setup. Caviti

B. X. Wang, C. Y. Zhao

Topological phonon polaritons (TPhPs) are promising optical modes relevant in long-range radiative heat transfer, information processing and infrared sensing, whose topological protection is expected to enable their robust existence and transport. In this work we show that TPhPs can be supported in one-dimensional (1D) bichromatic silicon carbide nanoparticl

Jianwei Zhang, Lei Zhang, Junyou Wang, Xin Wei

Acne detection is crucial for interpretative diagnosis and precise treatment of skin disease. The arbitrary boundary and small size of acne lesions lead to a significant number of poor-quality proposals in two-stage detection. In this paper, we propose a novel head structure for Region Proposal Network to improve the proposals' quality in two ways. At first,

Chenran Li, Tu Trinh, Letian Wang, Changliu Liu

Planning under social interactions with other agents is an essential problem for autonomous driving. As the actions of the autonomous vehicle in the interactions affect and are also affected by other agents, autonomous vehicles need to efficiently infer the reaction of the other agents. Most existing approaches formulate the problem as a generalized Nash equ

Zhaojia Huang, Liang Zhang, Tianhao Zhi

There is a rapid development and commercialization of new Energy Vehicles (NEV) in recent years. Although traditional fuel vehicles (TFV) still occupy a majority share of the market, it is generally believed that NEV is more efficient, more environmental friendly, and has a greater potential of a Schumpeterian "creative destruction" that may lead to a paradi

Shenghao Jin, Ming Xiao, Wenbin Zhang, Boxiang Wang

Daytime radiative cooling is a promising passive cooling technology for combating global warming. Existing daytime radiative coolers usually show whitish colors due to their broadband high solar reflectivity, which severely impedes applications in real-life situations with aesthetic demands and effective display. However, there is a trade-off between vivid c

Nic Ezzell, Bibek Pokharel, Lina Tewala, Gregory Quiroz

Dynamical Decoupling (DD) is perhaps the simplest and least resource-intensive error suppression strategy for improving quantum computer performance. Here we report on a large-scale survey of the performance of 60 different DD sequences from 10 families, including basic as well as advanced sequences with high order error cancellation properties and built-in

Hyuntai Kim

In this paper, a mode decomposition (MD) method for degenerated modes has been studied. Convolution neural network (CNN) has been applied for image training and predicting the mode coefficients. Four-fold degenerated $LP_{11}$ series has been the target to be decomposed. Multiple images are regarded as an input to decompose the degenerate modes. Total of sev

Jingwei Li

In this paper, we study two problems: determining action model equivalence and minimizing the event space of an action model under certain structural relationships. The Kripke model equivalence is perfectly caught by the structural relationship called bisimulation. In this paper, we propose the generalized action emulation perfectly catching the action model

Lupei Qin, Luting Xu, Xin-Qi Li

We present a theoretical analysis for the metrology quality of joint weak measurements (JWM), in close comparison with the weak-value-amplification (WVA) technique. We point out that the difference probability function employed in the JWM scheme cannot be used to calculate the uncertainty variance and Fisher information (FI). In order to carry out the metrol

Jip Kim, Siddharth Bhela, James Anderson, Gil Zussman

The urgent need for the decarbonization of power girds has accelerated the integration of renewable energy. Concurrently the increasing distributed energy resources (DER) and advanced metering infrastructures (AMI) have transformed the power grids into a more sophisticated cyber-physical system with numerous communication devices. While these transitions pro

Jiaxin Ai, Zhongyuan Wang, Baojin Huang, Zhen Han

Deepfake face not only violates the privacy of personal identity, but also confuses the public and causes huge social harm. The current deepfake detection only stays at the level of distinguishing true and false, and cannot trace the original genuine face corresponding to the fake face, that is, it does not have the ability to trace the source of evidence. T

William Savoie, Harry Tuazon, M. Saad Bhamla, Daniel I. Goldman

The design of amorphous entangled systems, specifically from soft and active materials, has the potential to open exciting new classes of active, shape-shifting, and task-capable 'smart' materials. However, the global emergent mechanics that arises from the local interactions of individual particles are not well understood. In this study, we examine the emer

Natsuho Maeda, Keiji Ohtsuki, Takayuki Tanigawa, Masahiro N. Machida

Gas accretion onto the circumplanetary disks and the source region of accreting gas are important to reveal dust accretion that leads to satellite formation around giant planets. We performed local three-dimensional high-resolution hydrodynamic simulations of isothermal and inviscid gas flow around a planet to investigate planetary-mass dependence of gas acc

Hideto Asashiba, Emerson G. Escolar, Ken Nakashima, Michio Yoshiwaki

In topological data analysis, two-parameter persistence can be studied using the representation theory of the 2d commutative grid, the tensor product of two Dynkin quivers of type A. In a previous work, we defined interval approximations using restrictions to essential vertices of intervals together with Mobius inversion. In this work, we consider homologica

Qi Heng Ho, Roland B. Ilyes, Zachary N. Sunberg, Morteza Lahijanian

This paper presents an algorithmic framework for control synthesis of continuous dynamical systems subject to signal temporal logic (STL) specifications. We propose a novel algorithm to obtain a time-partitioned finite automaton from an STL specification, and introduce a multi-layered framework that utilizes this automaton to guide a sampling-based search tr

Jiyou Li, Sicheng Zhao

In a recent paper, Andrews and Merca investigated the number of even parts in all partitions of $n$ into distinct parts, which arise naturally from the Euler-Glaisher bijective proof. They obtained new combinatorial interpretations for this number by using generating functions. We obtain a new direct combinatorial proof in this note.

Xiaosheng Lin, Dachun Yang, Sibei Yang, Wen Yuan

Let $L$ be a homogeneous divergence form higher order elliptic operator with complex bounded measurable coefficients on $\mathbb{R}^n$ and $X$ a ball quasi-Banach function space on $\mathbb{R}^n$ satisfying some mild assumptions. Denote by $H_{X,\, L}(\mathbb{R}^n)$ the Hardy space, associated with both $L$ and $X$, which is defined via the Lusin area functi

D. Osin

Let $\mathcal G_n$ denote the space of $n$-generated marked groups. We prove that, for every $n\ge 2$, there exist $2^{\aleph_0}$ non-atomic, $Out(F_n)$-invariant, mixing probability measures on $\mathcal G_n$. On the other hand, there are non-empty closed subsets of $\mathcal G_n$ that admit no $Out(F_n)$-invariant probability measure. Acylindrical hyperbol

Aparajita Dasgupta, Santosh Kumar Nayak

In this paper, we study harmonic analysis on the affine Poincar\'e group $\mathcal{P}_{aff}$, which is a non-unimodular group, and obtain pseudo-differential operators with operator valued symbols. More precisely, we study the boundedness properties of pseudo-differential operators on $\mathcal{P}_{aff}$. We also provide a necessary and sufficient condition

Keisuke Uchimura

Chebyshev polynomials in one variable are typical chaotic maps on the complex 1-space. Chebyshev endomorphisms f on the complex n-space A are also chaotic. The endomorphisms f induce mappings on the quotient space A/G, where G is the dihedral group of order 2(n+1). Using invariant theory we embed A/G as an affine subvariety X in the complex m-space. Then we

Hoang-Anh Pham, Thao Minh Le, Vuong Le, Tu Minh Phuong

It would be a technological feat to be able to create a system that can hold a meaningful conversation with humans about what they watch. A setup toward that goal is presented as a video dialog task, where the system is asked to generate natural utterances in response to a question in an ongoing dialog. The task poses great visual, linguistic, and reasoning