July 2022 arXiv papers — page 33

Jie Wang, Xiao-Lei Zhang

Unsupervised domain adaptation (UDA) transfers knowledge from a label-rich source domain to a different but related fully-unlabeled target domain. To address the problem of domain shift, more and more UDA methods adopt pseudo labels of the target samples to improve the generalization ability on the target domain. However, inaccurate pseudo labels of the targ

Keehang Kwon

Inspired by computability logic\cite{Jap03}, we refine recursive function definitions into two kinds: blindly-quantified (BQ) ones and parallel universally quantified (PUQ) ones. BQ definitions corresponds to the traditional ones where recursive definitions are $not$ evolving. PUQ definitions are {\it evolving} in the course of computation, leading to automa

Rosa Donat, Sergio López Ureña

General purpose optimization techniques can be used to solve many problems in engineering computations, although their cost is often prohibitive when the number of degrees of freedom is very large. We describe a multilevel approach to speed up the computation of the solution of a large-scale optimization problem by a given optimization technique. By embeddin

Navin Raj Prabhu, Nale Lehmann-Willenbrock, Timo Gerkmann

As different people perceive others' emotional expressions differently, their annotation in terms of arousal and valence are per se subjective. To address this, these emotion annotations are typically collected by multiple annotators and averaged across annotators in order to obtain labels for arousal and valence. However, besides the average, also the uncer

Mikkel Have Eriksen, Jakob E. Olsen, Christian Wolff, Joel D. Cox

We explore the emergence and active control of optical bistability in a two-level atom near a graphene sheet. Our theory incorporates self-interaction of the optically-driven atom and its coupling to electromagnetic vacuum modes, both of which are sensitive to the electrically-tunable interband transition threshold in graphene. We show that electro-optical b

Suraj Prasad, Neelkamal Mallick, Sushanta Tripathy, Raghunath Sahoo

Anisotropic flow is accredited to have effects from the initial state geometry and fluctuations in the nuclear overlap region. The elliptic flow ($v_2$) and triangular flow ($v_3$) coefficients of the final state particles are expected to have influenced by eccentricity ($\varepsilon_2$) and triangularity ($\varepsilon_3$) of the participants, respectively.

Lucian Cristian Iacob, Roland Tóth, Maarten Schoukens

The Koopman framework proposes a linear representation of finite-dimensional nonlinear systems through a generally infinite-dimensional globally linear embedding. Originally, the Koopman formalism has been derived for autonomous systems. In applications for systems with inputs, generally a linear time invariant (LTI) form of the Koopman model is assumed, as

Camila Paiva Novaes, Jiajun Zhang, Eduardo J. de Mericia, Filipe B. Abdalla

A new and promising technique for observing the Universe and study the dark sector is the intensity mapping of the redshifted 21cm line of neutral hydrogen (HI). The BINGO radio telescope will use the 21cm line to map the Universe in the redshift range $0.127 \le z \le 0.449$, in a tomographic approach, with the main goal of probing BAO. This work presents t

Xiaoting Feng, Hengying Zhang, Cunfeng Feng, Lingling Ma

LHAASO KM2A consists of 5915 scintillation detectors and 1188 muon detectors, and the muon detectors cover 4% area of the whole array with 30 m spacing. The muon number of air shower events, with very high energy, is investigated with the data recorded by muon detector of the 3/4 LHAASO array in 2021. The attenuation length of muon number in the air shower i

M. Kögl, P. Soubelet, M. Brotons-Gisbert, A. V. Stier

Large scale two-dimensional (2D) moir\'e superlattices are driving a revolution in designer quantum materials. The electronic interactions in these superlattices, strongly dependent on the periodicity and symmetry of the moir\'e pattern, critically determine the emergent properties and phase diagrams. To date, the relative twist angle between two layers has

Nguyen Quang Hieu, Diep N. Nguyen, Dinh Thai Hoang, Eryk Dutkiewicz

Rate Splitting Multiple Access (RSMA) has emerged as an effective interference management scheme for applications that require high data rates. Although RSMA has shown advantages in rate enhancement and spectral efficiency, it has yet not to be ready for latency-sensitive applications such as virtual reality streaming, which is an essential building block of

Huy V. Vo, Oriane Siméoni, Spyros Gidaris, Andrei Bursuc

Object detectors trained with weak annotations are affordable alternatives to fully-supervised counterparts. However, there is still a significant performance gap between them. We propose to narrow this gap by fine-tuning a base pre-trained weakly-supervised detector with a few fully-annotated samples automatically selected from the training set using ``box-

Yuhao Yi, Yuan Wang, Xingkang He, Stacy Patterson

One of the intensely studied concepts of network robustness is $r$-robustness, which is a network topology property quantified by an integer $r$. It is required by mean subsequence reduced (MSR) algorithms and their variants to achieve resilient consensus. However, determining $r$-robustness is intractable for large networks. In this paper, we propose a samp

José Niño-Mora

This paper considers a Markovian model for the optimal dynamic routing of homogeneous traffic to parallel heterogeneous queues, each having its own finite input buffer and server pool, where buffer and server-pool sizes, as well as service rates, may differ across queues. The main goal is to identify a heuristic index-based routing policy with low complexity

Mengyu Cheng, Zimo Hao, Michael Röckner

In this paper, we study the averaging principle for distribution dependent stochastic differential equations with drift in localized $L^p$ spaces. Using Zvonkin's transformation and estimates for solutions to Kolmogorov equations, we prove that the solutions of the original system strongly and weakly converge to the solution of the averaged system as the tim

David Greenblatt, Hanns Müller-Vahl, Christoph Strangfeld

The effect of high-amplitude harmonic surging on airfoil laminar separation bubbles was investigated theoretically, and experimentally in a dedicated surging-flow wind tunnel. A generalized pressure coefficient was developed that accounts for local static pressure variations due to surging. This generalization, never previously implemented, facilitated direc

Dang Nguyen, Sunil Gupta, Kien Do, Svetha Venkatesh

Knowledge distillation (KD) is an efficient approach to transfer the knowledge from a large "teacher" network to a smaller "student" network. Traditional KD methods require lots of labeled training samples and a white-box teacher (parameters are accessible) to train a good student. However, these resources are not always available in real-world applications.

Hongbo Bo, Ryan McConville, Jun Hong, Weiru Liu

Online social network platforms have a problem with misinformation. One popular way of addressing this problem is via the use of machine learning based automated misinformation detection systems to classify if a post is misinformation. Instead of post hoc detection, we propose to predict if a user will engage with misinformation in advance and design an effe

Zitong Huang, Yiping Bao, Bowen Dong, Erjin Zhou

Weakly-supervised object detection (WSOD) aims to train an object detector only requiring the image-level annotations. Recently, some works have managed to select the accurate boxes generated from a well-trained WSOD network to supervise a semi-supervised detection framework for better performance. However, these approaches simply divide the training set int

Matheus H. Martins Costa, Jeroen van den Brink, Flavio S. Nogueira, Gastão Krein

The entanglement between momentum modes of a quantum field theory at different scales is not as well studied as its counterpart in real space, despite the natural connection with the Wilsonian idea of integrating out the high-momentum degrees of freedom. Here, we push such connection further by developing a novel method to calculate the R\'enyi and entanglem

Kazuo Muroi

This article discusses the reasons for the choice of the sexagesimal system by ancient Sumerians. It is shown that Sumerians chose this specific numeral system based on logical and practical reasons which enabled them to deal with big numbers easily and even perform the multiplications and divisions in this system. I shall also discuss how the Sumerians calc

Pietro Bongini, Federico Becattini, Alberto Del Bimbo

The use of Deep Learning and Computer Vision in the Cultural Heritage domain is becoming highly relevant in the last few years with lots of applications about audio smart guides, interactive museums and augmented reality. All these technologies require lots of data to work effectively and be useful for the user. In the context of artworks, such data is annot

Yunsheng Pang, Qiuhong Ke, Hossein Rahmani, James Bailey

Human interaction recognition is very important in many applications. One crucial cue in recognizing an interaction is the interactive body parts. In this work, we propose a novel Interaction Graph Transformer (IGFormer) network for skeleton-based interaction recognition via modeling the interactive body parts as graphs. More specifically, the proposed IGFor

Soumya Chatterjee, Sumana Ghosh, D. Mitra

Multiple ionisation and electron capture are found to be vital mechanisms for K and L x-ray emissions along with the direct coulomb ionisation in heavy ion-atom collisions. Naturally, these two mechanisms may also be significant for M x-ray emissions also. However, these mechanisms associating with the M-shell are highly complex and not yet studied convincin

Yusuke Kimura, Hidetoshi Nishimori

We derive a generic bound on the rate of decrease of transverse field for quantum annealing to converge to the ground state of a generic Ising model when quantum annealing is formulated as an infinite-time process. Our theorem is based on a rigorous upper bound on the excitation probability in the infinite-time limit and is a mathematically rigorous counterp

Prajwal Padmanabha, Daniel Maria Busiello, Amos Maritan, Deepak Gupta

Out-of-equilibrium systems continuously generate entropy, with its rate of production being a fingerprint of non-equilibrium conditions. In small-scale dissipative systems subject to thermal noise, fluctuations of entropy production are significant. Hitherto, mean and variance have been abundantly studied, even if higher moments might be important to fully c

J. Kariuki Chege, C. H. Jordan, C. Lynch, C. M. Trott

The redshifted cosmological 21 cm signal emitted by neutral hydrogen during the first billion years of the universe is much fainter relative to other galactic and extragalactic radio emissions, posing a great challenge towards detection of the signal. Therefore, precise instrumental calibration is a vital prerequisite for the success of radio interferometers

Azhar Mohammed, Dang Nguyen, Bao Duong, Thin Nguyen

Data augmentation is one of the most successful techniques to improve the classification accuracy of machine learning models in computer vision. However, applying data augmentation to tabular data is a challenging problem since it is hard to generate synthetic samples with labels. In this paper, we propose an efficient classifier with a novel data augmentati

Tianying Wang, Iuliana Ionita-Laza, Ying Wei

Transcriptome-wide association studies (TWAS) are powerful tools for identifying gene-level associations by integrating genome-wide association studies and gene expression data. However, most TWAS methods focus on linear associations between genes and traits, ignoring the complex nonlinear relationships that may be present in biological systems. To address t

Esteve Valls Mascaro, Hyemin Ahn, Dongheui Lee

To anticipate how a human would act in the future, it is essential to understand the human intention since it guides the human towards a certain goal. In this paper, we propose a hierarchical architecture which assumes a sequence of human action (low-level) can be driven from the human intention (high-level). Based on this, we deal with Long-Term Action Anti

Sayantan Mitra, Dipa Saha, Ankur Sensharma

Site percolation in a distorted simple cubic lattice is characterized numerically employing the Newman-Ziff algorithm. Distortion is administered in the lattice by systematically and randomly dislocating its sites from their regular positions. The amount of distortion is tunable by a parameter called the distortion parameter. In this model, two occupied neig

Debasis Sen, Gopal Chandra Dutta

We introduce a nested sequence of monoids related to self-homotopy equivalences of fibrewise pointed spaces, such that the limit is the group of homotopy classes of fibrewise pointed self-equivalences. We explore this monoid for the fibred product in terms of individual spaces. Further we study two related invariants associated to these monoids: self closene

Jiannan Yang

The eigenvalues and eigenvectors of the Fisher information matrix (FIM) can reveal the most and least sensitive directions of a system and it has wide application across science and engineering. We present a symplectic variant of the eigenvalue decomposition for the FIM and extract the sensitivity information with respect to two-parameter conjugate pairs. Th

Martin Dindoš, Erika Nyström, Martin Ulmer

In the present paper we study perturbation theory for the $L^p$ Dirichlet problem on bounded chord arc domains for elliptic operators in divergence form with potentially unbounded antisymmetric part in BMO. Specifically, given elliptic operators $L_0 = \mbox{div}(A_0\nabla)$ and $L_1 = \mbox{div}(A_1\nabla)$ such that the $L^p$ Dirichlet problem for $L_0$ is

Shashank A. Deshpande, Ankur A. Kulkarni

It is known in the context of decentralised control that there exist control strategies consistent with the requirements of a given information structure, yet physically unimplementable through any amount of passive common randomness. This imposes a natural set of limitations on what is achievable through common randomness in both cooperative and competitive

Xinxing Chen, Chuheng Chen, Yuxuan Wang, Bowen Yang

Gait phase-based control is a trending research topic for walking-aid robots, especially robotic lower-limb prostheses. Gait phase estimation is a challenge for gait phase-based control. Previous researches used the integration or the differential of the human's thigh angle to estimate the gait phase, but accumulative measurement errors and noises can affect

G. Castignani, M. Radovich, F. Combes, P. Salomé

Brightest Cluster Galaxies (BCGs) are typically massive ellipticals at the centers of clusters. They are believed to experience strong environmental processing, and their mass assembly and star formation history are still debated. We have selected three star forming BCGs in the equatorial field of the Kilo-Degree Survey (KiDS) at intermediate redshifts. We h

Yonggeun Cho, Seokchang Hong, Tohru Ozawa

We study the Cauchy problems for the Hartree-type nonlinear Dirac equations with Yukawa-type potential derived from pseudoscalar field. We establish scattering for large data but with a relatively small part of initial data associated with charge conjugation by exploiting null structure induced by chirality.

Andrea Meo, Carenza E. Cronshaw, Sarah Jenkins, Amelia Lees

Dynamic simulations of spin-transfer and spin-orbit torques are increasingly important for a wide range of spintronic devices including magnetic random access memory, spin-torque nano-oscillators and electrical switching of antiferromagnets. Here we present a computationally efficient method for the implementation of spin-transfer and spin-orbit torques with

Erik Busley, Leon Espert Miranda, Christian Kurtscheid, Frederik Wolf

The Liouville theorem states that the phase-space volume of an ensemble in a closed system remains constant. While gases of material particles can efficiently be cooled by sympathetic or laser cooling techniques, allowing for large phase-space compression, for light both the absence of an internal structure, as well as the usual non-conservation of particle

Ting-Ting Liu, Su-Yan Pei, Wei Li, Meng Han

By adopting the relativistic Bethe-Salpeter method, the OZI allowed strong decays of the $2^+$ state $B^{*}_{2}(5747)^{0}$ are studied, emphasis is paid to the relativistic corrections. We first study the partial waves in the wave functions used, find that there are $P$, $D$ and $F$ partial waves in $B^{*}_{2}(5747)^{0}$ meson, and the ratios $P:D:F=1:0.421:

Pouya Narimani, Seyed Amin Habibi, Mohammad Ali Akhaee

Various studies among side-channel attacks have tried to extract information through leakages from electronic devices to reach the instruction flow of some appliances. However, previous methods highly depend on the resolution of traced data. Obtaining low-noise traces is not always feasible in real attack scenarios. This study proposes two deep models to ext

Jan G. Rittig, Qinghe Gao, Manuel Dahmen, Alexander Mitsos

Molecular property prediction is of crucial importance in many disciplines such as drug discovery, molecular biology, or material and process design. The frequently employed quantitative structure-property/activity relationships (QSPRs/QSARs) characterize molecules by descriptors which are then mapped to the properties of interest via a linear or nonlinear m

Gourab Kumar Sar, Dibakar Ghosh

Swarmalators have emerged as a new paradigm for dynamical collective behavior of multi-agent systems due to the interplay of synchronization and swarming that they inherently incorporate. Their dynamics have been explored with different coupling topologies, interaction functions, external forcing, noise, competitive interactions, and from other important vie

Hamza Keurti, Hsiao-Ru Pan, Michel Besserve, Benjamin F. Grewe

How can agents learn internal models that veridically represent interactions with the real world is a largely open question. As machine learning is moving towards representations containing not just observational but also interventional knowledge, we study this problem using tools from representation learning and group theory. We propose methods enabling an

William Jaco, J. Hyam Rubinstein, Jonathan Spreer, Stephan Tillmann

Let $M$ be a compact 3--manifold with boundary a single torus. We present upper and lower complexity bounds for closed 3--manifolds obtained as even Dehn fillings of $M.$ As an application, we characterise some infinite families of even Dehn fillings of $M$ for which our method determines the complexity of its members up to an additive constant. The constant

Toni Peter, Ralf S. Klessen, Guido Kanschat, Simon C. O. Glover

We introduce the radiative transfer code Sweep for the cosmological simulation suite Arepo. Sweep is a discrete ordinates method in which the radiative transfer equation is solved under the infinite speed of light, steady state assumption by a transport sweep across the entire computational grid. Since Arepo is based on an adaptive, unstructured grid, the de

Tarun Krishna, Ayush K. Rai, Yasser A. D. Djilali, Alan F. Smeaton

Whilst computer vision models built using self-supervised approaches are now commonplace, some important questions remain. Do self-supervised models learn highly redundant channel features? What if a self-supervised network could dynamically select the important channels and get rid of the unnecessary ones? Currently, convnets pre-trained with self-supervisi

Pavel Pokhilko, Dominika Zgid

We adopt a broken-symmetry strategy for evaluating effective magnetic constants $J$ within the fully self-consistent GW method. To understand the degree of spin contamination present in broken-symmetry periodic solutions, we propose several size-extensive quantities that demonstrate that the unrestricted self-consistent GW preserves well the broken-symmetry

Payam Zahadat, Ada Diaconescu

In many self-organising systems the ability to extract necessary resources from the external environment is essential to the system's growth and survival. Examples include the extraction of sunlight and nutrients in organic plants, of monetary income in business organisations and of mobile robots in swarm intelligence actions. When operating within competiti

Achkan Salehi, Steffen Rühl, Stephane Doncieux

Model-based Reinforcement Learning and Control have demonstrated great potential in various sequential decision making problem domains, including in robotics settings. However, real-world robotics systems often present challenges that limit the applicability of those methods. In particular, we note two problems that jointly happen in many industrial systems:

Yajing Kong, Liu Liu, Zhen Wang, Dacheng Tao

Continual learning is a learning paradigm that learns tasks sequentially with resources constraints, in which the key challenge is stability-plasticity dilemma, i.e., it is uneasy to simultaneously have the stability to prevent catastrophic forgetting of old tasks and the plasticity to learn new tasks well. In this paper, we propose a new continual learning

Matthias Häußler, Robin Terhaar, Martin A. Wolff, Helge Gehring

Superconducting nanowire single-photon detectors are an enabling technology for modern quantum information science and are gaining attractiveness for the most demanding photon counting tasks in other fields. Embedding such detectors in photonic integrated circuits enables additional counting capabilities through nanophotonic functionalization. Here we show h

Tuomas Kangas, Lin Yan, Steve Schulze, Claes Fransson

We present a sample of 14 hydrogen-rich superluminous supernovae (SLSNe II) from the Zwicky Transient Facility (ZTF) between 2018 and 2020. We include all classified SLSNe with peaks $M_{g}<-20$ mag and with observed \emph{broad} but not narrow Balmer emission, corresponding to roughly 20 per cent of all hydrogen-rich SLSNe in ZTF phase I. We examine the lig

Sujata S Kulkarni, Raviraj Dave, Udit Bhatia, Rohini Kumar

Rising economic instability and continuous evolution in international relations demand a self-reliant trade and commodity flow networks at regional scales to efficiently address the growing human needs of a nation. Despite its importance in securing India's food security, the potential advantages of inland trade remain unexplored. Here we perform a comprehen

Gaofeng Huang, Frank Kutzschebauch, Josua Schott

In this article we deduce some algebraic properties for the group $\mathrm{Sp}_{2n} (\mathcal{O}(X))$ of holomorphic symplectic matrices on a Stein space $X$: holomorphic factorization, exponential factorization, and Kazhdan's property (T). In holomorphic factorization we combine a recent result of the third author and K-theory tools to give explicit bounds

Chong Wang, Rongkai Zhang, Saiprasad Ravishankar, Bihan Wen

Image restoration schemes based on the pre-trained deep models have received great attention due to their unique flexibility for solving various inverse problems. In particular, the Plug-and-Play (PnP) framework is a popular and powerful tool that can integrate an off-the-shelf deep denoiser for different image restoration tasks with known observation models

Robert Cardona, Cédric Oms

A $b$-contact structure on a $b$-manifold $(M,Z)$ is a Jacobi structure on $M$ satisfying a transversality condition along the hypersurface $Z$. We show that, in three dimensions, $b$-contact structures with overtwisted three-dimensional leaves satisfy an existence $h$-principle that allows prescribing the induced singular foliation. We give a method to clas

Luka Abb, Jana-Rebecca Rehse

User interaction (UI) logs are high-resolution event logs that record low-level activities performed by a user during the execution of a task in an information system. Each event in a UI log corresponds to a single interaction between the user and the interface, such as clicking a button or entering a string into a text field. UI logs are used for purposes l

Ze-Cheng Zou, Yong-Feng Huang, Xiao-Li Zhang

According to the strange quark matter hypothesis, pulsars may actually be strange stars composed of self-bound strange quark matter. The normal matter crust of a strange star, unlike that of a normal neutron star, is supported by a strong electric field. A gap is then presented between the crust and the strange quark core. Therefore, peculiar core-crust osci

Constantine Maganaris, Eftychios Protopapadakis, Nikolaos Bakalos, Nikolaos Doulamis

In this paper, we investigate the transferability limitations when using deep learning models, for semantic segmentation of pneumonia-infected areas in CT images. The proposed approach adopts a 4 channel input; 3 channels based on Hounsfield scale, plus one channel (binary) denoting the lung area. We used 3 different, publicly available, CT datasets. If the

Ali Faqihi, Shah J Miah

AI has the potential to improve approaches to talent management enabling dynamic provisions through implementing advanced automation. This study aims to identify the new requirements for developing AI-oriented artifacts to address talent management issues. Focusing on enhancing interactions between professional assessment and planning attributes, the design

Laura Stops, Roel Leenhouts, Qinghe Gao, Artur M. Schweidtmann

Process synthesis experiences a disruptive transformation accelerated by digitization and artificial intelligence. We propose a reinforcement learning algorithm for chemical process design based on a state-of-the-art actor-critic logic. Our proposed algorithm represents chemical processes as graphs and uses graph convolutional neural networks to learn from p

Christoph Adelsberger, Stefano Bosco, Jelena Klinovaja, Daniel Loss

Hole semiconductor nanowires (NW) are promising platforms to host spin qubits and Majorana bound states for topological qubits because of their strong spin-orbit interactions (SOI). The properties of these systems depend strongly on the design of the cross section and on strain, as well as on external electric and magnetic fields. In this work, we analyze in

Wenjie Pei, Shuang Wu, Dianwen Mei, Fanglin Chen

While fine-tuning based methods for few-shot object detection have achieved remarkable progress, a crucial challenge that has not been addressed well is the potential class-specific overfitting on base classes and sample-specific overfitting on novel classes. In this work we design a novel knowledge distillation framework to guide the learning of the object

Nuno Souto, João Carlos Silva

In recent years there has been a growing interest in reconfigurable intelligent surfaces (RISs) as enablers for the realization of smart radio propagation environments which can provide performance improvements with low energy consumption in future wireless networks. However, to reap the potential gains of RIS it is crucial to jointly design both the transmi

Valery Lunts, Špela Špenko, Michel Van den Bergh

We find an explicit $S_n$-equivariant bijection between the integral points in a certain zonotope in $\mathbb{R}^n$, combinatorially equivalent to the permutahedron, and the set of $m$-parking functions of length $n$. This bijection restricts to a bijection between the regular $S_n$-orbits and $(m,n)$-Dyck paths, the number of which is given by the Fuss-Cata

Ayush Aniket, Arpan Chattopadhyay

We study learning in periodic Markov Decision Process(MDP), a special type of non-stationary MDP where both the state transition probabilities and reward functions vary periodically, under the average reward maximization setting. We formulate the problem as a stationary MDP by augmenting the state space with the period index, and propose a periodic upper con

Lijuan Liu, Zhenjun Zhou, Yuming Wang, Xudong Sun

Rapid increase of horizontal magnetic field ($B_h$) around the flaring polarity inversion line is the most prominent photospheric field change during flares. It is considered to be caused by the contraction of flare loops, the details behind which is still not fully understood. Here we investigate the $B_h$-increase in 35 major flares using HMI high-cadence

Robert Carruthers, Isabel Straw, James K Ruffle, Daniel Herron

Equity is widely held to be fundamental to the ethics of healthcare. In the context of clinical decision-making, it rests on the comparative fidelity of the intelligence -- evidence-based or intuitive -- guiding the management of each individual patient. Though brought to recent attention by the individuating power of contemporary machine learning, such epis

Dongli Xu, Jinhong Deng, Wen Li

Average precision (AP) loss has recently shown promising performance on the dense object detection task. However,a deep understanding of how AP loss affects the detector from a pairwise ranking perspective has not yet been developed.In this work, we revisit the average precision (AP)loss and reveal that the crucial element is that of selecting the ranking pa

Ennio Cascetta, Marcello Montanino

A wide range of price-based congestion management schemes were proposed in the literature ranging from marginal cost road pricing to trip based multimodal pricing. The underlying models were formulated under different theoretical assumptions and with varying, and sometimes conflicting objectives. This paper presents a unifying framework under which different

Valery Lunts, Špela Špenko, Michel Van den Bergh

We give a brief review of the cohomological Hall algebra CoHA $\mathcal{H}$ and the K-theoretical Hall algebra KHA $\mathcal{R}$ associated to quivers. In the case of symmetric quivers, we show that there exists a homomorphism of algebras (obtained from a Chern character map) $\mathcal{R}\to \hat{\mathcal{H}}^{\sigma}$ where $\hat{\mathcal{H}}^{\sigma}$ is a

Ciarán Dunne, J. B. Wells

A generalized set theory (GST) is like a standard set theory but also can have non-set structured objects that can contain other structured objects including sets. This paper presents Isabelle/HOL support for GSTs, which are treated as type classes that combine features that specify kinds of mathematical objects, e.g., sets, ordinal numbers, functions, etc.

Dror Ozeri

Given an affine transformation $T$, we define its Fisher distortion $Dist_F(T)$. We show that the Fisher distortion has Riemannian metric structure and provide an algorithm for finding mean distorting transformation -- namely -- for a given set $\{T_{i}\}_{i=1}^N$ of affine transformations, find an affine transformation $T$ that minimize the overall distorti

Paolo Baldi, Barbara Pacchiarotti

We investigate the Large Deviation behavior in small time of continuous Gaussian processes. We introduce a general procedure allowing to derive Large Deviation Principles in small time starting from the well understood context of Large Deviation Principles with a small parameter, going beyond the self-similar case. Several motivating examples are also treate

D. P. Bourne, M. Pearce, S. M. Roper

In this paper we describe a fast algorithm for generating periodic RVEs of polycrystalline materials. In particular, we use the damped Newton method from semi-discrete optimal transport theory to generate 3D periodic Laguerre tessellations (or power diagrams) with cells of given volumes. Complex, polydisperse RVEs with up to 100,000 grains of prescribed volu

Georgi Karadzhov, Tom Stafford, Andreas Vlachos

People leverage group discussions to collaborate in order to solve complex tasks, e.g. in project meetings or hiring panels. By doing so, they engage in a variety of conversational strategies where they try to convince each other of the best approach and ultimately reach a decision. In this work, we investigate methods for detecting what makes someone change

Xin Qin, Songbai Chen, Zelin Zhang, Jiliang Jing

The polarized images of a synchrotron emitting ring are studied in the spacetime of a rotating black hole in the Scalar-Tensor-Vector-Gravity (STVG) theory. The black hole owns an additional dimensionless MOG parameter described its deviation from Kerr black hole. The effects of the MOG parameter on the observed polarization vector and Strokes $Q-U$ loops de

Karin Sevegnani, Arjun Seshadri, Tian Wang, Anurag Beniwal

Recommender systems and search are both indispensable in facilitating personalization and ease of browsing in online fashion platforms. However, the two tools often operate independently, failing to combine the strengths of recommender systems to accurately capture user tastes with search systems' ability to process user queries. We propose a novel remedy to

Shiyu Gao, Zhaoxin Li, Zhaoqi Wang

Multi-view stereo is an important research task in computer vision while still keeping challenging. In recent years, deep learning-based methods have shown superior performance on this task. Cost volume pyramid network-based methods which progressively refine depth map in coarse-to-fine manner, have yielded promising results while consuming less memory. Howe

Yipeng An, Juncai Chen, Zhengxuan Wang, Jie Li

Recently the kagome compounds have inspired enormous interest and made some great progress such as in the field of superconductivity and topology. Here we predict a different kagome magnesium triboride (MgB3) superconductor with a calculated Tc ~12.2 K and Tc ~15.4 K by external stress, the potentially highest among the reported diverse kagome-type supercond

Guangjing Huang, Xu Chen, Tao Ouyang, Qian Ma

Federated learning (FL) is a promising distributed framework for collaborative artificial intelligence model training while protecting user privacy. A bootstrapping component that has attracted significant research attention is the design of incentive mechanism to stimulate user collaboration in FL. The majority of works adopt a broker-centric approach to he

Ruibo Wang, Mustafa A. Kishk, Mohamed-Slim Alouini

This paper investigates the accuracy of recently proposed stochastic geometry-based modeling of low earth orbit (LEO) satellite networks. In particular, we use the Wasserstein Distance-inspired method to analyze the distances between different models, including Fibonacci lattice and orbit models. We propose an algorithm to calculate the distance between the

Chanho Park, Rehan Ahmad, Thomas Hain

This paper proposes an unsupervised data selection method by using a submodular function based on contrastive loss ratios of target and training data sets. A model using a contrastive loss function is trained on both sets. Then the ratio of frame-level losses for each model is used by a submodular function. By using the submodular function, a training set fo

Weifeng Zeng, Huanhui Cao, Wenjie Lu, Hao Xiong

Researchers have developed various cascaded controllers and non-cascaded controllers for the navigation and control of quadrotors in recent years. It is vital to ensure the safety of a quadrotor both in normal state and in abnormal state if a controller tends to make the quadrotor unsafe. To this end, this paper proposes a non-cascaded Control Barrier Functi

Celestine Angla, Benoit Larrat, Jean-Luc Gennisson, Sylvain Chatillon

Transcranial ultrasound is more and more used for therapy and imaging of the brain. However, the skull is a highly attenuating and aberrating medium, with different structures and acoustic properties between samples and even within a sample. Thus, case-specific simulations are needed to perform transcranial focused ultrasound interventions safely. In this ar

Joydeep Chowdhury, Probal Chaudhuri

We consider an analysis of variance type problem, where the sample observations are random elements in an infinite dimensional space. This scenario covers the case, where the observations are random functions. For such a problem, we propose a test based on spatial signs. We develop an asymptotic implementation as well as a bootstrap implementation and a perm

K. Sysoliatina, A. Just

We present a generalised version of the semi-analytic Just-Jahreiss (JJ) model of the Galactic disk that incorporates our findings for the solar neighbourhood and is applicable to a wide range of galactocentric distances, 4 kpc $\lesssim R \lesssim $ 14 kpc. The JJ model is a flexible tool for stellar population synthesis with a fine age resolution of 25 Myr

Robert Ernö Csetnek, Mikhail A. Karapetyants

In a Hilbert setting we aim to study a second order in time differential equation, combining viscous and Hessian-driven damping, containing a time scaling parameter function and a Tikhonov regularization term. The dynamical system is related to the problem of minimization of a nonsmooth convex function. In the formulation of the problem as well as in our ana

K. Victor Sam Moses Babu, Satya Surya Vinay K, Pratyush Chakraborty

Sharing economy has become a socio-economic trend in transportation and housing sectors. It develops business models leveraging underutilized resources. Like those sectors, power grid is also becoming smarter with many flexible resources, and researchers are investigating the impact of sharing resources here as well that can help to reduce cost and extract v

Ethan A. Chi, Ashwin Paranjape, Abigail See, Caleb Chiam

We present Chirpy Cardinal, an open-domain social chatbot. Aiming to be both informative and conversational, our bot chats with users in an authentic, emotionally intelligent way. By integrating controlled neural generation with scaffolded, hand-written dialogue, we let both the user and bot take turns driving the conversation, producing an engaging and soci

Wang Lu, Jindong Wang, Haoliang Li, Yiqiang Chen

Deep learning has achieved great success in the past few years. However, the performance of deep learning is likely to impede in face of non-IID situations. Domain generalization (DG) enables a model to generalize to an unseen test distribution, i.e., to learn domain-invariant representations. In this paper, we argue that domain-invariant features should be

Alexander W. Wray, Radu Cimpeanu, Susana N. Gomes

A robust control scheme is derived and tested for the Navier-Stokes equations for two-dimensional multiphase flow of a thin film underneath an inclined solid surface. Control is exerted via the use of an electrode parallel to the substrate, which induces an electric field in the gas phase, and a resultant Maxwell stress at the liquid-gas interface. The impos

Jiro Kikkawa, Masao Takaku, Fuyuki Yoshikane

Digital Object Identifiers (DOIs) are regarded as persistent; however, they are sometimes deleted. Deleted DOIs are an important issue not only for persistent access to scholarly content but also for bibliometrics, because they may cause problems in correctly identifying scholarly articles. However, little is known about how much of deleted DOIs and what cau

Antonio Victor da Silva, Nicholas Braun Rodrigues

We prove the existence of approximate solutions in the (regular) Denjoy-Carleman sense for some systems of smooth complex vector fields. Such approximate solutions provide a well defined notion of Denjoy-Carleman wave front set of distributions on maximally real submanifolds in complex space which can be characterized in terms of the decay of the Fourier-Bro

Hildegard Müller, Stefan Weinzierl

In this talk we discuss the interplay of two elliptic curves, which occur in different sub-sectors of Feynman integrals. We analyse a particular Feynman integral depending on two elliptic curves and derive an associated differential equation in $\varepsilon$-form. We discuss the mixed entries of the differential equation, which depend on both elliptic curves

Lorenzo Rovigatti, Francesco Sciortino

Single-chain nanoparticles (SCNP) are a new class of bio and soft-matter polymeric objects in which a fraction of the monomers are able to form equivalently intra- or inter-polymer bonds. Here we numerically show that a fully-entropic gas-liquid phase separation can take place in SCNP systems. Control over the discontinuous (first-order) change -- from a pha

Xabier Telleria-Allika, Miguel Escobar Azor, Grégoire François, Gian Luigi Bendazzoli

approaches. We demonstrate that the Wigner regime can be reached using small values of the confinement parameter. To obtain physical insight in our results we analyze them with a semi-analytical model for two electrons. Thanks to electronic-structure properties such as the one-body density and the particle-hole entropy, we are able to define a path that conn

Markus Zopf

How to aggregate information from multiple instances is a key question multiple instance learning. Prior neural models implement different variants of the well-known encoder-decoder strategy according to which all input features are encoded a single, high-dimensional embedding which is then decoded to generate an output. In this work, inspired by Choquet cap