November 2022 arXiv papers — page 28

Dan Filipescu, Ioana Gheorghe, Konstantin Stopani, Sergey Belyshev

Intense, quasi-monochromatic, polarized $\gamma$-ray beams produced by Compton scattering of laser photons against relativistic electrons are used for fundamental studies and applications. Following a series of photoneutron cross section measurements in the Giant Dipole Resonance energy region performed at the NewSUBARU synchrotron radiation facility, we hav

Élisabeth Remy, Paul Ruet

The canalizing properties of biological functions have been mainly studied in the context of Boolean modelling of gene regulatory networks. An important mathematical consequence of canalization is a low average sensitivity, which ensures in particular the expected robustness to noise. In certain situations, the Boolean description is too crude, and it may be

Priya Sundaresan, Suneel Belkhale, Dorsa Sadigh

Acquiring food items with a fork poses an immense challenge to a robot-assisted feeding system, due to the wide range of material properties and visual appearances present across food groups. Deformable foods necessitate different skewering strategies than firm ones, but inferring such characteristics for several previously unseen items on a plate remains no

Amro M. Farid, Muhannad Alshareef, Parupkar Singh Badhesha, Chiara Boccaletti

At the turn of the 21st century, urban development has experienced a paradigm shift so that the quest for smarter cities has become a priority agenda, with the direct participation of industry, policymakers, practitioners, and the scientific community alike. The 2008 financial crisis, the exodus from rural areas, and the densification of urban centers couple

Haocheng Xiao, Sam Ainsworth

Side-channel attacks pose serious threats to many security models, especially sandbox-based browsers. While transient-execution side channels in out-of-order processors have previously been blamed for vulnerabilities such as Spectre and Meltdown, we show that in fact, the capability of out-of-order execution \emph{itself} to cause mayhem is far more general.

Sriram Balasubramanian, Soheil Feizi

The ability to remove features from the input of machine learning models is very important to understand and interpret model predictions. However, this is non-trivial for vision models since masking out parts of the input image typically causes large distribution shifts. This is because the baseline color used for masking (typically grey or black) is out of

David Martínez-Rubio, Sebastian Pokutta

We propose a globally-accelerated, first-order method for the optimization of smooth and (strongly or not) geodesically-convex functions in a wide class of Hadamard manifolds. We achieve the same convergence rates as Nesterov's accelerated gradient descent, up to a multiplicative geometric penalty and log factors. Crucially, we can enforce our method to stay

Saeid Abedi, Mehdi Tarighi Ahmadpour, Samira Baninajarian, Hamideh Kahnouji

The band gap is an important parameter of semiconductor materials that influences several functional properties, in particular optical properties. However, a fast and reliable first-principles prediction of band gaps remains a challenging problem. Standard DFT approximations tend to strongly underestimate band gaps, while the more accurate $GW$ and hybrid fu

Mathias N. Larrouturou, Andrew J. Higgins, Jeffrey K. Greason

A technique by which a spacecraft can interact with flows of ionized gas in space (the solar wind or interstellar medium) to be accelerated to velocities greater than the wind velocity is explored. Inspired by the dynamic soaring maneuvers performed by sea birds and gliders in which differences in wind speed are exploited to gain velocity, in the proposed te

Moses Ike, Kandy Phan, Keaton Sadoski, Romuald Valme

Modern Industrial Control Systems (ICS) attacks evade existing tools by using knowledge of ICS processes to blend their activities with benign Supervisory Control and Data Acquisition (SCADA) operation, causing physical world damages. We present SCAPHY to detect ICS attacks in SCADA by leveraging the unique execution phases of SCADA to identify the limited s

S. N. Paneru, C. R. Brune, D. Connolly, D. Odell

Measurements of the elastic scattering cross section of 3He and 4He are important in order to improve constraints on theoretical models of 4He(3He,g)7Be, a key reaction in Big Bang nucleosynthesis and solar neutrino production. The astrophysical S-factor for this reaction is a significant source of uncertainty in the standard solar-model prediction of the 7B

Samuel Epstein

We provide another proof to the EL Theorem. We show the tradeoff between compressibility of codebooks and their communication capacity. A resource bounded version of the EL Theorem is proven. This is used to prove three instances of resource bounded derandomization. This paper is in support of the general claim that if the existence of an object can be prove

Kenan Tang, Hanchun Jiang

Masked language models pick up gender biases during pre-training. Such biases are usually attributed to a certain model architecture and its pre-training corpora, with the implicit assumption that other variations in the pre-training process, such as the choices of the random seed or the stopping point, have no effect on the biases measured. However, we show

Zuhui Wang

Microscopy images from different imaging conditions, organs, and tissues often have numerous cells with various shapes on a range of backgrounds. As a result, designing a deep learning model to count cells in a source domain becomes precarious when transferring them to a new target domain. To address this issue, manual annotation costs are typically the norm

Mingchao Liu, Jinhua He, Jixing Ge, Tie Liu

Although episodic star formation (SF) had been suggested for nearby SF regions, a panoramic view to the latest episodic SF history in the solar neighborhood is still missing. By uniformly constraining the slope $\alpha$ of infrared spectral energy distributions (SEDs) of young stellar objects (YSOs) in the 13 largest Gould's Belt (GB) protoclusters surveyed

José Ignacio Illana, Alejandro Jiménez Cano

The Standard Model of the electroweak and strong interactions of particle physics is a quantum field theory. Elementary particles are not indivisible `pieces' of matter but energy bundles of fields, whose properties and interactions are a consequence of the principles of symmetry. These lecture notes provide a brief introduction to the construction of the St

Alexandra Yang

The theory of divide-and-conquer parallelization has been well-studied in the past, providing a solid basis upon which to explore different approaches to the parallelization of merge sort in Python. Python's simplicity and extensive selection of libraries make it the most popular scientific programming language, so it is a fitting language in which to implem

Konstantin A. Beyer, Subir Sarkar

We revisit the domain wall problem for QCD axion models with more than one quark charged under the Peccei-Quinn symmetry. Symmetry breaking during or after inflation results in the formation of a domain wall network which would cause cosmic catastrophe if it comes to dominate the Universe. The network may be made unstable by invoking a `tilt' in the axion po

Jonathan Libgober, Ruozi Song

This paper shows that a non-price intervention which increased the prevalence of a new technology facilitated its further adoption. The BlueLA program put Electric Vehicles (EVs) for public use in many heavily trafficked areas, primarily (but not exclusively) aimed at low-to-middle income households. We show, using data on subsidies for these households and

Congxi Xiao, Jingbo Zhou, Jizhou Huang, Hengshu Zhu

Urban villages (UVs) refer to the underdeveloped informal settlement falling behind the rapid urbanization in a city. Since there are high levels of social inequality and social risks in these UVs, it is critical for city managers to discover all UVs for making appropriate renovation policies. Existing approaches to detecting UVs are labor-intensive or have

Sayandev Mukherjee, Bernardo A. Huberman

We argue that many properties of fully-connected feedforward neural networks (FCNNs), also called multi-layer perceptrons (MLPs), are explainable from the analysis of a single pair of operations, namely a random projection into a higher-dimensional space than the input, followed by a sparsification operation. For convenience, we call this pair of successive

Zhora Nikoghosyan

Two new sufficient conditions for generalized cycles (including Hamilton and dominating cycles as special cases) in an arbitrary k-connected graph (k=1,2,...) are derived, which prove the truth of Bondy's (1980) famous conjecture for some variants significantly improving the result expected by the given hypothesis. Similarly, two new lower bounds for the cir

Marius Sajgalik, Michal Barla, Maria Bielikova

Word feature vectors have been proven to improve many NLP tasks. With recent advances in unsupervised learning of these feature vectors, it became possible to train it with much more data, which also resulted in better quality of learned features. Since it learns joint probability of latent features of words, it has the advantage that we can train it without

Mark Berezovik, Roman Karasev

We state a conjecture about the volume of symplectically self-polar convex bodies and show that it is equivalent to Mahler's conjecture concerning the volume of a convex body and its Euclidean polar. We also establish lower and upper bounds for symplectic capacities of symplectically self-polar bodies.

Simonas Gervė, Andrius Grigutis

In this paper, we set up the distribution function $$ \varphi(u)=\mathbb{P}\left(\sup_{n\geqslant 1}\sum_{i=1}^{n}\left(X_i-\kappa\right)<u\right), $$ and the generating function of $\varphi(u+1)$, where $u\in\mathbb{N}_0$, $\kappa\in\mathbb{N}$, the random walk $\left\{\sum_{i=1}^{n}X_i, n\in\mathbb{N}\right\},$ consists of $N\in\mathbb{N}$ periodically occ

Paolo Marimon

A recent article of Chernikov, Hrushovski, Kruckman, Krupinski, Moconja, Pillay and Ramsey finds the first examples of simple structures with formulas which do not fork over $\emptyset$ but are universally measure zero. In this article we give the first known simple $\omega$-categorical counterexamples. These happen to be various $\omega$-categorical Hrushov

Ghada Sokar, Zahra Atashgahi, Mykola Pechenizkiy, Decebal Constantin Mocanu

A new line of research for feature selection based on neural networks has recently emerged. Despite its superiority to classical methods, it requires many training iterations to converge and detect informative features. The computational time becomes prohibitively long for datasets with a large number of samples or a very high dimensional feature space. In t

M. S. Anwar, T. Nakamura, R. Ishiguro, S. Arif

Non-reciprocal electronic transport in a material occurs if both time reversal and inversion symmetries are broken. The superconducting diode effect (SDE) is an exotic manifestation of this type of behavior where the critical current for positive and negative currents are mismatched, as recently observed in some non-centrosymmetric superconductors with a mag

Fan Ge

Let $U\in U(N)$ be a random unitary matrix of size $N$, distributed with respect to the Haar measure on $U(N)$. Let $P(z)=P_U(z)$ be the characteristic polynomial of $U$. We prove that for $z$ close to the unit circle, $ \frac{P'}{P}(z) $ can be approximated using zeros of $P$ very close to $z$, with a typically controllable error term. This is an analogue o

Bhavesh Jaiswal

Imaging the planets that orbit around other stars requires blocking the host star which is usually 8-10 orders of magnitude brighter than the planets. This is achieved with the help of a stellar coronagraph. In the current work, a concept of a new type of stellar coronagraph is introduced where the star light is blocked by a linear polarizer in the collimate

Ke Di, Shuai Tan, Liyong Wang, Anyu Cheng

Quantum noise suppression and phase-sensitive modulation of continuously variable in vacuum and squeezed fields in a hybrid resonant cavity system are investigated theoretically. Multiple dark windows similar to electromagnetic induction transparency (EIT) are observed in quantum noise fluctuation curve. The effects of pumping light on both suppression of qu

Cristian Cazacu, Joshua Flynn, Nguyen Lam, Guozhen Lu

We set up a one-parameter family of inequalities that contains both the Hardy inequalities (when the parameter is 1) and the Caffarelli-Kohn-Nirenberg inequalities (when the parameter is optimal). Moreover, we study these results with the exact remainders to provide direct understandings to the sharp constants, as well as the existence and non-existence of t

Claire Burrin, Samantha Fairchild, Jon Chaika

Let $\Lambda_1$, $\Lambda_2$ be two discrete orbits under the linear action of a lattice $\Gamma<\mathrm{SL}_2(\mathbb{R})$ on the Euclidean plane. We prove a Siegel$-$Veech-type integral formula for the averages $$ \sum_{\mathbf{x}\in\Lambda_1} \sum_{\mathbf{y}\in\Lambda_2} f(\mathbf{x}, \mathbf{y}) $$ from which we derive new results for the set $S_M$ of h

Sonia Petrini, Ramon Ferrer-i-Cancho

The syntactic structure of a sentence can be represented as a graph, where vertices are words and edges indicate syntactic dependencies between them. In this setting, the distance between two linked words is defined as the difference between their positions. Here we wish to contribute to the characterization of the actual distribution of syntactic dependency

Ashutosh Kumar Singh, Deepika Saxena, Jitendra Kumar, Vrinda Gupta

This work presents a novel Evolutionary Quantum Neural Network (EQNN) based workload prediction model for Cloud datacenter. It exploits the computational efficiency of quantum computing by encoding workload information into qubits and propagating this information through the network to estimate the workload or resource demands with enhanced accuracy proactiv

Mousomi Bhakta, Debdip Ganguly, Debabrata Karmakar, Saikat Mazumdar

A classical result owing to Mancini and Sandeep [Ann. Sc. Norm. Super. Pisa Cl. Sci. 7 (2008)] asserts that all positive solutions of the Poincar\'e-Sobolev equation on the hyperbolic space $$ -\Delta_{\mathbb{B}^n} u-\lambda u = |u|^{p-1}u, \quad u\in H^1(\mathbb{B}^n), $$ are unique up to hyperbolic isometries where $n \geq 3,$ $1 < p \leq \frac{n+2}{n-2}

Simeon Brüggenjürgen, Nina Schaaf, Pascal Kerschke, Marco F. Huber

This work introduces a novel interpretable machine learning method called Mixture of Decision Trees (MoDT). It constitutes a special case of the Mixture of Experts ensemble architecture, which utilizes a linear model as gating function and decision trees as experts. Our proposed method is ideally suited for problems that cannot be satisfactorily learned by a

Fei Fang, Zhong Tan, Huiru Xiong

In this paper, the following problem in the hyperbolic space $\mathbb{B}^N$ will be considered \begin{equation*} -\Delta_{\mathbb{B}^N} u=f(x,u), \mathrm{in} \ \mathbb{B}^N.\eqno{(1)} \end{equation*} where, $\Delta_{\mathbb{B}^N}$ denotes the Laplace Beltrami operator on $\mathbb{B}^N$. And this problem can be converted into the following Euclidean problem \

Bojan Nikolić, Boris Šobot

In this paper we consider measures of similarity between two sets of strings built up using the Hamming distance and tools of persistence homology as a basis. First we describe the construction of the \v Cech filtration adjoined to the set of strings, the persistence module corresponding to this filtration and its barcode structure. Using these means, we int

Wei Wang

In this paper, we study the estimates of resolvents $ R(\lambda,\mathcal{L}_{\varepsilon})=(\mathcal{L}_{\varepsilon}-\lambda I)^{-1} $, where $$ \mathcal{L}_{\varepsilon}=-\operatorname{div}(A(x/\varepsilon)\nabla) $$ is a family of second elliptic operators with symmetric, periodic and oscillating coefficients defined on a bounded domain $ \Omega $ with $

Gabriel Istrate

Almost periodicity has been considered in Formal Language Theory in connection with some topics in Symbolic Dynamics. In (P\u{a}un and Marcus, Bulletin of EATCS 53 (1994)) some problems concerning this property are raised. For instance it is asked whether there exists some almost periodic word $\alpha$ such that $Sub(\alpha)$, the set of its finite factors,

Francisco Guillén-González, André Luiz Corrêa Vianna Filho

In the present work we investigate an optimal control problem related to the following chemotaxis-consumption model in a bounded domain $\Omega\subset \mathbb{R}^3$: $$\partial_t u - \Delta u = - \nabla \cdot (u \nabla v), \quad \partial_t v - \Delta v = - u^s v + f \,v\, 1_{\Omega_c},$$ with $s \geq 1$, endowed with isolated boundary conditions and initial

Mohammad Hossein Jarrahi, Ali Memariani, Shion Guha

Data is a crucial infrastructure to how artificial intelligence (AI) systems learn. However, these systems to date have been largely model-centric, putting a premium on the model at the expense of the data quality. Data quality issues beset the performance of AI systems, particularly in downstream deployments and in real-world applications. Data-centric AI (

Mala Virdee, Markus Kaiser, Emily Shuckburgh, Carl Henrik Ek

Adaptation-relevant predictions of climate change are often derived by combining climate model simulations in a multi-model ensemble. Model evaluation methods used in performance-based ensemble weighting schemes have limitations in the context of high-impact extreme events. We introduce a locally time-invariant method for evaluating climate model simulations

Philipp Reiser, David J. Wraith

We prove a canonical variation-type result for submersion metrics with positive intermediate Ricci curvatures. This can then be used in conjunction with surgery techniques to establish the existence of metrics with positive intermediate Ricci curvatures on a wide range of examples which had previously only been known to admit positive Ricci curvature, such a

Jiyang Li, Wei Wang, Kratika Bhagtani, Yincheng Jin

With the increasing demands of emotion comprehension and regulation in our daily life, a customized music-based emotion regulation system is introduced by employing current EEG information and song features, which predicts users' emotion variation in the valence-arousal model before recommending music. The work shows that: (1) a novel music-based emotion reg

Jiyang Li, Ann Gina Konnayil, Adam Russell, Dingran Wang

Self-tracking has been long discussed, which can monitor daily activities and help users to recall previous experiences. Such data-capturing technique is no longer limited to photos, text messages, or personal diaries in recent years. With the development of wearable EEG devices, we introduce a novel modality of logging EEG data while listening to music, and

Somoy Subandhu Barua, Imam Mohammad Zulkarnain, Abhishek Roy, Md. Golam Rabiul Alam

For a full-stack web or app development, it requires a software firm or more specifically a team of experienced developers to contribute a large portion of their time and resources to design the website and then convert it to code. As a result, the efficiency of the development team is significantly reduced when it comes to converting UI wireframes and datab

Pablo Rocha

In this article we give a molecular reconstruction theorem for $H_{\omega}^{p(\cdot)}(\mathbb{R}^{n})$. As an application of this result and the atomic decomposition developed in [5] we show that classical singular integrals can be extended to bounded operators on $H_{\omega}^{p(\cdot)}(\mathbb{R}^{n})$. We also prove, for certain exponents $q(\cdot)$ and ce

H. Linander, O. Balabanov, H. Yang, B. Mehlig

Bayesian inference can quantify uncertainty in the predictions of neural networks using posterior distributions for model parameters and network output. By looking at these posterior distributions, one can separate the origin of uncertainty into aleatoric and epistemic contributions. One goal of uncertainty quantification is to inform on prediction accuracy.

Ramana Sundararaman, Riccardo Marin, Emanuele Rodola, Maks Ovsjanikov

In this work we present a novel approach for computing correspondences between non-rigid objects, by exploiting a reduced representation of deformation fields. Different from existing works that represent deformation fields by training a general-purpose neural network, we advocate for an approximation based on mesh-free methods. By letting the network learn

Xinyu Huang, Yu Huang, Miaowen Wen, Nan Yang

This paper designs a molecule harvesting transmitter (TX) model, where the surface of a spherical TX is covered by heterogeneous receptors with different sizes and arbitrary locations. If molecules hit any receptor, they are absorbed by the TX immediately. Within the TX, molecules are stored in vesicles that are continuously generated and released by the TX

Seyedeh Azadeh Fallah Mortezanejad

How to find unknown distributions is questioned in many pieces of research. There are several ways to figure them out, but the main question is which acts more reasonably than others. In this paper, we focus on the maximum entropy principle as a suitable method of discovering the unknown distribution, which recommends some prior information based on the avai

David Jaramillo Duque, Amir-Kian Kashani-Poor

We study the elliptic genera of the non-critical strings of six dimensional superconformal field theories from the point of view of the strings' worldsheet theory. We formulate a general ansatz for these in terms of characters of the affine Lie algebra associated to the 6d gauge group at negative level, and provide ample evidence for the validity of this ans

Kristóf Kanalas

We prove analogues of model theory results for $\mathcal{C}\to \mathcal{D}$ coherent functors, including variants of the omitting types theorem and some results on ultraproduct constructions. We introduce a distributive lattice valued invariant of $\mathcal{C}\to \mathbf{Set}$ coherent functors that vanishes precisely on positively closed models, then we stu

Seyedsaman Emami, Gonzalo Martínez-Muñoz

This paper presents a computationally efficient variant of gradient boosting for multi-class classification and multi-output regression tasks. Standard gradient boosting uses a 1-vs-all strategy for classifications tasks with more than two classes. This strategy translates in that one tree per class and iteration has to be trained. In this work, we propose t

Artur Hideyuki Tomita, Juliane Trianon-Fraga

In 1990, Comfort asked: is there, for every cardinal number $\alpha \leq 2^{\mathfrak{c}}$, a topological group $G$ such that $G^\gamma$ is countably compact for all cardinals $\gamma<\alpha$, but $G^\alpha$ is not countably compact? A similar question can also be asked for countably pracompact groups: for which cardinals $\alpha$ is there a topological grou

I. Mohelsky, J. Wyzula, B. A. Piot, G. D. Gu

Using Landau level spectroscopy, we determine the temperature dependence of the energy band gap in zirconium pentatelluride (ZrTe$_5$). We find that the band gap reaches $E_g=(5 \pm 1)$ meV at low temperatures and increases monotonously when the temperature is raised. This implies that ZrTe$_5$ is a weak topological insulator, with non-inverted ordering of e

Daehan Kim, Minseok Seo, YoungJin Jeon, Dong-Geol Choi

The Visual Domain Adaptation(VisDA) 2022 Challenge calls for an unsupervised domain adaptive model in semantic segmentation tasks for industrial waste sorting. In this paper, we introduce the SIA_Adapt method, which incorporates several methods for domain adaptive models. The core of our method in the transferable representation from large-scale pre-training

Nicholas Hagedorn

In 2013, Adams introduced the $n$-crossing number of a knot $K$, denoted by $c_n(K)$. Inequalities between the $2$-, $3$-, $4$-, and $5$-crossing numbers have been previously established. We prove $c_9(K)\leq c_3(K)-2$ for all knots $K$ that are not the trivial, trefoil, or figure-eight knot. We show this inequality is optimal and obtain previously unknown v

Zhengang Zhong, Ehecatl Antonio del Rio-Chanona, Panagiotis Petsagkourakis

Model predictive control (MPC) is an effective approach to control multivariable dynamic systems with constraints. Most real dynamic models are however affected by plant-model mismatch and process uncertainties, which can lead to closed-loop performance deterioration and constraint violations. Methods such as stochastic MPC (SMPC) have been proposed to allev

Yuhui Li, Zejia Wu, Chao Zhang, Hongyang Zhang

We study the problem of out-of-distribution (o.o.d.) generalization where spurious correlations of attributes vary across training and test domains. This is known as the problem of correlation shift and has posed concerns on the reliability of machine learning. In this work, we introduce the concepts of direct and indirect effects from causal inference to th

Xiaoqing Chi, Hui Zhang, Xiaoyun Jiang

In this paper, we first establish a new fractional magnetohydrodynamic (MHD) coupled flow and heat transfer model for a generalized second-grade fluid. This coupled model consists of a fractional momentum equation and a heat conduction equation with a generalized form of Fourier law. The second-order fractional backward difference formula is applied to the t

Turibius Rozario, Arjun Trivedi, Ankit Goel

This paper presents a compact, matrix-based representation of neural networks in a self-contained tutorial fashion. Specifically, we develop neural networks as a composition of several vector-valued functions. Although neural networks are well-understood pictorially in terms of interconnected neurons, neural networks are mathematical nonlinear functions cons

Nicolas Berthier, Steven De Oliveira, Nikolai Kosmatov, Delphine Longuet

Dynamic symbolic execution (DSE) is a powerful test generation approach based on an exploration of the path space of the program under test. Well-adapted for path coverage, this approach is however less efficient for conditions, decisions, advanced coverage criteria (such as multiple conditions, weak mutations, boundary testing) or user-provided test objecti

Philipp Siebers, Christian Janiesch, Patrick Zschech

In recent years, with the advent of highly scalable artificial-neural-network-based text representation methods the field of natural language processing has seen unprecedented growth and sophistication. It has become possible to distill complex linguistic information of text into multidimensional dense numeric vectors with the use of the distributional hypot

David C. Baughman, Peter A. Becker

The variability of the X-ray emission from active galactic nuclei is often characterized using time lags observed between soft and hard energy bands in the detector. The time lags are usually computed using the complex cross spectrum, which is based on the Fourier transforms of the hard and soft time series data. It has been noted that some active galactic n

Jianfeng Zhang, Zihang Jiang, Dingdong Yang, Hongyi Xu

Unsupervised generation of 3D-aware clothed humans with various appearances and controllable geometries is important for creating virtual human avatars and other AR/VR applications. Existing methods are either limited to rigid object modeling, or not generative and thus unable to generate high-quality virtual humans and animate them. In this work, we propose

Fucai Lin, Qiyun Wu, Chuan Liu

A topological space is called {\it dense-separable} if each dense subset of its is separable. Therefore, each dense-separable space is separable. We establish some basic properties of dense-separable topological groups. We prove that each separable space with a countable tightness is dense-separable, and give a dense-separable topological group which is not

Jun Zhao, Jingya Zhu, Pengxuan Zhu, Rui Zhu

Light higgsinos below several hundred GeV are favored or required by the naturalness of low energy supersymmetry. If only higgsinos are light while other sparticles are sufficiently heavy, we have the so-called light higgsino scenario. Confronted with the muon $g-2$ data, this scenario is examined in this work. Since in this scenario the LSP (lightest sparti

Alexander Stehli, Jan David Brehm, Tim Wolz, Andre Schneider

Quantum simulation is one of the most promising near term applications of quantum computing. Especially, systems with a large Hilbert space are hard to solve for classical computers and thus ideal targets for a simulation with quantum hardware. In this work, we study experimentally the transient dynamics in the multistate Landau-Zener model as a function of

Derek K. Thomas

For convex univalent functions we give instances where the sharp bound for various coefficient functionals are identical to those for the corresponding bound for the inverse function. We give instances where the sharp bounds differ and also suggest some significant open problems.

Haoxian Chen, Lan Lu, Brendan Massey, Yuepeng Wang

Smart contracts manage a large number of digital assets nowadays. Bugs in these contracts have led to significant financial loss. Verifying the correctness of smart contracts is, therefore, an important task. This paper presents an automated safety verification tool, DCV, that targets declarative smart contracts written in DeCon, a logic-based domain-specifi

Niels Langeveld, Tony Samuel

We show that every intermediate $\beta$-transformation is topologically conjugate to a greedy $\beta$-transformation with a hole at zero, and provide a counterexample illustrating that the correspondence is not one-to-one. This characterisation is employed to (1) build a Krieger embedding theorem for intermediate $\beta$-transformation, complementing the res

Jonathan B. Freund

Numerical simulations in two space dimensions are used to examine the dynamics, transport, and equilibrium behaviors of a neutrally buoyant circular object immersed in an active suspension within a larger closed circular container. The continuum model of Gao et al. (Phys. Rev. Fluids, 2017) represents the suspension of non-interacting, immotile, extensor-typ

Zhengjie Huang, Yunyang Huang, Peng Qian, Jianhai Chen

Bitcoin is one of the decentralized cryptocurrencies powered by a peer-to-peer blockchain network. Parties who trade in the bitcoin network are not required to disclose any personal information. Such property of anonymity, however, precipitates potential malicious transactions to a certain extent. Indeed, various illegal activities such as money laundering,

Alexander Premet, David Stewart

In this paper we describe the number of multiplicity-free primitive ideals associated with the rigid nilpotent orbits in finite-dimensional simple Lie algebras. Thanks to the results obtained earlier we need to solve the problem for the two largest rigid nilpotent orbits in Lie algebras of type ${\rm E}_8$. As a corollary we compute the number of small modul

Jean-Philippe Nicolas, Grigalius Taujanskas

We construct a complete conformal scattering theory for finite energy Maxwell potentials on a class of curved, asymptotically flat spacetimes with prescribed smoothness of null infinity and a non-zero ADM mass. In order to define the full set of scattering data, we construct a Lorenz-like gauge which makes the field equations hyperbolic and non-singular up t

Jiayu Huang, Mingqiu Wang, Yuanshan Wu

Transfer learning has become an essential technique to exploit information from the source domain to boost performance of the target task. Despite the prevalence in high-dimensional data, heterogeneity and heavy tails are insufficiently accounted for by current transfer learning approaches and thus may undermine the resulting performance. We propose a transf

Jianya Lu, Yingjun Mo, Zhijie Xiao, Lihu Xu

The generative adversarial networks (GANs) have recently been applied to estimating the distribution of independent and identically distributed data, and have attracted a lot of research attention. In this paper, we use the blocking technique to demonstrate the effectiveness of GANs for estimating the distribution of stationary time series. Theoretically, we

Yifan Zuo, Jiacheng Xie, Yuming Fang, Yan Huang

A mainstream type of the state of the arts (SOTAs) based on convolutional neural network (CNN) for real image denoising contains two sub-problems, i.e., noise estimation and non-blind denoising. This paper considers real noise approximated by heteroscedastic Gaussian/Poisson Gaussian distributions with in-camera signal processing pipelines. The related works

Aram Davtyan, Sepehr Sameni, Paolo Favaro

We introduce a novel generative model for video prediction based on latent flow matching, an efficient alternative to diffusion-based models. In contrast to prior work, we keep the high costs of modeling the past during training and inference at bay by conditioning only on a small random set of past frames at each integration step of the image generation pro

Yousef Alamri, David I. Ketcheson

A numerical search approach is used to design high-order diagonally implicit Runge-Kutta (DIRK) schemes equipped with embedded error estimators, some of which have identical diagonal elements (SDIRK) and explicit first stage (ESDIRK). In each of these classes, we present new A-stable schemes of order six (the highest order of previously known A-stable DIRK-t

Takehiro Aoshima, Takashi Matsubara

Semantic editing of images is the fundamental goal of computer vision. Although deep learning methods, such as generative adversarial networks (GANs), are capable of producing high-quality images, they often do not have an inherent way of editing generated images semantically. Recent studies have investigated a way of manipulating the latent variable to dete

Azzam Habib

A strongly connected graph is strongly biconnected if after ignoring the direction of its edges we have an undirected graph with no articulation points. A 3-vertex strongly biconnected graph is a strongly biconnected digraph that has the property that deleting any two vertices in this graph leaves a strongly binconnected subgraph. Jaberi [11] presented appro

Irina Agadzhanian, Mikhail Rybakov

The paper proves PSPACE-hardness of variable-free fragments of all logics between K and wGrz.

Reetta-Kaisa Ghezzi, Minnamaria Korhonen, Hannu Vilpponen, Tommi Mikkonen

Public sector is a large consumer of ICT systems and services, used for various public services. Tendering for such systems is governed by laws aimed at eliminating unfair advantages and offering all possible parties equal opportunities to participate in the tendering process. In this article, we study in-house rpocurement, where the acquiring organization i

Zhaojian Wang, Wei Wei, John Zhen Fu Pang, Feng Liu

Traditionally, offline optimization of power systems is acceptable due to the largely predictable loads and reliable generation. The increasing penetration of fluctuating renewable generation and Internet-of-Things devices allowing for fine-grained controllability of loads have led to the diminishing applicability of offline optimization in the power systems

Jihoon Ko, Shinhwan Kang, Taehyung Kwon, Heechan Moon

Continual Learning (CL) is the process of learning ceaselessly a sequence of tasks. Most existing CL methods deal with independent data (e.g., images and text) for which many benchmark frameworks and results under standard experimental settings are available. Compared to them, however, CL methods for graph data (graph CL) are relatively underexplored because

Ryan Martin

Bayesian inference requires specification of a single, precise prior distribution, whereas frequentist inference only accommodates a vacuous prior. Since virtually every real-world application falls somewhere in between these two extremes, a new approach is needed. This series of papers develops a new framework that provides valid and efficient statistical i

Hajar Dkhissi, Allal Ghanmi

We provide a theoretical study of a new family of orthogonal functions on the punctured complex plane solving the eigenvalue problems for some magnetic Laplacian perturbed by a singular vector potential with zero magnetic field modeling the Aharonov-Bohm effect. The functions are defined by their $\beta$-modified Rodrigues type formula and extend the poly-an

Francesco Linsalata, Nassar Ksairi

The combination of the effects of Doppler frequency shifts (due to mobility) and phase noise (due to the imperfections of oscillators operating at a high carrier frequency) poses serious challenges to Orthogonal Frequency Division Multiplexing (OFDM) wireless transmissions in terms of channel estimation and phase noise tracking performance and the associated

Guangze Zheng, Changhong Fu, Junjie Ye, Bowen Li

Although the manipulating of the unmanned aerial manipulator (UAM) has been widely studied, vision-based UAM approaching, which is crucial to the subsequent manipulating, generally lacks effective design. The key to the visual UAM approaching lies in object tracking, while current UAM tracking typically relies on costly model-based methods. Besides, UAM appr

Arushi Goel, Basura Fernando, Frank Keller, Hakan Bilen

Coreference resolution aims to identify words and phrases which refer to same entity in a text, a core task in natural language processing. In this paper, we extend this task to resolving coreferences in long-form narrations of visual scenes. First we introduce a new dataset with annotated coreference chains and their bounding boxes, as most existing image-t

Junmou Chen, Kaoru Hagiwara, Junichi Kanzaki, Kentarou Mawatari

Recently proposed Feynman-diagram (FD) gauge propagator for massless and massive gauge bosons is obtained from a light-cone (LC) gauge propagator, by choosing the gauge vector along the opposite direction of the gauge boson three-momentum. We implement a general LC gauge propagator for all the gauge bosons of the Standard Model (SM) in the HELicity Amplitude

Shrobona Bagchi, Abhay Srivastav, Arun Kumar Pati

The quantum speed limit provides a fundamental bound on how fast a quantum system can evolve between the initial and the final states under any physical operation. The celebrated Mandelstam-Tamm (MT) bound has been widely studied for various quantum systems undergoing unitary time evolution. Here, we prove a new quantum speed limit using the tighter uncertai

Caio Vitor Beojone, Nikolas Geroliminis

Dynamic network-level models directly addressing ride-sourcing services can support the development of efficient strategies for both congestion alleviation and promotion of more sustainable mobility. Recent developments presented models focusing on ride-hailing (solo rides), but no work addressed ridesplitting (shared rides) in dynamic contexts. Here, we sou

Junlin Hou, Jilan Xu, Nan Zhang, Yuejie Zhang

This paper presents our solution for the 2nd COVID-19 Severity Detection Competition. This task aims to distinguish the Mild, Moderate, Severe, and Critical grades in COVID-19 chest CT images. In our approach, we devise a novel infection-aware 3D Contrastive Mixup Classification network for severity grading. Specifcally, we train two segmentation networks to

SeungHeon Doh, Minz Won, Keunwoo Choi, Juhan Nam

This paper introduces effective design choices for text-to-music retrieval systems. An ideal text-based retrieval system would support various input queries such as pre-defined tags, unseen tags, and sentence-level descriptions. In reality, most previous works mainly focused on a single query type (tag or sentence) which may not generalize to another input t

Junlin Hou, Jilan Xu, Nan Zhang, Yi Wang

This paper presents our solution for the 2nd COVID-19 Competition, occurring in the framework of the AIMIA Workshop at the European Conference on Computer Vision (ECCV 2022). In our approach, we employ the winning solution last year which uses a strong 3D Contrastive Mixup Classifcation network (CMC v1) as the baseline method, composed of contrastive represe