May 2022 arXiv papers — page 23

Kaiyuan Tian, Bin Duo, Xiaojun Yuan, Wu Luo

This letter considers the reconfigurable intelligent surface (RIS)-aided unmanned aerial vehicle (UAV) communication systems in urban areas under the general Rician fading channel. A hybrid offline-online design is proposed to improve the system performance by leveraging both the statistical channel state information (S-CSI) and instantaneous channel state i

Machteld E. Kamminga, Jonas Okkels Birk, Jari í Hjøllum, Henrik Jacobsen

The universal theory of critical phase transitions describes the critical behavior at second-order phase transitions in infinitely large systems. With the increased contemporary interest in nanoscale materials, we investigated CoO nanoparticles by means of neutron scattering and found how the theory of critical phenomena breaks down in the nanoscale regime.

Xiaofei Wang, Fangxing Li, Linquan Bai, Xin Fang

Traditionally, the electric distribution system operates with uniform energy prices across all system nodes. However, as the adoption of distributed energy resources (DERs) propels a shift from passive to active distribution network (ADN) operation, a distribution-level electricity market has been proposed to manage new complexities efficiently. In addition,

Thomas Fernique, Carole Porrier

Ammann bars are formed by segments (decorations) on the tiles of a tiling such that forming straight lines with them while tiling forces non-periodicity. Only a few cases are known, starting with Robert Ammann's observations on Penrose tiles, but there is no general explanation or construction. In this article we propose a general method for cut and project

Aoqi Zuo, Susan Wei, Tongliang Liu, Bo Han

Fair machine learning aims to avoid treating individuals or sub-populations unfavourably based on \textit{sensitive attributes}, such as gender and race. Those methods in fair machine learning that are built on causal inference ascertain discrimination and bias through causal effects. Though causality-based fair learning is attracting increasing attention, c

Hernan B. Xavier, Rodrigo G. Pereira

Junctions of one-dimensional systems are of great interest to the development of synthetic materials that harbor topological phases. We study a junction of three gapless spin-1 chains described by the $\mathrm{SU(2)}_{2}$ Wess-Zumino-Wittten model and coupled by exchange and chiral three-spin interactions. We show that a chiral fixed point appears as a speci

Marco Conti, Andrea Passarella

The cyber-physical convergence, the fast expansion of the Internet at its edge, and tighter interactions between human users and their personal mobile devices push towards a data-centric Internet where the human user becomes more central than ever. We argue that this will profoundly impact primarily on the way data should be handled in the Next Generation In

D. O. Skanchenko, E. V. Altynbaev, V. A. Sidorov, G. Chaboussant

We report on a comprehensive experimental and theoretical study of Fe$_{1-x}$Rh$_{x}$Ge compounds, within the entire concentration range $x \in \left[0.0 - 1.0\right]$, using X-Ray diffraction, small-angle neutron scattering, magnetometry and theoretical calculations. While FeGe and RhGe are single phase helimagnet and unconventional superconductor, respecti

Roger de Belsunce, Steven Gratton, George Efstathiou

We present a Bayesian parametric component separation method for polarised microwave sky maps. We solve jointly for the primary cosmic microwave background (CMB) signal and the main Galactic polarised foreground components. For the latter, we consider electron-synchrotron radiation and thermal dust emission, modelled in frequency as a power law and a modifie

Sérgio S. Rodrigues, Dagmawi A. Seifu

It is shown that an oblique projection based feedback control is able to stabilize the state of the Kuramoto-Sivashinsky equation, evolving in rectangular domains, to a given time-dependent trajectory. The number of actuators is finite and consists of a finite number of indicator functions supported in small subdomains. Simulations are presented, in the one-

Annika Bach, Teresa Esposito, Roberta Marziani, Caterina Ida Zeppieri

In this paper we study the asymptotic behaviour of phase-field functionals of Am brosio and Tortorelli type allowing for small-scale oscillations both in the volume and in the diffuse surface term. The functionals under examination can be interpreted as an instance of a static gradient damage model for heterogeneous materials. Depending on the mutual vanishi

Bosai Lyu, Jiajun Chen, Shuo Lou, Can Li

Graphene nanoribbons (GNRs) with widths of a few nanometres are promising candidates for future nano-electronic applications due to their structurally tunable bandgaps, ultrahigh carrier mobilities, and exceptional stability. However, the direct growth of micrometre-long GNRs on insulating substrates, which is essential for the fabrication of nano-electronic

Pedro Liendo, Junchen Rong, Haoyu Zhang

We study conformal field theories with finite group symmetries with spontaneous symmetry breaking (SSB) phases that persist at all temperatures. We work with two $\lambda \phi^4$ theories coupled through their mass terms. The two $\lambda \phi^4$ theories are chosen to preserve either the cubic symmetry group or the tetrahedral symmetry group. A one-loop cal

Ayesha Afzal, Georg Hager, Gerhard Wellein, Stefano Markidis

This paper studies the utility of using data analytics and machine learning techniques for identifying, classifying, and characterizing the dynamics of large-scale parallel (MPI) programs. To this end, we run microbenchmarks and realistic proxy applications with the regular compute-communicate structure on two different supercomputing platforms and choose th

Johannes R. Eskilt, Eiichiro Komatsu

The observed pattern of linear polarization of the cosmic microwave background (CMB) photons is a sensitive probe of physics violating parity symmetry under inversion of spatial coordinates. A new parity-violating interaction might have rotated the plane of linear polarization by an angle $\beta$ as the CMB photons have been traveling for more than 13 billio

Xiliang Zhu, Shayna Gardiner, David Rossouw, Tere Roldán

Automatic Speech Recognition (ASR) systems typically produce unpunctuated transcripts that have poor readability. In addition, building a punctuation restoration system is challenging for low-resource languages, especially for domain-specific applications. In this paper, we propose a Spanish punctuation restoration system designed for a real-time customer su

Nahid Vasim

We revisited the first saturation correction to projectile in nucleus-nucleus collisions \cite{Chirilli:2015tea} within diagrammatic light cone perturbation theory (LCPT). To get an analytic expression for the saturation correction in projectile authors in \cite{Chirilli:2015tea} calculated complete ${\cal{O}}(g^3)$ amplitudes, where the main results are giv

Jonas Krook, Robi Malik, Sahar Mohajerani, Martin Fabian

This paper proposes a method to synthesise controllers for cyber-physical systems such that the controlled systems satisfy specifications given as linear temporal logic formulas. The focus is on systems with disturbance, where future states cannot be predicted exactly due to uncertainty in the environment. The approach used to solve this problem is to first

Hayssam Dahrouj, Shasha Liu, Mohamed-Slim Alouini

Integrated space-air-ground networks promise to offer a valuable solution space for empowering the sixth generation of communication networks (6G), particularly in the context of connecting the unconnected and ultraconnecting the connected. Such digital inclusion thrive makes resource management problems, especially those accounting for load-balancing consid

Mei Wang, Weihong Deng

Domain adaptation aims to leverage a labeled source domain to learn a classifier for the unlabeled target domain with a different distribution. Previous methods mostly match the distribution between two domains by global or class alignment. However, global alignment methods cannot achieve a fine-grained class-to-class overlap; class alignment methods supervi

Brit Youngmann, Sihem Amer-Yahia, Aurélien Personnaz

Data summarization is the process of producing interpretable and representative subsets of an input dataset. It is usually performed following a one-shot process with the purpose of finding the best summary. A useful summary contains k individually uniform sets that are collectively diverse to be representative. Uniformity addresses interpretability and dive

Federico Miretti, Daniela Misul, Giulio Gennaro, Antonio Ferrari

Hybrid-electric powertrains are among the most promising technologies for abating emissions from marine vessels in sensitive areas. However, their effectiveness strongly depends on the context they operate into. This paper attempts to evaluate the potential impact on air quality of hybridizing the diesel-powered waterbuses that currently operate in the city

Bin Lu, Xiaoying Gan, Lina Yang, Weinan Zhang

With the tremendous expansion of graphs data, node classification shows its great importance in many real-world applications. Existing graph neural network based methods mainly focus on classifying unlabeled nodes within fixed classes with abundant labeling. However, in many practical scenarios, graph evolves with emergence of new nodes and edges. Novel clas

Xinyi Li, Zijie Zhuang

We investigate random interlacements on $\mathbb{Z}^d$ with $d \geq 3$, and derive the large deviation rate for the probability that the capacity of the interlacement set in a macroscopic box is much smaller than that of the box. As an application, we obtain the large deviation rate for the probability that two independent interlacements have empty intersect

Guido Martinelli, Manuel Naviglio, Silvano Simula, Ludovico Vittorio

We present the results of the application of the unitarity-based Dispersion Matrix approach to semileptonic charged-current $B$ decays. This method allows to achieve a non-perturbative and completely model-independent determination of the hadronic form factors. Starting from lattice results available at large values of the momentum transfer, the behaviour of

Rakesh K. Prasad, Dilip K. Singh

The growth of large crystallite continuous monolayer materials like molybdenum disulfide (MoS$_2$) with desired morphology via chemical vapor deposition (CVD) remains a challenge. In CVD, the complex interplay of various factors like growth temperature, precursors, and nature of the substrate decides the crystallinity, crystallite size, and coverage area of

Yuriy Yerin, A. A. Varlamov, Caterina Petrillo

Recently it was demonstrated that the long-known transition between the gap and gapless superconducting states in the Abrikosov-Gor'kov theory of superconducting alloy with paramagnetic impurities is of the Lifshitz's type, i.e. at zero temperature this is the $2\frac12$ order phase transition. Since transitions of this kind in a normal metal are always asso

Romain Laroche, Remi Tachet des Combes, Jacob Buckman

A central object of study in Reinforcement Learning (RL) is the Markovian policy, in which an agent's actions are chosen from a memoryless probability distribution, conditioned only on its current state. The family of Markovian policies is broad enough to be interesting, yet simple enough to be amenable to analysis. However, RL often involves more complex po

Hugo Mlodecki

By Foissy's work, the bidendriform structure of the Word Quasisymmetric Functions Hopf algebra (WQSym) implies that it is isomorphic to its dual. However, the only known explicit isomorphism due to Vargas does not respect the bidendriform structure. This structure is entirely determined by so-called totally primitive elements (elements such that the two half

Bowen Zhao, Wei-Neng Chen, Feng-Feng Wei, Ximeng Liu

Evolutionary algorithms (EAs), such as the genetic algorithm (GA), offer an elegant way to handle combinatorial optimization problems (COPs). However, limited by expertise and resources, most users do not have enough capability to implement EAs to solve COPs. An intuitive and promising solution is to outsource evolutionary operations to a cloud server, whils

Bin Lu, Xiaoying Gan, Weinan Zhang, Huaxiu Yao

Spatio-temporal graph learning is a key method for urban computing tasks, such as traffic flow, taxi demand and air quality forecasting. Due to the high cost of data collection, some developing cities have few available data, which makes it infeasible to train a well-performed model. To address this challenge, cross-city knowledge transfer has shown its prom

Simon Allais

In this article, we study conjectures of Sandon on the minimal number of translated points in the special case of the unit tangent bundle of a Riemannian manifold. We restrict ourselves to contactomorphisms of $SM$ that lift diffeomorphisms of $M$ homotopic to identity. We prove that there exist sequences $(p_n,t_n)$ where $p_n$ is a translated point of time

Zhe Liu, Chunyang Chen, Junjie Wang, Yuekai Huang

Graphical User Interface (GUI) provides a visual bridge between a software application and end users, through which they can interact with each other. With the upgrading of mobile devices and the development of aesthetics, the visual effects of the GUI are more and more attracting, and users pay more attention to the accessibility and usability of applicatio

Ankita Tondwalkar, Andres Kwasinski

This paper presents a novel deep reinforcement learning-based resource allocation technique for the multi-agent environment presented by a cognitive radio network where the interactions of the agents during learning may lead to a non-stationary environment. The resource allocation technique presented in this work is distributed, not requiring coordination wi

Siyuan Li, Di Wu, Fang Wu, Zelin Zang

Masked image modeling, an emerging self-supervised pre-training method, has shown impressive success across numerous downstream vision tasks with Vision transformers. Its underlying idea is simple: a portion of the input image is masked out and then reconstructed via a pre-text task. However, the working principle behind MIM is not well explained, and previo

Nicolas Boursin, Carl Remlinger, Joseph Mikael, Carol Anne Hargreaves

Driven by the good results obtained in computer vision, deep generative methods for time series have been the subject of particular attention in recent years, particularly from the financial industry. In this article, we focus on commodity markets and test four state-of-the-art generative methods, namely Time Series Generative Adversarial Network (GAN) Yoon

Carles Domingo-Enrich, Youssef Mroueh

Differential privacy (DP) is the de facto standard for private data release and private machine learning. Auditing black-box DP algorithms and mechanisms to certify whether they satisfy a certain DP guarantee is challenging, especially in high dimension. We propose relaxations of differential privacy based on new divergences on probability distributions: the

Rahul Gupta, Shashi Bhushan Pandey, Amit Kumar, Amar Aryan

In this article, we present multi-band photometric observations and analysis of the host galaxies for a sample of five interesting gamma-ray bursts (GRBs) observed using the 3.6m Devasthal Optical Telescope (DOT) and the back-end instruments. The host galaxy observations of GRBs provide unique opportunities to estimate the stellar mass, ages, star-formation

T. Chaumont-Frelet

We derive an equilibrated a posteriori error estimator for the space (semi) discretization of the scalar wave equation by finite elements. In the idealized setting where time discretization is ignored and the simulation time is large, we provide fully-guaranteed upper bounds that are asymptotically constant-free and show that the proposed estimator is effici

Shao-Peng Jia, Baijun Li, Ya-Feng Jiao, Hui Jing

We study Einstein-Podolsky-Rosen (EPR) steering properties of quantum phases in the generalized Dicke model (GDM) generated by a cavity Bose-Einstein condensate (BEC) doped with a single impurity. It is shown that the normal and superradiant phases of the GDM exhibit much rich EPR steerability. In the normal phase, there exist one-way EPR steering from the c

Mei Wang, Weihong Deng

Despite great progress in face recognition tasks achieved by deep convolution neural networks (CNNs), these models often face challenges in real world tasks where training images gathered from Internet are different from test images because of different lighting condition, pose and image quality. These factors increase domain discrepancy between training (so

Julian Siegl, Jordi Picó-Cortés, Milena Grifoni

The combined action of a DC bias and a microwave drive on the transport characteristic of a superconductor-quantum dot-superconductor junction is investigated. To cope with time dependent non-equilibrium effects and interactions in the quantum dot, we develop a general formalism for the dynamics of the density operator based on a particle conserving approach

Rickard K. A. Karlsson, Jesse H. Krijthe

A common assumption in causal inference from observational data is that there is no hidden confounding. Yet it is, in general, impossible to verify this assumption from a single dataset. Under the assumption of independent causal mechanisms underlying the data-generating process, we demonstrate a way to detect unobserved confounders when having multiple obse

Ashutosh Singh, Gyan Prakash Singh, Anirudh Pradhan

We investigate the cosmic dynamics of Rastall gravity in non-flat Friedmann-Robertson-Walker (FRW) space-time with barotropic fluid. In this context, we are concerned about the class of model satisfying the affine equation of state. We derive the autonomous system for the Rastall model with barotropic fluid. We apply the derived system to investigate the cri

Rym Jaroudi, Lukáš Malý, Gabriel Eilertsen, B. Tomas Johansson

This paper presents the Standalone Neural ODE (sNODE), a continuous-depth neural ODE model capable of describing a full deep neural network. This uses a novel nonlinear conjugate gradient (NCG) descent optimization scheme for training, where the Sobolev gradient can be incorporated to improve smoothness of model weights. We also present a general formulation

Nicola Bastianello, Ruggero Carli, Sandro Zampieri

In this paper we propose a model-based approach to the design of online optimization algorithms, with the goal of improving the tracking of the solution trajectory (trajectories) w.r.t. state-of-the-art methods. We focus first on quadratic problems with a time-varying linear term, and use digital control tools (a robust internal model principle) to propose a

Luis A. Anchordoqui

Very recently, it was pointed out that when basic ideas of the Swampland program are combined with the cosmological hierarchy problem (i.e. the smallness of the dark energy in Planck units) and together are confronted to experiment lead to the prediction of the existence of a single extra-dimension (dubbed the dark dimension) with characteristic length-scale

Siddharth Barman, Arindam Khan, Arnab Maiti, Ayush Sawarni

We extend the notion of regret with a welfarist perspective. Focussing on the classic multi-armed bandit (MAB) framework, the current work quantifies the performance of bandit algorithms by applying a fundamental welfare function, namely the Nash social welfare (NSW) function. This corresponds to equating algorithm's performance to the geometric mean of its

Paul Brookes, Tikai Chang, Marzena Szymanska, Eytan Grosfeld

Symmetry considerations are key towards our understanding of the fundamental laws of Nature. The presence of a symmetry implies that a physical system is invariant under specific transformations and this invariance may have deep consequences. For instance, symmetry arguments state that a system will remain in its initial state if incentives to actions are eq

Deeksha Varshney, Akshara Prabhakar, Asif Ekbal

Grounding dialogue on external knowledge and interpreting linguistic patterns in dialogue history context, such as ellipsis, anaphora, and co-references is critical for dialogue comprehension and generation. In this paper, we present a novel open-domain dialogue generation model which effectively utilizes the large-scale commonsense and named entity based kn

Jörg K. H. Franke, Frederic Runge, Frank Hutter

Our world is ambiguous and this is reflected in the data we use to train our algorithms. This is particularly true when we try to model natural processes where collected data is affected by noisy measurements and differences in measurement techniques. Sometimes, the process itself is ambiguous, such as in the case of RNA folding, where the same nucleotide se

Tom Bachmann

We show that if G is a finite constant group acting on a scheme X such that the order of G is invertible in the residue fields of X, then the G-equivariant motivic stable homotopy category of X is equivalent to the stabilization of the category of motivic G-spaces with finite \'etale transfers over X at the trivial representation sphere. Along the way we obt

Lin Wang, YongXin Guo, Tao Lin, Xiaoying Tang

Partial client participation has been widely adopted in Federated Learning (FL) to reduce the communication burden efficiently. However, an inadequate client sampling scheme can lead to the selection of unrepresentative subsets, resulting in significant variance in model updates and slowed convergence. Existing sampling methods are either biased or can be fu

Gergely Neu, Julia Olkhovskaya, Matteo Papini, Ludovic Schwartz

We study the Bayesian regret of the renowned Thompson Sampling algorithm in contextual bandits with binary losses and adversarially-selected contexts. We adapt the information-theoretic perspective of \cite{RvR16} to the contextual setting by considering a lifted version of the information ratio defined in terms of the unknown model parameter instead of the

Rajsuryan Singh, Eita Nakamura

We investigate the dynamics of music creation style distributions to understand cultural evolution involving advanced intelligence. Using statistical modelling methods and several musical statistics extracted from datasets of popular music created in Japan and the United States (the US), we explored the dynamics of cluster structures and constructed a fitnes

Jilan Xu, Junlin Hou, Yuejie Zhang, Rui Feng

Weakly Supervised Object Localization (WSOL) aims to localize objects with image-level supervision. Existing works mainly rely on Class Activation Mapping (CAM) derived from a classification model. However, CAM-based methods usually focus on the most discriminative parts of an object (i.e., incomplete localization problem). In this paper, we empirically prov

Woojung Kim, Keondo Park, Kihyuk Sohn, Raphael Shu

With the increasing computing power of edge devices, Federated Learning (FL) emerges to enable model training without privacy concerns. The majority of existing studies assume the data are fully labeled on the client side. In practice, however, the amount of labeled data is often limited. Recently, federated semi-supervised learning (FSSL) is explored as a w

Guo-Qiang Zhang, Wei Feng, Wei Xiong, Qi-Ping Su

Very recently, dissipative coupling was discovered, which develops and broadens methods for controlling and utilizing light-matter interactions. Here, we propose a scheme to generate the tripartite $W$ state in a dissipatively coupled system, where one qubit and two resonators simultaneously interact with a common reservoir. With appropriate parameters, we f

Ramin Safaeian, Saber Salehkaleybar, Mahmoud Tabandeh

Causal relationships among a set of variables are commonly represented by a directed acyclic graph. The orientations of some edges in the causal DAG can be discovered from observational/interventional data. Further edges can be oriented by iteratively applying so-called Meek rules. Inferring edges' orientations from some previously oriented edges, which we c

Yuto Shibata, Manfred Sigrist

We present a study of strain modulation effects on electronic structures of a two-dimensional single-band chiral p-wave superconductor within the BCS mean-field scheme. We employ a lattice model and numerically solve the corresponding Bogolyubov-de Gennes equations. Assuming that strain modulation only modifies hopping amplitudes, we observe the emergence of

Eri Tatsumi, Fernando Tinaut-Ruano, Julia de León, Javier Licandro

Aims. The aim of our study is to obtain the near-ultraviolet to visible (NUV-VIS, 0.35 - 0.95 micron) reflectance spectra of primitive asteroids with a focus on members of the Themis and Polana-Eulalia complex families. This characterization allows us to discuss the origin of two recent sample return mission target asteroids, (162173) Ryugu and (101955) Benn

Duncan Adamson

Unlabelled Necklaces are an equivalence class of cyclic words under both the rotation (cyclic shift) and the relabelling operations. The relabelling of a word is a bijective mapping from the alphabet to itself. The main result of the paper is the first polynomial-time algorithm for ranking unlabelled necklaces of a binary alphabet. The time-complexity of the

Shan Tharanga, Eyyub Selim Unlu, Yongli Hu, Muhammad Farhan Sjaugi

Sequence diversity is one of the major challenges in the design of diagnostic, prophylactic and therapeutic interventions against viruses. DiMA is a novel tool that is big data-ready and designed to facilitate the dissection of sequence diversity dynamics for viruses. DiMA stands out from other diversity analysis tools by offering various unique features. Di

Biao Zhang, Matthias Nießner, Peter Wonka

We propose a new representation for encoding 3D shapes as neural fields. The representation is designed to be compatible with the transformer architecture and to benefit both shape reconstruction and shape generation. Existing works on neural fields are grid-based representations with latents defined on a regular grid. In contrast, we define latents on irreg

Zhishu Sun, Zhifeng Shen, Luojun Lin, Yuanlong Yu

Domain generalization (DG) is a fundamental yet very challenging research topic in machine learning. The existing arts mainly focus on learning domain-invariant features with limited source domains in a static model. Unfortunately, there is a lack of training-free mechanism to adjust the model when generalized to the agnostic target domains. To tackle this p

Zhijie Chen, Chang-Shou Lin, Yifan Yang

In this paper, we consider the following curvature equation $$\Delta u+{\rm e}^u=4\pi\biggl((\theta_0-1)\delta_0+(\theta_1-1)\delta_1 +\sum_{j=1}^{n+m}\bigl(\theta_j'-1\bigr)\delta_{t_j}\biggr)\qquad \text{in}\ \mathbb R^2,$$ $$u(x)=-2(1+\theta_\infty)\ln|x|+O(1)\qquad \text{as} \ |x|\to\infty,$$ where $\theta_0$, $\theta_1$, $\theta_\infty$, and $\theta_{j}

Qinghua Liu, Valeria Vitelli, Carlo Mannino, Arnoldo Frigessi

We propose the Pseudo-Mallows distribution over the set of all permutations of $n$ items, to approximate the posterior distribution with a Mallows likelihood. The Mallows model has been proven to be useful for recommender systems where it can be used to learn personal preferences from highly incomplete data provided by the users. Inference based on MCMC is h

Arya Akhavan, Evgenii Chzhen, Massimiliano Pontil, Alexandre B. Tsybakov

This work studies online zero-order optimization of convex and Lipschitz functions. We present a novel gradient estimator based on two function evaluations and randomization on the $\ell_1$-sphere. Considering different geometries of feasible sets and Lipschitz assumptions we analyse online dual averaging algorithm with our estimator in place of the usual gr

Miklós Z. Horváth, Mark Niklas Müller, Marc Fischer, Martin Vechev

Tree-based models are used in many high-stakes application domains such as finance and medicine, where robustness and interpretability are of utmost importance. Yet, methods for improving and certifying their robustness are severely under-explored, in contrast to those focusing on neural networks. Targeting this important challenge, we propose deterministic

Gopendra Vikram Singh, Priyanshu Priya, Mauajama Firdaus, Asif Ekbal

The long-standing goal of Artificial Intelligence (AI) has been to create human-like conversational systems. Such systems should have the ability to develop an emotional connection with the users, hence emotion recognition in dialogues is an important task. Emotion detection in dialogues is a challenging task because humans usually convey multiple emotions w

Yusuke Hama, Hirofumi Nishi

Near-term quantum computers have been built as intermediate-scale quantum devices and are fragile against quantum noise effects, namely, NISQ devices. Traditional quantum-error-correcting codes are not implemented on such devices and to perform quantum computation in good accuracy with these machines we need to develop alternative approaches for mitigating q

David Mundelius

Consider a Noetherian domain $R$ and a finite group $G \subseteq Gl_n(R)$. We prove that if the ring of invariants $R[x_1, \ldots, x_n]^G$ is a Cohen-Macaulay ring, then it is generated as an $R$-algebra by elements of degree at most $\max(|G|,n(|G|-1))$. As an intermediate result we also show that if $R$ is a Noetherian local ring with infinite residue fiel

Iliya Bluskov, Jan de Heer, Alexander Sidorenko

Tur\'{a}n number $T(n,5,3)$ is the minimum size of a system of triples out of a base set $X$ of $n$ elements such that every quintuple in $X$ contains a triple from the system. The exact values of $T(n,5,3)$ are known for $n \leq 17$. Tur\'{a}n conjectured that $T(2m,5,3) = 2\binom{m}{3}$, and no counterexamples have been found so far. If this conjecture is

Edson Nobuyuki Egashira, Diana Pamela Moya Osorio, Nhan Thanh Nguyen, Markku Juntti

The hybrid relay-reflecting intelligent surface (HR-RIS) has been recently introduced as an efficient solution to overcome the double path loss and limited beamforming diversity of the conventional fully passive reflecting surface. This motivates us to investigate the application of the HR-RIS in improving the secrecy capacity of millimeter wave multiple-inp

Andrea De Domenico, Ali Farjami, Krishna Manoorkar, Alessandra Palmigiano

We establish a novel connection between two research areas in non-classical logics which have been developed independently of each other so far: on the one hand, input/output logic, introduced within a research program developing logical formalizations of normative reasoning in philosophical logic and AI; on the other hand, subordination algebras, investigat

Alexandre Maraval, Matthieu Zimmer, Antoine Grosnit, Rasul Tutunov

Faced with problems of increasing complexity, recent research in Bayesian Optimisation (BO) has focused on adapting deep probabilistic models as flexible alternatives to Gaussian Processes (GPs). In a similar vein, this paper investigates the feasibility of employing state-of-the-art probabilistic transformers in BO. Upon further investigation, we observe tw

Axel Wassington, Sergi Abadal

In general, to draw robust conclusions from a dataset, all the analyzed population must be represented on said dataset. Having a dataset that does not fulfill this condition normally leads to selection bias. Additionally, graphs have been used to model a wide variety of problems. Although synthetic graphs can be used to augment available real graph datasets

Gregor Bachmann, Lorenzo Noci, Thomas Hofmann

While Bayesian neural networks (BNNs) provide a sound and principled alternative to standard neural networks, an artificial sharpening of the posterior usually needs to be applied to reach comparable performance. This is in stark contrast to theory, dictating that given an adequate prior and a well-specified model, the untempered Bayesian posterior should ac

Lorena C. Bianchet, Natalia Alves, Laura Zarraoa, Tomas Lamich

We present precise, sub-wavelength optical intensity measurement using a single trapped $^{87}$Rb atom as a sensor. The intensity is measured by the scalar ac Stark shift it produces on the $F=1 \rightarrow F'=2$ hyperfine transition of the D$_{2}$ line, chosen for its $F' = F+1$ structure and very small tensor polarizability. To boost signal and reduce meas

Santeri Karppinen, Sumeetpal S. Singh, Matti Vihola

Conditional particle filters (CPFs) with backward/ancestor sampling are powerful methods for sampling from the posterior distribution of the latent states of a dynamic model such as a hidden Markov model. However, the performance of these methods deteriorates with models involving weakly informative observations and/or slowly mixing dynamics. Both of these c

Sunita Khod, Akshay Dvivedi, Mayank Goswami

The 3D printing process flow requires several inputs for the best printing quality. These settings may vary from sample to sample, printer to printer, and depend upon users' previous experience. The involved operational parameters for 3D Printing are varied to test the optimality. Thirty-eight samples are printed using four commercially available 3D printers

Ransell D'Souza, José D. Querales-Flores, Jiang Cao, Stephen Fahy

Achieving high valley degeneracy (i.e. "band convergence") in a material usually results in considerably enhanced thermoelectric properties. However, it is still unclear why this strategy of designing efficient thermoelectric materials is so successful, since the benefit of increased density of states may be severely degraded by intervalley scattering. Using

Michaela Mihoková

Recurrence quantification analysis is a method for measuring the complexity of dynamical systems. Recurrence determinism is a fundamental characteristic of it, closely related to correlation sum. In this paper, we study asymptotic behavior of these quantities for interval maps. We show for which cases the asymptotic correlation sum exists. An example of an i

Chandra Shekhar, Jagnyashini Debadarshini, Sudipta Saha

Detection and classification of vehicles are very significant components in an Intelligent-Transportation System. Existing solutions not only use heavy-weight and costly equipment, but also largely depend on constant cloud (Internet) connectivity, as well as adequate uninterrupted power-supply. Such dependencies make these solutions fundamentally impractical

Sergei P. Maydanyuk, Kostiantyn A. Shaulskyi

Pycnonuclear reactions in the compact stars at zero temperatures are studied on quantum mechanical basis in the paper. Formalism of multiple internal reflections is generalized for analysis, that was developed for nuclear decays and captures by nuclei with high precision and tests. For the chosen reaction $^{12}$C + $^{12}$C = $^{24}$Mg, we find the followin

Sanne ter Horst, Alma van der Merwe

Nevanlinna-Pick interpolation developed from a topic in classical complex analysis to a useful tool for solving various problems in control theory and electrical engineering. Over the years many extensions of the original problem were considered, including extensions to different function spaces, nonstationary problems, several variable settings and interpol

Yanqing Wang, Yike Huang, Wei Wei, Huan Yu

In this paper, we establish several new anisotropic Hardy-Sobolev inequalities in mixed Lebesgue spaces and mixed Lorentz spaces, which covers many known corresponding results. As an application, this type of inequalities allows us to generalize some regularity criteria of the 3D axisymmetric Navier-Stokes equations.

Runlin Lei, Zhen Wang, Yaliang Li, Bolin Ding

Graph Neural Networks (GNNs) have received extensive research attention for their promising performance in graph machine learning. Despite their extraordinary predictive accuracy, existing approaches, such as GCN and GPRGNN, are not robust in the face of homophily changes on test graphs, rendering these models vulnerable to graph structural attacks and with

Yongyi Yang, Zengfeng Huang, David Wipf

Deep learning models such as the Transformer are often constructed by heuristics and experience. To provide a complementary foundation, in this work we study the following problem: Is it possible to find an energy function underlying the Transformer model, such that descent steps along this energy correspond with the Transformer forward pass? By finding such

Tuomas Orponen, Pablo Shmerkin

We prove two new exceptional set estimates for radial projections in the plane. If $K \subset \mathbb{R}^{2}$ is a Borel set with $\dim_{\mathrm{H}} K > 1$, then $$\dim_{\mathrm{H}} \{x \in \mathbb{R}^{2} \, \setminus \, K : \dim_{\mathrm{H}} \pi_{x}(K) \leq \sigma\} \leq \max\{1 + \sigma - \dim_{\mathrm{H}} K,0\}, \qquad \sigma \in [0,1).$$ If $K \subset \m

Maciej Rybczyński, Zbigniew Włodarczyk

The initial time-dependence of a state in circumstances where it makes transitions to, or decay to, a second state has been investigated. In classical stochastic processes, the observed time dependence of transition or decay proportional to $t^2$ is attributed to the noise with memory. In contrast to quantum mechanics, the quadratic form of initial decay is

Jiawei Liu, Guopeng Zhang, Kezhi Wang, Kun Yang

Federated learning (FL) has been proposed as a popular learning framework to protect the users' data privacy but it has difficulties in motivating the users to participate in task training. This paper proposes a Bertrand-game-based framework for FL in wireless networks, where the model server as a resource buyer can issue an FL task, whereas the employed use

Mahmoud Saad Mehany, Qing-Wen Wang

This paper investigates the necessary and sufficient algebraic conditions to a constrained system of Sylvester-type quaternion tensor equations. An explicit formula of the general solution regarding the Moore-Penrose inverses of some block given tensors is obtained. As an application of a particular case, we establish the solvability conditions and the gener

J. M. Alcaraz-Pelegrina, M. C. Gordillo

We used a diffusion Monte Carlo technique to describe the properties of fully-heavy compact arrangements (no dibaryon molecules) including six quarks and no antiquarks within the framewok of a constituent quark model. Only arrangements whose wavefunctions were eigenvectors of $L^2$ with eigenvalue $\ell$ = 0 were taken into account, what means that we only c

Vera Delfavero, Richard O'Shaughnessy, Daniel Wysocki, Anjali Yelikar

Gravitational-wave observations of quasicircular compact binary mergers imply complicated posterior measurements of their parameters. Though Gaussian approximations to the pertinent likelihoods have decades of history in the field, the relative generality and practical utility of these approximations hasn't been appreciated, given focus on careful, comprehen

Myrsini Gkolemi, Panagiotis Papadopoulos, Evangelos P. Markatos, Nicolas Kourtellis

In the last years, hundreds of new Youtube channels have been creating and sharing videos targeting children, with themes related to animation, superhero movies, comics, etc. Unfortunately, many of these videos are inappropriate for consumption by their target audience, due to disturbing, violent, or sexual scenes. In this paper, we study YouTube channels fo

Shrey Gupta, Anmol Agarwal, Manas Gaur, Kaushik Roy

Conversational Agents (CAs) powered with deep language models (DLMs) have shown tremendous promise in the domain of mental health. Prominently, the CAs have been used to provide informational or therapeutic services to patients. However, the utility of CAs to assist in mental health triaging has not been explored in the existing work as it requires a control

Dinara Rzayeva, Saber Malekzadeh

Detection of a Fraud transaction on credit cards became one of the major problems for financial institutions, organizations and companies. As the global financial system is highly connected to non-cash transactions and online operations fraud makers invent more effective ways to access customers' finances. The main problem in credit card fraud detection is t

Emetis Niazmand, Gezim Sejdiu, Damien Graux, Maria-Esther Vidal

Knowledge Graphs (KGs) integrate heterogeneous data, but one challenge is the development of efficient tools for allowing end users to extract useful insights from these sources of knowledge. In such a context, reducing the size of a Resource Description Framework (RDF) graph while preserving all information can speed up query engines by limiting data shuffl

George Kappos, Haaroon Yousaf, Rainer Stütz, Sofia Rollet

One of the defining features of Bitcoin and the thousands of cryptocurrencies that have been derived from it is a globally visible transaction ledger. While Bitcoin uses pseudonyms as a way to hide the identity of its participants, a long line of research has demonstrated that Bitcoin is not anonymous. This has been perhaps best exemplified by the developmen