July 2022 arXiv papers — page 68

Luca Benatti, Mattia Fogagnolo, Lorenzo Mazzieri

We study the asymptotic behaviour of the $p$-capacitary potential and of the weak Inverse Mean Curvature Flow of a bounded set along the ends of an Asymptotically Conical Riemannian manifolds with asymptotically nonnegative Ricci curvature.

Mohammad Amin Zare Pour, Oleksandr Romanyuk, Dominik C. Moritz, Agnieszka Paszuk

Lattice matched n-type AlInP(100) charge selective contacts are commonly grown on n-p GaInP(100) top absorbers in high-efficiency III-V multijunction solar or photoelectrochemical cells. The cell performance can be greatly limited by the electron selectivity and valance band offset at this heterointerface. Understanding of the atomic and electronic propertie

Subhankar Roy, Mingxuan Liu, Zhun Zhong, Nicu Sebe

We study the new task of class-incremental Novel Class Discovery (class-iNCD), which refers to the problem of discovering novel categories in an unlabelled data set by leveraging a pre-trained model that has been trained on a labelled data set containing disjoint yet related categories. Apart from discovering novel classes, we also aim at preserving the abil

Xinglin Wand, Wenxiang Yan, Yuan Gao, Zheng Yuan

Structured optical fields embedded with polarization singularities (PSs) have attracted extensive attention due to their capability to retain topological invariance during propagation. Many advances in PSs research have been made over the past 20 years in the areas of mathematical description, generation and detection technologies, propagation dynamics, and

Fabio Massimo Zennaro

Structural causal models (SCMs) are a widespread formalism to deal with causal systems. A recent direction of research has considered the problem of relating formally SCMs at different levels of abstraction, by defining maps between SCMs and imposing a requirement of interventional consistency. This paper offers a review of the solutions proposed so far, foc

Yinghui Xing, Yan Zhang, Houjun He, Xiuwei Zhang

The process of fusing a high spatial resolution (HR) panchromatic (PAN) image and a low spatial resolution (LR) multispectral (MS) image to obtain an HRMS image is known as pansharpening. With the development of convolutional neural networks, the performance of pansharpening methods has been improved, however, the blurry effects and the spectral distortion s

Ri Cheng, Yuqi Sun, Bo Yan, Weimin Tan

Recent multi-view multimedia applications struggle between high-resolution (HR) visual experience and storage or bandwidth constraints. Therefore, this paper proposes a Multi-View Image Super-Resolution (MVISR) task. It aims to increase the resolution of multi-view images captured from the same scene. One solution is to apply image or video super-resolution

Remo Burn, Alexandre Emsenhuber, Jesse Weder, Oliver Völkel

Millimeter astronomy provides valuable information on the birthplaces of planetary systems. In order to compare theoretical models with observations, the dust component has to be carefully calculated. Here, we aim to study the effects of dust entrainment in photoevaporative winds and the ejection and drag of dust due to effects caused by radiation from the c

Richard Comploi-Taupe, Giulia Francescutto, Gottfried Schenner

In this paper, we apply incremental answer set solving to product configuration. Incremental answer set solving is a step-wise incremental approach to Answer Set Programming (ASP). We demonstrate how to use this technique to solve product configurations problems incrementally. Every step of the incremental solving process corresponds to a predefined configur

Niclas Eich, Martin Erdmann, Peter Fackeldey, Benjamin Fischer

The development of an LHC physics analysis involves numerous investigations that require the repeated processing of terabytes of data. Thus, a rapid completion of each of these analysis cycles is central to mastering the science project. We present a solution to efficiently handle and accelerate physics analyses on small-size institute clusters. Our solution

Hossein Hajiabolhassan, Zahra Taheri, Ali Hojatnia, Yavar Taheri Yeganeh

Learning expressive molecular representations is crucial to facilitate the accurate prediction of molecular properties. Despite the significant advancement of graph neural networks (GNNs) in molecular representation learning, they generally face limitations such as neighbors-explosion, under-reaching, over-smoothing, and over-squashing. Also, GNNs usually ha

Wenjie Liu, Jian Sun, Gang Wang, Francesco Bullo

Self-triggered control, a well-documented technique for reducing the communication overhead while ensuring desired system performance, is gaining increasing popularity. However, existing methods for self-triggered control require explicit system models that are assumed perfectly known a priori. An end-to-end control paradigm known as data-driven control lear

Debraj Chandra, Nur Alam

The star versions of the Scheepers property, namely star-Scheepers, strongly star-Scheepers and new star-Scheepers property have been introduced. We explore further ramifications concerning critical cardinalities. Quite a few interesting observations are obtained while dealing with the Isbell-Mr\'{o}wka spaces, Niemytzki plane and Alexandroff duplicates. The

E. S. Andriyakhina, I. S. Burmistrov

The interplay of Anderson localization and electron-electron interactions is known to lead to enhancement of superconductivity due to multifractality of electron wave functions. We develop the theory of multifractally-enhanced superconducting states in two-dimensional systems in the presence of spin-orbit coupling. Using the Finkel'stein nonlinear sigma mode

Hua-Xing Chen, Yi-Xin Yan, Wei Chen

We study the decay behaviors of the fully-bottom tetraquark states within the diquark-antidiquark picture, and calculate their relative branching ratios through the Fierz rearrangement. Our results suggest that the $C=+$ states can be searched for in the $\mu^+ \mu^- \Upsilon(1S)$ and $\mu^+ \mu^- \Upsilon(2S)$ channels with the relative branching ratio $\ma

Tal Amir, Shahar Kovalsky, Nadav Dym

The classical $\textit{Procrustes}$ problem is to find a rigid motion (orthogonal transformation and translation) that best aligns two given point-sets in the least-squares sense. The $\textit{Robust Procrustes}$ problem is an important variant, in which a power-1 objective is used instead of least squares to improve robustness to outliers. While the optimal

Wenyi Li, Shengjie Zheng, Yufan Liao, Rongqi Hong

Decoding images from brain activity has been a challenge. Owing to the development of deep learning, there are available tools to solve this problem. The decoded image, which aims to map neural spike trains to low-level visual features and high-level semantic information space. Recently, there are a few studies of decoding from spike trains, however, these s

Pascal Grange

The voter model is a toy model of consensus formation based on nearest-neighbor interactions. A voter sits at each vertex in a hypercubic lattice (of dimension $d$) and is in one of two possible opinion states. The opinion state of each voter flips randomly, at a rate proportional to the fraction of the nearest neighbors that disagree with the voter. If the

Xu Chen, Junjie Wang, Tianping Ying, Dajian Huang

MPX3 (M=metals, X=S or Se) represents a large family of van der Waals (vdW) materials featuring with P-P dimers of ~2.3 {\AA} separation. Its electrical transport property and structure can hardly be tuned by the intentional chemical doping and ionic intercalation. Here, we employ an entropy-enhancement strategy to successfully obtain a series of medium-entr

Asil Koc, Tho Le-Ngoc

This paper proposes a novel nature-inspired $\alpha$-fair hybrid precoding (NI-$\alpha$HP) technique for millimeter-wave multi-user massive multiple-input multiple-output systems. Unlike the existing HP literature, we propose to apply $\alpha$-fairness for maintaining various fairness expectations (e.g., sum-rate maximization, proportional fairness, max-min

Iain Hammond, Valentin Christiaens, Daniel J. Price, Maria Giulia Ubeira-Gabellini

We present new high-contrast images in near-infrared wavelengths ($\lambda$ = 1.04, 1.24, 1.62, 2.18 and 3.78$\mu$m) of the young variable star CQ Tau, aiming to constrain the presence of companions in the protoplanetary disc. We reached a Ks-band contrast of 14 magnitudes with SPHERE/IRDIS at separations greater than 0."4 from the star. Our mass sensitivity

Peter Marvin Müller, Martin Siebenborn, Thomas Rung

The paper is concerned with the minimal drag problem in shape optimization of merchant ships exposed to turbulent two-phase flows. Attention is directed to the solution of Reynolds Averaged Navier-Stokes equations using a Finite Volume method. Central aspects are the use of a p-Laplacian relaxed steepest descent direction and the introduction of crucial tech

Pranay Gorantla, Ho Tat Lam, Shu-Heng Shao

We introduce two exotic lattice models on a general spatial graph. The first one is a matter theory of a compact Lifshitz scalar field, while the second one is a certain rank-2 $U(1)$ gauge theory of fractons. Both lattice models are defined via the discrete Laplacian operator on a general graph. We unveil an intriguing correspondence between the physical ob

Xuan Chen, Thomas Gehrmann, Nigel Glover, Alexander Huss

Charged current Drell-Yan production at hadron colliders is a benchmark electroweak process. A recent measurement of the W boson mass by the CDF experiment displays a large deviation from the Standard Model prediction. To enable precision phenomenology for this process, we compute the third-order (N$^3$LO) QCD corrections to the rapidity distribution in W bo

Domenic Donato, Lei Yu, Wang Ling, Chris Dyer

We introduce a new distributed policy gradient algorithm and show that it outperforms existing reward-aware training procedures such as REINFORCE, minimum risk training (MRT) and proximal policy optimization (PPO) in terms of training stability and generalization performance when optimizing machine translation models. Our algorithm, which we call MAD (on acc

Ryan Cardman, Georg Raithel

We measure the hyperfine structure of $nP_{1/2}$ Rydberg states using mm-wave spectroscopy on an ensemble of laser-cooled $^{85}$Rb atoms. Systematic uncertainties in our measurement from the Zeeman splittings induced by stray magnetic fields and dipole-dipole interactions between two Rydberg atoms are factored in with the obtained statistical uncertainty. O

Qingyang Zhong, Jifan Yu, Zheyuan Zhang, Yiming Mao

Adaptive learning aims to stimulate and meet the needs of individual learners, which requires sophisticated system-level coordination of diverse tasks, including modeling learning resources, estimating student states, and making personalized recommendations. Existing deep learning methods have achieved great success over statistical models; however, they sti

Judith Sáinz-Pardo Díaz, Álvaro López García

Federated learning is a data decentralization privacy-preserving technique used to perform machine or deep learning in a secure way. In this paper we present theoretical aspects about federated learning, such as the presentation of an aggregation operator, different types of federated learning, and issues to be taken into account in relation to the distribut

Simran Arora, Monal Kashav, Surender Verma, B. C. Chauhan

The latest results of CDF-II collaboration show a discrepancy of $7\sigma$ with standard model expectations. There is, also, a $4.2\sigma$ discrepancy in the measurement of muon magnetic moment reported by Fermilab. We study the connection between neutrino masses, dark matter, muon ($g-2$) and W-boson mass anomaly within a single coherent framework based on

Jorge Fandinno, Vladimir Lifschitz

Theory of stable models is the mathematical basis of answer set programming. Several results in that theory refer to the concept of the positive dependency graph of a logic program. We describe a modification of that concept and show that the new understanding of positive dependency makes it possible to strengthen some of these results. Under consideration i

R. P. L. Azevedo

In the standard model of cosmology, the background evolution of the Universe can in general be adequately described by general relativity and a uniform and isotropic metric minimally coupled with a collection of perfect fluids. These fluids are usually described by their energy-momentum tensor, which can be derived from the fluid's Lagrangian density. Under

Zakariae Aznay, Abdelmalek Ouahab, Hassan Zariouh

We study the stability of certain spectra under some algebraic conditions weaker than the commutativity and we generalize many known commutative perturbation results.

Haoyu Ren, Kirill Dorofeev, Darko Anicic, Youssef Hammad

Internet of Things (IoT) is transforming the industry by bridging the gap between Information Technology (IT) and Operational Technology (OT). Machines are being integrated with connected sensors and managed by intelligent analytics applications, accelerating digital transformation and business operations. Bringing Machine Learning (ML) to industrial devices

Méril Reboud

I present EOS, an open-source software dedicated to a variety of tasks in the processing of flavor physics observables. EOS is written in C++ and offers both a C++ and a Python interface. It is developed for three main tasks, the production of theoretical predictions for flavor physics observables; the inference of theoretical parameters from an extensible d

Andronikos Paliathanasis

The Noether symmetry analysis is applied for the analysis of the field equations in an anisotropic background in $f(T,B)$-theory. We consider the $f\left( T,B\right) =T+F\left( B\right) $ which describes a small deviation from TEGR introduced by the boundary scalar $B$. For the Bianchi\ I, Bianchi III and Kantowski-Sachs geometries there exists a minisupersp

David Cohen, Tal Shnitzer, Yuval Kluger, Ronen Talmon

In this paper, we present a new method for few-sample supervised feature selection (FS). Our method first learns the manifold of the feature space of each class using kernels capturing multi-feature associations. Then, based on Riemannian geometry, a composite kernel is computed, extracting the differences between the learned feature associations. Finally, a

Sergio Da Silva, Megumi Harada

We initiate a study of the Gr\"obner geometry of local defining ideals of Hessenberg varieties by studying the special case of regular nilpotent Hessenberg varieties in Lie type A, and focusing on the affine coordinate chart on $\mathrm{Flags}(\mathbb{C}^n) \cong GL_n(\mathbb{C})/B$ corresponding to the longest element $w_0$ of the Weyl group $S_n$ of $GL_n(

Ján Komara

This report presents an elementary theory of unification for positive conjunctive queries. A positive conjunctive query is a formula constructed from propositional constants, equations and atoms using the conjunction $\wedge$ and the existential quantifier $\exists$. In particular, empty queries correspond to existentially quantified systems of equations --

Genly Leon, Andronikos Paliathanasis

We study the asymptotic dynamics of $f(T, B)$-theory in an anisotropic Bianchi III background geometry. We show that an attractor always exists for the field equations, which depends on a free parameter provided by the specific $f(T, B)$ functional form. The attractor is an accelerated spatially flat FLRW or non-accelerated LRS Bianchi III geometry. Conseque

Genly Leon, Andronikos Paliathanasis

In the context of the modified teleparallel $f(T, B)$-theory of gravity, we consider a homogeneous and anisotropic background geometry described by the Kantowski-Sachs line element. We derive the field equations and investigate the existence of exact solutions. Furthermore, the evolution of the trajectories for the field equations is studied by deriving the

Dimitrios Konstantinidis, Ilias Papastratis, Kosmas Dimitropoulos, Petros Daras

Vision Transformers are very popular nowadays due to their state-of-the-art performance in several computer vision tasks, such as image classification and action recognition. Although their performance has been greatly enhanced through highly descriptive patch embeddings and hierarchical structures, there is still limited research on utilizing additional dat

Zhi-zhong Xing

The present neutrino oscillation data allow $m^{}_1 = 0$ (or $m^{}_3 = 0$) for the neutrino mass spectrum and support $\theta^{}_{23} \simeq \pi/4$ and $\delta \simeq -\pi/2$ as two good approximations for the PMNS lepton flavor mixing matrix $U$. We show that these intriguing possibilities can be a very natural consequence of the {\it translational} $\mu$-$

Andronikos Paliathanasis

That is the first part of a series of studies on analyzing the higher-order teleparallel theory of gravity known as the $f(T, B)$-theory. This work attempts to understand how the anisotropic spacetimes are involved in the $f(T, B)$-theory. In this work, we review the previous analysis of $f(T, B)$-gravity, and we investigate the global dynamics in the case o

Lucas Winter, Sebastian Großenbach, Ulrich Nowak, Levente Rózsa

It was demonstrated recently that on ultrashort time scales magnetization dynamics does not only exhibit precession but also nutation. Here, we investigate how nutation can contribute to spin switching leading towards ultrafast data writing. We use analytic theory and atomistic spin simulations to discuss the behavior of ferromagnets and antiferromagnets in

Vladimir V. Bytev, Bernd A. Kniehl, Oleg L. Veretin

Starting from the Mellin-Barnes integral representation of a Feynman integral depending on set of kinematic variables $z_i$, we derive a system of partial differential equations w.r.t.\ new variables $x_j$, which parameterize the differentiable constraints $z_i=y_i(x_j)$. In our algorithm, the powers of propagators can be considered as arbitrary parameters.

Sameh A. Eisa, Sameer Pokhrel

For centuries, soaring birds -- such as albatrosses and eagles -- have been mysterious and intriguing for biologists, physicists, aeronautical/control engineers, and applied mathematicians. These fascinating biological organisms have the ability to fly for long-duration while spending little to no energy. This flight technique/maneuver is called dynamic soar

Teng Ji, Xiang-Kun Dong, Miguel Albaladejo, Meng-Lin Du

Recently, the LHCb Collaboration reported a near-threshold enhancement, $X(3960)$, in the $D_s^+D_s^-$ invariant mass distribution. We show that the data can be well described by either a bound or a virtual state below the $D_s^+D_s^-$ threshold. The mass given by the pole position is $(3928\pm3)$ MeV. Using this mass and the existing information on the $X(3

Haoran Luo, Haihong E, Ling Tan, Gengxian Zhou

In the field of representation learning on knowledge graphs (KGs), a hyper-relational fact consists of a main triple and several auxiliary attribute-value descriptions, which is considered more comprehensive and specific than a triple-based fact. However, currently available hyper-relational KG embedding methods in a single view are limited in application be

Aikaterini Mandilara, Daniil Fedotov, Vladimir M. Akulin

Many quantum algorithms can be seen as a transition from a well-defined initial quantum state of a complex quantum system, to an unknown target quantum state, corresponding to a certain eigenvalue either of the Hamiltonian or of a transition operator. Often such a target state corresponds to the minimum energy of a band of states. In this context, approximat

Zixing Lei, Shunli Ren, Yue Hu, Wenjun Zhang

Collaborative perception has recently shown great potential to improve perception capabilities over single-agent perception. Existing collaborative perception methods usually consider an ideal communication environment. However, in practice, the communication system inevitably suffers from latency issues, causing potential performance degradation and high ri

S. A. Seyed Fakhari

Assume that $G$ is a graph with edge ideal $I(G)$ and matching number ${\rm match}(G)$. For every integer $s\geq 1$, we denote the $s$-th squarefree power of $I(G)$ by $I(G)^{[s]}$. It is shown that for every positive integer $s\leq {\rm match}(G)$, the inequality ${\rm reg}(I(G)^{[s]})\leq {\rm match}(G)+s$ holds provided that $G$ belongs to either of the f

Georg Engelhardt, Sayan Choudhury, W. Vincent Liu

Periodically-driven quantum systems can exhibit a plethora of intriguing non-equilibrium phenomena that can be analyzed using Floquet theory. Naturally, Floquet theory is employed to describe the dynamics of atoms interacting with intense laser fields. However, this semiclassical analysis can not account for quantum-optical phenomena that rely on the quantiz

Ahmad Shapiro, Ayman Khalafallah, Marwan Torki

Online presence on social media platforms such as Facebook and Twitter has become a daily habit for internet users. Despite the vast amount of services the platforms offer for their users, users suffer from cyber-bullying, which further leads to mental abuse and may escalate to cause physical harm to individuals or targeted groups. In this paper, we present

Xudong Pan, Qifan Xiao, Mi Zhang, Min Yang

Self-driving cars (SDC) commonly implement the perception pipeline to detect the surrounding obstacles and track their moving trajectories, which lays the ground for the subsequent driving decision making process. Although the security of obstacle detection in SDC is intensively studied, not until very recently the attackers start to exploit the vulnerabilit

Nils Berglund, Tom Klose

We give a relatively short, almost self-contained proof of the fact that the partition function of the suitably renormalised $\Phi^4_3$ measure admits an asymptotic expansion, the coefficients of which converge as the ultraviolet cut-off is removed. We also examine the question of Borel summability of the asymptotic series. The proofs are based on Wiener cha

Lorenzo Carlucci, Leonardo Mainardi

When the Canonical Ramsey's Theorem by Erd\H{o}s and Rado is applied to regressive functions one obtains the Regressive Ramsey's Theorem by Kanamori and McAloon. Taylor proved a "canonical" version of Hindman's Theorem, analogous to the Canonical Ramsey's Theorem. We introduce the restriction of Taylor's Canonical Hindman's Theorem to a subclass of the regre

Bernd A. Kniehl, Oleg L. Veretin

We consider the renormalization of the three-quark operators without derivatives at next-to-next-to-leading order in QCD perturbation theory at the symmetric subtraction point. This allows us to obtain conversion factors between the $\bar{\rm MS}$ scheme and the regularization invariant symmetric MOM (RI/SMOM, RI${}'$/SMOM) schemes. The results are presented

Han-Jie Zhu, Xiao-Ming Zhao, Jin-Kui Zhao, Lin Zhuang

We investigate polariton-phonon hybrid excitations, which describe the collective excitations of emitter-photon polaritons and vibrational phonons, in a periodic array of vibrating two-level emitters interacting with waveguide photons. We demonstrate the emergence of an interaction-induced quasiperiodic structure caused by the interplay between phonon scatte

Mareike Hasenpflug, Daniel Rudolf, Björn Sprungk

For $\ell\colon \mathbb{R}^d \to [0,\infty)$ we consider the sequence of probability measures $\left(\mu_n\right)_{n \in \mathbb{N}}$, where $\mu_n$ is determined by a density that is proportional to $\exp(-n\ell)$. We allow for infinitely many global minimal points of $\ell$, as long as they form a finite union of compact manifolds. In this scenario, we sho

Uwe Schwiegelshohn

We investigate deterministic non-preemptive online scheduling with delayed commitment for total completion time minimization on parallel identical machines. In this problem, jobs arrive one-by-one and their processing times are revealed upon arrival. An online algorithm can assign a job to a machine at any time after its arrival. We neither allow preemption

Xinyu Shi, Dong Wei, Yu Zhang, Donghuan Lu

Research into Few-shot Semantic Segmentation (FSS) has attracted great attention, with the goal to segment target objects in a query image given only a few annotated support images of the target class. A key to this challenging task is to fully utilize the information in the support images by exploiting fine-grained correlations between the query and support

Manu Joseph, Harsh Raj

We propose a novel high-performance, interpretable, and parameter \& computationally efficient deep learning architecture for tabular data, Gated Adaptive Network for Deep Automated Learning of Features (GANDALF). GANDALF relies on a new tabular processing unit with a gating mechanism and in-built feature selection called Gated Feature Learning Unit (GFLU) a

Hegui Zhu, Zhan Gao, Jiayi Wang, Yange Zhou

Traditional fine-grained image classification typically relies on large-scale training samples with annotated ground-truth. However, some sub-categories have few available samples in real-world applications, and current few-shot models still have difficulty in distinguishing subtle differences among fine-grained categories. To solve this challenge, we propos

Mukesh Singh, Alok Shukla, Brahmananda Charkraborty

Expanding pollution and rapid consumption of natural reservoirs (gas, oil, and coal) led humankind to explore alternative energy fuels like hydrogen fuel. Solid-state hydrogen storage is most desirable because of its usefulness in the onboard vehicle. In this work, we explored the yttrium decorated ultra porous, two-dimensional holey-graphyne for hydrogen st

Ludovico Marini, Stefano Meda, Stefano Pigola, Giona Veronelli

In this paper we investigate the validity of first and second order $L^{p}$ estimates for the solutions of the Poisson equation depending on the geometry of the underlying manifold. We first present $L^{p}$ estimates of the gradient under the assumption that the Ricci tensor is lower bounded in a local integral sense and construct the first counterexample sh

Caglar Demir, Axel-Cyrille Ngonga Ngomo

Knowledge graph embedding research has mainly focused on learning continuous representations of knowledge graphs towards the link prediction problem. Recently developed frameworks can be effectively applied in research related applications. Yet, these frameworks do not fulfill many requirements of real-world applications. As the size of the knowledge graph g

Samson Saneblidze, Ronald Umble

We prove that the formula for the diagonal approximation $\Delta_{K}$ on J. Stasheff's $n$-dimensional associahedron $K_{n+2}$ derived by the current authors in 2004 agrees with the "magical formula" for the diagonal approximation $\Delta_{K}^{\prime}$ derived by Markl and Shnider in 2006, by Loday in 2011, and by Masuda, Thomas, Tonks, and Vallette in 2021.

S. Ren

We consider the random hypergraph on a finite vertex set by choosing each set of vertices as an hyperedge independently at random. We express the probability distributions of the (lower-)associated simplicial complex and the (lower-)associated independence hypergraph of the random hypergraph in terms of the probability distributions of certain random simplic

Maryam Mohammadi Saem, Ionel-Dumitrel Ghiba, Patrizio Neff

Using $\Gamma$-convergence arguments, we construct a nonlinear membrane-like Cosserat shell model on a curvy reference configuration starting from a geometrically nonlinear, physically linear three-dimensional isotropic Cosserat model. Even if the theory is of order $O(h)$ in the shell thickness $h$, by comparison to the membrane shell models proposed in cla

Wei Jiang, Gang Li, Yibo Wang, Lijun Zhang

Variance reduction techniques such as SPIDER/SARAH/STORM have been extensively studied to improve the convergence rates of stochastic non-convex optimization, which usually maintain and update a sequence of estimators for a single function across iterations. What if we need to track multiple functional mappings across iterations but only with access to stoch

Stefan Schacht

Recent LHCb data shows that the direct CP asymmetries of the decay modes $D^0\rightarrow \pi^+\pi^-$ and $D^0\rightarrow K^+K^-$ have the same sign, violating an improved $U$-spin limit sum rule in an unexpected way at $2.1\sigma$. From the new data, we determine for the first time the imaginary part of the CKM-subleading, $U$-spin breaking $\Delta U=1$ corr

E. Oset, L. Roca

We evaluate theoretically the interaction of the open bottom and strange systems $\bar B\bar K$, $\bar B^* \bar K$, $\bar B\bar K^*$ and $\bar B^*\bar K^*$ to look for possible bound states which could correspond to exotic non--quark-antiquark mesons since they would contain at least one $b$ and one $s$ quarks. The s-wave scattering matrix is evaluated imple

Alexey Kurennoy, John Coleman, Ian Harris, Alice Lynch

Pairwise debiasing is one of the most effective strategies in reducing position bias in learning-to-rank (LTR) models. However, limiting the scope of this strategy, are the underlying assumptions required by many pairwise debiasing approaches. In this paper, we develop an approach based on a minimalistic set of assumptions that can be applied to a much broad

Zequn Qin, Jingyu Chen, Chao Chen, Xiaozhi Chen

Bird's eye view (BEV) representation is a new perception formulation for autonomous driving, which is based on spatial fusion. Further, temporal fusion is also introduced in BEV representation and gains great success. In this work, we propose a new method that unifies both spatial and temporal fusion and merges them into a unified mathematical formulation. T

Yilin Li, Wang Miao, Ilya Shpitser, Eric J. Tchetgen Tchetgen

We introduce a self-censoring model for multivariate nonignorable nonmonotone missing data, where the missingness process of each outcome is affected by its own value and is associated with missingness indicators of other outcomes, while conditionally independent of the other outcomes. The self-censoring model complements previous graphical approaches for th

Or Alon-Ronen, Yosi Shrem, Yossi Keshet, Eva Gilboa-Schechtman

Background - Social anxiety (SA) is a common and debilitating condition, negatively affecting life quality even at sub-diagnostic thresholds. We sought to characterize SA's acoustic signature using hypothesis-testing and machine learning (ML) approaches. Methods - Participants formed spontaneous utterances responding to instructions to refuse or consent to c

Yi Zeng, Dongcheng Zhao, Feifei Zhao, Guobin Shen

Spiking neural networks (SNNs) have attracted extensive attentions in Brain-inspired Artificial Intelligence and computational neuroscience. They can be used to simulate biological information processing in the brain at multiple scales. More importantly, SNNs serve as an appropriate level of abstraction to bring inspirations from brain and cognition to Artif

Sadra Boreiri, Antoine Girardin, Bora Ulu, Patryk Lypka-Bartosik

The network scenario offers interesting new perspectives on the phenomenon of quantum nonlocality. Notably, when considering networks with independent sources, it is possible to demonstrate quantum nonlocality without the need for measurements inputs, i.e. with all parties performing a fixed quantum measurement. Here we aim to find minimal examples of this e

Liang Peng, Xiaopei Wu, Zheng Yang, Haifeng Liu

Monocular 3D detection has drawn much attention from the community due to its low cost and setup simplicity. It takes an RGB image as input and predicts 3D boxes in the 3D space. The most challenging sub-task lies in the instance depth estimation. Previous works usually use a direct estimation method. However, in this paper we point out that the instance dep

D. M. Bowman

In recent years, it has been discovered that massive stars commonly exhibit a non-coherent form of variability in their light curves referred to as stochastic low frequency (SLF) variability. Various physical mechanisms can produce SLF variability in such stars, including stochastic gravity waves excited at the interface of convective and radiative regions,

Pierre Talbot, Frédéric Pinel, Pascal Bouvry

The number of cores on graphical computing units (GPUs) is reaching thousands nowadays, whereas the clock speed of processors stagnates. Unfortunately, constraint programming solvers do not take advantage yet of GPU parallelism. One reason is that constraint solvers were primarily designed within the mental frame of sequential computation. To solve this issu

Kai Töpfer, Andrea Pasti, Anuradha Das, Seyedeh Maryam Salehi

The spectroscopy and structural dynamics of a deep eutectic mixture (KSCN/acetamide) with varying water content is investigated from 2D IR (with the C-N stretch vibration of the SCN$^-$ anions as the reporter) and THz spectroscopy. Molecular dynamics simulations correctly describe the non-trivial dependence of both spectroscopic signatures depending on water

Jonas Bekaert, Levie Bringmans, Milorad Milosevic

We determine the energy of an interface between a multiband superconducting and a normal half-space, in presence of an applied magnetic field, based on a multiband Ginzburg-Landau (GL) approach. We obtain that the multiband surface energy is fully determined by the critical temperature, electronic densities of states, and superconducting gap functions associ

Ivan Kryven, Rik Versendaal

Complex systems, ranging from soft materials to wireless communication, are often organised as random geometric networks in which nodes and edges evenly fill up the volume of some space. Studying such networks is difficult because they inherit their properties from the embedding space as well as from the constraints imposed on the network's structure by desi

D. M. Bowman, D. Lecoanet, T. Van Reeth

Spectropolarimetic campaigns have established that large-scale magnetic fields are present at the surfaces of approximately 10% of massive dwarf stars. However, there is a dearth of magnetic field measurements for their deep interiors. Asteroseismology of gravity-mode pulsations combined with rotating magneto-hydrodynamical calculations of the early-B main-s

Bohua Peng, Mobarakol Islam, Mei Tu

Curriculum learning needs example difficulty to proceed from easy to hard. However, the credibility of image difficulty is rarely investigated, which can seriously affect the effectiveness of curricula. In this work, we propose Angular Gap, a measure of difficulty based on the difference in angular distance between feature embeddings and class-weight embeddi

Johannes Bluemlein, Marco Saragnese, Carsten Schneider

We present recent computer algebra methods that support the calculations of (multivariate) series solutions for (certain coupled systems of partial) linear differential equations. The summand of the series solutions may be built by hypergeometric products and more generally by indefinite nested sums defined over such products. Special cases are hypergeometri

Joao Fonseca, Fernando Bacao

In the Machine Learning research community, there is a consensus regarding the relationship between model complexity and the required amount of data and computation power. In real world applications, these computational requirements are not always available, motivating research on regularization methods. In addition, current and past research have shown that

Clara C. Wanjura, Jesse J. Slim, Javier del Pino, Matteo Brunelli

Nonreciprocity means that the transmission of a signal depends on its direction of propagation. Despite vastly different platforms and underlying working principles, the realisations of nonreciprocal transport in linear, time-independent systems rely on Aharonov-Bohm interference among several pathways and require breaking time-reversal symmetry. Here we ext

Yue Li, Carolina Scarton, Xingyi Song, Kalina Bontcheva

Vaccine hesitancy is widespread, despite the government's information campaigns and the efforts of the World Health Organisation (WHO). Categorising the topics within vaccine-related narratives is crucial to understand the concerns expressed in discussions and identify the specific issues that contribute to vaccine hesitancy. This paper addresses the need fo

D. M. Bowman, B. Vandenbussche, H. Sana, A. Tkachenko

The ESA/KU Leuven CubeSpec mission is specifically designed to provide low-cost space-based high-resolution optical spectroscopy. Here we highlight the science requirements and capabilities of CubeSpec. The primary science goal is to perform pulsation mode identification from spectroscopic line profile variability and empower asteroseismology of massive star

Yann Guggisberg, Fabian Ziltener

We give the first concrete examples of symplectic capacities that are not target-representable. This provides some answers to a question by Cieliebak, Hofer, Latschev, and Schlenk.

M. Grayling, C. P. Gutiérrez, M. Sullivan, P. Wiseman

We present the luminosity functions and host galaxy properties of the Dark Energy Survey (DES) core-collapse supernova (CCSN) sample, consisting of 69 Type II and 50 Type Ibc spectroscopically and photometrically-confirmed supernovae over a redshift range $0.045<z<0.25$. We fit the observed DES $griz$ CCSN light-curves and K-correct to produce rest-frame $R$

Guangyu Wu, Anders Lindquist

Non-Gaussian Bayesian filtering is a core problem in stochastic filtering. The difficulty of the problem lies in parameterizing the state estimates. However the existing methods are not able to treat it well. We propose to use power moments to obtain a parameterization. Unlike the existing parametric estimation methods, our proposed algorithm does not requir

Moein Heidari, Amirhossein Kazerouni, Milad Soltany, Reza Azad

Convolutional neural networks (CNNs) have been the consensus for medical image segmentation tasks. However, they suffer from the limitation in modeling long-range dependencies and spatial correlations due to the nature of convolution operation. Although transformers were first developed to address this issue, they fail to capture low-level features. In contr

E. R. Carreón Ruiz, J. Lee, J. I. Márquez Damián, M. Strobl

We present spectroscopic neutron imaging (SNI), a bridge between imaging and scattering techniques, for the analysis of hydrogenated molecules in lithium-ion cells. The scattering information of CHn-based organic solvents and electrolytes was mapped in two-dimensional space by investigating the wavelength-dependent property of hydrogen atoms through time-of-

Marek Kryspin, Janusz Mierczyński

We show the continuous dependence of solutions of linear nonautonomous second order parabolic partial differential equations (PDEs) with bounded delay on coefficients and delay. The assumptions are very weak: only convergence in the weak-* topology of delay coefficients is required. The results are important in the applications of the theory of Lyapunov expo

Aishwarya Bhatta, Rukmani Mohanta

Recently, several indications of lepton non-universality observables have been perceived in semileptonic $B$ meson decay processes, both in the neutral-current ($b \to s ll $) and charged-current ($b \to c l \bar \nu_l$) transitions. Influenced by these fascinating quotients, we examine the semileptonic decays involving the $b \to c l \bar \nu_l$ quark level

Richard Price, Gaurav Khanna

Recent work on improved efficiency of calculations for extreme mass ratio inspirals has produced the useful byproduct of comparisons of inspirals of comparable mass by particle perturbation (PP) methods and by numerical relativty (NR). Here we point out: (1) In choosing the rescaling of the masses, consideration must be given to the differences in the PP and

Sergei D. Odintsov, Diego Saez-Chillon Gomez, German S. Sharov

Some models within the framework of Gauss-Bonnet gravities are considered in the presence of a non-minimally coupled scalar field. By imposing a particular constraint on the scalar field coupling, an extension of the called Einstein-Gauss-Bonnet gravity that keeps the correct speed of propagation for gravitational waves, is considered. The cosmological evolu