March 2023 arXiv papers — page 66

G. Vinelli, F. Castelli, R. Ferragut, M. Romé

This paper reports the study of a new interferometric configuration to measure the effect of gravity on positronium. A Mach-Zehnder matter-wave interferometer has been designed to operate with single-photon transitions and to transfer high momentum to a 200 eV positronium beam. The work shows the results and methods used to simulate the interferometer and es

Seokju Cho, Heeseong Shin, Sunghwan Hong, Anurag Arnab

Open-vocabulary semantic segmentation presents the challenge of labeling each pixel within an image based on a wide range of text descriptions. In this work, we introduce a novel cost-based approach to adapt vision-language foundation models, notably CLIP, for the intricate task of semantic segmentation. Through aggregating the cosine similarity score, i.e.,

Chengyu Sun, Xing Ai, Zhihong Zhang, Edwin R Hancock

In recent years, kernel methods are widespread in tasks of similarity measuring. Specifically, graph kernels are widely used in fields of bioinformatics, chemistry and financial data analysis. However, existing methods, especially entropy based graph kernels are subject to large computational complexity and the negligence of node-level information. In this p

Rina Anno, Timothy Logvinenko

We define unbounded twisted complexes and bicomplexes generalising the notion of a (bounded) twisted complex over a DG category [BK90]. These need to be considered relative to another DG category $B$ admitting countable direct sums and shifts. The resulting DG category of unbounded twisted complexes has a fully faithful convolution functor into Mod-$B$ which

Alice Barbora Tumpach, Tomasz Goliński

We construct a Banach Poisson-Lie group structure on the unitary group of a separable complex Hilbert space.

Dávid Hovančík, Marie Kratochvílová, Tetiana Haidamak, Petr Doležal

We studied magnetic states and phase transitions in the van der Waals antiferromagnet VBr3 by specific heat and magnetization measurements of single crystals in high magnetic fields and by ab initio density functional theory calculations focused on exchange interactions. The magnetization behavior resembles Ising antiferromagnets with magnetic moments kept i

Binh M. Le, Shahroz Tariq, Simon S. Woo

Deep neural networks, particularly in vision tasks, are notably susceptible to adversarial perturbations. To overcome this challenge, developing a robust classifier is crucial. In light of the recent advancements in the robustness of classifiers, we delve deep into the intricacies of adversarial training and Jacobian regularization, two pivotal defenses. Our

Mani A

A number of generalizations of stochastic and information-theoretic randomness are known in the literature. However, they are not compatible with handling meaning in vague and dynamic contexts of rough reasoning (and therefore explainable artificial intelligence and machine learning). In this research, new concepts of rough randomness that are neither stocha

R. A. Battye, M. J. Keith, J. I. McDonald, S. Srinivasan

Axion dark matter can be converted into photons in the magnetospheres of neutron stars leading to a spectral line centred on the Compton wavelength of the axion. Due to the rotation of the star and the plasma effects in the magnetosphere the signal is predicted to be periodic with significant time variation - a unique smoking gun for axion dark matter. As a

Yihao Wang, Zhigang Wang, Bin Zhao, Dong Wang

Non-line-of-sight (NLOS) tracking has drawn increasing attention in recent years, due to its ability to detect object motion out of sight. Most previous works on NLOS tracking rely on active illumination, e.g., laser, and suffer from high cost and elaborate experimental conditions. Besides, these techniques are still far from practical application due to ove

Ali Kashefi, Tapan Mukerji

ChatGPT is a large language model recently released by the OpenAI company. In this technical report, we explore for the first time the capability of ChatGPT for programming numerical algorithms. Specifically, we examine the capability of GhatGPT for generating codes for numerical algorithms in different programming languages, for debugging and improving writ

Anwai Archit, Constantin Pape

Segmentation is a crucial analysis task in biomedical imaging. Given the diverse experimental settings in this field, the lack of generalization limits the use of deep learning in practice. Domain adaptation is a promising remedy: it involves training a model for a given task on a source dataset with labels and adapts it to a target dataset without additiona

Yu Dong, Christy Jie Liang, Yi Chen, Jie Hua

The visual modeling method enables flexible interactions with rich graphical depictions of data and supports the exploration of the complexities of epidemiological analysis. However, most epidemiology visualizations do not support the combined analysis of objective factors that might influence the transmission situation, resulting in a lack of quantitative a

Andrii Shcherbakov, Kevin Synnatschke, Stanislav Bodnar, Johnathan Zerhoch

Antiferromagnets are promising materials for future opto-spintronic applications since they show spin dynamics in the THz range and no net magnetization. Recently, layered van der Waals (vdW) antiferromagnets have been reported, which combine low-dimensional excitonic properties with complex spin-structure. While various methods for the fabrication of vdW 2D

Manuel Katzer, Malte Selig, Lukas Sigl, Mirco Troue

The question of macroscopic occupation and spontaneous emergence of coherence for exciton ensembles has gained renewed attention due to the rise of van der Waals heterostructures made of atomically thin semiconductors. The hosted interlayer excitons exhibit nanosecond lifetimes, long enough to allow for excitonic thermalization in time. Several experimental

Haipeng Zhang, Ran Li, Yan Chen, Zhongda Chu

The objective-based forecasting considers the asymmetric and non-linear impacts of forecasting errors on decision objectives, thus improving the effectiveness of its downstream decision-making process. However, existing objective-based forecasting methods are risk-neutral and not suitable for tasks like power system inertia management and unit commitment, of

Jian-Ming Shen, Bing-Hai Qin, Jiang Yan, Sheng-Quan Wang

We present a novel method for precisely determining the QCD running coupling from $R_{\rm uds}$ measurements in electron-positron annihilation. When calculating the fixed-order perturbative QCD (pQCD) approximant of $R_{\rm uds}$, its effective coupling constant $\alpha_s(Q_*^2)$ is determined by using the principle of maximum conformality, a systematic scal

Amartya Bose

Simulation of non-adiabatic dynamics of a quantum system coupled to dissipative environments poses significant challenges. New sophisticated methods are regularly being developed with an eye towards moving to larger systems and more complicated description of solvents. Many of these methods, however, are quite difficult to implement and debug. Furthermore, t

Yuhao Yang, Chao Huang, Lianghao Xia, Chunzhen Huang

Current sequential recommender systems are proposed to tackle the dynamic user preference learning with various neural techniques, such as Transformer and Graph Neural Networks (GNNs). However, inference from the highly sparse user behavior data may hinder the representation ability of sequential pattern encoding. To address the label shortage issue, contras

I. B. Okon, C. N. Isonguyo, C. A. Onate, A. D. Antia

In this work, we obtained analytical bound state solution of the Schr\"odinger equation with Manning Rosen plus exponential Yukawa Potential using parametric Nikiforov-Uvarov method (NU). We obtained the normalized wave function in terms of Jacobi polynomial. The energy eigen equation was determined and presented in a compact form. The study also includes th

Minati De, Satyam Singh

We consider the online hitting set problem for the range space $\Sigma=(\cal X,\cal R)$, where the point set $\cal X$ is known beforehand, but the set $\cal R$ of geometric objects is not known in advance. Here, objects from $\cal R$ arrive one by one. The objective of the problem is to maintain a hitting set of the minimum cardinality by taking irrevocable

V. N. Mantsevich, D. S. Smirnov

Weak spin-orbit coupling produces very limited current induced spin accumulation in semiconductor nanostructures. We demonstrate a possibility to increase parametrically the spin polarization using the Kondo effect. As a model object we consider a quantum dot side coupled to a quantum wire taking into account the spin dependent electron tunneling from the wi

Matteo Barigozzi

We review Quasi Maximum Likelihood estimation of factor models for high-dimensional panels of time series. We consider two cases: (1) estimation when no dynamic model for the factors is specified (Bai and Li, 2012, 2016); (2) estimation based on the Kalman smoother and the Expectation Maximization algorithm thus allowing to model explicitly the factor dynami

S. Bagnulo, Z. Gray, M. Granvik, A. Cellino

We have monitored the Didymos-Dimorphos binary asteroid in spectropolarimetric mode in the optical range before and after the DART impact. The ultimate goal was to obtain constraints on the characteristics of the ejected dust for modelling purposes. Before impact, Didymos exhibited a linear polarization rapidly increasing with phase angle, reaching a level o

Jiaqi Yan, Hideaki Ishii

In this paper, we consider the problem of distributed parameter estimation in sensor networks. Each sensor makes successive observations of an unknown $d$-dimensional parameter, which might be subject to Gaussian random noises. They aim to infer true value of the unknown parameter by cooperating with each other. To this end, we first generalize the so-called

Maciej Skorski, Alessandro Temperoni

This paper revisits the performance of Rademacher random projections, establishing novel statistical guarantees that are numerically sharp and non-oblivious with respect to the input data. More specifically, the central result is the Schur-concavity property of Rademacher random projections with respect to the inputs. This offers a novel geometric perspectiv

Ruth Mitze, Michal Kvasnica, Martin Mönnigmann

This paper studies symmetric constrained linear-quadratic optimal control problems and their parametric solutions. The parametric solution of such a problem is a piecewise-affine feedback law that can be equivalently expressed as a set of active sets. We show symmetries of the optimal control problem entail symmetries of the active sets, which can be used to

Tomas Hlavacek, Haya Shulman, Niklas Vogel, Michael Waidner

IP prefix hijacks allow adversaries to redirect and intercept traffic, posing a threat to the stability and security of the Internet. To prevent prefix hijacks, networks should deploy RPKI and filter bogus BGP announcements with invalid routes. In this work we evaluate the impact of RPKI deployments on the security and resilience of the Internet. We aim to u

Youngjoon Jang, Youngtaek Oh, Jae Won Cho, Myungchul Kim

The goal of this work is to develop self-sufficient framework for Continuous Sign Language Recognition (CSLR) that addresses key issues of sign language recognition. These include the need for complex multi-scale features such as hands, face, and mouth for understanding, and absence of frame-level annotations. To this end, we propose (1) Divide and Focus Con

D. M. K. K. Venkateswara Rao, Hamed Habibi, Jose Luis Sanchez-Lopez, Prathyush P. Menon

In this paper, an adaptive super-twisting controller is designed for an agile maneuvering quadrotor unmanned aerial vehicle to achieve accurate trajectory tracking in the presence of external disturbances. A cascaded control architecture is designed to determine the desired accelerations using the proposed controller and subsequently used to compute the desi

Kishan Kumar Dayaram, Amartya Goswami, Zurab Janelidze, Diana Ferreira Rodelo

Through abelian categories, homological lemmas for modules admit a self-dual treatment, where half of the proof of a lemma is sufficient to prove the full lemma. In this paper, we show how the context of a `noetherian form', recently introduced by the second and third authors, allows a self-dual treatment of these lemmas even in the case of non-abelian categ

Christopher Caruvana

In this note, we characterize when the Vietoris space of compact subsets of a given space has the Hurewicz property in terms of a selection principle on the given space itself using $k$-covers and the notion of groupability introduced by Ko\v{c}inac and Scheepers. We comment that the same technique establishes another equivalent condition to a space being Hu

Kalman Szenes, Niccolò Discacciati, Luca Bonaventura, William Sawyer

We assess two domain-specific languages included in the GridTools ecosystem as tools for implementing a high-order Discontinuous Galerkin discretization of the shallow water equations. Equations in spherical geometry are considered, thus providing a blueprint for the application of domain-specific languages to the development of global atmospheric models. Th

Tung Nguyen, Alex Scott, Paul Seymour

We prove that for every path H, and every integer d, there is a polynomial f such that every graph G with chromatic number greater than f(t) either contains H as an induced subgraph, or contains as a subgraph the complete d-partite graph with parts of cardinality t. For t = 1 and general d this is a classical theorem of Gy\'arf\'as, and for d = 2 and general

Achu Wilson, Helen Jiang, Wenzhao Lian, Wenzhen Yuan

Manipulating cables is challenging for robots because of the infinite degrees of freedom of the cables and frequent occlusion by the gripper and the environment. These challenges are further complicated by the dexterous nature of the operations required for cable routing and assembly, such as weaving and inserting, hampering common solutions with vision-only

Graziano Crasta, Ilaria Fragalà

We introduce an evolution model \`a la Firey for a convex stone which tumbles on a beach and undertakes an erosion process depending on some variational energy, such as torsional rigidity, principal Dirichlet Laplacian eigenvalue, or Newtonian capacity. Relying on the assumption of existence of a solution to the corresponding parabolic flow, we prove that th

Najam Us Saqib, Shumei Hou, Sung Ho Chae, Sang-Woon Jeon

We consider reconfigurable intelligent surface (RIS) aided sixth-generation (6G) terahertz (THz) communications for indoor environment in which a base station (BS) wishes to send independent messages to its serving users with the help of multiple RISs. For indoor environment, various obstacles such as pillars, walls, and other objects can result in no line-o

S. Diehl, N. Trotta, K. Joo, P. Achenbach

The polarized cross section ratio $\sigma_{LT'}/\sigma_{0}$ from hard exclusive $\pi^{-} \Delta^{++}$ electroproduction off an unpolarized hydrogen target has been extracted based on beam-spin asymmetry measurements using a 10.2 GeV / 10.6 GeV incident electron beam and the CLAS12 spectrometer at Jefferson Lab. The study, which provides the first observation

Jacopo Tagliabue, Hugo Bowne-Anderson, Ville Tuulos, Savin Goyal

As Machine Learning (ML) gains adoption across industries and new use cases, practitioners increasingly realize the challenges around effectively developing and iterating on ML systems: reproducibility, debugging, scalability, and documentation are elusive goals for real-world pipelines outside tech-first companies. In this paper, we review the nature of ML-

Erol Gelenbe, Mert Nakıp

This paper presents several novel algorithms for real-time cyberattack detection using the Auto-Associative Deep Random Neural Network, which were developed in the HORIZON 2020 IoTAC Project. Some of these algorithms require offline learning, while others require the algorithm to learn during its normal operation while it is also testing the flow of incoming

Onyekachukwu R. Okonji

Malaria is usually diagnosed by a microbiologist by examining a small sample of blood smear. Reducing mortality from malaria infection is possible if it is diagnosed early and followed with appropriate treatment. While the WHO has set audacious goals of reducing malaria incidence and mortality rates by 90% in 2030 and eliminating malaria in 35 countries by t

Cheng Zhang, Pengfei Liang, Neill Lambert, Mauro Cirio

We study a generalization of a recently introduced Dicke trimer model [Phys. Rev. Lett. 128, 163601, Phys. Rev. Research 5, L042016], which allows for cavity losses and unbalanced light-matter interactions (in which rotating and counter-rotating terms can be tuned independently). We find that in the extreme unbalanced limit, the $U(1)$ symmetry of the Tavis-

Shengze Zhong, Parinya Punpongsanon, Daisuke Iwai, Kosuke Sato

Nature evolves structures like honeycombs at optimized performance with limited material. These efficient structures can be artificially created with the collaboration of structural topology optimization and additive manufacturing. However, the extensive computation cost of topology optimization causes low mesh resolution, long solving time, and rough bounda

Johan Vertens, Nicolai Dorka, Tim Welschehold, Michael Thompson

The safe deployment of autonomous vehicles relies on their ability to effectively react to environmental changes. This can require maneuvering on varying surfaces which is still a difficult problem, especially for slippery terrains. To address this issue we propose a new approach that learns a surface-aware dynamics model by conditioning it on a latent varia

Gefen Dawidowicz, Elad Hirsch, Ayellet Tal

Medical imaging analysis plays a critical role in the diagnosis and treatment of various medical conditions. This paper focuses on chest X-ray images and their corresponding radiological reports. It presents a new model that learns a joint X-ray image & report representation. The model is based on a novel alignment scheme between the visual data and the text

Haitz Sáez de Ocáriz Borde, Álvaro Arroyo, Ingmar Posner

Graph Neural Networks leverage the connectivity structure of graphs as an inductive bias. Latent graph inference focuses on learning an adequate graph structure to diffuse information on and improve the downstream performance of the model. In this work we employ stereographic projections of the hyperbolic and spherical model spaces, as well as products of Ri

Adarsh Kumar, Ali Ismail Awad, Gaurav Sharma, Rajalakshmi Krishnamurthi

Quantum communication is the most secure technique of transmitting data available today. Fiber communication lines and satellite-to-ground links have served as the basis for the most successful quantum networks that have been developed so far. Using a UAV, satellite or both for free-space quantum communication reduces the need for permanent ground connection

Artur O. Lopes, Marcos Sebastian, Victor Vargas

We consider topological groupoids in finite and also in a compact settings. In the initial sections, we introduce definitions of typical observables and we studied them in the context of statistical mechanics and quantum mechanics. We exhibit explicit examples and one of them will be the so-called quantum ratchet. This is related to Schwinger's algebra of se

Mohamed Amine Ferrag, Merouane Debbah, Muna Al-Hawawreh

The next generation of cellular technology, 6G, is being developed to enable a wide range of new applications and services for the Internet of Things (IoT). One of 6G's main advantages for IoT applications is its ability to support much higher data rates and bandwidth as well as to support ultra-low latency. However, with this increased connectivity will com

Lei Lin, Shuangtao Li, Xiaodong Shi

Simultaneous machine translation, which aims at a real-time translation, is useful in many live scenarios but very challenging due to the trade-off between accuracy and latency. To achieve the balance for both, the model needs to wait for appropriate streaming text (READ policy) and then generates its translation (WRITE policy). However, WRITE policies of pr

Zhenyu Wang, Yali Li, Xi Chen, Ser-Nam Lim

In this paper, we formally address universal object detection, which aims to detect every scene and predict every category. The dependence on human annotations, the limited visual information, and the novel categories in the open world severely restrict the universality of traditional detectors. We propose UniDetector, a universal object detector that has th

Malcolm Crowe, Fritz Laux

This paper reviews suggestions for changes to database technology coming from the work of many researchers, particularly those working with evolving big data. We discuss new approaches to remote data access and standards that better provide for durability and auditability in settings including business and scientific computing. We propose ways in which the l

Leif Lönnblad, Harsh Shah

We present an updated version of the QCD-based colour reconnection model in PYTHIA8, where we constrain the range in impact parameter for which reconnections are allowed. In this way, we can introduce more realistic colour reconnections in the Angantyr model for heavy ion collisions, where previously only reconnections within separate nucleon sub-collisions

Alessandro Speciale, Greta Vallero, Luca Vassio, Marco Mellia

The Reading&Machine project exploits the support of digitalization to increase the attractiveness of libraries and improve the users' experience. The project implements an application that helps the users in their decision-making process, providing recommendation system (RecSys)-generated lists of books the users might be interested in, and showing them thro

Mohamed Amine Ferrag, Burak Kantarci, Lucas C. Cordeiro, Merouane Debbah

Federated edge learning can be essential in supporting privacy-preserving, artificial intelligence (AI)-enabled activities in digital twin 6G-enabled Internet of Things (IoT) environments. However, we need to also consider the potential of attacks targeting the underlying AI systems (e.g., adversaries seek to corrupt data on the IoT devices during local upda

Shweta Choudhary, Pranjal Srivastava, Harleen Dahiya

We compute the light antiquark flavor asymmetry in the proton using the Chiral Quark Model ($\chi_{\rm QM}$). The distribution functions for the light antiquarks $\bar{d}(x)$ and $\bar{u}(x)$ have been extracted with the help of experimental data from NuSea/E866 and HERMES for the Bjorken$-x$ range $0.015 < x < 0.35$ as well from the most recent SeaQuest dat

Giuseppe Fanizza, Giovanni Marozzi, Matheus Medeiros

We derive the expressions on the observed light-cone for some relevant cosmological gauge invariant variables, such as the Mukhanov-Sasaki variable and $E$- and $B$- modes of the tensor perturbations. Since the structure of the light-cone does not reflect in a direct way the FLRW symmetries, we develop a formalism which is coordinate independent and classifi

Marcin Hoffmann, Pawel Kryszkiewicz

The Massive Multiple-Input Multiple-Output (M-MIMO) is considered as one of the key technologies in 5G, and future 6G networks. From the perspective of, e.g., channel estimation, especially for high-speed users it is easier to implement an M-MIMO network exploiting a static set of beams, i.e., Grid of Beams (GoB). While considering GoB it is important to pro

Maya Briani, Emiliano Cristiani, Elia Onofri

In this paper, we aim at developing new methods to join machine learning techniques and macroscopic differential models for vehicular traffic estimation and forecast. It is well known that data-driven and model-driven approaches have (sometimes complementary) advantages and drawbacks. We consider here a dataset with flux and velocity data of vehicles moving

Athanassios Tzouvaras

We reformulate slightly Russell's notion of typicality, so as to eliminate its circularity and make it applicable to elements of any first-order structure. We argue that the notion parallels Martin-L\"{o}f (ML) randomness, in the sense that it uses definable sets in place of computable ones and sets of ``small'' cardinality (i.e., strictly smaller than that

Giulio Peruginelli

Let $p\in\mathbb Z$ be a prime, $\overline{\mathbb Q_p}$ a fixed algebraic closure of the field of $p$-adic numbers and $\overline{\mathbb Z_p}$ the absolute integral closure of the ring of $p$-adic integers. Given a residually algebraic torsion extension $W$ of $\mathbb Z_{(p)}$ to $\mathbb Q(X)$, by Kaplansky's characterization of immediate extensions of v

Maria Leyva-Vallina, Nicola Strisciuglio, Nicolai Petkov

Visual place recognition (VPR) is a fundamental task of computer vision for visual localization. Existing methods are trained using image pairs that either depict the same place or not. Such a binary indication does not consider continuous relations of similarity between images of the same place taken from different positions, determined by the continuous na

Rebecca Robinson, Guillaume Aulanier, Mats Carlsson

Low-altitude twisted magnetic fields may be relevant to atmospheric heating in the quiet Sun, but the exact role, topology, and formation of these twisted fields remains to be studied. We investigate the formation and evolution of a preflare flux rope in a stratified, 3D MHD simulation. One puzzle is that this modelled flux rope does not form by the usual me

Patrice Salzenstein

Better knowing the precision on the measured value of the Brillouin peak frequencies is essen-tial in order to use it to deduce parameters related to studied materials. Modern methods for evaluating uncertainties are based on the recommendations of the Guide to the expression of uncertainty in measurement. After checking the agreement between the measured 15

G. Yang, K. I. Caputi, C. Papovich, P. Arrabal Haro

Mid-infrared observations are powerful in identifying heavily obscured Active Galactic Nuclei (AGN) which have weak emission in other wavelengths. Data from the Mid-Infrared Instrument (MIRI) onboard JWST provides an excellent opportunity to perform such studies. We take advantage of the MIRI imaging data from the Cosmic Evolution Early Release Science Surve

Hans-Martin Rieser, Frank Köster, Arne Peter Raulf

Once developed for quantum theory, tensor networks have been established as a successful machine learning paradigm. Now, they have been ported back to the quantum realm in the emerging field of quantum machine learning to assess problems that classical computers are unable to solve efficiently. Their nature at the interface between physics and machine learni

Kenyu Kobayashi, Renata Khasanova, Arno Schneuwly, Felix Schmidt

Autoencoders are a powerful and versatile tool often used for various problems such as anomaly detection, image processing and machine translation. However, their reconstructions are not always trivial to explain. Therefore, we propose a fast explainability solution by extending the Layer-wise Relevance Propagation method with the help of Deep Taylor Decompo

Pooja Yadav, Mamta Kamra

In this article, we demonstrate the common fixed point theorems for three transformations on vector S-metric space by utilizing weakly compatible and point of coincidence. Moreover, some of our results generalize the existing results in the literature.

Karthick Panner Selvam, Mats Brorsson

Deep Learning (DL) has developed to become a corner-stone in many everyday applications that we are now relying on. However, making sure that the DL model uses the underlying hardware efficiently takes a lot of effort. Knowledge about inference characteristics can help to find the right match so that enough resources are given to the model, but not too much.

Chen Ju, Zeqian Li, Peisen Zhao, Ya Zhang

In this paper, we consider the problem of temporal action localization under low-shot (zero-shot & few-shot) scenario, with the goal of detecting and classifying the action instances from arbitrary categories within some untrimmed videos, even not seen at training time. We adopt a Transformer-based two-stage action localization architecture with class-agnost

Kieran Leach, Philip Cass, Steven Robson, Eimantas Kazakevicius

The ARCHER2 service, a CPU based HPE Cray EX system with 750,080 cores (5,860 nodes), has been deployed throughout 2020 and 2021, going into full service in December of 2021. A key part of the work during this deployment was the integration of ARCHER2 into our local monitoring systems. As ARCHER2 was one of the very first large-scale EX deployments, this inv

Jingyi Xu, Tushar Vaidya, Yufei Wu, Saket Chandra

We introduce algebraic machine reasoning, a new reasoning framework that is well-suited for abstract reasoning. Effectively, algebraic machine reasoning reduces the difficult process of novel problem-solving to routine algebraic computation. The fundamental algebraic objects of interest are the ideals of some suitably initialized polynomial ring. We shall ex

Yue Xie, Xianxin Wu, Zhong Fang, Zhijun Wang

Type-II Dirac semimetals exhibit a unique Fermi surface topology, which allows them to host novel topological superconductivity (TSC). We reveal a novel inter-orbital superconducting state, corresponding to the B1u and B2u pairings under the D4h point group. Intriguingly, we find that both first- and second-order TSC coexist in this novel state. It is induce

SeokYeong Lee, JunYong Choi, Seungryong Kim, Ig-Jae Kim

In this paper, we introduce a new challenge for synthesizing novel view images in practical environments with limited input multi-view images and varying lighting conditions. Neural radiance fields (NeRF), one of the pioneering works for this task, demand an extensive set of multi-view images taken under constrained illumination, which is often unattainable

Bing-Zhong Hu, Zu-Quan Zhang, Lei-Lei Nian, Jing-Tao Lü

We study angular momentum radiation from electrically-biased chiral single molecular junctions using the nonequilibrium Green's function method. Using single helical chains as examples, we make connections between the ability of a chiral molecule to emit photons with angular momentum to the geometrical factors of the molecule. We point out that the mechanism

Xiaoxuan Ma, Jiajun Su, Chunyu Wang, Wentao Zhu

Inspired by the success of volumetric 3D pose estimation, some recent human mesh estimators propose to estimate 3D skeletons as intermediate representations, from which, the dense 3D meshes are regressed by exploiting the mesh topology. However, body shape information is lost in extracting skeletons, leading to mediocre performance. The advanced motion captu

Alessandro Navone, Mauro Martini, Simone Angarano, Marcello Chiaberge

Modern robotic platforms need a reliable localization system to operate daily beside humans. Simple pose estimation algorithms based on filtered wheel and inertial odometry often fail in the presence of abrupt kinematic changes and wheel slips. Moreover, despite the recent success of visual odometry, service and assistive robotic tasks often present challeng

Linda-Sophie Schneider, Mareike Thies, Christopher Syben, Richard Schielein

We present a method for selecting valuable projections in computed tomography (CT) scans to enhance image reconstruction and diagnosis. The approach integrates two important factors, projection-based detectability and data completeness, into a single feed-forward neural network. The network evaluates the value of projections, processes them through a differe

Franco Blanchini, Elisa Franco, Giulia Giordano, Dino Osmanovic

The interaction of phase-separating systems with chemical reactions is of great interest in various contexts, from biology to material science. In biology, phase separation is thought to be the driving force behind the formation of biomolecular condensates, i.e. organelles without a membrane that are associated with cellular metabolism, stress response, and

Shuzhou Yang, Moxuan Ding, Yanmin Wu, Zihan Li

The following three factors restrict the application of existing low-light image enhancement methods: unpredictable brightness degradation and noise, inherent gap between metric-favorable and visual-friendly versions, and the limited paired training data. To address these limitations, we propose an implicit Neural Representation method for Cooperative low-li

Yiqi Liu, Yuan Qi

We discuss estimation and inference of conditional treatment effects in regression discontinuity (RD) designs with multiple scores. In addition to local linear regressions and the minimax-optimal estimator more recently proposed by Imbens and Wager (2019), we argue that two variants of random forests, honest regression forests and local linear forests, shoul

Kamil Adamczewski, Christos Sakaridis, Vaishakh Patil, Luc Van Gool

Lidar is a vital sensor for estimating the depth of a scene. Typical spinning lidars emit pulses arranged in several horizontal lines and the monetary cost of the sensor increases with the number of these lines. In this work, we present the new problem of optimizing the positioning of lidar lines to find the most effective configuration for the depth complet

Julien Duron, Frédéric Havet, Florian Hörsch, Clément Rambaud

The {\it inversion} of a set $X$ of vertices in a digraph $D$ consists of reversing the direction of all arcs of $D\langle X\rangle$. We study $sinv'_k(D)$ (resp. $sinv_k(D)$) which is the minimum number of inversions needed to transform $D$ into a $k$-arc-strong (resp. $k$-strong) digraph and $sinv'_k(n) = \max\{sinv'_k(D) \mid D~\mbox{is a $2k$-edge-connec

R. L. Seeger, F. Millo, A. Mouhoub, G. de Loubens

Controlling the uniaxial magnetic anisotropy is of practical interest to a wide variety of applications. We study Co$_{40}$Fe$_{40}$B$_{20}$ single films grown on various crystalline orientations of LiNbO$_3$ substrates and on oxidized silicon. We identify the annealing conditions that are appropriate to induce or suppress uniaxial anisotropy. Anisotropy fie

Chaoning Zhang, Chenshuang Zhang, Sheng Zheng, Yu Qiao

As ChatGPT goes viral, generative AI (AIGC, a.k.a AI-generated content) has made headlines everywhere because of its ability to analyze and create text, images, and beyond. With such overwhelming media coverage, it is almost impossible for us to miss the opportunity to glimpse AIGC from a certain angle. In the era of AI transitioning from pure analysis to cr

Dapeng Li, Feiyang Pan, Jia He, Zhiwei Xu

In high-dimensional time-series analysis, it is essential to have a set of key factors (namely, the style factors) that explain the change of the observed variable. For example, volatility modeling in finance relies on a set of risk factors, and climate change studies in climatology rely on a set of causal factors. The ideal low-dimensional style factors sho

Shaohan Huang, Yi Liu, Carol Fung, Jiaxing Qi

Modern systems produce a large volume of logs to record run-time status and events. System operators use these raw logs to track a system in order to obtain some useful information to diagnose system anomalies. One of the most important problems in this area is to help operators find the answers to log-based questions efficiently and user-friendly. In this w

Yuan Chen, Wei Guo, Kai Shi, Hongbao Zhang

The algebraic approach to the spectrum of quasinormal modes has been made as simple as possible for the BTZ black hole by the strategy developed in \cite{Zhang}. By working with the self-dual warped AdS black hole, we demonstrate in an explicit way that such a strategy can be well adapted to those warped AdS balck holes with the $SL(2,R)\times U(1)$ isometry

Enrico Barausse

These lecture notes collect the material that I have been using over the years for various short courses on the physics of gravitational waves, first at the Institut d'Astrophysique de Paris (France), and then at SISSA (Italy) and various summer/winter schools. The level should be appropriate for PhD students in physics or for MSc students that have taken a

Emilio Gamba, Bart Bogaerts, Tias Guns

We build on a recently proposed method for stepwise explaining solutions of Constraint Satisfaction Problems (CSP) in a human-understandable way. An explanation here is a sequence of simple inference steps where simplicity is quantified using a cost function. The algorithms for explanation generation rely on extracting Minimal Unsatisfiable Subsets (MUS) of

Md Sabir Ali, Hasan El Moumni, Jamal Khalloufi, Karima Masmar

We start with a brief overview of the basic thermodynamic properties of the Born-Infeld metric in AdS spacetime. Using the concept of the enthalpy characterizing the total mass of the black hole, in our present paper, we probe the thermal phase transition structure, the dynamic and kinetic behavior of the Born-Infeld-AdS black hole. The emergence of the trip

Kestutis Cesnavicius, Alex Youcis

The $B_{\mathrm{dR}}^+$-affine Grassmannian was introduced by Scholze in the context of the geometric local Langlands program in mixed characteristic and is the Fargues-Fontaine curve analogue of the equal characteristic Beilinson-Drinfeld affine Grassmannian. For a reductive group $G$, it is defined as the \'{e}tale (equivalently, $v$-) sheafification of th

Jacek Szczepkowskia, Marcin Gronowski, Anna Grochola, Włodzimierz Jastrzebski

Despite its apparently simple nature with four valence electrons, the strontium dimer constitutes a challenge for modern electronic structure theory. Here we focus on excited electronic states of Sr$_2$, which we investigate theoretically up to 25000 cm$^{-1}$ above the ground state, to guide and explain new spectroscopic measurements. In particular, we focu

Gabriel Skantze, A. Seza Doğruöz

There is a surge in interest in the development of open-domain chatbots, driven by the recent advancements of large language models. The "openness" of the dialogue is expected to be maximized by providing minimal information to the users about the common ground they can expect, including the presumed joint activity. However, evidence suggests that the effect

Anna Jenčová

A quantum channel is sufficient with respect to a set of input states if it can be reversed on this set. In the approximate version, the input states can be recovered within an error bounded by the decrease of the relative entropy under the channel. Using a new integral representation of the relative entropy in arXiv:2208.12194, we present an easy proof of a

Alexis Derumigny, Johannes Schmidt-Hieber

In nonparametric statistics, rate-optimal estimators typically balance bias and stochastic error. The recent work on overparametrization raises the question whether rate-optimal estimators exist that do not obey this trade-off. In this work we consider pointwise estimation in the Gaussian white noise model with regression function $f$ in a class of $\beta$-H

Amer Delilbasic, Bertrand Le Saux, Morris Riedel, Kristel Michielsen

In recent years, the development of quantum annealers has enabled experimental demonstrations and has increased research interest in applications of quantum annealing, such as in quantum machine learning and in particular for the popular quantum SVM. Several versions of the quantum SVM have been proposed, and quantum annealing has been shown to be effective

Chunliang Li, Shuhui Yang, Yan Lin

Via the new weight $A_{\vec p}^{\theta }(\varphi )$, the authors introduce a new class of multilinear square operators. The boundedness on the weighted Lebesgue space and the weighted Morrey space is obtained, respectively. Our results include the known results of the standard multilinear square operator and the weight $A_{\vec p}$. Moreover, the results in

Argyrios Deligkas, Michelle Döring, Eduard Eiben, Tiger-Lily Goldsmith

We consider the influence maximization problem over a temporal graph, where there is a single fixed source. We deviate from the standard model of influence maximization, where the goal is to choose the set of most influential vertices. Instead, in our model we are given a fixed vertex, or source, and the goal is to find the best time steps to transmit so tha

Emile Reyn Engelbrecht, Johan du Preez

This study investigates the relationship between semi-supervised learning (SSL, which is training off partially labelled datasets) and open-set recognition (OSR, which is classification with simultaneous novelty detection) under the context of generative adversarial networks (GANs). Although no previous study has formally linked SSL and OSR, their respective