November 2022 arXiv papers — page 61

Ziyi Dong, Pengxu Wei, Liang Lin

State-of-the-arts text-to-image generation models such as Imagen and Stable Diffusion Model have succeed remarkable progresses in synthesizing high-quality, feature-rich images with high resolution guided by human text prompts. Since certain characteristics of image content \emph{e.g.}, very specific object entities or styles, are very hard to be accurately

Jiyao Liu

In this paper, the problem of orientation correction in cardiac MRI images is investigated and a framework for orientation recognition via deep neural networks is proposed. For multi-modality MRI, we introduce a transfer learning strategy to transfer our proposed model from single modality to multi-modality. We embed the proposed network into the orientation

Zhen Zhao, Sifan Long, Jimin Pi, Jingdong Wang

Recently, semi-supervised semantic segmentation has achieved promising performance with a small fraction of labeled data. However, most existing studies treat all unlabeled data equally and barely consider the differences and training difficulties among unlabeled instances. Differentiating unlabeled instances can promote instance-specific supervision to adap

Karthik Shenoy, Akshit Saradagi, Ramkrishna Pasumarthy, Vijaysekhar Chellaboina

In this article, we present data-driven feedback linearization for nonlinear systems with periodic orbits in the zero-dynamics. This scenario is challenging for data-driven control design because the higher order terms of the internal dynamics in the discretization appear as disturbance inputs to the controllable subsystem of the normal form. Our design cons

Wyatt Vine, Mykhailo Savytskyi, Daniel Parker, James Slack-Smith

The use of superconducting micro-resonators in combination with quantum-limited Josephson parametric amplifiers has in recent years lead to more than four orders of magnitude improvement in the sensitivity of pulsed Electron Spin Resonance (ESR) measurements. So far, the microwave resonators and amplifiers have been designed as separate components, largely d

Ana Kostovska, Diederick Vermetten, Carola Doerr, Saso Džeroski

Many optimization algorithm benchmarking platforms allow users to share their experimental data to promote reproducible and reusable research. However, different platforms use different data models and formats, which drastically complicates the identification of relevant datasets, their interpretation, and their interoperability. Therefore, a semantically ri

Maarten Molendijk, Floran de Putter, Manil Gomony, Pekka Jääskeläinen

Recently, accelerators for extremely quantized deep neural network (DNN) inference with operand widths as low as 1-bit have gained popularity due to their ability to largely cut down energy cost per inference. In this paper, a flexible SoC with mixed-precision support is presented. Contrary to the current trend of fixed-datapath accelerators, this architectu

Inge S. Helland

The Nobel prize in physics for 2022 was given for performing Bell experiments with varying degree of sophistication. The interpretation of this experiment is discussed by first recalling Bell's simple argument behind his inequalities, in particular the CHSH inequality. It is argued that any independent observer must have a limitation: He is not able to keep

Jianliang Li, Jiaqing Yang

Consider the inverse acoustic scattering of time-harmonic point sources by an unbounded locally rough interface with bounded obstacles embedded in the lower half-space. A novel version of reverse time migration is proposed to reconstruct both the locally rough interface and the embedded obstacle. By a modified Helmholtz-Kirchhoff identity associated with a p

Michael Kapralov, Hannah Lawrence, Mikhail Makarov, Cameron Musco

We present a sublinear query algorithm for outputting a near-optimal low-rank approximation to any positive semidefinite Toeplitz matrix $T \in \mathbb{R}^{d \times d}$. In particular, for any integer rank $k \leq d$ and $\epsilon,\delta > 0$, our algorithm makes $\tilde{O} \left (k^2 \cdot \log(1/\delta) \cdot \text{poly}(1/\epsilon) \right )$ queries to th

Damiano Rossi

We give new evidences to the fact that the structure of a solvable group can be controlled by irreducible monomial characters. In particular we inspect the role of monomial characters in Isaacs-Navarro-Wolf's conjecture and in Gluck's conjecture.

Samuele Maschio

Every partial applicative structure gives rise to an indexed binary relation, that is a contravariant functor from the category of sets to the category of sets endowed with binary relations and maps preserving them. In this paper we characterize those partial applicative structures giving rise to indexed relations satisfying certain elementary properties in

Jianliang Li, Hao Wu, Jiaqing Yang

Consider the inverse scattering of time-harmonic acoustic scattering by an infinite rough surface which is supposed to be a local perturbation of a plane. A novel version of reverse time migration (RTM) is proposed to reconstruct the shape and location of the rough surface. The method is based on a modified Helmholtz-Kirchhoff identity associated with a spec

Weiqi Sun, Rui Su, Qian Yu, Dong Xu

Weakly supervised temporal action localization (WTAL) aims to localize actions in untrimmed videos with only weak supervision information (e.g. video-level labels). Most existing models handle all input videos with a fixed temporal scale. However, such models are not sensitive to actions whose pace of the movements is different from the ``normal" speed, espe

James Chapman, Ana Lawry Aguila, Lennie Wells

Generalized Eigenvalue Problems (GEPs) encompass a range of interesting dimensionality reduction methods. Development of efficient stochastic approaches to these problems would allow them to scale to larger datasets. Canonical Correlation Analysis (CCA) is one example of a GEP for dimensionality reduction which has found extensive use in problems with two or

Ilaria Perissi, Aled Jones

In the present study, for the first time, an effort sharing approach based on Inertia and Capability principles is proposed to assess European Union (EU27) carbon budget distribution among the Member States. This is done within the context of achieving the Green Deal objective and EU27 carbon neutrality by 2050. An in-depth analysis is carried out about the

Sunder Ali Khowaja, Parus Khuwaja, Kapal Dev, Angelos Antonopoulos

The applications concerning vehicular networks benefit from the vision of beyond 5G and 6G technologies such as ultra-dense network topologies, low latency, and high data rates. Vehicular networks have always faced data privacy preservation concerns, which lead to the advent of distributed learning techniques such as federated learning. Although federated le

Decai Chen, Peng Zhang, Ingo Feldmann, Oliver Schreer

Recent works on implicit neural representations have made significant strides. Learning implicit neural surfaces using volume rendering has gained popularity in multi-view reconstruction without 3D supervision. However, accurately recovering fine details is still challenging, due to the underlying ambiguity of geometry and appearance representation. In this

Ajay Jain, Amber Xie, Pieter Abbeel

Diffusion models have shown impressive results in text-to-image synthesis. Using massive datasets of captioned images, diffusion models learn to generate raster images of highly diverse objects and scenes. However, designers frequently use vector representations of images like Scalable Vector Graphics (SVGs) for digital icons or art. Vector graphics can be s

Begoña Cano, Marí a Jesús Moreta

In this paper a technique is given to recover the classical order of the method when explicit exponential Runge-Kutta methods integrate reaction-diffusion problems. Although methods of high stiff order for problems with vanishing boundary conditions can be constructed, that may imply increasing the number of stages and therefore, the computational cost seems

Xuan Zhang, Shiyu Li, Xi Li, Ping Huang

Visual anomaly detection, an important problem in computer vision, is usually formulated as a one-class classification and segmentation task. The student-teacher (S-T) framework has proved to be effective in solving this challenge. However, previous works based on S-T only empirically applied constraints on normal data and fused multi-level information. In t

Wei Chen, Yansheng Li, Bo Dang, Yongjun Zhang

This paper presents EHSNet, a new end-to-end segmentation network designed for the holistic learning of large-size remote sensing image semantic segmentation (LRISS). Large-size remote sensing images (LRIs) can lead to GPU memory exhaustion due to their extremely large size, which has been handled in previous works through either global-local fusion or multi

Sifan Long, Zhen Zhao, Jimin Pi, Shengsheng Wang

Vision transformers have achieved significant improvements on various vision tasks but their quadratic interactions between tokens significantly reduce computational efficiency. Many pruning methods have been proposed to remove redundant tokens for efficient vision transformers recently. However, existing studies mainly focus on the token importance to prese

Juan Cruz Viotti, Mital Kinderkhedia

In this paper, we present the JSON Stats Analyzer, a free-to-use open-source web-based JavaScript tool and module that provides JSON document analysis. We explain how the JSON Stats Analyzer works, its usage alongside the demonstration of eleven JSON documents from Tier 1, Tier 2 and Tier 3 from our proposed taxonomy that categorizes JSON documents according

Nicolas Marsic, Wolfgang F. O. Müller, Volker Tympel, Thomas Stöhlker

This paper studies the impact of mechanical deformations on the performance of a coaxial-type cryogenic current comparator (CCC). Such deformations may become a concern as the size of the CCC increases (e.g. when used as a diagnostic device in a particle accelerator facility involving beamlines with a large diameter). In addition to static deformations, this

Yunfeng Diao, He Wang, Tianjia Shao, Yong-Liang Yang

Human Activity Recognition (HAR) has been employed in a wide range of applications, e.g. self-driving cars, where safety and lives are at stake. Recently, the robustness of skeleton-based HAR methods have been questioned due to their vulnerability to adversarial attacks. However, the proposed attacks require the full-knowledge of the attacked classifier, whi

Jasper van der Kolk, Guillermo García-Pérez, Nikos E. Kouvaris, M. Ángeles Serrano

Turing patterns, arising from the interplay between competing species of diffusive particles, has long been an important concept for describing non-equilibrium self-organization in nature, and has been extensively investigated in many chemical and biological systems. Historically, these patterns have been studied in extended systems and lattices. Recently, t

Chun-Wang Liu, Ye Liu, Lei Du, Wan-Jun Su

We study enhanced sensing of optomechanically induced nonlinearity (OMIN) in a cavity-waveguide coupled system. The Hamiltonian of the system is anti-PT symmetric with the two involved cavities being dissipatively coupled via the waveguide. When a weak waveguide-mediated coherent coupling is introduced, the anti-PT symmetry may break down. However, we find a

Changlin Li, Guangyang Wu, Yanan Sun, Xin Tao

Capitalizing on the rapid development of neural networks, recent video frame interpolation (VFI) methods have achieved notable improvements. However, they still fall short for real-world videos containing large motions. Complex deformation and/or occlusion caused by large motions make it an extremely difficult problem in video frame interpolation. In this pa

Dominik Stallmann, Philip Kenneweg, Barbara Hammer

Transfer learning schemes based on deep networks which have been trained on huge image corpora offer state-of-the-art technologies in computer vision. Here, supervised and semi-supervised approaches constitute efficient technologies which work well with comparably small data sets. Yet, such applications are currently restricted to application domains where s

Anna Chiara Lai, Paola Loreti

We investigate optimal expansions of Kakeya sequences for the representation of real numbers. Expansions of Kakeya sequences generalize the expansions in non-integer bases and they display analogous redundancy phenomena. In this paper, we characterize optimal expansions of Kakeya sequences, and we provide conditions for the existence of unique expansions wit

Sheng Yu, Xiao Pan, Anastasis Georgiou, Boli Chen

The recent advancement in vehicular networking technology provides novel solutions for designing intelligent and sustainable vehicle motion controllers. This work addresses a car-following task, where the feedback linearisation method is combined with a robust model predictive control (RMPC) scheme to safely, optimally and efficiently control a connected ele

Guido Martinelli, Manuel Naviglio, Silvano Simula, Ludovico Vittorio

We present an application of the unitarity-based dispersion matrix (DM) approach to the extraction of the CKM matrix element $|V_{cb}|$ from the experimental data on the exclusive semileptonic $B_{(s)} \to D_{(s)}^{(*)} \ell \nu_\ell$ decays. The DM method allows to achieve a non-perturbative, model-independent determination of the momentum dependence of the

Ting Han, Kunhao Pan, Xinyu Chen, Dingjie Song

Bidirectional Encoder Representations from Transformers or BERT~\cite{devlin-etal-2019-bert} has been one of the base models for various NLP tasks due to its remarkable performance. Variants customized for different languages and tasks are proposed to further improve the performance. In this work, we investigate supervised continued pre-training~\cite{gurura

Maryam Parvizi, Amirreza Khodadadian, Sven Beuchler, Thomas Wick

The time-harmonic Maxwell equations are used to study the effect of electric and magnetic fields on each other. Although the linear systems resulting from solving this system using FEMs are sparse, direct solvers cannot reach the linear complexity. In fact, due to the indefinite system matrix, iterative solvers suffer from slow convergence. In this work, we

Ruili Feng, Kecheng Zheng, Kai Zhu, Yujun Shen

This work presents two astonishing findings on neural networks learned for large-scale image classification. 1) Given a well-trained model, the logits predicted for some category can be directly obtained by linearly combining the predictions of a few other categories, which we call \textbf{neural dependency}. 2) Neural dependencies exist not only within a si

Jun Xu, Jinlin Han, Pengfei Wang, Yi Yan

The Five-hundred-meter Aperture Spherical radio Telescope (FAST) is the most sensitive radio telescope for pulsar observations. We make polarimetric measurements of a large number of faint and distant pulsars using the FAST. We present the new measurements of Faraday rotation for 134 faint pulsars in the Galactic halo. Significant improvements are also made

Muhammad Junaid Haris, Aanchal Upreti, Melih Kurtaran, Filip Ginter

The problem of gender bias is highly prevalent and well known. In this paper, we have analysed the portrayal of gender roles in English movies, a medium that effectively influences society in shaping people's beliefs and opinions. First, we gathered scripts of films from different genres and derived sentiments and emotions using natural language processing t

L. G. Hou, J. L. Han, Tao Hong, X. Y. Gao

As one of the major components of the interstellar medium, the ionized gas in our Milky Way, especially the low-density diffuse component, has not been extensively observed in the radio band. The Galactic Plane Pulsar Snapshot (GPPS) survey covers the sky area within the Galactic latitude of $\pm10^\circ$ around the Galactic plane visible by the Five-hundred

Qianhui Wu, Huiqiang Jiang, Haonan Yin, Börje F. Karlsson

Self-supervised representation learning has proved to be a valuable component for out-of-distribution (OoD) detection with only the texts of in-distribution (ID) examples. These approaches either train a language model from scratch or fine-tune a pre-trained language model using ID examples, and then take the perplexity output by the language model as OoD sc

Tao Hong, J. L. Han, L. G. Hou, X. Y. Gao

Neutral hydrogen (HI) is the fundamental component of the interstellar medium. The Galactic Plane Pulsar Snapshot (GPPS) survey is designed for hunting pulsars by using the Five-hundred-meter Aperture Spherical radio Telescope (FAST) from the visible Galactic plane within $|b| \leq 10^{\circ}$. The survey observations are conducted with the L-band 19-beam re

Chloe Paliard, Nils Thuerey, Kiwon Um

We explore training deep neural network models in conjunction with physics simulations via partial differential equations (PDEs), using the simulated degrees of freedom as latent space for a neural network. In contrast to previous work, this paper treats the degrees of freedom of the simulated space purely as tools to be used by the neural network. We demons

Qi Jia, Yizhu Liu, Haifeng Tang, Kenny Q. Zhu

Curriculum learning has shown promising improvements in multiple domains by training machine learning models from easy samples to hard ones. Previous works which either design rules or train models for scoring the difficulty highly rely on task-specific expertise, and cannot generalize. Inspired by the "easy-to-hard" intuition, we propose to do in-sample cur

Nicolas Larue, Ngoc-Son Vu, Vitomir Struc, Peter Peer

Modern deepfake detectors have achieved encouraging results, when training and test images are drawn from the same data collection. However, when these detectors are applied to images produced with unknown deepfake-generation techniques, considerable performance degradations are commonly observed. In this paper, we propose a novel deepfake detector, called S

Alessio Morea, Michele N. Notarnicola, Stefano Olivares

The task of preserving entanglement against noises is of crucial importance for both quantum communication and quantum information transfer. To this aim, quantum error correction (QEC) codes may be employed to compensate, at least partially, the detriments induced by environmental noise that can be modelled as a bit-flip or a phase-flip error channel. In thi

Kasper Claes, Valentina Ticcinelli, Reham Badawy, Yordan P. Raykov

Digital sensors are increasingly being used to monitor the change over time of physiological processes in biological health and disease, often using wearable devices. This generates very large amounts of digital sensor data, for which, a consensus on a common storage, exchange and archival data format standard, has yet to be reached. To address this gap, we

Hao Shi, Qi Jiang, Kailun Yang, Xiaoting Yin

Vision sensors are widely applied in vehicles, robots, and roadside infrastructure. However, due to limitations in hardware cost and system size, camera Field-of-View (FoV) is often restricted and may not provide sufficient coverage. Nevertheless, from a spatiotemporal perspective, it is possible to obtain information beyond the camera's physical FoV from pa

Sheng Hu, Song Gao, Wei Luo, Liang Wu

The unprecedented urbanization in China has dramatically changed the urban spatial structure of cities. With the proliferation of individual-level geospatial big data, previous studies have widely used the network abstraction model to reveal the underlying urban spatial structure. However, the construction of network abstraction models primarily focuses on t

Qian Chen, Yuxuan Liu, Yu Tian, Xiaoning Wu

In this work, we investigate the real-time dynamics of quenching a state from phase separation in a holographic model of first-order phase transition. In addition to the typical phase-separated and high-energy final states, we have discovered a novel dynamical process that drives the system to a low-temperature supercooled final state within a narrow range o

Sebastian Schlor, Robin Strässer, Frank Allgöwer

The security of public-key cryptosystems relies on computationally hard problems, that are classically analyzed by number theoretic methods. In this paper, we introduce a new perspective on cryptosystems by interpreting the Diffie-Hellman key exchange as a nonlinear dynamical system. Employing Koopman theory, we transfer this dynamical system into a higher-d

Jin Woo Jang, Juan J. L. Velázquez

In this paper, we study the temperature distribution of a body when the heat is transmitted only by radiation. The heat transmitted by convection and conduction is ignored. We consider the stationary radiative transfer equation in the local thermodynamic equilibrium. We prove that the stationary radiative transfer equation coupled with the non-local temperat

Rachid Benbrik, Mohammed Boukidi

In this contribution, we discuss the light charged Higgs boson production via $pp \to \bar{t} bH^\pm$ at the Large Hadron Collider (LHC) in the Two-Higgs Doublet Model (2HDM) Type-III. We explore the prospect of looking the aforementioned Higgs boson production channel followed by $H^\pm\mu\nu$ signal. The latter has the potential to be overwhelmingly strong

Josua Unger, Anette Messinger, Benjamin E. Niehoff, Michael Fellner

We present a construction for circuits with low gate count and depth, implementing three- and four-body Pauli-Z product operators as they appear in the form of plaquette-shaped constraints in QAOA when using the parity mapping. The circuits can be implemented on any quantum device with nearest-neighbor connectivity on a square-lattice, using only one gate ty

Hao-Hao Peng, Sihao Wu, Ren-jie Wang, Duan She

We have derived the solutions of the relativistic anomalous magnetohydrodynamics with longitudinal Bjorken boost invariance and transverse electromagnetic fields in the presence of temperature or energy density dependent electric conductivity. We consider the equations of states in a high temperature limit or in a high chiral chemical potential limit. We obt

Chern Chuang, Arie Kapulkin, Arjendu K. Pattanayak, Paul Brumer

We demonstrate that non-equilibrium steady states of the dissipative Rabi model show dramatic spikes in transport rates over narrow parameter ranges. Similar results are found for the Holstein and Dicke models. This is found to be due to avoided energy level crossings in the corresponding closed systems, and correlates with spikes in the entanglement entropy

Sergio Barrachina-Muñoz, Jorge Baranda, Miquel Payaró, Josep Mangues-Bafalluy

The need of mobile network operators for cost-effectiveness is driving 5G and beyond networks towards highly flexible and agile deployments to adapt to dynamic and resource-constrained scenarios while meeting a myriad of user network stakeholders' requirements. In this setting, we consider that zero-touch orchestration schemes based on cloud-native deploymen

Ketan M. Patel, Saurabh K. Shukla

Incorporation of the standard model Yukawa interactions in a grand unified theory (GUT) often predicts varieties of new scalars that couple to the fermions and lead to some novel observational effects. We assess such a possibility for the colour sextet diquark scalars within the realistic renormalizable models based on $SO(10)$ GUT. The spectrum consists of

Bo Yan, Xingran Zhao, Yadong Li, Hongbin Wang

Instance segmentation is applied widely in image editing, image analysis and autonomous driving, etc. However, insufficient data is a common problem in practical applications. The Visual Inductive Priors(VIPriors) Instance Segmentation Challenge has focused on this problem. VIPriors for Data-Efficient Computer Vision Challenges ask competitors to train model

Shiqiang Zhu, Ting Yu, Tao Xu, Hongyang Chen

Computing is a critical driving force in the development of human civilization. In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence and internet-of-things with new computing theories, archite

Olga Boyko, Olga Martynyuk, Vyacheslav Pivovarchik

Spectral problems are considered generated by the Sturm-Liouville equation on equilateral trees with the Dirichlet boundary conditions at the pendant vertices and continuity and Kirchhoff's conditions at the interior vertices. It is proved that there are no co-spectral (i.e., having the same spectrum of such problem) among equilateral trees of less or equal

Yu. Sotnikova, T. Mufakharov, R. Udovitskiy, M. Mingaliev

In this paper we present the RATAN-600 multi-frequency catalogue of blazars, an updated version of the BLcat: the RATAN-600 multi-frequency catalogue of BL Lacertae objects. The main novelty in the catalogue is an extension of the sample with flat-spectrum radio quasars (FSRQs), thus currently it contains more than 1700 blazars of different types. The main f

Luca De Luigi, Ren Li, Benoît Guillard, Mathieu Salzmann

Recent approaches to drape garments quickly over arbitrary human bodies leverage self-supervision to eliminate the need for large training sets. However, they are designed to train one network per clothing item, which severely limits their generalization abilities. In our work, we rely on self-supervision to train a single network to drape multiple garments.

Yuanbo Li, Yiqin Wang, Yi Chen, Ziming Yu

With abundant bandwidth resource, the Terahertz band (0.1~THz to 10~THz) is envisioned as a key technology to realize ultra-high data rates in the 6G and beyond mobile communication systems. However, moving to the THz band, existing channel models dedicated for microwave or millimeter-wave bands are ineffective. To fill this research gap, extensive channel m

Qiushi Zhu, Long Zhou, Ziqiang Zhang, Shujie Liu

Although speech is a simple and effective way for humans to communicate with the outside world, a more realistic speech interaction contains multimodal information, e.g., vision, text. How to design a unified framework to integrate different modal information and leverage different resources (e.g., visual-audio pairs, audio-text pairs, unlabeled speech, and

CLAS Collaboration, G. Christiaens, M. Defurne, D. Sokhan

Deeply virtual Compton scattering (DVCS) allows one to probe Generalized Parton Distributions (GPDs) describing the 3D structure of the nucleon. We report the first measurement of the DVCS beam-spin asymmetry using the CLAS12 spectrometer with a 10.2 and 10.6 GeV electron beam scattering from unpolarised protons. The results greatly extend the $Q^2$ and Bjor

Robert Brandenberger

I present some new perspectives on Dark Matter, Dark Energy and the origin of structure in the Universe. First, I argue that in order to understand the two latter issues, one needs to go beyond a standard point particle effective field theory analysis. Next, I review recent work attempting to construct a unified dark sector model from Heterotic superstring t

Fabian Zierler, Suchita Kulkarni, Axel Maas, Seán Mee

The stable hadronic bound states in a hidden new non-Abelian gauge sector provide interesting candidates for strongly-interacting Dark Matter (DM). A particular example are theories in which DM is made up of dark pions which set the DM relic abundance through self-annihilation. One of the simplest realizations is $Sp(4)_c$ gauge theory with two Dirac fermion

Xiaoyu Chen, Feng Yan, Menghan Hu, Zihuai Lin

This paper examines the energy efficiency optimization problem of intelligent reflective surface (IRS)-assisted multi-user rate division multiple access (RSMA) downlink systems under terahertz propagation. The objective function for energy efficiency is optimized using the salp swarm algorithm (SSA) and compared with the successive convex approximation (SCA)

Cheng Guo, Xiuhua Jiang

High dynamic range (HDR) image is widely-used in graphics and photography due to the rich information it contains. Recently the community has started using deep neural network (DNN) to reconstruct standard dynamic range (SDR) images into HDR. Albeit the superiority of current DNN-based methods, their application scenario is still limited: (1) heavy model imp

Xin-Yue Kang, Chunmei Zhang, Mingxing Chen, Si Li

The hourglass fermions in solid-state materials have been attracting significant interest recently. However, realistic two-dimensional (2D) materials with hourglass-shaped band structures are still very scarce. Here, through the first-principles calculations, we identify the monolayer Pb(ClO$_{2}$)$_{2}$ and Sr(ClO$_{2}$)$_{2}$ materials as the new realistic

Jean-Guillaume Malherbe, Vincent Russier, Juan-Jose Alonso

We study the magnetic phase diagram of an ensemble of dipolar hard spheres (DHS) with or without uniaxial anisotropy and frozen in position on a disordered structure by tempered Monte Carlo simulations. The crucial point is to consider an anisotropic structure, obtained from the liquid state of the dipolar hard spheres fluid, frozen in its polarized state at

Nicolas Marsic, Wolfgang F. O. Müller, Herbert De Gersem, Matthias Schmelz

This paper presents a new shield configuration for cryogenic current comparators (CCCs), namely the folded coaxial geometry. An analytical model describing its shielding performance is first developed, and then validated by means of finite element simulations. Thanks to this model, the fundamental properties of the new shield are highlighted. Additionally, t

Chang-Xu Yan, Furu Zhang, Chao-Yang Tan, Hao-Ran Chang

The over-tilting of Dirac cones has led to various fascinating quantum phenomena. Here we find that two anomalous acoustic plasmons (AAPs) are dictated by the distinct geometry of two-dimensional (2D) type-II Dirac cones, far beyond the conventional $\sqrt{q}$ plasmon. One AAP originates from the strong hybridization of two pockets with large velocity anisot

E. Pariat, P. F. Wyper, L. Linan

While free/non-potential magnetic energy is a necessary element of any active phenomenon in the solar corona, its role as a marker of the trigger of eruptive process remains elusive. Based on the unique decomposition of the magnetic field into potential and non-potential components, magnetic energy and helicity can also both be uniquely decomposed into two q

Jonas Tauch, Saba Zia Hassan, Markus Noetzold, Eric S. Endres

The study of cold and controlled molecular ions is pivotal for fundamental research in modern physics and chemistry. Investigations into cooling molecular anions, in particular, have proven to be of key consequence for the production of cold antihydrogen, the creation, and study of anionic Coulomb crystals as well as in atmospheric research and astrochemistr

J. Waghorne, C. Howard, H. Hu, J. Pang

Complex soft tissues, for example the knee meniscus, play a crucial role in mobility and joint health, but when damaged are incredibly difficult to repair and replace. This is due to their highly hierarchical and porous nature which in turn leads to their unique mechanical properties. In order to design tissue substitutes, the internal architecture of the na

Natalia S. Salakhova, Ilia M. Fradkin, Sergey A. Dyakov, Nikolay A. Gippius

Multilayer stacks of twisted optical metasurfaces are considered as a prospective platform for chiral nanophotonic devices. Such structures are primarily used for the realization of circularly polarized light sources, artificial optical rotation, and circular dichroism. At the same time, the behavior of their hybrid photonic modes is strongly affected by the

Zelin Zang, Lei Shang, Senqiao Yang, Fei Wang

Unsupervised domain adaptation (UDA) has proven to be highly effective in transferring knowledge from a label-rich source domain to a label-scarce target domain. However, the presence of additional novel categories in the target domain has led to the development of open-set domain adaptation (ODA) and universal domain adaptation (UNDA). Existing ODA and UNDA

Andreas Höring, Thomas Peternell

Let $M$ be a smooth Fano threefold such that a canonical extension of the tangent bundle is an affine manifold. We show that $M$ is rational homogeneous.

Robert Tjarko Lange, Tom Schaul, Yutian Chen, Tom Zahavy

Optimizing functions without access to gradients is the remit of black-box methods such as evolution strategies. While highly general, their learning dynamics are often times heuristic and inflexible - exactly the limitations that meta-learning can address. Hence, we propose to discover effective update rules for evolution strategies via meta-learning. Concr

Shakthi Weerasinghe, Arkady Zaslavsky, Seng W. Loke, Alireza Hassani

Context information is in demand more than ever with the rapid increase in the number of context-aware Internet of Things applications developed worldwide. Research in context and context-awareness is being conducted to broaden its applicability in light of many practical and technical challenges. One of the challenges is improving performance when respondin

Muhammad Umar B. Niazi, Philip E. Paré, Karl H. Johansson

This paper proposes a feedback design that effectively copes with uncertainties for reliable epidemic monitoring and control. There are several optimization-based methods to estimate the parameters of an epidemic model by utilizing past reported data. However, due to the possibility of noise in the data, the estimated parameters may not be accurate, thereby

Qi Jiang, Hao Shi, Shaohua Gao, Jiaming Zhang

Semantic scene understanding with Minimalist Optical Systems (MOS) in mobile and wearable applications remains a challenge due to the corrupted imaging quality induced by optical aberrations. However, previous works only focus on improving the subjective imaging quality through the Computational Imaging (CI) technique, ignoring the feasibility of advancing s

Guimin Hu, Ting-En Lin, Yi Zhao, Guangming Lu

Multimodal sentiment analysis (MSA) and emotion recognition in conversation (ERC) are key research topics for computers to understand human behaviors. From a psychological perspective, emotions are the expression of affect or feelings during a short period, while sentiments are formed and held for a longer period. However, most existing works study sentiment

Luping Liu, Yi Ren, Xize Cheng, Rongjie Huang

Out-of-distribution (OOD) detection is a crucial task for ensuring the reliability and safety of deep learning. Currently, discriminator models outperform other methods in this regard. However, the feature extraction process used by discriminator models suffers from the loss of critical information, leaving room for bad cases and malicious attacks. In this p

Livia Terlizzi

ALICE (A Large Ion Collider Experiment) at the CERN Large Hadron Collider (LHC) is designed to study p-p and Pb-Pb collisions at ultra-relativistic energies. ALICE is equipped with a Muon Spectrometer (MS) to study the heavy charmonia in p-p and heavy ion collisions via their muonic decay. At first, in the LHC Run 1 and 2 the selection of interesting events

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

Based on electron positron collision data collected with the BESIII detector operating at the BEPCII storage rings, the differential cross sections of inclusive $\pi^0$ and $K^0_S$ production as a function of hadron momentum, normalized by the total cross section of the $e^{+}e^{-} \to$ hadrons process, are measured at six center-of-mass energies from 2.2324

Lukas Pukelis, Nuria Bautista-Puig, Gustė Statulevičiūtė, Vilius Stančiauskas

Despite concrete indicators and targets, monitoring the progress of the UN Sustainable Development Goals (SDGs) remains a challenge, given the many different actors, initiatives, and institutions involved. OSDG, an open-source classification tool aims to help navigate the SDG related ambiguities through a simple and easy to use application. The tool allows t

Belle Collaboration, K. -N. Chu, Y. -R. Lin, M. -Z. Wang

We search for the tree-diagram dominated process $B^+ \rightarrow p \overline{n} \pi^0$, using a data sample of $772 \times 10^6~B\overline B$ pairs collected at the $\Upsilon(4S)$ resonance with the Belle detector at the KEKB asymmetric-energy $e^+ e^-$ collider. This is the first search with the Belle detector for a decay mode including an anti-neutron. No

Ahad Hamednia, Jimmy Forsman, Nikolce Murgovski, Viktor Larsson

This paper investigates battery preheating before fast charging, for a battery electric vehicle (BEV) driving in a cold climate. To prevent the battery from performance degradation at low temperatures, a thermal management (TM) system has been considered, including a high-voltage coolant heater (HVCH) for the battery and cabin compartment heating. Accordingl

Jun-Hao Huang, Fan-Yun Hung, Pei-Xin Liang, Chia-Fu Yu

A multinorm one torus associated to a commutative \'etale algebra $L$ over a global field $k$ is of Kummer type if each factor of $L$ is a cyclic Kummer extension. In this paper we compute the Tate-Shafarevich group of such tori based on recent works of Bayer-Fluckiger, T.-Y. Lee and Parimala, and of T.-Y.~Lee. We also implement an effective algorithm using

Le Zhuo, Zhaokai Wang, Baisen Wang, Yue Liao

Music is essential when editing videos, but selecting music manually is difficult and time-consuming. Thus, we seek to automatically generate background music tracks given video input. This is a challenging task since it requires music-video datasets, efficient architectures for video-to-music generation, and reasonable metrics, none of which currently exist

Zelin Zang, Shenghui Cheng, Linyan Lu, Hanchen Xia

Dimension reduction (DR) is commonly utilized to capture the intrinsic structure and transform high-dimensional data into low-dimensional space while retaining meaningful properties of the original data. It is used in various applications, such as image recognition, single-cell sequencing analysis, and biomarker discovery. However, contemporary parametric-fr

Jiachen Qian, Peihu Duan, Zhisheng Duan, Ling shi

The coupled Riccati equations are cosisted of multiple Riccati-like equations with solutions coupled with each other, which can be applied to depict the properties of more complex systems such as markovian systems or multi-agent systems. This paper manages to formulate and investigate a new kind of coupled Riccati equations, called harmonic-coupled Riccati e

Bousselham El Haddaoui, Raddouane Chiheb, Rdouan Faizi, Abdellatif El Afia

Deep learning techniques have proven their effectiveness for Sentiment Analysis (SA) related tasks. Recurrent neural networks (RNN), especially Long Short-Term Memory (LSTM) and Bidirectional LSTM, have become a reference for building accurate predictive models. However, the models complexity and the number of hyperparameters to configure raises several ques

Matteo Tanzi

In this review we survey the literature on mean-field coupled maps. We start with the early works from the physics literature, arriving to some recent results from ergodic theory studying the thermodynamic limit of globally coupled maps and the associated self-consistent transfer operators. We also give few pointers to related research fields dealing with me

Patrick Sieberer, Chenfan Zhang, Thomas Bergauer, Raimon Casanova Mohr

The CERN-RD50 CMOS working group develops the RD50-MPWseries of monolithic high-voltage CMOS pixel sensors for potential use in future high luminosity experiments such as the HL-LHC and FCC-hh. In this contribution, the design of the latest prototype in this series, RD50-MPW3, is presented. An overview of its pixel matrix and digital readout periphery is giv

Xueyang Tang, Song Guo, Jie Zhang

Recently, data heterogeneity among the training datasets on the local clients (a.k.a., Non-IID data) has attracted intense interest in Federated Learning (FL), and many personalized federated learning methods have been proposed to handle it. However, the distribution shift between the training dataset and testing dataset on each client is never considered in

Jiaru Jia, Mingzhe Liu, Jiake Xie, Xin Chen

Generating semantic segmentation datasets has consistently been laborious and time-consuming, particularly in the context of large models or specialized domains(i.e. Medical Imaging or Remote Sensing). Specifically, large models necessitate a substantial volume of data, while datasets in professional domains frequently require the involvement of domain exper

Derong Kong, Beibei Sun

Given $\rho\in (0,1/4]$, the four corner Cantor set $E\subset \mathbb{R}^{2}$ is a self-similar set generated by the iterated function system \[ \left\{(\rho x, \rho y), \quad(\rho x, \rho y+1-\rho),\quad (\rho x+1-\rho, \rho y),\quad(\rho x+1-\rho,\rho y+1-\rho)\right\}. \] For $\theta\in[0,\pi)$ let $E_\theta$ be the orthogonal projection of $E$ onto a lin