May 2023 arXiv papers — page 75

Thomas Fernique

A packing of disks in the plane is a set of disks with disjoint interiors. This paper is a survey of some open questions about such packings. It is organized into five themes: compacity, conjugacy, density, uniformity and computability.

Claudio Paonessa, Dominik Frefel, Manfred Vogel

We introduce Parallel Paraphrasing ($\text{Para}_\text{both}$), an augmentation method for translation metrics making use of automatic paraphrasing of both the reference and hypothesis. This method counteracts the typically misleading results of speech translation metrics such as WER, CER, and BLEU if only a single reference is available. We introduce two ne

Tian Lan, Jing-Ren Zhou

We establish the formulation for quantum current. Given a symmetry group $G$, let $\mathcal{C}:=\mathrm{Rep} G$ be its representation category. Physically, symmetry charges are objects of $\mathcal{C}$ and symmetric operators are morphisms in $\mathcal{C}$. The addition of charges is given by the tensor product of representations. For any symmetric operator

Yuze Lu, Hailong Zhang, Qiwen Guo

Applications of deep learning in financial market prediction has attracted huge attention from investors and researchers. In particular, intra-day prediction at the minute scale, the dramatically fluctuating volume and stock prices within short time periods have posed a great challenge for the convergence of networks result. Informer is a more novel network,

Thi-Hoa Nguyen, René R. Hiemstra, Sascha Eisenträger, Dominik Schillinger

We present a mass lumping approach based on an isogeometric Petrov-Galerkin method that preserves higher-order spatial accuracy in explicit dynamics calculations irrespective of the polynomial degree of the spline approximation. To discretize the test function space, our method uses an approximate dual basis, whose functions are smooth, have local support an

Tamoghna Das

Two typical morphology of two-dimensional aggregates are considered: compact crystalline clusters and string-like non-compact conformations. Simulated trajectories of both types of aggregates are analysed with fine spatial resolution. While the long-time geometry of such trajectories appears to be statistically identical for two conformations, the self-overl

Omar Ghazal, Simranjeet Singh, Tousif Rahman, Shengqi Yu

In-memory computing for Machine Learning (ML) applications remedies the von Neumann bottlenecks by organizing computation to exploit parallelism and locality. Non-volatile memory devices such as Resistive RAM (ReRAM) offer integrated switching and storage capabilities showing promising performance for ML applications. However, ReRAM devices have design chall

Valerii Chernov, Alexey Matveev

An underactuated nonholonomic Dubins-vehicle-like robot with a lower-limited turning radius travels with a constant speed in a plane, which hosts unknown complex objects. The robot has to approach and then circumnavigate all objects, with maintaining a given distance to the currently nearest of them. So the ideal targeted path is the equidistant curve of the

Wujian Peng, Zejia Weng, Hengduo Li, Zuxuan Wu

Exploring a substantial amount of unlabeled data, semi-supervised learning (SSL) boosts the recognition performance when only a limited number of labels are provided. However, traditional methods assume that the data distribution is class-balanced, which is difficult to achieve in reality due to the long-tailed nature of real-world data. While the data imbal

Anani Kwassi

This paper aims at obtaining, by means of integral transforms, analytical approximations in short times of solutions to boundary value problems for the one-dimensional reaction-diffusion equation with constant coefficients. The general form of the equation is considered on a bounded generic interval and the three classical types of boundary conditions, i.e.,

Elena Rufeil Fiori, Adolfo J. Banchio

Integral equation theories (IETs) based on the Ornstein-Zernike (OZ) relation can be used as an analytical tool to predict structural and thermodynamic properties and phase behavior of fluids with low numerical cost. However, there are no studies of the IETs for the dipolar density interaction potential in 2D systems, a relevant inter-domain interaction in l

Jianchun Chu, Jintian Zhu

In this paper, we investigate the weighted mass for weighted manifolds. By establishing a version of density theorem and generalizing Geroch conjecture in the setting of $P$-scalar curvature, we are able to prove the positive weighted mass theorem for weighted manifolds, which generalizes the result of Baldauf-Ozuch to non-spin manifolds.

Miriam Anschütz, Joshua Oehms, Thomas Wimmer, Bartłomiej Jezierski

Automatic text simplification systems help to reduce textual information barriers on the internet. However, for languages other than English, only few parallel data to train these systems exists. We propose a two-step approach to overcome this data scarcity issue. First, we fine-tuned language models on a corpus of German Easy Language, a specific style of G

Julian Coda-Forno, Marcel Binz, Zeynep Akata, Matthew Botvinick

Large language models have shown tremendous performance in a variety of tasks. In-context learning -- the ability to improve at a task after being provided with a number of demonstrations -- is seen as one of the main contributors to their success. In the present paper, we demonstrate that the in-context learning abilities of large language models can be rec

BoYang Zheng

Adversarial attacks against Deep Neural Networks(DNN) have been a crutial topic ever since \cite{goodfellow} purposed the vulnerability of DNNs. However, most prior works craft adversarial examples in the pixel space, following the $l_p$ norm constraint. In this paper, we give intuitional explain about why crafting adversarial examples in the latent space is

Christopher A. Schroeder

Let $G$ be a finite group and let $p$ be a prime. In this paper, we study the structure of finite groups with a large number of $p$-regular conjugacy classes or, equivalently, a large number of irreducible $p$-modular representations. We prove sharp lower bounds for this number in terms of $p$ and the $p'$-part of the order of $G$ which ensure that $G$ is $p

Erkko Lehtonen

The near-unanimity-closed minions of Boolean functions, i.e., the clonoids whose target algebra contains a near-unanimity function, are completely described. The key concept towards this result is the minorant-minor partial order and its order ideals.

Shentong Mo, Jing Shi, Yapeng Tian

Text-to-audio (TTA) generation is a recent popular problem that aims to synthesize general audio given text descriptions. Previous methods utilized latent diffusion models to learn audio embedding in a latent space with text embedding as the condition. However, they ignored the synchronization between audio and visual content in the video, and tended to gene

Chi-Yee Cheung

We note that the proof of the no-go theorem of unconditionally secure quantum bit commitment is based on a model which is not universal. For protocols not described by the model, this theorem does not apply. Using unstable particles and a modified double-slit setup, we construct such a protocol and show that it is unconditionally secure. In this protocol, th

Yu-Shan Tai, Ming-Guang Lin, An-Yeu, Wu

Vision transformers (ViTs) have achieved remarkable performance in various computer vision tasks. However, intensive memory and computation requirements impede ViTs from running on resource-constrained edge devices. Due to the non-normally distributed values after Softmax and GeLU, post-training quantization on ViTs results in severe accuracy degradation. Mo

Jennifer D'Souza, Moussab Hrou, Sören Auer

There have been many recent investigations into prompt-based training of transformer language models for new text genres in low-resource settings. The prompt-based training approach has been found to be effective in generalizing pre-trained or fine-tuned models for transfer to resource-scarce settings. This work, for the first time, reports results on adopti

L. Olano-Vegas, I. Pardo, S. Leardini, M. Morales

We present a ceramic material based on hematite (Fe$_2$O$_3$) and zirconia stabilized with yttria at 8% molar (YSZ), that exhibits stable electrical properties with transported charge and that can be tuned to the resistivities necessary to induce spark-quenching in gaseous detectors ($\rho = 10^9-10^{12}$ $\Omega \cdot$cm), from room temperature down to the

Konstantin Kraus, Kathrin Klamroth, Michael Stiglmayr

We consider an online version of a longest path problem in an undirected and planar graph that is motivated by a location and routing problem occurring in the board game "Turn & Taxis". Path extensions have to be selected based on only partial knowledge on the order in which nodes become available in later iterations. Besides board games, online path extensi

Henning Bruhn, Raphael Steck

We show that walls of size at least $6 \times 4$ do not have the edge-Erd\H{o}s-P\'{o}sa property.

Reisel Millan, Estefania Bello-Jurado, Manual Moliner, Mercedes Boronat

Cu-exchanged zeolites rely on mobile solvated Cu+ cations for their catalytic activity, but the role of framework composition on transport is not fully understood. Ab initio molecular dynamics simulations can provide quantitative atomistic insight but are too computationally expensive to explore large length- and time-scales or diverse compositions. We repor

Qizhang Feng, Ninghao Liu, Fan Yang, Ruixiang Tang

Graph Neural Networks (GNNs) are gaining extensive attention for their application in graph data. However, the black-box nature of GNNs prevents users from understanding and trusting the models, thus hampering their applicability. Whereas explaining GNNs remains a challenge, most existing methods fall into approximation based and perturbation based approache

Owen Henkel, Libby Hills

Machine Learning models have many potentially beneficial applications in education settings, but a key barrier to their development is securing enough data to train these models. Labelling educational data has traditionally relied on highly skilled raters using complex, multi-class rubrics, making the process expensive and difficult to scale. An alternative,

R. S. Tessinari, R. I. Woodward, A. J. Shields

We propose and implement a software-defined network architecture that integrates the QKD SDN Controller within the QKD node, enabling it to use quantum keys to secure its communication with SDN agents while optimizing QKD-keys consumption.

Daniel Casanueva-Morato, Alvaro Ayuso-Martinez, Juan P. Dominguez-Morales, Angel Jimenez-Fernandez

The brain has a great capacity for computation and efficient resolution of complex problems, far surpassing modern computers. Neuromorphic engineering seeks to mimic the basic principles of the brain to develop systems capable of achieving such capabilities. In the neuromorphic field, navigation systems are of great interest due to their potential applicabil

Jun Peng, Xing-Hui Feng

In this paper, we investigate the Blandford-Znajek (BZ) process within the framework of Einsteinian cubic gravity (ECG). To analytically study the BZ process using the split monopole configuration, we construct a slowly rotating black hole in ECG up to cubic order in small spin, considering the leading order in small coupling constant of higher curvature ter

Marko Staric

At the Belle II experiment a Time-of-Propagation (TOP) counter is used for particle identification in the barrel region. This novel type of particle identification device combines the Cherenkov ring imaging technique with the time-of-flight and therefore it relies on a precise knowledge of the time of collision in each triggered event. We discuss the perform

Andrea Merlo, Andrea Pavone, Daniel Böckenhoff, Ekkehard Pasch

High-$\langle \beta \rangle$ operations require a fast and robust inference of plasma parameters with a self-consistent MHD equilibrium. Precalculated MHD equilibria are usually employed at W7-X due to the high computational cost. To address this, we couple a physics-regularized NN model that approximates the ideal-MHD equilibrium with the Bayesian modeling

Werner Hofmann, Roberta Zanin

The Cherenkov Telescope Array Observatory (CTAO) is a next-generation facility for ground-based very high energy gamma ray astronomy. CTAO will be operated as an open observatory. With two sites, in the northern and southern hemispheres, the Cherenkov Telescope Array CTA will provide full-sky coverage, improving sensitivity by an order of magnitude over curr

Mireille Fares, Catherine Pelachaud, Nicolas Obin

In this study, we address the importance of modeling behavior style in virtual agents for personalized human-agent interaction. We propose a machine learning approach to synthesize gestures, driven by prosodic features and text, in the style of different speakers, even those unseen during training. Our model incorporates zero-shot multimodal style transfer u

Dionis Totsila, Konstantinos Chatzilygeroudis, Denis Hadjivelichkov, Valerio Modugno

State-of-the-art sensorimotor learning algorithms offer policies that can often produce unstable behaviors, damaging the robot and/or the environment. Traditional robot learning, on the contrary, relies on dynamical system-based policies that can be analyzed for stability/safety. Such policies, however, are neither flexible nor generic and usually work only

Raphael Bennett-Tennenhaus

For a path algebra over a noetherian local ground ring, the notion of an admissible ideal was defined by Raggi-C{\'a}rdenas and Salmer{\'o}n. We provide sufficient conditions for admissibility and use them to study semiperfect module-finite algebras over local rings whose quotient by the radical is a product of copies of the residue field. We define string a

Cody B. Anderson, Giovanni Picotti, Michael E. Cholette, Bruce Leslie

This paper presents a novel methodology for characterizing soiling losses through experimental measurements. Soiling predictions were obtained by calibrating a soiling model based on field measurements from a 50 MW modular solar tower project in Mount Isa, Australia. The study found that the mean predicted soiling rate for horizontally fixed mirrors was 0.12

Monalisa Chatterjee, Manoranjan Kumar, Zoltán G. Soos

The magnetization $M(h)$ is used to identify three singlet quantum phases of the ladder with isotropic exchange interactions. The Dimer phase with frustrated F exchanges in rungs and legs has a first-order $M(h)$ transition at $0$ K from singlet to ferromagnetic at the saturation field $h_s$. The Haldane-DAF phase with strong F exchange in rungs and net AF e

Sungyoon Lee, Sokbae Lee

In recent years, there has been a significant growth in research focusing on minimum $\ell_2$ norm (ridgeless) interpolation least squares estimators. However, the majority of these analyses have been limited to an unrealistic regression error structure, assuming independent and identically distributed errors with zero mean and common variance. In this paper

Dmitry Kobyakov

Earlier, the screening condition in neutron star core has been formulated as equality of velocities of superconducting protons and the electrons $\mathbf{v}_p=\mathbf{u}_e$ at wavenumbers $q\ll\lambda^{-1}$ ($\lambda$ is the London penetration depth) and has been used to derive the force exerted by the electrons on a moving flux tube. By calculating the curr

Jiazhi Guan, Tianshu Hu, Hang Zhou, Zhizhi Guo

The issue of detecting deepfakes has garnered significant attention in the research community, with the goal of identifying facial manipulations for abuse prevention. Although recent studies have focused on developing generalized models that can detect various types of deepfakes, their performance is not always be reliable and stable, which poses limitations

Philipp Sadler, Sherzod Hakimov, David Schlangen

The ability to pick up on language signals in an ongoing interaction is crucial for future machine learning models to collaborate and interact with humans naturally. In this paper, we present an initial study that evaluates intra-episodic feedback given in a collaborative setting. We use a referential language game as a controllable example of a task-oriente

Andrei Agrachev

We consider a smooth system of the form $\dot q=f_0(q)+\sum\limits_{i=1}^k u_i f_i(q)$, $q\in M,\ u_i\in\mathbb R,$ and study controllability issues on the group of diffeomorphisms of $M$. It is well-known that the system can arbitrarily well approximate the movement in the direction of any Lie bracket polynomial of $f_1,\ldots,f_k$. Any Lie bracket polynomi

Guangsheng Bao, Zhiyang Teng, Hao Zhou, Jianhao Yan

Non-autoregressive translation (NAT) models achieve comparable performance and superior speed compared to auto-regressive translation (AT) models in the context of sentence-level machine translation (MT). However, their abilities are unexplored in document-level MT, hindering their usage in real scenarios. In this paper, we conduct a comprehensive examinatio

Aleksander Ćwiszewski, Piotr Kokocki

We show the existence of standing waves for the nonlinear Schr\"{o}dinger equation with Kato-Rellich type potential. We consider both resonant with the nonlinearity satisfying one of Landesman-Lazer type or sign conditions and non-resonant case where the linearization at infinity has zero kernel. The approach relies on the geometric and topological analysis

Kezhou Lin, Xiaohan Wang, Linchao Zhu, Ke Sun

In this paper, we tackle the problem of sign language translation (SLT) without gloss annotations. Although intermediate representation like gloss has been proven effective, gloss annotations are hard to acquire, especially in large quantities. This limits the domain coverage of translation datasets, thus handicapping real-world applications. To mitigate thi

Fadi Jebali, Atreya Majumdar, Clément Turck, Kamel-Eddine Harabi

Memristor-based neural networks provide an exceptional energy-efficient platform for artificial intelligence (AI), presenting the possibility of self-powered operation when paired with energy harvesters. However, most memristor-based networks rely on analog in-memory computing, necessitating a stable and precise power supply, which is incompatible with the i

Ricky Hutchins, Olga Maleva

We show that for each fixed non-constant complex polynomial $P$ of the plane there exists a homeomorphism $h$ such that $P\circ h$ is a Lipschitz quotient mapping. This corrects errors in the construction given earlier by Johnson et. al. [Michigan Math. J. $\textbf{47}$ (2000), 15-31]. Further we introduce a stronger notion of pointwise co-Lipschitzness and

Joaquim Martín, Walter A. Ortiz

We introduce Poincar\'e type inequalities based on rearrangement invariant spaces in the setting of metric measure spaces and analyze when they imply the doubling condition on the underline measure.

Patrick Barth, Eva E. Stüeken, Christiane Helling, Lukas Rossmanith

Bioavailable nitrogen is thought to be a requirement for the origin and sustenance of life. Before the onset of biological nitrogen fixation, abiotic pathways to fix atmospheric N2 must have been prominent to provide bioavailable nitrogen to Earth's earliest ecosystems. Lightning has been shown to produce fixed nitrogen as nitrite and nitrate in both modern

Simin Li, Jun Guo, Jingqiao Xiu, Ruixiao Xu

In this study, we explore the robustness of cooperative multi-agent reinforcement learning (c-MARL) against Byzantine failures, where any agent can enact arbitrary, worst-case actions due to malfunction or adversarial attack. To address the uncertainty that any agent can be adversarial, we propose a Bayesian Adversarial Robust Dec-POMDP (BARDec-POMDP) framew

Fabien Casenave, Brian Staber, Xavier Roynard

When learning simulations for modeling physical phenomena in industrial designs, geometrical variabilities are of prime interest. While classical regression techniques prove effective for parameterized geometries, practical scenarios often involve the absence of shape parametrization during the inference stage, leaving us with only mesh discretizations as av

Yuxin Jiang, Chunkit Chan, Mingyang Chen, Wei Wang

The practice of transferring knowledge from a sophisticated, proprietary large language model (LLM) to a compact, open-source LLM has garnered considerable attention. Previous works have focused on a unidirectional knowledge distillation way by aligning the responses of the student model with those of the teacher model to a set of instructions. Nevertheless,

B. K. Sartayev

It is shown that the variety of transposed Poisson algebras coincides with the variety of Gelfand-Dorfman algebras in which the Novikov multiplication is commutative. The Gr\"obner-Shirshov basis for the transposed Poisson operad is calculated up to degree 4. Furthermore, we demonstrate that every transposed Poisson algebra is F-manifold. We verify that the

Zhen Ye, Wei Xue, Xu Tan, Qifeng Liu

Developing digital sound synthesizers is crucial to the music industry as it provides a low-cost way to produce high-quality sounds with rich timbres. Existing traditional synthesizers often require substantial expertise to determine the overall framework of a synthesizer and the parameters of submodules. Since expert knowledge is hard to acquire, it hinders

Andres F. Ducuara, Paul Skrzypczyk

We establish fundamental connections between utility theories of wealth from the economic sciences and information-theoretic quantities. In particular, we introduce operational tasks based on betting where both gambler and bookmaker have access to side information, or betting tasks with double side information for short. In order to characterise these operat

David Könen, Michael Stiglmayr

This paper addresses the problem of enumerating all supported efficient solutions for a linear multi-objective integer minimum cost flow problem (MOIMCF). It derives an output-polynomial time algorithm to determine all supported efficient solutions for MOIMCF problems. This is the first approach to solve this general problem in output-polynomial time. Moreov

Zehan Wang, Yang Zhao, Xize Cheng, Haifeng Huang

Multi-modal Contrastive Representation learning aims to encode different modalities into a semantically aligned shared space. This paradigm shows remarkable generalization ability on numerous downstream tasks across various modalities. However, the reliance on massive high-quality data pairs limits its further development on more modalities. This paper propo

Andrew Lord, Robert Woodward, Shinya Murai, Hideaki Sato

We describe a London Quantum-Secured Metro Network using Quantum Key Distribution between three London nodes together with customer access tails. The commercially- eady solution is fully integrated into the BT network and on-boarded its first customer.

Weisong Sun, Chunrong Fang, Yudu You, Yun Miao

To support software developers in understanding and maintaining programs, various automatic code summarization techniques have been proposed to generate a concise natural language comment for a given code snippet. Recently, the emergence of large language models (LLMs) has led to a great boost in the performance of natural language processing tasks. Among th

Nirbhay Patil, Jean-Pierre Nadal, Jean-Philippe Bouchaud

We analyse the income distributions of cities in France and the scaling of the income of different deciles as a function of the population. We find a significant difference in the scaling exponents for the richer and poorer parts of the population, implying an unequivocal rise in inequalities in larger cities, made worse by living costs that are disproportio

Simin Li, Shuing Zhang, Gujun Chen, Dong Wang

Physical world adversarial attack is a highly practical and threatening attack, which fools real world deep learning systems by generating conspicuous and maliciously crafted real world artifacts. In physical world attacks, evaluating naturalness is highly emphasized since human can easily detect and remove unnatural attacks. However, current studies evaluat

Shuqin Gao, Costas A. Courcoubetis, Lingjie Duan

Given the proximity of many wireless users and their diversity in consuming local resources (e.g., data-plans, computation and energy resources), device-to-device (D2D) resource sharing is a promising approach towards realizing a sharing economy. This paper adopts an easy-to-implement greedy matching algorithm with distributed fashion and only sub-linear O(l

Victor Franken, Hervé Partouche, François Rondeau, Nicolaos Toumbas

In the context of de Sitter static-patch holography, two prescriptions have been put forward for holographic entanglement entropy computations, the monolayer and bilayer proposals. In this paper, we reformulate both prescriptions in a covariant way and extend them to include quantum corrections. We argue that the bilayer proposal is self-consistent, while th

Zili Tang, Junfeng Chen, Meng Guo

Multi-agent systems can be extremely efficient when working concurrently and collaboratively, e.g., for transportation, maintenance, search and rescue. Coordination of such teams often involves two aspects: (i) selecting appropriate sub-teams for different tasks; (ii) designing collaborative control strategies to execute these tasks. The former aspect can be

Pia Hanfeld, Marina M. -C. Höhne, Michael Bussmann, Wolfgang Hönig

Autonomous flying robots, e.g. multirotors, often rely on a neural network that makes predictions based on a camera image. These deep learning (DL) models can compute surprising results if applied to input images outside the training domain. Adversarial attacks exploit this fault, for example, by computing small images, so-called adversarial patches, that ca

Valeriy I. Kondratyev, Dmitry V. Permyakov, Tatyana V. Ivanova, Ivan V. Iorsh

Guided 2D exciton-polaritons, resulting from the strong coupling of excitons in semiconductors with non-radiating waveguide modes, provide an attractive approach towards developing novel on-chip optical devices. These quasiparticles are characterized by long propagation distances and efficient nonlinear interaction but cannot be directly accessed from the fr

Stefania Miricola, Armando Rungi, Gianluca Santoni

This study examines how multinational enterprises structure ownership chains to coordinate subsidiaries across multiple national borders. Using a unique global dataset, we first document key stylized facts: 54% of subsidiaries are controlled through indirect ownership, and ownership chains can span up to seven countries. In particular, we find that subsidiar

Xiaokai Hou, Qingyu Li, Man-Hong Yung, Xusheng Xu

Variational quantum algorithms have been a promising candidate to utilize near-term quantum devices to solve real-world problems. The powerfulness of variational quantum algorithms is ultimately determined by the expressiveness of the underlying quantum circuit ansatz for a given problem. In this work, we propose a sequentially generated circuit ansatz, whic

Ram Prasad

MQ-6 Semiconductor Sensor for Combustible Gas detection is a Sensitive Gas sensor. The sensitive material of this MQ-6 gas sensor is SnO2, which works with lower conductivity in clean air. When the target combustible gas exist, the sensors conductivity is higher along with the gas concentration rising. As the conductivity increases the current in the circuit

Sven Dummer, Nicola Strisciuglio, Christoph Brune

Diffeomorphic registration frameworks such as Large Deformation Diffeomorphic Metric Mapping (LDDMM) are used in computer graphics and the medical domain for atlas building, statistical latent modeling, and pairwise and groupwise registration. In recent years, researchers have developed neural network-based approaches regarding diffeomorphic registration to

Jinglin Zhan, Tiejun Liu, Rengang Li, Jingwei Zhang

Data and model are the undoubtable two supporting pillars for LiDAR object detection. However, data-centric works have fallen far behind compared with the ever-growing list of fancy new models. In this work, we systematically study the synthesis-based LiDAR data augmentation approach (so-called GT-Aug) which offers maxium controllability over generated data

Luzhe Huang, Jianing Li, Xiaofu Ding, Yijie Zhang

Uncertainty estimation is critical for numerous applications of deep neural networks and draws growing attention from researchers. Here, we demonstrate an uncertainty quantification approach for deep neural networks used in inverse problems based on cycle consistency. We build forward-backward cycles using the physical forward model available and a trained d

Renlong Jie, Xiaojun Meng, Lifeng Shang, Xin Jiang

This study proposes a multitask learning architecture for extractive summarization with coherence boosting. The architecture contains an extractive summarizer and coherent discriminator module. The coherent discriminator is trained online on the sentence vectors of the augmented textual input, thus improving its general ability of judging whether the input s

Yu. S. Tsyganov, D. Ibadullayev, A. N. Polyakov, A. A. Voinov

In this paper, we present the results of the first beam tests of the detection system at the focal plane of the Dubna Gas-Filled Recoil Separator-2 (DGFRS-2), which receives beams from the DC-280 FLNR cyclotron. The high beam intensity of $^{48}$Ca$^{+10}$ heavy ions from the cyclotron enables us to obtain a number of superheavy recoils sufficient to compare

Jin Won Kim, Prashant G. Mehta

This paper is concerned with the problem of nonlinear (stochastic) filter stability of a hidden Markov model (HMM) with white noise observations. A contribution is the variance decay property which is used to conclude filter stability. For this purpose, a new notion of the Poincar\'e inequality (PI) is introduced for the nonlinear filter. PI is related to bo

Alexandr Valyuzhenich

In this work, we prove a general version of the reduction lemmas for eigenfunctions of graphs admitting involutive automorphisms of a special type.

M. A. Kaliteevsky, V. V. Enaldiev, V. I. Fal'ko

Lattice relaxation in twistronic bilayers with close lattice parameters and almost perfect crystallographic alignment of the layers results in the transformation of moir\'e pattern into a sequence of preferential stacking domains and domain wall networks. Here, we show that reconstructed moir\'e superlattices of the perfectly aligned heterobilayers of same-c

Xuemei Li, Dongsheng Li

In this paper, we establish pointwise boundary ${{C}^{1,\alpha}}$ estimates for viscosity solutions of some degenerate fully nonlinear elliptic equations on ${C^{1,\alpha}}$ domains. Instead of straightening out the boundary, we utilize the perturbation and compactness techniques.

Felix Bestehorn, Christoph Hansknecht, Christian Kirches, Paul Manns

The combinatorial integral approximation (CIA) is a solution technique for integer optimal control problems. In order to regularize the solutions produced by CIA, one can minimize switching costs in one of its algorithmic steps. This leads to combinatorial optimization problems, which are called switching cost aware rounding problems (SCARP). They can be sol

Chenhang Cui, Jinyu Xie, Yechenhao Yang

Multispectral methods have gained considerable attention due to their promising performance across various fields. However, most existing methods cannot effectively utilize information from two modalities while optimizing time efficiency. These methods often prioritize accuracy or time efficiency, leaving room for improvement in their performance. To this en

Md. Alamin Talukder, Md. Manowarul Islam, Md Ashraf Uddin

Brain tumors present a grave risk to human life, demanding precise and timely diagnosis for effective treatment. Inaccurate identification of brain tumors can significantly diminish life expectancy, underscoring the critical need for precise diagnostic methods. Manual identification of brain tumors within vast Magnetic Resonance Imaging (MRI) image datasets

Han Jiang, Ruoxuan Li, Haosen Sun, Yu-Wing Tai

No significant work has been done to directly merge two partially overlapping scenes using NeRF representations. Given pre-trained NeRF models of a 3D scene with partial overlapping, this paper aligns them with a rigid transform, by generalizing the traditional registration pipeline, that is, key point detection and point set registration, to operate on 3D d

Zhiqiang Wei, Fan Liu, Chang Liu, Zai Yang

This paper proposes an integrated sensing, navigation, and communication (ISNC) framework for safeguarding unmanned aerial vehicle (UAV)-enabled wireless networks against a mobile eavesdropping UAV (E-UAV). To cope with the mobility of the E-UAV, the proposed framework advocates the dual use of artificial noise transmitted by the information UAV (I-UAV) for

Ellis L Thompson, Yan Xu, Peng Wei

In the UAM space, strategic deconfliction provides an all-essential layer to airspace automation by providing safe, pre-emptive deconfliction or assignment of airspace resources to airspace users pre-flight. Strategic deconfliction approaches provide an elegant solution to pre-flight deconfliction operations. This overall creates safer and more efficient air

Kazuo Iwama, Shuichi Miyazaki

This paper has two objectives. One is to give a linear time algorithm that solves the stable roommates problem (i.e., obtains one stable matching) using the stable marriage problem. The idea is that a stable matching of a roommate instance $I$ is a stable matching (that however must satisfy a certain condition) of some marriage instance $I'$. $I'$ is obtaine

Xiaodong Zhang, Weihua Zhang, Jiongning Che, Barbara Dietz

We report on an experimental investigation of the transition of a quantum system with integrable classical dynamics to one with violated time-reversal (T) invariance and chaotic classical counterpart. High-precision experiments are performed with a flat superconducting microwave resonator with circular shape in which T-invariance violation and chaoticity are

Shilin Zhou, Zhenghua Li, Yu Hong, Min Zhang

End-to-end automatic speech recognition (ASR) systems have made significant progress in general scenarios. However, it remains challenging to transcribe contextual named entities (NEs) in the contextual ASR scenario. Previous approaches have attempted to address this by utilizing the NE dictionary. These approaches treat entities as individual tokens and gen

Yafeng Chen, Siqi Zheng, Hui Wang, Luyao Cheng

Effective fusion of multi-scale features is crucial for improving speaker verification performance. While most existing methods aggregate multi-scale features in a layer-wise manner via simple operations, such as summation or concatenation. This paper proposes a novel architecture called Enhanced Res2Net (ERes2Net), which incorporates both local and global f

Zhangming Chan, Yu Zhang, Shuguang Han, Yong Bai

Conversion rate (CVR) prediction is one of the core components in online recommender systems, and various approaches have been proposed to obtain accurate and well-calibrated CVR estimation. However, we observe that a well-trained CVR prediction model often performs sub-optimally during sales promotions. This can be largely ascribed to the problem of the dat

M. C. Crabb

We establish a stable homotopy-theoretic version of a recent result of Farber and Weinberger on the fibrewise topological complexity of sphere bundles and prove, by closely parallel methods, a similar result for real, complex and quaternionic projective bundles. The symmetrized invariant introduced by Farber and Grant is also considered.

Siyi Liu, Hongming Zhang, Hongwei Wang, Kaiqiang Song

Researchers have proposed various information extraction (IE) techniques to convert news articles into structured knowledge for news understanding. However, none of the existing methods have explicitly addressed the issue of framing bias that is inherent in news articles. We argue that studying and identifying framing bias is a crucial step towards trustwort

Stefan Schnabel, Wolfhard Janke

We derive a formula that expresses the density of states of a system with continuous degrees of freedom as a function of microcanonical averages of squared gradient and Laplacian of the Hamiltonian. This result is then used to propose a novel flat-histogram Monte Carlo algorithm, which is tested on a three-dimensional system of interacting Lennard-Jones part

Na Dong, Yongqiang Zhang, Mingli Ding, Gim Hee Lee

Real-world data tends to follow a long-tailed distribution, where the class imbalance results in dominance of the head classes during training. In this paper, we propose a frustratingly simple but effective step-wise learning framework to gradually enhance the capability of the model in detecting all categories of long-tailed datasets. Specifically, we build

Claudia Garetto, Bolys Sabitbek

In this paper we continue the analysis of non-diagonalisable hyperbolic systems initiated in \cite{GarJRuz, GarJRuz2}. Here we assume that the system has discontinuous coefficients or more in general distributional coefficients. Well-posedness is proven in the very weak sense for systems with singularities with respect to the space variable or the time varia

Yidi Jiang, Ruijie Tao, Zexu Pan, Haizhou Li

In active speaker detection (ASD), we would like to detect whether an on-screen person is speaking based on audio-visual cues. Previous studies have primarily focused on modeling audio-visual synchronization cue, which depends on the video quality of the lip region of a speaker. In real-world applications, it is possible that we can also have the reference s

Sourav Pal, Rickmoy Samanta, Supratik Pal

Massive neutrinos are well-known to cause a characteristic suppression in the growth of structures at scales below the neutrino free-streaming length. A detailed understanding of this suppression is essential in the era of precision cosmology we are entering into, enabling us to better constrain the total neutrino mass and possibly probe (beyond)-$\Lambda$CD

Fatma Elsafoury, Stamos Katsigiannis

Language models are the new state-of-the-art natural language processing (NLP) models and they are being increasingly used in many NLP tasks. Even though there is evidence that language models are biased, the impact of that bias on the fairness of downstream NLP tasks is still understudied. Furthermore, despite that numerous debiasing methods have been propo

Huan Liang, Xiang Li, Dunyan Yan

In this paper, we study m-linear n-demensional Hardy-Littlewood-P\'{o}lya operator and m-linear n-demensional Hilbert operator on Heisenberg group BMO space. We obtain that the above two $m$-linear n-demensional operators is bounded in the BMO space of the Heisenberg group.

Guillermo Ortiz-Jimenez, Alessandro Favero, Pascal Frossard

Task arithmetic has recently emerged as a cost-effective and scalable approach to edit pre-trained models directly in weight space: By adding the fine-tuned weights of different tasks, the model's performance can be improved on these tasks, while negating them leads to task forgetting. Yet, our understanding of the effectiveness of task arithmetic and its un