April 2023 arXiv papers — page 53

Velmathi G, Kaushal Goyal

Deaf individuals confront significant communication obstacles on a daily basis. Their inability to hear makes it difficult for them to communicate with those who do not understand sign language. Moreover, it presents difficulties in educational, occupational, and social contexts. By providing alternative communication channels, technology can play a crucial

Xu Zhang, Xinzheng Niu, Philippe Fournier-Viger, Xudong Dai

Image-text retrieval is one of the major tasks of cross-modal retrieval. Several approaches for this task map images and texts into a common space to create correspondences between the two modalities. However, due to the content (semantics) richness of an image, redundant secondary information in an image may cause false matches. To address this issue, this

Max F. Burg, Florian Wenzel, Dominik Zietlow, Max Horn

Many approaches have been proposed to use diffusion models to augment training datasets for downstream tasks, such as classification. However, diffusion models are themselves trained on large datasets, often with noisy annotations, and it remains an open question to which extent these models contribute to downstream classification performance. In particular,

Theo Torres

The quasi-normal mode (QNM) spectrum of black holes is unstable under small perturbation of the potential and has observational consequences in time signals. Such signals might be experimentally difficult to observe and probing this instability will be a technical challenge. Here we investigate the spectral instability of time-independent data. This leads us

Rüdiger Brecht, Alex Bihlo

Precipitation forecasts are less accurate compared to other meteorological fields because several key processes affecting precipitation distribution and intensity occur below the resolved scale of global weather prediction models. This requires to use higher resolution simulations. To generate an uncertainty prediction associated with the forecast, ensembles

Wentian Xu, Jianbo Jiao

Implicit Neural Representation (INR) has been emerging in computer vision in recent years. It has been shown to be effective in parameterising continuous signals such as dense 3D models from discrete image data, e.g. the neural radius field (NeRF). However, INR is under-explored in 2D image processing tasks. Considering the basic definition and the structure

Mohamed El Amine Seddik, José Henrique de Morais Goulart, Maxime Guillaud

This paper studies the deflation algorithm when applied to estimate a low-rank symmetric spike contained in a large tensor corrupted by additive Gaussian noise. Specifically, we provide a precise characterization of the large-dimensional performance of deflation in terms of the alignments of the vectors obtained by successive rank-1 approximation and of thei

Philipp Rigoll, Patrick Petersen, Hanno Stage, Lennart Ries

Handling large amounts of data has become a key for developing automated driving systems. Especially for developing highly automated driving functions, working with images has become increasingly challenging due to the sheer size of the required data. Such data has to satisfy different requirements to be usable in machine learning-based approaches. Thus, eng

Jiezhu Cheng, Kaizhu Huang, Zibin Zheng

In the stock market, a successful investment requires a good balance between profits and risks. Based on the learning to rank paradigm, stock recommendation has been widely studied in quantitative finance to recommend stocks with higher return ratios for investors. Despite the efforts to make profits, many existing recommendation approaches still have some l

Baris Kayalibay, Atanas Mirchev, Ahmed Agha, Patrick van der Smagt

Partially-observable problems pose a trade-off between reducing costs and gathering information. They can be solved optimally by planning in belief space, but that is often prohibitively expensive. Model-predictive control (MPC) takes the alternative approach of using a state estimator to form a belief over the state, and then plan in state space. This ignor

Gaurav N. Gadbail, Ameya Kolhatkar, Sanjay Mandal, P. K. Sahoo

Symmetric teleparallel gravity offers to reformulate the gravitational formalism without the presence of curvature and torsion with the help of non-metricity tensors. Interestingly, Symmetric teleparallel gravity can be formulated equivalently to teleparallel gravity or general relativity for an appropriate setup. In this study, our aim lies in exploring the

Hang Wang, Xuanhong Chen, Bingbing Ni, Yutian Liu

While lightweight ViT framework has made tremendous progress in image super-resolution, its uni-dimensional self-attention modeling, as well as homogeneous aggregation scheme, limit its effective receptive field (ERF) to include more comprehensive interactions from both spatial and channel dimensions. To tackle these drawbacks, this work proposes two enhance

Chiara Cappello, Reza Naserasr, Eckhard Steffen, Zhouningxin Wang

Extending the notion of maxcut, the study of the frustration index of signed graphs is one of the basic questions in the theory of signed graphs. Recently two of the authors initiated the study of critically frustrated signed graphs. That is a signed graph whose frustration index decreases with the removal of any edge. The main focus of this study is on crit

Fanny Lehmann, Filippo Gatti, Michaël Bertin, Didier Clouteau

With the recent rise of neural operators, scientific machine learning offers new solutions to quantify uncertainties associated with high-fidelity numerical simulations. Traditional neural networks, such as Convolutional Neural Networks (CNN) or Physics-Informed Neural Networks (PINN), are restricted to the prediction of solutions in a predefined configurati

Victor J. B. Jung, Arne Symons, Linyan Mei, Marian Verhelst

To meet the growing need for computational power for DNNs, multiple specialized hardware architectures have been proposed. Each DNN layer should be mapped onto the hardware with the most efficient schedule, however, SotA schedulers struggle to consistently provide optimum schedules in a reasonable time across all DNN-HW combinations. This paper proposes SALS

Yongming Yang, Shuwei Shao, Tao Yang, Peng Wang

Monocular depth estimation is critical for endoscopists to perform spatial perception and 3D navigation of surgical sites. However, most of the existing methods ignore the important geometric structural consistency, which inevitably leads to performance degradation and distortion of 3D reconstruction. To address this issue, we introduce a gradient loss to pe

Bonan Zhang, David E. A. Castillo, Ana G. Grunfeld, Marco Ruggieri

We study the potential of the Quantum Chromodynamics axion in hot and/or dense quark matter, within a Nambu-Jona-Lasinio-like model that includes the coupling of the axion to quarks. Differently from previous studies, we implement local electrical neutrality and $\beta-$equilibrium, which are relevant for the description of the quark matter in the core of co

Alexander Söderberg Rousu

We study fusion of two scalar Wilson defects. We propose that fusion holds at a quantum level by showing that bare one-point functions stay invariant. This is an expected result as the path integral stays invariant under fusion of the two defects. The difference instead lies in renormalization of local quantities on the defects. Those on the fused defect tak

Ahmed S. Dalaq, Shadi Khazaaleh, Mohammed F. Daqaq

Current architectured cellular cushion materials rely mainly on damage and/or unpredictable collapse of their unit cells to absorb and dissipate energy under impact. This prevents shape recovery and produces undesirable force fluctuations that limit reusability and reduce energy absorption efficiency. Here, we propose to combine advanced manufacturing techno

Benito Arnoldi, Sara L. Zachritz, Sebastian Hedwig, Martin Aeschlimann

One of the key challenges for spintronic and novel quantum technologies is to achieve active control of the spin angular momentum of electrons in nanoscale materials on ultrafast, femtosecond timescales. While conventional ferromagnetic materials and materials supporting spin texture suffer both from conceptional limitations in miniaturization and in efficie

Raphael Holzinger, Nico S. Bassler, Helmut Ritsch, Claudiu Genes

We study the photophysics of molecular aggregates from a quantum optics perspective, with emphasis on deriving scaling laws for the fast non-radiative relaxation of collective electronic excitations, referred to as Kasha's rule. Aggregates exhibit an energetically broad manifold of collective states with delocalized electronic excitations originating from ne

Claude Marion, Pedro V. Silva, Gareth Tracey

We prove that, given a finitely generated subgroup $H$ of a free group $F$, the following questions are decidable: is $H$ closed (dense) in $F$ for the pro-(met)abelian topology? is the closure of $H$ in $F$ for the pro-(met)abelian topology finitely generated? We show also that if the latter question has a positive answer, then we can effectively construct

Vignesh V Menon, Reza Farahani, Prajit T Rajendran, Mohammed Ghanbari

In recent years, video streaming applications have proliferated the demand for Video Quality Assessment VQA). Reduced reference video quality assessment (RR-VQA) is a category of VQA where certain features (e.g., texture, edges) of the original video are provided for quality assessment. It is a popular research area for various applications such as social me

Marianne Vestergaard, Kayhan Gültekin

Since a black hole does not emit light from its interior, nor does it have a surface on which light from nearby sources can be reflected, observational study of black hole physics requires observing the gravitational impact of the black hole on its surroundings. A massive black hole leaves a dynamical imprint on stars and gas close by. Gas in the immediate v

Christian Berger, Lukas Birkemeyer

Evaluating the performance of software for automated vehicles is predominantly driven by data collected from the real world. While professional test drivers are supported with technical means to semi-automatically annotate driving maneuvers to allow better event identification, simple data loggers in large vehicle fleets typically lack automatic and detailed

Dezső Horváth, Zoltán Trócsányi

We review the concept of chirality and charge for particles and antiparticles. We point out that the commonly accepted equivalence of particles and antiparticles - with difference only in the opposite signs of their charges, which follows from the CPT invariance - is valid only for free non-chiral particles. We show that with the weak interaction turned on t

Claude Marion, Pedro V. Silva, Gareth Tracey

We determine the closure of a cyclic subgroup $H$ of a free group for the pro-{\bf V} topology when {\bf V} is an extension-closed pseudovariety of finite groups. We show that $H$ is always closed for the pro-nilpotent topology and compute its closure for the pro-$\mathbf{G}_p$ and pro-$\mathbf{V}_p$ topologies, where $\mathbf{G}_p$ and $\mathbf{V}_p$ denote

Leigh N. Fletcher, Thibault Cavalié, Davide Grassi, Ricardo Hueso

ESA's Jupiter Icy Moons Explorer (JUICE) will provide a detailed investigation of the Jovian system in the 2030s, combining a suite of state-of-the-art instruments with an orbital tour tailored to maximise observing opportunities. We review the Jupiter science enabled by the JUICE mission, building on the legacy of discoveries from the Galileo, Cassini, and

David Vasak, Jürgen Struckmeier

The framework of the Covariant Canonical Gauge theory of Gravity (CCGG) is described in detail. CCGG emerges naturally in the Palatini formulation, where the vierbein and the spin connection are independent fields. Neither torsion nor non-metricity are excluded. The manifestly covariant gauge process is based on canonical transformations in the De Donder-Wey

Hao-Cheng Weng, Jorge Monroy-Ruz, Jonathan C. F. Matthews, John G. Rarity

Diamond colour centres are promising optically-addressable solid state spins that can be matter-qubits, mediate deterministic interaction between photons and act as single photon emitters. Useful quantum computers will comprise millions of logical qubits. To become useful in constructing quantum computers, spin-photon interfaces must therefore become scalabl

Zheren Li, Zhiming Cui, Lichi Zhang, Sheng Wang

The deep learning technique has been shown to be effectively addressed several image analysis tasks in the computer-aided diagnosis scheme for mammography. The training of an efficacious deep learning model requires large data with diverse styles and qualities. The diversity of data often comes from the use of various scanners of vendors. But, in practice, i

Hyeong-Ohk Bae, Seung Yeon Cho, Jane Yoo, Seok-Bae Yun

In this work, we suggest a system of differential equations that quantitatively models the formulation and evolution of a trend cycle through the consideration of underlying dynamics between the trend participants. Our model captures the five stages of a trend cycle, namely, the onset, rise, peak, decline, and obsolescence. It also provides a unified mathema

Haoyang Peng, Baopu Li, Bo Zhang, Xin Chen

Point cloud based 3D deep model has wide applications in many applications such as autonomous driving, house robot, and so on. Inspired by the recent prompt learning in natural language processing, this work proposes a novel Multi-view Vision-Prompt Fusion Network (MvNet) for few-shot 3D point cloud classification. MvNet investigates the possibility of lever

Raf Bocklandt, Jasper van de Kreeke

In this paper we calculate the Hochschild cohomology of gentle $A_\infty$-algebras of arc collections on marked surfaces without boundary components. When the underlying arc collection has no loops or two-cycles, we show that the dgla structure of the Hochschild complex is formal and give an explicit realization of all deformations up to gauge equivalence.

Ye Yang, Fanghang Yu, Xikai Wen, Zhigang Gui

The insulator-metal transition in Mott insulators, known as the Mott transition, is usually accompanied with various novel quantum phenomena, such as unconventional superconductivity, non-Fermi liquid behavior and colossal magnetoresistance. Here, based on high-pressure electrical transport and XRD measurements, and first-principles calculations, we find tha

Raquel Galazo García, Philippe Brax, Patrick Valageas

We study the formation and evolution of solitons supported by repulsive self-interactions inside extended halos, for scalar-field dark matter scenarios. We focus on the semiclassical regime where the quantum pressure is typically much smaller than the self-interactions. We present numerical simulations, with initial conditions where the halo is described by

Xiaokang Liu, Jianquan Li, Jingjing Mu, Min Yang

Open intent classification, which aims to correctly classify the known intents into their corresponding classes while identifying the new unknown (open) intents, is an essential but challenging task in dialogue systems. In this paper, we introduce novel K-center contrastive learning and adjustable decision boundary learning (CLAB) to improve the effectivenes

Naomi Clare Robertson, Cristóbal Sifón, Marika Asgari, Nicholas Battaglia

We present weak gravitational lensing measurements of a sample of 157 clusters within the Kilo Degree Survey (KiDS), detected with a $>5\sigma$ thermal Sunyaev-Zel'dovich (SZ) signal by the Atacama Cosmology Telescope (ACT). Using a halo-model approach we constrain the average total cluster mass, $M_{\rm WL}$, accounting for the ACT cluster selection functio

Sounak Kar, Jean-Yves Le Boudec

The BB84 QKD protocol is based on the idea that the sender and the receiver can reconcile a certain fraction of the teleported qubits to detect eavesdropping or noise and decode the rest to use as a private key. Under the present hardware infrastructure, decoherence of quantum states poses a significant challenge to performing perfect or efficient teleportat

Adrian Padellaro, Rajath Radhakrishnan, Sanjaye Ramgoolam

Integrality properties of partial sums over irreducible representations, along columns of character tables of finite groups, were recently derived using combinatorial topological string theories (CTST). These CTST were based on Dijkgraaf-Witten theories of flat $G$-bundles for finite groups $G$ in two dimensions, denoted $G$-TQFTs. We define analogous combin

Yusser Al Ghussin, Jingyi Zhang, Josef van Genabith

Document-level neural machine translation (NMT) has outperformed sentence-level NMT on a number of datasets. However, document-level NMT is still not widely adopted in real-world translation systems mainly due to the lack of large-scale general-domain training data for document-level NMT. We examine the effectiveness of using Paracrawl for learning document-

Han Gao, Peiyao Zhao, Zhengshuo Li

In an integrated electricity-gas system (IEGS), the tight coupling of power and natural gas systems is embodied by frequent changes in gas withdrawal from gas-fired units to provide regulation services for the power system to handle uncertainty, which may in turn endanger the secure operation of the natural gas system and ultimately affect the safety of the

Hiroki Ishizaka

We studied an anisotropic modified Crouzeix--Raviart finite element method for the rotational form of a stationary incompressible Navier--Stokes equation with large irrotational body forces. We present an anisotropic $H^1$ error estimate for the velocity of the modified Crouzeix--Raviart finite element method for the Navier--Stokes equation. The modified Cro

Scott Harper

By a classical theorem of Jordan, every faithful transitive action of a nontrivial finite group has a derangement (an element with no fixed points). The existence of derangements with additional properties has attracted much attention, especially for faithful primitive actions of almost simple groups. In this paper, we show that an almost simple group can ha

Michael Kopp

Recent progress in deep learning, a special form of machine learning, has led to remarkable capabilities machines can now be endowed with: they can read and understand free flowing text, reason and bargain with human counterparts, translate texts between languages, learn how to take decisions to maximize certain outcomes, etc. Today, machines have revolution

Nilayan Paul, Basudev Nag Chowdhury, Sanatan Chattopadhyay

The current work explores a geometrically engineered dual gate GaAs nanowire FET with state of the art miniaturized dimensions for high performance charge qubit operation at room temperature. Relevant gate voltages in such device can create two voltage tunable quantum dots (VTQDs) underneath the gates, as well as can manipulate their eigenstate detuning and

Sami Barchid, Benjamin Allaert, Amel Aissaoui, José Mennesson

Facial Expression Recognition (FER) is an active research domain that has shown great progress recently, notably thanks to the use of large deep learning models. However, such approaches are particularly energy intensive, which makes their deployment difficult for edge devices. To address this issue, Spiking Neural Networks (SNNs) coupled with event cameras

Sishu Shankar Muni, Soumitro Banerjee

In this paper, we report the bifurcations of mode-locked periodic orbits occurring in maps of three or higher dimensions. The `torus' is represented by a closed loop in discrete time, which contains stable and unstable cycles of the same periodicity, and the unstable manifolds of the saddle. We investigate two types of `doubling' of such loops: in (a) two di

Ilia Kopchinskii, Petr Satunin

The paper studies resonant generation of higher-order harmonics in a closed cavity in Euler-Heisenberg electrodynamics from the point of view of pure quantum field theory. We consider quantum states of the electromagnetic field in a rectangular cavity with conducting boundary conditions, and calculate the cross-section for the merging of three quanta of cavi

Marcin Stawiski

Karo\'nski, {\L}uczak and Thomason conjectured in 2004 that for every finite graph without isolated edge, the edges can be assigned weights from $\{1,2,3\}$ in such a way that the endvertices of each edge have different sums of incident edge weights. This is known as the 1-2-3 Conjecture, and it was only recently proved by Keusch. We extend this result to in

Tae Yeob Kang, Haebom Lee, Sungho Suh

Fault detection and diagnosis of the interconnects are crucial for prognostics and health management (PHM) of electronics. Traditional methods, which rely on electronic signals as prognostic factors, often struggle to accurately identify the root causes of defects without resorting to destructive testing. Furthermore, these methods are vulnerable to noise in

Stamen I. Dolaptchiev, Peter Spichtinger, Manuel Baumgartner, Ulrich Achatz

We present an asymptotic approach for the systematic investigation of the effect of gravity waves (GW) on ice clouds formed through homogeneous nucleation. In particular, we consider high- and mid-frequency GW in the tropopause region driving the formation of ice clouds, modeled with a double-moment bulk ice microphysics scheme. The asymptotic approach allow

Jordi-Lluís Figueras, Alex Haro

In this paper we present an a-posteriori KAM theorem for the existence of an $(n-d)$-parameters family of $d$-dimensional isotropic invariant tori with Diophantine frequency vector $\omega\in \mathbb R^d$, of type $(\gamma,\tau)$, for $n$ degrees of freedom Hamiltonian systems with $(n-d)$ independent first integrals in involution. If the first integrals ind

Muhammad Atif Ur Rehman, Muhammad Salahuddin, Spyridon Mastorakis, Byung-Seo Kim

The recent advances aiming to enable in-network service provisioning are empowering a plethora of smart infrastructure developments, including smart cities, and intelligent transportation systems. Although edge computing in conjunction with roadside units appears as a promising technology for proximate service computations, the rising demands for ubiquitous

Seonghwan Kim, Jeheon Woo, Woo Youn Kim

The exploration of transition state (TS) geometries is crucial for elucidating chemical reaction mechanisms and modeling their kinetics. Recently, machine learning (ML) models have shown remarkable performance for prediction of TS geometries. However, they require 3D conformations of reactants and products often with their appropriate orientations as input,

Tom Savage, Antonio del Rio Chanona, Gbemi Oluleye

Increasing the adoption of alternative technologies is vital to ensure a successful transition to net-zero emissions in the manufacturing sector. Yet there is no model to analyse technology adoption and the impact of policy interventions in generating sufficient demand to reduce cost. Such a model is vital for assessing policy-instruments for the implementat

Jiangdong Ai, Stefanie Gerke, Gregory Gutin, Anders Yeo

We obtain lower and upper bounds for the maximum weight of a directed cut in the classes of weighted digraphs and weighted acyclic digraphs as well as in some of their subclasses. We compare our results with those obtained for the maximum size of a directed cut in unweighted digraphs. In particular, we show that a lower bound obtained by Alon, Bollobas, Gyaf

Savvas Papaioannou, Panayiotis Kolios, Theocharis Theocharides, Christos G. Panayiotou

Nowadays, unmanned aerial vehicles or UAVs are being used for a wide range of tasks, including infrastructure inspection, automated monitoring and coverage. This paper investigates the problem of 3D inspection planning with an autonomous UAV agent which is subject to dynamical and sensing constraints. We propose a receding horizon 3D inspection planning cont

Jerome Nowak, Anesta Kothari, Hongquan Li, Jaspreet Pannu

Microscopy plays a crucial role in hematology and diagnosis of infectious diseases worldwide. For malaria alone, more than 200 million slides are read by manual microscopists every year. High quality thin blood smears are essential for subsequent microscopy examinations including malaria microscopy, but are hard to make in field settings. Existing devices fo

Yuyuan Li, Chaochao Chen, Xiaolin Zheng, Yizhao Zhang

Recent regulations on the Right to be Forgotten have greatly influenced the way of running a recommender system, because users now have the right to withdraw their private data. Besides simply deleting the target data in the database, unlearning the associated data lineage e.g., the learned personal features and preferences in the model, is also necessary fo

Chenchen Cao, Zhenfeng Wu, Chi Zhang

A normal subgroup $E$ of a group $G$ is said to be hypercyclically embedded in $G$ if either $E=1$ or $E\neq 1$ and every chief factor of $G$ below $E$ is cyclic. In this article, we present some new characterizations of a normal subgroup to be hypercyclically embedded. Some recent results in this literature are generalized and unified.

Naresh Dadhich, Rituparno Goswami, Chevarra Hansraj

In this paper, we geometrically establish yet another correspondence between Newtonian mechanics and general relativity by connecting the Buchdahl bound and the Virial theorem. Buchdahl stars are defined by the saturation of the Buchdahl bound, $\Phi(R) \leq 4/9$ where $\Phi(R)$ is the gravitational potential felt by a radially falling particle. An interesti

Tomáš Jakl, Dan Marsden, Nihil Shah

We present a categorical theory of the composition methods in finite model theory -- a key technique enabling modular reasoning about complex structures by building them out of simpler components. The crucial results required by the composition methods are Feferman-Vaught-Mostowski (FVM) type theorems, which characterize how logical equivalence behaves under

Guangyuan Ma, Xing Wu, Peng Wang, Songlin Hu

Passage retrieval aims to retrieve relevant passages from large collections of the open-domain corpus. Contextual Masked Auto-Encoding has been proven effective in representation bottleneck pre-training of a monolithic dual-encoder for passage retrieval. Siamese or fully separated dual-encoders are often adopted as basic retrieval architecture in the pre-tra

Wenyu Song, Yuhao Wang, Wentao Miao, Zehao Yu

PbTe nanowires coupled to a superconductor have recently been proposed as a potential Majorana platform. The hallmark of the one-dimensional nature of ballistic nanowires is their quantized conductance. Here, we report the observation of conductance plateaus at multiples of the quantized value $2e^2/h$ in PbTe nanowires at finite magnetic fields. The quantiz

Yuichiro Sato, Takanao Tsuyuki

We study time-dependent compactification of extra dimensions. We assume that the spacetime is spatially homogeneous, and solve the vacuum Einstein equations without cosmological constant in more than three dimensions. We consider globally hyperbolic spacetimes in which almost Abelian Lie groups act on the spaces isometrically and simply transitively. We give

Zhao-An Wang, Yu Meng, Zheng-Hao Liu, Yi-Tao Wang

Causal inference revealing causal dependencies between variables from empirical data has found applications in multiple sub-fields of scientific research. A quantum perspective of correlations holds the promise of overcoming the limitation by Reichenbach's principle and enabling causal inference with only the observational data. However, it is still not clea

Tao Sun, Bojian Yin, Sander Bohte

Spiking neural networks (SNNs) have gained attention as models of sparse and event-driven communication of biological neurons, and as such have shown increasing promise for energy-efficient applications in neuromorphic hardware. As with classical artificial neural networks (ANNs), predictive uncertainties are important for decision making in high-stakes appl

Souneil Park, Pavol Mulinka, Diego Perino

Internet access is a special resource of which needs has become universal across the public whereas the service is operated in the private sector. Mobile Network Operators (MNOs) put efforts for management, planning, and optimization; however, they do not link such activities to socioeconomic fairness. In this paper, we make a first step towards understandin

Wei Yi Yeoh, Bo Lan, Michael J. S. Lowe

The presence of macrozones (or micro-textured regions) in Ti-6Al-4V (Ti64) was shown to be a potential cause to the onset of cold dwell fatigue which reduces fatigue life significantly. Past research has demonstrated the potential of using ultrasonic testing for macrozone characterisation, with the variation of ultrasound attenuation, backscatter, and veloci

Jian-Kun Huang, Xiao-Li Huang, Ji-Gui Cheng, Jia Ren

The observed hard TeV gamma-ray spectrum of the nearby gamma-ray burst (GRB) 190829A may challenge the conventional leptonic GRB afterglow model. It has been proposed that an ultra-high-energy (UHE; $\varepsilon^{'}_{\rm p}\sim 10^{20}$ eV) proton population can be pre-accelerated by internal shocks in GRB jets. We study possible signatures of the UHE proton

Liam Hockley, Waseem Kamleh, Derek Leinweber, Anthony Thomas

A variational analysis is performed within the framework of lattice QCD to extract the masses of the spin-3/2 positive parity $ \Delta^+ $ baryons, including radial excitations. $2+1$ flavour dynamical gauge-field configurations provided by the PACS-CS collaboration via the ILDG are considered. To improve our interpolator basis, we perform an iterative proce

Minho Kim

We characterized the combinatorial structure of the Voronoi cell of the $A_n$ lattice in arbitrary dimensions. Based on the well-known fact that the Voronoi cell is the disjoint union of $(n+1)!$ congruent simplices, we show that it is the disjoint union of $(n+1)$ congruent hyper-rhombi, which are the generalized rhombi or trigonal trapezohedra. The explici

I. Bailleul, N. V. Dang, L. Ferdinand, T. D. Tô

We construct the $\Phi^4_3$ measure on an arbitrary 3-dimensional compact Riemannian manifold without boundary as an invariant probability measure of a singular stochastic partial differential equation. Proving the nontriviality and the covariance under Riemannian isometries of that measure gives a non-perturbative, non-topological interacting Euclidean quan

Christopher Chung, Berta Hudak

We determine the representation type for block algebras of the quiver Hecke algebras $R^{\Lambda_k}(\beta)$ of type $C^{(1)}_\ell$ for all $k$, generalising results of Ariki and Park for $\Lambda = \Lambda_0$.

Etienne Mémin, Long Li, Noé Lahaye, Gilles Tissot

In this paper, we investigate the wave solutions of a stochastic rotating shallow water model. This approximate model provides an interesting simple description of the interplay between waves and random forcing ensuing either from the wind or coming as the feedback of the ocean on the atmosphere and leading in a very fast way to the selection of some wavelen

Ville Paananen, Jonas Oppenlaender, Aku Visuri

The recent progress of text-to-image generation has been recognized in architectural design. Our study is the first to investigate the potential of text-to-image generators in supporting creativity during the early stages of the architectural design process. We conducted a laboratory study with 17 architecture students, who developed a concept for a culture

Antoine Gonon, Léon Zheng, Clément Lalanne, Quoc-Tung Le

This article measures how sparsity can make neural networks more robust to membership inference attacks. The obtained empirical results show that sparsity improves the privacy of the network, while preserving comparable performances on the task at hand. This empirical study completes and extends existing literature.

Vladyslav M. Kuchkin, Nikolai S. Kiselev, Filipp N. Rybakov, Igor S. Lobanov

Cubic chiral magnets exhibit a remarkable diversity of two-dimensional topological magnetic textures, including skyrmions. However, the experimental confirmation of topological states localized in all three spatial dimensions remains challenging. In this paper, we investigate a three-dimensional topological state called a heliknoton, which is a hopfion embed

Rahat Masum

Industry 4.0 is a blend of the hyper-connected digital industry within two world of Information Technology (IT) and Operational Technology (OT). With this amalgamate opportunity, smart manufacturing involves production assets with the manufacturing equipment having its own intelligence, while the system-wide intelligence is provided by the cyber layer. Howev

Yushuang Wu, Zizheng Yan, Ce Chen, Lai Wei

3D shape completion from point clouds is a challenging task, especially from scans of real-world objects. Considering the paucity of 3D shape ground truths for real scans, existing works mainly focus on benchmarking this task on synthetic data, e.g. 3D computer-aided design models. However, the domain gap between synthetic and real data limits the generaliza

Luca Binci, Nicola Marzari

The magnetic, noncollinear parametrization of Dudarev's DFT+$U$ method is generalized to fully-relativistic ultrasoft pseudopotentials. We present the definition of the DFT+$U$ total energy functional, and the calculation of forces and stresses in the case of orthogonalized atomic orbitals defining the localised Hubbard manifold, where additional contributio

Léo Jacqmin, Lucas Druart, Yannick Estève, Benoît Favre

Though Dialogue State Tracking (DST) is a core component of spoken dialogue systems, recent work on this task mostly deals with chat corpora, disregarding the discrepancies between spoken and written language.In this paper, we propose OLISIA, a cascade system which integrates an Automatic Speech Recognition (ASR) model and a DST model. We introduce several a

Zhicheng Sun, Yadong Mu, Gang Hua

Continual learning aims to learn on non-stationary data streams without catastrophically forgetting previous knowledge. Prevalent replay-based methods address this challenge by rehearsing on a small buffer holding the seen data, for which a delicate sample selection strategy is required. However, existing selection schemes typically seek only to maximize the

Steffen Gracla, Edgar Beck, Carsten Bockelmann, Armin Dekorsy

Questions remain on the robustness of data-driven learning methods when crossing the gap from simulation to reality. We utilize weight anchoring, a method known from continual learning, to cultivate and fixate desired behavior in Neural Networks. Weight anchoring may be used to find a solution to a learning problem that is nearby the solution of another lear

David Gordon, Panayiotis Petousis, Anders O. Garlid, Keith Norris

Several algorithms for learning the structure of dynamic Bayesian networks (DBNs) require an a priori ordering of variables, which influences the determined graph topology. However, it is often unclear how to determine this order if feature importance is unknown, especially as an exhaustive search is usually impractical. In this paper, we introduce Ranking A

G. I. Lehrer, R. B. Zhang

We introduce a diagram category, study its structure, and investigate some of its applications to the representation theory of Lie algebras and Lie superalgebras. The morphisms of the category, which contains a subcategory isomorphic to the Brauer category, are linear combinations of `polar enhancements' of Brauer diagrams. The endomorphism algebra of each o

Roberto Ortiz, Martin A Guerrero

Many asymptotic giant branch (AGB) and supergiant stars exhibit extended detached shells in the far-infrared, resembling rings or arcs. These structures have long been interpreted as the bow shock formed in the interface between the stellar wind and the interstellar medium, the astrosphere. To date, only a few AGB stars have been observed showing an extended

Chaabane Rejeb

Let $\Delta_k$ be the Dunkl Laplacian relative to a fixed root system $\mathcal{R}$ in $\mathbb{R}^d$, $d\geq2$, and to a nonnegative multiplicity function $k$ on $\mathcal{R}$. Our first purpose in this paper is to solve the $\Delta_k$-Dirichlet problem for annular regions. Secondly, we introduce and study the $\Delta_k$-Green function of the annulus and we

Fang-Yuan Wang, Jun-Ping Yang, Xiang Chen, Ying Cui

In this work, we first analyze the correlations among different nuclear matter parameters which are obtained by the 119 effective Skyrme interaction sets. The values of the correlation coefficients illustrate that the magnitude of the effective mass splitting is crucial for the tight constraints of the symmetry energy via HICs. Thus, the main object of this

Lina Putze, Lukas Westhofen, Tjark Koopmann, Eckard Böde

Automated driving systems are safety-critical cyber-physical systems whose safety of the intended functionality (SOTIF) can not be assumed without proper argumentation based on appropriate evidences. Recent advances in standards and regulations on the safety of driving automation are therefore intensely concerned with demonstrating that the intended function

Antal A. Járai, Christian Mönch, Lorenzo Taggi

We consider an elementary model for self-organised criticality, the activated random walk on the complete graph. We introduce a discrete time Markov chain as follows. At each time step, we add an active particle at a random vertex and let the system stabilise following the activated random walk dynamics, obtaining a particle configuration with all sleeping p

Istasis Mishra, Arpan Dasgupta, Pratik Jawanpuria, Bamdev Mishra

Extreme multi-label (XML) classification refers to the task of supervised multi-label learning that involves a large number of labels. Hence, scalability of the classifier with increasing label dimension is an important consideration. In this paper, we develop a method called LightDXML which modifies the recently developed deep learning based XML framework b

Yue Gao, Yuan Zhou, Jinglu Wang, Xiao Li

Talking head generation is to generate video based on a given source identity and target motion. However, current methods face several challenges that limit the quality and controllability of the generated videos. First, the generated face often has unexpected deformation and severe distortions. Second, the driving image does not explicitly disentangle movem

Ruiqing Sun, Delong Yang, Yao Hu, Qun Hao

Fourier Ptychographic Microscopy (FPM) is a computational technique that achieves a large space-bandwidth product imaging. It addresses the challenge of balancing a large field of view and high resolution by fusing information from multiple images taken with varying illumination angles. Nevertheless, conventional FPM framework always suffers from long acquis

Florian Frohn, Jürgen Giesl

We recently proposed Acceleration Driven Clause Learning (ADCL), a novel calculus to analyze satisfiability of Constrained Horn Clauses (CHCs). Here, we adapt ADCL to transition systems and introduce ADCL-NT, a variant for disproving termination. We implemented ADCL-NT in our tool LoAT and evaluate it against the state of the art.

Nikolay Tzvetkov

We show that the recent work by G{\'e}rard-Kappeler-Topalov can be used in order to construct new non degenerate invariant measures for the Benjamin-Ono equation on the Sobolev spaces H s , s > --1/2.

Wonseong Kim, Jan Frederic Spörer, Siegfried Handschuh

This research article analyzes the language used in the official statements released by the Federal Open Market Committee (FOMC) after its scheduled meetings to gain insights into the impact of FOMC official statements on financial markets and economic forecasting. The study reveals that the FOMC is careful to avoid expressing emotion in their sentences and

Vsevolod Voronov

The work is devoted to one of the variations of the Hadwiger--Nelson problem on the chromatic number of the plane. In this formulation one needs to find for arbitrarily small $\varepsilon$ the least possible number of colors needed to color a Euclidean plane in such a way that any two points, the distance between which belongs to the interval $[1-\varepsilon

Vlad-Raul Constantinescu, Ionel Popescu

In this paper, we prove that in the overparametrized regime, deep neural network provide universal approximations and can interpolate any data set, as long as the activation function is locally in $L^1(\RR)$ and not an affine function. Additionally, if the activation function is smooth and such an interpolation networks exists, then the set of parameters whi