March 2023 arXiv papers — page 33

Wei-Hang Zhang, Ying-Hao Ye, Lei Zeng, En-Ze Li

One of the major difficulties in realizing a high-dimensional frequency converter for conventional optical vortex (COV) stems from the difference in ring diameter of COV modes with different topological charge numbers l. Here, we implement a high-dimensional frequency convertor for perfect optical vortex (POV) modes with invariant size through the four-wave

Pinaki Nath Chowdhury, Ayan Kumar Bhunia, Aneeshan Sain, Subhadeep Koley

Sketches are highly expressive, inherently capturing subjective and fine-grained visual cues. The exploration of such innate properties of human sketches has, however, been limited to that of image retrieval. In this paper, for the first time, we cultivate the expressiveness of sketches but for the fundamental vision task of object detection. The end result

Johanna Henrich

Quantum computers could break currently used asymmetric cryptographic schemes in a few years using Shor's algorithm. They are used in numerous protocols and applications to secure authenticity as well as key agreement, and quantum-safe alternatives are urgently needed. NIST therefore initiated a standardization process. This requires intensive evaluation, al

Mohammad Rezaei, Farnaz Farahanipad, Alex Dillhoff, Vassilis Athitsos

Despite the significant progress that depth-based 3D hand pose estimation methods have made in recent years, they still require a large amount of labeled training data to achieve high accuracy. However, collecting such data is both costly and time-consuming. To tackle this issue, we propose a semi-supervised method to significantly reduce the dependence on l

Haibo Gou, Huanyin Chen

In this paper we investigate Hirano invertibility of anti-triangular matrix over a Banach algebra. Let $a\in {\mathcal A}^H, b\in {\mathcal A}^{sD}.$ If $b^Da=0, bab^{\pi}=0,$ we prove that $\begin{pmatrix} a&1\\ b&0 \end{pmatrix}\in M_2(\mathcal A)^H.$ Moreover, we considered Hirano invertibility of anti-triangular matrices under commutative-like conditions

Diana Savin

Let $A$ be a unitary ring and let $(\mathbf{I(A),\subseteq })$ be the lattice of ideals of the ring $A.$ In this article we will study the property of the lattice $(\mathbf{I(A),\subseteq})$ to be Noetherian or not, for various types of rings $A$. In the last section of the article we study certain rings that are not Boolean rings, but all their ideals are i

Youngwook Kee, MungSoo Kang, Seongho Jeong, Gerald Behr

We propose a novel respiratory motion-resolved MR image reconstruction method that jointly treats multi-echo k-space raw data. Continuously acquired non-Cartesian multi-echo/multi-coil k-space data with free breathing are sorted/binned into the motion states from end-expiratory to end-inspiratory phases based on a respiratory motion signal. Temporal total va

He Fang, Zhenlong Xiao, Xianbin Wang, Li Xu

The conventional device authentication of wireless networks usually relies on a security server and centralized process, leading to long latency and risk of single-point of failure. While these challenges might be mitigated by collaborative authentication schemes, their performance remains limited by the rigidity of data collection and aggregated result. The

Georgii Oblapenko

In the present work, the Tensor-Train decomposition algorithm is applied to reduce the memory footprint of a stochastic discrete velocity solver for rarefied gas dynamics simulation. An energy-conserving modification to the algorithm is proposed, along with an interleaved collision/convection routine which allows for easy application of higher-order convecti

Carlos H. Verbeno, Jakub Zázvorka, Lukáš Nowak, Martin Veis

Growth optimization of multilayers is a topic of interest due to their unique physical properties. Systems containing magnetic materials, such as platinum-cobalt, have been studied because of their potential for technological applications, e.g. spintronics, magnetic storage and magnetic sensors. Since the magnetic properties of thin layers are strongly relat

Zhikang Liu, Yiming Zhou, Yuansheng Xu, Zilei Wang

We propose a simple and application-friendly network (called SimpleNet) for detecting and localizing anomalies. SimpleNet consists of four components: (1) a pre-trained Feature Extractor that generates local features, (2) a shallow Feature Adapter that transfers local features towards target domain, (3) a simple Anomaly Feature Generator that counterfeits an

Sergei M. Kuzenko, I. N. McArthur

A decade ago, it was shown that associated with any model for $\mathsf{U}(1)$ duality-invariant nonlinear electrodynamics there is a unique $\mathsf{U}(1)$ duality-invariant supersymmetric nonlinear sigma model formulated in terms of chiral and complex linear superfields. Here we study the ${\cal N}=1$ superconformal $\sigma$-model analogue of the conformal

Stephen J. Hegner, M. Andrea Rodriguez

In a multigranular framework, the two most important binary predicates are those for subsumption and disjointness. In the first part of this work, a sound and complete inference system for assertions using these predicates is developed. It is customized for the granular framework; particularly, it models both bottom and top granules correctly, and it require

T Anuradha, Ayan Patra, Rivu Gupta, Amit Rai

Starting with a product initial state, squeezed (squeezed coherent) state in one of the modes, and vacuum in the rest, we report that a circular waveguide comprising modes coupled with varying coupling strength is capable of producing genuine multimode entanglement (GME), quantified via the generalized geometric measure (GGM). We demonstrate that for a fixed

Kai Zhou, Lingxiao Wang, Long-Gang Pang, Shuzhe Shi

In recent years, machine learning has emerged as a powerful computational tool and novel problem-solving perspective for physics, offering new avenues for studying strongly interacting QCD matter properties under extreme conditions. This review article aims to provide an overview of the current state of this intersection of fields, focusing on the applicatio

Lorenzo Zambon, Arianna Agosto, Paolo Giudici, Giorgio Corani

Reconciliation enforces coherence between hierarchical forecasts, in order to satisfy a set of linear constraints. While most works focus on the reconciliation of the point forecasts, we consider probabilistic reconciliation and we analyze the properties of the distributions reconciled via conditioning. We provide a formal analysis of the variance of the rec

Benjamin Bedert

Let $A$ be a subset of the cyclic group $\mathbf{Z}/p\mathbf{Z}$ with $p$ prime. It is a well-studied problem to determine how small $|A|$ can be if there is no unique sum in $A+A$, meaning that for every two elements $a_1,a_2\in A$, there exist $a_1',a_2'\in A$ such that $a_1+a_2=a_1'+a_2'$ and $\{a_1,a_2\}\neq \{a_1',a_2'\}$. Let $m(p)$ be the size of a sm

Finn Årup Nielsen

I present an agile method and a tool to display data from Wikidata and other Wikibase instances via SPARQL queries. The work-in-progress combines ideas from the Scholia Web application and the Listeria tool.

Zhongcan Li, Ping Huang, Chao Wen, Filipe Rodrigues

Railway operations involve different types of entities (stations, trains, etc.), making the existing graph/network models with homogenous nodes (i.e., the same kind of nodes) incapable of capturing the interactions between the entities. This paper aims to develop a heterogeneous graph neural network (HetGNN) model, which can address different types of nodes

Srinath Tankasala, Long Chen, Andreas Stolcke, Anirudh Raju

We propose a novel approach for ASR N-best hypothesis rescoring with graph-based label propagation by leveraging cross-utterance acoustic similarity. In contrast to conventional neural language model (LM) based ASR rescoring/reranking models, our approach focuses on acoustic information and conducts the rescoring collaboratively among utterances, instead of

Huiwen Yang, Lingying Huang, Yuzhe Li, Subhrakanti Dey

In this paper, we consider using simultaneous wireless information and power transfer (SWIPT) to recharge the sensor in the LQG control, which provides a new approach to prolonging the network lifetime. We analyze the stability of the proposed system model and show that there exist two critical values for the power splitting ratio {\alpha}. Then, we propose

Jacob Bramley, Dejice Jacob, Andrei Lascu, Jeremy Singer

Several open-source memory allocators have been ported to CHERI, a hardware capability platform. In this paper we examine the security and performance of these allocators when run under CheriBSD on Arm's experimental Morello platform. We introduce a number of security attacks and show that all but one allocator are vulnerable to some of the attacks - includi

Rushuang Zhou, Weishan Ye, Zhiguo Zhang, Yanyang Luo

Electroencephalography (EEG) is an objective tool for emotion recognition and shows promising performance. However, the label scarcity problem is a main challenge in this field, which limits the wide application of EEG-based emotion recognition. In this paper, we propose a novel semi-supervised learning framework (EEGMatch) to leverage both labeled and unlab

Lukas Köhldorfer, Peter Balazs

Frames have been investigated frequently over the last few decades due to their valuable properties, which are desirable for various applications as well as interesting for theory. Some applications additionally require distributed processing techniques, which naturally leads to the concept of fusion frames and fusion frame systems. The latter consists of a

Mehmet Mueller, Timo Häckel, Philipp Meyer, Franz Korf

Automotive softwarization is progressing and future cars are expected to operate a Service-Oriented Architecture on multipurpose compute units, which are interconnected via a high-speed Ethernet backbone. The AUTOSAR architecture foresees a universal middleware called SOME/IP that provides the service primitives, interfaces, and application protocols on top

Tianrui Qin, Xitong Gao, Juanjuan Zhao, Kejiang Ye

Unlearnable example attacks are data poisoning techniques that can be used to safeguard public data against unauthorized use for training deep learning models. These methods add stealthy perturbations to the original image, thereby making it difficult for deep learning models to learn from these training data effectively. Current research suggests that adver

Zehan Zheng, Danni Wu, Ruisi Lu, Fan Lu

In recent years, there has been a significant increase in focus on the interpolation task of computer vision. Despite the tremendous advancement of video interpolation, point cloud interpolation remains insufficiently explored. Meanwhile, the existence of numerous nonlinear large motions in real-world scenarios makes the point cloud interpolation task more c

Tae Soo Kim, Arghya Sarkar, Yoonjoo Lee, Minsuk Chang

Large language models (LLMs) can enhance writing by automating or supporting specific tasks in writers' workflows (e.g., paraphrasing, creating analogies). Leveraging this capability, a collection of interfaces have been developed that provide LLM-powered tools for specific writing tasks. However, these interfaces provide limited support for writers to creat

Xuechen Guo, Wenhao Hu, Chiming Ni, Wenhao Chai

Medical images often incorporate doctor-added markers that can hinder AI-based diagnosis. This issue highlights the need of inpainting techniques to restore the corrupted visual contents. However, existing methods require manual mask annotation as input, limiting the application scenarios. In this paper, we propose a novel blind inpainting method that automa

Miha Srdinšek, Tomaž Prosen, Spyros Sotiriadis

The validity of the ergodic hypothesis in quantum systems can be rephrased in the form of the eigenstate thermalisation hypothesis (ETH), a set of statistical properties for the matrix elements of local observables in energy eigenstates, which is expected to hold in any ergodic system. We test the ETH in a nonintegrable model of relativistic quantum field th

Mausoom Sarkar, Nikitha SR, Mayur Hemani, Rishabh Jain

Face parsing is defined as the per-pixel labeling of images containing human faces. The labels are defined to identify key facial regions like eyes, lips, nose, hair, etc. In this work, we make use of the structural consistency of the human face to propose a lightweight face-parsing method using a Local Implicit Function network, FP-LIIF. We propose a simple

Amirhossein Mohammadi, Sara Hajiaghajani, Mohammad Bahrani

Swarm Intelligence algorithms have gained significant attention in recent years as a means of solving complex and non-deterministic problems. These algorithms are inspired by the collective behavior of natural creatures, and they simulate this behavior to develop intelligent agents for computational tasks. One such algorithm is Ant Colony Optimization (ACO),

Andrea Chiuri, Marco Barbieri, Iole Venditti, Federico Angelini

Assessing the presence of chemical, biological, radiological and nuclear threats is a crucial task which is usually dealt with by analyzing the presence of spectral features in a measured absorption profile. The use of quantum light allows to perform these measurements remotely without compromising the measurement accuracy through ghost spectrometry. However

Nan Qiao, Sheng Yue, Yongmin Zhang, Ju Ren

Freshness-aware computation offloading has garnered great attention recently in the edge computing arena, with the aim of promptly obtaining up-to-date information and minimizing the transmission of outdated data. However, most of the existing work assumes that wireless channels are reliable and neglect the dynamics and stochasticity thereof. In addition, va

D. M. Barreiros, H. B. Câmara, F. R. Joaquim

We propose a model based on the interplay between the type-II seesaw and scotogenic neutrino mass generation mechanisms. The setup features a $\mathbb{Z}_8$ discrete flavour symmetry which is broken down to a residual $\mathbb{Z}_2$ responsible for stabilising dark matter. A singlet scalar field is introduced to implement spontaneous CP violation. The effect

Nitesh Kumar, Anupam Bhardwaj, Harinder P. Singh, Susmita Das

We present a new technique to generate the light curves of RRab stars in different photometric bands ($I$ and $V$ bands) using Artificial Neural Networks (ANN). A pre-computed grid of models was used to train the ANN, and the architecture was tuned using the $I$ band light curves. The best-performing network was adopted to make the final interpolators in the

Mengtian Yin, Llewellyn Tang, Chris Webster, Shen Xu

Being able to efficiently retrieve the required building information is critical for construction project stakeholders to carry out their engineering and management activities. Natural language interface (NLI) systems are emerging as a time and cost-effective way to query Building Information Models (BIMs). However, the existing methods cannot logically comb

Martina Lippi, Michael C. Welle, Andrea Gasparri, Danica Kragic

Planning in learned latent spaces helps to decrease the dimensionality of raw observations. In this work, we propose to leverage the ensemble paradigm to enhance the robustness of latent planning systems. We rely on our Latent Space Roadmap (LSR) framework, which builds a graph in a learned structured latent space to perform planning. Given multiple LSR fram

Aidana Massalimova, Maikel Timmermans, Nicola Cavalcanti, Daniel Suter

Pedicle drilling is a complex and critical spinal surgery task. Detecting breach or penetration of the surgical tool to the cortical wall during pilot-hole drilling is essential to avoid damage to vital anatomical structures adjacent to the pedicle, such as the spinal cord, blood vessels, and nerves. Currently, the guidance of pedicle drilling is done using

Fajar J. Ekaputra, Majlinda Llugiqi, Marta Sabou, Andreas Ekelhart

In line with the general trend in artificial intelligence research to create intelligent systems that combine learning and symbolic components, a new sub-area has emerged that focuses on combining machine learning (ML) components with techniques developed by the Semantic Web (SW) community - Semantic Web Machine Learning (SWeML for short). Due to its rapid g

Xinnan Zhang, Yuanbo Cheng, Xiaolei Shang, Jun liu

We consider a mixed analog-to-digital converter (ADC) based architecture for direction of arrival (DOA) estimation using a uniform linear array (ULA). We derive the Cram{\'e}r-Rao bound (CRB) of the DOA under the optimal time-varying threshold, and find that the asymptotic CRB is related to the arrangement of high-precision and one-bit ADCs for a fixed numbe

Shaozhe Hao, Kai Han, Kwan-Yee K. Wong

Compositional zero-shot learning (CZSL) aims at learning visual concepts (i.e., attributes and objects) from seen compositions and combining concept knowledge into unseen compositions. The key to CZSL is learning the disentanglement of the attribute-object composition. To this end, we propose to exploit cross-attentions as compositional disentanglers to lear

Elizaveta Korotkova, Isaac Chung

The rapid growth in user generated content on social media has resulted in a significant rise in demand for automated content moderation. Various methods and frameworks have been proposed for the tasks of hate speech detection and toxic comment classification. In this work, we combine common datasets to extend these tasks to brand safety. Brand safety aims t

Xiangyuan Yang, Jie Lin, Hanlin Zhang, Xinyu Yang

In the transfer-based adversarial attacks, adversarial examples are only generated by the surrogate models and achieve effective perturbation in the victim models. Although considerable efforts have been developed on improving the transferability of adversarial examples generated by transfer-based adversarial attacks, our investigation found that, the big de

Sang-Hun Han, Min-Gyu Park, Ju Hong Yoon, Ju-Mi Kang

High-quality 3D human body reconstruction requires high-fidelity and large-scale training data and appropriate network design that effectively exploits the high-resolution input images. To tackle these problems, we propose a simple yet effective 3D human digitization method called 2K2K, which constructs a large-scale 2K human dataset and infers 3D human mode

Baichun Wei, Chunzhi Yi, Qi Zhang, Haiqi Zhu

Deep learning-based human activity recognition (HAR) methods have shown great promise in the applications of smart healthcare systems and wireless body sensor network (BSN). Despite their demonstrated performance in laboratory settings, the real-world implementation of such methods is still hindered by the cross-subject issue when adapting to new users. To s

Mihály A. Csirik, Andre Laestadius

In a series of two articles, we propose a comprehensive mathematical framework for Coupled-Cluster-type methods. In this second part, we analyze the nonlinear equations of the single-reference Coupled-Cluster method using topological degree theory. We establish existence results and qualitative information about the solutions of these equations that also she

Qiming Zhang, Jing Zhang, Yufei Xu, Dacheng Tao

Window-based attention has become a popular choice in vision transformers due to its superior performance, lower computational complexity, and less memory footprint. However, the design of hand-crafted windows, which is data-agnostic, constrains the flexibility of transformers to adapt to objects of varying sizes, shapes, and orientations. To address this is

Souhaib Attaiki, Lei Li, Maks Ovsjanikov

Transfer learning is fundamental for addressing problems in settings with little training data. While several transfer learning approaches have been proposed in 3D, unfortunately, these solutions typically operate on an entire 3D object or even scene-level and thus, as we show, fail to generalize to new classes, such as deformable organic shapes. In addition

Zhiquan Tan, Yifan Zhang, Jingqin Yang, Yang Yuan

Contrastive learning is a powerful self-supervised learning method, but we have a limited theoretical understanding of how it works and why it works. In this paper, we prove that contrastive learning with the standard InfoNCE loss is equivalent to spectral clustering on the similarity graph. Using this equivalence as the building block, we extend our analysi

Simon Dupourqué, Nicolas Clerc, Etienne Pointecouteau, Dominique Eckert

Turbulent processes at work in the intracluster medium perturb this environment, displacing gas, and creating local density fluctuations that can be quantified via X-ray surface brightness fluctuation analyses. Improved knowledge of these phenomena would allow for a better determination of the mass of galaxy clusters, as well as a better understanding of the

Zongrui Li, Qian Zheng, Boxin Shi, Gang Pan

Uncalibrated photometric stereo (UPS) is challenging due to the inherent ambiguity brought by the unknown light. Although the ambiguity is alleviated on non-Lambertian objects, the problem is still difficult to solve for more general objects with complex shapes introducing irregular shadows and general materials with complex reflectance like anisotropic refl

Christos Theodoropoulos, Marie-Francine Moens

Current research on the advantages and trade-offs of using characters, instead of tokenized text, as input for deep learning models, has evolved substantially. New token-free models remove the traditional tokenization step; however, their efficiency remains unclear. Moreover, the effect of tokenization is relatively unexplored in sequence tagging tasks. To t

Konrad Kułakowski, Jacek Szybowski, Jiri Mazurek, Sebastian Ernst

In decision-making methods, it is common to assume that the experts are honest and professional. However, this is not the case when one or more experts in the group decision making framework, such as the group analytic hierarchy process (GAHP), try to manipulate results in their favor. The aim of this paper is to introduce two heuristics in the GAHP, setting

Haoyu Geng, Guanjie Zheng, Zhengqing Han, Hua Wei

Recently, the world has witnessed the most severe pandemic (COVID-19) in this century. Studies on epidemic prediction and simulation have received increasing attention. However, the current methods suffer from three issues. First, most of the current studies focus on epidemic prediction, which can not provide adequate support for intervention policy making.

Kohsuke Takaoka, Ayumu Kuwahara, Shigeru Ida, Hiroyuki Kurokawa

We investigate the spin state of a protoplanet during the pebble accretion influenced by the gas flow in the gravitational potential of the protoplanet and how it depends on the planetary mass, the headwind speed, the distance from the host star, and the pebble size. We perform nonisothermal three-dimensional hydrodynamical simulations in a local frame to ob

Sandra Staudt, Viktor Unterberger, Markus Gölles, Michael Wernhart

Absorption heat pumping devices (AHPDs, comprising absorption heat pumps and chillers) are devices that use thermal energy instead of electricity to generate heating and cooling, thereby facilitating the use of waste heat and renewable energy sources such as solar or geothermal energy. Despite this benefit, widespread use of AHPDs is still limited. One reaso

Shangkun Weng, Zihao Zhang

In this paper, we establish the existence and uniqueness of subsonic flows with a contact discontinuity in a two-dimensional finitely long slightly curved nozzle by prescribing the entropy, the Bernoulli's quantity and the horizontal mass flux distribution at the entrance and the flow angle at the exit. The problem is formulated as a nonlinear boundary value

Zoltán M. Balogh, Tamás Titkos, Dániel Virosztek

Our purpose in this paper is to study isometries and isometric embeddings of the $p$-Wasserstein space $\mathcal{W}_p(\mathbb{H}^n)$ over the Heisenberg group $\mathbb{H}^n$ for all $p>1$ and for all $n\geq 1$. First, we create a link between optimal transport maps in the Euclidean space $\mathbb{R}^{2n}$ and the Heisenberg group $\mathbb{H}^n$. Then we use

Garrett Gould

The static, spherical solutions that exhibit an antisymmetry between the temporal and radial coordinates in $f(R)$ gravity theories are presented. I present the constraint for this antisymmetry and show that pure $R^2$ models produce these solutions. An asymptotic expansion approach is taken to find the solutions, presenting first and second order correction

Vasileios Sevetlidis, George Pavlidis, Vasiliki Balaska, Athanasios Psomoulis

In many real-world scenarios, obtaining large amounts of labeled data can be a daunting task. Weakly supervised learning techniques have gained significant attention in recent years as an alternative to traditional supervised learning, as they enable training models using only a limited amount of labeled data. In this paper, the performance of a weakly super

Linda A. Khachatryan

The purpose of this note is to recall one remarkable theorem of Khinchin about the special role of the Gaussian distribution. This theorem allows us to give a new interpretation of the Lindeberg condition: it guarantees the uniform integrability of the squares of normed sums of random variables and, thus, the passage to the limit under the expectation sign.

Emil Jeřábek

We present a streamlined and simplified exponential lower bound on the length of proofs in intuitionistic implicational logic, adapted to Gordeev and Haeusler's dag-like natural deduction.

Lili Chen, Jingge Zhu, Jamie Evans

Due to mutual interference between users, power allocation problems in wireless networks are often non-convex and computationally challenging. Graph neural networks (GNNs) have recently emerged as a promising approach to tackling these problems and an approach that exploits the underlying topology of wireless networks. In this paper, we propose a novel graph

M. G. Elsheikh, Y. Abdella, T. El-Ashram, W. M. Moslem

In this manuscript, a fluid model is utilized to calculate the density of plasma species assuming geometrically symmetric Ar/CF4 Radiofrequency Capacitively Coupled Plasmas. The electrodes are driven by a sinusoidal waefront with an amplitude of 200 V and a frequency of 13.56 MHz. The gap between the electrodes is 5cm. The plasma species density is calculate

Ibrahem Yakzan Hasan, Rudra Narayan Padhan

In this article, we discuss the category $\mathcal{SN}_2$ where the objects are finite-dimensional nilpotent Lie superalgebras of class two and the category $\mathcal{SSKE}$ where the objects are skew-supersymmetric bilinear maps. We establish relation between $\mathcal{SN}_2$ and $\mathcal{SSKE}$. As a result, we discuss the capability of nilpotent Lie supe

Ebrahim Balouji, Jonas Sjöblom, Nikolce Murgovski, Morteza Haghir Chehreghani

In this paper, we propose machine learning solutions to predict the time of future trips and the possible distance the vehicle will travel. For this prediction task, we develop and investigate four methods. In the first method, we use long short-term memory (LSTM)-based structures specifically designed to handle multi-dimensional historical data of trip time

Davide Moltisanti, Frank Keller, Hakan Bilen, Laura Sevilla-Lara

The goal of this work is to understand the way actions are performed in videos. That is, given a video, we aim to predict an adverb indicating a modification applied to the action (e.g. cut "finely"). We cast this problem as a regression task. We measure textual relationships between verbs and adverbs to generate a regression target representing the action c

Felix Fuhrmann, Sven Becker, Akashdeep Akashdeep, Zengyao Ren

In this study, we investigate the dynamic response of a Y$_3$Fe$_5$O$_{12}$ (YIG)/ Gd$_3$Fe$_5$O$_{12}$ (GdIG)/ Pt trilayer system by measurements of the ferromagnetic resonance (FMR) and the pumped spin current detected by the inverse spin Hall effect. This trilayer system offers the unique opportunity to investigate the spin dynamics of the ferrimagnetic G

Sara Giarrusso, Pierre-François Loos

The exact functionals associated with the (singlet) ground and the two singlet excited states of the asymmetric Hubbard dimer at half-filling are calculated using both Levy's constrained search and Lieb's convex formulation. While the ground-state functional is, as commonly known, a convex function with respect to the density (or, more precisely, the site oc

Shengchao Zhou, Weizhou Liu, Chen Hu, Shuchang Zhou

In the field of 3D object detection for autonomous driving, the sensor portfolio including multi-modality and single-modality is diverse and complex. Since the multi-modal methods have system complexity while the accuracy of single-modal ones is relatively low, how to make a tradeoff between them is difficult. In this work, we propose a universal cross-modal

Onur Tanil Doganay, Kathrin Klamroth, Bruno Lang, Michael Stiglmayr

We suggest a global perspective on dynamic network flow problems that takes advantage of the similarities to port-Hamiltonian dynamics. Dynamic minimum cost flow problems are formulated as open-loop optimal control problems for general port-Hamiltonian systems with possibly state-dependent system matrices. We prove well-posedness of these systems and charact

Siqi Chen, Xueming Li, Xianlin Zhang, Mingdao Wang

Exemplar-based video colorization is an essential technique for applications like old movie restoration. Although recent methods perform well in still scenes or scenes with regular movement, they always lack robustness in moving scenes due to their weak ability in modeling long-term dependency both spatially and temporally, leading to color fading, color dis

Angelica Psaridi, François Bouchy, Monika Lendl, Babatunde Akinsanmi

While the sample of confirmed exoplanets continues to increase, the population of transiting exoplanets around early-type stars is still limited. These planets allow us to investigate the planet properties and formation pathways over a wide range of stellar masses and study the impact of high irradiation on hot Jupiters orbiting such stars. We report the dis

Simone Cito, Gloria Paoli, Gianpaolo Piscitelli

Let $\Omega=\Omega_0\setminus \overline{\Theta}\subset \mathbb{R}^n$, $n\geq 2$, where $\Omega_0$ and $\Theta$ are two open, bounded and convex sets such that $\overline{\Theta}\subset \Omega_0$ and let $\beta<0$ be a given parameter. We consider the eigenvalue problem for the Laplace operator associated to $\Omega$, with Robin boundary condition on $\partia

Ning Wu, Ming Gong, Linjun Shou, Shining Liang

Text summarization has a wide range of applications in many scenarios. The evaluation of the quality of the generated text is a complex problem. A big challenge to language evaluation is that there is a clear divergence between existing metrics and human evaluation. A document summary's quality can be assessed by human annotators on various criteria, both ob

V. Gayathri, E. P. Amaladass, A. T. Sathyanarayana, T. Geetha Kumary

The effect of proximity of the magnetism of the Pr-based manganite (Pr0.6Sr0.4MnO3) on the superconductivity of Bi-based high-temperature superconductor (Bi1.75Pb0.25Sr2Ca2Cu3O10+d) was studied based on the results obtained from the magnetotransport and magnetization measurements. Decrease in the values of the upper critical field (HC2(0)) and an increase in

Andreas Link, a Irene Luna Pardo, Bernd Porr, Thomas Franke

The flow dynamics of red blood cells in vivo in blood capillaries and in vitro in microfluidic channels is complex. Cells can obtain differnet shapes such as discoid, parachute, slipper-like shapes and various intermediate states depending on flow conditions and their viscoelastic properties. We use artificial intelligence based analysis of red blood cells (

Yanli Wang

In this paper, we determine all conformal Ricci collineations associated to the Levi-Civita connection on three-dimensional Lorentzian Lie groups .

Francisco Caldas, Cláudia Soares, Cláudia Nunes, Marta Guimarães

Space debris is a major problem in space exploration. International bodies continuously monitor a large database of orbiting objects and emit warnings in the form of conjunction data messages. An important question for satellite operators is to estimate when fresh information will arrive so that they can react timely but sparingly with satellite maneuvers. W

Yuzhou Qiu, E. Alper Yıldırım

We study linear programming relaxations of nonconvex quadratic programs given by the reformulation-linearization technique (RLT), referred to as RLT relaxations. We investigate the relations between the polyhedral properties of the feasible regions of a quadratic program and its RLT relaxation. We establish various connections between recession directions, b

Yi-Ting Lee, Da-Yi Wu, Chih-Chun Yang, Shou-De Lin

The goal of this paper is to report certain scientific discoveries about a Seq2Seq model. It is known that analyzing the behavior of RNN-based models at the neuron level is considered a more challenging task than analyzing a DNN or CNN models due to their recursive mechanism in nature. This paper aims to provide neuron-level analysis to explain why a vanilla

Luojia Wang, Da-Wei Wang, Luqi Yuan, Yaping Yang

Atomic arrays provide an important quantum optical platform with photon-mediated dipoledipole interactions, which can be engineered to realize key applications in quantum information processing. A major obstacle for such application is the fast decay of the excited states. By controlling two-band Bloch oscillations in an atomic array under external magnetic

Sebastian Blauth, Kevin Sturm

The ability to efficiently solve topology optimization problems is of great importance for many practical applications. Hence, there is a demand for efficient solution algorithms. In this paper, we propose novel quasi-Newton methods for solving PDE-constrained topology optimization problems. Our approach is based on and extends the popular solution algorithm

Geoffrey R. Hosack

A statistical method for the elicitation of priors in Bayesian generalised linear models (GLMs) and extensions is proposed. Probabilistic predictions are elicited from the expert to parametrise a multivariate t prior distribution for the unknown linear coefficients of the GLM and an inverse gamma prior for the dispersion parameter, if unknown. The elicited p

Firas Bayram, Bestoun S. Ahmed, Erik Hallin, Anton Engman

Data quality assessment has become a prominent component in the successful execution of complex data-driven artificial intelligence (AI) software systems. In practice, real-world applications generate huge volumes of data at speeds. These data streams require analysis and preprocessing before being permanently stored or used in a learning task. Therefore, si

Petra Števuliáková, Petr Hurtik

We focus on the construction of a loss function for the bounding box regression. The Intersection over Union (IoU) metric is improved to converge faster, to make the surface of the loss function smooth and continuous over the whole searched space, and to reach a more precise approximation of the labels. The main principle is adding a smoothing part to the or

Thais Lemos, Ruchika, Joel C. Carvalho, Jailson Alcaniz

Measurements of the characteristic length scale $r_s$ of the baryon acoustic oscillations (BAO) provide a robust determination of the distance-redshift relation. Currently, the best (sub-per cent) estimate of $r_s$ at the drag epoch is provided by Cosmic Microwave Background (CMB) observations assuming the validity of the standard $\Lambda$CDM model at $z \s

Julian McGinnis, Suprosanna Shit, Hongwei Bran Li, Vasiliki Sideri-Lampretsa

Clinical routine and retrospective cohorts commonly include multi-parametric Magnetic Resonance Imaging; however, they are mostly acquired in different anisotropic 2D views due to signal-to-noise-ratio and scan-time constraints. Thus acquired views suffer from poor out-of-plane resolution and affect downstream volumetric image analysis that typically require

S. Valère Bitseki Penda

We study the kernel estimator of the transition density of bifurcating Markov chains. Under some ergodic and regularity properties, we prove that this estimator is consistent and asymptotically normal. Next, in the numerical studies, we propose two data-driven methods to choose the bandwidth parameters. These methods are based on the so-called two bandwidths

Katharina Noesges, Maximilian Klich, Aranka Derzsi, Benedek Horváth

In capacitively coupled radio frequency (CCRF) discharges, the interaction of the plasma and the surface boundaries is linked to a variety of highly relevant phenomena for technological processes. One possible plasma-surface interaction is the generation of secondary electrons (SEs), which significantly influence the discharge when accelerated in the sheath

Beomyoung Kim, Joonhyun Jeong, Dongyoon Han, Sung Ju Hwang

In this paper, we introduce a novel learning scheme named weakly semi-supervised instance segmentation (WSSIS) with point labels for budget-efficient and high-performance instance segmentation. Namely, we consider a dataset setting consisting of a few fully-labeled images and a lot of point-labeled images. Motivated by the main challenge of semi-supervised a

ATLAS Collaboration

This paper presents the observation of four-top-quark ($t\bar{t}t\bar{t}$) production in proton-proton collisions at the LHC. The analysis is performed using an integrated luminosity of 140 fb$^{-1}$ at a centre-of-mass energy of 13 TeV collected using the ATLAS detector. Events containing two leptons with the same electric charge or at least three leptons (

Jaehoon Choi, Dongki Jung, Taejae Lee, Sangwook Kim

We present a new pipeline for acquiring a textured mesh in the wild with a single smartphone which offers access to images, depth maps, and valid poses. Our method first introduces an RGBD-aided structure from motion, which can yield filtered depth maps and refines camera poses guided by corresponding depth. Then, we adopt the neural implicit surface reconst

Robert Schüler

We shortly present two small results regarding the study formal duality in finite abelian groups as introduced by Cohn, Kumar, Reiher and Sch\"urmann. In particular, we give a new example of a formally self dual set in $\mathbb{Z}_2^2\times\mathbb{Z}_4^2$ and computed nonexistence of primitive formally dual sets of size $8$ in $\mathbb{Z}_8^2$.

Eugen Rogozinnikov

This article is an extended version of the talk given by the author in the seminar Th\'eorie Spectrale et G\'eom\'etrie at the Institut Fourier in March 2022. We present some results from the author's doctoral thesis, extended by several results from other papers. We give a parametrization of the space of maximal framed representations of the fundamental gro

Cheng Wang, Jacek Golebiowski

Model miscalibration has been frequently identified in modern deep neural networks. Recent work aims to improve model calibration directly through a differentiable calibration proxy. However, the calibration produced is often biased due to the binning mechanism. In this work, we propose to learn better-calibrated models via meta-regularization, which has two

Fabrizio Gilardi, Meysam Alizadeh, Maël Kubli

Many NLP applications require manual data annotations for a variety of tasks, notably to train classifiers or evaluate the performance of unsupervised models. Depending on the size and degree of complexity, the tasks may be conducted by crowd-workers on platforms such as MTurk as well as trained annotators, such as research assistants. Using a sample of 2,38

Anna Galler, Angel Rubio, Ofer Neufeld

Ultrafast nonlinear optical phenomena in solids have been attracting major interest as novel methodologies for femtosecond spectroscopy of electron dynamics and control of material properties. Here, we theoretically investigate strong-field nonlinear optical transitions in a prototypical two-dimensional material, hBN, and show that the k-resolved conduction

Minani Iragi, David Holgate

In a category $\mathcal{C}$ with a proper $(\mathcal{E}, \mathcal{M})$-factorization system, we study the notions of strict, co-strict, initial and final morphisms with respect to a topogenous order. Besides showing that they allow simultaneous study of four classes of morphisms obtained separately with respect to closure, interior and neighbourhood operator