April 2023 arXiv papers — page 36

Yinchuan Li, Zhigang Li, Wenqian Li, Yunfeng Shao

Many score-based active learning methods have been successfully applied to graph-structured data, aiming to reduce the number of labels and achieve better performance of graph neural networks based on predefined score functions. However, these algorithms struggle to learn policy distributions that are proportional to rewards and have limited exploration capa

Seok-Hyung Lee, Hyunseok Jeong

Graph states are versatile resources for various quantum information processing tasks, including measurement-based quantum computing and quantum repeaters. Although the type-II fusion gate enables all-optical generation of graph states by combining small graph states, its non-deterministic nature hinders the efficient generation of large graph states. In thi

Andrei Paleyes, Neil D. Lawrence

Dataflow computing was shown to bring significant benefits to multiple niches of systems engineering and has the potential to become a general-purpose paradigm of choice for data-driven application development. One of the characteristic features of dataflow computing is the natural access to the dataflow graph of the entire system. Recently it has been obser

R. Deb, A. K. Das

In this article we consider the sparse solutions of the tensor complementarity problem (TCP) which are the solutions of the smallest cardinality. We establish a connection between the least element of the feasible solution set of TCP and sparse solution for $Z$-tensor. We propose a $p$ norm regularized minimization model when $p\in (0,1)$ and show that it ca

Mohan Li, Rama Doddipatla, Catalin Zorila

This paper proposes a self-regularised minimum latency training (SR-MLT) method for streaming Transformer-based automatic speech recognition (ASR) systems. In previous works, latency was optimised by truncating the online attention weights based on the hard alignments obtained from conventional ASR models, without taking into account the potential loss of AS

Junyi Ji, Guoliang Yu, Changsong Xu, H. J. Xiang

The physical properties of crystals are governed by their symmetry according to Neumann's principle. However, we present a case that contradicts this principle wherein the polarization is not invariant under its symmetry. We term this phenomenon as unconventional ferroelectricity in violation of Neumann's principle (UFVNP). Our group theory analysis reveals

Vivek Shahare, Milind Chabbi, Nikhil Hegde

The lock is a building-block synchronization primitive that enables mutually exclusive access to shared data in shared-memory parallel programs. Mutual exclusion is typically achieved by guarding the code that accesses the shared data with a pair of lock() and unlock() operations. Concurrency bugs arise when this ordering of operations is violated. In this p

Lev Teshler, Hannes Weisbrich, Jonathan Sturm, Raffael L. Klees

In recent years, various classes of systems were proposed to realize topological states of matter. One of them are multiterminal Josephson junctions where topological Andreev bound states are constructed in the synthetic space of superconducting phases. Crucially, the topology in these systems results in a quantized transconductance between two of its termin

Pradeep R. Nair

Optical pump-probe techniques like absorption spectroscopy and microwave conductivity are widely used to characterize the carrier dynamics in perovskites for optoelectronic applications. In contrast to the prevalent assumption of exponentials, here we predict the possibility of trap emission limited logarithmic and power-law transients. These predictions are

Eline Tolstoy, Ása Skúladóttir, Giuseppina Battaglia, Anthony G. A. Brown

We present a new homogeneous survey of VLT/FLAMES LR8 line-of-sight radial velocities (vlos) for 1604 resolved red giant branch stars in the Sculptor dwarf spheroidal galaxy. In addition, we provide reliable Ca II triplet metallicities, [Fe/H], for 1339 of these stars. From this combination of new observations (2257 individual spectra) with ESO archival data

Yan Zhou, Jie Guo, Hao Sun, Bin Song

The main idea of multimodal recommendation is the rational utilization of the item's multimodal information to improve the recommendation performance. Previous works directly integrate item multimodal features with item ID embeddings, ignoring the inherent semantic relations contained in the multimodal features. In this paper, we propose a novel and effectiv

A. Zaborowska, Ł. Kurpaska, E. Wyszkowska, A. Azarov

It is well known that ion irradiation can be successfully used to reproduce microstructural features triggered by neutron irradiation. Even though the irradiation process brings many benefits, it is also associated with several drawbacks. For example, the penetration depth of the ion in the material is very limited. This is particularly important for energie

The PANDA Collaboration

We present a detailed simulation study of the signatures from the sequential decays of the triple-strange pbar p -> {\Omega}+{\Omega}- -> K+{\Lambda}barK- {\Lambda} -> K+pbar{\pi}+K-p{\pi}- process in the PANDA central tracking system with focus on hit patterns and precise time measurement. We present a systematic approach for studying physics channels at th

Kenichi Fujita, Takanori Ashihara, Hiroki Kanagawa, Takafumi Moriya

This paper proposes a zero-shot text-to-speech (TTS) conditioned by a self-supervised speech-representation model acquired through self-supervised learning (SSL). Conventional methods with embedding vectors from x-vector or global style tokens still have a gap in reproducing the speaker characteristics of unseen speakers. A novel point of the proposed method

Yin-Dong Zheng, Guo Chen, Minglei Yuan, Tong Lu

Action detection is a challenging video understanding task, requiring modeling spatio-temporal and interaction relations. Current methods usually model actor-actor and actor-context relations separately, ignoring their complementarity and mutual support. To solve this problem, we propose a novel network called Multi-Relation Support Network (MRSN). In MRSN,

H. Buglia, M. Jarmolovicius, L. Galdino, R. I. Killey

A closed-form model for the nonlinear interference (NLI) in Raman amplified links is presented, the formula accounts for both forward (FW) and backward (BW) pumping schemes and inter-channel stimulated Raman scattering (ISRS) effect. The formula also accounts for an arbitrary number of pumps, wavelength-dependent fibre parameters, launch-power profiles, and

A. Zaborowska, Ł. Kurpaska, M. Clozel, E. J. Olivier

In this study structural and mechanical properties of a 1 um thick Al2O3 coating, deposited on 316L stainless steel by Pulsed Laser Deposition (PLD), subjected to high energy ion irradiation were assessed. Mechanical properties of pristine and ion-modified specimens were investigated using the nanoindentation technique. A comprehensive characterization combi

S. E. Hadjadj, C. González-Orellana, J. Lawrence, D. Bikaljević

Magnetic two-dimensional (2D) semiconductors have attracted a lot of attention because modern preparation techniques are capable of providing single crystal films of these materials with precise control of thickness down to the single-layer limit. It opens up a way to study rich variety of electronic and magnetic phenomena with promising routes towards poten

Dániel Keliger, László Lovász, Tamás Móri, Gergely Ódor

We study SIR type epidemics on graphs in two scenarios: (i) when the initial infections start from a well connected central region, (ii) when initial infections are distributed uniformly. Previously, \'Odor et al. demonstrated on a few random graph models that the expectation of the total number of infections undergoes a switchover phenomenon; the central re

Zerui Chen, Shizhe Chen, Cordelia Schmid, Ivan Laptev

Signed distance functions (SDFs) is an attractive framework that has recently shown promising results for 3D shape reconstruction from images. SDFs seamlessly generalize to different shape resolutions and topologies but lack explicit modelling of the underlying 3D geometry. In this work, we exploit the hand structure and use it as guidance for SDF-based shap

Ziqi Xu, Debo Cheng, Jiuyong Li, Jixue Liu

An essential problem in causal inference is estimating causal effects from observational data. The problem becomes more challenging with the presence of unobserved confounders. When there are unobserved confounders, the commonly used back-door adjustment is not applicable. Although the instrumental variable (IV) methods can deal with unobserved confounders,

Jinyu Yang, Mingqi Gao, Zhe Li, Shang Gao

Recently, the Segment Anything Model (SAM) gains lots of attention rapidly due to its impressive segmentation performance on images. Regarding its strong ability on image segmentation and high interactivity with different prompts, we found that it performs poorly on consistent segmentation in videos. Therefore, in this report, we propose Track Anything Model

David Beyer, Christian Holm

We introduce a Monte-Carlo method that allows for the simulation of a polymeric phase containing a weak polyelectrolyte, which is coupled to a reservoir at a fixed pH, salt concentration and total concentration of a weak polyprotic acid. The method generalizes the established Grand-Reaction Method by Landsgesell et al. [Macromolecules 53, 3007-3020 (2020)] a

Wenwen Yu, Mingyu Liu, Mingrui Chen, Ning Lu

Reading seal title text is a challenging task due to the variable shapes of seals, curved text, background noise, and overlapped text. However, this important element is commonly found in official and financial scenarios, and has not received the attention it deserves in the field of OCR technology. To promote research in this area, we organized ICDAR 2023 c

Pierre Nazé

The optimal protocols for the irreversible work achieve their maximum usefulness if their work fluctuations are the smallest ones. In this work, for classical and isothermal processes subjected to finite-time and weak drivings, I show that the optimal protocol for the irreversible work is the same for the variance of work. This conclusion is based on the flu

Shuhei Yokoo, Peifei Zhu, Junki Ishikawa, Rintaro Hasegawa

This paper presents our 3rd place solution in both Descriptor Track and Matching Track of the Meta AI Video Similarity Challenge (VSC2022), a competition aimed at detecting video copies. Our approach builds upon existing image copy detection techniques and incorporates several strategies to exploit on the properties of video data, resulting in a simple yet p

Zhuoxuan Li, Zhongda Chu, Fei Teng

Recently, frequency security is challenged by high uncertainty and low inertia in power system with high penetration of Renewable Energy Sources (RES). In the context of Unit Commitment (UC) problems, frequency security constraints represented by neural networks have been developed and embedded into the optimization problem to represent complicated frequency

ATLAS Collaboration

A search for high-mass charged and neutral bosons decaying to $W\gamma$ and $Z\gamma$ final states is presented in this paper. The analysis uses a data sample of $\sqrt{s} = 13$ TeV proton-proton collisions with an integrated luminosity of 139 fb$^{-1}$ collected by the ATLAS detector during LHC Run 2 operation. The sensitivity of the search is determined us

Sebastian Berns, Simon Colton, Christian Guckelsberger

Large data-driven image models are extensively used to support creative and artistic work. Under the currently predominant distribution-fitting paradigm, a dataset is treated as ground truth to be approximated as closely as possible. Yet, many creative applications demand a diverse range of output, and creators often strive to actively diverge from a given d

Philipp Kuehn, Mike Schmidt, Markus Bayer, Christian Reuter

Publicly available information contains valuable information for Cyber Threat Intelligence (CTI). This can be used to prevent attacks that have already taken place on other systems. Ideally, only the initial attack succeeds and all subsequent ones are detected and stopped. But while there are different standards to exchange this information, a lot of it is s

Jinghua Zhang, Li Liu, Kai Gao, Dewen Hu

Automatic Pill Recognition (APR) systems are crucial for enhancing hospital efficiency, assisting visually impaired individuals, and preventing cross-infection. However, most existing deep learning-based pill recognition systems can only perform classification on classes with sufficient training data. In practice, the high cost of data annotation and the con

Takeyuki Sasai

We tackle estimating sparse coefficients in a linear regression when the covariates are sampled from an $L$-subexponential random vector. This vector belongs to a class of distributions that exhibit heavier tails than Gaussian random vector. Previous studies have established error bounds similar to those derived for Gaussian random vectors. However, these me

Yixing Huang, Ahmed Gomaa, Sabine Semrau, Marlen Haderlein

The potential of large language models in medicine for education and decision making purposes has been demonstrated as they achieve decent scores on medical exams such as the United States Medical Licensing Exam (USMLE) and the MedQA exam. In this work, we evaluate the performance of ChatGPT-4 in the specialized field of radiation oncology using the 38th Ame

Thomas Borsoni

We establish a connection between the relative Classical entropy and the relative Fermi-Dirac entropy, allowing to transpose, in the context of the Boltzmann or Landau equation, any entropy-entropy production inequality from one case to the other; therefore providing entropy-entropy production inequalities for the Boltzmann-Fermi-Dirac operator, similar to t

Yaxin Shi, Xiaowei Zhou, Ping Liu, Ivor W. Tsang

In the field of Image-to-Image (I2I) translation, ensuring consistency between input images and their translated results is a key requirement for producing high-quality and desirable outputs. Previous I2I methods have relied on result consistency, which enforces consistency between the translated results and the ground truth output, to achieve this goal. How

Chenlin Zhou, Liutao Yu, Zhaokun Zhou, Han Zhang

Spiking neural networks (SNNs) offer a promising energy-efficient alternative to artificial neural networks, due to their event-driven spiking computation. However, some foundation SNN backbones (including Spikformer and SEW ResNet) suffer from non-spike computations (integer-float multiplications) caused by the structure of their residual connections. These

Debora Impera, Niels Martin Møller, Michele Rimoldi

We prove a rigidity result for mean curvature self-translating solitons, characterizing the grim reaper cylinder as the only finite entropy self-translating 2-surface in $\mathbb{R}^3$ of width $\pi$ and bounded from below. The proof makes use of parabolicity in a weighted setting applied to a suitable universally $L$-superharmonic function defined on transl

Emma Caizergues, François Durand, Fabien Mathieu

Chjara, breeder in Carg{\`e}se, has n wild pigs. She would like to sort her herd by weight to better meet the demands of her buyers. Each beast has a distinct weight, alas unknown to Chjara. All she has at her disposal is a Roberval scale, which allows her to compare two pigs only at the cost of an acrobatic manoeuvre. The balance, quite old, can break at an

Ke Zhang

Unlike developed market, some emerging markets are dominated by retail and unprofessional trading. China A share market is a good and fitting example in last 20 years. Meanwhile, lots of research show professional investor in China A share market continuously generate excess return compare with total market index. Specifically, this excess return mostly come

Timothy R. Law, Philip T. Barton

We present a practical cell-centred volume-of-fluid method developed within a pure Eulerian setting for the simulation of compressible solid-fluid problems. The method builds on a previously published diffuse-interface Godunov-type scheme through the addition of a specialised mixed-cell update that is capable of maintaining sharp interfaces indefinitely. The

Edgar Gasperin, Rafael Pinto

The NP constants of massless spin-0 fields propagating in Minkowski spacetime are computed close to spatial and null infinity by means of Friedrich's \emph{$i^0$-cylinder}. Assuming certain regularity condition on the initial data ensuring that the field extends analytically to the critical sets, it is shown that the NP constants at future $\mathscr{I}^{+}$

Bo Xiong, Mojtaba Nayyeri, Ming Jin, Yunjie He

Geometric relational embeddings map relational data as geometric objects that combine vector information suitable for machine learning and structured/relational information for structured/relational reasoning, typically in low dimensions. Their preservation of relational structures and their appealing properties and interpretability have led to their uptake

Tao Yu, J. W. Rao

A perspective on non-Hermitian physics in magnetic systems is addressed in this short article, including exceptional points, exceptional nodal phases, the non-Hermitian SSH model, and the non-Hermitian skin effect.

Zefeng Chen, Wensheng Gan, Gengsen Huang, Yan Li

Sequential pattern mining (SPM) has excellent prospects and application spaces and has been widely used in different fields. The non-overlapping SPM, as one of the data mining techniques, has been used to discover patterns that have requirements for gap constraints in some specific mining tasks, such as bio-data mining. And for the non-overlapping sequential

Gianluca Faraco

For every $g\ge 2$ and $n\ge4$, we provide an $n-$manifold $M$ and a continuous $2-$sided map $f\colon S\longrightarrow M$, where $S$ is a closed genus $g$ surface, such that no simple loop is contained in $\text{ker}(\,f_*\,)$. This provides a counterexample to the the classical simple loop conjecture for surfaces to manifolds of dimensions at least four.

Benoît Bonnet-Weill, Hélène Frankowska

In this article, we derive conditions for the existence of solutions to state-constrained continuity inclusions in Wasserstein spaces whose right-hand sides may be discontinuous in time. These latter are based on a fine investigation of the infinitesimal behaviour of the underlying reachable sets, through which we show that up to a negligible set of times, e

Xiaolong Zhang, Vadim Nikolayev

Dewetting of liquid films on solid surfaces in the presence of evaporation is a common phenomenon and has been studied by many researchers. The previous numerical approach has revealed that evaporation accelerates the dewetting speed of the triple contact line and established correlations between the dewetting speed and the surface wettability and superheati

Feng-Feng Song, Guang-Ming Zhang

We have developed a tensor network approach to the two-dimensional fully frustrated classical XY spin model on the kagome lattice, and clarified the nature of the possible phase transitions of various topological excitations.We find that the standard tensor network representation for the partition function does not work due to the strong frustrations in the

Martin Ludvigsen, Markus Grasmair

The idea of adversarial learning of regularization functionals has recently been introduced in the wider context of inverse problems. The intuition behind this method is the realization that it is not only necessary to learn the basic features that make up a class of signals one wants to represent, but also, or even more so, which features to avoid in the re

Haitao Li, Qingyao Ai, Jingtao Zhan, Jiaxin Mao

Recent studies have shown that Dense Retrieval (DR) techniques can significantly improve the performance of first-stage retrieval in IR systems. Despite its empirical effectiveness, the application of DR is still limited. In contrast to statistic retrieval models that rely on highly efficient inverted index solutions, DR models build dense embeddings that ar

Even Marius Nordhagen, Henrik Andersen Sveinsson, Anders Malthe-Sørenssen

Friction is the force resisting relative motion of objects. The force depends on material properties, loading conditions and external factors such as temperature and humidity, but also contact aging has been identified as a primary factor. Several aging mechanisms have been proposed, including increased "contact quantity" due to plastic or elastic creep and

Arjun Parthasarathy, Bhaskar Krishnamachari

Edge inference has become more widespread, as its diverse applications range from retail to wearable technology. Clusters of networked resource-constrained edge devices are becoming common, yet no system exists to split a DNN across these clusters while maximizing the inference throughput of the system. Additionally, no production-ready orchestration system

Arthur Vervaet

Within today's large-scale systems, one anomaly can impact millions of users. Detecting such events in real-time is essential to maintain the quality of services. It allows the monitoring team to prevent or diminish the impact of a failure. Logs are a core part of software development and maintenance, by recording detailed information at runtime. Such log da

Tianzhen Zhang, Yining Hu, Wei Su, Chen Chen

Revealing the energy and spatial characteristics of impurity induced states in superconductors is essential for understanding their mechanism and fabricating new quantum state by manipulating impurities. Here by using high-resolution scanning tunneling microscopy/spectroscopy, we investigated the spatial distribution and magnetic field response of the impuri

Haoye Tian, Weiqi Lu, Tsz On Li, Xunzhu Tang

Recently, the ChatGPT LLM has received great attention: it can be used as a bot for discussing source code, prompting it to suggest changes, provide descriptions or even generate code. Typical demonstrations generally focus on existing benchmarks, which may have been used in model training (i.e., data leakage). To assess the feasibility of using an LLM as a

M. M. Serim, Ç. K. Dönmez, D. Serim, L. Ducci

We present a timing and noise analysis of the Be/X-ray binary system Swift J0243.6+6124 during its 2017-2018 super-Eddington outburst using NICER/XTI observations. We apply a synthetic pulse timing analysis to enrich the Fermi/GBM spin frequency history of the source with the new measurements from NICER/XTI. We show that the pulse profiles switch from double

Boquan Ren, A. A. Arkhipova, Yiqi Zhang, Y. V. Kartashov

Introduction of controllable deformations into periodic materials that lead to disclinations in their structure opens novel routes for construction of higher-order topological insulators hosting topological states at disclinations. Appearance of these topological states is consistent with the bulk-disclination correspondence principle, and is due to the fill

Dušan Tomko, Luboš Neslušan

After the prediction of meteor showers in the Earth's atmosphere caused by the particles originating in the nucleus of comet 21P/Giacobini-Zinner, we went on with the prediction of showers on the other three terrestrial planets. Based on our modeling of theoretical stream of the parent comet, we predicted several related meteorite (on Mercury) or meteor (on

Matteo Bizzarri, Fabrizio Panebianco, Paolo Pin

We analyze the effect of homophily in the diffusion of a harmful state between two groups of agents that differ in immunization rates. Homophily has a very different impact on the steady state infection level (that is increasing in homophily when homophily is small, and decreasing when high), and on the cumulative number of infections generated by a deviatio

Feng Liu

The Su-Schrieffer-Heeger (SSH) model is fundamental in topological insulators and relevant to understanding higher-order topological phases. This study explores the relationship between the $n$-dimensional SSH model and its $(n-1)$-dimensional counterpart, identifying a hierarchical structure in the Hamiltonian that allows us to solve an arbitrary $n$-dimens

Philippe Schnoebelen, Julien Veron

Using arch-jumping functions and properties of the arch factorization of words, we propose a new algorithm for computing the subword circular universality index of words. We also introduce the subword universality signature for words, that leads to simple algorithms for the universality indexes of SLP-compressed words.

Arthur Vervaet, Raja Chiky, Mar Callau-Zori

Logs are a fundamental component of modern computer systems. They enable the analysis and monitoring teams to understand any abnormal or malicious behavior that may have occurred. The continuous increase in the volume of logs generated by these systems made it unsuitable for manual inspection and represents a real challenge with regard to process automation.

Pengcheng Ai, Le Xiao, Zhi Deng, Yi Wang

Pulse timing is an important topic in nuclear instrumentation, with far-reaching applications from high energy physics to radiation imaging. While high-speed analog-to-digital converters become more and more developed and accessible, their potential uses and merits in nuclear detector signal processing are still uncertain, partially due to associated timing

Albert Elias-López, Fabio Del Sordo, Daniele Viganò

This work concentrates on the effect of an irrotational forcing on a magnetized flow in the presence of rotation, baroclinicity, shear, or a combination of them. By including magnetic field in the model we can evaluate the occurrence of dynamo on both small and large scales. We aim at finding what are the minimum ingredients needed to trigger a dynamo instab

Manuel Schwonberg, Joshua Niemeijer, Jan-Aike Termöhlen, Jörg P. Schäfer

Deep neural networks (DNNs) have proven their capabilities in many areas in the past years, such as robotics, or automated driving, enabling technological breakthroughs. DNNs play a significant role in environment perception for the challenging application of automated driving and are employed for tasks such as detection, semantic segmentation, and sensor fu

Yongjie Yang

Over the past few years, the (parameterized) complexity landscape of constructive control for many prevalent approval-based multiwinner voting (ABMV) rules has been explored. We expand these results in two directions. First, we study constructive control for sequential Thiele's rules. Second, we study destructive counterparts of these problems. Our explorati

Arthur Vervaet, Raja Chiky, Mar Callau-Zori

Logs record valuable system information at runtime. They are widely used by data-driven approaches for development and monitoring purposes. Parsing log messages to structure their format is a classic preliminary step for log-mining tasks. As they appear upstream, parsing operations can become a processing time bottleneck for downstream applications. The qual

Jakob Schyga, Markus Knitt, Johannes Hinckeldeyn, Jochen Kreutzfeldt

Various applications leverage location data to increase transparency, efficiency, and safety in intralogistics. There are several properties of location data, such as the data's degrees of freedom, system latency, update rate, or accuracy. To select a suitable indoor localization system, corresponding data requirements must be derived by analyzing the consid

Ioannis K. Gallos, Daniel Lehmberg, Felix Dietrich, Constantinos Siettos

We propose a machine-learning approach to model long-term out-of-sample dynamics of brain activity from task-dependent fMRI data. Our approach is a three stage one. First, we exploit Diffusion maps (DMs) to discover a set of variables that parametrize the low-dimensional manifold on which the emergent high-dimensional fMRI time series evolve. Then, we constr

Timo Hromadka, Timotej Smolen, Tomas Remis, Branislav Pecher

This paper presents the best-performing solution to the SemEval 2023 Task 3 on the subtask 3 dedicated to persuasion techniques detection. Due to a high multilingual character of the input data and a large number of 23 predicted labels (causing a lack of labelled data for some language-label combinations), we opted for fine-tuning pre-trained transformer-bas

Long Liu, Tong Li, Hui Cheng

Existing knowledge distillation methods generally use a teacher-student approach, where the student network solely learns from a well-trained teacher. However, this approach overlooks the inherent differences in learning abilities between the teacher and student networks, thus causing the capacity-gap problem. To address this limitation, we propose a novel m

Hosein Azarbonyad, Zubair Afzal, George Tsatsaronis

In this paper, we describe Topic Pages, an inventory of scientific concepts and information around them extracted from a large collection of scientific books and journals. The main aim of Topic Pages is to provide all the necessary information to the readers to understand scientific concepts they come across while reading scholarly content in any scientific

Keichi Takahashi, Soya Fujimoto, Satoru Nagase, Yoko Isobe

Data movement is a key bottleneck in terms of both performance and energy efficiency in modern HPC systems. The NEC SX-series supercomputers have a long history of accelerating memory-intensive HPC applications by providing sufficient memory bandwidth to applications. In this paper, we analyze the performance of a prototype SX-Aurora TSUBASA supercomputer eq

Victor Guyomard, Françoise Fessant, Thomas Guyet, Tassadit Bouadi

Counterfactual explanations have become a mainstay of the XAI field. This particularly intuitive statement allows the user to understand what small but necessary changes would have to be made to a given situation in order to change a model prediction. The quality of a counterfactual depends on several criteria: realism, actionability, validity, robustness, e

Tony Lindeberg

This paper presents a theoretical analysis of the orientation selectivity of simple and complex cells that can be well modelled by the generalized Gaussian derivative model for visual receptive fields, with the purely spatial component of the receptive fields determined by oriented affine Gaussian derivatives for different orders of spatial differentiation.

J. Viquerat, E. Hachem

The coupling of deep reinforcement learning to numerical flow control problems has recently received a considerable attention, leading to groundbreaking results and opening new perspectives for the domain. Due to the usually high computational cost of fluid dynamics solvers, the use of parallel environments during the learning process represents an essential

Alexandros Zeakis, George Papadakis, Dimitrios Skoutas, Manolis Koubarakis

Many recent works on Entity Resolution (ER) leverage Deep Learning techniques involving language models to improve effectiveness. This is applied to both main steps of ER, i.e., blocking and matching. Several pre-trained embeddings have been tested, with the most popular ones being fastText and variants of the BERT model. However, there is no detailed analys

Sergey Antipov, Chao Li

The PETRA IV upgrade project is aiming at building a 6 GeV diffraction-limited light source. The storage ring`s off-axis accumulation injection scheme will allow generating a wide range of filling patterns for the needs of photon science users. To reserve high beam quality and low transverse emittances it is imperative to ensure beam stability against collec

Kouhei Washiyama, Kenichi Yoshida

Background: Non-yrast states in neutron-rich nuclei are being investigated experimentally. These states reveal various aspects and details of the nuclear structure, such as the fluctuation around the axially symmetric shape. Purpose: The beyond-mean-field effects in neutron-rich nuclei with $N \simeq 28$ are investigated. We focus on the role of collective m

Seyyed Sadegh Gholami, Yousef Zamani

Let V be a unitary space. Suppose G is a subgroup of the full symmetric group S_m and X is an irreducible unitary representation of G. In this paper, we introduce the generalized Cartesian symmetry class over V associated with G and X. Then we investigate some important properties of this vector space. Also, we study some basic properties of the induced line

Diancong Jin, Derui Sheng

In this work, we establish the Freidlin--Wentzell large deviations principle (LDP) of the stochastic Cahn--Hilliard equation with small noise, which implies the one-point LDP. Further, we give the one-point LDP of the spatial finite difference method (FDM) for the stochastic Cahn--Hilliard equation. Our main result is the convergence of the one-point large d

J. Fiaschi, B. Fuks, M. Klasen, A. Neuwirth

Due to the greater experimental precision expected from the currently ongoing LHC Run 3, equally accurate theoretical predictions are essential. We update the documentation of the Resummino package, a program dedicated to precision cross section calculations for the production of a pair of sleptons, electroweakinos, and leptons in the presence of extra gauge

Nachiketa Sarkar

We investigate, in addition to the experimentally established hadrons, how the inclusion of extra resonance states, through the Hagedorn mass spectrum (HS) or Quark Model (QM) predicated states, affects the thermodynamic and transport quantities of the hadronic system in the van der Waals hadron resonance gas (VDWHRG) model. We found that the VDWHRG model wi

Matthias Kraus, Ron Riekenbrauck, Wolfgang Minker

The concept of a Human-AI team has gained increasing attention in recent years. For effective collaboration between humans and AI teammates, proactivity is crucial for close coordination and effective communication. However, the design of adequate proactivity for AI-based systems to support humans is still an open question and a challenging topic. In this pa

C. H. Kim, S. Ahn, K. Y. Chae, J. Hooker

Pile-up signals are frequently produced in experimental physics. They create inaccurate physics data with high uncertainty and cause various problems. Therefore, the correction to pile-up signals is crucially required. In this study, we implemented a deep learning method to restore the original signals from the pile-up signals. We showed that a deep learning

Francesco Verde, Donatella Darsena, Vincenzo Galdi

Until now, researchers in wireless communications have mainly focused their attention on slowly time-varying designs of reconfigurable intelligent surfaces (RISs), where the spatial-phase gradient across the RIS is varied at the rate equal to the inverse of the channel coherence time. Additional degrees of freedom for controlling EM waves can be gained by ap

Francisco Prada, Peter Behroozi, Tomoaki Ishiyama, Anatoly Klypin

In their recent study, Labb\'e et al. used multi-band infrared images captured by the James Webb Space Telescope (JWST) to discover a population of red massive galaxies that formed approximately 600 million years after the Big Bang. The authors reported an extraordinarily large density of these galaxies, with stellar masses exceeding $10^{10}$ solar masses,

Julian Senoner, Bernhard Kratzwald, Milan Kuzmanovic, Torbjørn H. Netland

To meet order fulfillment targets, manufacturers seek to optimize production schedules. Machine learning can support this objective by predicting throughput times on production lines given order specifications. However, this is challenging when manufacturers produce customized products because customization often leads to changes in the probability distribut

Cillian Cockrell, Oliver Dicks, Ilian T. Todorov, Alin M. Elena

Fluidity, the ability of liquids to flow, is the key property distinguishing liquids from solids. This fluidity is set by the mobile transit atoms moving from one quasi-equilibrium point to the next. The nature of this transit motion is unknown. Here, we show that flow-enabling transits form a dynamically distinct sub-ensemble where atoms move on average fas

Joachim Marco Hermansen, Frederik Laust Durhuus, Cathrine Frandsen, Marco Beleggia

A permanent magnet can be levitated simply by placing it in the vicinity of another permanent magnet that rotates in the order of 200 Hz. This surprising effect can be easily reproduced in the lab with off-the-shelf components. Here we investigate this novel type of magnetic levitation experimentally and clarify the underlying physics. Using a 19 mm diameter

Daniele Boffi, Andrea Cangiani, Marco Feder, Lucia Gastaldi

We perform a systematic comparison of various numerical schemes for the approximation of interface problems. We consider unfitted approaches in view of their application to possibly moving configurations. Particular attention is paid to the implementation aspects and to the analysis of the costs related to the different phases of the simulations.

Yuan Xie, Tianyu Chen, Ji Xu

Underwater acoustic recognition for ship-radiated signals has high practical application value due to the ability to recognize non-line-of-sight targets. However, due to the difficulty of data acquisition, the collected signals are scarce in quantity and mainly composed of mechanical periodic noise. According to the experiments, we observe that the repeatabi

Hanqing Sun, Yanwei Pang, Jiale Cao, Jin Xie

Transformers have shown promising progress in various visual object detection tasks, including monocular 2D/3D detection and surround-view 3D detection. More importantly, the attention mechanism in the Transformer model and the 3D information extraction in binocular stereo are both similarity-based. However, directly applying existing Transformer-based detec

Rui Hao, Linmei Hu, Weijian Qi, Qingliu Wu

Dialogue-based language models mark a huge milestone in the field of artificial intelligence, by their impressive ability to interact with users, as well as a series of challenging tasks prompted by customized instructions. However, the prevalent large-scale dialogue-based language models like ChatGPT still have room for improvement, such as unstable respons

Yu-Xuan Zhang, Zhengchun Zhou, Xingxing He, Avik Ranjan Adhikary

Multi-instance learning (MIL) is a widely-applied technique in practical applications that involve complex data structures. MIL can be broadly categorized into two types: traditional methods and those based on deep learning. These approaches have yielded significant results, especially with regards to their problem-solving strategies and experimental validat

Erik Skibsted, Xue Ping Wang

This book provides a systematic study of spectral and scattering theory for many-body Schr\"odinger operators at two-cluster thresholds. While the two-body problem (reduced after separation of the center of mass motion to a one-body problem at zero energy) is a well-studied subject, the literature on the many-body problem is sparse. However our analysis cove

Paul Hoyer

I consider the frame dependence of QCD bound states in the presence of a confining, spatially constant gluon field energy density. The states are quantized at equal time in $A^0=0$ (temporal) gauge. I derive the frame dependence of the wave functions, and demonstrate the Lorentz covariance of the electromagnetic (transition) form factors for states of any sp

Omar Mustafa

A fine tuned rainbow gravity describes both relativistic quantum particles and anti-particles alike. That is, the ratio $y=E/E_{P}$ in the rainbow functions $g_{_{0}}\left( y\right) $ and $% g_{_{1}}\left( y\right) $ should be fine tuned into $0\leq y=E/E_{P}\leq 1\Rightarrow y=\left\vert E\right\vert /E_{P}$, otherwise rainbow gravity will only secure Planc

Nilotpal Sanyal

We propose an iterative variable selection method for the accelerated failure time model using high-dimensional survival data. Our method pioneers the use of the recently proposed structured screen-and-select framework for survival analysis. We use the marginal utility as the measure of association to inform the structured screening process. For the selectio

Marek Kozoň, Rutger Schrijver, Matthias Schlottbom, Jaap J. W. van der Vegt

We employ unsupervised machine learning to enhance the accuracy of our recently presented scaling method for wave confinement analysis [1]. We employ the standard k-means++ algorithm as well as our own model-based algorithm. We investigate cluster validity indices as a means to find the correct number of confinement dimensionalities to be used as an input to