May 2023 arXiv papers — page 36

Jixiang Li, Jiahao Pi, Guohang Yan, Yikang Li

With the development of autonomous driving technology, sensor calibration has become a key technology to achieve accurate perception fusion and localization. Accurate calibration of the sensors ensures that each sensor can function properly and accurate information aggregation can be achieved. Among them, camera calibration based on surround view has receive

U. Lebreuilly, M. -M. Mac Low, B. Commerçon, D. S. Ebel

Context: Chondrules originate from the reprocessing of dust grains. They are key building blocks of telluric planets, yet their formation, which must happen in strongly localized regions of high temperature, remains poorly understood. Aims: We examine the dust spatial distribution near regions of strong local heating produced by current sheets, as a step tow

Antonio J. Cuesta, José I. Illana, Manuel Masip

We investigate how the resonant conversion at a temperature $\bar{T}=25$-$65$ keV of a fraction of the CMB photons into an axion-like majoron affects BBN. The scenario, that assumes the presence of a primordial magnetic field and the subsequent decay of the majorons into neutrinos at $T\approx 1$ eV, has been proposed to solve the $H_0$ tension. We find two

Giriprasad Sridhara, Ranjani H. G., Sourav Mazumdar

ChatGPT (Chat Generative Pre-trained Transformer) is a chatbot launched by OpenAI on November 30, 2022. OpenAI's GPT-3 family of large language models serve as the foundation for ChatGPT. ChatGPT is fine-tuned with both supervised and reinforcement learning techniques and has received widespread attention for its articulate responses across diverse domains o

Brandon J. O'Connell, Timothy J. Rogers

Modal parameter estimation of operational structures is often a challenging task when confronted with unwanted distortions (outliers) in field measurements. Atypical observations present a problem to operational modal analysis (OMA) algorithms, such as stochastic subspace identification (SSI), severely biasing parameter estimates and resulting in misidentifi

Pinxue Guo, Tony Huang, Peiyang He, Xuefeng Liu

Open-vocabulary Video Instance Segmentation (OpenVIS) can simultaneously detect, segment, and track arbitrary object categories in a video, without being constrained to categories seen during training. In this work, we propose InstFormer, a carefully designed framework for the OpenVIS task that achieves powerful open-vocabulary capabilities through lightweig

Fabian David Schmidt, Ivan Vulić, Goran Glavaš

Massively multilingual language models have displayed strong performance in zero-shot (ZS-XLT) and few-shot (FS-XLT) cross-lingual transfer setups, where models fine-tuned on task data in a source language are transferred without any or with only a few annotated instances to the target language(s). However, current work typically overestimates model performa

Wei Chen, Shiqi Wei, Zhongyu Wei, Xuanjing Huang

Symptom diagnosis in medical conversations aims to correctly extract both symptom entities and their status from the doctor-patient dialogue. In this paper, we propose a novel framework called KNSE for symptom status recognition (SSR), where the SSR is formulated as a natural language inference (NLI) task. For each mentioned symptom in a dialogue window, we

Jacek Piłka, Michał Kwaśny, Magdalena Czerniewicz, Mirosław Karpierz

Vortex beams are a type of structured light characterized by phase rotation around the propagation axis, resulting in orbital angular momentum. Their properties make them useful in various applications such as high-resolution microscopy, optical tweezing, and telecommunications. This has led to a comprehensive development of methods for their generation, ran

Matan Even Tzur, Oren Cohen

The motion of laser-driven electrons quivers with an average energy termed pondermotive energy. We explore electron dynamics driven by bright squeezed vacuum (BSV), finding that BSV induces width oscillations, akin to electron quivering in laser light, with an equivalent ponderomotive energy. In the case of bound electrons, width oscillations may lead to tun

Sanket Shah, Andrew Perrault, Bryan Wilder, Milind Tambe

Predict-then-Optimize is a framework for using machine learning to perform decision-making under uncertainty. The central research question it asks is, "How can the structure of a decision-making task be used to tailor ML models for that specific task?" To this end, recent work has proposed learning task-specific loss functions that capture this underlying s

Zaibin Zhang, Yuanhang Zhang, Lijun Wang, Yifan Wang

A popular approach for constructing bird's-eye-view (BEV) representation in 3D detection is to lift 2D image features onto the viewing frustum space based on explicitly predicted depth distribution. However, depth distribution can only characterize the 3D geometry of visible object surfaces but fails to capture their internal space and overall geometric stru

Fay Dowker, Rafael D. Sorkin

Relativistic causality (RC) is the principle that no cause can act outside its future lightcone, but any attempt to formulate this principle more precisely will depend on the foundational framework that one adopts for quantum theory. Adopting a histories-based (or "path integral") framework, we relate RC to a condition we term "Persistence of Zero" (PoZ), ac

Florian Huber, Gary Koop

The shocks which hit macroeconomic models such as Vector Autoregressions (VARs) have the potential to be non-Gaussian, exhibiting asymmetries and fat tails. This consideration motivates the VAR developed in this paper which uses a Dirichlet process mixture (DPM) to model the shocks. However, we do not follow the obvious strategy of simply modeling the VAR er

Heejin Do, Yunsu Kim, Gary Geunbae Lee

Automated essay scoring (AES) aims to score essays written for a given prompt, which defines the writing topic. Most existing AES systems assume to grade essays of the same prompt as used in training and assign only a holistic score. However, such settings conflict with real-education situations; pre-graded essays for a particular prompt are lacking, and det

Anubhab Dey, Nathan Cottam, Oleg Makarovskiy, Wenjing Yan

The quantum Hall effect is widely used for the investigation of fundamental phenomena, ranging from topological phases to composite fermions. In particular, the discovery of a room temperature resistance quantum in graphene is significant for compact resistance standards that can operate above cryogenic temperatures. However, this requires large magnetic fie

Sabila Parveen, Kiran Sharma, Sudhanwa Patra, Poonam Mehta

While most of the results of the neutrino oscillation experiments can be accommodated within the standard paradigm of three active flavor, there are tantalizing hints of an light eV-scale sterile neutrino from anomalous results of a few short baseline experiments. This additional light sterile neutrino is expected to leave an imprint on the physics observabl

Gaole Dai, Wei Wu, Ziyu Wang, Jie Fu

Learned optimizers are a crucial component of meta-learning. Recent advancements in scalable learned optimizers have demonstrated their superior performance over hand-designed optimizers in various tasks. However, certain characteristics of these models, such as an unstable learning curve, limited ability to handle unseen tasks and network architectures, dif

Christoforos Vasilatos, Manaar Alam, Talal Rahwan, Yasir Zaki

As the use of Large Language Models (LLMs) in text generation tasks proliferates, concerns arise over their potential to compromise academic integrity. The education sector currently tussles with distinguishing student-authored homework assignments from AI-generated ones. This paper addresses the challenge by introducing HowkGPT, designed to identify homewor

Marco Anisetti, Claudio A. Ardagna, Nicola Bena, Ernesto Damiani

Machine Learning (ML) is increasingly used to implement advanced applications with non-deterministic behavior, which operate on the cloud-edge continuum. The pervasive adoption of ML is urgently calling for assurance solutions assessing applications non-functional properties (e.g., fairness, robustness, privacy) with the aim to improve their trustworthiness.

Maria N. Anastasiadou, Philippos Isaia, Panayiotis Kolios, Christos Charalambous

The COVID-19 pandemic has exposed several weaknesses in the public health infrastructure, including supply chain mechanisms and public health ICT systems. The expansion of testing and contact tracing has been key to identifying and isolating infected individuals, as well as tracking and containing the spread of the virus. Digital technologies, such as teleme

Pranav Ajit Nair, Sukomal Pal, Pradeepika Verma

Domain generalization is hitherto an underexplored area applied in abstractive summarization. Moreover, most existing works on domain generalization have sophisticated training algorithms. In this paper, we propose a lightweight, weight averaging based, Domain Aligned Prefix Averaging approach to domain generalization for abstractive summarization. Given a n

Julius Steen, Juri Opitz, Anette Frank, Katja Markert

Conditional language models still generate unfaithful output that is not supported by their input. These unfaithful generations jeopardize trust in real-world applications such as summarization or human-machine interaction, motivating a need for automatic faithfulness metrics. To implement such metrics, NLI models seem attractive, since they solve a strongly

H M Sabbir Ahmad, Ehsan Sabouni, Wei Xiao, Christos G. Cassandras

We address the security of a network of Connected and Automated Vehicles (CAVs) cooperating to navigate through a conflict area. Adversarial attacks such as Sybil attacks can cause safety violations resulting in collisions and traffic jams. In addition, uncooperative (but not necessarily adversarial) CAVs can also induce similar adversarial effects on the tr

Damien Teney, Jindong Wang, Ehsan Abbasnejad

Mixup is a highly successful technique to improve generalization of neural networks by augmenting the training data with combinations of random pairs. Selective mixup is a family of methods that apply mixup to specific pairs, e.g. only combining examples across classes or domains. These methods have claimed remarkable improvements on benchmarks with distribu

Longshen Ou, Xichu Ma, Min-Yen Kan, Ye Wang

The development of general-domain neural machine translation (NMT) methods has advanced significantly in recent years, but the lack of naturalness and musical constraints in the outputs makes them unable to produce singable lyric translations. This paper bridges the singability quality gap by formalizing lyric translation into a constrained translation probl

Xiuge Chen, Rajesh Chitnis, Patrick Eades, Anthony Wirth

In this paper, we design sub-linear space streaming algorithms for estimating three fundamental parameters -- maximum independent set, minimum dominating set and maximum matching -- on sparse graph classes, i.e., graphs which satisfy $m=O(n)$ where $m,n$ is the number of edges, vertices respectively. Each of the three graph parameters we consider can have si

Mario Gattobigio, Alejandro Kievsky

We investigate the properties of the excited state of $^4\mathrm{He}$, $^4\mathrm{He}^*$, within the framework of Efimov physics and its connection to the unitary point of the nuclear interaction. We explore two different approaches to track the trajectory of $^4\mathrm{He}^*$ as it crosses the $^3\mathrm{H}$+p threshold and potentially becomes a resonant st

C. Cardoso, A. T. Costa, A. H. MacDonald, J. Fernández-Rossier

We propose a new class of magnetic proximity effects based on the spin dependent hybridization between the electronic states at the Fermi energy in a non-magnetic conductor and the narrow spin split bands of a ferromagnetic insulator. Unlike conventional exchange proximity, we show this hybridization proximity effect has a very strong influence on the non-ma

Han Liu, Sen Chen, Ruitao Feng, Chengwei Liu

Quality assurance (QA) tools are receiving more and more attention and are widely used by developers. Given the wide range of solutions for QA technology, it is still a question of evaluating QA tools. Most existing research is limited in the following ways: (i) They compare tools without considering scanning rules analysis. (ii) They disagree on the effecti

Zhangyin Feng, Yuchen Ren, Xinmiao Yu, Xiaocheng Feng

Diffusion models developed on top of powerful text-to-image generation models like Stable Diffusion achieve remarkable success in visual story generation. However, the best-performing approach considers historically generated results as flattened memory cells, ignoring the fact that not all preceding images contribute equally to the generation of the charact

Shu-Lin Cheng, Da-Shin Lee, Kin-Wang Ng

It is known that the single-field inflation with a transient ultra-slow-roll phase can produce a large curvature perturbation at small scales for the formation of primordial black holes. In our previous work, we have considered quantum loop corrections to the curvature perturbation and found that the growth of these small-scale modes would affect the curvatu

Arun Balajiee Lekshmi Narayanan, Ligia E. Gomez, Martha Michelle Soto Fernandez, Tri Nguyen

When caregivers ask open--ended questions to motivate dialogue with children, it facilitates the child's reading comprehension skills.Although there is scope for use of technological tools, referred here as "intelligent tutoring systems", to scaffold this process, it is currently unclear whether existing intelligent systems that generate human--language like

Lennart Jaretzki

In this paper, we introduce a novel way to use geometric deep learning for knot data by constructing a functor that takes knots to graphs and using graph neural networks. We will attempt to predict several knot invariants with this approach. This approach demonstrates high generalization capabilities.

Daiki Miyake, Akihiro Iohara, Yu Saito, Toshiyuki Tanaka

In image editing employing diffusion models, it is crucial to preserve the reconstruction fidelity to the original image while changing its style. Although existing methods ensure reconstruction fidelity through optimization, a drawback of these is the significant amount of time required for optimization. In this paper, we propose negative-prompt inversion,

Vikas Raunak, Arul Menezes, Matt Post, Hany Hassan Awadalla

Large Language Models (LLMs) such as GPT-3 have emerged as general-purpose language models capable of addressing many natural language generation or understanding tasks. On the task of Machine Translation (MT), multiple works have investigated few-shot prompting mechanisms to elicit better translations from LLMs. However, there has been relatively little inv

J. M. García-Regaña, I. Calvo, F. I. Parra, H. Thienpondt

A systematic study of the impact of impurities on the turbulent heat fluxes is presented for the stellarator Wendelstein 7-X (W7-X) and, for comparison, the Large Helical Device and ITER. By means of nonlinear multispecies gyrokinetic simulations, it is shown that impurities, depending on the sign of their density gradient, can significantly enhance or reduc

Tai-Yu Pan, Qing Liu, Wei-Lun Chao, Brian Price

Segmenting object parts such as cup handles and animal bodies is important in many real-world applications but requires more annotation effort. The largest dataset nowadays contains merely two hundred object categories, implying the difficulty to scale up part segmentation to an unconstrained setting. To address this, we propose to explore a seemingly simpli

Lei Guan, Dongsheng Li, Yanqi Shi, Jian Meng

In this paper, we propose a general deep learning training framework XGrad which introduces weight prediction into the popular gradient-based optimizers to boost their convergence and generalization when training the deep neural network (DNN) models. In particular, ahead of each mini-batch training, the future weights are predicted according to the update ru

Lenka Tětková, Thea Brüsch, Teresa Karen Scheidt, Fabian Martin Mager

Current work on human-machine alignment aims at understanding machine-learned latent spaces and their correspondence to human representations. G{\"a}rdenfors' conceptual spaces is a prominent framework for understanding human representations. Convexity of object regions in conceptual spaces is argued to promote generalizability, few-shot learning, and interp

Salvatore Tirone, Raffaele Salvia, Stefano Chessa, Vittorio Giovannetti

Quantum work capacitances and maximal asymptotic work/energy ratios are figures of merit characterizing the robustness against noise of work extraction processes in quantum batteries formed by collections of quantum systems. In this paper we establish a direct connection between these functionals and, exploiting this result, we analyze different types of noi

F Napolitano, M Bazzi, M Bragadireanu, M Cargnelli

In this work, we present novel Machine Learning and Differentiable Programming enhanced calibration techniques used to improve the energy resolution of the Silicon Drift Detectors (SDDs) of the VIP-2 underground experiment at the Gran Sasso National Laboratory (LNGS). We achieve for the first time a Full Width at Half Maximum (FWHM) in VIP-2 below 180 eV at

Anran Liu, Engui Fan

In this paper, we address the existence of global solutions to the Cauchy problem for the integrable nonlocal modified Korteweg-de vries (nonlocal mKdV) equation with the initial data $u_0 \in H^{3}(\mathbb{R}) \cap H^{1,1}(\mathbb{R}) $ with the $L^1(\mathbb{R})$ small-norm assumption. A Lipschitz $L^2$-bijection map between potential and reflection coeffic

Evangelos G. Sartinas, Emmanouil Z. Psarakis, Dimitrios I. Kosmopoulos

An interesting problem in many video-based applications is the generation of short synopses by selecting the most informative frames, a procedure which is known as video summarization. For sign language videos the benefits of using the $t$-parameterized counterpart of the curvature of the 2-D signer's wrist trajectory to identify keyframes, have been recentl

Antonio J. Rivera, Miguel A. Dávila, David Elizondo, María J. del Jesus

Resampling algorithms are a useful approach to deal with imbalanced learning in multilabel scenarios. These methods have to deal with singularities in the multilabel data, such as the occurrence of frequent and infrequent labels in the same instance. Implementations of these methods are sometimes limited to the pseudocode provided by their authors in a paper

Jiakai Sun, Weijing Zhang, Zhanjie Zhang, Tianyi Chu

Traditional inverse rendering techniques are based on textured meshes, which naturally adapts to modern graphics pipelines, but costly differentiable multi-bounce Monte Carlo (MC) ray tracing poses challenges for modeling global illumination. Recently, neural fields has demonstrated impressive reconstruction quality but falls short in modeling indirect illum

Gabriella Skitalinskaya, Henning Wachsmuth

Optimizing the phrasing of argumentative text is crucial in higher education and professional development. However, assessing whether and how the different claims in a text should be revised is a hard task, especially for novice writers. In this work, we explore the main challenges to identifying argumentative claims in need of specific revisions. By learnin

Yue Feng, Yunlong Jiao, Animesh Prasad, Nikolaos Aletras

User Satisfaction Modeling (USM) is one of the popular choices for task-oriented dialogue systems evaluation, where user satisfaction typically depends on whether the user's task goals were fulfilled by the system. Task-oriented dialogue systems use task schema, which is a set of task attributes, to encode the user's task goals. Existing studies on USM negle

Loukas Ilias, Spiros Mouzakitis, Dimitris Askounis

In today's fast-paced world, the rates of stress and depression present a surge. Social media provide assistance for the early detection of mental health conditions. Existing methods mainly introduce feature extraction approaches and train shallow machine learning classifiers. Other researches use deep neural networks or transformers. Despite the fact that t

S. Akshay, Krishnendu Chatterjee, Tobias Meggendorfer, Đorđe Žikelić

Markov decision processes can be viewed as transformers of probability distributions. While this view is useful from a practical standpoint to reason about trajectories of distributions, basic reachability and safety problems are known to be computationally intractable (i.e., Skolem-hard) to solve in such models. Further, we show that even for simple example

Ossi Räisä, Joonas Jälkö, Antti Honkela

Generating synthetic data, with or without differential privacy, has attracted significant attention as a potential solution to the dilemma between making data easily available, and the privacy of data subjects. Several works have shown that consistency of downstream analyses from synthetic data, including accurate uncertainty estimation, requires accounting

Xinchi Qiu, Heng Pan, Wanru Zhao, Yan Gao

Most work in privacy-preserving federated learning (FL) has focused on horizontally partitioned datasets where clients hold the same features and train complete client-level models independently. However, individual data points are often scattered across different institutions, known as clients, in vertical FL (VFL) settings. Addressing this category of FL n

Xikun Jiang, Chenhao Ying, Lei Li, Boris Düdder

Mobile crowd sensing (MCS) has emerged as an increasingly popular sensing paradigm due to its cost-effectiveness. This approach relies on platforms to outsource tasks to participating workers when prompted by task publishers. Although incentive mechanisms have been devised to foster widespread participation in MCS, most of them focus only on static tasks (i.

Minwoo Jung, Sangwoo Jung, Ayoung Kim

In recent years, multiple Light Detection and Ranging (LiDAR) systems have grown in popularity due to their enhanced accuracy and stability from the increased field of view (FOV). However, integrating multiple LiDARs can be challenging, attributable to temporal and spatial discrepancies. Common practice is to transform points among sensors while requiring st

Linus Bleistein, Agathe Guilloux

Neural Controlled Differential Equations (NCDEs) are a state-of-the-art tool for supervised learning with irregularly sampled time series (Kidger, 2020). However, no theoretical analysis of their performance has been provided yet, and it remains unclear in particular how the irregularity of the time series affects their predictions. By merging the rich theor

Petri J. Käpylä, Matthew K. Browning, Allan Sacha Brun, Gustavo Guerrero

We review the state of the art of three dimensional numerical simulations of solar and stellar dynamos. We summarize fundamental constraints of numerical modelling and the techniques to alleviate these restrictions. Brief summary of the relevant observations that the simulations seek to capture is given. We survey the current progress of simulations of solar

Xi Weng, Yunhao Ni, Tengwei Song, Jie Luo

Whitening loss offers a theoretical guarantee against feature collapse in self-supervised learning (SSL) with joint embedding architectures. Typically, it involves a hard whitening approach, transforming the embedding and applying loss to the whitened output. In this work, we introduce Spectral Transformation (ST), a framework to modulate the spectrum of emb

Habib Ammari, Bryn Davies, Erik Orvehed Hiltunen

We show that resonant frequencies of a system of coupled resonators in a truncated periodic lattice converge to the essential spectrum of corresponding infinite lattice. We use the capacitance matrix as a model for fully coupled resonators with long-range interactions in three spatial dimensions. For one-, two- or three-dimensional lattices embedded in three

Hannah E. Brinkman, C. L. Doherty, M. Pignatari, O. R. Pols

Many of the short-lived radioactive nuclei that were present in the early Solar System can be produced in massive stars. In the first paper in this series (Brinkman et al. 2019), we focused on the production of $^{26}$Al in massive binaries. In our second paper (Brinkman et al. 2021), we considered rotating single stars, two more short-lived radioactive nucl

Yogesh Chandola, Chao-Wei Tsai, Di Li, Chandreyee Sengupta

We present the results from deep 21 cm H I mapping of two nearby blue compact dwarf galaxies (BCDGs), W1016+3754 and W2326+0608, using the Giant Metrewave Radio Telescope (GMRT). These BCDGs are bright in mid-infrared (MIR) data and undergoing active star formation. With the GMRT observations, we investigate the role of cold neutral gas as the fuel resource

Zhitong Chen, Richard Obenchain, Richard E. Wirz

Plasma medicine is an emerging and innovative interdisciplinary research field combining biology, chemistry, physics, engineering, and medicine. However, the safe clinical application of cold atmospheric plasma (CAP) technology is still a challenge. Here, we examine the use of aluminum (Al) foam with three pores-per-inch (PPI) ratings in clinical plasma appl

J. Loizu, D. Bonfiglio

We show that the saturation of resistive tearing modes in a cylindrical tokamak, as well as the corresponding island width, can be directly calculated with an MHD equilibrium code without solving the dynamics and without considering resistivity. The results are compared to initial value resistive MHD simulations and to an analytical nonlinear theory. For sma

Dongqi Liu, Yifan Wang, Vera Demberg

For text summarization, the role of discourse structure is pivotal in discerning the core content of a text. Regrettably, prior studies on incorporating Rhetorical Structure Theory (RST) into transformer-based summarization models only consider the nuclearity annotation, thereby overlooking the variety of discourse relation types. This paper introduces the '

Roland Becker, Malte Braack

We provide a concise proof of existence for nonlinear operator equations in separable Banach spaces. Notably, the operator is not assumed to be monotone. Instead, our main hypotheses consist of a continuity assumption and a generalized coercivity property. Mapped coercivity is a generalization of the usual coercivity property for nonlinear operators. In the

Matteo Nurisso, Annalisa Celotti, Andrea Mignone, Gianluigi Bodo

We present a new algorithm for the identification and physical characterization of current sheets and reconnection sites in 2D and 3D large scale relativisticmagnetohydrodynamic numerical simulations. This has been implemented in the PLUTO code and tested in the cases of a single current sheet, a 2D jet and a 3D unstable plasma column. Its main features are:

Krzysztof Jodłowski

Inelastic Dark Matter (iDM) is an interesting thermal DM scenario that can pose challenges for conventional detection methods. However, recent studies demonstrated that iDM coupled to a photon by electric or magnetic dipole moments can be effectively constrained by intensity frontier experiments using the displaced single-photon decay signature. In this work

Kai Zhao, Qiyu Kang, Yang Song, Rui She

Graph neural networks (GNNs) have shown promising results across various graph learning tasks, but they often assume homophily, which can result in poor performance on heterophilic graphs. The connected nodes are likely to be from different classes or have dissimilar features on heterophilic graphs. In this paper, we propose a novel GNN that incorporates the

S. M. Mewes, G. J. Boyle, A. Ferran Pousa, R. J. Shalloo

In recent years, hydrodynamic optical-field-ionized (HOFI) channels have emerged as a promising technique to create laser waveguides suitable for guiding tightly-focused laser pulses in a plasma, as needed for laser-plasma accelerators. While experimental advances in HOFI channels continue to be made, the underlying mechanisms and the roles of the main param

Jeongmin Lee, Minji Lee, Dongjun Lee

For many robotic manipulation and contact tasks, it is crucial to accurately estimate uncertain object poses, for which certain geometry and sensor information are fused in some optimal fashion. Previous results for this problem primarily adopt sampling-based or end-to-end learning methods, which yet often suffer from the issues of efficiency and generalizab

Yihong Huang, Yuang Zhang, Liping Wang, Xuemin Lin

Outlier detection (OD) has received continuous research interests due to its wide applications. With the development of deep learning, increasingly deep OD algorithms are proposed. Despite the availability of numerous deep OD models, existing research has reported that the performance of deep models is extremely sensitive to the configuration of hyperparamet

Patrick Linker, Cenap Ozel, Alexander Pigazzini, Monika Sati

We will classify physically admissible manifold structures by the use of Waldhausen categories. These categories give rise to algebraic K-Theory. Moreover, we will show that a universal K-spectrum is necessary for a physical manifold being admissible. Application to the generalized structure of D-branes are also provided. This might give novel insights in ho

Rohit Tyagi, Abhijeet Bhogale, Sandeep Bari, L. C. Tribedi

We have measured absolute total cross section for LMM Auger electron emission of Cl in chlorinated methane and benzene chloride in collision with H+ ion. Projectile energy dependence of the total yield as well as the angular distribution has been studied. Incident proton energy has been varied from 125 keV to 275 keV in steps of 50 keV. C KLL Auger yield hav

Ranit Das, Luigi Favaro, Theo Heimel, Claudius Krause

Well-trained classifiers and their complete weight distributions provide us with a well-motivated and practicable method to test generative networks in particle physics. We illustrate their benefits for distribution-shifted jets, calorimeter showers, and reconstruction-level events. In all cases, the classifier weights make for a powerful test of the generat

Fernando Lledó, Ignacio Sevillano

In this article we give a geometrical description of the (in general non-selfadjoint) in/out Laplacian $\mathcal{L}^{+/-} = (d^{+/-})^* d$ and adjacency matrix on digraphs with arbitrary weights, where $(d^{+/-})^*$ is the adjoint of the evaluation map $d^{+/-}$ on the terminal/initial vertex of each arc and $d = d^+ + d^-$ denotes the discrete gradient. We

Chuanyu Xue, Tianyu Zhang, Yuanbin Zhou, Mark Nixon

Time-Sensitive Networking (TSN) has been recognized as one of the key enabling technologies for Industry 4.0 and has been deployed in many mission- and safety-critical applications e.g., automotive and aerospace systems. Given the stringent real-time requirements of these applications, the Time-Aware Shaper (TAS) draws special attention among TSN's many traf

Puning Zhao, Zhiguo Wan

This paper studies robust nonparametric regression, in which an adversarial attacker can modify the values of up to $q$ samples from a training dataset of size $N$. Our initial solution is an M-estimator based on Huber loss minimization. Compared with simple kernel regression, i.e. the Nadaraya-Watson estimator, this method can significantly weaken the impac

Binyang Song, Chenyang Yuan, Frank Permenter, Nikos Arechiga

Generative AI models have made significant progress in automating the creation of 3D shapes, which has the potential to transform car design. In engineering design and optimization, evaluating engineering metrics is crucial. To make generative models performance-aware and enable them to create high-performing designs, surrogate modeling of these metrics is n

Huayang Song, Hao Sun, Jiang-Hao Yu

With the help of Young tensor technique, we enumerate the complete and independent set of effective operators up to $dim$-8 for the extension of the standard model with a Goldsonte boson by further imposing the Adler's zero condition in the soft momentum limit. Such basis can be reduced to describe the axion or majoron effective Lagrangian if further (symmet

Daniel Adams, Márton Balázs, Jessica Jay

We consider any fixed $d\in\mathbb{Z}_{>0}$ number of second class particles in the asymmetric simple exclusion process (ASEP), constructed via a basic coupling of two ASEPs. We give the joint distribution of the positions of the second class particles and also the probability of there being a second class particle at a given site, under the natural blocking

Fred Philippy, Siwen Guo, Shohreh Haddadan

In recent years, pre-trained Multilingual Language Models (MLLMs) have shown a strong ability to transfer knowledge across different languages. However, given that the aspiration for such an ability has not been explicitly incorporated in the design of the majority of MLLMs, it is challenging to obtain a unique and straightforward explanation for its emergen

Joseph Peterson, Ioannis Bagkeris, Vipin Michael

When processing dense emulsions, complex flows stretch and deform droplets to the point of breakup, changing the droplet size distribution and the mechanical properties of the final product. For steady homogeneous flows, a droplet's shape and proclivity to breakup can be inferred from a Capillary number, comparing the strain rate with the typical shape relax

K. K. Dudek, L. Mizzi, J. A. Iglesias Martínez, A. Spaggiari

The ability to control Poisson's ratio of functional materials has been one of the main objectives of researchers attempting to develop structures efficient from the perspective of protective, biomedical and soundproofing devices. This task becomes even more challenging at small scales, such as the microscale, where the possibility to control mechanical prop

Niful Islam, Debopom Sutradhar, Humaira Noor, Jarin Tasnim Raya

ChatGPT is a conversational artificial intelligence that is a member of the generative pre-trained transformer of the large language model family. This text generative model was fine-tuned by both supervised learning and reinforcement learning so that it can produce text documents that seem to be written by natural intelligence. Although there are numerous a

John Hewitt, John Thickstun, Christopher D. Manning, Percy Liang

We present Backpacks: a new neural architecture that marries strong modeling performance with an interface for interpretability and control. Backpacks learn multiple non-contextual sense vectors for each word in a vocabulary, and represent a word in a sequence as a context-dependent, non-negative linear combination of sense vectors in this sequence. We find

Zishen Chen, Ricardo García-Mayoral

Turbulent flows over canopies of rigid filaments with different densities, $\lambda_f$, are studied using direct simulations at Reynolds numbers $Re_\tau\approx550-1000$. The canopies have heights $h^+\approx110-220$, and are an instance of obstructing substrate. We show that conventional methods used to determine the zero-plane displacement can be at odds w

Jacob Focke, Dániel Marx, Fionn Mc Inerney, Daniel Neuen

For a well-studied family of domination-type problems, in bounded-treewidth graphs, we investigate whether it is possible to find faster algorithms. For sets $\sigma,\rho$ of non-negative integers, a $(\sigma,\rho)$-set of a graph $G$ is a set $S$ of vertices such that $|N(u)\cap S|\in \sigma$ for every $u\in S$, and $|N(v)\cap S|\in \rho$ for every $v\not\i

Jun-xiang Shao, Wei-jie Fu, Yu-xin Liu

We study the freeze-out parameters in a QCD-assisted effective theory that accurately captures the quantum and in-medium effects of QCD at low energies. Functional renormalization group approach is implemented in our work to incorporate the non-perturbative quantum, thermal and density fluctuations. By analyzing the calculated baryon number susceptibility ra

G. L. Klimchitskaya, V. M. Mostepanenko

The spatially nonlocal response functions of graphene obtained on the basis of first principles of quantum field theory using the polarization tensor are considered in the areas of both the on-the-mass-shell and off-the-mass-shell waves. It s shown that at zero frequency the longitudinal permittivity of graphene is the regular function, whereas the transvers

Stephan I. Tzenov, Zhichu Chen

A relativistic quantum mechanical model to describe the quantum FEL dynamics has been developed. Neglecting the spin of electrons in the impacting beam, this model is based on the Klein-Gordon equation coupled to the Poisson equation for the space-charge potential and the wave equation for the transverse components of the radiation field. Furthermore, a syst

Cuauhtemoc Gomez-Navarro, Edgardo Roldán-Pensado

Let $K$ be a compact convex set in $\mathbb{R}^2$ and let $\mathcal{F}_1, \mathcal{F}_2, \mathcal{F}_3$ be finite families of translates of $K$ such that $A \cap B \neq \emptyset$ for every $A \in \mathcal{F}_i$ and $B \in \mathcal{F}_j$ with $i \neq j$. A conjecture by Dolnikov is that, under these conditions, there is always some $j \in \lbrace 1,2,3 \rbra

Takato Yoshikawa, Yuki Endo, Yoshihiro Kanamori

This paper tackles text-guided control of StyleGAN for editing garments in full-body human images. Existing StyleGAN-based methods suffer from handling the rich diversity of garments and body shapes and poses. We propose a framework for text-guided full-body human image synthesis via an attention-based latent code mapper, which enables more disentangled cont

Wei-Zhu Yeoh, Michal Kepkowski, Gunnar Heide, Dali Kaafar

Web authentication is a critical component of today's Internet and the digital world we interact with. The FIDO2 protocol enables users to leverage common devices to easily authenticate to online services in both mobile and desktop environments following the passwordless authentication approach based on cryptography and biometric verification. However, there

Martin Perešíni, Ivan Homoliak, Federico Matteo Benčić, Martin Hrubý

Several blockchain consensus protocols proposed to use of Directed Acyclic Graphs (DAGs) to solve the limited processing throughput of traditional single-chain Proof-of-Work (PoW) blockchains. Many such protocols utilize a random transaction selection (RTS) strategy (e.g., PHANTOM, GHOSTDAG, SPECTRE, Inclusive, and Prism) to avoid transaction duplicates acro

Kunal Marwaha

We prove an inequality related to arctanh, resolving a conjecture of Gu and Polyanskiy [arXiv:2303.14689].

Nicolò Tamagnone, Selim Fekih, Ximena Contla, Nayid Orozco

Accurate and rapid situation analysis during humanitarian crises is critical to delivering humanitarian aid efficiently and is fundamental to humanitarian imperatives and the Leave No One Behind (LNOB) principle. This data analysis can highly benefit from language processing systems, e.g., by classifying the text data according to a humanitarian ontology. Ho

Hiba Arnaout, Simon Razniewski

We examine the ability of large language models (LLMs) to generate salient (interesting) negative statements about real-world entities; an emerging research topic of the last few years. We probe the LLMs using zero- and k-shot unconstrained probes, and compare with traditional methods for negation generation, i.e., pattern-based textual extractions and knowl

W. Q. Su, J. L. Han, P. F. Wang, J. P. Yuan

Timing observations are crucial for determining the basic parameters of newly discovered pulsars. Using the Five-hundred-meter Aperture Spherical radio Telescope (FAST) with the L-band 19-beam receiver covering the frequency range of 1.0--1.5 GHz, the FAST Galactic Plane Pulsar Snapshot (GPPS) Survey has discovered more than 600 faint pulsars with flux densi

Łukasz Zinkiewicz, Milena Królikowska, Alexander Krupiński-Ptaszek, Piotr Wasylczyk

Prototype optical microscopes, built to pursue developments in advanced imaging techniques, need specific optomechanical constructions: preferably with high flexibility in the elements arrangement, easy access to the optical paths, straightforward integration with external optical subsystems - light sources and detectors - as well as good mechanical stabilit

Enoch Hsin-Ho Huang, Rong Chao, Yu Tsao, Chao-Min Wu

ElectrodeNet, a deep learning based sound coding strategy for the cochlear implant (CI), is proposed to emulate the advanced combination encoder (ACE) strategy by replacing the conventional envelope detection using various artificial neural networks. The extended ElectrodeNet-CS strategy further incorporates the channel selection (CS). Network models of deep

Severin Bals, Alexandros Evangelidis, Jan Křetínský, Jakob Waibel

We present MULTIGAIN 2.0, a major extension to the controller synthesis tool MULTIGAIN, built on top of the probabilistic model checker PRISM. This new version extends MULTIGAIN's multi-objective capabilities, by allowing for the formal verification and synthesis of controllers for probabilistic systems with multi-dimensional long-run average reward structur