May 2023 arXiv papers — page 108

Johnny Tian-Zheng Wei, Frederike Zufall, Robin Jia

The Digital Services Act, recently adopted by the EU, requires social media platforms to report the "accuracy" of their automated content moderation systems. The colloquial term is vague, or open-textured -- the literal accuracy (number of correct predictions divided by the total) is not suitable for problems with large class imbalance, and the ground truth

Ashutosh Dutta, Milan Jain, Arif Khan, Arun Sathanur

Collisions, crashes, and other incidents on road networks, if left unmitigated, can potentially cause cascading failures that can affect large parts of the system. Timely handling such extreme congestion scenarios is imperative to reduce emissions, enhance productivity, and improve the quality of urban living. In this work, we propose a Deep Reinforcement Le

Mercedes Martín-Benito, Rita B. Neves, Javier Olmedo

In this work we investigate observational signatures of a primordial power spectrum with exponential infrared suppression, motivated by the choice of a non-oscillatory vacuum in a bouncing and inflationary geometry within Loop Quantum Cosmology (LQC). We leave the parameter that defines the scale at which suppression occurs free and perform a Bayesian analys

bo wang, Heyan Huang, Xiaochi Wei, Ge Shi

Event extraction aims to recognize pre-defined event triggers and arguments from texts, which suffer from the lack of high-quality annotations. In most NLP applications, involving a large scale of synthetic training data is a practical and effective approach to alleviate the problem of data scarcity. However, when applying to the task of event extraction, re

Antonio Bianchi, Walter Venturini Delsolaro

Since the late '80s, CERN has pioneered the development of niobium thin film radio-frequency (RF) cavities deposited on copper substrates for several particle accelerator applications. However, niobium thin film cavities historically feature a progressive performance degradation as the accelerating field increases. In this study, we describe a temperature ma

Prithivraj G, Alka Kumari

NASA's Kepler Space Telescope has been instrumental in the task of finding the presence of exoplanets in our galaxy. This search has been supported by computational data analysis to identify exoplanets from the signals received by the Kepler telescope. In this paper, we consider building upon some existing work on exoplanet identification using residual netw

Alexander Braverman, David Kazhdan, Alexander Polishchuk, Ka Fai Wong

We study Hecke operators associated with curves over a non-archimedean local field $K$ and over the rings $O/{\mathfrak m}^N$, where $O\subset K$ is the ring of integers. Our main result is commutativity of a certain "small" local Hecke algebra over $O/{\mathfrak m}^N$, associated with a connected split reductive group $G$ such that $[G,G]$ is simple and sim

Ahsan Adeel, Junaid Muzaffar, Fahad Zia, Khubaib Ahmed

Going beyond 'dendritic democracy', we introduce a 'democracy of local processors', termed Cooperator. Here we compare their capabilities when used in permutation invariant neural networks for reinforcement learning (RL), with machine learning algorithms based on Transformers, such as ChatGPT. Transformers are based on the long standing conception of integra

Luke Puppo, Weng-Keen Wong, Bechir Hamdaoui, Abdurrahman Elmaghbub

New capabilities in wireless network security have been enabled by deep learning, which leverages patterns in radio frequency (RF) data to identify and authenticate devices. Open-set detection is an area of deep learning that identifies samples captured from new devices during deployment that were not part of the training set. Past work in open-set detection

J. Gregory Pauloski, Valerie Hayot-Sasson, Logan Ward, Nathaniel Hudson

Advances in networks, accelerators, and cloud services encourage programmers to reconsider where to compute -- such as when fast networks make it cost-effective to compute on remote accelerators despite added latency. Workflow and cloud-hosted serverless computing frameworks can manage multi-step computations spanning federated collections of cloud, high-per

Amin Sarihi, Ahmad Patooghy, Peter Jamieson, Abdel-Hameed A. Badawy

Current Hardware Trojan (HT) detection techniques are mostly developed based on a limited set of HT benchmarks. Existing HT benchmark circuits are generated with multiple shortcomings, i.e., i) they are heavily biased by the designers' mindset when created, and ii) they are created through a one-dimensional lens, mainly the signal activity of nets. We introd

Sara Giarrusso, Aurora Pribram-Jones

In recent years, Adiabatic Connection Interpolations developed within Density Functional Theory (DFT) have been found to provide satisfactory performances in the calculation of interaction energies when used with Hartree-Fock (HF) ingredients. The physical and mathematical reasons for such unanticipated performance have been clarified, to some extent, by stu

Miguel A. Cascales Sandoval, A. Hierro-Rodríguez, S. Ruiz-Gómez, L. Skoric

In this work we present a detailed analysis on the performance of X-ray magnetic circular dichroism photo-emission electron microscopy (XMCD-PEEM) as a tool for vector reconstruction of the magnetization. For this, we choose 360$^{\circ}$ domain wall ring structures which form in a synthetic antiferromagnet as our model to conduct the quantitative analysis.

Marcus Engsig, Alejandro Tejedor, Yamir Moreno, Efi Foufoula-Georgiou

Determining the key elements of interconnected infrastructure and complex systems is paramount to ensure system functionality and integrity. This work quantifies the dominance of the networks' nodes in their respective neighborhoods, introducing a novel centrality metric, DomiRank, that integrates local and global topological information via a tunable parame

Lopamudra Kundu, Xingqin Lin, Elena Agostini, Vikrama Ditya

Radio access networks (RAN) are going through a paradigm shift towards interoperable, intelligent, software-defined, and cloud-native open RAN solutions. A key challenge towards the adoption and deployment of open RAN at scale is performance. Hence, it is critical to leverage the power of hardware acceleration to offload compute-heavy RAN workloads to specia

J. Mourad, A. Sagnotti

Internal intervals spanned by finite ranges of a conformal coordinate $z$ and terminating at a pair of singularities are a common feature of many string compactifications with broken supersymmetry. The squared masses emerging in lower-dimensional Minkowski spaces are then eigenvalues of Schr\"odinger-like operators, whose potentials have double poles at the

Hualei Sun, Mengwu Huo, Xunwu Hu, Jingyuan Li

High-transition-temperature (high-T_c) superconductivity in cuprates has been discovered for more than three decades, but the underlying mechanism remains a mystery. Cuprates are the only unconventional superconducting family that host bulk superconductivity with T_cs above the liquid nitrogen boiling temperature at 77 Kelvin. Here we report an observation o

Wieke Prummel, Jhony H. Giraldo, Anastasia Zakharova, Thierry Bouwmans

Moving Object Segmentation (MOS) is a challenging problem in computer vision, particularly in scenarios with dynamic backgrounds, abrupt lighting changes, shadows, camouflage, and moving cameras. While graph-based methods have shown promising results in MOS, they have mainly relied on transductive learning which assumes access to the entire training and test

Thomas Lips, Francis wyffels

Robots that assist humans will need to interact with articulated objects such as cabinets or microwaves. Early work on creating systems for doing so used proprioceptive sensing to estimate joint mechanisms during contact. However, nowadays, almost all systems use only vision and no longer consider proprioceptive information during contact. We believe that pr

Maitreyi Jayaseelan, Andrew P. Rotunno, Nikunjkumar Prajapati, Samuel Berweger

We examine spectral signatures of Rydberg atoms driven with near-resonant dual-tone radio-frequency (RF) fields in the regime of strong driving. We experimentally demonstrate and theoretically model a variety of nonlinear and multiphoton phenomena in the atomic Rydberg response that manifest in the EIT spectra. Our results echo previous studies of two-level

Theodore D. Drivas, Tarek M. Elgindi, In-Jee Jeong

We introduce a notion of stability for non-autonomous Hamiltonian flows on two-dimensional annular surfaces. This notion of stability is designed to capture the sustained twisting of particle trajectories. The main Theorem is applied to establish a number of results that reveal a form of irreversibility in the Euler equations governing the motion of an incom

F. Peña, C. A. Lindstrøm, J. Beinortaitė, J. Björklund Svensson

For plasma-wakefield accelerators to fulfil their potential for cost effectiveness, it is essential that their energy-transfer efficiency be maximized. A key aspect of this efficiency is the near-complete transfer of energy, or depletion, from the driver electrons to the plasma wake. Achieving full depletion is limited by the process of re-acceleration, whic

Gus Henry Smith, Ben Kushigian, Vishal Canumalla, Andrew Cheung

Compiler backends should be automatically generated from hardware design language (HDL) models of the hardware they target. Generating compiler components directly from HDL can provide stronger correctness guarantees, ease development effort, and encourage hardware exploration. Past work has already championed this idea; here we argue that advances in progra

Moni Naor, Kobbi Nissim, Uri Stemmer, Chao Yan

A private learner is trained on a sample of labeled points and generates a hypothesis that can be used for predicting the labels of newly sampled points while protecting the privacy of the training set [Kasiviswannathan et al., FOCS 2008]. Research uncovered that private learners may need to exhibit significantly higher sample complexity than non-private lea

Jamie M. Taylor, Manuela Bastidas, David Pardo, Ignacio Muga

Solving PDEs with machine learning techniques has become a popular alternative to conventional methods. In this context, Neural networks (NNs) are among the most commonly used machine learning tools, and in those models, the choice of an appropriate loss function is critical. In general, the main goal is to guarantee that minimizing the loss during training

Yuefeng Yu, Jan N. Kirchhof, Aleksei Tsarapkin, Victor Deinhart

Phononic crystals (PnCs) are artificially patterned media exhibiting bands of allowed and forbidden zones for phonons. Many emerging applications of PnCs from solid-state simulators to quantum memories could benefit from the on-demand tunability of the phononic band structure. Here, we demonstrate the fabrication of suspended graphene PnCs in which the phono

Hicham Ait Laasri, Eliane Bsaibess, Fabian Delorme, Guillaume F. Nataf

$BaWO_{4}$, $Ce_{2/3}\square_{1/3}WO_{4}$ and $La_{2/3}\square_{1/3}WO_{4}$ polycrystalline ceramics were synthesized by conventional solid-state reaction route. The effect of cation-deficiency on the crystallographic structure, microstructure and thermal properties of these scheelite-type compounds were investigated. X-ray diffraction was used to identify t

Zhi-Yu Xiao, Hao Shi, Shiwei Zhang

We present an approach to interface branching random walks with Markov chain Monte Carlo sampling, and to switch seamlessly between the two. The approach is discussed in the context of auxiliary-field quantum Monte Carlo (AFQMC) but is applicable to other Monte Carlo calculations or simulations. In AFQMC, the formulation of branching random walks along imagi

Wei Du, Peixuan Li, Boqun Li, Haodong Zhao

Backdoors implanted in pre-trained language models (PLMs) can be transferred to various downstream tasks, which exposes a severe security threat. However, most existing backdoor attacks against PLMs are un-targeted and task-specific. Few targeted and task-agnostic methods use manually pre-defined triggers and output representations, which prevent the attacks

Sanna J. Ali, Angèle Christin, Andrew Smart, Riitta Katila

Amidst decline in public trust in technology, computing ethics have taken center stage, and critics have raised questions about corporate ethics washing. Yet few studies examine the actual implementation of AI ethics values in technology companies. Based on a qualitative analysis of technology workers tasked with integrating AI ethics into product developmen

Dimitrios Tsapetis, Michael D. Shields, Dimitris G. Giovanis, Audrey Olivier

This paper presents the latest improvements introduced in Version 4 of the UQpy, Uncertainty Quantification with Python, library. In the latest version, the code was restructured to conform with the latest Python coding conventions, refactored to simplify previous tightly coupled features, and improve its extensibility and modularity. To improve the robustne

Farita Tasnim, David H. Wolpert

Many dynamical systems consist of multiple, co-evolving subsystems (degrees of freedom). These subsystems often depend upon each other in a way that restricts the overall system's dynamics. How does this network of dependencies affect the system's thermodynamics? Prior studies in the stochastic thermodynamics of multipartite processes (MPPs) have approached

A. Arbuzov, S. Bondarenko, L. Kalinovskaya, R. Sadykov

Electroweak effects in the $e^+e^- \to t \bar{t}$ annihilation process are described with taking into account polarization of the initial and final particles. We investigate the effects of complete one-loop electroweak radiative corrections and higher-order radiative effects to the total cross section and analyze different types of asymmetries for polarized

James R. Maddison

Automated code generation allows for a separation between the development of a model, expressed via a domain specific language, and lower level implementation details. Algorithmic differentiation can be applied symbolically at the level of the domain specific language, and the code generator reused to implement code required for an adjoint calculation. Howev

Mariana Frank, Nima Pourtolami, Manuel Toharia

We consider a minimal extension of the Standard Model in warped extra dimensions, with fields propagating in the bulk including a bulk SM-like Higgs doublet. We show that the Higgs can acquire a non-trivial oscillatory VEV, strongly localized towards the TeV brane, but such that its value at that brane could be highly suppressed due to its oscillatory behavi

T. Berriel Martins, Javier Civera

In this paper we propose the Ray-Patch querying, a novel model to efficiently query transformers to decode implicit representations into target views. Our Ray-Patch decoding reduces the computational footprint and increases inference speed up to one order of magnitude compared to previous models, without losing global attention, and hence maintaining specifi

Elias Eulig, Atalanti A. Mastakouri, Patrick Blöbaum, Michaela Hardt

Understanding causal relationships among the variables of a system is paramount to explain and control its behavior. For many real-world systems, however, the true causal graph is not readily available and one must resort to predictions made by algorithms or domain experts. Therefore, metrics that quantitatively assess the goodness of a causal graph provide

Mathieu Molitor

We consider K\"ahler toric manifolds $N$ that are torifications of statistical manifolds $\mathcal{E}$ in the sense of [M. Molitor, "K\"ahler toric manifolds from dually flat spaces", arXiv:2109.04839], and prove a geometric analogue of the spectral decomposition theorem in which Hermitian matrices are replaced by K\"ahler functions on $N$. The notion of "sp

Qiyuan Pang, Haizhao Yang

While orthogonalization exists in current dimensionality reduction methods in spectral clustering on undirected graphs, it does not scale in parallel computing environments. We propose four orthogonalization-free methods for spectral clustering. Our methods optimize one of two objective functions with no spurious local minima. In theory, two methods converge

Maame G. Asante-Mensah, Anh Huy Phan, Salman Ahmadi-Asl, Zaher Al Aghbari

This paper presents a pixel selection method for compact image representation based on superpixel segmentation and tensor completion. Our method divides the image into several regions that capture important textures or semantics and selects a representative pixel from each region to store. We experiment with different criteria for choosing the representative

Dimitris Korobilis, Maximilian Schröder

We propose a multicountry quantile factor augmeneted vector autoregression (QFAVAR) to model heterogeneities both across countries and across characteristics of the distributions of macroeconomic time series. The presence of quantile factors allows for summarizing these two heterogeneities in a parsimonious way. We develop two algorithms for posterior infere

S. Lin, J. K. Baldwin, M. Blatnik, S. M. Clayton

High spatial resolution of ultracold neutron (UCN) measurement is of growing interest to UCN experiments such as UCN spectrometers, UCN polarimeters, quantum physics of UCNs, and quantum gravity. Here we utilize physics-informed deep learning to enhance the experimental position resolution and to demonstrate sub-micron spatial resolutions for UCN position me

Ji Jiang, Qipeng Yan, Ye Li, Min Lu

It is the first step for understanding how RNA structure folds from base sequences that to know how its secondary structure is formed. Traditional energy-based algorithms are short of precision, particularly for non-nested sequences, while learning-based algorithms face challenges in obtaining high-quality training data. Recently, quantum annealer has rapidl

G V Naidis, N Yu Babaeva

Recently published results of numerical simulations of positive and negative streamers propagating in uniform electric fields in air are analyzed here in the framework of an analytical approach. Obtained approximate relations between the streamer radius, velocity and length, depending on the value of applied electric field, are in reasonable agreement with t

Anoubhav Agarwaal, Prabhat Kanaujia, Sartaki Sinha Roy, Susmita Ghose

This research paper presents a novel audio fingerprinting system for Automatic Content Recognition (ACR). By using signal processing techniques and statistical transformations, our proposed method generates compact fingerprints of audio segments that are robust to noise degradations present in real-world audio. The system is designed to be highly scalable, w

Corrado Carlo Maria Capriata, Bengt Gunnar Malm, Andrew D. Kent, Gabriel D. Chaves-O'Flynn

Thermally-induced transitions between bistable magnetic states of magnetic tunnel junctions (MTJ) are of interest for generating random bitstreams and for applications in stochastic computing. An applied field transverse to the easy axis of a perpendicularly magnetized MTJ (pMTJ) can lower the energy barrier ($E_b$) to these transitions leading to faster flu

Lin Chen, Gang Fu, Amin Karbasi, Vahab Mirrokni

Protecting user privacy is a major concern for many machine learning systems that are deployed at scale and collect from a diverse set of population. One way to address this concern is by collecting and releasing data labels in an aggregated manner so that the information about a single user is potentially combined with others. In this paper, we explore the

Liya Wang, Jason Chou, Dave Rouck, Alex Tien

Learning effective sentence representations is crucial for many Natural Language Processing (NLP) tasks, including semantic search, semantic textual similarity (STS), and clustering. While multiple transformer models have been developed for sentence embedding learning, these models may not perform optimally when dealing with specialized domains like aviation

Qian Song, Conrad M Albrecht, Zhitong Xiong, Xiao Xiang Zhu

We propose a tree-level biomass estimation model approximating allometric equations by LiDAR data. Since tree crown diameters estimation is challenging from spaceborne LiDAR measurements, we develop a model to correlate tree height with biomass on the individual tree level employing a Gaussian process regressor. In order to validate the proposed model, a set

M. N. Chen, Yu Zhou

In this work, we present an analytical study on the surface plasmon polaritons in a two dimensional parity anomaly Chern insulator. The connections between the topology in the bulk implied by the BHZ model and the dispersion relations of the surface plasmons have been revealed. Anisotropy has been considered during the calculations of the dispersion relation

Farshad Rostami Ghadi, Kai-Kit Wong, F. Javier Lopez-Martinez, Kin-Fai Tong

In this letter, we study the performance of a single-user fluid antenna system (FAS) under arbitrary fading distributions, in which the fading channel coefficients over the ports are correlated. We adopt copula theory to model the structure of dependency between fading coefficients. Specifically, we first derive an exact closed-from expression for the outage

Lintong Zhang, Tejaswi Digumarti, Georgi Tinchev, Maurice Fallon

Localization for autonomous robots in prior maps is crucial for their functionality. This paper offers a solution to this problem for indoor environments called InstaLoc, which operates on an individual lidar scan to localize it within a prior map. We draw on inspiration from how humans navigate and position themselves by recognizing the layout of distinctiv

Rainer Kartmann, Tamim Asfour

Humans use semantic concepts such as spatial relations between objects to describe scenes and communicate tasks such as "Put the tea to the right of the cup" or "Move the plate between the fork and the spoon." Just as children, assistive robots must be able to learn the sub-symbolic meaning of such concepts from human demonstrations and instructions. We addr

Ajinkya Deshmukh, Saumya Banthia, Anantha Sharma

Protecting sensitive information is crucial in today's world of Large Language Models (LLMs) and data-driven services. One common method used to preserve privacy is by using data perturbation techniques to reduce overreaching utility of (sensitive) Personal Identifiable Information (PII) data while maintaining its statistical and semantic properties. Data pe

Navid Madani, Rabiraj Bandyopadhyay, Briony Swire-Thompson, Michael Miller Yoder

Social media platforms provide users with a profile description field, commonly known as a ``bio," where they can present themselves to the world. A growing literature shows that text in these bios can improve our understanding of online self-presentation and behavior, but existing work relies exclusively on keyword-based approaches to do so. We here propose

Sai Swagat Mishra, Sanjay Mandal, P. K. Sahoo

Gravitational baryogenesis is one of the mechanisms which help us to explore more about our early universe, especially baryon-anti-baryon asymmetry. As we know, modified theories of gravity are very successful in describing the present accelerated scenario of the universe. Therefore, in this letter, we aim to constrain the generalized torsion-based modified

Stanisław Cichomski, Adam Osękowski

Let $\mathcal{C}$ denote the family of all coherent distributions on the unit square $[0,1]^2$, i.e. all those probability measures $\mu$ for which there exists a random vector $(X,Y)\sim \mu$, a pair $(\mathcal{G},\mathcal{H})$ of $\sigma$-fields and an event $E$ such that $X=\mathbb{P}(E|\mathcal{G})$, $Y=\mathbb{P}(E|\mathcal{H})$ almost surely. In this p

Ahmed Samir Imam Mahmoud, Alexander Nolte, Dietmar Pfahl

Background: OSS projects face various challenges. One major challenge is to onboard and integrate newcomers to the project. Aim: We aim to understand and discuss the challenges newcomers face when joining an OSS project and present evidence on how hackathons can mitigate those challenges. Method: We conducted two searches on digital libraries to (1) explore

Massimo Bartoletti, Riccardo Marchesin, Roberto Zunino

Most blockchain platforms from Ethereum onwards render smart contracts as stateful reactive objects that update their state and transfer crypto-assets in response to transactions. A drawback of this design is that when users submit a transaction, they cannot predict in which state it will be executed. This exposes them to transaction-ordering attacks, a wide

Philip Beltracchi, Camilo Posada

We reconsider the problem of a slowly rotating homogeneous star, or Schwarzschild star, when its compactness goes beyond the Buchdahl bound and approaches the gravastar limit $R\to 2M$. We compute surface and integral properties of such configuration by integrating the Hartle-Thorne structure equations for slowly rotating relativistic masses, at second order

Xinliang Zhou, Chenyu Liu, Jiaping Xiao, Yang Liu

Sleep staging is critical for assessing sleep quality and diagnosing sleep disorders. However, capturing both the spatial and temporal relationships within electroencephalogram (EEG) signals during different sleep stages remains challenging. In this paper, we propose a novel framework called the Hybrid Attention EEG Sleep Staging (HASS) Framework. Specifical

Adam S. R. Parker, Michael R. Dawson, Patrick M. Pilarski

Artificial limbs are sophisticated devices to assist people with tasks of daily living. Despite advanced robotic prostheses demonstrating similar motion capabilities to biological limbs, users report them difficult and non-intuitive to use. Providing more effective feedback from the device to the user has therefore become a topic of increased interest. In pa

Norsang Lama, R. Joe Stanley, Anand Nambisan, Akanksha Maurya

Deep learning implemented with convolutional network architectures can exceed specialists' diagnostic accuracy. However, whole-image deep learning trained on a given dataset may not generalize to other datasets. The problem arises because extra-lesional features - ruler marks, ink marks, and other melanoma correlates - may serve as information leaks. These e

Simone Di Cataldo, Lilia Boeri

In this paper we present a first-principles study of the high-pressure superconducting phase diagram of calcium alanates (Ca-Al-H), based on ab-initio crystal structure prediction and anisotropic Migdal-Eliashberg Theory. Calcium alanates have been intensively studied at ambient pressure for their hydrogen-storage properties, but their high-pressure behavior

Yucheng Hao, Zhiping Yang, Zeyu Li, Xi Kong

Interfacial interactions are crucial in a variety of fields and can greatly affect the electric, magnetic, and chemical properties of materials. Among them, interface orbital hybridization plays a fundamental role in the properties of surface electrons such as dispersion, interaction, and ground states. Conventional measurements of electronic states at inter

Yi Huang, Asim Kadav, Farley Lai, Deep Patel

Action recognition is an important problem that requires identifying actions in video by learning complex interactions across scene actors and objects. However, modern deep-learning based networks often require significant computation, and may capture scene context using various modalities that further increases compute costs. Efficient methods such as those

Fabian Reiter

We extend classical methods of computational complexity to the realm of distributed computing, where they sometimes prove more effective than in their original context. Our focus is on decision problems in the LOCAL model, a setting in which networked computers use synchronous message passing to collectively answer questions about their network topology. We

Kunihiro Miyazaki, Taichi Murayama, Takayuki Uchiba, Jisun An

The emergence of generative AI has sparked substantial discussions, with the potential to have profound impacts on society in all aspects. As emerging technologies continue to advance, it is imperative to facilitate their proper integration into society, managing expectations and fear. This paper investigates users' perceptions of generative AI using 3M post

Lars Henry Berge Olsen, Ingrid Kristine Glad, Martin Jullum, Kjersti Aas

Shapley values originated in cooperative game theory but are extensively used today as a model-agnostic explanation framework to explain predictions made by complex machine learning models in the industry and academia. There are several algorithmic approaches for computing different versions of Shapley value explanations. Here, we focus on conditional Shaple

Jose Alvarez Colmenares

The field of Artificial Intelligence (AI) is focusing on creating automated decision-making (ADM) systems that operate as close as possible to human-like intelligence. This effort has pushed AI researchers into exploring cognitive fields like psychology. The work of Daniel Kahneman and the late Amos Tversky on biased human decision-making, including the stud

Fritz Hohl, Nianheng Wu, Martina Galetti, Remi van Trijp

Despite enormous progress in Natural Language Processing (NLP), our field is still lacking a common deep semantic representation scheme. As a result, the problem of meaning and understanding is typically sidestepped through more simple, approximative methods. This paper argues that in order to arrive at such a scheme, we also need a common modelling scheme.

Yun Liu, Zhongsheng Yan, Sixiang Chen, Tian Ye

Nighttime image dehazing is a challenging task due to the presence of multiple types of adverse degrading effects including glow, haze, blurry, noise, color distortion, and so on. However, most previous studies mainly focus on daytime image dehazing or partial degradations presented in nighttime hazy scenes, which may lead to unsatisfactory restoration resul

Shuwei Feng, Tianyang Zhan, Zhanming Jie, Trung Quoc Luong

This paper presents GenDoc, a general sequence-to-sequence document understanding model pre-trained with unified masking across three modalities: text, image, and layout. The proposed model utilizes an encoder-decoder architecture, which allows for increased adaptability to a wide range of downstream tasks with diverse output formats, in contrast to the enco

Line Jelver, Joel D. Cox

Phosphorene has emerged as an atomically-thin platform for optoelectronics and nanophotonics due to its excellent nonlinear optical properties and the possibility of actively tuning light-matter interactions through electrical doping. While phosphorene is a two-dimensional semiconductor, plasmon resonances characterized by pronounced anisotropy and strong op

Denny Puntel, Wibke Bronsch, Manuel Tuniz, Mingu Kang

CoSn is a prototypical kagome compound showing lattice-born flat bands with suppressed bandwidth over large parts of the Brillouin zone. Here, by means of time- and angle-resolved photoelectron spectroscopy, we provide direct evidence of the response to photoexcitation of the flat bands, that underlie information about localization in real space. In particul

Antonio Štrkalj, Xi-Rong Chen, Wei Chen, D. Y. Xing

The identification of topological superconductors usually involves searching for in-gap modes that are protected by topology. However, in the current experimental settings, the smoking-gun evidence of these in-gap modes is still lacking. In this work, we propose to distinguish between two-dimensional conventional s-wave and topological p-wave superconductors

J. M. Becker, G. M. Koutentakis, P. Schmelcher

We investigate spin-charge separation of a spin-1/2 Fermi system confined in a triple well where multiple bands are occupied. We assume that our finite fermionic system is close to fully spin polarized while being doped by a hole and an impurity fermion with opposite spin. Our setup involves ferromagnetic couplings among the particles in different bands, lea

Dominik Muhle, Lukas Koestler, Krishna Murthy Jatavallabhula, Daniel Cremers

We propose a differentiable nonlinear least squares framework to account for uncertainty in relative pose estimation from feature correspondences. Specifically, we introduce a symmetric version of the probabilistic normal epipolar constraint, and an approach to estimate the covariance of feature positions by differentiating through the camera pose estimation

Velio Tralli, Enrico Paolini

A framework for the analysis of synchronous grant-free massive multiple access schemes based on the irregular repetition slotted ALOHA (IRSA) protocol and operating over the Gaussian multiple access channel is presented. IRSA-based schemes are considered here as an instance of the class of unsourced slotted random access codes, operating over a frame partiti

Nicolas Wijsen, David Lario, Beatriz Sánchez-Cano, Immanuel C. Jebaraj

We present simulation results of a gradual solar energetic particle (SEP) event detected on 2021 October 9 by multiple spacecraft, including BepiColombo (Bepi) and near-Earth spacecraft such as the Advanced Composition Explorer (ACE). A peculiarity of this event is that the presence of a high speed stream (HSS) affected the low-energy ion component ($\lesssi

M. Benfatto, E. Pace, I. Davoli, Massimiliano Lucci

Biophotons are an ultra-weak emission of photons in the visible energy range from living matter. In this work, we study the emission from germinating seeds using an experimental technique designed to detect light of extremely small intensity. The emission from lentil seeds and single bean was analyzed during the whole germination process in terms of both the

Huan Mao, Yulin Chen, Zongtan Li, Feng Chen

Detection-based tracking is one of the main methods of multi-object tracking. It can obtain good tracking results when using excellent detectors but it may associate wrong targets when facing overlapping and low-confidence detections. To address this issue, this paper proposes a multi-object tracker based on shape constraint and confidence named SCTracker. I

Andrei V. Korol, Gennady B. Sushko, Andrey V. Solov'yov

We review achievements made in recent years in the field of numerical modeling of ultra-relativistic projectiles propagation in oriented crystals, radiation emission and related phenomena. This topic is highly relevant to the problem of designing novel gamma-ray light sources based on the exposure of oriented crystals to the beams of ultra-relativistic charg

Max Hausmann, Fabien Evrard, Berend van Wachem

We propose a novel model to obtain the subgrid-scale velocity in the context of large-eddy simulation (LES) of particle-laden turbulent flows, to recover accurate particle statistics. In the new wavelet enrichment model, the subgrid-scale velocity is discretized with a divergence-free wavelet vector basis, and the coefficients of the expansion are obtained b

Ruoxi Xu, Hongyu Lin, Xinyan Guan, Xianpei Han

Understanding documents is central to many real-world tasks but remains a challenging topic. Unfortunately, there is no well-established consensus on how to comprehensively evaluate document understanding abilities, which significantly hinders the fair comparison and measuring the progress of the field. To benchmark document understanding researches, this pa

Moritz Pilarski, Kirill Solovev, Nicolas Pröllochs

Deploying links to fact-checking websites (so-called "snoping") is a common intervention that can be used by social media users to refute misleading claims. However, its real-world effect may be limited as it suffers from low visibility and distrust towards professional fact-checkers. As a remedy, Twitter launched its community-based fact-checking system Com

Olivier Ezratty

In 2017, John Preskill defined Noisy Intermediate Scale Quantum (NISQ) computers as an intermediate step on the road to large scale error corrected fault-tolerant quantum computers (FTQC). The NISQ regime corresponds to noisy qubit quantum computers with the potential to solve actual problems of some commercial value faster than conventional supercomputers,

Georgia L. Jones, Qiaozhou Xiong, Xinyu Liu, Brett E. Bouma

Intravascular polarimetry with catheter-based polarization-sensitive optical coherence tomography (PS-OCT) complements the high-resolution structural tomograms of OCT with morphological contrast available through polarimetry. Its clinical translation has been complicated by the need for modification of conventional OCT hardware to enable polarimetric measure

Abbas Ali, P. P. Abdul Salih

The field theoretic ADHM instantons have stringy generalizations as linear sigma models. These were constructed by Witten in 1995. Recently Ali and Ilahi constructed a complementary version related to Witten's construction by a duality. In this note we generalize these to a complete ADHM instanton linear sigma model, as suggested by Witten, in which above du

Tong Wu, Zhihao Fan, Xiao Liu, Yeyun Gong

Diffusion models have gained significant attention in the realm of image generation due to their exceptional performance. Their success has been recently expanded to text generation via generating all tokens within a sequence concurrently. However, natural language exhibits a far more pronounced sequential dependency in comparison to images, and the majority

Esteban Marulanda, Edgar Rueda

In this paper, we present an analytical study of the relationship between the statistical distribution of a physical parameter and the uncertainties in the physical quantities used to determine it through indirect measurement. We investigate two possible methods for determining the physical quantity: linear regression and inversion of the equation in the par

Lewis Y. Geer, Joel Lapin, Douglas J. Slotta, Tytus D. Mak

The unbounded permutations of biological molecules, including proteins and their constituent peptides, presents a dilemma in identifying the components of complex biosamples. Sequence search algorithms used to identify peptide spectra can be expanded to cover larger classes of molecules, including more modifications, isoforms, and atypical cleavage, but at t

Yunlong Dong, Xiaohong Liu, Yixuan Gao, Xunchu Zhou

Recently, Users Generated Content (UGC) videos becomes ubiquitous in our daily lives. However, due to the limitations of photographic equipments and techniques, UGC videos often contain various degradations, in which one of the most visually unfavorable effects is the underexposure. Therefore, corresponding video enhancement algorithms such as Low-Light Vide

Gonçalo das Neves Carneiro, Carlos Conceição António

In probabilistic structural mechanics, the Hasofer-Lind reliability index problem is a paradigmatic equality constrained problem of searching for the minimum distance from a point to a surface. In practical engineering problems, such surface is defined implicitly, requiring the solution of a boundary-value problem. Recently, it was proposed in the literature

Shubham Agrawal, Nikhil Chavan-Dafle, Isaac Kasahara, Selim Engin

Robotic manipulation systems operating in complex environments rely on perception systems that provide information about the geometry (pose and 3D shape) of the objects in the scene along with other semantic information such as object labels. This information is then used for choosing the feasible grasps on relevant objects. In this paper, we present a novel

Yue Deng, Wenxuan Zhang, Sinno Jialin Pan, Lidong Bing

Cross-domain aspect-based sentiment analysis (ABSA) aims to perform various fine-grained sentiment analysis tasks on a target domain by transferring knowledge from a source domain. Since labeled data only exists in the source domain, a model is expected to bridge the domain gap for tackling cross-domain ABSA. Though domain adaptation methods have proven to b

Benito van der Zander, Markus Bläser, Maciej Liśkiewicz

We study formal languages which are capable of fully expressing quantitative probabilistic reasoning and do-calculus reasoning for causal effects, from a computational complexity perspective. We focus on satisfiability problems whose instance formulas allow expressing many tasks in probabilistic and causal inference. The main contribution of this work is est

Ashley Béguin, Tangui Rodzinka, Léo Calmels, Baptiste Allard

We report here on the realization of light-pulse atom interferometers with Large-momentum-transfer atom optics based on a sequence of Bragg transitions. We demonstrate momentum splitting up to 200 photon recoils in an ultra-cold atom interferometer. We highlight a new mechanism of destructive interference of the losses leading to a sizeable efficiency enhanc

Yago Fontenla-Seco, Alberto Bugarín-Diz, Manuel Lama

In this paper, we propose a series of fuzzy temporal protoforms in the framework of the automatic generation of quantitative and qualitative natural language descriptions of processes. The model includes temporal and causal information from processes and attributes, quantifies attributes in time during the process life-span and recalls causal relations and t

Peter Gibson

Evaluation of a product integral with values in the Lie group SU(1,1) yields the explicit solution to the impedance form of the Schr\"odinger equation. Explicit formulas for the transmission coefficient and $S$-matrix of the classical one-dimensional Schr\"odinger operator with arbitrary compactly supported potential are obtained as a consequence. The formul