May 2023 arXiv papers — page 66

Chaoran Chen, Tanja Stadler

Introduction: The COVID-19 pandemic highlighted the importance of making epidemiological data and scientific insights easily accessible and explorable for public health agencies, the general public, and researchers. State-of-the-art approaches for sharing data and insights included regularly updated reports and web dashboards. However, they face a trade-off

Laurie Burchell, Alexandra Birch, Nikolay Bogoychev, Kenneth Heafield

Language identification (LID) is a fundamental step in many natural language processing pipelines. However, current LID systems are far from perfect, particularly on lower-resource languages. We present a LID model which achieves a macro-average F1 score of 0.93 and a false positive rate of 0.033 across 201 languages, outperforming previous work. We achieve

Ales Kubicek, Athanasios Stratikopoulos, Juan Fumero, Nikos Foutris

In this article, we present TornadoQSim, an open-source quantum circuit simulation framework implemented in Java. The proposed framework has been designed to be modular and easily expandable for accommodating different user-defined simulation backends, such as the unitary matrix simulation technique. Furthermore, TornadoQSim features the ability to interchan

Yi Huang, Jiancheng Huang, Jianzhuang Liu, Mingfu Yan

Latest diffusion-based methods for many image restoration tasks outperform traditional models, but they encounter the long-time inference problem. To tackle it, this paper proposes a Wavelet-Based Diffusion Model (WaveDM). WaveDM learns the distribution of clean images in the wavelet domain conditioned on the wavelet spectrum of degraded images after wavelet

Alexander Henzi, Michael Law

We consider the problem of independence testing for two univariate random variables in a sequential setting. By leveraging recent developments on safe, anytime-valid inference, we propose a test with time-uniform type I error control and derive explicit bounds on the finite sample performance of the test. We demonstrate the empirical performance of the proce

Christel Gérardin, Perceval Wajsbürt, Basile Dura, Alice Calliger

Objective:Develop and validate an algorithm for analyzing the layout of PDF clinical documents to improve the performance of downstream natural language processing tasks. Materials and Methods: We designed an algorithm to process clinical PDF documents and extract only clinically relevant text. The algorithm consists of several steps: initial text extraction

Akshay Kumar, Varun Jindal

For an extended locally convex space $(X,\tau)$, in [8], the authors studied the finest locally convex topology (flc topology) $\tau_F$ on $X$ coarser than $\tau$. One can often prove facts about $(X, \tau)$ by applying classical locally convex space theory on $(X, \tau_F)$. This paper employs the flc topology to analyze barreled extended locally convex spac

Can Huang, Bingjie Liu, LingZi Jiang, Yanfei Pan

The two-dimensional 1T-CrTe$_2$ has been an attractive room-temperature van der Waals magnet which has a potential application in spintronic devices. Although it was recognized as a ferromagnetism in the past, the monolayer 1T-CrTe$_2$ was recently found to exhibit zigzag antiferromagnetism with the easy axis oriented at $70^\circ$ to the perpendicular direc

Xuecheng Xu, Yanmei Jiao, Sha Lu, Xiaqing Ding

This paper investigates the advantages of using Bird's Eye View (BEV) representation in 360-degree visual place recognition (VPR). We propose a novel network architecture that utilizes the BEV representation in feature extraction, feature aggregation, and vision-LiDAR fusion, which bridges visual cues and spatial awareness. Our method extracts image features

Nayan Adhikary, Anima Nagar

We consider the topological dynamics of closed relations(CR) by studying one of the oldest dynamical property - `transitivity'. We investigate the two kinds of (closed relation) CR-dynamical systems - $(X,G)$ where the relation $G \subseteq X \times X$ is closed and $(X,G, \bullet)$ giving the `suitable dynamics' for a suitable closed relation $G$, where $X$

Harman Singh, Pengchuan Zhang, Qifan Wang, Mengjiao Wang

Contrastively trained vision-language models have achieved remarkable progress in vision and language representation learning, leading to state-of-the-art models for various downstream multimodal tasks. However, recent research has highlighted severe limitations of these models in their ability to perform compositional reasoning over objects, attributes, and

Ignacio Breva Ribes, Raúl Oset Sinha

For function germs $g:(\mathbb C^n,0)\to (\mathbb C,0)$ it is well known that $1\leq\frac{\mu(g)}{\tau(g)}$ and it has recently been proved by Liu that $\frac{\mu(g)}{\tau(g)}\leq n$. We give an upper bound for the codimension of map-germs $f:(\mathbb C^n,0)\to (\mathbb C^p,0)$ given as augmentations of other map-germs with which we prove the analog to the f

Shashank M. S. Panga, Siddhartha S. Borkotoky

We present a transmission scheme aimed at integrating wake-up radios (WuR) into LoRa sensor networks featuring UAV-mounted gateways. The sensors are informed of the UAV's arrival with the help of WuR, followed by sensor-to-UAV data transfer with frequency and spreading-factor hopping. The proposed scheme provides significant energy savings at the sensors whi

Oliver Lorscheid, Koen Thas

This text is dedicated to Jacques Tits's ideas on geometry over F1, the field with one element. In a first part, we explain how thin Tits geometries surface as rational point sets over the Krasner hyperfield, which links these ideas to combinatorial flag varieties in the sense of Borovik, Gelfand and White and F1-geometry in the sense of Connes and Consani.

Dongryeol Lee, Segwang Kim, Minwoo Lee, Hwanhee Lee

Ambiguous questions persist in open-domain question answering, because formulating a precise question with a unique answer is often challenging. Previously, Min et al. (2020) have tackled this issue by generating disambiguated questions for all possible interpretations of the ambiguous question. This can be effective, but not ideal for providing an answer to

Eyal Ackerman, Balázs Keszegh

Let $\cC$ be a set of curves in the plane such that no three curves in $\cC$ intersect at a single point and every pair of curves in $\cC$ intersect at exactly one point which is either a crossing or a touching point. J\'anos Pach conjectured that the number of pairs of curves in $\cC$ that touch each other is $O(|\cC|)$. We prove this conjecture for $x$-mon

Shuji Shinohara, Daiki Morita, Nobuhito Manome, Ryota Hayashi

The Levy walk in which the frequency of occurrence of step lengths follows a power-law distribution, can be observed in the migratory behavior of organisms at various levels. Levy walks with power exponents close to 2 are observed, and the reasons are unclear. This study aims to propose a model that universally generates inverse square Levy walks (called Cau

Rakesh Pandey, Saurabh Sharma, Lokesh Dewangan, Aayushi Verma

Using the multiwavelength data sets, we studied the star formation activity in H II region Sh 2-61 (hereafter S61). We identified a clustering in the region and estimated the membership using the Gaia proper motion data. The physical environment of S61 is inspected using infrared to radio wavelength images. We also determined the Lyman continuum flux associa

Zilong Wang, Jingbo Shang

The rapid growth of web pages and the increasing complexity of their structure poses a challenge for web mining models. Web mining models are required to understand the semi-structured web pages, particularly when little is known about the subject or template of a new page. Current methods migrate language models to the web mining by embedding the XML source

Sibo Gai, Donglin Wang, Li He

The capability of continuously learning new skills via a sequence of pre-collected offline datasets is desired for an agent. However, consecutively learning a sequence of offline tasks likely leads to the catastrophic forgetting issue under resource-limited scenarios. In this paper, we formulate a new setting, continual offline reinforcement learning (CORL),

Xiaolong Liu, Lujun Li, Chao Li, Anbang Yao

Existing feature distillation methods commonly adopt the One-to-one Representation Matching between any pre-selected teacher-student layer pair. In this paper, we present N-to-One Representation (NORM), a new two-stage knowledge distillation method, which relies on a simple Feature Transform (FT) module consisting of two linear layers. In view of preserving

Zerun Wang, Ling Xiao, Liuyu Xiang, Zhaotian Weng

Open-set semi-supervised object detection (OSSOD) task leverages practical open-set unlabeled datasets that comprise both in-distribution (ID) and out-of-distribution (OOD) instances for conducting semi-supervised object detection (SSOD). The main challenge in OSSOD is distinguishing and filtering the OOD instances (i.e., outliers) during pseudo-labeling sin

Naoto Ohsaka, Riku Togashi

Diversification of recommendation results is a promising approach for coping with the uncertainty associated with users' information needs. Of particular importance in diversified recommendation is to define and optimize an appropriate diversity objective. In this study, we revisit the most popular diversity objective called intra-list distance (ILD), define

Haiwei Wu, Jiantao Zhou, Shile Zhang

The heightened realism of AI-generated images can be attributed to the rapid development of synthetic models, including generative adversarial networks (GANs) and diffusion models (DMs). The malevolent use of synthetic images, such as the dissemination of fake news or the creation of fake profiles, however, raises significant concerns regarding the authentic

Hongtao Wang, Jiangshe Zhang, Xiaoli Wei, Li Long

In seismic exploration, first break (FB) picking is a crucial aspect in the determination of subsurface velocity models, significantly influencing the placement of wells. Many deep neural networks (DNNs)-based automatic picking methods have been proposed to accelerate this processing. Significantly, the segmentation-based DNN methods provide a segmentation m

Hannes Malcha

In this thesis, we study the Nicolai maps of the 2-dimensional Wess-Zumino model, $\mathcal{N}=1$ super Yang-Mills and $\mathcal{N}=4$ super Yang-Mills. We compute the Nicolai map of the 2-dimensional Wess-Zumino model up to the fifth order in the coupling. In $\mathcal{N}=1$ super Yang-Mills, we introduce the notion of on- and off-shell Nicolai maps. The on

Hugues Van Assel, Titouan Vayer, Rémi Flamary, Nicolas Courty

Many approaches in machine learning rely on a weighted graph to encode the similarities between samples in a dataset. Entropic affinities (EAs), which are notably used in the popular Dimensionality Reduction (DR) algorithm t-SNE, are particular instances of such graphs. To ensure robustness to heterogeneous sampling densities, EAs assign a kernel bandwidth p

Zhibin Qiu, Mengfan Fu, Fuchun Sun, Gulila Altenbek

We propose SE-Bridge, a novel method for speech enhancement (SE). After recently applying the diffusion models to speech enhancement, we can achieve speech enhancement by solving a stochastic differential equation (SDE). Each SDE corresponds to a probabilistic flow ordinary differential equation (PF-ODE), and the trajectory of the PF-ODE solution consists of

Sumeet Batra, Bryon Tjanaka, Matthew C. Fontaine, Aleksei Petrenko

Training generally capable agents that thoroughly explore their environment and learn new and diverse skills is a long-term goal of robot learning. Quality Diversity Reinforcement Learning (QD-RL) is an emerging research area that blends the best aspects of both fields -- Quality Diversity (QD) provides a principled form of exploration and produces collectio

Andreas Schwarz, Di He, Maarten Van Segbroeck, Mohammed Hethnawi

Streaming Automatic Speech Recognition (ASR) in voice assistants can utilize prefetching to partially hide the latency of response generation. Prefetching involves passing a preliminary ASR hypothesis to downstream systems in order to prefetch and cache a response. If the final ASR hypothesis after endpoint detection matches the preliminary one, the cached r

Haigang Li, Longjuan Xu, Peihao Zhang

It is an interesting and important topic to study the motion of small particles in a viscous liquid in current applied research. In this paper we assume the particles are convex with arbitrary shapes and mainly investigate the interaction between the rigid particles and the domain boundary when the distance tends to zero. In fact, even though the domain and

Pooria Namyar, Arvin Ghavidel, Daniel Crankshaw, Daniel S. Berger

Cloud providers install mitigations to reduce the impact of network failures within their datacenters. Existing network mitigation systems rely on simple local criteria or global proxy metrics to determine the best action. In this paper, we show that we can support a broader range of actions and select more effective mitigations by directly optimizing end-to

Fabien Le Floc'h

This paper generalizes the local variance gamma model of Carr and Nadtochiy, to a piecewise quadratic local variance function. The formulation encompasses the piecewise linear Bachelier and piecewise linear Black local variance gamma models. The quadratic local variance function results in an arbitrage-free interpolation of class C3. The increased smoothness

Gilles Dowek

The goal of this note is to compare two notions, one coming from the theory of rewrite systems and the other from proof theory: confluence and cut elimination. We show that to each rewrite system on terms, we can associate a logical system: asymmetric deduction modulo this rewrite system and that the confluence property of the rewrite system is equivalent to

Haigang Li, Yan Zhao

The Minneart resonance is a low frequency resonance in which the wavelength is much larger than the size of the resonators. It is interesting to study the interaction between two adjacent bubbles when they are brought close together. Because the bubbles are usually compressible, in this paper we mainly investigate resonant modes of two general convex resonat

Noah Lee, Na Min An, James Thorne

Large language models (LLMs) have shown impressive achievements in solving a broad range of tasks. Augmented by instruction fine-tuning, LLMs have also been shown to generalize in zero-shot settings as well. However, whether LLMs closely align with the human disagreement distribution has not been well-studied, especially within the scope of natural language

Timothée Audinet, Julien Toulouse

We consider a one-dimensional effective quantum electrodynamics (QED) model of the relativistic hydrogen-like atom using delta-potential interactions. We discuss the general exact theory and the Hartree-Fock approximation. The present one-dimensional effective QED model shares the essential physical feature of the three-dimensional theory: the nuclear charge

Viorica Pătrăucean, Lucas Smaira, Ankush Gupta, Adrià Recasens Continente

We propose a novel multimodal video benchmark - the Perception Test - to evaluate the perception and reasoning skills of pre-trained multimodal models (e.g. Flamingo, SeViLA, or GPT-4). Compared to existing benchmarks that focus on computational tasks (e.g. classification, detection or tracking), the Perception Test focuses on skills (Memory, Abstraction, Ph

Danqing Luo, Chen Zhang, Jiahui Xu, Bin Wang

Training or finetuning large-scale language models (LLMs) such as GPT-3 requires substantial computation resources, motivating recent efforts to explore parameter-efficient adaptation to downstream tasks. One practical area of research is to treat these models as black boxes and interact with them through their inference APIs. In this paper, we investigate h

Hiroki Kojima, Takashi Ikegami

The relationship between reaction-diffusion (RD) systems, characterized by continuous spatiotemporal states, and cellular automata (CA), marked by discrete spatiotemporal states, remains poorly understood. This paper delves into this relationship through an examination of a recently developed CA known as Lenia. We demonstrate that asymptotic Lenia, a variant

Shuqiao Huang, Xiru Wu, Guoming Huang

Due to the vastly different energy consumption between up-slope and down-slope, a path with the shortest length on a complex off-road terrain environment (2.5D map) is not always the path with the least energy consumption. For any energy-sensitive vehicle, realizing a good trade-off between distance and energy consumption in 2.5D path planning is significant

Sherzod Hakimov, David Schlangen

Large language models have demonstrated robust performance on various language tasks using zero-shot or few-shot learning paradigms. While being actively researched, multimodal models that can additionally handle images as input have yet to catch up in size and generality with language-only models. In this work, we ask whether language-only models can be uti

W. Esmail, A. Hammad, S. Moretti

The $A\to Z^{(*)}h$ decay signature has been highlighted as possibly being the first testable probe of the Standard Model (SM) Higgs boson discovered in 2012 ($h$) interacting with Higgs companion states, such as those existing in a 2-Higgs Doublet Model (2HDM), chiefly, a CP-odd one ($A$). The production mechanism of the latter at the Large Hadron Collider

Chris Whittle, Ge Yang, Matthew Evans, Lisa Barsotti

Machine learning has become an effective tool for processing the extensive data sets produced by large physics experiments. Gravitational-wave detectors are now listening to the universe with quantum-enhanced sensitivity, accomplished with the injection of squeezed vacuum states. Squeezed state preparation and injection is operationally complicated, as well

Sooyeob Jung, Seongah Jeong, Jinkyu Kang, Joon Gyu Ryu

This paper presents a novel transceiver design aimed at enabling Direct-to-Satellite Internet of Things (DtS-IoT) systems based on long range-frequency hopping spread spectrum (LR-FHSS). Our focus lies in developing an accurate transmission method through the analysis of the frame structure and key parameters outlined in Long Range Wide-Area Network (LoRaWAN

Jianing Li, Bowen Chen, Zhiyong Wang, Honghai Liu

Given an untrimmed video, repetitive actions counting aims to estimate the number of repetitions of class-agnostic actions. To handle the various length of videos and repetitive actions, also optimization challenges in end-to-end video model training, down-sampling is commonly utilized in recent state-of-the-art methods, leading to ignorance of several repet

Jinheng Xie, Kai Ye, Yudong Li, Yuexiang Li

Various stuff and things in visual data possess specific traits, which can be learned by deep neural networks and are implicitly represented as the visual prior, e.g., object location and shape, in the model. Such prior potentially impacts many vision tasks. For example, in conditional image synthesis, spatial conditions failing to adhere to the prior can re

Rishabh Gupta, Shaily Desai, Manvi Goel, Anil Bandhakavi

Counterspeech has been demonstrated to be an efficacious approach for combating hate speech. While various conventional and controlled approaches have been studied in recent years to generate counterspeech, a counterspeech with a certain intent may not be sufficient in every scenario. Due to the complex and multifaceted nature of hate speech, utilizing multi

Michal Štefánik, Marek Kadlčík

Many recent language models (LMs) of Transformers family exhibit so-called in-context learning (ICL) ability, manifested in the LMs' ability to modulate their function by a task described in a natural language input. Previous work curating these models assumes that ICL emerges from vast over-parametrization or the scale of multi-task training. However, a com

Jiangyan Yi, Jianhua Tao, Ruibo Fu, Xinrui Yan

Audio deepfake detection is an emerging topic in the artificial intelligence community. The second Audio Deepfake Detection Challenge (ADD 2023) aims to spur researchers around the world to build new innovative technologies that can further accelerate and foster research on detecting and analyzing deepfake speech utterances. Different from previous challenge

Dong Wei, Xiaoning Sun, Huaijiang Sun, Bin Li

The emergence of text-driven motion synthesis technique provides animators with great potential to create efficiently. However, in most cases, textual expressions only contain general and qualitative motion descriptions, while lack fine depiction and sufficient intensity, leading to the synthesized motions that either (a) semantically compliant but uncontrol

Bernhard Maschke, Arjan van der Schaft

In this paper we extend the previously introduced class of boundary port-Hamiltonian systems to boundary control systems where the variational derivative of the Hamiltonian functional is replaced by a pair of reciprocal differential operators. In physical systems modelling, these differential operators naturally represent the constitutive relations associate

Anatoliy Tugay, Mariusz Tarnopolski

Simulated galaxy distributions are suitable for developing filament detection algorithms. However, samples of observed galaxies, being of limited size, cause difficulties that lead to a discontinuous distribution of filaments. We created a new galaxy filament catalog composed of a continuous cosmic web with no lone filaments. The core of our approach is a ri

Yuekun Dai, Chongyi Li, Shangchen Zhou, Ruicheng Feng

Developing and integrating advanced image sensors with novel algorithms in camera systems are prevalent with the increasing demand for computational photography and imaging on mobile platforms. However, the lack of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mo

Shayan Alipour, Niccolò Di Marco, Michele Avalle, Gabriele Etta

The web radically changed the dissemination of information and the global spread of news. In this study, we aim to reconstruct the connectivity patterns within nations shaping news propagation globally in 2022. We do this by analyzing a dataset of unprecedented size, containing 140 million news articles from 183 countries and related to 37,802 domains in the

Jérôme Bolte, Edouard Pauwels, Samuel Vaiter

In appropriate frameworks, automatic differentiation is transparent to the user at the cost of being a significant computational burden when the number of operations is large. For iterative algorithms, implicit differentiation alleviates this issue but requires custom implementation of Jacobian evaluation. In this paper, we study one-step differentiation, al

Yuki Wakata, Xiaoliang Chen, Ning Zhu, Sijia Lyu

In this Letter, we systematically investigate the Leidenfrost temperature for hot solid substrates with various thermal diffusivities and surface roughnesses. Based on the experimental results, we build a phenomenological model that considers the thermal diffusivity of a solid substrate and derive a relationship between the surface roughness and the resultin

Élisabeth Remy, Paul Ruet

Nested canalization (NC) is a property of Boolean functions which has been recently extended to multivalued functions. We study the effect of the Van Ham mapping (from multivalued to Boolean functions) on this property. We introduce the class of softly nested canalizing (SNC) multivalued functions, and prove that the Van Ham mapping sends SNC multivalued fun

Luke K. Topham, Wasiq Khan, Dhiya Al-Jumeily, Abir Hussain

Person identification is a problem that has received substantial attention, particularly in security domains. Gait recognition is one of the most convenient approaches enabling person identification at a distance without the need of high-quality images. There are several review studies addressing person identification such as the utilization of facial images

Julian Lienen, Eyke Hüllermeier

Label noise poses an important challenge in machine learning, especially in deep learning, in which large models with high expressive power dominate the field. Models of that kind are prone to memorizing incorrect labels, thereby harming generalization performance. Many methods have been proposed to address this problem, including robust loss functions and m

Mainak Poddar, Anoop Singh

We investigate relative holomorphic connections on a principal bundle over a family of compact complex manifolds. A sufficient condition is given for the existence of a relative holomorphic connection on a holomorphic principal bundle over a complex analytic family. We also introduce the notion of relative equivariant bundles and establish its relation with

Anirudh Palutla, Shivansh Seth, S. S. Ashwin, Marimuthu Krishnan

Criticality, observed during second-order phase transitions, is an emergent phenomenon. The brain operates near criticality, where complex systems exhibit high correlations. The critical brain hypothesis suggests that the brain becomes an efficient learning system in this state but poor in memory, while sub-criticality enhances memory but inhibits learning.

Salih Kibaroğlu

This study investigates the possibility of a homogeneous and isotropic cosmological solution within the context of the Maxwell-Weyl gauge theory of gravity. To achieve this, we utilize the Einstein-Yang-Mills theory as an analogy and represent the Maxwell gauge field in terms of two time-dependent scalar fields. We derive the modified Friedmann equations, in

K. Piasecki, P. Piotrowski

The parametrizations of experimental yields of K$^{\pm,0}$, $\phi$ and $\Lambda+\Sigma^0$ are proposed as function of available energy, $\sqrt{s_\mathrm{NN}}$, and number of participants, $\langle A_\mathrm{part} \rangle_\mathrm{b}$, for $\sqrt{s_\mathrm{NN}}$ from 2.15 to 3 GeV. For all the dataset the $\langle A_\mathrm{part} \rangle_\mathrm{b}$ was extrac

Tobias Buck, Aura Obreja, Bridget Ratcliffe, Yuxi

Recent observations of the Milky Way (MW) found an unexpected steepening of the star-forming gas metallicity gradient around the time of the Gaia-Sausage-Enceladus (GSE) merger event. Here we investigate the influence of early ($t_{\mathrm{merger}}\lesssim5$ Gyr) massive ($M_{\mathrm{gas}}^{\mathrm{merger}}/M_{\mathrm{gas}}^{\mathrm{main}}(t_{\mathrm{merger}

Hyemi Kim, Jiyun Park, Taegyun Kwon, Dasaem Jeong

While piano music transcription models have shown high performance for solo piano recordings, their performance degrades when applied to ensemble recordings. This study aims to analyze the impact of different data augmentation methods on piano transcription performance, specifically focusing on mixing techniques applied to violin-piano ensembles. We apply mi

Simon Tarboush, Anum Ali, Tareq Y. Al-Naffouri

The large bandwidth combined with ultra-massive multiple-input multiple-output (UM-MIMO) arrays enables terahertz (THz) systems to achieve terabits-per-second throughput. The THz systems are expected to operate in the near, intermediate, as well as the far-field. As such, channel estimation strategies suitable for the near, intermediate, or far-field have be

Bach Kim, Jose Dolz, Pierre-Marc Jodoin, Christian Desrosiers

Privacy protection in medical data is a legitimate obstacle for centralized machine learning applications. Here, we propose a client-server image segmentation system which allows for the analysis of multi-centric medical images while preserving patient privacy. In this approach, the client protects the to-be-segmented patient image by mixing it to a referenc

Feng Jiang, Longwang He, Peifeng Li, Qiaoming Zhu

Discourse parsing, the task of analyzing the internal rhetorical structure of texts, is a challenging problem in natural language processing. Despite the recent advances in neural models, the lack of large-scale, high-quality corpora for training remains a major obstacle. Recent studies have attempted to overcome this limitation by using distant supervision,

Taichi Kato

Using ASAS-SN Sky Patrol Photometic Database and Asteroid Terrestrial-impact Last Alert System (ATLAS) data, I found that the high-field polar AR UMa entered a long-lasting high state in 2022 October. This object is renowned for its small duty cycle, and short-lived high states have only been occasionally seen since the discovery. It appears that the present

Tianya Li, Yongpeng Wu, Wenjun Zhang, Xiang-Gen Xia

This paper investigates the multiple-input-multiple-output (MIMO) massive unsourced random access in an asynchronous orthogonal frequency division multiplexing (OFDM) system, with both timing and frequency offsets (TFO) and non-negligible user collisions. The proposed coding framework splits the data into two parts encoded by sparse regression code (SPARC) a

Haochen Wang, Yujun Shen, Jingjing Fei, Wei Li

Domain adaptive semantic segmentation aims to transfer knowledge from a labeled source domain to an unlabeled target domain. However, existing methods primarily focus on directly learning qualified target features, making it challenging to guarantee their discrimination in the absence of target labels. This work provides a new perspective. We observe that th

Linghao Jin, Jacqueline He, Jonathan May, Xuezhe Ma

Context-aware neural machine translation involves leveraging information beyond sentence-level context to resolve inter-sentential discourse dependencies and improve document-level translation quality, and has given rise to a number of recent techniques. However, despite well-reasoned intuitions, most context-aware translation models show only modest improve

Y. -T. Cheng, C. -H. Chien, K. -M. Hsieh, Y. -H. Huang

A coherent electromagnetic field can be described by its amplitude, frequency, and phase. All these properties can influence the interaction between the field and an atom. Here we demonstrate the phase shaping of microwaves that are scattered by a superconducting artificial atom coupled to the end of a semi-infinite 1D transmission line. In particular, we in

Zihan Wang, Jingbo Shang, Ruiqi Zhong

Unsupervised clustering is widely used to explore large corpora, but existing formulations neither consider the users' goals nor explain clusters' meanings. We propose a new task formulation, "Goal-Driven Clustering with Explanations" (GoalEx), which represents both the goal and the explanations as free-form language descriptions. For example, to categorize

Pavel Koten, Lukáš Shrbený, Pavel Spurný, Jiří Borovička

A tau-Herculid meteor outburst or even a storm was predicted by several models to occur around 5~UT on 31~May, 2022 as a consequence of the break-up of comet 73P/Schwassmann-Wachmann 3 in 1995. The multi-instrument and multi-station experiment was carried-out within the Czech Republic to cover possible earlier activity of the shower between 21 and 1 UT on 30

Ruiyang Xu, Jalaj Bhandari, Dmytro Korenkevych, Fan Liu

Auction-based recommender systems are prevalent in online advertising platforms, but they are typically optimized to allocate recommendation slots based on immediate expected return metrics, neglecting the downstream effects of recommendations on user behavior. In this study, we employ reinforcement learning to optimize for long-term return metrics in an auc

Zhenyu Ma, Xin Zhang, Pu Liu, Yong Deng

The investigation of chemical reactions during the ion irradiation is a frontier for the study of the ion-material interaction. In order to derive the contribution of bond formation to chemistry of ion produced nanoclusters, the valence electron energy loss spectroscopy (VEELS) was exploited to investigate the Ga$^+$ ion damage in Al$_2$O$_3$, InP and InGaAs

Cunshi Wang, Yu Bai, Xinrui Song, Jiacheng Xu

The classification of stellar light curves has become a key task in modern time-domain astronomy, fueled by the rapid growth of data from large-scale surveys such as Kepler and TESS. Although deep learning models have achieved high accuracy in this area, their computational costs can limit scalability. To tackle this issue, we propose LightCurve MoE, a Mixtu

Shimon Arie Haver, Eran Ginossar, Sebastian E. de Graaf, Eytan Grosfeld

Exploring the interplay between topological phases and photons opens new avenues for investigating novel quantum states. Here we show that superconducting resonators can serve as sensitive probes for properties of topological insulator nanowires (TINWs) embedded within them. By combining a static, controllable magnetic flux threading the TINW with an additio

Qiushi Sun, Nuo Chen, Jianing Wang, Xiang Li

Code pre-trained models (CodePTMs) have recently demonstrated a solid capacity to process various software intelligence tasks, e.g., code clone detection, code translation, and code summarization. The current mainstream method that deploys these models to downstream tasks is to fine-tune them on individual tasks, which is generally costly and needs sufficien

Partha Sarkar, Kshitij Khare, Malay Ghosh

The Inverse-Wishart (IW) distribution is a standard and popular choice of priors for covariance matrices and has attractive properties such as conditional conjugacy. However, the IW family of priors has crucial drawbacks, including the lack of effective choices for non-informative priors. Several classes of priors for covariance matrices that alleviate these

P. Gavignet, F. Mondain, E. Pincemin, A. J. Grant

We report the co-propagation, over 50 km of SSMF, of the quantum channel (1310 nm) of a QKD system with ~17 dBm total power of DWDM data channels (1550 nm range). A metric to evaluate Co-propagation Efficiency is proposed.

François Renaville, Fabienne Prosmans

No library can buy or hold everything its patrons need. At a certain point, librarians need to pool their resources and collaborate to provide access to what they don't have: Collaboration and partnership, centralized and shared collection storage, digitization projects, interlibrary loan and resource sharing, purchase on demand, PDA and EBA are notably key

Kibeom Kim, Hyundo Lee, Min Whoo Lee, Moonheon Lee

Tasks that involve interaction with various targets are called multi-target tasks. When applying general reinforcement learning approaches for such tasks, certain targets that are difficult to access or interact with may be neglected throughout the course of training - a predicament we call Under-explored Target Problem (UTP). To address this problem, we pro

Yiming Ai, Zhiwei He, Kai Yu, Rui Wang

Tense inconsistency frequently occurs in machine translation. However, there are few criteria to assess the model's mastery of tense prediction from a linguistic perspective. In this paper, we present a parallel tense test set, containing French-English 552 utterances. We also introduce a corresponding benchmark, tense prediction accuracy. With the tense tes

Guodong Li, Chao-Wei Tsai, Daniel Stern, Jingwen Wu

We report the discovery of the hyperluminous, highly obscured AGN WISE J190445.04+485308.9 (W1904+4853 hereafter, $L_{bol} = 1.1 \times 10^{13} \ L_{\odot}$) at z=0.415. Its well-sampled spectral energy distribution (SED) is dominated by infrared dust emission, though broad emission lines are detected in the optical spectra. These features suggest that W1904

Yuwei Fang, Mahmoud Khademi, Chenguang Zhu, Ziyi Yang

Artificial General Intelligence (AGI) requires comprehensive understanding and generation capabilities for a variety of tasks spanning different modalities and functionalities. Integrative AI is one important direction to approach AGI, through combining multiple models to tackle complex multimodal tasks. However, there is a lack of a flexible and composable

Yongqian Han

The incompressible Navier-Stokes equations are considered. We find that these equations have symplectic symmetry structures. Two linearly independent symplectic symmetries form moving frame. The velocity vector possesses symplectic representation in a moving frame. The symplectic representation of two-dimensional Navier-Stokes equations holds radial symmetry

Ivan Duchemin, Antoine Levitt

We present a method to compute the photoionization spectra of atoms and molecules in linear response time-dependent density functional theory. The electronic orbital variations corresponding to ionized electrons are expanded on a basis set of delocalized functions obtained as the solution of the inhomogeneous Helmholtz equation with gaussian basis set functi

Sungdong Kim, Sanghwan Bae, Jamin Shin, Soyoung Kang

Aligning large language models (LLMs) to human values has become increasingly important as it enables sophisticated steering of LLMs. However, it requires significant human demonstrations and feedback or distillation from proprietary LLMs such as ChatGPT. In this work, we propose a novel alignment learning framework with synthetic feedback not dependent on e

Baihong Li, Changhua Chen, Boxin Yuan, Xiangying Hao

We theoretically propose a novel quantum interferometer in which the NOON state interferometer (NOONI) is combined with the Hong-Ou-Mandel interferometer (HOMI). This interferometer combined the advantages of both the NOONI that depends on biphoton frequency sum, and the HOMI that depends on biphoton frequency difference into a single interferometer. It can

Zhi Cao

This work conducts a comprehensive exploration into the proficiency of OpenAI's ChatGPT-4 in sourcing scientific references within an array of research disciplines. Our in-depth analysis encompasses a wide scope of fields including Computer Science (CS), Mechanical Engineering (ME), Electrical Engineering (EE), Biomedical Engineering (BME), and Medicine, as

Rui Wang, Hongru Wang, Fei Mi, Yi Chen

Numerous works are proposed to align large language models (LLMs) with human intents to better fulfill instructions, ensuring they are trustful and helpful. Nevertheless, some human instructions are often malicious or misleading and following them will lead to untruthful and unsafe responses. Previous work rarely focused on understanding how LLMs manage inst

Ao Liu, Shaoshi Yang, Jingsheng Tan, Zongze Liang

Containers are used by an increasing number of Internet service providers to deploy their applications in multi-access edge computing (MEC) systems. Although container-based virtualization technologies significantly increase application availability, they may suffer expensive communication overhead and resource use imbalances. However, so far there has been

Jiacheng Li, Ming Wang, Jin Li, Jinmiao Fu

Sequential recommendation aims to model dynamic user behavior from historical interactions. Existing methods rely on either explicit item IDs or general textual features for sequence modeling to understand user preferences. While promising, these approaches still struggle to model cold-start items or transfer knowledge to new datasets. In this paper, we prop

Hieu T. Ngo

We study a matrix analog of the Erd\H{o}s-Falconer distance problems in vector spaces over finite fields. There arises an interesting analysis of certain quadratic matrix Gauss sums.

Sukmin Cho, Soyeong Jeong, Jeongyeon Seo, Jong C. Park

Re-rankers, which order retrieved documents with respect to the relevance score on the given query, have gained attention for the information retrieval (IR) task. Rather than fine-tuning the pre-trained language model (PLM), the large-scale language model (LLM) is utilized as a zero-shot re-ranker with excellent results. While LLM is highly dependent on the

Victor P. Ruban

Coupled nonlinear Schrodinger equations for paraxial optics with two circular polarizations of light in a defocusing Kerr medium with anomalous dispersion coincide in form with the Gross-Pitaevskii equations for a binary Bose-Einstein condensate (BEC) of cold atoms in the phase separation regime. In this case, the helical symmetry of an optical waveguide cor

Aitor Ormazabal, Mikel Artetxe, Eneko Agirre

Methods for adapting language models (LMs) to new tasks and domains have traditionally assumed white-box access to the model, and work by modifying its parameters. However, this is incompatible with a recent trend in the field, where the highest quality models are only available as black-boxes through inference APIs. Even when the model weights are available