October 2023 arXiv papers — page 120

Patrick Kramer, Alexander Kreiß

We study regression discontinuity designs with the use of additional covariates for estimation of the average treatment effect. We provide a detailed proof of asymptotic normality of the covariate-adjusted estimator under minimal assumptions, which may serve as an accessible text to the mathematics behind regression discontinuity with covariates. In addition

Haoran Luo, Haihong E, Zichen Tang, Shiyao Peng

Knowledge Base Question Answering (KBQA) aims to answer natural language questions over large-scale knowledge bases (KBs), which can be summarized into two crucial steps: knowledge retrieval and semantic parsing. However, three core challenges remain: inefficient knowledge retrieval, mistakes of retrieval adversely impacting semantic parsing, and the complex

Petr Mánek, Petr Burian, Eric David-Bosne, Petr Smolyanskiy

Fast, incremental evolution of physics instrumentation raises the question of efficient software abstraction and transferability of algorithms across similar technologies. This contribution aims to provide an answer by introducing Track Lab, a modern data acquisition program focusing on extensibility and high performance. Shipping with documented API and mor

Hashem Fatemi, Hamidreza Khalilian, Jalal Sarabadani, M. Reza Shaebani

Understanding interactions between chiral active particles -- self-propelling and self-rotating entities -- is crucial for uncovering how chiral active matter self-organizes into dynamic structures. Although fluctuation-induced forces in nonequilibrium active systems can drive structure formation, the role of chirality remains largely unexplored. We investig

Alexandru Dimca

In a recent paper, after introducing the notion of plus-one generated hyperplane arrangements, Takuro Abe has shown that if we add (resp. delete) a line to (resp. from) a free line arrangement, then the resulting line arrangement is either free or plus-one generated. In this note we prove that the same properties hold when we replace the line arrangement by

Chen Du, Yong-Feng Huang, Zhi-Bin Zhang, Alexander Rodin

Fast Radio Bursts (FRBs) are bright radio transients with millisecond durations which typically occur at extragalactic distances. The association of FRB 20200428 with the Galactic magnetar SGR J1935+2154 strongly indicates that they could originate from neutron stars, which naturally leads to the expectation that periodicity connected with the spinning of ma

In-Hwan Lee, Hoang-Anh Le, S. -R. Eric Yang

Graphene zigzag nanoribbons, initially in a topologically ordered state, undergo a topological phase transition into crossover phases distinguished by quasi-topological order. We computed mutual information for both the topologically ordered phase and its crossover phases, revealing the following results: (i) In the topologically ordered phase, A-chirality c

Sergio Blanes, Fernando Casas, Cesáreo González, Mechthild Thalhammer

The present work is concerned with the extension of modified potential operator splitting methods to specific classes of nonlinear evolution equations. The considered partial differential equations of Schr{\"o}dinger and parabolic type comprise the Laplacian, a potential acting as multiplication operator, and a cubic nonlinearity. Moreover, an invariance pri

Ye Sun, Fabio Caccioli, Xiancheng Li, Giacomo Livan

The Great Gatsby Curve measures the relationship between income inequality and intergenerational income persistence. By utilizing genealogical data of over 245,000 mentor-mentee pairs and their academic publications from 22 different disciplines, this study demonstrates that an academic Great Gatsby Curve exists as well, in the form of a positive correlation

Maxime Bouthors, Josep Crego, François Yvon

Retrieval-Augmented Machine Translation (RAMT) is attracting growing attention. This is because RAMT not only improves translation metrics, but is also assumed to implement some form of domain adaptation. In this contribution, we study another salient trait of RAMT, its ability to make translation decisions more transparent by allowing users to go back to ex

Satya N. Majumdar, Francesco Mori, Pierpaolo Vivo

We consider the Nonlinear-Cost Random Walk model in discrete time introduced in [Phys. Rev. Lett. 130, 237102 (2023)], where a fee is charged for each jump of the walker. The nonlinear cost function is such that slow/short jumps incur a flat fee, while for fast/long jumps the cost is proportional to the distance covered. In this paper we compute analytically

Arafat Abbar, Clément Coine, Colin Petitjean

Fix a metric space $M$ and let $\mathrm{Lip}_0(M)$ be the Banach space of complex-valued Lipschitz functions defined on $M$. A weighted composition operator on $\mathrm{Lip}_0(M)$ is an operator of the kind $wC_f : g \mapsto w \cdot g \circ f$, where $w : M \to \mathbb C$ and $f: M \to M$ are any map. When such an operator is bounded, it is actually the adjo

M. Urai, K. Miyagawa, S. A. Torunova, N. Drichko

We investigated the local magnetism of the organic conductor, $\kappa$-(ET)$_{2}$Hg(SCN)$_{2}$Cl, with a quasi-triangular lattice of weakly dimerized molecules through $^{13}$C NMR spectroscopy. The NMR spectra and nuclear relaxation show that charge disproportionation occurs, associated with the metal-insulator transition at 31 K. The relaxation rate indica

Étienne Burle, Philippe Gaborit, Younes Hatri, Ayoub Otmani

In rank-metric cryptography, a vector from a finite dimensional linear space over a finite field is viewed as the linear space spanned by its entries. The rank decoding problem which is the analogue of the problem of decoding a random linear code consists in recovering a basis of a random noise vector that was used to perturb a set of random linear equations

Qian Chen, Zilong Wang, Jiaqi Hu, Haonan Yan

Federated learning (FL) is becoming a major driving force behind machine learning as a service, where customers (clients) collaboratively benefit from shared local updates under the orchestration of the service provider (server). Representing clients' current demands and the server's future demand, local model personalization and global model generalization

Hancheng Zhu, Yuanwei Liu, Yik Chung Wu, Vincent K. N. Lau

Simultaneously transmitting and reflecting (STAR) reconfigurable intelligent surface (RIS), which serves users located on both sides of the surface, has recently emerged as a promising enhancement to the traditional reflective only RIS. Due to the lack of a unified comparison of communication systems equipped with different modes of STAR-RIS and the performa

P. Bonnefis, J. Sierra-Ausin, D. Fabre, J. Magnaudet

The first stages of the path instability phenomenon known to affect the buoyancy-driven motion of gas bubbles rising in weakly or moderately viscous liquids are examined thanks to a recently developed numerical tool designed to assess the global linear stability of incompressible flows involving freely-evolving interfaces. Predictions for the critical bubble

Chen Zhang, Luis Fernando D'Haro, Chengguang Tang, Ke Shi

Recent advancements in reference-free learned metrics for open-domain dialogue evaluation have been driven by the progress in pre-trained language models and the availability of dialogue data with high-quality human annotations. However, current studies predominantly concentrate on English dialogues, and the generalization of these metrics to other languages

Mingfei Qu, Dou Li, Chenhong Li, Kangqi Liu

A fully stabilized soliton microcomb is critical for many applications of optical frequency comb based on microresonators. However, the current approaches for full frequency stabilization require either external acousto-optic or electro-optic devices or auxiliary lasers and multiple phase-locked loops, which compromises the convenience of the system. This st

Yufei Wang, Bo Li, Ge Zhang, Qi Liu

Existing deep learning-based depth completion methods generally employ massive stacked layers to predict the dense depth map from sparse input data. Although such approaches greatly advance this task, their accompanied huge computational complexity hinders their practical applications. To accomplish depth completion more efficiently, we propose a novel light

Marco Menale, Romina Travaglini

In this paper, we develop a nonconservative kinetic framework to be applied to the study of immune system dysregulation. From the modeling viewpoint, the model regards a system composed of stochastically interacting agents, under the action of an eternal force field. According to the application perspectives of this paper, the external force field has a spec

S. Gupta, R. Ohshima, Y. Ando, T. Endo

Tungsten diselenide, WSe2 shows excellent properties and become very promising material among two dimensional semiconductors. Wide band gap and large spin-orbit coupling along with naturally lacking inversion symmetry in the monolayer WSe2 make it efficient material for spintronics, optoelectronics and valleytronics applications. In this work, we report elec

Antonin Sulc, Annika Eichler, Tim Wilksen

In this paper, we show a textual analysis of past ICALEPCS and IPAC conference proceedings to gain insights into the research trends and topics discussed in the field. We use natural language processing techniques to extract meaningful information from the abstracts and papers of past conference proceedings. We extract topics to visualize and identify trends

Li-Chin Yeh, Ing-Guey Jiang, Napaporn A-thano

Transit observations have become an important technique to probe exoplanets. Therefore, there are many projects carrying on organized observations of transit events, which make a huge amount of light-curve and transit timing data available. We consider this as an excellent opportunity to search for possible orbital decays of exoplanets from this big number o

Hendrik van Eerten, Geoffrey Ryan

Models for gamma-ray burst afterglow dynamics and synchrotron spectra are known to exhibit various scale invariances, owing to the scale-free nature of fluid dynamics and the power-law shape of synchrotron spectra. Since GRB 170817A, off-axis jet models including a lateral energy structure in the initial outflow geometry have gained in prominence. Here we de

Antonin Sulc, Annika Eichler, Tim Wilksen

This article introduces a novel method for detecting anomalies within log data from control system nodes at the European XFEL accelerator. Effective anomaly detection is crucial for providing operators with a clear understanding of each node's availability, status, and potential problems, thereby ensuring smooth accelerator operation. Traditional and learnin

Jian Guan, Youde Liu, Qiuqiang Kong, Feiyang Xiao

Unsupervised anomalous sound detection (ASD) aims to detect unknown anomalous sounds of devices when only normal sound data is available. The autoencoder (AE) and self-supervised learning based methods are two mainstream methods. However, the AE-based methods could be limited as the feature learned from normal sounds can also fit with anomalous sounds, reduc

Xiangyu Zhao, Bo Liu, Qijiong Liu, Guangyuan Shi

We present EasyGen, an efficient model designed to enhance multimodal understanding and generation by harnessing the capabilities of diffusion models and large language models (LLMs), Unlike existing multimodal models that predominately depend on encoders like CLIP or ImageBind and need ample amounts of training data to bridge modalities,EasyGen leverages Bi

Xin Luo, Fang-Yi Liang, Jiale Liu, Yu-Wei Zhan

As Web technology continues to develop, it has become increasingly common to use data stored on different clients. At the same time, federated learning has received widespread attention due to its ability to protect data privacy when let models learn from data which is distributed across various clients. However, most existing works assume that the client's

Hildeberto Jardón-Kojakhmetov, Christian Kuehn

Network dynamics is nowadays of extreme relevance to model and analyze complex systems. From a dynamical systems perspective, understanding the local behavior near equilibria is of utmost importance. In particular, equilibria with at least one zero eigenvalue play a crucial role in bifurcation analysis. In this paper, we want to shed some light on nilpotent

Roland Backhouse, Walter Guttmann, Michael Winter

An equivalence relation can be constructed from a given (homogeneous, binary) relation in two steps: first, construct the smallest reflexive and transitive relation containing the given relation (the "star" of the relation) and, second, construct the largest symmetric relation that is included in the result of the first step. The fact that the final result i

Andrei Kulikovsky

A model for impedance of a PEM fuel cell cathode catalyst layer under simultaneous application of potential and oxygen concentration perturbations is developed and solved. The resulting expression demonstrates dramatic lowering of the layer impedance under increase in the amplitude of the oxygen concentration perturbation. In--phase oscillations of the overp

Carel van Niekerk, Christian Geishauser, Michael Heck, Shutong Feng

Supervised neural approaches are hindered by their dependence on large, meticulously annotated datasets, a requirement that is particularly cumbersome for sequential tasks. The quality of annotations tends to deteriorate with the transition from expert-based to crowd-sourced labelling. To address these challenges, we present CAMEL (Confidence-based Acquisiti

Chenxu Yang, Zheng Lin, Lanrui Wang, Chong Tian

Knowledge-grounded dialogue generation aims to mitigate the issue of text degeneration by incorporating external knowledge to supplement the context. However, the model often fails to internalize this information into responses in a human-like manner. Instead, it simply inserts segments of the provided knowledge into generic responses. As a result, the gener

Philipp Trunschke, Anthony Nouy, Martin Eigel

Approximation of high-dimensional functions is a problem in many scientific fields that is only feasible if advantageous structural properties, such as sparsity in a given basis, can be exploited. A relevant tool for analysing sparse approximations is Stechkin's lemma. In its standard form, however, this lemma does not allow to explain convergence rates for

Jennifer López-Viejobueno, Leire Beitia-Antero, Ana I. Gómez de Castro

Tons of space particles enter the Earth atmosphere every year, being detected when they produce fireballs, meteor showers, or when they impact the Earth surface. Particle detection in the showers could also be attempted from space using satellites in low Earth orbit. Measuring the polarization would provide extra crucial information on the dominant alignment

Francesco Giglio, Giulio Landolfi, Luigi Martina

We investigate real solutions of a C-integrable non-evolutionary partial differential equation in the form of a scalar conservation law where the flux density depends both on the density and on its first derivatives with respect to the local variables. By performing a similarity reduction dictated by one of its local symmetry generators, a nonlinear ordinary

Guillaume Sagnol, Luc Pronzato

The problems of Lasso regression and optimal design of experiments share a critical property: their optimal solutions are typically \emph{sparse}, i.e., only a small fraction of the optimal variables are non-zero. Therefore, the identification of the support of an optimal solution reduces the dimensionality of the problem and can yield a substantial simplifi

Ilia Sokolovskii, Gerrit Groenhof

Placing a material inside an optical cavity can enhance transport of excitation energy by hybridizing excitons with confined light modes into polaritons, which have a dispersion that provides these light-matter quasi-particles with low effective masses and very high group velocities. While in experiments polariton propagation is typically initiated with lase

Vladimir Pokrovskii

An heuristic model of the society, as an assembly of weakly interacting individuals, is discussed. The model allows to connect macroscopic phenomena with features of relations between individuals. Addressing to the problem of inequality, a non-equilibrium situation is considered. The calculated income distribution function, which coincides with the Pareto di

Jiajun Zhang, Baitian Tang, Jiang Chang, Xiangxiang Xue

During the long term evolution of globular clusters (GCs), a part of member stars are lost to the field. The recently found nitrogen-rich (N-rich) metal-poor field stars are promising candidates of these GC escapees, since N enhancement is the fingerprint of chemically enhanced populations in GCs. In this work, we discuss the possibility of identifying N-ric

Huaijin Liang, Zengjing Chen

The 1936 Mills Futurity slot machine had the feature that, if a player loses 10 times in a row, the 10 lost coins are returned. Ethier and Lee (2010) studied a generalized version of this machine, with 10 replaced by deterministic parameter J. They established the Parrondo effect for a hypothetical two-armed machine with the Futurity award. Specifically, arm

Rukun Qiao, Hiroshi Kawasaki, Hongbin Zha

In recent years, deep neural networks have shown remarkable progress in dense disparity estimation from dynamic scenes in monocular structured light systems. However, their performance significantly drops when applied in unseen environments. To address this issue, self-supervised online adaptation has been proposed as a solution to bridge this performance ga

Bronisław Jakubczyk, Wojciech Kryński

We study the geometry of dynamic pairs $(X,\mathcal{V})$ on a manifold $M$, where $X$ is a vector field and $\mathcal{V}$ is a distribution on $M$, both satisfying a regularity condition. Special cases are pairs defined by systems of second order ODEs, geodesic sprays in Riemannian, Finslerian and Lagranian geometries, semi-Hamiltonian systems and control-af

Rukun Qiao, Hiroshi Kawasaki, Hongbin Zha

We introduced Temporally Incremental Disparity Estimation Network (TIDE-Net), a learning-based technique for disparity computation in mono-camera structured light systems. In our hardware setting, a static pattern is projected onto a dynamic scene and captured by a monocular camera. Different from most former disparity estimation methods that operate in a fr

Long Chen, Jan Rottmayer, Lisa Kusch, Nicolas R. Gauger

We formulate and solve data-driven aerodynamic shape design problems with distributionally robust optimization (DRO) approaches. Building on the findings of the work \cite{gotoh2018robust}, we study the connections between a class of DRO and the Taguchi method in the context of robust design optimization. Our preliminary computational experiments on aerodyna

Teng Zhang

In this paper, we provide a simple proof of a generalization of the Gauss-Lucas theorem. By using methods of D-companion matrix, we get the majorization relationship between the zeros of convex combinations of incomplete polynomials and an origin polynomial. Moreover, we prove that the set of all zeros of all convex combinations of incomplete polynomials coi

Ruijie Zheng, Khanh Nguyen, Hal Daumé, Furong Huang

Assistant AI agents should be capable of rapidly acquiring novel skills and adapting to new user preferences. Traditional frameworks like imitation learning and reinforcement learning do not facilitate this capability because they support only low-level, inefficient forms of communication. In contrast, humans communicate with progressive efficiency by defini

Jinwoo Kim, Janghyuk Choi, Jaehyun Kang, Changyeon Lee

The binding problem in artificial neural networks is actively explored with the goal of achieving human-level recognition skills through the comprehension of the world in terms of symbol-like entities. Especially in the field of computer vision, object-centric learning (OCL) is extensively researched to better understand complex scenes by acquiring object re

Jiamei Liu, Han Sun, Yizhen Jia, Jie Qin

Domain adaptation aims to alleviate the domain shift when transferring the knowledge learned from the source domain to the target domain. Due to privacy issues, source-free domain adaptation (SFDA), where source data is unavailable during adaptation, has recently become very demanding yet challenging. Existing SFDA methods focus on either self-supervised lea

Ollie Burke, Gabriel Andres Piovano, Niels Warburton, Philip Lynch

We investigate the impact of post-adiabatic (1PA) terms on parameter estimation for extreme and intermediate mass-ratio inspirals using state-of-the-art waveform models. Our analysis is the first to employ Bayesian inference to assess systematic errors for 1PA waveforms. We find that neglecting 1PA terms introduces significant biases for the (small) mass rat

Tuomas Hytönen

We consider the action of finitely truncated singular integral operators on functions taking values in a Banach space. Such operators are bounded for any Banach space, but we show a quantitative improvement over the trivial bound in any space with an equivalent uniformly convex norm. This answers a question asked by Naor and the author, who previously proved

Nilanjana Nandi, Arunima Banerjee, Ganesh Narayanan

Ultra-diffuse galaxies (UDGs) exhibit morphological similarities with other low-luminosity galaxies indicating a possible evolutionary connection. We investigate for common dynamical characteristics of isolated, HI-rich UDGs with other low luminosity field galaxies, namely the low surface brightness galaxies (LSBs) and the dwarf irregulars (dIrrs). From the

Shuo Zou, Bo Zhou, Qi Xuan

Degree correlation is an important characteristic of networks, which is usually quantified by the assortativity coefficient. However, concerns arise about changing the assortativity coefficient of a network when networks suffer from adversarial attacks. In this paper, we analyze the factors that affect the assortativity coefficient and study the optimization

Hongfu Liu, Ye Wang

Large Language models (LLMs) possess the capability to engage In-context Learning (ICL) by leveraging a few demonstrations pertaining to a new downstream task as conditions. However, this particular learning paradigm suffers from high instability stemming from substantial variances induced by factors such as the input distribution of selected examples, their

Yicheng Feng, Yuxuan Wang, Jiazheng Liu, Sipeng Zheng

Recently, various studies have leveraged Large Language Models (LLMs) to help decision-making and planning in environments, and try to align the LLMs' knowledge with the world conditions. Nonetheless, the capacity of LLMs to continuously acquire environmental knowledge and adapt in an open world remains uncertain. In this paper, we propose an approach to spu

Shuang Song, Yuanbang Liang, Jing Wu, Yu-Kun Lai

Despite the success of StyleGAN in image synthesis, the images it synthesizes are not always perfect and the well-known truncation trick has become a standard post-processing technique for StyleGAN to synthesize high-quality images. Although effective, it has long been noted that the truncation trick tends to reduce the diversity of synthesized images and un

Ryoma Sato, Yuki Takezawa, Han Bao, Kenta Niwa

We propose Easymark, a family of embarrassingly simple yet effective watermarks. Text watermarking is becoming increasingly important with the advent of Large Language Models (LLM). LLMs can generate texts that cannot be distinguished from human-written texts. This is a serious problem for the credibility of the text. Easymark is a simple yet effective solut

Ákos G. Horváth

In classical geometry, there is no such well-known and much-studied topic as the construction of conic sections (or briefly conics) from its five points. Its importance in many applications of mechanical engineering, civil engineering and architectural engineering, as well as other applied sciences is clear. The beauty of the topic is that it raises difficul

Jacek Gulgowski, Piotr Kasprzak, Piotr Maćkowiak

In this paper we provide a full characterization of linear integral operators acting from the space of functions of bounded Jordan variation to the space of functions of bounded Schramm variation in terms of their generating kernels.

Ling Yue, Yongqi Zhang, Quanming Yao, Yong Li

Knowledge graph (KG) embedding is a fundamental task in natural language processing, and various methods have been proposed to explore semantic patterns in distinctive ways. In this paper, we propose to learn an ensemble by leveraging existing methods in a relation-aware manner. However, exploring these semantics using relation-aware ensemble leads to a much

Imen Hnid, Ali Yassin, Imane Arbouch, David Guérin

Molecular electronics targets tiny devices exploiting the electronic properties of the molecular orbitals, which can be tailored and controlled by the chemical structure/conformation of the molecules. Many functional devices have been experimentally demonstrated; however, these devices were operated in the low frequency domain (mainly, dc to MHz). This repre

Yuxin Zhang, Lirui Zhao, Mingbao Lin, Yunyun Sun

The ever-increasing large language models (LLMs), though opening a potential path for the upcoming artificial general intelligence, sadly drops a daunting obstacle on the way towards their on-device deployment. As one of the most well-established pre-LLMs approaches in reducing model complexity, network pruning appears to lag behind in the era of LLMs, due m

Sandipan Dhar, Anuvab Sen, Aritra Bandyopadhyay, Nanda Dulal Jana

Speech Command Recognition (SCR), which deals with identification of short uttered speech commands, is crucial for various applications, including IoT devices and assistive technology. Despite the promise shown by Convolutional Neural Networks (CNNs) in SCR tasks, their efficacy relies heavily on hyper-parameter selection, which is typically laborious and ti

Zhenyu Han, Qianyue Hao, Qiwei He, Katherine Budeski

Background: Despite the consensus that vaccines play an important role in combating the global spread of infectious diseases, vaccine inequity is still rampant with deep-seated mentality of self-priority. This study aims to evaluate the existence and possible outcomes of a more equitable global vaccine distribution and explore a concrete incentive mechanism

Ahmed El Alaoui, Andrea Montanari, Mark Sellke

We consider Ising mixed $p$-spin glasses at high-temperature and without external field, and study the problem of sampling from the Gibbs distribution $\mu$ in polynomial time. We develop a new sampling algorithm with complexity of the same order as evaluating the gradient of the Hamiltonian and, in particular, at most linear in the input size. We prove that

Hiroto Ishida

We consider Poisson problems $-\Delta u^\varepsilon=f$ on perforated domains, and characterize the limit of $u^\varepsilon$ as the solution to $(-\Delta+\mu)u=f$ on domain $\Omega\subset\mathbb{R}^d$ with some potential $\mu\in W^{-1,\infty}(\Omega).$ It is known that $\mu$ is related to the capacity of holes when $\mu\in L^\infty(\Omega).$ In this paper, we

Amelie Royer, Tijmen Blankevoort, Babak Ehteshami Bejnordi

Training a single model on multiple input domains and/or output tasks allows for compressing information from multiple sources into a unified backbone hence improves model efficiency. It also enables potential positive knowledge transfer across tasks/domains, leading to improved accuracy and data-efficient training. However, optimizing such networks is a cha

Andrea Bernini, Fabrizio Silvestri, Gabriele Tolomei

Community detection techniques are useful for social media platforms to discover tightly connected groups of users who share common interests. However, this functionality often comes at the expense of potentially exposing individuals to privacy breaches by inadvertently revealing their tastes or preferences. Therefore, some users may wish to preserve their a

Xinyi Yang, Runzhe Zhan, Derek F. Wong, Junchao Wu

The large language model (LLM) has garnered significant attention due to its in-context learning mechanisms and emergent capabilities. The research community has conducted several pilot studies to apply LLMs to machine translation tasks and evaluate their performance from diverse perspectives. However, previous research has primarily focused on the LLM itsel

Shilun Jin, Noam Soker

The common envelop jet supernovae (CEJSN) r-process scenario has been proposed as an r-process nucleosynthesis site in the past decade. Jets launched by a neutron star that spirals-in inside the core of a red supergiant star in a common envelope evolution supply the proper conditions for the formation of elements heavier than iron through the rapid neutron c

Mohit Kumar Jat, Shubhankar Mishra, Harsimran Kaur Mann, Robin Bajaj

In this Letter, we present experimental findings on electron-electron scattering in a two-dimensional moir'e heterostructure with tunable Fermi wave vector, reciprocal lattice vector, and band gap. We achieve this in high-mobility aligned heterostructures of bilayer graphene (BLG) and hBN. Around half-filling, the primary contribution to the resistance of BL

Valera Berestovskii, Irina Zubareva

The Lie subgroup ${\rm GL}^{+}(2,\mathbb{C})$ of all matrices in the Lie group ${\rm GL}(2,\mathbb{C})$ with positive real determinant is equipped with a left-invariant sub-Lorentzian (anti)metric defined by the natural structure of the 4-dimensional Minkowski space-time on the subspace of Hermitian matrices in its Lie algebra. In base of the corresponding t

Marco Baldan, Sebastian Blauth, Dušan Bošković, Christian Leithäuser

Diazo compounds are gathering interest for their potential in promoting greener synthesis routes. We investigate, at a lab-scale, the continuous synthesis of diazo acetonitrile (DAN) using a micro-structured flow reactor and a flow reaction calorimeter. Data concerning DAN formation in the former, and relative to reaction heat and gas flow rate in the latter

Bare Luka Žagar, Tim Hertel, Mingyu Liu, Ekim Yurtsever

Deformable linear objects are vastly represented in our everyday lives. It is often challenging even for humans to visually understand them, as the same object can be entangled so that it appears completely different. Examples of deformable linear objects include blood vessels and wiring harnesses, vital to the functioning of their corresponding systems, suc

Pengyu Wang, Linyang Li, Ke Ren, Botian Jiang

Widely applied large language models (LLMs) can generate human-like content, raising concerns about the abuse of LLMs. Therefore, it is important to build strong AI-generated text (AIGT) detectors. Current works only consider document-level AIGT detection, therefore, in this paper, we first introduce a sentence-level detection challenge by synthesizing a dat

Xuezheng Wang, Wu Jiang, Zhiqiang Shen, Zhen Yan

Through very long baseline interferometry observations of one of the closest low-luminosity active galactic nuclei M81* at multifrequencies of 8.8, 22 and 44GHz, a bright discrete knot with an unusual low apparent speed $\sim$0.1c was detected. Combining with the contemporary monitoring of X-rays data at 2-10keV, it indicates that a moderate X-ray flare happ

Gabriel Mukobi, Hannah Erlebach, Niklas Lauffer, Lewis Hammond

The growing capabilities and increasingly widespread deployment of AI systems necessitate robust benchmarks for measuring their cooperative capabilities. Unfortunately, most multi-agent benchmarks are either zero-sum or purely cooperative, providing limited opportunities for such measurements. We introduce a general-sum variant of the zero-sum board game Dip

Kaori Obayashi, Ayumu Toriyama, Mayu Murakoshi, Yuri Sato

We present a possible theoretical interpretation of the observed afterglow emission of long gamma-ray burst GRB 080710. While its prompt GRB emission properties are normal, the afterglow light curves in the optical and infrared bands are exceptional in two respects. One is that the observed light curves of different wavelengths have maximum at the same time,

Hao Liu, Matei Zaharia, Pieter Abbeel

Training large transformers using next-token prediction has given rise to groundbreaking advancements in AI. While this generative AI approach has produced impressive results, it heavily leans on human supervision. Even state-of-the-art AI models like ChatGPT depend on fine-tuning through human demonstrations, demanding extensive human input and domain exper

Shengsheng Liu, Caishi Wang, Jijun Zhao

Of a quantum walk, its stationary measures play an important role in understanding its evolution behavior. In this paper we investigate stationary measures of two models of space-inhomogeneous three-state quantum walk on the line. By using the method of reduced matrix, we find out stationary measures of the two models under some mild conditions. Our results

Jina Lee, Youngtaek Hong, Dawun Jeong, Yeonggul Jang

Radiomics, a medical imaging technique, extracts quantitative handcrafted features from images to predict diseases. Harmonization in those features ensures consistent feature extraction across various imaging devices and protocols. Methods for harmonization include standardized imaging protocols, statistical adjustments, and evaluating feature robustness. My

Dan-Xuan Liu, Yu-Ran Gu, Chao Qian, Xin Mu

Migration has been a universal phenomenon, which brings opportunities as well as challenges for global development. As the number of migrants (e.g., refugees) increases rapidly in recent years, a key challenge faced by each country is the problem of migrant resettlement. This problem has attracted scientific research attention, from the perspective of maximi

Yuzhe Zhang, Qin Wang, Shiping Chen, Chen Wang

This paper centers around a simple yet crucial question for everyday users: How should one choose their delegated validators within proof-of-stake (PoS) protocols, particularly in the context of Ethereum 2.0? This has been a long-overlooked gap, as existing studies have primarily focused on inter-committee (validator set) behaviors and activities, while negl

Elizabath Peter, K. K. Krishnan Namboodiri, B. Sundar Rajan

This work explores a multiple transmit antenna setting in a multi-access coded caching (MACC) network where each user accesses more than one cache. A MACC network has $K$ users and $K$ caches, and each user has access to $r < K$ consecutive caches in a cyclic wrap-around manner. There are $L$ antennas at the server, and each cache has a normalized size of $M

Zhengguang Liu, Yanrong Zhang, Xiaoli Li

The scalar auxiliary variable (SAV)-type methods are very popular techniques for solving various nonlinear dissipative systems. Compared to the semi-implicit method, the baseline SAV method can keep a modified energy dissipation law but doubles the computational cost. The general SAV approach does not add additional computation but needs to solve a semi-impl

Takumi Nishiyasu, Wataru Shimoda, Yoichi Sato

Image cropping is essential in image editing for obtaining a compositionally enhanced image. In display media, image cropping is a prospective technique for automatically creating media content. However, image cropping for media contents is often required to satisfy various constraints, such as an aspect ratio and blank regions for placing texts or objects.

Ramnath Kumar, Anshul Mittal, Nilesh Gupta, Aditya Kusupati

Dense embedding-based retrieval is widely used for semantic search and ranking. However, conventional two-stage approaches, involving contrastive embedding learning followed by approximate nearest neighbor search (ANNS), can suffer from misalignment between these stages. This mismatch degrades retrieval performance. We propose End-to-end Hierarchical Indexin

Tyler R. Fairnington, Emma Nabbie, Chelsea X. Huang, George Zhou

We present the confirmation of a hot super-Neptune with an exterior Neptune companion orbiting a bright (V = 10.1 mag) F-dwarf identified by the $\textit{Transiting Exoplanet Survey Satellite}$ ($\textit{TESS}$). The two planets, observed in sectors 45, 46 and 48 of the $\textit{TESS}$ extended mission, are $4.74^{+0.16}_{-0.14}$ $R_{\oplus}$ and $3.86^{+0.1

Linyang Li, Ke Ren, Yunfan Shao, Pengyu Wang

With the rapid development of neural network applications in NLP, model robustness problem is gaining more attention. Different from computer vision, the discrete nature of texts makes it more challenging to explore robustness in NLP. Therefore, in this paper, we aim to connect discrete perturbations with continuous perturbations, therefore we can use such c

Mahin Khan Mahadi, Abdullah Abdullah, Jamal Uddin, Asif Newaz

Alzheimer's disease (AD) is the most common long-term illness in elderly people. In recent years, deep learning has become popular in the area of medical imaging and has had a lot of success there. It has become the most effective way to look at medical images. When it comes to detecting AD, the deep neural model is more accurate and effective than general m

Seohong Park, Oleh Rybkin, Sergey Levine

Unsupervised pre-training strategies have proven to be highly effective in natural language processing and computer vision. Likewise, unsupervised reinforcement learning (RL) holds the promise of discovering a variety of potentially useful behaviors that can accelerate the learning of a wide array of downstream tasks. Previous unsupervised RL approaches have

Roland R Netz

When analyzing experimental or simulation time-series data, the question arises whether it is possible to tell from a one-dimensional time-dependent trajectory whether the system is in equilibrium or not. We here consider the non-equilibrium version of the generalized Langevin equation for a Gaussian observable and show that i) the multi-point joint distribu

Willy Chung, Samuel Cahyawijaya, Bryan Wilie, Holy Lovenia

Large language models (LLMs) have been used for diverse tasks in natural language processing (NLP), yet remain under-explored for task-oriented dialogue systems (TODS), especially for end-to-end TODS. We present InstructTODS, a novel off-the-shelf framework for zero-shot end-to-end task-oriented dialogue systems that can adapt to diverse domains without fine

Zehan Wang, Ziang Zhang, Luping Liu, Yang Zhao

Multi-modal contrastive representation (MCR) of more than three modalities is critical in multi-modal learning. Although recent methods showcase impressive achievements, the high dependence on large-scale, high-quality paired data and the expensive training costs limit their further development. Inspired by recent C-MCR, this paper proposes Extending Multimo

Priyotosh Bandyopadhyay, Mariana Frank, Snehashis Parashar, Chandrima Sen

We study the interaction between the inert Higgs doublet (IDM) dark matter and a vector-like $SU(2)$ triplet lepton (VLL), both of which are $Z_2$-odd. The vector current of the VLL with the $Z$-boson rules out a fermionic or two-component dark matter scenario. However, a compressed mass spectrum and a sufficiently small Yukawa coupling allows co-annihilatio

Panu Lahti, Andrea Pinamonti, Xiaodan Zhou

We study the asymptotic behavior of three classes of nonlocal functionals in complete metric spaces equipped with a doubling measure and supporting a Poincar\'e inequality. We show that the limits of these nonlocal functionals are comparable to the variation $\| Df\|(\Omega)$ or the Sobolev semi-norm $\int_\Omega g_f^p\, d\mu$, which extends Euclidean result

Giannis Fikioris, Siddhartha Banerjee, Éva Tardos

We study the allocation of shared resources over multiple rounds among competing agents, via the dynamic max-min fair (DMMF) mechanism: the good in each round is allocated to the requesting agent with the least number of allocations received to date. We show that in large markets when an agent has i.i.d. values across rounds, under mild distributional assump

Zi-Ming Zhou, Chang-Xiang He, Hai-Ying Shan

For a graph $G$, let $S_2(G)$ be the sum of the first two largest signless Laplacian eigenvalues of $G$, and $f(G)=e(G)+3-S_2(G)$. Oliveira, Lima, Rama and Carvalho conjectured that $K^+_{1,n-1}$ (the star graph with an additional edge) is the unique graph with minimum value of $f(G)$ on $n$ vertices. In this paper, we prove this conjecture, which also confi

Quanjun Zhang, Tongke Zhang, Juan Zhai, Chunrong Fang

Large Language Models (LLMs) have been gaining increasing attention and demonstrated promising performance across a variety of Software Engineering (SE) tasks, such as Automated Program Repair (APR), code summarization, and code completion. For example, ChatGPT, the latest black-box LLM, has been investigated by numerous recent research studies and has shown