arXiv papers from February 2024

Mathias Viborg Andersen, Ross Greer, Andreas Møgelmose, Mohan Trivedi

Advanced Driver Assistance Systems (ADAS) in intelligent vehicles rely on accurate driver perception within the vehicle cabin, often leveraging a combination of sensing modalities. However, these modalities operate at varying rates, posing challenges for real-time, comprehensive driver state monitoring. This paper addresses the issue of missing data due to s

Alessandra F. Lütz, Lucas Wardil

Pluralistic ignorance is a social-psychological phenomenon that occurs when individuals privately hold beliefs that differ from perceived group norms. Traditional models, based on opinion dynamics with private and public states, fail to account for a key aspect: when nonexpression aligns with normative behavior, initial social pressure can induce pluralistic

Benjamin Cohen-Wang, Joshua Vendrow, Aleksander Madry

Pre-training is a widely used approach to develop models that are robust to distribution shifts. However, in practice, its effectiveness varies: fine-tuning a pre-trained model improves robustness significantly in some cases but not at all in others (compared to training from scratch). In this work, we seek to characterize the failure modes that pre-training

Bin Yuan, Tianbo Song

The study utilizes a comprehensive dataset informed by IPv6 routing information to provide statistics, degree distribution, joint degree distribution, and clustering analysis of the IPv6 Internet's structure and resilience.The dataset includes 17,232 unique ASes and 10,000 unique IPv6 prefixes. Analysis reveals an interconnected network with an average path

Lev Tauz, Debarnab Mitra, Jayanth Shreekumar, Murat Can Sarihan

Quantum key distribution (QKD) is a popular protocol that provides information theoretically secure keys to multiple parties. Two important post-processing steps of QKD are 1) the information reconciliation (IR) step, where parties reconcile mismatches in generated keys through classical communication, and 2) the privacy amplification (PA) step, where partie

Hamza Jnane, Simon C Benjamin

Electron spins in semiconductor devices are highly promising building blocks for quantum processors (QPs). Commercial semiconductor foundries can create QPs using the same processes employed for conventional chips, once the QP design is suitably specified. There is a vast accessible design space; to identify the most promising options for fabrication, one re

Bin Yuan, Tianbo Song, Jerry Yao

This study presents an integrated approach for identifying key nodes in information propagation networks using advanced artificial intelligence methods. We introduce a novel technique that combines the Decision-making Trial and Evaluation Laboratory (DEMATEL) method with the Global Structure Model (GSM), creating a synergistic model that effectively captures

Yuanwei Liu, Chongjun Ouyang, Zhiguo Ding, Robert Schober

The evolution of wireless communications has been significantly influenced by remarkable advancements in multiple access (MA) technologies over the past five decades, shaping the landscape of modern connectivity. Within this context, a comprehensive tutorial review is presented, focusing on representative MA techniques developed over the past 50 years. The f

Kate Donahue, Nicole Immorlica, Meena Jagadeesan, Brendan Lucier

When deployed in the world, a learning agent such as a recommender system or a chatbot often repeatedly interacts with another learning agent (such as a user) over time. In many such two-agent systems, each agent learns separately and the rewards of the two agents are not perfectly aligned. To better understand such cases, we examine the learning dynamics of

Takahiro Miki, Joonho Lee, Lorenz Wellhausen, Marco Hutter

Legged robots have the potential to traverse complex terrain and access confined spaces beyond the reach of traditional platforms thanks to their ability to carefully select footholds and flexibly adapt their body posture while walking. However, robust deployment in real-world applications is still an open challenge. In this paper, we present a method for le

Nicolas Marie, Amélie Rosier

This paper deals with a nonparametric warped kernel estimator $\widehat b$ of the drift function computed from independent continuous observations of a diffusion process. A risk bound on $\widehat b$ is established. The paper also deals with an extension of the PCO bandwidth selection method for $\widehat b$. Finally, some numerical experiments are provided.

D. R. Ernst, A. Bortolon, C. S. Chang, S. Ku

Multi-machine empirical scaling predicts an extremely narrow heat exhaust layer in future high magnetic field tokamaks, producing high power densities that require mitigation. In the experiments presented, the width of this exhaust layer is nearly doubled using actuators to increase turbulent transport in the plasma edge. This is achieved in low collisionali

Xumei Xi, Christina Lee Yu, Yudong Chen

Low-rank matrix completion concerns the problem of estimating unobserved entries in a matrix using a sparse set of observed entries. We consider the non-uniform setting where the observed entries are sampled with highly varying probabilities, potentially with different asymptotic scalings. We show that under structured sampling probabilities, it is often bet

Maryam Soltani, Ghasem Akbari, Nader Montazerin

Particle Image Velocimetry (PIV) data is a valuable asset in fluid mechanics. It is capable of visualizing flow structures even in complex physics scenarios, such as the flow at the exit of the rotor of a centrifugal fan. Machine learning is also a successful companion to PIV in order to increase data resolution or impute experimental gaps. While classical a

Ali Beikmohammadi, Sarit Khirirat, Sindri Magnússon

Data similarity assumptions have traditionally been relied upon to understand the convergence behaviors of federated learning methods. Unfortunately, this approach often demands fine-tuning step sizes based on the level of data similarity. When data similarity is low, these small step sizes result in an unacceptably slow convergence speed for federated metho

Carlos Ansótegui, Jordi Levy

Quantum Annealers are basically quantum computers that with high probability can optimize certain quadratic functions on Boolean variables in constant time. These functions are basically the Hamiltonian of Ising models that reach the ground energy state, with a high probability, after an annealing process. They have been proposed as a way to solve SAT. These

Kyle M. Kabasares, Jonathan H. Cohn, Aaron J. Barth, Benjamin D. Boizelle

We present molecular gas-dynamical mass measurements of the central black holes in the giant elliptical galaxies NGC 4786 and NGC 5193, based on CO(2$-$1) observations from the Atacama Large Millimeter/submillimeter Array (ALMA) and Hubble Space Telescope near-infrared imaging. The central region in each galaxy contains a circumnuclear disk that exhibits ord

Karina Halevy, Anna Sotnikova, Badr AlKhamissi, Syrielle Montariol

Model editing has emerged as a cost-effective strategy to update knowledge stored in language models. However, model editing can have unintended consequences after edits are applied: information unrelated to the edits can also be changed, and other general behaviors of the model can be wrongly altered. In this work, we investigate how model editing methods u

Jimin Mun, Cathy Buerger, Jenny T. Liang, Joshua Garland

Counterspeech, i.e., direct responses against hate speech, has become an important tool to address the increasing amount of hate online while avoiding censorship. Although AI has been proposed to help scale up counterspeech efforts, this raises questions of how exactly AI could assist in this process, since counterspeech is a deeply empathetic and agentic pr

Zijie Huang, Jeehyun Hwang, Junkai Zhang, Jinwoo Baik

Real-world multi-agent systems are often dynamic and continuous, where the agents co-evolve and undergo changes in their trajectories and interactions over time. For example, the COVID-19 transmission in the U.S. can be viewed as a multi-agent system, where states act as agents and daily population movements between them are interactions. Estimating the coun

Keying Kuang, Frances Dean, Jack B. Jedlicki, David Ouyang

A digital twin is a virtual replica of a real-world physical phenomena that uses mathematical modeling to characterize and simulate its defining features. By constructing digital twins for disease processes, we can perform in-silico simulations that mimic patients' health conditions and counterfactual outcomes under hypothetical interventions in a virtual se

Wei Niu, Gagan Agrawal, Bin Ren

Though many compilation and runtime systems have been developed for DNNs in recent years, the focus has largely been on static DNNs. Dynamic DNNs, where tensor shapes and sizes and even the set of operators used are dependent upon the input and/or execution, are becoming common. This paper presents SoD$^2$, a comprehensive framework for optimizing Dynamic DN

Chenhao Fang, Xiaohan Li, Zezhong Fan, Jianpeng Xu

Product attribute value extraction is a pivotal component in Natural Language Processing (NLP) and the contemporary e-commerce industry. The provision of precise product attribute values is fundamental in ensuring high-quality recommendations and enhancing customer satisfaction. The recently emerging Large Language Models (LLMs) have demonstrated state-of-th

Safouane El Ghazouali, Youssef Mhirit, Ali Oukhrid, Umberto Michelucci

In the realm of computer vision, the integration of advanced techniques into the processing of RGB-D camera inputs poses a significant challenge, given the inherent complexities arising from diverse environmental conditions and varying object appearances. Therefore, this paper introduces FusionVision, an exhaustive pipeline adapted for the robust 3D segmenta

Matthew Danish, SM Labib, Britta Ricker, Marco Helbich

Street View Imagery (SVI) is a valuable data source for studies (e.g., environmental assessments, green space identification or land cover classification). While commercial SVI is available, such providers commonly restrict copying or reuse in ways necessary for research. Open SVI datasets are readily available from less restrictive sources, such as Mapillar

Dimitrios Giannakis, Mohammad Javad Latifi Jebelli

We define the notion of a thick open set $\Omega$ in a Euclidean space and show that a local Hardy-Littlewood inequality holds in $L^p(\Omega)$, $p \in (1, \infty]$. We then establish pointwise and $L^p(\Omega)$ convergence for families of convolution operators with a Markov normalization on $\Omega$. We demonstrate application of such smoothing operators to

Zhiyu An, Xianzhong Ding, Wan Du

Recent research has shown the potential of Model-based Reinforcement Learning (MBRL) to enhance energy efficiency of Heating, Ventilation, and Air Conditioning (HVAC) systems. However, existing methods rely on black-box thermal dynamics models and stochastic optimizers, lacking reliability guarantees and posing risks to occupant health. In this work, we over

Santiago Núñez-Corrales, Eric Jakobsson

Understanding realistic complex systems requires confronting significant conceptual, theoretical and experimental limitations rooted in the persistence of views that originated in the mechanics of simple moving bodies. We define the category of complex multiscale stochastic systems as a useful device for capturing the minimally required complexity of many ty

M. Baldini, G. Chlachidze, G. Apollinari, J. Dimarco

The High Luminosity upgrade of the Large Hadron Collider (HL-LHC) at CERN will include eight cryo-assemblies that are expected to be fabricated and delivered to CERN by the US HL-LHC Accelerator Upgrade Project (AUP) as part of the U.S. contributions to the HL-LHC. These cryostat assemblies are the quadrupole magnetic components of the HL-LHC Q1 and Q3 inner

Guanxuan Wu, Allison Sullivan

Writing declarative models has numerous benefits, ranging from automated reasoning and correction of design-level properties before systems are built to automated testing and debugging of their implementations after they are built. Unfortunately, the model itself needs to be correct to gain these benefits. Alloy is a commonly used modeling language that has

Kangfeng Ye, Fang Yan, Simos Gerasimou

Probabilistic model checking is a widely used formal verification technique to automatically verify qualitative and quantitative properties for probabilistic models. However, capturing such systems, writing corresponding properties, and verifying them require domain knowledge. This makes it not accessible for researchers and engineers who may not have the re

Nicolas Clozeau, Antoine Gloria, Siguang Qi

We establish quantitative homogenization results for the popular log-normal coefficients. Since the coefficients are neither bounded nor uniformly elliptic, standard proofs do not apply directly. Instead, we take inspiration from the approach developed for the nonlinear setting by the first two authors and capitalize on large-scale regularity results by Bell

Yuta Kimoto, Hidetoshi Masuda, Takeshi Seki, Yoichi Nii

We found signatures of current-induced sliding motion in helimagnetic $\mathrm{Mn}\mathrm{Au}_2$ thin films. An abrupt change in differential resistivity occurred at a threshold bias current in the helimagnetic state, whereas it was absent in the induced ferromagnetic state. Broadband voltage noise also emerged above the threshold current in the helimagnetic

Johannes H. Uhl, Stefan Leyk

The accuracy assessment of remote-sensing derived built-up land data represents a specific case of binary map comparison, where class imbalance varies considerably across rural-urban trajectories. Thus, local accuracy characterization of such datasets requires specific strategies that are robust to low sample sizes and different levels of class imbalance. He

Yunyi Zhang, Ruozhen Yang, Xueqiang Xu, Rui Li

Hierarchical text classification aims to categorize each document into a set of classes in a label taxonomy, which is a fundamental web text mining task with broad applications such as web content analysis and semantic indexing. Most earlier works focus on fully or semi-supervised methods that require a large amount of human annotated data which is costly an

Giovanni P. Galdi, Tatsuki Yamamoto

We study the nonhomogeneous boundary value problem for the steady-state Navier-Stokes equations under the slip boundary conditions in two-dimensional multiply-connected bounded domains. Employing the approach of Korobkov-Pileckas-Russo (Ann. Math. 181(2), 769-807, 2015), we prove that this problem has a solution if the friction coefficient is sufficiently la

Nishchay Suri, Jason Saied, Davide Venturelli

We present a general condition to obtain subspaces that decay uniformly in a system governed by the Lindblad master equation and use them to perform error mitigated quantum computation. The expectation values of dynamics encoded in such subspaces are unbiased estimators of noise-free expectation values. In analogy to the decoherence free subspaces which are

Luciana Ebani

We investigated Relations Among Green Functions defined in an alternative strategy for coping with the divergences, also called the Implicit Regularization Method (IREG): the mathematical content (divergent and finite) will remain intact until the calculations end. The divergent part will be organized through standardized objects free of physical quantities.

Johannes H. Uhl, Stefan Leyk

This paper presents a method for thematic agreement assessment of geospatial data products of different semantics and spatial granularities, which may be affected by spatial offsets between test and reference data. The proposed method uses a multi-scale framework allowing for a probabilistic evaluation whether thematic disagreement between datasets is induce

Olivier Berné, Emilie Habart, Els Peeters, Ilane Schroetter

Most low-mass stars form in stellar clusters that also contain massive stars, which are sources of far-ultraviolet (FUV) radiation. Theoretical models predict that this FUV radiation produces photo-dissociation regions (PDRs) on the surfaces of protoplanetary disks around low-mass stars, impacting planet formation within the disks. We report JWST and Atacama

Ivan Rosas-Soto

In the present article we define an integral analogue of Chow-K\"unneth decomposition for \'etale motives. By using families of conservative functors we are able to establish a decomposition of the \'etale motive of commutative group schemes over a base and we relate to an integral \'etale Chow-K\"unneth decomposition of abelian varieties. For a projective v

Raj Agrawal, Sam Witty, Andy Zane, Eli Bingham

Many practical problems involve estimating low dimensional statistical quantities with high-dimensional models and datasets. Several approaches address these estimation tasks based on the theory of influence functions, such as debiased/double ML or targeted minimum loss estimation. This paper introduces \textit{Monte Carlo Efficient Influence Functions} (MC-

Ziqin Chen, Yongqiang Wang

Distributed optimization and learning has recently garnered great attention due to its wide applications in sensor networks, smart grids, machine learning, and so forth. Despite rapid development, existing distributed optimization and learning algorithms require each agent to exchange messages with its neighbors, which may expose sensitive information and ra

Nicole Sabina Ticea, Srinivas Raghu, Yi-Ming Wu

An indispensable ingredient for pair density wave (PDW) superconductivity is the presence of an attractive pairing interaction at finite momentum. Here, we show how this condition can be met with straightforward electron-density interactions in multiband systems. The electron-density interaction, when projected to the band basis, acquires form factors with n

Mahsa Mozafari-Nia, Salimeh Yasaei Sekeh

Despite the impressive performance of deep neural networks (DNNs), their computational complexity and storage space consumption have led to the concept of network compression. While DNN compression techniques such as pruning and low-rank decomposition have been extensively studied, there has been insufficient attention paid to their theoretical explanation.

Yu Wang

Interest is increasing among political scientists in leveraging the extensive information available in images. However, the challenge of interpreting these images lies in the need for specialized knowledge in computer vision and access to specialized hardware. As a result, image analysis has been limited to a relatively small group within the political scien

Karan Ahuja

A long-standing vision in computer science has been to evolve computing devices into proactive assistants that enhance our productivity, health and wellness, and many other facets of our lives. User digitization is crucial in achieving this vision as it allows computers to intimately understand their users, capturing activity, pose, routine, and behavior. To

Ambroise Müller, Thomas Ayral, Corentin Bertrand

Matrix product density operators (MPDOs) are tensor network representations of locally purified density matrices where each physical degree of freedom is associated to an environment degree of freedom. MPDOs have interesting properties for mixed state representations: guaranteed positivity by construction, efficient conservation of the trace and computation

Dmitry A. Garanin, Eugene M. Chudnovsky

We report Monte-Carlo studies of the orientational order and melting of a 2D skyrmion lattice containing more than one million spins. Two models have been investigated, a microscopic model of lattice spins with Dzyaloshinskii-Moryia interaction that possesses skyrmions, and the model in which skyrmions are treated as point particles with repulsive interactio

José Concepción Torres-Guzmán, Alfredo Díaz-de-Anda, Jesús Arriaga

In this work, we obtain closed expressions for the transfer matrix and the transmittance of electromagnetic waves propagating in finite 1D anisotropic periodic stratified media with an arbitrary number of cells. By invoking the Cayley-Hamilton theorem on the transfer matrix for the electromagnetic field in a periodic stratified media formed by N cells, we ob

Nadav Kohen

Many famous integer sequences including the Catalan numbers and the Motzkin numbers can be expressed in the form $ConstantTermOf\left[P(x)^nQ(x)\right]$ for Laurent polynomials $Q$, and symmetric Laurent trinomials $P$. In this paper we characterize the primes for which sequences of this form are uniformly recurrent modulo $p$. For all other primes, we show

Marios Constantinides, Mohammad Tahaei, Daniele Quercia, Simone Stumpf

With the upcoming AI regulations (e.g., EU AI Act) and rapid advancements in generative AI, new challenges emerge in the area of Human-Centered Responsible Artificial Intelligence (HCR-AI). As AI becomes more ubiquitous, questions around decision-making authority, human oversight, accountability, sustainability, and the ethical and legal responsibilities of

Pavel Dvurechensky, Jia-Jie Zhu

By choosing a suitable function space as the dual to the non-negative measure cone, we study in a unified framework a class of functional saddle-point optimization problems, which we term the Mixed Functional Nash Equilibrium (MFNE), that underlies several existing machine learning algorithms, such as implicit generative models, distributionally robust optim

Christian Morsbach, Bjoern F. Klose, Michael Bergmann, Felix M. Möller

We revisit recently published high-fidelity implicit large eddy simulation datasets obtained with a high-order discontinuous Galerkin spectral element method and analyse them using Proper Orthogonal Decomposition (POD) as well as Spectral Proper Orthogonal Decomposition (SPOD). The first configuration is the MTU T161 low-pressure turbine cascade with resolve

Malak Sadek, Marios Constantinides, Daniele Quercia, Céline Mougenot

Value Sensitive Design (VSD) is a framework for integrating human values throughout the technology design process. In parallel, Responsible AI (RAI) advocates for the development of systems aligning with ethical values, such as fairness and transparency. In this study, we posit that a VSD approach is not only compatible, but also advantageous to the developm

Yuqiao Wen, Behzad Shayegh, Chenyang Huang, Yanshuai Cao

The ability of zero-shot translation emerges when we train a multilingual model with certain translation directions; the model can then directly translate in unseen directions. Alternatively, zero-shot translation can be accomplished by pivoting through a third language (e.g., English). In our work, we observe that both direct and pivot translations are nois

Behzad Shayegh, Yuqiao Wen, Lili Mou

We address unsupervised discontinuous constituency parsing, where we observe a high variance in the performance of the only previous model in the literature. We propose to build an ensemble of different runs of the existing discontinuous parser by averaging the predicted trees, to stabilize and boost performance. To begin with, we provide comprehensive compu

Louis Davis, Boris Baeumer, Ting Wang

This paper extends the existing fractional Hawkes process to better model mainshock-aftershock sequences of earthquakes. The fractional Hawkes process is a self-exciting point process model with temporal decay kernel being a Mittag-Leffler function. A maximum likelihood estimation scheme is developed and its consistency is checked. It is then compared to the

Joykirat Singh, Sehban Fazili, Rohan Jain, Md Shad Akhtar

Privacy policy documents have a crucial role in educating individuals about the collection, usage, and protection of users' personal data by organizations. However, they are notorious for their lengthy, complex, and convoluted language especially involving privacy-related entities. Hence, they pose a significant challenge to users who attempt to comprehend o

Polina Arsenteva, Mohamed Amine Benadjaoud, Hervé Cardot

In modern experimental science, there is a common problem of estimating the coefficients of a linear regression in a context where the variables of interest cannot be observed simultaneously. When there is a categorical variable that is observed on all statistical units, we consider two estimators of linear regression that take this additional information in

Tim Leung, Matthew Lorig, Yoshihiro Shirai

This paper analyzes a problem of optimal static hedging using derivatives in incomplete markets. The investor is assumed to have a risk exposure to two underlying assets. The hedging instruments are vanilla options written on a single underlying asset. The hedging problem is formulated as a utility maximization problem whereby the form of the optimal static

Justyn Snarski-Adamski, Mirosław Werwiński

New hard magnetic materials with zero or low rare earth content are in demand due to the high prices of the rare earth metals. Among the candidates for such materials, we consider MnB, FeB and their alloys, because previous experiments suggest that FeB has a relatively high magnetic hardness of about 0.83 at room temperature. Using first-principles calculati

Robert Nimmo, Marios Constantinides, Ke Zhou, Daniele Quercia

As Artificial Intelligence (AI) becomes ubiquitous, the need for Explainable AI (XAI) has become critical for transparency and trust among users. A significant challenge in XAI is catering to diverse users, such as data scientists, domain experts, and end-users. Recent research has started to investigate how users' characteristics impact interactions with an

Mohammadali Saffary, Nishan Inampudi, Joshua E. Siegel

As highly automated vehicles reach higher deployment rates, they find themselves in increasingly dangerous situations. Knowing that the consequence of a crash is significant for the health of occupants, bystanders, and properties, as well as to the viability of autonomy and adjacent businesses, we must search for more efficacious ways to comprehensively and

J. A. Oller

When a resonance lies near the threshold of a heavier channel, an interesting feature can occur. The paradigmatic example employed here is the scalar isoscalar $f_0(980)$ resonance that couples to the lighter $\pi\pi$ and heavier $K\bar{K}$ channels. It is shown that the decay width is given by the sum or subtraction of the partial decay widths depending on

Tao Jiang, Wei Yu

This paper addresses the design of transmit precoder and receive combiner matrices to support $N_{\rm s}$ independent data streams over a time-division duplex (TDD) point-to-point massive multiple-input multiple-output (MIMO) channel with either a fully digital or a hybrid structure. The optimal precoder and combiner design amounts to finding the top-$N_{\rm

Georges Dupret, Konstantin Sozinov, Carmen Barcena Gonzalez, Ziggy Zacks

Making ideal decisions as a product leader in a web-facing company is extremely difficult. In addition to navigating the ambiguity of customer satisfaction and achieving business goals, one must also pave a path forward for ones' products and services to remain relevant, desirable, and profitable. Data and experimentation to test product hypotheses are key t

Justin Kalloor, Mathias Weiden, Ed Younis, John Kubiatowicz

The design space of current quantum computers is expansive with no obvious winning solution. This leaves practitioners with a clear question: "What is the optimal system configuration to run an algorithm?". This paper explores hardware design trade-offs across NISQ systems to guide algorithm and hardware design choices. The evaluation is driven by algorithmi

Shanghua Gao, Teddy Koker, Owen Queen, Thomas Hartvigsen

Although pre-trained transformers and reprogrammed text-based LLMs have shown strong performance on time series tasks, the best-performing architectures vary widely across tasks, with most models narrowly focused on specific areas, such as time series forecasting. Unifying predictive and generative time series tasks within a single model remains challenging.

Yungang Lu

This paper primarily focuses on the investigation of the distribution of certain crucial operators with respect to significant states on the (q,2)-Fock space, for instance, the vacuum distribution of the field operator.

Joscha Diehl, Kurusch Ebrahimi-Fard, Fabian Harang, Samy Tindel

Over the past decade, the importance of the 1D signature which can be seen as a functional defined along a path, has been pivotal in both path-wise stochastic calculus and the analysis of time series data. By considering an image as a two-parameter function that takes values in a $d$-dimensional space, we introduce an extension of the path signature to image

Amartya Shankha Biswas, Ruidi Cao, Cassandra Marcussen, Edward Pyne

We initiate the study of Local Computation Algorithms on average case inputs. In the Local Computation Algorithm (LCA) model, we are given probe access to a huge graph, and asked to answer membership queries about some combinatorial structure on the graph, answering each query with sublinear work. For instance, an LCA for the $k$-spanner problem gives access

Bryan Habas, Bo Cheng

Inverted landing is a routine behavior among a number of animal fliers. However, mastering this feat poses a considerable challenge for robotic fliers, especially to perform dynamic perching with rapid body rotations (or flips) and landing against gravity. Inverted landing in flies have suggested that optical flow senses are closely linked to the precise tri

Sui He

Prompt engineering has shown potential for improving translation quality in LLMs. However, the possibility of using translation concepts in prompt design remains largely underexplored. Against this backdrop, the current paper discusses the effectiveness of incorporating the conceptual tool of translation brief and the personas of translator and author into p

Xiaoqiang Wang, Lingfei Wu, Tengfei Ma, Bang Liu

Large language models (LLMs) are primarily evaluated by overall performance on various text understanding and generation tasks. However, such a paradigm fails to comprehensively differentiate the fine-grained language and cognitive skills, rendering the lack of sufficient interpretation to LLMs' capabilities. In this paper, we present FAC$^2$E, a framework f

Miao Li, Ming-Bin Chen, Bo Tang, Shengbin Hou

We present NewsBench, a novel evaluation framework to systematically assess the capabilities of Large Language Models (LLMs) for editorial capabilities in Chinese journalism. Our constructed benchmark dataset is focused on four facets of writing proficiency and six facets of safety adherence, and it comprises manually and carefully designed 1,267 test sample

Yuan Wang, Lokesh Kumar Sambasivan, Mingang Fu, Prakhar Mehrotra

Generative AI applications, such as ChatGPT or DALL-E, have shown the world their impressive capabilities in generating human-like text or image. Diving deeper, the science stakeholder for those AI applications are Deep Generative Models, a.k.a DGMs, which are designed to learn the underlying distribution of the data and generate new data points that are sta

Jan Jaśkowiec, N. Sukumar

In this paper, we present a new high-order discontinuous Galerkin (DG) method, in which neither a penalty parameter nor a stabilization parameter is needed. We refer to this method as penalty-free DG (\PFDG). In this method, the trial and test functions belong to the broken Sobolev space, in which the functions are in general discontinuous on the mesh skelet

Subhrajyoti Roy, Rhombik Roy, Arnaldo Gammal, Barnali Chakrabarti

We explore the ground states of strongly interacting bosons in the vanishingly small and weak lattices using the multiconfiguration time-dependent Hartree method for bosons (MCTDHB) which calculate numerically exact many-body wave function. Two new many-body phases: fragmented or quasi superfluid (QSF) and incomplete fragmented Mott or quasi Mott insulator (

Rouven Essig, Ryan Plestid, Aman Singal

Solid-state detectors with a low energy threshold have several applications, including searches of non-relativistic halo dark-matter particles with sub-GeV masses. When searching for relativistic, beyond-the-Standard-Model particles with enhanced cross sections for small energy transfers, a small detector with a low energy threshold may have better sensitivi

Jonathan B VanGeest, Kieran J Fogarty, William G Hervey, Robert A Hanson

Quantum technologies, including quantum computing, cryptography, and sensing, among others, are set to revolutionize sectors ranging from materials science to drug discovery. Despite their significant potential, the implications for public health have been largely overlooked, highlighting a critical gap in recognition and preparation. This oversight necessit

Maria Emelianenko, Guy B. Oldaker

While there exists a rich array of matrix column subset selection problem (CSSP) algorithms for use with interpolative and CUR-type decompositions, their use can often become prohibitive as the size of the input matrix increases. In an effort to address these issues, the authors in \cite{emelianenko2024adaptive} developed a general framework that pairs a col

Sabrina Drammis, Bowen Zheng, Karthik Srinivasan, Robert C. Berwick

A feedforward neural network using rectified linear units constructs a mapping from inputs to outputs by partitioning its input space into a set of convex regions where points within a region share a single affine transformation. In order to understand how neural networks work, when and why they fail, and how they compare to biological intelligence, we need

Derek Garton, Jeffrey Lin Thunder, Colin Weir

In this paper we present a new approach to counting the proportion of hyperelliptic curves of genus $g$ defined over a finite field $\mathbb{F}_q$ with a given $a$-number. In characteristic three this method gives exact probabilities for curves of the form $Y^2=f(X)$ with $f(X)\in\mathbb{F}_q[X]$ monic and cubefree, probabilities that match the data presente

Rana Badreddine

We study the zero-dispersion limit of the Calogero-Moser derivative NLS equation $$i\partial_tu+\partial_x^2 u \pm\,2D\Pi(|u|^2)u=0, \qquad x\in\mathbb{R},$$ starting from an initial data $u_0\in L^2_+(\mathbb{R})\cap L^\infty (\mathbb{R}),$ where $D=-i\partial_x,$ and $\Pi$ is the Szeg\H{o} projector defined as $\widehat{\Pi u}(\xi)=1_{[0,+\infty)}(\xi)\wid

Nadiia M. Kostogryz, Alexander I. Shapiro, Veronika Witzke, Robert H. Cameron

Stars appear darker at their limbs than at their disk centers because at the limb we are viewing the higher and cooler layers of stellar photospheres. Limb darkening derived from state-of-the-art stellar atmosphere models systematically fails to reproduce recent transiting exoplanet light curves from the Kepler, TESS, and JWST telescopes -- stellar brightnes

Lior Michaeli, Ramon Gao, Michael D. Kelzenberg, Claudio U. Hail

Ultrathin lightsails propelled by laser radiation pressure to relativistic speeds are currently the most promising route for flyby-based exoplanet exploration. However, there has been a notable lack of experimental characterization of key parameters essential for lightsail propulsion. Therefore, a model platform for optomechanical characterization of lightsa

Ethan Blaser, Chuanhao Li, Hongning Wang

The demand for collaborative and private bandit learning across multiple agents is surging due to the growing quantity of data generated from distributed systems. Federated bandit learning has emerged as a promising framework for private, efficient, and decentralized online learning. However, almost all previous works rely on strong assumptions of client hom

Markus Bläser, Julian Dörfler, Gorav Jindal

The problem PosSLP is the problem of determining whether a given straight-line program (SLP) computes a positive integer. PosSLP was introduced by Allender et al. to study the complexity of numerical analysis (Allender et al., 2009). PosSLP can also be reformulated as the problem of deciding whether the integer computed by a given SLP can be expressed as the

Christophe Lacave, Matthieu Ménard, Catherine Sulem

A central object in the analysis of the water wave problem is the Dirichlet-Neumann operator. This paper is devoted to the study of its spectrum in the context of the water wave system linearized near equilibrium in a domain with a variable bottom, assumed to be a $C^2$ periodic function. We use the analyticity of the Dirichlet-Neumann operator with respect

Nikita Rybin, Alexander Shapeev

Calculations of heat transport in crystalline materials have recently become mainstream, thanks to machine-learned interatomic potentials that allow for significant computational cost reductions while maintaining the accuracy of first-principles calculations. Moment tensor potentials (MTP) are among the most efficient and accurate models in this regard. In t

Hector Linares, Eduard Masana Salvador J Ribas, Manuel García-Gil, Martin Aubé

Zenith sky brightness maps in the V and B bands of the region of Catalonia are presented in this paper. For creating them we have used the light pollution numerical model Illumina v2. The maps have a sampling of 5x5 km for the whole region with an improved resolution of 1x1 km for one of the provinces within Catalonia, Tarragona. Before creating the final ma

Michael Unterkalmsteiner, Waleed Abdeen

Introduction: Taxonomies capture knowledge about a particular domain in a succinct manner and establish a common understanding among peers. Researchers use taxonomies to convey information about a particular knowledge area or to support automation tasks, and practitioners use them to enable communication beyond organizational boundaries. Aims: Despite this i

Michele L. Silverstein, Thomas Barclay, Joshua E. Schlieder, Karen A. Collins

The nearby LHS 1678 (TOI-696) system contains two confirmed planets and a wide-orbit, likely-brown-dwarf companion, which orbit an M2 dwarf with a unique evolutionary history. The host star occupies a narrow "gap" in the HR diagram lower main sequence, associated with the M dwarf fully convective boundary and long-term luminosity fluctuations. This system is

Gregory Moille, Pradyoth Shandilya, Alioune Niang, Curtis Menyuk

Kerr-induced synchronization (KIS) provides a new key tool for the control and stabilization of the repetition rate of a cavity soliton frequency comb. It enables direct external control of a given comb tooth of a dissipative Kerr soliton (DKS) thanks to its capture by an injected reference laser. Efficient KIS requires its coupling energy to be sufficiently

Hongyi Liu, Shaochen Zhong, Xintong Sun, Minghao Tian

Finetuning LLMs with LoRA has gained significant popularity due to its simplicity and effectiveness. Often, users may even find pluggable, community-shared LoRAs to enhance their base models for a specific downstream task of interest; enjoying a powerful, efficient, yet customized LLM experience with negligible investment. However, this convenient share-and-

Yurui Huang, Xuesen Cheng, Chaolin Tian, Xunyi Jiang

This study aims to investigate the influence of cross-border recruitment program in China, which confers scientists with a 'talent hat' including a startup package comprising significant bonuses, pay, and funding, on their future performance and career development. By curating a unique dataset from China's 10-year talent recruitment program, we employed mult

Jonathan Zong, Isabella Pedraza Pineros, Mengzhu Katie Chen, Daniel Hajas

We present Umwelt, an authoring environment for interactive multimodal data representations. In contrast to prior approaches, which center the visual modality, Umwelt treats visualization, sonification, and textual description as coequal representations: they are all derived from a shared abstract data model, such that no modality is prioritized over the oth

Alexander Asemota, Giles Hooker

Counterfactual explanations are a common approach to providing recourse to data subjects. However, current methodology can produce counterfactuals that cannot be achieved by the subject, making the use of counterfactuals for recourse difficult to justify in practice. Though there is agreement that plausibility is an important quality when using counterfactua

Iasonas Nikolaou, Evimaria Terzi

In this work, we formulate the problem of team formation amidst conflicts. The goal is to assign individuals to tasks, with given capacities, taking into account individuals' task preferences and the conflicts between them. Using dependent rounding schemes as our main toolbox, we provide efficient approximation algorithms. Our framework is extremely versatil