February 2024 arXiv papers — page 170

Joshua Park, Priyanshu Mahey, Ore Adeniyi

Electroencephalography (EEG) plays a vital role in recording brain activities and is integral to the development of brain-computer interface (BCI) technologies. However, the limited availability and high variability of EEG signals present substantial challenges in creating reliable BCIs. To address this issue, we propose a practical solution drawing on the l

Yuan Gao, Haokun Chen, Xiang Wang, Zhicai Wang

Machine learning models have demonstrated remarkable efficacy and efficiency in a wide range of stock forecasting tasks. However, the inherent challenges of data scarcity, including low signal-to-noise ratio (SNR) and data homogeneity, pose significant obstacles to accurate forecasting. To address this issue, we propose a novel approach that utilizes artific

Jiayi Deng, Mehdi Molaei, Nicholas G. Chisholm, Kathleen J. Stebe

The behavior of fluid interfaces far from equilibrium plays central roles in nature and in industry. Active swimmers trapped at interfaces can alter transport at fluid boundaries with far reaching implications. Swimmers can become trapped at interfaces in diverse configurations and swim persistently in these surface adhered states. The self-propelled motion

Chit Siong Lau, Sarthak Das, Ivan A. Verzhbitskiy, Ding Huang

Despite over a decade of intense research efforts, the full potential of two-dimensional transition metal dichalcogenides continues to be limited by major challenges. The lack of compatible and scalable dielectric materials and integration techniques restrict device performances and their commercial applications Conventional dielectric integration techniques

Tomoyuki Hanawa, Antonio Garufi, Linda Podio, Claudio Codella

DG Tau is a nearby T Tauri star associated with a collimated jet, a circumstellar disk and a streamer a few hundred au long. The streamer connects to the disk at $\sim$50 au from DG Tau. At this location SO emission is observed, likely due to the release of sulphur from dust grains caused by the shock of the impact of the accretion streamer onto the disk. We

Enneng Yang, Li Shen, Zhenyi Wang, Guibing Guo

Multi-task learning (MTL) compresses the information from multiple tasks into a unified backbone to improve computational efficiency and generalization. Recent work directly merges multiple independently trained models to perform MTL instead of collecting their raw data for joint training, greatly expanding the application scenarios of MTL. However, by visua

Shanshan Wang, Ruoyou Wu, Sen Jia, Alou Diakite

Deep learning (DL) has emerged as a leading approach in accelerating MR imaging. It employs deep neural networks to extract knowledge from available datasets and then applies the trained networks to reconstruct accurate images from limited measurements. Unlike natural image restoration problems, MR imaging involves physics-based imaging processes, unique dat

Shu-Yi Kong, Jun-Tao Zhu, Shu Chen, Jun He

This study investigates the production of open-charm pentaquark molecular states, specifically $N\bar{D}^*$ and $\bar{N}\bar{D}^*$, within the $B^0 \rightarrow \bar{D}^0 p \bar{p}$ decay. We examine the invariant mass spectra of $p\bar{D}^0$ and $\bar{p}\bar{D}^0$, incorporating the rescattering process through a quasipotential Bethe-Salpeter equation approa

Guanbo Wang, Alexander Levis, Jon Steingrimsson, Issa Dahabreh

When extending inferences from a randomized trial to a new target population, the transportability condition for conditional difference effect measures is invoked to identify the marginal causal mean difference in the target population. However, many clinical investigators believe that conditional relative effect measures are more likely to be "transportable

Jiafei Lyu, Le Wan, Xiu Li, Zongqing Lu

Recently, there are many efforts attempting to learn useful policies for continuous control in visual reinforcement learning (RL). In this scenario, it is important to learn a generalizable policy, as the testing environment may differ from the training environment, e.g., there exist distractors during deployment. Many practical algorithms are proposed to ha

Junze Deng, Yuan Cheng, Shaofeng Zou, Yingbin Liang

Contextual Markov decision processes (CMDPs) describe a class of reinforcement learning problems in which the transition kernels and reward functions can change over time with different MDPs indexed by a context variable. While CMDPs serve as an important framework to model many real-world applications with time-varying environments, they are largely unexplo

Zhenyu Zhou, Junhui Chen, Namin Wang, Lantian Li

Data augmentation (DA) has gained widespread popularity in deep speaker models due to its ease of implementation and significant effectiveness. It enriches training data by simulating real-life acoustic variations, enabling deep neural networks to learn speaker-related representations while disregarding irrelevant acoustic variations, thereby improving robus

Shicong Cen, Jincheng Mei, Hanjun Dai, Dale Schuurmans

Stochastic dominance serves as a general framework for modeling a broad spectrum of decision preferences under uncertainty, with risk aversion as one notable example, as it naturally captures the intrinsic structure of the underlying uncertainty, in contrast to simply resorting to the expectations. Despite theoretical appeal, the application of stochastic do

Zenan Ling, Longbo Li, Zhanbo Feng, Yixuan Zhang

Deep equilibrium models (DEQs), as a typical implicit neural network, have demonstrated remarkable success on various tasks. There is, however, a lack of theoretical understanding of the connections and differences between implicit DEQs and explicit neural network models. In this paper, leveraging recent advances in random matrix theory (RMT), we perform an

Raha Moraffah, Paras Sheth, Saketh Vishnubhatla, Huan Liu

Machine Learning (ML) has become an integral aspect of many real-world applications. As a result, the need for responsible machine learning has emerged, focusing on aligning ML models to ethical and social values, while enhancing their reliability and trustworthiness. Responsible ML involves many issues. This survey addresses four main issues: interpretabili

Raha Moraffah, Huan Liu

Sentence-level attacks craft adversarial sentences that are synonymous with correctly-classified sentences but are misclassified by the text classifiers. Under the black-box setting, classifiers are only accessible through their feedback to queried inputs, which is predominately available in the form of class probabilities. Even though utilizing class probab

Jisheng Bai, Mou Wang, Haohe Liu, Han Yin

Acoustic scene classification (ASC) is a crucial research problem in computational auditory scene analysis, and it aims to recognize the unique acoustic characteristics of an environment. One of the challenges of the ASC task is the domain shift between training and testing data. Since 2018, ASC challenges have focused on the generalization of ASC models acr

Sakaé Fuchino, Toshimichi Usuba

The Recurrence Axiom for a class $\mathcal{P}$ of \pos\ and a set $A$ of parameters is an axiom scheme in the language of ZFC asserting that if a statement with parameters from $A$ is forced by a poset in $\mathcal{P}$, then there is a ground containing the parameters and satisfying the statement. The tightly super-$C^{(\infty)}$-$\mathcal{P}$-Laver generic

Alan Chung, Amin Saberi, Morgane Austern

This paper derives statistical guarantees for the performance of Graph Neural Networks (GNNs) in link prediction tasks on graphs generated by a graphon. We propose a linear GNN architecture (LG-GNN) that produces consistent estimators for the underlying edge probabilities. We establish a bound on the mean squared error and give guarantees on the ability of L

Imaad Zaffar Khan, Amaan Aijaz Sheikh, Utkarsh Sinha

With the abundance of data and information in todays time, it is nearly impossible for man, or, even machine, to go through all of the data line by line. What one usually does is to try to skim through the lines and retain the absolutely important information, that in a more formal term is called summarization. Text summarization is an important task that ai

Arkadeep Datta, Arani Mukhopadhyay, Amitava Datta, Ranjan Ganguly

The COVID-19 pandemic has profoundly reshaped our lives, prompting a search for solutions to its far-reaching effects. Vaccines emerged as a beacon of hope, yet reaching remote areas faces last-mile hurdles and cost issues due to loss of vaccine potency due to poor temperature regulation of the storage units and unanticipated vaccine wastage en route, a comm

Zeinab Salehi, Yijun Chen, Ian R. Petersen, Elizabeth L. Ratnam

In this paper, we consider microgrids that interconnect prosumers with distributed energy resources and dynamic loads. Prosumers are connected through the microgrid to trade energy and gain profit while respecting the network constraints. We establish a local energy market by defining a competitive equilibrium which balances energy and satisfies voltage cons

Daniel W. Cranston, Jiaao Li, Zhouningxin Wang, Chunyan Wei

We study the problem of finding homomorphisms into odd cycles from planar graphs with high odd-girth. The Jaeger-Zhang conjecture states that every planar graph of odd-girth at least $4k+1$ admits a homomorphism to the odd cycle $C_{2k+1}$. The $k=1$ case is the well-known Gr\"otzsch's $3$-coloring theorem. For general $k$, in 2013 Lov\'asz, Thomassen, Wu, a

Zijian Zhang, Jieao Zhu, Linglong Dai, Robert W. Heath

Fluid antenna systems (FASs) can reconfigure their locations freely within a spatially continuous space. To keep favorable antenna positions, the channel state information (CSI) acquisition for FASs is essential. While some techniques have been proposed, most existing FAS channel estimators require several channel assumptions, such as slow variation and angu

Xiaoxing Wang, Jiaxing Li, Chao Xue, Wei Liu

BayesianOptimization(BO) is a sample-efficient black-box optimizer, and extensive methods have been proposed to build the absolute function response of the black-box function through a probabilistic surrogate model, including Tree-structured Parzen Estimator (TPE), random forest (SMAC), and Gaussian process (GP). However, few methods have been explored to es

Weihan Li, Chengrui Li, Yule Wang, Anqi Wu

Studying the complex interactions between different brain regions is crucial in neuroscience. Various statistical methods have explored the latent communication across multiple brain regions. Two main categories are the Gaussian Process (GP) and Linear Dynamical System (LDS), each with unique strengths. The GP-based approach effectively discovers latent vari

Saeid Mehrdad, Seyed AmirHossein Janani

Nonlinear hyperspectral unmixing has recently received considerable attention, as linear mixture models do not lead to an acceptable resolution in some problems. In fact, most nonlinear unmixing methods are designed by assuming specific assumptions on the nonlinearity model which subsequently limits the unmixing performance. In this paper, we propose an unsu

Payal Mohapatra, Lixu Wang, Qi Zhu

Pattern recognition is a fundamental task in continuous sensing applications, but real-world scenarios often experience distribution shifts that necessitate learning generalizable representations for such tasks. This challenge is exacerbated with time-series data, which also exhibit inherent nonstationarity--variations in statistical and spectral properties

Zheng-Yang Zhuang, Zhongbo Yan

Two-dimensional systems with honeycomb lattice are known to be a paradigmatic platform to explore the various types of Hall effects, owing to that the interplay of lattice geometry, spin-orbit coupling and magnetism can give rise to very rich features in the quantum geometry of wave functions. In this work, we consider honeycomb topological antiferromagets t

Guanbo Wang, Alexander Levis, Jon Steingrimsson, Issa Dahabreh

Investigators often use multi-source data (e.g., multi-center trials, meta-analyses of randomized trials, pooled analyses of observational cohorts) to learn about the effects of interventions in subgroups of some well-defined target population. Such a target population can correspond to one of the data sources of the multi-source data or an external populati

Giuseppe Mingione

Nonuniform ellipticity is a classical topic in the theory of partial differential equations. While several results in regularity theory have been adding up over decades, many basic issues, as for instance the validity of Schauder theory and sharp dependence of regularity upon data, remained opened for a while. In these notes we give an overview of recent res

Ceren Cengiz, Shima Shahab

Acoustic holographic lenses (AHLs) show great potential for sound manipulation. These lenses store the phase and amplitude profile of the desired wavefront when illuminated by a single acoustic source to reconstruct focused ultrasound (FUS) pressure fields, induce localized heating, and achieve temporal and spatial thermal effects in acousto-thermal material

Raha Moraffah, Shubh Khandelwal, Amrita Bhattacharjee, Huan Liu

Adversarial purification is a defense mechanism for safeguarding classifiers against adversarial attacks without knowing the type of attacks or training of the classifier. These techniques characterize and eliminate adversarial perturbations from the attacked inputs, aiming to restore purified samples that retain similarity to the initially attacked ones and

YuQing Xie, Tess Smidt

Equivariant neural networks (ENNs) have been shown to be extremely effective in applications involving underlying symmetries. By construction ENNs cannot produce lower symmetry outputs given a higher symmetry input. However, symmetry breaking occurs in many physical systems and we may obtain a less symmetric stable state from an initial highly symmetric one.

Rohin Manvi, Samar Khanna, Marshall Burke, David Lobell

Large Language Models (LLMs) inherently carry the biases contained in their training corpora, which can lead to the perpetuation of societal harm. As the impact of these foundation models grows, understanding and evaluating their biases becomes crucial to achieving fairness and accuracy. We propose to study what LLMs know about the world we live in through t

Boris S. Kalita, John D. Silverman, Emanuele Daddi, Wilfried Mercier

Resolved stellar morphology of $z>1$ galaxies was inaccessible before JWST. This limitation, due to the impact of dust on rest-frame UV light, had withheld major observational conclusions required to understand the importance of clumps in galaxy evolution. Essentially independent of this issue, we use the rest-frame near-IR for a stellar-mass dependent clump

Daisuke Takahashi, Shohei Shimizu, Takuma Tanaka

Explainable artificial intelligence (XAI) has helped elucidate the internal mechanisms of machine learning algorithms, bolstering their reliability by demonstrating the basis of their predictions. Several XAI models consider causal relationships to explain models by examining the input-output relationships of prediction models and the dependencies between fe

Konstantinos Kravvaris, Sofia Quaglioni, Petr Navratil

The no-core shell model (NCSM) is an \textit{ab initio} method that solves the nuclear many-body problem by expanding the many-particle wave function into a (typically) harmonic oscillator basis and minimizing the energy to obtain the expansion coefficients. Extensions of the NCSM, such as its coupling with microscopic-cluster basis states, further allow for

Thomas Lancaster

Generative AI is changing the way in which humans seek to find answers to questions in different fields including on the gig economy and labour markets, but there is limited information available about closely ChatGPT simulated output matches that obtainable from existing question and answer platforms. This paper uses ChatGPT as a research assistant to explo

Tobin South, Alexander Camuto, Shrey Jain, Shayla Nguyen

In a world of increasing closed-source commercial machine learning models, model evaluations from developers must be taken at face value. These benchmark results-whether over task accuracy, bias evaluations, or safety checks-are traditionally impossible to verify by a model end-user without the costly or impossible process of re-performing the benchmark on b

Minju Sim, Hongjun An, Zorawar Wadiasingh

We investigated the multiband emission from the pulsar binaries XSS J12270-4859, PSR J2039-5617, and PSR J2339-0533, which exhibit orbital modulation in the X-ray and gamma-ray bands. We constructed the sources' broadband spectral energy distributions and multiband orbital light curves by supplementing our X-ray measurements with published gamma-ray results,

Shengjie Gong, Lingxiao Huang, Shuangping Huang, Yuyi Wang

Multi-winner voting plays a crucial role in selecting representative committees based on voter preferences. Previous research has predominantly focused on single-stage voting rules, which are susceptible to manipulation during preference collection. In order to mitigate manipulation and increase the cost associated with it, we propose the introduction of mul

Yuji Kawamata, Ryoki Motai, Yukihiko Okada, Akira Imakura

The estimation of conditional average treatment effects (CATEs) is an important topic in many scientific fields. CATEs can be estimated with high accuracy if data distributed across multiple parties are centralized. However, it is difficult to aggregate such data owing to confidentiality or privacy concerns. To address this issue, we propose data collaborati

Y. Gua, C. T. Kong, D. D Zhangc, Y. J Baid

AI-driven TMD diagnostic system uses AI segmentation method to diagnose Temporomandibular Joint Disorders (TMD). By using segmentation, three important parts: temporal bone, temporomandibular joint (TMJ) disc and the condyle can be identified. The location and the size of each segment are used as the basic information to determine if the patient has a high c

Thiago Castilho de Mello, Felipe Yukihide Yasumura

We study the graded polynomial identities with a homogeneous involution on the algebra of upper triangular matrices endowed with a fine group grading. We compute their polynomial identities and a basis of the relatively free algebra, considering an arbitrary base field. We obtain the asymptotic behaviour of the codimension sequence when the characteristic of

Paul Ramond

In general relativity, the motion of an extended test body is influenced by its proper rotation, or spin. We present a covariant and physically self-consistent Hamiltonian framework to study this motion, up to quadratic order in the body's spin, including a spin-induced quadrupole, and in an arbitrary background spacetime. The choice of spin supplementary co

Rio Aguina-Kang, Maxim Gumin, Do Heon Han, Stewart Morris

We present a system for generating indoor scenes in response to text prompts. The prompts are not limited to a fixed vocabulary of scene descriptions, and the objects in generated scenes are not restricted to a fixed set of object categories -- we call this setting indoor scene generation. Unlike most prior work on indoor scene generation, our system does no

Ella Xi Wang, Thomas Nordlander, Sven Buder, Ioana Ciucă

Lithium's susceptibility to burning in stellar interiors makes it an invaluable tracer for delineating the evolutionary pathways of stars, offering insights into the processes governing their development. Observationally, the complex Li production and depletion mechanisms in stars manifest themselves as Li plateaus, and as Li-enhanced and Li-depleted regions

Lei Yang, Yossi Gilad, Mohammad Alizadeh

Set reconciliation, where two parties hold fixed-length bit strings and run a protocol to learn the strings they are missing from each other, is a fundamental task in many distributed systems. We present Rateless Invertible Bloom Lookup Tables (Rateless IBLT), the first set reconciliation protocol, to the best of our knowledge, that achieves low computation

Guangtao Zhang, Jiani Lin, Qijia Zhai, Huiyu Yang

Physics-Informed Neural Networks (PINNs) encounter accuracy limitations when solving the Allen--Cahn (AC) and Cahn--Hilliard (CH) partial differential equations (PDEs). To overcome this, we employ a novel loss function, Residuals-weighted Region Activation Evaluation (Residuals-RAE), featuring a { pre-training weight update scheme}. { Unlike conventional sel

John Bryant, Tahu Kukutai

Data on historical populations often extends no further than numbers of people by broad age-sex group, with nothing on numbers of births or deaths. Demographers studying these populations have experimented with methods that use the data on numbers of people to infer birth and death rates. These methods have, however, received little attention since they were

Peter Vamplew, Cameron Foale, Conor F. Hayes, Patrick Mannion

Research in multi-objective reinforcement learning (MORL) has introduced the utility-based paradigm, which makes use of both environmental rewards and a function that defines the utility derived by the user from those rewards. In this paper we extend this paradigm to the context of single-objective reinforcement learning (RL), and outline multiple potential

Pashmeen Kaur, Peter F. Craigmile

A popular and flexible time series model for counts is the generalized integer autoregressive process of order $p$, GINAR($p$). These Markov processes are defined using thinning operators evaluated on past values of the process along with a discretely-valued innovation process. This class includes the commonly used INAR($p$) process, defined with binomial th

Ricardo Silva

Blanket statements of equivalence between causal concepts and purely probabilistic concepts should be approached with care. In this short note, I examine a recent claim that counterfactual fairness is equivalent to demographic parity. The claim fails to hold up upon closer examination. I will take the opportunity to address some broader misunderstandings abo

Eric Yang Yu, Christopher Liao, Sathvik Ravi, Theodoros Tsiligkaridis

Recent advances in vision-language models have combined contrastive approaches with generative methods to achieve state-of-the-art (SOTA) on downstream inference tasks like zero-shot image classification. However, a persistent issue of these models for image classification is their out-of-distribution (OOD) generalization capabilities. We first show that whe

Mark Drvodelic, Mingming Gong, Andrew I. Webb

GraphRT is a graph based deep learning model that predicts the retention time (RT) of peptides in liquid chromatography tandem mass spectrometry (LC MSMS) experiments. Each amino acid is represented as a graph, capturing its atomic and structural properties through a graph neural network. This enables the model to understand not just the chemical composition

Kosei Matsumoto, Hiroyuki Hirashita, Kentaro Nagamine, Stefan van der Giessen

We aim to provide observational signatures of the dust size evolution in the ISM. In particular, we explore indicators of the polycyclic aromatic hydrocarbon (PAH) mass fraction ($q_{PAH}$), defined as the mass fraction of PAHs relative to total dust grains. In addition, we validate our dust evolution model by comparing the observational signatures from our

Zihan Wang, Yunxuan Li, Yuexin Wu, Liangchen Luo

Process supervision, using a trained verifier to evaluate the intermediate steps generated by a reasoner, has demonstrated significant improvements in multi-step problem solving. In this paper, to avoid the expensive effort of human annotation on the verifier training data, we introduce Model-induced Process Supervision (MiPS), a novel method for automating

Yan-Jun Zhao, Yu-Qi Wang, Yang Xue, Xun-Wei Xu

We investigate a square-lattice architecture of superconducting transmon qubits with inter-qubit interactions mediated by inductive couplers. Therein, the inductive couling between the qubit and couplers is suggested to be designed into the gradiometer form to intigimate the flux noise orginating from the environment. Via periodically modulating the couplers

Satpreet Harcharan Singh, Kevin Jiang, Kanchan Bhasin, Ashutosh Sabharwal

Semi-structured interviews (SSIs) are a commonly employed data-collection method in healthcare research, offering in-depth qualitative insights into subject experiences. Despite their value, the manual analysis of SSIs is notoriously time-consuming and labor-intensive, in part due to the difficulty of extracting and categorizing emotional responses, and chal

Thinh Phuoc Ngo, Khoa Tran Anh Dang, Son T. Luu, Kiet Van Nguyen

This paper presents the development process of a Vietnamese spoken language corpus for machine reading comprehension (MRC) tasks and provides insights into the challenges and opportunities associated with using real-world data for machine reading comprehension tasks. The existing MRC corpora in Vietnamese mainly focus on formal written documents such as Wiki

Bo Fu, Kai-Zhi Bai, Shun-Qing Shen

We report the discovery of the half-quantized mirror Hall effect, a novel quantum-anomaly induced by mirror symmetry in a strong topological insulator (TI) film. These films are known to host a pair of gapless Dirac cones associated with surface electrons. Our findings reveal that mirror symmetry assigns a unique mirror parity to each Dirac cone, resulting i

Haodong Lu, Dong Gong, Shuo Wang, Jason Xue

Out-of-distribution (OOD) detection aims to detect testing samples far away from the in-distribution (ID) training data, which is crucial for the safe deployment of machine learning models in the real world. Distance-based OOD detection methods have emerged with enhanced deep representation learning. They identify unseen OOD samples by measuring their distan

Zhuo Bin Siu, Anirban Kundu, Mansoor B. A. Jalil

Certain non-centrosymmetric materials with broken time-reversal symmetry may exhibit non-reciprocal transport behavior under an applied electric field in which the charge and spin currents contain components that are second order in the electric field. In this study, we investigate the second-order spin accumulation and charge and spin responses in the LaAlO

William Chen, Oier Mees, Aviral Kumar, Sergey Levine

Humans can quickly learn new behaviors by leveraging background world knowledge. In contrast, agents trained with reinforcement learning (RL) typically learn behaviors from scratch. We thus propose a novel approach that uses the vast amounts of general and indexable world knowledge encoded in vision-language models (VLMs) pre-trained on Internet-scale data f

M. Berrocoso, A. Fernández-Ros, M. E. Ramírez, J. M. Salamanca

Since 1987, Spain has been continuously developing several scientific projects, mainly based on Earth Sciences, in Geodesy, Geochemistry, Geology or Volcanology. The need of a geodetic reference frame when doing hydrographic and topographic mapping meant the organization of the earlier campaigns with the main goals of updating the existing cartography and of

Suraj Mishra, Danny Z. Chen

Medical image segmentation using deep neural networks has been highly successful. However, the effectiveness of these networks is often limited by inadequate dense prediction and inability to extract robust features. To achieve refined dense prediction, we propose densely decoded networks (ddn), by selectively introducing 'crutch' network connections. Such '

Jinwoo Ahn, Kyuseung Shin

Large Language Models (LLMs) frequently struggle with complex reasoning tasks, failing to construct logically sound steps towards the solution. In response to this behavior, users often try prompting the LLMs repeatedly in hopes of reaching a better response. This paper studies such repetitive behavior and its effect by defining a novel setting, Chain-of-Fee

Akash Venkateshwaran, Indu Kant Deo, Jasmin Jelovica, Rajeev K. Jaiman

The underwater radiated noise (URN) emanating from ships presents a significant threat to marine mammals, given their heavy reliance on hearing for essential life activities. The intensity of URN from ships is directly correlated to the speed, making speed reduction a crucial operational mitigation strategy. This paper presents a new multi-objective optimiza

Paolo Aluffi, Stephanie Chen, Matilde Marcolli

Using a known recursive formula for the Grothendieck classes of the moduli spaces $\overline{\mathcal M}_{0,n}$, we prove that they satisfy an asymptotic form of ultra-log-concavity as polynomials in the Lefschetz class. We also observe that these polynomials are $\gamma$-positive. Both properties, along with numerical evidence, support the conjecture that t

A. Sánchez-Alzola, J. Martí, A. García-Yeguas, A. J. Gil

In this paper we present the current crustal deformation model of Tenerife Island derived from daily CGPS time series processing (2008 to 2015). Our results include the position time series, a global velocity estimation and the current crustal deformation on the island in terms of strain tensors. We detect a measurable subsidence of 1.5 to 2 mm/yr. in the pr

Michael Wornow, Alejandro Lozano, Dev Dash, Jenelle Jindal

Matching patients to clinical trials is a key unsolved challenge in bringing new drugs to market. Today, identifying patients who meet a trial's eligibility criteria is highly manual, taking up to 1 hour per patient. Automated screening is challenging, however, as it requires understanding unstructured clinical text. Large language models (LLMs) offer a prom

Giuseppe Meneghini, Samuel Brem, Ermin Malic

Twisted van der Waals heterostructures show an intriguing interface exciton physics including hybridization effects and emergence of moiré potentials. Recent experiments have revealed that moiré-trapped excitons exhibit a remarkable dynamics, where excited states show lifetimes that are several orders of magnitude longer than those in monolayers. The origin

Soham Mandal, Manish Jain, Prabal K. Maiti

The occurrence of thermal transport phenomena is widespread, exerting a pivotal influence on the functionality of diverse electronic and thermo-electric energy-conversion devices. The traditional first-principles theory governing the thermal and thermodynamic characteristics of insulators relies on the perturbative treatment of interatomic potential and ad-h

Karin Baur, Colin Krawchuk

In this article we introduce a gluing operation on dimer models. This allows us to construct dimer quivers on arbitrary surfaces. We study how the associated dimer and boundary algebras behave under the gluing and how to determine them from the gluing components. We also use this operation to construct homogeneous dimer quivers on annuli.

Wenqi Li

The fusion rules in $\mathrm{Rep}_f D(G)$ for a finite group $G$ can be computed in terms of character inner products. Using an explicit formula for these fusion rules, we show that $\mathrm{Rep}_f D(G)$ is multiplicity free for two infinite families of finite groups: the Dihedral groups and the Dicyclic groups. In fact, we will compute all fusion rules in t

Edwin V. Bonilla, Pantelis Elinas, He Zhao, Maurizio Filippone

Estimating the structure of Bayesian networks as directed acyclic graphs (DAGs) from observational data is a fundamental challenge, particularly in causal discovery. Bayesian approaches excel by quantifying uncertainty and addressing identifiability, but key obstacles remain: (i) representing distributions over DAGs and (ii) estimating a posterior in the und

Paramita Barai

CMOS VLSI technology is the most dominant integration methodology prevailing in the world today. Various signal-processing blocks are made using analog or digital design techniques in MOS VLSI. An important component is the Memory unit used to store data. In the project a memory cell has been built up using analog design method. A capacitor is used as the ba

Xuanhe Zhou, Xinyang Zhao, Guoliang Li

Machine learning (ML) techniques for optimizing data management problems have been extensively studied and widely deployed in recent five years. However traditional ML methods have limitations on generalizability (adapting to different scenarios) and inference ability (understanding the context). Fortunately, large language models (LLMs) have shown high gene

Aditya Thimmaiah, Leonidas Lampropoulos, Christopher J. Rossbach, Milos Gligoric

We introduce Object Graph Programming (OGO), which enables reading and modifying an object graph (i.e., the entire state of the object heap) via declarative queries. OGO models the objects and their relations in the heap as an object graph thereby treating the heap as a graph database: each node in the graph is an object (e.g., an instance of a class or an i

Daniel Bienstock, Matias Villagra

We present a linear cutting-plane relaxation approach that rapidly proves tight lower bounds for the Alternating Current Optimal Power Flow Problem (ACOPF). Our method leverages outer-envelope linear cuts for well-known second-order cone relaxations for ACOPF along with modern cut management techniques. These techniques prove effective on a broad family of A

Xin Xia, Dejing Du, Xiaoshan Jiang, Yong Liu

A pico-second timing (PIST) front-end electronic chip has been developed using $55~\mathrm{nm}$ CMOS technology for future electron-positron collider experiments (namely Higgs factories). Extensive tests have been performed to evaluate the timing performance of a dedicated SiPM-readout system equipped with a PIST chip. The results show that the system timing

J. Wu, M. P. Carpenter, F. G. Kondev, R. V. F. Janssens

Two 0$^{+}$ states at 1294.5 and 1774.0 keV, together with three 2$^{+}$ and one 4$^{+}$ levels, were identified or unambiguously spin-parity assigned for the first time in $^{100}$Zr utilizing $\gamma$-ray spectroscopy and $\gamma$-$\gamma$ angular correlation techniques with the Gammasphere spectrometer, following the $\beta^{-}$ decay of neutron-rich, mas

Ned Cooper, Courtney Heldreth, Ben Hutchinson

Indigenous languages are historically under-served by Natural Language Processing (NLP) technologies, but this is changing for some languages with the recent scaling of large multilingual models and an increased focus by the NLP community on endangered languages. This position paper explores ethical considerations in building NLP technologies for Indigenous

Sabir Umarov

This paper is devoted to the general theory of systems of time-fractional differential-operator equations. The representation formulas for solutions of systems of ordinary differential equations with single (commensurate) fractional order is known through the matrix valued Mittag-Leffler function. Multi-order (incommensurate) systems with rational components

Yuka Hashimoto, Masahiro Ikeda, Hachem Kadri

Machine learning has a long collaborative tradition with several fields of mathematics, such as statistics, probability and linear algebra. We propose a new direction for machine learning research: $C^*$-algebraic ML $-$ a cross-fertilization between $C^*$-algebra and machine learning. The mathematical concept of $C^*$-algebra is a natural generalization of

Gaël Gendron, Bao Trung Nguyen, Alex Yuxuan Peng, Michael Witbrock

Despite impressive performance on language modelling and complex reasoning tasks, Large Language Models (LLMs) fall short on the same tasks in uncommon settings or with distribution shifts, exhibiting a lack of generalisation ability. By contrast, systems such as causal models, that learn abstract variables and causal relationships, can demonstrate increased

Ekzhin Ear, Brandon Bailey, Shouhuai Xu

Space is an emerging domain critical to humankind. Correspondingly, space cybersecurity is an emerging field with much research to be done. To help space cybersecurity practitioners better manage cyber risks, The Aerospace Corporation proposed Notional Risk Scores (NRS) within their Space Attack Research and Tactic Analysis (SPARTA) framework, which can be a

Bin Ren, Yawei Li, Jingyun Liang, Rakesh Ranjan

While it is crucial to capture global information for effective image restoration (IR), integrating such cues into transformer-based methods becomes computationally expensive, especially with high input resolution. Furthermore, the self-attention mechanism in transformers is prone to considering unnecessary global cues from unrelated objects or regions, intr

Eric Khiu, Hasti Toossi, David Anugraha, Jinyu Liu

Fine-tuning and testing a multilingual large language model is expensive and challenging for low-resource languages (LRLs). While previous studies have predicted the performance of natural language processing (NLP) tasks using machine learning methods, they primarily focus on high-resource languages, overlooking LRLs and shifts across domains. Focusing on LR

Nafiseh Nikeghbal, Amir Hossein Kargaran, Abbas Heydarnoori

Platforms such as GitHub and GitLab introduce Issue Report Templates (IRTs) to enable more effective issue management and better alignment with developer expectations. However, these templates are not widely adopted in most repositories, and there is currently no tool available to aid developers in generating them. In this work, we introduce GIRT-Model, an a

Yifeng He, Jiabo Huang, Yuyang Rong, Yiwen Guo

The remarkable capability of large language models (LLMs) in generating high-quality code has drawn increasing attention in the software testing community. However, existing code LLMs often demonstrate unsatisfactory capabilities in generating accurate and complete tests since they were trained on code snippets collected without differentiating between code

Justin S. Kang, Yigit E. Erginbas, Landon Butler, Ramtin Pedarsani

One of the key challenges in machine learning is to find interpretable representations of learned functions. The M\"obius transform is essential for this purpose, as its coefficients correspond to unique importance scores for sets of input variables. This transform is closely related to widely used game-theoretic notions of importance like the Shapley and Bh

Scott L. Dyer, Christian A. Femrite, Joshua D. Guttman, Julian P. Lanson

Assured Remote Execution on a device is the ability of suitably authorized parties to construct secure channels with known processes -- i.e. processes executing known code -- running on it. Assured Remote Execution requires a hardware basis including cryptographic primitives. In this paper, we show that a simple hardware-level mechanism called Cryptographica

Chen Feng, Ziquan Liu, Zhuo Zhi, Ilija Bogunovic

It is widely known that state-of-the-art machine learning models, including vision and language models, can be seriously compromised by adversarial perturbations. It is therefore increasingly relevant to develop capabilities to certify their performance in the presence of the most effective adversarial attacks. Our paper offers a new approach to certify the

Ruben Lier

Odd viscosity is a transport coefficient that can occur when fluids experience breaking of parity and time-reversal symmetry. Previous knowledge indicates that cylinders in incompressible odd viscous fluids, under no-slip boundary conditions, do not exhibit lift force, a phenomenon that poses challenges for the experimental detection of odd viscosity. This s

Neisarg Dave, Daniel Kifer, C. Lee Giles, Ankur Mali

This paper analyzes two competing rule extraction methodologies: quantization and equivalence query. We trained $3600$ RNN models, extracting $18000$ DFA with a quantization approach (k-means and SOM) and $3600$ DFA by equivalence query($L^{*}$) methods across $10$ initialization seeds. We sampled the datasets from $7$ Tomita and $4$ Dyck grammars and traine

Richard Demsyn-Jones

The purpose of modeling document relevance for search engines is to rank better in subsequent searches. Document-specific historical click-through rates can be important features in a dynamic ranking system which updates as we accumulate more sample. This paper describes the properties of several such features, and tests them in controlled experiments. Exten

Razvan-Gabriel Dumitru, Darius Peteleaza, Mihai Surdeanu

We introduce the concept of multiple temporal perspectives, a novel approach applicable to Recurrent Neural Network (RNN) architectures for enhancing their understanding of sequential data. This method involves maintaining diverse temporal views of previously encountered text, significantly enriching the language models' capacity to interpret context. To sho

Baha Zarrouki, Marios Spanakakis, Johannes Betz

Determining the optimal cost function parameters of Model Predictive Control (MPC) to optimize multiple control objectives is a challenging and time-consuming task. Multiobjective Bayesian Optimization (BO) techniques solve this problem by determining a Pareto optimal parameter set for an MPC with static weights. However, a single parameter set may not deliv