February 2024 arXiv papers — page 115

Taisuke Hosaka, Renato Portugal, Etsuo Segawa

In this paper, we analyze the dynamics of quantum walks on a graph structure resulting from the integration of a main connected graph $G$ and a secondary connected graph $G'$. This composite graph is formed by a disjoint union of $G$ and $G'$, followed by the contraction of a selected pair of vertices creating a cut vertex $v^*$ and leading to a unique form

Andrew Hundt, Julia Schuller, Severin Kacianka

Machine Learning (ML) and 'Artificial Intelligence' ('AI') methods tend to replicate and amplify existing biases and prejudices, as do Robots with AI. For example, robots with facial recognition have failed to identify Black Women as human, while others have categorized people, such as Black Men, as criminals based on appearance alone. A 'culture of modulari

Jin Li, Shoujin Wang, Qi Zhang, Longbing Cao

Recommender Systems (RS) have significantly advanced online content filtering and personalized decision-making. However, emerging vulnerabilities in RS have catalyzed a paradigm shift towards Trustworthy RS (TRS). Despite substantial progress on TRS, most efforts focus on data correlations while overlooking the fundamental causal nature of recommendations. T

Bo-Yang Zhang, Tao Zhu, Jing-Fei Zhang, Xin Zhang

Violation of Lorentz symmetry can result in two distinct effects in the propagation of the gravitational waves (GWs). One is a modified dispersion relation and another is a frequency-dependent damping of GWs. While the former has been extensively studied in the literature, in this paper we concentrate on the frequency-dependent damping effect that arises fro

Hirokazu Iwasawa, Yoshihiro Matsumori

This paper discusses the foundation of methods for accurately grasping interaction effects. The partial dependence (PD) and accumulated local effects (ALE) methods, which capture interaction effects as terms, are known as global model-agnostic methods in the interpretable machine learning field. ALE provides a functional decomposition of the prediction funct

Zhouyou Gu, Wibowo Hardjawana, Branka Vucetic

This paper considers opportunistic scheduler (OS) design using statistical channel state information~(CSI). We apply max-weight schedulers (MWSs) to maximize a utility function of users' average data rates. MWSs schedule the user with the highest weighted instantaneous data rate every time slot. Existing methods require hundreds of time slots to adjust the M

Srimanta Banerjee, Gulab C. Dewangan, Christian Knigge, Maria Georganti

We present a comprehensive multi-wavelength spectral analysis of the black hole X-ray binary MAXI J1820+070 during its 2018 outburst, utilizing AstroSat far UV, soft and hard X-ray data, along with (quasi-)simultaneous optical and X-ray data from Las Cumbres Observatory and NICER, respectively. In the soft state, we detect soft X-ray and UV/optical excess co

Siqi Peng, Hongyuan Yang, Akihiro Yamamoto

We propose BERT4FCA, a novel method for link prediction in bipartite networks, using formal concept analysis (FCA) and BERT. Link prediction in bipartite networks is an important task that can solve various practical problems like friend recommendation in social networks and co-authorship prediction in author-paper networks. Recent research has found that in

Zhaoming Kong, Xiaowei Yang

Noise removal in the standard RGB (sRGB) space remains a challenging task, in that the noise statistics of real-world images can be different in R, G and B channels. In fact, the green channel usually has twice the sampling rate in raw data and a higher signal-to-noise ratio than red/blue ones. However, the green channel prior (GCP) is often understated or i

Weifan Liu, Longqian Xu, Zezhou Yang, Xudong Zhang

Electro-sorption (ES) is a research frontier in electrochemical separation, with proven potential applications in desalination, wastewater treatment, and selective resource extraction. However, due to the limited adsorption capacity of film electrodes, ES requires short circuiting or circuit reversal, accompanied by solution-switch between the feed solution

Fabian Krause, Jan-Peter Calliess

In Statistical Arbitrage (StatArb), classical mean reversion trading strategies typically hinge on asset-pricing or PCA based models to identify the mean of a synthetic asset. Once such a (linear) model is identified, a separate mean reversion strategy is then devised to generate a trading signal. With a view of generalising such an approach and turning it t

David Gamarnik, Devin Smedira

We design a Quasi-Polynomial time deterministic approximation algorithm for computing the integral of a multi-dimensional separable function, supported by some underlying hyper-graph structure, appropriately defined. Equivalently, our integral is the partition function of a graphical model with continuous potentials. While randomized algorithms for high-dime

Meesam Jafri, Suraj Srivastava, Sunil Kumar, Aditya K. Jagannatham

Asynchronous distributed hybrid beamformers (ADBF) are conceived for minimizing the total transmit power subject to signal-to-interference-plus-noise ratio (SINR) constraints at the users. Our design requires only limited information exchange between the base stations (BSs) of the mmWave multi-cell coordinated (MCC) networks considered. To begin with, a semi

Mobeen Mahmood, Yuanxing Zhang, Robert Morawski, Tho Le-Ngoc

This study employs a uniform rectangular array (URA) sub-connected hybrid beamforming (SC-HBF) architecture to provide a novel self-interference (SI) suppression scheme in a full-duplex (FD) massive multiple-input multiple-output (mMIMO) system. Our primary objective is to mitigate the strong SI through the design of RF beamforming stages for uplink and down

Davin Choo, Kirankumar Shiragur, Caroline Uhler

Causal graph discovery is a significant problem with applications across various disciplines. However, with observational data alone, the underlying causal graph can only be recovered up to its Markov equivalence class, and further assumptions or interventions are necessary to narrow down the true graph. This work addresses the causal discovery problem under

Kai Guo, Hongzhi Wen, Wei Jin, Yaming Guo

Graph neural networks (GNNs) have exhibited remarkable performance under the assumption that test data comes from the same distribution of training data. However, in real-world scenarios, this assumption may not always be valid. Consequently, there is a growing focus on exploring the Out-of-Distribution (OOD) problem in the context of graphs. Most existing e

Yixiang Yao, Fei Wang, Srivatsan Ravi, Muhao Chen

Language Models as a Service (LMaaS) offers convenient access for developers and researchers to perform inference using pre-trained language models. Nonetheless, the input data and the inference results containing private information are exposed as plaintext during the service call, leading to privacy issues. Recent studies have started tackling the privacy

Jean-Baptiste Waring, Christophe Pere, Sebastien Le Beux

In order to enter the era of utility, noisy intermediate-scale quantum (NISQ) devices need to enable long-range entanglement of large qubit chains. However, due to the limited connectivity of superconducting NISQ devices, long-range entangling gates are realized in linear depth. Furthermore, a time-dependent degradation of the average CNOT gate fidelity is o

Kyle O'Brien, Nathan Ng, Isha Puri, Jorge Mendez

Machine learning models for text classification often excel on in-distribution (ID) data but struggle with unseen out-of-distribution (OOD) inputs. Most techniques for improving OOD robustness are not applicable to settings where the model is effectively a black box, such as when the weights are frozen, retraining is costly, or the model is leveraged via an

Jiancheng An, Chau Yuen, Yong Liang Guan, Marco Di Renzo

Stacked intelligent metasurfaces (SIM) are capable of emulating reconfigurable physical neural networks by relying on electromagnetic (EM) waves as carriers. They can also perform various complex computational and signal processing tasks. A SIM is fabricated by densely integrating multiple metasurface layers, each consisting of a large number of small meta-a

Alireza Fallah, Michael I. Jordan, Ali Makhdoumi, Azarakhsh Malekian

We study a producer's problem of selling a product to a continuum of privacy-conscious consumers, where the producer can implement third-degree price discrimination, offering different prices to different market segments. We consider a privacy mechanism that provides a degree of protection by probabilistically masking each market segment. We establish that t

Lei Fang, Yue Wang, Chenglong Ye

The intricate interplay between host organisms and their gut microbiota has catalyzed research into the microbiome's role in disease, shedding light on novel aspects of disease pathogenesis. However, the mechanisms through which the microbiome exerts its influence on disease remain largely unclear. In this study, we first introduce a structural equation mode

Xiaohe Li, Feilong Huang, Zide Fan, Fangli Mou

Trajectory prediction has garnered widespread attention in different fields, such as autonomous driving and robotic navigation. However, due to the significant variations in trajectory patterns across different scenarios, models trained in known environments often falter in unseen ones. To learn a generalized model that can directly handle unseen domains wit

Rachel Anderson, Alberto Avila, Bin Fu, Timothy Gomez

We introduce a new model of \emph{step} Chemical Reaction Networks (step CRNs), motivated by the step-wise addition of materials in standard lab procedures. Step CRNs have ordered reactants that transform into products via reaction rules over a series of steps. We study an important subset of weak reaction rules, \emph{void} rules, in which chemical species

Haotian Sun, Yuchen Zhuang, Wei Wei, Chao Zhang

Adapting state-of-the-art Large Language Models (LLMs) like GPT-4 and Gemini for specific tasks is challenging. Due to the opacity in their parameters, embeddings, and even output probabilities, existing fine-tuning adaptation methods are inapplicable. Consequently, adapting these black-box LLMs is only possible through their API services, raising concerns a

C. Chong, A. Geisler, P. G. Kevrekidis, G. Biondini

In the present work we revisit the shock wave dynamics in a granular chain with precompression. By approximating the model by an $\alpha$-Fermi-Pasta-Ulam-Tsingou chain, we leverage the connection of the latter in the strain variable formulation to two separate integrable models, one continuum, namely the KdV equation, and one discrete, namely the Toda latti

Kassie Povinelli, Yuhang Zhao

Social virtual reality (VR) serves as a vital platform for transgender individuals to explore their identities through avatars and foster personal connections within online communities. However, it presents a challenge: the disconnect between avatar embodiment and voice representation, often leading to misgendering and harassment. Prior research acknowledges

Sheng Liu, Zihan Wang, Yuxiao Chen, Qi Lei

Reconstruction attacks and defenses are essential in understanding the data leakage problem in machine learning. However, prior work has centered around empirical observations of gradient inversion attacks, lacks theoretical grounding, and cannot disentangle the usefulness of defending methods from the computational limitation of attacking methods. In this w

Jingyang Zhao, Mingyu Xiao

Given an edge-weighted metric complete graph with $n$ vertices, the maximum weight metric triangle packing problem is to find a set of $n/3$ vertex-disjoint triangles with the total weight of all triangles in the packing maximized. Several simple methods can lead to a 2/3-approximation ratio. However, this barrier is not easy to break. Chen et al. proposed a

Jozsef Solymosi

We describe constructions of infinite graphs which are not representable as integral graphs in the plane, addressing a question of Erd\H{o}s. We also mention some related problems.

Meng-Hua Chen, Li-Xin Li, Qiu-Hong Chen, Rui-Chong Hu

The discovery of the radioactively powered kilonova AT2017gfo, associated with the short-duration gamma-ray burst GRB 170817A and the gravitational wave source GW170817, has provided the first direct evidence supporting binary neutron star mergers as crucial astrophysical sites for the synthesis of heavy elements beyond iron through $r$-process nucleosysthes

Haoran Wang

We prove the dispersive and Strichartz estimates for solutions to the wave equation with a class of many-electric potentials in spatial dimension three. To obtain the desired dispersive estimate, based on the spectral properties of the Schr\"odinger operator involved, we subsequently prove the dispersive estimate for the corresponding Schr\"odinger semigroup

Sizhe Yang, Qian Luo, Anumpam Pani, Yanchao Yang

Embodied agents capable of complex physical skills can improve productivity, elevate life quality, and reshape human-machine collaboration. We aim at autonomous training of embodied agents for various tasks involving mainly large foundation models. It is believed that these models could act as a brain for embodied agents; however, existing methods heavily re

Aaron Traylor, Jack Merullo, Michael J. Frank, Ellie Pavlick

Models based on the Transformer neural network architecture have seen success on a wide variety of tasks that appear to require complex "cognitive branching" -- or the ability to maintain pursuit of one goal while accomplishing others. In cognitive neuroscience, success on such tasks is thought to rely on sophisticated frontostriatal mechanisms for selective

Mohammad Ghazi Vakili, Christoph Gorgulla, AkshatKumar Nigam, Dmitry Bezrukov

The discovery of small molecules with therapeutic potential is a long-standing challenge in chemistry and biology. Researchers have increasingly leveraged novel computational techniques to streamline the drug development process to increase hit rates and reduce the costs associated with bringing a drug to market. To this end, we introduce a quantum-classical

Hiroyuki Namba, Shota Horiguchi, Masaki Hamamoto, Masashi Egi

Data cleansing aims to improve model performance by removing a set of harmful instances from the training dataset. Data Shapley is a common theoretically guaranteed method to evaluate the contribution of each instance to model performance; however, it requires training on all subsets of the training data, which is computationally expensive. In this paper, we

Mandar Pitale, Alireza Abbaspour, Devesh Upadhyay

This paper explores the role and challenges of Artificial Intelligence (AI) algorithms, specifically AI-based software elements, in autonomous driving systems. These AI systems are fundamental in executing real-time critical functions in complex and high-dimensional environments. They handle vital tasks like multi-modal perception, cognition, and decision-ma

Jiachen Lu, Ming Nie, Bozhou Zhang, Reyuan Peng

The extraction of road network is essential for the generation of high-definition maps since it enables the precise localization of road landmarks and their interconnections. However, generating road network poses a significant challenge due to the conflicting underlying combination of Euclidean (e.g., road landmarks location) and non-Euclidean (e.g., road t

Cosme Louart

Following the concentration of the measure theory formalism, we consider the transformation $\Phi(Z)$ of a random variable $Z$ having a general concentration function $\alpha$. If the transformation $\Phi$ is $\lambda$-Lipschitz with $\lambda>0$ deterministic, the concentration function of $\Phi(Z)$ is immediately deduced to be equal to $\alpha(\cdot/\lambda

Linh Trinh, Alif Anzuman, Eric Batkhuu, Dychen Chan

TurtleRabbit is a new RoboCup SSL team from Western Sydney University. This team description paper presents our approach in navigating some of the challenges in developing a new SSL team from scratch. SSL is dominated by teams with extensive experience and customised equipment that has been developed over many years. Here, we outline our approach in overcomi

Wen Teng

In this paper, we first introduce the concept and representations of modified $\lambda$-differential Lie-Yamaguti algebras. We then establish the cohomology of a modified $\lambda$-differential Lie-Yamaguti algebra with coefficients in a representation. As applications, we investigate the formal deformations and abelian extensions of modified $\lambda$-diffe

Eduardo Fernández

We prove the existence of a subclass of overtwisted contact structures, called strongly overtwisted, on a 3-manifold that satisfy a complete h-principle without prescribing the contact structures over any subset of the 3-manifold. As a consequence, the homotopy type of the space of overtwisted disk embeddings into a strongly overtwisted contact 3-manifold is

Malcolm S. Carroll, James R. Wootton, Andrew W. Cross

Logical qubits encoded into a quantum code exhibit improved error rates when the physical error rates are sufficiently low, below the pseudothreshold. Logical error rates and pseudothresholds can be estimated for specific circuits and noise models, and these estimates provide approximate goals for qubit performance. However, estimates often assume uniform er

Lingyun Zhong

Financial fraud detection poses a typical challenge characterized by class imbalance, where instances of fraud are extremely rare but can lead to unpredictable economic losses if misidentified. Precisely classifying these critical minority samples represents a challenging task within the classification. The primary difficulty arises from mainstream classifie

Mohammad Mehrabi, Stefan Wager

Doubly robust methods hold considerable promise for off-policy evaluation in Markov decision processes (MDPs) under sequential ignorability: They have been shown to converge as $1/\sqrt{T}$ with the horizon $T$, to be statistically efficient in large samples, and to allow for modular implementation where preliminary estimation tasks can be executed using sta

AprilPyone MaungMaung, Huy H. Nguyen, Hitoshi Kiya, Isao Echizen

We propose a method for generating spurious features by leveraging large-scale text-to-image diffusion models. Although the previous work detects spurious features in a large-scale dataset like ImageNet and introduces Spurious ImageNet, we found that not all spurious images are spurious across different classifiers. Although spurious images help measure the

A. Schmidt, A. W. Denniston, E. M. Seroka, N. Barnea

We study the relation between measured high-$x_B$, high-$Q^2$, Helium-3 to Tritium, $(e,e')$ inclusive-scattering cross-section ratios and the relative abundance of high-momentum neutron-proton ($np$) and proton-proton ($pp$) short-range correlated (SRC) nucleon pairs in three-body ($A=3$) nuclei. Analysis of this data using a simple pair-counting cross-sect

Lirong Wu, Yufei Huang, Cheng Tan, Zhangyang Gao

Compound-Protein Interaction (CPI) prediction aims to predict the pattern and strength of compound-protein interactions for rational drug discovery. Existing deep learning-based methods utilize only the single modality of protein sequences or structures and lack the co-modeling of the joint distribution of the two modalities, which may lead to significant pe

Anatoli Ivanov, Sergiy Shelyag

We construct stable periodic solutions for a simple form nonlinear delay differential equation (DDE) with a periodic coefficient. The equation involves one underlying nonlinearity with the multiplicative periodic coefficient. The well-known idea of reduction to interval maps is used in the case under consideration, when both the defining nonlinearity and the

Qingchun Ji, Jun Yao

In this paper, we develop $L^2$ theory for Riemannian and Hermitian foliations on manifolds with basic boundary. We establish a decomposition theorem, various vanishing theorems, a twisted duality theorem for basic cohomologies and an extension theorem for basic forms of induced Riemannian foliation on the boundary. We prove the complex analogues for Hermiti

Janani Kugarajeevan, Thanikasalam Kokul, Amirthalingam Ramanan, Subha Fernando

One-stream Transformer trackers have shown outstanding performance in challenging benchmark datasets over the last three years, as they enable interaction between the target template and search region tokens to extract target-oriented features with mutual guidance. Previous approaches allow free bidirectional information flow between template and search toke

Andrea Coladangelo, Saachi Mutreja

It is well-known that digital signatures can be constructed from one-way functions in a black-box way. While one-way functions are essentially the minimal assumption in classical cryptography, this is not the case in the quantum setting. A variety of qualitatively weaker and inherently quantum assumptions (e.g. EFI pairs, one-way state generators, and pseudo

Dan MacKinlay, Russell Tsuchida, Dan Pagendam, Petra Kuhnert

Efficient inference in high-dimensional models is a central challenge in machine learning. We introduce the Gaussian Ensemble Belief Propagation (GEnBP) algorithm, which combines the strengths of the Ensemble Kalman Filter (EnKF) and Gaussian Belief Propagation (GaBP) to address this challenge. GEnBP updates ensembles of prior samples into posterior samples

Tzu-Chien Hsueh, Yeshaiahu Fainman, Bill Lin

A system-on-chip (SoC) photonic-electronic linear-algebra accelerator with the features of wavelength-division-multiplexing (WDM) based broadband photodetections and high-dimensional matrix-inversion operations fabricated in advanced monolithic silicon-photonics (M-SiPh) semiconductor process technology is proposed to achieve substantial leaps in computation

Wilbert Pumacay, Ishika Singh, Jiafei Duan, Ranjay Krishna

To realize effective large-scale, real-world robotic applications, we must evaluate how well our robot policies adapt to changes in environmental conditions. Unfortunately, a majority of studies evaluate robot performance in environments closely resembling or even identical to the training setup. We present THE COLOSSEUM, a novel simulation benchmark, with 2

Xiao-Gang He, Zhong-Lv Huang, Ming-Wei Li, Chia-Wei Liu

The recent measurements of $h \to Z \gamma$ from ATLAS and CMS show an excess of the signal strength $\mu_Z = (\sigma\cdot{\cal B})_{\mathrm{obs}}/(\sigma\cdot{\cal B})_{\mathrm{SM}}=2.2\pm 0.7$, normalized as 1 in the standard model~(SM). If confirmed, it would be a signal of new physics (NP) beyond the SM. We study NP explanation for this excess. In genera

Karthik Sreedhar, Lydia Chilton

When creating policies, plans, or designs for people, it is challenging for designers to foresee all of the ways in which people may reason and behave. Recently, Large Language Models (LLMs) have been shown to be able to simulate human reasoning. We extend this work by measuring LLMs ability to simulate strategic reasoning in the ultimatum game, a classic ec

Chia Min Tan, Emmanuel Fonseca, Kathryn Crowter, Fengqiu Adam Dong

We performed near-daily observations on the binary pulsars PSR J0218+4232, PSR J1518+4904 and PSR J2023+2853 with the Canadian Hydrogen Intensity Mapping Experiment (CHIME). For the first time, we detected the Shapiro time delay in all three pulsar-binary systems, using only 2--4 years of CHIME/Pulsar timing data. We measured the pulsar masses to be $1.49^{+

Sung Woong Cho, Jae Yong Lee, Hyung Ju Hwang

Scientific computing using deep learning has seen significant advancements in recent years. There has been growing interest in models that learn the operator from the parameters of a partial differential equation (PDE) to the corresponding solutions. Deep Operator Network (DeepONet) and Fourier Neural operator, among other models, have been designed with str

Alessandro Alla, Agnese Pacifico

We address the control of Partial Differential equations (PDEs) with unknown parameters. Our objective is to devise an efficient algorithm capable of both identifying and controlling the unknown system. We assume that the desired PDE is observable provided a control input and an initial condition. The method works as follows, given an estimated parameter con

Minjong Cheon, Daehyun Kang, Yo-Hwan Choi, Seon-Yu Kang

Modern deep learning techniques, which mimic traditional numerical weather prediction (NWP) models and are derived from global atmospheric reanalysis data, have caused a significant revolution within a few years. In this new paradigm, our research introduces a novel strategy that deviates from the common dependence on high-resolution data, which is often con

Ayesha Siddika Nipu, Siming Liu, Anthony Harris

Multi-Agent Reinforcement Learning (MARL) algorithms are widely adopted in tackling complex tasks that require collaboration and competition among agents in dynamic Multi-Agent Systems (MAS). However, learning such tasks from scratch is arduous and may not always be feasible, particularly for MASs with a large number of interactive agents due to the extensiv

Chenghao Xiao, Zhuoxu Huang, Danlu Chen, G Thomas Hudson

Pretrained language models are long known to be subpar in capturing sentence and document-level semantics. Though heavily investigated, transferring perturbation-based methods from unsupervised visual representation learning to NLP remains an unsolved problem. This is largely due to the discreteness of subword units brought by tokenization of language models

Ray Coden Mercurius, Ehsan Ahmadi, Soheil Mohamad Alizadeh Shabestary, Amir Rasouli

Accurate prediction of pedestrians' future motions is critical for intelligent driving systems. Developing models for this task requires rich datasets containing diverse sets of samples. However, the existing naturalistic trajectory prediction datasets are generally imbalanced in favor of simpler samples and lack challenging scenarios. Such a long-tail effec

Fan Lyu, Kaile Du, Yuyang Li, Hanyu Zhao

Continual Test-Time Adaptation (CTTA) task investigates effective domain adaptation under the scenario of continuous domain shifts during testing time. Due to the utilization of solely unlabeled samples, there exists significant uncertainty in model updates, leading CTTA to encounter severe error accumulation issues. In this paper, we introduce VCoTTA, a var

Ryoya Fukasaku, Kei Hirose, Yutaro Kabata, Keisuke Teramoto

In exploratory factor analysis, model parameters are usually estimated by maximum likelihood method. The maximum likelihood estimate is obtained by solving a complicated multivariate algebraic equation. Since the solution to the equation is usually intractable, it is typically computed with continuous optimization methods, such as Newton-Raphson methods. Wit

Shinsaku Sakaue, Han Bao, Taira Tsuchiya, Taihei Oki

This paper studies online structured prediction with full-information feedback. For online multiclass classification, Van der Hoeven (2020) established \emph{finite} surrogate regret bounds, which are independent of the time horizon, by introducing an elegant \emph{exploit-the-surrogate-gap} framework. However, this framework has been limited to multiclass c

Liuxian Zhao, Xuxu Zhuang, Hao Guo, Chuanxing Bi

In this paper, a novel structural Luneburg lens with local resonators is proposed. This lens allows for the realization of subwavelength focusing in low frequency range. The lens is achieved by graded refractive index from the lens centre to the outer surface. Numerical simulations are conducted to obtain data on wave propagation waveform, maximum displaceme

Ryoko Sakuma, Motoki Asano, Hiroshi Yamaguchi, Hajime Okamoto

Raman-gain-enhanced near-field optomechanical transduction between a movable optical cavity and SiN-membrane resonator is demonstrated. The Raman gain compensates for the intrinsic loss of the cavity and amplifies the optomechanical transduction, through which the membrane vibration is sensed using a high-Q whispering-gallery-mode optical cavity evanescently

Jae-Woo Choi, Youngwoo Yoon, Hyobin Ong, Jaehong Kim

Large language models (LLMs) have recently received considerable attention as alternative solutions for task planning. However, comparing the performance of language-oriented task planners becomes difficult, and there exists a dearth of detailed exploration regarding the effects of various factors such as pre-trained model selection and prompt construction.

Garth Warner

Here one will find a rigorous treatment of the simplest situation in Surface Area Theory, viz. the nonparametric case with domain the unit square in the plane. This is installment IV of a four part discussion of certain aspects of Real Analysis: Functions of a Single Variable, Curves and Length, Functions of Several Variables, and Surfaces and Area.

Muxin Han

A family of infinite-dimensional irreducible $*$-representations on $\mathcal{H}\simeq L^2(\mathbb{R})\otimes\mathbb{C}^N$ is defined for a quantum-deformed Lorentz algebra $\mathscr{U}_{\bf q}(sl_2)\otimes \mathscr{U}_{\widetilde{\bf {q}}}(sl_2)$, where $\mathbf{q}=\exp[\frac{\pi i}{N}(1+b^2)]$ and $\tilde{\mathbf{q}}=\exp[\frac{\pi i}{N}(1+b^{-2})]$ with $

Bilal Tasdelen, Mustafa Utkur, Asli Alpman, Can Baris Top

In magnetic particle imaging (MPI), simultaneous excitation and signal acquisition leads to direct feedthrough interference. While this interference can be mitigated up to some extent with passive compensation, its time-varying nature necessitates active compensation methods to achieve the sensitivity levels needed for applications such as stem cell tracking

Minsang Kim, Seungjun Baek

Learning positional information of nodes in a graph is important for link prediction tasks. We propose a representation of positional information using representative nodes called landmarks. A small number of nodes with high degree centrality are selected as landmarks, which serve as reference points for the nodes' positions. We justify this selection strate

Qijia Zhai, Shiquan Zhang, Pengtao Sun, Xiaoping Xie

In this paper, a type of novel projection-based, time-segmented reduced order model (ROM) is proposed for dynamic fluid-structure interaction (FSI) problems based upon the arbitrary Lagrangian--Eulerian (ALE)-finite element method (FEM) in a monolithic frame, where spatially, each variable is separated from others in terms of their attribution (fluid/structu

David Gray Widder

What counts as legitimate AI ethics labor, and consequently, what are the epistemic terms on which AI ethics claims are rendered legitimate? Based on 75 interviews with technologists including researchers, developers, open source contributors, and activists, this paper explores the various epistemic bases from which AI ethics is discussed and practiced. In t

Runjin Chen, Tong Zhao, Ajay Jaiswal, Neil Shah

Graph Neural Networks (GNNs) have empowered the advance in graph-structured data analysis. Recently, the rise of Large Language Models (LLMs) like GPT-4 has heralded a new era in deep learning. However, their application to graph data poses distinct challenges due to the inherent difficulty of translating graph structures to language. To this end, we introdu

J. Socorro, J. Juan Rosales, Leonel Toledo-Sesma

In this work we shall explore the effects of non commutativity in fractional classical and quantum schemes using the flat Friedmmann-Robertson-Walker (FRW) cosmological model coupled to a scalar field in the K-essence formalism. In previous work we have obtained the commutative solutions in both regimes into the fractional framework. Here we introduce noncom

Yiruo Lin

A nonlocality anomaly in which a partially entangled state can outperform a maximally entangled state in a task exploiting nonlocality and several ways to remove the anomaly are discussed. A necessary condition for the anomaly to occur is given in terms of joint probabilities of local measurements. By extending semiquantum games to include classical communic

Nana Liu, Huanpeng Liang, Liu Tan, Kaijian Chen

Conventionally, intrinsic orbital angular momentum (OAM) is associated with phase vortices. However, our investigation into the propagation dynamics of 2D superimposed catastrophe beams, termed cyclone catastrophe beams (CCBs), reveals that these beams inherently exhibit rotation and possess OAM, distinct from the typical connection to phase vortices. Our ob

Yiruo Lin

Some progress is reported on conditions for convertibility among bipartite 2x2 entangled states: An inconvertibility condition related to the rank of an entangled state is given that it is impossible to convert to an entangled state with lower rank under separable operations; a particular set of local operations and classical communication (LOCC) is used to

David Gray Widder, Claire Le Goues

Considerable effort in software research and practice is spent on bugs. Finding, reporting, tracking, triaging, attempting to fix them automatically, detecting "bug smells" -these comprise a substantial portion of large projects' time and development cost, and are of significant interest to researchers in Software Engineering, Programming Languages, and beyo

Binghui Peng, Srini Narayanan, Christos Papadimitriou

What are the root causes of hallucinations in large language models (LLMs)? We use Communication Complexity to prove that the Transformer layer is incapable of composing functions (e.g., identify a grandparent of a person in a genealogy) if the domains of the functions are large enough; we show through examples that this inability is already empirically pres

J. Pace VanDevender, T. Sloan, Michael Glissman

A search has been carried out for Magnetized Quark Nuggets (MQNs) accumulating in iron ore over geologic time. MQNs, which are theoretically consistent with the Standard Models of Physics and of Cosmology, have been suggested as dark-matter candidates. Indirect evidence of MQNs has been previously inferred from observations of magnetars and of non-meteorite

Martin Herschend, Hiroyuki Minamoto

In this paper we study a certain class of central extensions of preprojective algebras of quivers under the name quiver Heisenberg algebras (QHA). There are several classes of algebras introduced before by different researchers from different view points, which have the QHA as a special case. While these have mainly been studied in characteristic zero, we al

Joanna Piper Morgan, Alexander Mote, Samuel Lee Pasmann, Gavin Ridley

The Monte Carlo Computational Summit was held on the campus of the University of Notre Dame in South Bend, Indiana, USA on 25--26 October 2023. The goals of the summit were to discuss algorithmic and software alterations required for successfully porting respective code bases to exascale-class computing hardware, compare software engineering techniques used

Jianqiang Zhao

In recent years, a variety of variants of multiple zeta values (MZVs) have been defined and studied. One way to produce these variants is to restrict the indices in the definition of MZVs to some fixed parity pattern, which include Hoffman's multiple $t$-values, Kaneko and Tsumura's multiple $T$-values, and Xu and the author's multiple $S$-values. We have al

Dennis Hein, Grant Stevens, Adam Wang, Ge Wang

X-ray computed tomography (CT) is widely used for medical diagnosis and treatment planning; however, concerns about ionizing radiation exposure drive efforts to optimize image quality at lower doses. This study introduces Poisson Flow Consistency Models (PFCM), a novel family of deep generative models that combines the robustness of PFGM++ with the efficient

Yoo Jung Kim, Michael P. Fitzgerald, Jonathan Lin, Steph Sallum

Photonic Lanterns (PLs) are tapered waveguides that gradually transition from a multi-mode fiber geometry to a bundle of single-mode fibers (SMFs). They can efficiently couple multi-mode telescope light into a multi-mode fiber entrance at the focal plane and convert it into multiple single-mode beams. Thus, each SMF samples its unique mode (lantern principal

Julio Soria, Shahram Karami, Callum Atkinson, Minghang Li

Direct numerical simulation of a turbulent thermal boundary layer (TTBL) can perform the role of an analogy to simulate bushfires that can serve as a testbed for artificial intelligence (AI) enhanced remote sensing of bushfire propagation. By solving the Navier-Stokes equations for a turbulent flow, DNS predicts the flow field and allows for a detailed study

Lin Tian, Xiuzhen Zhang, Jey Han Lau

We apply causal mediation analysis to explain the decision-making process of neural models for rumour detection on Twitter. Interventions at the input and network level reveal the causal impacts of tweets and words in the model output. We find that our approach CMA-R -- Causal Mediation Analysis for Rumour detection -- identifies salient tweets that explain

Gregory Moille, Usman A. Javid, Michal Chojnacky, Pradyoth Shandilya

Kerr-induced synchronization (KIS) [1] involves the capture of a dissipative Kerr soliton (DKS) microcomb [2] tooth by a reference laser injected into the DKS resonator. This phase-locking behavior is described by an Adler equation whose analogous form describes numerous other physical systems [3], such as Josephson junctions [4]. We present an AC version of

Tatsuro Kawakami

Let $(X,B)$ be a pair of a normal surface over a perfect field of characteristic $p>0$ and an effective $\mathbb{Q}$-divisor $B$ on $X$. We prove that Steenbrink-type vanishing holds for $(X,B)$ if it is log canonical and $p>5$, or it is $F$-pure. We also show that rational surface singularities satisfying the vanishing are $F$-injective.

Tatsuro Kawakami, Teppei Takamatsu

We show that a plt surface singularity $(P\in X,B)$ is $F$-liftable if and only if it is $F$-pure and is not a rational double point of type $E_8^1$ in characteristic $p=5$. As a consequence, we prove the logarithmic extension theorem for $F$-pure surface pairs and Bogomolov-Sommese vanishing for globally $F$-split surface pairs. These results were previousl

Ethan Kane Waters, Carla Chia-Ming Chen, Mostafa Rahimi Azghadi

Research into large-scale crop monitoring has flourished due to increased accessibility to satellite imagery. This review delves into previously unexplored and under-explored areas in sugarcane health monitoring and disease/pest detection using satellite-based spectroscopy and Machine Learning (ML). It discusses key considerations in system development, incl

Joshua C Chang, Xiangting Li, Tianyi Su, Shixin Xu

Importance sampling (IS) is an efficient stand-in for model refitting in performing (LOO) cross-validation (CV) on a Bayesian model. IS inverts the Bayesian update for a single observation by reweighting posterior samples. The so-called importance weights have high variance -- we resolve this issue through adaptation by transformation. We observe that removi

Jie Peng, W. Ryan Deskins, Maria Helena Braga, Anter El-Azab

In this work, the effect of electron-phonon (e-ph) coupling on both electron and phonon transport of metals is investigated via first principles calculations. A Monte-Carlo (MC) approach for solving the coupled electron-phonon Boltzmann transport equations is developed to investigate thermal conductivity of metals. In this approach, the anisotropic electron

Alex Laguë, Fiona McCarthy, Mathew Madhavacheril, J. Colin Hill

The predicted present-day amplitude of matter fluctuations based on cosmic microwave background (CMB) anisotropy data has sometimes been found discrepant with more direct measurements of late-time structure. This has motivated many extensions to the standard cosmological model, including kinetic interactions between dark matter and dark energy that introduce

Morgan Jones, Yuanbo Nie, Matthew M. Peet

In this paper, we propose a novel method for addressing Optimal Control Problems (OCPs) with input-affine dynamics and cost functions. This approach adopts a Model Predictive Control (MPC) strategy, wherein a controller is synthesized to handle an approximated OCP within a finite time horizon. Upon reaching this horizon, the controller is re-calibrated to ta

David Brandfonbrener, Simon Henniger, Sibi Raja, Tarun Prasad

Large Language Models (LLMs) can generate useful code, but often the code they generate cannot be trusted to be sound. In this paper, we present VerMCTS, an approach to begin to resolve this issue by generating verified programs in Dafny and Coq. VerMCTS uses a logical verifier in concert with an LLM to guide a modified Monte Carlo Tree Search (MCTS). This a