April 2024 arXiv papers — page 152

Songhui Yue

While AI is extensively transforming Software Engineering (SE) fields, SE is still in need of a framework to overall consider all phases to facilitate Automated Software Evolution (ASEv), particularly for intelligent applications that are context-rich, instead of conquering each division independently. Its complexity comes from the intricacy of the intellige

Megha Rayer, Charul Rajput, B. Sundar Rajan

In Pliable Private Information Retrieval (PPIR) with a single server, messages are partitioned into $\Gamma$ non-overlapping classes. The user wants to retrieve a message from its desired class without revealing the identity of the desired class to the server. In S. A. Obead, H. Y. Lin and E. Rosnes, Single-Server Pliable Private Information Retrieval With S

Hyeongjin Nam, Daniel Sungho Jung, Gyeongsik Moon, Kyoung Mu Lee

Human-object contact serves as a strong cue to understand how humans physically interact with objects. Nevertheless, it is not widely explored to utilize human-object contact information for the joint reconstruction of 3D human and object from a single image. In this work, we present a novel joint 3D human-object reconstruction method (CONTHO) that effective

Shezheng Song, Shasha Li, Shan Zhao, Xiaopeng Li

Multimodal entity linking (MEL) aims to utilize multimodal information (usually textual and visual information) to link ambiguous mentions to unambiguous entities in knowledge base. Current methods facing main issues: (1)treating the entire image as input may contain redundant information. (2)the insufficient utilization of entity-related information, such a

Yukti Makhija, Priyanka Agrawal, Rishi Saket, Aravindan Raghuveer

Large language models (LLMs) are being increasingly tuned to power complex generation tasks such as writing, fact-seeking, querying and reasoning. Traditionally, human or model feedback for evaluating and further tuning LLM performance has been provided at the response level, enabling faster and more cost-effective assessments. However, recent works (Amplayo

Yechun Yu, Han Zhang, Bai-Cian Ke, Yao Yu

Our main objective is to derive the decay rate for the semileptonic decays $D\to V\ell^+\nu_{\ell}\,(\ell=e,\mu)$, where $V$ represents a vector particle. In these decays, the vector particle $V$ decays into three pseudo-scalar particles. To accomplish this, we evaluate the phase-space factor for the five-body decay with a set of eight independent variables

Hongzheng Chen, Niansong Zhang, Shaojie Xiang, Zhichen Zeng

Special-purpose hardware accelerators are increasingly pivotal for sustaining performance improvements in emerging applications, especially as the benefits of technology scaling continue to diminish. However, designers currently lack effective tools and methodologies to construct complex, high-performance accelerator architectures in a productive manner. Exi

Yi Zhang, Dongyuan Lu, Jitao Sang

Machine learning models often make predictions based on biased features such as gender, race, and other social attributes, posing significant fairness risks, especially in societal applications, such as hiring, banking, and criminal justice. Traditional approaches to addressing this issue involve retraining or fine-tuning neural networks with fairness-aware

Tyler Grover, Kory Stiffler, Patrick Vecera

Thomas-Whitehead (TW) gravity is a recently formulated projectively invariant extension of Einstein-Hilbert gravity. Projective geometry was used long ago by Thomas et. al. to succinctly package equivalent paths encoded by the geodesic equation. Projective invariance in gravity has further origins in string theory through a geometric action constructed from

Robin Y. Wen, Henry S. Grasshorn Gebhardt, Chen Heinrich, Olivier Doré

The three-dimensional galaxy power spectrum is a powerful probe of primordial non-Gaussianity and additional general relativistic effects, which become important on large scales. At the same time, wide-angle (WA) effects due to differing lines-of-sight (LOS) on the curved sky also become important with large angular separation. In this work, we accurately mo

Joshua N. Benabou, Isabel Sands, Henry S. Grasshorn Gebhardt, Chen Heinrich

As galaxy redshift surveys expand to larger areas on the sky, effects coming from the curved nature of the sky become important, introducing wide-angle (WA) corrections to the power spectrum multipoles at large galaxy-pair separations. These corrections particularly impact the measurement of physical effects that are predominantly detected on large scales, s

Yuqi Song, Rongzhi Dong, Lai Wei, Qin Li

Computational prediction of stable crystal structures has a profound impact on the large-scale discovery of novel functional materials. However, predicting the crystal structure solely from a material's composition or formula is a promising yet challenging task, as traditional ab initio crystal structure prediction (CSP) methods rely on time-consuming global

Raphaël Merx, Aso Mahmudi, Katrina Langford, Leo Alberto de Araujo

This study explores the use of large language models (LLMs) for translating English into Mambai, a low-resource Austronesian language spoken in Timor-Leste, with approximately 200,000 native speakers. Leveraging a novel corpus derived from a Mambai language manual and additional sentences translated by a native speaker, we examine the efficacy of few-shot LL

Qiaole Dong, Yanwei Fu

Optical flow is a classical task that is important to the vision community. Classical optical flow estimation uses two frames as input, whilst some recent methods consider multiple frames to explicitly model long-range information. The former ones limit their ability to fully leverage temporal coherence along the video sequence; and the latter ones incur hea

Xuan Sun, Zhanfu An, Yuyu Liu

We investigated domain adaptive semantic segmentation in foggy weather scenarios, which aims to enhance the utilization of unlabeled foggy data and improve the model's adaptability to foggy conditions. Current methods rely on clear images as references, jointly learning defogging and segmentation for foggy images. Despite making some progress, there are stil

Gaiane Panina, Rade Živaljević

The classic Ky Fan theorem is a combinatorial equivalent of Borsuk-Ulam theorem. It is a generalization and extension of Tucker's lemma and, just like its predecessor, it pinpoints important properties of antipodal colorings of vertices of a triangulated sphere $S^n$. Here we describe generalizations of Ky Fan theorem for the case when the sphere is replaced

Kevin Cahill

The action of general relativity with fermions has two independent symmetries: general coordinate invariance and local Lorentz invariance. General coordinate transformations act on coordinates and tensor indices, while local Lorentz transformations act on Dirac and Lorentz indices, much like a noncompact internal symmetry. \par The internal-symmetry characte

Norihiro Iizuka, Mitsuhiro Nishida

We propose that the logarithmic singularities of the Renyi entropy of local-operator-excited states for replica index $n$ can be a sign of quantum chaos. As concrete examples, we analyze the logarithmic singularities of the Renyi entropy in various two-dimensional conformal field theories. We show that there are always logarithmic singularities of the Renyi

Jinlong Li, Baolu Li, Zhengzhong Tu, Xinyu Liu

Vision-centric perception systems for autonomous driving have gained considerable attention recently due to their cost-effectiveness and scalability, especially compared to LiDAR-based systems. However, these systems often struggle in low-light conditions, potentially compromising their performance and safety. To address this, our paper introduces LightDiff,

Rui-Hao Bi, Yu Su, Yao Wang, Lei Sun

Fermi's golden rule (FGR) offers an empirical framework for understanding the dynamics of spin-lattice relaxation in magnetic molecules, encompassing mechanisms like direct (one-phonon) and Raman (two-phonon) processes. These principles effectively model experimental longitudinal relaxation rates, denoted as $T_1^{-1}$. However, under scenarios of increased

Amir E. Bazkiaei, Lee S. Kelvin, Sarah Brough, Simon J. O'Toole

We present the Bright Star Subtraction (BSS) pipeline for the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST). This pipeline generates an extended PSF model using observed stars and subtracts the model from the bright stars in LSST data. When testing the pipeline on Hyper Suprime-Cam (HSC) data, we find that the shape of the extended PSF m

Zhen Cao, F. Aharonian, Q. An, Axikegu

KM2A is one of the main sub-arrays of LHAASO, working on gamma ray astronomy and cosmic ray physics at energies above 10 TeV. Detector simulation is the important foundation for estimating detector performance and data analysis. It is a big challenge to simulate the KM2A detector in the framework of Geant4 due to the need to track numerous photons from a lar

Yukun Yang, Naihao Wang, Haixin Yang, Ruirui Li

Label noise is a common issue in real-world datasets that inevitably impacts the generalization of models. This study focuses on robust classification tasks where the label noise is instance-dependent. Estimating the transition matrix accurately in this task is challenging, and methods based on sample selection often exhibit confirmation bias to varying degr

Zhimeng Xin, Shiming Chen, Tianxu Wu, Yuanjie Shao

Object detection as a subfield within computer vision has achieved remarkable progress, which aims to accurately identify and locate a specific object from images or videos. Such methods rely on large-scale labeled training samples for each object category to ensure accurate detection, but obtaining extensive annotated data is a labor-intensive and expensive

Linbin Wang, Rowena Ball, Hongzhang Xu

People typically consider only European mathematics as orthodox, often intentionally or unintentionally overlooking the existence of mathematics from non-European societies. Inspired by Maria Ascher's two well-known papers on sand drawings in Oceania and Africa, this paper focuses on the strong link between modern mathematics and the mathematics behind the s

Hongliang Lu, Xinxin Ma

Let $G$ be a graph and $g,f:V(G)\to2^N$ be two set functions such that $g(v)\le f(v)$ and $g(v)\equiv f(v)\pmod 2$ for every $v\in V(G)$. An orientation $O$ of $G$ is called a $(g,f)$-parity orientation if $g(v)\le d^+_O(v)\le f(v)$ and $g(v)\equiv d^+_O(v)\pmod 2$ for every $v\in V(G)$. In this paper, we give a Tutte-type characterization for a graph to hav

Tatsuro Kawakami, Hiromu Tanaka

We show that smooth del Pezzo varieties in positive characteristic are quasi-$F$-split. To this end, we introduce weak quasi-$F$-splitting and we prove that general ladders of smooth del Pezzo varieties are normal.

Karthik C. S., Saladi Rahul

In this work, we present a plethora of results for the range longest increasing subsequence problem (Range-LIS) and its variants. The input to RLIS is a sequence $S$ of $n$ real numbers and a collection $Q$ of $m$ query ranges, and for each query in $Q$, the goal is to report the LIS of the sequence $S$ restricted to that query. Our two main results are for

Maosen Peng, Yan Li, Chong Wu, Liang Li

The propensity score is widely used for causal inference in observational studies, but common parametric estimators can produce biased and inefficient effect estimates when model assumptions are violated. Nonparametric approaches reduce sensitivity to misspecification but often yield unstable weights and inadequate covariate balance. We propose Local Balance

Zihao Wang, Bin Cui, Shaoduo Gan

Optimizing the Key-Value (KV) cache of the Large Language Model (LLM) has been considered critical to saving the cost of inference. Most of the existing KV-cache compression algorithms attempted to sparsify the sequence of tokens by taking advantage of the different importance of tokens. However, most of these methods treat all layers equally, allocating the

Xiaoteng Shen, Rui Zhang, Xiaoyan Zhao, Jieming Zhu

The emergence of large language models (LLMs) has revolutionized the capabilities of text comprehension and generation. Multi-modal generation attracts great attention from both the industry and academia, but there is little work on personalized generation, which has important applications such as recommender systems. This paper proposes the first method for

Longwei Zou, Han Zhang, Yangdong Deng

The fast growing capabilities of large-scale deep learning models, such as Bert, GPT and ViT, are revolutionizing the landscape of NLP, CV and many other domains. Training such models, however, poses an unprecedented demand for computing power, which incurs exponentially increasing energy cost and carbon dioxide emissions. It is thus critical to develop effi

Runzhen Xue, Mingyu Yan, Dengke Han, Yihan Teng

Heterogeneous Graph Neural Networks (HGNNs) have broadened the applicability of graph representation learning to heterogeneous graphs. However, the irregular memory access pattern of HGNNs leads to the buffer thrashing issue in HGNN accelerators. In this work, we identify an opportunity to address buffer thrashing in HGNN acceleration through an analysis of

Min Bao, Nanase Harada, Kotaro Kohno, Yuki Yoshimura

The physical properties of galactic molecular outflows are important as they could constrain outflow formation mechanisms. We study the properties of the southwest (SW) outflow streamer including gas kinematics, optical depth, dense gas fraction, and shock strength in the central molecular zone of the starburst galaxy NGC 253. We image the molecular emission

Tatsuro Kawakami, Hiromu Tanaka

Let $k$ be an algebraically closed field of characteristic $p>0$. Let $X$ be a normal projective surface over $k$ with canonical singularities whose anti-canonical divisor is nef and big. We prove that $X$ is globally $F$-regular except for the following cases: (1) $K_X^2=4$ and $p=2$, (2) $K_X^2=3$ and $p \in \{2, 3\}$, (3) $K_X^2=2$ and $p \in \{2, 3\}$, (

Ying-Ming Xie, Hiroki Isobe, Naoto Nagaosa

The incommensurate charge density wave states (CDWs) can exhibit steady motion in the flow limit after depinning, behaving as a nonequilibrium system with time-dependent states. Since the moving CDW, like an electric current, breaks both time-reversal and inversion symmetries, one may speculate the emergence of nonreciprocal nonlinear responses from such mot

David P. Blecher, Raphaël Clouâtre

We study projections in the bidual of a $C^*$-algebra $B$ that are null with respect to a subalgebra $A$, that is projections $p\in B^{**}$ satisfying $|\phi|(p)=0$ for every $\phi\in B^*$ annihilating $A$. In the separable case, $A$-null projections are precisely the peak projections in the bidual of $A$ at which the subalgebra $A$ interpolates the entire $

Zhe Zhang, Rong-Gang Ping, Tianbo Liu, Jiao Jiao Song

We developed a method to analyze the polarization correlations of two baryons $B_{1}\bar{B}_{2}$ with various spin combinations in the annihilation process. We established spin density matrices for arbitrary spins in standard and Cartesian forms, and demonstrated their application in the helicity formalism. This paper provides parametrization schemes for the

Bikai Gao, Yan Yan, Masayasu Harada

The recent discovery of a central compact object (CCO) within the supernova remnant HESS J1731-347, characterized by a mass of approximately $0.77^{+0.20}_{-0.17} M_{\odot}$ and a radius of about $10.4^{+0.86}_{-0.78}$ km, has opened up a new window for the study of compact objects. This CCO is particularly intriguing because it is the lightest and smallest

Guangyuan Li, Chen Rao, Juncheng Mo, Zhanjie Zhang

Recently, diffusion models (DM) have been applied in magnetic resonance imaging (MRI) super-resolution (SR) reconstruction, exhibiting impressive performance, especially with regard to detailed reconstruction. However, the current DM-based SR reconstruction methods still face the following issues: (1) They require a large number of iterations to reconstruct

Yixuan Huang, Jie Yang, Wankai Tang, Chao-Kai Wen

Radio imaging is rapidly gaining prominence in the design of future communication systems, with the potential to utilize reconfigurable intelligent surfaces (RISs) as imaging apertures. Although the sparsity of targets in three-dimensional (3D) space has led most research to adopt compressed sensing (CS)-based imaging algorithms, these often require substant

Alexander Rabinovich, Daniel Fattal

The Church Problem asks for the construction of a procedure which, given a logical specification A(I,O) between input omega-strings I and output omega-strings O, determines whether there exists an operator F that implements the specification in the sense that A(I, F(I)) holds for all inputs I. Buchi and Landweber provided a procedure to solve the Church prob

Cui Yuanping, Li Xiaoyue

Based on the assumption of the existence and uniqueness of the invariant measure for McKean-Vlasov stochastic differential equations (MV-SDEs), a self-interacting process that depends only on the current and historical information of the solution is constructed for MV-SDEs. The convergence rate of the weighted empirical measure of the self-interacting proces

Arjun Suresh, Michael R. Blanton

We examine the demographics of radio-emitting active galactic nuclei (AGN) in the local universe as a function of host galaxy properties, most notably both stellar mass and star formation rate. Radio AGN activity is theoretically implicated in helping reduce star formation rates of galaxies, and therefore it is natural to investigate the relationship between

Christian Claudel

In late 2022, ICAO member states adopted a long-term global aspirational goal (LTAG) to achieve net zero carbon emissions from international aviation by 2050. To date however, no economically scalable solution to the aviation decarbonization problem has been proposed. Despite considerable research on potential alternative fuels including e-fuels, Sustainable

Yi-Heng Chi, Han-Xiao Chen, Yang Chen, Yi-Fan Meng

While 30 Dor C is a unique superbubble in the Large Magellanic Cloud for its luminous non-thermal X-ray emission, the thermal X-ray emission it emanates has not yet been thoroughly investigated and well constrained. Based on the separate ~1 Ms deep XMM-Newton and Chandra observations, we report the discovery of the thermally-emitting plasma in some portions

Kazue Matsuyama, Jeff Greensite

We consider a refinement of the usual Hartree-Fock method applied to the 2D Hubbard model, in Nambu spinor formulation. The new element is the addition of a "condensate inducing" term proportional to a variational parameter h to the Hartree-Fock Hamiltonian, which generates an s- or d-wave condensate at zero temperature. This modified Hartree-Fock Hamiltonia

Reza Rafie Borujeny, Susanna E. Rumsey, Stark C. Draper, Frank R. Kschischang

For applications in concatenated coding for optical communications systems, we examine soft-demapping of short spherical codes constructed as constant-energy shells of the Cartesian power of pulse amplitude modulation constellations. These are unions of permutation codes having the same average power. We construct a list decoder for permutation codes by adap

Chen Wang

In the framework of BEPU (Best Estimate plus Uncertainty) methodology, the uncertainties involved in the simulations must be quantified to prove that the investigated design is acceptable. The output uncertainties are usually calculated by propagating input uncertainties through the simulation model, which requires knowledge of the model input uncertainties.

I. M. Suslov

Localization of electrons in 1D disordered systems is usually described in the random phase approximation, when distributions of phases \varphi and \theta, entering the transfer matrix, are considered as uniform. In the general case, the random phase approximation is violated, and the evolution equations (when the system length L is increased) contain three

Shi Li

We revisit the unrelated machine scheduling problem with the weighted completion time objective. It is known that independent rounding achieves a 1.5 approximation for the problem, and many prior algorithms improve upon this ratio by leveraging strong negative correlation schemes. On each machine $i$, these schemes introduce strong negative correlation betwe

Zheng Wu, Yichuan Li, Wei Zhan, Changliu Liu

The development of robotic systems for palletization in logistics scenarios is of paramount importance, addressing critical efficiency and precision demands in supply chain management. This paper investigates the application of Reinforcement Learning (RL) in enhancing task planning for such robotic systems. Confronted with the substantial challenge of a vast

Antonius J. Renders, David Gustavsson, Marcus Lindén, Andreas Walther

Distortion free negative group velocity pulse propagation is demonstrated in a rare-earth-ion-doped-crystal (RE) through the creation of a carefully designed spectral absorption structure in the inhomogeneous profile of Eu:YSO and subsequently inverting it. The properties of the RE system make it particularly well suited for this since it supports the creati

Md Nayem Uddin, Enfa Rose George, Eduardo Blanco, Steven Corman

This paper presents multiple question generation strategies for document-level event argument extraction. These strategies do not require human involvement and result in uncontextualized questions as well as contextualized questions grounded on the event and document of interest. Experimental results show that combining uncontextualized and contextualized qu

Forrest McKee, David Noever

The widespread adoption of voice-activated systems has modified routine human-machine interaction but has also introduced new vulnerabilities. This paper investigates the susceptibility of automatic speech recognition (ASR) algorithms in these systems to interference from near-ultrasonic noise. Building upon prior research that demonstrated the ability of ne

Ariful Islam, Nathan David Rock, Michael A. Scarpulla

In ultrawide bandgap (UWBG) nitride and oxide semiconductors, increased bandgap (Eg) correlates with greater ionicity and strong electron-phonon coupling. This limits mobility through polar optical phonon scattering, localizes carriers via polarons and self-trapping, broadens optical transitions via dynamic disorder, and modifies the breakdown field. Herein,

Hyunsuk Kim, Sridhar Venkatesh

We study the Hodge filtration of the intersection cohomology Hodge module for toric varieties. More precisely, we study the cohomology sheaves of the graded de Rham complex of the intersection cohomology Hodge module and give a precise formula relating it with the stalks of the intersection cohomology as a constructible complex. The main idea is to use the I

Garth Warner

The purpose of this book is to lay out certain aspects of descriptive set theory. After initially establishing notation and generalities we proceed to the following topics: partitions, semirings, rings, $\sigma$-rings, $\delta$-rings, products and sums, extension and generation. Extensive references and historical comments are included at the end of each sec

Cankut Tasci, Leonardo A. Cunha, Johannes Flick

We introduce an electron-photon exchange-correlation functional for quantum electrodynamical density-functional theory (QEDFT). The approach, photon MBD (pMBD), is inspired by the many-body dispersion (MBD) method for weak intermolecular interactions, which is generalized to include both electronic and photonic (electromagnetic) degrees of freedom on the sam

Tatsuro Kawakami, Hiromu Tanaka

In our series of papers, we prove that smooth Fano threefolds in positive characteristic lift to the ring of Witt vectors. Moreover, we show that they satisfy Akizuki-Nakano vanishing, $E_1$-degeneration of the Hodge to de Rham spectral sequence, and torsion-freeness of Crystalline cohomologies. In this paper, we establish these results except when $|-K_X|$

Hritik Bansal, Po-Nien Kung, P. Jeffrey Brantingham, Kai-Wei Chang

Multimodal event argument role labeling (EARL), a task that assigns a role for each event participant (object) in an image is a complex challenge. It requires reasoning over the entire image, the depicted event, and the interactions between various objects participating in the event. Existing models heavily rely on high-quality event-annotated training data

A. J. Battisti, M. B. Bagley, M. Rafelski, I. Baronchelli

We present reduced images and catalogues of photometric and emission line data ($\sim$230,000 and $\sim$8,000 sources, respectively) for the WFC3 Infrared Spectroscopic Parallel (WISP) Survey. These data are made publicly available on the Mikulski Archive for Space Telescopes (MAST) and include reduced images from various facilities: ground-based $ugri$, HST

Elenna Capote, Louis Dartez, Derek Davis

The next generation of ground-based gravitational-wave interferometers is expected to generate a bounty of new astrophysical discoveries, with sensitivities and bandwidths greatly improved compared to current-generation detectors. These detectors will allow us to make exceptional advancements in our understanding of fundamental physics, the dynamics of dense

Mark Moeller, Jules Jacobs, Olivier Savary Belanger, David Darais

We develop new data structures and algorithms for checking verification queries in NetKAT, a domain-specific language for specifying the behavior of network data planes. Our results extend the techniques obtained in prior work on symbolic automata and provide a framework for building efficient and scalable verification tools. We present KATch, an implementat

Busayo Awobade, Mardiyyah Oduwole, Steven Kolawole

Compression techniques have been crucial in advancing machine learning by enabling efficient training and deployment of large-scale language models. However, these techniques have received limited attention in the context of low-resource language models, which are trained on even smaller amounts of data and under computational constraints, a scenario known a

Shunrui Li, Yang Liu

Double Field Theory suggests that people can view the whole massless NS-NS sector as the gravitational unity. The $O(D,D)$ covariance and the doubled diffeomorphisms determine precisely how the Standard Model as well as a relativistic point particle should couple to the NS-NS sector. The theory also refines the notion of singularity. In [1], the authors deri

Chu Guo, Ruofan Chen

The Grassmann time-evolving matrix product operator (GTEMPO) method has proven to be an accurate and efficient numerical method for the real-time dynamics of quantum impurity problems. Whereas its application for imaginary-time calculations is much less competitive compared to well-established methods such as the continuous-time quantum Monte Carlo (CTQMC).

Atsuhide Ishida, Masaki Kawamoto

In this paper, we study the linear and nonlinear Schr\"odinger equations with a time-decaying harmonic oscillator and inverse-square potential. This model retains a form of scale invariance, and using this property, we demonstrate the asymptotic completeness of wave operators and Strichartz estimates for linear propagators.

Sayun Mao, Tom Chou, Maria D'Orsogna

More than 60% of individuals recovering from substance use disorder relapse within one year. Some will resume drug consumption even after decades of abstinence. The cognitive and psychological mechanisms that lead to relapse are not completely understood, but stressful life experiences and external stimuli that are associated with past drug-taking are known

David Beltran, Jennifer Duncan, Jonathan Hickman

We revisit certain localised variants of the Bennett-Carbery-Tao multilinear restriction theorem, recently proved by Bejenaru. We give a new proof of Bejenaru's theorem, relating the estimates to the theory of Kakeya-Brascamp-Lieb inequalities. Moreover, the new proof allows for a substantial generalisation, exploiting the full power of the Kakeya-Brascamp-L

Magnus Åström, Philipp Gentner, Omer Haliloglu, Behrooz Makki

Reconfigurable intelligent surface (RIS) has been suggested to be a key 6G feature and was suggested to be considered as a study-item in both 3GPP Releases 18 and 19. However, in both releases, it has been decided not to continue with it as a study-item, and to leave it for possible future specification. In this paper, we present the rationale for such a dec

Peihan Li, Vishnu Menon, Bhavanaraj Gudiguntla, Daniel Ting

Flocking is a behavior where multiple agents in a system attempt to stay close to each other while avoiding collision and maintaining a desired formation. This is observed in the natural world and has applications in robotics, including natural disaster search and rescue, wild animal tracking, and perimeter surveillance and patrol. Recently, large language m

Mohammadreza Zeinali

This paper describes a SIMO DC-DC converter capable of generating all the required voltages (buck and boost) utilizing n-type transistors for the output switches. The design incorporates only one shared bootstrap capacitor and a single pad to connect the top plate of the off-chip bootstrap capacitor to the on-chip drivers. By utilizing one off-chip bootstrap

Eric Horvitz, Vincent Conitzer, Sheila McIlraith, Peter Stone

Advances in artificial intelligence (AI) will transform many aspects of our lives and society, bringing immense opportunities but also posing significant risks and challenges. The next several decades may well be a turning point for humanity, comparable to the industrial revolution. We write to share a set of recommendations for moving forward from the persp

Tomos Parry

We use the Petrow-Young [10] subconvexity bound for Dirichlet $L$-functions to show that $d_4(n)$ has exponent of distribution $4/7$ when we allow an average over $a$ mod $q$, thereby giving an equidistribution result for $d_4(n)$ which goes past the $1/2$ barrier for the first time.

Hongchuan Zeng, Hongshen Xu, Lu Chen, Kai Yu

Large Language Models (LLMs) have ushered in a new era in Natural Language Processing, but their massive size demands effective compression techniques for practicality. Although numerous model compression techniques have been investigated, they typically rely on a calibration set that overlooks the multilingual context and results in significant accuracy deg

Tomos Parry

We give a relatively simple proof that \[ \int _0^1\left |\sum _{n\leq x}d(n)e(n\alpha )\right |d\alpha \asymp \sqrt x.\]

Sajid Husain, Isaac Harris, Peter Meisenheimer, Sukriti Mantri

Antiferromagnets have attracted significant attention in the field of magnonics, as promising candidates for ultralow-energy carriers for information transfer for future computing. The role of crystalline orientation distribution on magnon transport has received very little attention. In multiferroics such as BiFeO$_3$ the coupling between antiferromagnetic

Hao Li, Xiang Chen, Jiangxin Dong, Jinhui Tang

The key success of existing video super-resolution (VSR) methods stems mainly from exploring spatial and temporal information, which is usually achieved by a recurrent propagation module with an alignment module. However, inaccurate alignment usually leads to aligned features with significant artifacts, which will be accumulated during propagation and thus a

Tao Chen, Miqing Li

When tuning software configuration for better performance (e.g., latency or throughput), an important issue that many optimizers face is the presence of local optimum traps, compounded by a highly rugged configuration landscape and expensive measurements. To mitigate these issues, a recent effort has shifted to focus on the level of optimization model (calle

Ziteng Wang, Shankara Pailoor, Aaryan Prakash, Yuepeng Wang

Online streaming algorithms, tailored for continuous data processing, offer substantial benefits but are often more intricate to design than their offline counterparts. This paper introduces a novel approach for automatically synthesizing online streaming algorithms from their offline versions. In particular, we propose a novel methodology, based on the noti

A. V. Malyshev, E. A. Prokhorchuk, V. M. Shabaev

Methods of bound-state QED that treat the self-energy contributions to the Lamb shift within the partial-wave expansion usually face the problem of slow convergence of the latter. Inspired by an approach formulated in [J. Sapirstein and K. T. Cheng, Phys. Rev. A 108, 042804 (2023)], we propose a modification of the standard procedure for calculating the cont

Seung-Kyun Lee, Yihe Hua

Eddy current shielding by a Faraday cage is an effective way to shield alternating-current (AC) magnetic fields in scientific instrumentation. In a strong static magnetic field, however, the eddy current in the conductive shield is subject to the Lorentz force which causes the shield to vibrate. In addition to mechanical issues, such vibration induces motion

Stewart Pearson, Edward Malthouse

This editorial outlines an expanded scope for the next (fifth) generation of integrated marketing communication. It identifies key market forces that gave rise to this evolution and describes a trajectory of where Integrated Marketing Communication (IMC) has been and where it is going. The central shift is moving from primarily focusing on one stakeholder to

Maxwell Schneider, Cody McCarthy, Michael G. Maxwell, Joshua Pfeffer

We detail the mathematical formulation of the line of "functional quantizer" modules developed by the Mathematics and Music Lab (MML) at Michigan Technological University, for the VCV Rack software modular synthesizer platform, which allow synthesizer players to tune oscillators to new musical scales based on mathematical functions. For example, we describe

Danielle Van Boxel

We make Bayesian Additive Regression Networks (BARN) available as a Python package, \texttt{barmpy}, with documentation at \url{https://dvbuntu.github.io/barmpy/} for general machine learning practitioners. Our object-oriented design is compatible with SciKit-Learn, allowing usage of their tools like cross-validation. To ease learning to use \texttt{barmpy},

Laurel Ohm

We consider a simplified extensible version of a dynamic free boundary problem for a thin filament with radius $\epsilon>0$ immersed in 3D Stokes flow. The 3D fluid is coupled to the quasi-1D filament dynamics via a novel type of angle-averaged Neumann-to-Dirichlet operator for the Stokes equations, and much of the difficulty in the analysis lies in understa

Iury B. de A. Santos, André C. P. L. F. de Carvalho

The adoption of Deep Learning algorithms in the medical imaging field is a prominent area of research, with high potential for advancing AI-based Computer-aided diagnosis (AI-CAD) solutions. However, current solutions face challenges due to a lack of interpretability features and high data demands, prompting recent efforts to address these issues. In this st

Bin Lei, Yi Zhang, Shan Zuo, Ali Payani

Recent advancements in large language models, such as GPT-4, have demonstrated remarkable capabilities in processing standard queries. Despite these advancements, their performance substantially declines in \textbf{advanced mathematical problems requiring complex, multi-step logical reasoning}. To enhance their inferential capabilities, current research has

Tin Barisin, Illia Horenko

Convolutional neural networks (CNNs) are reported to be overparametrized. The search for optimal (minimal) and sufficient architecture is an NP-hard problem as the hyperparameter space for possible network configurations is vast. Here, we introduce a layer-by-layer data-driven pruning method based on the mathematical idea aiming at a computationally-scalable

J. Grace Clark, Kamil Hornoch, Allen W. Shafter, Hana Kučáková

The results of a two decade long $R$-band photometric survey of novae in M31 are presented. From these data, $R$-band light curves have been determined for 180 novae with data sufficient for estimating peak brightness and subsequent rate of decline. The data show a weak correlation of peak brightness with fade rate consistent with the well-known Maximum Magn

Suparna Seshadri, Jie Wang, Andrew M. Weiner

Efficient spatiotemporal control of optical beams is of paramount importance in diverse technological domains. Conventional systems focusing on quasi-static beam control demand precise phase or wavelength tuning for steering. This work presents a time-efficient solution for dynamic beam steering, emphasizing high-duty-cycle operation with fast scan rates, an

Chuqin Geng, Haolin Ye, Yihan Zhang, Brigitte Pientka

Computing differences between tree-structured data is a critical but challenging problem in software analysis. In this paper, we propose a novel tree diffing approach called SatDiff, which reformulates the structural diffing problem into a MaxSAT problem. By encoding the necessary transformations from the source tree to the target tree, SatDiff generates cor

Jonathan Beardsley, So Nakamura

We describe a fully faithful embedding of projective geometries, given in terms of closure operators, into $\mathbb{F}_1$-modules, in the sense of Connes and Consani. This factors through a faithful functor out of simple pointed matroids. This follows from our construction of a fully faithful embedding of weakly unital, commutative hypermagmas into $\fun$-mo

Stéphane Chrétien, Ben Gao, Astrid Thebault-Guiochon, Rémi Vaucher

Anomaly detection in multivariate signals is a task of paramount importance in many disciplines (epidemiology, finance, cognitive sciences and neurosciences, oncology, etc.). In this perspective, Topological Data Analysis (TDA) offers a battery of "shape" invariants that can be exploited for the implementation of an effective detection scheme. Our contributi

Agron Gemajli, Shivam Patel, Phillip G. Bradford

Proof of Work (PoW) blockchains burn a lot of energy. Proof-of-work algorithms are expensive by design and often only serve to compute blockchains. In some sense, carbon-based and non-carbon based regional electric power is fungible. So the total carbon and non-carbon electric power mix plays a role. Thus, generally PoW algorithms have large CO$_2$ footprint

Anubhav Jangra, Jamshid Mozafari, Adam Jatowt, Smaranda Muresan

Digital education has gained popularity in the last decade, especially after the COVID-19 pandemic. With the improving capabilities of large language models to reason and communicate with users, envisioning intelligent tutoring systems (ITSs) that can facilitate self-learning is not very far-fetched. One integral component to fulfill this vision is the abili

David Freire-Obregón, Daniel Hernández-Sosa, Oliverio J. Santana, Javier Lorenzo-Navarro

Emotion classification through EEG signals plays a significant role in psychology, neuroscience, and human-computer interaction. This paper addresses the challenge of mapping human emotions using EEG data in the Mapping Human Emotions through EEG Signals FG24 competition. Subjects mimic the facial expressions of an avatar, displaying fear, joy, anger, sadnes

Nils Schlüter, Junsoo Kim, Moritz Schulze Darup

The growing interconnectivity in control systems due to robust wireless communication and cloud usage paves the way for exciting new opportunities such as data-driven control and service-based decision-making. At the same time, connected systems are susceptible to cyberattacks and data leakages. Against this background, encrypted control aims to increase the

Azaan Rehman, Alexander Zhovmer, Ryo Sato, Yosuke Mukoyama

Deep neural networks have been applied to improve the image quality of fluorescence microscopy imaging. Previous methods are based on convolutional neural networks (CNNs) which generally require more time-consuming training of separate models for each new imaging experiment, impairing the applicability and generalization. Once the model is trained (typically