February 2024 arXiv papers — page 93

Elad Aigner-Horev, Daniel Rosenberg, Roi Weiss

The {\em resilience} of a Rademacher chaos is the maximum number of adversarial sign-flips that the chaos can sustain without having its largest atom probability significantly altered. Inspired by probabilistic lower-bound guarantees for the resilience of linear Rademacher chaos (aka. resilience of the Littlewood-Offord problem), obtained by Bandeira, Ferber

Ansgar Denner, Stefan Rode

At energies that are large with respect to the electroweak scale, the electroweak corrections to scattering processes involve large logarithms that have to be resummed to obtain decent predictions. Soft-collinear effective theory (SCET) has been proposed as a suitable framework to allow for this resummation, while retaining non-logarithmic corrections in a c

Purba Mukherjee, Konstantinos F. Dialektopoulos, Jackson Levi Said, Jurgen Mifsud

The strengthening of tensions in the cosmological parameters has led to a reconsideration of fundamental aspects of standard cosmology. The tension in the Hubble constant can also be viewed as a tension between local and early Universe constraints on the absolute magnitude $M_B$ of Type Ia supernova. In this work, we reconsider the possibility of a variation

Jean-Luc Lehners

In this note, we revisit and extend the analysis of the no-boundary wave function for the minisuperspace model in which the universe is described by a biaxial Bianchi IX metric. As matter content, we simply assume a positive cosmological constant. We find that two Stokes phenomena occur, at large squashing parameters of the spatial section of the universe. T

Nirjhar Das, Souradip Chakraborty, Aldo Pacchiano, Sayak Ray Chowdhury

Large Language Models (LLMs) aligned using Reinforcement Learning from Human Feedback (RLHF) have shown remarkable generation abilities in numerous tasks. However, collecting high-quality human preferences creates costly bottlenecks in practical deployments, and hence, training data are often budgeted. In these scenarios, it is crucial to collect training da

Himanshu Chaudhary, Shibesh Kumar Jas Pacif, G. Mustafa, Amine Bouali

This study examines five models derived from the Pacif parametrization scheme of the Hubble parameter ($H$), yielding various linear to quintic forms of the deceleration parameter (DP). Our goal is to explore the impact of these DP variations on late-time evolution and their potential to alleviate cosmological tensions. To enhance model constraints, we intro

Matthew Hase-Liu

Browning and Vishe used the Hardy-Littlewood circle method to show the moduli space of rational curves on smooth hypersurfaces of low degree is irreducible and of the expected dimension. We reinterpret the circle method geometrically and prove a generalization for higher genus smooth projective curves. In particular, we explain how the geometry of numbers ca

V. I. Romansky, A. M. Bykov, S. M. Osipov

Fast blue optical transients (FBOTs) represent a new class of highly energetic sources observed from radio to X-rays. High luminosity, light curves and spectra of the sources can be understood if they are associated with supernova-like or tidal disruption events. Radio observations of the transient sources revealed a mildly relativistic expansion of some of

Siyuan Zhang, Yongbo Huang, Miao He, Chengfeng Yang

A 20-kiloton liquid scintillator detector is designed in the Jiangmen Underground Neutrino Observatory (JUNO) for multiple physics purposes, including the determination of the neutrino mass ordering through reactor neutrinos, as well as measuring supernova neutrinos, solar neutrinos, and atmosphere neutrinos to explore different physics topics. Efficient rec

Bart Rienties, John Domingue, Subby Duttaroy, Christothea Herodotou

With the release of Generative AI systems such as ChatGPT, an increasing interest in using Artificial Intelligence (AI) has been observed across domains, including higher education. While emerging statistics show the popularity of using AI amongst undergraduate students, little is yet known about students' perceptions regarding AI including self-reported ben

Haoqiang Kang, Terra Blevins, Luke Zettlemoyer

While many hallucination detection techniques have been evaluated on English text, their effectiveness in multilingual contexts remains unknown. This paper assesses how well various factual hallucination detection metrics (lexical metrics like ROUGE and Named Entity Overlap, and Natural Language Inference (NLI)-based metrics) identify hallucinations in gener

Qiongyao Wang, Jing Bai, Luting Xu, Wei Feng

We perform simulations for transport and nontrivial fusion of Majorana zero modes in topological superconducting quantum wires. We uncover interesting behaviors of nonadiabatic transition associated with the transport through mini-gate-controlled multiple-segments modulations. Owing to breaking of the initial fermion parity induced by nonadiabatic transition

Hugo Férée, Iris van der Giessen, Sam van Gool, Ian Shillito

The uniform interpolation property in a given logic can be understood as the definability of propositional quantifiers. We mechanise the computation of these quantifiers and prove correctness in the Coq proof assistant for three modal logics, namely: (1) the modal logic K, for which a pen-and-paper proof exists; (2) G\"odel-L\"ob logic GL, for which our form

Wenwei Liu, Hui Feng, Feng Ji, Bo Hu

The theory of sampling and recovery of bandlimited graph signals has been extensively studied. However, in many cases, the observation of a signal is quite coarse. For example, users only provide simple comments such as "like" or "dislike" for a product on an e-commerce platform. This is a particular scenario where only the sign information of a graph signal

Tewodrose Altaye

This research utilized three types of artificial neural network (ANN) methodologies, namely Backpropagation Neural Network (BPNN) with varied training, transfer, divide, and learning functions; Radial Basis Function Neural Network (RBFNN); and General Regression Neural Network (GRNN), to forecast the severity of stem rust. It considered parameters such as me

Lanqing Guo, Yingqing He, Haoxin Chen, Menghan Xia

Diffusion models have proven to be highly effective in image and video generation; however, they encounter challenges in the correct composition of objects when generating images of varying sizes due to single-scale training data. Adapting large pre-trained diffusion models to higher resolution demands substantial computational and optimization resources, ye

Emil Babić, Ignacio A. Figueroa, Vesna Mikšić Trontl, Petar Pervan

The valence band (VB) structure of an Al0.5TiZrPdCuNi high-entropy alloy (HEA) obtained from X-ray photoelectron spectroscopy has been compared to that recently calculated by Odbadrakh et al, 2019. Both experimental and theoretical VBs show split-band structures typical of alloys composed from the early (TE) and late (TL) transition metals. Accordingly, seve

Ionut Munteanu

It is well known that if $A\in\mathbb{C}^{N\times N}$ and $B\in\mathbb{C}^{N\times M}$ form a controllable pair (in the sense that the Kalman matrix $[B\ |\ AB\ | \ \dots\ |\ A^{N-1}B]$ has full rank) then, there exists $K\in\mathbb{C}^{M\times N}$ such that the matrix $A+BK$ has only eigenvalues with negative real parts. The matrix $K$ is not unique, and is

Zhichao Peng

Applications such as uncertainty quantification and optical tomography, require solving the radiative transfer equation (RTE) many times for various parameters. Efficient solvers for RTE are highly desired. Source Iteration with Synthetic Acceleration (SISA) is a popular and successful iterative solver for RTE. Synthetic Acceleration (SA) acts as a precondit

Chin-Chia Michael Yeh, Yujie Fan, Xin Dai, Uday Singh Saini

Spatial-temporal forecasting systems play a crucial role in addressing numerous real-world challenges. In this paper, we investigate the potential of addressing spatial-temporal forecasting problems using general time series forecasting models, i.e., models that do not leverage the spatial relationships among the nodes. We propose a all-Multi-Layer Perceptro

B. Akinsanmi, S. C. C. Barros, M. Lendl, L. Carone

Ultra-hot Jupiters present a unique opportunity to understand the physics and chemistry of planets at extreme conditions. WASP-12b stands out as an archetype of this class of exoplanets. We performed comprehensive analyses of the transits, occultations, and phase curves of WASP-12b by combining new CHEOPS observations with previous TESS and Spitzer data to m

Rustem Khasanov, Thomas J. Hicken, Dariusz J. Gawryluk, Vahid Sazgari

The observation of superconductivity in La$_3$Ni$_2$O$_{7-\delta}$ under pressure, following the suppression of a high-temperature density wave state, has attracted considerable attention. The nature of this density wave order was not clearly identified. Here, we probe the magnetic response of the zero-pressure phase of La$_3$Ni$_2$O$_{7-\delta}$ as hydrosta

Benjamin Brück, Kevin I. Piterman, Volkmar Welker

For a finite-dimensional vector space $V$, the common basis complex of $V$ is the simplicial complex whose vertices are the proper non-zero subspaces of $V$, and $\sigma$ is a simplex if and only if there exists a basis $B$ of $V$ that contains a basis of $S$ for all $S\in \sigma$. This complex was introduced by Rognes in 1992 in connection with stable build

Haimin Luo, Min Ouyang, Zijun Zhao, Suyi Jiang

Hairstyle reflects culture and ethnicity at first glance. In the digital era, various realistic human hairstyles are also critical to high-fidelity digital human assets for beauty and inclusivity. Yet, realistic hair modeling and real-time rendering for animation is a formidable challenge due to its sheer number of strands, complicated structures of geometry

Hyeonsu Jeong, Hye Won Chung

We investigate the mechanisms of self-distillation in multi-class classification, particularly in the context of linear probing with fixed feature extractors where traditional feature learning explanations do not apply. Our theoretical analysis reveals that multi-round self-distillation effectively performs label averaging among instances with high feature c

Xiaorui Zuo, Yao-Tsung Chen, Wolfgang Karl Härdle

In the burgeoning realm of cryptocurrency, social media platforms like Twitter have become pivotal in influencing market trends and investor sentiments. In our study, we leverage GPT-4 and a fine-tuned transformer-based BERT model for a multimodal sentiment analysis, focusing on the impact of emoji sentiment on cryptocurrency markets. By translating emojis i

Ruixia Wang, Xiaosi Xu, Fei Yan, Xiaoxiao Xiao

Noise in quantum information processing poses a significant obstacle to achieving precise results. Quantum error mitigation techniques are crucial for improving the accuracy of experimental expectation values in this process. In the experiments, it is commonly observed that some measured events violate certain principles, such as symmetry constraints. These

Homa Shababi, Tanwi Bandyopadhyay, Ujjal Debnath

In this work, we investigate the thermal properties of black holes using a new class of generalized entropy functions [K. Ourabah, Class. Quantum Grav., 41, 015010 (2024)]. At the fundamental level, these entropic forms are associated with alternative gravitational laws, within an entropic gravity framework. Our investigation revolves around three distinct e

Ishan Rajendrakumar Dave, Tristan de Blegiers, Chen Chen, Mubarak Shah

Malaria is a major health issue worldwide, and its diagnosis requires scalable solutions that can work effectively with low-cost microscopes (LCM). Deep learning-based methods have shown success in computer-aided diagnosis from microscopic images. However, these methods need annotated images that show cells affected by malaria parasites and their life stages

Genki Osada, Tsubasa Takahashi, Takashi Nishide

Out-of-distribution (OOD) detection is crucial to safety-critical machine learning applications and has been extensively studied. While recent studies have predominantly focused on classifier-based methods, research on deep generative model (DGM)-based methods have lagged relatively. This disparity may be attributed to a perplexing phenomenon: DGMs often ass

Zuntao Liu, Yaohui Li, Chenming Hu, Delei Kong

Event cameras are ideal for visual place recognition (VPR) in challenging environments due to their high temporal resolution and high dynamic range. However, existing methods convert sparse events into dense frame-like representations for Artificial Neural Networks (ANNs), ignoring event sparsity and incurring high computational cost. Spiking Neural Networks

Jaewook Lee, Hanseul Cho, Chulhee Yun

The Gradient Descent-Ascent (GDA) algorithm, designed to solve minimax optimization problems, takes the descent and ascent steps either simultaneously (Sim-GDA) or alternately (Alt-GDA). While Alt-GDA is commonly observed to converge faster, the performance gap between the two is not yet well understood theoretically, especially in terms of global convergenc

Reza Ghane, Danil Akhtiamov, Babak Hassibi

We study the use of linear regression for multiclass classification in the over-parametrized regime where some of the training data is mislabeled. In such scenarios it is necessary to add an explicit regularization term, $\lambda f(w)$, for some convex function $f(\cdot)$, to avoid overfitting the mislabeled data. In our analysis, we assume that the data is

Songjie Xie, Youlong Wu, Jiaxuan Li, Ming Ding

As machine learning (ML) becomes more prevalent in human-centric applications, there is a growing emphasis on algorithmic fairness and privacy protection. While previous research has explored these areas as separate objectives, there is a growing recognition of the complex relationship between privacy and fairness. However, previous works have primarily focu

Eranda Dhananjaya, Wei-Tian Li

An undirected graph $G$ is said to admit an antimagic orientation if there exist an orientation $D$ and a bijection between $E(G)$ and $\{1,2,\ldots,|E(G)|\}$ such that any two vertices have distinct vertex sums, where the vertex sum of a vertex is the sum of the labels of the in-edges minus that of the out-edges incident to the vertex. It is conjectured by

Ning Chen, Ying-nan Mao, Zhaolong Teng

The observed Standard Model (SM) quark/lepton mass hierarchies and the Cabibbo-Kobayashi-Maskawa (CKM) mixing pattern are described in an ${\rm SU}(8)$ theory through its realistic symmetry breaking pattern with three intermediate stages, which rely on a set of $d=5$ gravity-induced operators that break the emergent global symmetries in the chiral fermion se

Kuniaki Saito, Kihyuk Sohn, Chen-Yu Lee, Yoshitaka Ushiku

Large language models require updates to remain up-to-date or adapt to new domains by fine-tuning them with new documents. One key is memorizing the latest information in a way that the memorized information is extractable with a query prompt. However, LLMs suffer from a phenomenon called perplexity curse; despite minimizing document perplexity during fine-t

Soichiro Kumano, Hiroshi Kera, Toshihiko Yamasaki

It is not fully understood why adversarial examples can deceive neural networks and transfer between different networks. To elucidate this, several studies have hypothesized that adversarial perturbations, while appearing as noises, contain class features. This is supported by empirical evidence showing that networks trained on mislabeled adversarial example

Ryan M. Aronson, Nicola Castelletto, François P. Hamon, J. A. White

We consider the numerical behavior of the fixed-stress splitting method for coupled poromechanics as undrained regimes are approached. We explain that pressure stability is related to the splitting error of the scheme, not the fact that the discrete saddle point matrix never appears in the fixed-stress approach. This observation reconciles previous results r

Xinjian Zhao, Liang Zhang, Yang Liu, Ruocheng Guo

Graph contrastive learning (GCL) has emerged as a pivotal technique in the domain of graph representation learning. A crucial aspect of effective GCL is the caliber of generated positive and negative samples, which is intrinsically dictated by their resemblance to the original data. Nevertheless, precise control over similarity during sample generation prese

Namrata Arvind, Saikat Panja

For a group $G$ and a character $\chi$ of $G$, let $c(\chi)$ denote the set of all irreducible characters of $G$, occurring in $\chi$. We prove that whenever $q\geq 8$, all non-trivial irreducible character $\chi$ of $\mathrm{PSL}_2(q)$ satisfies $c(\chi^4)=\mathrm{Irr}\left(\mathrm{PSL}_2(q)\right)$ if $q=2^{2m+1}$ and $c(\chi^3)=\mathrm{Irr}\left(\mathrm{P

Zekun Li, Zhiyu Zoey Chen, Mike Ross, Patrick Huber

Large language models (LLMs) are increasingly prevalent in conversational systems due to their advanced understanding and generative capabilities in general contexts. However, their effectiveness in task-oriented dialogues (TOD), which requires not only response generation but also effective dialogue state tracking (DST) within specific tasks and domains, re

Anuj Kumar Bhagat, Ritumoni Sarma, Vidya Sagar

A non-zero $\mathbb{F}$-linear map from a finite-dimensional commutative $\mathbb{F}$-algebra to $\mathbb{F}$ is called an $\mathbb{F}$-valued trace if its kernel does not contain any non-zero ideals. In this article, we utilize an $\mathbb{F}_2$-valued trace of the $\mathbb{F}_2$-algebra $\mathcal{R}_2:=\mathbb{F}_2[x]/\langle x^3-x\rangle$ to study binary

Sichang He, Beilong Tang, Boyan Zhang, Jiaoqi Shao

We present FedKit, a federated learning (FL) system tailored for cross-platform FL research on Android and iOS devices. FedKit pipelines cross-platform FL development by enabling model conversion, hardware-accelerated training, and cross-platform model aggregation. Our FL workflow supports flexible machine learning operations (MLOps) in production, facilitat

Jun Weng, Aiqiang Zhang, Qi Wu, Lishuang Ma

The atomic layer deposition(ALD) coating lengthens the lifetime of microchannel plates(MCP), which are used as the electron amplifier of the photomultiplier tubes(PMT). In the Jinping Neutrino Experiment, the newly developed 8-inch MCP-PMT achieves high collection efficiency by coating with high secondary emission materials. The resulting single electron res

Xiaohong Wang, Lijia Han

We consider the global existence and scattering for solutions of magnetic Zakharov system in three-dimensional space. When the initial data is small, we prove the existence of smooth global solutions and scattering results, by combining the space--time resonance method, weighted Sobolev space and dispersive estimates. Moreover, the decay rates for the soluti

Hossein Rajabzadeh, Mojtaba Valipour, Tianshu Zhu, Marzieh Tahaei

Finetuning large language models requires huge GPU memory, restricting the choice to acquire Larger models. While the quantized version of the Low-Rank Adaptation technique, named QLoRA, significantly alleviates this issue, finding the efficient LoRA rank is still challenging. Moreover, QLoRA is trained on a pre-defined rank and, therefore, cannot be reconfi

Shristi Bist, Ratnesh K. Pandey, Sejal Shah, Parswajit Kalita

Tungsten carbide (WC) stands out as a crucial material for exploration in extreme environments due to its resistance to radiation and impressive mechanical strength. Widely utilized in cutting tools, high-wear components, and as a potential contender for plasma-facing material in nuclear reactors, WC's erosion behavior under surrogate irradiations is a subje

Supriya Mishra

Modern databases typically makes use of the Log Structured Merge-Tree for organizing data in indexes, which is a kind of disk-based data structure. It was proposed to efficiently handle frequent update queries (also called update intensive workloads) databases. In recent years, LSM-Tree has gained popularity and has been adopted by a number of NoSql database

Jin Saito, Zhibang Yao, Tsutomu Kobayashi

We study the post-Newtonian limit of higher-order scalar-tensor theories that are degenerate in the unitary gauge. They can be conveniently described by the effective field theory (EFT) of dark energy. We determine all the parametrized post-Newtonian (PPN) parameters in terms of the EFT of dark energy parameters. Experimental bounds on the PPN parameters are

Qing-Geng Yang, Meng Yao, Da Wang, Qiang-Hua Wang

The effects of optical bond phonons coupled to electrons in two-dimensional lattices have attracted much interest recently, with the hope to explore unconventional superconducting mechanism and pairing symmetries. Here we conduct a systematic investigation of such phonon modes in the kagome lattice at and around the upper van Hove filling, in order to unrave

Chenhui Hu, Pengfei Cao, Yubo Chen, Kang Liu

Knowledge editing aims to rectify inaccuracies in large language models (LLMs) without costly retraining for outdated or erroneous knowledge. However, current knowledge editing methods primarily focus on single editing, failing to meet the requirements for lifelong editing. This study reveals a performance degradation encountered by knowledge editing in life

Chunkai Fu, Brandon G. Nguyen, Jung Hoon Seo, Ryan Zesch

We study the integration of machine learning advice to improve upon traditional data structure designed for efficient search queries. Although there has been recent effort in improving the performance of binary search trees using machine learning advice, e.g., Lin et. al. (ICML 2022), the resulting constructions nevertheless suffer from inherent weaknesses o

Wenhui Sophia Lu, Chenyang Zhong, Wing Hung Wong

The generation of synthetic data with distributions that faithfully emulate the underlying data-generating mechanism holds paramount significance. Wasserstein Generative Adversarial Networks (WGANs) have emerged as a prominent tool for this task; however, due to the delicate equilibrium of the minimax formulation and the instability of Wasserstein distance i

Yi-Qun Liu, Yan-Bin Liu, Wan-Sheng Wang, Da Wang

We study the electronic orders at the lower van Hove filling in the kagome lattice. In the weak limit of the Hubbard interaction $U$ versus the hopping parameter $t$, we find that the system develops itinerant ferromagnetism; In the intermediate range of $U$, we find the system develops noncollinear magnetic order with orthogonal spin moments on nearest-neig

Mahapara Khurshid, Mayank Vatsa, Richa Singh

The rising global prevalence of skin conditions, some of which can escalate to life-threatening stages if not timely diagnosed and treated, presents a significant healthcare challenge. This issue is particularly acute in remote areas where limited access to healthcare often results in delayed treatment, allowing skin diseases to advance to more critical stag

Gagan Bhatia, El Moatez Billah Nagoudi, Hasan Cavusoglu, Muhammad Abdul-Mageed

We introduce FinTral, a suite of state-of-the-art multimodal large language models (LLMs) built upon the Mistral-7b model and tailored for financial analysis. FinTral integrates textual, numerical, tabular, and image data. We enhance FinTral with domain-specific pretraining, instruction fine-tuning, and RLAIF training by exploiting a large collection of text

Navid Madani, Sougata Saha, Rohini Srihari

In this study, we address the challenge of enabling large language models (LLMs) to consistently adhere to emotional support strategies in extended conversations. We focus on the steerability of the Llama-2 and Llama-3 suite of models, examining their ability to maintain these strategies throughout interactions. To assess this, we introduce the Strategy Rele

Qiuyu Ren, Michael Willis

We show that the Khovanov-Rozansky $\mathfrak{gl}_2$ skein lasagna module distinguishes the exotic pair of knot traces $X_{-1}(-5_2)$ and $X_{-1}(P(3,-3,-8))$, an example first discovered by Akbulut. This gives the first analysis-free proof of the existence of exotic compact orientable $4$-manifolds. We also present a family of exotic knot traces that seem n

Susumu Kubo, Kazuhisa Makino, Souta Sakamoto

We consider composition orderings for linear functions of one variable. Given $n$ linear functions $f_1,\dots,f_n$ and a constant $c$, the objective is to find a permutation $\sigma$ that minimizes/maximizes $f_{\sigma(n)}\circ\dots\circ f_{\sigma(1)}(c)$. It was first studied in the area of time-dependent scheduling, and known to be solvable in $O(n\log n)$

Ruijie Zheng, Ching-An Cheng, Hal Daumé, Furong Huang

Temporal action abstractions, along with belief state representations, are a powerful knowledge sharing mechanism for sequential decision making. In this work, we propose a novel view that treats inducing temporal action abstractions as a sequence compression problem. To do so, we bring a subtle but critical component of LLM training pipelines -- input token

Patrick Del Vecchio, Oussama Moutanabbir

The selective confinement of light holes (LHs) in a tensile-strained germanium (Ge) quantum well is studied by mapping the electronic structure of Ge$_{1-x}$Sn$_x$/Ge/Ge$_{1-x}$Sn$_x$ heterostructures as a function of Sn content, residual strain, and Ge well thickness. It is shown that above $12\,\text{at.}\%$ Sn and below $0.4\%$ residual compressive strain

Shinjan Mandal, Indrajit Maity, H. R. Krishnamurthy, Manish Jain

We present a computational study of the phonon linewidths in twisted bilayer graphene arising from electron-phonon interactions and anharmonic effects. The electronic structure is calculated using distance-dependent transfer integrals based on the atomistic Slater-Koster tight-binding formalism, including electron-electron interactions treated at the Hartree

Aliakbar Daemi, Nobuo Iida, Christopher Scaduto

We show that the knot group of any knot in any integer homology sphere admits a non-abelian representation into $SU(3)$ such that meridians are mapped to matrices whose eigenvalues are the three distinct third roots of unity. This answers the $N=3$ case of a question posed by Xie and the first author. We also characterize when a $PU(3)$-bundle admits a flat

Shengjie Qiu, Junhao Zheng, Zhen Liu, Yicheng Luo

The incremental sequence labeling task involves continuously learning new classes over time while retaining knowledge of the previous ones. Our investigation identifies two significant semantic shifts: E2O (where the model mislabels an old entity as a non-entity) and O2E (where the model labels a non-entity or old entity as a new entity). Previous research h

XENON Collaboration, E. Aprile, J. Aalbers, K. Abe

The multi-staged XENON program at INFN Laboratori Nazionali del Gran Sasso aims to detect dark matter with two-phase liquid xenon time projection chambers of increasing size and sensitivity. The XENONnT experiment is the latest detector in the program, planned to be an upgrade of its predecessor XENON1T. It features an active target of 5.9 tonnes of cryogeni

Yuyang Deng, Mingda Qiao

We study a variant of Collaborative PAC Learning, in which we aim to learn an accurate classifier for each of the $n$ data distributions, while minimizing the number of samples drawn from them in total. Unlike in the usual collaborative learning setup, it is not assumed that there exists a single classifier that is simultaneously accurate for all distributio

Weimin Yi, P. B. Hall, Zunlin Yuan, W. N. Brandt

We present an observational study of wind acceleration based on four low-ionization broad absorption line (LoBAL) quasars (J0136, J1238, J1259, J1344). J0136 and J1344 (group-1) are radio quiet and show large BAL-velocity shifts as opposed to stable line-locking associated absorption lines (AALs). Notably, J1344 displays a linear relation between BAL-velocit

Donal F Connon

In his book entitled Divergent Series, Hardy makes various references to divergent series of sine functions. In this paper, we show how such series may be treated rigorously and, in particular, we revisit Entry 17(v) in Ramanujan's Notebooks.

Lei Zheng, Rui Yang, Michael Yu Wang, Jun Ma

Enforcing safety while preventing overly conservative behaviors is essential for autonomous vehicles to achieve high task performance. In this paper, we propose a barrier-enhanced parallel homotopic trajectory optimization (BPHTO) approach with the over-relaxed alternating direction method of multipliers (ADMM) for real-time integrated decision-making and pl

Bishoy M. Kousa, Nemin Wei, Allan H. MacDonald

The lowest Landau level of bilayer graphene has an octet of internal degrees of freedom, composed from spin, valley and orbital two-level systems. Dominance of $n=0$ orbitals over $n=1$ orbitals in low energy quantum fluctuations leads to distinct fractional quantum Hall characteristics compared dominance of $n=1$ over $n=0$. The competition between $n=0$ an

Bhaskar Ray Chaudhury, Christian Kroer, Ruta Mehta, Tianlong Nan

We study the computation of competitive equilibrium for Fisher markets with $n$ agents and $m$ divisible chores. Competitive equilibria for chores are known to correspond to the nonzero KKT points of a program that minimizes the product of agent disutilities, which is a non-convex program whose zero points foil iterative optimization methods. We introduce a

Yuanlong Wang, Gerardo A. Paz-Silva

Characterizing noise is key to the optimal control of the quantum system it affects. Using a single-qubit probe and appropriate sequences of $\pi$ and non-$\pi$ pulses, we show how one can characterize the noise a quantum bath generates across a wide range of frequencies -- including frequencies below the limit set by the probe's $\mathbb{T}_2$ time. To do s

Ara Basmajian, Robert Suzzi Valli

For a fixed cusp neighborhood (determined by depth D) of the modular surface, we investigate the class of reciprocal geodesics that enter this neighborhood (called a cusp excursion) a fixed number of times.

Wenchao Dong, Assem Zhunis, Hyojin Chin, Jiyoung Han

We explored cultural biases-individualism vs. collectivism-in ChatGPT across three Western languages (i.e., English, German, and French) and three Eastern languages (i.e., Chinese, Japanese, and Korean). When ChatGPT adopted an individualistic persona in Western languages, its collectivism scores (i.e., out-group values) exhibited a more negative trend, surp

Xin Zhang, Keren Fu, Qijun Zhao

Person re-identification (re-ID) continues to pose a significant challenge, particularly in scenarios involving occlusions. Prior approaches aimed at tackling occlusions have predominantly focused on aligning physical body features through the utilization of external semantic cues. However, these methods tend to be intricate and susceptible to noise. To addr

Xu Zheng, Tianchun Wang, Wei Cheng, Aitian Ma

Modern techniques like contrastive learning have been effectively used in many areas, including computer vision, natural language processing, and graph-structured data. Creating positive examples that assist the model in learning robust and discriminative representations is a crucial stage in contrastive learning approaches. Usually, preset human intuition d

Jiarui Lu, Zuobai Zhang, Bozitao Zhong, Chence Shi

The protein dynamics are common and important for their biological functions and properties, the study of which usually involves time-consuming molecular dynamics (MD) simulations in silico. Recently, generative models has been leveraged as a surrogate sampler to obtain conformation ensembles with orders of magnitude faster and without requiring any simulati

Kalman J. Knizhnik, Micah J. Weberg, Elena Provornikova, Harry P. Warren

The launches of Parker Solar Probe (Parker) and Solar Orbiter (SolO) are enabling a new era of solar wind studies that track the solar wind from its origin at the photosphere, through the corona, to multiple vantage points in the inner heliosphere. A key ingredient for these models is the input photospheric magnetic field map that provides the boundary condi

Jun Li, Biwei Jiang, He Zhao, Xi Chen

The far-infrared (FIR) opacity of dust in dark clouds within the Ophiuchus molecular cloud is investigated through multi-wavelength infrared observations from UKIDSS, Spitzer and Herschel. Employing the infrared color excess technique with both near-infrared (NIR) and mid-infrared (MIR) photometric data, a high-resolution extinction map in the $K$ band ($A_K

Dheeraj Mekala, Alex Nguyen, Jingbo Shang

Instruction-tuning language models has become a crucial step in aligning them for general use. Typically, this process involves extensive training on large datasets, incurring high training costs. In this paper, we introduce a novel training data selection based on the learning percentage of the samples. We assert that current language models possess the cap

Kyoungseok Jang, Junpei Komiyama, Kazutoshi Yamazaki

We consider the fixed-confidence best arm identification (FC-BAI) problem in the Bayesian setting. This problem aims to find the arm of the largest mean with a fixed confidence level when the bandit model has been sampled from the known prior. Most studies on the FC-BAI problem have been conducted in the frequentist setting, where the bandit model is predete

Qianyu Chen

We prove that the minimal exponent for local complete intersections satisfies an Inversion-of-Adjunction property. As a result, we also obtain the Inversion of Adjunction for higher Du Bois and higher rational singularities for local complete intersections.

Muqiao Yang, Xiang Li, Umberto Cappellazzo, Shinji Watanabe

Continual learning has emerged as an increasingly important challenge across various tasks, including Spoken Language Understanding (SLU). In SLU, its objective is to effectively handle the emergence of new concepts and evolving environments. The evaluation of continual learning algorithms typically involves assessing the model's stability, plasticity, and g

Mikhail Kazdagli, Mohit Tiwari, Akshat Kumar

Cloud computing services provide scalable and cost-effective solutions for data storage, processing, and collaboration. With their growing popularity, concerns about security vulnerabilities are increasing. To address this, first, we provide a formal model, called CloudLens, that expresses relations between different cloud objects such as users, datastores,

Herun Wan, Shangbin Feng, Zhaoxuan Tan, Heng Wang

Large language models are limited by challenges in factuality and hallucinations to be directly employed off-the-shelf for judging the veracity of news articles, where factual accuracy is paramount. In this work, we propose DELL that identifies three key stages in misinformation detection where LLMs could be incorporated as part of the pipeline: 1) LLMs coul

Hannah G. Mason, Jack H. Noble

Cochlear implants (CIs) are neural prosthetics used to treat patients with severe-to-profound hearing loss. Patient-specific modeling of CI stimulation of the auditory nerve fiber (ANFs) can help audiologists improve the CI programming. These models require localization of the ANFs relative to surrounding anatomy and the CI. Localization is challenging becau

Ting-Rui Chiang, Dani Yogatama

In this work, we propose a simple theoretical framework, Pelican Soup, aiming to better understand how pretraining allows LLMs to (1) generalize to unseen instructions and (2) perform in-context learning, even when the verbalizers are irrelevant to the task. To this end, in our framework, we introduce the notion of "knowledge base" and "reference-sense assoc

Kenneth Odoh

Our work focuses on understanding the underpinning mechanism of dataset condensation by drawing connections with ($\epsilon$, $\delta$)-differential privacy where the optimal noise, $\epsilon$, is chosen by adversarial uncertainty \cite{Grining2017}. We can answer the question about the inner workings of the dataset condensation procedure. Previous work \cit

Ioannis Tsiamas, Gerard I. Gállego, José A. R. Fonollosa, Marta R. Costa-jussà

Data scarcity and the modality gap between the speech and text modalities are two major obstacles of end-to-end Speech Translation (ST) systems, thus hindering their performance. Prior work has attempted to mitigate these challenges by leveraging external MT data and optimizing distance metrics that bring closer the speech-text representations. However, achi

Pengfei Cai, Anas Abdallah, Pratheepa Jeganathan

In the property and casualty (P&C) insurance industry, reserves comprise most of a company's liabilities. These reserves are the best estimates made by actuaries for future unpaid claims. Notably, reserves for different lines of business (LOBs) are related due to dependent events or claims. While the actuarial industry has developed both parametric and non-p

Daniel Perry

The goal of this paper is to define and inspect a metric version of the universal path space and study its application to purely 2-unrectifiable spaces, in particular the Heisenberg group $\mathbb{H}^1$. The construction of the universal Lipschitz path space, as the metric version is called, echoes the construction of the universal cover for path-connected,

Vinay Kumar Chapala, Pratham Sharma, Sameer Sharma, S. M. Zafaruddin

The present path loss models for wireless systems employing reconfigurable intelligent surfaces (RIS) do not account for the elevation of the transmitter, receiver, and RIS module. In this paper, we develop an analytical model for path loss of a wireless system utilizing an NxM-element RIS module positioned above the ground surface with elevated transmitter

Nikolas Koumpis, Dionysis Kalogerias

We leverage the duality between risk-averse and distributionally robust optimization (DRO) to devise a distributionally robust estimator that strictly outperforms the empirical average for all probability distributions with negative excess kurtosis. The aforesaid estimator solves the $\chi^{2}-$robust mean squared error problem in closed form.

H. E. Camblong, A. Chakraborty, P. Lopez-Duque, C. R. Ordóñez

Entanglement degradation appears to be a generic prediction in relativistic quantum information whenever horizons restrict access to a region of spacetime. This property has been previously explored in connection with the Unruh effect, where a bipartite entangled system composed of an inertial observer (Alice) and a uniformly accelerated observer (Rob) was s

Lance Ying, Tan Zhi-Xuan, Lionel Wong, Vikash Mansinghka

Despite the fact that beliefs are mental states that cannot be directly observed, humans talk about each others' beliefs on a regular basis, often using rich compositional language to describe what others think and know. What explains this capacity to interpret the hidden epistemic content of other minds? In this paper, we take a step towards an answer by gr

Xuan Fang, Haomiao Huang, Martin A. Guerrero, Letizia Stanghellini

We report spectroscopic surveys of planetary nebulae (PNe) in the Milky Way and Andromeda (M31), using the 10.4-m Gran Telescopio Canarias (GTC). The spectra are of high quality and cover the whole optical range, mostly from 3650 \r{A} to beyond 1 micron, enabling detection of nebular emission lines critical for spectral analysis as well as photoionization m

Jiancheng Wu, Qingwen Wu, Chichuan Jin, Jianfeng Wu

Weak-line quasars (WLQs) are a notable group of active galactic nuclei (AGNs) that show unusually weak UV lines even though their optical-UV continuum shapes are similar to those of typical quasars. The physical mechanism for WLQs is an unsolved puzzle in the AGN unified model. We explore the properties of UV emission lines by performing extensive photoioniz

Harbir Antil, Daiki Mizuno, Ken Shirakawa

The original KWC-system is widely used in materials science. It was proposed in [Kobayashi et al, Physica D, 140, 141--150 (2000)] and is based on the phase field model of planar grain boundary motion. This model suffers from two key challenges. Firstly, it is difficult to establish its relation to physics, in particular, a variational model. Secondly, it la

Jiaheng Wei, Yuanshun Yao, Jean-Francois Ton, Hongyi Guo

LLM hallucination, i.e. generating factually incorrect yet seemingly convincing answers, is currently a major threat to the trustworthiness and reliability of LLMs. The first step towards solving this complicated problem is to measure it. However, existing hallucination metrics require having a benchmark dataset with gold-standard answers, i.e. "best" or "co