May 2023 arXiv papers — page 98

Jiankui Zhou, Yue Qiu

Stein Variational Gradient Descent (SVGD) is a popular particle-based method for Bayesian inference. However, its convergence suffers from the variance collapse, which reduces the accuracy and diversity of the estimation. In this paper, we study the isotropy property of finite particles during the convergence process and show that SVGD of finite particles ca

Edgar Shaghoulian

The measurement problem in quantum mechanics is almost exclusively discussed in situations where gravity is ignored. We discuss some recent developments in our understanding of quantum gravity and argue that they significantly alter the problem. Quantum gravity may even resolve one of the thorniest questions in discussions of the measurement problem: who col

Krishna Pillutla, Vincent Roulet, Sham Kakade, Zaid Harchaoui

Gauss-Newton methods and their stochastic version have been widely used in machine learning and signal processing. Their nonsmooth counterparts, modified Gauss-Newton or prox-linear algorithms, can lead to contrasting outcomes when compared to gradient descent in large-scale statistical settings. We explore the contrasting performance of these two classes of

Alex Damian, Eshaan Nichani, Rong Ge, Jason D. Lee

We focus on the task of learning a single index model $\sigma(w^\star \cdot x)$ with respect to the isotropic Gaussian distribution in $d$ dimensions. Prior work has shown that the sample complexity of learning $w^\star$ is governed by the information exponent $k^\star$ of the link function $\sigma$, which is defined as the index of the first nonzero Hermite

Xudong Liu, Daosong Yu, Chuanfu Sun, Zhijie Mei

Phase modulation plays a crucial role in various terahertz applications, including biomedical imaging, high-rate communication, and radar detection. Existing terahertz phase shifters typically rely on tuning the resonant effect of metamaterial structures to achieve a narrow bandwidth phase shift. However, the terahertz band offers a wide bandwidth resource,

Yu Xiao, Xin Yang, Sijuan Huang, Lihua Guo

Medical image segmentation is particularly critical as a prerequisite for relevant quantitative analysis in the treatment of clinical diseases. For example, in clinical cervical cancer radiotherapy, after acquiring subabdominal MRI images, a fast and accurate image segmentation of organs and tumors in MRI images can optimize the clinical radiotherapy process

Nicole Rodriguez Cavero, Lorenzo Marra, Henric Krawczynski, Michal Dovčiak

The Imaging X-ray Polarimetry Explorer (IXPE) observed the black hole X-ray binary 4U 1630-47 in the steep power law (or very high) state. The observations reveal a linear polarization degree of the 2-8 keV X-rays of 6.8 +/- 0.2 % at a position angle of 21{\deg}.3 +/- 0{\deg}.9 East of North (all errors at 1{\sigma} confidence level). Whereas the polarizatio

Sin-Ei Takahasi, Kiyoshi Shirayanagi, Makoto Tsukada

An endo-commutative algebra is a nonassociative algebra in which the square mapping preserves multiplication. In this paper, we give a complete classification of 2-dimensional endo-commutative straight algebras of rank one over an arbitrary non-trivial field, where a straight algebra of dimension 2 satisfies the condition that there exists an element $x$ suc

P. L. Krapivsky, S. Redner

We highlight intriguing features of complex networks that are grown by \emph{redirection}. In this mechanism, a target node is chosen uniformly at random from the pre-existing network nodes and the new node attaches either to this initial target or to a neighbor of this target. This exceedingly simple algorithm generates preferential attachment networks in a

Kaho Tse, Alexander Heger, Ryosuke Hirai, Duncan K. Galloway

We construct a new formulation that allows efficient exploration of steady-state accretion processes onto compact objects. Accretion onto compact objects is a common scenario in astronomy. These systems serve as laboratories to probe the nuclear burning of the accreted matter. Conventional stellar evolution codes have been developed to simulate in detail the

Jiannan Xiang, Tianhua Tao, Yi Gu, Tianmin Shu

While large language models (LMs) have shown remarkable capabilities across numerous tasks, they often struggle with simple reasoning and planning in physical environments, such as understanding object permanence or planning household activities. The limitation arises from the fact that LMs are trained only on written text and miss essential embodied knowled

Arghya Datta, Subhrangshu Nandi, Jingcheng Xu, Greg Ver Steeg

Deep Neural Networks (DNNs) are becoming integral components of real world services relied upon by millions of users. Unfortunately, architects of these systems can find it difficult to ensure reliable performance as irrelevant details like random initialization can unexpectedly change the outputs of a trained system with potentially disastrous consequences.

Nihar Shah, Lucas Baker, Suraj Srinivasan, Alex Toberoff

This short note illustrates the theoretical solution to a trader determining how to optimally swap her wealth into a target asset through on-chain operations. It offers the framework to solve optimal slippage parameters and optimal trade size.

A. C. Alvarez, J. Bruining, D. Marchesin

Carbonated water flooding (CWI) increases oil production due to favorable dissolution effects and viscosity reduction. Accurate modeling of CWI performance requires a simulator with the ability to capture the true physics of such process. In this study, compositional modeling coupled with surface complexation modeling (SCM) are done, allowing a unified study

Brandon Livio Annesi, Clarissa Lauditi, Carlo Lucibello, Enrico M. Malatesta

Empirical studies on the landscape of neural networks have shown that low-energy configurations are often found in complex connected structures, where zero-energy paths between pairs of distant solutions can be constructed. Here we consider the spherical negative perceptron, a prototypical non-convex neural network model framed as a continuous constraint sat

Devvrat Tiwari, Subhashish Banerjee

Here we study the impact of non-Markovian evolution on prominent characteristics of quantum thermodynamics, such as ergotropy and power. These are benchmarked by the behavior of the quantum speed limit time. We make use of both geometric-based, particularly quantum Fisher and Wigner-Yanase information metric, and physical properties based-measures, particula

Yulin Sun, Qingming Qu, Chenxingyu Zhao, Arvind Krishnamurthy

Cloud-native containerized applications constantly seek high-performance and easy-to-operate container network solutions. RDMA network is a potential enabler with higher throughput and lower latency than the standard TCP/IP network stack. However, several challenges remain in equipping containerized applications with RDMA network: 1) How to deliver transpare

Pedram Rabiee, Jesse B. Hoagg

This paper presents two new control approaches for guaranteed safety (remaining in a safe set) subject to actuator constraints (the control is in a convex polytope). The control signals are computed using real-time optimization, including linear and quadratic programs subject to affine constraints, which are shown to be feasible. The first control method rel

K. Imamura, S. Suetsugu, Y. Mizukami, Y. Yoshida

The field-induced quantum disordered state of layered honeycomb magnet $\alpha$-RuCl$_3$ is a prime candidate for Kitaev spin liquids hosting Majorana fermions and non-Abelian anyons. Recent observations of anomalous planar thermal Hall effect demonstrate a topological edge mode, but whether it originates from Majorana fermions or bosonic magnons remains con

MohammadTaghi Hajiaghayi, Dariusz R. Kowalski, Jan Olkowski

Fault-tolerant consensus is about reaching agreement on some of the input values in a limited time by non-faulty autonomous processes, despite of failures of processes or communication medium. This problem is particularly challenging and costly against an adaptive adversary with full information. Bar-Joseph and Ben-Or (PODC'98) were the first who proved an a

Igor Bandos, Tomas Ortin

We study the invariance of N=1,d=4 supergravity solutions under diffeormophisms and show that, in order to obtain consistent conditions (``Killing equations'') invariant under local supersymmetry transformations, one has to perform supersymmetry transformations generated by the superpartner of the vector that generates standard diffeomorphisms, just as a sup

Abanoub Ghobrial, Samuel Budgett, Dieter Balemans, Hamid Asgari

There is a lot of ongoing research effort into developing different techniques for neural networks compression. However, the community lacks standardised evaluation metrics, which are key to identifying the most suitable compression technique for different applications. This paper reviews existing neural network compression evaluation metrics and implements

Jiatong Shi, Dan Berrebbi, William Chen, Ho-Lam Chung

Speech processing Universal PERformance Benchmark (SUPERB) is a leaderboard to benchmark the performance of Self-Supervised Learning (SSL) models on various speech processing tasks. However, SUPERB largely considers English speech in its evaluation. This paper presents multilingual SUPERB (ML-SUPERB), covering 143 languages (ranging from high-resource to end

Hugo Cui, Lenka Zdeborová

We address the problem of denoising data from a Gaussian mixture using a two-layer non-linear autoencoder with tied weights and a skip connection. We consider the high-dimensional limit where the number of training samples and the input dimension jointly tend to infinity while the number of hidden units remains bounded. We provide closed-form expressions for

Mohamed Elobaid, Giulio Romualdi, Gabriele Nava, Lorenzo Rapetti

In this paper we consider the problem of allowing a humanoid robot that is subject to a persistent disturbance, in the form of a payload-carrying task, to follow given planned footsteps. To solve this problem, we combine an online nonlinear centroidal Model Predictive Controller - MPC with a contact stable force parametrization. The cost function of the MPC

Jan Hladký, Eng Keat Hng

Grebík and Rocha [Fractional Isomorphism of Graphons, Combinatorica 42, pp 365-404 (2022)] extended the well studied notion of fractional isomorphism of graphs to graphons. We prove that fractionally isomorphic graphons can be approximated in the cut distance by fractionally isomorphic finite graphs. This answers the main question from ibid. As an easy but c

Byung-Doh Oh, William Schuler

While there is much recent interest in studying why Transformer-based large language models make predictions the way they do, the complex computations performed within each layer have made their behavior somewhat opaque. To mitigate this opacity, this work presents a linear decomposition of final hidden states from autoregressive language models based on eac

Dong-Ho Lee, Kian Ahrabian, Woojeong Jin, Fred Morstatter

Temporal knowledge graph (TKG) forecasting benchmarks challenge models to predict future facts using knowledge of past facts. In this paper, we apply large language models (LLMs) to these benchmarks using in-context learning (ICL). We investigate whether and to what extent LLMs can be used for TKG forecasting, especially without any fine-tuning or explicit m

Syuzanna Asadulina, Andrey Bogdanov, Stanislav Glybovski, Oleh Yermakov

Guided waves are the perfect carriers of electromagnetic signals in planar miniaturized devices due to their high localization and controlled propagation direction. However, it is still a challenge to control the polarization of propagating guided waves. In this work, we discover both theoretically and experimentally the broadband polarization TE-TM degenera

Jiajun Huang, Kaiming Ouyang, Yujia Zhai, Jinyang Liu

In the exascale computing era, optimizing MPI collective performance in high-performance computing (HPC) applications is critical. Current algorithms face performance degradation due to system call overhead, page faults, or data-copy latency, affecting HPC applications' efficiency and scalability. To address these issues, we propose PiP-MColl, a Process-in-P

Pratik Fegade, Tianqi Chen, Phillip B. Gibbons, Todd C. Mowry

Dynamic control flow is an important technique often used to design expressive and efficient deep learning computations for applications such as text parsing, machine translation, exiting early out of deep models and so on. The control flow divergence resulting from dynamic control flow makes batching, an important optimization enabling high throughput and h

Saeth Wannasuphoprasit, Yi Zhou, Danushka Bollegala

Cosine similarity between two words, computed using their contextualised token embeddings obtained from masked language models (MLMs) such as BERT has shown to underestimate the actual similarity between those words (Zhou et al., 2022). This similarity underestimation problem is particularly severe for highly frequent words. Although this problem has been no

Li Qiao, Zhen Gao, Zhongxiang Li, Deniz Gündüz

Over-the-air computation (OAC) is a promising technique to achieve fast model aggregation across multiple devices in federated edge learning (FEEL). In addition to the analog schemes, one-bit digital aggregation (OBDA) scheme was proposed to adapt OAC to modern digital wireless systems. However, one-bit quantization in OBDA can result in a serious informatio

Erica Cooper, Junichi Yamagishi

Mean Opinion Score (MOS) is a popular measure for evaluating synthesized speech. However, the scores obtained in MOS tests are heavily dependent upon many contextual factors. One such factor is the overall range of quality of the samples presented in the test -- listeners tend to try to use the entire range of scoring options available to them regardless of

Matthew Tirona

This narrative review evaluates the effect of chromium supplementation on glycemia and serum lipids, with an emphasis on patients with type 2 diabetes mellitus (T2DM). Additionally, this narrative review evaluates the essentiality of the trace mineral chromium to human nutrition. Meta-analyses and reviews were included, while certain clinical trials were spe

Stephen Whitelam

Time-dependent protocols that perform irreversible logical operations, such as memory erasure, cost work and produce heat, placing bounds on the efficiency of computers. Here we use a prototypical computer model of a physical memory to show that it is possible to learn feedback-control protocols to do fast memory erasure without input of work or production o

Julianna Winnik, Damian Suski, Piotr Zdańkowski, Luiza Stanaszek

We propose a speed-up method for the in-focus plane detection in digital holographic microscopy that can be applied to a broad class of autofocusing algorithms that involve repetitive propagation of an object wave to various axial locations to decide the in-focus position. The classical autofocusing algorithms apply a uniform search strategy, i.e., they prob

Anna Kamiśka, Mariusz Żyluk

We investigate here the density of the set of the restrictions from $C_C^\infty(\mathbb{R}^d)$ to $C_C^\infty(\Omega)$ in the Musielak-Orlicz-Sobolev space $W^{1,\Phi}(\Omega)$. It is a continuation of article \cite{KamZyl3}, where we have studied density of $C_C^\infty(\mathbb{R}^d)$ in $W^{k, \Phi}(\mathbb{R}^d)$ for $k\in\mathbb{N}$. The main theorem stat

Yuri Berest, Ajay C. Ramadoss

This is the first in a series of papers, where we introduce and study topological spaces that realize the algebras of quasi-invariants of finite reflection groups. Our result can be viewed as a generalization of a well-known theorem of A. Borel that realizes the ring of invariant polynomials a Weyl group $W$ as a cohomology ring of the classifying space $BG$

Leonidas Daskalakis

We establish weak-type $(1,1)$ bounds for the maximal function associated with ergodic averaging operators modeled on a wide class of thin deterministic sets $B$. As a corollary we obtain the corresponding pointwise convergence result on $L^1$. This contributes yet another counterexample for the conjecture of Rosenblatt and Wierdl from 1991 asserting the fai

Anna Kaminśka, Mariusz Żyluk

We consider here Musielak-Orlicz Sobolev (MOS) spaces $W^{k,\Phi}(\Omega)$, where $\Omega$ is an open subset of $\mathbb{R}^d$, $k\in\mathbb{N}$ and $\Phi$ is a Musielak-Orlicz function. The main outcomes consist of the results on density of the space of compactly supported smooth functions $C_C^\infty(\Omega)$ in $W^{k,\Phi}(\Omega)$. One section is devoted

Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran

Language models are increasingly being deployed for general problem solving across a wide range of tasks, but are still confined to token-level, left-to-right decision-making processes during inference. This means they can fall short in tasks that require exploration, strategic lookahead, or where initial decisions play a pivotal role. To surmount these chal

Michael K. -H. Kiessling

This note advertises a simple necessary condition for optimality that any list $N\mapsto v^x(N)$ of computer-generated putative lowest average pair energies $v^x(N)$ of clusters that consist of $N$ monomers has to satisfy, whenever the monomers interact with each other through pair forces satisfying Newton's ``actio equals re-actio.'' These can be quite comp

Adrian Kummerländer, Samuel J. Avis, Halim Kusumaatmaja, Fedor Bukreev

OpenLB is an object-oriented implementation of LBM. It is the first implementation of a generic platform for LBM programming, which is shared with the open source community (GPLv2). Since the first release in 2007, the code has been continuously improved and extended which is documented by thirteen releases as well as the corresponding release notes which ar

Justin Lasker

I prove closed-form identities that I discovered for the first and second derivatives of the $q$-rationals at $q = 1$, in which the $q$-rationals are defined as they are in arXiv:1812.00170.

Edward Misback, Caleb C. Chan, Brett Saiki, Eunice Jun

In recent years, researchers have proposed a number of automated tools to identify and improve floating-point rounding error in mathematical expressions. However, users struggle to effectively apply these tools. In this paper, we work with novices, experts, and tool developers to investigate user needs during the expression rewriting process. We find that us

Theo McKenzie, John Urschel

Analyzing nodal domains is a way to discern the structure of eigenvectors of operators on a graph. We give a new definition extending the concept of nodal domains to arbitrary signed graphs, and therefore to arbitrary symmetric matrices. We show that for an arbitrary symmetric matrix, a positive fraction of eigenbases satisfy a generalized version of known n

Himanshu Gupta, Saurabh Arjun Sawant, Swaroop Mishra, Mutsumi Nakamura

Instruction tuning of language models has demonstrated the ability to enhance model generalization to unseen tasks via in-context learning using a few examples. However, typical supervised learning still requires a plethora of downstream training data for finetuning. Often in real-world situations, there is a scarcity of data available for finetuning, fallin

Gabriel Jung, Andrea Ravenni, Marco Baldi, William R. Coulton

We study signatures of primordial non-Gaussianity (PNG) in the redshift-space halo field on non-linear scales, using a combination of three summary statistics, namely the halo mass function (HMF), power spectrum, and bispectrum. The choice of adding the HMF to our previous joint analysis of power spectrum and bispectrum is driven by a preliminary field-level

Kunle Adegoke, Robert Frontczak

We study an elementary series that can be considered a relative of a series studied by Ramanujan in Part 1 of his Lost Notebooks. We derive a closed form for this series in terms of the inverse hyperbolic arctangent and the polylogarithm. Special cases will follow in terms of the Riemann zeta and the alternating Riemann zeta function. In addition, some trigo

Penny Haxell, Ronen Wdowinski

An \emph{independent transversal} (IT) in a graph $G$ with a given vertex partition $P$ is an independent set of vertices of $G$ (i.e. it induces no edges), that consists of one vertex from each part (\emph{block}) of $P$. Over the years, various criteria have been established that guarantee the existence of an IT, often given in terms of $P$ being $t$-\emph

Peng Jiang, Srikanth Saripalli

LIDAR and RADAR are two commonly used sensors in autonomous driving systems. The extrinsic calibration between the two is crucial for effective sensor fusion. The challenge arises due to the low accuracy and sparse information in RADAR measurements. This paper presents a novel solution for 3D RADAR-LIDAR calibration in autonomous systems. The method employs

Lian Liu, liguo Zhou

CNN-based face detection methods have achieved significant progress in recent years. In addition to the strong representation ability of CNN, post-processing methods are also very important for the performance of face detection. In general, the face detection method predicts several candidate bounding-boxes for one face. NMS is used to filter out inaccurate

Junhyeok Hur, Wonjun Lee, Kiryang Kwon, SeungJung Huh

Exotic quantum many-body states, such as Haldane and spin liquid phases, can exhibit remarkable features like fractional excitations and non-abelian statistics and offer new understandings of quantum entanglement in many-body quantum systems. These phases are classified by non-local correlators that can be directly measured in atomic analog quantum simulatin

Hikaru Kawai, Nobuyoshi Ohta

The importance of the proper treatment of the wave function renormalization in the renormalization group analysis of quantum gravity is pointed out. The renormalization factor, sometimes called an inessential coupling, can be used to fix any one of the coupling constants, with the exception of the coupling constants that remain unchanged by the rescaling of

Mi-Xiang Lan, Xue-Feng Wu, Zi-Gao Dai

The environment of gamma-ray burst (GRB) has an important influence on the evolution of jet dynamics and of its afterglow. Here we investigate the afterglow polarizations in a stratified medium with the equal arrival time surface (EATS) effect. Polarizations of multi-band afterglows are predicted. The effects of the parameters of the stratified medium on the

Shuang Chen, Amir Atapour-Abarghouei, Edmond S. L. Ho, Hubert P. H. Shum

We present a software that predicts non-cleft facial images for patients with cleft lip, thereby facilitating the understanding, awareness and discussion of cleft lip surgeries. To protect patients privacy, we design a software framework using image inpainting, which does not require cleft lip images for training, thereby mitigating the risk of model leakage

Carina Kauf, Anna Ivanova

Estimating the log-likelihood of a given sentence under an autoregressive language model is straightforward: one can simply apply the chain rule and sum the log-likelihood values for each successive token. However, for masked language models (MLMs), there is no direct way to estimate the log-likelihood of a sentence. To address this issue, Salazar et al. (20

John S. Schreck, William Petzke, Pedro A. Jimenez, Thomas Brummet

Monitoring the fuel moisture content (FMC) of vegetation is crucial for managing and mitigating the impact of wildland fires. The combination of in situ FMC observations with numerical weather prediction (NWP) models and satellite retrievals has enabled the development of machine learning (ML) models to estimate dead FMC retrievals over the contiguous US (CO

Gláucia Murta, Flavio Baccari

Here we consider the task of device-independent certification of the quantum state distributed in a network when some of the nodes in this network may collude and act dishonestly. We introduce the paradigm of self-testing with dishonest parties and present a protocol to self-test the GHZ state in this framework. We apply this result for state certification i

M. O. Nestoklon, Erik Kirstein, D. R. Yakovlev, E. A. Zhukov

The tunability of the optical properties of lead halide perovskite nanocrystals makes them highly appealing for applications. Both, halide anion exchange and quantum confinement pave the way for tailoring their band gap energy. For spintronics applications, the Land\'e g-factors of electrons and hole are of great importance. By means of the empirical tight-b

Yi Li, Jia-Hui Zhang, Feng Mei, Biye Xie

Topological corner states are exotic topological boundary states that are bounded to zero-dimensional geometry even the dimension of systems is large than one. As an elegant physical correspondence, their numbers are dictated by the bulk topological invariants. So far, all previous realizations of HOTIs are hallmarked by $\mathbb{Z}_2$ topological invariants

Giovanni Bellettini, Simone Carano, Riccardo Scala

Given a bounded open set $\Omega \subset \mathbb{R}^2$, we study the relaxation of the nonparametric area functional in the strict topology in $BV(\Omega;\mathbb{R}^2)$, and compute it for vortex-type maps, and more generally for maps in $W^{1,1}(\Omega;\mathcal{S}^1)$ having a finite number of topological singularities. We also extend the analysis to some s

Blanche Buet, Xavier Pennec

By interpreting the product of the Principal Component Analysis, that is the covariance matrix, as a sequence of nested subspaces naturally coming with weights according to the level of approximation they provide, we are able to embed all $d$--dimensional Grassmannians into a stratified space of covariance matrices. We observe that Grassmannians constitute t

Anil Kunwar, Haoran Ma, Johan Hektor

In the electronic packaging and energy storage sectors, the study of Cu6Sn5 intermetallic compound (IMC) is getting more attention. At temperatures above 186 oC, this IMC exists in a hexagonal closed packed (HCP) crystalline structure. Crystal plasticity finite element simulations are performed on Cu6Sn5 IMC by taking the information about its lattice parame

Bob Briscoe, Olga Albisser

This paper aims to provide a robust grounding for the additive increase factor used in the `TCP-Friendly' mode of the CUBIC congestion control algorithm.

Juncheng Li, David J. Cappelleri

In this paper, we present Sim-MEES: a large-scale synthetic dataset that contains 1,550 objects with varying difficulty levels and physics properties, as well as 11 million grasp labels for mobile manipulators to plan grasps using different gripper modalities in cluttered environments. Our dataset generation process combines analytic models and dynamic simul

Dominik Zimny, Artur Kasymov, Adam Kania, Jacek Tabor

NeRF is a popular model that efficiently represents 3D objects from 2D images. However, vanilla NeRF has some important limitations. NeRF must be trained on each object separately. The training time is long since we encode the object's shape and color in neural network weights. Moreover, NeRF does not generalize well to unseen data. In this paper, we present

S. Hekker, Y. Elsworth, T. A. M. Braun, S. Basu

Global stellar oscillations probe the internal structure of stars. In low- to intermediate-mass red giants, these oscillations provide signatures from both the outer regions of the star as well as from the core. These signatures are imprinted in e.g. the frequency of maximum oscillation power, and in the differences in periods of non-radial oscillations (per

Yutong Qiu, Yihang Shen, Carl Kingsford

The graph traversal edit distance (GTED), introduced by Ebrahimpour Boroojeny et al.~(2018), is an elegant distance measure defined as the minimum edit distance between strings reconstructed from Eulerian trails in two edge-labeled graphs. GTED can be used to infer evolutionary relationships between species by comparing de Bruijn graphs directly without the

Arpita Misra, Marek Jamrozy, Marek Weżgowiec

Galaxies exhibiting a specific large-scale extended radio emission, such as X-shaped radio galaxies, belong to a rare class of winged radio galaxies. The morphological evolution of these radio sources is explained using several theoretical models, including galaxy mergers. However, such a direct link between a perturbed radio morphology and a galaxy merger r

Nikhil Srivastava

We survey recent progress on efficient algorithms for approximately diagonalizing a square complex matrix in the models of rational (variable precision) and finite (floating point) arithmetic. This question has been studied across several research communities for decades, but many mysteries remain. We present several open problems which we hope will be of br

Jeanne Colbois, Nicolas Laflorencie

Despite a very good understanding of single-particle Anderson localization in one-dimensional (1D) disordered systems, many-body effects are still full of surprises, a famous example being the interaction-driven many-body localization (MBL) problem, about which much has been written, and perhaps the best is yet to come. Interestingly enough the non-interacti

A. V. Astashenok, A. S. Tepliakov

The model of generalized Tsallis holographic dark energy (which is known to be particular representative of Nojiri-Odintsov HDE) with event horizon as cut-off is investigated using methods of dynamical analysis. We take into consideration possible interaction with dark energy and matter in various forms. Critical points are determined. Cosmological evolution

Frank Qiu

Following up on a previous analysis of graph embeddings, we generalize and expand some results to the general setting of vector symbolic architectures (VSA) and hyperdimensional computing (HDC). Importantly, we explore the mathematical relationship between superposition, orthogonality, and tensor product. We establish the tensor product representation as the

Debanjana Kundu, Florian Sprung

For an elliptic curve, we study how many Selmer groups are cotorsion over the anti-cyclotomic $\mathbb{Z}_p$-extension as one varies the prime $p$ or the quadratic imaginary field in question.

M. Klen, A. A. Semenov

Atmospheric turbulence is one of the lead disturbance factors for free-space quantum communication. The quantum states of light in such channels are affected by fluctuating losses characterized by the probability distribution of transmittance (PDT). We obtain the PDT for different horizontal links via numerical simulations of light transmission through the a

Francesca De Benetti, Walter Simson, Magdalini Paschali, Hasan Sari

Dynamic positron emission tomography imaging (dPET) provides temporally resolved images of a tracer enabling a quantitative measure of physiological processes. Voxel-wise physiologically-based pharmacokinetic (PBPK) modeling of the time activity curves (TAC) can provide relevant diagnostic information for clinical workflow. Conventional fitting strategies fo

Mohamed Amine Aouichaoui, Mohammed Hichem Mortad

In this note, simple proofs of certain well-known results involving the positive square root of positive matrices are given.

Jordan Coil, Vered Shwartz

Noun compound interpretation is the task of expressing a noun compound (e.g. chocolate bunny) in a free-text paraphrase that makes the relationship between the constituent nouns explicit (e.g. bunny-shaped chocolate). We propose modifications to the data and evaluation setup of the standard task (Hendrickx et al., 2013), and show that GPT-3 solves it almost

David Kalaj, Miodrag Mateljević, Iosif Pinelis

Assume that $f$ is a real $\rho$-harmonic function of the unit disk $\mathbb{D}$ onto the interval $(-1,1)$, where $\rho(u,v)=R(u)$ is a metric defined in the infinite strip $(-1,1)\times \mathbb{R}$. Then we prove that $|\nabla f(z)|(1-|z|^2)\le \frac{4}{\pi}(1-f(z)^2)$ for all $z\in\mathbb{D}$, provided that $\rho$ has a non-negative Gaussian curvature. Th

Luhang Sun, Mian Wei, Yibing Sun, Yoo Ji Suh

Generative AI models like DALL-E 2 can interpret textual prompts and generate high-quality images exhibiting human creativity. Though public enthusiasm is booming, systematic auditing of potential gender biases in AI-generated images remains scarce. We addressed this gap by examining the prevalence of two occupational gender biases (representational and pres

Mohammed Nasereddin, Mert Nakıp, Erol Gelenbe

The IoT is vulnerable to network attacks, and Intrusion Detection Systems (IDS) can provide high attack detection accuracy and are easily installed in IoT Servers. However, IDS are seldom evaluated in operational conditions which are seriously impaired by attack overload. Thus a Local Area Network testbed is used to evaluate the impact of UDP Flood Attacks o

Yo Joong Choe, Aditya Gangrade, Aaditya Ramdas

Abstaining classifiers have the option to abstain from making predictions on inputs that they are unsure about. These classifiers are becoming increasingly popular in high-stakes decision-making problems, as they can withhold uncertain predictions to improve their reliability and safety. When evaluating black-box abstaining classifier(s), however, we lack a

Honggen Zhang, June Zhang, Igor Molybog

We consider a contrastive learning approach to knowledge graph embedding (KGE) via InfoNCE. For KGE, efficient learning relies on augmenting the training data with negative triples. However, most KGE works overlook the bias from generating the negative triples-false negative triples (factual triples missing from the knowledge graph). We argue that the genera

Zhaozhuo Xu, Zirui Liu, Beidi Chen, Yuxin Tang

While the numerous parameters in Large Language Models (LLMs) contribute to their superior performance, this massive scale makes them inefficient and memory-hungry. Thus, they are hard to deploy on commodity hardware, such as one single GPU. Given the memory and power constraints of such devices, model compression methods are widely employed to reduce both t

Wayne Barrett, Shaun Fallat, Veronika Furst, Shahla Nasserasr

For an $n \times n$ matrix $A$, let $q(A)$ be the number of distinct eigenvalues of $A$. If $G$ is a connected graph on $n$ vertices, let $\mathcal{S}(G)$ be the set of all real symmetric $n \times n$ matrices $A=[a_{ij}]$ such that for $i\neq j$, $a_{ij}=0$ if and only if $\{i,j\}$ is not an edge of $G$. Let $q(G)={\rm min}\{q(A)\,:\,A \in \mathcal{S}(G)\}$

Chris Jenkins, Shantanu Agarwal, Joel Barry, Steven Fincke

In this paper, we present ISI-Clear, a state-of-the-art, cross-lingual, zero-shot event extraction system and accompanying user interface for event visualization & search. Using only English training data, ISI-Clear makes global events available on-demand, processing user-supplied text in 100 languages ranging from Afrikaans to Yiddish. We provide multiple e

Irene Ferreira, Chris Harbron, Alex Hughes, Tamsin Sargood

Secondary use of data already collected in clinical studies has become more and more popular in recent years, with the commitment of the pharmaceutical industry and many academic institutions in Europe and the US to provide access to their clinical trial data. Whilst this clearly provides societal benefit in helping to progress medical research, this has to

Joseph Slote, Alexander Volberg, Haonan Zhang

For any $K>2$ and the multiplicative cyclic group $\Omega_K$ of order $K$, consider any function $f:\Omega_K^n\to\mathbf{C}$ and its Fourier expansion $f(z)=\sum_{\alpha\in\{0,1,\ldots,K-1\}^n}a_\alpha z^\alpha$, with $d:=\text{deg}(f)$ denoting its degree as a multivariate polynomial. We prove a Bohnenblust--Hille (BH) inequality in this setting: the $\ell_

Elena Giacomazzi, Felix Haag, Konstantin Hopf

Recent developments related to the energy transition pose particular challenges for distribution grids. Hence, precise load forecasts become more and more important for effective grid management. Novel modeling approaches such as the Transformer architecture, in particular the Temporal Fusion Transformer (TFT), have emerged as promising methods for time seri

Vasil Rokaj, Jie Wang, John Sous, Markus Penz

We study the quantum Hall effect in a two-dimensional homogeneous electron gas coupled to a quantum cavity field. As initially pointed out by Kohn, Galilean invariance for a homogeneous quantum Hall system implies that the electronic center of mass (CM) decouples from the electron-electron interaction, and the energy of the CM mode, also known as Kohn mode,

Baikjin Jung, Myungji Lee, Jong-Hyeok Lee, Yunsu Kim

Automatic postediting (APE) is an automated process to refine a given machine translation (MT). Recent findings present that existing APE systems are not good at handling high-quality MTs even for a language pair with abundant data resources, English-to-German: the better the given MT is, the harder it is to decide what parts to edit and how to fix these err

Shulu Chen, Antony Evans, Marc Brittain, Peng Wei

Urban air mobility (UAM) has the potential to revolutionize our daily transportation, offering rapid and efficient deliveries of passengers and cargo between dedicated locations within and around the urban environment. Before the commercialization and adoption of this emerging transportation mode, however, aviation safety must be guaranteed, i.e., all the ai

Erin E Gabriel, Michael C Sachs, Andreas Kryger Jensen

The probability of benefit is a valuable and important measure of treatment effect, which has advantages over the average treatment effect. Particularly for an ordinal outcome, it has a better interpretation and can make apparent different aspects of the treatment impact. Unfortunately, this measure, and variations of it, are not identifiable even in randomi

Lin Duan, Jingwei Sun, Yiran Chen, Maria Gorlatova

Edge-cloud collaborative inference empowers resource-limited IoT devices to support deep learning applications without disclosing their raw data to the cloud server, thus preserving privacy. Nevertheless, prior research has shown that collaborative inference still results in the exposure of data and predictions from edge devices. To enhance the privacy of co

Fabio Palmese, Alessandro E. C. Redondi

The Internet of Things (IoT) has boomed in recent years, with an ever-growing number of connected devices and a corresponding exponential increase in network traffic. As a result, IoT devices have become potential witnesses of the surrounding environment and people living in it, creating a vast new source of forensic evidence. To address this need, a new fie

Igor Sfiligoi, Emily A. Belli, Jeff Candy, Reuben D. Budiardja

NVIDIA has been the main provider of GPU hardware in HPC systems for over a decade. Most applications that benefit from GPUs have thus been developed and optimized for the NVIDIA software stack. Recent exascale HPC systems are, however, introducing GPUs from other vendors, e.g. with the AMD GPU-based OLCF Frontier system just becoming available. AMD GPUs can

Georg Wölflein, Lucie Charlotte Magister, Pietro Liò, David J. Harrison

Traditional supervised learning tasks require a label for every instance in the training set, but in many real-world applications, labels are only available for collections (bags) of instances. This problem setting, known as multiple instance learning (MIL), is particularly relevant in the medical domain, where high-resolution images are split into smaller p

Fabio Andrijauskas, Igor Sfiligoi, Frank Würthwein

Compute resource providers often put in place batch compute systems to maximize the utilization of such resources. However, compute nodes in such clusters, both physical and logical, contain several complementary resources, with notable examples being CPUs, GPUs, memory and ephemeral storage. User jobs will typically require more than one such resource, resu