May 2023 arXiv papers — page 105

Ahmed J. Afifi, Samuel T. Thiele, Aldino Rizaldy, Sandra Lorenz

The increasing use of deep learning techniques has reduced interpretation time and, ideally, reduced interpreter bias by automatically deriving geological maps from digital outcrop models. However, accurate validation of these automated mapping approaches is a significant challenge due to the subjective nature of geological mapping and the difficulty in coll

Vesselin Drensky, Nurlan Ismailov, Manat Mustafa, Bekzat Zhakhayev

We introduce the variety ${\mathfrak B}_{\textrm{sup}}$ of bicommutative superalgebras over an arbitrary field of characteristic different from 2. The variety consists of all nonassociative ${\mathbb Z}_2$-graded algebras satisfying the polynomial super-identities of super- left- and right-commutativity \[ x(yz)= (-1)^{\overline{x}\,\overline{y}} y(xz)\text{

Jian Liang, Linjie Song

We are interested in the following semilinear elliptic problem: \begin{equation*} \begin{cases} -\Delta u + \lambda u = u^{p-1} \ \text{in} \ T,\\ u > 0, u = 0 \ \text{on} \ \partial T,\\ \int_{T}u^{2} \, dx= c \end{cases} \end{equation*} where $T = \{x \in \mathbb{R}^{N}: 1 < |x| < 2\}$ is an annulus in $\mathbb{R}^{N}$, $N \geq 2$, $p > 1$ is Sobolev-subcr

Kathryn Gray, Mingwei Li, Reyan Ahmed, Md. Khaledur Rahman

Large tree structures are ubiquitous and real-world relational datasets often have information associated with nodes (e.g., labels or other attributes) and edges (e.g., weights or distances) that need to be communicated to the viewers. Yet, scalable, easy to read tree layouts are difficult to achieve. We consider tree layouts to be readable if they meet some

Hao Zheng, Jinbao Wang, Xiantong Zhen, Hong Chen

Recently, Transformers have emerged as the go-to architecture for both vision and language modeling tasks, but their computational efficiency is limited by the length of the input sequence. To address this, several efficient variants of Transformers have been proposed to accelerate computation or reduce memory consumption while preserving performance. This p

Mohammadreza Mohammadi, Heath Smith, Lareb Khan, Ramtin Zand

Facial Expression Recognition (FER) plays an important role in human-computer interactions and is used in a wide range of applications. Convolutional Neural Networks (CNN) have shown promise in their ability to classify human facial expressions, however, large CNNs are not well-suited to be implemented on resource- and energy-constrained IoT devices. In this

Shuai Ma, Ruixin Yang, Chun Du, Hang Li

Integrated visible light positioning and communication (VLPC), capable of combining advantages of visible light communications (VLC) and visible light positioning (VLP), is a promising key technology for the future Internet of Things. In VLPC networks, positioning and communications are inherently coupled, which has not been sufficiently explored in the lite

Jordan T. Bishop, Marcus Gallagher, Will N. Browne

Reinforcement learning (RL) is experiencing a resurgence in research interest, where Learning Classifier Systems (LCSs) have been applied for many years. However, traditional Michigan approaches tend to evolve large rule bases that are difficult to interpret or scale to domains beyond standard mazes. A Pittsburgh Genetic Fuzzy System (dubbed Fuzzy MoCoCo) is

J. R. Fuentes, Evan H. Anders, Andrew Cumming, Bradley W. Hindman

Recent measurements of Jupiter's gravitational moments by the Juno spacecraft and seismology of Saturn's rings suggest that the primordial composition gradients in the deep interior of these planets have persisted since their formation. One possible explanation is the presence of a double-diffusive staircase below the planet's outer convection zone, which in

Wai-Pan Ng, Qiujiang Liang, Jun Yang

Accurate ab-initio prediction of electronic energies is very expensive for macromolecules by explicitly solving post-Hartree-Fock equations. We here exploit the physically justified local correlation feature in compact basis of small molecules, and construct an expressive low-data deep neural network (dNN) model to obtain machine-learned electron correlation

Sohan Ghodla, J. J. Eldridge

Recently, it was shown that the formation of a photon-trapping surface might not be sufficient to ensure unimpeded super-Eddington (SE) accretion. In light of this finding, here we derive a condition such that sustained and unimpeded SE accretion could be achieved in optically thick slim accretion disks surrounding neutron stars (NSs) and black holes (BHs).

Dan Luo, Lixin Zou, Qingyao Ai, Zhiyu Chen

The goal of unbiased learning to rank (ULTR) is to leverage implicit user feedback for optimizing learning-to-rank systems. Among existing solutions, automatic ULTR algorithms that jointly learn user bias models (i.e., propensity models) with unbiased rankers have received a lot of attention due to their superior performance and low deployment cost in practi

Abhijit Chakraborty, Tetsuo Hatsuda, Yuichi Ikeda

Cryptoassets are growing rapidly worldwide. One of the large cap cryptoassets is XRP. In this article, we focus on analyzing transaction data for the 2017-2018 period that consist one of the significant XRP market price bursts. We construct weekly weighted directed networks of XRP transactions. These weekly networks are embedded on continuous vector space us

Zhou Xian, Theophile Gervet, Zhenjia Xu, Yi-Ling Qiao

This document serves as a position paper that outlines the authors' vision for a potential pathway towards generalist robots. The purpose of this document is to share the excitement of the authors with the community and highlight a promising research direction in robotics and AI. The authors believe the proposed paradigm is a feasible path towards accomplish

K. Abe, N. Akhlaq, R. Akutsu, H. Alarakia-Charles

Muon neutrino and antineutrino disappearance probabilities are identical in the standard three-flavor neutrino oscillation framework, but CPT violation and non-standard interactions can violate this symmetry. In this work we report the measurements of $\sin^{2} \theta_{23}$ and $\Delta m_{32}^2$ independently for neutrinos and antineutrinos. The aforemention

I. I. Gontchar, M. V. Chushnyakova

Theoretical modeling of nucleus-nucleus collision often is based on the nucleus-nucleus potential. One of the advanced methods for constructing this potential is the semi-microscopical double-folding model with the M3Y-Paris NN-forces. Proton and neutron densities are significant ingredient of this model. Correct nucleon density (ND) must reproduce experimen

Ziang Zhang, Patrick Brown, Jamie Stafford

Quasi-periodicity refers to a pattern in a function where it appears periodic but has evolving amplitudes over time. This is often the case in practical settings such as the modeling of case counts of infectious disease or the carbon dioxide (CO2) concentration over time. In this paper, we introduce a class of Gaussian processes, called seasonal Gaussian Pro

Karine Karine, Predrag Klasnja, Susan A. Murphy, Benjamin M. Marlin

Just-in-Time Adaptive Interventions (JITAIs) are a class of personalized health interventions developed within the behavioral science community. JITAIs aim to provide the right type and amount of support by iteratively selecting a sequence of intervention options from a pre-defined set of components in response to each individual's time varying state. In thi

Karol Kowalski, Bo Peng, Nicholas P. Bauman

In this paper, we evaluate the accuracy of the Hermitian form of the downfolding procedure utilizing the double unitary coupled cluster Ansatz (DUCC) on the H6 and H8 benchmark systems. The computational infrastructure employs the occupation-number-representation codes to construct the matrix representation of arbitrary second-quantized operators, enabling t

Markus Holzer, Jayesh Badwaik, Radim Vavrik, Gabriel Staffelbach

The primary goal of the EuroHPC JU project SCALABLE is to develop an industrial Lattice Boltzmann Method (LBM)-based computational fluid dynamics (CFD) solver capable of exploiting current and future extreme scale architectures, expanding current capabilities of existing industrial LBM solvers by at least two orders of magnitude in terms of processor cores a

Koudai Sugimoto, Satoshi Ejima

By utilizing time-dependent tensor-network algorithms in the infinite matrix-product-state representation, we theoretically investigate the pump-probe spectroscopy of the one-dimensional extended Hubbard model at half filling. Our focus lies on nonequilibrium optical conductivity and single-particle excitation spectra. In the spin-density-wave (SDW) phase, w

Vivek Yelleti, Ch Priyanka

In the era of real-time data, traditional methods often struggle to keep pace with the dynamic nature of streaming environments. In this paper, we proposed a hybrid framework where in (i) stage-I follows a traditional approach where the model is built once and evaluated in a real-time environment, and (ii) stage-II employs an incremental learning approach wh

P. M. Aronow, Haoge Chang, Patrick Lopatto

Given a randomized experiment with binary outcomes, exact confidence intervals for the average causal effect of the treatment can be computed through a series of permutation tests. This approach requires minimal assumptions and is valid for all sample sizes, as it does not rely on large-sample approximations such as those implied by the central limit theorem

Aysajan Abidin, Karim Eldefrawy, Dave Singelee

Mutual distance bounding (DB) protocols enable two distrusting parties to establish an upper-bound on the distance between them. DB has been so far mainly considered in classical settings and for classical applications, especially in wireless settings, e.g., to prevent relay attacks in wireless authentication and access control systems, and for secure locali

Arthur Castello B. de Oliveira, Milad Siami, Eduardo D. Sontag

Recent research in neural networks and machine learning suggests that using many more parameters than strictly required by the initial complexity of a regression problem can result in more accurate or faster-converging models -- contrary to classical statistical belief. This phenomenon, sometimes known as ``benign overfitting'', raises questions regarding in

Shahab Asoodeh, Mario Diaz

The Noisy-SGD algorithm is widely used for privately training machine learning models. Traditional privacy analyses of this algorithm assume that the internal state is publicly revealed, resulting in privacy loss bounds that increase indefinitely with the number of iterations. However, recent findings have shown that if the internal state remains hidden, the

Chris Budd, Rachel Kuske

We study the behaviour at tipping points close to (smoothed) non-smooth fold bifurcations in one-dimensional oscillatory forced systems. The focus is the Stommel-Box, and related climate models, which are piecewise-smooth continuous dynamical systems, modelling thermohaline circulation. These exhibit non-smooth fold bifurcations which arise when a saddle-poi

Yushen Huang, Ertai Luo, Stanley Bak, Yifan Sun

Polynomial zonotopes, a non-convex set representation, have a wide range of applications from real-time motion planning and control in robotics, to reachability analysis of nonlinear systems and safety shielding in reinforcement learning. Despite this widespread use, a frequently overlooked difficulty associated with polynomial zonotopes is intersection chec

Sourya Basu, Pulkit Katdare, Prasanna Sattigeri, Vijil Chenthamarakshan

Efficient transfer learning algorithms are key to the success of foundation models on diverse downstream tasks even with limited data. Recent works of Basu et al. (2023) and Kaba et al. (2022) propose group averaging (equitune) and optimization-based methods, respectively, over features from group-transformed inputs to obtain equivariant outputs from non-equ

Wenhan Guo, Tzu-Chieh Wei

We present a comprehensive study on the extraction of CFT data using tensor network methods, specially, from the fixed-point tensor of the linearized tensor renormalization group (lTRG) for the 2D classical Ising model near the critical temperature. Utilizing two different methods, we extract operator scaling dimensions and operator-product-expansion (OPE) c

Jeewoo Sul, Yong Suk Choi

An important problem of the sequence-to-sequence neural models widely used in abstractive summarization is exposure bias. To alleviate this problem, re-ranking systems have been applied in recent years. Despite some performance improvements, this approach remains underexplored. Previous works have mostly specified the rank through the ROUGE score and aligned

Yuheng Jia, Chongjie Si, Min-ling Zhang

In partial label learning (PLL), each training sample is associated with a set of candidate labels, among which only one is valid. The core of PLL is to disambiguate the candidate labels to get the ground-truth one. In disambiguation, the existing works usually do not fully investigate the effectiveness of the non-candidate label set (a.k.a. complementary la

Boyue Li, Yuejie Chi

Achieving communication efficiency in decentralized machine learning has been attracting significant attention, with communication compression recognized as an effective technique in algorithm design. This paper takes a first step to understand the role of gradient clipping, a popular strategy in practice, in decentralized nonconvex optimization with communi

Ryosuke Satoh

Quantum Repeaters are one critical technology for scalable quantum networking. One of the key challenges regarding quantum repeaters is their management of how they provide quantum entanglement for distant quantum computers. We focus on the RuleSet architecture, which is a decentralized way to manage repeaters. The RuleSet concept is designed to scale up the

Jiashi Yang

We study frequency shifts in a piezoelectric quartz resonator induced by a surface mass layer for sensor applications. The scalar differential equations for thickness-shear modes in a quartz plate are used. A first-order perturbation analysis is performed. The frequency shift is obtained and is expressed by a perturbation integral. It produces the well-known

Kuiliang Gao, Anzhu Yu, Xiong You, Wenyue Guo

In the existing unsupervised domain adaptation (UDA) methods for remote sensing images (RSIs) semantic segmentation, class symmetry is an widely followed ideal assumption, where the source and target RSIs have exactly the same class space. In practice, however, it is often very difficult to find a source RSI with exactly the same classes as the target RSI. M

Jinghao Deng, Fanqi Wan, Tao Yang, Xiaojun Quan

Contrastive learning has been widely studied in sentence representation learning. However, earlier works mainly focus on the construction of positive examples, while in-batch samples are often simply treated as negative examples. This approach overlooks the importance of selecting appropriate negative examples, potentially leading to a scarcity of hard negat

Shuaishuai Guo, Kaiqian Qu

The spatial degrees of freedom (DoFs) greatly increase in the near-field region of millimeter wave or terahertz multiple-input multiple-output communications with extremely large antenna arrays (XL-MIMO). To employ the increased spatial DoFs, a beamspace modulation (BM) strategy is introduced to the near field of XL-MIMO. BM can work with a fixed small numbe

Young-Joo Han, Ha-Jin Yu

Recently, numerous studies have been conducted on supervised learning-based image denoising methods. However, these methods rely on large-scale noisy-clean image pairs, which are difficult to obtain in practice. Denoising methods with self-supervised training that can be trained with only noisy images have been proposed to address the limitation. These metho

Ana Aleksandric, Henry Isaac Anderson, Anisha Dangal, Gabriela Mustata Wilson

Abortion remains one of the most controversial topics, especially after overturning Roe v. Wade ruling in the United States. Previous literature showed that the illegality of abortion could have serious consequences, as women might seek unsafe pregnancy terminations leading to increased maternal mortality rates and negative effects on their reproductive heal

Junsup Shim, Changbom Park, Juhan Kim, Sungwook E. Hong

We identify cosmic voids from galaxy density fields under the theory of void-cluster correspondence. We extend the previous novel void-identification method developed for the matter density field to the galaxy density field for practical applications. From cosmological N-body simulations, we construct galaxy number- and mass-weighted density fields to identi

Jiong Zhu, Aishwarya Reganti, Edward Huang, Charles Dickens

Distributed training of GNNs enables learning on massive graphs (e.g., social and e-commerce networks) that exceed the storage and computational capacity of a single machine. To reach performance comparable to centralized training, distributed frameworks focus on maximally recovering cross-instance node dependencies with either communication across instances

Harish Kishnani, Rijubrata Kundu, Sumit Chandra Mishra

Let $k$ be a field with $u$-invariant $\leq2$. Assume further that $k$ is not quadratically closed, $\mathsf{char}(k)\neq 2$ and $|k|\geq 5$. It is known that the covering number of both $\text{SL}_2(k)$ and $\text{PSL}_2(k)$ is three, while their extended covering number is four. In this article, we completely describe the product of two conjugacy classes i

Jakub Konieczny

We study the notion of an asymptotically automatic sequence, which generalises the notion of an automatic sequence. While $k$-automatic sequences are characterised by finiteness of $k$-kernels, the $k$-kernels of asymptotically $k$-automatic sequences are only required to be finite up to equality almost everywhere. We prove basic closure properties and a lin

Khadija F. Zaidi, Michelle Harris-Love

Upper extremity motor impairment affects about 80\% of persons after strokes. For stroke rehabilitation, upper limb kinematic assessments have increasingly been used as primary or secondary outcome measures. Studying the upper extremity provides a valuable tool for assessing limb coordination, mal-adaptations, and recovery. There is currently no universal st

Rie Matsumoto, Shiniji Yuasa, Hiroshi Imamura

Voltage-controlled magnetoresistive random access memory (VC-MRAM) based on voltage-induced dynamic switching in magnetic tunnel junctions (MTJs) is a promising ultimate non-volatile memory with ultralow power consumption. However, the dynamic switching in a conventional MTJ is accompanied by a relatively high write error rate (WER), hindering the reliable o

David Zegeye, Thomas Crawford, Wayne Hu

The thermal Sunyaev-Zel'dovich (tSZ) effect is a spectral distortion of the cosmic microwave background (CMB) resulting from inverse Compton scattering of CMB photons with electrons in the medium of galaxy clusters. The spectrum of the tSZ effect is typically calculated assuming the spectrum of the CMB is a blackbody. However, energy or photon number injecti

Fangxiu Meng, Kang Xie, Peng Liu, Huazhou Chen

Northeast China is the learding food productive base of China. The extreme precipitation (EP) event seriously impacts agricultural production and social life. Given the limited understanding of the EP in Northeast China, we investigate the trend and potential risk of the EP in Northeast China(107 stations) during 1959-2017, especially in early and later summ

Asifullah Khan, Zunaira Rauf, Anabia Sohail, Abdul Rehman

Vision transformers have become popular as a possible substitute to convolutional neural networks (CNNs) for a variety of computer vision applications. These transformers, with their ability to focus on global relationships in images, offer large learning capacity. However, they may suffer from limited generalization as they do not tend to model local correl

Maxim Lyutikov, Ahmad Ibrahim

We point out the dominant importance of plasma injection effects for relativistic winds from pulsars and black holes. We demonstrate that outside the light cylinder the magnetically dominated outflows while sliding along the helical magnetic field move in fact nearly radially with very large Lorentz factors $\gamma_0 \gg 1 $, imprinted into the flow during p

Domingos Djinja, Sergei Silvestrov, Alex Behakanira Tumwesigye

Representations of polynomial covariance type commutation relations are constructed on Banach spaces $L_p$ and $C[\alpha, \beta],\ \alpha,\beta\in \mathbb{R}$. Representations involve operators with piecewise functions, multiplication operators and inner superposition operators.

Yuanyuan Tian, Wenwen Li, Sizhe Wang, Zhining Gu

Initiated by the University Consortium of Geographic Information Science (UCGIS), GIS&T Body of Knowledge (BoK) is a community-driven endeavor to define, develop, and document geospatial topics related to geographic information science and technologies (GIS&T). In recent years, GIS&T BoK has undergone rigorous development in terms of its topic re-organizatio

Ankang Liu, Alexander M. Finkel'stein

The magnonic crystal, which has a spatial modulation wave vector $q$, couples the spin wave with wave vector $k$ to the one with wave vector $k-q$. For a conventional magnonic crystal with direct current (dc) supply, the spin waves around $q/2$ are resonantly coupled to the waves near $-q/2$, and a band gap is opened at $k=\pm q/2$. If instead of the dc curr

Dong Woo Yoo, Sakib Reza, Nicholas Wilson, Kemi Jona

This research seeks to explore how Augmented Reality (AR) can support learning psychomotor tasks that involve complex manipulation and reasoning processes. The AR prototype was created using Unity and used on HoloLens 2 headsets. Here, we explore the potential of AR as a training or assistive tool for spatial tasks and the need for intelligent mechanisms to

Hyeonggeun Yun, Younggeol Cho, Jinwon Lee, Arim Ha

Unmanned ground vehicles (UGVs) in unstructured environments mostly operate through teleoperation. To enable stable teleoperated driving in unstructured environments, some research has suggested driver assistance and evaluation methods that involve user studies, which can be costly and require lots of time and effort. A simulation model-based approach has be

Valerio De Angeis

A concise and elementary derivation of the complete asymptotic expansion for the factorial function $n!$ is presented. This treatment produces a new expression for the coefficients, and it brings to light the simple relationship between the asymptotic expansions of $n!$ and $1/n!$.

Christopher Sims

The engineering of new states of matter through Floquet driving has revolutionized the field of condensed matter physics. This technique enables the creation of hybrid topological states and ordered phases that are absent in normal systems. Crystalline structures, exemplifying spatially ordered systems under periodic driving, have been extensively studied. H

Ke Liu, Behrouz Movahhed Nouri, Elham Heidari, Hamed Dalir

Photonic signal processing requires efficient on-chip light sources with higher modulation bandwidths. Todays conventional fastest semiconductor diode lasers exhibit modulation speeds only on the order of a few tens of GHz due to gain compression effects and parasitic electrical capacitances. Here we theoretically show an electrically-driven Carbon nanotube

Ya-Yen Tsai, Bidan Huang, Yu Zheng, Lei Han

Tactile sensors are believed to be essential in robotic manipulation, and prior works often rely on experts to reason the sensor feedback and design a controller. With the recent advancement in data-driven approaches, complicated manipulation can be realised, but an accurate and efficient tactile simulation is necessary for policy training. To this end, we p

Zhihong Fang, Shaolin Tan, Yaonan Wang

In this paper, we consider the problem of inferring the sign of a link based on limited sign data in signed networks. Regarding this link sign prediction problem, SDGNN (Signed Directed Graph Neural Networks) provides the best prediction performance currently to the best of our knowledge. In this paper, we propose a different link sign prediction architectur

Kenneth Bogert, Matthew Kothe

The Principle of Maximum Entropy is a rigorous technique for estimating an unknown distribution given partial information while simultaneously minimizing bias. However, an important requirement for applying the principle is that the available information be provided error-free (Jaynes, 1982). We relax this requirement using a memoryless communication channel

Keqi Wang, Wei Xie, Hua Zheng

To facilitate a rapid response to pandemic threats, this paper focuses on developing a mechanistic simulation model for in vitro transcription (IVT) process, a crucial step in mRNA vaccine manufacturing. To enhance production and support industry 4.0, this model is proposed to improve the prediction and analysis of IVT enzymatic reaction network. It incorpor

Aline V. Andrade, Danilo R. Santiago, Danilo D. Silva, Luiz C. S. Sobral

We investigate rank $3$ instanton vector bundles on $\mathbb{P}^3$ of charge $n$ and its correspondence with rational curves of degree $n+3$. For $n=2$ we present a correspondence between stable rank $3$ instanton bundles and stable rank $2$ reflexive linear sheaves of Chern classes $(c_1,c_2,c_3)=(-1,3,3)$ and we use this correspondence to compute the dimen

Tatsunari Watanabe

In this paper, we will compute the non-abelian cohomology of the universal complete curve in positive characteristic. This extends Hain's result on the non-abelian cohomology of generic curves in characteristic zero to positive characteristics. Furthermore, we will prove that the exact sequence of etale fundamental groups of the universal n-punctured curve i

Jason Mars, Yiping Kang, Roland Daynauth, Baichuan Li

Today's production scale-out applications include many sub-application components, such as storage backends, logging infrastructure and AI models. These components have drastically different characteristics, are required to work in collaboration, and interface with each other as microservices. This leads to increasingly high complexity in developing, optimiz

Yifan Tang, M. Rahmani Dehaghani, G. Gary Wang

Transfer learning (TL) based additive manufacturing (AM) modeling is an emerging field to reuse the data from historical products and mitigate the data insufficiency in modeling new products. Although some trials have been conducted recently, the inherent challenges of applying TL in AM modeling are seldom discussed, e.g., which source domain to use, how muc

Chandan Singh, Aliyah R. Hsu, Richard Antonello, Shailee Jain

Large language models (LLMs) have demonstrated remarkable prediction performance for a growing array of tasks. However, their rapid proliferation and increasing opaqueness have created a growing need for interpretability. Here, we ask whether we can automatically obtain natural language explanations for black box text modules. A "text module" is any function

Alon Harell, Yalda Foroutan, Ivan V. Bajic

Compression for machines is an emerging field, where inputs are encoded while optimizing the performance of downstream automated analysis. In scalable coding for humans and machines, the compressed representation used for machines is further utilized to enable input reconstruction. Often performed by jointly optimizing the compression scheme for both machine

Uroš A. Colović, Branislav I. Prvulović

The aim of this paper is to prove a conjecture made by T. Fukaya in 2008. This conjecture concerns the exact value of the $\mathbb Z_2$-cup-length of the Grassmann manifold $\widetilde G_{n,3}$ of oriented $3$-planes in $\mathbb R^n$. Along the way, we calculate the heights of the Stiefel--Whitney classes of the canonical vector bundle over $\widetilde G_{n,

A. Albert, R. Alfaro, C. Alvarez, J. C. Arteaga-Velazquez

The Galactic Halo is a key target for indirect dark matter detection. The High Altitude Water Cherenkov (HAWC) observatory is a high-energy (~300 GeV to >100 TeV) gamma-ray detector located in central Mexico. HAWC operates via the water Cherenkov technique and has both a wide field of view of 2 sr and a >95% duty cycle, making it ideal for analyses of highly

Markus Freitag, Behrooz Ghorbani, Patrick Fernandes

Recent advances in machine translation (MT) have shown that Minimum Bayes Risk (MBR) decoding can be a powerful alternative to beam search decoding, especially when combined with neural-based utility functions. However, the performance of MBR decoding depends heavily on how and how many candidates are sampled from the model. In this paper, we explore how dif

Niloofar Mireshghallah, Justus Mattern, Sicun Gao, Reza Shokri

With the advent of fluent generative language models that can produce convincing utterances very similar to those written by humans, distinguishing whether a piece of text is machine-generated or human-written becomes more challenging and more important, as such models could be used to spread misinformation, fake news, fake reviews and to mimic certain autho

Jiao Chen, Luyi Ma, Xiaohan Li, Nikhil Thakurdesai

Knowledge Graphs (KGs) play a crucial role in enhancing e-commerce system performance by providing structured information about entities and their relationships, such as complementary or substitutable relations between products or product types, which can be utilized in recommender systems. However, relation labeling in KGs remains a challenging task due to

Vipul Raheja, Dhruv Kumar, Ryan Koo, Dongyeop Kang

We introduce CoEdIT, a state-of-the-art text editing system for writing assistance. CoEdIT takes instructions from the user specifying the attributes of the desired text, such as "Make the sentence simpler" or "Write it in a more neutral style," and outputs the edited text. We present a large language model fine-tuned on a diverse collection of task-specific

Kenneth B. A. Benicio, André L. F. de Almeida, Bruno Sokal, Fazal-E-Asim

This letter proposes a model for symbol detection in the uplink of IRS-assisted networks in the presence of channel aging. During the first stage, we model the received pilot signal as a tensor, which serves as a basis for both estimating the channel and configuring the IRS. In the second stage, the proposed tensor approach tracks the aging process to detect

Grigoris Tsopouridis, Andreas-Alexandros Vasilakis, Ioannis Fudos

We present a machine learning approach for efficiently computing order independent transparency (OIT). Our method is fast, requires a small constant amount of memory (depends only on the screen resolution and not on the number of triangles or transparent layers), is more accurate as compared to previous approximate methods, works for every scene without setu

Zhangchen Zhou, Hanxu Zhou, Yuqing Li, Zhi-Qin John Xu

Previous research has shown that fully-connected networks with small initialization and gradient-based training methods exhibit a phenomenon known as condensation during training. This phenomenon refers to the input weights of hidden neurons condensing into isolated orientations during training, revealing an implicit bias towards simple solutions in the para

Fan Xing, Zeyang Liao, Xue-hua Wang

Quantum light sources play a vital role in various aspects of quantum information science, but on-demand high-efficient generation of arbitrary multiphoton states which can be easily integrated is still challenging. Here, we propose a chip-integrable scheme to deterministically generate a group of n photons with very high fidelity based on the long-range col

Victoria Huang, Shaleeza Sohail, Michael Mayo, Tania Lorido Botran

Federated learning (FL), as an emerging artificial intelligence (AI) approach, enables decentralized model training across multiple devices without exposing their local training data. FL has been increasingly gaining popularity in both academia and industry. While research works have been proposed to improve the fault tolerance of FL, the real impact of unre

Tasdiqul Islam, Engin Arslan

Long-distance quantum communication presents a significant challenge as maintaining the fidelity of qubits can be difficult. This issue can be addressed through the use of quantum repeaters to transmit entanglement information through Bell measurements. However, despite its necessity to enable wide-area quantum internet, the deployment cost of quantum repeat

Peter Olamide Olanipekun

We establish a rigidity result for the critical points, with boundary, of a four dimensional Willmore energy. These critical points satisfy a 4-Willmore equation which is a sixth order nonlinear elliptic partial differential equation. We establish several curvature estimates and prove that four dimensional Willmore submanifold with totally geodesic boundary

Patrick I. Babb, S. Farzad Ahmadi, Forrest Brent, Ruby Gans

Passive solar desalination is an emerging low-cost technology for fresh water production. State of the art desalinators typically evaporate water using wicking structures to achieve high solar-to-vapor efficiency by minimizing heat loss. However, wicking structures cannot reject salt continuously which limits the operating duration of the desalinators to sev

Marco Bagnara, Mario Maurelli, Fanhui Xu

By employing a suitable multiplicative It\^o noise with radial structure and with more than linear growth, we show the existence of a unique, global-in-time, strong solution for the stochastic Euler equations in two and three dimensions. More generally, we consider a class of stochastic partial differential equations (SPDEs) with a superlinear growth drift a

Domingos Djinja, Sergei Silvestrov, Alex Behakanira Tumwesigye

Representations by linear integral operators on $L_p$ spaces over measure spaces are investigated for the polynomial covariance type commutation relations and more general two-sided generalizations of covariance commutation relations extending simultaneously the covariance and the reciprocal covariance type commutation relations. Necessary and sufficient con

Ruokai Yin, Yuhang Li, Abhishek Moitra, Priyadarshini Panda

We propose Multiplier-less INTeger (MINT) quantization, a uniform quantization scheme that efficiently compresses weights and membrane potentials in spiking neural networks (SNNs). Unlike previous SNN quantization methods, MINT quantizes memory-intensive membrane potentials to an extremely low precision (2-bit), significantly reducing the memory footprint. M

Sergio Nava-Muñoz, Mario Graff Guerrero, Hugo Jair Escalante

In recent decades, challenges have become very popular in scientific research as these are crowdsourcing schemes. In particular, challenges are essential for developing machine learning algorithms. For the challenges settings, it is vital to establish the scientific question, the dataset (with adequate quality, quantity, diversity, and complexity), performan

Sinya Aoki, Tetsuya Onogi, Tatsuya Yamaoka

In this paper, we investigate relations or differences among various conserved quantities which involve the matter Energy Momentum Tensor (EMT) in general relativity. These charges include the energy with Einstein's pseudo EMT, the generalized Komar integral, or the ADM energy, all of which can be derived from Noether's second theorem, as well as an extra co

Andrea F. Daniele

Accessibility is one of the most important features in the design of robots and their interfaces. This thesis proposes methods that improve the accessibility of robots for three different target audiences: consumers, researchers, and learners. In order for humans and robots to work together effectively, they both must be able to communicate with each other.

Pareesa Ameneh Golnari, Zhewei Yao, Yuxiong He

This study examines the impact of optimizing the Stable Diffusion (SD) guided inference pipeline. We propose optimizing certain denoising steps by limiting the noise computation to conditional noise and eliminating unconditional noise computation, thereby reducing the complexity of the target iterations by 50%. Additionally, we demonstrate that later iterati

Zihao He, Jonathan May, Kristina Lerman

Detecting norm violations in online communities is critical to maintaining healthy and safe spaces for online discussions. Existing machine learning approaches often struggle to adapt to the diverse rules and interpretations across different communities due to the inherent challenges of fine-tuning models for such context-specific tasks. In this paper, we in

Jesús M. F. Castillo, Manuel González, Raúl Pino

We study the structure of the Rochberg Banach spaces $\mathfrak Z_n$ associated to the interpolation pair $(\ell_\infty, \ell_1)$ at $1/2$, and the operators defined on them

Daniel Martin

In this paper, we give a definition for the Bartnik mass of a domain whose extensions are asymptotically hyperbolic manifolds. With this definition, we show that asymptotically hyperbolic admissible extensions of a domain that achieve the Bartnik mass must admit a static potential. Given a non-static admissible extension of a domain, we are able to construct

Bruce Fegley, Katharina Lodders, Nathan S. Jacobson

The chemical equilibrium distribution of 69 elements between gas and melt is modeled for bulk silicate Earth (BSE) material from 1000 - 4500 K and 1e-6 to 100 bar. The BSE melt is modeled as a non-ideal solution and the effects of different activity coefficients and ideal solution are studied. Results include 50% condensation temperatures, major gases of eac

Jennifer Paykin, Albert T. Schmitz, Mohannad Ibrahim, Xin-Chuan Wu

This paper presents the Pauli-based Circuit Optimization, Analysis, and Synthesis Toolchain (PCOAST), a framework for quantum circuit optimizations based on the commutative properties of Pauli strings. Prior work has demonstrated that commuting Clifford gates past Pauli rotations can expose opportunities for optimization in unitary circuits. PCOAST extends t

Albert T. Schmitz, Mohannad Ibrahim, Nicolas P. D. Sawaya, Gian Giacomo Guerreschi

The Pauli-based Circuit Optimization, Analysis and Synthesis Toolchain (PCOAST) was recently introduced as a framework for optimizing quantum circuits. It converts a quantum circuit to a Pauli-based graph representation and provides a set of optimization subroutines to manipulate that internal representation as well as methods for re-synthesizing back to a q

Harbir Antil, Madhu Gupta, Randy Price

Empirical interpolation method (EIM) is a well-known technique to efficiently approximate parameterized functions. This paper proposes to use EIM algorithm to efficiently reduce the dimension of the training data within supervised machine learning. This is termed as DNN-EIM. Applications in data science (e.g., MNIST) and parameterized (and time-dependent) pa

Sehyun Ji

We prove a lower bound for the entropy dissipation of the Landau equation with Coulomb potentials by a weighted Lebesgue norm $L^3_{-5/3}$. In particular, we enhance the weight exponent from $-5$, which was established by Desvillettes, to $-5/3$. Moreover, we prove that the weighted Lebesgue norm $L^3_{-5/3}$ is optimal for both exponents.

Stephen Wissow, Masataro Asai

Balancing exploration and exploitation has been an important problem in both game tree search and automated planning. However, while the problem has been extensively analyzed within the Multi-Armed Bandit (MAB) literature, the planning community has had limited success when attempting to apply those results. We show that a more detailed theoretical understan

André Luís Peixoto Considera, Simon Thalabard

Spontaneous stochasticity is a modern paradigm for turbulent transport at infinite Reynolds numbers. It suggests that tracer particles advected by rough turbulent flows and subject to additional thermal noise, remain non-deterministic in the limit where the random input, namely the thermal noise, vanishes. Here, we investigate the fate of spontaneous stochas

James E. Kostas, Scott M. Jordan, Yash Chandak, Georgios Theocharous

Coagent networks for reinforcement learning (RL) [Thomas and Barto, 2011] provide a powerful and flexible framework for deriving principled learning rules for arbitrary stochastic neural networks. The coagent framework offers an alternative to backpropagation-based deep learning (BDL) that overcomes some of backpropagation's main limitations. For example, co