May 2023 arXiv papers — page 39

Jiaxuan Li, Lang Yu, Allyson Ettinger

Current pre-trained language models have enabled remarkable improvements in downstream tasks, but it remains difficult to distinguish effects of statistical correlation from more systematic logical reasoning grounded on the understanding of real world. We tease these factors apart by leveraging counterfactual conditionals, which force language models to pred

Conghao Zhou, Jie Gao, Mushu Li, Nan Cheng

In this paper, we design a 3D map management scheme for edge-assisted mobile augmented reality (MAR) to support the pose estimation of individual MAR device, which uploads camera frames to an edge server. Our objective is to minimize the pose estimation uncertainty of the MAR device by periodically selecting a proper set of camera frames for uploading to upd

Amir A. Khodahami, Azizollah Azizi

The Page curve exhibits a sharp peak at the Page time corresponding to a phase transition from an empty quantum extremal surface to a non-empty one. This study delves into the impact of this phase transition on the informational content of black hole radiation through constructing a smooth Page curve which represents a gradual transition instead. We utilize

Jongmin Lee, Ernest K. Ryu

Value Iteration (VI) is foundational to the theory and practice of modern reinforcement learning, and it is known to converge at a $\mathcal{O}(\gamma^k)$-rate, where $\gamma$ is the discount factor. Surprisingly, however, the optimal rate for the VI setup was not known, and finding a general acceleration mechanism has been an open problem. In this paper, we

Ying Tang, Ryan Hare

We provide ongoing results from the development of a personalized learning system integrated into a serious game. Given limited instructor resources, the use of computerized systems to help tutor students offers a way to provide higher quality education and to improve educational efficacy. Personalized learning systems like the one proposed in this paper off

Emily Liu, Michael Noseworthy, Nicholas Roy

In this paper, we investigate a scenario in which a robot learns a low-dimensional representation of a door given a video of the door opening or closing. This representation can be used to infer door-related parameters and predict the outcomes of interacting with the door. Current machine learning based approaches in the doors domain are based primarily on l

Zheng Li, Caili Guo, Xin Wang, Zerun Feng

Image-Text Retrieval (ITR) is essentially a ranking problem. Given a query caption, the goal is to rank candidate images by relevance, from large to small. The current ITR datasets are constructed in a pairwise manner. Image-text pairs are annotated as positive or negative. Correspondingly, ITR models mainly use pairwise losses, such as triplet loss, to lear

Wenjing Chen, Zexi Wang

In this article, we study the existence of normalized ground state solutions for the following biharmonic nonlinear Schr\"{o}dinger equation with combined nonlinearities \begin{equation*} \Delta^2u=\lambda u+\mu|u|^{q-2}u+|u|^{p-2}u,\quad \text {in $\mathbb{R}^N$} \end{equation*} having prescribed mass \begin{equation*} \int_{\mathbb{R}^N}|u|^2dx=a^2, \end{e

Victor-Alexandru Pădurean, Georgios Tzannetos, Adish Singla

Generative neural models hold great promise in enhancing programming education by synthesizing new content. We seek to design neural models that can automatically generate programming tasks for a given specification in the context of visual programming domains. Despite the recent successes of large generative models like GPT-4, our initial results show that

Mrigank Dhingra, Omer San, Anne E. Staples

Simulating turbulence to stationarity is a major bottleneck in many engineering problems of practical importance. The problem can be cast as a multiscale problem involving energy redistribution processes that take place on the long large eddy turnover time scale and chaotic processes that take the much shorter time scale of the turbulent fluctuations. But th

Mohamed Belmoubarik, Muftah Al-Mahdawi, George Machado, Tomohiro Nozaki

Ferroelectric memristors have attracted much attention as a type of nonvolatile resistance switching memories in neuromorphic computing, image recognition, and information storage. Their resistance switching mechanisms have been studied several times in perovskite and complicated materials systems. It was interpreted as the modulation of carrier transport by

Anton Tsitsulin, Marina Munkhoeva, Bryan Perozzi

Unsupervised learning has recently significantly gained in popularity, especially with deep learning-based approaches. Despite numerous successes and approaching supervised-level performance on a variety of academic benchmarks, it is still hard to train and evaluate SSL models in practice due to the unsupervised nature of the problem. Even with networks trai

Bingjie Hao, István A. Kovács

Studying significant network patterns, known as graphlets (or motifs), has been a popular approach to understand the underlying organizing principles of complex networks. Statistical significance is routinely assessed by comparing to null models that randomize the connections while preserving some key aspects of the data. However, in signed networks, capturi

Maxwell Aifer, Juzar Thingna, Sebastian Deffner

Quantum synchronization is crucial for understanding complex dynamics and holds potential applications in quantum computing and communication. Therefore, assessing the thermodynamic resources required for finite-time synchronization in continuous-variable systems is a critical challenge. In the present work, we find these resources to be extensive for large

Minqian Liu, Lifu Huang

Class-incremental learning (CIL) aims to develop a learning system that can continually learn new classes from a data stream without forgetting previously learned classes. When learning classes incrementally, the classifier must be constantly updated to incorporate new classes, and the drift in decision boundary may lead to severe forgetting. This fundamenta

Nan Zhou, Xinghui Tao, Xi Chen

We introduce CONA, a novel context-aware instruction paradigm for effective knowledge dissemination using generative pre-trained transformer (GPT) models. CONA is a flexible framework designed to leverage the capabilities of Large Language Models (LLMs) and incorporate DIKW (Data, Information, Knowledge, Wisdom) hierarchy to automatically instruct and optimi

Maxence Noble, Valentin De Bortoli, Arnaud Doucet, Alain Durmus

Multi-marginal Optimal Transport (mOT), a generalization of OT, aims at minimizing the integral of a cost function with respect to a distribution with some prescribed marginals. In this paper, we consider an entropic version of mOT with a tree-structured quadratic cost, i.e., a function that can be written as a sum of pairwise cost functions between the node

Sayna Ebrahimi, Sercan O. Arik, Yihe Dong, Tomas Pfister

Multimodal large-scale pretraining has shown impressive performance for unstructured data such as language and image. However, a prevalent real-world scenario involves structured data types, tabular and time-series, along with unstructured data. Such scenarios have been understudied. To bridge this gap, we propose LANISTR, an attention-based framework to lea

Ali Zia, Renuka Sharma, Reza Arablouei, Greg Bishop-Hurley

Existing image/video datasets for cattle behavior recognition are mostly small, lack well-defined labels, or are collected in unrealistic controlled environments. This limits the utility of machine learning (ML) models learned from them. Therefore, we introduce a new dataset, called Cattle Visual Behaviors (CVB), that consists of 502 video clips, each fiftee

Hao Liu, Pieter Abbeel

Large transformer models powered by diverse data and model scale have dominated natural language modeling and computer vision and pushed the frontier of multiple AI areas. In reinforcement learning (RL), despite many efforts into transformer-based policies, a key limitation, however, is that current transformer-based policies cannot learn by directly combini

Paul Charbonneau, Dmitry Sokoloff

In this paper, written as a general historical and technical introduction to the various review papers collected in the special issue ``Solar and Stellar Dynamo: A New Era'', we review the evolution and current state of dynamo theory and modelling, with emphasis on the solar dynamo. Starting with a historical survey, we then focus on a set of ``tension point

Kadri Ozdemir

The comparison of the kinematic properties of different Vector Boson Scattering (VBS) and Vector Boson Fusion (VBF) processes using multivariate discriminator is presented. The search is performed to identify common features in the the polarized $W^{\pm}W^{\pm}jj$ channel, mainly central and forward region jets kinematic variables such as eta, phi, mass and

Yuqing Cheng, Xingyu Gao, Qiong Li, Yu Liu

The transport properties of matter have been widely investigated. In particular, shear viscosity over a wide parameter space is crucial for various applications, such as designing inertial confinement fusion (ICF) targets and determining the Rayleigh-Taylor instability. In this work, an extended random-walk shielding-potential viscosity model (ext-RWSP-VM) b

Gwen McKinley, Sam Spiro

Given a graph $F$, we define $\operatorname{ex}(G_{n,p},F)$ to be the maximum number of edges in an $F$-free subgraph of the random graph $G_{n,p}$. Very little is known about $\operatorname{ex}(G_{n,p},F)$ when $F$ is bipartite, with essentially tight bounds known only when $F$ is either $C_4, C_6, C_{10}$, or $K_{s,t}$ with $t$ sufficiently large in terms

Anmin Mao, Qian Zhang

We study a class of Schr\"{o}dinger-Kirchhoff system involving critical exponent. We aim to find suitable conditions to assure the existence of a positive ground state solution of Nehari-Poho\u{z}aev type $u_{\varepsilon}$ with exponential decay at infinity for $\varepsilon$ and $ u_{\varepsilon}$ concentrates around a global minimum point of $ V$ as $ \vare

Rongxin Zhu, Jianzhong Qi, Jey Han Lau

A series of datasets and models have been proposed for summaries generated for well-formatted documents such as news articles. Dialogue summaries, however, have been under explored. In this paper, we present the first dataset with fine-grained factual error annotations named DIASUMFACT. We define fine-grained factual error detection as a sentence-level multi

Ayres Freitas, Qian Song, Keping Xie

Recently, the next-to-next-to-leading order (NNLO) electroweak corrections with fermion loops to the Higgsstrahling process were computed. Here we present numerical results for polarized electron/positron beams, as well as for two input parameter schemes known as the $\alpha(0)$ and $G_\mu$ schemes. The size of the NNLO corrections strongly depends on the be

Davi Guimarães da Silva, Anderson Alvarenga de Moura Meneses

Electric consumption prediction methods are investigated for many reasons such as decision-making related to energy efficiency as well as for anticipating demand in the energy market dynamics. The objective of the present work is the comparison between two Deep Learning models, namely the Long Short-Term Memory (LSTM) and Bi-directional LSTM (BLSTM) for univ

Benjamin Coleman, David Torres Ramos, Vihan Lakshman, Chen Luo

Lookup tables are a fundamental structure in many data processing and systems applications. Examples include tokenized text in NLP, quantized embedding collections in recommendation systems, integer sketches for streaming data, and hash-based string representations in genomics. With the increasing size of web-scale data, such applications often require compr

Nicholas A. Gabriel, David A. Broniatowski, Neil F. Johnson

Influence operations are large-scale efforts to manipulate public opinion. The rapid detection and disruption of these operations is critical for healthy public discourse. Emergent AI technologies may enable novel operations which evade current detection methods and influence public discourse on social media with greater scale, reach, and specificity. New me

Shuhei Watanabe, Archit Bansal, Frank Hutter

The recent rise in popularity of Hyperparameter Optimization (HPO) for deep learning has highlighted the role that good hyperparameter (HP) space design can play in training strong models. In turn, designing a good HP space is critically dependent on understanding the role of different HPs. This motivates research on HP Importance (HPI), e.g., with the popul

Yimin Chen, Juncheol Pyo

In this paper, we prove a Heintze-Karcher type inequality for capillary hypersurfaces supported on various hypersurfaces in the hyperbolic space. The equality case only occurs on capillary totally umbilical hypersurfaces. Then we apply this result to prove the Alexandrov type theorem for embedded capillary hypersurfaces in the hyperbolic space. In addition,

Yixiu Zhao, Scott W. Linderman

Structured variational autoencoders (SVAEs) combine probabilistic graphical model priors on latent variables, deep neural networks to link latent variables to observed data, and structure-exploiting algorithms for approximate posterior inference. These models are particularly appealing for sequential data, where the prior can capture temporal dependencies. H

Natalie Behague, Gabriel Crudele, Jonathan A. Noel, Lina M. Simbaqueba

Given two non-empty graphs $H$ and $T$, write $H\succcurlyeq T$ to mean that $t(H,G)^{|E(T)|}\geq t(T,G)^{|E(H)|}$ for every graph $G$, where $t(\cdot,\cdot)$ is the homomorphism density function. We obtain various necessary and sufficient conditions for two trees $H$ and $T$ to satisfy $H\succcurlyeq T$ and determine all such pairs on at most 8 vertices. Th

Rui Tuo, Haoyuan Chen, Raktim Bhattacharya

We propose a novel theoretical and methodological framework for Gaussian process regression subject to privacy constraints. The proposed method can be used when a data owner is unwilling to share a high-fidelity supervised learning model built from their data with the public due to privacy concerns. The key idea of the proposed method is to add synthetic noi

Xuan Zhang, Shenglong Xu, Shuiwang Ji

Quantum Monte Carlo coupled with neural network wavefunctions has shown success in computing ground states of quantum many-body systems. Existing optimization approaches compute the energy by sampling local energy from an explicit probability distribution given by the wavefunction. In this work, we provide a new optimization framework for obtaining propertie

Mitre C. Dourado, Vitor S. Ponciano, Rômulo L. O. da Silva

In this corrigendum, we give a counterexample to Theorem 5.2 in "On the monophonic rank of a graph" [Discrete Math. Theor. Comput. Sci. 24:2 (2022) #3]. We also present a polynomial-time algorithm for computing the monophonic rank of a starlike graph.

Zhenyuan Zhang, Aaditya Ramdas, Ruodu Wang

Given a composite null $ \mathcal P$ and composite alternative $ \mathcal Q$, when and how can we construct a p-value whose distribution is exactly uniform under the null, and stochastically smaller than uniform under the alternative? Similarly, when and how can we construct an e-value whose expectation exactly equals one under the null, but its expected log

Victoria García

Here we study the behaviour of the horocyclic orbit of a vector on the unit tangent bundle of a geometrically infinite surface with variable negative curvature, when the corresponding geodesic ray is almost minimizing and the injectivity radius is finite.

Kazuki Oshita, Masao Tsuzuki

We introduce a local zeta-function for an irreducible admissible supercuspidal representation $\pi$ of the metaplectic double cover of $\SL_2$ over a non-archimedean local field of characteristic zero. We prove a functional equation of the local zeta-functions showing that the gamma factor is given by a Mellin type transform of the Bessel function of $\pi$.

Yihao Xue, Siddharth Joshi, Eric Gan, Pin-Yu Chen

Contrastive learning (CL) has emerged as a powerful technique for representation learning, with or without label supervision. However, supervised CL is prone to collapsing representations of subclasses within a class by not capturing all their features, and unsupervised CL may suppress harder class-relevant features by focusing on learning easy class-irrelev

Daniel B. Seaton, Amir Caspi, Roberto Casini, Cooper Downs

Heliophysics image data largely relies on a forty-year-old ecosystem built on the venerable Flexible Image Transport System (FITS) data standard. While many in situ measurements use newer standards, they are difficult to integrate with multiple data streams required to develop global understanding. Additionally, most data users still engage with data in much

Joseph Shenouda, Rahul Parhi, Kangwook Lee, Robert D. Nowak

This paper introduces a novel theoretical framework for the analysis of vector-valued neural networks through the development of vector-valued variation spaces, a new class of reproducing kernel Banach spaces. These spaces emerge from studying the regularization effect of weight decay in training networks with activations like the rectified linear unit (ReLU

Amir Caspi, Daniel B. Seaton, Roberto Casini, Cooper Downs

COMPLETE is a flagship mission concept combining broadband spectroscopic imaging and comprehensive magnetography from multiple viewpoints around the Sun to enable tomographic reconstruction of 3D coronal magnetic fields and associated dynamic plasma properties, which provide direct diagnostics of energy release. COMPLETE re-imagines the paradigm for solar re

Amir Caspi, Daniel B. Seaton, Roberto Casini, Cooper Downs

The coronal magnetic field is the prime driver behind many as-yet unsolved mysteries: solar eruptions, coronal heating, and the solar wind, to name a few. It is, however, still poorly observed and understood. We highlight key questions related to magnetic energy storage, release, and transport in the solar corona, and their relationship to these important pr

Amir Samadi, Konstantinos Koufos, Kurt Debattista, Mehrdad Dianati

Deep Reinforcement Learning (DRL) has demonstrated promising capability in solving complex control problems. However, DRL applications in safety-critical systems are hindered by the inherent lack of robust verification techniques to assure their performance in such applications. One of the key requirements of the verification process is the development of ef

Han Lin Shang, Kaiying Ji

Intraday financial data often take the form of a collection of curves that can be observed sequentially over time, such as intraday stock price curves. These curves can be viewed as a time series of functions observed on equally spaced and dense grids. Due to the curse of dimensionality, high-dimensional data poses challenges from a statistical aspect; howev

Nuojin Cheng, Osman Asif Malik, Subhayan De, Stephen Becker

Quantifying the uncertainty of quantities of interest (QoIs) from physical systems is a primary objective in model validation. However, achieving this goal entails balancing the need for computational efficiency with the requirement for numerical accuracy. To address this trade-off, we propose a novel bi-fidelity formulation of variational auto-encoders (BF-

Jefferson Bastos, Claudio Buzzi, Paulo Santana

The introduction of concepts of Game Theory and Ordinary Differential Equations into Biology gave birth to the field of Evolutionary Stable Strategies, with applications in Biology, Genetics, Politics, Economics and others. In special, the model composed by two players having two pure strategies each results in a planar polynomial vector field with an invari

Mahmoud Jalali Mehrabad, Sunil Mittal, Mohammad Hafezi

Topological photonics is emerging as a new paradigm for the development of both classical and quantum photonic architectures. What makes topological photonics remarkably intriguing is the built-in protection as well as intrinsic unidirectionality of light propagation, which originates from the robustness of global topological invariants. In this Perspective,

Jose Blanchet, Haoxuan Chen, Yiping Lu, Lexing Ying

This paper studies the use of a machine learning-based estimator as a control variate for mitigating the variance of Monte Carlo sampling. Specifically, we seek to uncover the key factors that influence the efficiency of control variates in reducing variance. We examine a prototype estimation problem that involves simulating the moments of a Sobolev function

Daniel Schug, Sai Yerramreddy, Rich Caruana, Craig Greenberg

As the deployment of computer vision technology becomes increasingly common in science, the need for explanations of the system and its output has become a focus of great concern. Driven by the pressing need for interpretable models in science, we propose the use of Explainable Boosting Machines (EBMs) for scientific image data. Inspired by an important appl

Nikolaos Tziavelis, Nofar Carmeli, Wolfgang Gatterbauer, Benny Kimelfeld

We present efficient algorithms for Quantile Join Queries, abbreviated as %JQ. A %JQ asks for the answer at a specified relative position (e.g., 50% for the median) under some ordering over the answers to a Join Query (JQ). Our goal is to avoid materializing the set of all join answers, and to achieve quasilinear time in the size of the database, regardless

Robin Cockett, Jean-Simon Pacaud Lemay

In the category of sets and partial functions, $\mathsf{PAR}$, while the disjoint union $\sqcup$ is the usual categorical coproduct, the Cartesian product $\times$ becomes a restriction categorical analogue of the categorical product: a restriction product. Nevertheless, $\mathsf{PAR}$ does have a usual categorical product as well in the form $A \& B := A \s

Omar de J. Cabrera-Rosas, Tonatiuh Matos

One of the most challenging open questions in physics today is discovering the nature of dark matter. In this work we study the imaging formation in dark matter (DM) halos due to an external light source using some DM profiles for comparison with astronomical observations. Approaching these models on a small scale, we analyze the images generated on the lens

Mauro David, Ismael C. Doganlar, Daniele Nazzari, Elena Arigliani

Liquid spectroscopy in the mid-infrared spectral range is a very powerful, yet premature technique for selective and sensitive molecule detection. Due to the lack of suitable concepts and materials for versatile miniaturized sensors, it is often still limited to bulky systems and offline analytics. Mid-infrared plasmonics is a promising field of current rese

Christopher Clarke, Yuzhao Heng, Yiping Kang, Krisztian Flautner

Conventional approaches to text classification typically assume the existence of a fixed set of predefined labels to which a given text can be classified. However, in real-world applications, there exists an infinite label space for describing a given text. In addition, depending on the aspect (sentiment, topic, etc.) and domain of the text (finance, legal,

Jinyoung Park, Michail Sarantis, Prasad Tetali

We give a short and self-contained argument that shows that, for any positive integers $t$ and $n$ with $t =O\Bigl(\frac{n}{\log n}\Bigr)$, the number $\alpha([t]^n)$ of antichains of the poset $[t]^n$ is at most \[\exp_2\Bigl(1+O\Bigl(\Bigl(\frac{t\log^3 n}{n}\Bigr)^{1/2}\Bigr)\Bigr)N(t,n)\,,\] where $N(t,n)$ is the size of a largest level of $[t]^n$. This,

Sabrina Chiesurin, Dimitris Dimakopoulos, Marco Antonio Sobrevilla Cabezudo, Arash Eshghi

Large language models are known to produce output which sounds fluent and convincing, but is also often wrong, e.g. "unfaithful" with respect to a rationale as retrieved from a knowledge base. In this paper, we show that task-based systems which exhibit certain advanced linguistic dialog behaviors, such as lexical alignment (repeating what the user said), ar

Neil Epstein

Let D be a Euclidean domain, with fraction field K. Let R(D) be the subring of K generated by the reciprocals of the nonzero elements of D. The main theorem states that if R(D) is not equal to K, then R(D) is a rank 1 discrete valuation ring that contains a field consisting of the units of D along with 0. Connections are made to ideas from medieval Italian m

Siddhartha Chattopadhyay, Carlos Marante, Barry Schneider, Luca Argenti

As a step toward the full \emph{ab-initio} description of two-photon double ionization processes, we present a finite-pulse version of the virtual-sequential model for polyelectronic atoms. The model relies on the \emph{ab initio} description of the single ionization scattering states of both the neutral and ionized target system. As a proof of principle and

Alexander Clow, Neil McKay

In this paper we consider ordinal sums of combinatorial games where each summand is a number, not necessarily in canonical form. In doing so we give formulas for the value of an ordinal sum of numbers where the literal form of the base has certain properties. These formulas include a closed form of the value of any ordinal sum of numbers where the base is in

Anton Pakhomov, Mikhail Arkhipov, Nikolay Rosanov, Rostislav Arkhipov

The generalization of the area theorem is derived for the case of a pulse circulating inside a ring laser cavity. In contrast to the standard area theorem, which is valid for a single pass of a traveling pulse through a resonant medium, the obtained generalized area theorem takes into account the medium-assisted nonlinear self-action effects through the medi

Shiqi Yu

The IceCube Neutrino Observatory is a Cherenkov detector located at the South Pole. Its main component consists of an in-ice array of optical modules instrumenting one cubic kilometer of deep Glacial ice. The DeepCore sub-detector is a denser in-fill array with a lower energy threshold, allowing us to study atmospheric neutrinos oscillations with energy belo

Roman Snytsar

Sliding window sums are widely used for string indexing, hashing and time series analysis. We have developed a family of the generic vectorized sliding sum algorithms that provide speedup of O(P/w) for window size $w$ and number of processors P. For a sum with a commutative operator the speedup is improved to O(P/log(w)). Even more important, our algorithms

Hyeong Jin Kim, Binay P. Nayak, Honghu Zhang, Benjamin M. Ocko

Hypothesis: Introducing charged terminal groups to polymers that graft nanoparticles enables Coulombic control over their assembly by tuning the pH and salinity of aqueous suspensions. Experiments: Gold nanoparticles (AuNPs) are grafted with poly(ethylene glycol) (PEG) terminated with CH3 (charge-neutral), COOH (negatively charged), or NH2 (positively charge

Gustavo M. Monteiro, Dylan Reynolds, Paolo Glorioso, Sriram Ganeshan

In this letter, we investigate the dissipative dynamics at the edge of Laughlin fractional quantum Hall (FQH) states starting from the hydrodynamic framework of the composite Boson theory recently developed in arXiv:2203.06516. Critical to this description is the choice of boundary conditions, which ultimately stems from the choice of hydrodynamic variables

Mihir Kulkarni, Theodor J. L. Forgaard, Kostas Alexis

Developing learning-based methods for navigation of aerial robots is an intensive data-driven process that requires highly parallelized simulation. The full utilization of such simulators is hindered by the lack of parallelized high-level control methods that imitate the real-world robot interface. Responding to this need, we develop the Aerial Gym simulator

Ming-Chang Lee, Jia-Chun Lin

A multivariate time series refers to observations of two or more variables taken from a device or a system simultaneously over time. There is an increasing need to monitor multivariate time series and detect anomalies in real time to ensure proper system operation and good service quality. It is also highly desirable to have a lightweight anomaly detection s

Amit Daniely, Nathan Srebro, Gal Vardi

We present a PTAS for learning random constant-depth networks. We show that for any fixed $\epsilon>0$ and depth $i$, there is a poly-time algorithm that for any distribution on $\sqrt{d} \cdot \mathbb{S}^{d-1}$ learns random Xavier networks of depth $i$, up to an additive error of $\epsilon$. The algorithm runs in time and sample complexity of $(\bar{d})^{\

Noah Goss, Samuele Ferracin, Akel Hashim, Arnaud Carignan-Dugas

Quantum computing with qudits is an emerging approach that exploits a larger, more-connected computational space, providing advantages for many applications, including quantum simulation and quantum error correction. Nonetheless, qudits are typically afflicted by more complex errors and suffer greater noise sensitivity which renders their scaling difficult.

Özge Sürer, Matthew Plumlee, Stefan M. Wild

Simulation models of critical systems often have parameters that need to be calibrated using observed data. For expensive simulation models, calibration is done using an emulator of the simulation model built on simulation output at different parameter settings. Using intelligent and adaptive selection of parameters to build the emulator can drastically impr

Cevahir Koprulu, Ufuk Topcu

Self-paced reinforcement learning (RL) aims to improve the data efficiency of learning by automatically creating sequences, namely curricula, of probability distributions over contexts. However, existing techniques for self-paced RL fail in long-horizon planning tasks that involve temporally extended behaviors. We hypothesize that taking advantage of prior k

Qiantong Xu, Fenglu Hong, Bo Li, Changran Hu

Recent studies on software tool manipulation with large language models (LLMs) mostly rely on closed model APIs. The industrial adoption of these models is substantially constrained due to the security and robustness risks in exposing information to closed LLM API services. In this paper, we ask can we enhance open-source LLMs to be competitive to leading cl

Abhinav Jain, Chima Adiole, Swarat Chaudhuri, Thomas Reps

Large Language Models (LLMs) pre-trained on code have recently emerged as the dominant approach to program synthesis. However, these models are trained using next-token prediction, which ignores the syntax and semantics of code. We propose RLCF, that further trains a pre-trained LLM via reinforcement learning, using feedback from a grounding function that sc

Xuanli He, Jun Wang, Benjamin Rubinstein, Trevor Cohn

Backdoor attacks are an insidious security threat against machine learning models. Adversaries can manipulate the predictions of compromised models by inserting triggers into the training phase. Various backdoor attacks have been devised which can achieve nearly perfect attack success without affecting model predictions for clean inputs. Means of mitigating

Ifueko Igbinedion, Sertac Karaman

Robots operating alongside humans often encounter unfamiliar environments that make autonomous task completion challenging. Though improving models and increasing dataset size can enhance a robot's performance in unseen environments, data collection and model refinement may be impractical in every environment. Approaches that utilize human demonstrations thr

Han Shao, Avrim Blum, Omar Montasser

We study the fundamental mistake bound and sample complexity in the strategic classification, where agents can strategically manipulate their feature vector up to an extent in order to be predicted as positive. For example, given a classifier determining college admission, student candidates may try to take easier classes to improve their GPA, retake SAT and

Zeyad M. Manaa, Mohammed R. Elbalshy, Ayman M. Abdallah

Dynamical systems provide a mathematical framework for understanding complex physical phenomena. The mathematical formulation of these systems plays a crucial role in numerous applications; however, it often proves to be quite intricate. Fortunately, data can be readily available through sensor measurements or numerical simulations. In this study, we employ

Naman K. Gupta, Ronny Sutarto, Rantong Gong, Stefan Idziak

Unidirectional spin and charge density wave order in the cuprates is known to compete with superconductivity. In the stripe order (La,M)$_2$CuO$_4$ family of cuprates, spin and charge order occur as unidirectional order that can be stabilized by symmetry breaking structural distortions, such as the low temperature tetragonal (LTT) phase. Here we examine the

Joe Watson, Sandy H. Huang, Nicolas Heess

Imitation learning methods seek to learn from an expert either through behavioral cloning (BC) of the policy or inverse reinforcement learning (IRL) of the reward. Such methods enable agents to learn complex tasks from humans that are difficult to capture with hand-designed reward functions. Choosing BC or IRL for imitation depends on the quality and state-a

Marcin Pietron, Dominik Zurek, Kamil Faber, Roberto Corizzo

Anomaly detection tools and methods present a key capability in modern cyberphysical and failure prediction systems. Despite the fast-paced development in deep learning architectures for anomaly detection, model optimization for a given dataset is a cumbersome and time consuming process. Neuroevolution could be an effective and efficient solution to this pro

Kohei Kawabata, Ramanjit Sohal, Shinsei Ryu

The Lieb-Schultz-Mattis (LSM) theorem provides a general constraint on quantum many-body systems and plays a significant role in the Haldane gap phenomena and topological phases of matter. Here, we extend the LSM theorem to open quantum systems and establish a general theorem that restricts the steady state and spectral gap of Liouvillians based solely on sy

Jiayin Dong, Songhu Wang, Malena Rice, George Zhou

Warm Jupiters are close-in giant planets with relatively large planet-star separations (i.e., $10< a/R_\star <100$). Given their weak tidal interactions with their host stars, measurements of stellar obliquity may be used to probe the initial obliquity distribution and dynamical history for close-in gas giants. Using spectroscopic observations, we confirm th

Haotian Xue, Alexandre Araujo, Bin Hu, Yongxin Chen

Neural networks are known to be susceptible to adversarial samples: small variations of natural examples crafted to deliberately mislead the models. While they can be easily generated using gradient-based techniques in digital and physical scenarios, they often differ greatly from the actual data distribution of natural images, resulting in a trade-off betwe

Birendra Kumar, Harish Chandr Chauhan, Ajay Baro, Jyoti Saini

Here, we report the origin of magnetic anisotropy in Sr-doped infinite layer manganites $La_{(1\-x)}Sr_{x}MnO_{3}$ (0.125 \leq x \leq 0.400). Magnetic anisotropy is responsible for the large difference in the temperature dependence of field-cooled and zero-field-cooled magnetization. Translational symmetry breaking in the context of spins around the boundary

David Azatyan

Stroke is one of two main causes of death worldwide. Many individuals suffer from ischemic stroke every year. Only in US more over 700,000 individuals meet ischemic stroke due to blood clot blocking an artery to the brain every year. The paper describes particular approach how to apply Artificial Intelligence for purposes of separating two major acute ischem

Abdullah Alomar, Munther Dahleh, Sean Mann, Devavrat Shah

The well-established practice of time series analysis involves estimating deterministic, non-stationary trend and seasonality components followed by learning the residual stochastic, stationary components. Recently, it has been shown that one can learn the deterministic non-stationary components accurately using multivariate Singular Spectrum Analysis (mSSA)

Chu Fei Luo, Rohan Bhambhoria, Samuel Dahan, Xiaodan Zhu

Deep learning has made significant progress in the past decade, and demonstrates potential to solve problems with extensive social impact. In high-stakes decision making areas such as law, experts often require interpretability for automatic systems to be utilized in practical settings. In this work, we attempt to address these requirements applied to the im

Basel Elkhapery, Robert Pěnička, Michal Němec, Mohsin Siddiqui

This paper introduces a wall construction planner for Unmanned Aerial Vehicles (UAVs), which uses a Greedy Randomized Adaptive Search Procedure (GRASP) metaheuristic to generate near-time-optimal building plans for even large walls within seconds. This approach addresses one of the most time-consuming and labor-intensive tasks, while also minimizing workers'

Morgan R. Edwards, Jaime Garibay-Rodriguez, Jacob Shimkus Erickson, Muhammad Shayan

Heat pumps are an energy-efficient and increasingly cost-effective solution for reducing greenhouse gas emissions in the building sector. However, other clean energy technologies such as rooftop solar are less likely to be adopted in underserved communities, and thus policies incentivizing their adoption may funnel tax dollars to well-resourced communities.

Rawal Khirodkar, Aayush Bansal, Lingni Ma, Richard Newcombe

We present EgoHumans, a new multi-view multi-human video benchmark to advance the state-of-the-art of egocentric human 3D pose estimation and tracking. Existing egocentric benchmarks either capture single subject or indoor-only scenarios, which limit the generalization of computer vision algorithms for real-world applications. We propose a novel 3D capture s

Paul Corbae, Nicolai Taufertshöfer, Ellis Kennedy, Mary Scott

Strong disorder has a crucial effect on the electronic structure in quantum materials by increasing localization, interactions, and modifying the density of states. Bi$_x$TeI films grown at room temperature and \SI{230}{K} exhibit dramatic magnetotransport effects due to disorder, localization and electron correlation effects, including a MIT at a compositio

Shaun M. Fallat, Prateek Kumar Vishwakarma

A real linear combination of products of minors which is nonnegative over all totally nonnegative (TN) matrices is called a determinantal inequality for these matrices. It is referred to as multiplicative when it compares two collections of products of minors and additive otherwise. Set theoretic operations preserving the class of TN matrices naturally trans

Iordanis Fostiropoulos, Jiaye Zhu, Laurent Itti

In Continual Learning (CL), a model is required to learn a stream of tasks sequentially without significant performance degradation on previously learned tasks. Current approaches fail for a long sequence of tasks from diverse domains and difficulties. Many of the existing CL approaches are difficult to apply in practice due to excessive memory cost or train

Honghao Wei, Xin Liu, Weina Wang, Lei Ying

This paper considers a class of reinforcement learning problems, which involve systems with two types of states: stochastic and pseudo-stochastic. In such systems, stochastic states follow a stochastic transition kernel while the transitions of pseudo-stochastic states are deterministic given the stochastic states/transitions. We refer to such systems as mix

Michael T. McCann, Hyungjin Chung, Jong Chul Ye, Marc L. Klasky

This paper explores the use of score-based diffusion models for Bayesian image reconstruction. Diffusion models are an efficient tool for generative modeling. Diffusion models can also be used for solving image reconstruction problems. We present a simple and flexible algorithm for training a diffusion model and using it for maximum a posteriori reconstructi

Zhengyang Lou, Huan Xu, Fangzhou Mu, Yanli Liu

Deep models have demonstrated recent success in single-image dehazing. Most prior methods consider fully supervised training and learn from paired clean and hazy images, where a hazy image is synthesized based on a clean image and its estimated depth map. This paradigm, however, can produce low-quality hazy images due to inaccurate depth estimation, resultin

Joseph A. Smiga, Marco Radaelli, Felix C. Binder, Gabriel T. Landi

We study the problem of parameter estimation in time series stemming from general stochastic processes, where the outcomes may exhibit arbitrary temporal correlations. In particular, we address the question of how much Fisher information is lost if the stochastic process is compressed into a single histogram, known as the empirical distribution. As we show,

Suchindra, Preetam Nagaraj

DNA sequence alignment is important today as it is usually the first step in finding gene mutation, evolutionary similarities, protein structure, drug development and cancer treatment. Covid-19 is one recent example. There are many sequencing algorithms developed over the past decades but the sequence alignment using expert systems is quite new. To find DNA