October 2022 arXiv papers — page 56

Kohei Ichikawa, Kunihiko Kaneko

Various animals, including humans, have been suggested to perform Bayesian inferences to handle noisy, time-varying external information. In performing Bayesian inference, the prior distribution must be shaped by sampling noisy external inputs. However, the mechanism by which neural activities represent such distributions has not yet been elucidated. In this

Tao Ge, Jaideep Pathak, Akshay Subramaniam, Karthik Kashinath

Data-driven models, such as FourCastNet (FCN), have shown exemplary performance in high-resolution global weather forecasting. This performance, however, is based on supervision on mesh-gridded weather data without the utilization of raw climate observational data, the gold standard ground truth. In this work we develop a methodology to correct, remap, and f

Jin Hyung To

A holomorphic chain on a compact Riemann surface is a tuple of vector bundles together with homomorphisms between them. We show that the moduli space of holomorphic chains of rank one is identified with a fiber product of projective space bundles. We compute the Euler characteristic of the moduli space. The stability of chains involves real vector parameters

Igor Araujo, Kareem Benaissa, Richard Bi, Sean English

A path in an edge-colored graph is said to be rainbow if no color repeats on it. An edge-colored graph is said to be rainbow $k$-connected if every pair of vertices is connected by $k$ internally disjoint rainbow paths. The rainbow $k$-connection number $\mathrm{rc}_k(G)$ is the minimum number of colors $\ell$ such that there exists a coloring with $\ell$ co

Matt Bowen, Marcin Sabok

We show that for every coloring of the rationals into finitely many colors, one of the colors contains a set of the form $\{x,y,xy,x+y\}$ for some nonzero $x$ and $y$.

Alex Creiner, Stephen Jackson

We prove two sets of results concerning computational complexity classes. The first concerns a variation of the random oracle hypothesis posed by Bennett and Gill after they showed that relative to a randomly chosen oracle, P not equal NP with probability 1. This hypothesis was quickly disproven in several ways, most famously in 1992 with the result that IP

Samantha Chen, Puoya Tabaghi, Yusu Wang

Optimal transport provides a metric which quantifies the dissimilarity between probability measures. For measures supported in discrete metric spaces, finding the optimal transport distance has cubic time complexity in the size of the space. However, measures supported on trees admit a closed-form optimal transport that can be computed in linear time. In thi

Jacob Golomb, Colm Talbot

The spins of black holes in merging binaries can reveal information related to the formation and evolution of these systems through their gravitational wave emission. Combining events to infer the astrophysical distribution of black hole spins allows us to determine the relative contribution from different formation scenarios to the population. Many previous

Lukas Mauth

A. Poltotaski proved an analog of Carleson's Theorem on almost everywhere convergence of Fourier series for a version of the non-linear Fourier transform. We aim to present his proof in full detail and elaborate on the ideas behind each step.

Haochen Li, Chunyan Miao, Cyril Leung, Yanxian Huang

Code search, which aims at retrieving the most relevant code fragment for a given natural language query, is a common activity in software development practice. Recently, contrastive learning is widely used in code search research, where many data augmentation approaches for source code (e.g., semantic-preserving program transformation) are proposed to learn

Siddarth Srinivasan, Bibek Pokharel, Gregory Quiroz, Byron Boots

Measurement error mitigation (MEM) techniques are postprocessing strategies to counteract systematic read-out errors on quantum computers (QC). Currently used MEM strategies face a tradeoff: methods that scale well with the number of qubits return negative probabilities, while those that guarantee a valid probability distribution are not scalable. Here, we p

Albert Q. Jiang, Sean Welleck, Jin Peng Zhou, Wenda Li

The formalization of existing mathematical proofs is a notoriously difficult process. Despite decades of research on automation and proof assistants, writing formal proofs remains arduous and only accessible to a few experts. While previous studies to automate formalization focused on powerful search algorithms, no attempts were made to take advantage of ava

Alexandre Piche, Valentin Thomas, Joseph Marino, Rafael Pardinas

Bootstrapping is behind much of the successes of Deep Reinforcement Learning. However, learning the value function via bootstrapping often leads to unstable training due to fast-changing target values. Target Networks are employed to stabilize training by using an additional set of lagging parameters to estimate the target values. Despite the popularity of T

Albert Chau, Ben Weinkove

We consider a well-known quasi-static model for the shape of a liquid droplet. The solution can be described in terms of time-evolving domains in $\mathbb{R}^n$. We give an example to show that convexity of the domain can be instantaneously broken.

Pier Fiedorowicz, Eduardo Rozo, Supranta S. Boruah

We present KaRMMa 2.0, an updated version of the mass map reconstruction code introduced in Fiedorowicz et al. (2022). KaRMMa is a full-sky Bayesian algorithm for reconstructing weak lensing mass maps from shear data. It forward-models the convergence field as a realization of a lognormal field. The corresponding shear map is calculated using the standard Ka

Ababacar Sadikhe Djité, Diaraf Seck

In this paper, we give a simple control on how an optimal shape can be characterized. The framework of Riemannian manifold of infinite dimension is essential. And the covariant derivative plays a key role in the computation and in the analysis of qualitative properties from the shape hessian. The control depends only on the mean curvature of the domain which

Tuluhan Akbulut, Max Merlin, Shane Parr, Benedict Quartey

Reinforcement learning has been demonstrated as a flexible and effective approach for learning a range of continuous control tasks, such as those used by robots to manipulate objects in their environment. But in robotics particularly, real-world rollouts are costly, and sample efficiency can be a major limiting factor when learning a new skill. In game envir

Haoyu Jiang, Jason Xu

Stochastic versions of proximal methods have gained much attention in statistics and machine learning. These algorithms tend to admit simple, scalable forms, and enjoy numerical stability via implicit updates. In this work, we propose and analyze a stochastic version of the recently proposed proximal distance algorithm, a class of iterative optimization meth

Ning Shi, Bin Tang, Bo Yuan, Longtao Huang

Text editing, such as grammatical error correction, arises naturally from imperfect textual data. Recent works frame text editing as a multi-round sequence tagging task, where operations -- such as insertion and substitution -- are represented as a sequence of tags. While achieving good results, this encoding is limited in flexibility as all actions are boun

Antonio S. Hales, SebastiÁn Marino, Patrick D. Sheehan, Silvio Ulloa

We present Atacama Large Millimeter Array (ALMA) observations of the young, gas-rich debris disk around HD110058 at 0.3-0.6\arcsec resolution. The disk is detected in the 0.85 and 1.3~mm continuum, as well as in the J=2-1 and J=3-2 transitions of $^{12}$CO and $^{13}$CO. The observations resolve the dust and gas distributions and reveal that this is the smal

Ivan Conjeaud, Philipp Lorenz-Spreen, Argyris Kalogeratos

This paper investigates how interacting agents arrive to a consensus or a polarized state. We study the opinion formation process under the effect of a global steering mechanism (GSM), which aggregates the opinion-driven stochastic agent states at the network level and feeds back to them a form of global information. We also propose a new two-layer agent-bas

Raiomond Doctor, Alexander Gutkin, Cibu Johny, Brian Roark

Since its original appearance in 1991, the Perso-Arabic script representation in Unicode has grown from 169 to over 440 atomic isolated characters spread over several code pages representing standard letters, various diacritics and punctuation for the original Arabic and numerous other regional orthographic traditions. This paper documents the challenges tha

Alexandre Piche, Rafael Pardinas, David Vazquez, Igor Mordatch

Offline Reinforcement Learning (RL) via Supervised Learning is a simple and effective way to learn robotic skills from a dataset collected by policies of different expertise levels. It is as simple as supervised learning and Behavior Cloning (BC), but takes advantage of return information. On datasets collected by policies of similar expertise, implicit BC h

Matthias Beck, Danai Deligeorgaki, Max Hlavacek, Jerónimo Valencia-Porras

The Ehrhart polynomial $\text{ehr}_P(n)$ of a lattice polytope $P$ counts the number of integer points in the $n$-th integral dilate of $P$. The $f^*$-vector of $P$, introduced by Felix Breuer in 2012, is the vector of coefficients of $\text{ehr}_P(n)$ with respect to the binomial coefficient basis $ \left\{\binom{n-1}{0},\binom{n-1}{1},...,\binom{n-1}{d}\ri

Chen Chen, Matin Yarmand, Zhuoqun Xu, Varun Singh

Accurately drawing non-planar 3D curves in immersive Virtual Reality (VR) is indispensable for many precise 3D tasks. However, due to lack of physical support, limited depth perception, and the non-planar nature of 3D curves, it is challenging to adjust mid-air strokes to achieve high precision. Instead of creating new interaction techniques, we investigated

George Spahn, Doron Zeilberger

We explore both automated and human approaches to the generalized Missionaries and Cannibals problem.

Benedict Quartey, Tuluhan Akbulut, Wasiwasi Mgonzo, Zheng Xin Yong

This project presents an exploration into 3D scene reconstruction of synthetic and real-world scenes using Neural Radiance Field (NeRF) approaches. We primarily take advantage of the reduction in training and rendering time of neural graphic primitives multi-resolution hash encoding, to reconstruct static video game scenes and real-world scenes, comparing an

Arthur D. Yaghjian

Using realistic classical models of microscopic electric-charge electric dipoles and electric-current (Amperian) magnetic dipoles, it is proven that the Einstein-Laub macroscopic electromagnetic force on a macroscopic-continuum volume of these classical dipoles equals the sum of the microscopic electromagnetic forces on the discrete classical dipoles in that

Ross J. Jennings, James M. Cordes, Shami Chatterjee, Maura A. McLaughlin

The millisecond pulsar J1713+0747 underwent a sudden and significant pulse shape change between April 16 and 17, 2021 (MJDs 59320 and 59321). Subsequently, the pulse shape gradually recovered over the course of several months. We report the results of continued multi-frequency radio observations of the pulsar made using the Canadian Hydrogen Intensity Mappin

Kabir Ahuja, Sunayana Sitaram, Sandipan Dandapat, Monojit Choudhury

Massively Multilingual Language Models (MMLMs) have recently gained popularity due to their surprising effectiveness in cross-lingual transfer. While there has been much work in evaluating these models for their performance on a variety of tasks and languages, little attention has been paid on how well calibrated these models are with respect to the confiden

Chris Nagele, John D. Silverman, Tilman Hartwig, Junyao Li

The conditions under which galactic nuclear regions become active are largely unknown, although it has been hypothesized that secular processes related to galaxy morphology could play a significant role. We investigate this question using optical i-band images of 3096 SDSS quasars and galaxies at 0.3<z<0.6 from the Hyper Suprime-Cam Subaru Strategic Program,

Nils Asmussen, En-Hung Chao, Antoine Gérardin, Jeremy R. Green

Hadronic light-by-light scattering is one of the virtual processes that causes the gyromagnetic factor $g$ of the muon to deviate from the value of two predicted by Dirac's theory. This process makes one of the largest contributions to the uncertainty of the Standard Model prediction for the muon $(g-2)$. Lattice QCD allows for a first-principles approach to

Mengdi Xu, Peide Huang, Yaru Niu, Visak Kumar

One key challenge for multi-task Reinforcement learning (RL) in practice is the absence of task indicators. Robust RL has been applied to deal with task ambiguity, but may result in over-conservative policies. To balance the worst-case (robustness) and average performance, we propose Group Distributionally Robust Markov Decision Process (GDR-MDP), a flexible

Yue Yang, Wenlin Yao, Hongming Zhang, Xiaoyang Wang

Large-scale pretrained language models have made significant advances in solving downstream language understanding tasks. However, they generally suffer from reporting bias, the phenomenon describing the lack of explicit commonsense knowledge in written text, e.g., ''an orange is orange''. To overcome this limitation, we develop a novel approach, Z-LaVI, to

Matthew R. Carbone, Stepan Fomichev, Andrew J. Millis, Mona Berciu

We present an efficient implementation of the Generalized Green's function Cluster Expansion (GGCE), which is a new method for computing the ground-state properties and dynamics of polarons (single electrons coupled to lattice vibrations) in model electron-phonon systems. The GGCE works at arbitrary temperature and is well suited for a variety of electron-ph

Abhilash Reddy Shankarampeta, Vivek Gupta, Shuo Zhang

Recent methods based on pre-trained language models have exhibited superior performance over tabular tasks (e.g., tabular NLI), despite showing inherent problems such as not using the right evidence and inconsistent predictions across inputs while reasoning over the tabular data. In this work, we utilize Pattern-Exploiting Training (PET) (i.e., strategic MLM

Rick Presman, Jason Xu

Constrained learning is prevalent in many statistical tasks. Recent work proposes distance-to-set penalties to derive estimators under general constraints that can be specified as sets, but focuses on obtaining point estimates that do not come with corresponding measures of uncertainty. To remedy this, we approach distance-to-set regularization from a Bayesi

Shirley Wu, Jiaxuan You, Jure Leskovec, Rex Ying

Despite the success of automated machine learning (AutoML), which aims to find the best design, including the architecture of deep networks and hyper-parameters, conventional AutoML methods are computationally expensive and hardly provide insights into the relations of different model design choices. To tackle the challenges, we propose FALCON, an efficient

Sebastian G. Gruber, Florian Buettner

Reliably estimating the uncertainty of a prediction throughout the model lifecycle is crucial in many safety-critical applications. The most common way to measure this uncertainty is via the predicted confidence. While this tends to work well for in-domain samples, these estimates are unreliable under domain drift and restricted to classification. Alternativ

V. Cuomo, V. P. Debattista, S. Racz, S. R. Anderson

The short-lived buckling instability is responsible for the formation of at least some box/peanut (B/P) shaped bulges, which are observed in most massive, $z=0$, barred galaxies. Nevertheless, it has also been suggested that B/P bulges form via the slow trapping of stars onto vertically extended resonant orbits. The key difference between these two scenarios

Vikram Voleti, Christopher Pal, Adam Oberman

Generative models based on denoising diffusion techniques have led to an unprecedented increase in the quality and diversity of imagery that is now possible to create with neural generative models. However, most contemporary state-of-the-art methods are derived from a standard isotropic Gaussian formulation. In this work we examine the situation where non-is

Will Pazner, Tzanio Kolev, Jean-Sylvain Camier

In this paper, we present algorithms and implementations for the end-to-end GPU acceleration of matrix-free low-order-refined preconditioning of high-order finite element problems. The methods described here allow for the construction of effective preconditioners for high-order problems with optimal memory usage and computational complexity. The precondition

Shivang Agarwal, Amartya S. Banerjee

We formulate and implement a spectral method for solving the Schrodinger equation, as it applies to quasi-one-dimensional materials and structures. This allows for computation of the electronic structure of important technological materials such as nanotubes (of arbitrary chirality), nanowires, nanoribbons, chiral nanoassemblies, nanosprings and nanocoils, i

Guangyue Han, Brian Marcus, Chengyu Wu

In this paper, we first give a necessary and sufficient condition for a factor code with an unambiguous symbol to admit a subshift of finite type restricted to which it is one-to-one and onto. We then give a necessary and sufficient condition for the standard factor code on a spoke graph to admit a subshift of finite type restricted to which it is finite-to-

Christopher Agia, Toki Migimatsu, Jiajun Wu, Jeannette Bohg

Advances in robotic skill acquisition have made it possible to build general-purpose libraries of learned skills for downstream manipulation tasks. However, naively executing these skills one after the other is unlikely to succeed without accounting for dependencies between actions prevalent in long-horizon plans. We present Sequencing Task-Agnostic Policies

Constanza Riera, Pantelimon Stanica, Haode Yan

In the paper, we concentrate on the map $x\mapsto x^{\frac{p^n+1}{2}}$ on $\mathbb{F}_{p^n}$ and using combinatorial and number theory techniques, we compute its detailed $c$-differential spectrum for all values of $c\neq 1$ (the spectrum for $c=1$ is known).

Vladimir Kozhevnikov

Electrodynamics of superconductors is primarily the electrodynamics of the Meissner state, a state characterized by zero magnetic induction of a superconducting fraction of conduction electrons. Simultaneously, the Meissner state is characterized by zero resistivity and zero entropy of these electrons. The latter means that the temperature of an ensemble of

xiangyang Ju, Yunsong Wang, Daniel Murnane, Nicholas Choma

Many artificial intelligence (AI) devices have been developed to accelerate the training and inference of neural networks models. The most common ones are the Graphics Processing Unit (GPU) and Tensor Processing Unit (TPU). They are highly optimized for dense data representations. However, sparse representations such as graphs are prevalent in many domains,

Liam Walsh, Juergen Dingel, Karim Jahed

In this paper, we propose a general architecture for designing language servers for hybrid modeling languages, that is, modeling languages that contain both textual and graphical representations. The architecture consists of a textual language server, a graphical language server, and a client that communicates with the two servers. The servers are implemente

Haozhe He, Ryan J. Kramer, Brian J. Soden, Nadir Jeevanjee

When evaluating the effect of CO2 changes on the earth's climate, it is widely assumed that instantaneous radiative forcing from a doubling of a given CO2 concentration (IRF2xCO2) is constant and that variances in climate sensitivity arise from differences in radiative feedbacks, or a dependence of these feedbacks on the climatological base-state. In this pa

Amir Kazemi, Hadi Meidani

A framework is proposed for the unconditional generation of synthetic time series based on learning from a single sample in low-data regime case. The framework aims at capturing the distribution of patches in wavelet scalogram of time series using single image generative models and producing realistic wavelet coefficients for the generation of synthetic time

He Guo

A generalization of the famous Caccetta--H\"aggkvist conjecture, suggested by Aharoni [Rainbow triangles and the Caccetta-H\"aggkvist conjecture, J. Graph Theory (2019)], is that any family $\mathcal{F}=(F_1, \ldots,F_n)$ of sets of edges in $K_n$, each of size $k$, has a rainbow cycle of length at most $\lceil \frac{n}{k}\rceil$. In [Rainbow cycles for fami

Kush Attal, Brian Ondov, Dina Demner-Fushman

Though exponentially growing health-related literature has been made available to a broad audience online, the language of scientific articles can be difficult for the general public to understand. Therefore, adapting this expert-level language into plain language versions is necessary for the public to reliably comprehend the vast health-related literature.

Oliver Boyne, James Charles, Roberto Cipolla

In this paper we present a high fidelity and articulated 3D human foot model. The model is parameterised by a disentangled latent code in terms of shape, texture and articulated pose. While high fidelity models are typically created with strong supervision such as 3D keypoint correspondences or pre-registration, we focus on the difficult case of little to no

Antonio Amariti, Simone Rota

We compute the Lens space index for 4d supersymmetric gauge theories involving symplectic gauge groups. This index can distinguish between different gauge groups from a given algebra and it matches across theories related by supersymmetric dualities. We provide explicit calculations for $\mathcal{N}=4$ SYM and for classes of $\mathcal{N}=2$ and $\mathcal{N}=

Matthew Dirks, David Poole

We consider energy-dispersive X-ray Fluorescence (EDXRF) applications where the fundamental parameters method is impractical such as when instrument parameters are unavailable. For example, on a mining shovel or conveyor belt, rocks are constantly moving (leading to varying angles of incidence and distances) and there may be other factors not accounted for (

Hong Ye Tan, Subhadip Mukherjee, Junqi Tang, Andreas Hauptmann

Learning-to-optimize is an emerging framework that leverages training data to speed up the solution of certain optimization problems. One such approach is based on the classical mirror descent algorithm, where the mirror map is modelled using input-convex neural networks. In this work, we extend this functional parameterization approach by introducing moment

Sven Hirsch

We introduce new systems of PDE on initial data sets $(M,g,k)$ whose solutions model double-null foliations. This allows us to generalize Geroch's monotonicity formula for the Hawking mass under inverse mean curvature flow to initial data sets satisfying the dominant energy condition. We study the existence theory of these systems and give geometric applicat

Andreas Munk, Alexander Mead, Frank Wood

We consider the problem of performing Bayesian inference in probabilistic models where observations are accompanied by uncertainty, referred to as "uncertain evidence." We explore how to interpret uncertain evidence, and by extension the importance of proper interpretation as it pertains to inference about latent variables. We consider a recently-proposed me

Xianyang Zhang, Trisha Dawn

One common approach to detecting change-points is minimizing a cost function over possible numbers and locations of change-points. The framework includes several well-established procedures, such as the penalized likelihood and minimum description length. Such an approach requires finding the cost value repeatedly over different segments of the data set, whi

Le Peng, Yash Travadi, Rui Zhang, Ying Cui

We propose performing imbalanced classification by regrouping majority classes into small classes so that we turn the problem into balanced multiclass classification. This new idea is dramatically different from popular loss reweighting and class resampling methods. Our preliminary result on imbalanced medical image classification shows that this natural ide

Kalyani Asthana, Zhouhang Xie, Wencong You, Adam Noack

We introduce the Text Classification Attack Benchmark (TCAB), a dataset for analyzing, understanding, detecting, and labeling adversarial attacks against text classifiers. TCAB includes 1.5 million attack instances, generated by twelve adversarial attacks targeting three classifiers trained on six source datasets for sentiment analysis and abuse detection in

Yuhang Zhao, Yaxing Yao, Jiaru Fu, Nihan Zhou

Camera-based technology can be privacy-invasive, especially for bystanders who can be captured by the cameras but do not have direct control or access to the devices. The privacy threats become even more significant to bystanders with visual impairments (BVI) since they cannot visually discover the use of cameras nearby and effectively avoid being captured.

Andrew Bai, Cho-Jui Hsieh, Wendy Kan, Hsuan-Tien Lin

Generative adversarial network (GAN) continues to be a popular research direction due to its high generation quality. It is observed that many state-of-the-art GANs generate samples that are more similar to the training set than a holdout testing set from the same distribution, hinting some training samples are implicitly memorized in these models. This memo

Jianbin Fang, Peng Zhang, Chun Huang, Tao Tang

As the hardware industry moves towards using specialized heterogeneous many-cores to avoid the effects of the power wall, software developers are finding it hard to deal with the complexity of these systems. This article shares our experience when developing a programming model and its supporting compiler and libraries for Matrix-3000, which is designed for

Sotiris Moschoyiannis, Evangelos Chatzaroulas, Vytenis Sliogeris, Yuhu Wu

The ability to direct a Probabilistic Boolean Network (PBN) to a desired state is important to applications such as targeted therapeutics in cancer biology. Reinforcement Learning (RL) has been proposed as a framework that solves a discrete-time optimal control problem cast as a Markov Decision Process. We focus on an integrative framework powered by a model

Bowen Zhao, Jiuding Sun, Bin Xu, Xingyu Lu

Web and artificial intelligence technologies, especially semantic web and knowledge graph (KG), have recently raised significant attention in educational scenarios. Nevertheless, subject-specific KGs for K-12 education still lack sufficiency and sustainability from knowledge and data perspectives. To tackle these issues, we propose EDUKG, a heterogeneous sus

Bernd Wolf, Felix Spathelf, Jan Zimmermann, Theresa Lundbeck

We present results of magnetic susceptibility and thermal expansion measurements performed on high-quality single crystals of EuPd$_2$(Si$_{1-x}$Ge$_x$)$_2$ for 0 $\leq$ x $\leq$ 0.2 and temperatures 2 K $\leq T \leq$ 300 K. Data were taken at ambient pressure and finite He-gas pressure $p$ $\leq$ 0.5 GPa. For x = 0 and ambient pressure we observe a pronounc

Andrew Chael, Sara Issaoun, Dominic W. Pesce, Michael D. Johnson

The Event Horizon Telescope (EHT) has produced images of the plasma flow around the supermassive black holes in Sgr A* and M87* with a resolution comparable to the projected size of their event horizons. Observations with the next-generation Event Horizon Telescope (ngEHT) will have significantly improved Fourier plane coverage and will be conducted at multi

Norihiko Minami

This is the first of a series of papers, where we investigate hierarchies of generalized {L}\"{u}roth problems on the hierarchy of rationality, starting with the obvious hierarchy between the rationality and the ruledness. Our primary goal here was to construct very general necessary conditions for a smooth, not necessary proper, scheme of finite type over t

Sevim Polat Genlik, Roberto C. Myers, Maryam Ghazisaeidi

We study the electronic properties of the glide set of dislocations in diamond from first principles using hybrid exchange correlation functionals and find that the atomic-scale dislocation core states give rise to a prototypical one-dimensional (1D) band structure, i.e. natural quantum wires. The position and character of the core states varies strongly wit

Florian Lux, Julia Koch, Ngoc Thang Vu

While neural methods for text-to-speech (TTS) have shown great advances in modeling multiple speakers, even in zero-shot settings, the amount of data needed for those approaches is generally not feasible for the vast majority of the world's over 6,000 spoken languages. In this work, we bring together the tasks of zero-shot voice cloning and multilingual low-

Torrey Cullen, Scott Aronson, Ron Pagano, Jonathan Cripe

Quantum mechanics places noise limits and sensitivity restrictions on physical measurements. The balance between unwanted backaction and the precision of optical measurements impose a standard quantum limit (SQL) on interferometric systems. In order to realize a sensitivity below the SQL, it is necessary to leverage a back-action evading measurement techniqu

Yanghyeon Cho, Emily Berg

A difficulty in MSE estimation occurs because we do not specify a full distribution for the survey weights. This obfuscates the use of fully parametric bootstrap procedures. To overcome this challenge, we develop a novel MSE estimator. We estimate the leading term in the MSE, which is the MSE of the best predictor (constructed with the true parameters), usin

Caitlin F. Harrigan, Gabriela Morgenshtern, Anna Goldenberg, Fanny Chevalier

Clinician-facing predictive models are increasingly present in the healthcare setting. Regardless of their success with respect to performance metrics, all models have uncertainty. We investigate how to visually communicate uncertainty in this setting in an actionable, trustworthy way. To this end, we conduct a qualitative study with cardiac critical care cl

Florian Lux, Ching-Yi Chen, Ngoc Thang Vu

Embedding paralinguistic properties is a challenging task as there are only a few hours of training data available for domains such as emotional speech. One solution to this problem is to pretrain a general self-supervised speech representation model on large amounts of unlabeled speech. This pretrained model is then finetuned to a specific task. Paralinguis

Arjun Parthasarathy, Bhaskar Krishnamachari

Edge inference has become more widespread, as its diverse applications range from retail to wearable technology. Clusters of networked resource-constrained edge devices are becoming common, yet no system exists to split a DNN across these clusters while maximizing the inference throughput of the system. We present an algorithm which partitions DNNs and distr

Arjun Parthasarathy, Bhaskar Krishnamachari

Edge inference is becoming ever prevalent through its applications from retail to wearable technology. Clusters of networked resource-constrained edge devices are becoming common, yet there is no production-ready orchestration system for deploying deep learning models over such edge networks which adopts the robustness and scalability of the cloud. We presen

Oyvind Tafjord, Bhavana Dalvi Mishra, Peter Clark

Our goal is a question-answering (QA) system that can show how its answers are implied by its own internal beliefs via a systematic chain of reasoning. Such a capability would allow better understanding of why a model produced the answer it did. Our approach is to recursively combine a trained backward-chaining model, capable of generating a set of premises

Jonathan Wang, Kesheng Wu, Alex Sim, Seongwook Hwangbo

To ensure reliability, power transformers are monitored for partial discharge (PD) events, which are symptoms of transformer failure. Since failures can have catastrophic cascading consequences, it is critical to preempt them as early as possible. Our goal is to classify PDs as corona, floating, particle, or void, to gain an understanding of the failure loca

Akshat Gahoi, Jayant Duneja, Anshul Padhi, Shivam Mangale

Code-mixed machine translation has become an important task in multilingual communities and extending the task of machine translation to code mixed data has become a common task for these languages. In the shared tasks of WMT 2022, we try to tackle the same for both English + Hindi to Hinglish and Hinglish to English. The first task dealt with both Roman and

Thien Nguyen, Nathalie Tran, Liuhui Deng, Thiago Fraga da Silva

Code-switching describes the practice of using more than one language in the same sentence. In this study, we investigate how to optimize a neural transducer based bilingual automatic speech recognition (ASR) model for code-switching speech. Focusing on the scenario where the ASR model is trained without supervised code-switching data, we found that semi-sup

Zekun Li, Jina Kim, Yao-Yi Chiang, Muhao Chen

Named geographic entities (geo-entities for short) are the building blocks of many geographic datasets. Characterizing geo-entities is integral to various application domains, such as geo-intelligence and map comprehension, while a key challenge is to capture the spatial-varying context of an entity. We hypothesize that we shall know the characteristics of a

Yifei Wang, Mert Pilanci

We propose a fast algorithm for computing the entire ridge regression regularization path in nearly linear time. Our method constructs a basis on which the solution of ridge regression can be computed instantly for any value of the regularization parameter. Consequently, linear models can be tuned via cross-validation or other risk estimation strategies with

Victoria DiTomasso, Chantanelle Nava, Mercedes López-Morales, Allyson Bieryla

We present high-precision radial velocities (RVs) from the HARPS-N spectrograph for HD79210 and HD79211, two M0V members of a gravitationally-bound binary system. We detect a planet candidate with a period of $24.421^{+0.016}_{-0.017}$ days around HD79211 in these HARPS-N RVs, validating the planet candidate originally identified in CARMENES RV data alone. U

Gabrielle Beaudin, Alexandre Désilets-Benoit, Andrea Daniele Bianchi, Robert Arnold

We conducted a small-angle neutron scattering experiments (SANS) on the ferromagnetic semi-metal EuB$_6$, where we observed direct evidence for the presence of magnetic polarons. We carried out SANS experiments over a large range of scattering vectors $|\vec{q}|$ from 0.006 to 0.140~\AA$^{-1}$ and 2 to 60~K. Just above $T_{\mathrm{C}}$ our experiments show m

Adam Fishman, Adithyavairan Murali, Clemens Eppner, Bryan Peele

Collision-free motion generation in unknown environments is a core building block for robot manipulation. Generating such motions is challenging due to multiple objectives; not only should the solutions be optimal, the motion generator itself must be fast enough for real-time performance and reliable enough for practical deployment. A wide variety of methods

Frederic Heihoff

It has been well established that, in attraction-repulsion Keller-Segel systems of the form\begin{equation*} \left\{ \begin{aligned} u_t &= \Delta u - \chi \nabla \cdot (u\nabla v) + \xi \nabla \cdot (u\nabla w), \\ \tau v_t &= \Delta v + \alpha u - \beta v,\\ \tau w_t &= \Delta w + \gamma u - \delta w \end{aligned} \right. \end{equation*} in a smooth bounde

Rebecca A. Booth, Roland Kothes, Tom Landecker, Jo-Anne Brown

Supernova remnants (SNRs) are significant contributors of matter and energy to the interstellar medium. Understanding the impact and the mechanism of this contribution requires knowledge of the physical size, energy, and expansion rate of individual SNRs, which can only come if reliable distances can be obtained. We aim to determine the distance to the SNR D

Filip Klubička, John D. Kelleher

Improving our understanding of how information is encoded in vector space can yield valuable interpretability insights. Alongside vector dimensions, we argue that it is possible for the vector norm to also carry linguistic information. We develop a method to test this: an extension of the probing framework which allows for relative intrinsic interpretations

Juraj Lorincik, Vanessa Polito, Bart De Pontieu, Sijie Yu

We report on observations of highly-varying Si IV 1402.77 line profiles observed with the Interface Region Imaging Spectrograph (IRIS) during the M-class flare from 2022 January 18 at an unprecedented 0.8 s cadence. Moment analysis of this line observed in flare ribbon kernels showed that the intensity, Doppler velocity, and non-thermal broadening exhibited

Indrajit Badvaram, Brian A. Camley

Membrane curvature sensing is essential for a diverse range of biological processes. Recent experiments have revealed that a single nanometer-sized septin protein can distinguish between membrane-coated glass beads of one micron and three micron diameters, even though the septin is orders of magnitude smaller than the beads. This sensing ability is especiall

Charles P. Boyer, Hongnian Huang, Eveline Legendre, Christina W. Tønnesen-Friedman

We discuss the existence and non-existence of constant scalar curvature, as well as extremal, Sasaki metrics. We prove that the natural Sasaki-Boothby-Wang manifold over the admissible projective bundles over local products of non-negative CSC K\"ahler metrics, as described in https://link-springer-com.libproxy.unm.edu/article/10.1007/s00222-008-0126-x, alwa

Lu Sang, Bjoern Haefner, Xingxing Zuo, Daniel Cremers

Fine-detailed reconstructions are in high demand in many applications. However, most of the existing RGB-D reconstruction methods rely on pre-calculated accurate camera poses to recover the detailed surface geometry, where the representation of a surface needs to be adapted when optimizing different quantities. In this paper, we present a novel multi-view RG

Sajjad Ghanizadeh Zare, Kazem Haghnejad Azar, Mina Matin, Somayeh Hazrati

Let $E$ be a sublattice of a vector lattice $F$. A continuous operator $T$ from the vector lattice $E$ into a normed vector space $X$ is said to be $\tilde{o}$rder-norm continuous whenever $x_\alpha\xrightarrow{Fo}0$ implies $Tx_\alpha\xrightarrow{\Vert.\Vert}0$ for each $(x_\alpha)_\alpha\subseteq E$. Our mean from the convergence $ x_\alpha\stackrel{Fo} {\

Hudson Griffith

In this work, the researcher presents a novel approach to calculating melodic originality based on the research by Simonton (1994). This novel formula is then applied to a dataset of 428 classical music pieces from the Romantic period to analyze the relationship between melodic originality and thematic fame.

Lionel Riou-Durand, Pavel Sountsov, Jure Vogrinc, Charles C. Margossian

Hamiltonian Monte Carlo (HMC) is a widely used sampler for continuous probability distributions. In many cases, the underlying Hamiltonian dynamics exhibit a phenomenon of resonance which decreases the efficiency of the algorithm and makes it very sensitive to hyperparameter values. This issue can be tackled efficiently, either via the use of trajectory leng

Justin Atsushi Otter, Kate Rowlands, Katherine Alatalo, Ho-Hin Leung

Post-starburst galaxies (PSBs) have recently and rapidly quenched their star-formation, thus they are an important way to understand how galaxies transition from star-forming late-types to quiescent early-types. The recent discovery of large cold gas reservoirs in PSBs calls into question the theory that galaxies must lose their gas to become quiescent. Opti

Hossein Esfandiari, Vahab Mirrokni, Jon Schneider

In this work, we present and study a new framework for online learning in systems with multiple users that provide user anonymity. Specifically, we extend the notion of bandits to obey the standard $k$-anonymity constraint by requiring each observation to be an aggregation of rewards for at least $k$ users. This provides a simple yet effective framework wher

Oren Sultan, Dafna Shahaf

Analogy-making gives rise to reasoning, abstraction, flexible categorization and counterfactual inference -- abilities lacking in even the best AI systems today. Much research has suggested that analogies are key to non-brittle systems that can adapt to new domains. Despite their importance, analogies received little attention in the NLP community, with most