October 2022 arXiv papers — page 76

Rui Ai, Boxiang Lyu, Zhaoran Wang, Zhuoran Yang

We study reserve price optimization in multi-phase second price auctions, where the seller's prior actions affect the bidders' later valuations through a Markov Decision Process (MDP). Compared to the bandit setting in existing works, the setting in ours involves three challenges. First, from the seller's perspective, we need to efficiently explore the envir

Ki-Seok Kim, Mitsuhiro Nishida, Yoonseok Choun

Implementing the Wilsonian renormalization group (RG) transformation in a nonperturbative way, we construct an effective holographic dual description with an emergent extradimension identified with an RG scale. Taking the large$-N$ limit, we obtain an equation of motion of an order-parameter field, here the chiral condensate for our explicit demonstration. I

Shuanglin Yan, Neng Dong, Liyan Zhang, Jinhui Tang

TIReID aims to retrieve the image corresponding to the given text query from a pool of candidate images. Existing methods employ prior knowledge from single-modality pre-training to facilitate learning, but lack multi-modal correspondences. Besides, due to the substantial gap between modalities, existing methods embed the original modal features into the sam

Sean Kulinski, David I. Inouye

A distribution shift can have fundamental consequences such as signaling a change in the operating environment or significantly reducing the accuracy of downstream models. Thus, understanding distribution shifts is critical for examining and hopefully mitigating the effect of such a shift. Most prior work focuses on merely detecting if a shift has occurred a

Huiran Li, Yanwu Yang

In sponsored search advertising (SSA), advertisers need to select keywords and determine matching types for selected keywords simultaneously, i.e., keyword targeting. An optimal keyword targeting strategy guarantees reaching the right population effectively. This paper aims to address the keyword targeting problem, which is a challenging task because of the

Kosuke Karube, Victor Ukleev, Fumitaka Kagawa, Yoshinori Tokura

Intermetallic Pd-doped (Fe,Ni)$_3$P, that crystalizes in a non-centrosymmetric tetragonal structure with $S_4$ symmetry, has recently been discovered to host magnetic antiskyrmions, antivortex-like topological spin textures. In this material, uniaxial magnetic anisotropy and dipolar interactions play a significant role, giving rise to finely branched magneti

Jinwon Sohn, Seonghyun Jeong, Young Min Cho, Taeyoung Park

In the analysis of binary longitudinal data, it is of interest to model a dynamic relationship between a response and covariates as a function of time, while also investigating similar patterns of time-dependent interactions. We present a novel generalized varying-coefficient model that accounts for within-subject variability and simultaneously clusters vary

H. Wang, S. Karami, O. Dia, H. Ritter

A backdoor or Trojan attack is an important type of data poisoning attack against deep neural network (DNN) classifiers, wherein the training dataset is poisoned with a small number of samples that each possess the backdoor pattern (usually a pattern that is either imperceptible or innocuous) and which are mislabeled to the attacker's target class. When trai

Rui-Qin Wang, Ji-Peng Lv, Yan-Hao Li, Jun Song

We study the production of light nuclei in the coalescence mechanism in Au-Au collisions at midrapidity at $\sqrt{s_{NN}}=3$ GeV. We derive analytic formulas of momentum distributions of two bodies, three bodies and four nucleons coalescing into light nuclei, respectively. We naturally explain the transverse momentum spectra of the deuteron ($d$), triton ($t

Nirmal J. Nair, Andres Goza

A bio-inspired, passively deployable flap attached to an airfoil by a torsional spring of fixed stiffness can provide significant lift improvements at post-stall angles of attack. In this work, we describe a hybrid active-passive variant to this purely passive flow control paradigm, where the stiffness of the hinge is actively varied in time to yield passive

Victoria M Chayes

We offer a new proof of uniform convexity inequalities for the Finsler manifold of nonpositive curvature taken on the space of positive-semidefinite matrices with the weighted matrix geometric mean defining the geodesic between two points. Using the technique of log majorization, we are able to characterize that the equality cases of said equalities occur if

Dung Le, Huy Nguyen, Khai Nguyen, Trang Nguyen

Generalized sliced Wasserstein distance is a variant of sliced Wasserstein distance that exploits the power of non-linear projection through a given defining function to better capture the complex structures of the probability distributions. Similar to sliced Wasserstein distance, generalized sliced Wasserstein is defined as an expectation over random projec

Tsunehiko Tanaka, Daiki Kimura, Michiaki Tatsubori

Text-based games are becoming commonly used in reinforcement learning as real-world simulation environments. They are usually imperfect information games, and their interactions are only in the textual modality. To challenge these games, it is effective to complement the missing information by providing knowledge outside the game, such as human common sense.

Jaejeong Shin, Shi Chang, Matthew Bays, Joshua Weaver

We propose a novel method to generate underwater object imagery that is acoustically compliant with that generated by side-scan sonar using the Unreal Engine. We describe the process to develop, tune, and generate imagery to provide representative images for use in training automated target recognition (ATR) and machine learning algorithms. The methods provi

Tetsuya Sakai, Sijie Tao, Maria Maistro, Zhumin Chu

Unfortunately, the official English (sub)task results reported in the NTCIR-14 WWW-2, NTCIR-15 WWW-3, and NTCIR-16 WWW-4 overview papers are incorrect due to noise in the official qrels files; this paper reports results based on the corrected qrels files. The noise is due to a fatal bug in the backend of our relevance assessment interface. More specifically,

Shupei Liu, Linfeng Feng, Yijun Gong, Chengdong Liang

While deep-learning-based speaker localization has shown advantages in challenging acoustic environments, it often yields only direction-of-arrival (DOA) cues rather than precise two-dimensional (2D) coordinates. To address this, we propose a novel deep-learning-based 2D speaker localization method leveraging ad-hoc microphone arrays, where an ad-hoc microph

Jianfei Li, Han Feng, Ding-Xuan Zhou

Deep neural networks (DNNs) have garnered significant attention in various fields of science and technology in recent years. Activation functions define how neurons in DNNs process incoming signals for them. They are essential for learning non-linear transformations and for performing diverse computations among successive neuron layers. In the last few years

Andres Pulido, Ruoyao Qin, Antonio Diaz, Andrew Ortega

Generating 3D point cloud (PC) data from noisy sonar measurements is a problem that has potential applications for bathymetry mapping, artificial object inspection, mapping of aquatic plants and fauna as well as underwater navigation and localization of vehicles such as submarines. Side-scan sonar sensors are available in inexpensive cost ranges, especially

Hideyoshi Yanagisawa, Xiaoxiang Wu, Kazutaka Ueda, Takeo Kato

An appropriate level of arousal induces positive emotions, and a high arousal potential may provoke negative emotions. To explain the effect of arousal on emotional valence, we propose a novel mathematical framework of arousal potential variations in the dual process of human cognition: automatic and controlled. A suitable mathematical formulation to explain

Peilin Du, Yu Wang, Kui Liu, Rongguo Yang

Large-scale continuous variable (CV) cluster state is necessary in quantum information processing based on measurement-based quantum computing (MBQC). Specially, generating large-scale CV cluster state multiplexed in time domain is easier to implement and has strong scalability in experiment. Here one-dimensional (1D) large-scale dual-rail CV cluster states

Xueru Wen, Changjiang Zhou, Haotian Tang, Luguang Liang

Named entity recognition is a traditional task in natural language processing. In particular, nested entity recognition receives extensive attention for the widespread existence of the nesting scenario. The latest research migrates the well-established paradigm of set prediction in object detection to cope with entity nesting. However, the manual creation of

Enrico Celestino Colón, John Urschel

In this note, we prove that the $\lceil \tfrac{1}{2} \sqrt{n} \log_2^2 n \rceil^{th}$ power of a connected $n$-vertex Eulerian digraph is Hamiltonian, and provide an infinite family of digraphs for which the $\lfloor \sqrt{n}/2 \rfloor^{th}$ power is not.

Zhaofeng Wu, Robert L. Logan, Pete Walsh, Akshita Bhagia

Recently introduced language model prompting methods can achieve high accuracy in zero- and few-shot settings while requiring few to no learned task-specific parameters. Nevertheless, these methods still often trail behind full model finetuning. In this work, we investigate if a dedicated continued pretraining stage could improve "promptability", i.e., zero-

Malcolm Hoong Wai Chen, Angelina Yan Mui Chin, Ta Sheng Tan

Let $f(x)=x^8+ax^4+b \in \mathbb{Q}[x]$ be an irreducible polynomial where $b$ is a square. We give a method that completely describes the factorization patterns of a linear resolvent of $f(x)$ using simple arithmetic conditions on $a$ and $b$. As a result, we determine the exact six possible Galois groups of $f(x)$ and completely classify all of them. As an

Shuyuan Xu, Da Xu, Evren Korpeoglu, Sushant Kumar

A fundamental challenge of recommendation systems (RS) is understanding the causal dynamics underlying users' decision making. Most existing literature addresses this problem by using causal structures inferred from domain knowledge. However, there are numerous phenomenons where domain knowledge is insufficient, and the causal mechanisms must be learnt from

Trond I. Andersen, Yuri D. Lensky, Kostyantyn Kechedzhi, Ilya Drozdov

Indistinguishability of particles is a fundamental principle of quantum mechanics. For all elementary and quasiparticles observed to date - including fermions, bosons, and Abelian anyons - this principle guarantees that the braiding of identical particles leaves the system unchanged. However, in two spatial dimensions, an intriguing possibility exists: braid

Lars Lindemann, Matthew Cleaveland, Gihyun Shim, George J. Pappas

We propose a framework for planning in unknown dynamic environments with probabilistic safety guarantees using conformal prediction. Particularly, we design a model predictive controller (MPC) that uses i) trajectory predictions of the dynamic environment, and ii) prediction regions quantifying the uncertainty of the predictions. To obtain prediction regions

Joan Puigcerver, Rodolphe Jenatton, Carlos Riquelme, Pranjal Awasthi

Adversarial robustness is a key desirable property of neural networks. It has been empirically shown to be affected by their sizes, with larger networks being typically more robust. Recently, Bubeck and Sellke proved a lower bound on the Lipschitz constant of functions that fit the training data in terms of their number of parameters. This raises an interest

Anupama Chingacham, Vera Demberg, Dietrich Klakow

In noisy environments, speech can be hard to understand for humans. Spoken dialog systems can help to enhance the intelligibility of their output, either by modifying the speech synthesis (e.g., imitate Lombard speech) or by optimizing the language generation. We here focus on the second type of approach, by which an intended message is realized with words t

Sankalpa Timilsina, Justin Presley, David Reddick, Susmit Shannigrahi

As the growth of genomics samples rapidly expands due to increased access to high resolution DNA sequencing technology, the need for a scalable platform to aggregate dispersed datasets enable easy access to the vast wealth of DNA sequences available is paramount. In this work, we introduce and demonstrate a novel way to use Named Data Networking (NDN) in con

Huafu Li, Liqin Ding, Yang Wang, Zhenyong Wang

We investigate the effect of channel aging on multi-cell massive multiple-input multiple-output (MIMO) vehicular networks in a generic non-isotropic scattering environment. Based on the single cluster scattering assumption and the von Mises distribution assumptions of the scatterers' angles, an aging channel model is established to capture the joint effect o

Wei Dai, Daniel Berleant

Image quality is important, and can affect overall performance in image processing and computer vision as well as for numerous other reasons. Image quality assessment (IQA) is consequently a vital task in different applications from aerial photography interpretation to object detection to medical image analysis. In previous research, the BRISQUE algorithm an

Himanshi Garg, Christophe Pinte, Iain Hammond, Richard Teague

We present ALMA band 6 images of the 12CO, 13CO and C18O J=2-1 line emissions for the circumstellar disc around HD 169142, at ~8 au spatial resolution. We resolve a central gas depleted cavity, along with two independent near-symmetric ring-like structures in line emission: a well-defined inner gas ring [~25 au] and a second relatively fainter and diffuse ou

Love Trivedi, Raviit Vij

With advancements in computer vision taking place day by day, recently a lot of light is being shed on activity recognition. With the range for real-world applications utilizing this field of study increasing across a multitude of industries such as security and healthcare, it becomes crucial for businesses to distinguish which machine learning methods perfo

Muralidhar Andoorveedu, Zhanda Zhu, Bojian Zheng, Gennady Pekhimenko

Training deep learning models can be computationally expensive. Prior works have shown that increasing the batch size can potentially lead to better overall throughput. However, the batch size is frequently limited by the accelerator memory capacity due to the activations/feature maps stored for the training backward pass, as larger batch sizes require large

Henry W. Robbins, Samuel C. Gutekunst, David B. Shmoys, David P. Williamson

The Simplex algorithm for solving linear programs-one of Computing in Science & Engineering's top 10 most influential algorithms of the 20th century-is an important topic in many algorithms courses. While the Simplex algorithm relies on intuitive geometric ideas, the computationally-involved mechanics of the algorithm can obfuscate a geometric understanding.

Jihua Wang

In this paper, we propose an analytical non-polynomial potential system which has infinitely many critical periodic orbits in phase plane. By showing the existence of infinitely many $2\pi-$ periodic solutions, the proof bases on variational methods and the properties of Bessel function. The result provides an affirmative example to Dumortier's conjecture [N

Shaoying Cai, Yingjiu Li, Changshe Ma, Sherman S. M. Chow

Radio Frequency Identification (RFID) is a key technology used in many applications. In the past decades, plenty of secure and privacy-preserving RFID tag/mutual authentication protocols as well as formal frameworks for evaluating them have been proposed. However, we notice that a property, namely proof of possession (PoP), has not been rigorously studied ti

Abdus Salam Azad, Izzeddin Gur, Jasper Emhoff, Nathaniel Alexis

Reinforcement Learning (RL) algorithms are often known for sample inefficiency and difficult generalization. Recently, Unsupervised Environment Design (UED) emerged as a new paradigm for zero-shot generalization by simultaneously learning a task distribution and agent policies on the generated tasks. This is a non-stationary process where the task distributi

R. Kadono, M. Hiraishi, H. Okabe, A. Koda

Magnesium hydride has great potential as a solid hydrogen (H) storage material because of its high H storage capacity of 7.6 wt%. However, its slow hydrogenation and dehydrogenation kinetics and the high temperature of 300 $^\circ$C required for decomposition are major obstacles to small-scale applications such as automobiles. The local electronic structure

Haiquan Lu, Yong Zeng, Shi Jin, Rui Zhang

Delay alignment modulation (DAM) is a promising technology to achieve ISI-free wideband communication, by leveraging delay compensation and path-based beamforming, rather than the conventional channel equalization or multi-carrier transmission. In particular, when there exist a few strong time-dispersive channel paths, DAM can effectively align different pro

Xueru Wen, Changjiang Zhou, Haotian Tang, Luguang Liang

Named entity recognition is a fundamental task in natural language processing, identifying the span and category of entities in unstructured texts. The traditional sequence labeling methodology ignores the nested entities, i.e. entities included in other entity mentions. Many approaches attempt to address this scenario, most of which rely on complex structur

Amar Ali-bey, Brahim Chaib-draa, Philippe Giguère

This paper aims to investigate representation learning for large scale visual place recognition, which consists of determining the location depicted in a query image by referring to a database of reference images. This is a challenging task due to the large-scale environmental changes that can occur over time (i.e., weather, illumination, season, traffic, oc

Shengzhi Wang, Xiangyu Chu, K. W. Samuel Au

Projected Inverse Dynamics Control (PIDC) is commonly used in robots subject to contact, especially in quadrupedal systems. Many methods based on such dynamics have been developed for quadrupedal locomotion tasks, and only a few works studied simple interactions between the robot and environment, such as pressing an E-stop button. To facilitate the interacti

Shamsi Soleimani, Kasra Rouhi, Ali Momeni

The ability to control waves at the nanoscale has attracted considerable attention to ultrathin metasurface lenses (metalenses) in optical imaging and encryption systems. We propose an approach to active tuning metasurfaces by integrating an ultrathin layer of indium-tin-oxide (ITO) into a unit cell as an electro-optically tunable material. A proposed design

Sami Assaf, Anne Dranowski, Nicolle Gonzalez

Demazure crystals are subcrystals of highest weight irreducible $\mathfrak{g}$-crystals. In this article, we study tensor products of a larger class of subcrystals, called extremal, and give a local characterization for exactly when the tensor product of Demazure crystals is extremal. We then show that tensor products of Demazure crystals decompose into dire

Ryo Kawaguchi, Katsushi Hashimoto, Toshiyuki Kakudate, Keiichi Katoh

The spintronic properties of magnetic molecules have attracted significant scientific attention. Special emphasis has been placed on the qubit for quantum information processing. The single molecule magnet, bis(phthalocyaninato (Pc)) Tb(III) (TbPc2), is one of the best examined cases in which the delocalized {\pi}-radical electron spin of the Pc ligand plays

Martijn S. S. L. Oei, Reinout J. van Weeren, Aivin R. D. J. G. I. B. Gast, Andrea Botteon

Radio galaxies are luminous structures created by the jets of supermassive black holes, and consist of atomic nuclei, relativistic electrons, and magnetic fields. In exceptional cases, radio galaxies attain cosmological, megaparsec extents - and thus turn into giants. Giants embody the most extreme known mechanism through which galaxies can impact the Cosmic

Samia Sultana, Boshir Ahmed, Manoranjan Paul, Muhammad Rafiqul Islam

Lane marking detection is fundamental for both advanced driving assistance systems. However, detecting lane is highly challenging when the visibility of a road lane marking is low due to real-life challenging environment and adverse weather. Most of the lane detection methods suffer from four types of challenges: (i) light effects i.e., shadow, glare of ligh

Deanna Wilborne

Using a Genetic Algorithm and Decision Tree Classifier, the features of the NSL-KDD dataset are reduced using combinatorial optimization to determine the minimum features required to accurately classify Denial of Service attacks within the NSL-KDD dataset.

Mostafa Shahin, Beena Ahmed, Julien Epps

One of the major challenges in acoustic modelling of child speech is the rapid changes that occur in the children's articulators as they grow up, their differing growth rates and the subsequent high variability in the same age group. These high acoustic variations along with the scarcity of child speech corpora have impeded the development of a reliable spee

Eduardo Villasenor, Robert Malaney

Quantum states of light being transmitted via realistic free-space channels often suffer erasure errors due to several factors such as coupling inefficiencies between transmitter and receiver. In this work, an error correction code capable of protecting a single-mode quantum state against erasures is presented. Our three-mode code protects a single-mode Cont

Sam Edwards, Minju Lee, Hee Oh

For any $d\geq 1$, we obtain counting and equidistribution results for tori with small volume for a class of $d$-dimensional torus packings, invariant under a self-joining $\Gamma_\rho<\prod_{i=1}^d\mathrm{PSL}_2(\mathbb{C})$ of a Kleinian group $\Gamma$ formed by a $d$-tuple of convex cocompact representations $\rho=(\rho_1, \cdots, \rho_d)$. More precisely

S. G. Barwick, W. -A. Jackson, P. Wild

In this article we look at the geometric structure of the feet of an orthogonal Buekenhout-Metz unital U in PG(2,q^2). We show that the feet of each point form a set of type (0,1,2,4). Further, we discuss the structure of any 4-secants, and determine exactly when the feet form an arc.

Kalpa Gunaratna, Vijay Srinivasan, Akhila Yerukola, Hongxia Jin

Joint intent detection and slot filling is a key research topic in natural language understanding (NLU). Existing joint intent and slot filling systems analyze and compute features collectively for all slot types, and importantly, have no way to explain the slot filling model decisions. In this work, we propose a novel approach that: (i) learns to generate a

Zillur Rahman, Amit Mazumder Ami, Muhammad Ahsan Ullah

Wrong-way driving is one of the main causes of road accidents and traffic jam all over the world. By detecting wrong-way vehicles, the number of accidents can be minimized and traffic jam can be reduced. With the increasing popularity of real-time traffic management systems and due to the availability of cheaper cameras, the surveillance video has become a b

Mumin Tolga Emirler, Bilin Aksun Guvenc

Vehicle stability control systems are important components of active safety systems for road transport. The problem of vehicle lateral stability control is addressed in this paper using active front wheel steering and individual braking. Vehicle lateral stability control means keeping the vehicle yaw rate and the vehicle side slip angle in desired values. Fo

David J. Moss

Mode splitting induced by coherent optical mode interference in coupled resonant cavities is a key phenomenon in photonic resonators that can lead to powerful and versatile filtering functions, in close analogy to electromagnetically-induced-transparency, Autler-Townes splitting, Fano resonances, and dark states. It can not only break the dependence between

Tianyang Liu, Chong Wang, Kun Huang, Peng Liang

$\textbf{Context}$: The release planning of mobile apps has become an area of active research, with most studies centering on app analysis through release notes in the Apple App Store and tracking user reviews via issue trackers. However, the correlation between these release notes and user reviews in App Store remains understudied. $\textbf{Objective}$: In

Holger Goetz, Thorsten Pöschel

For a wide range of applications, we need DEM simulations of granular matter in contact with flexible elastic boundaries. We present a novel method to describe the interaction between granular particles and a flexible elastic membrane. Here, the standard mass-spring model approach is supplemented by surface patches given by triangulation of the membrane. In

Yuki Tanaka, Shuhei M. Yoshida, Makoto Terao

We propose a non-iterative method to optimize pseudo-labeling thresholds for learning object detection from a collection of low-cost datasets, each of which is annotated for only a subset of all the object classes. A popular approach to this problem is first to train teacher models and then to use their confident predictions as pseudo ground-truth labels whe

Belle II Collaboration, F. Abudinén, I. Adachi, K. Adamczyk

We measure the photon-energy spectrum in radiative bottom-meson ($B$) decays into inclusive final states involving a strange hadron and a photon. We use SuperKEKB electron-positron collisions corresponding to $189~\mathrm{fb}^{-1}$ of integrated luminosity collected at the $\Upsilon(4S)$ resonance by the Belle II experiment. The partner $B$ candidates are fu

Alfred Q. R. Baron, Daisuke Ishikawa

Background rates in several pixel array detectors are investigated with an eye toward using them with hard, >6 keV, x-rays in very low-rate experiments - e.g. at signal rates <0.1/s/cm^2. Measured background event rates for a detector with an unshielded 0.75 mm thick CdTe sensor on the experimental floor at SPring-8 varied from 0.4/s/cm^2 with a 6 keV lower-

Han Shu, Jacob Mays

Liberalized electricity markets often include resource adequacy mechanisms that require consumers to contract with generation resources well in advance of real-time operations. While administratively defined mechanisms have most commonly taken the form of a capacity obligation, efficient markets would feature a broad array of arrangements adapted to the risk

Joshua W. Burby, Nathan Duignan, James D. Meiss

A simple property of magnetic fields that minimizes bouncing to passing type transitions of guiding center orbits is defined and discussed. This property, called isoprominence, is explored through the framework of a near-axis expansion. It is shown that isoprominent magnetic fields for a toroidal configuration exist to all orders in a formal expansion about

Nicholas A. Moskovitz, Lawrence Wasserman, Brian Burt, Robert Schottland

The astorb database at Lowell Observatory is an actively curated catalog of all known asteroids in the Solar System. astorb has heritage dating back to the 1970's and has been publicly accessible since the 1990's. Beginning in 2015 work began to modernize the underlying database infrastructure, operational software, and associated web applications. That effo

MicroBooNE collaboration, P. Abratenko, D. Andrade Aldana, J. Anthony

We present a search for eV-scale sterile neutrino oscillations in the MicroBooNE liquid argon detector, simultaneously considering all possible appearance and disappearance effects within the $3+1$ active-to-sterile neutrino oscillation framework. We analyze the neutrino candidate events for the recent measurements of charged-current $\nu_e$ and $\nu_{\mu}$

Yifeng Huang, Ruofan Jiang

We investigate the algebra and combinatorics of an analogue of the Hermite normal form that classifies finite-index submodules of $\mathbb F_q[[T]]^d$. We identity both normal forms as instances of Gr\"obner basis theory under different monomial orders, where the Hermite normal form corresponds to the lex order, and the new normal form the hlex order. We not

S. J. Frank, J. C. Wright, P. T. Bonoli

Treatments of plasma waves usually assume homogeneity, but the parallel gradients ubiquitous in plasmas can modify wave propagation and absorption. We derive a quasilocal inhomogeneous correction to the plasma dielectric for arbitrary distributions by expanding the phase correlation integral and develop a novel integration technique that allows our correctio

J Félix Salazar, Thomas Zannias

We introduce a class of relativistic fluid states satisfying the relativistic local thermodynamical equilibrium postulate (abbreviated as relativistic (LTE) postulate). States satisfying this postulate, are states "near equilibrium" (a term defined precisely in the course of the paper) and permit us to attach a fictitious "local thermodynamical equilibrium"

Wim Boes, Hugo Van hamme

In this technical report, the systems we submitted for subtask 1B of the DCASE 2021 challenge, regarding audiovisual scene classification, are described in detail. They are essentially multi-source transformers employing a combination of auditory and visual features to make predictions. These models are evaluated utilizing the macro-averaged multi-class cros

Yuanzhao Zhang, Sean P. Cornelius

Reservoir Computing (RC) is a simple and efficient model-free framework for forecasting the behavior of nonlinear dynamical systems from data. Here, we show that there exist commonly-studied systems for which leading RC frameworks struggle to learn the dynamics unless key information about the underlying system is already known. We focus on the important pro

Philip Greengard, Manas Rachh, Alex Barnett

We introduce a Fourier-based fast algorithm for Gaussian process regression in low dimensions. It approximates a translationally-invariant covariance kernel by complex exponentials on an equispaced Cartesian frequency grid of $M$ nodes. This results in a weight-space $M\times M$ system matrix with Toeplitz structure, which can thus be applied to a vector in

Prateek Yadav, Mohit Bansal

Continual Learning (CL) methods focus on accumulating knowledge over time while avoiding catastrophic forgetting. Recently, Wortsman et al. (2020) proposed a CL method, SupSup, which uses a randomly initialized, fixed base network (model) and finds a supermask for each new task that selectively keeps or removes each weight to produce a subnetwork. They preve

Wim Boes, Hugo Van hamme

In this technical report, the systems we submitted for subtask 4 of the DCASE 2021 challenge, regarding sound event detection, are described in detail. These models are closely related to the baseline provided for this problem, as they are essentially convolutional recurrent neural networks trained in a mean teacher setting to deal with the heterogeneous ann

Denizalp Goktas, Amy Greenwald

Pseudo-games are a natural and well-known generalization of normal-form games, in which the actions taken by each player affect not only the other players' payoffs, as in games, but also the other players' strategy sets. The solution concept par excellence for pseudo-games is the generalized Nash equilibrium (GNE), i.e., a strategy profile at which each play

James Sunseri, Zachary Slepian, Stephen Portillo, Jiamin Hou

We present a new $\texttt{python}$ package SARABANDE for measuring 3 & 4 Point Correlation Functions (3/4 PCFs) in $\mathcal{O}(N_{\rm g} \log N_{\rm g})$ time using Fast Fourier Transforms (FFTs), with $N_{\rm g}$ the number of grid points used for the FFT. SARABANDE can measure both projected and full 3 and 4 PCFs on gridded 2D and 3D datasets. The general

Eric Luhman, Troy Luhman

While hierarchical variational autoencoders (VAEs) have achieved great density estimation on image modeling tasks, samples from their prior tend to look less convincing than models with similar log-likelihood. We attribute this to learned representations that over-emphasize compressing imperceptible details of the image. To address this, we introduce a KL-re

Hai-Chao Zhang

In the framework of special relativity (SR), I propose that matter conformally couples to a scalar field through the Lagrangian density of matter, whether matter is characterized by classical or by quantum (statistical) mechanics. The largest interaction strength of the scalar-mediated force can achieve the order of $1/\Lambda_E^2$ with the cosmological cons

David Biagioni, Xiangyu Zhang, Christiane Adcock, Michael Sinner

Grid-interactive building control is a challenging and important problem for reducing carbon emissions, increasing energy efficiency, and supporting the electric power grid. Currently researchers and practitioners are confronted with a choice of control strategies ranging from model-free (purely data-driven) to model-based (directly incorporating physical kn

Qi Heng Ho, Zachary N. Sunberg, Morteza Lahijanian

This paper presents a new multi-layered algorithm for motion planning under motion and sensing uncertainties for Linear Temporal Logic specifications. We propose a technique to guide a sampling-based search tree in the combined task and belief space using trajectories from a simplified model of the system, to make the problem computationally tractable. Our m

Haiyu Lu, Makoto Hashimoto, Su-Di Chen, Shigeyuki Ishida

Identifying quantum critical points (QCPs) and their associated fluctuations may hold the key to unraveling the unusual electronic phenomena observed in cuprate superconductors. Recently, signatures of quantum fluctuations associated with charge order (CO) have been inferred from the anomalous enhancement of CO excitations that accompany the reduction of the

Llion Jones, Richard Sproat, Haruko Ishikawa, Alexander Gutkin

If one sees the place name Houston Mercer Dog Run in New York, how does one know how to pronounce it? Assuming one knows that Houston in New York is pronounced "how-ston" and not like the Texas city, then one can probably guess that "how-ston" is also used in the name of the dog park. We present a novel architecture that learns to use the pronunciations of n

Samuel Daulton, Xingchen Wan, David Eriksson, Maximilian Balandat

Optimizing expensive-to-evaluate black-box functions of discrete (and potentially continuous) design parameters is a ubiquitous problem in scientific and engineering applications. Bayesian optimization (BO) is a popular, sample-efficient method that leverages a probabilistic surrogate model and an acquisition function (AF) to select promising designs to eval

Dongha Lee, Jiaming Shen, Seonghyeon Lee, Susik Yoon

Topic taxonomies display hierarchical topic structures of a text corpus and provide topical knowledge to enhance various NLP applications. To dynamically incorporate new topic information, several recent studies have tried to expand (or complete) a topic taxonomy by inserting emerging topics identified in a set of new documents. However, existing methods foc

M. T. P. Liska, N. Kaaz, G. Musoke, A. Tchekhovskoy

In many black hole systems, the accretion disk is expected to be misaligned with respect to the black hole spin axis. If the scale height of the disk is much smaller than the misalignment angle, the spin of the black hole can tear the disk into multiple, independently precessing `sub-disks'. This is most likely to happen during outbursts in black hole X-Ray

Adrian van Kan, François Pétrélis

On-off intermittency occurs in nonequilibrium physical systems close to bifurcation points and is characterised by an aperiodic switching between a large-amplitude "on" state and a small-amplitude "off" state. L\'evy on-off intermittency is a recently introduced generalisation of on-off intermittency to multiplicative L\'evy noise, which depends on a stabili

Youshan Zhang, Jialu Li

Audio denoising has been explored for decades using both traditional and deep learning-based methods. However, these methods are still limited to either manually added artificial noise or lower denoised audio quality. To overcome these challenges, we collect a large-scale natural noise bird sound dataset. We are the first to transfer the audio denoising prob

Mark Koch

The field of quantum machine learning (QML) explores how quantum computers can be used to more efficiently solve machine learning problems. As an application of hybrid quantum-classical algorithms, it promises a potential quantum advantages in the near term. In this thesis, we use the ZXW-calculus to diagrammatically analyse two key problems that QML applica

Peide Huang, Mengdi Xu, Jiacheng Zhu, Laixi Shi

Curriculum Reinforcement Learning (CRL) aims to create a sequence of tasks, starting from easy ones and gradually learning towards difficult tasks. In this work, we focus on the idea of framing CRL as interpolations between a source (auxiliary) and a target task distribution. Although existing studies have shown the great potential of this idea, it remains u

Shentong Mo, Zhun Sun, Chao Li

Contrastive self-supervised learning (CSL) with a prototypical regularization has been introduced in learning meaningful representations for downstream tasks that require strong semantic information. However, to optimize CSL with a loss that performs the prototypical regularization aggressively, e.g., the ProtoNCE loss, might cause the "coagulation" of examp

Ziang Liu, Ayush Bhandari, Bruno Clerckx

Massive multiple-input multiple-output (M-MIMO) architecture is the workhorse of modern communication systems. Currently, two fundamental bottlenecks, namely, power consumption and receiver saturation, limit the full potential achievement of this technology. These bottlenecks are intricately linked with the analog-to-digital converter (ADC) used in each radi

Jeremias Arf

We consider and discretize a mixed formulation for linear elasticity with weakly imposed symmetry in two and three dimensions. Whereas existing methods mainly deal with simplicial or polygonal meshes, we take advantage of isogeometric analysis (IGA) and consequently allow for shapes with curved boundaries. To introduce the discrete spaces we use isogeometric

Changhan Wang, Hirofumi Inaguma, Peng-Jen Chen, Ilia Kulikov

The amount of labeled data to train models for speech tasks is limited for most languages, however, the data scarcity is exacerbated for speech translation which requires labeled data covering two different languages. To address this issue, we study a simple and effective approach to build speech translation systems without labeled data by leveraging recent

Mia Sauda Bovill, Massimo Stiavelli, Alessa Ibrahim Wiggins, Massimo Ricotti

The first Pop III stars formed out of primordial, metal free gas, in minihalos at z>20, and kickstarted the cosmic processes of reionizaton and enrichment. While these stars are likely more massive than their enriched counterparts, the current unknowns of their astrophysics include; when the first Pop III stars ignited, how massive they were, and when and ho

Luis A. Martínez-Martínez, Tzu-Ching Yen, Artur F. Izmaylov

Solving the electronic structure problem via unitary evolution of the electronic Hamiltonian is one of the promising applications of digital quantum computers. One of the practical strategies to implement the unitary evolution is via Trotterization, where a sequence of short-time evolutions of fast-forwardable (i.e. efficiently diagonalizable) Hamiltonian fr

Jean-Charles Layoun, Alexis Roger, Irina Rish

The goal of vision-language modeling is to allow models to tie language understanding with visual inputs. The aim of this paper is to evaluate and align the Visual Language Model (VLM) called Multimodal Augmentation of Generative Models through Adapter-based finetuning (MAGMA) with human values. MAGMA is a VLM that is capable of image captioning and visual q

Lorenzo Laneve, Francesco Tacchino, Ivano Tavernelli

Random walks (or Markov chains) are models extensively used in theoretical computer science. Several tools, including analysis of quantities such as hitting and mixing times, are helpful for devising randomized algorithms. A notable example is Sch\"oning's algorithm for the satisfiability (SAT) problem. In this work, we use the density-matrix formalism to de

Luke Vilnis, Yury Zemlyanskiy, Patrick Murray, Alexandre Passos

Decoding methods for large language models often trade-off between diversity of outputs and parallelism of computation. Methods such as beam search and Gumbel top-k sampling can guarantee a different output for each element of the beam, but are not easy to parallelize. Alternatively, methods such as temperature sampling and its modifications (top-k sampling,

Samantha Wu, Jim Fuller

Many core collapse supernovae (SNe) with hydrogen-poor and low-mass ejecta, such as ultra-stripped SNe and type Ibn SNe, are observed to interact with dense circumstellar material (CSM). These events likely arise from the core-collapse of helium stars which have been heavily stripped by a binary companion and ejected significant mass during the last weeks to