October 2022 arXiv papers — page 104

Ye-Ryoung Lee, Wonjun Choi, Seungwon Jeong, Sungsam Kang

Shaping the wavefront of an incident wave to a complex scattering medium has demonstrated interesting possibilities, such as sub-diffraction wave focusing and enhancing light energy delivery. However, wavefront shaping has mainly been based on the control of transmitted waves that are inaccessible in most realistic applications. Here, we investigate the effe

Viqar Husain, Muhammad Muzammil

We study the classical-quantum (CQ) hybrid dynamics of homogeneous cosmology from a Hamiltonian perspective where the classical gravitational phase space variables and matter state evolve self-consistently with full backreaction. We compare numerically the classical and CQ dynamics for isotropic and anisotropic models, including quantum scalar-field induced

Yoon Bae Jun, Chae Young Lim, Kun Ho Kim

Autocovariance of the error term in a time series model plays a key role in the estimation and inference for the model that it belongs to. Typically, some arbitrary parametric structure is assumed upon the error to simplify the estimation, which inevitably introduces potential model-misspecification. We thus conduct nonparametric estimation of it. To avoid t

Xiudi Li, Abolfazl Safikhani, Ali Shojaie

Regime shifts in high-dimensional time series arise naturally in many applications, from neuroimaging to finance. This problem has received considerable attention in low-dimensional settings, with both Bayesian and frequentist methods used extensively for parameter estimation. The EM algorithm is a particularly popular strategy for parameter estimation in lo

Shuxin Zhang, Jinlei Zhang, Lixing Yang, Chengcheng Wang

Accurate passenger flow prediction of urban rail transit is essential for improving the performance of intelligent transportation systems, especially during the epidemic. How to dynamically model the complex spatiotemporal dependencies of passenger flow is the main issue in achieving accurate passenger flow prediction during the epidemic. To solve this issue

Zexue He, Yu Wang, Julian McAuley, Bodhisattwa Prasad Majumder

Recent work on reducing bias in NLP models usually focuses on protecting or isolating information related to a sensitive attribute (like gender or race). However, when sensitive information is semantically entangled with the task information of the input, e.g., gender information is predictive for a profession, a fair trade-off between task performance and b

Wenhan Xian, Feihu Huang, Heng Huang

Distributed data mining is an emerging research topic to effectively and efficiently address hard data mining tasks using big data, which are partitioned and computed on different worker nodes, instead of one centralized server. Nevertheless, distributed learning methods often suffer from the communication bottleneck when the network bandwidth is limited or

Jonathan Pilault, Michael Galkin, Bahare Fatemi, Perouz Taslakian

Recent work on Graph Neural Networks has demonstrated that self-supervised pretraining can further enhance performance on downstream graph, link, and node classification tasks. However, the efficacy of pretraining tasks has not been fully investigated for downstream large knowledge graph completion tasks. Using a contextualized knowledge graph embedding appr

Zachary Stone, Yue Shen

Neustadt & Kochanek (2022, hereafter NK22) proposed a new method to reconstruct the temperature perturbation map (as functions of time and disc radius) of AGN accretion discs using multi-wavelength photometric light curves. We apply their technique to 100 quasars at $z=0.5-2$ from the Sloan Digital Sky Survey Reverberation Mapping project, using multi-epoch

Tariq M. Khan, Syed S. Naqvi, Antonio Robles-Kelly, Erik Meijering

Compression of convolutional neural network models has recently been dominated by pruning approaches. A class of previous works focuses solely on pruning the unimportant filters to achieve network compression. Another important direction is the design of sparsity-inducing constraints which has also been explored in isolation. This paper presents a novel trai

Noriaki Hirose, Dhruv Shah, Ajay Sridhar, Sergey Levine

Machine learning techniques rely on large and diverse datasets for generalization. Computer vision, natural language processing, and other applications can often reuse public datasets to train many different models. However, due to differences in physical configurations, it is challenging to leverage public datasets for training robotic control policies on n

Saikat Dey, Sonal Jha, Wu-chun Feng

Many data-mining applications use dynamic attributed graphs to represent relational information; but due to security and privacy concerns, there is a dearth of available datasets that can be represented as dynamic attributed graphs. Even when such datasets are available, they do not have ground truth that can be used to train deep-learning models. Thus, we p

Stefan Larson, Gordon Lim, Yutong Ai, David Kuang

The ability of a document classifier to handle inputs that are drawn from a distribution different from the training distribution is crucial for robust deployment and generalizability. The RVL-CDIP corpus is the de facto standard benchmark for document classification, yet to our knowledge all studies that use this corpus do not include evaluation on out-of-d

Ying Su, Hongming Zhang, Yangqiu Song, Tong Zhang

As a key natural language processing (NLP) task, word sense disambiguation (WSD) evaluates how well NLP models can understand the lexical semantics of words under specific contexts. Benefited from the large-scale annotation, current WSD systems have achieved impressive performances in English by combining supervised learning with lexical knowledge. However,

Takashi Hamana

An excess up-scattering mass bias on a weak lensing cluster mass estimate is a statistical bias that an observed weak lensing mass ($M_{\rm obs}$) of a cluster of galaxies is, in a statistical sense, larger than its true mass ($M_{\rm true}$) because of a higher chance of up-scattering than that of down-scattering due to random noises in a weak lensing clust

Qinda Guo, Maciej Dendzik, Magnus H. Berntsen, Antonija Grubišić-Čabo

Ultrafast light sources have become an indispensable tool to access and understand transient phenomenon in material science. However, a simple and easy-to-implement method for harmonic selection, with high transmission efficiency and pulse duration conservation, is still a challenge. Here we showcase and compare two approaches for selecting the desired harmo

Jérôme Vétois

On a smooth, closed Riemannian manifold $(M,g)$ of dimension $n\ge3$ with positive scalar curvature and not conformally diffeomorphic to the standard sphere, we prove that the only conformal metrics to $g$ with constant Q-curvature of order 4 are the metrics $\lambda g$ with $\lambda>0$ constant.

Kang Liu, Feng Xue, Dan Guo, Le Wu

In most E-commerce platforms, whether the displayed items trigger the user's interest largely depends on their most eye-catching multimodal content. Consequently, increasing efforts focus on modeling multimodal user preference, and the pressing paradigm is to incorporate complete multimodal deep features of the items into the recommendation module. However,

Minghua Liu, Xuanlin Li, Zhan Ling, Yangyan Li

We study how choices of input point cloud coordinate frames impact learning of manipulation skills from 3D point clouds. There exist a variety of coordinate frame choices to normalize captured robot-object-interaction point clouds. We find that different frames have a profound effect on agent learning performance, and the trend is similar across 3D backbone

Zizhang Chen, Peizhao Li, Hongfu Liu, Pengyu Hong

Influence function, a method from robust statistics, measures the changes of model parameters or some functions about model parameters concerning the removal or modification of training instances. It is an efficient and useful post-hoc method for studying the interpretability of machine learning models without the need for expensive model re-training. Recent

Bodhisattwa Prasad Majumder, Zexue He, Julian McAuley

Debiasing methods in NLP models traditionally focus on isolating information related to a sensitive attribute (e.g., gender or race). We instead argue that a favorable debiasing method should use sensitive information 'fairly,' with explanations, rather than blindly eliminating it. This fair balance is often subjective and can be challenging to achieve algor

Navid Hashemi, Xin Qin, Jyotirmoy V. Deshmukh, Georgios Fainekos

In this paper, we consider the problem of synthesizing a controller in the presence of uncertainty such that the resulting closed-loop system satisfies certain hard constraints while optimizing certain (soft) performance objectives. We assume that the hard constraints encoding safety or mission-critical task objectives are expressed using Signal Temporal Log

Sri Sakti Swarup Anupindi, Michael Alphonse

In this paper, we define several types of maximal operators on sequence spaces occuring in Harmonic analysis and present various connections between them.

Berivan Isik, Francisco Pernice, Tsachy Weissman

We consider the maximum coding rate achievable by uniformly-random codes for the deletion channel. We prove an upper bound that's within 0.1 of the best known lower bounds for all values of the deletion probability $d,$ and much closer for small and large $d.$ We give simulation results which suggest that our upper bound is within 0.05 of the exact value for

Mingze Xi, Ashfaqur Rahman, Chuong Nguyen, Stuart Arnold

Understanding the growth and distribution of the prawns is critical for optimising the feed and harvest strategies. An inadequate understanding of prawn growth can lead to reduced financial gain, for example, crops are harvested too early. The key to maintaining a good understanding of prawn growth is frequent sampling. However, the most commonly adopted sam

Ryan Griffiths, Jack Naylor, Donald G. Dansereau

There are a multitude of emerging imaging technologies that could benefit robotics. However the need for bespoke models, calibration and low-level processing represents a key barrier to their adoption. In this work we present NOCaL, Neural odometry and Calibration using Light fields, a semi-supervised learning architecture capable of interpreting previously

M. Popa, K. Szpojankowski, P. -L. Tseng

We study asymptotic infinitesimal distributions of Gaussian Unitary Ensembles with permuted entries. We show that for random uniform permutations, the asymptotically permuted GUE matrix has a null infinitesimal distribution. Moreover, we show that asymptotically different permutations of the same GUE matrix are infinitesimally free. Besides this we study par

Sung Joon Maeng, Ozgur Ozdemir, İsmail Güvenç, Mihail Sichitiu

In this paper, we introduce AERIQ: a software-defined radio (SDR) based I/Q measurement and analysis framework for wireless signals for aerial experimentation. AERIQ is integrated into controllable aerial vehicles, it is flexible, repeatable, and provides raw I/Q samples for post-processing the data to extract various key parameters of interest (KPIs) over a

Albert Wilcox, Ashwin Balakrishna, Jules Dedieu, Wyame Benslimane

Providing densely shaped reward functions for RL algorithms is often exceedingly challenging, motivating the development of RL algorithms that can learn from easier-to-specify sparse reward functions. This sparsity poses new exploration challenges. One common way to address this problem is using demonstrations to provide initial signal about regions of the s

Jingyu Zhang, James Glass, Tianxing He

Existing work on controlled text generation (CTG) assumes a control interface of categorical attributes. In this work, we propose a natural language (NL) interface, where we craft a PCFG to embed the control attributes into natural language commands, and propose variants of existing CTG models that take commands as input. In our experiments, we design tailor

Abhishek Abhishek, Eric Drechsler, Wojciech Fedorko, Bernd Stelzer

Calorimeter simulation is the most computationally expensive part of Monte Carlo generation of samples necessary for analysis of experimental data at the Large Hadron Collider (LHC). The High-Luminosity upgrade of the LHC would require an even larger amount of such samples. We present a technique based on Discrete Variational Autoencoders (DVAEs) to simulate

A. C. Aguilar, M. N. Ferreira, B. M. Oliveira, J. Papavassiliou

We study a special Schwinger-Dyson equation in the context of a pure SU(3) Yang-Mills theory, formulated in the background field method. Specifically, we consider the corresponding equation for the vertex that governs the interaction of two background gluons with a ghost-antighost pair. By virtue of the background gauge invariance, this vertex satisfies a na

Kumar Shashwat

A robot finds it really hard to learn creatively and adapt to new unseen challenges. This is mainly because of the minimal information it has access to or experience towards. Paulius et al. [1] presented a way to construct functional graphs that encapsulate. Sakib et al. [2] further expanded FOON objects for robotic cooking. This paper presents a comparative

C. R. Muniz, H. R. Christiansen, M. S. Cunha, J. Furtado

In this paper, we study quantum relativistic features of a scalar field around the Rindler-Schwarzschild wormhole. First, we introduce this new class of spacetime, investigating some energy conditions and verifying their violation in a region nearby the wormhole throat, which means that the object has to have an exotic energy in order to prevent its collapse

Ademi Adeniji, Amber Xie, Pieter Abbeel

While unsupervised skill discovery has shown promise in autonomously acquiring behavioral primitives, there is still a large methodological disconnect between task-agnostic skill pretraining and downstream, task-aware finetuning. We present Intrinsic Reward Matching (IRM), which unifies these two phases of learning via the $\textit{skill discriminator}$, a p

Xiaoyan Huang, Weijun Yuan, Aaron Holman, Minho Kwon

We propose metasurface holograms as a novel platform to generate optical trap arrays for cold atoms with high fidelity, efficiency, and thermal stability. We developed design and fabrication methodologies to create dielectric, phase-only metasurface holograms based on titanium dioxide. We experimentally demonstrated optical trap arrays of various geometries,

Sreevidya Ayyar, Yukitoshi Matsushita, Taisuke Otsu

This paper extends validity of the conditional likelihood ratio (CLR) test developed by Moreira (2003) to instrumental variable regression models with unknown error variance and many weak instruments. In this setting, we argue that the conventional CLR test with estimated error variance loses exact similarity and is asymptotically invalid. We propose a modif

S. Erokhin, D. Berkov, A. Michels

Motivated by recent experimental polarized neutron results, we present a numerical micromagnetic study of the interfacial (intergrain) Dzyaloshinskii-Moriya interaction (DMI) in nanocrystalline terbium. We demonstrate that the DMI-induced spin misalignment between adjacent nanograins is the reason for the formation of the asymmetric positive-negative pattern

YuXuan Liu, Nikhil Mishra, Maximilian Sieb, Yide Shentu

3D bounding boxes are a widespread intermediate representation in many computer vision applications. However, predicting them is a challenging task, largely due to partial observability, which motivates the need for a strong sense of uncertainty. While many recent methods have explored better architectures for consuming sparse and unstructured point cloud da

ATHENA Collaboration, J. Adam, L. Adamczyk, N. Agrawal

ATHENA has been designed as a general purpose detector capable of delivering the full scientific scope of the Electron-Ion Collider. Careful technology choices provide fine tracking and momentum resolution, high performance electromagnetic and hadronic calorimetry, hadron identification over a wide kinematic range, and near-complete hermeticity. This article

Jing Huang, Kevin J Liang, Rama Kovvuri, Tal Hassner

Most existing OCR methods focus on alphanumeric characters due to the popularity of English and numbers, as well as their corresponding datasets. On extending the characters to more languages, recent methods have shown that training different scripts with different recognition heads can greatly improve the end-to-end recognition accuracy compared to combinin

Nahuel Mirón Granese, Alejandra Kandus, Esteban Calzetta

Just as non relativistic fluids, oftentimes we find relativistic fluids in situations where random fluctuations cannot be ignored, thermal and turbulent fluctuations being the most relevant examples. Because of the theory's inherent nonlinearity, fluctuations induce deep and complex changes in the dynamics of the system. The Martin-Siggia-Rose technique is a

Sergey Vasilyev, Andrey Muraviev, Dmitrii Konnov, Mike Mirov

We report molecular detection using dual frequency-comb spectroscopy with highly coherent broadband (6.6-11.4 {\m}m) long-wavelength infrared (LWIR) combs. The combs were produced via intra-pulse difference frequency generation (IDFG) in ZGP crystals using sub-three-cycle (20 fs) driving pulses from mode-locked Cr:ZnS lasers at the central wavelength of 2.4

Wen-Xiang Chen, Jun-Xian Li, Jing-Yi Zhang

This paper attempted to apply the RVB method for calculating the Hawking temperatures of black holes under f(R) gravity. In calculating the Hawking temperature, we found a difference in the integration constant between the RVB and general methods.

Wisdom C. Agboh, Satvik Sharma, Kishore Srinivas, Mallika Parulekar

We consider a decluttering problem where multiple rigid convex polygonal objects rest in randomly placed positions and orientations on a planar surface and must be efficiently transported to a packing box using both single and multi-object grasps. Prior work considered frictionless multi-object grasping. In this paper, we introduce friction to increase the n

Sarra Ahallal, Said Mennou, Ali Kacha

The direct calculation of the Generalized operator entropy proves difficult by the appearance of rational exponents of matrices. The main motivation of this work is to overcome these difficulties and to present a practical and efficient method for this calculation using its representation by the matrix continued fraction. At the end of our paper, we deduce a

Edward D. Lee, Alan P. Kwan, Rudolf Hanel, Anjali Bhatt

Social and biological collectives need to exchange information to persist and to function. This happens across internal networks, whose structure represents static channels through which information flows. Less studied is the quantity and variety of information transmitted. We characterize a part of the information flow, the information going into organizati

Yiwen Song, Arkka Bhattacharyya, Anwarul Karim, Daniel Shoemaker

Ulta-wide bandgap semiconductors based on $\beta$-Ga$_2$O$_3$ offer the potential to achieve higher power switching performance, efficiency, and lower manufacturing cost than today's wide bandgap power semiconductors. However, the most critical challenge to the commercialization of Ga$_2$O$_3$ electronics is overheating, which impacts the device's performanc

Minh Nguyen, Gia H. Ngo, Mert R. Sabuncu

The Granger framework is useful for discovering causal relations in time-varying signals. However, most Granger causality (GC) methods are developed for densely sampled timeseries data. A substantially different setting, particularly common in medical imaging, is the longitudinal study design, where multiple subjects are followed and sparsely observed over t

Negar Mokhberian, Frederic R. Hopp, Bahareh Harandizadeh, Fred Morstatter

Morality plays an important role in culture, identity, and emotion. Recent advances in natural language processing have shown that it is possible to classify moral values expressed in text at scale. Morality classification relies on human annotators to label the moral expressions in text, which provides training data to achieve state-of-the-art performance.

Hamed Nilforoshan, Wenli Looi, Emma Pierson, Blanca Villanueva

A long-standing expectation is that large, dense, and cosmopolitan areas support socioeconomic mixing and exposure between diverse individuals. It has been difficult to assess this hypothesis because past approaches to measuring socioeconomic mixing have relied on static residential housing data rather than real-life exposures between people at work, in plac

Masanori Koyama, Takeru Miyato, Kenji Fukumizu

Recent years have witnessed the effectiveness of contrastive learning in obtaining the representation of dataset that is useful in interpretation and downstream tasks. However, the mechanism that describes this effectiveness have not been thoroughly analyzed, and many studies have been conducted to investigate the data structures captured by contrastive lear

Aikata Aikata, Ahmet Can Mert, David Jacquemin, Amitabh Das

We propose design methodologies for building a compact, unified and programmable cryptoprocessor architecture that computes post-quantum key agreement and digital signature. Synergies in the two types of cryptographic primitives are used to make the cryptoprocessor compact. As a case study, the cryptoprocessor architecture has been optimized targeting the si

Tengfei Xue, Fan Zhang, Leo R. Zekelman, Chaoyi Zhang

Neuroimaging-based prediction of neurocognitive measures is valuable for studying how the brain's structure relates to cognitive function. However, the accuracy of prediction using popular linear regression models is relatively low. We propose a novel deep regression method, namely TractoSCR, that allows full supervision for contrastive learning in regressio

Jarosław Pawłowski, Mateusz Krawczyk

Quantum entanglement is a fundamental property commonly used in various quantum information protocols and algorithms. Nonetheless, the problem of identifying entanglement has still not reached a general solution for systems larger than $2\times3$. In this study, we use deep convolutional NNs, a type of supervised machine learning, to identify quantum entangl

E. de Lera Acedo, D. I. L. de Villiers, N. Razavi-Ghods, W. Handley

Observations of the 21-cm line from primordial hydrogen promise to be one of the best tools to study the early epochs of the Universe: the Dark Ages, the Cosmic Dawn, and the subsequent Epoch of Reionization. In 2018, the EDGES experiment caught the attention of the cosmology community with a potential detection of an absorption feature in the sky-averaged r

Yorick A. Birkhölzer, Kai Sotthewes, Nicolas Gauquelin, Lars Riekehr

Vanadium dioxide (VO2) is a popular candidate for electronic and optical switching applications due to its well-known semiconductor-metal transition. Its study is notoriously challenging due to the interplay of long and short range elastic distortions, as well as the symmetry change, and the electronic structure changes. The inherent coupling of lattice and

Sevvandi Kandanaarachchi, Rob J Hyndman

Detecting anomalies from a series of temporal networks has many applications, including road accidents in transport networks and suspicious events in social networks. While there are many methods for network anomaly detection, statistical methods are under utilised in this space even though they have a long history and proven capability in handling temporal

K. Monkman, J. Sirker

For a many-body system of arbitrary dimension, we consider fermionic ground states of non-interacting Hamiltonians invariant under a $C_2$ cyclic group. The absolute difference $\Delta$ between the number of occupied symmetric and anti-symmetric single-particle states is an adiabatic invariant. We prove lower bounds on the configurational and the number entr

Vitali Kapovitch, Xingyu Zhu

We show that if an Alexandrov space $X$ has an Alexandrov subspace $\bar \Omega$ of the same dimension disjoint from the boundary of $X$, then the topological boundary of $\bar \Omega$ coincides with its Alexandrov boundary. Similarly, if a noncollapsed RCD(K,N) space $X$ has a noncollapsed RCD(K,N) subspace $\bar \Omega$ disjoint from boundary of $X$ and wi

Satoshi Akasaki, Naoki Yoshinaga, Masashi Toyoda

We make decisions by reacting to changes in the real world, in particular, the emergence and disappearance of impermanent entities such as events, restaurants, and services. Because we want to avoid missing out on opportunities or making fruitless actions after they have disappeared, it is important to know when entities disappear as early as possible. We th

Brittany J. Leathers

The Immersed Boundary (IB) method of Peskin (J. Comput. Phys., 1977) is useful for problems involving fluid-structure interactions or complex geometries. By making use of a regular grid that is independent of the geometry, the IB framework yields a robust numerical scheme that can efficiently handle immersed deformable structures. The IB method has also been

Ramy F. Taki Eldin

Characterizing the duals of linear codes with rich algebraic structures received great interest in recent decades. The beginning was by representing cyclic codes over finite fields as ideals in the polynomial ring. Subsequently, studying the duals of constacyclic, quasi-cyclic, quasi-twisted, generalized quasi-cyclic, and multi-twisted codes appeared extensi

Adam Sanchez, François G. Meyer

This work addresses the rising demand for novel tools in statistical and machine learning for "graph-valued random variables" by proposing a fast algorithm to compute the sample Frechet mean, which replaces the concept of sample mean for graphs (or networks). We use convolutional neural networks to learn the morphology of the graphs in a set of graphs. Our e

Akash Nagaraj, Mukund Sood, Chetna Sureka, Gowri Srinivasa

In this paper we present a three-stream algorithm for real-time action recognition and a new dataset of handwash videos, with the intent of aligning action recognition with real-world constraints to yield effective conclusions. A three-stream fusion algorithm is proposed, which runs both accurately and efficiently, in real-time even on low-powered systems su

Agnese Barbensi, Daniele Celoria

Grid diagrams are a combinatorial version of classical link diagrams, widely used in theoretical, computational and applied knot theory. Motivated by questions from (bio)-physical knot theory, we introduce GridPyM, a Sage compatible Python module that handles grid diagrams. GridPyM focuses on generating and simplifying grids, and on modelling local transform

Mayara Duarte de Araujo Caldas, Ricardo Miranda Martins

The goal of this article is to study the existence of closed trajectories for the differential equation $\dddot{z}+a\ddot{z}+b\dot{z}+abz=\varepsilon F(z,\dot{z},\ddot{z})$ in two situations. In the first situation, we consider $F(z,\dot{z},\ddot{z})=1$ and $b={\rm sgn}(h(z,\dot{z},\ddot{z}))$, where $h(z,\dot{z},\ddot{z})=z^2+(\dot{z})^2+(\ddot{z})^2-1$. We

Akash Nagaraj, Mukund Sood, Bhagya M Patil

One of the biggest hurdles robotics faces is the facet of sophisticated and hard-to-engineer behaviors. Reinforcement learning offers a set of tools, and a framework to address this problem. In parallel, the misgivings of robotics offer a solid testing ground and evaluation metric for advancements in reinforcement learning. The two disciplines go hand-in-han

Benjamin Feuer, Ameya Joshi, Chinmay Hegde

Vision language (VL) models like CLIP are robust to natural distribution shifts, in part because CLIP learns on unstructured data using a technique called caption supervision; the model inteprets image-linked texts as ground-truth labels. In a carefully controlled comparison study, we show that caption-supervised CNNs trained on a standard cross-entropy loss

Mohammad Farhat, Antranik Sefilian, Jihad Touma

Debris disc architecture presents [exo-]planetary scientists with precious clues for processes of planet formation and evolution, including constraints on planetary mass perturbers. This is particularly true of the disc in HD 106906, which in early HST, then follow up polarimetric observations, presented asymmetries and needle-like features that have been at

Zhouxing Shi, Yihan Wang, Huan Zhang, Zico Kolter

Lipschitz constants are connected to many properties of neural networks, such as robustness, fairness, and generalization. Existing methods for computing Lipschitz constants either produce relatively loose upper bounds or are limited to small networks. In this paper, we develop an efficient framework for computing the $\ell_\infty$ local Lipschitz constant o

Jason B. Cho, Sven Serneels, David S. Matteson

Non-fungible tokens (NFT) have recently emerged as a novel blockchain hosted financial asset class that has attracted major transaction volumes. Investment decisions rely on data and adequate preprocessing and application of analytics to them. Both owing to the non-fungible nature of the tokens and to a blockchain being the primary data source, NFT transacti

Thomas R. Shultz, Ardavan S. Nobandegani, Zilong Wang

We propose a relatively simple computational neural-network model of number comparison. Training on comparisons of the integers 1-9 enable the model to efficiently and accurately simulate a wide range of phenomena, including distance and ratio effects and robust generalization to multidigit integers, negative numbers, and decimal numbers. An accompanying log

Chen Heinrich, Trey Driskell, Chris Heinrich

The next-generation CMB experiments are expected to constrain the tensor-to-scalar ratio $r$ with high precision. Delensing is an important process as the observed CMB $B$-mode polarization that contains the primordial tensor perturbation signal is dominated by a much larger contribution due to gravitational lensing. To do so successfully, it is useful to ex

Chinonso Onah

Useful relations describing arbitrary parameters of given quantum systems can be derived from simple physical constraints imposed on the vectors in the corresponding Hilbert space. This is well known and it usually proceeds by partitioning the large dimensional Hilbert space into relevant sub spaces and relating points in the Hilbert space to the expectation

Adam Abrams, Svetlana Katok, Ilie Ugarcovici

We study the topological entropy of a two-parameter family of maps related to (a,b)-continued fraction algorithms and prove that it is constant on a square within the parameter space (two vertices of this square correspond to well-studied continued fraction algorithms). The proof uses conjugation to maps of constant slope. We also present experimental eviden

Elias Maciel, Inocencio Ortiz, Christian E. Schaerer

We propose a numerical scheme for the time-integration of nonholonomic mechanical systems, both conservative and nonconservative. The scheme is obtained by simultaneously discretizing the constraint equations and the Herglotz variational principle. We validate the method using numerical simulations and contrast them against the results of standard methods fr

Axel Levy, Gordon Wetzstein, Julien Martel, Frederic Poitevin

Cryo-electron microscopy (cryo-EM) is an imaging modality that provides unique insights into the dynamics of proteins and other building blocks of life. The algorithmic challenge of jointly estimating the poses, 3D structure, and conformational heterogeneity of a biomolecule from millions of noisy and randomly oriented 2D projections in a computationally eff

Michele Sorelli, Marco Marchetti, Pietro Ricci, Domenico Alfieri

Radio-frequency (RF) signal generators are standard laboratory equipment and a wide-range of open-source and commercial devices exists to address their many applications. Nonetheless, only few expensive and proprietary solutions can be re-configured within a wide frequency band and triggered on a micro-second timescale. Such specifications are required for a

Lening Li, Haoxiang Ma, Shuo Han, Jie Fu

This paper studies the deployment of joint moving target defense (MTD) and deception against multi-stage cyberattacks. Given the system equipped with MTD that randomizes between different configurations, we investigate how to allocate a bounded number of sensors in each configuration to optimize the attack detection rate before the attacker achieves its obje

Ha Tran, Thanh Le, Suresh Singh

There is a growing interest in exploiting the terahertz frequency band for future communication systems that demand high data rates. Given the complex propagation behavior of this frequency band, various researchers have developed channel models that can be utilized in the development of communication systems. These models however do not include a crucial as

Zarathustra Brady

These are notes from a multi-year learning seminar on the algebraic approach to Constraint Satisfaction Problems (CSPs). The main topics covered are the theory of algebraic structures with few subpowers, the theory of absorbing subalgebras and its applications to studying CSP templates which can be solved by local consistency methods, and the dichotomy theor

Ruoyao Wang, Peter Jansen, Marc-Alexandre Côté, Prithviraj Ammanabrolu

In this work, we explore techniques for augmenting interactive agents with information from symbolic modules, much like humans use tools like calculators and GPS systems to assist with arithmetic and navigation. We test our agent's abilities in text games -- challenging benchmarks for evaluating the multi-step reasoning abilities of game agents in grounded,

Sabin Roman, Francesco Bertolotti

We propose a new mechanism for generating power laws. Starting from a random walk, we first outline a simple derivation of the Fokker-Planck equation. By analogy, starting from a certain Markov chain, we derive a master equation for power laws that describes how the number of cascades changes over time (cascades are consecutive transitions that end when the

Daniela Teodorescu, Saif M. Mohammad

Automatically generated emotion arcs -- that capture how an individual or a population feels over time -- are widely used in industry and research. However, there is little work on evaluating the generated arcs. This is in part due to the difficulty of establishing the true (gold) emotion arc. Our work, for the first time, systematically and quantitatively e

Herman Geuvers, Komi Golov

Labelled transitions systems can be studied in terms of modal logic and in terms of bisimulation. These two notions are connected by Hennessy-Milner theorems, that show that two states are bisimilar precisely when they satisfy the same modal logic formulas. Recently, apartness has been studied as a dual to bisimulation, which also gives rise to a dual versio

Anna Wigren, Fredrik Lindsten

We consider Bayesian inference from multiple time series described by a common state-space model (SSM) structure, but where different subsets of parameters are shared between different submodels. An important example is disease-dynamics, where parameters can be either disease or location specific. Parameter inference in these models can be improved by system

Jorge Romero, Merced Montesinos, Ricardo Escobedo

We study minimal and nonminimal couplings of fermions to the Palatini action in $n$ dimensions ($n\geq 3$) from the Lagrangian and Hamiltonian viewpoints. The Lagrangian action considered is not, in general, equivalent to the Einstein-Dirac action principle. However, by choosing properly the coupling parameters, it is possible to give a first-order action fu

Tracey N. Bell, Aubrey E. Kusi-Appiaha, Pengfei Lyu, L. Zhu

High throughput screening of small molecules and natural products is costly, requiring significant amounts of time, reagents, and operating space. Although microarrays have proven effective in the miniaturization of screening for certain biochemical assays, such as nucleic acid hybridization or antibody binding, they are not widely used for drug discovery in

Yaniv Ben-Itzhak, Helen Möllering, Benny Pinkas, Thomas Schneider

Secure aggregation is commonly used in federated learning (FL) to alleviate privacy concerns related to the central aggregator seeing all parameter updates in the clear. Unfortunately, most existing secure aggregation schemes ignore two critical orthogonal research directions that aim to (i) significantly reduce client-server communication and (ii) mitigate

Domenico Valloni

In this paper, we study maps between moduli spaces of lattice-polarized K3 surfaces induced by sublattices of prime index. We show that these maps can be used to determine if a rational point of the moduli space belongs to the Noether-Lefschetz locus. As an application, we prove that the Bombieri-Lang conjecture implies non-density statements for the rationa

Yanbo Zhang, Sara Imari Walker

The high-dimesionality, non-linearity and emergent properties of complex systems pose a challenge to identifying general laws in the same manner that has been so successful in simpler physical systems. In Anderson's seminal work on why "more is different" he pointed to how emergent, macroscale patterns break symmetries of the underlying microscale laws. Yet,

Zdeněk Kasner, Ioannis Konstas, Ondřej Dušek

Pretrained language models (PLMs) for data-to-text (D2T) generation can use human-readable data labels such as column headings, keys, or relation names to generalize to out-of-domain examples. However, the models are well-known in producing semantically inaccurate outputs if these labels are ambiguous or incomplete, which is often the case in D2T datasets. I

Pei Sun, Mingxing Tan, Weiyue Wang, Chenxi Liu

3D object detection in point clouds is a core component for modern robotics and autonomous driving systems. A key challenge in 3D object detection comes from the inherent sparse nature of point occupancy within the 3D scene. In this paper, we propose Sparse Window Transformer (SWFormer ), a scalable and accurate model for 3D object detection, which can take

Shuyan Zhang, Jie Gong, Sharon Chu, Daniel Xiao

Feature extraction and a neural network model are applied to predict the defect types and concentrations in experimental TiO$_2$ samples. A dataset of TiO$_2$ structures with vacancies and interstitials of oxygen and titanium is built and the structures are relaxed using energy minimization. The features of the calculated pair distribution functions (PDFs) o

Machel Reid, Victor Zhong, Suchin Gururangan, Luke Zettlemoyer

We present M2D2, a fine-grained, massively multi-domain corpus for studying domain adaptation in language models (LMs). M2D2 consists of 8.5B tokens and spans 145 domains extracted from Wikipedia and Semantic Scholar. Using ontologies derived from Wikipedia and ArXiv categories, we organize the domains in each data source into 22 groups. This two-level hiera

Luccas Campos, Ademir Pastor

We consider the following Scr\"odinger system $$\begin{cases}\displaystyle i\partial_t u + \Delta u +(|u|^2+\beta |v|^2) u= 0, \\ \displaystyle i\partial_t v + \Delta v +(|v|^2+\beta |u|^2) v = 0,\end{cases}$$ with initial data $(u_0,v_0) \in H^1(\mathbb{R} ^3)\times H^1(\mathbb{R}^3)$ at the so-called \textit{mass-energy threshold}, i.e., such that %$\mathc

Yong Suk Moon

In this short note, we prove a purity result for crystalline local systems on a smooth $p$-adic affine formal scheme. Our method is based on the prismatic description of crystalline local systems.

Chiara Marletto, Vlatko Vedral

We revisit the vexed question of how unpredictability can arise in a deterministic universe, focusing on unitary quantum theory. We discuss why quantum unpredictability is irrelevant for the possibility of what some people call `free-will', and why existing `free-will' arguments are themselves irrelevant to argue for or against a physical theory.

Abaz Kryemadhi, Marios Maroudas, Andreas Mastronikolis, Konstantin Zioutas

Cosmological simulations for cold dark matter (DM) indicate that a large number of streams might exist in our Galaxy. The present work incorporates gravitational focusing (GF) effects on streaming DM constituents by the Sun and the Earth preceding their encounter with Earth bound detectors. For streaming DM, the GF gives rise to spatiotemporal flux enhanceme