Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comprehensive Survey on Traffic Prediction

Traffic prediction plays an essential role in intelligent transportation system. Accurate traffic prediction can assist route planing, guide vehicle dispatching, and mitigate traffic congestion. This problem is challenging due to the complicated and dynamic spatio-temporal dependencies between different regions in the road network. Recently, a significant amount of research efforts have been devoted to this area, greatly advancing traffic prediction abilities. The purpose of this paper is to provide a comprehensive survey for traffic prediction. Specifically, we first summarize the existing traffic prediction methods, and give a taxonomy of them. Second, we list the common applications of traffic prediction and the state-of-the-art in these applications. Third, we collect and organize widely used public datasets in the existing literature. Furthermore, we give an evaluation by conducting extensive experiments to compare the performance of methods related to traffic demand and speed prediction respectively on two datasets. Finally, we discuss potential future directions.
Predicting Station-Level Bike-Sharing Demands Using Graph Convolutional Neural Network

This study proposes a novel Graph Convolutional Neural Network with Data-driven Graph Filter (GCNN-DDGF) model that can learn hidden heterogeneous pairwise correlations among stations to predict station-level hourly demand in a large-scale bike-sharing network. Two architectures of the GCNN-DDGF model are explored: GCNNreg-DDGF is a regular GCNN-DDGF model which contains the convolution and feedforward blocks; GCNNrec-DDGF additionally contains a recurrent block from the Long Short-term Memory neural network to capture temporal dependencies in bike-sharing demand series. Furthermore, four GCNN models are proposed whose adjacency matrices are based on various bike-sharing system data, including Spatial Distance matrix (SD), Demand matrix (DE), Average Trip Duration matrix (ATD), and Demand Correlation matrix (DC). These six GCNN models along with seven other benchmark models are built and compared using the Citi Bike dataset from New York City, which includes 272 stations and over 28 million transactions from 2013 to 2016. Results show that the GCNNrec-DDGF performs the best in terms of the Root Mean Square Error, the Mean Absolute Error, and the coefficient of determination (R2), followed by the GCNNreg-DDGF. They outperform the other models.
Determination of Bayesian optimal warranty length under Type-II unified hybrid censoring scheme

ABSTRACT We consider determination of optimal warranty length for the combined free replacement and pro-rata warranty (FRW-PRW) policy based on the Type-II unified hybrid censored data by Bayesian approach. A non-linear pro-rata rebate cost function is proposed based on which warranty cost is computed. It is assumed that the lifetime follows log normal distribution. The optimal warranty length is obtained by maximizing an expected utility function consisting of three cost functions such as economic benefit function, warranty cost function and dissatisfaction cost function. The expectation is taken with respect to the posterior predictive model for the time-to-failure data. It is observed that the non-linear pro-rata rebate cost function gives a larger warranty length with maximum profit as compared to linear pro-rata rebate cost function. A real-data set is analyzed in order to illustrate the proposed methodology of finding optimal warranty length.
A short note on solving box inequality and linear equality constrained optimization problem

This paper discusses a special kind of convex constrained optimization problem, whose constraints consist of box inequalities and linear equalities. For this problem, in addition to general optimization algorithms such as exact penalty algorithm and interior point algorithm, there is a simple iterative algorithm that is simple to implement, which is favored by machine learning practitioners.
CatNet: Class Incremental 3D ConvNets for Lifelong Egocentric Gesture Recognition

Egocentric gestures are the most natural form of communication for humans to interact with wearable devices such as VR/AR helmets and glasses. A major issue in such scenarios for real-world applications is that may easily become necessary to add new gestures to the system e.g., a proper VR system should allow users to customize gestures incrementally. Traditional deep learning methods require storing all previous class samples in the system and training the model again from scratch by incorporating previous samples and new samples, which costs humongous memory and significantly increases computation over time. In this work, we demonstrate a lifelong 3D convolutional framework - c(C)la(a)ss increment(t)al net(Net)works (CatNet), which considers temporal information in videos and enables life-long learning for egocentric gesture video recognition by learning the feature representation of an exemplar set selected from previous class samples. Importantly, we propose a two-stream CatNet, which deploys RGB and depth modalities to train two separate networks. We evaluate Cat- Nets on a publicly available dataset - EgoGesture dataset, and show that CatNets can learn many classes incrementally over a long period of time. Results also demonstrate that the two-stream architecture achieves the best performance on both joint training and class incremental training compared to 3 other one-stream architectures. The codes and pre-trained models used in this work are provided at https://github.com/villawang/CatNet.
Analyzing the discrepancy principle for kernelized spectral filter learning algorithms

We investigate the construction of early stopping rules in the non-parametric regression problem where iterative learning algorithms are used and the optimal iteration number is unknown. More precisely, we study the discrepancy principle, as well as modifications based on smoothed residuals, for kernelized spectral filter learning algorithms including gradient descent. Our main theoretical bounds are oracle inequalities established for the empirical estimation error (fixed design), and for the prediction error (random design). From these finite-sample bounds it follows that the classical discrepancy principle is statistically adaptive for slow rates occurring in the hard learning scenario, while the smoothed discrepancy principles are adaptive over ranges of faster rates (resp. higher smoothness parameters). Our approach relies on deviation inequalities for the stopping rules in the fixed design setting, combined with change-of-norm arguments to deal with the random design setting.
Model-driven Engineering of Safety and Security Systems: A Systematic Mapping Study

This paper presents a systematic mapping study on the model-driven engineering of safety and security concerns in systems. Integrated modeling and development of both safety and security concerns is an emerging field of research. Our mapping study provides an overview of the current state-of-the-art in this field. Through a rigorous and systematic process, this study carefully selected 95 publications out of 17,927 relevant papers published between 1992 and 2018. This paper then proposes and answers several relevant research questions about frequently used methods, development stages where these concerns are typically investigated in, or application domains. Additionally, we identify the community's preference for publication venues and trends.
Human Activity Recognition Using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey

In the last decade, Human Activity Recognition (HAR) has become a vibrant research area, especially due to the spread of electronic devices such as smartphones, smartwatches and video cameras present in our daily lives. In addition, the advance of deep learning and other machine learning algorithms has allowed researchers to use HAR in various domains including sports, health and well-being applications. For example, HAR is considered as one of the most promising assistive technology tools to support elderly’s daily life by monitoring their cognitive and physical function through daily activities. This survey focuses on critical role of machine learning in developing HAR applications based on inertial sensors in conjunction with physiological and environmental sensors.
SCUBA-2 overdensities associated with candidate protoclusters selected from Planck data

We measure the 850-μm source densities of 46 candidate protoclusters selected from the Planck high-z catalogue (PHz) and the Planck Catalogue of Compact Sources (PCCS) that were followed up with Herschel-SPIRE and SCUBA-2. This paper aims to search for overdensities of 850-μm sources in order to select the fields that are most likely to be genuine protoclusters. Of the 46 candidate protoclusters, 25 have significant overdensities (>5 times the field counts), 11 have intermediate overdensities (3–5 times the field counts), and 10 have no overdensity (<3 times the field counts) of 850-μm sources. We find that the enhanced number densities are unlikely to be the result of sample variance. Compared with the number counts of another sample selected from Planck’s compact source catalogues, this [PHz + PCCS]-selected sample has a higher fraction of candidate protoclusters with significant overdensities, though both samples show overdensities of 850-μm sources above intermediate level. Based on the estimated star formation rate densities (SFRDs), we suggest that both samples can efficiently select protoclusters with starbursting galaxies near the redshift at which the global field SFRD peaks (2 < z < 3). Based on the confirmation of overdensities found here, future follow-up observations on other PHz targets may greatly increase the number of genuine dusty star-forming galaxy-rich clusters/protoclusters.
Compositionality and Generalization In Emergent Languages

Natural language allows us to refer to novel composite concepts by combining expressions denoting their parts according to systematic rules, a property known as compositionality. In this paper, we study whether the language emerging in deep multi-agent simulations possesses a similar ability to refer to novel primitive combinations, and whether it accomplishes this feat by strategies akin to human-language compositionality. Equipped with new ways to measure compositionality in emergent languages inspired by disentanglement in representation learning, we establish three main results: First, given sufficiently large input spaces, the emergent language will naturally develop the ability to refer to novel composite concepts. Second, there is no correlation between the degree of compositionality of an emergent language and its ability to generalize. Third, while compositionality is not necessary for generalization, it provides an advantage in terms of language transmission: The more compositional a language is, the more easily it will be picked up by new learners, even when the latter differ in architecture from the original agents. We conclude that compositionality does not arise from simple generalization pressure, but if an emergent language does chance upon it, it will be more likely to survive and thrive.
Search for the imprint of axion-like particles in the highest-energy photons of hard γ-ray blazars

Axion-like particles (ALPs), predicted in theories beyond the Standard Model, can have observational effects on the transparency of the Universe to γ rays in the presence of magnetic fields. In this work, we search for effects compatible with the existence of ALPs with 80 months of data from the Fermi Large Area Telescope, by comparing the distributions of observed highest energy photons from sources beyond redshifts of z ⩾ 0.1 with theoretical predictions in the presence of ALPs. We find no evidence for an increased γ-ray transparency due to ALPs and therefore we set limits on the ALPs parameters assuming a value of the intergalactic magnetic field strength of 1 nG. Photon-ALP couplings above 10−11 GeV−1 are excluded for ALP masses ma ≲ 3.0 neV . As the allowed magnetic field parameter space is large, we also test lower magnetic field strengths and no constraints can be set for B⩽0.1 nG below the CAST limit. These constraints exclude a region of the parameter space not covered by other γ-ray telescopes and are compatible with limits imposed by other experiments.
The duty cycle of radio galaxies revealed by LOFAR: remnant and restarted radio source populations in the Lockman Hole

Feedback from radio jets associated with Active Galactic Nuclei (AGN) plays a profound role in the evolution of galaxies. Kinetic power of these radio jets appears to show temporal variation, but the mechanism(s) responsible for this process are not yet clear. Recently, the LOw Frequency ARray (LOFAR) has uncovered large populations of active, remnant and restarted radio jet populations. By focusing on LOFAR data in the Lockman Hole, in this work we use the Radio AGN in Semi-Analytic Environments (RAiSE) dynamical model to present the first self-consistent modelling analysis of active, remnant and restarted radio source populations. Consistent with other recent work, our models predict that remnant radio lobes fade quickly. Any high (> 10 percent) observed fraction of remnant and restarted sources therefore requires a dominant population of short-lived jets. We speculate that this could plausibly be provided by feedback-regulated accretion.
On ageing star clusters using red supergiants independent of the fraction of interacting binary stars

We use the Binary Population and Spectral Synthesis models to test the recent suggestion that red supergiants can provide an accurate age estimate of a coeval stellar population that is unaffected by interacting binary stars. Ages are estimated by using both the minimum luminosity of red supergiants and the mean luminosity of red supergiants in a cluster. We test these methods on a number of observed star clusters and find our results in agreement with previous estimates. Importantly, we find the difference between the ages derived from stellar population models with and without a realistic population of interacting binary stars is only a few hundred thousand years at most. We find that the mean luminosity of red supergiants in a cluster is the best method to determine the age of a cluster because it is based on the entire red supergiant population rather than using only the least luminous red supergiant.
Alias-Free, Matrix-Free, and Quadrature-Free Discontinuous Galerkin Algorithms for (Plasma) Kinetic Equations

Understanding fundamental kinetic processes is important for many problems, from plasma physics to gas dynamics. A first-principles approach to these problems requires a statistical description via the Boltzmann equation, coupled to appropriate field equations. In this paper we present a novel version of the discontinuous Galerkin (DG) algorithm to solve such kinetic equations. Unlike Monte-Carlo methods, we use a continuum scheme in which we directly discretize the 6D phase-space using discontinuous basis functions. Our DG scheme eliminates counting noise and aliasing errors that would otherwise contaminate the delicate field-particle interactions. We use modal basis functions with reduced degrees of freedom to improve efficiency while retaining a high formal order of convergence. Our implementation incorporates a number of software innovations: use of JIT compiled top-level language, automatically generated computational kernels and a sophisticated shared-memory MPI implementation to handle velocity space parallelization.
Coherent dynamics in a dual-coupling spin-boson model

We study the dynamics of a particle tunneling between the ground states of a symmetric double potential well system, in the presence of simultaneous diagonal and non-diagonal couplings to an Ohmic oscillator bath. We use the non-interacting-blip-approximation to investigate coherence effects across a wide range of system-environment couplings at physiological temperatures. We show how the presence of a non-diagonal coupling mechanism dramatically alters the dynamics of the tunneling particle, leading to a revival in coherent oscillations despite strong thermal fluctuations. Our model demonstrates that fluctuations in both the particle's polarization, as well as tunneling energies, lead to competing influences on the coherent nature of the particle dynamics, with non-diagonal couplings reducing the decoherence time by up to an order of magnitude in the intermediate coupling regime.
Probing entanglement in a spinor condensate near the single atom level

Using parametric conversion induced by a Shapiro-type resonance, we produce and characterize a two-mode squeezed vacuum state in a sodium spin 1 Bose-Einstein condensate. Spin changing collisions generate correlated pairs of atoms in the $m=\pm 1$ Zeeman states out of a condensate with initially all atoms in $m=0$. A novel fluorescence imaging technique with sensitivity $\Delta N \sim 1.6$ atom enables us to demonstrate the role of quantum fluctuations in the initial dynamics and to characterize the full distribution of the final state, which reaches a squeezing level of $16.8\,$dB.
Energy-Based Imitation Learning

We tackle a common scenario in imitation learning (IL), where agents try to recover the optimal policy from expert demonstrations without further access to the expert or environment reward signals. The classical inverse reinforcement learning (IRL) solution involves bi-level optimization and is of high computational cost. Recent generative adversarial methods formulate the IL problem as occupancy measure matching, which, however, suffer from the notorious training instability and mode-dropping problems. Inspired by recent progress in energy-based model (EBM), in this paper, we propose a novel IL framework named Energy-Based Imitation Learning (EBIL), solving the IL problem via estimating the expert energy as the surrogate reward function through score matching. EBIL combines the idea of both EBM and occupancy measure matching, which enjoys: (1) high model flexibility for expert policy distribution estimation; (2) efficient computation that avoids the previous alternate training fashion. Though motivated by matching the policy between the expert and the agent, we surprisingly find a non-trivial connection between EBIL and the classic Max-Entropy IRL (MaxEnt IRL) approach, and further show that EBIL can be seen as a simpler and more efficient solution of MaxEnt IRL. Extensive experiments show that EBIL can always achieve comparable performance against state-of-the-art methods with less computation cost.
Towards Understanding Normalization in Neural ODEs

Normalization is an important and vastly investigated technique in deep learning. However, its role for Ordinary Differential Equation based networks (neural ODEs) is still poorly understood. This paper investigates how different normalization techniques affect the performance of neural ODEs. Particularly, we show that it is possible to achieve 93% accuracy in the CIFAR-10 classification task, and to the best of our knowledge, this is the highest reported accuracy among neural ODEs tested on this problem.
A Universal Approximation Theorem of Deep Neural Networks for Expressing Distributions

This paper studies the universal approximation property of deep neural networks for representing probability distributions. Given a target distribution $\pi$ and a source distribution $p_z$ both defined on $\mathbb{R}^d$, we prove under some assumptions that there exists a deep neural network $g:\mathbb{R}^d\rightarrow \mathbb{R}$ with ReLU activation such that the push-forward measure $(\nabla g)_\# p_z$ of $p_z$ under the map $\nabla g$ is arbitrarily close to the target measure $\pi$. The closeness are measured by three classes of integral probability metrics between probability distributions: $1$-Wasserstein distance, maximum mean distance (MMD) and kernelized Stein discrepancy (KSD). We prove upper bounds for the size (width and depth) of the deep neural network in terms of the dimension $d$ and the approximation error $\varepsilon$ with respect to the three discrepancies. In particular, the size of neural network can grow exponentially in $d$ when $1$-Wasserstein distance is used as the discrepancy, whereas for both MMD and KSD the size of neural network only depends on $d$ at most polynomially. Our proof relies on convergence estimates of empirical measures under aforementioned discrepancies and semi-discrete optimal transport.
Analysis of the galaxy size versus stellar mass relation

The scatter in the galaxy size versus stellar mass (M⋆) relation gets largely reduced when, rather than the half-mass radius Re, the size at a fixed surface density is used. Here we address why this happens. We show how a reduction is to be expected because any two galaxies with the same M⋆ have at least one radius with identical surface density, where the galaxies have identical size. However, the reason why the scatter is reduced to the observed level is not trivial, and we pin it down to the galaxy surface density profiles approximately following Sersic profiles with their Re and Sersic index (n) anti-correlated (i.e., given M⋆, n increases when Re decreases). Our analytical results describe very well the behavior of the observed galaxies as portrayed in the NASA Sloan Atlas (NSA), which contains more than half a million local objects with 7 < log (M⋆/M⊙) < 11.5. The comparison with NSA galaxies also allows us to find the optimal values for the mass surface density ($2.4_{-0.9}^{+1.3}\, M_\odot \, {\rm pc}^{-2}$) and surface brightness (r-band 24.7 ± 0.5 mag arcsec−2) that minimize the scatter, although the actual values depend somehow on the subset of NSA galaxies used for optimization. The physical reason for the existence of optimal values is unknown but, as Trujillo et al. (2020) point out, they are close to the gas surface density threshold to form stars and thus may trace the physical end of a galaxy. Our NSA-based size–mass relation agrees with theirs on the slope as well as on the magnitude of the scatter. As a by-product of the narrowness of the size–mass relation (only 0.06 dex), we propose to use the size of a galaxy to measure its stellar mass. In terms of observing time, it is not more demanding than the usual photometric techniques and may present practical advantages in particular cases.
Accuracy and Conditioning of Surface-Source Based Near-Field to Far-Field Transformations

The conditioning and accuracy of various inverse surface-source formulations are investigated. First, the normal systems of equations are discussed. Second, different implementations of the zero-field condition are analyzed regarding their effect on solution accuracy, conditioning, and source ambiguity. The weighting of the Love-current side constraint is investigated in order to provide an accurate problem-independent methodology. The transformation results for simulated and measured near-field data show a comparable behavior regarding accuracy and conditioning for most of the formulations. Advantages of the Love-current solutions are found only in diagnostic capabilities. Regardless of this, the Love side constraint is a computationally costly way to influence the iterative solver threshold, which is more conveniently controlled with the appropriate type of normal equation. The solution behavior of the inverse surface-source formulations is mostly influenced by the choice of the reconstruction surface. A spherical Huygens’ surface leads to the best conditioning, whereas the most accurate solutions are found with a tight, possibly convex hull around the antenna under test.