Miracle-dz/UniFAR-FTUs huggingface dataset:
POLY-HOOT: Monte-Carlo Planning in Continuous Space MDPs with Non-Asymptotic Analysis

Monte-Carlo planning, as exemplified by Monte-Carlo Tree Search (MCTS), has demonstrated remarkable performance in applications with finite spaces. In this paper, we consider Monte-Carlo planning in an environment with continuous state-action spaces, a much less understood problem with important applications in control and robotics. We introduce POLY-HOOT, an algorithm that augments MCTS with a continuous armed bandit strategy named Hierarchical Optimistic Optimization (HOO) (Bubeck et al., 2011). Specifically, we enhance HOO by using an appropriate polynomial, rather than logarithmic, bonus term in the upper confidence bounds. Such a polynomial bonus is motivated by its empirical successes in AlphaGo Zero (Silver et al., 2017b), as well as its significant role in achieving theoretical guarantees of finite space MCTS (Shah et al., 2019). We investigate, for the first time, the regret of the enhanced HOO algorithm in non-stationary bandit problems. Using this result as a building block, we establish non-asymptotic convergence guarantees for POLY-HOOT: the value estimate converges to an arbitrarily small neighborhood of the optimal value function at a polynomial rate. We further provide experimental results that corroborate our theoretical findings.
Hybrid Model for Anomaly Detection on Call Detail Records by Time Series Forecasting

Mobile network operators store an enormous amount of information like log files that describe various events and users' activities. Analysis of these logs might be used in many critical applications such as detecting cyber-attacks, finding behavioral patterns of users, security incident response, network forensics, etc. In a cellular network Call Detail Records (CDR) is one type of such logs containing metadata of calls and usually includes valuable information about contact such as the phone numbers of originating and receiving subscribers, call duration, the area of activity, type of call (SMS or voice call) and a timestamp. With anomaly detection, it is possible to determine abnormal reduction or increment of network traffic in an area or for a particular person. This paper's primary goal is to study subscribers' behavior in a cellular network, mainly predicting the number of calls in a region and detecting anomalies in the network traffic. In this paper, a new hybrid method is proposed based on various anomaly detection methods such as GARCH, K-means, and Neural Network to determine the anomalous data. Moreover, we have discussed the possible causes of such anomalies.
Near equilibrium fluctuations for supermarket models with growing choices

We consider the supermarket model in the usual Markovian setting where jobs arrive at rate $n \lambda_n$ for some $\lambda_n > 0$, with $n$ parallel servers each processing jobs in its queue at rate 1. An arriving job joins the shortest among $d_n \le n$ randomly selected service queues. We show that when $d_n \to \infty$ and $\lambda_n \to \lambda \in (0, \infty)$, under natural conditions on the initial queues, the state occupancy process converges in probability, in a suitable path space, to the unique solution of an infinite system of constrained ordinary differential equations parametrized by $\lambda$. Our main interest is in the study of fluctuations of the state process about its near equilibrium state in the critical regime, namely when $\lambda_n \to 1$. Previous papers have considered the regime $\frac{d_n}{\sqrt{n}\log n} \to \infty$ while the objective of the current work is to develop diffusion approximations for the state occupancy process that allow for all possible rates of growth of $d_n$. In particular we consider the three canonical regimes (a) ${d_n}/{\sqrt{n}} \to 0$; (b) ${d_n}/{\sqrt{n}} \to c\in (0,\infty)$ and, (c) ${d_n}/{\sqrt{n}} \to \infty$. In all three regimes we show, by establishing suitable functional limit theorems, that (under conditions on $\lambda_n$) fluctuations of the state process about its near equilibrium are of order $n^{-1/2}$ and are governed asymptotically by a one dimensional Brownian motion. The forms of the limit processes in the three regimes are quite different; in the first case we get a linear diffusion; in the second case we get a diffusion with an exponential drift; and in the third case we obtain a reflected diffusion in a half space. In the special case ${d_n}/({\sqrt{n}\log n}) \to \infty$ our work gives alternative proofs for the universality results established by Mukherjee et al in 2018.
Deep Learning Enabled Preamble Collision Resolution in Distributed Massive MIMO

Preamble collision is a bottleneck that impairs the performance of random access (RA) user equipment (UE) in grant-free RA (GFRA). In this paper, by leveraging distributed massive multiple input multiple output (mMIMO) together with deep learning, a novel access point (AP) clustering scheme is proposed to mitigate the preamble collision problem in GFRA. The key idea is to identify and employ the neighboring APs of a collided RA UE for its data decoding rather than all the APs, so that the mutual interference among collided RA UEs can be effectively mitigated. To this end, we first design a tailored deep neural network (DNN) to enable the preamble multiplicity estimation in GFRA. With the estimated preamble multiplicity, we then propose a K-means AP clustering algorithm to cluster the neighboring APs of collided RA UEs and organize each AP cluster to decode the received data individually. Simulation results show that a decent performance of preamble multiplicity estimation in terms of accuracy and reliability can be achieved by the proposed DNN, and confirm that the proposed DNN based AP clustering scheme is effective in preamble collision resolution in GFRA, which is able to achieve a near-optimal performance in terms of uplink achievable rate per collided RA UE, and offer significant performance improvement over traditional schemes.
Quarantine Fatigue: first-ever decrease in social distancing measures after the COVID-19 pandemic outbreak before reopening United States

By the emergence of the novel coronavirus disease (COVID-19) in Wuhan, China, and its rapid outbreak worldwide, the infectious illness has changed our everyday travel patterns. In this research, our team investigated the changes in the daily mobility pattern of people during the pandemic by utilizing an integrated data panel. To incorporate various aspects of human mobility, the team focused on the Social Distancing Index (SDI) that was calculated based on five basic mobility measures. SDI patterns showed a plateau stage in the beginning of April that lasted for about two weeks. This phenomenon then followed by a universal decline of SDI, increased number of trips and reduction in percentage of people staying at home. We called the latter observation: Quarantine Fatigue. The Rate of Change (ROC) method was employed to trace back the start date of quarantine fatigue which was indicated to be April 15th. Our analysis showed that despite substantial differences between states, most states’ residents started experiencing a quarantine fatigue phenomenon in the same period. This observation became more important by knowing that none of the states had officially announced the reopening until late April showing that people decide to loosen up their social distancing practices before the official reopening announcement. Moreover, our analysis indicated that official reopening led to a faster decline in SDI, raising the concern of a second wave of outbreak. The synchronized trend among states also emphasizes the importance of a more nationwide decision making attitude for the future as the condition of each state depends on the behavior of other states.
Sparse Error Localization in Complex Dynamic Networks

Understanding the dynamics of complex systems is a central task in many different areas ranging form biology via epidemics to economics and engineering. Unexpected behaviour of dynamic systems or even systems failure is sometimes difficult to comprehend. Such unexpected dynamics can be caused by systematic model errors, unknown inputs from the environment and systems faults. Localizing the root cause of these errors or faults and reconstructing their dynamics is only possible if the measured outputs of the system are sufficiently informative. Here, we present a mathematical theory for the measurements required to localize the position of error sources in large dynamic networks. We assume, that faults or errors occur at a limited number of positions in the network. This sparsity assumption facilitates the accurate reconstruction of the dynamic timecourses of the errors by solving a convex optimal control problem. For cases, where the sensor measurements are not sufficiently informative to pinpoint the error position exactly, we provide methods to restrict the error location to a smaller subset of network nodes. We also suggest strategies to efficiently select additional measurements for narrowing down the error location.
Improving Inference for Neural Image Compression

We consider the problem of lossy image compression with deep latent variable models. State-of-the-art methods build on hierarchical variational autoencoders (VAEs) and learn inference networks to predict a compressible latent representation of each data point. Drawing on the variational inference perspective on compression, we identify three approximation gaps which limit performance in the conventional approach: (i) an amortization gap, (ii) a discretization gap, and (iii) a marginalization gap. We propose improvements to each of these three shortcomings based on ideas related to iterative inference, stochastic annealing for discrete optimization, and bits-back coding, resulting in the first application of bits-back coding to lossy compression. In our experiments, which include extensive baseline comparisons and ablation studies, we achieve new state-of-the-art performance on lossy image compression using an established VAE architecture, by changing only the inference method.
Sparse representation for damage identification of structural systems

Identifying damage of structural systems is typically characterized as an inverse problem which might be ill-conditioned due to aleatory and epistemic uncertainties induced by measurement noise and modeling error. Sparse representation can be used to perform inverse analysis for the case of sparse damage. In this article, we propose a novel two-stage sensitivity analysis–based framework for both model updating and sparse damage identification. Specifically, an ℓ 2 Bayesian learning method is first developed for updating the intact model and uncertainty quantification so as to set forward a baseline for damage detection. A sparse representation pipeline built on a quasi- ℓ 0 method, for example, sequential threshold least squares regression, is then presented for damage localization and quantification. In addition, Bayesian optimization together with cross-validation is developed to heuristically learn hyperparameters from data, which saves the computational cost of hyperparameter tuning and produces more reliable identification result. The proposed framework is verified by three examples, including a 10-story shear-type building, a complex truss structure, and a shake-table test of an eight-story steel frame. Results show that the proposed approach is capable of both localizing and quantifying structural damage with high accuracy.
Multi-scale classification for electro-sensing

This paper introduces premier and innovative (real-time) multi-scale method for target classification in electro-sensing. The intent is that of mimicking the behavior of the weakly electric fish, which is able to retrieve much more information about the target by approaching it. The method is based on a family of transform-invariant shape descriptors computed from generalized polarization tensors (GPTs) reconstructed at multiple scales. The evidence provided by the different descriptors at each scale is fused using Dempster-Shafer Theory. Numerical simulations show that the recognition algorithm we proposed performs undoubtedly well and yields a robust classification.
Skill Discovery of Coordination in Multi-agent Reinforcement Learning

Unsupervised skill discovery drives intelligent agents to explore the unknown environment without task-specific reward signal, and the agents acquire various skills which may be useful when the agents adapt to new tasks. In this paper, we propose "Multi-agent Skill Discovery"(MASD), a method for discovering skills for coordination patterns of multiple agents. The proposed method aims to maximize the mutual information between a latent code Z representing skills and the combination of the states of all agents. Meanwhile it suppresses the empowerment of Z on the state of any single agent by adversarial training. In another word, it sets an information bottleneck to avoid empowerment degeneracy. First we show the emergence of various skills on the level of coordination in a general particle multi-agent environment. Second, we reveal that the "bottleneck" prevents skills from collapsing to a single agent and enhances the diversity of learned skills. Finally, we show the pretrained policies have better performance on supervised RL tasks.
Fully Convolutional Mesh Autoencoder using Efficient Spatially Varying Kernels

Learning latent representations of registered meshes is useful for many 3D tasks. Techniques have recently shifted to neural mesh autoencoders. Although they demonstrate higher precision than traditional methods, they remain unable to capture fine-grained deformations. Furthermore, these methods can only be applied to a template-specific surface mesh, and is not applicable to more general meshes, like tetrahedrons and non-manifold meshes. While more general graph convolution methods can be employed, they lack performance in reconstruction precision and require higher memory usage. In this paper, we propose a non-template-specific fully convolutional mesh autoencoder for arbitrary registered mesh data. It is enabled by our novel convolution and (un)pooling operators learned with globally shared weights and locally varying coefficients which can efficiently capture the spatially varying contents presented by irregular mesh connections. Our model outperforms state-of-the-art methods on reconstruction accuracy. In addition, the latent codes of our network are fully localized thanks to the fully convolutional structure, and thus have much higher interpolation capability than many traditional 3D mesh generation models.
Pulsar polarimetry with the Parkes ultra-wideband receiver

Pulsar radio emission and its polarization are observed to evolve with frequency. This frequency dependence is key to the emission mechanism and the structure of the radio beam. With the new Ultra-Wideband receiver (UWL) on the Parkes radio telescope we are able, for the first time, to observe how pulsar profiles evolve over a broad continuous bandwidth of 700-4000 MHz. We describe here a technique for processing broadband polarimetric observations to establish a meaningful alignment and visualize the data across the band. We apply this to observations of PSRs J1056-6258 and J1359-6038, chosen due to previously unresolved questions about the frequency evolution of their emission. Application of our technique reveals that it is possible to align the polarization position angle (PA) across a broad frequency range when constrained to applying only corrections for dispersion and Faraday rotation to do so. However, this does not correspond to aligned intensity profiles for these two sources. We find that it is possible to convert these misalignments into emission height range estimates that are consistent with published and simulated values, suggesting that they can be attributed to relativistic effects in the magnetosphere. We discuss this work in the context of the radio beam structure and prepare the ground for a wider study of pulsar emission using broadband polarimetric data.
Toward a Better Understanding and Evaluation of Tree Structures on Flash SSDs

Solid-state drives (SSDs) are extensively used to deploy persistent data stores, as they provide low latency random access, high write throughput, high data density, and low cost. Tree-based data structures are widely used to build persistent data stores, and indeed they lie at the backbone of many of the data management systems used in production and research today. We show that benchmarking a persistent tree-based data structure on an SSD is a complex process, which may easily incur subtle pitfalls that can lead to an inaccurate performance assessment. At a high-level, these pitfalls stem from the interaction of complex software running on complex hardware. On the one hand, tree structures implement internal operations that have non-trivial effects on performance. On the other hand, SSDs employ firmware logic to deal with the idiosyncrasies of the underlying flash memory, which are well known to also lead to complex performance dynamics. We identify seven benchmarking pitfalls using RocksDB and WiredTiger, two widespread implementations of an LSM-Tree and a B+Tree, respectively. We show that such pitfalls can lead to incorrect measurements of key performance indicators, hinder the reproducibility and the representativeness of the results, and lead to suboptimal deployments in production environments. We also provide guidelines on how to avoid these pitfalls to obtain more reliable performance measurements, and to perform more thorough and fair comparisons among different design points.
Extended superconducting dome of electron-doped cuprates after protect annealing revealed by ARPES

The electron-doped cuprates are usually characterized by a more robust antiferromagnetic phase and a much narrower superconducting (SC) dome than those of the hole-doped counterparts. Recently, bulk single crystals of Pr1.3-xLa0.7CexCuO4 (PLCCO) prepared by the protect annealing method have been studied extensively and revealed many intriguing properties that were different from those obtained from samples annealed by the conventional methods. Here, we report on a systematic angle-resolved photoemission spectroscopy study of PLCCO single crystals after protect annealing. The results indicate that the actual electron concentration (nFS ) estimated from the Fermi-surface area is significantly larger than the Ce concentration x and the new nFS-based SC dome of PLCCO is more extended towards the overdoped side than the x-based SC dome derived for samples prepared using the conventional annealing method. The similarity between the new nFS-based SC dome and that of the hole-doped cuprate La2-xSrxCuO4 further provides a clue for understanding the reported electron-hole symmetry/asymmetry of the cuprate phase diagram.
Fusion Recurrent Neural Network

Considering deep sequence learning for practical application, two representative RNNs - LSTM and GRU may come to mind first. Nevertheless, is there no chance for other RNNs? Will there be a better RNN in the future? In this work, we propose a novel, succinct and promising RNN - Fusion Recurrent Neural Network (Fusion RNN). Fusion RNN is composed of Fusion module and Transport module every time step. Fusion module realizes the multi-round fusion of the input and hidden state vector. Transport module which mainly refers to simple recurrent network calculate the hidden state and prepare to pass it to the next time step. Furthermore, in order to evaluate Fusion RNN's sequence feature extraction capability, we choose a representative data mining task for sequence data, estimated time of arrival (ETA) and present a novel model based on Fusion RNN. We contrast our method and other variants of RNN for ETA under massive vehicle travel data from DiDi Chuxing. The results demonstrate that for ETA, Fusion RNN is comparable to state-of-the-art LSTM and GRU which are more complicated than Fusion RNN.
A ROS-based Framework for Monitoring Human and Robot Conditions in a Human-Multi-robot Team

This paper presents a framework for monitoring human and robot conditions in human multi-robot interactions. The proposed framework consists of four modules: 1) human and robot conditions monitoring interface, 2) synchronization time filter, 3) data feature extraction interface, and 4) condition monitoring interface. The framework is based on Robot Operating System (ROS), and it supports physiological and behavioral sensors and devices and robot systems, as well as custom programs. Furthermore, it allows synchronizing the monitoring conditions and sharing them simultaneously. In order to validate the proposed framework, we present experiment results and analysis obtained from the user study where 30 human subjects participated and simulated robot experiments.
Unsupervised Graph Representation by Periphery and Hierarchical Information Maximization

Deep representation learning on non-Euclidean data types, such as graphs, has gained significant attention in recent years. Invent of graph neural networks has improved the state-of-the-art for both node and the entire graph representation in a vector space. However, for the entire graph representation, most of the existing graph neural networks are trained on a graph classification loss in a supervised way. But obtaining labels of a large number of graphs is expensive for real world applications. Thus, we aim to propose an unsupervised graph neural network to generate a vector representation of an entire graph in this paper. For this purpose, we combine the idea of hierarchical graph neural networks and mutual information maximization into a single framework. We also propose and use the concept of periphery representation of a graph and show its usefulness in the proposed algorithm which is referred as GraPHmax. We conduct thorough experiments on several real-world graph datasets and compare the performance of GraPHmax with a diverse set of both supervised and unsupervised baseline algorithms. Experimental results show that we are able to improve the state-of-the-art for multiple graph level tasks on several real-world datasets, while remain competitive on the others.
Energy cascades in Rayleigh-Taylor turbulence.

We analyze the scale-to-scale energy transfer in 2D and 3D compressible Rayleigh-Taylor (RT) turbulence using the coarse-graining approach. Based on the Favre scale decomposition, we outline the energy pathways in RT flows which include transfer between potential energy, kinetic energy at different scales, and internal energy. In particular, two flux terms are responsible for kinetic energy transfer across scales, namely baropycnal work $\Lambda_\ell$, which arises from work done by pressure gradients on the turbulent mass flux, and deformation work $\Pi_\ell$, which is solely due to multi-scale turbulent velocity fields. Within the energy pathways, we can identity a range of scales in which the kinetic energy fluxes $\Lambda_\ell$ and $\Pi_\ell$ are not directly affected by either external inputs at large scales or by dissipation at small scales. We call this range of scales the inertial range in turbulent RT flows over which kinetic energy cascades. We carry out a detailed analysis of energy budgets, including the cascades, as a function of scale using high resolution turbulent RT simulations in 2D and 3D. The paper then characterizes the anisotropic energy cascades and anisotropic spectra of density and velocity in RT flows. Furthermore, the paper discusses similarities and differences between two-species incompressible RT turbulence and our single-species compressible RT turbulence.