Miracle-dz/UniFAR-FTUs huggingface dataset:
Threshold-Based Fast Successive-Cancellation Decoding of Polar Codes

Fast SC decoding overcomes the latency caused by the serial nature of the SC decoding by identifying new nodes in the upper levels of the SC decoding tree and implementing their fast parallel decoders. In this work, we first present a novel sequence repetition node corresponding to a particular class of bit sequences. Most existing special node types are special cases of the proposed sequence repetition node. Then, a fast parallel decoder is proposed for this class of node. To further speed up the decoding process of general nodes outside this class, a threshold-based hard-decision-aided scheme is introduced. The threshold value that guarantees a given error-correction performance in the proposed scheme is derived theoretically. Analysis and hardware implementation results on a polar code of length 1024 with code rates 1/4, 1/2, and 3/4 show that our proposed algorithm reduces the required clock cycles by up to 8%, and leads to a 10% improvement in the maximum operating frequency compared to state-of-the-art decoders without tangibly altering the error-correction performance. In addition, using the proposed threshold-based hard-decision-aided scheme, the decoding latency can be further reduced by 57% at $\mathrm {E_{b}}/\mathrm {N_{0}} = 5.0$ dB.
Generic Detectability and Isolability of Topology Failures in Networked Linear Systems

This article studies the possibility of detecting and isolating topology failures (including link and node failures) of a networked system from subsystem measurements, in which subsystems are of fixed high-order linear dynamics, and the exact interaction weights among them are unknown. We prove that in such a class of networked systems with the same network topologies, the detectability and isolability of a given topology failure (set) are generic properties, indicating that it is the network topology that dominates the property of being detectable or isolable for a failure (set). We first give algebraic conditions for detectability, and isolability of arbitrary parameter perturbations for a lumped plant and then derive graph-theoretical necessary and sufficient conditions for generic detectability and isolability of topology failures for the networked systems. On the basis of these results, we consider the problems of deploying the smallest set of sensors for generic detectability and isolability. We reduce the associated sensor placement problems to the hitting set problems, which can be effectively solved by greedy algorithms with guaranteed approximation performances.
Understanding Learners' Problem-Solving Strategies in Concurrent and Parallel Programming: A Game-Based Approach

Concurrent and parallel programming (CPP) is an increasingly important subject in Computer Science Education. However, the conceptual shift from sequential programming is notoriously difficult to make. Currently, relatively little research exists on how people learn CPP core concepts. This paper presents our results of using Parallel, an educational game about CPP, focusing on the learners' self-efficacy and how they learn CPP concepts. Based on a study of 44 undergraduate students, our research shows that (a) self-efficacy increased significantly after playing the game; (b) the problem-solving strategies employed by students playing the game can be classified in three main types: trial and error, single-thread, and multi-threaded strategies, and (c) that self-efficacy is correlated with the percentage of time students spend in multithreaded problem-solving.
Commonsense Evidence Generation and Injection in Reading Comprehension

Human tackle reading comprehension not only based on the given context itself but often rely on the commonsense beyond. To empower the machine with commonsense reasoning, in this paper, we propose a Commonsense Evidence Generation and Injection framework in reading comprehension, named CEGI. The framework injects two kinds of auxiliary commonsense evidence into comprehensive reading to equip the machine with the ability of rational thinking. Specifically, we build two evidence generators: one aims to generate textual evidence via a language model; the other aims to extract factual evidence (automatically aligned text-triples) from a commonsense knowledge graph after graph completion. Those evidences incorporate contextual commonsense and serve as the additional inputs to the reasoning model. Thereafter, we propose a deep contextual encoder to extract semantic relationships among the paragraph, question, option, and evidence. Finally, we employ a capsule network to extract different linguistic units (word and phrase) from the relations, and dynamically predict the optimal option based on the extracted units. Experiments on the CosmosQA dataset demonstrate that the proposed CEGI model outperforms the current state-of-the-art approaches and achieves the highest accuracy (83.6%) on the leaderboard.
A Survey on Sampling and Profiling over Big Data (Technical Report)

Due to the development of internet technology and computer science, data is exploding at an exponential rate. Big data brings us new opportunities and challenges. On the one hand, we can analyze and mine big data to discover hidden information and get more potential value. On the other hand, the 5V characteristic of big data, especially Volume which means large amount of data, brings challenges to storage and processing. For some traditional data mining algorithms, machine learning algorithms and data profiling tasks, it is very difficult to handle such a large amount of data. The large amount of data is highly demanding hardware resources and time consuming. Sampling methods can effectively reduce the amount of data and help speed up data processing. Hence, sampling technology has been widely studied and used in big data context, e.g., methods for determining sample size, combining sampling with big data processing frameworks. Data profiling is the activity that finds metadata of data set and has many use cases, e.g., performing data profiling tasks on relational data, graph data, and time series data for anomaly detection and data repair. However, data profiling is computationally expensive, especially for large data sets. Therefore, this paper focuses on researching sampling and profiling in big data context and investigates the application of sampling in different categories of data profiling tasks. From the experimental results of these studies, the results got from the sampled data are close to or even exceed the results of the full amount of data. Therefore, sampling technology plays an important role in the era of big data, and we also have reason to believe that sampling technology will become an indispensable step in big data processing in the future.
Compressing Recurrent Neural Networks Using Hierarchical Tucker Tensor Decomposition

Recurrent Neural Networks (RNNs) have been widely used in sequence analysis and modeling. However, when processing high-dimensional data, RNNs typically require very large model sizes, thereby bringing a series of deployment challenges. Although the state-of-the-art tensor decomposition approaches can provide good model compression performance, these existing methods are still suffering some inherent limitations, such as restricted representation capability and insufficient model complexity reduction. To overcome these limitations, in this paper we propose to develop compact RNN models using Hierarchical Tucker (HT) decomposition. HT decomposition brings strong hierarchical structure to the decomposed RNN models, which is very useful and important for enhancing the representation capability. Meanwhile, HT decomposition provides higher storage and computational cost reduction than the existing tensor decomposition approaches for RNN compression. Our experimental results show that, compared with the state-of-the-art compressed RNN models, such as TT-LSTM, TR-LSTM and BT-LSTM, our proposed HT-based LSTM (HT-LSTM), consistently achieves simultaneous and significant increases in both compression ratio and test accuracy on different datasets.
Semi-Supervised Dialogue Policy Learning via Stochastic Reward Estimation

Dialogue policy optimization often obtains feedback until task completion in task-oriented dialogue systems. This is insufficient for training intermediate dialogue turns since supervision signals (or rewards) are only provided at the end of dialogues. To address this issue, reward learning has been introduced to learn from state-action pairs of an optimal policy to provide turn-by-turn rewards. This approach requires complete state-action annotations of human-to-human dialogues (i.e., expert demonstrations), which is labor intensive. To overcome this limitation, we propose a novel reward learning approach for semi-supervised policy learning. The proposed approach learns a dynamics model as the reward function which models dialogue progress (i.e., state-action sequences) based on expert demonstrations, either with or without annotations. The dynamics model computes rewards by predicting whether the dialogue progress is consistent with expert demonstrations. We further propose to learn action embeddings for a better generalization of the reward function. The proposed approach outperforms competitive policy learning baselines on MultiWOZ, a benchmark multi-domain dataset.
In‐Memory Eigenvector Computation in Time O(1)

In‐memory computing with cross‐point resistive memory arrays has gained enormous attention to accelerate the matrix‐vector multiplication in the computation of data‐centric applications. By combining a cross‐point array and feedback amplifiers, it is possible to compute matrix eigenvectors in one step without algorithmic iterations. Herein, time complexity of the eigenvector computation is investigated, based on the feedback analysis of the cross‐point circuit. The results show that the computing time of the circuit is determined by the mismatch degree of the eigenvalues implemented in the circuit, which controls the rising speed of output voltages. For a dataset of random matrices, the time for computing the dominant eigenvector in the circuit is constant for various matrix sizes; namely, the time complexity is O(1). The O(1) time complexity is also supported by simulations of PageRank of real‐world datasets. This work paves the way for fast, energy‐efficient accelerators for eigenvector computation in a wide range of practical applications.
COVID-19 growth prediction using multivariate long short term memory

Coronavirus disease (COVID-19) spread forecasting is an important task to track the growth of the pandemic. Existing predictions are merely based on qualitative analyses and mathematical modeling. The use of available big data with machine learning is still limited in COVID-19 growth prediction even though the availability of data is abundance. To make use of big data in the prediction using deep learning, we use long short-term memory (LSTM) method to learn the correlation of COVID-19 growth over time. The structure of an LSTM layer is searched heuristically until the best validation score is achieved. First, we trained training data containing confirmed cases from around the globe. We achieved favorable performance compared with that of the recurrent neural network (RNN) method with a comparable low validation error. The evaluation is conducted based on graph visualization and root mean squared error (RMSE). We found that it is not easy to achieve the same quantity of confirmed cases over time. However, LSTM provide a similar pattern between the actual cases and prediction. In the future, our proposed prediction can be used for anticipating forthcoming pandemics. The code is provided here: this https URL
The Barker proposal: Combining robustness and efficiency in gradient‐based MCMC

There is a tension between robustness and efficiency when designing Markov chain Monte Carlo (MCMC) sampling algorithms. Here we focus on robustness with respect to tuning parameters, showing that more sophisticated algorithms tend to be more sensitive to the choice of step‐size parameter and less robust to heterogeneity of the distribution of interest. We characterise this phenomenon by studying the behaviour of spectral gaps as an increasingly poor step‐size is chosen for the algorithm. Motivated by these considerations, we propose a novel and simple gradient‐based MCMC algorithm, inspired by the classical Barker accept‐reject rule, with improved robustness properties. Extensive theoretical results, dealing with robustness to tuning, geometric ergodicity and scaling with dimension, suggest that the novel scheme combines the robustness of simple schemes with the efficiency of gradient‐based ones. We show numerically that this type of robustness is particularly beneficial in the context of adaptive MCMC, giving examples where our proposed scheme significantly outperforms state‐of‐the‐art alternatives.
Access structures determined by uniform polymatroids

Abstract In this article, all multipartite access structures obtained from uniform integer polymatroids were investigated using the method developed by Farràs, Martí-Farré, and Padró. They are matroid ports, i.e., they satisfy the necessary condition to be ideal. Moreover, each uniform integer polymatroid defines some ideal access structures. Some objects in this family can be useful for the applications of secret sharing. The method presented in this article is universal and can be continued with other classes of polymatroids in further similar studies. Here, we are especially interested in hierarchy of participants determined by the access structure, and we distinguish two main classes: they are compartmented and hierarchical access structures. The main results obtained for access structures determined by uniform integer polymatroids and a monotone increasing family Δ \Delta can be summarized as follows. If the increment sequence of the polymatroid is non-constant, then the access structure is connected. If Δ \Delta does not contain any singletons or the height of the polymatroid is maximal and its increment sequence is not constant starting from the second element, then the access structure is compartmented. If Δ \Delta is generated by a singleton or the increment sequence of the polymatroid is constant starting from the second element, then the obtained access structures are hierarchical. They are proven to be ideal, and their hierarchical orders are completely determined. Moreover, if the increment sequence of the polymatroid is constant and ∣ Δ ∣ > 1 | \Delta | \gt 1 , then the hierarchical order is not antisymmetric, i.e., some different blocks are equivalent. The hierarchical order of access structures obtained from uniform integer polymatroids is always flat, that is, every hierarchy chain has at most two elements.
Hunting for Runaways from the Orion Nebula Cluster

We use Gaia DR2 to hunt for runaway stars from the Orion Nebula Cluster (ONC). We search a region extending 45° around the ONC and out to 1 kpc to find sources that have overlapped in angular position with the cluster in the last ∼10 Myr. We find ∼17,000 runaway/walkaway candidates that satisfy this 2D traceback condition. Most of these are expected to be contaminants, e.g., caused by Galactic streaming motions of stars at different distances. We thus examine six further tests to help identify real runaways, namely: (1) possessing young stellar object (YSO) colors and magnitudes based on Gaia optical photometry; (2) having IR excess consistent with YSOs based on 2MASS and Wide-field Infrared Survey Explorer photometry; (3) having a high degree of optical variability; (4) having closest approach distances well-constrained to within the cluster half-mass radius; (5) having ejection directions that avoid the main Galactic streaming contamination zone; and (6) having a required radial velocity (RV) for 3D overlap of reasonable magnitude (or, for the 7% of candidates with measured RVs, satisfying 3D traceback). Thirteen sources, not previously noted as Orion members, pass all these tests, while another twelve are similarly promising, except they are in the main Galactic streaming contamination zone. Among these 25 ejection candidates, ten with measured RVs pass the most restrictive 3D traceback condition. We present full lists of runaway/walkaway candidates, estimate the high-velocity population ejected from the ONC, and discuss its implications for cluster formation theories via comparison with numerical simulations.
Optimal Obfuscation Mechanisms via Machine Learning

We consider the problem of obfuscating sensitive information while preserving utility, and we propose a machine-learning approach inspired by the generative adversarial networks paradigm. The idea is to set up two nets: the generator, that tries to produce an optimal obfuscation mechanism to protect the data, and the classifier, that tries to de-obfuscate the data. By letting the two nets compete against each other, the mechanism improves its degree of protection, until an equilibrium is reached. We apply our method to the case of location privacy, and we perform experiments on synthetic data and on real data from the Gowalla dataset. We evaluate the privacy of the mechanism not only by its capacity to defeat the classifier, but also in terms of the Bayes error, which represents the strongest possible adversary. We compare the privacy-utility tradeoff of our method with that of the planar Laplace mechanism used in geo-indistinguishability, showing favorable results. Like the Laplace mechanism, our system can be deployed at the user end for protecting his location.
Optical Variability of the Dwarf AGN NGC 4395 from the Transiting Exoplanet Survey Satellite

We present optical light curves from the Transiting Exoplanet Survey Satellite (TESS) for the archetypical dwarf active galactic nucleus (AGN) in the nearby galaxy NGC 4395 hosting a ∼105 M⊙ supermassive black hole (SMBH). Significant variability is detected on timescales from weeks to hours before reaching the background noise level. The ∼month-long, 30 minute-cadence, high-precision TESS light curve can be well fit by a simple damped random walk (DRW) model, with the damping timescale τDRW constrained to be days (1σ). NGC 4395 lies almost exactly on the extrapolation of the relation measured for AGNs with BH masses that are more than three orders of magnitude larger. The optical variability periodogram can be well fit by a broken power law with the high-frequency slope (−1.88 ± 0.15) and the characteristic timescale ( days) consistent with the DRW model within 1σ. This work demonstrates the power of TESS light curves in identifying low-mass accreting SMBHs with optical variability, and a potential global relation that can be used to estimate SMBH masses with optical variability measurements.
Determining a Piecewise Conductive Medium Body by a Single Far-Field Measurement

We are concerned with the inverse problem of recovering a conductive medium body. The conductive medium body arises in several applications of practical importance, including the modeling of an electromagnetic object coated with a thin layer of a highly conducting material and the magnetotellurics in geophysics. We consider the determination of the material parameters inside the body as well as on the conductive interface by the associated electromagnetic far-field measurement. Under the transverse-magnetic polarisation, we derive two novel unique identifiability results in determining a 2D piecewise conductive medium body associated with a polygonal-nest or a polygonal-cell geometry by a single active or passive far-field measurement.
On Subgraph Signal Processing

Graph signal processing allows us to analyze a graph signal by transforming it into the frequency domain. In general, the procedure requires one to know the signal value at every node. In real situations, it is possible that only partial observations are made, called subgraph signals. In this paper, we introduce a subgraph signal processing framework. It allows us to define Fourier transform and the notion of frequency domain with signals available on a subset of nodes. As a consequence, we are able to give meaningful frequency interpretation of subgraph signals and perform standard signal processing tasks.
Faster classical Boson Sampling

Since its introduction Boson Sampling has been the subject of intense study in the world of quantum computing. The task is to sample independently from the set of all $n \times n$ submatrices built from possibly repeated rows of a larger $m \times n$ complex matrix according to a probability distribution related to the permanents of the submatrices. Experimental systems exploiting quantum photonic effects can in principle perform the task at great speed. In the framework of classical computing, Aaronson and Arkhipov (2011) showed that exact Boson Sampling problem cannot be solved in polynomial time unless the polynomial hierarchy collapses to the third level. Indeed for a number of years the fastest known exact classical algorithm ran in $O({m+n-1 \choose n} n 2^n )$ time per sample, emphasising the potential speed advantage of quantum computation. The advantage was reduced by Clifford and Clifford (2018) who gave a significantly faster classical solution taking $O(n 2^n + \operatorname{poly}(m,n))$ time and linear space, matching the complexity of computing the permanent of a single matrix when $m$ is polynomial in $n$. We continue by presenting an algorithm for Boson Sampling whose average-case time complexity is much faster when $m$ is proportional to $n$. In particular, when $m = n$ our algorithm runs in approximately $O(n\cdot1.69^n)$ time on average. This result further increases the problem size needed to establish quantum computational supremacy via Boson Sampling.
PriLok: Citizen-protecting distributed epidemic tracing

Contact tracing is an important instrument for national health services to fight epidemics. As part of the COVID-19 situation, many proposals have been made for scaling up contract tracing capacities with the help of smartphone applications, an important but highly critical endeavor due to the privacy risks involved in such solutions. Extending our previously expressed concern, we clearly articulate in this article, the functional and non-functional requirements that any solution has to meet, when striving to serve, not mere collections of individuals, but the whole of a nation, as required in face of such potentially dangerous epidemics. We present a critical information infrastructure, PriLock, a fully-open preliminary architecture proposal and design draft for privacy preserving contact tracing, which we believe can be constructed in a way to fulfill the former requirements. Our architecture leverages the existing regulated mobile communication infrastructure and builds upon the concept of "checks and balances", requiring a majority of independent players to agree to effect any operation on it, thus preventing abuse of the highly sensitive information that must be collected and processed for efficient contact tracing. This is enforced with a largely decentralised layout and highly resilient state-of-the-art technology, which we explain in the paper, finishing by giving a security, dependability and resilience analysis, showing how it meets the defined requirements, even while the infrastructure is under attack.