OpenRaiser/Intern-Atlas huggingface dataset:
Scheduled Intrinsic Drive: A Hierarchical Take on Intrinsically Motivated Exploration
Venue: arXiv cs.LG 2019
Year: 2019

Exploration in sparse reward reinforcement learning remains an open challenge. Many state-of-the-art methods use intrinsic motivation to complement the sparse extrinsic reward signal, giving the agent more opportunities to receive feedback during exploration. Commonly these signals are added as bonus rewards, which results in a mixture policy that neither conducts exploration nor task fulfillment resolutely. In this paper, we instead learn separate intrinsic and extrinsic task policies and schedule between these different drives to accelerate exploration and stabilize learning. Moreover, we introduce a new type of intrinsic reward denoted as successor feature control (SFC), which is general and not task-specific. It takes into account statistics over complete trajectories and thus differs from previous methods that only use local information to evaluate intrinsic motivation. We evaluate our proposed scheduled intrinsic drive (SID) agent using three different environments with pure visual inputs: VizDoom, DeepMind Lab and DeepMind Control Suite. The results show a substantially improved exploration efficiency with SFC and the hierarchical usage of the intrinsic drives. A video of our experimental results can be found at https://youtu.be/b0MbY3lUlEI.
Dissecting the NVidia Turing T4 GPU via Microbenchmarking
Venue: arXiv cs.DC 2019
Year: 2019

In 2019, the rapid rate at which GPU manufacturers refresh their designs, coupled with their reluctance to disclose microarchitectural details, is still a hurdle for those software designers who want to extract the highest possible performance. Last year, these very reasons motivated us to dissect the Volta GPU architecture using microbenchmarks. The introduction in August 2018 of Turing, NVidia's latest architecture, pressed us to update our study. In this report, we examine Turing and compare it quantitatively against previous NVidia GPU generations. Specifically, we study the T4 GPU: a low-power board aiming at inference applications. We describe its improvements against its inference-oriented predecessor: the P4 GPU based on the Pascal architecture. Both T4 and P4 GPUs achieve significantly higher frequency-per-Watt figures than their full-size counterparts. We study the performance of the T4's TensorCores, finding a much higher throughput on low-precision operands than on the P4 GPU. We reveal that Turing introduces new instructions that express matrix math more succinctly. We map Turing's instruction space, finding the same encoding as Volta, and additional instructions. We reveal that the Turing TU104 chip has the same memory hierarchy depth as the Volta GV100; cache levels sizes on the TU104 are frequently twice as large as those found on the Pascal GP104. We benchmark each constituent of the T4 memory hierarchy and find substantial overall performance improvements over its P4 predecessor. We studied how clock throttling affects compute-intensive workloads that hit power or thermal limits. Many of our findings are novel, published here for the first time. All of them can guide high-performance software developers get closer to the GPU's peak performance.
Forecasting the Progression of Alzheimer's Disease Using Neural Networks and a Novel Pre-Processing Algorithm
Venue: arXiv cs.LG 2019
Year: 2019

Alzheimer's disease (AD) is the most common neurodegenerative disease in older people. Despite considerable efforts to find a cure for AD, there is a 99.6% failure rate of clinical trials for AD drugs, likely because AD patients cannot easily be identified at early stages. This project investigated machine learning approaches to predict the clinical state of patients in future years to benefit AD research. Clinical data from 1737 patients was obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database and was processed using the "All-Pairs" technique, a novel methodology created for this project involving the comparison of all possible pairs of temporal data points for each patient. This data was then used to train various machine learning models. Models were evaluated using 7-fold cross-validation on the training dataset and confirmed using data from a separate testing dataset (110 patients). A neural network model was effective (mAUC = 0.866) at predicting the progression of AD on a month-by-month basis, both in patients who were initially cognitively normal and in patients suffering from mild cognitive impairment. Such a model could be used to identify patients at early stages of AD and who are therefore good candidates for clinical trials for AD therapeutics.
PZnet: Efficient 3D ConvNet Inference on Manycore CPUs
Venue: arXiv cs.CV 2019
Year: 2019

Convolutional nets have been shown to achieve state-of-the-art accuracy in many biomedical image analysis tasks. Many tasks within biomedical analysis domain involve analyzing volumetric (3D) data acquired by CT, MRI and Microscopy acquisition methods. To deploy convolutional nets in practical working systems, it is important to solve the efficient inference problem. Namely, one should be able to apply an already-trained convolutional network to many large images using limited computational resources. In this paper we present PZnet, a CPU-only engine that can be used to perform inference for a variety of 3D convolutional net architectures. PZNet outperforms MKL-based CPU implementations of PyTorch and Tensorflow by more than 3.5x for the popular U-net architecture. Moreover, for 3D convolutions with low featuremap numbers, cloud CPU inference with PZnet outperfroms cloud GPU inference in terms of cost efficiency.
Counting independent sets and colorings on random regular bipartite graphs
Venue: arXiv cs.DS 2019
Year: 2019

We give a fully polynomial-time approximation scheme (FPTAS) to count the number of independent sets on almost every $Δ$-regular bipartite graph if $Δ\ge 53$. In the weighted case, for all sufficiently large integers $Δ$ and weight parameters $λ=\tildeΩ\left(\frac{1}Δ\right)$, we also obtain an FPTAS on almost every $Δ$-regular bipartite graph. Our technique is based on the recent work of Jenssen, Keevash and Perkins (SODA, 2019) and we also apply it to confirm an open question raised there: For all $q\ge 3$ and sufficiently large integers $Δ=Δ(q)$, there is an FPTAS to count the number of $q$-colorings on almost every $Δ$-regular bipartite graph.
Should Citations be Counted Separately from Each Originating Section
Venue: arXiv cs.DL 2019
Year: 2019

Articles are cited for different purposes and differentiating between reasons when counting citations may therefore give finer-grained citation count information. Although identifying and aggregating the individual reasons for each citation may be impractical, recording the number of citations that originate from different article sections might illuminate the general reasons behind a citation count (e.g., 110 citations = 10 Introduction citations + 100 Methods citations). To help investigate whether this could be a practical and universal solution, this article compares 19 million citations with DOIs from six different standard sections in 799,055 PubMed Central open access articles across 21 out of 22 fields. There are apparently non-systematic differences between fields in the most citing sections and the extent to which citations from one section overlap with citations from another, with some degree of overlap in most cases. Thus, at a science-wide level, section headings are partly unreliable indicators of citation context, even if they are more standard within individual fields. They may still be used within fields to help identify individual highly cited articles that have had one type of impact, especially methodological (Methods) or context setting (Introduction), but expert judgement is needed to validate the results.
A constructive proof of dependent choice in classical arithmetic via memoization
Venue: arXiv cs.LO 2019
Year: 2019

In a recent paper, Herbelin developed dPA${^ω}$, a calculus in which constructive proofs for the axioms of countable and dependent choices could be derived via the memoization of choice functions. However, the property of normalization (and therefore the one of soundness) was only conjectured. The difficulty for the proof of normalization is due to the simultaneous presence of dependent types (for the constructive part of the choice), of control operators (for classical logic), of coinductive objects (to encode functions of type ${\mathbb{N} \to A}$ into streams (${a_0},{a_1},...$)) and of lazy evaluation with sharing (for memoizing these coinductive objects). Elaborating on previous works, we introduce in this paper a variant of dPA${^ω}$ presented as a sequent calculus. On the one hand, we take advantage of a variant of Krivine classical realizability that we developed to prove the normalization of classical call-by-need. On the other hand, we benefit from dL${_{\hat{tp}}}$, a classical sequent calculus with dependent types in which type safety is ensured by using delimited continuations together with a syntactic restriction. By combining the techniques developed in these papers, we manage to define a realizability interpretation à la Krivine of our calculus that allows us to prove normalization and soundness. This paper goes over the whole process, starting from Herbelin's calculus dPA${^ω}$ until our introduction of its sequent calculus counterpart dLPA${^ω}$.
A Method for Reducing the Complexity of Model Predictive Control in Robotics Applications
Venue: arXiv cs.SY 2019
Year: 2019

This article describes an approach for parametrizing input and state trajectories in model predictive control. The parametrization is designed to be invariant to time shifts, which enables warm-starting the successive optimization problems and reduces the computational complexity of the online optimization. It is shown that in certain cases (e.g. for linear time-invariant dynamics with input and state constraints) the parametrization leads to inherent stability and recursive feasibility guarantees without additional terminal set constraints. Due to the fact that the number of decision variables are greatly reduced through the parametrization, while the warm-starting capabilities are preserved, the approach is suitable for applications where the available computational resources (memory and CPU-power) are limited.
Downlink transceiver beamforming and admission control for massive MIMO cognitive radio networks
Venue: arXiv cs.IT 2019
Year: 2019

In this paper, an optimization framework is proposed for joint transceiver beamforming and admission control in massive MIMO cognitive radio networks. The objective of the optimization problem is to support maximum number of secondary users in downlink transmission with constraints on the total power allocated to users, the rate achieved at secondary users and the interference at the primary nodes. The proposed framework takes into account the imperfect knowledge of the channels between the secondary and the primary nodes and also mitigates the interference caused by the primary users at secondary receivers. In order to solve the underlying NP-hard problem, we propose a three-step algorithm with two alternative schemes for power allocation, namely: equal power and equal rate. In addition, we provide a solution by reducing the problem to an Integer Linear Program (ILP). The performances of equal rate, equal power, and ILP methods are studied in under different constraints.
Recommending Comprehensive Solutions for Programming Tasks by Mining Crowd Knowledge
Venue: arXiv cs.SE 2019
Year: 2019

Developers often search for relevant code examples on the web for their programming tasks. Unfortunately, they face two major problems. First, the search is impaired due to a lexical gap between their query (task description) and the information associated with the solution. Second, the retrieved solution may not be comprehensive, i.e., the code segment might miss a succinct explanation. These problems make the developers browse dozens of documents in order to synthesize an appropriate solution. To address these two problems, we propose CROKAGE (Crowd Knowledge Answer Generator), a tool that takes the description of a programming task (the query) and provides a comprehensive solution for the task. Our solutions contain not only relevant code examples but also their succinct explanations. Our proposed approach expands the task description with relevant API classes from Stack Overflow Q&A threads and then mitigates the lexical gap problems. Furthermore, we perform natural language processing on the top quality answers and then return such programming solutions containing code examples and code explanations unlike earlier studies. We evaluate our approach using 48 programming queries and show that it outperforms six baselines including the state-of-art by a statistically significant margin. Furthermore, our evaluation with 29 developers using 24 tasks (queries) confirms the superiority of CROKAGE over the state-of-art tool in terms of relevance of the suggested code examples, benefit of the code explanations and the overall solution quality (code + explanation).
Self-Dual Skew Cyclic Codes over $\mathbb{F}_{q}+u\mathbb{F}_{q}$
Venue: arXiv cs.IT 2019
Year: 2019

In this paper, we give conditions for the existence of Hermitian self-dual $Θ-$cyclic and $Θ-$negacyclic codes over the finite chain ring $\mathbb{F}_q+u\mathbb{F}_q$. By defining a Gray map from $R=\mathbb{F}_q+u\mathbb{F}_q$ to $\mathbb{F}_{q}^{2}$, we prove that the Gray images of skew cyclic codes of odd length $n$ over $R$ with even characteristic are equivalent to skew quasi-twisted codes of length $2n$ over $\mathbb{F}_q$ of index $2$. We also extend an algorithm of Boucher and Ulmer \cite{BF3} to construct self-dual skew cyclic codes based on the least common left multiples of non-commutative polynomials over $\mathbb{F}_q+u\mathbb{F}_q$.
A Parallel Data Compression Framework for Large Scale 3D Scientific Data
Venue: arXiv cs.DC 2019
Year: 2019

Large scale simulations of complex systems ranging from climate and astrophysics to crowd dynamics, produce routinely petabytes of data and are projected to reach the zettabytes level in the coming decade. These simulations enable unprecedented insights but at the same their effectiveness is hindered by the enormous data sizes associated with the computational elements and respective output quantities of interest that impose severe constraints on storage and I/O time. In this work, we address these challenges through a novel software framework for scientific data compression. The software (CubismZ) incorporates efficient wavelet based techniques and the state-of-the-art ZFP, SZ and FPZIP floating point compressors. The framework relies on a block-structured data layout, benefits from OpenMP and MPI and targets supercomputers based on multicores. CubismZ can be used as a tool for ex situ (offline) compression of scientific datasets and supports conventional Computational Fluid Dynamics (CFD) file formats. Moreover, it provides a testbed of comparison, in terms of compression factor and peak signal-to-noise ratio, for a number of available data compression methods. The software yields in situ compression ratios of 100x or higher for fluid dynamics data produced by petascale simulations of cloud cavitation collapse using $\mathcal{O}(10^{11})$ grid cells, with negligible impact on the total simulation time.
Dynamic Deep Networks for Retinal Vessel Segmentation
Venue: arXiv cs.CV 2019
Year: 2019

Segmenting the retinal vasculature entails a trade-off between how much of the overall vascular structure we identify vs. how precisely we segment individual vessels. In particular, state-of-the-art methods tend to under-segment faint vessels, as well as pixels that lie on the edges of thicker vessels. Thus, they underestimate the width of individual vessels, as well as the ratio of large to small vessels. More generally, many crucial bio-markers---including the artery-vein (AV) ratio, branching angles, number of bifurcation, fractal dimension, tortuosity, vascular length-to-diameter ratio and wall-to-lumen length---require precise measurements of individual vessels. To address this limitation, we propose a novel, stochastic training scheme for deep neural networks that better classifies the faint, ambiguous regions of the image. Our approach relies on two key innovations. First, we train our deep networks with dynamic weights that fluctuate during each training iteration. This stochastic approach forces the network to learn a mapping that robustly balances precision and recall. Second, we decouple the segmentation process into two steps. In the first half of our pipeline, we estimate the likelihood of every pixel and then use these likelihoods to segment pixels that are clearly vessel or background. In the latter part of our pipeline, we use a second network to classify the ambiguous regions in the image. Our proposed method obtained state-of-the-art results on five retinal datasets---DRIVE, STARE, CHASE-DB, AV-WIDE, and VEVIO---by learning a robust balance between false positive and false negative rates. In addition, we are the first to report segmentation results on the AV-WIDE dataset, and we have made the ground-truth annotations for this dataset publicly available.
Low-Rank Discriminative Least Squares Regression for Image Classification
Venue: arXiv cs.CV 2019
Year: 2019

Latest least squares regression (LSR) methods mainly try to learn slack regression targets to replace strict zero-one labels. However, the difference of intra-class targets can also be highlighted when enlarging the distance between different classes, and roughly persuing relaxed targets may lead to the problem of overfitting. To solve above problems, we propose a low-rank discriminative least squares regression model (LRDLSR) for multi-class image classification. Specifically, LRDLSR class-wisely imposes low-rank constraint on the intra-class regression targets to encourage its compactness and similarity. Moreover, LRDLSR introduces an additional regularization term on the learned targets to avoid the problem of overfitting. These two improvements are helpful to learn a more discriminative projection for regression and thus achieving better classification performance. Experimental results over a range of image databases demonstrate the effectiveness of the proposed LRDLSR method.
NeuralHydrology -- Interpreting LSTMs in Hydrology
Venue: arXiv cs.LG 2019
Year: 2019

Despite the huge success of Long Short-Term Memory networks, their applications in environmental sciences are scarce. We argue that one reason is the difficulty to interpret the internals of trained networks. In this study, we look at the application of LSTMs for rainfall-runoff forecasting, one of the central tasks in the field of hydrology, in which the river discharge has to be predicted from meteorological observations. LSTMs are particularly well-suited for this problem since memory cells can represent dynamic reservoirs and storages, which are essential components in state-space modelling approaches of the hydrological system. On basis of two different catchments, one with snow influence and one without, we demonstrate how the trained model can be analyzed and interpreted. In the process, we show that the network internally learns to represent patterns that are consistent with our qualitative understanding of the hydrological system.
Efficient Consensus-based Formation Control With Discrete-Time Broadcast Updates
Venue: arXiv cs.SY 2019
Year: 2019

This paper presents a consensus-based formation control strategy for autonomous agents moving in the plane with continuous-time single integrator dynamics. In order to save wireless resources (bandwidth, energy, etc), the designed controller exploits the superposition property of the wireless channel. A communication system, which is based on the Wireless Multiple Access Channel (WMAC) model and can deal with the presence of a fading channel is designed. Agents access the channel with simultaneous broadcasts at synchronous update times. A continuous-time controller with discrete-time updates is proposed. A proof of convergence is given and simulations are shown, demonstrating the effectiveness of the suggested approach.
Improving Video Compression With Deep Visual-Attention Models
Venue: arXiv cs.CV 2019
Year: 2019

Recent advances in deep learning have markedly improved the quality of visual-attention modelling. In this work we apply these advances to video compression. We propose a compression method that uses a saliency model to adaptively compress frame areas in accordance with their predicted saliency. We selected three state-of-the-art saliency models, adapted them for video compression and analyzed their results. The analysis includes objective evaluation of the models as well as objective and subjective evaluation of the compressed videos. Our method, which is based on the x264 video codec, can produce videos with the same visual quality as regular x264, but it reduces the bitrate by 25% according to the objective evaluation and by 17% according to the subjective one. Also, both the subjective and objective evaluations demonstrate that saliency models can compete with gaze maps for a single observer. Our method can extend to most video bitstream formats and can improve video compression quality without requiring a switch to a new video encoding standard.
Complexity of the dynamics of reaction systems
Venue: arXiv cs.FL 2019
Year: 2019

Reaction systems are discrete dynamical systems inspired by bio-chemical processes, whose dynamical behaviour is expressed by set-theoretic operations on finite sets. Reaction systems thus provide a description of bio-chemical phenomena that complements the more traditional approaches, for instance those based on differential equations. A comprehensive list of decision problems about the dynamical behavior of reaction systems (such as cycles and fixed/periodic points, attractors, and reachability) is provided along with the corresponding computational complexity, which ranges from tractable problems to PSPACE-complete problems.
A Choquet Fuzzy Integral Vertical Bagging Classifier for Mobile Telematics Data Analysis
Venue: arXiv cs.LG 2019
Year: 2019

Mobile app development in recent years has resulted in new products and features to improve human life. Mobile telematics is one such development that encompasses multidisciplinary fields for transportation safety. The application of mobile telematics has been explored in many areas, such as insurance and road safety. However, to the best of our knowledge, its application in gender detection has not been explored. This paper proposes a Choquet fuzzy integral vertical bagging classifier that detects gender through mobile telematics. In this model, different random forest classifiers are trained by randomly generated features with rough set theory, and the top three classifiers are fused using the Choquet fuzzy integral. The model is implemented and evaluated on a real dataset. The empirical results indicate that the Choquet fuzzy integral vertical bagging classifier outperforms other classifiers.
Tackling Initial Centroid of K-Means with Distance Part (DP-KMeans)
Venue: arXiv cs.LG 2019
Year: 2019

The initial centroid is a fairly challenging problem in the k-means method because it can affect the clustering results. In addition, choosing the starting centroid of the cluster is not always appropriate, especially, when the number of groups increases.