OpenRaiser/Intern-Atlas huggingface dataset:
WordNet-feelings: A linguistic categorisation of human feelings
Venue: arXiv cs.CL 2018
Year: 2018

In this article, we present the first in depth linguistic study of human feelings. While there has been substantial research on incorporating some affective categories into linguistic analysis (e.g. sentiment, and to a lesser extent, emotion), the more diverse category of human feelings has thus far not been investigated. We surveyed the extensive interdisciplinary literature around feelings to construct a working definition of what constitutes a feeling and propose 9 broad categories of feeling. We identified potential feeling words based on their pointwise mutual information with morphological variants of the word `feel' in the Google n-gram corpus, and present a manual annotation exercise where 317 WordNet senses of one hundred of these words were categorised as `not a feeling' or as one of the 9 proposed categories of feeling. We then proceeded to annotate 11386 WordNet senses of all these words to create WordNet-feelings, a new affective dataset that identifies 3664 word senses as feelings, and associates each of these with one of the 9 categories of feeling. WordNet-feelings can be used in conjunction with other datasets such as SentiWordNet that annotate word senses with complementary affective properties such as valence and intensity.
Semantic Term "Blurring" and Stochastic "Barcoding" for Improved Unsupervised Text Classification
Venue: arXiv cs.CL 2018
Year: 2018

The abundance of text data being produced in the modern age makes it increasingly important to intuitively group, categorize, or classify text data by theme for efficient retrieval and search. Yet, the high dimensionality and imprecision of text data, or more generally language as a whole, prove to be challenging when attempting to perform unsupervised document clustering. In this thesis, we present two novel methods for improving unsupervised document clustering/classification by theme. The first is to improve document representations. We look to exploit "term neighborhoods" and "blur" semantic weight across neighboring terms. These neighborhoods are located in the semantic space afforded by "word embeddings." The second method is for cluster revision, based on what we deem as "stochastic barcoding", or "S- Barcode" patterns. Text data is inherently high dimensional, yet clustering typically takes place in a low dimensional representation space. Our method utilizes lower dimension clustering results as initial cluster configurations, and iteratively revises the configuration in the high dimensional space. We show with experimental results how both of the two methods improve the quality of document clustering. While this thesis elaborates on the two new conceptual contributions, a joint thesis by David Yan details the feature transformation and software architecture we developed for unsupervised document classification.
Quantizers with Parameterized Distortion Measures
Venue: arXiv cs.IT 2018
Year: 2018

In many quantization problems, the distortion function is given by the Euclidean metric to measure the distance of a source sample to any given reproduction point of the quantizer. We will in this work regard distortion functions, which are additively and multiplicatively weighted for each reproduction point resulting in a heterogeneous quantization problem, as used for example in deployment problems of sensor networks. Whereas, normally in such problems, the average distortion is minimized for given weights (parameters), we will optimize the quantization problem over all weights, i.e., we tune or control the distortion functions in our favor. For a uniform source distribution in one-dimension, we derive the unique minimizer, given as the uniform scalar quantizer with an optimal common weight. By numerical simulations, we demonstrate that this result extends to two-dimensions where asymptotically the parameter optimized quantizer is the hexagonal lattice with common weights. As an application, we will determine the optimal deployment of unmanned aerial vehicles (UAVs) to provide a wireless communication to ground terminals under a minimal communication power cost. Here, the optimal weights relate to the optimal flight heights of the UAVs.
Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-based Sequence to Sequence Network
Venue: arXiv cs.CV 2018
Year: 2018

Exploring contextual information in the local region is important for shape understanding and analysis. Existing studies often employ hand-crafted or explicit ways to encode contextual information of local regions. However, it is hard to capture fine-grained contextual information in hand-crafted or explicit manners, such as the correlation between different areas in a local region, which limits the discriminative ability of learned features. To resolve this issue, we propose a novel deep learning model for 3D point clouds, named Point2Sequence, to learn 3D shape features by capturing fine-grained contextual information in a novel implicit way. Point2Sequence employs a novel sequence learning model for point clouds to capture the correlations by aggregating multi-scale areas of each local region with attention. Specifically, Point2Sequence first learns the feature of each area scale in a local region. Then, it captures the correlation between area scales in the process of aggregating all area scales using a recurrent neural network (RNN) based encoder-decoder structure, where an attention mechanism is proposed to highlight the importance of different area scales. Experimental results show that Point2Sequence achieves state-of-the-art performance in shape classification and segmentation tasks.
Online Off-policy Prediction
Venue: arXiv cs.LG 2018
Year: 2018

This paper investigates the problem of online prediction learning, where learning proceeds continuously as the agent interacts with an environment. The predictions made by the agent are contingent on a particular way of behaving, represented as a value function. However, the behavior used to select actions and generate the behavior data might be different from the one used to define the predictions, and thus the samples are generated off-policy. The ability to learn behavior-contingent predictions online and off-policy has long been advocated as a key capability of predictive-knowledge learning systems but remained an open algorithmic challenge for decades. The issue lies with the temporal difference (TD) learning update at the heart of most prediction algorithms: combining bootstrapping, off-policy sampling and function approximation may cause the value estimate to diverge. A breakthrough came with the development of a new objective function that admitted stochastic gradient descent variants of TD. Since then, many sound online off-policy prediction algorithms have been developed, but there has been limited empirical work investigating the relative merits of all the variants. This paper aims to fill these empirical gaps and provide clarity on the key ideas behind each method. We summarize the large body of literature on off-policy learning, focusing on 1- methods that use computation linear in the number of features and are convergent under off-policy sampling, and 2- other methods which have proven useful with non-fixed, nonlinear function approximation. We provide an empirical study of off-policy prediction methods in two challenging microworlds. We report each method's parameter sensitivity, empirical convergence rate, and final performance, providing new insights that should enable practitioners to successfully extend these new methods to large-scale applications.[Abridged abstract]
DeepDPM: Dynamic Population Mapping via Deep Neural Network
Venue: arXiv cs.CV 2018
Year: 2018

Dynamic high resolution data on human population distribution is of great importance for a wide spectrum of activities and real-life applications, but is too difficult and expensive to obtain directly. Therefore, generating fine-scaled population distributions from coarse population data is of great significance. However, there are three major challenges: 1) the complexity in spatial relations between high and low resolution population; 2) the dependence of population distributions on other external information; 3) the difficulty in retrieving temporal distribution patterns. In this paper, we first propose the idea to generate dynamic population distributions in full-time series, then we design dynamic population mapping via deep neural network(DeepDPM), a model that describes both spatial and temporal patterns using coarse data and point of interest information. In DeepDPM, we utilize super-resolution convolutional neural network(SRCNN) based model to directly map coarse data into higher resolution data, and a time-embedded long short-term memory model to effectively capture the periodicity nature to smooth the finer-scaled results from the previous static SRCNN model. We perform extensive experiments on a real-life mobile dataset collected from Shanghai. Our results demonstrate that DeepDPM outperforms previous state-of-the-art methods and a suite of frequent data-mining approaches. Moreover, DeepDPM breaks through the limitation from previous works in time dimension so that dynamic predictions in all-day time slots can be obtained.
Automatic Assessment of Full Left Ventricular Coverage in Cardiac Cine Magnetic Resonance Imaging with Fisher-Discriminative 3D CNN
Venue: arXiv cs.CV 2018
Year: 2018

Cardiac magnetic resonance (CMR) images play a growing role in the diagnostic imaging of cardiovascular diseases. Full coverage of the left ventricle (LV), from base to apex, is a basic criterion for CMR image quality and necessary for accurate measurement of cardiac volume and functional assessment. Incomplete coverage of the LV is identified through visual inspection, which is time-consuming and usually done retrospectively in the assessment of large imaging cohorts. This paper proposes a novel automatic method for determining LV coverage from CMR images by using Fisher-discriminative three-dimensional (FD3D) convolutional neural networks (CNNs). In contrast to our previous method employing 2D CNNs, this approach utilizes spatial contextual information in CMR volumes, extracts more representative high-level features and enhances the discriminative capacity of the baseline 2D CNN learning framework, thus achieving superior detection accuracy. A two-stage framework is proposed to identify missing basal and apical slices in measurements of CMR volume. First, the FD3D CNN extracts high-level features from the CMR stacks. These image representations are then used to detect the missing basal and apical slices. Compared to the traditional 3D CNN strategy, the proposed FD3D CNN minimizes within-class scatter and maximizes between-class scatter. We performed extensive experiments to validate the proposed method on more than 5,000 independent volumetric CMR scans from the UK Biobank study, achieving low error rates for missing basal/apical slice detection (4.9\%/4.6\%). The proposed method can also be adopted for assessing LV coverage for other types of CMR image data.
View Inter-Prediction GAN: Unsupervised Representation Learning for 3D Shapes by Learning Global Shape Memories to Support Local View Predictions
Venue: arXiv cs.CV 2018
Year: 2018

In this paper we present a novel unsupervised representation learning approach for 3D shapes, which is an important research challenge as it avoids the manual effort required for collecting supervised data. Our method trains an RNN-based neural network architecture to solve multiple view inter-prediction tasks for each shape. Given several nearby views of a shape, we define view inter-prediction as the task of predicting the center view between the input views, and reconstructing the input views in a low-level feature space. The key idea of our approach is to implement the shape representation as a shape-specific global memory that is shared between all local view inter-predictions for each shape. Intuitively, this memory enables the system to aggregate information that is useful to better solve the view inter-prediction tasks for each shape, and to leverage the memory as a view-independent shape representation. Our approach obtains the best results using a combination of L_2 and adversarial losses for the view inter-prediction task. We show that VIP-GAN outperforms state-of-the-art methods in unsupervised 3D feature learning on three large scale 3D shape benchmarks.
Gravitational octree code performance evaluation on Volta GPU
Venue: arXiv cs.MS 2018
Year: 2018

In this study, the gravitational octree code originally optimized for the Fermi, Kepler, and Maxwell GPU architectures is adapted to the Volta architecture. The Volta architecture introduces independent thread scheduling requiring either the insertion of the explicit synchronizations at appropriate locations or the enforcement of the same implicit synchronizations as do the Pascal or earlier architectures by specifying \texttt{-gencode arch=compute\_60,code=sm\_70}. The performance measurements on Tesla V100, the current flagship GPU by NVIDIA, revealed that the $N$-body simulations of the Andromeda galaxy model with $2^{23} = 8388608$ particles took $3.8 \times 10^{-2}$~s or $3.3 \times 10^{-2}$~s per step for each case. Tesla V100 achieves a 1.4 to 2.2-fold acceleration in comparison with Tesla P100, the flagship GPU in the previous generation. The observed speed-up of 2.2 is greater than 1.5, which is the ratio of the theoretical peak performance of the two GPUs. The independence of the units for integer operations from those for floating-point number operations enables the overlapped execution of integer and floating-point number operations. It hides the execution time of the integer operations leading to the speed-up rate above the theoretical peak performance ratio. Tesla V100 can execute $N$-body simulation with up to $25 \times 2^{20} = 26214400$ particles, and it took $2.0 \times 10^{-1}$~s per step. It corresponds to $3.5$~TFlop/s, which is 22\% of the single-precision theoretical peak performance.
Modeling Personalized Dynamics of Social Network and Opinion at Individual Level
Venue: arXiv cs.SI 2018
Year: 2018

Network dynamics has always been a meaningful topic deserving exploration in the realm of academy. previous network models contain two parts: (1) generating structure as per user property; (2) changing property as per network structure. Properties in these models, however, cannot be interpreted to concept in prevalent social theories or empirical truth. Also, they usually treat everyone in an uniform fashion. While such assumption is quite misguiding, and thus saliently limits their performance. To overcome these flaws, we devise a personalized evolving model for social network and opinion (PENO), where citizens' ideology is revealed by variable opinions and four dimensions of personality are considered for each entity - leadership, openness, agreeableness, and neuroticism. Opinion propagates via social tie, tie generates from opinion affinity, and personalities integrally work with opinion and tie across evolution. To our best knowledge, PENO is the first attempt to introduce personality impact in network dynamics and verify social science with reasonable visualization during simulation. We also present its probabilistic graph and conceive iterative learning algorithm. Experiments show PENO outperforms several state-of-the-art baselines over two typical prediction tasks - congress voting prediction for legislative bills and friendship prediction on a book-commenting website. Finally, we discuss its scalability to do multi-task learning and transfer learning in daily scenarios.
Image Smoothing via Unsupervised Learning
Venue: arXiv cs.CV 2018
Year: 2018

Image smoothing represents a fundamental component of many disparate computer vision and graphics applications. In this paper, we present a unified unsupervised (label-free) learning framework that facilitates generating flexible and high-quality smoothing effects by directly learning from data using deep convolutional neural networks (CNNs). The heart of the design is the training signal as a novel energy function that includes an edge-preserving regularizer which helps maintain important yet potentially vulnerable image structures, and a spatially-adaptive Lp flattening criterion which imposes different forms of regularization onto different image regions for better smoothing quality. We implement a diverse set of image smoothing solutions employing the unified framework targeting various applications such as, image abstraction, pencil sketching, detail enhancement, texture removal and content-aware image manipulation, and obtain results comparable with or better than previous methods. Moreover, our method is extremely fast with a modern GPU (e.g, 200 fps for 1280x720 images). Our codes and model are released in https://github.com/fqnchina/ImageSmoothing.
Unification in Matching Logic - Extended Version
Venue: arXiv cs.LO 2018
Year: 2018

Matching Logic is a framework for specifying programming language semantics and reasoning about programs. Its formulas are called patterns and are built with variables, symbols, connectives and quantifiers. A pattern is a combination of structural components (term patterns), which must be matched, and constraints (predicate patterns), which must be satisfied. Dealing with more than one structural component in a pattern could be cumbersome because it involves multiple matching operations. A source for getting patterns with many structural components is the conjunction of patterns. Here, we propose a method that uses a syntactic unification algorithm to transform conjunctions of structural patterns into equivalent patterns having only one structural component and some additional constraints. We prove the soundness of our approach, we discuss why the approach is not complete and we provide sound strategies to generate certificates for the equivalences, validated using Coq.
Effects of Dataset properties on the training of GANs
Venue: arXiv cs.LG 2018
Year: 2018

Generative Adversarial Networks are a new family of generative models, frequently used for generating photorealistic images. The theory promises for the GAN to eventually reach an equilibrium where generator produces pictures indistinguishable for the training set. In practice, however, a range of problems frequently prevents the system from reaching this equilibrium, with training not progressing ahead due to instabilities or mode collapse. This paper describes a series of experiments trying to identify patterns in regard to the effect of the training set on the dynamics and eventual outcome of the training.
Player Availability Rating (PAR) - A Tool for Quantifying Skater Performance for NHL General Managers
Venue: arXiv cs.CY 2018
Year: 2018

This project aims to assess the performance of various regression models in predicting the performance of hockey players. The measure of performance is chosen to be points scored (sum of goals scored and assists made) by individual players per game (PPG). This paper thus considers the offensive performance of the players and uses PPG as a metric to assign a value to the player. This predicted value can be used to rank players and is similar to what TSN.com and NHL.com use to rank players before each hockey season. A combination of physical characteristics, shooting percentage, and usage during games are used as training features. A novel metric, Player Availability Rating (PAR) is proposed which utilizes the offensive predictions to predict the availability of a player during a season. These metrics can be valuable in the NHL as it would allow for general managers to track a players performance and their availability during the course of a season.
Certified Ethical Hacker v.10 Online Course - a Case Study
Venue: arXiv cs.CY 2018
Year: 2018

CEH v.10 Certification Self-study Course is an online course preparing learners for one of the most prestige cyber security certifications in the world - the Certified Ethical Hacker (CEH) v.10 Certification. Due to a pay wall and the practical rather than theoretical nature, most researchers have limited exposure to this course. For the first time, this paper will analyze the course's instructional design based on the highest national standards and related peer-reviewed published research works. The sole intention is to push the course to a higher ground, making it the best online course for cyber security. More importantly, the paper's instructional design evaluation strategy can well be extended and applied to any other online course' instructional design review and/or evaluation process.
Model Inconsistent but Correlated Noise: Multi-view Subspace Learning with Regularized Mixture of Gaussians
Venue: arXiv cs.LG 2018
Year: 2018

Multi-view subspace learning (MSL) aims to find a low-dimensional subspace of the data obtained from multiple views. Different from single view case, MSL should take both common and specific knowledge among different views into consideration. To enhance the robustness of model, the complexity, non-consistency and similarity of noise in multi-view data should be fully taken into consideration. Most current MSL methods only assume a simple Gaussian or Laplacian distribution for the noise while neglect the complex noise configurations in each view and noise correlations among different views of practical data. To this issue, this work initiates a MSL method by encoding the multi-view-shared and single-view-specific noise knowledge in data. Specifically, we model data noise in each view as a separated Mixture of Gaussians (MoG), which can fit a wider range of complex noise types than conventional Gaussian/Laplacian. Furthermore, we link all single-view-noise as a whole by regularizing them by a common MoG component, encoding the shared noise knowledge among them. Such regularization component can be formulated as a concise KL-divergence regularization term under a MAP framework, leading to good interpretation of our model and simple EM-based solving strategy to the problem. Experimental results substantiate the superiority of our method.
Scalable and Energy-Efficient Millimeter Massive MIMO Architectures: Reflect-Array and Transmit-Array Antennas
Venue: arXiv cs.IT 2018
Year: 2018

Hybrid analog-digital architectures are considered as promising candidates for implementing millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems since they enable a considerable reduction of the required number of costly radio frequency (RF) chains by moving some of the signal processing operations into the analog domain. However, the analog feed network, comprising RF dividers, combiners, phase shifters, and line connections, of hybrid MIMO architectures is not scalable due to its prohibitively high power consumption for large numbers of transmit antennas. Motivated by this limitation, in this paper, we study novel massive MIMO architectures, namely reflect-array (RA) and transmit-array (TA) antennas. We show that the precoders for RA and TA antennas have to meet different constraints compared to those for conventional MIMO architectures. Taking these constraints into account and exploiting the sparsity of mmWave channels, we design an efficient precoder for RA and TA antennas based on the orthogonal matching pursuit algorithm. Furthermore, in order to fairly compare the performance of RA and TA antennas with conventional fully-digital and hybrid MIMO architectures, we develop a unified power consumption model. Our simulation results show that unlike conventional MIMO architectures, RA and TA antennas are highly energy efficient and fully scalable in terms of the number of transmit antennas.