OpenRaiser/Intern-Atlas huggingface dataset:
Nonlinear Modal Decoupling Based Power System Transient Stability Analysis
Venue: arXiv cs.SY 2018
Year: 2018

Nonlinear modal decoupling (NMD) was recently proposed to nonlinearly transform a multi-oscillator system into a number of decoupled oscillators which together behave the same as the original system in an extended neighborhood of the equilibrium. Each oscillator has just one degree of freedom and hence can easily be analyzed to infer the stability of the original system associated with one electromechanical mode. As the first attempt of applying the NMD methodology to realistic power system models, this paper proposes an NMD-based transient stability analysis approach. For a multi-machine power system, the approach first derives decoupled nonlinear oscillators by a coordinates transformation, and then applies Lyapunov stability analysis to oscillators to assess the stability of the original system. Nonlinear modal interaction is also considered. The approach can be efficiently applied to a large-scale power grid by conducting NMD regarding only selected modes. Case studies on a 3-machine 9-bus system and an NPCC 48-machine 140-bus system show the potentials of the approach in transient stability analysis for multi-machine systems.
Global sensitivity analysis based on DIRECT-KG-HDMR and thermal optimization of pin-fin heat sink for the platform inertial navigation system
Venue: arXiv cs.CE 2018
Year: 2018

In this study, in order to reduce the local high temperature of the platform in inertial navigation system (PINS), a pin-fin heat sink with staggered arrangement is designed. To reduce the dimension of the inputs and improve the efficiency of optimization, a feasible global sensitivity analysis (GSA) based on Kriging-High Dimensional Model Representation with DIviding RECTangles sampling strategy (DIRECT-KG-HDMR) is proposed. Compared with other GSA methods, the proposed method can indicate the effects of the structural and the material parameters on the maximum temperature at the bottom of the heat sink by using both sensitivity and coupling coefficients. From the results of GSA, it can be found that the structural parameters have greater effects on thermal performance than the material ones. Moreover, the coupling intensities between the structural and material parameters are weak. Therefore, the structural parameters are selected to optimize the thermal performance of the heat sink, and several popular optimization algorithms such as GA, DE, TLBO, PSO and EGO are used for the optimization. Moreover, steady thermal response of the PINS with the optimized heat sink is also studied, and its result shows that the maximum temperature of high temperature region of the platform is reduced by 1.09 degree Celsius compared with the PINS without the heat sink.
Parallel processing area extraction and data transfer number reduction for automatic GPU offloading of IoT applications
Venue: arXiv cs.DC 2018
Year: 2018

For Open IoT, we have proposed Tacit Computing technology to discover the devices that have data users need on demand and use them dynamically and an automatic GPU offloading technology as an elementary technology of Tacit Computing. However, it can improve limited applications because it only optimizes parallelizable loop statements extraction. Thus, in this paper, to improve performances of more applications automatically, we propose an improved method with reduction of data transfer between CPU and GPU. We evaluate our proposed offloading method by applying it to Darknet and find that it can process it 3 times as quickly as only using CPU.
Collaboratively Learning the Best Option on Graphs, Using Bounded Local Memory
Venue: arXiv cs.DC 2018
Year: 2018

We consider multi-armed bandit problems in social groups wherein each individual has bounded memory and shares the common goal of learning the best arm/option. We say an individual learns the best option if eventually (as $t\to \infty$) it pulls only the arm with the highest expected reward. While this goal is provably impossible for an isolated individual due to bounded memory, we show that, in social groups, this goal can be achieved easily with the aid of social persuasion (i.e., communication) as long as the communication networks/graphs satisfy some mild conditions. To deal with the interplay between the randomness in the rewards and in the social interaction, we employ the {\em mean-field approximation} method. Considering the possibility that the individuals in the networks may not be exchangeable when the communication networks are not cliques, we go beyond the classic mean-field techniques and apply a refined version of mean-field approximation: (1) Using coupling we show that, if the communication graph is connected and is either regular or has doubly-stochastic degree-weighted adjacency matrix, with probability $\to 1$ as the social group size $N \to \infty$, every individual in the social group learns the best option. (2) If the minimum degree of the graph diverges as $N \to \infty$, over an arbitrary but given finite time horizon, the sample paths describing the opinion evolutions of the individuals are asymptotically independent. In addition, the proportions of the population with different opinions converge to the unique solution of a system of ODEs. In the solution of the obtained ODEs, the proportion of the population holding the correct opinion converges to $1$ exponentially fast in time. Notably, our results hold even if the communication graphs are highly sparse.
Looking Deeper into Deep Learning Model: Attribution-based Explanations of TextCNN
Venue: arXiv cs.IR 2018
Year: 2018

Layer-wise Relevance Propagation (LRP) and saliency maps have been recently used to explain the predictions of Deep Learning models, specifically in the domain of text classification. Given different attribution-based explanations to highlight relevant words for a predicted class label, experiments based on word deleting perturbation is a common evaluation method. This word removal approach, however, disregards any linguistic dependencies that may exist between words or phrases in a sentence, which could semantically guide a classifier to a particular prediction. In this paper, we present a feature-based evaluation framework for comparing the two attribution methods on customer reviews (public data sets) and Customer Due Diligence (CDD) extracted reports (corporate data set). Instead of removing words based on the relevance score, we investigate perturbations based on embedded features removal from intermediate layers of Convolutional Neural Networks. Our experimental study is carried out on embedded-word, embedded-document, and embedded-ngrams explanations. Using the proposed framework, we provide a visualization tool to assist analysts in reasoning toward the model's final prediction.
Imprecise dynamic walking with time-projection control
Venue: arXiv cs.RO 2018
Year: 2018

We present a new walking foot-placement controller based on 3LP, a 3D model of bipedal walking that is composed of three pendulums to simulate falling, swing and torso dynamics. Taking advantage of linear equations and closed-form solutions of the 3LP model, our proposed controller projects intermediate states of the biped back to the beginning of the phase for which a discrete LQR controller is designed. After the projection, a proper control policy is generated by this LQR controller and used at the intermediate time. This control paradigm reacts to disturbances immediately and includes rules to account for swing dynamics and leg-retraction. We apply it to a simulated Atlas robot in position-control, always commanded to perform in-place walking. The stance hip joint in our robot keeps the torso upright to let the robot naturally fall, and the swing hip joint tracks the desired footstep location. Combined with simple Center of Pressure (CoP) damping rules in the low-level controller, our foot-placement enables the robot to recover from strong pushes and produce periodic walking gaits when subject to persistent sources of disturbance, externally or internally. These gaits are imprecise, i.e., emergent from asymmetry sources rather than precisely imposing a desired velocity to the robot. Also in extreme conditions, restricting linearity assumptions of the 3LP model are often violated, but the system remains robust in our simulations. An extensive analysis of closed-loop eigenvalues, viable regions and sensitivity to push timings further demonstrate the strengths of our simple controller.
A generic framework for privacy preserving deep learning
Venue: arXiv cs.LG 2018
Year: 2018

We detail a new framework for privacy preserving deep learning and discuss its assets. The framework puts a premium on ownership and secure processing of data and introduces a valuable representation based on chains of commands and tensors. This abstraction allows one to implement complex privacy preserving constructs such as Federated Learning, Secure Multiparty Computation, and Differential Privacy while still exposing a familiar deep learning API to the end-user. We report early results on the Boston Housing and Pima Indian Diabetes datasets. While the privacy features apart from Differential Privacy do not impact the prediction accuracy, the current implementation of the framework introduces a significant overhead in performance, which will be addressed at a later stage of the development. We believe this work is an important milestone introducing the first reliable, general framework for privacy preserving deep learning.
Splenomegaly Segmentation on Multi-modal MRI using Deep Convolutional Networks
Venue: arXiv cs.CV 2018
Year: 2018

The findings of splenomegaly, abnormal enlargement of the spleen, is a non-invasive clinical biomarker for liver and spleen disease. Automated segmentation methods are essential to efficiently quantify splenomegaly from clinically acquired abdominal magnetic resonance imaging (MRI) scans. However, the task is challenging due to (1) large anatomical and spatial variations of splenomegaly, (2) large inter- and intra-scan intensity variations on multi-modal MRI, and (3) limited numbers of labeled splenomegaly scans. In this paper, we propose the Splenomegaly Segmentation Network (SS-Net) to introduce the deep convolutional neural network (DCNN) approaches in multi-modal MRI splenomegaly segmentation. Large convolutional kernel layers were used to address the spatial and anatomical variations, while the conditional generative adversarial networks (GAN) were employed to leverage the segmentation performance of SS-Net in an end-to-end manner. A clinically acquired cohort containing both T1-weighted (T1w) and T2-weighted (T2w) MRI splenomegaly scans was used to train and evaluate the performance of multi-atlas segmentation (MAS), 2D DCNN networks, and a 3D DCNN network. From the experimental results, the DCNN methods achieved superior performance to the state-of-the-art MAS method. The proposed SS-Net method achieved the highest median and mean Dice scores among investigated baseline DCNN methods.
Understanding and Predicting Links in Graphs: A Persistent Homology Perspective
Venue: arXiv cs.SI 2018
Year: 2018

Persistent Homology is a powerful tool in Topological Data Analysis (TDA) to capture topological properties of data succinctly at different spatial resolutions. For graphical data, shape, and structure of the neighborhood of individual data items (nodes) is an essential means of characterizing their properties. In this paper, we propose the use of persistent homology methods to capture structural and topological properties of graphs and use it to address the problem of link prediction. We evaluate our approach on seven different real-world datasets and offer directions for future work.
A note on simultaneous representation problem for interval and circular-arc graphs
Venue: arXiv cs.DM 2018
Year: 2018

In this short note, we show two NP-completeness results regarding the \emph{simultaneous representation problem}, introduced by Lubiw and Jampani. The simultaneous representation problem for a given class of intersection graphs asks if some $k$ graphs can be represented so that every vertex is represented by the same interval in each representation. We prove that it is NP-complete to decide this for the class of interval and circular-arc graphs in the case when $k$ is a part of the input and graphs are not in a sunflower position.
Multiple People Tracking Using Hierarchical Deep Tracklet Re-identification
Venue: arXiv cs.CV 2018
Year: 2018

The task of multiple people tracking in monocular videos is challenging because of the numerous difficulties involved: occlusions, varying environments, crowded scenes, camera parameters and motion. In the tracking-by-detection paradigm, most approaches adopt person re-identification techniques based on computing the pairwise similarity between detections. However, these techniques are less effective in handling long-term occlusions. By contrast, tracklet (a sequence of detections) re-identification can improve association accuracy since tracklets offer a richer set of visual appearance and spatio-temporal cues. In this paper, we propose a tracking framework that employs a hierarchical clustering mechanism for merging tracklets. To this end, tracklet re-identification is performed by utilizing a novel multi-stage deep network that can jointly reason about the visual appearance and spatio-temporal properties of a pair of tracklets, thereby providing a robust measure of affinity. Experimental results on the challenging MOT16 and MOT17 benchmarks show that our method significantly outperforms state-of-the-arts.
Reasoning over RDF Knowledge Bases using Deep Learning
Venue: arXiv cs.LG 2018
Year: 2018

Semantic Web knowledge representation standards, and in particular RDF and OWL, often come endowed with a formal semantics which is considered to be of fundamental importance for the field. Reasoning, i.e., the drawing of logical inferences from knowledge expressed in such standards, is traditionally based on logical deductive methods and algorithms which can be proven to be sound and complete and terminating, i.e. correct in a very strong sense. For various reasons, though, in particular, the scalability issues arising from the ever-increasing amounts of Semantic Web data available and the inability of deductive algorithms to deal with noise in the data, it has been argued that alternative means of reasoning should be investigated which bear high promise for high scalability and better robustness. From this perspective, deductive algorithms can be considered the gold standard regarding correctness against which alternative methods need to be tested. In this paper, we show that it is possible to train a Deep Learning system on RDF knowledge graphs, such that it is able to perform reasoning over new RDF knowledge graphs, with high precision and recall compared to the deductive gold standard.
Computational Thinking with the Web Crowd using CodeMapper
Venue: arXiv cs.CY 2018
Year: 2018

It has been argued that computational thinking should precede computer programming in the course of a career in computing. This argument is the basis for the slogan "logic first, syntax later" and the development of many cryptic syntax removed programming languages such as Scratch!, Blockly and Visual Logic. The goal is to focus on the structuring of the semantic relationships among the logical building blocks to yield solutions to computational problems. While this approach is helping novice programmers and early learners, the gap between computational thinking and professional programming using high level languages such as C++, Python and Java is quite wide. It is wide enough for about one third students in first college computer science classes to drop out or fail. In this paper, we introduce a new programming platform, called the CodeMapper, in which learners are able to build computational logic in independent modules and aggregate them to create complex modules. Code{\em Mapper} is an abstract development environment in which rapid visual prototyping of small to substantially large systems is possible by combining already developed independent modules in logical steps. The challenge we address involves supporting a visual development environment in which "annotated code snippets" authored by the masses in social computing sites such as SourceForge, StackOverflow or GitHub can also be used as is into prototypes and mapped to real executable programs. CodeMapper thus facilitates soft transition from visual programming to syntax driven programming without having to practice syntax too heavily.
Power Normalizing Second-order Similarity Network for Few-shot Learning
Venue: arXiv cs.CV 2018
Year: 2018

Second- and higher-order statistics of data points have played an important role in advancing the state of the art on several computer vision problems such as the fine-grained image and scene recognition. However, these statistics need to be passed via an appropriate pooling scheme to obtain the best performance. Power Normalizations are non-linear activation units which enjoy probability-inspired derivations and can be applied in CNNs. In this paper, we propose a similarity learning network leveraging second-order information and Power Normalizations. To this end, we propose several formulations capturing second-order statistics and derive a sigmoid-like Power Normalizing function to demonstrate its interpretability. Our model is trained end-to-end to learn the similarity between the support set and query images for the problem of one- and few-shot learning. The evaluations on Omniglot, miniImagenet and Open MIC datasets demonstrate that this network obtains state-of-the-art results on several few-shot learning protocols.
Efficient Spiking Neural Networks with Logarithmic Temporal Coding
Venue: arXiv cs.NE 2018
Year: 2018

A Spiking Neural Network (SNN) can be trained indirectly by first training an Artificial Neural Network (ANN) with the conventional backpropagation algorithm, then converting it into an SNN. The conventional rate-coding method for SNNs uses the number of spikes to encode magnitude of an activation value, and may be computationally inefficient due to the large number of spikes. Temporal-coding is typically more efficient by leveraging the timing of spikes to encode information. In this paper, we present Logarithmic Temporal Coding (LTC), where the number of spikes used to encode an activation value grows logarithmically with the activation value; and the accompanying Exponentiate-and-Fire (EF) spiking neuron model, which only involves efficient bit-shift and addition operations. Moreover, we improve the training process of ANN to compensate for approximation errors due to LTC. Experimental results indicate that the resulting SNN achieves competitive performance at significantly lower computational cost than related work.
Efficiently Approximating Edit Distance Between Pseudorandom Strings
Venue: arXiv cs.DS 2018
Year: 2018

We present an algorithm for approximating the edit distance $\operatorname{ed}(x, y)$ between two strings $x$ and $y$ in time parameterized by the degree to which one of the strings $x$ satisfies a natural pseudorandomness property. The pseudorandomness model is asymmetric in that no requirements are placed on the second string $y$, which may be constructed by an adversary with full knowledge of $x$. We say that $x$ is \emph{$(p, B)$-pseudorandom} if all pairs $a$ and $b$ of disjoint $B$-letter substrings of $x$ satisfy $\operatorname{ed}(a, b) \ge pB$. Given parameters $p$ and $B$, our algorithm computes the edit distance between a $(p, B)$-pseudorandom string $x$ and an arbitrary string $y$ within a factor of $O(1/p)$ in time $\tilde{O}(nB)$, with high probability. Our algorithm is robust in the sense that it can handle a small portion of $x$ being adversarial (i.e., not satisfying the pseudorandomness property). In this case, the algorithm incurs an additive approximation error proportional to the fraction of $x$ which behaves maliciously. The asymmetry of our pseudorandomness model has particular appeal for the case where $x$ is a \emph{source string}, meaning that $\operatorname{ed}(x, y)$ will be computed for many strings $y$. Suppose that one wishes to achieve an $O(α)$-approximation for each $\operatorname{ed}(x, y)$ computation, and that $B$ is the smallest block-size for which the string $x$ is $(1/α, B)$-pseudorandom. We show that without knowing $B$ beforehand, $x$ may be preprocessed in time $\tilde{O}(n^{1.5}\sqrt{B})$, so that all future computations of the form $\operatorname{ed}(x, y)$ may be $O(α)$-approximated in time $\tilde{O}(nB)$. Furthermore, for the special case where only a single $\operatorname{ed}(x, y)$ computation will be performed, we show how to achieve an $O(α)$-approximation in time $\tilde{O}(n^{4/3}B^{2/3})$.
Towards Governing Agent's Efficacy: Action-Conditional $β$-VAE for Deep Transparent Reinforcement Learning
Venue: arXiv cs.LG 2018
Year: 2018

We tackle the blackbox issue of deep neural networks in the settings of reinforcement learning (RL) where neural agents learn towards maximizing reward gains in an uncontrollable way. Such learning approach is risky when the interacting environment includes an expanse of state space because it is then almost impossible to foresee all unwanted outcomes and penalize them with negative rewards beforehand. Unlike reverse analysis of learned neural features from previous works, our proposed method \nj{tackles the blackbox issue by encouraging} an RL policy network to learn interpretable latent features through an implementation of a disentangled representation learning method. Toward this end, our method allows an RL agent to understand self-efficacy by distinguishing its influences from uncontrollable environmental factors, which closely resembles the way humans understand their scenes. Our experimental results show that the learned latent factors not only are interpretable, but also enable modeling the distribution of entire visited state space with a specific action condition. We have experimented that this characteristic of the proposed structure can lead to ex post facto governance for desired behaviors of RL agents.
Neural Generative Models for 3D Faces with Application in 3D Texture Free Face Recognition
Venue: arXiv cs.CV 2018
Year: 2018

Using heterogeneous depth cameras and 3D scanners in 3D face verification causes variations in the resolution of the 3D point clouds. To solve this issue, previous studies use 3D registration techniques. Out of these proposed techniques, detecting points of correspondence is proven to be an efficient method given that the data belongs to the same individual. However, if the data belongs to different persons, the registration algorithms can convert the 3D point cloud of one person to another, destroying the distinguishing features between the two point clouds. Another issue regarding the storage size of the point clouds. That is, if the captured depth image contains around 50 thousand points in the cloud for a single pose for one individual, then the storage size of the entire dataset will be in order of giga if not tera bytes. With these motivations, this work introduces a new technique for 3D point clouds generation using a neural modeling system to handle the differences caused by heterogeneous depth cameras, and to generate a new face canonical compact representation. The proposed system reduces the stored 3D dataset size, and if required, provides an accurate dataset regeneration. Furthermore, the system generates neural models for all gallery point clouds and stores these models to represent the faces in the recognition or verification processes. For the probe cloud to be verified, a new model is generated specifically for that particular cloud and is matched against pre-stored gallery model presentations to identify the query cloud. This work also introduces the utilization of Siamese deep neural network in 3D face verification using generated model representations as raw data for the deep network, and shows that the accuracy of the trained network is comparable all published results on Bosphorus dataset.