OpenRaiser/Intern-Atlas huggingface dataset:
MOOSE: Enabling Massively Parallel Multiphysics Simulation
Venue: arXiv cs.MS 2019
Year: 2019

Harnessing modern parallel computing resources to achieve complex multi-physics simulations is a daunting task. The Multiphysics Object Oriented Simulation Environment (MOOSE) aims to enable such development by providing simplified interfaces for specification of partial differential equations, boundary conditions, material properties, and all aspects of a simulation without the need to consider the parallel, adaptive, nonlinear, finite-element solve that is handled internally. Through the use of interfaces and inheritance, each portion of a simulation becomes reusable and composable in a manner that allows disparate research groups to share code and create an ecosystem of growing capability that lowers the barrier for the creation of multiphysics simulation codes. Included within the framework is a unique capability for building multiscale, multiphysics simulations through simultaneous execution of multiple sub-applications with data transfers between the scales. Other capabilities include automatic differentiation, scaling to a large number of processors, hybrid parallelism, and mesh adaptivity. To date, MOOSE-based applications have been created in areas of science and engineering such as nuclear physics, geothermal science, magneto-hydrodynamics, seismic events, compressible and incompressible fluid flow, microstructure evolution, and advanced manufacturing processes.
Reinforcement-Learning-Based Variational Quantum Circuits Optimization for Combinatorial Problems
Venue: arXiv cs.LG 2019
Year: 2019

Quantum computing exploits basic quantum phenomena such as state superposition and entanglement to perform computations. The Quantum Approximate Optimization Algorithm (QAOA) is arguably one of the leading quantum algorithms that can outperform classical state-of-the-art methods in the near term. QAOA is a hybrid quantum-classical algorithm that combines a parameterized quantum state evolution with a classical optimization routine to approximately solve combinatorial problems. The quality of the solution obtained by QAOA within a fixed budget of calls to the quantum computer depends on the performance of the classical optimization routine used to optimize the variational parameters. In this work, we propose an approach based on reinforcement learning (RL) to train a policy network that can be used to quickly find high-quality variational parameters for unseen combinatorial problem instances. The RL agent is trained on small problem instances which can be simulated on a classical computer, yet the learned RL policy is generalizable and can be used to efficiently solve larger instances. Extensive simulations using the IBM Qiskit Aer quantum circuit simulator demonstrate that our trained RL policy can reduce the optimality gap by a factor up to 8.61 compared with other off-the-shelf optimizers tested.
A Fast Macromodeling Approach to Efficiently Simulate Inhomogeneous Electromagnetic Surfaces
Venue: arXiv cs.CE 2019
Year: 2019

The full-wave simulation of complex electromagnetic surfaces such as reflectarrays and metasurfaces is a challenging problem. In this paper, we present a macromodeling approach to efficiently simulate complex electromagnetic surfaces composed of PEC traces, possibly with fine features, on a finite-sized multilayer dielectric substrate. In our approach, we enclose each element of the structure with a fictitious surface. By applying the equivalence principle on each surface, we derive a macromodel for each element of the array. This macromodel consists of a linear operator that relates the equivalent electric and magnetic current densities introduced on the fictitious surface. Mutual coupling between the elements of the structure is captured by the equivalent current densities in a fully accurate way. The crux of the proposed technique is to solve for equivalent current densities on the fictitious surface instead of directly solving for the actual current densities on the original scatterer. When simulating complex surfaces, this approach leads to fewer unknowns and better conditioning. We also propose a rigorous acceleration algorithm based on the fast Fourier transform to simulate electrically large surfaces. Numerical results demonstrate that the proposed approach is significantly faster and requires less memory than commercial solvers based on the surface integral equation method, while giving accurate results.
Recurrence Extraction for Functional Programs through Call-by-Push-Value (Extended Version)
Venue: arXiv cs.PL 2019
Year: 2019

The main way of analyzing the complexity of a program is that of extracting and solving a recurrence that expresses its running time in terms of the size of its input. We develop a method that automatically extracts such recurrences from the syntax of higher-order recursive functional programs. The resulting recurrences, which are programs in a call-by-name language with recursion, explicitly compute the running time in terms of the size of the input. In order to achieve this in a uniform way that covers both call-by-name and call-by-value evaluation strategies, we use Call-by-Push-Value (CBPV) as an intermediate language. Finally, we use domain theory to develop a denotational cost semantics for the resulting recurrences.
Fast Stochastic Peer Selection in Proof-of-Stake Protocols
Venue: arXiv cs.DC 2019
Year: 2019

The problem of peer selection, which randomly selects a peer from a set, is commonplace in Proof-of-Stake (PoS) protocols. In PoS, peers are chosen randomly with probability proportional to the amount of stake that they possess. This paper presents an approach that relates PoS peer selection to Roulette-wheel selection, which is frequently used in genetic and evolutionary algorithms or complex network modelling. In particular, we introduce the use of stochastic acceptance algorithm [6] for fast peer selection. The roulette-wheel selection algorithm [6] achieves O(1) complexity based on stochastic acceptance, whereas searching based algorithms may take O(N ) or O(logN ) complexity in a network of N peers.
Scheme Macros for Non-linear Pattern Matching with Backtracking for Non-free Data Types
Venue: arXiv cs.PL 2019
Year: 2019

Pattern matching is an important feature of programming languages for data abstraction. Many pattern-matching extensions have been proposed and implemented for extending the range of data types to which pattern matching is applicable. Among them, the pattern-matching system proposed by Egi and Nishiwaki features practical pattern matching for non-free data types by providing an extensible non-linear pattern-matching facility with backtracking. However, they implemented their proposal only in an interpreter of the Egison programming language, and a method for compiling pattern-matching expressions of Egison was not discussed. This paper proposes a method for translating a program that contains pattern-matching expressions of Egison to a program that contains only the existing syntax constructs of functional programming languages. This method is based on the three key ideas: (i) transformation of a matcher to a function that takes a pattern and a target, and returns lists of triples of a pattern, a matcher, and a target; (ii) compilation of match-all to application of the map function; (iii) transformation of a value pattern to a function that takes an intermediate pattern-matching result and returns a value. This paper shows the proposed method works by showing Scheme macros that provide the users the pattern-matching facility of Egison. This paper also presents benchmark results that show Egison pattern-matching embedded in Gauche Scheme is faster than the original Egison interpreter.
Hyper-Sphere Quantization: Communication-Efficient SGD for Federated Learning
Venue: arXiv cs.LG 2019
Year: 2019

The high cost of communicating gradients is a major bottleneck for federated learning, as the bandwidth of the participating user devices is limited. Existing gradient compression algorithms are mainly designed for data centers with high-speed network and achieve $O(\sqrt{d} \log d)$ per-iteration communication cost at best, where $d$ is the size of the model. We propose hyper-sphere quantization (HSQ), a general framework that can be configured to achieve a continuum of trade-offs between communication efficiency and gradient accuracy. In particular, at the high compression ratio end, HSQ provides a low per-iteration communication cost of $O(\log d)$, which is favorable for federated learning. We prove the convergence of HSQ theoretically and show by experiments that HSQ significantly reduces the communication cost of model training without hurting convergence accuracy.
WiredSwarm: High Resolution Haptic Feedback Provided by a Swarm of Drones to the User's Fingers for VR interaction
Venue: arXiv cs.RO 2019
Year: 2019

We propose a concept of a novel interaction strategy for providing rich haptic feedback in Virtual Reality (VR), when each user's finger is connected to micro-quadrotor with a wire. Described technology represents the first flying wearable haptic interface. The solution potentially is able to deliver high resolution force feedback to each finger during fine motor interaction in VR. The tips of tethers are connected to the centers of quadcopters under their bottom. Therefore, flight stability is increasing and the interaction forces are becoming stronger which allows to use smaller drones.
Uniform Partition in Population Protocol Model under Weak Fairness
Venue: arXiv cs.DC 2019
Year: 2019

We focus on a uniform partition problem in a population protocol model. The uniform partition problem aims to divide a population into k groups of the same size, where k is a given positive integer. In the case of k=2 (called uniform bipartition), a previous work clarified space complexity under various assumptions: 1) an initialized base station (BS) or no BS, 2) weak or global fairness, 3) designated or arbitrary initial states of agents, and 4) symmetric or asymmetric protocols, except for the setting that agents execute a protocol from arbitrary initial states under weak fairness in the model with an initialized base station. In this paper, we clarify the space complexity for this remaining setting. In this setting, we prove that P states are necessary and sufficient to realize asymmetric protocols, and that P+1 states are necessary and sufficient to realize symmetric protocols, where P is the known upper bound of the number of agents. From these results and the previous work, we have clarified the solvability of the uniform bipartition for each combination of assumptions. Additionally, we newly consider an assumption on a model of a non-initialized BS and clarify solvability and space complexity in the assumption. Moreover, the results in this paper can be applied to the case that k is an arbitrary integer (called uniform k-partition).
Data-Free Point Cloud Network for 3D Face Recognition
Venue: arXiv cs.CV 2019
Year: 2019

Point clouds-based Networks have achieved great attention in 3D object classification, segmentation and indoor scene semantic parsing. In terms of face recognition, 3D face recognition method which directly consume point clouds as input is still under study. Two main factors account for this: One is how to get discriminative face representations from 3D point clouds using deep network; the other is the lack of large 3D training dataset. To address these problems, a data-free 3D face recognition method is proposed only using synthesized unreal data from statistical 3D Morphable Model to train a deep point cloud network. To ease the inconsistent distribution between model data and real faces, different point sampling methods are used in train and test phase. In this paper, we propose a curvature-aware point sampling(CPS) strategy replacing the original furthest point sampling(FPS) to hierarchically down-sample feature-sensitive points which are crucial to pass and aggregate features deeply. A PointNet++ like Network is used to extract face features directly from point clouds. The experimental results show that the network trained on generated data generalizes well for real 3D faces. Fine tuning on a small part of FRGCv2.0 and Bosphorus, which include real faces in different poses and expressions, further improves recognition accuracy.
Prediction of Missing Semantic Relations in Lexical-Semantic Network using Random Forest Classifier
Venue: arXiv cs.CL 2019
Year: 2019

This study focuses on the prediction of missing six semantic relations (such as is_a and has_part) between two given nodes in RezoJDM a French lexical-semantic network. The output of this prediction is a set of pairs in which the first entries are semantic relations and the second entries are the probabilities of existence of such relations. Due to the statement of the problem we choose the random forest (RF) predictor classifier approach to tackle this problem. We take for granted the existing semantic relations, for training/test dataset, gathered and validated by crowdsourcing. We describe how all of the mentioned ideas can be followed after using the node2vec approach in the feature extraction phase. We show how this approach can lead to acceptable results.
Grouping Capsules Based Different Types
Venue: arXiv cs.CV 2019
Year: 2019

Capsule network was introduced as a new architecture of neural networks, it encoding features as capsules to overcome the lacking of equivariant in the convolutional neural networks. It uses dynamic routing algorithm to train parameters in different capsule layers, but the dynamic routing algorithm need to be improved. In this paper, we propose a novel capsule network architecture and discussed the effect of initialization method of the coupling coefficient $c_{ij}$ on the model. First, we analyze the rate of change of the initial value of $c_{ij}$ when the dynamic routing algorithm iterates. The larger the initial value of $c_{ij}$, the better effect of the model. Then, we proposed improvement that training different types of capsules by grouping capsules based different types. And this improvement can adjust the initial value of $c_{ij}$ to make it more suitable. We experimented with our improvements on some computer vision datasets and achieved better results than the original capsule network
Anomaly Detection for Industrial Control Systems Using Sequence-to-Sequence Neural Networks
Venue: arXiv cs.CR 2019
Year: 2019

This study proposes an anomaly detection method for operational data of industrial control systems (ICSs). Sequence-to-sequence neural networks were applied to train and predict ICS operational data and interpret their time-series characteristic. The proposed method requires only a normal dataset to understand ICS's normal state and detect outliers. This method was evaluated with SWaT (secure water treatment) dataset, and 29 out of 36 attacks were detected. The reported method also detects the attack points, and 25 out of 53 points were detected. This study provides a detailed analysis of false positives and false negatives of the experimental results.
Minimal representations and algebraic relations for single nested products
Venue: arXiv cs.SC 2019
Year: 2019

Recently, it has been shown constructively how a finite set of hypergeometric products, multibasic hypergeometric products or their mixed versions can be modeled properly in the setting of formal difference rings. Here special emphasis is put on robust constructions: whenever further products have to be considered, one can reuse --up to some mild modifications-- the already existing difference ring. In this article we relax this robustness criteria and seek for another form of optimality. We will elaborate a general framework to represent a finite set of products in a formal difference ring where the number of transcendental product generators is minimal. As a bonus we are able to describe explicitly all relations among the given input products.
Detecting Network Disruptions At Colocation Facilities
Venue: arXiv cs.NI 2019
Year: 2019

Colocation facilities and Internet eXchange Points (IXPs) provide neutral places for concurrent networks to daily exchange terabytes of data traffic. Although very reliable, these facilities are not immune to failure and may experience difficulties that can have significant impacts on exchanged traffic. In this paper we devise a methodology to identify collocation facilities in traceroute data and to monitor delay and routing patterns between facilities. We also present an anomaly detection technique to report abnormal traffic changes usually due to facilities outages. We evaluate this method with eight months of traceroute data from the RIPE Atlas measurement platform and manually inspect the most prominent events, that are: an IXP outage, a DDoS attack, and a power failure in a facility. These case studies validate the benefits of the proposed system to detect real world outages from traceroute data. We also investigate the impact of anomalies at the metropolitan-level and identify outages that span across up to eight facilities.
Deep-Aligned Convolutional Neural Network for Skeleton-based Action Recognition and Segmentation
Venue: arXiv cs.LG 2019
Year: 2019

Convolutional neural networks (CNNs) are deep learning frameworks which are well-known for their notable performance in classification tasks. Hence, many skeleton-based action recognition and segmentation (SBARS) algorithms benefit from them in their designs. However, a shortcoming of such applications is the general lack of spatial relationships between the input features in such data types. Besides, non-uniform temporal scalings is a common issue in skeleton-based data streams which leads to having different input sizes even within one specific action category. In this work, we propose a novel deep-aligned convolutional neural network (DACNN) to tackle the above challenges for the particular problem of SBARS. Our network is designed by introducing a new type of filters in the context of CNNs which are trained based on their alignments to the local subsequences in the inputs. These filters result in efficient predictions as well as learning interpretable patterns in the data. We empirically evaluate our framework on real-world benchmarks showing that the proposed DACNN algorithm obtains a competitive performance compared to the state-of-the-art while benefiting from a less complicated yet more interpretable model.
Multi-Step Chord Sequence Prediction Based on Aggregated Multi-Scale Encoder-Decoder Network
Venue: arXiv cs.LG 2019
Year: 2019

This paper studies the prediction of chord progressions for jazz music by relying on machine learning models. The motivation of our study comes from the recent success of neural networks for performing automatic music composition. Although high accuracies are obtained in single-step prediction scenarios, most models fail to generate accurate multi-step chord predictions. In this paper, we postulate that this comes from the multi-scale structure of musical information and propose new architectures based on an iterative temporal aggregation of input labels. Specifically, the input and ground truth labels are merged into increasingly large temporal bags, on which we train a family of encoder-decoder networks for each temporal scale. In a second step, we use these pre-trained encoder bottleneck features at each scale in order to train a final encoder-decoder network. Furthermore, we rely on different reductions of the initial chord alphabet into three adapted chord alphabets. We perform evaluations against several state-of-the-art models and show that our multi-scale architecture outperforms existing methods in terms of accuracy and perplexity, while requiring relatively few parameters. We analyze musical properties of the results, showing the influence of downbeat position within the analysis window on accuracy, and evaluate errors using a musically-informed distance metric.
Using musical relationships between chord labels in automatic chord extraction tasks
Venue: arXiv cs.SD 2019
Year: 2019

Recent researches on Automatic Chord Extraction (ACE) have focused on the improvement of models based on machine learning. However, most models still fail to take into account the prior knowledge underlying the labeling alphabets (chord labels). Furthermore, recent works have shown that ACE performances are converging towards a glass ceiling. Therefore, this prompts the need to focus on other aspects of the task, such as the introduction of musical knowledge in the representation, the improvement of the models towards more complex chord alphabets and the development of more adapted evaluation methods. In this paper, we propose to exploit specific properties and relationships between chord labels in order to improve the learning of statistical ACE models. Hence, we analyze the interdependence of the representations of chords and their associated distances, the precision of the chord alphabets, and the impact of the reduction of the alphabet before or after training of the model. Furthermore, we propose new training losses based on musical theory. We show that these improve the results of ACE systems based on Convolutional Neural Networks. By performing an in-depth analysis of our results, we uncover a set of related insights on ACE tasks based on statistical models, and also formalize the musical meaning of some classification errors.
Multi-hop Convolutions on Weighted Graphs
Venue: arXiv cs.CV 2019
Year: 2019

Graph Convolutional Networks (GCNs) have made significant advances in semi-supervised learning, especially for classification tasks. However, existing GCN based methods have two main drawbacks. First, to increase the receptive field and improve the representation capability of GCNs, larger kernels or deeper network architectures are used, which greatly increases the computational complexity and the number of parameters. Second, methods working on higher order graphs computed directly from adjacency matrices may alter the relationship between graph nodes, particularly for weighted graphs. In addition, the direct construction of higher-order graphs introduces redundant information, which may result in lower network performance. To address the above weaknesses, in this paper, we propose a new method of multi-hop convolutional network on weighted graphs. The proposed method consists of multiple convolutional branches, where each branch extracts node representation from a $k$-hop graph with small kernels. Such design systematically aggregates multi-scale contextual information without adding redundant information. Furthermore, to efficiently combine the extracted information from the multi-hop branches, an adaptive weight computation (AWC) layer is proposed. We demonstrate the superiority of our MultiHop in six publicly available datasets, including three citation network datasets and three medical image datasets. The experimental results show that our proposed MultiHop method achieves the highest classification accuracy and outperforms the state-of-the-art methods. The source code of this work have been released on GitHub (https://github.com/ahukui/Multi-hop-Convolutions-on-Weighted-Graphs).