OpenRaiser/Intern-Atlas huggingface dataset:
Transient dynamics of pulse-driven memristors in the presence of a stable fixed point
Venue: arXiv cs.ET 2019
Year: 2019

Some memristors are quite interesting from the point of view of dynamical systems. When driven by narrow pulses of alternating polarities, their dynamics has a stable fixed point, which may be useful for future applications. We study the transient dynamics of two types of memristors characterized by a stable fixed point using a time-averaged evolution equation. Time-averaged trajectories of the Biolek window function memristor and resistor-threshold type memristor circuit (an effective memristor) are determined analytically, and the times of relaxation to the stable fixed point are found. Our analytical results are in perfect agreement with the results of numerical simulations.
Bayesian policy selection using active inference
Venue: arXiv cs.LG 2019
Year: 2019

Learning to take actions based on observations is a core requirement for artificial agents to be able to be successful and robust at their task. Reinforcement Learning (RL) is a well-known technique for learning such policies. However, current RL algorithms often have to deal with reward shaping, have difficulties generalizing to other environments and are most often sample inefficient. In this paper, we explore active inference and the free energy principle, a normative theory from neuroscience that explains how self-organizing biological systems operate by maintaining a model of the world and casting action selection as an inference problem. We apply this concept to a typical problem known to the RL community, the mountain car problem, and show how active inference encompasses both RL and learning from demonstrations.
Coherent Detection for Short-Packet Physical-Layer Network Coding with FSK Modulation
Venue: arXiv cs.IT 2019
Year: 2019

This paper investigates coherent detection for physical-layer network coding (PNC) with short packet transmissions in a two-way relay channel (TWRC). PNC turns superimposed EM waves into network-coded messages to improve throughput in a relay system. To achieve this, accurate channel information at the relay is a necessity. Much prior work applies preambles to estimate the channel. For long packets, the preamble overhead is low because of the large data payload. For short packets, that is not the case. To avoid excessive overhead, we consider a set-up in which short packets do not have preambles. A key challenge is how the relay can estimate the channel and detect the network-coded messages jointly based on the received signals from the two end users. We design a coherent detector that makes use of a belief propagation (BP) algorithm to do so. For concreteness, we focus on frequency-shift-keying (FSK) modulation. We show how the BP algorithm can be simplified and made practical with Gaussian-mixture passing. In addition, we demonstrate that prior knowledge on the channel distribution is not needed with our framework. Benchmarked against the detector with prior knowledge of the channel distribution, numerical results show that our detector can have nearly the same performance without such prior knowledge.
Happiness and the productivity of software engineers
Venue: arXiv cs.SE 2019
Year: 2019

Software companies and startups often follow the idea of flourishing happiness among developers. Perks, playground rooms, free breakfast, remote office options, sports facilities near the companies, company retreats, you name it. The rationale is that happy developers should be more productive and also retained. But is it the case that happy software engineers are more productive? Moreover, are perks the way to go to make developers happy? Are developers happy at all? What are the consequences of unhappiness among software engineers? These questions are important to ask both from the perspective of productivity and from the perspective of sustainable software development and well-being in the workplace. Managers, team leaders, as well as team members should be interested in these concerns. This chapter provides an overview of our studies on the happiness of software developers. You will learn why it is important to make software developers happy, how happy they really are, what makes them unhappy, and what is expected regarding happiness and productivity while developing software.
Bonsai -- Diverse and Shallow Trees for Extreme Multi-label Classification
Venue: arXiv cs.LG 2019
Year: 2019

Extreme multi-label classification (XMC) refers to supervised multi-label learning involving hundreds of thousand or even millions of labels. In this paper, we develop a suite of algorithms, called Bonsai, which generalizes the notion of label representation in XMC, and partitions the labels in the representation space to learn shallow trees. We show three concrete realizations of this label representation space including : (i) the input space which is spanned by the input features, (ii) the output space spanned by label vectors based on their co-occurrence with other labels, and (iii) the joint space by combining the input and output representations. Furthermore, the constraint-free multi-way partitions learnt iteratively in these spaces lead to shallow trees. By combining the effect of shallow trees and generalized label representation, Bonsai achieves the best of both worlds - fast training which is comparable to state-of-the-art tree-based methods in XMC, and much better prediction accuracy, particularly on tail-labels. On a benchmark Amazon-3M dataset with 3 million labels, \bonsai outperforms a state-of-the-art one-vs-rest method in terms of prediction accuracy, while being approximately 200 times faster to train. The code for Bonsai is available at \url{https://github.com/xmc-aalto/bonsai}
USE-Net: incorporating Squeeze-and-Excitation blocks into U-Net for prostate zonal segmentation of multi-institutional MRI datasets
Venue: arXiv cs.CV 2019
Year: 2019

Prostate cancer is the most common malignant tumors in men but prostate Magnetic Resonance Imaging (MRI) analysis remains challenging. Besides whole prostate gland segmentation, the capability to differentiate between the blurry boundary of the Central Gland (CG) and Peripheral Zone (PZ) can lead to differential diagnosis, since tumor's frequency and severity differ in these regions. To tackle the prostate zonal segmentation task, we propose a novel Convolutional Neural Network (CNN), called USE-Net, which incorporates Squeeze-and-Excitation (SE) blocks into U-Net. Especially, the SE blocks are added after every Encoder (Enc USE-Net) or Encoder-Decoder block (Enc-Dec USE-Net). This study evaluates the generalization ability of CNN-based architectures on three T2-weighted MRI datasets, each one consisting of a different number of patients and heterogeneous image characteristics, collected by different institutions. The following mixed scheme is used for training/testing: (i) training on either each individual dataset or multiple prostate MRI datasets and (ii) testing on all three datasets with all possible training/testing combinations. USE-Net is compared against three state-of-the-art CNN-based architectures (i.e., U-Net, pix2pix, and Mixed-Scale Dense Network), along with a semi-automatic continuous max-flow model. The results show that training on the union of the datasets generally outperforms training on each dataset separately, allowing for both intra-/cross-dataset generalization. Enc USE-Net shows good overall generalization under any training condition, while Enc-Dec USE-Net remarkably outperforms the other methods when trained on all datasets. These findings reveal that the SE blocks' adaptive feature recalibration provides excellent cross-dataset generalization when testing is performed on samples of the datasets used during training.
Amobee at SemEval-2019 Tasks 5 and 6: Multiple Choice CNN Over Contextual Embedding
Venue: arXiv cs.CL 2019
Year: 2019

This article describes Amobee's participation in "HatEval: Multilingual detection of hate speech against immigrants and women in Twitter" (task 5) and "OffensEval: Identifying and Categorizing Offensive Language in Social Media" (task 6). The goal of task 5 was to detect hate speech targeted to women and immigrants. The goal of task 6 was to identify and categorized offensive language in social media, and identify offense target. We present a novel type of convolutional neural network called "Multiple Choice CNN" (MC-CNN) that we used over our newly developed contextual embedding, Rozental et al. (2019). For both tasks we used this architecture and achieved 4th place out of 69 participants with an F1 score of 0.53 in task 5, in task 6 achieved 2nd place (out of 75) in Sub-task B - automatic categorization of offense types (our model reached places 18/2/7 out of 103/75/65 for sub-tasks A, B and C respectively in task 6).
Usage of Decision Support Systems for Conflicts Modelling during Information Operations Recognition
Venue: arXiv cs.AI 2019
Year: 2019

Application of decision support systems for conflict modeling in information operations recognition is presented. An information operation is considered as a complex weakly structured system. The model of conflict between two subjects is proposed based on the second-order rank reflexive model. The method is described for construction of the design pattern for knowledge bases of decision support systems. In the talk, the methodology is proposed for using of decision support systems for modeling of conflicts in information operations recognition based on the use of expert knowledge and content monitoring.
Compute-and-forward relaying with LDPC codes over QPSK scheme
Venue: arXiv cs.IT 2019
Year: 2019

In this paper, we study a compute-and-forward (CAF) relaying scheme with low-density parity-check (LDPC) codes, a special case of physical layer network coding, under the quadrature phase shift keying (QPSK) modulation. The novelty of this paper is the theoretical analysis of decoding performance of the CAF scheme and traditional separation decoding (SD) scheme with joint decoding or with successive interference cancellation (SIC) decoding when the reception powers from both senders are not equal but close to each other. First, we study the decoding performance of linear random coding (LRC) in the CAF scheme whose decoder is based on the degraded channel. When rotation angles of constellations of two senders are controlled, we show that they can achieve rates well beyond the multiple access channel (MAC) with LRC with optimal rotation angles. Second, we analyze the practical feasibility of CAF using LDPC codes in terms of computational costs and decoding performance of belief propagation (BP) decoders. The calculation complexity of the BP decoder for the CAF scheme is almost equal to that of the SIC BP decoder, and smaller than the joint BP decoder in the SD scheme. Decoding performance is evaluated by the asymptotic decodable region using density evolution. The results show that, with code rate fixed, the CAF scheme has better performance than the SD scheme with joint BP decoding and SIC BP decoding in the high rate region.
LegenDary 2012 Soccer 2D Simulation Team Description Paper
Venue: arXiv cs.MA 2019
Year: 2019

In LegenDary project, we started a new research based on Agent2D in RoboCup 2D soccer simulation. In this paper, we mainly present the team algorithms and structures which we used to develop our team in separated section. We have focused on passing, dribbling and blocking skills. We improved them and made the team ready for this competition. Through pass is the most important part of our team that we work a lot on it.
Truxen: A Trusted Computing Enhanced Blockchain
Venue: arXiv cs.DC 2019
Year: 2019

Truxen is a Trusted Computing enhanced blockchain that uses Proof of Integrity protocol as the consensus. Proof of Integrity protocol is derived from Trusted Computing and associated Remote Attestations, that can be used to vouch a node's identity and integrity to all of the other nodes in the blockchain network. In this paper we describe how Trusted Computing and Proof of Integrity can be used to enhance blockchain in the areas of mining block, executing transaction and smart contract, protecting sensitive data. Truxen presents a Single Execution Model, that can verify and execute transaction and smart contract in a solo node, consequently enables remote calls to off-chain applications and performs in-deterministic tasks.
An Extended Game-Theoretic Model for Aggregate Lane Choice Behavior of Vehicles at Traffic Diverges with a Bifurcating Lane
Venue: arXiv cs.GT 2019
Year: 2019

Road network junctions, such as merges and diverges, often act as bottlenecks that initiate and exacerbate congestion. More complex junction configurations lead to more complex driver behaviors, resulting in aggregate congestion patterns that are more difficult to predict and mitigate. In this paper, we discuss diverge configurations where vehicles on some lanes can enter only one of the downstream roads, but vehicles on other lanes can enter one of several downstream roads. Counterintuitively, these bifurcating lanes, rather than relieving congestion (by acting as a versatile resource that can serve either downstream road as the demand changes), often cause enormous congestion due to lane changing. We develop an aggregate lane--changing model for this situation that is expressive enough to model drivers' choices and the resultant congestion, but simple enough to easily analyze. We use a game-theoretic framework to model the aggregate lane choice behavior of selfish vehicles as a Wardrop equilibrium (an aggregate type of Nash equilibrium). We then establish the existence and uniqueness of this equilibrium. We explain how our model can be easily calibrated using simulation data or real data, and we present results showing that our model successfully predicts the aggregate behavior that emerges from widely-used behavioral lane-changing models. Our model's expressiveness, ease of calibration, and accuracy may make it a useful tool for mitigating congestion at these complex diverges.
Render4Completion: Synthesizing Multi-View Depth Maps for 3D Shape Completion
Venue: arXiv cs.CV 2019
Year: 2019

We propose a novel approach for 3D shape completion by synthesizing multi-view depth maps. While previous work for shape completion relies on volumetric representations, meshes, or point clouds, we propose to use multi-view depth maps from a set of fixed viewing angles as our shape representation. This allows us to be free of the limitations of memory for volumetric representations and point clouds by casting shape completion into an image-to-image translation problem. Specifically, we render depth maps of the incomplete shape from a fixed set of viewpoints, and perform depth map completion in each view. Different from image-to-image translation network that completes each view separately, our novel network, multi-view completion net (MVCN), leverages information from all views of a 3D shape to help the completion of each single view. This enables MVCN to leverage more information from different depth views to achieve high accuracy in single depth view completion and keep the consistency among the completed depth images in different views. Benefited by the multi-view representation and the novel network structure, MVCN significantly improves the accuracy of 3D shape completion in large-scale benchmarks compared to the state of the art.
Vid2Game: Controllable Characters Extracted from Real-World Videos
Venue: arXiv cs.LG 2019
Year: 2019

We are given a video of a person performing a certain activity, from which we extract a controllable model. The model generates novel image sequences of that person, according to arbitrary user-defined control signals, typically marking the displacement of the moving body. The generated video can have an arbitrary background, and effectively capture both the dynamics and appearance of the person. The method is based on two networks. The first network maps a current pose, and a single-instance control signal to the next pose. The second network maps the current pose, the new pose, and a given background, to an output frame. Both networks include multiple novelties that enable high-quality performance. This is demonstrated on multiple characters extracted from various videos of dancers and athletes.
A Faster Local Algorithm for Detecting Bounded-Size Cuts with Applications to Higher-Connectivity Problems
Venue: arXiv cs.DS 2019
Year: 2019

Consider the following "local" cut-detection problem in a directed graph: We are given a starting vertex $s$ and need to detect whether there is a cut with at most $k$ edges crossing the cut such that the side of the cut containing $s$ has at most $Δ$ interior edges. If we are given query access to the input graph, then this problem can in principle be solved in sublinear time without reading the whole graph and with query complexity depending on $k$ and $Δ$. We design an elegant randomized procedure that solves a slack variant of this problem with $O(k^2 Δ)$ queries, improving in particular a previous bound of $O((2(k+1))^{k+2} Δ)$ by Chechik et al. [SODA 2017]. In this slack variant, the procedure must successfully detect a component containing $s$ with at most $k$ outgoing edges and $Δ$ interior edges if such a component exists, but the component it actually detects may have up to $O(k Δ)$ interior edges. Besides being of interest on its own, such cut-detection procedures have been used in many algorithmic applications for higher-connectivity problems. Our new cut-detection procedure therefore almost readily implies (1) a faster vertex connectivity algorithm which in particular has nearly linear running time for polylogarithmic value of the vertex connectivity, (2) a faster algorithm for computing the maximal $k$-edge connected subgraphs, and (3) faster property testing algorithms for higher edge and vertex connectivity, which resolves two open problems, one by Goldreich and Ron [STOC '97] and one by Orenstein and Ron [TCS 2011].
Learning Interpretable Disentangled Representations using Adversarial VAEs
Venue: arXiv cs.LG 2019
Year: 2019

Learning Interpretable representation in medical applications is becoming essential for adopting data-driven models into clinical practice. It has been recently shown that learning a disentangled feature representation is important for a more compact and explainable representation of the data. In this paper, we introduce a novel adversarial variational autoencoder with a total correlation constraint to enforce independence on the latent representation while preserving the reconstruction fidelity. Our proposed method is validated on a publicly available dataset showing that the learned disentangled representation is not only interpretable, but also superior to the state-of-the-art methods. We report a relative improvement of 81.50% in terms of disentanglement, 11.60% in clustering, and 2% in supervised classification with a few amounts of labeled data.
Performance Evalution of 3D Keypoint Detectors and Descriptors for Plants Health Classification
Venue: arXiv cs.CV 2019
Year: 2019

Plant Phenomics based on imaging based techniques can be used to monitor the health and the diseases of plants and crops. The use of 3D data for plant phenomics is a recent phenomenon. However, since 3D point cloud contains more information than plant images, in this paper, we compare the performance of different keypoint detectors and local feature descriptors combinations for the plant growth stage and it's growth condition classification based on 3D point clouds of the plants. We have also implemented a modified form of 3D SIFT descriptor, that is invariant to rotation and is computationally less intense than most of the 3D SIFT descriptors reported in the existing literature. The performance is evaluated in terms of the classification accuracy and the results are presented in terms of accuracy tables. We find the ISS-SHOT and the SIFT-SIFT combinations consistently perform better and Fisher Vector (FV) is a better encoder than Vector of Linearly Aggregated (VLAD) for such applications. It can serve as a better modality.
Machine Vision for Natural Gas Methane Emissions Detection Using an Infrared Camera
Venue: arXiv cs.CV 2019
Year: 2019

It is crucial to reduce natural gas methane emissions, which can potentially offset the climate benefits of replacing coal with gas. Optical gas imaging (OGI) is a widely-used method to detect methane leaks, but is labor-intensive and cannot provide leak detection results without operators' judgment. In this paper, we develop a computer vision approach to OGI-based leak detection using convolutional neural networks (CNN) trained on methane leak images to enable automatic detection. First, we collect ~1 M frames of labeled video of methane leaks from different leaking equipment for building CNN model, covering a wide range of leak sizes (5.3-2051.6 gCH4/h) and imaging distances (4.6-15.6 m). Second, we examine different background subtraction methods to extract the methane plume in the foreground. Third, we then test three CNN model variants, collectively called GasNet, to detect plumes in videos taken at other pieces of leaking equipment. We assess the ability of GasNet to perform leak detection by comparing it to a baseline method that uses optical-flow based change detection algorithm. We explore the sensitivity of results to the CNN structure, with a moderate-complexity variant performing best across distances. We find that the detection accuracy can reach as high as 99%, the overall detection accuracy can exceed 95% for a case across all leak sizes and imaging distances. Binary detection accuracy exceeds 97% for large leaks (~710 gCH4/h) imaged closely (~5-7 m). At closer imaging distances (~5-10 m), CNN-based models have greater than 94% accuracy across all leak sizes. At farthest distances (~13-16 m), performance degrades rapidly, but it can achieve above 95% accuracy to detect large leaks (>950 gCH4/h). The GasNet-based computer vision approach could be deployed in OGI surveys to allow automatic vigilance of methane leak detection with high detection accuracy in the real world.
Dynamic Gesture Recognition by Using CNNs and Star RGB: a Temporal Information Condensation
Venue: arXiv cs.CV 2019
Year: 2019

Due to the advance of technologies, machines are increasingly present in people's daily lives. Thus, there has been more and more effort to develop interfaces, such as dynamic gestures, that provide an intuitive way of interaction. Currently, the most common trend is to use multimodal data, as depth and skeleton information, to enable dynamic gesture recognition. However, using only color information would be more interesting, since RGB cameras are usually available in almost every public place, and could be used for gesture recognition without the need of installing other equipment. The main problem with such approach is the difficulty of representing spatio-temporal information using just color. With this in mind, we propose a technique capable of condensing a dynamic gesture, shown in a video, in just one RGB image. We call this technique star RGB. This image is then passed to a classifier formed by two Resnet CNNs, a soft-attention ensemble, and a fully connected layer, which indicates the class of the gesture present in the input video. Experiments were carried out using both Montalbano and GRIT datasets. For Montalbano dataset, the proposed approach achieved an accuracy of 94.58%. Such result reaches the state-of-the-art when considering this dataset and only color information. Regarding the GRIT dataset, our proposal achieves more than 98% of accuracy, recall, precision, and F1-score, outperforming the reference approach by more than 6%.