OpenRaiser/Intern-Atlas huggingface dataset:
Deep Learning Based Early Diagnostics of Parkinsons Disease
Venue: arXiv cs.CV 2020
Year: 2020

In the world, about 7 to 10 million elderly people are suffering from Parkinson's Disease (PD) disease. Parkinson's disease is a common neurological degenerative disease, and its clinical characteristics are Tremors, rigidity, bradykinesia, and decreased autonomy. Its clinical manifestations are very similar to Multiple System Atrophy (MSA) disorders. Studies have shown that patients with Parkinson's disease often reach an irreparable situation when diagnosed, so As Parkinson's disease can be distinguished from MSA disease and get an early diagnosis, people are constantly exploring new methods. With the advent of the era of big data, deep learning has made major breakthroughs in image recognition and classification. Therefore, this study proposes to use The deep learning method to realize the diagnosis of Parkinson's disease, multiple system atrophy, and healthy people. This data source is from Istanbul University Cerrahpasa Faculty of Medicine Hospital. The processing of the original magnetic resonance image (Magnetic Resonance Image, MRI) is guided by the doctor of Istanbul University Cerrahpasa Faculty of Medicine Hospital. The focus of this experiment is to improve the existing neural network so that it can obtain good results in medical image recognition and diagnosis. An improved algorithm was proposed based on the pathological characteristics of Parkinson's disease, and good experimental results were obtained by comparing indicators such as model loss and accuracy.
High resolution neural texture synthesis with long range constraints
Venue: arXiv cs.CV 2020
Year: 2020

The field of texture synthesis has witnessed important progresses over the last years, most notably through the use of Convolutional Neural Networks. However, neural synthesis methods still struggle to reproduce large scale structures, especially with high resolution textures. To address this issue, we first introduce a simple multi-resolution framework that efficiently accounts for long-range dependency. Then, we show that additional statistical constraints further improve the reproduction of textures with strong regularity. This can be achieved by constraining both the Gram matrices of a neural network and the power spectrum of the image. Alternatively one may constrain only the autocorrelation of the features of the network and drop the Gram matrices constraints. In an experimental part, the proposed methods are then extensively tested and compared to alternative approaches, both in an unsupervised way and through a user study. Experiments show the interest of the multi-scale scheme for high resolution textures and the interest of combining it with additional constraints for regular textures.
The Intersection of $3$-Maximal Submonids
Venue: arXiv cs.FL 2020
Year: 2020

Very little is known about the structure of the intersection of two $k$-generated monoids of words, even for $k=3$. Here we investigate the case of $k$-maximal monoids, that is, monoids whose basis of cardinality $k$ cannot be non-trivially decomposed into at most $k$ words. We characterize the intersection in the case of two $3$-maximal monoids.
Robust Reinforcement Learning using Adversarial Populations
Venue: arXiv cs.LG 2020
Year: 2020

Reinforcement Learning (RL) is an effective tool for controller design but can struggle with issues of robustness, failing catastrophically when the underlying system dynamics are perturbed. The Robust RL formulation tackles this by adding worst-case adversarial noise to the dynamics and constructing the noise distribution as the solution to a zero-sum minimax game. However, existing work on learning solutions to the Robust RL formulation has primarily focused on training a single RL agent against a single adversary. In this work, we demonstrate that using a single adversary does not consistently yield robustness to dynamics variations under standard parametrizations of the adversary; the resulting policy is highly exploitable by new adversaries. We propose a population-based augmentation to the Robust RL formulation in which we randomly initialize a population of adversaries and sample from the population uniformly during training. We empirically validate across robotics benchmarks that the use of an adversarial population results in a more robust policy that also improves out-of-distribution generalization. Finally, we demonstrate that this approach provides comparable robustness and generalization as domain randomization on these benchmarks while avoiding a ubiquitous domain randomization failure mode.
Optimum Reconfiguration of Routing Interconnection Network in APSoC Fabrics
Venue: arXiv cs.AR 2020
Year: 2020

This paper presents an automated algorithm for optimum configuration of routing interconnection network in Xilinx Zynq-7000 All programmable system-on-chip (APSoC) fabrics. A method to configure circuits with optimum routing resources is presented along with their performance parameters with and without the proposed algorithm. The proposed algorithm enables full control over routing resources for using different interconnection types in order to create routing-based circuit-under-test. The algorithm proposes the routing techniques through the 2-D array of switch matrices inside the interconnection network and automatically identifies the involved programmable interconnection points associated with a node. An experimental setup is proposed to measure the performance parameters such as slack time and power with and without the applied algorithm on the APSoC routing resources. The proposed setup requires no external equipment such as manufactured equipments or external instruments for performance measurement.
Implicit Saliency in Deep Neural Networks
Venue: arXiv cs.CV 2020
Year: 2020

In this paper, we show that existing recognition and localization deep architectures, that have not been exposed to eye tracking data or any saliency datasets, are capable of predicting the human visual saliency. We term this as implicit saliency in deep neural networks. We calculate this implicit saliency using expectancy-mismatch hypothesis in an unsupervised fashion. Our experiments show that extracting saliency in this fashion provides comparable performance when measured against the state-of-art supervised algorithms. Additionally, the robustness outperforms those algorithms when we add large noise to the input images. Also, we show that semantic features contribute more than low-level features for human visual saliency detection.
A Novel Approach for the Process Planning and Scheduling Problem Using the Concept of Maximum Weighted Independent Set
Venue: arXiv cs.DC 2020
Year: 2020

Process Planning and Scheduling (PPS) is an essential and practical topic but a very intractable problem in manufacturing systems. Many research use iterative methods to solve such problems; however, they cannot achieve satisfactory results in both quality and computational speed. Other studies formulate scheduling problems as a graph coloring problem (GCP) or its extensions, but these formulations are limited to certain types of scheduling problems. In this paper, we propose a novel approach to formulate a general type of the PPS problem with resource allocation and process planning integrated towards a typical objective, minimizing the makespan. The PPS problem is formulated into an undirected weighted conflicting graph, where nodes represent operations and their resources; edges represent constraints, and weight factors are guidelines for the node selection at each time slot. Then, the Maximum Weighted Independent Set (MWIS) problem can be solved to find the best set of operations with their desired resources for each discrete time slot. This proposed approach solves the PPS problem directly with minimum iterations. We establish that the proposed approach always returns a feasible optimum or near-optimum solution to the PPS problem. The different weight configurations of the proposed approach for solving the PPS problem are tested on a real-world PPS example and further designated test instances to evaluate the scalability, accuracy, and robustness.
An Algorithm Framework for the Exact Solution and Improved Approximation of the Maximum Weighted Independent Set Problem
Venue: arXiv cs.DS 2020
Year: 2020

The Maximum Weighted Independent Set (MWIS) problem, which considers a graph with weights assigned to nodes and seeks to discover the "heaviest" independent set, that is, a set of nodes with maximum total weight so that no two nodes in the set are connected by an edge. The MWIS problem arises in many application domains, including the resource-constrained scheduling, error-correcting coding, complex system analysis and optimization, and communication networks. Since solving the MWIS problem is the core function for finding the optimum solution of our novel graph-based formulation of the resource-constrained Process Planning and Scheduling (PPS) problem, it is essential to have "good-performance" algorithms to solve the MWIS problem. In this paper, we propose a Novel Hybrid Heuristic Algorithm (NHHA) framework in a divide-and-conquer structure that yields optimum feasible solutions to the MWIS problem. The NHHA framework is optimized to minimize the recurrence. Using the NHHA framework, we also solve the All Maximal Independent Sets Listing (AMISL) problem, which can be seen as the subproblem of the MWIS problem. Moreover, building composed MWIS algorithms that utilizing fast approximation algorithms with the NHHA framework is an effective way to improve the accuracy of approximation MWIS algorithms (e.g., GWMIN and GWMIN2 (Sakai et al., 2003)). Eight algorithms for the MWIS problem, the exact MWIS algorithm, the AMISL algorithm, two approximation algorithms from the literature, and four composed algorithms, are applied and tested for solving the graph-based formulation of the resource-constrained PPS problem to evaluate the scalability, accuracy, and robustness.
F2GAN: Fusing-and-Filling GAN for Few-shot Image Generation
Venue: arXiv cs.CV 2020
Year: 2020

In order to generate images for a given category, existing deep generative models generally rely on abundant training images. However, extensive data acquisition is expensive and fast learning ability from limited data is necessarily required in real-world applications. Also, these existing methods are not well-suited for fast adaptation to a new category. Few-shot image generation, aiming to generate images from only a few images for a new category, has attracted some research interest. In this paper, we propose a Fusing-and-Filling Generative Adversarial Network (F2GAN) to generate realistic and diverse images for a new category with only a few images. In our F2GAN, a fusion generator is designed to fuse the high-level features of conditional images with random interpolation coefficients, and then fills in attended low-level details with non-local attention module to produce a new image. Moreover, our discriminator can ensure the diversity of generated images by a mode seeking loss and an interpolation regression loss. Extensive experiments on five datasets demonstrate the effectiveness of our proposed method for few-shot image generation.
Analytical Study of Incremental Approach for Information Dissemination in Wireless Networks
Venue: arXiv cs.NI 2020
Year: 2020

In many scenarios, control information dissemination becomes a bottleneck, which limits the scalability and the performance of wireless networks. Such a problem is especially crucial in mobile ad hoc networks, dense networks, networks of vehicles and drones, sensor networks. In other words, this problem occurs in any scenario with frequent changes in topology or interference level on one side and with strong requirements on delay, reliability, power consumption, or capacity on the other side. If the control information changes partially, it may be worth sending only differential updates instead of messages containing full information to reduce overhead. However, such an approach needs accurate tuning of dissemination parameters, since it is necessary to guarantee information relevance in error-prone wireless networks. In the paper, we provide a deep study of two approaches for generating differential updates - namely, incremental and cumulative - and compare their efficiency. We show that the incremental approach allows significantly reducing the amount of generated control information compared to the cumulative one, while providing the same level of information relevance. We develop an analytical model for the incremental approach and propose an algorithm which allows tuning its parameters, depending on the number of nodes in the network, their mobility, and wireless channel quality. Using the developed analytical model, we show that the incremental approach is very useful for static dense network deployments and networks with low and medium mobility, since it allows us to significantly reduce the amount of control information compared to the classical full dump approach.
A variable rest length impedance grasping strategy in the port-Hamiltonian framework
Venue: arXiv cs.RO 2020
Year: 2020

This work is devoted to an impedance grasping strategy for a class of standard mechanical systems in the port-Hamiltonian framework. We embed a variable rest-length of the springs of the existing impedance grasping strategy in order to achieve a stable non-contact to contact transition, and a desired grasping force. We utilize the port-Hamiltonian structure of standard mechanical systems. First, we utilize a change of variables that transforms the port-Hamiltonian system into one with a constant mass-inertia matrix. We then achieve impedance grasping control via a \emph{virtual spring} with a variable rest-length. The force that is exerted by the virtual spring leads to a dissipation term in the impedance grasping controller, which is needed to obtain a smoother non-contact to contact transition. Simulations and experimental results are given in order to motivate our results.
SpinAPS: A High-Performance Spintronic Accelerator for Probabilistic Spiking Neural Networks
Venue: arXiv cs.NE 2020
Year: 2020

We discuss a high-performance and high-throughput hardware accelerator for probabilistic Spiking Neural Networks (SNNs) based on Generalized Linear Model (GLM) neurons, that uses binary STT-RAM devices as synapses and digital CMOS logic for neurons. The inference accelerator, termed "SpinAPS" for Spintronic Accelerator for Probabilistic SNNs, implements a principled direct learning rule for first-to-spike decoding without the need for conversion from pre-trained ANNs. The proposed solution is shown to achieve comparable performance with an equivalent ANN on handwritten digit and human activity recognition benchmarks. The inference engine, SpinAPS, is shown through software emulation tools to achieve 4x performance improvement in terms of GSOPS/W/mm2 when compared to an equivalent SRAM-based design. The architecture leverages probabilistic spiking neural networks that employ first-to-spike decoding rule to make inference decisions at low latencies, achieving 75% of the test performance in as few as 4 algorithmic time steps on the handwritten digit benchmark. The accelerator also exhibits competitive performance with other memristor-based DNN/SNN accelerators and state-of-the-art GPUs.
Densely Connected Residual Network for Attack Recognition
Venue: arXiv cs.CR 2020
Year: 2020

High false alarm rate and low detection rate are the major sticking points for unknown threat perception. To address the problems, in the paper, we present a densely connected residual network (Densely-ResNet) for attack recognition. Densely-ResNet is built with several basic residual units, where each of them consists of a series of Conv-GRU subnets by wide connections. Our evaluation shows that Densely-ResNet can accurately discover various unknown threats that appear in edge, fog and cloud layers and simultaneously maintain a much lower false alarm rate than existing algorithms.
Fully Automated and Standardized Segmentation of Adipose Tissue Compartments by Deep Learning in Three-dimensional Whole-body MRI of Epidemiological Cohort Studies
Venue: arXiv cs.CV 2020
Year: 2020

Purpose: To enable fast and reliable assessment of subcutaneous and visceral adipose tissue compartments derived from whole-body MRI. Methods: Quantification and localization of different adipose tissue compartments from whole-body MR images is of high interest to examine metabolic conditions. For correct identification and phenotyping of individuals at increased risk for metabolic diseases, a reliable automatic segmentation of adipose tissue into subcutaneous and visceral adipose tissue is required. In this work we propose a 3D convolutional neural network (DCNet) to provide a robust and objective segmentation. In this retrospective study, we collected 1000 cases (66$\pm$ 13 years; 523 women) from the Tuebingen Family Study and from the German Center for Diabetes research (TUEF/DZD), as well as 300 cases (53$\pm$ 11 years; 152 women) from the German National Cohort (NAKO) database for model training, validation, and testing with a transfer learning between the cohorts. These datasets had variable imaging sequences, imaging contrasts, receiver coil arrangements, scanners and imaging field strengths. The proposed DCNet was compared against a comparable 3D UNet segmentation in terms of sensitivity, specificity, precision, accuracy, and Dice overlap. Results: Fast (5-7seconds) and reliable adipose tissue segmentation can be obtained with high Dice overlap (0.94), sensitivity (96.6%), specificity (95.1%), precision (92.1%) and accuracy (98.4%) from 3D whole-body MR datasets (field of view coverage 450x450x2000mm${}^3$). Segmentation masks and adipose tissue profiles are automatically reported back to the referring physician. Conclusion: Automatic adipose tissue segmentation is feasible in 3D whole-body MR data sets and is generalizable to different epidemiological cohort studies with the proposed DCNet.
Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection
Venue: arXiv cs.LG 2020
Year: 2020

Network attacks have been very prevalent as their rate is growing tremendously. Both organization and individuals are now concerned about their confidentiality, integrity and availability of their critical information which are often impacted by network attacks. To that end, several previous machine learning-based intrusion detection methods have been developed to secure network infrastructure from such attacks. In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique to tune the parameters of Support Vector Machine with Gaussian Kernel (SVM-RBF), Random Forest (RF), and k-Nearest Neighbor (k-NN) algorithms. The performance of the considered algorithms is evaluated using the ISCX 2012 dataset. Experimental results show the effectiveness of the proposed framework in term of accuracy rate, precision, low-false alarm rate, and recall.
Cross-Model Image Annotation Platform with Active Learning
Venue: arXiv cs.CV 2020
Year: 2020

We have seen significant leapfrog advancement in machine learning in recent decades. The central idea of machine learnability lies on constructing learning algorithms that learn from good data. The availability of more data being made publicly available also accelerates the growth of AI in recent years. In the domain of computer vision, the quality of image data arises from the accuracy of image annotation. Labeling large volume of image data is a daunting and tedious task. This work presents an End-to-End pipeline tool for object annotation and recognition aims at enabling quick image labeling. We have developed a modular image annotation platform which seamlessly incorporates assisted image annotation (annotation assistance), active learning and model training and evaluation. Our approach provides a number of advantages over current image annotation tools. Firstly, the annotation assistance utilizes reference hierarchy and reference images to locate the objects in the images, thus reducing the need for annotating the whole object. Secondly, images can be annotated using polygon points allowing for objects of any shape to be annotated. Thirdly, it is also interoperable across several image models, and the tool provides an interface for object model training and evaluation across a series of pre-trained models. We have tested the model and embeds several benchmarking deep learning models. The highest accuracy achieved is 74%.
Studying Politeness across Cultures Using English Twitter and Mandarin Weibo
Venue: arXiv cs.SI 2020
Year: 2020

Modeling politeness across cultures helps to improve intercultural communication by uncovering what is considered appropriate and polite. We study the linguistic features associated with politeness across US English and Mandarin Chinese. First, we annotate 5,300 Twitter posts from the US and 5,300 Sina Weibo posts from China for politeness scores. Next, we develop an English and Chinese politeness feature set, `PoliteLex'. Combining it with validated psycholinguistic dictionaries, we then study the correlations between linguistic features and perceived politeness across cultures. We find that on Mandarin Weibo, future-focusing conversations, identifying with a group affiliation, and gratitude are considered to be more polite than on English Twitter. Death-related taboo topics, lack of or poor choice of pronouns, and informal language are associated with higher impoliteness on Mandarin Weibo compared to English Twitter. Finally, we build language-based machine learning models to predict politeness with an F1 score of 0.886 on Mandarin Weibo and a 0.774 on English Twitter.
Unravelling the Architecture of Membrane Proteins with Conditional Random Fields
Venue: arXiv cs.LG 2020
Year: 2020

In this paper, we will show that the recently introduced graphical model: Conditional Random Fields (CRF) provides a template to integrate micro-level information about biological entities into a mathematical model to understand their macro-level behavior. More specifically, we will apply the CRF model to an important classification problem in protein science, namely the secondary structure prediction of proteins based on the observed primary structure. A comparison on benchmark data sets against twenty-eight other methods shows that not only does the CRF model lead to extremely accurate predictions but the modular nature of the model and the freedom to integrate disparate, overlapping and non-independent sources of information, makes the model an extremely versatile tool to potentially solve many other problems in bioinformatics.
Deep Reinforcement Learning based Local Planner for UAV Obstacle Avoidance using Demonstration Data
Venue: arXiv cs.RO 2020
Year: 2020

In this paper, a deep reinforcement learning (DRL) method is proposed to address the problem of UAV navigation in an unknown environment. However, DRL algorithms are limited by the data efficiency problem as they typically require a huge amount of data before they reach a reasonable performance. To speed up the DRL training process, we developed a novel learning framework which combines imitation learning and reinforcement learning and building upon Twin Delayed DDPG (TD3) algorithm. We newly introduced both policy and Q-value network are learned using the expert demonstration during the imitation phase. To tackle the distribution mismatch problem transfer from imitation to reinforcement learning, both TD-error and decayed imitation loss are used to update the pre-trained network when start interacting with the environment. The performances of the proposed algorithm are demonstrated on the challenging 3D UAV navigation problem using depth cameras and sketched in a variety of simulation environments.