Reviewerly/exHarmony huggingface dataset:
Robust differential abundance test in compositional data

Summary Differential abundance tests for compositional data are essential and fundamental in various biomedical applications, such as single-cell, bulk RNA-seq and microbiome data analysis. However, because of the compositional constraint and the prevalence of zero counts in the data, differential abundance analysis on compositional data remains a complicated and unsolved statistical problem. This article proposes a new differential abundance test, the robust differential abundance test, to address these challenges. Compared with existing methods, the robust differential abundance test is simple and computationally efficient, is robust to prevalent zero counts in compositional datasets, can take the data’s compositional nature into account, and has a theoretical guarantee of controlling false discoveries in a general setting. Furthermore, in the presence of observed covariates, the robust differential abundance test can work with covariate-balancing techniques to remove potential confounding effects and draw reliable conclusions. The proposed test is applied to several numerical examples, and its merits are demonstrated using both simulated and real datasets.
Real-Time Enumeration of Metro Passenger Volume Using Anchor-Free Object Detection Network on Edge Devices

Enumeration of metro passenger volume is essential in providing effective passenger guidance and improving the usage rate of each carriage.However, existing methods cannot provide the accurate number of alighting and boarding passengers at each gate on the platform.For existing visual methods, the occlusion problem seriously affected the results caused by the angle of view.In this study, we introduce a real-time metro passenger volume enumerating algorithm that is simple, effective, and fast enough to run on edge devices mounted above the platform gate.First, we capture videos from the cameras and design an anchor-free object detection network called CircleDet to detect passengers' heads.CircleDet predicts a circle to localize and bound the target instead of traditional bounding box.Then, we apply a simple but effective circle IoU-based method to identify and track passengers in the videos.CircleDet can achieve up to 111 frames per second (FPS) running on NVIDIA RTX 2080 and 7.8 FPS on an NVIDIA Jetson Nano device.The accuracy of enumeration is as high as 97.1% on our own metro object detection (MOD) dataset.
The Other Side of the Coin: Risks of the Libra Blockchain

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
Global Registration of 3D Cerebral Vessels to Its 2D Projections by a New Branch-and-Bound Algorithm

Endovascular interventions are usually guided by intraoperative 2D images, which cannot fully reflect the 3D structure of vessels and sometimes cause ambiguity. Registering preoperative 3D images to intraoperative 2D images can help eliminate this ambiguity. Most existing methods can only converge to a local optimal solution. Similar to POSE [1], we formulate the problem as 2D-3D point set registration, and develop a fast and accurate global registration method. We propose a novel objective function using consensus set between the 3D points and the projection lines of the 2D points in the 3D space, and introduce a new global optimal rotation search algorithm. The translation search problem is tackled by synchronized grid search in the translation space. Furthermore, we extend the algorithm to register 3D vessels to its two projections, which only increases the runtime slightly but improves the 3D registration accuracy significantly. The proposed method is approximately two times faster than POSE. Experiments on real data show that the mean 3D rotation error is reduced from 5.55 degrees of POSE to 1.62 degrees and the mean translation error is reduced from 8.60 mm of POSE to 1.06 mm by the proposed double-plane method.
Image-level supervised segmentation for human organs with confidence cues

Image segmentation for human organs is an important task for the diagnosis and treatment of diseases. Current deep learning-based methods are fully supervised and need pixel-level labels. Since the medical images are highly specialized and complex, the work of delineating pixel-level segmentation masks is very time-consuming. Weakly supervised methods are then chosen to lighten the workload, which only needs physicians to determine whether an image contains the organ regions of interest. These weakly supervised methods have a common drawback, in that they do not incorporate prior knowledge that alleviates the lack of pixel-level information for segmentation. In this work, we propose a weakly supervised method based on prior knowledge for the segmentation of human organs. The proposed method was validated on three data sets of human organ segmentation. Experimental results show that the proposed image-level supervised segmentation method outperforms several state-of-the-art methods.
Certified Robustness of Graph Classification against Topology Attack with Randomized Smoothing

Graph classification has practical applications in diverse fields. Recent studies show that graph-based machine learning models are especially vulnerable to adversarial perturbations due to the non i.i. d nature of graph data. By adding or deleting a small number of edges in the graph, adversaries could greatly change the graph label predicted by a graph classification model. In this work, we propose to build a smoothed graph classification model with certified robustness guarantee. We have proven that the resulting graph classification model would output the same prediction for a graph under l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> bounded adversarial perturbation. We also evaluate the effectiveness of our approach under graph convolutional network (GCN) based multi-class graph classification model.
Identification of potential targets for kimberlite exploration using satellite imagery and map combination approach in the Lesotho Kimberlite Province

This study presents the use of satellite data and map combination approach for kimberlite exploration to identify follow-up exploration targets. The study area falls in the Lesotho Kimberlite Province, which hosts diamondiferous kimberlite pipes such as Letseng and Kao in Lesotho, and Monastery and Jagersfontein in South Africa. This study focusses on processing and integration of evidential map layers (geomorphic features, linear structures and kimberlite indicator minerals) that have known correlation with kimberlite occurrence. The evidential map layers were acquired from satellite platforms such as ASTER and SRTM DEM. These layers were integrated to identify favourable sites for detailed follow-up exploration by subdividing the area into high, moderate and low prospective regions based on the number of controlling evidences operating in each sub-region. A follow-up sampling and ground magnetic survey were carried out at one of the high prospective zones to validate the results of the present approach. Kimberlite indicator minerals such as garnet, diopside, ilmenite and chromite were recovered from the collected sample. Several garnets show adhering materials known as kelyphite rims, which are typical surface features of peridotitic garnets having close relationship with diamondiferous kimberlite. Keating correlation coefficient, which uses a simple pattern recognition to locate magnetic anomalies that resemble the response of modelled kimberlite pipes, was applied to ground magnetic data. We used a series of reference kimberlite models composed of homogeneous vertical cylinders of various radius ranging from 50 to 200 m and depth varying from 50 to 500 m. The correlation coefficients between analytical signal grid of modelled kimberlite pipe and observed magnetic data were computed. Following this approach, kimberlite model with correlation coefficients exceeding 80% was selected. By matching the response of modelled kimberlite pipe with the magnetic anomalies, four pipe-like targets were located. The target(s) may be a potential source of the recovered kimberlite indicator minerals since the morphology of these minerals suggest a proximal kimberlitic source.
Algorithmic Fairness in Mortgage Lending: From Absolute Conditions to Relational Trade-Offs

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI
A Path Planning Optimization Algorithm Based on Particle Swarm Optimization for UAVs for Bird Monitoring and Repelling – Simulation Results

Bird damage to orchards causes large monetary losses to farmers. The application of traditional methods such as bird cannons and tree netting became inefficient in the long run, along with its high maintenance and reduced mobility. Due to their versatility, Unmanned Aerial Vehicles (UAVs) can be very useful to solve this problem. However, due to their low autonomy it is necessary to evolve flight planning. In this article, an optimization algorithm for path planning of UAVs based on Particle Swarm Optimization (PSO) is presented. This technique was used due to the need of an entry optimization algorithm to start the initial tests. The PSO algorithm is a simple and has few control parameters while maintaining a good performance. This path planning optimization algorithm aims to manage the drone's distance and flight time, applying optimization and randomness techniques, to be able to overcome the disadvantages of other systems. The performance of the proposed algorithm was tested in a tree case simulation that represents all the possible cases.
Power-Efficient Deep Convolutional Neural Network Design Through Zero-Gating PEs and Partial-Sum Reuse Centric Dataflow

Convolution neural networks (CNNs) have shown great success in many areas such as object detection and pattern recognition at the cost of extreme high computation complexity and significant external memory access, which makes state-of-the-art deep CNNs difficult to be implemented on resource-constrained portable/wearable devices with limited capacity of battery. To address this design challenge, a power-efficient CNN design through zero-gating processing elements (PEs) and partial-sum reuse centric dataflow is proposed in this paper. Unlike the existing works which either only consider the zeros in activation maps or use off-chip training process for on-chip computation reduction, a zero-gating PE design is proposed to avoid unnecessary on-chip computation by taking advantages of the large number of zeros in both the filter's weights of pre-trained models and the activation maps. Furthermore, a partial-sum reuse centric dataflow is also proposed for off-chip DRAM access reduction. The evaluation results show that the overall power consumption of PE arrays with our proposal can be reduced by 37% and 14% at the cost of 8% and 1% area overhead when compared to the baseline PE design and the existing only-activation-gated design (i.e. that in Eyeriss), respectively. Moreover, the proposed method can achieve 35% and 47% DRAM access reduction with the corresponding 14% and 49% energy savings for AlexNet and VGG-16 when compared to that in Eyeriss.
Understanding Estimation and Generalization Error of Generative Adversarial Networks

This article investigates the estimation and generalization errors of the generative adversarial network (GAN) training. On the statistical side, we develop an upper bound as well as a minimax lower bound on the estimation error for training GANs. The upper bound incorporates the roles of both the discriminator and the generator of GANs, and matches the minimax lower bound in terms of the sample size and the norm of the parameter matrices of neural networks under ReLU activation. On the algorithmic side, we develop a generalization error bound for the stochastic gradient method (SGM) in training GANs. Such a bound justifies the generalization ability of the GAN training via SGM after multiple passes over the data and reflects the interplay between the discriminator and the generator. Our results imply that the training of the generator requires more samples than the training of the discriminator. This is consistent with the empirical observation that the training of the discriminator typically converges faster than that of the generator. The experiments validate our theoretical results.
How Human Communication Influences Virtual Personal Assistants

Virtual Personal Assistants (VPI) are becoming part of our daily lives. The possibility of voice communication marks a new stage in the development of the interaction between people and computers. New challenges are associated not only with security issues and lack of the VPIs ability to give relevant answers to critical questions but also with the fact that the new artificial interlocutor does not always meet people’s expectations. The article discusses how the answers to the tricky questions of the virtual assistant Alice have been changing from the moment of its creation in 2017 to 2020. It was revealed that Alice not only developed the ability to respond to questions more correctly and avoid problematic topics (including those that could offend a human interlocutor), but also became more “modest” and less gender-oriented. Alice’s small talk and answers to personal questions have changed, thus the VPI hesitates to express her own opinion and make free jokes, begins to make excuses, and promises to improve, which was not observed earlier.
The Explanation Game: A Formal Framework for Interpretable Machine Learning

We propose a formal framework for interpretable machine learning. Combining elements from statistical learning, causal interventionism, and decision theory, we design an idealized explanation game in which players collaborate to find the best explanation(s) for a given algorithmic prediction. Through an iterative procedure of questions and answers, the players establish a three-dimensional Pareto frontier that describes the optimal trade-offs between explanatory accuracy, simplicity, and relevance. Multiple rounds are played at different levels of abstraction, allowing the players to explore overlapping causal patterns of variable granularity and scope. We characterize the conditions under which such a game is almost surely guaranteed to converge on a (conditionally) optimal explanation surface in polynomial time, and highlight obstacles that will tend to prevent the players from advancing beyond certain explanatory thresholds. The game serves a descriptive and a normative function, establishing a conceptual space in which to analyse and compare existing proposals, as well as design new and improved solutions.
Remarks on Control of a Robot Manipulator using a Quaternion Recurrent Neural-Network-Based Compensator

This paper presents and investigates practical applications of a recurrent quaternion neural network in controlling a robot manipulator within a desired trajectory. To effectively track the robot manipulator's end-effector position against the desired trajectory, computed torque control is used to design a control system, and the recurrent quaternion neural network is used to synthesise an external input to compensate for the system's control input. Computational experiments for controlling a 3-link robot manipulator are conducted using the control system. Experimental results demonstrate the effectiveness of using the recurrent quaternion neural network for practical control applications in a control system.
RobotDrlSim: A Real Time Robot Simulation Platform for Reinforcement Learning and Human Interactive Demonstration Learning

Abstract Deep reinforcement learning (DRL) techniques give robotics research an AI boost in many applications. In order to simultaneously accommodate the complex robotic behaviour simulation and DRL algorithm verification, a new simulation platform, namely the RobotDrlSim, is proposed. First, we design a standardized API interfacing mechanism for coordinating diverse environments on RobotDrlSim platform, where PyBullet simulator is equipped with an API to form a physical engine for robotics simulation. Second, benchmark DRL models are included in the baseline library for evaluation. Third, real-time human-robot interactions can be captured and imported to drive the RobotDrlSim tasks, which provide big data-stream for reinforcement learning. Experimentations show that cutting-edge DRL algorithms developed in python can be seamlessly deployed to the robots, and human interactions can be availed in training the robots. RobotDrlSim is valid for efficiently developing DRL algorithms for artificial intelligence models of robots, and it is especially suitable for the robot educational purposes.
A Clustering Analysis Method With High Reliability Based on Wilcoxon-Mann-Whitney Testing

As a core step in clustering analysis, distance measurement results can influence clustering accuracy. Existing measurement methods are mostly based on cluster feature information. However, these cluster features may be insufficient and result in losing data information for clusters containing a number of objects. To improve measurement accuracy, we make full use of the distribution characteristics of objects in clusters, i.e., we use descriptive statistics and the Wilcoxon-Mann-Whitney rank sum test in nonparametric statistics to measure distances during clustering. Furthermore, we propose a two-stage clustering algorithm to improve clustering analysis performance. In terms of avoiding preliminarily assuming the number of clusters, with the proposed distance measurement method, the clustering algorithm can discover clusters with arbitrary shapes and improve clustering accuracy. Experiments with multiple datasets compared with other clustering algorithms illustrate the accuracy and efficiency of the proposed clustering algorithm.
Adaptive Stochastic Filtration Based on the Estimation of the Covariance Matrix of Measurement Noises Using Irregular Accurate Observations

In measurement systems operating under various disturbances the probabilistic characteristics of measurement noises are usually known approximately. To improve the observation accuracy, a new approach to the Kalman’s filter adaptation is proposed. In this approach, the Covariance Matrix of Measurement Noises (CMMN) is estimated by accurate measurements detected irregularly by the mobile object observation system (from radiofrequency identifiers, etalon reference, fixed points etc.). The problem of adaptive estimation of the observer’s noises covariance matrix in the Kalman filter is solved analytically for two cases: mutual noises correlation, and its absence. The numerical example for adaptive filtration of complexing navigation system parameters of a mobile object using irregular accurate measurements is given to illustrate the effectiveness of the proposed algorithm. Coordinate estimating errors have changed in comparison with the traditional scheme from 100 m to 2 m in latitude, and from 200 m to 1.5 m in longitude.
Deep neural network for coded mask cryptographical imaging

We proposed a novel cryptographic imaging scheme that is the combination of optical encryption and computational decryption. To prevent personal privacy from being spied upon amid the imaging formation process, in this study we applied a coded mask to optically encrypt the scene and utilized the deep neural network for computational decryption. For encryption, the sensor recorded a new representation of the original signal, not being distinguishable by humans on purpose. For decryption, we successfully reconstructed the image with the mean squared error equal to 0.028, and 100% for the classification through the Japanese Female Facial Expression dataset. By means of the feature visualization, we found that the coded mask served as a linear operator to synthesize the spatial fidelity of the original scene, but kept the features for the post-recognition process. We believe the proposed framework can inspire more possibilities for the unconventional imaging system.
Gauge: An Interactive Data-Driven Visualization Tool for HPC Application I/O Performance Analysis

Understanding and alleviating I/O bottlenecks in HPC system workloads is difficult due to the complex, multilayered nature of HPC I/O subsystems. Even with full visibility into the jobs executed on the system, the lack of tooling makes debugging I/O problems difficult. In this work, we introduce Gauge, an interactive, data-driven, web-based visualization tool for HPC I/O performance analysis. Gauge aids in the process of visualizing and analyzing, in an interactive fashion, large sets of HPC application execution logs. It performs a number of functions met to significantly reduce the cognitive load of navigating these sets - some worth many years of HPC logs. For instance, as its first step in many processing chains, it arranges unordered sets of collected HPC logs into a hierarchy of clusters for later analysis. This clustering step allows application developers to quickly navigate logs, find how their jobs compare to those of their peers in terms of I/O utilization, as well as how to improve their future runs. Similarly, facility operators can use Gauge to `get a pulse' on the workloads running on their HPC systems, find clusters of under performing applications, and diagnose the reason for poor I/O throughput. In this work, we describe how Gauge arrives at the HPC jobs clustering, how it presents data about the jobs, and how it can be used to further narrow down and understand behavior of sets of jobs. We also provide a case study on using Gauge from the perspective of a facility operator.
A Fuzzy Clustering Based Collaborative Filtering Algorithm for Time-aware POI Recommendation

Abstract Location based social network develops and gets widely concern along with the population and widespread use of mobile. Point of interest(POI) recommendation become one of the most widely application among location-based service. To get better POI recommendation performance, a fuzzy clustering based collaborative filtering algorithm (FCCF) for time-aware POI recommendation is proposed in this paper. It first constructs the user feature vector from users’ check-in behaviours. Individual’s check-in behaviour can be under the influence of location region and time slots, so user’s feature consists of two parts. One is the vising frequency of each user in different location regions, and the other is the vising frequency of each user in different time slots. Next fuzzy c-means is adopted due to its simplicity to group users according to user feature vector. Then the user similarity computation can be limited in the similar small user groups. In the end, a collaborative filtering algorithm is applied to recommend a number of top-N POIs at a given time for the target user. Some experiments are conducted and the comparative results on Foursquare and Gowalla show that FCCF has higher precision and recall value than the comparative algorithms.
Deep Learning Techniques for Optical Character Recognition

Handwritten character recognition (HWCR) is an area of pattern recognition and optical character recognition (OCR) to identify and recognize the characters by the system or any electronic device. Optical character recognition is the process that converts handwritten text into machine-encoded text. In this paper, OCR techniques such as digitization, preprocessing, segmentation, feature extraction, and recognition are discussed. HWCR using different machine learning algorithms like Bayesian classifier, Bayesian decision theory, and SVM are discussed. Recent techniques of HWCR are analyzed for native language and compared their functionality and features. The comparative study of these recent techniques is summarized in table format.