Miracle-dz/UniFAR-FTUs huggingface dataset:
Does Impedance Matter When Recording Spikes With Polytrodes?

Extracellular microelectrodes have been widely used to measure brain activity, yet there are still basic questions about the requirements for a good extracellular microelectrode. One common source of confusion is how an electrode’s impedance affects the amplitude of extracellular spikes and background noise. Here we discuss how an electrode’s impedance affects data quality in extracellular recordings, which is crucial for both the detection of spikes and their assignment to the correct neurons. This study employs commercial polytrodes containing 32 electrodes (177 μm2) arranged in a dense array. This allowed us to directly compare, side-by-side, the same extracellular signals measured by modified low impedance (~100 kOhm) microelectrodes with unmodified high impedance (~1 MOhm) microelectrodes. We begin with an evaluation of existing protocols to lower the impedance of the electrodes. The poly(3,4-ethylenedioxythiophene)-polystyrene sulfonate (PEDOT-PSS) electrodeposition protocol is a simple, stable, and reliable method for decreasing the impedance of a microelectrode up to tenfold. We next record in vivo using polytrodes that are modified in a ‘chess board’ pattern, such that the signal of one neuron is detected by multiple coated and non-coated electrodes. The performance of the coated and non-coated electrodes is then compared on measures of background noise and amplitude of the detected action potentials. If the proper recording system is used, then the impedance of a microelectrode within the range of standard polytrodes (~ 0.1 to 2 MOhm) does not significantly affect data quality and spike sorting. This study should encourage neuroscientists to stop worrying about one more unknown.
A Path Optimization Algorithm for Motion Planning with the Moving Target

There are many algorithms based on RRT* algorithm for solving robotic motion planning problems, but most of them deal with cases with the static target. In this paper, we improved the bidirectional RRT* algorithm and proposed a path optimization algorithm tailored to the moving target. Our algorithm can find the first feasible path more rapidly and optimize the path in real-time as the target moves. We compared RRT*, bidirectional RRT* and our optimized algorithm in simulation experiments. The experiment results show that our algorithms find the first feasible solution in shorter time and has faster rate of convergence in the environment with different obstacle placements. We implemented our algorithm on UR5 robot with RG2 grasper in V-rep simulations and the robot grasped the moving object successfully.
Classifier Ensemble by Exploring Supplementary Ordering Information

Supplementary information has been proven to be particularly useful in many machine learning tasks. In ensemble learning for a set of trained base classifiers, there also exists abundant implicit supplementary information about the performance orderings for the trained base classifiers in previous literature. However, few classifier ensemble studies consider exploring and utilizing supplementary information. The current study proposes a new learning method for stack classifier ensembles by considering the implicit supplementary ordering information regarding a set of trained classifiers. First, a new metric learning algorithm for measuring the similarities between two arbitrary learning tasks is introduced. Second, supplementary ordering information for the trained classifiers of a given learning task is inferred based on the learned similarities and related performance results reported in the previous literature. Third, a set of ordered soft constraints is generated based on the supplementary ordering information, and achieving the optimal combination weights of the trained classifiers is formalized into a goal programming problem. The optimal combination weights are then obtained. Finally, the experimental results verify the effectiveness of the proposed new classifier ensemble method.
Protein Complex Detection Based on Semi-Supervised Matrix Factorization

The identification of protein complexes is important for understanding the organization and function within cells. Owing to the quick development of high-throughput experimental techniques, an increasing number of protein-protein interaction (PPI) datasets are accumulated, which makes it possible to detect protein complexes from PPI networks. However, most of the currently available protein complex detection methods are unsupervised learning methods and can not utilize the information of the known complexes. In this paper, we present a novel semi-supervised matrix factorization method, called Constrained Overlapping Complex Detection Model (COCDM), which incorporates the known complexes as additional constrains. Experimental results demonstrate that COCDM performs much better than most state-of-the-art complex detection techniques.
Tasks Scheduling and Resource Allocation in Fog Computing Based on Containers for Smart Manufacturing

Fog computing has been proposed as an extension of cloud computing to provide computation, storage, and network services in network edge. For smart manufacturing, fog computing can provide a wealth of computational and storage services, such as fault detection and state analysis of devices in assembly lines, if the middle layer between the industrial cloud and the terminal device is considered. However, limited resources and low-delay services hinder the application of new virtualization technologies in the task scheduling and resource management of fog computing. Thus, we build a new task-scheduling model by considering the role of containers. Then, we construct a task-scheduling algorithm to ensure that the tasks are completed on time and the number of concurrent tasks for the fog node is optimized. Finally, we propose a reallocation mechanism to reduce task delays in accordance with the characteristics of the containers. The results showed that our proposed task-scheduling algorithm and reallocation scheme can effectively reduce task delays and improve the concurrency number of the tasks in fog nodes.
Medical Image Classification Based on Deep Features Extracted by Deep Model and Statistic Feature Fusion with Multilayer Perceptron‬

Medical image classification is a key technique of Computer-Aided Diagnosis (CAD) systems. Traditional methods rely mainly on the shape, color, and/or texture features as well as their combinations, most of which are problem-specific and have shown to be complementary in medical images, which leads to a system that lacks the ability to make representations of high-level problem domain concepts and that has poor model generalization ability. Recent deep learning methods provide an effective way to construct an end-to-end model that can compute final classification labels with the raw pixels of medical images. However, due to the high resolution of the medical images and the small dataset size, deep learning models suffer from high computational costs and limitations in the model layers and channels. To solve these problems, in this paper, we propose a deep learning model that integrates Coding Network with Multilayer Perceptron (CNMP), which combines high-level features that are extracted from a deep convolutional neural network and some selected traditional features. The construction of the proposed model includes the following steps. First, we train a deep convolutional neural network as a coding network in a supervised manner, and the result is that it can code the raw pixels of medical images into feature vectors that represent high-level concepts for classification. Second, we extract a set of selected traditional features based on background knowledge of medical images. Finally, we design an efficient model that is based on neural networks to fuse the different feature groups obtained in the first and second step. We evaluate the proposed approach on two benchmark medical image datasets: HIS2828 and ISIC2017. We achieve an overall classification accuracy of 90.1% and 90.2%, respectively, which are higher than the current successful methods.
Brain Pacemakers in Consumer Medical Electronics Improve Quality of Life-Part II: The Need for Patient Feedback in the Product Lifecycle Management

The following is a continuation of the interview "Brain Pacemakers in Consumer Medical Electronics Improve Quality of Life," published in the July 2018 issue of IEEE Consumer Electronics Magazine. It spotlights Gary Olhoeft, who, in 2009, had a deep-brain stimulation (DBS) device implanted to help him combat Parkinson's disease. Dr. Olhoeft is a retired emeritus professor of geophysics at the Colorado School of Mines, Golden. He previously taught a number of subjects related to advanced electrical and electromagnetic methods, antennas, near-surface field methods, ground-penetrating radar, and complex resistivity.
Stability Analysis of Genetic Regulatory Networks with Dual Regulation Strategies

This paper analyzes the stability characteristic of a small genetic regulatory network with dual regulatory processes (both transcriptional and post-transcriptional regulation). Comparing to the traditional dynamic analysis only focusing on single transcriptional process, dual regulatory processes can help us understand the regulatory mechanism of genetic regulatory networks more precise. The existence of positive equilibriums is discussed for this genetic structure, and stability conditions are established for each equilibrium. Finally, numerical simulations are adopted to prove our analysis.
Energy-based decision engine for household human activity recognition

We propose a framework for energy-based human activity recognition in a household environment. We apply machine learning techniques to infer the state of household appliances from their energy consumption data and use rule- based scenarios that exploit these states to detect human activity. Our decision engine achieved a 99.1% accuracy for real-world data collected in the kitchens of two smart homes.
Component Analysis of Errors in Four GPM-Based Precipitation Estimations over Mainland China

As the Global Precipitation Measurement (GPM) Core Observatory satellite continues its mission, the latest GPM-era satellite-based precipitation estimations, including Global Satellite Mapping of Precipitation (GSMaP) and Integrated Multi-satellitE Retrievals for the GPM (IMERG), have been released. However, few studies have systematically evaluated these products over mainland China, although this is very important for both the end users and data developers. To these ends, the final-run uncalibrated IMERG V05 (V05UC), gauge-calibrated IMERG V05 (V05C) and IMERG V04 (V04C), and latest gauge-calibrated GSMaP V7 (GSMaP) are systematically evaluated and mutually compared against a merged product obtained from the China Meteorological Data Service Center via continuous statistical indices and an error decomposition analysis technology suite over mainland China from April 2014 to December 2016 at a 3 hourly scale and 0.1° × 0.1° resolution. The results show that, irrespective of the slight overestimation in the southeast and underestimation in the northern Tibetan Plateau, all four GSPEs could generally capture the spatial patterns of precipitation over mainland China. Meanwhile, the overall quality of the GSMaP is slightly superior to the IMERG products in east and south China; however, it also suffers from an overestimation of light rain and an underestimation of heavy rain. Such overestimation and underestimation are primarily from a large false precipitation in light rain and a negative hit bias in heavy rain, respectively. The latest IMERG V05 products have not shown significant improvement over the earlier version (V04C) in east and south China, but the calibrated V05C can best reproduce the probability density function in terms of precipitation intensity. Furthermore, V04C shows remarkable underestimation over the Tibetan Plateau, while this shortcoming has been resolved significantly in V05C. Alternately, the effects of the gauge calibration algorithm (GCA) used in IMERG are examined by comparison of V05UC and V05C. The results indicate that GCA cannot reduce the missed precipitation, and even enlarges the false precipitation over some regions. This reveals that GCA cannot effectively alleviate the bias resulting from the rain areas’ delineation and raining or not-raining detection. In addition, all of the products’ performance can be improved, particularly in the dry climate and high-latitude regions. This is a systematic estimation for GSPEs, providing deep insight into the characteristics and sources of error, and it could be valuable as a reference for both algorithm developers and data users, as well as for associated global products and various applications.
Hierarchical Differences in Supervisor Support and Coworker Support

Reference [1]stated that when supervisors act as agents of the organization, then employees develop global views concerning the degree to which supervisors value their contributions and care about their well-being. Reference [2] indicated that strength of influence on the perceived organizational support (POS), follows to the status of supervisors. Reference [3] reported coworkers' influence on POS through social networking. However, there are few studies on PSS according to hierarchy and PCS. This study investigated the strength of employees' perceptions of support from coworker and from supervisors, and the correlation between perceived coworker support, supervisor support with affective commitment, and citizenship behavior. Research subjects included are 83 technical employees working in two factories belonging in a steel manufacturing group located in the region in Japan. We analyzed the data using regression analysis and covariance structure analysis. We found that (1) perceived support from coworkers had greater influence than that from the factory manager and immediate supervisors. (2) Perceived support from upper manager influence more than that from the factory manager and immediate supervisors. (3) Affective commitment (AC) mediated between perceived coworkers and supervisor support and citizenship behavior. The study suggested that the perceived support of coworkers and upper managers has a strong influence on employees.
Stability and performance guarantees in networks with cyclic dependencies

With the development of real-time networks such as reactive embedded systems, there is a need to compute deterministic performance bounds. This paper focuses on the performance guarantees and stability conditions in networks with cyclic dependencies in the network calculus framework. We first propose an algorithm that computes tight backlog bounds in tree networks for any set of flows crossing a server. Then, we show how this algorithm can be applied to improve bounds from the literature fir any topology, including cyclic networks. In particular, we show that the ring is stable in the network calculus framework.
Exponential Synchronization of Nonlinear Systems with Delay via Aperiodically Intermittent Control

In the paper, the synchronization control scheme of nonlinear systems with delay are introduced by using aperiodically intermittent control. When control ratios of control width to total time width are equal in any time interval, feasibility of the control scheme is studied. Moreover, the rigorous mathematical proofs of the global exponential synchronization are given. Delayed Chua's circuit and a neural network are employed to evaluate the performances of the control scheme. The experimental results indicate that the proposed scheme successfully achieves synchronizations with flexibility and effectiveness.
A Direct Dense Visual Servoing Approach Using Photometric Moments

In this paper, visual servoing based on photometric moments is advocated. A direct approach is chosen by which the extraction of geometric primitives, visual tracking and image matching steps of a conventional visual servoing pipeline can be bypassed. A vital challenge in photometric methods is the change in the image resulting from the appearance and disappearance of portions of the scene from the camera field of view during the servo. To tackle this issue, a general model for the photometric moments enhanced with spatial weighting is proposed. The interaction matrix for these spatially weighted photometric moments is derived in an analytical form. The correctness of the modeling, effectiveness of the proposed strategy in handling the exogenous regions, and improved convergence domain are demonstrated with a combination of simulation and the experimental results.
Passivity-Based Output Synchronization of Discrete-Time Heterogeneous Multi-Agent Systems

In this paper, we consider the distributed output synchronization problem of heterogeneous discrete-time nonlinear multi-agent systems with input shortage passivity. We propose fully distributed synchronization protocols based on the outputs of neighboring agents. By using the passivity-based control, we show that the multi-agent system can be driven to synchronization provided that the coupling strengths are weak enough. In order to reduce the communication burden and the update frequency of controllers, an event-triggered distributed synchronization protocol is proposed where the transmission of output states and the updates of controllers are only needed at event instants. Finally, a simulation example is carried out to illustrate the effectiveness of the proposed protocols.
Equine asthma: Integrative biologic relevance of a recently proposed nomenclature

The term “equine asthma” has been proposed as a unifying descriptor of inflammatory airway disease (IAD), recurrent airway obstruction (RAO), and summer pasture‐associated obstructive airway disease. Whilst the term will increase comprehensibility for both the lay and scientific communities, its biologic relevance must be compared and contrasted to asthma in human medicine, recognizing the limited availability of peer‐reviewed equine‐derived data, which are largely restricted to clinical signs, measures of airway obstruction and inflammation and response to therapy. Such limitations constrain meaningful comparisons with human asthma phenotypes. Suggested minimum inclusion criteria supporting the term asthma, as well as similarities and differences between IAD, RAO, and multiple human asthma phenotypes are discussed. Furthermore, differences between phenotype and severity are described, and typical features for equine asthma subcategories are proposed. Based on shared features, we conclude that mild/moderate (IAD) and severe (RAO) equine asthma are biologically appropriate models for both allergic and non‐allergic human asthma, with RAO (severe equine asthma) also being an appropriate model for late‐onset asthma. With the development of new biologic treatments in humans and the application of more targeted therapeutic approaches in the horse, it would appear appropriate to further investigate the allergic (Th‐2) and non‐allergic (non‐Th‐2) phenotypes of equine asthma. Further research is required to more fully determine the potential clinical utility of phenotype classification.
Time to Discover and Fix Software Vulnerabilities in Open Source Software Projects: Notes on Measurement and Data Availability

Reducing the time taken to discover and fix vulnerabilities in open source software projects is increasingly relevant to technology entrepreneurs and technology managers at all levels of industry. Rigorous research requires access to valid and reliable data on when vulnerabilities were introduced, discovered, and closed. This article offers three contributions about measurement and data availability: (1) an approach to measuring the time to discover and time to fix vulnerabilities in open source software projects, (2) evidence that combining project release histories and metrics from two online databases can provide reliable proxy dates for vulnerability introduction and fix, but not discovery, and (3) possible technical and open collaboration solutions to the data availability limitations of current databases. These results were part of a larger mixed-method study on the relationship between open source project and community attributes and software vulnerabilities with a data set of 1268 vulnerabilities affecting the software produced by 60 open source projects.
Robust Disturbance- Rejection for a Class of Nonlinear Systems

This paper presents a robust disturbance-rejection method for a system with a time-variant nonlinearity and a disturbance. The nonlinear dynamics which satisfies Lipschitz condition is unknown. The disturbance is also unknown but bounded. Taking advantage of stable inversion, an improved EID (IEID) is utilized to deal with the total effect of the nonlinearity and disturbance. To reduce the conservativeness of system design, a feedback control gain is obtained in prior to the control gains of the observer and the IEID estimator. Then a Lyapunov functional considering the information of the nonlinearity is constructed and based on that, a linear-matrix-inequality (LMI)-based stability condition is derived to acquire the gains of the observer and the IEID estimator. Two tuning parameters are introduced in the LMI to enable the adjustment of disturbance-rejection performance. Finally, simulations for a flight control system show that the developed method has good robustness. Comparisons with disturbance-observer-based control (DOBC), composite DOBC and $H$ oo control, and conventional EID-based control methods demonstrate the effectiveness of the developed method and its advantages over others.
Improved Astar Algorithm for Path Planning of Marine Robot

Marine robot plays an important role in the marine research due to its good application prospects. Path planning provides the necessary information for marine robot to accomplish missions, and classic methods for path planning can be roughly divided into pre-planning and realtime planning, in wich Astar is an algorithm widely applied in path pre-planning of mobile robot. Classic Astar only generates a series of way-points for robots in formation of Descartes coordinate point which is based on a two-value grid map. The result has approximately optimal distance, however, the path does not conform with the motion constraint of robot. This paper proposes an improved algorithm in consideration of the orientation constraint of marine robot for Astar algorithm, and introduces related work about environment modeling. Path generated via this proposed method is more appropriate than classic Astar's in practical application.
Regulation of Lymphatic GM-CSF Expression by the E3 Ubiquitin Ligase Cbl-b

Genome-wide association studies as well as lymphatic expression analyses have linked both Cbl-b and GM-CSF to human multiple sclerosis as well as other autoimmune diseases. Both Cbl-b and GM-CSF have been shown to play a prominent role in the development of murine encephalomyelitis; however, no functional connection between the two has yet been established. In this study, we show that Cblb knockout mice demonstrated significantly exacerbated severity of experimental autoimmune encephalomyelitis (EAE), augmented T cell infiltration into the central nervous system (CNS) and strongly increased production of GM-CSF in T cells in vitro and in vivo.GM-CSF neutralization demonstrated that the increased susceptibility of Cblb−/− mice to EAE was dependent on GM-CSF. Mechanistically, p50 binding to the GM-CSF promoter and the IL-3/GM-CSF enhancer element “CNSa” was strongly increased in nuclear extracts from Cbl-b-deficient T cells. This study suggests that Cbl-b limits autoimmunity by preventing the pathogenic effects of GM-CSF overproduction in T cells.