Miracle-dz/UniFAR-FTUs huggingface dataset:
Disruption in structural–functional network repertoire and time-resolved subcortical fronto-temporoparietal connectivity in disorders of consciousness

Understanding recovery of consciousness and elucidating its underlying mechanism is believed to be crucial in the field of basic neuroscience and medicine. Ideas such as the global neuronal workspace and the mesocircuit theory hypothesize that failure of recovery in conscious states coincide with loss of connectivity between subcortical and frontoparietal areas, a loss of the repertoire of functional networks states and metastable brain activation. We adopted a time-resolved functional connectivity framework to explore these ideas and assessed the repertoire of functional network states as a potential marker of consciousness and its potential ability to tell apart patients in the unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS). In addition, prediction of these functional network states by underlying hidden spatial patterns in the anatomical network, i.e. so-called eigenmodes, were supplemented as potential markers. By analysing time-resolved functional connectivity from fMRI data, we demonstrated a reduction of metastability and functional network repertoire in UWS compared to MCS patients. This was expressed in terms of diminished dwell times and loss of nonstationarity in the default mode network and fronto-parietal subcortical network in UWS compared to MCS patients. We further demonstrated that these findings co-occurred with a loss of dynamic interplay between structural eigenmodes and emerging time-resolved functional connectivity in UWS. These results are, amongst others, in support of the global neuronal workspace theory and the mesocircuit hypothesis, underpinning the role of time-resolved thalamo-cortical connections and metastability in the recovery of consciousness.
Evaluating the L4S Architecture in Cellular Networks with a Programmable Switch

Low-Iatency applications, such as cloud gaming or cloud robotics are very demanding in terms of network latency. The IETF defines the L4S (Low Latency Low Loss Scalable throughput) architecture, to enable the delivery of high-bitrate low-latency applications without degrading the quality of other services. To deploy and upgrade L4S in network equipment to follow the transport protocols entering an age of quick evolutions, P4 (Programming Protocol-Independent Packet Processor), a programmable data plane concept, can help since it facilitates the deployment of networking software. In this paper, we propose a P4-based L4S solution and the performed evaluation proves our system behaves as expected. Furthermore, to be largely deployed, L4S should also be efficient under real cellular network conditions, which can vary over time and where the main network bottlenecks are. However, our evaluation shows limitations of L4S in providing high throughput while ensuring low latency delivery with time varying network conditions.
Learning Multi-Pitch Estimation from Weakly Aligned Score-Audio Pairs Using a Multi-Label CTC Loss

Detecting the simultaneous activity of pitches in music audio recordings is a central task within music processing, commonly known as multi-pitch estimation or frame-wise polyphonic music transcription. Deep-learning approaches recently achieved major improvements for this task, but the lack of annotated, large-size datasets beyond the piano solo scenario is still a limitation for fully exploiting their potential. In this paper, we propose a strategy for training a CNN-based multi-pitch estimator on weakly aligned score-audio pairs of pieces in different instrumentations. To this end, we make use of a multi-label variant of the connectionist temporal classification loss (MCTC), recently proposed for image recognition tasks. We re-formalize the MCTC loss to be applicable for multi-pitch estimation and perform several systematic experiments to analyze its behavior and robustness to training conditions. Finally, we report on multi-pitch estimation results for common datasets using weakly aligned training with MCTC, which performs similar than systems trained on strongly aligned scores.
In-Network Data Aggregation for Information-Centric WSNs using Unsupervised Machine Learning Techniques

IoT applications are changing our daily lives. These innovative applications are supported by new communication technologies and protocols. Particularly, the information-centric network (ICN) paradigm is well suited for many IoT application scenarios that involve large-scale wireless sensor networks (WSNs). Even though the ICN approach can significantly reduce the network traffic by optimizing the process of information recovery from network nodes, it is also possible to apply data aggregation strategies. This paper proposes an unsupervised machine learning-based data aggregation strategy for multi-hop information-centric WSNs. The results show that the proposed algorithm can significantly reduce the ICN data traffic while having reduced information degradation.
Parking Guidance System: San Jose, California

A feasibility analysis and concept plan was recently developed for a parking guidance system (PGS) in downtown San Jose, California. As San Jose continues to grow, real-time parking management is needed to manage the increased demand. A PGS will direct motorists to city and privately owned parking facilities. The system will inform drivers of parking locations and availability with electronic message signs located at key locations on major streets and freeway exit ramps. Having timely and accurate information will allow drivers to make informed decisions about where to park, thus increasing the efficiency of parking facilities. The concept was developed under the direction of the city's Department of Transportation and was finalized in September 2000. With the support of city officials and downtown stakeholders, the development of plans, specifications, and engineering began in late 2000. Funding was set aside for the design and initial implementation of the program. The concept, design, and issues associated with implementation of a PGS are discussed. Following deployment, San Jose's PGS will be one of the largest applications of real-time parking management in North America.
a high-Quality Fuels dataBase OF phOtOs and inFormation

Photo series and their associated data provide a quick and easy way for managers to quantify and describe fuel and vegetation properties, such as loading of dead and down woody material, tree density, or height of understory vegetation. This information is critical for making fuel management decisions and for predicting fire behavior and fire effects. The Digital Photo Series (DPS) is a user-friendly, Web-based application that displays data and images from all 16 currently published volumes of the Natural Fuels Photo Series (NFPS) (42 different photo series for a total of 438 sites). The database format of DPS enables searching, downloading, customized site generation, and side-by-side comparison of data and images. DPS follows the published volumes in both content and presentation.
4.4.3 A Visual Explanation of Development Methods and Strategies including the Waterfall, Spiral, Vee, Vee+, and Vee++ Models

Considerable confusion continues relative to development methods, development strategies, and delivery strategies. This confusion is prevalent in routine engineering discussions and in recent textbooks. Method models such as the Waterfall, Spiral, Vee, Vee+, and Vee++ offer a variety of software and system development approaches each with a specific emphasis. Each of these models can be applied to an incremental or evolutionary strategy according to the nature of the project and each of these in turn can be produced in single or multiple deliveries.
An Efficient method for the advancement of performance of wireless sensor network

In this paper, the main focus or emphasis is given on the upgrdation of the performance of the wireless sensor network or it may be a general wireless network . The main issue is towards the monitoring of the delay issue & network lifetime issue.wireless sensor network also suffer from the different problems or vulnerabilities. WSN suffer from attacks due to which there is an impact on the performance of the wireless sensor network.our main aim in this paper is towards the advancement in the performance of the wireless network.Also towards the maximization of network lifetime & minimization of delay term. Wireless sensor networks consists of many sensing nodes that captures the changes in the environment enclose data in data packets and gives these packets to sink node present in the network. A WSN consists of a number of sensors spread across a geographical area. Each sensor has wireless communication capability and sufficient intelligence for signal processing and networking of the data. The network lifetime is usually defined as the time until the first node fails because of energy depletion. So sleep-wake scheduling is effective mechanism to increase network lifetime. Sleep-wake scheduling is efficient to increase network lifetime but it could result insubstantial delays because a transmitting node needs to wait for its next-hop relay node to wake up. Anycast forwarding schemes to forward the data packet to next hop node which minimizes the expected packet-delivery delays from the sensor nodes to the sink node.CMAC avoid the synchronization while supporting the latency.
Artificial Intelligence-Based Power System Virtual Seats Key Technology Research and Application

Virtual agent technology plays a significant role in improving the quality of power customer service in the power industry. Through the virtual agent technology, combined with speech recognition technology, knowledge mapping research and multi round human-computer dialogue technology, the ability of the power industry to cope with the increase of the number of power users, the increase of power consumption and the diversification of services is promoted. This paper first summarizes the key technologies of virtual agent, then discusses the application scenarios of virtual agent in power system, and finally prospects its development.
Gastric Location Classification During Esophagogastroduodenoscopy Using Deep Neural Networks

Esophagogastroduodenoscopy (EGD) is a common procedure that visualizes the esophagus, stomach, and the duodenum by inserting a camera, attached to a long flexible tube, into the patient's mouth and down the stomach. A comprehensive EGD needs to examine all gastric locations, but since the camera is controlled manually, it is easy to miss some surface area and create diagnostic blind spots, which often result in life-costing oversights of early gastric cancer and other serious illnesses. In order to address this problem, we train a convolutional neural network to classify gastric locations based on the camera feed during an EGD, and based on the classifier and a triggering algorithm we propose, we build a video processing system that checks off each location as visited, allowing human operators to keep track of which locations they have visited and which they have not. Based on collected clinical patient reports, we consider six gastric locations, and we add a background class to our classifier to accomodate for the frames in EGD videos that do not resemble the six defined classes (including when the camera is outside of the patient body). Our best classifier achieves 98 % accuracy within the six gastric locations and 88 % accuracy including the background class, and our video processing system clearly checks off gastric locations in an expected order when being tested on recorded EGD videos. Lastly, we use class activation mapping to provide human-readable insight into how our trained classifier works.
Transcriptional and Cytotoxic Responses of Human Intestinal Organoids to Interferon Types I, II, and III

The three types of interferon (IFN) have roles in antimicrobial immunity and inflammation that must be properly balanced to maintain tissue homeostasis. For example, IFNs are elevated in the context of inflammatory bowel disease and may synergize with inflammatory cytokines such as tumor necrosis factor alpha (TNFα) to promote tissue damage. Prior studies suggest that in mouse intestinal epithelial cells (IECs), type III IFNs are preferentially produced during viral infections and are less cytotoxic than type I IFN. Here, we generated human IEC organoid lines from biopsies of ileum, ascending colon, and sigmoid colon of three healthy subjects to establish the baseline responses of normal human IECs to types I, II, and III IFN. We found that all IFN types elicited responses that were qualitatively consistent across intestinal biopsy sites. However, IFN types differed in magnitude of STAT1 phosphorylation and identity of genes in their downstream transcriptional programs. Specifically, there was a core transcriptional module shared by IFN types, but types I and II IFN stimulated unique transcriptional modules beyond this core gene signature. The transcriptional modules of type I and II IFN included pro-apoptotic genes, and expression of these genes correlated with potentiation of TNFα cytotoxicity. These data define the response profiles of healthy human IEC organoids across IFN types, and suggest that cytotoxic effects mediated by TNFα in inflamed tissues may be amplified by a simultaneous high-magnitude IFN response.
Predictive Analytics to Support Health Informatics on COVID-19 Data

Bioinformatics and health informatics-in conjection with data science, data mining and machine learning-have been applied in numerous real-life applications including disease and healthcare analytics, such as predictive analytics of coronavirus disease 2019 (COVID-19). Many of these existing works usually require large volumes of data train the classification and prediction models. However, these data (e.g., computed tomography (CT) scan images, viral/molecular test results) that can be expensive to produce and/or not easily accessible. For instance, partially due to privacy concerns and other factors, the volume of available disease data can be limited. Hence, in this paper, we present a predictive analytics system to support health analytics. Specifically, the system make good use of autoencoder and few-shot learning to train the prediction model with only a few samples of more accessible and less expensive types of data (e.g., serology/antibody test results from blood samples), which helps to support prediction on classification of potential patients (e.g., potential COVID-19 patients). Moreover, the system also provides users (e.g., healthcare providers) with predictions on hospitalization status and clinical outcomes of COVID-19 patients. This provides healthcare administrators and staff with a good estimate on the demand for healthcare support. With this system, users could then focus and provide timely treatment to the true patients, thus preventing them for spreading the disease in the community. The system is helpful, especially for rural areas, when sophisticated equipment (e.g., CT scanners) may be unavailable. Evaluation results on a real-life datasets demonstrate the effectiveness of our digital health system in health analytics, especially in classifying patients and their medical needs.
Particle swarm optimization of L‐probe fed E‐shaped patch antenna using single‐objective function

An application of particle swarm optimization (PSO) for wideband patch antenna design is presented in this article. A PSO kernel is built on a MATLAB‐driven MoM‐based EM simulator FEKO. The chosen antenna geometry in this study is the E‐shaped patch but excited by an L‐probe feed. Ten geometrical parameters are optimized simultaneously with the use of a single‐objective: impedance bandwidth broadening. The desired center frequency of this ceiling mounted WLAN (IEEE 802.11a/n) antenna is set at 5.5 GHz with butterfly beam operation. E‐shaped patch geometry of higher order mode is obtained from the PSO‐EM optimizer. The simulated results are verified by the experimental results that a ∼3.5 dBi gain at the elevation angles of ±36° and a >50% of 10‐dB return‐loss bandwidth are obtained at the price of high cross‐polarization levels. This suggests that a multiobjective particle swarm optimization should be used for the wideband antenna design in the future study. © 2010 Wiley Periodicals, Inc. Microwave Opt Technol Lett 52: 702–706, 2010; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.25016
A Common Bus Voltage Compensation Method for Microgrid Based on Voltage Drop Estimation

When multiple distributed renewable energy sources with the characteristics of virtual synchronous generator are paralleled in microgrid, the power distribution of the parallel inverter is unbalanced due to the mismatch of feeder impedance, and the common bus voltage drop problem. In this paper, a mathematical model of the parallel system is established to analyze the conditions of power sharing, and the virtual impedance method is used to improve the accuracy of power sharing of the inverter. However, the virtual impedance in series will increase the voltage drop of the line and cause the voltage of the common bus to deviate from the rated value, thus affecting the power quality. Therefore, based on the theoretical analysis of the voltage drop caused by the virtual impedance method, a voltage compensation method based on the voltage drop estimation for the common bus of microgrid is proposed. Finally, using MATLAB/SIMULINK software to build a distributed sources parallel system simulation model to verify the correctness of the virtual impedance to achieve power balance distribution and common bus voltage compensation methods.
Urban design quality and real estate value: in search of a methodological framework

Abstract This paper presents a critical interdisciplinary examination of the interface between the urban design and real estate disciplines and provides a modus operandi for estimating different aspects of quality design. The purpose of this paper is to provide a framework for further developing hedonic modelling of real estate markets to value urban design quality. It achieves this by investigating in detail the concepts of urban design quality and real estate value, and by advocating the need for an overarching conceptual approach through the application of utility theory that provides the theoretical underpinnings of the framework.
Design and Simulation of a Solar PV System for a University Building

System Advisor Model (SAM) software was used to analyze a grid-connected solar photovoltaic system on the rooftop of the Student Union Building (SUB) at Texas Tech University. The SUB has an average daily load requirement of 7917 kWh/day, and the average load per year is 2.89 GWh. A simulation of a PV system under various load circumstances was conducted. A 327 Wp monocrystalline silicon module was utilized to reach a total power of 141.6 kW, using a rooftop area of 2168 m2. The system can provide about 12 % of the required energy for the SUB, resulting in energy cost savings.
Deep Learning based CSI Reconstruction with Limited Feedback for Massive MIMO Systems

Channel state information (CSI) acquisition is one of the major challenges for massive MIMO systems to enable high efficiency MIMO transmissions. Limited feedback schemes, which quantize CSI into limited number of bits and feed these bits back to the base station (BS), are widely used in real-life mobile networks for BSs to obtain downlink CSI. Practical limited feedback schemes usually have large feedback granularity and introduce severe nonlinear quantization error due to the constraint on feedback overhead. It is hard for legacy signal processing technologies to reconstruct CSI accurately for high efficiency downlink transmissions. Different from the legacy signal processing technologies, deep neural networks trained with big data have strong abilities to fit any nonlinear functions and learn the complex signal model behind the big data, which is a useful tool to solve problems that are nonlinear or hard to be described mathematically. Motivated by these observations, we consider a deep learning based approach for BS receivers to reconstruct CSI based on the limited feedback bits from user equipment (UE). The CSI reconstruction is similar with the image super-resolution which reconstructs a high resolution image from a low resolution one based on the structure information learned by the neural network itself. Performance evaluations demonstrate that the proposed method outperforms the legacy algorithms. The improvement on spectrum efficiency is about 25%. It can also reconstruct the CSI with lower density reference signal (RS) than that in current specification, e.g., the RS density can be 1/32 of current one. Therefore, the reference signal and feedback overhead can be reduced with our method. These results show the potential gain of applying deep learning to physical layer signal processing at the receiver side only, which has little air-interface impact and does not require much effort on enhancing network specifications. Therefore, it is feasible to be deployed in current 5th generation mobile networks with the pace of network evolutions, and opens the gate of intelligent physical layer design for future generations of mobile networks.
Network intrusion detection based on BiSRU and CNN

In recent years, with the continuous development of artificial intelligence algorithms, their applications in network intrusion detection have become more and more widespread. However, as the network speed continues to increase, network traffic increases dramatically, and the drawbacks of traditional machine learning methods such as high false alarm rate and long training time are gradually revealed. CNN(Convolutional Neural Networks) can only extract spatial features of data, which is obviously insufficient for network intrusion detection. In this paper, we propose an intrusion detection model that combines CNN and BiSRU (Bi-directional Simple Recurrent Unit) to achieve the goal of intrusion detection by processing network traffic logs. First, we extract the spatial features of the original data using CNN, after that we use them as input, further extract the temporal features using BiSRU, and finally output the classification results by softmax to achieve the purpose of intrusion detection.
Syslog Anomaly Detection Using Supervised Machine Learning Models

Nowadays, detecting anomalies is crucial for managing every network. Massive logs are produced by modern large-scale distributed systems. These logs contain useful information regarding network behavior. Traditionally, developers detect anomalies by complex coded scripts. However, such approach is not efficient for large-scale systems where they generate thousands of logs. Thus, syslog anomaly detection tool has been proposed in this paper by using supervised machine learning (ML) models. As a source of dataset for the ML models, syslog generator was developed to generate the desired dataset. A comparative study about many supervised ML methods has been evaluated in this paper using different amount of datasets. The target was to check the impact of enlargement of datasets on the performance of the anomaly detections.