Miracle-dz/UniFAR-FTUs huggingface dataset:
Market Mechanisms for Energy Storage Planning and Operation in a Power Network

We conduct a comparative analysis on three joint market mechanisms for energy storage investment and operation under locational marginal pricing: i) socially optimal storage investment with centralized operation, ii) profit-maximizing storage investment with centralized operation, and iii) profit-maximizing storage investment with deregulated operation. For the first mechanism (according to which a social planner centrally optimizes the sitting, sizing, and operation of batteries), we show that the revenue collected from storage operation exactly covers the investment cost at a social optimum. Under the last two mechanisms, a profit maximizing firm strategically determines the sitting and sizing of batteries, and the storage operation is either centrally optimized for cost minimization (mechanism ii) or completely controlled by the storage owner for profit maximization (mechanism iii). Numerical results on the IEEE 57-bus test system reveal that under profit-maximizing investment, the centralized and deregulated storage operations usually lead to almost identical outcomes (on installed storage capacity, social cost, and storage profit).
Dual-Graph Regularized Discriminative Multitask Tracker

Multitask and low-rank learning methods have attracted increasing attention for visual tracking. However, most trackers only focus on learning appearance subspace basis or the sparse low rankness of representation and, thus, do not make full use of the structure information among and inside target candidates (or samples). In this paper, we propose a dual-graph regularized discriminative low-rank learning for a multitask tracker, which integrates the discriminative subspace and intrinsic geometric structures among tasks. By constructing dual-graph regulations from two views of multitask observation, the developed model not only exploits the intrinsic relationship among tasks, and preserves the spatial layout structure among the local patches inside each candidate, but also learns the salient features of the target samples. This operation has the benefit of having good target representation and improving the performance of the tracker. Moreover, our developed tracker is a collaborate multitask tracking model and learns the discriminative subspace with adaptive dimension and optimal classifier simultaneously. Then, a collaborate metric is developed to find the best candidate, which integrates both classification reliability and representation accuracy. Encouraging experimental results on a large set of public video sequences justify that our tracker performs favorably against many other state-of-the-art trackers.
Traits of criticality in membrane potential fluctuations of pyramidal neurons in the CA1 region of rat hippocampus

Evidence that neural circuits are operating near criticality has been provided at various levels of brain organisation with a presumed role in maximising information processing and multiscale activity association. Criticality has been linked to excitation at both the single‐cell and network levels, as action potential generation marks an obvious phase transition from a resting to an excitable state. Using in vitro intracellular recordings, we examine irregular, small amplitude membrane potential fluctuations from CA1 pyramidal neurons of Wistar male rats. We show that these fluctuations, confounded with noise, carry information relevant to the neuronal state. The underlying dynamics exhibit intermittent characteristics shown to describe the temporal fluctuations of the order parameter of a macroscopic system at its critical point even in the absence of firing. An externally applied stimulus serves as the control parameter, driving the system in and out of its critical state. Based on our experimental observations we calculate the equivalent of the isothermal critical exponent δh finding a value which depends on the applied stimulus. For each neuron there is a stimulus amplitude for which the critical behaviour becomes most pronounced. The corresponding mean value of δh in the considered ensemble of neurons is δh ≈ 1.89, close to theoretical predictions for critical networks. Finally, we show that the firing rate of a neuron decreases exponentially with δh.
Multi-objective Optimal Tuning of the Multi-loop Pitch Control Systems of a Wind Turbine

The control of three-bladed, variable-speed, horizontal-axis large-sized wind turbines, in case of overrated wind speed (i.e. Region III) is based on the equal actuation of the all three blades. This is knows as collective pitch control (CPC). The pitch actuation produces additional tower oscillations that will require an active tower damping control (ATDC). In order to mitigate loads, the pitch angles provided by the CPC can be slightly individually modified according to measurements of the root bending moments. In total, six coupled controllers have to be tuned, where to find the best possible compromise between all controllers is a very important goal. In the current work, all controllers are designed by using a cooperative game-theoretic approach, whose solution is found by using multi-objective parametric optimization. Simulation results show that the proposed method yields a satisfactory performance for all control loops.
Early Plasma Matrix Metalloproteinase Profiles. A Novel Pathway in Pediatric Acute Respiratory Distress Syndrome

Rationale: MMPs (Matrix metalloproteinases) and their endogenous tissue inhibitors may contribute to lung injury through extracellular matrix degradation and modulation of inflammation and fibrosis. Objectives: To test for an association between MMP pathway proteins and inflammation, endothelial dysfunction, and clinical outcomes. Methods: We measured MMPs in plasma collected on acute respiratory distress syndrome (ARDS) Day 1 from 235 children at five hospitals between 2008 and 2017. We used latent class analysis to identify patients with distinct MMP profiles and then associated those profiles with markers of inflammation (IL‐1RA, ‐6, ‐8, ‐10, and ‐18; macrophage inflammatory protein‐1&agr; and ‐1&bgr;; tumor necrosis factor‐&agr; and ‐R2), endothelial injury (angiopoietin‐2, von Willebrand factor, soluble thrombomodulin), impaired oxygenation (PaO2/FiO2 [P/F] ratio, oxygenation index), morbidity, and mortality. Measurements and Main Results: In geographically distinct derivation and validation cohorts, approximately one‐third of patients demonstrated an MMP profile characterized by elevated MMP‐1, ‐2, ‐3, ‐7, and ‐8 and tissue inhibitor of metalloproteinase‐1 and ‐2; and depressed active and total MMP‐9. This MMP profile was associated with multiple markers of inflammation, endothelial injury, and impaired oxygenation on Day 1 of ARDS, and conferred fourfold increased odds of mortality or severe morbidity independent of the P/F ratio and other confounders (95% confidence interval, 2.1‐7.6; P < 0.001). Logistic regression using both the P/F ratio and MMP profiles was superior to the P/F ratio alone in prognosticating mortality or severe morbidity (area under the receiver operating characteristic curve, 0.75; 95% confidence interval, 0.68‐0.82 vs. area under the receiver operating characteristic curve, 0.66; 95% confidence interval, 0.58‐0.73; P = 0.009). Conclusions: Pediatric patients with ARDS have specific plasma MMP profiles associated with inflammation, endothelial injury, morbidity, and mortality. MMPs may play a role in the pathobiology of children with ARDS.
Compact Branch-Line Coupler Based on an Inductor and a U-Shaped Capacitor

This paper describes the analysis of a compact branch-line coupler design. The miniature structure is obtained by substituting the quarter-wave sections with equivalent circuits consisting of a microstrip line and a U-shaped capacitor connected to it. Such structure possesses similar characteristics with those of a substituted segment at the central frequency and its surroundings. It is worth noticing that the compact layout has technically feasible dimensions and characteristics, comparable to those of standard layout. The standard and compact branch-line coupler layouts were modeled using the AWR DE electrodynamic simulation software. Using this PCB etching method a prototype was obtained, operating at the central frequency of 900 MHz. Afterwards the frequency dependencies of the compact branch-line coupler were obtained using R&S vector network analyzer. Thus, using the suggested miniaturization method it was possible to reduce the conventional branch-line coupler surface area by 77.6%. Meanwhile the working band got reduced by only 3%.
Third-Order Volterra MVDR Beamforming for Non-Gaussian and Potentially Non-Circular Interference Cancellation

Linear beamformers are optimal, in a mean square (MS) sense, when the signal of interest (SOI) and observations are jointly Gaussian and circular. Otherwise, linear beamformers become suboptimal. When the SOI and observations are zero-mean, jointly Gaussian and noncircular, optimal beamformers become widely linear (WL). They become nonlinear with a structure depending on the unknown joint probability distribution of the SOI and observations when the latter are jointly nonGaussian, assumption which is very common in radiocommunications. In this context, the paper aims at introducing, for small-scale systems, third-order Volterra minimum variance distortionless response (MVDR) beamformers, for the reception of an SOI, whose waveform is unknown but whose steering vector is known, corrupted by nonGaussian and potentially noncircular interference, omnipresent in practical situations. Properties, performance, complexity, and adaptive implementation of these beamformers in the presence of nonGaussian and potentially noncircular interference are analyzed in this paper. These new beamformers are shown to always improve, in the steady state, the performance of Capon beamformer for nonGaussian/circular interference, whereas some of them improve the performance of the WL MVDR beamformer for nonGaussian/noncircular interference. These new beamformers open new perspectives for spectrum monitoring of nonGaussian signals and for radiocommunication networks using such signals.
Visual Analysis of Text Annotations for Stance Classification with ALVA

The automatic detection and classification of stance taking in text data using natural language processing and machine learning methods create an opportunity to gain insight about the writers' feelings and attitudes towards their own and other people's utterances. However, this task presents multiple challenges related to the training data collection as well as the actual classifier training. In order to facilitate the process of training a stance classifier, we propose a visual analytics approach called ALVA for text data annotation and visualization. Our approach supports the annotation process management and supplies annotators with a clean user interface for labeling utterances with several stance categories. The analysts are provided with a visualization of stance annotations which facilitates the analysis of categories used by the annotators. ALVA is already being used by our domain experts in linguistics and computational linguistics in order to improve the understanding of stance phenomena and to build a stance classifier for applications such as social media monitoring.
How Do Dancers Learn To Dance?: A first-person perspective of dance acquisition by expert contemporary dancers

We are interested in supporting motor skill acquisition in highly creative skilled practices such as dance. We conducted semi-structured interviews with 11 professional dancers to better understand how they learn new dance movements. We found that each dancer engages in a set of operations, including imitation, segmentation, marking, or applying movement variations. We also found a progression in learning dance movements composed of three steps consistently reported by dancers: analysis, integration and personalization. In this study, we aim to provide an empirical foundation to better understand the acquisition of dance movements from the perspective of the learner.
Online Learning of Automotive Gasoline Engine Model Using Robust Recursive Gaussian Process

The dynamical model structure based on the Nonlinear Auto-Regressive with eXogenous inputs (NARX) model is effective as a control model of automotive engine systems. In this paper, we propose an identification method of the NARX model using the Gaussian processes (GP). The first main result is the proposal of a robust online model updating method against outliers. In this approach, we apply the concept of robust Kalman filter to the GP-NARX model. The second main result is to propose a new dynamical Design of Experiment (DoE) algorithm with GP. We propose an input design method for acquiring training data of GP-NARX model. The effectiveness of the modeling strategy is demonstrated by an engine benchmark problem provided by the joint research committee of Society of Automotive Engineers and Society of Instrument (JSAE) and Control Engineers (SICE).
Process-Aware Side Channel Shaping and Watermarking for Cyber-Physical Systems

This paper considers process-aware modifications to real-time code running on embedded devices in cyber-physical systems (CPS) so as to create signatures in analog side channel signals without impacting process characteristics. We consider side channel signals emanating from the digital components of the CPS as well as from physical instrumentation such as actuators. We show that code in an embedded device can be instrumented to shape the side channel signals so as to carry auxiliary information without appreciably altering the stability and performance characteristics of the closed-loop process. Inserting auxiliary information into analog side channels can be used both for attack (e.g., leaking information) and defense (e.g., remote real-time monitoring) purposes. The proposed process-aware side channel shaping approach is demonstrated on the Tennessee Eastman process control benchmark implemented in a hardware-in-the-loop simulator.
Tests for Pilots under Simulated Hypergravity Conditions - Technological Challenges and Tesearch Methodology

The paper presents the application of the orthostatic test and LBNP (lower body negative pressure) test for pilots. The former is used for the assessment of the condition of the cardiovascular system when shifting from a supine position to a head up tilt. The latter consists in applying negative pressure to the lower parts of the body. The paper also describes the existing solutions as regards tilt tables and LBNP chambers with particular emphasis on the original system solution, used in tests for pilots and aviation candidates under simulated hypergravity conditions: ORTO-LBNP. The designed original system is equipped with specially suited measurement modules to record physiological parameters, with a focus on cardiovascular system parameters. Dedicated measurement modules are integrated in one measurement cartridge and allow for simultaneous recording of physiological parameters during the orthostatic and LBNP tests.
Effects of low volume isometric leg press complex training at two knee angles on force-angle relationship and rate of force development

Abstract This study compared knee angle-specific neuromuscular adaptations after two low-volume isometric leg press complex training programmes performed at different muscle lengths. Fifteen young males were divided into two groups and trained three times per week for 6 weeks. One group (n = 8) performed 5–7 sets of 3 s maximum isometric leg press exercise, with 4 min recovery, with knee angle at 85° ± 2° (longer muscle-tendon unit length; L-MTU). The other group (n = 7) performed the same isometric training at a knee angle of 145° ± 2° (180° = full extension; shorter muscle-tendon unit length; S-MTU). During the recovery after each set of isometric exercise, participants performed two CMJ every minute, as a form of complex training. Maximum isometric force (MIF) and rate of force development (RFD) were measured over a wide range of knee angles. Countermovement jump (CMJ) performance and maximum half-squat strength (1RM) were also assessed. Training at S-MTU induced a large increase of MIF (22–58%, p < 0.02) and RFD (18–43%, p < 0.05 to 0.001) at knee angles close to the training angle and resulted in a 14° ± 9° shift of the force vs. knee joint angle relationship towards extended knee joint angles (p = 0.001). In contrast, training at L-MTU, resulted in a moderate and similar (≈12.3%, p = 0.028) improvement of force at all knee angles. CMJ performance and 1RM were equally increased in both groups after training by 10.4% ± 8.3% and 7.8% ± 4.7% (p < 0.001), respectively. Low-volume maximal isometric leg-press complex training at S-MTU causes angle-specific adaptations in isometric strength and RFD, while dynamic muscle performance is independent of muscle length during training.
Study on the Optimum Co-Located MIMO Scheme for LDM in ATSC 3.0: Use Cases and Core Layer Performance

In this paper, the joint transmission of ATSC 3.0 Layered Division Multiplexing (LDM) with co-located Multi-Antenna schemes (MIMO) is investigated for the mobile or Core Layer (CL). Previous works have already assessed the ergodic capacity of LDM plus MIMO, but the performance over realistic channels has not been performed yet. Four potential system models for the CL are compared, depending on the number of mobile receiving antennas and the use of Transmit Diversity Code Filter Sets (TDCFS). Mobile layer performance is evaluated by means of physical layer simulations. The main results obtained show that system models with one antenna receivers provide similar performance as more-complex implementations with two receiving antennas. The use of TDCFS is also recommended for mobile channels.
Mobility Modeling and Analysis in Mobile Communication Networks

In the previous work by Wang et al. [9], they presented the analytical results for cell or location area (LA) border crossing rate and LA residence time of user equipment (UE) by adopting fluid-flow model. They showed that the LA residence time follows an exponential distribution if cell residence time is exponentially distributed. Note that the LA residence time summed up the cell residence time cannot follow an exponential distribution. In this paper, we obtain computer simulation results by using RAPTOR visual tool [6], and compare the crossing rate and LA residence time. Then, through the analysis of coefficient of variation, the distribution of simulated LA residence time is estimated.
Towards Application-Aware Networking: ML-Based End-to-End Application KPI/QoE Metrics Characterization in SDN

Software Defined Networking (SDN) presents a unique networking paradigm that facilitates the development of network innovations. This paper aims to improve application awareness by incorporating Machine Learning (ML) techniques within an open source SDN architecture. The paper explores how end-to-end application Key Performance Indicator (KPI) metrics can be designed and utilized for the purpose of application awareness in networks. The main goal of this research is to characterize application KPI metrics using a suitable ML approach based on available network data. Resource allocation and network orchestration tasks can be automated based on the findings. A key facet of this research is introducing a novel feedback interface to the SDN's Northbound Interface that receives realtime performance feedback from applications. This paper aim to show how could we exploit the applications feedback to determine useful characteristics of an application's traffic. A mapping application with a defined KPI is used for experimentation. Linear multiple regression is used to derive a characteristic relationship between the application KPI and the network metrics.
Interactive Exploration of Student Generated Content presented in Blogs

Nowadays blogs are regarded as tools for communication as well as an important source for spreading information in almost every subject. In recent years, school teachers have started to take advantage of this technology in order to support their educational practices. In this paper we focus on the data generated by a project involving more than 50 Swedish schools where teachers and pupils are posting content related to their astronomy class activities in their blogs with the aims of improving the teaching process. The challenge here is to find suitable methods to explore all these blogs in an interactive and discovery fashion. Our proposed solution to this challenge is to provide a visual and interactive tool for the exploration of blog corpora by teachers, pupils, project managers and parents.
Learning Hand Movement Interaction Control Using RNNs: From HHI to HRI

A key problem in robotics is enabling an autonomous agent to perform human-like arm movements in close proximity to another human. However, modeling the human decision and control process of the movement during dyadic interaction presents a challenge. Although, most prior approaches rely on multicomponent robot motion planning architectures, we use data of two humans performing interfering arm reaching movements to extract and transfer interaction behavior control skill to a robotic agent. A recurrent neural network-based framework is constructed to learn a policy that computes control signals for a robot end effector in order to replace one human. The learned policy is benchmarked against unseen interaction data and a state-of-the-art learning from demonstration framework in simulated scenarios. We compare several architectures and investigate a new activation function of three stacked tanh(). The results show that the proposed framework successfully learns a policy to imitate human movement behavior control during dyadic interaction. The policy is transferred to a real robot and its feasibility for close-proximity human-robot interaction is shown.
The Sialic Acid Binding Activity of Human Parainfluenza Virus 3 and Mumps Virus Glycoproteins Enhances the Adherence of Group B Streptococci to HEp-2 Cells

In the complex microenvironment of the human respiratory tract, different kinds of microorganisms may synergistically interact with each other resulting in viral-bacterial co-infections that are often associated with more severe diseases than the respective mono-infections. Human respiratory paramyxoviruses, for example parainfluenza virus type 3 (HPIV3), are common causes of respiratory diseases both in infants and a subset of adults. HPIV3 recognizes sialic acid (SA)-containing receptors on host cells. In contrast to human influenza viruses which have a preference for α2,6-linked sialic acid, HPIV3 preferentially recognize α2,3-linked sialic acids. Group B streptococci (GBS) are colonizers in the human respiratory tract. They contain a capsular polysaccharide with terminal sialic acid residues in an α2,3-linkage. In the present study, we report that HPIV3 can recognize the α2,3-linked sialic acids present on GBS. The interaction was evident not only by the binding of virions to GBS in a co-sedimentation assay, but also in the GBS binding to HPIV3-infected cells. While co-infection by GBS and HPIV3 had a delaying effect on the virus replication, it enhanced GBS adherence to virus-infected cells. To show that other human paramyxoviruses are also able to recognize the capsular sialic acid of GBS we demonstrate that GBS attaches in a sialic acid-dependent way to transfected BHK cells expressing the HN protein of mumps virus (MuV) on their surface. Overall, our results reveal a new type of synergism in the co-infection by respiratory pathogens, which is based on the recognition of α2,3-linked sialic acids. This interaction between human paramyxoviruses and GBS enhances the bacterial adherence to airway cells and thus may result in more severe disease.