Miracle-dz/UniFAR-FTUs huggingface dataset:
Cloud-aided collaborative estimation by ADMM-RLS algorithms for connected diagnostics and prognostics

As the connectivity of consumer devices is rapidly growing and cloud computing technologies are becoming more widespread, cloud-aided algorithms for parameter estimation can be developed to exploit the theoretically unlimited storage memory and computational power of the “cloud”, while relying on information provided by multiple sources. With the ultimate goal of developing monitoring, diagnostic and prognostic strategies, this paper focuses on the design of a Recursive Least-Squares (RLS) based estimator for identification over a multitude of similar devices (such as a mass production) connected to the cloud. The proposed approach, that relies on Node-to-Cloud-to-Node (N2C2N) transmissions, is designed so that: (i) estimates of the unknown parameters are computed locally and (ii) the local estimates are refined on the cloud by exploiting the additional information that the devices have similar characteristics. The proposed approach requires minimal changes to local (pre-existing) RLS estimators.
An Alternative Generic Transformation for Optimal Repair Bandwidth and Rebuilding Access in MDS Codes

In last ISIT, we reported a generic transformation on maximum distance separable (MDS) codes, which can convert any non-binary (k+r, k) MDS code into another (k+r, k) MDS code such that an arbitrarily chosen $r$ nodes will have the optimal repair bandwidth and the optimal rebuilding access by modifying their data. However, the resultant code thus obtained is no longer in a systematic form if we wish to optimal repair $r$ systematic nodes, and another linear transformation is needed to convert it into one, which may break the inherent simplicity in the decoding and repair procedure. In this work, we propose an alternative generic transformation to solve this issue. In the alternative generic transformation, any $r$ systematic nodes can be optimally repaired with their data keeping unchanged through instead modifying the data on the $r$ parity nodes. As a result, by applying multiple times the two transformations in combination, we can directly obtain systematic MDS codes with optimal rebuilding access for all nodes or for a subset of nodes from any non-binary scalar MDS codes, which have the optimal sub-packatization level as well.
Decentralized Chernoff Test in Sensor Networks

We propose a decentralized, sequential and adaptive hypothesis test in sensor networks, which extends Chernoff's test to a decentralized setting. We show that the proposed test achieves the same asymptotic optimality of the original one, minimizing the expected cost required to reach a decision plus the expected cost of making a wrong decision, when the observation cost per unit time tends to zero. We also show that the proposed test is parsimonious in terms of communications. Namely, in the regime of vanishing observation cost per unit time, the expected number of channel uses required by each sensor to complete the test converges to four.
Molecular and Morphological Discrimination of Dicrocoeliid Larvae (Trematoda: Digenea) from Terrestrial Mollusks in Japan

ABSTRACT Trematodes of the family Dicrocoeliidae commonly use terrestrial mollusks as the first intermediate host. Despite abundant studies on the adult worms in birds and mammals, few reports exist on their larval stage in snail intermediate hosts. A present survey of mollusks in Japan led us to the discovery of dicrocoeliid sporocysts with cercariae in 16 of 303 individuals, encompassing 8 snail species and 1 slug species. A DNA barcoding based on sequencing of mitochondrial cytochrome c oxidase subunit 1 showed that the larvae consisted of 5 species. Phylogenetic trees of nuclear 18S and 28S ribosomal DNAs confirmed the 5 species to be members of the Dicrocoeliidae. These were temporarily termed dicrocoeliid species 1 to 5, because conclusive identification was impossible without adult worms. These unknown species were phylogenetically related to each other, except for sp. 5. The phylogenetic trees demonstrated close genetic relationships between sp. 3 and the genus Lutztrema and between sp. 5 and the genus Lyperosomum. The phylogenetic analysis also suggests a possibility that the currently accepted macrotaxonomy of the Dicrocoeliidae is problematic, due to the paraphyly of the subfamilies Dicrocoeliinae and Leipertrematinae. Morphological characterization of the cercariae and their DNA barcodes provide a primary platform for differentiating dicrocoeliids from various mollusks in Japan. The DNA barcodes, in particular, will enable tracing the parasite life cycles, in case of finding metacercariae and adults from presently unknown hosts.
Modeling of Pneumatic Artificial Muscle with Kinetic Friction and Sliding Mode Control

A nonlinear lumped-parameter state-space model of a pneumatic artificial muscle that accounts for kinetic friction is developed. Model simulations are reported for square-wave command signals at different frequencies. Comparisons to experimental results demonstrate the fidelity of the model. A new sliding mode control tuning parameter is introduced that increases the gradient of the error dynamic poles of the sliding surface with respect to lower order errors. With this method input-output feedback linearization and model observation are not needed. A third-order integral sliding mode control law exhibits steady-state errors of $\pm 15\ \mu \mathbf{m}$ or less with a maximum error of 0.29 mm or less when tracking a 7th-order square-wave position trajectory with an amplitude of 5.40 mm. This simplified sliding mode control law shows advantages compared to a conventional approach.
A Nonlinear Model-Predictive Control Scheme for a Heavy Duty Truck's Waste Heat Recovery System Featuring Moving Horizon Estimation

In this paper, a nonlinear model predictive control scheme for the control of a waste heat recovery system (WHR) based on the organic Rankine cycle for a heavy duty truck is presented. For dynamic state estimation, a scheme based on the moving horizon estimation technique is used. As a result, the system is controlled using easily measurable quantities as inputs. System nonlinearities, operational constraints and performance optimization criteria are explicitly taken into account. The proposed scheme enables the WHR to generate around 2 [%] more energy than standard PI and LQG controllers.
Joint Coverage, Connectivity, and Charging Strategies for Distributed UAV Networks

This paper proposes deployment strategies for consumer unmanned aerial vehicles (UAVs) to maximize the stationary coverage of a target area and to guarantee the continuity of the service through energy replenishment operations at ground charging stations. The three main contributions of our work are as follows. 1) A centralized optimal solution is proposed for the joint problem of UAV positioning for a target coverage ratio and scheduling the charging operations of the UAVs that involves travel to the ground station. 2) A distributed game-theory-based scheduling strategy is proposed using normal-form games with rigorous analysis on performance bounds. Furthermore, a bio-inspired scheme using attractive/repulsive spring actions are used for distributed positioning of the UAVs. 3) The cost-benefit tradeoffs of different levels of cooperation among the UAVs for the distributed charging operations is analyzed. This paper demonstrates that the distributed deployment using only 1-hop messaging achieves approximation of the centrally computed optimum, in terms of coverage and lifetime.
A Regional Topic Model Using Hybrid Stochastic Variational Gibbs Sampling for Real-Time Video Mining

The events location and real-time computational performance of crowd scenes continuously challenge the field of video mining. In this paper, we address these two problems based on a regional topic model. In the process of video topic modeling, region topic model can simultaneously cluster motion words of video into motion topics, and the locations of motion into motion regions, where each motion topic associates with its region. Meanwhile, a hybrid stochastic variational Gibbs sampling algorithm is developed for inference of our region topic model, which has the ability of inferring in real time with massive video stream dataset. We evaluate our method on simulate and real datasets. The comparison with the Gibbs sampling algorithm shows the superiorities of proposed model and its online inference algorithm in terms of anomaly detection.
Blind Group Testing

The main goal in group testing is to recover a small subset of defective items from a larger population, while efficiently reducing the total number of (possibly noisy) required tests/measurements. In this paper, we analyze the fundamental limits of a general noisy group testing problem when the channel law is unknown. Specifically, we obtain sufficient conditions on the number of tests required for exact recovery using two decoders; the first is based on joint-decoding (inefficient), and the second is a based on separate-decoding (efficient). We show that in several scenarios, our decoders achieve the same performance as if the channel was known, implying that the proposed decoders are robust/universal.
Tracking Fatty Acids From Phytoplankton to Jellyfish Polyps Under Different Stress Regimes: A Three Trophic Levels Experiment

The impacts of biochemicals driving food web processes are under investigation for just the last few decades. In addition, as jellyfish are drawing increasing attentions because of their mass developments and of their potential capacity of driving food web structures and energy flow by ‘top-down’ and ‘bottom-up’ controls. We here show that the provision with the biochemical complex thiamin (vitamin B1) to the common phytoplankton Rhodomonas baltica altered its fatty acid (FA) pattern towards 3-highly-unsaturated FAs (3-HUFA) and that this pattern was further transferred up to the zooplankton consumer, the copepod Acartia tonsa. However, polyps of the Jellyfish Aurelia aurita feeding on A. tonsa only had a low relative 3-HUFA content, especially due to a reduction in 22:63 (DHA), but elevated levels of 20:46 (ARA). The high proportion of the -6 HUFA, ARA in polyps may provide evidence for preferential conversion of ARA in polyps, eventually from DHA in a so far unknown pathway. In contrast to A. tonsa, newly hatched A. salina nauplii used as food for A. aurita polyps were almost devoid of HUFA, but contained high levels of C18 polyunsaturated FAs (C18-PUFA). Consequently, polyps feeding on them contained few HUFA, while high levels of C18-PUFA predominated. This suggests that A. aurita polyps cannot efficiently convert 3 C18-PUFA to 3-HUFA. In addition, besides a decrease in saturated FAs, especially an increase in HUFA in A. aurita polyps with decreasing temperature was observed, for which the dietary provision with HUFA seemed to be critical. Altering the FA pattern as a response of temperature reflects an adaptation to seasonal changes and may be related to their life history plasticity.
Predictive tracking control of network-based agents with communication delays

This paper investigates the remote tracking control problem of Network-based Agents with communication delays existing in both forward and feedback communication channels. A networked predictive tracking controller is proposed to compensate the negative effects caused by bilateral time-delays in a wireless network. Furthermore, the problem of consecutive data loss in the feedback channel is solved using aforementioned controller, where lateral movement perturbations are introduced. Simulations and experiments are provided for several cases, which verify the realizability and effectiveness of the proposed controller.
Double-matching resource allocation strategy in fog computing networks based on cost efficiency

Fog computing is an advanced technique to decrease latency and network congestion, and provide economical gains for Internet of Things (IoT) networks. In this paper, we investigate the computing resource allocation problem in three-layer fog computing networks. We first formulated the resource allocation problem as a double two-sided matching optimization problem. Then, we propose a double-matching strategy for the resource allocation problem in fog computing networks based on cost efficiency, which is derived by analysing the utility and cost in fog computing networks. The proposed double-matching strategy is an extension of the deferred acceptance algorithm from two-side matching to three-side matching. Numerical results show that high cost efficiency performance can be achieved by adopting the proposed strategy. Furthermore, by using the proposed strategy, the three participants in the fog computing networks could achieve stable results that each participant cannot change its paired partner unilaterally for more cost efficiency.
Simulation-Based Packet Delivery Performance Evaluation with Different Parameters in Flying Ad-Hoc Network (FANET) Using OLSR

The article presents the results of experimental study, conducted to determine UAV ad hoc network parameters providing the probability of link connectivity guaranteeing PDR (Packet Delivery Ratio) not less than 90% in the given area. For the research purpose the network structure imitation model was built in ns-2. OLSR is used as a routing protocol to discover paths between the nodes. The dependence of the nodes connectivity on such network parameters as the nodes number and transmission range is determined.
Control-Theoretical Software Adaptation: A Systematic Literature Review

Modern software applications are subject to uncertain operating conditions, such as dynamics in the availability of services and variations of system goals. Consequently, runtime changes cannot be ignored, but often cannot be predicted at design time. Control theory has been identified as a principled way of addressing runtime changes and it has been applied successfully to modify the structure and behavior of software applications. Most of the times, however, the adaptation targeted the resources that the software has available for execution (CPU, storage, etc.) more than the software application itself. This paper investigates the research efforts that have been conducted to make software adaptable by modifying the software rather than the resource allocated to its execution. This paper aims to identify: the focus of research on control-theoretical software adaptation; how software is modeled and what control mechanisms are used to adapt software; what software qualities and controller guarantees are considered. To that end, we performed a systematic literature review in which we extracted data from 42 primary studies selected from 1,512 papers that resulted from an automatic search. The results of our investigation show that even though the behavior of software is considered non-linear, research efforts use linear models to represent it, with some success. Also, the control strategies that are most often considered are classic control, mostly in the form of Proportional and Integral controllers, and Model Predictive Control. The paper also discusses sensing and actuating strategies that are prominent for software adaptation and the (often neglected) proof of formal properties. Finally, we distill open challenges for control-theoretical software adaptation.
RheoScale: A tool to aggregate and quantify experimentally determined substitution outcomes for multiple variants at individual protein positions

Human mutations often cause amino acid changes (variants) that can alter protein function or stability. Some variants fall at protein positions that experimentally exhibit “rheostatic” mutation outcomes (different amino acid substitutions lead to a range of functional outcomes). In ongoing studies of rheostat positions, we encountered the need to aggregate experimental results from multiple variants, to describe the overall roles of individual positions. Here, we present “RheoScale” which generates quantitative scores to discriminate rheostat positions from those with “toggle” (most substitutions abolish function) or “neutral” (most substitutions have wild‐type function) outcomes. RheoScale scores facilitate correlations of experimental data (such as binding affinity or stability) with structural and bioinformatic analyses. The RheoScale calculator is encoded into a Microsoft Excel workbook and an R script. Example analyses are shown for three model protein systems, including one assessed via deep mutational scanning. The RheoScale calculator quickly and efficiently provided quantitative descriptions that were in good agreement with prior qualitative observations. As an example application, scores were compared to the example proteins’ structures; strong rheostat positions tended to occur in dynamic locations. In the future, RheoScale scores can be easily integrated into computational studies to facilitate improved algorithms for predicting outcomes of human variants.
Novel Automatic Human-Height Measurement Using a Digital Camera

Nowadays, automatic object-size measurement (AOSM) techniques are in high demand since there are numerous applications including three-dimensional multimedia entertainment games, cyber-physical interaction, and real-time geographical survey. When a scene is complex with many objects, the AOSM problem is quite challenging because it is very hard to describe the object of interest a priori. In this paper, we present a novel efficient automatic human-height measurement scheme using a single camera. Our proposed new scheme is very robust so that the heights of multiple persons within the scene can be estimated automatically and simultaneously. The experimental results demonstrate that the average error percentage is less than 2% in general.
Integral Input-to-State Stability of Cascaded Systems and Observer-Based Feedback Design

For nonlinear systems, stability is not always preserved under cascade interconnection. Saturation in decay dynamics and bilinearity in component systems hinder the preservation, although they do not necessarily prevent component systems from being integral input-to-state stable (iISS). This paper develops a stability condition under which cascade of iISS systems remains stable. The established stability is not only internal property, but also boundedness and convergence properties in the presence of disturbances acting on all component systems. It is shown how the cascade stability criterion is beneficial for addressing saturation and bilinearity in observer-based feedback design, whereas previously existing criteria need to invoke more restrictive assumptions and settings.
OCC Protocol for connected LEDs in Optical Camera Communication Networks

Nowadays, and Internet of Lights (IoL) can be used to help a variety of lights in home, building, factory, city, and so on. The achievement of wireless communication technologies have brought convenience and amazing services to users. The hybrid use of wireless technologies in a system is not a novel idea, however, in this paper, we introduce the feasible OCC protocol based on Wi-Fi technology and OCC technology that is emerging technology nowadays. Various type of LEDs that implemented based on OCC technologies are connected via an MQTT (Message Queue Telemetry Transportation) broker to bring a promising service of light that allows smartphone users to receiving, control and monitoring data with augmented reality user experience. Demonstrations of the system along with implemented software and hardware are given to illustrate the feasibility of our system.
Indoor Localization with WiFi Fingerprinting Using Convolutional Neural Network

Indoor localization has been an active research field for decades, due to its wide range of applications. WiFi fingerprinting, which estimates the user's locations using pre-collecting WiFi signals as references, is of particular interest since these days, every user can easily access to WiFi networks. Among numerous methods, deep-neural-network (DNN) based methods have shown an attractive performance but their major drawback is the sensitivity to the fluctuation of received signals (caused by multipaths). To yield a satisfactory performance, thus, a sufficiently large number of possible cases should be trained, which costs a lot. In this paper, we address the above problem by presenting a convolutional neural network (CNN) based localization method. As success in image classifications, the proposed method can be robust to the small changes of received signals as it exploits the topology of a radio map as well as signal strengths. Via experimental results, we demonstrate that the proposed CNN method can outperform the other DNN-based methods using publicly available datasets provided in IPIN 2015.
The Impact of co-morbid severe mental illness and HIV upon mental and physical health and social outcomes: a systematic review

ABSTRACT Our aim was to review the evidence related to the impact of co-morbid severe mental illness SMI (schizophrenia, schizoaffective and bipolar disorder) and HIV upon mental health, physical health and social outcomes. We carried out a systematic review of scientific evidence, searching online databases (MEDLINE, PsychInfo, EMBASE, Global Health and Scopus) for studies between 1983 and 2017 using search terms for SMI and HIV. Studies were included if they compared health or social outcomes between people living with co-morbid SMI and HIV and people living with either: a) HIV only; or b) SMI only. Outcomes of interest were: mortality, health service use, HIV/SMI-related, co-morbidities, and social outcomes. We identified 20 studies which met our inclusion criteria. Although studies were generally high quality, there was heterogeneity in both selection of outcomes and choice of measure. It was therefore difficult to draw strong conclusions regarding the impact of co-morbid SMI and HIV across any outcome. We found little evidence that co-morbid SMI and HIV were associated with lower levels of treatment, care or poorer clinical outcomes compared to people living with SMI or HIV alone. However, mortality appeared to be higher among the co-morbid group in three out of four analyses identified. Physical and mental co-morbidities and social outcomes were rarely measured. Limited data mean that the impact of co-morbid SMI and HIV is uncertain. In order to develop evidence-based guidelines, there is an urgent need for further research. This may be realized by exploring opportunities for using data from existing cohort studies, routinely collected data and data linkage to investigate important questions relating to this neglected but potentially important area.