Miracle-dz/UniFAR-FTUs huggingface dataset:
A Robust Spectral Estimator With Application to a Noise-Corrupted Process

When a dataset is corrupted by noise, the model for data generating process is misspecified and can cause parameter-estimation problems. For example, in the case of a Gaussian autoregressive (AR) process corrupted by noise, data are more accurately modeled as an AR–moving-average process rather than an AR process. This misspecification leads to bias, and hence, low resolution in AR spectral estimation. However, a new parametric spectral estimator, the realizable information-theoretic estimator (RITE) based on a nonhomogeneous Poisson spectral representation, is shown by simulation to be more robust to white noise than the asymptotic maximum likelihood estimator (MLE). We, therefore, conducted an in-depth investigation and analyzed the statistics of RITE and the asymptotic MLE for the misspecified model. For large data records, both RITE and the asymptotic MLE are asymptotically normally distributed. The asymptotic MLE has a slightly lower variance, but RITE exhibits much less bias. Simulation examples of a white-noise-corrupted AR process are provided to support the theoretical properties. This advantage of RITE increases as the signal-to-noise-ratio decreases.
Electromyographic Connectivity of Masseter Muscle with Different Retentive Attachments for Implant Overdentures in Patients with Atrophied Mandibular Ridges: A Crossover Study.

PURPOSE The aim of this crossover study was to evaluate electromyographic (EMG) connectivity of masseter muscle with different attachments used to retain implant overdentures in patients with atrophied mandibular ridges. MATERIALS AND METHODS Twenty-four edentulous participants with atrophic mandibular ridges received conventional dentures (control). Three months after the adaptation period, two implants were placed in the canine areas of the mandible. After osseointegration, each participant was successively given the following prostheses in a crossover manner: (1) ball-retained overdentures, (2) bar-retained overdentures, and (3) -retained overdentures. The EMG parameters (amplitude, chewing area, chewing rate, duration of chewing cycle, duration of chewing burst, and chewing time) were measured 3 months after wearing the following prostheses: conventional dentures, ball overdentures, bar overdentures, and Locator overdentures. Measurements were made during chewing of hard (carrot) and soft (cake) foods. RESULTS The highest EMG activity/amplitude, chewing area, duration of chewing cycle, and duration of chewing burst were noted with ball overdentures, followed by bar overdentures and Locator overdentures, and the lowest values were observed with conventional dentures. The highest chewing rate and masticatory time were noted with conventional dentures and the lowest values were observed with ball overdentures. With the exception of chewing area, no significant differences in all tested parameters between bar overdentures and Locator overdentures were observed. Except for duration of chewing cycle, all tested parameters were significantly higher during chewing of hard food than soft food. CONCLUSION Within the limitations of this study, two-implant overdentures recorded higher muscle functions compared to conventional dentures regardless of the type of attachment used. For such overdentures, ball attachment may be recommended over bar and Locator attachments, as it was associated with improved muscle activity and function.
Optimizing thermodynamic trajectories using evolutionary reinforcement learning

Author(s): Beeler, Chris; Yahorau, Uladzimir; Coles, Rory; Mills, Kyle; Whitelam, Stephen; Tamblyn, Isaac | Abstract: Using a model heat engine we show that neural network-based reinforcement learning can identify thermodynamic trajectories of maximal efficiency. We use an evolutionary learning algorithm to evolve a population of neural networks, subject to a directive to maximize the efficiency of a trajectory composed of a set of elementary thermodynamic processes; the resulting networks learn to carry out the maximally-efficient Carnot, Stirling, or Otto cycles. Given additional irreversible processes this evolutionary scheme learns a hitherto unknown thermodynamic cycle. Our results show how the reinforcement learning strategies developed for game playing can be applied to solve physical problems conditioned upon path-extensive order parameters.
Parent's perception of constraint induced movement therapy in cerebral palsy management in rehabilitation centers of Lahore.

OBJECTIVE To assess the perceptions of parents about constraint-induced movement therapy to treat their children with cerebral palsy. METHODS The qualitative study was conducted in three rehabilitation centres situated in Lahore, Pakistan, from January to July 2014, and comprised parents with enough experience of constraint-induced movement therapy. Therapy session was given to children with hemiplegic spastic cerebral palsy for 4 5 hours per day for at least a month. The interview was recorded and then transcribed verbatim. RESULTS There were 5 sets of parents whose children were aged 4-12 years. Five main themes emerged from the interviews and they were: child behaviour towards the therapy is variable; increase in cognitive level of the child; group therapy is more beneficial than individual therapy; after therapy the children developed a habit to use the affected hand in their activities; and other than change in the physical condition, parents also observed change in the social behaviour. CONCLUSIONS Constraint-induced movement therapy seemed to offer a promising opportunity to tackle and promote both social and physical outcomes in cerebral palsy children.
PREDICTIVE EQUATIONS OF MAXIMUM OXYGEN CONSUMPTION BY SHUTTLE RUN TEST IN CHILDREN AND ADOLESCENTS: A SYSTEMATIC REVIEW

ABSTRACT Objective: To systematically review the literature as for the level of evidence of predictive equations of VO2peak through the 20-meter shuttle run test (20m-SRT) in children and adolescents. Data sources: Searches were conducted independently by two researchers, according to the procedures adopted by PRISMA, in the electronic databases MEDLINE via PubMed, ScienceDirect, Web of Science, LILACS and SciELO, for articles published until September 2017 in English and Portuguese. The inclusion criteria were: original studies, abstract available, using predictive equations of VO2peak through 20m-SRT, conducted with adolescents and/or children, non-athletes, and mentioning correlation analysis between predicted and measured VO2peak. The level of evidence of equations was based on the risk of bias of the studies using the following criteria: sample number, sample characteristics, and statistical analysis. Data synthesis: Eighteen studies were selected, in which fifteen equations were found and analyzed. The studies had been conducted with samples composed of subjects of both sexes, aged 8 to 19 years. Equations of Léger and Matsuzaka had their level of evidence classified as high, and estimation ranged between r=0.54-0.90 and r=0.65-0.90. Equations by Ruiz, Barnett and Matsuzaka had their level of evidence classified as moderate, and estimation ranged between r=0.75-0.96, r=0.66-0.84 and r=0.66-0.89, respectively. Conclusions: Matsuzaka’s equation presented satisfactory parameters for estimates of VO2peak in children and adolescents. Although not explored in equations, body adiposity and pubertal stage are significantly associated with cardiorespiratory fitness in children and adolescents.
DeepVID: Deep Visual Interpretation and Diagnosis for Image Classifiers via Knowledge Distillation

Deep Neural Networks (DNNs) have been extensively used in multiple disciplines due to their superior performance. However, in most cases, DNNs are considered as black-boxes and the interpretation of their internal working mechanism is usually challenging. Given that model trust is often built on the understanding of how a model works, the interpretation of DNNs becomes more important, especially in safety-critical applications (e.g., medical diagnosis, autonomous driving). In this paper, we propose DeepVID, a Deep learning approach to Visually Interpret and Diagnose DNN models, especially image classifiers. In detail, we train a small locally-faithful model to mimic the behavior of an original cumbersome DNN around a particular data instance of interest, and the local model is sufficiently simple such that it can be visually interpreted (e.g., a linear model). Knowledge distillation is used to transfer the knowledge from the cumbersome DNN to the small model, and a deep generative model (i.e., variational auto-encoder) is used to generate neighbors around the instance of interest. Those neighbors, which come with small feature variances and semantic meanings, can effectively probe the DNN's behaviors around the interested instance and help the small model to learn those behaviors. Through comprehensive evaluations, as well as case studies conducted together with deep learning experts, we validate the effectiveness of DeepVID.
Mongoose RTOS based IoT Implementation of Surveillance System

This paper proposes a smart surveillance system based on Mongoose RTOS which can be remotely monitored by the owner. When a trespass is detected, an alert is send to the owner’s phone and it can also be observed on the application log of Losant Platform. This paper proposed a system for monitoring the motion of a person using PIR motion sensor and ESP8266 NodeMCU WiFi micro-controller. The eminent feature of the system is that it uses Mongoose RTOS which is reliable to run in production environment and the owner can receive alerts even if his mobile phone is not connected to the Internet. The sytem is inexpensive as it uses low cost Wi-Fi enabled micro-controller ESP8266 NodeMCU and PIR sensor which is very cheap compared to other devices for motion detection.
Stereoselective Synthesis of Molecular Square and Granny Knots

We report on the stereoselective synthesis of both molecular granny and square knots through the use of lanthanide-complexed overhand knots of specific handedness as three-crossing “entanglement synthons”. The composite knots are assembled by combining two entanglement synthons (of the same chirality for a granny knot; of opposite handedness for a square knot) in three synthetic steps: first, a CuAAC reaction joins together one end of each overhand knot. Ring-closing olefin metathesis (RCM) then affords the closed-loop knot, locking the topology. This allows the lanthanide ions necessary for stabilizing the entangled conformation of the synthons to subsequently be removed. The composite knots were characterized by 1H and 13C NMR spectroscopy and mass spectrometry and the chirality of the knot stereoisomers compared by circular dichroism. The synthetic strategy of combining building blocks of defined stereochemistry (here overhand knots of Λ- or Δ-handed entanglement) is reminiscent of the chiron approach of using minimalist chiral synthons in the stereoselective synthesis of molecules with multiple asymmetric centers.
Future suitability of habitat in a migratory ungulate under climate change

With climate change, the effect of global warming on snow cover is expected to cause range expansion and enhance habitat suitability for species at their northern distribution limits. However, how this depends on landscape topography and sex in size-dimorphic species remains uncertain, and is further complicated for migratory animals following climate-driven seasonal resource fluctuations across vast landscapes. Using 11 years of data from a partially migratory ungulate at their northern distribution ranges, the red deer (Cervus elaphus), we predicted sex-specific summer and winter habitat suitability in diverse landscapes under medium and severe global warming. We found large increases in future winter habitat suitability, resulting in expansion of winter ranges as currently unsuitable habitat became suitable. Even moderate warming decreased snow cover substantially, with no suitability difference between warming scenarios. Winter ranges will hence not expand linearly with warming, even for species at their northern distribution limits. Although less pronounced than in winter, summer ranges also expanded and more so under severe warming. Summer habitat suitability was positively correlated with landscape topography and ranges expanded more for females than males. Our study highlights the complexity of predicting future habitat suitability for conservation and management of size-dimorphic, migratory species under global warming.
Cognitive remediation therapy of working memory in addictive disorders: An individualized, tailored, and recovery-oriented approach

Substance use disorders (SUD) constitute a major public health problem. Despite the availability of psychological and pharmacological interventions, SUD are still commonly associated with chronic courses, frequent relapses, and social negative consequences [1–5]. Consequently, developing innovative and complementary evidence-based therapeutic strategies to prevent or reduce the personal, social, and economic burden associated with addictions is of paramount importance. Neurocognitive impairments in addiction have gained much attention in recent years as both determinants and consequences of drug use. Nevertheless, regardless of whether these impairments are consideredprimary or secondary to SUD, they constitute relevant transdiagnostic targets for treatment [6,7]. More specifically, many individuals with SUD present neurocognitive deficits across a wide range of functions, including planning, attention, memory, response inhibition, emotion recognition, and decision making [8–10]. In particular, several core executive functions, such asworkingmemory (the ability tomaintain andmanipulate limited amounts of information to serve current goals), are frequently impaired in SUD [11]. Such impairments have been linked to poor self-regulation and decisionmaking [12], which are hallmarks of addictive disorders. Crucially, working memory impairments could interfere with patients’ daily life activities and social inclusion (e.g. managing finance, interacting with others, finding or holding a job) and impact on key clinical variables (e.g. attendance at sessions, compliance with medications, dropout rates, low willingness to change, relapse rate) [13]. For instance, such impairments may prevent patients from accurately appraising high-risk situations in daily life and fully assimilating and applying information or techniques provided in the course of therapy. This may, in turn, result in greater difficulties in preventing relapse and/or attaining meaningful personal goals. However, despite their relevance, working memory impairments are not yet a specific target of addiction treatment. By providing structured cognitive exercises and skills training, cognitive remediation therapy constitutes a promising approach to decrease working memory impairments and improve clinical and functional outcomes in patients with addictive disorders. Although working memory training has been examined in various clinical and nonclinical samples, including addiction-related disorders, controversies regarding its efficacy remain [14]. Therefore, our aim is to underline the limitations of cognitive remediation therapy as it is commonly implemented and to provide relevant avenues for improving its efficacy in patients with addictive disorders.
Fault-Tolerant Consensus Tracking Control for Linear Multiagent Systems Under Switching Directed Network

In this paper, for linear leader–follower networks with multiple heterogeneous actuator faults, including partial loss of effectiveness fault and actuator bias fault, a cooperative fault-tolerant control (CFTC) approach is developed. Assume that the interaction network topology among all nodes is a switching directed graph. To address the difficulty of designing the distributed compensation control laws under the time-varying asymmetrical network structure, a novel distributed-reference-observer-based fault-tolerant tracking control approach is established, under which the global tracking errors are proved to be asymptotically convergent in the presence of actuator failures. First, by constructing a group of distributed reference observers based on neighborhood state information, all followers can estimate the leader’s state trajectories directly. Second, a decentralized adaptive fault-tolerant tracking controller via local estimation is designed to achieve the global synchronization. Furthermore, the reliable coordination problem under switching directed topology with intermittent communications is solved by utilizing the presented CFTC approach. Finally, the effectiveness of the proposed coordination control protocol is illustrated by its applications to a networked aircraft system.
Machine Learning Based Novel Approach for Intrusion Detection and Prevention System: A Tool Based Verification

Now a day, Wireless Sensor Networks are widely used in military applications by its applications, it is extended to healthcare, industrial environments and many more. As we know that, there are some unique features of WSNs such as limited power supply, minimum bandwidth and limited energy. So, to secure traditional network, multiple techniques are available, but we can't use same techniques to secure WSNs. So to increase the overall security of WSNs, we required new ideas as well as new approaches. In general, intrusion prevention is the primary issue in WSNs and intrusion detection already reached to saturation. Thus, we need an efficient solution for proactive intrusion prevention towards WSNs. Thus, formal validation of protocols in WSN is an essential area of research. This research paper aims to formally verify as well as model some protocol used for intrusion detection using AVISPA tool and HLPSL language. In this research paper, the results of authentication and DoS attacks were detected is presented, but there is a need to prevent such type of attacks. In this research paper, a system is proposed in order to avoid intrusion using machine learning for the wireless sensor network. So, the proposed system will be used for intrusion prevention in a wireless sensor network.
Enhanced Performance of MSM UV Photodetectors by Molecular Modification of Gallium Nitride Using Porphyrin Organic Molecules

In this brief, we present a novel surface modification process for gallium nitride (GaN) epitaxial films leading to enhanced ultraviolet (UV) photodetection. The adsorption of a layer of thiol-functionalized porphyrin-based organic molecules on the GaN surface has been carried out. The effect of surface modification was seen in the form of a significant reduction in the surface potential of GaN by ~250 mV and five-fold enhancement in the near-band-edge photoluminescence intensity, both indicating surface passivation of GaN. Consequently, reverse current for Nickel (Ni) Schottky contacts on molecularly modified GaN was decreased by 3 orders of magnitude, in dark at room temperature. Upon illumination by UV light, Ni/molecular layer/GaN interdigitated structures showed considerably improved photodetector (PD) characteristics such as responsivity for the visible-blind spectral region, photo-to-dark current ratio, and UV-to-visible rejection ratio. Such metal-molecular layer semiconductor device structures can be useful for the fabrication of more efficient GaN-based UV PDs, mitigating the adverse effects of electronic surface states in these materials.
Methodological considerations for studies of brain glycogen

Glycogen stores in the brain have been recognized for decades, but the underlying physiological function of this energy reserve remains elusive. This uncertainty stems in part from several technical challenges inherent in the study of brain glycogen metabolism. These include low glycogen content in the brain, non‐homogeneous labeling of glycogen by radiotracers, rapid glycogenolysis during postmortem tissue handling, and effects of the stress response on brain glycogen turnover. Here we briefly review the aspects of the glycogen structure and metabolism that bear on these technical challenges and present ways they can be addressed.
Influence Analysis of Typical Objects in Rural Railway Environments at 28 GHz

Millimeter wave (mmWave) is a key to realize high data-rate communication and enable smart railway services. There are more and more research work focusing on the algorithm and system design for railway communications in the mmWave band. A correct understanding of the mmWave channel characteristics for railway scenario is important for the estimation and evaluation of designed technologies. In this paper, the influence of typical objects in rural railway environment to the mmWave propagation channel is analyzed. Based on the channel measurement conducted at 28 GHz with train-to-infrastructure deployment in the rural railway environment, a ray tracing (RT) simulator is calibrated in terms of environment modeling and electromagnetic (EM) calculation. The dominant multipath components of the measurement are tracked and matched with corresponding objects in the propagation environment. Accordingly, dominant propagation mechanisms (direct, penetration, reflection, scattering, etc.) are determined, the three-dimensional environment model and the EM parameters of different objects are calibrated. To overcome the constraints of the measurement and analyze the influence more practically, the measurement campaign is extended to four different environment cases and four T2I deployments. With the predetermined dominant propagation mechanisms and calibrated EM parameters, reliable RT simulations are conducted. The influence of typical objects are analyzed for the considered environment cases and deployments. The analysis of this paper not only helps to understand the important influential factors of propagation channel at the object level, but also can be useful in guiding deployment of mmWave communication system. The provided EM parameters and environment modeling suggestions will enable reliable RT based channel realization in rural railway environments.
Experimental Investigation of Remote Coulomb Scattering on Mobility Degradation of Ge pMOSFET by Various PDA Ambiences

The impact of various postdeposition annealing (PDA) ambiences (N<sub>2</sub>, O<sub>2</sub>, and NH<sub>3</sub>) on the hole mobility of germanium (Ge) pMOSFET with GeO<sub>2</sub>/Al<sub>2</sub>O<sub>3</sub> gate-stack is investigated. It is found that the mobility is about 10% higher after N<sub>2</sub> PDA, while it is about 10% smaller after O<sub>2</sub> and NH<sub>3</sub> PDA than that without PDA. The physical origin is attributed to the remote Coulomb scattering. The Ge/GeO<sub>2</sub> interface charge Q<sub>1</sub> decreases, but the GeO<sub>2</sub>/Al<sub>2</sub>O<sub>3</sub> interface dipole Q<sub>dipole</sub> increases after PDA in N<sub>2</sub>, O<sub>2</sub>, and NH<sub>3</sub>. The higher mobility after N<sub>2</sub> PDA is due to a smaller Q<sub>1</sub> and Q<sub>dipole</sub>, while the lower mobility after O<sub>2</sub> and NH<sub>3</sub> PDA is due to a larger Q<sub>dipole</sub>. All these three PDA ambiences are beneficial to reduce the gate leakage current. Therefore, PDA in N<sub>2</sub> is a balance approach for both the hole mobility improvement and gate leakage current reduction.