Miracle-dz/UniFAR-FTUs huggingface dataset:
Fiber-tip bubble-structure microcavity sensor

A Fabry-Perot interferometer sensor based on a fiber-tip bubble-structure micro-cavity is proposed, fabricated, and demonstrated for hydrostatic pressure sensing and transverse load sensing. A segment of a well-cleaved multimode fiber with a core diameter of 62. 5μm is processed with chemical etching based on a solution of HF 40% and the bubblestructure micro-cavity is fabricated by using arc discharge at the end of the processed multimode fiber. The sensor can be considered as a two-beam Fabry-Perot interferometer with one beam from the silica-air interface on the left side of the bubble and the other from the air-silica interface on the right side of the bubble. The broadband light is injected into the fiber-tip bubble-structure micro-cavity by splicing the multimode fiber with the bubble-structure micro-cavity to a 3- dB optical coupler and the reflective spectrum of the bubble-structure micro-cavity is measured by an optical spectrum analyzer. Both hydrostatic pressure sensing with a sensitivity of ~0.1 nm/MPa and transverse load sensing with sensitivity of 3.64 nm/N are experimentally demonstrated based the proposed fiber-tip bubble-structure micro-cavity sensor. The proposed sensor is demonstrated with a relative low temperature sensitivity of about 2 pm/°C. Properties of the fiber-tip bubble-structure micro-cavitys with different sizes are investigated. The sensor has the advantages of lowcost, ease of fabrication and compact size, which make it a promising candidate for hydrostatic pressure sensing or transverse load sensing in harsh environments.
Employing social network analysis for disease biomarker detection

Detection of disease biomarkers in general and cancer biomarkers in particular is an important task which has received considerable attention in the area of in silico genomic experiments. We describe a new approach for detecting cancer biomarkers based on genomic microarray data; it is characterised by employing Social Network Analysis (SNA) techniques. Through social interaction perspective, we can have genes as actors in a social network, where similarities between genes can be described as connections between these actors. The correct determination of biomarkers out of huge genomic data dramatically decreases the number of features. It is also possible to achieve the same or better classification performance compared to using the whole data. The minimum number of biomarkers can be researched further biologically to reduce the numerous time-consuming in vitro experiments. Results of the conducted experiments with selected biomarkers are promising and efficient.
The Role of piRNA-Mediated Epigenetic Silencing in the Population Dynamics of Transposable Elements in Drosophila melanogaster

The piwi-interacting RNAs (piRNA) are small RNAs that target selfish transposable elements (TEs) in many animal genomes. Until now, piRNAs’ role in TE population dynamics has only been discussed in the context of their suppression of TE transposition, which alone is not sufficient to account for the skewed frequency spectrum and stable containment of TEs. On the other hand, euchromatic TEs can be epigenetically silenced via piRNA-dependent heterochromatin formation and, similar to the widely known “Position-effect variegation”, heterochromatin induced by TEs can “spread” into nearby genes. We hypothesized that the piRNA-mediated spread of heterochromatin from TEs into adjacent genes has deleterious functional effects and leads to selection against individual TEs. Unlike previously identified deleterious effects of TEs due to the physical disruption of DNA, the functional effect we investigated here is mediated through the epigenetic influences of TEs. We found that the repressive chromatin mark, H3K9me, is elevated in sequences adjacent to euchromatic TEs at multiple developmental stages in Drosophila melanogaster. Furthermore, the heterochromatic states of genes depend not only on the number of and distance from adjacent TEs, but also on the likelihood that their nearest TEs are targeted by piRNAs. These variations in chromatin status probably have functional consequences, causing genes near TEs to have lower expression. Importantly, we found stronger selection against TEs that lead to higher H3K9me enrichment of adjacent genes, demonstrating the pervasive evolutionary consequences of TE-induced epigenetic silencing. Because of the intrinsic biological mechanism of piRNA amplification, spread of TE heterochromatin could result in the theoretically required synergistic deleterious effects of TE insertions for stable containment of TE copy number. The indirect deleterious impact of piRNA-mediated epigenetic silencing of TEs is a previously unexplored, yet important, element for the evolutionary dynamics of TEs.
Fatigue Damage Behavior of Freestanding 40 µm‐Thick Nickel Foils for MEMS Applications

Fatigue damage behavior of freestanding 40 µm‐thick Ni foils was investigated. We found that there were evident differences in fatigue behaviors of the foils loaded normal to and parallel to the rolling direction. EBSD analyses revealed that fatigue cracking along grain boundaries (GBs) or slip bands in two kinds of the foils depended on the difference in Schmid factor of the grains along both sides of the GBs and the misorientation angle of the GB. Fatigue damage mechanism was discussed.
THz probe studies of MBE grown epitaxial GaAs

We have built a THz time-domain spectrometer driven by a sub-15fs pulse duration mode-locked Ti:Al203 laser. Using THz time-domain spectroscopy with photoconductive antenna for THz generation and electro-optic sampling for detection as well as photoexcited THz spectroscopy, we measured the carrier concentrations and mobilities of epitaxially grown undoped GaAs samples to be used in photoconductive antenna production. The samples were grown at 600 °C to 1 μm effective layer thickness on top of a 650 μm SI-GaAs wafer. The resistivities, mobilities and the carrier concentrations were measured and calculated by the van der Pauw method under the magnetic field. These Hall effect measurements and the THz probe studies were compared with each other. The measurements and calculations obtained electronically are compared optically using the Drude Model for the conductivity and mobility.
Scalable Support Vector Machine for Semi-supervised Learning

Owing to the prevalence of unlabeled data, semisupervised learning has been one of the most prominent machine learning paradigms, and applied successfully in many real-world applications. However, most of existing semi-supervised learning methods are neither computationally efficient nor economic in memory usage. In this paper, we present the Graph-based Semisupervised Kernel Machine (GKM), a method that leverages the generalization ability of kernel-based method with the geometrical and distributive information carried in a spectral graph induced from data for semi-supervised learning purpose. Our proposed GKM can be solved directly in the primal form using the Stochastic Gradient Descent method with the ideal convergence rate $O(\frac{1}{T})$. Besides, our formulation is suitable for a wide spectrum of important loss functions in the literature of machine learning (e.g., Hinge, smooth Hinge, Logistic, L1, and {\epsilon}-insensitive) and smoothness functions (i.e., $l_p(t) = |t|^p$ with $p\ge1$). We note that the well-known Laplacian Support Vector Machine falls into the spectrum of gS3VM corresponding to the combination of the Hinge loss and the smoothness function $l_2(.)$. We further show that the well-known Laplacian Support Vector Machine is a special case of our formulation. We validate our proposed method on several benchmark datasets to demonstrate that GKM is appropriate for the large-scale datasets since it is optimal in memory usage and yields superior classification accuracy whilst simultaneously achieving a significant computation speed-up in comparison with the state-of-the-art baselines.
Self-Organizing Distinctive-State Abstraction For Learning Robot Navigation ∗

A major challenge in reinforcement learning research is to extend methods that have worked well on discrete, shortrange, low-dimensional problems to continuous, high-diameter, high-dimensional problems, such as robot navigation using high-resolution sensors. Self-Organizing Distinctive-state Abstraction (SODA) is a new, generic method by which a robot in a continuous world can better learn to navigate by learning a set of high-level features and building temporally-extended actions to carry it between distinctive states based on those features. A SODA agent first uses a self-organizing feature map to develop a set of high-level perceptual features while exploring the environment with primitive, local actions. The agent then builds a set of high-level actions composed of generic trajectory-following and hill-climbing control laws that carry it between the states at local maxima of feature activations. In an experiment on a simulated robot navigation task, the SODA agent learns to perform a task requiring 300 small-scale, local actions using as few as 9 new, temporally-extended actions, significantly improving learning time over navigating with the local actions.
M-ary Orthogonal Coded/Balanced UWB Transmitted-Reference System

A new M-ary orthogonal coded/balanced signaling is proposed to realize high rate UWB transmitted-reference (TR) systems. Especially, the inter-pulse interference by an overlap of multipath-delayed pulses is mitigated by combining the signaling and a pair of balanced matched filters in a joint manner, so as to permit a shorter time delay between the reference and data pulses in TR systems. To evaluate an achievable information rate increase relative to conventional TR, the symbol error probability (SEP) is theoretically derived for the M-ary balanced TR system in dense multipath.
Post-Fire Recovery in Coastal Sage Scrub: Seed Rain and Community Trajectory

Disturbance is a primary mechanism structuring ecological communities. However, human activity has the potential to alter the frequency and intensity of natural disturbance regimes, with subsequent effects on ecosystem processes. In Southern California, human development has led to increased fire frequency close to urban areas that can form a positive feedback with invasive plant spread. Understanding how abiotic and biotic factors structure post-fire plant communities is a critical component of post-fire management and restoration. In this study we considered a variety of mechanisms affecting post-fire vegetation recovery in Riversidean sage scrub. Comparing recently burned plots to unburned plots, we found that burning significantly reduced species richness and percent cover of exotic vegetation the first two years following a 100-hectare wildfire. Seed rain was higher in burned plots, with more native forb seeds, while unburned plots had more exotic grass seeds. Moreover, there were significant correlations between seed rain composition and plant cover composition the year prior and the year after. Collectively, this case study suggests that fire can alter community composition, but there was not compelling evidence of a vegetation-type conversion. Instead, the changes in the community composition were temporary and convergence in community composition was apparent within two years post-fire.
Mobility Prediction for Efficient Resources Management in Vehicular Cloud Computing

Vehicular Cloud Computing (VCC) has becomea significant research area recently, due to its potentialadvantages and applications, especially in the field ofIntelligent Transportation Systems (ITS). However, thehigh mobility of vehicular environment poses crucial challengesto resources allocation and management in VCC, which makesits implementation more complex than conventional clouds. Many works have been introduced to address various issuesand aspects of VCC, including resources management andVirtual Machine Migration in vehicular clouds. However, usingmobility prediction in VCC has not been studied previously. Inthis paper, we introduce a novel solution to reduce the effect ofresources mobility on the performance of vehicular cloud, usingan efficient resources management scheme based on vehiclesmobility prediction. This approach enables the vehicular cloudto take pre-planned procedures, based on the output of anArtificial Neural Network (ANN) mobility prediction model. The aim is to reduce the negative impact of sudden changes invehicles locations on vehicular cloud performance. A simulationscenario is introduced to compare between the performanceof our resources management scheme and other resourcesmanagement approaches introduced in the literature. Thesimulation environment is based on Nagel-Shreckenberg cellularautomata (CA) discrete model for traffic simulation. Simulationresults show that our proposed approach has leveraged theperformance of vehicular cloud effectively without overusingavailable vehicular cloud resources.
The Metabolic Syndrome in Children and Adolescents: Shifting the Focus to Cardiometabolic Risk Factor Clustering

Metabolic syndrome (MetS) was developed by the National Cholesterol Education Program Adult Treatment Panel III, identifying adults with at least 3 of 5 cardiometabolic risk factors (hyperglycemia, increased central adiposity, elevated triglycerides, decreased high-density lipoprotein cholesterol, and elevated blood pressure) who are at increased risk of diabetes and cardiovascular disease. The constellation of MetS component risk factors has a shared pathophysiology and many common treatment approaches grounded in lifestyle modification. Several attempts have been made to define MetS in the pediatric population. However, in children, the construct is difficult to define and has unclear implications for clinical care. In this Clinical Report, we focus on the importance of screening for and treating the individual risk factor components of MetS. Focusing attention on children with cardiometabolic risk factor clustering is emphasized over the need to define a pediatric MetS.
TrueNorth: A High-Performance, Low-Power Neurosynaptic Processor for Multi-Sensory Perception, Action, and Cognition

Abstract : IBMs TrueNorth neurosynaptic processor is a radical departure from decades of traditional von Neumann computing. Containing 5.4 billion transistors and fabricated in a 28nm low-power CMOS process technology, TrueNorth contains 1 million neurons and 256 million synapses. With applications ranging from embedded and embodied intelligence to large-scale perceptual analysis of streaming multi-sensory data, this massively parallel processor consumes only 65mW typically.
Secure Routing in Intelligent Device-to-Device Communications

Device-to-Device (D2D) communications have received exceptional attention nowadays due to the overabundance number of applications and services. Therefore, D2D is expected to be a vital technical component in Internet of Things and to play a significant role with the next generation 5G. On the other hand, due to the growth demand of D2D, it becomes an ideal target for attackers. Moreover, the rapid rise in mobile capabilities opens the door to the cyber criminals that explore new avenues for malware attacks. In spite of the fact that the literature proposed security schemes for malware attacks. However, the research field is still immature and unexplored in depth due to the fast evolution of malware. Accordingly, malware attacks formalize security risk that threatens the mobile network. A noteworthy concern is that the malware attacks are going on at a rate far surpassing the development of safety techniques. The fundamental goal of our thesis is to propose a novel secure, energy-aware stochastic routing protocol based on a game-theoretic approach for security improvement against malware attacks in Device-to- Device network. The proposed protocol considers for the security requirement, as well as the energy system constraints. As a first step toward thwarting the success of the malware attacks, we try to hinder the malware infection by detecting the malware before it infects the targeted devices. Moreover, the proposed routing protocol considers the attacker’s behavior and the computation of decision makers’ strategies. The effectiveness of the proposed routing protocol has been evaluated using network simulator. Through extensive simulations, we have validated the effectiveness of proposed protocol by comparing its performance with the traditional routing protocols and with another strategic customized protocol. Results are presented to illustrate the efficiency of the proposed routing protocol regarding the detection rate and overall expected payoff compared with traditional routing protocols and another strategic customized protocol in case of three different attacks Distribution.
Probing Rad51-DNA interactions by changing DNA twist

In eukaryotes, Rad51 protein is responsible for the recombinational repair of double-strand DNA breaks. Rad51 monomers cooperatively assemble on exonuclease-processed broken ends forming helical nucleo-protein filaments that can pair with homologous regions of sister chromatids. Homologous pairing allows the broken ends to be reunited in a complex but error-free repair process. Rad51 protein has ATPase activity but its role is poorly understood, as homologous pairing is independent of adenosine triphosphate (ATP) hydrolysis. Here we use magnetic tweezers and electron microscopy to investigate how changes of DNA twist affect the structure of Rad51-DNA complexes and how ATP hydrolysis participates in this process. We show that Rad51 protein can bind to double-stranded DNA in two different modes depending on the enforced DNA twist. The stretching mode is observed when DNA is unwound towards a helical repeat of 18.6 bp/turn, whereas a non-stretching mode is observed when DNA molecules are not permitted to change their native helical repeat. We also show that the two forms of complexes are interconvertible and that by enforcing changes of DNA twist one can induce transitions between the two forms. Our observations permit a better understanding of the role of ATP hydrolysis in Rad51-mediated homologous pairing and strand exchange.
Where is Family in the Family Nurse Practitioner Program? Results of a U.S. Family Nurse Practitioner Program Survey

Though recent progress in family nursing science can serve the family nurse practitioner (FNP) to intervene in the regulation of family health, whether those advances are taught to FNP students has been unclear. All 266 FNP programs in the United States were invited to participate in a survey to assess the content and clinical application of family nursing theories in the curriculum. The majority of FNP programs frame family as the context of care for the individual. Though FNP students receive a foundation in family nursing theory in core courses, they are not usually expected to use family assessment methods in clinical practicum courses or to plan interventions for the family as the unit of care. The authors challenge educators to consider family nursing science as an essential component of the FNP program as the Doctor of Nursing Practice (DNP) evolves and becomes requisite for entry into advanced practice.
A novel corpus of children 2 s disordered speech.

This paper introduces the acquisition, evaluation and baseline Automatic Speech Recognition (ASR) experiments of a novel corpus containing speech from a set of impaired and unimpaired young speakers. A group of 14 speakers with different speech disorders have uttered several sessions over a 57-word vocabulary in Spanish to gather more than 3 hours of speech. In addition to this work, a parallel corpus of speech from unimpaired young speakers has been recorded with more than 6 hours of speech with the same vocabulary. The impaired speech corpus has been evaluated through a manual labeling to detect the mispronunciations made by the speakers, and the outcome of this work show that 17.31% of the phonemes have been either mispronounced or deleted in an isolated work task. A baseline evaluation of the performance of an state-of-the-art ASR system shows a 35.02% of Word Error Rate (WER) when using Speaker Independent models based on adult speech. This WER is reduced to 27.60% using models based on children speech and further reduced to 15.35% using speaker dependent models. Finally, experiments on connected speech show how ASR performance degrades on 4 impaired speakers on the transition from isolated words to connected speech due to the language impairments of the speakers and the coarticulation in connected speech.
Amyloid polymorphisms constitute distinct clouds of conformational variants in different etiological subtypes of Alzheimer’s disease

Significance The clinical and pathological variability among patients with Alzheimer’s disease (AD) remains largely unexplained. Evidence is growing that this heterogeneity may be influenced by the heterogeneous molecular architecture of misfolded amyloid-β peptide (Aβ) in the brain. To test this hypothesis, we used unique fluorescent ligands to interrogate the molecular structure of Aβ in amyloid plaques from patients who had died with etiologically distinct subtypes of AD. We found that Aβ-amyloid plaques in the brain cluster as clouds of conformational variants that differ among certain subtypes of AD. The conformational features of AD plaques were partially transmissible to transgenic mice in a seeding paradigm, suggesting a mechanism whereby different molecular strains of Aβ propagate their features within the brain. The molecular architecture of amyloids formed in vivo can be interrogated using luminescent conjugated oligothiophenes (LCOs), a unique class of amyloid dyes. When bound to amyloid, LCOs yield fluorescence emission spectra that reflect the 3D structure of the protein aggregates. Given that synthetic amyloid-β peptide (Aβ) has been shown to adopt distinct structural conformations with different biological activities, we asked whether Aβ can assume structurally and functionally distinct conformations within the brain. To this end, we analyzed the LCO-stained cores of β-amyloid plaques in postmortem tissue sections from frontal, temporal, and occipital neocortices in 40 cases of familial Alzheimer’s disease (AD) or sporadic (idiopathic) AD (sAD). The spectral attributes of LCO-bound plaques varied markedly in the brain, but the mean spectral properties of the amyloid cores were generally similar in all three cortical regions of individual patients. Remarkably, the LCO amyloid spectra differed significantly among some of the familial and sAD subtypes, and between typical patients with sAD and those with posterior cortical atrophy AD. Neither the amount of Aβ nor its protease resistance correlated with LCO spectral properties. LCO spectral amyloid phenotypes could be partially conveyed to Aβ plaques induced by experimental transmission in a mouse model. These findings indicate that polymorphic Aβ-amyloid deposits within the brain cluster as clouds of conformational variants in different AD cases. Heterogeneity in the molecular architecture of pathogenic Aβ among individuals and in etiologically distinct subtypes of AD justifies further studies to assess putative links between Aβ conformation and clinical phenotype.
Heuristic-Based Automatic Online Proctoring System

This paper proposes a novel multi-modal method for automatic online proctoring using a combination of image processing, audio processing and PC monitoring techniques. In the proposed system the remote proctor only has to inspect the examination room. The proposed system is evaluated with a dataset mimicking the malpractice scenarios, the accuracy of the system is demonstrated with false positive rate of 0.08 and true negative rate of 0.13.