Miracle-dz/UniFAR-FTUs huggingface dataset:
Porous NiTi Particle Dispersed Mg-Zn-Ca Bulk Metallic Glass Matrix Composites

Even though the Mg-based bulk metallic glasses (BMGs) have shown superior anti-corrosion properties compared with their crystalline counterparts, the brittleness of them limits the widespread application of these materials. In the present study, we have firstly introduced porous NiTi shape memory alloy particles into an Mg-Zn-Ca BMG by the direct adding method. This composite showed both improved compressive strength and corrosion resistance in Hank’s solutions than its monolithic glassy counterpart. The NiTi dispersoids among the matrix were likely to hinder the main shear band propagation, and also acted as the corrosion barriers. Furthermore, the porous nature of present added particle could further increase the interface areas, which should enhance the reinforcing effects compared with solid ones. This low-cost, high-anticorrosive composite was a good candidate as an engineering material.
Scene Interpretation for Self-Aware Cognitive Robots

We propose a visual scene interpretation system for cognitive robots to maintain a consistent world model about their environments. This interpretation system is for our lifelong experimental learning framework that allows robots analyze failure contexts to ensure robustness in their future tasks. To efficiently analyze failure contexts, scenes should be interpreted appropriately. In our system, LINE-MOD and HS histograms are used to recognize objects with/without textures. Moreover, depth-based segmentation is applied for identifying unknown objects in the scene. This information is also used to augment the recognition performance. The world model includes not only the objects detected in the environment but also their spatial relations to efficiently represent contexts. Extracting unary and binary relations such as on, on_ground, clear and near is useful for symbolic representation of the scenes. We test the performance of our system on recognizing objects, determining spatial predicates, and maintaining consistency of the world model of the robot in the real world. Our preliminary results reveal that our system can be successfully used to extract spatial relations in a scene and to create a consistent model of the world by using the information gathered from the onboard RGB-D sensor as the robot explores its environment.
Reproducibility of the Evolution of Stride Biomechanics During Exhaustive Runs

Abstract Running biomechanics and its evolution that occurs over intensive trials are widely studied, but few studies have focused on the reproducibility of stride evolution in these runs. The purpose of this investigation was to assess the reproducibility of changes in eight biomechanical variables during exhaustive runs, using three-dimensional analysis. Ten male athletes (age: 23 ± 4 years; maximal oxygen uptake: 57.5 ± 4.4 ml02·min-1·kg-1; maximal aerobic speed: 19.3 ± 0.8 km·h-1) performed a maximal treadmill test. Between 3 to 10 days later, they started a series of three time-to-exhaustion trials at 90% of the individual maximal aerobic speed, seven days apart. During these trials eight biomechanical variables were recorded over a 20-s period every 4 min until exhaustion. The evolution of a variable over a trial was represented as the slope of the linear regression of these variables over time. Reproducibility was assessed with intraclass correlation coefficients and variability was quantified as standard error of measurement. Changes in five variables (swing duration, stride frequency, step length, centre of gravity vertical and lateral amplitude) showed moderate to good reproducibility (0.48 ≤ ICC ≤ 0.72), while changes in stance duration, reactivity and foot orientation showed poor reproducibility (-0.71 ≤ ICC ≤ 0.04). Fatigue-induced changes in stride biomechanics do not follow a reproducible course across the board; however, several variables do show satisfactory stability: swing duration, stride frequency, step length and centre of gravity shift.
Discourse in Multimedia: A Case Study in Information Extraction

To ensure readability, text is often written and presented with due formatting. These text formatting devices help the writer to effectively convey the narrative. At the same time, these help the readers pick up the structure of the discourse and comprehend the conveyed information. There have been a number of linguistic theories on discourse structure of text. However, these theories only consider unformatted text. Multimedia text contains rich formatting features which can be leveraged for various NLP tasks. In this paper, we study some of these discourse features in multimedia text and what communicative function they fulfil in the context. We examine how these multimedia discourse features can be used to improve an information extraction system. We show that the discourse and text layout features provide information that is complementary to lexical semantic information commonly used for information extraction. As a case study, we use these features to harvest structured subject knowledge of geometry from textbooks. We show that the harvested structured knowledge can be used to improve an existing solver for geometry problems, making it more accurate as well as more explainable.
Massively Parallel Databases and MapReduce Systems

Timely and cost-effective analytics over "big data" has emerged as a key ingredient for success in many businesses, scientific and engineering disciplines, and government endeavors. Web clicks, social media, scientific experiments, and datacenter monitoring are among data sources that generate vast amounts of raw data every day. The need to convert this raw data into useful information has spawned considerable innovation in systems for large-scale data analytics, especially over the last decade. This monograph covers the design principles and core features of systems for analyzing very large datasets using massively-parallel computation and storage techniques on large clusters of nodes. We first discuss how the requirements of data analytics have evolved since the early work on parallel database systems. We then describe some of the major technological innovations that have each spawned a distinct category of systems for data analytics. Each unique system category is described along a number of dimensions including data model and query interface, storage layer, execution engine, query optimization, scheduling, resource management, and fault tolerance. We conclude with a summary of present trends in large-scale data analytics.
Tick-borne encephalitis: A 43-year summary of epidemiological and clinical data from Latvia (1973 to 2016)

Background The incidence of tick-borne encephalitis (TBE) varies significantly over time. To better understand the annual incidence of all TBE cases in Latvia we investigated the disease burden in the country from 1973–2016 using several available sources and case definitions. Methods We identified cases of TBE from an electronic database (maintained by the Centre for Disease Prevention and Control of Latvia [CDPC]) by the use of ICD-10 diagnosis codes for TBE (A84; A84.0; A84.1; A84.8; A84.9). In addition, previously unreported TBE cases were found by review of TBE diagnoses according to ICD-10 codes in four hospital databases. Results From 1973 to 2016 a total of 15,193 TBE cases were reported to the CDPC, 2,819 of which were reported from January 2007 through December 2016, additionally for this time period, 104 cases were identified via hospital survey. From all 2,923 reported cases (2007–2016), 1,973 met TBE case definition criteria and were included in the TBE study analysis. The highest average 10 year incidence was observed from 1990–1999 (27.9 cases per 100,000; range 4.6–53.0), however, the average 10-year incidence from 2007–2016 using officially adopted TBE case definition was 9.6 cases per 100,000 (range 5.8–14.6). For this 10-year time period most cases were adults (95.1%) and male (52.2%). The most common clinical form of TBE was meningitis (90.6%). A tick bite prior to TBE onset was reported in 60.6% of TBE cases and 98.2% of cases were not vaccinated against TBE. Conclusion The data demonstrate that the incidence of TBE varies by about one third based on the case definition used. TBE occurs almost entirely in the unvaccinated population. Regular TBE awareness campaigns could encourage the population in Latvia to use protective measures to further control TBE in the country, either via vaccination or tick avoidance.
Realtime linear cartograms and metro maps

We introduce an efficient algorithm for drawing spatially-informative linear cartograms: transforming a geometric network such that given edge lengths are realised, while distorting edge directions as little as possible. Our algorithm is based on carefully linearised least squares optimisation, forgoing the need for an iterative solver. This is fast and ensures a well-defined result. The classic application of linear cartograms is drawing travel-time maps; we also discuss drawing schematised metro maps.
Using Bayesian networks to analyze expression data

DNA hybridization arrays simultaneously measure the expression level for thousands of genes. These measurements provide a “snapshot” of transcription levels within the cell. A major challenge in computational biology is to uncover, from such measurements, gene/protein interactions and key biological features of cellular systems. In this paper, we propose a new framework for discovering interactions between genes based on multiple expression measurements This framework builds on the use of Bayesian networks for representing statistical dependencies. A Bayesian network is a graph-based model of joint multi-variate probability distributions that captures properties of conditional independence between variables. Such models are attractive for their ability to describe complex stochastic processes, and for providing clear methodologies for learning from (noisy) observations. We start by showing how Bayesian networks can describe interactions between genes. We then present an efficient algorithm capable of learning such networks and statistical method to assess our confidence in their features. Finally, we apply this method to the S. cerevisiae cell-cycle measurements of Spellman et al. [35] to uncover biological features
ProstateGAN: Mitigating Data Bias via Prostate Diffusion Imaging Synthesis with Generative Adversarial Networks

Generative Adversarial Networks (GANs) have shown considerable promise for mitigating the challenge of data scarcity when building machine learning-driven analysis algorithms. Specifically, a number of studies have shown that GAN-based image synthesis for data augmentation can aid in improving classification accuracy in a number of medical image analysis tasks, such as brain and liver image analysis. However, the efficacy of leveraging GANs for tackling prostate cancer analysis has not been previously explored. Motivated by this, in this study we introduce ProstateGAN, a GAN-based model for synthesizing realistic prostate diffusion imaging data. More specifically, in order to generate new diffusion imaging data corresponding to a particular cancer grade (Gleason score), we propose a conditional deep convolutional GAN architecture that takes Gleason scores into consideration during the training process. Experimental results show that high-quality synthetic prostate diffusion imaging data can be generated using the proposed ProstateGAN for specified Gleason scores.
POLA KOMUNIKASI KELUARGA DALAM PEMBENTUKAN KEPRIBADIAN ANAK

Humans are social creatures who need a good relationship with other people, and have the urge to relate to other people and other impulses such as curiosity, self-actualization, and so on, it will be discarded by communicating with each other. Because humans can always develop and be able to carry out community life, of course, with communication, which can be done at any time, when, by whom and with anyone. Likewise with communication for a child in a family who has work to be used in the community. Parents in a family become a figure for children in the form of the same thing, behavior, speech words formed by parents of parents. The key pattern of communication in the family that is built will affect the parenting style of the children. With good communication patterns will create good parenting patterns, the formation of children in the family form as an effort of parents for the virtue of behaving that must be done early is the basis of education. The formation of awareness as the basis of education is a manifestation of the formation of a child's personality.
Contemporary Ribonomics Methods for Viral microRNA Target Analysis

Numerous cellular processes are regulated by microRNAs (miRNAs), both cellular and viral. Elucidating the targets of miRNAs has become an active area of research. An important method in this field is cross-linking and immunoprecipitation (CLIP), where cultured cells or tissues are UV-irradiated to cross-link protein and nucleic acid, the RNA binding protein of interest is immunoprecipitated, and the RNAs pulled down with the protein are isolated, reverse-transcribed, and analyzed by sequencing. CLIP using antibody against Argonaute (Ago), which binds to both miRNA and mRNA as they interact in RISC, has allowed researchers to uncover a large number of miRNA targets. Coupled with high-throughput sequencing, CLIP has been useful for revealing miRNA targetomes for the γ-herpesviruses Kaposi’s sarcoma-associated herpesvirus (KSHV) and Epstein-Barr virus (EBV). Variants on the CLIP protocol are described, with the benefits and drawbacks of each. In particular, the most recent methods involving RNA–RNA ligation to join the miRNA and its RNA target have aided in target identification. Lastly, data supporting biologically meaningful interactions between miRNAs and long non-coding RNAs (lncRNAs) are reviewed. In summary, ribonomics-based miRNA targetome analysis has expanded our understanding of miRNA targeting and has provided a rich resource for EBV and KSHV research with respect to pathogenesis and tumorigenesis.
Alkaloid-Rich Crude Extracts, Fractions and Piperamide Alkaloids of Piper guineense Possess Promising Antibacterial Effects

Piper guineense is a food and medicinal plant commonly used to treat infectious diseases in West-African traditional medicine. In a bid to identify new antibacterial compounds due to bacterial resistance to antibiotics, twelve extracts of P. guineense fruits and leaves, obtained by sequential extraction, as well as the piperine and piperlongumine commercial compounds were evaluated for antibacterial activity against human pathogenic bacteria. HPLC-DAD and UHPLC/Q-TOF MS analysis were conducted to characterize and identify the compounds present in the extracts with promising antibacterial activity. The extracts, with the exception of the hot water decoctions and macerations, contained piperamide alkaloids as their main constituents. Piperine, dihydropiperine, piperylin, dihydropiperylin or piperlonguminine, dihydropiperlonguminine, wisanine, dihydrowisanine and derivatives of piperine and piperidine were identified in a hexane extract of the leaf. In addition, some new piperamide alkaloids were identified, such as a piperine and a piperidine alkaloid derivative and two unknown piperamide alkaloids. To the best of our knowledge, there are no piperamides reported in the literature with similar UVλ absorption maxima and masses. A piperamide alkaloid-rich hexane leaf extract recorded the lowest MIC of 19 µg/mL against Sarcina sp. and gave promising growth inhibitory effects against S. aureus and E. aerogenes as well, inhibiting the growth of both bacteria with a MIC of 78 µg/mL. Moreover, this is the first report of the antibacterial activity of P. guineense extracts against Sarcina sp. and E. aerogenes. Marked growth inhibition was also obtained for chloroform extracts of the leaves and fruits against P. aeruginosa with a MIC value of 78 µg/mL. Piperine and piperlongumine were active against E. aerogenes, S. aureus, E. coli, S. enterica, P. mirabilis and B. cereus with MIC values ranging from 39–1250 µg/mL. Notably, the water extracts, which were almost devoid of piperamide alkaloids, were not active against the bacterial strains. Our results demonstrate that P. guineense contains antibacterial alkaloids that could be relevant for the discovery of new natural antibiotics.
Electrical Characteristics and pH Response of a Parylene-H Sensing Membrane in a Si-Nanonet Ion-Sensitive Field-Effect Transistor

We report the electrical characteristics and pH responses of a Si-nanonet ion-sensitive field-effect transistor with ultra-thin parylene-H as a gate sensing membrane. The fabricated device shows excellent DC characteristics: a low subthreshold swing of 85 mV/dec, a high current on/off ratio of ~107 and a low gate leakage current of ~10−10 A. The low interface trap density of 1.04 × 1012 cm−2 and high field-effect mobility of 510 cm2V−1s−1 were obtained. The pH responses of the devices were evaluated in various pH buffer solutions. A high pH sensitivity of 48.1 ± 0.5 mV/pH with a device-to-device variation of ~6.1% was achieved. From the low-frequency noise characterization, the signal-to-noise ratio was extracted as high as ~3400 A/A with the lowest noise equivalent pH value of ~0.002 pH. These excellent intrinsic electrical and pH sensing performances suggest that parylene-H can be promising as a sensing membrane in an ISFET-based biosensor platform.
Fog Robotics for Efficient, Fluent and Robust Human-Robot Interaction

Active communication between robots and humans is essential for effective human-robot interaction. To accomplish this objective, Cloud Robotics (CR) was introduced to make robots enhance their capabilities. It enables robots to perform extensive computations in the cloud by sharing their outcomes. Outcomes include maps, images, processing power, data, activities, and other robot resources. But due to the colossal growth of data and traffic, CR suffers from serious latency issues. Therefore, it is unlikely to scale a large number of robots particularly in human-robot interaction scenarios, where responsiveness is paramount. Furthermore, other issues related to security such as privacy breaches and ransomware attacks can increase. To address these problems, in this paper, we have envisioned the next generation of social robotic architectures based on Fog Robotics (FR) that inherits the strengths of Fog Computing to augment the future social robotic systems. These new architectures can escalate the dexterity of robots by shoving the data closer to the robot. Additionally, they can ensure that human-robot interaction is more responsive by resolving the problems of CR. Moreover, experimental results are further discussed by considering a scenario of FR and latency as a primary factor comparing to CR models.
MINT, the molecular interaction database: 2009 update

MINT (http://mint.bio.uniroma2.it/mint) is a public repository for molecular interactions reported in peer-reviewed journals. Since its last report, MINT has grown considerably in size and evolved in scope to meet the requirements of its users. The main changes include a more precise definition of the curation policy and the development of an enhanced and user-friendly interface to facilitate the analysis of the ever-growing interaction dataset. MINT has adopted the PSI-MI standards for the annotation and for the representation of molecular interactions and is a member of the IMEx consortium.
On the Performance of an Aerosol Electrometer with Enhanced Detection Limit

An aerosol electrometer with enhanced detection limit was developed for measuring the collected particles electrical current ranging from −50 pA to 50 pA with no range switching necessary. The detection limit was enhanced by suppressing the electric current measurement noise and improving the detection efficiency. A theoretical model for the aerosol electrometer has been established to investigate the noise effect factors and verified experimentally. The model showed that the noise was a function of ambient temperature, and it was affected by the characteristics of feedback resistor and operational amplifier simultaneously. The Faraday cup structure of the aerosol electrometer was optimized by adopting a newly designed cup-shaped metal filter which increased the surface area of the cup; thus the particle interception efficiency was improved. The aerosol electrometer performance-linearity, noise and the particle detection efficiency, were evaluated experimentally. When compared with TSI-3068B, a 99.4% (R2) statistical correlation was achieved. The results also showed that the root mean square noise and the peak-to-peak noise were 0.31 fA and 1.55 fA, respectively. The particle detection efficiency was greater than 99.3% when measuring particle diameter larger than 7.0 nm.
POMDPs for Risk-Aware Autonomy

Although we would like our robots to have completely autonomous behavior, this is often not possible. Some parts of a task might be hard to automate, perhaps due to hard-to-interpret sensor information, or a complex environment. In this case, using shared autonomy or teleoperation is preferable to an error-prone autonomous approach. However, the question of which parts of a task to allocate to the human, and which to the robot can often be tricky. In this work, we introduce A 3 P , a risk-aware task-level reinforcement learning algorithm. A 3 P represents a task-level state machine as a POMDP. In this paper, we introduce A 3 P , a risk-aware al-gorithm that discovers when to hand off subtasks to a human assistant. A 3 P models the task as a Partially Observably Markov Decision Process (POMDP) and explicitly represents failures as additional state-action pairs. Based on the model, the algorithm allows the user to allocate subtasks the robot or the human in such a way as to manage the worst-case performance time for the overall task.
ML-Net: multi-label classification of biomedical texts with deep neural networks

OBJECTIVE In multi-label text classification, each textual document is assigned 1 or more labels. As an important task that has broad applications in biomedicine, a number of different computational methods have been proposed. Many of these methods, however, have only modest accuracy or efficiency and limited success in practical use. We propose ML-Net, a novel end-to-end deep learning framework, for multi-label classification of biomedical texts. MATERIALS AND METHODS ML-Net combines a label prediction network with an automated label count prediction mechanism to provide an optimal set of labels. This is accomplished by leveraging both the predicted confidence score of each label and the deep contextual information (modeled by ELMo) in the target document. We evaluate ML-Net on 3 independent corpora in 2 text genres: biomedical literature and clinical notes. For evaluation, we use example-based measures, such as precision, recall, and the F measure. We also compare ML-Net with several competitive machine learning and deep learning baseline models. RESULTS Our benchmarking results show that ML-Net compares favorably to state-of-the-art methods in multi-label classification of biomedical text. ML-Net is also shown to be robust when evaluated on different text genres in biomedicine. CONCLUSION ML-Net is able to accuractely represent biomedical document context and dynamically estimate the label count in a more systematic and accurate manner. Unlike traditional machine learning methods, ML-Net does not require human effort for feature engineering and is a highly efficient and scalable approach to tasks with a large set of labels, so there is no need to build individual classifiers for each separate label.