Miracle-dz/UniFAR-FTUs huggingface dataset:
Zensei: Embedded, Multi-electrode Bioimpedance Sensing for Implicit, Ubiquitous User Recognition

Interactions and connectivity is increasingly expanding to shared objects and environments, such as furniture, vehicles, lighting, and entertainment systems. For transparent personalization in such contexts, we see an opportunity for embedded recognition, to complement traditional, explicit authentication. We introduce Zensei, an implicit sensing system that leverages bio-sensing, signal processing and machine learning to classify uninstrumented users by their body's electrical properties. Zensei could allow many objects to recognize users. E.g., phones that unlock when held, cars that automatically adjust mirrors and seats, or power tools that restore user settings. We introduce wide-spectrum bioimpedance hardware that measures both amplitude and phase. It extends previous approaches through multi-electrode sensing and high-speed wireless data collection for embedded devices. We implement the sensing in devices and furniture, where unique electrode configurations generate characteristic profiles based on user's unique electrical properties. Finally, we discuss results from a comprehensive longitudinal 22-day data collection experiment with 46 subjects. Our analysis shows promising classification accuracy and low false acceptance rate.
Subword Unit Concatenation for Malay Speech Synthesis

In this paper, we propose an idea to use subword synthesis units to generate novel word sound of Malay language. The subword synthesis units were carefully created based on Malay phoneme adjacency analysis. The phoneme adjacency analysis was used to find out which adjacent phonemes that would not introduce audible distortion if the phonemes were concatenated. Besides phoneme adjacency factor, we also include prosodic features in selecting the subword synthesis unit. Thus, by considering both phoneme adjacency and prosodic feature in selecting the subword units, a smooth sound of novel word can be obtained.
Fatigue crack propagation in discontinuously reinforced aluminum matrix composites

The propagation behavior of long and small fatigue cracks in an aluminum alloy (2024-T6) reinforced with 20 volume percent of SiC particles was investigated. The development of crack closure with crack propagation was measured through the compliance method. The propagation rate, dc/dN, of short cracks emanating from a sharp notch was higher than that predicted using the relation for long cracks. SiC particles cause the increase of roughness and plasticity induced crack closure. The relation between dc/dN and effective stress intensity range, AK^, for short cracks was identical to that obtained for long cracks.
Light-Weight Invariant Generation for Software Verification with CPAchecker

This thesis presents a light-weight approach to invariant generation in the context of an implementation of k-induction for software verification within the CPAchecker framework. CPAchecker is a tool for software verification, one of the grand challenges of computing research. Inductive methods to prove the correctness of loops have been applied for years, but rely on manual invariant annotation or complex and elaborate algorithms for automatic generation of invariants, because classic inductive methods often require strong invariants to be able to prove loop correctness. k-induction, however, is often successful with weaker invariants. The presented invariant generation algorithm supporting the k-induction implemented in the bounded model checking algorithm of CPAchecker is evaluated and it is shown that while k-induction certainly is no all-encompassing approach to software verification, there are promising results for a significant number of experiments, which might be an incentive to further research into the question what types of programs benefit most from a k-induction verification approach.
AUTONOMOUS MULTI-ROBOT BEHAVIOURS FOR SAFETY INSPECTION UNDER THE CONSTRAINTS OF UNDERGROUND MINE TERRAINS

The wide adoption of underground mining has contributed immensely to the rapid growth of the countries’ economy, job creation, gross domestic product, fixed capital formation, gross investment and merchandise exports on an enormous global scale. At the same time, the tedious and dangerous nature of underground mining makes it vulnerable to mine accidents and disasters, such as the fall of loose rocks, trapping of miners and miners’ death. Building upon a comprehensive survey of safety inspection issues in South Africa, this paper proposes an autonomous multi-robot cooperative behavioural model that can help to guide multi-robots in pre-entry safety inspection of the underground mines. We discuss in detail the different safety fears in underground mines and categorise these issues into mine-related and roboticrelated disasters, the latter being classified according to random component failures, human errors, software failures and systematic hardware faults. We present an empirical analysis of how our proposed model can be used or implemented through two real-life scenarios: a) the inspection of rock falls and b) the detection of gas levels in underground mines. This survey can be used as a reference guide to understand safety management and cooperative behavioural models for underground mines and to conduct further research on the existing models to make them more efficient, reliable and scalable, which can promote their use in larger mines and
Complete genome sequence of thermophilic Bacillus smithii type strain DSM 4216 T

Bacillus smithii is a facultatively anaerobic, thermophilic bacterium able to use a variety of sugars that can be derived from lignocellulosic feedstocks. Being genetically accessible, it is a potential new host for biotechnological production of green chemicals from renewable resources. We determined the complete genomic sequence of the B. smithii type strain DSM 4216, which consists of a 3,368,778 bp chromosome (GenBank accession number CP012024.1) and a 12,514 bp plasmid (GenBank accession number CP012025.1), together encoding 3880 genes. Genome annotation via RAST was complemented by a protein domain analysis. Some unique features of B. smithii central metabolism in comparison to related organisms included the lack of a standard acetate production pathway with no apparent pyruvate formate lyase, phosphotransacetylase, and acetate kinase genes, while acetate was the second fermentation product.
Inductive Model Generation for Text Categorization Using a Bipartite Heterogeneous Network

Usually, algorithms for categorization of numeric data have been applied for text categorization after a preprocessing phase which assigns weights for textual terms deemed as attributes. However, due to characteristics of textual data, some algorithms for data categorization are not efficient for text categorization. Characteristics of textual data such as sparsity and high dimensionality sometimes impair the quality of general purpose classifiers. Here, we propose a text classifier based on a bipartite heterogeneous network used to represent textual document collections. Such algorithm induces a classification model assigning weights to objects that represents terms of the textual document collection. The induced weights correspond to the influence of the terms in the classification of documents they appear. The least-mean-square algorithm is used in the inductive process. Empirical evaluation using a large amount of textual document collections shows that the proposed IMBHN algorithm produces significantly better results than the k-NN, C4.5, SVM and Naïve Bayes algorithms.
Cycle-to-cycle and within-cycle adaptive control of nozzle pressure during packing-holding for thermoplastic injection molding

Proper control of packing is critical to the quality of injection molded parts. To control the packing phase, nozzle pressure was chosen as a better indicator of the degree of packing than cavity pressure. This allows one nozzle pressure transducer to be used for different molds and is more suitable for industrial applications. The dynamics of nozzle pressure during the packing stage were studied and found to be both nonlinear and time varying. Therefore, an adaptive self-tuning controller was designed and implemented. Several improvements-an anti-windup estimate, an adaptive feedforward, and cycle-to-cycle adaptation-were incorporated to produce excellent packing pressure control results. The controller works well for a wide range of different conditions, such as set point profiles, barrel temperatures, molds, and materials.
Intelligent Feature Selection for Opinion Classification

References 1. T. Macer, M. Pearson, and F. Sebastiani, “Cracking the Code: What Customers Say, in their own Words,” Proc. 50th Ann. Conf. Market Research Soc. (MRS 07), MRS, 2007. 2. D. Giorgetti and F. Sebastiani, “Automating Survey Coding by Multiclass Text Categorization Techniques,” J. Am. Soc. Information Science and Technology, vol. 54, no. 14, 2003, pp. 1269–1277. 3. G. Forman, “Quantifying Counts and Costs via Classication,” Data Mining and Knowledge Discovery, vol. 17, no. 2, 2008, pp. 164–206. 4. Y. Rubner, C. Tomasi, and L.J. Guibas, “A Metric for Distributions with Applications to Image Databases,” Proc. 6th Int’l Conf. Vision (ICCV 98), IEEE CS Press, 1998, pp. 59–66. 5. T. Joachims, “A Support Vector Method for Multivariate Performance Measures,” Proc. 22nd Int’l Conf. Machine Learning (ICML 05), ACM Press, 2005, pp. 377–384.
Plant genome editing using engineered nucleases and success of CRISPR/Cas9 system

Development of new plant breeding techniques have facilitated easy manipulation of plants at genetic level. Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/CRISPR associated protein9 (Cas9) system is a valuable addition in programmable nucleases. The CRISPR/Cas9 system uses an RNA component to recognize a target DNA sequences and it has shown promising results with respect to simultaneous editing of multigenic plant traits. In this review, components of CRISPR/Cas9, their construction and its methods of delivery to plant cells are analyzed. Variation in nucleotide sequence of the protospacer adjacent motif, codon optimization and progress in web-based bioinformatic tools, will make CRISPR/Cas9 systems more efficient for plants. Development and optimization of protocols to efficiently targ et all plant species is still under development. Along with this, methods to inspect induced mutation and efficiency of the system have also been reviewed. Auxiliary improvements and understanding are still required to expand the CRISPR/Cas9 systems to target complex genome architectures and epigenetic elements.
Retrieving land surface soil parameters by using passive microwave remote sensing observations and land surface models

It is widely recognized that remote sensing data a very valuable source of information for the modeling of land-atmosphere interactions. During the last of couple decades, remote sensing data were used mainly to define the initial status of land surface models, to classify the land cover and land use type, and to correct the estimation of state variables in a data assimilation system. One relatively unexplored issue consists of the optimization of land surface parameters, such as, for example, soil texture, soil porosity, through remote sensing. This is due to the lack of a relationship which connecting remote sensing data with land surface parameters. The objective of this paper is to develop a systemic method to retrieve a number of land surface parameters through a combination of microwave remote sensing, radiative transfer modeling, land surface modeling and multi-objective optimizing. The microwave brightness temperature observations are used as the calibration references. The land surface model and radiative transfer model are used to determine the relationship between land surface parameters and the brightness temperature. The method is validated through a field experiment, in which a ground-based microwave radiometer is used to provide brightness temperature observations in a controllable footprint. Grand truth of soil parameters is also measured through intensive in situ samplings. The comparison of optimized parameters with the in situ observed ones indicates that our method has high potential to calibrate land surface parameters.
Effects of Nutrition Education on Nutrition Knowledge and Iron Status in Primary School Pupils of Gatanga District, Muranga County, Kenya

School-age children are both growing and learning, and anaemia can affect cognitive function, motor performance and educational achievements of this age group. Nutrition education has not been given the priority it deserves in primary schools due to the busy school curriculum. It is in this light this study was designed for one teaching calendar year. Subjects & methods: Pupils (n=601) covering the age 11-18 years were included. The main objective of this study was to evaluate the effects of three main Nutrition Education strategies on nutrition knowledge and iron status among primary schools children in Gatanga district. A baseline survey was conducted in 12 randomly selected schools for class six pupils and their households. Questionnaires and an interview schedule were used to collect data, with pre and post tests. The interventions schools were Mabanda, Kigio and Kirwara (experimental) and Gakurari (control school). Baseline data were analyzed by use of Statistical Package for Social Sciences (SPSS) and Nutri-Survey computer packages using both descriptive and inferential statistics. The data were coded to search for emerging themes. This led to the identification of variables and concepts of iron deficiency in the children, which was crucial to the design of the corrective measures model for the interventions. On average the mean mark, in nutrition knowledge at baseline was 30.05%. In the post-tests all experimental schools (n=154) significantly improved in nutrition knowledge, and the peer facilitated school performed best with (51.52+24.79) marks, Researcher facilitated school (48.39+22.23) and the agriculture staff (38.70+9.87). The pre-test post- test improvement in the control school (31.21+12.74) was however not significant (p>0.05). A total of 31.4 % pupils ( Sub sample n=89) were found to be anaemic after altitude adjustments at a calculated factor 0.5 for Gatanga altitude (2237m ASL). Pupils’ haemoglobin status were not significantly different (p>0.05) between the experimental and control schools at baseline, notable differences occurred in the experimental schools after the interventions. Relationship between nutrition knowledge and nutrient intake was positive and there was a statistically significant relationship between nutrition knowledge and hemoglobin levels at p<0.05 (R 2 =0.253, p=0.025). Anemia was found to be a significant problem and therefore the need for a comprehensive intervention strategy by all stakeholders to improve the iron status in this age group.
A fundamental bimodal role for neuropeptide Y1 receptor in the immune system

Psychological conditions, including stress, compromise immune defenses. Although this concept is not novel, the molecular mechanism behind it remains unclear. Neuropeptide Y (NPY) in the central nervous system is a major regulator of numerous physiological functions, including stress. Postganglionic sympathetic nerves innervating lymphoid organs release NPY, which together with other peptides activate five Y receptors (Y1, Y2, Y4, Y5, and y6). Using Y1-deficient (Y1−/−) mice, we showed that Y1−/− T cells are hyperresponsive to activation and trigger severe colitis after transfer into lymphopenic mice. Thus, signaling through Y1 receptor on T cells inhibits T cell activation and controls the magnitude of T cell responses. Paradoxically, Y1−/− mice were resistant to T helper type 1 (Th1) cell–mediated inflammatory responses and showed reduced levels of the Th1 cell–promoting cytokine interleukin 12 and reduced interferon γ production. This defect was due to functionally impaired antigen-presenting cells (APCs), and consequently, Y1−/− mice had reduced numbers of effector T cells. These results demonstrate a fundamental bimodal role for the Y1 receptor in the immune system, serving as a strong negative regulator on T cells as well as a key activator of APC function. Our findings uncover a sophisticated molecular mechanism regulating immune cell functions that can lead to stress-induced immunosuppression.
Learning Deep Structured Active Contours End-to-End

The world is covered with millions of buildings, and precisely knowing each instance's position and extents is vital to a multitude of applications. Recently, automated building footprint segmentation models have shown superior detection accuracy thanks to the usage of Convolutional Neural Networks (CNN). However, even the latest evolutions struggle to precisely delineating borders, which often leads to geometric distortions and inadvertent fusion of adjacent building instances. We propose to overcome this issue by exploiting the distinct geometric properties of buildings. To this end, we present Deep Structured Active Contours (DSAC), a novel framework that integrates priors and constraints into the segmentation process, such as continuous boundaries, smooth edges, and sharp corners. To do so, DSAC employs Active Contour Models (ACM), a family of constraint- and prior-based polygonal models. We learn ACM parameterizations per instance using a CNN, and show how to incorporate all components in a structured output model, making DSAC trainable end-to-end. We evaluate DSAC on three challenging building instance segmentation datasets, where it compares favorably against state-of-the-art. Code will be made available on https://github.com/dmarcosg/DSAC.
PaMM: Pose-Aware Multi-Shot Matching for Improving Person Re-Identification

Person re-identification is the problem of recognizing people across different images or videos with non-overlapping views. Although a significant progress has been made in person re-identification over the last decade, it remains a challenging task because the appearances of people can seem extremely different across diverse camera viewpoints and person poses. In this paper, we propose a novel framework for person re-identification by analyzing camera viewpoints and person poses called pose-aware multi-shot matching. It robustly estimates individual poses and efficiently performs multi-shot matching based on the pose information. The experimental results obtained by using public person re-identification data sets show that the proposed methods outperform the current state-of-the-art methods, and are promising for accomplishing person re-identification under diverse viewpoints and pose variances.
Effects of acetazolamide on the micro- and macro-vascular cerebral hemodynamics: a diffuse optical and transcranial doppler ultrasound study

Acetazolamide (ACZ) was used to stimulate the cerebral vasculature on ten healthy volunteers to assess the cerebral vasomotor reactivity (CVR). We have combined near infrared spectroscopy (NIRS), diffuse correlation spectroscopy (DCS) and transcranial Doppler (TCD) technologies to non-invasively assess CVR in real-time by measuring oxy- and deoxy-hemoglobin concentrations, using NIRS, local cerebral blood flow (CBF), using DCS, and blood flow velocity (CBFV) in the middle cerebral artery, using TCD. Robust and persistent increases in oxy-hemoglobin concentration, CBF and CBFV were observed. A significant agreement was found between macro-vascular (TCD) and micro-vascular (DCS) hemodynamics, between the NIRS and TCD data, and also within NIRS and DCS results. The relative cerebral metabolic rate of oxygen, rCMRO2, was also determined, and no significant change was observed. Our results showed that the combined diffuse optics-ultrasound technique is viable to follow (CVR) and rCMRO2 changes in adults, continuously, at the bed-side and in real time.
Development of a self-assessment score for metabolic syndrome risk in non-obese Korean adults.

BACKGROUND AND OBJECTIVES There is a need for simple risk scores that identify individuals at high risk for metabolic syndrome (MetS). Therefore, this study was performed to develop and validate a self-assessment score for MetS risk in non-obese Korean adults. METHODS AND STUDY DESIGN Data from the fourth Korea National Health and Nutrition Examination Survey (KNHANES IV), 2007-2009 were used to develop a MetS risk score. We included a total of 5,508 non-obese participants aged 19-64 years who were free of a self-reported diagnosis of diabetes, hyperlipidemia, hypertension, stroke, angina, or cancer. Multivariable logistic regression model coefficients were used to assign each variable category a score. The validity of the score was assessed in an independent population survey performed in 2010 and 2011, KNHANES V (n=3,892). RESULTS Age, BMI, physical activity, smoking, alcohol consumption, dairy consumption, dietary habit of eating less salty and food insecurity were selected as categorical variables. The MetS risk score value varied from 0 to 13, and a cut-point MetS risk score of >=7 was selected based on the highest Youden index. The cut-point provided a sensitivity of 81%, specificity of 61%, positive predictive value of 14%, and negative predictive value of 98%, with an area under the curve (AUC) of 0.78. Consistent results were obtained in the validation data sets. CONCLUSIONS This simple risk score may be used to identify individuals at high risk for MetS without laboratory tests among non-obese Korean adults. Further studies are needed to verify the usefulness and feasibility of this score in various settings.
An AP Endonuclease Functions in Active DNA Demethylation and Gene Imprinting in Arabidopsis

Active DNA demethylation in plants occurs through base excision repair, beginning with removal of methylated cytosine by the ROS1/DME subfamily of 5-methylcytosine DNA glycosylases. Active DNA demethylation in animals requires the DNA glycosylase TDG or MBD4, which functions after oxidation or deamination of 5-methylcytosine, respectively. However, little is known about the steps following DNA glycosylase action in the active DNA demethylation pathways in plants and animals. We show here that the Arabidopsis APE1L protein has apurinic/apyrimidinic endonuclease activities and functions downstream of ROS1 and DME. APE1L and ROS1 interact in vitro and co-localize in vivo. Whole genome bisulfite sequencing of ape1l mutant plants revealed widespread alterations in DNA methylation. We show that the ape1l/zdp double mutant displays embryonic lethality. Notably, the ape1l+/−zdp−/− mutant shows a maternal-effect lethality phenotype. APE1L and the DNA phosphatase ZDP are required for FWA and MEA gene imprinting in the endosperm and are important for seed development. Thus, APE1L is a new component of the active DNA demethylation pathway and, together with ZDP, regulates gene imprinting in Arabidopsis.
Intrinsic plasticity for reservoir learning algorithms

and Jochen Steil1- University of Bielefeld - Faculty of TechnologyUniversitaetsstrasse 25, 33615 Bielefeld - GermanyAbstract. One of the most diﬃcult problems in using dynamic reservoirslike echo state networks for signal processing is the choice of reservoir net-work parameters like connectivity or spectral radius of the weight matrix.In this article, we investigate the properties of an unsupervised intrinsicplasticity rule for signal speciﬁc adaptive shaping of the reservoir, whichis local in space and time and aims at maximizing input–to–output infor-mation transmission for each neuron. We show that the rule consistentlyregulates the neurons’ mean outputs and variances and is robust to learn-ing parameter changes. Simulations reveals that this reservoir adaptationrobustly enhances online learning of Backpropagation–Decorrelation recur-rent learning for a tenth–order nonlinear NARMA benchmark problem.