Miracle-dz/UniFAR-FTUs huggingface dataset:
A Health Smart Home System to Report Incidents for Disabled People

Health Smart Home (HSH) provides health care services for people with special needs and wish to be independent and remain in their own home. The residence is equipped with automatic devices and a range of sensors to ensure the safety of the patients at home and the supervision of their health status. These Health Smart Homes have been increasingly studied not only by the academic community but also by industry on account of the great potential it has, especially in applications that can help people with some type of physical disability such as visual or hearing impairment. We implemented a system witch monitors rooms and issues a warning whenever it detects a potential hazard. Our proposal relies on low-cost cutting-edge technology.
Amyloidosis, Synucleinopathy, and Prion Encephalopathy in a Neuropathic Lysosomal Storage Disease: The CNS-Biomarker Potential of Peripheral Blood

Mucopolysaccharidosis (MPS) IIIB is a devastating neuropathic lysosomal storage disease with complex pathology. This study identifies molecular signatures in peripheral blood that may be relevant to MPS IIIB pathogenesis using a mouse model. Genome-wide gene expression microarrays on pooled RNAs showed dysregulation of 2,802 transcripts in blood from MPS IIIB mice, reflecting pathological complexity of MPS IIIB, encompassing virtually all previously reported and as yet unexplored disease aspects. Importantly, many of the dysregulated genes are reported to be tissue-specific. Further analyses of multiple genes linked to major pathways of neurodegeneration demonstrated a strong brain-blood correlation in amyloidosis and synucleinopathy in MPS IIIB. We also detected prion protein (Prnp) deposition in the CNS and Prnp dysregulation in the blood in MPS IIIB mice, suggesting the involvement of Prnp aggregation in neuropathology. Systemic delivery of trans-BBB-neurotropic rAAV9-hNAGLU vector mediated not only efficient restoration of functional α-N-acetylglucosaminidase and clearance of lysosomal storage pathology in the central nervous system (CNS) and periphery, but also the correction of impaired neurodegenerative molecular pathways in the brain and blood. Our data suggest that molecular changes in blood may reflect pathological status in the CNS and provide a useful tool for identifying potential CNS-specific biomarkers for MPS IIIB and possibly other neurological diseases.
EXPLORING THE RELEVANCE OF ORGANIZATION THEORY PERSPECTIVES TO ENTREPRENEURSHIP

The theoretical analysis presented in this paper recognizes the foundation of four organization theories (resource based view, agency theory, prospect theory, and the upper echelon theory) and assesses their standing against the necessary assumptions bounding the opportunity-discovery view of entrepreneurship theory. The analysis results in the identification of contributing influences each organization theory may have in approaching entrepreneurship research questions during the entrepreneurial process.
Distributed Pattern Transformation-Invariant Recognition Scheme for Real-Time Sensory Applications

This paper describes the Cellular weighted Graph Neuron (CwGN) scheme, which demonstrates real-time transformation-invariant pattern recognition for resource constraint networks such as wireless sensor networks (WSN). This scheme can recognise patterns from a variety of perspectives in a fully distributed manner, which makes it highly responsive, scalable, and energy efficient. Learning operations are decoupled and reduced to relatively simple processing elements to achieve an economical parallelism. We show that by using only local computation, i.e., between adjacent nodes, we can fully describe patterns by their boundaries and then recognise the transformations of these patterns. Our scheme solves the invariant pattern recognition for the first time in a fully distributed manner within a predictable single cycle learning duration which allows the use of the scheme within resource-constrained networks such as wireless sensor networks for event-detection. Theoretical analysis and experimental results demonstrate online recognition of transformed patterns with competitive accuracy and a low energy budget.
Molecular Cloning and Expression Profile of a Halloween Gene Encoding Cyp307A1 From the Seabuckthorn Carpenterworm, Holcocerus hippophaecolus

Abstract 20-Hydroxyecdyone, an active form of ecdysteroid, is the key hormone in insect growth and development. Halloween genes encode ecdysteroidogenic enzymes, including cytochrome P450 monooxygenase. CYP307A1 (spook) is accepted as an enzyme acting in the so-called ‘black box’ that includes a series of hypothetical and unproven reactions that finally result in the oxidation of 7-dehydrocholesterol to diketol. In this study, the Holcocerus hippophaecolus Hua (Lepidoptera: Cossidae) CYP307A1 (HhSpo) gene was identified and characterized. The obtained cDNA sequence was 2084 base pairs with an open reading frame of 537 animo acids, in which existed conserved motifs of CYP450 enzymes. The transcript profiles of HhSpo were analyzed in various tissues of final instar larvae. The highest expression was observed in the prothoracic gland, while expression level was low but significant in other tissues. These results suggest that the sequence character and expression profile of HhSpo were well conserved and provided the basic information for its functional analysis.
Horacio Rodríguez: A Domain-Restricted Task-Guided Natural Language Interface Generator

Considerable attention has been paid in recent years to the subject of Natural Language Interfaces (NLI, see [4] for a survey on current trends in such technology). Starting with NL access to databases, NLI technology has evolved according to the needs of the different sectors involved. This evolution has followed several lines, improving both the quality and the capabilities of the interface functionality and the engineering features of the development process: the extension of the conceptual and linguistic coverage in order to deal with more complex domains and applications, the improvement of the friendliness of Person/Machine interaction, the integration of different modes of interaction leading to the multimodal/multimedia interfaces, the construction of transportable and integrated/modular systems, the reusing of software and linguistic components, etc.
Understanding Unemployment Hysteresis: A System-Based Econometric Approach to Changing Equilibria and Slow Adjustment

What explains the persistence of unemployment? The literature on hysteresis, which is based on unit root testing in autoregressive models, consists of a vast number of univariate studies, i.e. that analyze unemployment series in isolation, but few multivariate analyses that focus on the sources of hysteresis. As a result, this question remains largely unanswered. This paper presents a multivariate econometric framework for analyzing hysteresis, which allows one to test different hypotheses about non-stationarity of unemployment against one another. For example, whether this is due to a persistently changing equilibrium, slow adjustment towards the equilibrium (persistent ? uctuations), or perhaps even a combination of the two. Different hypotheses of slow adjustment, as implied by theories of hysteresis, nominal rigidities or labor hoarding can also be compared. A small illustrative application to UK quarterly data on prices, wages, output, unemployment and crude oil prices, suggests that, for the period 1988 up to the onset of the financial crisis, the non-stationarity of UK unemployment cannot be explained as a result of slow adjustment, including sluggish wage formation as emphasized by the hysteresis theories. Instead, it is the equilibrium that has evolved persistently as a consequence of exogenous oil prices shifting the price setting relation (in the unemployment-real wage space) in a non-stationary manner.
Patient Perspectives with Abbreviated versus Standard Pre-Test HIV Counseling in the Prenatal Setting: A Randomized-Controlled, Non-Inferiority Trial

Background In the US, an unacceptably high percentage of pregnant women do not undergo prenatal HIV testing. Previous studies have found increased uptake of prenatal HIV testing with abbreviated pre-test counseling, however little is known about patient decision making, testing satisfaction and knowledge in this setting. Methodology/Findings A randomized-controlled, non-inferiority trial was conducted from October 2006 through February 2008 at San Francisco General Hospital (SFGH), the public teaching hospital of the City and County of San Francisco. A total of 278 English- and Spanish-speaking pregnant women were randomized to receive either abbreviated or standard nurse-performed HIV test counseling at the initial prenatal visit. Patient decision making experience was compared between abbreviated versus standard HIV counseling strategies among a sample of low-income, urban, ethnically diverse prenatal patients. The primary outcome was the decisional conflict score (DCS) using O'Connor low-literacy scale and secondary outcomes included satisfaction with test decision, basic HIV knowledge and HIV testing uptake. We conducted an intention-to-treat analysis of 278 women – 134 (48.2%) in the abbreviated arm (AA) and 144 (51.8%) in the standard arm (SA). There was no significant difference in the proportion of women with low decisional conflict (71.6% in AA vs. 76.4% in SA, p = .37), and the observed mean difference between the groups of 3.88 (95% CI: −0.65, 8.41) did not exceed the non-inferiority margin. HIV testing uptake was very high (97. 8%) and did not differ significantly between the 2 groups (99.3% in AA vs. 96.5% in SA, p = .12). Likewise, there was no difference in satisfaction with testing decision (97.8% in AA vs. 99.3% in SA, p = .36). However, women in AA had significantly lower mean HIV knowledge scores (78.4%) compared to women in SA (83.7%, p<0.01). Conclusions/Significance This study suggests that streamlining the pre-test counseling process, while associated with slightly lower knowledge, does not compromise patient decision making or satisfaction regarding HIV testing. Trial Registration ClinicalTrials.gov NCT00503308
Specifying and Estimating New Keynesian Models with Instrument Rules and Optimal Monetary Policies

This paper estimates several popular sticky-price New Keynesian models in an effort to understand whether and under what circumstances these models can usefully describe observed outcomes. We estimate and compare specifications that contain different forms of habit formation, specifications that have either the gap or real marginal costs driving inflation, and specifications that use either optimal policymaking or a forward-looking Taylor-type rule to summarize monetary policy. Among other results, we find that the different forms of habit formation lead to very similar aggregate behavior, that optimal policymaking explains the data as well as a Taylor-type rule does, and that the data speak strongly against specifications that have real marginal costs as the driver in the Phillips curve.
Expression of a Targeted λ1 Light Chain Gene Is Developmentally Regulated and Independent of Igκ Rearrangements

Immunoglobulin light chain (IgL) rearrangements occur more frequently at Igκ than at Igλ. Previous results suggested that the unrearranged Igκ locus negatively regulates Igλ transcription and/or rearrangement. Here, we demonstrate that expression of a VJλ1-joint inserted into its physiological position in the Igλ locus is independent of Igκ rearrangements. Expression of the inserted VJλ1 gene segment is developmentally controlled like that of a VJκ-joint inserted into the Igκ locus and furthermore coincides developmentally with the occurrence of Igκ rearrangements in wild-type mice. We conclude that developmentally controlled transcription of a gene rearrangement in the Igλ locus occurs in the presence of an unrearranged Igκ locus and is therefore not negatively regulated by the latter. Our data also indicate light chain editing in ∼30% of λ1 expressing B cell progenitors.
Pooled Sample-Based GWAS: A Cost-Effective Alternative for Identifying Colorectal and Prostate Cancer Risk Variants in the Polish Population

Background Prostate cancer (PCa) and colorectal cancer (CRC) are the most commonly diagnosed cancers and cancer-related causes of death in Poland. To date, numerous single nucleotide polymorphisms (SNPs) associated with susceptibility to both cancer types have been identified, but their effect on disease risk may differ among populations. Methods To identify new SNPs associated with PCa and CRC in the Polish population, a genome-wide association study (GWAS) was performed using DNA sample pools on Affymetrix Genome-Wide Human SNP 6.0 arrays. A total of 135 PCa patients and 270 healthy men (PCa sub-study) and 525 patients with adenoma (AD), 630 patients with CRC and 690 controls (AD/CRC sub-study) were included in the analysis. Allele frequency distributions were compared with t-tests and χ2-tests. Only those significantly associated SNPs with a proxy SNP (p<0.001; distance of 100 kb; r2>0.7) were selected. GWAS marker selection was conducted using PLINK. The study was replicated using extended cohorts of patients and controls. The association with previously reported PCa and CRC susceptibility variants was also examined. Individual patients were genotyped using TaqMan SNP Genotyping Assays. Results The GWAS selected six and 24 new candidate SNPs associated with PCa and CRC susceptibility, respectively. In the replication study, 17 of these associations were confirmed as significant in additive model of inheritance. Seven of them remained significant after correction for multiple hypothesis testing. Additionally, 17 previously reported risk variants have been identified, five of which remained significant after correction. Conclusion Pooled-DNA GWAS enabled the identification of new susceptibility loci for CRC in the Polish population. Previously reported CRC and PCa predisposition variants were also identified, validating the global nature of their associations. Further independent replication studies are required to confirm significance of the newly uncovered candidate susceptibility loci.
An Efficient Semantic Web Through Semantic Mapping

The outgrowth of web usage leads to the expectation of faster and easier access of web resources. Experts and even naïve users wish to have more advanced features on search schemes to acquire accurate results within a short time span. Semantic web can bring this expectation to real practice. To bring Semantic web in true, efficient Semantic Mapping is must. In this paper, a novel Two step Semantic Mapping algorithm and few related Semantic Mapping schemes are detailed with their functions and features. It helps one to acquire more knowledge on Semantic Mapping of concepts.
Studies on the new-typed feed network with a single variable phase shifter for wireless base antennas

This paper analyzes the construction and the characteristics of the single variable phase-shifter feed network for wireless base antenna. The feed network, which is connected to a 2N-element array antenna, mainly contains a variable phase shifter, several 90°or 180°coupler hybrids, two power dividers and some fixed phase shifters. The properties of the above devices and the sensitivity of the array electrical tilt are studied in theory, which proves the feasibility of this new-typed and low cost feeding technology for wireless base antennas.
Spectral Clustering Ensemble Based on Synthetic Similarity

In this paper, a spectral clustering ensemble algorithm based on synthetic similarity (SCEBSS) is proposed to improve the performance of clustering. Multiple methods of vector similarity measurement are adopted to produce diverse similarity matrices of objects. Every similarity matrix is given a weight and then added as a synthetic similarity matrix. A spectral clustering algorithm is employed on the synthetic similarity matrix, and then a particle swarm optimization using normalized mutual information (NMI) as evaluation function is adopted to optimize the weights of similarity matrices to obtain the best clusters. Comparisons with other related clustering schemes demonstrate the better performance of SCEBSS in clustering data tasks and robustness to noise.
Built-In Self Optimization for Variation Resilience of Analog Filters

At deep sub-micron integrated circuit technologies and beyond, characteristics of analog circuits are increasingly affected by process variations and device aging. Although design-time approaches are able to take these effects into account, it is very difficult for a single static design to efficiently handle all kinds of variation scenarios. In this work, a built-in self optimization technique is developed such that each analog IC chip can autonomously improve its performance in the presence of significant variations. As a demonstration, an on-chip analog self-test platform is implemented to test an active-RC band-pass filter, whose components can be reconfigured to obtain different frequency responses. The desired response is found by a digital circuit implementation of multi-start meta-heuristic optimization. The proposed technique does not rely on external test equipment or any general purpose digital signal processor. Its effectiveness is confirmed by simulations as well as measurement results from a test-chip fabricated in 180 nm CMOS technology.
REST: A Toolkit for Resting-State Functional Magnetic Resonance Imaging Data Processing

Resting-state fMRI (RS-fMRI) has been drawing more and more attention in recent years. However, a publicly available, systematically integrated and easy-to-use tool for RS-fMRI data processing is still lacking. We developed a toolkit for the analysis of RS-fMRI data, namely the RESting-state fMRI data analysis Toolkit (REST). REST was developed in MATLAB with graphical user interface (GUI). After data preprocessing with SPM or AFNI, a few analytic methods can be performed in REST, including functional connectivity analysis based on linear correlation, regional homogeneity, amplitude of low frequency fluctuation (ALFF), and fractional ALFF. A few additional functions were implemented in REST, including a DICOM sorter, linear trend removal, bandpass filtering, time course extraction, regression of covariates, image calculator, statistical analysis, and slice viewer (for result visualization, multiple comparison correction, etc.). REST is an open-source package and is freely available at http://www.restfmri. net.
On the Complexity of Good Samples for Learning

In machine-learning, maximizing the sample margin can reduce the learning generalization-error. Thus samples on which the target function has a large margin (γ) convey more information so we expect fewer such samples. In this paper, we estimate the complexity of a class of sets of large-margin samples for a general learning problem over a finite domain. We obtain an explicit dependence of this complexity on γ and the sample size.
Improved JPEG Scheme Based on Adaptive Block Encoding

In this paper, a new adaptive image compression scheme based on JPEG was proposed to improve the image quality of the JPEG baseline sequential image compression codec. In this scheme, the Hough transformation technique is employed as an edge-oriented classifier to categorize image blocks into four classes. Different types of image blocks are processed by different quantization and encoding ways for achieving an effective compression performance. In order to preserve edge information, the adaptive quantization tables based on the human visual system and zig-zag scan sequences are used. For both objective and subjective visual image quality, simulation results show that the performance of the proposed scheme is comparable to that of other compared schemes.
Toward Autonomous Crowd-Powered Creation of Interactive Narratives

Interactive narrative is a form of storytelling that adapts to actions performed by users who assume the roles of story characters. To date, interactive narratives are built by hand. In this paper, we introduce Scheherazade, an intelligent system that automatically creates an interactive narrative about any topic from crowdsourced narratives. Our system leverages the experience and creativity of humans by crowdsourcing a corpus of linear narrative examples. It then constructs an executable plot graph, which is a knowledge structure that defines the legal space of an interactive narrative, by learning the plot events, execution precedence, and event separations. We demonstrate the system can successfully construct an interactive narrative based on noisy human input.
Micellar Catalytic Effect of Cetyltrimethylammonium Bromide on O-Allylation of Eugenol by Allyl Bromide

A simple and effective O-allylation reaction controlled micellar catalyst system was designed and synt hesized. In this system, cetyltrimethylammonium bromide (CTAB) was used for micellar catalytic O-allylation of eugenol. The reaction was carried out at room temperature to avoid Claisen rearrangement product. The results showed that the optimum concentration of CTAB as a catalyst was 1.21 × 10 mM with a yield 81.10%. GC-MS chromatogram revealed the major product is 4-allyl-1-(allyloxy)-2-methoxybenzene with 93.30% conversion. The applications of CTAB on the micellar catalytic O-allylation of eugenol wer e effective to reduce the reaction temperature and diminish the Claisen rearr angement product.
SDCache: Software Defined Data Caching Control for Cloud Services

The complexity of traditional control approaches typically features slow response times to system and component changes and runs the risk of accessing outdated information. Dynamic environments require fast control approaches to deal with variability in a reliable manner. We adopt the Software Defined System (SDSys) paradigm to handle this issue. The concept behind this fast emerging theme revolves around abstracting all controls as modules within a software control layer. In this paper, we exploit this concept to propose a new system architecture for data caching. We further develop and emulate a testbed environment to evaluate the proposed models by extending the elements of Mininet. Several metrics and multiple scenarios have been considered to capture processing costs and network congestion so as to reflect the merits of this approach. Our experiments validate that the proposed solution based on SDSys renders possible a simple programmable data caching control mechanism.
Comparison among T1-Weighted Magnetic Resonance Imaging, Modified Dixon Method, and Magnetic Resonance Spectroscopy in Measuring Bone Marrow Fat

Introduction. An increasing number of studies are utilizing different magnetic resonance (MR) methods to quantify bone marrow fat due to its potential role in osteoporosis. Our aim is to compare the measurements of bone marrow fat among T1-weighted magnetic resonance imaging (MRI), modified Dixon method (also called fat fraction MRI (FFMRI)), and magnetic resonance spectroscopy (MRS). Methods. Contiguous MRI scans were acquired in 27 Caucasian postmenopausal women with a modified Dixon method (i.e., FFMRI). Bone marrow adipose tissue (BMAT) of T1-weighted MRI and bone marrow fat fraction of the L3 vertebra and femoral necks were quantified using SliceOmatic and Matlab. MRS was also acquired at the L3 vertebra. Results. Correlation among the three MR methods measured bone marrow fat fraction and BMAT ranges from 0.78 to 0.88 (P < 0.001) in the L3 vertebra. Correlation between BMAT measured by T1-weighted MRI and bone marrow fat fraction measured by modified FFMRI is 0.86 (P < 0.001) in femoral necks. Conclusion. There are good correlations among T1-weighted MRI, FFMRI, and MRS for bone marrow fat quantification. The inhomogeneous distribution of bone marrow fat, the threshold segmentation of the T1-weighted MRI, and the ambiguity of the FFMRI may partially explain the difference among the three methods.