Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of a Direct Separation Method Based on Adaptive Chirplet Transform for Signals with Crossover Instantaneous Frequencies

In many applications, it is necessary to retrieve the sub-signal building blocks of a multi-component signal, which is usually non-stationary in real-world and real-life applications. Empirical mode decomposition (EMD), synchrosqueezing transform (SST), signal separation operation (SSO), and iterative filtering decomposition (IFD) have been proposed and developed for this purpose. However, these computational methods are restricted by the specification of well-separation of the sub-signal frequency curves for multi-component signals. On the other hand, the chirplet transform-based signal separation scheme (CT3S) that extends SSO from the two-dimensional"time-frequency"plane to the three-dimensional"time-frequency-chirp rate"space was recently proposed in our recent work to remove the frequency-separation specification, and thereby allowing"frequency crossing". The main objective of this present paper is to carry out an in-depth error analysis study of instantaneous frequency estimation and component recovery for the CT3S method.
Efficient Semantic Summary Graphs for Querying Large Knowledge Graphs

Knowledge Graphs (KGs) integrate heterogeneous data, but one challenge is the development of efficient tools for allowing end users to extract useful insights from these sources of knowledge. In such a context, reducing the size of a Resource Description Framework (RDF) graph while preserving all information can speed up query engines by limiting data shuffle, especially in a distributed setting. This paper presents two algorithms for RDF graph summarization: Grouping Based Summarization (GBS) and Query Based Summarization (QBS). The latter is an optimized and lossless approach for the former method. We empirically study the effectiveness of the proposed lossless RDF graph summarization to retrieve complete data, by rewriting an RDF Query Language called SPARQL query with fewer triple patterns using a semantic similarity. We conduct our experimental study in instances of four datasets with different sizes. Compared with the state-of-the-art query engine Sparklify executed over the original RDF graphs as a baseline, QBS query execution time is reduced by up to 80% and the summarized RDF graph is decreased by up to 99%.
Pruning has a disparate impact on model accuracy

Network pruning is a widely-used compression technique that is able to significantly scale down overparameterized models with minimal loss of accuracy. This paper shows that pruning may create or exacerbate disparate impacts. The paper sheds light on the factors to cause such disparities, suggesting differences in gradient norms and distance to decision boundary across groups to be responsible for this critical issue. It analyzes these factors in detail, providing both theoretical and empirical support, and proposes a simple, yet effective, solution that mitigates the disparate impacts caused by pruning.
An efficient tensor regression for high-dimensional data

Most currently used tensor regression models for high-dimensional data are based on Tucker decomposition, which has good properties but loses its efficiency in compressing tensors very quickly as the order of tensors increases, say greater than four or five. However, for the simplest tensor autoregression in handling time series data, its coefficient tensor already has the order of six. This paper revises a newly proposed tensor train (TT) decomposition and then applies it to tensor regression such that a nice statistical interpretation can be obtained. The new tensor regression can well match the data with hierarchical structures, and it even can lead to a better interpretation for the data with factorial structures, which are supposed to be better fitted by models with Tucker decomposition. More importantly, the new tensor regression can be easily applied to the case with higher order tensors since TT decomposition can compress the coefficient tensors much more efficiently. The methodology is also extended to tensor autoregression for time series data, and nonasymptotic properties are derived for the ordinary least squares estimations of both tensor regression and autoregression. A new algorithm is introduced to search for estimators, and its theoretical justification is also discussed. Theoretical and computational properties of the proposed methodology are verified by simulation studies, and the advantages over existing methods are illustrated by two real examples.
MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models

State-of-the-art causal discovery methods usually assume that the observational data is complete. However, the missing data problem is pervasive in many practical scenarios such as clinical trials, economics, and biology. One straightforward way to address the missing data problem is first to impute the data using off-the-shelf imputation methods and then apply existing causal discovery methods. However, such a two-step method may suffer from suboptimality, as the imputation algorithm may introduce bias for modeling the underlying data distribution. In this paper, we develop a general method, which we call MissDAG, to perform causal discovery from data with incomplete observations. Focusing mainly on the assumptions of ignorable missingness and the identifiable additive noise models (ANMs), MissDAG maximizes the expected likelihood of the visible part of observations under the expectation-maximization (EM) framework. In the E-step, in cases where computing the posterior distributions of parameters in closed-form is not feasible, Monte Carlo EM is leveraged to approximate the likelihood. In the M-step, MissDAG leverages the density transformation to model the noise distributions with simpler and specific formulations by virtue of the ANMs and uses a likelihood-based causal discovery algorithm with directed acyclic graph constraint. We demonstrate the flexibility of MissDAG for incorporating various causal discovery algorithms and its efficacy through extensive simulations and real data experiments.
TrackNet: A Triplet metric-based method for Multi-Target Multi-Camera Vehicle Tracking

We present TrackNet, a method for Multi-Target Multi-Camera (MTMC) vehicle tracking from traffic video sequences. Cross-camera vehicle tracking has proved to be a challenging task due to perspective, scale and speed variance, as well occlusions and noise conditions. Our method is based on a modular approach that first detects vehicles frame-by-frame using Faster R-CNN, then tracks detections through single camera using Kalman filter, and finally matches tracks by a triplet metric learning strategy. We conduct experiments on TrackNet within the AI City Challenge framework, and present competitive IDF1 results of 0.4733.
Contemporary European narrative jewellery : the prevalent themes, paradigms and the cognitive interaction between maker, wearer and viewer observed through the process, production and exhibition of narrative jewellery

The focus of this research lies within the field of contemporary studio jewellery, a discipline that has a variety of strands that includes the purely aesthetic, technique led, through concept based to architectonic. A further hybrid of contemporary jewellery is the narrative genre. More specifically this research examines how narrative might be defined and takes a position viewed from a European perspective. Chapter 1, Rationale & Contextual Review, examines contemporary studio jewellery and positions a definition of narrative jewellery within this wider framework. Chapter 2, Defining the Field, identifies global paradigms. The interrogation of the subject examines the correlation between themes and subject matter and the ethnographic factors that influence creative outcomes. The hypothesis proposes that these creative outcomes are universal in their translation whilst reflecting a relationship between environmental influence and ethnographic origin Chapter 3, Narrative Themes, presents an overview of current European makers. Through the author's role as exhibition curator, this chapter identifies prevalent themes and preoccupations selected from the exhibition ‘Maker-Wearer-Viewer’ which surveyed the work of over seventy makers from twenty European countries. Chapter 4, Source, Process & Practice, locates the author’s self-reflective practice within the field by examining the correlation of narrative subject matter, source material and the creative process employed. The importance of source material and its influence on the creative process is examined through reflexive practice. Further research examines the interactive relationship between the maker and wearer, and the response of the viewer. Through practice-based research, the design and making process, the authors’ narrative jewellery is clearly defined within a wider context.
Feature selection for prosthetic control using myoelectric signals

This thesis investigates selection of time domain (TD) signal features for myoelectric signal (MES) based control of motorised hand and wrist prostheses. A signal feature represents a distinguishing property of a MES to be used in pattern recognition algorithms. In particular, TD features reflect the mathematical functions and physical expression of the transient signal waveform with respect to time. Extracted features capture the structural details of a MES, minimise loss of information upon conversion, and simplify movement classification. The advantage of TD features is that they produce lower dimensional input vectors while maintaining sufficient accuracy of various movements if adequate information is provided. Feature sets as a solution to gather information in MES based control has not been thoroughly studied in the literature. We aim to develop methods to elevate the use of TD features and suggest a comprehensive feature set that is helpful in pattern recognition. Myolectric signals used in this study were from the BioPatRec database, an open source platform for research and control of artificial limbs via pattern recognition using bioelectric signals. This database is named as 10mov4chUntargetedForearm comprising data on10 hand and wrist movements acquired by 4 bipolar sEMG channels from the left or right forearm. Based on feature selection (FS) which preserves information of the MES, we propose three methods, namely a genetic algorithm (GA), class relevant criteria and a self-organising feature map (SOFM) to assemble feature sets from TD features of twenty one candidates. To evaluate these feature sets, we implemented three pattern recognition algorithms, particularly the Multilayer Perceptron (MLP), Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) algorithms. The reported movement accuracy and Wilcoxon p value demonstrated that the proposed feature sets consistently outperformed a typical feature set found in the literature; in particular, improved the accuracy of poor quality datasets from 85% to 93%. The thesis has made a thorough investigation of TD features contributing in three categories. Firstly, we developed a variety of independent methods for FS. It was noticed that FS has been limited in meta-heuristic searches in the literature. We have demonstrated that there are several solutions that use potential TD features to assemble a feature set to be used in pattern recognition. Secondly, we have shown that statistical tests can be successfully applied in FS. Thirdly, we explored an investigation of data along time series vectors instead of analysing it conventionally by time segmentation. The success of this method suggests a new way that may value further inspection. In brief, this thesis presents possible solutions for TD feature based pre-processing of the input of pattern recognition algorithms for prosthetic control. It provides immediate accuracy improvement through a replacement of feature sets and further implementation in methodology for FS.
Chemical Compositions and Internal Anatomy of the Gelated and Granulated Juice Sacs of Sanbokan (Citrus sulcata port, ex Takahashi) Fruits

In Sanbokan fruit, chemical compositions of the granulated juice sacs in the stemend region of segment and the gelated and granulated sacs in the middle region of segment were investigated. Some microscopical observations were also made on these juice sacs. By the microscopical observations, we found that the epidermis of the granulated juice sacs were extremely thicker than that of the healthy sacs in the stem-end region of segment, but, in the middle region of segment, it was slightly thicker than that of the healthy sacs. Cell wall polysaccharide contents of the epidermis and inner parenchyma were suggested to be higher in the granulated sacs than the healthy sacs by staining with PAS. Pulp and alcohol-insoluble solid (AIS) contents were higher in the granulated juice sacs than the healthy sacs. Cell wall polysaccharide content was also higher in the gelated and granulated sacs, and it was especially true for hot water, hemicellulose and cellulose fractions. In the stem-end region of segment, free acids, flavonoids, carotenoids and RNA were lower, while bound acids, vitamin C, total-N, AIS-N and amino-N were higher in the granulated sacs than the healthy sacs. Total sugars differed little between them. In the middle region of segment, all the chemical components measured were lower in the gelated sacs than the healthy sacs. In the granulated sacs, however, AIS-N and amino-N were higher than in the gelated and healthy sacs, and total sugars, bound acids, flavonoids, carotenoids, vitamin C and total-N were higher than in the gelated sacs but lower than in the healthy sacs. RNA was almost the same as, and free acids were lower than in the gelated sacs. In the stem-end region of segment, mineral components (crude ash, Ca, Mg, K, Na) were all higher in the granulated juice sacs than the healthy sacs. In the middle region of segment, they tended to be slightly lower in the gelated sacs than the healthy sacs. In the granulated sacs, Ca was higher, while K was lower than in the gelated sacs. On the basis of these results, we discussed the development of gelation and granulation in juice sacs of Sanbokan fruit.
Dinâmica evolutiva de anéis miméticos em comunidades ecológicas heterogêneas

3 Introduction . 5 Methods . 10 Results . 19 Discussion . 26 References . 32 Supplementary Materials . 42
THE INFLUENCE OF COMPANY IMAGE AND BUSINESS VALUE ON COMPANY PERFORMANCE OF TEXTILE INDUSTRY IN WEST JAVA, INDONESIA

The company performance of the textile industry in West Java tends less favorable as indicated by ROE companies that tend to decrease every year. It was thought to be caused by continued weakness in business value and image of the textile company at this time. The research objective was to study the effect of company image and business value on the performance of the textile companies in West Java. This study is verification. The unit of analysis in this research is industrial textile companies in West Java with the observation unit is the management of the company. The population in this study is textile company in West Java, amounting to 22 companies. This study uses the entire observation unit in the population to be
The Association Between Employee Voice, Pscyhological Capital And Well-Being Among Nato Workers: A Multinational Study From The Perspective Of Positive Organizational Behavior

The purpose of the present research is to investigate the effects of PsyCap and well-being (WB) on employee voice (EV) in a frame of integrated model on a multinational sample. The universe of the research was formed by the 4,000 employees of NATO operating in Afghanistan. Data were collected from 598 personnel from 13 different countries by using the questionnaire method. The data were analyzed with the Structural Equation Modeling (SEM) techniques by using SPSS.21. 0 and AMOS statistical program. The results indicated that that the PsyCap significantly affected the WB and the EV in the positive direction. Significant relationships were also found between PsyCap, WB subscales and EV. In examining the intermediary role, it was found that self-efficacy has a partial mediating role between positive well-being (PWB) and EV. Implications for future research and practice are discussed.
IMPROVEMENT OF BUS VOLTAGE PROFILES OF NIGERIAN POWER NETWORK IN THE PRESENCE OF STATIC SYNCHRONOUS COMPENSATOR (STATCOM) AND DOUBLY FED INDUCTION GENERATOR (DFIG)

Frequent blackouts and unstable supply of electricity show that the voltage instability problem has been one of the major challenges facing the power system network in Nigeria. This study investigates the voltage stability analysis of the Nigerian power network in the presence of renewable energy sources; FACTS device is used as a voltage controller. A 330kV, 28-bus power system network was studied using the PSS/E software-based Newton-Raphson load-flow technique. The results show that 10 out of the 28 buses had voltages lying below the statutory limit of 0.95  1.05 p.u. The application of STATCOM and DFIG devices on two of the weakest buses restored the voltages to acceptable statutory limits. The total active and reactive power losses were reduced to 18.76% and 18.82%
Big Data and IoT applications

Currently, the search for a competitive advantage is a reality in all business sectors. While several reports in the literature that address this theme, only a few discuss the relationship between competitive advantages and the implementation of Information and Communication Technologies. Thus, in the present study, we sought to investigate how the application of emerging ICTs, such as the IoT and Big Data, can provide a competitive advantage to organizations. To achieve this goal, we conducted a qualitative bibliographic survey of the literature, to identify and analyze the presently available publications on the subject of ICTs and organizational management. Additionally, we defined the elements that corroborated the conceptual construction of the results. Based on the literature, we were able to demonstrate that organizations that utilize Big Data and IoT applications can gain a competitive advantage over their competitors.
RELIABILITY AND VALIDITY OF THE BRIEF COPE SCALE AMONG OFFENDERS IN CHINA Authors:

The Brief Coping Orientation to Problems Experienced (B-COPE) Scale is a selfcompleted questionnaire measuring coping strategies, which contains 14 subscales. In the present study, the B-COPE Scale Chinese version were validated among 282 male offenders selected from a medium security prison in China. The results showed that internal consistencies ranged from 0.51-0.90. Exploratory Factor Analysis yielded eight factors with eigenvalues greater than 1.0, which together accounted for 61.98% of the variance in responding which was generally consistent with the original B-COPE scale. Significant correlation was found between the mean adaptive(/maladaptive) coping scores of the B-COPE and the mean positive(/negative) coping scores of Simplified Coping Style Questionnaire (SCSQ). The Chinese version B-COPE has satisfactory reliability and validity, and can be applied in Chinese offenders.
Detecting tandem repeat expansions in cohorts sequenced with short-read 1 sequencing data 2 3

25 Repeat expansions cause over 30, predominantly neurogenetic, inherited 26 disorders. These can present with overlapping clinical phenotypes, making molecular 27 diagnosis challenging. Single gene or small panel PCR-based methods are employed 28 to identify the precise genetic cause, but can be slow and costly, and often yield no 29 result. Genomic analysis via whole exome and whole genome sequencing (WES and 30 WGS) is being increasingly performed to diagnose genetic disorders. However, until 31 recently analysis protocols could not identify repeat expansions in these datasets. 32 A new method, called exSTRa (expanded Short Tandem Repeat algorithm) 33 for the identification of repeat expansions using either WES or WGS was developed 34 and performance of exSTRa was assessed in a simulation study. In addition, four 35 retrospective cohorts of individuals with eleven different known repeat expansion 36 disorders were analysed with the new method. Results were assessed by comparing to 37 known disease status. Performance was also compared to three other analysis methods 38 (ExpansionHunter, STRetch and TREDPARSE), which were developed specifically 39 for WGS data. Expansions in the STR loci assessed were successfully identified in 40 WES and WGS datasets by all four methods, with high specificity and sensitivity, 41 excepting the FRAXA STR where expansions were unlikely to be detected. Overall 42 exSTRa demonstrated more robust/superior performance for WES data in comparison 43 to the other three methods. exSTRa can be applied to existing WES or WGS data to 44 identify likely repeat expansions and can be used to investigate any STR of interest, 45 by specifying location and repeat motif. We demonstrate that methods such as 46 exSTRa can be effectively utilized as a screening tool to interrogate WES data 47 generated with PCR-based library preparations and WGS data generated using either 48 PCR-based or PCR-free library protocols, for repeat expansions which can then be 49 . CC-BY-NC-ND 4.0 International license is made available under a The copyright holder for this preprint (which was not peer-reviewed) is the author/funder. It . https://doi.org/10.1101/157792 doi: bioRxiv preprint
Implementation of a Network Provisioning System with User-driven and Trusty Protection Management

Proper management on user-driven virtual circuits (VCs) is essential for seamless operation of virtual networks. The Network Provisioning System (NPS) is useful software for creating user-driven VCs automatically and must take fault management into account for physical layer impairments on user-driven VCs. This paper addresses a user-driven and trusty protection management in an NPS with an open standard Network Service Interface (NSI), as a contribution to show how to implement the user-driven and trusty protection management required for user-driven VCs. In particular, it provides a RESTful web service Interface for Configuration and Event management (RICE) that enable management of a distinguished data and control plane VC status between Network Service Agents (NSAs) in the event of a node or link fault and repair in a domain. This capability represents a contribution to show how network and protection events in a domain can be monitored between NSAs (NPSs with the NSI) in multiple domains. The implemented NPS controls and manages both the primary and backup VC with disjoint path in a user-driven manner. A demonstration to verify RICE API"s capability is addressed for the trusty protection in the dynamic VC network.
Pax5 is required for recombination of transcribed, acetylated, 5 (cid:1) IgH V gene segments

Pax5 -deficient progenitor B (pro-B) cells are thought to be severely defective for recombination of all immunoglobulin heavy chain (IgH) V gene segments, but the mechanism by which Pax5 regulates this process has not been defined. To address this issue, we have examined the assembly of the IgH locus in Pax5 -deficient pro-B cells and find, unexpectedly, that 3 (cid:1) IgH V gene segments, which lie closest to the D-J-Cµ region, recombine efficiently, but progressively more distal V gene segments recombine progressively less efficiently. Histone acetylation and germ-line transcription correlate strongly with an open or an accessible chromatin structure thought to be permissive for V(D)J recombination, and defects in recombination are typically accompanied by deficits in these processes. We were therefore sur-prised to observe that distal V H gene segments in Pax5−/− pro-B cells exhibit no defect in these measures of accessibility. The finding of transcribed, histone acetylated gene segments that fail to recombine suggests that a Pax5 -dependent regulatory mechanism is required in addition to standard constraints of accessibility to control V H gene recombination. During lymphocyte development, V(D)J recombination assembles