Miracle-dz/UniFAR-FTUs huggingface dataset:
Rapid and Objective Assessment of Neural Function in Autism Spectrum Disorder Using Transient Visual Evoked Potentials

Objective There is a critical need to identify biomarkers and objective outcome measures that can be used to understand underlying neural mechanisms in autism spectrum disorder (ASD). Visual evoked potentials (VEPs) offer a noninvasive technique to evaluate the functional integrity of neural mechanisms, specifically visual pathways, while probing for disease pathophysiology. Methods Transient VEPs (tVEPs) were obtained from 96 unmedicated children, including 37 children with ASD, 36 typically developing (TD) children, and 23 unaffected siblings (SIBS). A conventional contrast-reversing checkerboard condition was compared to a novel short-duration condition, which was developed to enable objective data collection from severely affected populations who are often excluded from electroencephalographic (EEG) studies. Results Children with ASD showed significantly smaller amplitudes compared to TD children at two of the earliest critical VEP components, P60-N75 and N75-P100. SIBS showed intermediate responses relative to ASD and TD groups. There were no group differences in response latency. Frequency band analyses indicated significantly weaker responses for the ASD group in bands encompassing gamma-wave activity. Ninety-two percent of children with ASD were able to complete the short-duration condition compared to 68% for the standard condition. Conclusions The current study establishes the utility of a short-duration tVEP test for use in children at varying levels of functioning and describes neural abnormalities in children with idiopathic ASD. Implications for excitatory/inhibitory balance as well as the potential application of VEP for use in clinical trials are discussed.
A Two-Step Model for Defect Density Estimation

Identifying and locating defects in software projects is a difficult task. Further, estimating the density of defects is more difficult. Measuring software in a continuous and disciplined manner brings many advantages such as accurate estimation of project costs and schedules, and improving product and process qualities. Detailed analysis of software metric data gives significant clues about the locations and magnitude of possible defects in a program. The aim of this research is to establish an improved method for predicting software quality via identifying the defect density of fault prone modules using machine-learning techniques. We constructed a two-step model that predicts defect density by taking module metric data into consideration. Our proposed model utilizes classification and regression type learning methods consecutively. The results of the experiments on public data sets show that the two-step model enhances the overall performance measures as compared to applying only regression methods.
Interpreting recent changes in monetary aggregates

In 1994, broad monetary aggregates such as M2+ grew at an unusually slow rate, indicating a continuation of low inflation. Narrow money, M1, ballooned early in the year, partly for technical reasons. However, its overall deceleration for the year as a whole would be consistent with lower output growth in the first half of 1995 than was seen the year before. During the first half of 1994, there was a continued shift by investors from deposits into equity, bond and mortgage mutual funds. In the second half of the year, following a rise in interest rates and a fall in the yields posted by mutual funds, there was a movement back into M2+. In this annual review of the monetary aggregates, the author discusses the reasons for these shifts and their implications for M2+.
Software architecture recovery and restructuring through clustering techniques

1. ABSTRACT Capturing of software architecture is critical for maintenance and evolution. However, existing approaches often are limited only to software architecture recovery in the reverse engineering process. What is needed more is a systematic and effective approach to help the designer to restructure or reengineer an architecture for improvement. This paper presents an quantitative approach based on clustering techniques for software architecture restructuring and reengineering as well as for software architecture recovery. Clustering techniques are built on top of reverse engineering tools. The approach has been applied to several examples at various levels of abstraction. Two case studies are presented in this paper. One is an empirical study of a decoupling effort for a real-time telecommunications system. The other example shows a research potential to enforce the designer to improve an architecture by adopting a design pattern based on the clustering results. 1.1 Keywords Architecture recovery, restructuring, evolution, clustering, patterns
Comparison of field methods for measuring surface topography and their associations with a tropical reef fish assemblage

The relationships between 6 measures of substratum topography or vertical relief and a tropical fish assemblage were evaluated and the merits of the various measures were assessed. A method of quantifying surface topography in the field using a profile gauge is introduced and found to be rapid and to generate concise summary statistics. Of the 6 measures of substratum topography com- pared, 1 was novel, 2 were derivations of geomorphological techniques and 3 had been previously used in reef fish studies. Measures included the sum of consecutive substratum height differences, vector standard deviation (SD), substratum angle SD, substratum height SD, substratum height coefficient of variation (CV) and the ratio of contoured surface distance to linear distance ('chain-and-tape'). All but the CV were found to grossly differentiate among surfaces that varied in corrugation height and frequency. Consecutive substratum height difference, substratum angle SD and vector SD were best able to differentiate between 9 schematic profiles of varying topography. A field test of the methods was carried out on 100 quadrats (3 X 3 m) from the backreef of Lizard Island, northern Great Barrier Reef, Australia, in which the surface topography, fish and benthic communities had been sampled. The consecutive height difference method performed consistently well, scoring the highest average corre- lations with total fish abundance, diversity and the abundance of individual species. In an examination of the relationship between the backreef fish assemblage, substratum topography and the cover of benthic organisms, topography was only secondary in importance to depth in describing the patterns of damselfish abundance at the scale sampled.
GLIESE 581D IS THE FIRST DISCOVERED TERRESTRIAL-MASS EXOPLANET IN THE HABITABLE ZONE

It has been suggested that the recently discovered exoplanet GJ581d might be able to support liquid water due to its relatively low mass and orbital distance. However, GJ581d receives 35% less stellar energy than Mars and is probably locked in tidal resonance, with extremely low insolation at the poles and possibly a permanent night side. Under such conditions, it is unknown whether any habitable climate on the planet would be able to withstand global glaciation and/or atmospheric collapse. Here we present three-dimensional climate simulations which demonstrate that GJ581d will have a stable atmosphere and surface liquid water for a wide range of plausible cases, making it the first confirmed super-Earth (exoplanet of 2–10 Earth masses) in the habitable zone. We find that atmospheres with over 10 bar CO2 and varying amounts of background gas (e.g., N2) yield global mean temperatures above 0°C for both land and ocean-covered surfaces. Based on the emitted IR radiation calculated by the model, we propose observational tests that will allow these cases to be distinguished from other possible scenarios in the future.
‘They are harsher to me than to my friend who is blonde’. Police critique among ethnic minority youth in Sweden

ABSTRACT Young people belonging to categories and living in areas that are targeted by the police find more arguments for detailed police critique than others. When they narrate their assessments there is a series of experiential references being made, which can be used to sort out their police critique. This article draws on 20 interviews with ethnic minority youth in so-called vulnerable neighborhoods in Sweden. It shows how young people portray the police as sometimes profiling and racist, sometimes just and legitimate, and how they do so by comparisons and identifications. Comparisons within Sweden and identifications with police targets tend to result in more negative assessments whereas comparisons with countries of origin and identifications with non-targeted categories may result in more positive or mixed assessments. These young people also evaluate the police with reference to the stigmatization of their neighborhoods. To be stopped because you are associated with a residential area is described as deeply unfair, even if the police are polite, and observations of how the police change behavior in different areas promote critique. Our findings point to the importance of extending a criteria-based model of how today’s young people assess the police with a more social and ethnographic understanding.
Incremental learning from a single seed image for object detection

In this paper, we propose a novel online multiobject learning and detection algorithm. From single seed images of the target objects, our algorithm detects these objects in the input sequence, and incrementally updates the databases with the detection results. Reasonably sized databases are maintained as graphs of the registered images, while new views of the objects are added as the detection proceeds. The importance of the registered images is computed using our ranking algorithm, and redundant images are pruned from the database. The proposed method fully utilizes graphical representation to detect and recognize objects. A 3D model of a candidate object is built on-the-fly using the retrieved images, and initially undetected features are hallucinated for further matching and verification. This process improves the detection performance compared to the baseline algorithm. Object/background feature classification and object-likelihood maps effectively keep noisy background features from being added to the databases. The experimental results demonstrate that the proposed algorithm efficiently maintains the object databases and achieves better performance.
Modeling and Estimation of Transient Carrier Frequency Offset in Wireless Transceivers

Future wireless devices have to support many applications (e.g., remote robotics, wireless automation, and mobile gaming) with extremely low latency and reliability requirements over wireless connections. Optimizing wireless transceivers while switching between wireless connections with different circuit characteristics requires addressing many hardware impairments that have been overlooked previously. For instance, switching between transmission and reception radio functions to facilitate time-division duplexing can change the load on the power supply. As the supply voltage changes in response to the sudden change in load, the carrier frequency drifts. Such a drift results in transient carrier frequency offset (CFO) that cannot be estimated by conventional CFO estimators and is typically addressed by inserting or extending guard intervals. In this paper, we explore the modeling and estimation of the transient CFO, which is modeled as the response of an underdamped second order system. To compensate for the transient CFO, we propose a low complexity parametric estimation algorithm, which uses the null space of the Hankel-like matrix constructed from phase difference of the two halves of the repetitive training sequence. Furthermore, to minimize the mean squared error of the estimated parameters in noise, a weighted subspace fitting algorithm is derived with a slight increase in complexity. The Crámer-Rao bound for any unbiased estimator of the transient CFO parameters is derived. The performance of the proposed algorithms is also confirmed by the experimental results obtained from the real wireless transceivers.
Fast Classification of Empty and Occupied Parking Spaces Using Integral Channel Features

In this paper we present a novel, fast and accurate system for detecting the presence of cars in parking lots. The system is based on fast integral channel features and machine learning. The methods are well suited for running embedded on low performance platforms. The methods are tested on a database of nearly 700,000 images of parking spaces, where 48.5% are occupied and the rest are free. The experimental evaluation shows improved robustness in comparison to the baseline methods for the dataset.
Substitution in Amino Acid 70 of Hepatitis C Virus Core Protein Changes the Adipokine Profile via Toll-Like Receptor 2/4 Signaling

Background & Aims It has been suggested that amino acid (aa) substitution at position 70 from arginine (70R) to glutamine (70Q) in the genotype 1b hepatitis C virus (HCV) core protein is associated with insulin resistance and worse prognosis. However, the precise mechanism is still unclear. The aim of this study was to investigate the impact of the substitution at position 70 in HCV core protein on adipokine production by murine and human adipocytes. Methods The influence of treatment with HCV core protein (70R or 70Q) on adipokine production by both 3T3-L1 and human adipocytes were examined with real-time PCR and enzyme-linked immunosorbent assay (ELISA), and triglyceride content was also analyzed. The effects of toll-like receptor (TLR)2/4 inhibition on IL-6 production by 3T3-L1 induced by HCV core protein were examined. Results IL-6 production was significantly increased and adiponectin production was reduced without a change in triglyceride content by treatment with 70Q compared to 70R core protein in both murine and human adipocytes. IL-6 induction of 3T3-L1 cells treated by 70Q HCV core protein was significantly inhibited with anti-TLR2 antibody by 42%, and by TLR4 inhibitor by 40%. Conclusions Our study suggests that extracellular HCV core protein with substitution at position 70 enhanced IL-6 production and reduced adiponectin production from visceral adipose tissue, which can cause insulin resistance, hepatic steatosis, and ultimately development of HCC.
Relational aggression and prosocial behaviours in Australian preschool children

RELATIONAL AGGRESSION IS A subtle form of aggressive behaviour that uses dyadic relationships and manipulation as a vehicle of harm. Little is known about relational aggression in preschool-age children in cultural contexts outside the United States. This study examined relationally aggressive behaviours and prosocial behaviours in Australian preschoolers. The sample consisted of 60 children aged from three to five years (35 boys, 25 girls). Teachers rated children's social behaviour in terms of relational aggression and prosocial behaviour. Results indicated that teachers report significantly more relational aggression in the oldest age group of children (aged > 4.5 years). Relational aggression was related to lower scores of prosocial behaviour (p < 0.05). No significant differences were found between boys' and girls' engagement in relational aggression and prosocial behaviours. Results are discussed in terms of the importance of recognising the prevalence of these aggressive behaviours in Australian preschool-age children and the need for immediate intervention.
HIGH-FIDELITY RAPID PROTOTYPING OF THE REAL-TIME FAB OPERATIONS THROUGH DISCRETE EVENT SYSTEM MODELING

Traditionally, the semiconductor manufacturing industry has been driven by continuous technological advancement of the underlying production processes. Yet, as the industry matures, mere technology development is no longer sufficient. The effective deployment and exploitation of the system production capacity and operational capability become critical for competitive success. Hence, currently, there is an increasing interest towards the development of a control paradigm / framework that will allow the effective and efficient (re-)deployment and operation of contemporary fabs. The research program presented in this paper seeks to define a detailed modeling and control framework for the real-time fab operations, by exploiting and integrating emerging results in Discrete Event Systems theory. Furthermore, in addition to the development of the formal specification, the presented program will also implement the proposed fab modeling and control framework in a Web-based simulation platform, that will function as a fab (re)configuration and control synthesis tool for the fab control engineer, and as an educational tool for manufacturing system modeling and control.
Integration of GPS Precise Point Positioning and MEMS-Based INS Using Unscented Particle Filter

Integration of Global Positioning System (GPS) and Inertial Navigation System (INS) integrated system involves nonlinear motion state and measurement models. However, the extended Kalman filter (EKF) is commonly used as the estimation filter, which might lead to solution divergence. This is usually encountered during GPS outages, when low-cost micro-electro-mechanical sensors (MEMS) inertial sensors are used. To enhance the navigation system performance, alternatives to the standard EKF should be considered. Particle filtering (PF) is commonly considered as a nonlinear estimation technique to accommodate severe MEMS inertial sensor biases and noise behavior. However, the computation burden of PF limits its use. In this study, an improved version of PF, the unscented particle filter (UPF), is utilized, which combines the unscented Kalman filter (UKF) and PF for the integration of GPS precise point positioning and MEMS-based inertial systems. The proposed filter is examined and compared with traditional estimation filters, namely EKF, UKF and PF. Tightly coupled mechanization is adopted, which is developed in the raw GPS and INS measurement domain. Un-differenced ionosphere-free linear combinations of pseudorange and carrier-phase measurements are used for PPP. The performance of the UPF is analyzed using a real test scenario in downtown Kingston, Ontario. It is shown that the use of UPF reduces the number of samples needed to produce an accurate solution, in comparison with the traditional PF, which in turn reduces the processing time. In addition, UPF enhances the positioning accuracy by up to 15% during GPS outages, in comparison with EKF. However, all filters produce comparable results when the GPS measurement updates are available.
Probabilistic Lexical Modeling and Grapheme-based Automatic Speech Recognition

Standard hidden Markov model (HMM) based automatic speech recogni- tion (ASR) systems use phonemes as subword units. Thus, development of ASR system for a new language or domain depends upon the availability of a phoneme lexicon in the target language. In this paper, we introduce the notion of probabilistic lexical modeling and present an ASR approach where a) first, the relationship between acoustics and phonemes is learned on available acoustic and lexical resources (not necessarily from the target language or domain), and then b) probabilistic grapheme-to-phoneme rela- tionship is learned using the acoustic data of targeted language or domain. The resulting system is a grapheme-based ASR system. This brings in two potential advantages. First, development of lexicon for target language or domain becomes easy i.e., creation of a grapheme lexicon where each word is transcribed by its orthography. Second, the ASR system can exploit both acoustic and lexical resources of multiple languages and domains. We evaluate and show the potential of the proposed approach through a) an in-domain study, where acoustic and lexical resources of target language or domain are used to build an ASR system, b) a monolingual cross-domain study, where acoustic and lexical resources of another domain are used to build an ASR system for a new domain, and c) a multilingual cross-domain study, where acoustic and lexical resources of multiple languages are used to build multi-accent non-native speech recognition system.
Improving the Statistical Arbitrage Strategy in Intraday Trading by Combining Extreme Learning Machine and Support Vector Regression with Linear Regression Models

In this paper we investigate the statistical and economic performance for statistical arbitrage strategy using Extreme Learning Machine (ELM) and Support Vector Regression (SVR) models, and their forecast combination through four linear combination models. The application of the traditional Kalman Filter for the statistical arbitrage strategy improves the statistical performance of ELM and SVR individual forecasts. It is presented evidence that the financial performance for most of cointegrated pairs can be improved by at least one linear combination technique.
Inhibitory effect of quercetin on epithelial to mesenchymal transition in SK-MEL-28 human melanoma cells defined by in vitro analysis on 3D collagen gels

Considering the emerging concept of complementary and alternative medicine under the paucity of effective treatment for melanoma, we aimed to understand the effect of quercetin (Qu) on collagen I-induced epithelial–mesenchymal transition (EMT) in melanoma cells. To investigate the effect of Qu in melanoma cells, we used multiple methods, including real-time reverse transcription polymerase chain reaction, migration assay, and wound healing assay. We found that EMT was altered by Qu in melanoma cells. Qu-treated cells exhibited decreased migration and invasion activities. Mechanistically, a high expression of epithelial markers and a decrease in the expression of mesenchymal markers were found to be associated with reversal of EMT in melanoma cells. Time-dependent apoptosis was observed in Qu-treated melanoma cells, which was further confirmed by the upregulation in the protein levels of Caspase 3, a proapoptotic marker. Thus, our findings suggest Qu as a promising dietary compound under the new complementary and alternative medicine category of therapeutic drugs in the chemoprevention of melanoma.
Mid-infrared interferometry of massive young stellar objects. I. VLTI and Subaru observations of the

Context. Our knowledge of the inner structure of embedded massive young stellar objects is still quite limited. Thus, it is difficult to decide to what extent the mass accumulation onto forming massive stars differs from the process of low-mass star formation. Aims. We attempt to overcome the spatial resolution limitations of conventional thermal infrared imaging. Methods. We employed mid-infrared interferometry using the MIDI instrument on the ESO/VLTI facility to investigate M8E-IR, a well-known massive young stellar object suspected of containing a circumstellar disk. Spectrally dispersed visibilities in the 8-13 mu m range have been obtained at seven interferometric baselines. Results. We resolve the mid-infrared emission of M8E-IR and find typical sizes of the emission regions of the order of 30 milliarcseconds (= 45 AU). Radiative transfer simulations were performed to interpret the data. The fitting of the spectral energy distribution, in combination with the measured visibilities, does not provide evidence for an extended circumstellar disk with sizes greater than or similar to 100 AU but requires the presence of an extended envelope. The data are not able to constrain the presence of a small-scale disk in addition to an envelope. In either case, the interferometry measurements indicate the existence of a strongly bloated, relatively cool central object, possibly tracing the recent accretion history of M8E-IR. In addition, we present 24.5 mu m images that clearly distinguish between M8E-IR and the neighbouring ultracompact HII region and which show the cometary-shaped infrared morphology of the latter source. Conclusions. Our results show that IR interferometry, combined with radiative transfer modelling, can be a viable tool to reveal crucial structure information on embedded massive young stellar objects and to resolve ambiguities arising from fitting the SED.