Miracle-dz/UniFAR-FTUs huggingface dataset:
Additive, Epistatic, and Environmental Effects Through the Lens of Expression Variability QTL in a Twin Cohort

The expression of a gene can vary across individuals in the general population, as well as between monozygotic twins. This variable expression is assumed to be due to the influence of both genetic and nongenetic factors. Yet little evidence supporting this assumption has been obtained from empirical data. In this study, we used expression data from a large twin cohort to investigate the influences of genetic and nongenetic factors on variable gene expression. We focused on a set of expression variability QTL (evQTL)—i.e., genetic loci associated with the variance, as opposed to the mean, of gene expression. We identified evQTL for 99, 56, and 79 genes in lymphoblastoid cell lines, skin, and fat, respectively. The differences in gene expression, measured by the relative mean difference (RMD), tended to be larger between pairs of dizygotic (DZ) twins than between pairs of monozygotic (MZ) twins, showing that genetic background influenced the expression variability. Furthermore, a more profound RMD was observed between pairs of MZ twins whose genotypes were associated with greater expression variability than the RMD found between pairs of MZ twins whose genotypes were associated with smaller expression variability. This suggests that nongenetic (e.g., environmental) factors contribute to the variable expression. Lastly, we demonstrated that the formation of evQTL is likely due to partial linkages between eQTL SNPs that are additively associated with the mean of gene expression; in most cases, no epistatic effect is involved. Our findings have implications for understanding divergent sources of gene expression variability.
From Lattice-Reduction-Aided Detection Towards Maximum-Likelihood Detection in MIMO Systems

We consider the lattice-reduction-aided detection scheme of [2] based on the well-known LLL algorithm for MIMO systems with arbitrary number of dimensions. By considering a set of subspaces of the detection space we spend some additional computational effort in the detection scheme and show that this approach is extremely rewarding in terms of return in power efficiency gain. Additionally, the list of candidate solutions considered in the new approach can be used to calculate per-bit probability estimates.
Analysis of movement patterns and work-to-rest ratios for different panels of South African Rugby Union referees during matchrefereeing

The objective of this study was to determine the movement patterns, heart rate recordings and work-to-rest ratios for different refereeing panels of South African Rugby Union referees during match-refereeing. The referees were monitored during play by means of a video camera in a total of 16 matches within a tournament. The frequency and duration of the different movement patterns were analysed using different functions of the Dartfish 5.5 TeamPro software package. Heart rates (HR) were recorded by a Suunto Team pack HR monitor system to determine the intensity of the movement patterns. The mean frequency of movement activities revealed a moderate practical significant difference between referee panels for jogging (d=0.51) and the mean duration indicated a moderate practical significant difference for jogging (d=0.43) and sprinting (d=0.43). The intensity of movement activities indicated a large practical significant difference for the anaerobic threshold (d=3.68) and sub-threshold (d=1.36) heart rate zones. A work-to-rest ratio of 1:4 was revealed for both referee panels during match-refereeing. The study revealed that contender panel referees completed more jogging activities and spent more time on jogging and sprinting activities than the provincial referees, and regarding intensities the contender panel referees also spent more time in low intensity heart rate zones.
On the Supersymmetry of Non-BPS D-brane

In this paper we extend the boundary string field theory action for a non-BPS D-brane to the one including the target space fermions and the nonlinear supersymmetry with 32 supercharges up to some order. This is based on the idea that the vacuum with a non-BPS D-brane belongs to the spontaneously broken phase of the supersymmetry. As a result, we find that the action is almost uniquely determined up to the field redefinition ambiguities.
Towards analog and mixed-signal SOC design with systemC-AMS

Systems-on-Chip (SoCs) are heterogeneous by nature as they may integrate digital, analog, RF hardware as well as software components or non electrical parts such as sensors or actuators. The increasing level of complexity for designing SoCs in a reasonable amount of time and resources asks, among other capabilities, for powerful modeling and simulation means. SystemC is emerging as a de facto standard for digital system design, but is still lacking a standard support of continuous-time and mixed discrete-event/continuous-time systems. This paper presents the first elements of extensions to SystemC, called SystemC-AMS, that are proposed to fill the gap.
Explanation of Bayesian Networks and Influence Diagrams in Elvira

Bayesian networks (BNs) and influence diagrams (IDs) are probabilistic graphical models that are widely used for building diagnosis- and decision-support expert systems. Explanation of both the model and the reasoning is important for debugging these models, alleviating users' reluctance to accept their advice, and using them as tutoring systems. This paper describes some explanation options for BNs and IDs that have been implemented in Elvira and how they have been used for building medical models and teaching probabilistic reasoning to pre- and postgraduate students.
Future Design of Channel Codes: A Complex Network Perspective

Channel coding has been studied for over 60 years while complex network has not been intensively investigated until recently. In this review paper, we aim to establish a linkage between channel coding and complex networks -- two seemingly unrelated topics. We show by two examples how the characteristics of complex networks can inspire new ways of designing channel codes. Finally, we give some future directions in this promising research area.
Trinocular ystem for 3D motion and dense structure estimation

The relief of the seafloor is an important source of data for many scientists. In this paper we present an optical system to deal with underwater 3D reconstruction. This system is formed by three cameras that take images synchronously in a constant frame rate scheme. We use the images taken by these cameras to compute dense 3D reconstructions. We use Bundle Adjustment to estimate the motion of the trinocular rig. Given the path followed by the system, we get a dense map of the observed scene by registering the different dense local reconstructions in a unique and bigger one.
Constructing secure operating environments by co-locating multiple embedded operating systems

Embedded operating system (OS) kernels provide basic execution primitives that can be used commonly by many appliances. The recent emergence of digital appliances requires more advanced features, such as networking and GUI. Those features dramatically complicate the appliances' software systems and increase their code sizes. In order to develop such large and complex software systems, we propose a system architecture that co-locates multiple embedded operating systems on a microkernel. The proposed architecture employs a microkernel to provide protected execution environments for the existing embedded kernels. In each protected execution environment, a kernel and its applications run just as they run directly on hardware since they share the same protection domain. Our approach can achieve the maximum reusability of the existing software resources including embedded OS kernels and their applications; thus, it protects the existing software resources. We have developed the system that consists of TL4 microkernel and a /spl mu/ITRON embedded kernel, and shows the feasibility of our approach through evaluations.
Remote sensing of vertical distributions of smoke aerosol off the coast of Africa

In 2004 NASA plans to launch the Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observations — CALIPSO mission, with a two‐wavelength lidar aboard. CALIPSO will fly in formation with the Moderate Resolution Imaging Spectro‐Radiometer (MODIS) on the Aqua satellite. Here we present inversions of combined aircraft lidar and MODIS data to study the properties of smoke off the southwest coast of Southern Africa. The inversion derives profiles of the aerosol extinction due to fine and coarse particles. Comparisons with three sets of airborne in situ measurements show excellent agreement of the aerosol extinction profiles; however the inversion derives smaller spectral dependence of the extinction than the in situ measurements. The inversion is sensitive to the aerosol backscattering‐to‐extinction ratio (BER). Due to nonsphericity of the coarse aerosols, the range of BERs of the smoke aerosol is 0.014 to 0.021 sr−1 for the fine and coarse particles at 0.53 and 1.06 μm wavelengths, which do not differ much from the value for dust (0.016 sr−1) at these wavelengths.
Nonlinear Compensation of High Power Amplifier Distortion for Communication Using a Histogram-Based Method

A probabilistic approach to compensate the nonlinear distortion caused by a high power amplifier (HPA) in a communication system is proposed here. This is a nonparametric method that involves estimating two probabilistic cumulative distribution functions without any explicit parameter estimation as in the conventional compensation techniques. It is shown analytically that the maximum compensation error of the proposed method is bounded and small. An adaptive implementation is developed. Experiments are setup to collect the HPA data. Real data analysis shows that the proposed predistorter has an improved performance compared to the conventional polynomial and neural network predistortion techniques
A two-level checkpoint algorithm in a highly-available parallel single level store system

A parallel single level store system (PSLS) integrates a shared virtual memory and a parallel file system. Managing the data globally it provides programmers of scientific applications with the attractive shared memory programming model combined with a large and efficient file system in a cluster. We present a cheap and efficient two-level checkpointing approach enabling a PSLS to tolerate failures. The first level checkpointing algorithm is very efficient and saves data in memory but requires a large amount of memory space. When memories are saturated, an alternative algorithm, saving a checkpoint on disks is implemented. Performance results present the impact of different variants of the checkpointing algorithms.
Using Conditional Functional Dependency to Discover Abnormal Data in RDF Graphs

Many issues about data quality have been studied in relational data, such as data consistency, data deduplication, data accuracy, data completeness and so on. In this paper, we focus on the discovery of abnormal data in RDF graphs. As the amount of RDF data is increasing, data quality is becoming an important issue for usability of these RDF repositories. Although association rules have been used to find abnormals in RDF graph, existing solutions ignore the latent semantics of connected structures in RDF graphs. In order to detect latent dependencies in RDF graph, firstly, we innovatively define Graph-based Conditional Functional Dependency(GCFD) that can represent the attribute value and semantic structure dependencies of RDF data in a uniform manner. Then, we propose an efficient framework and some novel pruning rules to discover GCFD in large RDF graphs. Extensive experiments on several real-life RDF repositories confirm the superiority of our solution.
Association between synthetic cannabinoid use and depressive symptomology: Analysis of a US national survey of high-school seniors

ABSTRACT Introduction: Synthetic cannabinoid (SC) use among adolescents in the US is a major public health concern. SCs are considered by many teens to be a safe alternative to cannabis; however, little is known about the association between its use and depressive symptomology (DS). This study aims to examine the association between SC use and DS in US high-school seniors. Methods: Data from the 2011–2014 Monitoring the Future (MTF) survey for high-school seniors was analyzed (N = 9,272). Multinomial logistic regression analysis was used to examine the association between SC use (any use, ≤6 or >6 times in the last 12 months) and DS (low, medium, or high). Results: Approximately 8% of high-school seniors reported SC use in the last 12 months and 24% reported high levels of DS. Multinomial logistic regression analyses showed that in the unadjusted model and the model that controlled for sociodemographic factors, a significant association between SC use and DS was observed. However, upon controlling for other substance use, the association became markedly attenuated and lost its statistical significance. Conclusions: SC use was not associated with DS in high-school seniors. Future longitudinal studies are needed to further examine the relationship.
An integrated prediction model of sinter comprehensive performance

The process of sinter have a great influence on production and economic benefits of the iron-making, the prediction of sinter performance is very necessary. However, the sinter process is complex, nonlinear and hysteresis, and strong coupling, it's impossible to establish a mathematical model. In this paper, we constructed a prediction model of sinter comprehensive performance by integrating GM(1,1) and BPNN based on information entropy since a single model can't suit different sinter quality. The application results show that the prediction model has high accuracy rate, stability and efficiency.
DTD: A Novel Double-Track Approach to Clone Detection for RFID-Enabled Supply Chains

Toward improving the traditional clone detection technique whose performance may be affected by dynamic changes of supply chains and misread, we present a novel and effective clone detection approach, termed double-track detection, for radio frequency identification-enabled supply chains. As part of a tag’s attributes, verification information is written into tags so that the set of all verification information in the collected tag events forms a time series sequence. Genuine tags can be differentiated from clone tags due to the discrepancy in their verification sequences which are constructed as products flow along the supply chain. The verification sequence together with the sequence formed by business actions performed during the supply chains yield two tracks which can be assessed to detect the presence of clone tags. Theoretical analysis and experimental results show that our proposed mechanism is effective, reasonable, and has a relatively high clone detection rate when compared with a leading method in this area.
Spatial Transformation and Registration of Brain Images Using Elastically Deformable Models

The development of algorithms for the spatial transformation and registration of tomographic brain images is a key issue in several clinical and basic science medical applications, including computer-aided neurosurgery, functional image analysis, and morphometrics. This paper describes a technique for the spatial transformation of brain images, which is based on elastically deformable models. A deformable surface algorithm is used to find a parametric representation of the outer cortical surface and then to define a map between corresponding cortical regions in two brain images. Based on the resulting map, a three-dimensional elastic warping transformation is then determined, which brings two images into register. This transformation models images as inhomogeneous elastic objects which are deformed into registration with each other by external force fields. The elastic properties of the images can vary from one region to the other, allowing more variable brain regions, such as the ventricles, to deform more freely than less variable ones. Finally, the framework of prestrained elasticity is used to model structural irregularities, and in particular the ventricular expansion occurring with aging or diseases, and the growth of tumors. Performance measurements are obtained using magnetic resonance images.
A two-stage architecture for stock price forecasting by combining SOM and fuzzy-SVM

This paper proposed a model to predict the stock price based on combining Self-Organizing Map (SOM) and fuzzy - Support Vector Machines (f-SVM). Extraction of fuzzy rules from raw data based on the combining of statistical machine learning models is the base of this proposed approach. In the proposed model, SOM is used as a clustering algorithm to partition the whole input space into several disjoint regions. For each partition, a set of fuzzy rules is extracted based on a f-SVM combining model. Then fuzzy rules sets are used to predict the test data using fuzzy inference algorithms. The performance of the proposed approach is compared with other models using four data sets.
Influence of improved teaching practices on student satisfaction ratings for two undergraduate units at an Australian university

Abstract To track the quality of instruction delivered at the University of Western Australia, the university surveys all units using its Students’ Unit Reflective Feedback (SURF) metrics, and faculties use these metrics to benchmark student satisfaction. Consequently, teaching staff are actively encouraged to adopt teaching practices that will increase the average levels of these metrics. Using a ‘before-after’ approach, we compared these metrics before and after the implementation of improved teaching practices that addressed specific weaknesses identified through student responses for two undergraduate units. Despite the implementation of improved teaching practices, SURF scores did not increase significantly for the two units assessed, due possibly to the components of the academic programme covered by the SURF questions, the field-based nature of the units, and the difficulty in measuring an increase in the SURF scores of units which already score highly. This poor sensitivity of the SURF metrics to the implementation of improved teaching practices could have implications for their use for performance assessment by the university.
A Case Study on Information Extraction from the Internet : Populating a Movie Ontology

A method to extract information from the internet is discussed. We choose to formally represent information by ontologies, and the information extraction problem is reformulated as an ontology population problem. A case study on populating a Movie ontology is presented. We extract instances of some class of the ontology by analyzing query results from the search engine Google. Within the abstracts which Google generates, we interpret natural language expressions to find instances and their relations. We present the algorithm and discuss the results of our work. Ideas for future work are given as well.
Modeling supply chain's reconfigurability using fuzzy logic

To survive and gain a competitive advantage in today's ever-fast moving business environment, a company needs to constantly adapt its supply chain based on the changing market demands. Achieving an adaptive supply chain has therefore become crucial to its business success. For a company to adopt adaptive supply chain as its strategy, the most important metric to assess is reconfigurability -which is rather difficult to measure quantitatively like good, normal or poor and includes a number of second-level metrics. Therefore one of the key R&D challenges is how to model, measure and assess the reconfigurability of adaptive supply chain in a rational and flexible way so that such model could be adaptable to the changing needs of the company in various industry domains. This paper presents a fuzzy logic model for assessing the supply chain's reconfigurability, which could be further customized based on different measurement metrics, end-users' domain knowledge and self-defined business rules. This enables the reconfigurability metric to be measured and implemented, and expands the dimension of supply chain performance measurement.
The relationship between level of autistic traits and local bias in the context of the McGurk effect

The McGurk effect is a well-known illustration that demonstrates the influence of visual information on hearing in the context of speech perception. Some studies have reported that individuals with autism spectrum disorder (ASD) display abnormal processing of audio-visual speech integration, while other studies showed contradictory results. Based on the dimensional model of ASD, we administered two analog studies to examine the link between level of autistic traits, as assessed by the Autism Spectrum Quotient (AQ), and the McGurk effect among a sample of university students. In the first experiment, we found that autistic traits correlated negatively with fused (McGurk) responses. Then, we manipulated presentation types of visual stimuli to examine whether the local bias toward visual speech cues modulated individual differences in the McGurk effect. The presentation included four types of visual images, comprising no image, mouth only, mouth and eyes, and full face. The results revealed that global facial information facilitates the influence of visual speech cues on McGurk stimuli. Moreover, individual differences between groups with low and high levels of autistic traits appeared when the full-face visual speech cue with an incongruent voice condition was presented. These results suggest that individual differences in the McGurk effect might be due to a weak ability to process global facial information in individuals with high levels of autistic traits.
Providing and Analyzing NLP Terms for our Community

By its own nature, the Natural Language Processing (NLP) community is a priori the best equipped to study the evolution of its own publications, but works in this direction are rare and only recently have we seen a few attempts at charting the field. In this paper, we use the algorithms, resources, standards, tools and common practices of the NLP field to build a list of terms characteristic of ongoing research, by mining a large corpus of scientific publications, aiming at the largest possible exhaustivity and covering the largest possible time span. Study of the evolution of this term list through time reveals interesting insights on the dynamics of field and the availability of the term database and of the corpus (for a large part) make possible many further comparative studies in addition to providing a test field for a new graphic interface designed to perform visual time analytics of large sized thesauri.
Tabu Search Nds Global Optima of a Class of Ga-hard Problems

Tabu search (TS) is becoming increasingly recognised as an eecient way of nding high-quality solutions to hard combinatorial problems. It may be described as an intelligent meta-heuristic for controlling simpler local search procedures by the incorporation of simulated memory. Much of the development has been experimentally driven, and there is little in the way of theory to explain its undeniably good performance. Another popular approach to optimization is the use of genetic algorithms (GAs). Like TS, GAs can also be interpreted as a development of traditional neighbourhood search methods. Unlike TS, GA research has seen some interesting theoretical work in an attempt to explain more precisely why and how they work. One area which has seen exensive development is the investigation of problems which can be expected to be either hard of easy for GAs to solve. Classes of`GA-hard' problems have been constructed using the concept of deception. In this paper we show that a particular and important sub-class of deceptive problems is in fact easily solved using TS. These results may suggest ways in which TS research can develop in order to understand the method better. They also suggest that in attacking general optimization problems it may be important to use more than one technique.