Miracle-dz/UniFAR-FTUs huggingface dataset:
A star orbiting around a supermassive rotating black hole: free motion and corrections due to star–disc collisions

Our aim is to study the evolution of the orbit of a star under the influence of interactions with an accretion disc in an AGN. The model considered consists of a low-mass compact object orbiting a supermassive black hole and colliding periodically with the accretion disc. Approximate calculations based mostly on the Newtonian theory of gravity have been carried out by several authors, to estimate the effects of circularization of initially eccentric orbits and their dragging into the disc plane. Here, we present the first step towards a more adequate general relativistic approach in which the gravitational field of the nucleus is described by the Kerr metric
Revisiting Document Length Hypotheses: NTCIR-4 CLIR and Patent Experiments at Patolis

This paper describes Patolis NTCIR-4 experiments of CLIR and Patent tasks, focusing on comparative studies of two test-collections and two retrieval strategies in view of document length hypotheses. TF*IDF outperformed language modeling approach in the CLIR task while two approaches performed similarly in the Patent task. We assumed two different document length hypotheses behind two collections. Some other task specific techniques are evaluated and reported.
Femtocell Subband Selection Method for Managing Cross- and Co-tier Interference in a Femtocell Overlaid Cellular Network

The femtocell overlaid cellular network (FOCN) has been used to enhance the capacity of existing cellular systems. To obtain the desired system performance, both cross-tier interference and co-tier interference in an FOCN need to be managed. This paper proposes an interference management scheme that adaptively constructs a femtocell cluster, which is a group of femtocell base stations that share the same frequency band. The performance evaluation shows that the proposed scheme can enhance the performance of the macrocell-tier and maintain a greater signal to interference-plus-noise ratio than the outage level can for about 99% of femtocell users. Keywords—Clustering Method, Femtocell, Frequency Partition, Interference Management
Acceptable product pricing problem using L-localized solutions of max-plus interval linear equations

Product pricing is one of the most important strategies in doing any business. In this paper, we propose a specific marketing situation as an acceptable product pricing problem when the data on purchasing power and transportation cost cannot be measured exactly but can be shown as intervals of possible values. This problem is formulated as a minimization problem of a differentiable convex objective function with max-plus interval linear constraints A ⊗ x = b where x is called an L-localized solution. Its feasible region is nonconvex but could be viewed by the union of box constraints. The steepest descent algorithm is applied to optimize the objective function with respect to each of these box constraints and obtained an optimal solution from the best value.
The ACL RD-TEC: A Dataset for Benchmarking Terminology Extraction and Classification in Computational Linguistics

This paper introduces ACL RD-TEC: a dataset for evaluating the extraction and classification of terms from literature in the domain of computational linguistics. The dataset is derived from the Association for Computational Linguistics anthology reference corpus (ACL ARC). In its first release, the ACL RD-TEC consists of automatically segmented, part-of-speech-tagged ACL ARC documents, three lists of candidate terms, and more than 82,000 manually annotated terms. The annotated terms are marked as either valid or invalid, and valid terms are further classified as technology and non-technology terms. Technology terms signify methods, algorithms, and solutions in computational linguistics. The paper describes the dataset and reports the relevant statistics. We hope the step described in this paper encourages a collaborative effort towards building a full-fledged annotated corpus from the computational linguistics literature.
Maturation of brain function associated with response inhibition.

OBJECTIVE To investigate the developmental trajectory of response inhibition and, more specifically, whether there is a dissociation of function in the prefrontal cortex over the course of development of executive function and associated response inhibition abilities. METHOD Nineteen typically developing subjects, ranging in age from 8 to 20, performed a Go/NoGo task while behavioral and functional magnetic resonance imaging (fMRI) data were collected. RESULTS All subjects performed the task with few errors of omission and commission. No relationship between accuracy and age emerged, but the ability to inhibit responses significantly improved with age. Analyses of fMRI data revealed a positive correlation between activation and age in the left inferior frontal gyrus/insula/orbitofrontal gyrus, and a negative correlation between activation and age in the left middle/superior frontal gyri. CONCLUSION These data provide the first evidence of dissociable processes occurring in the prefrontal cortex during development of executive functions associated with response inhibition: (1) Younger subjects activate more extensively than older subjects in discrete regions of the prefrontal cortex, presumably due to increased demands and inefficient recruitment of brain regions subserving executive functions including working memory. (2) Older subjects show increasingly focal activation in specific regions thought to play a more critical role in response inhibition.
The design of two-layer flat lens

In this paper we present a two-layer flat lens design, which can simultaneously control the magnitude and phase of the transmitted wave passing through the lens. We introduce a method of dividing flat lens into several zones and adopt the transmission line approach to analyze the reflection and transmission coefficients of the proposed two-layer flat lens. A method is used to determine the distribution of the dielectric constants of the zones. Finally, we use HFSS validated the design methodology.
Evaluation of Interferon Resistance in Newly Established Genotype 1b Hepatitis C Virus Cell Culture System

Background and Aims: The hepatitis C virus (HCV) genotype 1b is known to exhibit treatment resistance with respect to interferon (IFN) therapy. Substitution of amino acids 70 and 91 in the core region of the 1b genotype is a significant predictor of liver carcinogenesis and poor response to pegylated-IFN-α and ribavirin therapy. However, the molecular mechanism has not yet been clearly elucidated because of limitations of the HCV genotype 1b infectious model. Recently, the TPF1-M170T HCV genotype 1b cell culture system was established, in which the clone successfully replicates and infects Huh-7-derived Huh7-ALS32.50 cells. Therefore, the purpose of this study was to compare IFN resistance in various HCV clones using this system. Methods: HCV core amino acid substitutions R70Q and L91M were introduced to the TPF1-M170T clone and then transfected into Huh7-ALS32.50 cells. To evaluate the production of each virus, intracellular HCV core antigens were measured. Results were confirmed with Western blot analysis using anti-NS5A antibodies, and IFN sensitivity was subsequently measured. Results: Each clone was transfected successfully compared with JFH-1, with a significant difference in intracellular HCV core antigen (p < 0.05), an indicator of continuous HCV replication. Among all clones, L91M showed the highest increase in the HCV core antigen and HCV protein. There was no significant resistance against IFN treatment in core substitutions; however, IFN sensitivity was significantly different between the wildtype core and JFH-1 (p < 0.05). Conclusions: A novel genotype 1b HCV cell culture was constructed with core amino acid substitutions, which demonstrated IFN resistance of genotype 1b. This system will be useful for future analyses into the mechanisms of HCV genotype 1b treatment.
Encoding and retrieval in human medial temporal lobes: An empirical investigation using functional magnetic resonance imaging (fMRI)

The precise functional role of the hippocampus in human episodic memory is an unresolved question though it has recently been suggested that distinct medial temporal lobe (MTL) regions are involved in encoding and retrieval operations respectively. For example, a recent meta‐analysis of positron emission tomography (PET) literature has suggested a rostral‐caudal functional division in the medial temporal lobes (MTL), with rostral MTL mediating encoding and caudal MTL retrieval operations. However, a review of the combined PET and fMRI literature, reported in the present issue, while noting systematic discrepancies between PET and fMRI, reaches a conclusion that posterior MTL is involved in encoding. Here we present fMRI data, from a modified artificial grammar learning paradigm, that examines two questions concerning the functional role of the hippocampus, and related MTL structures in episodic memory. Firstly, we test a hypothesis that anterior hippocampus is activated during encoding and that this response is greater for novel items. Secondly, we test whether increasing familiarity with stimulus material is associated with a posterior MTL neural response. Our empirical findings support both hypotheses in that we demonstrate a left anterior hippocampal response sensitive to encoding demands and a posterior parahippocampal response sensitive to retrieval demands. Furthermore, we show that both anterior and posterior hippocampal responses are modulated to the degree to which stimuli can be assimilated into a meaningful rule‐based framework. Hippocampus 1999;9:25–34. © 1999 Wiley‐Liss, Inc.
Indonesian Dependency Treebank: Annotation and Parsing

We introduce and describe ongoing work in our Indonesian dependency treebank. We described characteristics of the source data as well as describe our annotation guidelines for creating the dependency structures. Reported within are the results from the start of the Indonesian dependency treebank. We also show ensemble dependency parsing and self training approaches applicable to under-resourced languages using our manually annotated dependency structures. We show that for an under-resourced language, the use of tuning data for a meta classifier is more effective than using it as additional training data for individual parsers. This meta-classifier creates an ensemble dependency parser and increases the dependency accuracy by 4.92% on average and 1.99% over the best individual models on average. As the data sizes grow for the the under-resourced language a meta classifier can easily adapt. To the best of our knowledge this is the first full implementation of a dependency parser for Indonesian. Using self-training in combination with our Ensemble SVM Parser we show aditional improvement. Using this parsing model we plan on expanding the size of the corpus by using a semi-supervised approach by applying the parser and correcting the errors, reducing the amount of annotation time needed.
The ‘Within’ Journey: Assessment of the Online Indigenous Australian Cultural Competence Training Programme at Charles Sturt University

This paper explores the impact of self-reflective processes of staff at Charles Sturt University (CSU) while undertaking an online Indigenous Cultural Competency Program (ICCP). The ICCP was designed for completion by all university staff to enhance their knowledge and understanding of Indigenous cultures, histories and contemporary realities. Staff were requirement to complete and submit answers to an online quiz, and a written reflection on their learning. This paper reflects on the responses of 64 volunteers and indicates large positive shifts are self-reported knowledge and understanding. The reflective texts of staff provide a rich source of information on the ‘journey within’. In-depth thematic analysis (Braun & Clarke, 2006) identified three key themes: evaluation, the mapped learning journey and the personal learning journey. The participant feedback, not only pointed to the cognitive, attitudinal and emotional impact of the content and pedagogical approach of the programme, but also identified barriers and issues for programmes aimed at complex change in a contested space. Our programme assessment relied on self-reported individual perception that surfaced hidden assumptions about Indigenous cultural competency (Kumas-Tan¸ Beagan, Loppie, MacLeod, & Frank, 2007). We acknowledge in the longer term a range of comprehensive outcome measures are needed.
Constraints and design approaches in analog ICs forlmplantable medical devices

Active implantable medical devices (AIMDs) are microsystems requiring ultra low energy operation, which is a characteristic increasingly shared by several other applications. On the other hand, AIMDs must comply with several specific constraints imposed by the medical implantable context. This paper first summarizes, from the point of view of the analog IC designer, the state of the art of AIMDs and their specific constraints. Then, some general design techniques for analog ICs for AIMDs are highlighted and an analog front-end for neural devices is presented to illustrate current circuit and architecture approaches.
A hybrid method based on WordNet and Wikipedia for computing semantic relatedness between texts

In this article we present a new method for computing semantic relatedness between texts. For this purpose we use a tow-phase approach. The first phase involves modeling document sentences as a matrix to compute semantic relatedness between sentences. In the second phase, we compare text relatedness by using the relation of their sentences. Since Semantic relation between words must be searched in lexical semantic knowledge source, selecting a suitable source is very important, so that produced accurate results with correct selection. In this work, we attempt to capture the semantic relatedness between texts with a more accuracy. For this purpose, we use a collection of tow well known knowledge bases namely, WordNet and Wikipedia, so that provide more complete data source for calculate the semantic relatedness with a more accuracy. We evaluate our approach by comparison with other existing techniques (on Lee datasets).
A high-performance, memory-based interconnection system for multicomputer environments

The design and operation of a very-high-performance, memory-mapped interconnection system called Merlin is discussed. The design can be effectively utilized to interconnect processors in a wide variety of environments, ranging from closely-coupled, dedicated systems to distributed workstations. The system provides a uniform approach to parallel programming which is independent of interconnection topology, processing elements, and languages. By using dynamically mapped reflective memory, the system can support selectively shared virtual memory regions. This approach permits user-selected information to be shared at high speeds and low latency. There is no software involvement in the actual sharing of information, and the system overlaps computation and communication automatically, word-by-word, to the extent possible. <<ETX>>
An Analytical Planning Model to Estimate the Optimal Density of Charging Stations for Electric Vehicles

The charging infrastructure location problem is becoming more significant due to the extensive adoption of electric vehicles. Efficient charging station planning can solve deeply rooted problems, such as driving-range anxiety and the stagnation of new electric vehicle consumers. In the initial stage of introducing electric vehicles, the allocation of charging stations is difficult to determine due to the uncertainty of candidate sites and unidentified charging demands, which are determined by diverse variables. This paper introduces the Estimating the Required Density of EV Charging (ERDEC) stations model, which is an analytical approach to estimating the optimal density of charging stations for certain urban areas, which are subsequently aggregated to city level planning. The optimal charging station’s density is derived to minimize the total cost. A numerical study is conducted to obtain the correlations among the various parameters in the proposed model, such as regional parameters, technological parameters and coefficient factors. To investigate the effect of technological advances, the corresponding changes in the optimal density and total cost are also examined by various combinations of technological parameters. Daejeon city in South Korea is selected for the case study to examine the applicability of the model to real-world problems. With real taxi trajectory data, the optimal density map of charging stations is generated. These results can provide the optimal number of chargers for driving without driving-range anxiety. In the initial planning phase of installing charging infrastructure, the proposed model can be applied to a relatively extensive area to encourage the usage of electric vehicles, especially areas that lack information, such as exact candidate sites for charging stations and other data related with electric vehicles. The methods and results of this paper can serve as a planning guideline to facilitate the extensive adoption of electric vehicles.
Quantitative High-Resolution Genomic Analysis of Single Cancer Cells

During cancer progression, specific genomic aberrations arise that can determine the scope of the disease and can be used as predictive or prognostic markers. The detection of specific gene amplifications or deletions in single blood-borne or disseminated tumour cells that may give rise to the development of metastases is of great clinical interest but technically challenging. In this study, we present a method for quantitative high-resolution genomic analysis of single cells. Cells were isolated under permanent microscopic control followed by high-fidelity whole genome amplification and subsequent analyses by fine tiling array-CGH and qPCR. The assay was applied to single breast cancer cells to analyze the chromosomal region centred by the therapeutical relevant EGFR gene. This method allows precise quantitative analysis of copy number variations in single cell diagnostics.
Objective Language Feature Analysis in Children with Neurodevelopmental Disorders During Autism Assessment

Lexical planning is an important part of communication and is reflective of a speaker’s internal state that includes aspects of affect, mood, as well as mental health. Within the study of developmental disorders such as autism spectrum disorder (ASD), language acquisition and language use have been studied to assess disorder severity and expressive capability as well as to support diagnosis. In this paper, we perform a language analysis of children focusing on word usage, social and cognitive linguistic word counts, and a few recently proposed psycholinguistic norms. We use data from conversational samples of verbally fluent children obtained during Autism Diagnostic Observation Schedule (ADOS) sessions. We extract the aforementioned lexical cues from transcripts of session recordings and demonstrate their role in differentiating children diagnosed with Autism Spectrum Disorder from the rest. Further, we perform a correlation analysis between the lexical norms and ASD symptom severity. The analysis reveals an increased affinity by the interlocutor towards use of words with greater feminine association and negative valence.
A survey on job scheduling algorithms in Big data processing

Scheduling problem has been an active area of research in computing systems since their inception. The Apache Hadoop framework has emerged as most widely adopted framework for distributed data processing because of open source and allowing use of commodity hardware. Job scheduling has become an important factor to achieve high performance in Hadoop cluster. Several scheduling algorithms have been developed for Hadoop-MapReduce model which vary widely in design and behavior, handling different issues such as locality of data, user share fairness and resource awareness. This paper highlights fundamental issues in job scheduling, presents classification of Hadoop schedulers, and discusses presented survey of existing scheduling algorithm. Moreover paper also discusses features, advantages, and limitations of the scheduling algorithms. This paper also discusses about how various resource monitoring tools or frameworks help in achieving better result from MapReduce. It also discusses customized MapReduce frameworks used for improving the performance. This paper would be useful to beginners and researchers for understanding the state-of-the-art on scheduling in Big data processing.
CONSUMER BEHAVIOR, HORMONES, AND NEUROSCIENCE: INTEGRATED UNDERSTANDING OF FUNDAMENTAL MOTIVES WHY WE BUY

Consumer behaviors are shaped by fundamental motives: affiliation, self-protection, status attainment, mate attraction, mate retention, and child-rearing. It has been argued that each fundamental motive is activated by cues pertaining to threats or opportunities linked with each fundamental motive, and may be based on qualitatively different cognitive and neural systems. Steroid hormones influence specific neural systems and consumer behaviors rooted in diverse fundamental motives. By taking steroid sex hormones as examples of internal cues, we suggested that at least three fundamental motives (status attainment, mate attraction, and mate retention) may be explained by common cognitive and neural mechanisms. Consumer behaviors rooted in diverse fundamental motives, including status attainment, mate attraction, and mate retention, may be commonly explained by social motivations/vigilance (amygdala) and reward processing (reward-related brain regions). Neuroscientific tools may be useful for refining the fundamental motive framework, and for understanding more fully consumer behaviors rooted in evolutionary motives.
The Root Herbivore History of the Soil Affects the Productivity of a Grassland Plant Community and Determines Plant Response to New Root Herbivore Attack

Insect root herbivores can alter plant community structure by affecting the competitive ability of single plants. However, their effects can be modified by the soil environment. Root herbivory itself may induce changes in the soil biota community, and it has recently been shown that these changes can affect plant growth in a subsequent season or plant generation. However, so far it is not known whether these root herbivore history effects (i) are detectable at the plant community level and/or (ii) also determine plant species and plant community responses to new root herbivore attack. The present greenhouse study determined root herbivore history effects of click beetle larvae (Elateridae, Coleoptera, genus Agriotes) in a model grassland plant community consisting of six common species (Achillea millefolium, Plantago lanceolata, Taraxacum officinale, Holcus lanatus, Poa pratensis, Trifolium repens). Root herbivore history effects were generated in a first phase of the experiment by growing the plant community in soil with or without Agriotes larvae, and investigated in a second phase by growing it again in the soils that were either Agriotes trained or not. The root herbivore history of the soil affected plant community productivity (but not composition), with communities growing in root herbivore trained soil producing more biomass than those growing in untrained soil. Additionally, it influenced the response of certain plant species to new root herbivore attack. Effects may partly be explained by herbivore-induced shifts in the community of arbuscular mycorrhizal fungi. The root herbivore history of the soil proved to be a stronger driver of plant growth on the community level than an actual root herbivore attack which did not affect plant community parameters. History effects have to be taken into account when predicting the impact of root herbivores on grasslands.