Miracle-dz/UniFAR-FTUs huggingface dataset:
Brain Activity during the Observation, Imagery, and Execution of Tool Use: An fNIRS/EEG Study

This study used a functional near-infrared spectroscopy (fNIRS)/electroencephalogram (EEG) system to investigate brain activity during a daily living situation where subjects are engaged in the observation, imagery, and execution of tool use. The subjects were 24 healthy adults who signed an agreement to participate in the present study. All subjects were right-handed according to the Edinburgh Handedness Inventory. The subjects sat on a chair and participated in the observation, imagery, and execution of chopstick and hammer movements. Each trial consisted of observation, imagery, and execution and the trials were performed in triplicate. The timing protocol was 15.0, 15.0, 15.0, 15.0, and 15.0 s for rest, task, rest, task, and rest, respectively, and brain activity was measured during these periods. fNIRS and EEG were used to measure brain activity. Optical fibers of fNIRS were positioned over the frontoparietal area. Oxyhemoglobin served as a parameter, and NIRS-statistical parametric mapping (SPM) was used for data analysis. EEG measurements were made using 32 electrodes with an international 10–20 system. The event-related desynchronization of the Mu frequency band (8-12 Hz) was calculated by power spectrum analysis. The brain regions activated during the imagery and execution of the chopsticks movement were the dorsolateral prefrontal cortex, inferior frontal gyrus, premotor cortex, and primary somatosensory cortex, while the mu ERD values were obtained under both conditions. The brain regions that were mainly activated under all three conditions of the hammer movement, i.e., observation, imagery, and execution, were the dorsolateral prefrontal cortex, inferior frontal gyrus, and premotor cortex, while the mu ERD values were obtained under all three conditions. These results suggest that brain activity during the observation, imagery, and execution of tool use differed according to the sensory modality linked to a tool.
Regulation of Mycobacterium tuberculosis whiB3 in the Mouse Lung and Macrophages

ABSTRACT Mycobacterium tuberculosis is a highly successful human pathogen, with ∼2 × 109 individuals infected globally. To understand the responses of M. tuberculosis to the in vivo environment, we studied the in vivo regulation of M. tuberculosis genes whose M. marinum homologs are induced in chronically infected frog tissues. The expression of 16S rRNA was shown to remain constant in M. tuberculosis under in vivo and in vitro conditions and therefore could be used for internal normalization in quantitative reverse transcription-PCR assays. We found whiB3, a putative transcriptional regulator implicated in mediating tissue damage, to be maximally induced at 2 weeks postinfection in the lungs of wild-type and immunodeficient (gamma interferon receptor−/−, Rag1−/−, and tumor necrosis factor alpha−/−) mice. At later time points in wild-type mice, whiB3 induction was decreased and gradually declined over the course of infection. In immunodeficient mice, whiB3 induction declined rapidly and was completely abolished in moribund animals. whiB3 was also found to be induced in naïve bone marrow-derived macrophages after 6 h of infection. whiB3 expression in vivo and in vitro was found to be inversely correlated with bacterial density. These results indicate that M. tuberculosis regulates the expression of whiB3 in response to environmental signals present in vivo and are consistent with a model of regulation by quorum sensing.
The Design of a Network-Based Model for Business Performance Prediction

While much research work has been devoted to analysis and prediction of individuals’ behavior in social networks, very few studies about the analysis of business networks are conducted. Empowered by recent research on automated mining of business networks, this paper illustrates the design of a novel business network-based model called Energy Cascading Model (ECM) for the analysis and prediction of business performance using the proxies of stock prices. More specifically, the proposed prediction model takes into account both influential business relationships and twitter sentiments of firms to infer their stock price movements. Our empirical experiments based on a publicly available financial corpus and social media postings reveal that the proposed ECM model is effective for the prediction of directional stock price movements. The business implication of our research is that business managers can apply our design artifacts to more effectively analyze and predict the potential business performance of targeted firms.
An evaluation of software for computing eigenvalues of sparse nonsymmetric matrices Preprint MCS-P54

The past few years have seen a signiicant increase in research into numerical methods for computing selected eigenvalues of large sparse nonsymmetric matrices. This research has begun to lead to the development of high-quality mathematical software. The software includes codes that implement subspace iteration methods, Arnoldi-based algorithms , and nonsymmetric Lanczos methods. The aim of the current study is to evaluate this state-of-the-art software. In this study we consider subspace iteration and Arnoldi codes. We look at the key features of the codes and their ease of use. Then, using a wide range of test problems, we compare the performance of the codes in terms of storage requirements, execution times, accuracy, and reliability. We also consider their suitability for solving large-scale industrial problems. Based on our ndings, we suggest how improved software should be designed.
A Nucleolus-Predominant piggyBac Transposase, NP-mPB, Mediates Elevated Transposition Efficiency in Mammalian Cells

PiggyBac is a prevalent transposon system used to deliver transgenes and functionally explore the mammalian untouched genomic territory. The important features of piggyBac transposon are the relatively low insertion site preference and the ability of seamless removal from genome, which allow its potential uses in functional genomics and regenerative medicine. Efforts to increase its transposition efficiency in mammals were made through engineering the corresponding transposase (PBase) codon usage to enhance its expression level and through screening for mutant PBase variants with increased enzyme activity. To improve the safety for its potential use in regenerative medicine applications, site-specific transposition was achieved by using engineered zinc finger- and Gal4-fused PBases. An excision-prone PBase variant has also been successfully developed. Here we describe the construction of a nucleolus-predominant PBase, NP-mPB, by adding a nucleolus-predominant (NP) signal peptide from HIV-1 TAT protein to a mammalian codon-optimized PBase (mPB). Although there is a predominant fraction of the NP-mPB-tGFP fusion proteins concentrated in the nucleoli, an insertion site preference toward nucleolar organizer regions is not detected. Instead a 3–4 fold increase in piggyBac transposition efficiency is reproducibly observed in mouse and human cells.
Empathy as an Ability: Structure and Development during Training in Psychological Counseling

This article proposes a model for developing empathy as an ability. It introduces a model of empathic skills based on the abilities model of V.D. Shadrikov. It analyzes the structure of an empathic ability as consisting of functional, operational, and motivational components. The model is used as a basis for considering techniques and methods of developing empathy. The article presents the results of an experimental study of the development of empathy among undergraduates during training in psychological counseling in co-experiencing psychotherapy workshops.
Limk2 mediates semaphorin signalling in cortical interneurons migrating through the subpallium

Summary En route to the neocortex, interneurons migrate around and avoid the developing striatum. This is due to the chemorepulsive cues of class 3 semaphorins (Sema3A and Sema3F) acting through neuropilin and plexin co-receptors expressed in interneurons. In a recent genetic screen aimed at identifying novel components that may play a role in interneuron migration, we identified LIM-kinase 2 (Limk2), a kinase previously shown to be involved in cell movement and in Sema7A-PlexinC1 signalling. Here we show that Limk2 is differentially expressed in interneurons, with a higher expression in the subpallium compared to cortex, suggesting it may play a role in their migration through the subpallium. Chemotactic assays, carried out with small interfering RNAs (siRNAs), revealed that Limk2-siRNA transfected interneurons are less responsive to Sema3A, but respond to Sema3F. Lack of responsiveness to Sema3A resulted in their aberrant invasion of the developing striatum, as demonstrated in brain slice preparations and in in utero electroporated mouse embryos with the same siRNAs. Our results reveal a previously unknown role for Limk2 in interneuron migration and Sema3A signalling.
Finding frequent sub-trajectories with time constraints

With the advent of location-based social media and location-acquisition technologies, trajectory data are becoming more and more ubiquitous in the real world. Trajectory pattern mining has received a lot of attention in recent years. Frequent sub-trajectories, in particular, might contain very usable knowledge. In this paper, we define a new trajectory pattern called frequent sub-trajectories with time constraints (FSTTC) that requires not only the same continuous location sequence but also the similar staying time in each location. We present a two-phase approach to find FSTTCs based on suffix tree. Firstly, we select the spatial information from the trajectories and generate location sequences. Then the suffix tree is adopted to mine out the frequent location sequences. Secondly, we cluster all sub-trajectories with the same frequent location sequence with respect to the staying time using modified DBSCAN algorithm to find the densest clusters. Accordingly, the frequent sub-trajectories with time constraints, represented by the clusters, are identified. Experimental results show that our approach is efficient and can find useful and interesting information from the spatio-temporal trajectories.
Narrative Generation in Entertainment: Using Artificial Intelligence Planning

From the field of artificial intelligence (AI) there is a growing stream of technology capable of being embedded in software that will reshape the way we interact with our environment in our everyday lives. This ‘AI software’ is often used to tackle more mundane tasks that are otherwise dangerous or meticulous for a human to accomplish. One particular area, explored in this paper, is for AI software to assist in supporting the enjoyable aspects of the lives of humans. Entertainment is one of these aspects, and often includes storytelling in some form no matter what the type of media, including television, films, video games, etc. This paper aims to explore the ability of AI software to automate the story-creation and story-telling process. This is part of the field of Automatic Narrative Generator (ANG), which aims to produce intuitive interfaces to support people (without any previous programming experience) to use tools to generate stories, based on their ideas of the kind of characters, intentions, events and spaces they want to be in the story. The paper includes details of such AI software created by the author that can be downloaded and used by the reader for this purpose. Applications of this kind of technology include the automatic generation of story lines for ‘soap operas’.
Sympodial construction of Fibonacci-type leaf rosettes in Pinguicula moranensis (Lentibulariaceae).

BACKGROUND AND AIMS The leaf rosettes of the carnivorous Pinguicula moranensis follow a spiral phyllotaxis approaching a Fibonacci pattern while the stalked flowers arise from extra-axillary sites between the leaves. The organization of this rosette has been discussed by various authors, with various results. The aim of the present study was to clarify the development of the flowering rosettes of this species. METHODS The formation of the rosettes is shown with the aid of scanning electron microscopy. KEY RESULTS AND CONCLUSIONS The scanning electron micrographs show that each flower terminates an article (sympodial unit). The leaves of consecutive articles of such sympodially constructed rosettes are arranged along a spiral Fibonacci pattern (with divergence angles around 137 degrees). This results from homodromy of leaf initiation in consecutive articles with the first leaf (prophyll) of a new article inserted in an obliquely transverse position next to the floral scape that terminates the former article. Sympodial construction of flowering shoots and leaf rosettes is also known from Aloe, Gunnera and Philodendron. As a by-product of this study, the unidirectional development of the Pinguicula flower is confirmed and discussed.
Deporting “Bad Hombres”? The Profile of Deportees under Widespread Versus Prioritized Enforcement

US President Trump’s administration has vowed to boost immigration enforcement to get rid of “bad hombres,” or undocumented immigrants with criminal records. Using past data on the alleged detention motives for a representative sample of Mexican deportees, we evaluate how prior widespread and prioritized enforcement has fared in that regard. We find that while the early sweeping approach to enforcement raised deportees’ propensity of being detained for minor offenses, the trend reversed with prioritized enforcement. These findings inform policy tactics that, aside from proving effective in prioritizing serious criminal offenses, can also lead to significant savings to taxpayers.
Addressing Pre-Service Teachers' Misconceptions About Confidence Intervals

Addressing Pre-Service Teachers’ Misconceptions About Confidence Intervals Kiya Lynn Eliason Department of Mathematics Education, BYU Master of Arts Increased attention to statistical concepts has been a prevalent trend in revised mathematics curricula across grade levels. However, the preparation of secondary school mathematics educators has not received similar attention, and learning opportunities provided to these educators is oftentimes insufficient for teaching statistics well. The purpose of this study is to analyze pre-service teachers’ conceptions about confidence intervals. This research inquired about statistical reasoning from the perspective of students majoring in mathematics education enrolled in an undergraduate statistics education course who have previously completed an introductory course in statistics. We found common misconceptions among pre-service teachers participating in this study. An unanticipated finding is that all the pre-service teachers believed that the construction of a confidence interval relies on a sampling distribution that does not contain every possible sample. Instead, they believed it is necessary to take multiple samples and build a distribution of their means. I called this distribution the Multi-Sample Distribution (MSD).
Evaluating SPARQL Subqueries over P2P Overlay Networks

The introduction of subqueries is one of the most interesting features included in the latest SPARQL 1.1 specification. Existing distributed RDF storage and querying systems have not studied the evaluation of this newly included query feature. The evaluation of subqueries in distributed environment may be very inefficient and expensive in terms of query response time and bandwidth usage, particularly for the correlated subqueries where the inner query block is evaluated once for each solution of the outer query. In this paper, we study the problem of evaluating SPARQL subqueries over RDF data stored in 3nuts p2p network. We study semi join based optimization technique, and transformation algorithms to transform correlated queries to equivalent uncorrelated ones, that would make the distributed evaluation of such queries more efficient.
A Solution for Preventing Fraudulent Financial Reporting using Descriptive Data Mining Techniques

the present age of scams, financial statement fraud represents enormous cost to our economy. The deliberate misstatement of numbers in the accounting books with the help of well planned scheme by an intelligent squad of knowledgeable perpetrators in order to deceive the capital market participants is termed as financial statement fraud. In order to reduce fraud risk which comprehends both detection and prevention of financial statement fraud, this paper implements descriptive data mining techniques such as Association rules and clustering as opposed to predictive data mining techniques used in the literature. Each of these techniques is applied on dataset obtained from financial statements namely balance sheet, income statement and cash flow statement of 114 companies.
Highly linear and sub 120 fs Ron × Coff 130 nm RF SOI technology targeting 5G carrier aggregation RF switches and FEM SOC

RF front end modules (FEMs) are currently realized using a variety of technologies. However, since integration drives wireless business in order to achieve the appropriate cost and form factor, CMOS Silicon-on-insulator (SOI) has emerged over the past few years as the dominant technology for RF switches in RF FEMs for cell phones and WiFi [1]. While current performances available on RF SOI technology are already exceeding what was feasible using GaAs one, new cellular system such as carrier aggregation require even more stringent performances (linearity, power handling, insertion loss, isolation). To address those new requirements, RF SOI technology has to be improved. In this paper, the performances results of the latest generations of RF SOI switch technologies from STMicroelectronics are reviewed and technology elements that contribute to improved performance are discussed. Future improvements are also proposed, paving the way for RF SOI technology able to address 5G RF switches challenges.
Scalable Algorithms for Population Genomic Inference

Since the 1920s, researchers in population genetics have developed mathematical models to explain how a species evolves. With the rise of DNA sequencing over the past decade, we now have the data to use these models to answer real questions in evolutionary biology. However, the sheer amount of data and the time complexity of the models makes inference extremely challenging. Computer science has therefore become an essential tool for bridging theoretical models and modern sequencing data.In this thesis we present two novel algorithms that make use of DNA sequencing data in a principled yet practical way. The first method estimates the history of effective population sizes of a species using a coalescent hidden Markov model (HMM). Previous coalescent HMMs could only handle a few sequences, since the set of coalescent trees makes the state- space prohibitively large. Our algorithm uses a modified state-space to make inference computationally feasible while still retaining the essential genealogical features of a sample. We apply this algorithm, called diCal, to human data to learn more about major events in human history, such as the out-of-Africa migration. We also provide several extensions to diCal that make the computation faster, more automated, and applicable in a wider variety of scenarios.The second method is an algorithm for jointly estimating effective population size changes and natural selection. These two factors can leave similar traces in genomic data, and the models that would describe both are computationally intractable. Our method uses a machine learning technique called deep learning to make the inference procedure robust and efficient. Deep learning automatically teases out important features of the data, but previously had not been used in population genetics. We apply this method to African Drosophila melanogaster data to jointly infer their population size changes and classify each region of their genome as neutral or under natural selection. We considered three types of selection: hard sweeps, soft sweeps, and balancing selection. To create a sophisticated framework for population genomic inference, in the future it would be promising to combine machine learning algorithms with biologically-inspired coalescent modeling.
Volume regulation and shape bifurcation in the cell nucleus

ABSTRACT Alterations in nuclear morphology are closely associated with essential cell functions, such as cell motility and polarization, and correlate with a wide range of human diseases, including cancer, muscular dystrophy, dilated cardiomyopathy and progeria. However, the mechanics and forces that shape the nucleus are not well understood. Here, we demonstrate that when an adherent cell is detached from its substratum, the nucleus undergoes a large volumetric reduction accompanied by a morphological transition from an almost smooth to a heavily folded surface. We develop a mathematical model that systematically analyzes the evolution of nuclear shape and volume. The analysis suggests that the pressure difference across the nuclear envelope, which is influenced by changes in cell volume and regulated by microtubules and actin filaments, is a major factor determining nuclear morphology. Our results show that physical and chemical properties of the extracellular microenvironment directly influence nuclear morphology and suggest that there is a direct link between the environment and gene regulation. Highlighted Article: Depending on the physical environment of the cell, the nuclear volume can change dramatically. Cytoskeletal filaments and motors are involved in regulating nuclear volume.
Developing Nonstationary Noise Estimation for Application in Edge and Corner Detection

Accurate estimation of noise and signal power is of fundamental interest in a wide variety of vision applications as it is critical to thresholding and decision processes. This paper proposes two methods for the estimation of nonstationary noise based upon models of image structure which locally separate signal from noise. The resulting algorithms are noniterative and thereby fast. The accuracy of the proposed and existing methods is compared, first separately and then in application to two common image processing tasks: edge and corner detection. It is demonstrated that the proposed model can be used to improve the stability of both, in the presence of contrast change and nonstationary noise.
Packet switch architecture with multiple output queueing

In this paper, a new packet switch architecture with multiple output queuing (MOQ) is proposed. In this architecture, the nonblocking switch fabric, which has a capacity of N/spl times/N/sup 2/, and output buffers arranged into N separate queues for each output, are applied. Both switch fabric and buffers can operate at the same speed as input and output ports. This solution does not need any speedup in the switch fabric as well as arbitration logic for taking decisions which packets from inputs are to be transferred to outputs. The mean cell delay and cell loss probability, as performance measures for the proposed switch architecture, are evaluated and compared with the performance of the OQ architecture and VOQ architecture. The hardware complexity of the proposed switch is very similar to the VOQ switch but its performance is comparable to the OQ switch.
Robotic social imitation depends on self-embodiment and self-evaluation by direct teaching under multiple instructors

We have been worked about robotic social imitation in order to learn self-behavior depending on self-embodiment through interaction with multiple human. In this paper, we propose a learning method which allows to select behavioral patterns depending on self-embodiment and self-evaluation from multiple instructors by using the robot simulator Webots. We confirmed that results demonstrated that our proposal allows to improve the representative teaching data by the clustering includes two evaluation values (the distance moved forward and the impact shock for the body).