Miracle-dz/UniFAR-FTUs huggingface dataset:
Indoor 3D pedestrian tracking algorithm based on PDR using smarthphone

In this paper, we develop the indoor navigation system based on PDR (Pedestrian Dead Reckoning) using various sensors in smartphone. Usually PDR is consisted of step detection, step length estimation and heading estimation. The issue of PDR is step length estimation and to enhance the accuracy of step length, we apply the walking status recognition algorithm using ANN (Artificial Neuron Network). The features used in ANN are extracted through sensor signals of accelerometer and gyroscope. After recognizing the walking status, it is applied to estimate the step length. And when the status is recognized as stop, even if sensor signal is generated by redundant motion or movement of pedestrian, the moved distance is not calculated additionally and distance error is not increased. We use the barometric pressure sensor to extend the positioning area to whole building. To verify the proposed indoor navigation system, we implemented the application for android and conducted the experiment. Through the results, we demonstrated the accuracy of our system.
The research domain criteria framework in drug discovery for neuropsychiatric diseases: focus on negative valence

Drug discovery, particularly in the field of central nervous system, has had very limited success in the last few decades. A likely contributor is the poor translation between preclinical and clinical phases. The Research Domain Criteria of the National Institutes of Mental Health is a framework which aims to identify new ways of classifying mental illnesses that are based on observable behaviour and neurobiological measures, and to provide a guiding and evolving framework to improve the translation from preclinical to clinical research. At the core of the Research Domain Criteria approach is the assumption that the dimensional constructs described can be assessed across different units of analysis, thus enabling a more precise quantitative understanding of their neurobiological underpinnings, increasing the likelihood of identifying new and effective therapeutic approaches. In the present review, we discuss how the Research Domain Criteria can be applied to drug discovery with the domain Negative Valence, construct Potential Threat (‘Anxiety’) as an example. We will discuss the evidence supporting the utility of the Research Domain Criteria approach and evaluate how close we are to achieving a common thread of translational research from gene to self-report.
Perturbations in the spi1p GTPase cycle of Schizosaccharomyces pombe through its GTPase-activating protein and guanine nucleotide exchange factor components result in similar phenotypic consequences

spi1p of Schizosaccharomyces pombe is a structural homolog of the mammalian GTPase Ran. The distribution between the GTP- and GDP-bound forms of the protein is regulated by evolutionarily conserved gene products, rna1p and pim1p, functioning as GTPase-activating protein (GAP) and guanine nucleotide exchange factor (GEF), respectively. Antibodies to spi1p, pim1p, and rna1p were generated and used to demonstrate that pim1p is exclusively nuclear, while rna1p is cytoplasmic. A loss of pim1p GEF activity or an increase in the rna1p GAP activity correlates with a change in the localization of the GTPase from predominantly nuclear to uniformly distributed, suggesting that the two forms are topologically segregated and that the nucleotide-bound state of spi1p may dictate its intracellular localization. We demonstrate that the phenotype of cells overproducing the GAP resembles the previously reported phenotype of mutants with alterations in the GEF: the cells are arrested in the cell cycle as septated, binucleated cells with highly condensed chromatin, fragmented nuclear envelopes, and abnormally wide septa. Consistent with the expectation that either an increased dosage of the GAP or a mutation in the GEF would lead to an increase of the spi1p-GDP/spi1p-GTP ratio relative to that of wild-type cells, overexpression of the GAP together with a mutation in the GEF is synthetically lethal. The similar phenotypic consequences of altering the functioning of the nuclear GEF or the cytoplasmic GAP suggest that there is a single pool of the spi1p GTPase that shuttles between the nucleus and the cytoplasm. Phenotypically, rna1 null mutants, in which spi1p-GTP would be expected to accumulate, resemble pim1(ts) and rna1p-overproducing cells, in which spi1p-GDP would be expected to accumulate. Taken together, these results support the hypothesis that the balance between the GDP- and GTP-bound forms of spi1p mediates the host of nuclear processes that are adversely affected when the functioning of different components of this system is perturbed in various organisms.
Restoring functional PET Images usingAnatomical

In this paper we present a Bayesian method to enhance functional 3D PET images using apriori knowledge about the brain anatomy obtained from 3D MR images. We use a Markov Random Field as a prior model, which have the possibility of incorporating this information. We show both a small test example as well as a true PET-MR result, and further more show how to obtain the desired information from the MR images.
The Real maccoyii: Identifying Tuna Sushi with DNA Barcodes – Contrasting Characteristic Attributes and Genetic Distances

Background The use of DNA barcodes for the identification of described species is one of the least controversial and most promising applications of barcoding. There is no consensus, however, as to what constitutes an appropriate identification standard and most barcoding efforts simply attempt to pair a query sequence with reference sequences and deem identification successful if it falls within the bounds of some pre-established cutoffs using genetic distance. Since the Renaissance, however, most biological classification schemes have relied on the use of diagnostic characters to identify and place species. Methodology/Principal Findings Here we developed a cytochrome c oxidase subunit I character-based key for the identification of all tuna species of the genus Thunnus, and compared its performance with distance-based measures for identification of 68 samples of tuna sushi purchased from 31 restaurants in Manhattan (New York City) and Denver, Colorado. Both the character-based key and GenBank BLAST successfully identified 100% of the tuna samples, while the Barcode of Life Database (BOLD) as well as genetic distance thresholds, and neighbor-joining phylogenetic tree building performed poorly in terms of species identification. A piece of tuna sushi has the potential to be an endangered species, a fraud, or a health hazard. All three of these cases were uncovered in this study. Nineteen restaurant establishments were unable to clarify or misrepresented what species they sold. Five out of nine samples sold as a variant of “white tuna” were not albacore (T. alalunga), but escolar (Lepidocybium flavorunneum), a gempylid species banned for sale in Italy and Japan due to health concerns. Nineteen samples were northern bluefin tuna (T. thynnus) or the critically endangered southern bluefin tuna (T. maccoyii), though nine restaurants that sold these species did not state these species on their menus. Conclusions/Significance The Convention on International Trade Endangered Species (CITES) requires that listed species must be identifiable in trade. This research fulfills this requirement for tuna, and supports the nomination of northern bluefin tuna for CITES listing in 2010.
Aldehyde Dehydrogenase 1, a Potential Marker for Cancer Stem Cells in Human Sarcoma

Tumors contain a small population of cancer stem cells (CSC) proposed to be responsible for tumor maintenance and relapse. Aldehyde dehydrogenase 1 (ALDH1) activity has been used as a functional stem cell marker to isolate CSCs in different cancer types. This study used the Aldefluor® assay and fluorescence-activated cell sorting (FACS) analysis to isolate ALDH1high cells from five human sarcoma cell lines and one primary chordoma cell line. ALDH1high cells range from 0.3% (MUG-Chor1) to 4.1% (SW-1353) of gated cells. Immunohistochemical staining, analysis of the clone formation efficiency, and xCELLigence microelectronic sensor technology revealed that ALDH1high cells from all sarcoma cell lines have an increased proliferation rate compared to ALDH1low cells. By investigating of important regulators of stem cell biology, real-time RT-PCR data showed an increased expression of c-Myc, β-catenin, and SOX-2 in the ALDH1high population and a significant higher level of ABCG2. Statistical analysis of data demonstrated that ALDH1high cells of SW-982 and SW-1353 showed higher resistance to commonly used chemotherapeutic agents like doxorubicin, epirubicin, and cisplatin than ALDH1low cells. This study demonstrates that in different sarcoma cell lines, high ALDH1 activity can be used to identify a subpopulation of cells characterized by a significantly higher proliferation rate, increased colony forming, increased expression of ABC transporter genes and stemness markers compared to control cells. In addition, enhanced drug resistance was demonstrated.
Acquiring Knowledge About Explicit User Goals from Search Query Logs

Access to knowledge about user goals represents a critical component for realizing the vision of intelligent agents acting upon user intent on the web. Yet, the acquisition of knowledge about user goals represents a major challenge. In a departure from existing approaches, this paper proposes a novel perspective for knowledge acquisition: The utilization of search query logs for this task. The research presented in this paper makes the following contributions: (a) it presents an automatic method for the acquisition of user goals from search query logs with useful precision/recall scores (b) it provides insights into the nature and some characteristics of these goals and (c) it shows that the goals acquired from query logs exhibit traits of a long tail distribution, thereby providing access to a broad range of user goals. We conclude that our work has implications for open research problems such as common sense knowledge acquisition and studies of user intent in query logs.
Generation of animated panorama from single video sequence

Panoramic-image-based approach for representing environmental data has become a common method used by various virtual reality applications. It has many advantages over the traditional 3D modeling approaches, such as shorter generation times, faster rendering speeds, higher photorealism, and less storage needed. The drawback is however that it stores only static information of the scene. Panoramic video texture has been proposed to preserve dynamic characteristics of the natural environment, especially for outdoor sceneries. The idea is to create a seamless looping wide field-of-view video sequence. This paper presents a newly developed software program, which takes a single video sequence from a panning camera, and generates a cylindrical panorama embedded with video textures. Although the conceptual goal is different from the idea of panoramic video texture, the animated visual effects created by our approach are similar to result of panoramic video texture. While it is a fact that generating a panoramic video texture is a very time consuming task, we claim that the quality of our resulting animated panorama is satisfactory to virtual reality applications and the computation time is acceptable for practical use.
On the use of optical flow in mobile robot navigation: the search for a suitable algorithm

This work addresses the use of optical flow to supervise the navigation of mobile robots. The amount of computation, the number of image frames to be stored and the reliability of the optical flow vectors generated should be acquainted for by programming a suitable algorithm for the onboard calculation of the optical flow. Two good candidates to be this algorithm are selected from the literature and are here considered. A slight modification of one of them is here proposed, and thus three algorithms are evaluated. This evaluation is based on two experiments whose results are reported. The conclusion is that the new algorithm here proposed is more suitable for the application.
Error estimates for the logarithmic barrier method in stochastic linear quadratic optimal control problems

: We consider a linear quadratic stochastic optimal control problem whith non-negativity control constraints. The latter are penalized with the classical logarithmic barrier. Using a duality argument and the stochastic minimum principle, we provide an error estimate for the solution of the penalized problem which is the natural extension of the well known estimate in the deterministic framework.
Can we believe the DAGs? A comment on the relationship between causal DAGs and mechanisms

Directed acyclic graphs (DAGs) play a large role in the modern approach to causal inference. DAGs describe the relationship between measurements taken at various discrete times including the effect of interventions. The causal mechanisms, on the other hand, would naturally be assumed to be a continuous process operating over time in a cause–effect fashion. How does such immediate causation, that is causation occurring over very short time intervals, relate to DAGs constructed from discrete observations? We introduce a time-continuous model and simulate discrete observations in order to judge the relationship between the DAG and the immediate causal model. We find that there is no clear relationship; indeed the Bayesian network described by the DAG may not relate to the causal model. Typically, discrete observations of a process will obscure the conditional dependencies that are represented in the underlying mechanistic model of the process. It is therefore doubtful whether DAGs are always suited to describe causal relationships unless time is explicitly considered in the model. We relate the issues to mechanistic modeling by using the concept of local (in)dependence. An example using data from the Swiss HIV Cohort Study is presented.
Observational evidence against mountain‐wave generation of ice nuclei as a prerequisite for the formation of three solid nitric acid polar stratospheric clouds observed in the Arctic in early December 1999

[1] A number of recently published papers suggest that mountain-wave activity in the stratosphere, producing ice particles when temperatures drop below the ice frost point, may be the primary source of large NAT particles. In this paper we use measurements from the Advanced Very High Resolution Radiometer (AVHRR) instruments on board the National Oceanic and Atmospheric Administration (NOAA) polar-orbiting satellites to map out regions of ice clouds produced by stratospheric mountain-wave activity inside the Arctic vortex. Lidar observations from three DC-8 flights in early December 1999 show the presence of solid nitric acid (Type Ia or NAT) polar stratospheric clouds (PSCs). By using back trajectories and superimposing the position maps on the AVHRR cloud imagery products, we show that these observed NAT clouds could not have originated at locations of high-amplitude mountain-wave activity. We also show that mountain-wave PSC climatology data and Mountain Wave Forecast Model 2.0 (MWFM-2) raw hemispheric ray and grid box averaged hemispheric wave temperature amplitude hindcast data from the same time period are in agreement with the AVHRR data. Our results show that ice cloud formation in mountain waves cannot explain how at least three large-scale NAT clouds were formed in the stratosphere in early December 1999.
A Generic Framework for Approximate Simulation in Commonsense Reasoning Systems

This paper introduces the Slick architecture and outlines how it may be applied to solve the well known Egg-Cracking Problem. In contrast to other solutions to this problem that are based on formal logics, the Slick architecture is based on general-purpose and low-resolution quantitative simulations. On this benchmark problem, the Slick architecture offers greater elaboration tolerance and allows for faster elicitation of more
Can Adult Care Diminish the Effects of Friends’ Assaultive Behaviors on Bullying Perpetration among African American Youth?

ABSTRACT The aim of the present study is to explore whether caring relationships with parents and teachers might buffer the effects of friends’ assaultive behavior on bullying perpetration among urban African American adolescents. The study sample comprised 483 African American adolescents, 12–19 years of age, in Chicago’s Southside. Hierarchical multivariate regression analyses were conducted to explore the direct and indirect effects of parental care, parental monitoring, and teacher care, on the association between friends’ assaultive behavior and bullying perpetration. The findings suggest no direct association between parental care, parental monitoring, and bullying perpetration. However, higher level of teacher care was related to lower level of bullying perpetration. Additionally, an interaction term, teacher care × friends’ assaultive behavior was found to be associated with lower bullying perpetration. Implications for practice and research are discussed.
Sparse Manifold Learning with Applications to SAR Image Classification

Nonlinear data-driven dimensionality reduction techniques have recently gained popularity due to the emergence of high dimensional data sets. The algorithmic complexity and storage requirements of these techniques, however, can make them prohibitive in resource-limited applications. It is therefore beneficial to reduce the number of exemplar samples required for performing an out-of-sample extension to a test point. In this paper, we propose a novel method for selecting a minimal set of exemplars and performing the out-of-sample extension. In the case of two-class target recognition with synthetic aperture radar (SAR) data, we compare the efficacy of the proposed approach with other approaches for selecting a subset of the available training samples. We show that the proposed algorithm outperforms the existing methods by providing low-dimensional embeddings that maintain interclass separability using fewer retained exemplars.
Speed sensorless vector control of an induction motor using an extended Kalman filter

The vector control of an induction motor by speed estimation using an extended Kalman filter (EKF) is described. The rotor speed is regarded as a parameter, and the composite state is composed of the original state and the rotor speed. The EKF is employed to identify the speed of an induction motor and rotor magnetic flux based on the measured quantities such as stator currents and DC link voltage. The estimated speed is used for vector control and overall speed control. The current control is performed in the synchronous rotating reference frame, and the estimated speed information is used for reference frame transformation of the current controller. Computer simulation of the speed control has been carried out to confirm the usefulness of the speed estimation algorithm. The error between the real speed and the estimated speed is within a few RPM even in the low speed range of about 20 RPM. <<ETX>>
Three routes to the exact asymptotics for the one-dimensional quantum walk

We demonstrate an alternative method for calculating the asymptotic behaviour of the discrete one-coin quantum walk on the infinite line, via the Jacobi polynomials that arise in the path-integral representation. We calculate the asymptotics using a method that is significantly easier to use than the Darboux method. It also provides a single integral representation for the wavefunction that works over the full range of positions, n, including throughout the transitional range where the behaviour changes from oscillatory to exponential. Previous analyses of this system have run into difficulties in the transitional range, because the approximations on which they were based break down here. The fact that there are two different kinds of approach to this problem (path integral versus Schrodinger wave mechanics) is ultimately a manifestation of the equivalence between the path-integral formulation of quantum mechanics and the original formulation developed in the 1920s. We also discuss how and why our approach is related to the two methods that have already been used to analyse these systems.
BootCaT: Bootstrapping Corpora and Terms from the Web

This paper introduces the BootCaT toolkit, a suite of perl programs implementing an iterative procedure to bootstrap specialized corpora and terms from the web. The procedure requires only a small set of seed terms as input. The seeds are used to build a corpus via automated Google queries, and more terms are extracted from this corpus. In turn, these new terms are used as seeds to build a larger corpus via automated queries, and so forth. The corpus and the unigram terms are then used to extract multi-word terms. We conducted an evaluation of the tools by applying them to the construction of English and Italian corpora and term lists from the domain of psychiatry. The results illustrate the potential usefulness of the tools.
Automatic Dynamic Allocation of Cloud Storage for Scientific Applications

Particularly in scientific community the size of digital data to be stored is ramping up. For those application characterized by very dynamic workloads is difficult to estimate the real size of storage to be allocated and avoid over-provisioning, also in extremely elastic environments as cloud computing. DAViS (Dynamic Allocation of Virtual Storage) is a prototype of a system for dynamically providing virtual block storage to Virtual Machines (VMs), optimizing the physical resources utilization, through dynamic and autonomous resizing of the storage.
Effects of polycythemia and anemia on cardiac output and other circulatory factors

Normovolemic anemia and polycythemia were studied in 14 dogs. Cardiac outputs increased with anemia and fell with rises in hematocrit. Although many factors—such as chemical changes—may play an important role in these variations in cardiac output, there was an indication that viscosity alone may have a major effect. There was no significant association between changes in cardiac output and the various pressures—mean arterial, mean right atrial, mean pulmonary and mean circulatory. Although the pressures did not change significantly, there was a significant decrease in total peripheral resistance in anemia and a marked rise in polycythemia. It was also found that the maximum number of red cells present for oxygen transport to the tissues was near the mean normal hematocrit of 40.
Epochal Changes in ENSO-Streamflow Relationships in Sri Lanka

In an effort to use climate predictions for streamflow and malaria hazard prediction, the decadal variability of the El Nino–Southern Oscillation (ENSO) influence on streamflow and rainfall in the Kelani River in Sri Lanka was investigated based on records from 1925 to 1995. In the last half century, the warm ENSO phase was associated with decreased annual streamflow and the cold ENSO phase with increased streamflow. The annual streamflow had a negative correlation (warm ENSO associated with low streamflow) with the concurrent ENSO index of Nino-3.4 that was significant at the 5% level. This negative correlation with Nino-3.4 is enhanced to a 1% significance level if the aggregate streamflow from January to September alone is considered. There has been a transition in correlation between January–September streamflow and ENSO between the 1950s and 1970s from near or above zero to negative values that have 95% significance levels reminiscent of an epochal shift. This shift was evident when considering the period when the southwest monsoon dominates (April–September) or when correlations were undertaken between the seasonal streamflow and rainfall and the ENSO index in the month prior to each season. Since the relationship between ENSO and Sri Lankan streamflow has strengthened in recent decades the potential for ENSO-based prediction is retained. The epochal shift may also explain why malaria epidemics ceased to co-occur frequently with El Nino episodes after 1945.