Miracle-dz/UniFAR-FTUs huggingface dataset:
Appraising the Quality of Systematic Reviews

Systematic reviews have become increasingly popular across the allied health, education, and disability and rehabilitation fields. Unlike traditional narrative reviews, systematic reviews aim to minimize bias in locating, selecting, coding, and aggregating individual studies. This rigor in minimizing bias is what makes these reviews systematic. In a previous issue of Focus (no. 15), systematic reviews were introduced and described as instrumental for (a) implementing evidence-based practice (EBP), (b) taking stock relative to a particular question (or set of questions), and (c) for the shaping of future research. For EBP purposes, systematic reviews provide practitioners with pre-filtered evidence, save time, and minimize the need for appraisal expertise. In terms of taking stock, systematic reviews provide the “state-of-the-art” on a given question. Finally, because of their systematic nature, systematic reviews are uniquely suited to document data-based gaps in the research literature that could in turn guide future research.
Against All Probabilities: a Modeling Paradigm for Streaming Applications That Goes against Common Notions against All Probabilities a Modeling Paradigm for Streaming Applications That Goes against Common Notions

Hardware and software design requires the right portion of skills and mental faculties. The design of a good system is an exercise in rational thinking, engineering, and art. The design process is further complicated when we aspire to build systems that exploit parallelism or are targeted to be deployed on architecturally diverse computing devices, FPGAs or GPUs to name just a few. The need to develop systems that can take advantage of computing devices beyond general purpose CPUs is real. There are several application domains and research efforts that will simply not be able to adequately perform or yield answers in a reasonable amount of time otherwise. Developing a mathematical model of a system is a key stepping stone to a high performance system, but often is absent from the design process due to the complexity of the model development. In this paper we offer an easy, yet solid approach to the development of such mathematical models. This adds a little bit more weight to engineering side of the design process in the form of a quantifiable method that enables designers to reason about their systems, identify bottlenecks, and gain vital information for performance Type of Report: Other Department of Computer Science & Engineering Washington University in St. Louis Campus Box 1045 St. Louis, MO 63130 ph: (314) 935-6160 Undergraduate Honors Thesis Against All Probabilities A modeling paradigm for streaming applications that goes against common notions Rahav Dor Advisors: Roger D. Chamberlain and Mark A. Franklin Dept. of Computer Science and Engineering, Washington University in St. Louis Rahav.Dor@wustl.edu
Humans tracking in the complicated background by multi-cue integration

Human tracking, as a basic research in computer vision, is a key technology in many applications, such as robotics for home using, auto-vehicles in city road and automatic surveillance systems. Although it is always a hot topic in the world of computer vision, it still contains many opening areas for researchers, mainly because there is no single framework could solve all the four problems, occlusion, illumination variation, shadow, and zooming. This paper starts by a new series algorithms for illumination reconstruction, and then introduces a new framework which is called part tracking by multi-cue integration. Aiming at an on-line tracking system in complicated background, this framework is suitable for our application.
An Empirical Analysis of the Current Need for Teleneuromedical Care in German Hospitals without Neurology Departments

Indroduction. At present, modern telemedicine methods are being introduced, that may contribute to reducing lack of qualified stroke patient care, particularly in less populated regions. With the help of video conferencing systems, a so-called neuromedical teleconsultation is carried out. Methods. The study included a multicentered, completely standardized survey of physicians in hospitals by means of a computerized on-line questionnaire. Descriptive statistical methods were used for data analysis. Results. 119 acute hospitals without neurology departments were included in the study. The most important reasons for participating in a teleneuromedical network is seen as the improvement in the quality of treatment (82%), the ability to avoid unnecessary patient transport (76%), easier and faster access to stroke expertise (72%) as well as better competitiveness among medical services (67%). The most significant problem areas are the financing system of teleneuromedicine with regard to the acquisition costs of the technical equipment (43%) and the compensation for the stroke-unit center with the specialists' consultation service (31%) as well as legal aspects of teleneuromedicine (27%). Conclusions. This investigation showed that there is a high acceptance for teleneuromedicine among co-operating hospitals. However these facilities have goals in addition to improved quality in stroke treatment. Therefore the use of teleneuromedicine must be also associated with long term incentives for the overall health care system, particularly since the implementation of a teleneuromedicine network system is time consuming and associated with high implementation costs.
Shape from Mixed Polarization

Shape from Polarization (SfP) estimates surface normals using photos captured at different polarizer rotations. Fundamentally, the SfP model assumes that light is reflected either diffusely or specularly. However, this model is not valid for many real-world surfaces exhibiting a mixture of diffuse and specular properties. To address this challenge, previous methods have used a sequential solution: first, use an existing algorithm to separate the scene into diffuse and specular components, then apply the appropriate SfP model. In this paper, we propose a new method that jointly uses viewpoint and polarization data to holistically separate diffuse and specular components, recover refractive index, and ultimately recover 3D shape. By involving the physics of polarization in the separation process, we demonstrate competitive results with a benchmark method, while recovering additional information (e.g. refractive index).
Cellular Protein Modification by Poliovirus: the Two Faces of Poly(rC)-Binding Protein

ABSTRACT During picornavirus infection, several cellular proteins are cleaved by virus-encoded proteinases. Such cleavage events are likely to be involved in the changing dynamics during the intracellular viral life cycle, from viral translation to host shutoff to RNA replication to virion assembly. For example, it has been proposed that there is an active switch from poliovirus translation to RNA replication mediated by changes in RNA-binding protein affinities. This switch could be a mechanism for controlling template selection for translation and negative-strand viral RNA synthesis, two processes that use the same positive-strand RNA as a template but proceed in opposing directions. The cellular protein poly(rC)-binding protein (PCBP) was identified as a primary candidate for regulating such a mechanism. Among the four different isoforms of PCBP in mammalian cells, PCBP2 is required for translation initiation on picornavirus genomes with type I internal ribosome entry site elements and also for RNA replication. Through its three K-homologous (KH) domains, PCPB2 forms functional protein-protein and RNA-protein complexes with components of the viral translation and replication machinery. We have found that the isoforms PCBP1 and -2 are cleaved during the mid-to-late phase of poliovirus infection. On the basis of in vitro cleavage assays, we determined that this cleavage event was mediated by the viral proteinases 3C/3CD. The primary cleavage occurs in the linker between the KH2 and KH3 domains, resulting in truncated PCBP2 lacking the KH3 domain. This cleaved protein, termed PCBP2-ΔKH3, is unable to function in translation but maintains its activity in viral RNA replication. We propose that through the loss of the KH3 domain, and therefore loss of its ability to function in translation, PCBP2 can mediate the switch from viral translation to RNA replication.
Estimating Drivers' Stress from GPS Traces

Driving is known to be a daily stressor. Measurement of driver's stress in real-time can enable better stress management by increasing self-awareness. Recent advances in sensing technology has made it feasible to continuously assess driver's stress in real-time, but it requires equipping the driver with these sensors and/or instrumenting the car. In this paper, we present "GStress", a model to estimate driver's stress using only smartphone GPS traces. The GStress model is developed and evaluated from data collected in a mobile health user study where 10 participants wore physiological sensors for 7 days (for an average of 10.45 hours/day) in their natural environment. Each participant engaged in 10 or more driving episodes, resulting in a total of 37 hours of driving data. We find that major driving events such as stops, turns, and braking increase stress of the driver. We quantify their impact on stress and thus construct our GStress model by training a Generalized Linear Mixed Model (GLMM) on our data. We evaluate the applicability of GStress in predicting stress from GPS traces, and obtain a correlation of 0.72. By obviating any burden on the driver or the car, we believe, GStress can make driver's stress assessment ubiquitous.
Detecting Objects via Multiple Segmentations and Latent Topic Models

We present an object detection system and show results on the PASCAL 2006 dataset. We introduce a codebook defined over segment-level features and show how multiple segmentations and Latent Topic Models can be used to localize objects in images despite the bag-of-words assumption. We demonstrate how to train both the Latent Dirichlet Allocation model and the Correlated Topic Model in a supervised way and show how our approach is capable of detecting multiple instances of objects in images such as cars, horses, grass, sheep, etc.
Estrogen Stimulates Postsynaptic Density-95 Rapid Protein Synthesis via the Akt/Protein Kinase B Pathway

Estrogens induce synaptogenesis in the CA1 region of the dorsal hippocampus during the estrous cycle of the female rat. Functional consequences of such estrogen-mediated synaptogenesis include cyclic changes in neurotransmission and memory. At the molecular level, estrogen stimulates the rapid activation of specific signal transduction pathways, and of particular interest is the activation of Akt (protein kinase B), a key signal transduction intermediate that initiates protein translation by alleviating the downstream translational repression of eukaryotic initiation factor 4E-binding protein 1 (4E-BP1). Using a well established in vitromodel system of differentiated NG108-15 neurons to investigate such rapid signaling effects of estrogen, we show that estrogen stimulates the phosphorylation of Akt, an indication of kinase activation, as well as the phosphorylation of 4E-BP1. In turn, the activation of these signaling intermediates suggests a non-genomic mechanism by which estrogen might likewise lead to protein translation of dendrite-localized mRNA transcripts in the hippocampus in vivo. We therefore considered the translation of the dendritic spine scaffolding protein postsynaptic density-95 (PSD-95). Although estrogen does not stimulate a rapid increase in PSD-95 mRNA levels in NG108-15 neurons, we show here that estrogen does however stimulate a rapid increase in PSD-95 new protein synthesis in vitroand that this new protein synthesis is Akt dependent. These results demonstrate an essential role for Akt in estrogen-stimulated dendritic spine protein expression, describe for the first time a signal transduction pathway in PSD-95 expression, and delineate a novel, molecular mechanism by which ovarian hormones might translationally regulate synaptogenesis via activating protein synthesis for dendritic function.
Asynchronous Corona Training Protocols in Wireless Sensor and Actor Networks

Scalable energy-efficient training protocols are proposed for wireless networks consisting of sensors and a single actor, where the sensors are initially anonymous and unaware of their location. The protocols are based on an intuitive coordinate system imposed onto the deployment area, which partitions the sensors into clusters. The protocols are asynchronous, in the sense that the sensors wake up for the first time at random, then alternate between sleep and awake periods both of fixed length, and no explicit synchronization is performed between them and the actor. Theoretical properties are stated under which the training of all the sensors is possible. Moreover, both worst-case and average case analyses of the performance, as well as an experimental evaluation, are presented showing that the protocols are lightweight and flexible.
Efficient semantic annotation method for indexing large personal video database

As there is a large gap between high-level semantics and low-level features, it is difficult to automatically obtain high-accuracy video semantic annotation through general statistical learning based methods. In this paper, we propose a novel annotation framework based on active learning and semi-supervised ensemble method, which is specially designed for personal video database. To efficiently annotate the home video database, an initial training set is first elaborately constructed based on the distribution analysis of the entire video dataset. Then, both a semi-supervised ensemble based method and an active learning based method are proposed, which aims at minimizing a margin cost function of ensemble to ensure the generalization capacity. The experiment results on about 50 hours home videos show that the proposed method performs superior to both existing semi-supervised learning algorithms and the general active learning algorithms in terms of annotation accuracy and performance stability.
Near-infrared and optical broadband surface photometry of 86 face-on disk dominated galaxies. III. The statistics of disk and bulge parameters

The statistics of the fundamental bulge and disk parameters of galaxies and their relation to the Hubble sequence were investigated by an analysis of optical and near-infrared observations of 86 face-on spiral galaxies. The availability of near-infrared K-passband data made it possible for the first time to trace fundamental parameters related to the luminous mass while hardly being hampered by the effects of dust and stellar populations. The observed number frequency of galaxies was corrected for selection effects to calculate volume number densities of galaxies with respect to their fundamental parameters. The main conclusions of this investigation are: 1) Freeman's law has to be redefined. There is no single preferred value for the central surface brightnesses of disks in galaxies. There is only an upper limit to the central surface brightnesses of disks, while for lower central surface brightnesses the number of galaxies per volume element decreases only slowly as function of the central surface brightness. 2) The Hubble sequence type index correlates strongly with the effective surface brightness of the bulge, much better than with the bulge-to-disk ratio. 3) The disk and bulge scalelengths are correlated. 4) These scalelengths are not correlated with Hubble type. Hubble type is a lengthscale-free parameter and each type therefore comes in a range of magnitudes (and presumably a range of total masses). 5) Low surface brightness spiral galaxies are not a separate class of galaxies. In a number of aspects they are a continuation of a trend defined by the high surface brightness galaxies. Low surface brightness galaxies are in general of late Hubble type.
An adaptive system to control robots: ontology distribution and treatment

Computer science evolves ever more quickly towards autonomy: self-managing, self-configuring, or self-optimizing. This is an extreme view of automation. These features are crucial for the next generation of robots and software to help developing new tools. For these ambitious aims, there are different approaches such as datamining, expert systems, complex systems among many. Here, a multi-agent system to distribute ontology to control robots is presented. This project is developed with the Oz/Mozart system and a prototype is used to produce preliminary experiments. We present a method and its implementation for ontology distribution. This processing creates a scene description (a context in an environment) and makes a decision to follow a goal defined by different parameters.
Fourier Transformer Infra-Red Spectrophotometer Analysis of Urtica dioica Medicinal Herb Used for the Treatment of Diabetes , Malaria and Pneumonia in Kisii Region , Southwest Kenya

Medicinal herbs are an important source of phytochemicals that offer traditional medicinal treatment of various ailments. In Kisii region, southwest Kenya. Urtica dioica is amongst the herbs used as phytomedicine for the treatment of diabetes, malaria and pneumonia. A study was carried out on the plant in the year 2011 to 2012. The objective of the study was to identify the functional groups present in Urtica dioica by using Fourier transformer infra-red (FTIR) Spectrophotometer method of analysis. Results of the FTIR Spectra of the hexane, dichloromethane, ethyl acetate and ethanol crude extracts revealed the presence of different functional groups as follows: OH stretching for hydroxyl (3618.2-3359.8 cm ), C=O stretching for 1 carbonyls (1743.5.7-1654.8 cm ), C-O stretching for alcohols (1450.4-1049.2 cm ), carboxylic acid ( 1245.91 1 1222.8 cm ), carboxylic anhydrides (1245.9-1049.2 cm ), ethers (1245.9-1049.2 cm ), esters (1245.9-1049.2 1 1 1 cm ), C-N stretching for amines (1245.9-1049.2cm ), N-H stretching for amines (3618.2-3359.8 cm ), amides 1 1 1 (3359.8 cm ), C=C stretching for aromatic (1654.8-1531.4 cm ), C=N stretching for nitriles (2376.1.0-2248.8 1 1 cm ), N=C stretching for isocyanides (2140.8-2086.8cm ), C-H stretching for alkyl (2981.7-2931.6 cm ), C-H 1 1 1 bending for alkyl ( 1377.1-1330.8 cm ), C-H bending for methyl (1461.6-1392.5 cm ), S=O stretching for sulphur 1 1 derivatives (1377.1-1049.2 cm ), N=O stretching for nitro compounds (1377.1-1330.8 cm ), C-F stretching for 1 1 organic halogens (1245.9-1049.2 cm ), C-Cl stretching (723.9-621.0 cm ), C-Br stretching (671.2 cm ) and C-I 1 1 1 stretching (671.2 cm ). These findings indicate the presence of aldehydes, amines, amides, alcohols, phenols, 1 aromatics, carboxylic acids and anhydride, esters and lactones, ethers, nitriles, isonitriles, nitro compounds, sulphur derivatives, quinones, organic halogen compounds and carbohydrates in Urtica dioica. The medicinal value of the herb could be attributed to the presence of O-H, N-H, C-H, C=O, C-O, C-N, C=C, C=N, N=C, N=O and S=O bond stretching of the detected functional groups. The results confirm the presence of secondary plant metabolites viz., alkaloids, saponins, tannins, flavonoids, steroids and terpenoids, polyphenols and cardiac glycosides in the leaves of Urtica dioica plants. The results confirm the presence of secondary plant metabolites viz., alkaloids, saponins, tannins, flavonoids, steroids and terpenes, polyphenols and cardiac glycosides in the leaves of Urtica dioica.
Using Amazon Product Review Models to Characterize Amazon Reviewer Communities

More often than not, the time component of Amazon reviews is overlooked for the actual content or the characteristics of the reviewer. This paper examines the relationship between timestamp of reviews, reviewers, and products in a large dataset of Amazon reviews. Our model slices the graph into years, so that we can explore the development of Amazon user communities between 1996 and 2005. We create our graphs by generating edges between users who reviewed the same items, and we use review metadata to filter our edges in order to create models of helpful, positive, and negative reviewers. By applying community detection to these graphs, we explore the way these different communities grew and changed over time. We found communities around different product categories that changed over the years according to which items were more popular at the time.
Detecting Semantic Overlap and Discovering Precedents in the Biodiversity Research Literature Position Paper

Scientific literature on biodiversity is longevous, but even when legacy publications are available online, researchers often fail to search it adequately or effectively for prior publications; consequently, new research may replicate, or fail to adequately take into account, previously published research. The mechanisms of the Semantic Web and methods developed in contemporary research in natural language processing could be used, in the near-term future, as the basis for a precedent-finding system that would take the text of an author’s early draft (or a submitted manuscript) and find potentially related ideas in published work. Methods would include text-similarity metrics that take different terminologies, synonymy, paraphrase, discourse relations, and structure of argumentation into
Asymmetric Cell Divisions Sustain Long-Term Hematopoiesis from Single-sorted Human Fetal Liver Cells

Hematopoietic stem cells (HSCs) in adult marrow are believed to be derived from fetal liver precursors. To study cell kinetics involved in long-term hematopoiesis, we studied single-sorted candidate HSCs from fetal liver that were cultured in the presence of a mixture of stimulatory cytokines. After 8–10 d, the number of cells in primary cultures varied from <100 to >10,000 cells. Single cells in slow growing colonies were recloned upon reaching a 100–200 cell stage. Strikingly, the number of cells in subclones varied widely again. These results are indicative of asymmetric divisions in primitive hematopoietic cells in which proliferative potential and cell cycle properties are unevenly distributed among daughter cells. The continuous generation of functional heterogeneity among the clonal progeny of HSCs is in support of intrinsic control of stem cell fate and provides a model for the long-term maintenance of hematopoiesis in vitro and in vivo.
Enhanced Encryption schemes of video for real time applications

This work proposes light weight video encryption algorithms suitable for real time applications. The objective of these encryption algorithms is to reduce the total amount of time taken for encryption while encrypting the entire video which is necessary in certain applications like video conferencing. Two proposals are suggested in this work. In the first proposal, Intra frames and motion vectors are encrypted using Galois field polynomials. The second proposal consists of avoiding the computationally demanding motion compensation process, and the temporal redundancy is converted into spatial redundancy, and the converted frames are scrambled. This reduces the time taken for encryption, making it suitable for real time applications.
Learning Regular Languages via Alternating Automata

Nearly all algorithms for learning an unknown regular language, in particular the popular L* algorithm, yield deterministic finite automata. It was recently shown that the ideas of L* can be extended to yield non-deterministic automata, and that the respective learning algorithm, NL*, outperforms L* on randomly generated regular expressions. We conjectured that this is due to the existential nature of regular expressions, and NL* might not outperform L* on languages with a universal nature. In this paper we introduce UL* -- a learning algorithm for universal automata (the dual of non-deterministic automata); and AL* -- a learning algorithm for alternating automata (which generalize both universal and non-deterministic automata). Our empirical results illustrate the advantages and trade-offs among L*, NL*, UL* and AL*.