Miracle-dz/UniFAR-FTUs huggingface dataset:
A Model of Recurrent Networks that Learn the Finite Automaton from Given Input-Output Sequences

From the viewpoint of applying recurrent neural networks to AI, it is not suitable to use learning algorithms based on optimum control theory. One reason of this problem is lack of correspondence between these algorithms and traditional symbol processing. In this report, we propose a new algorithm for modi ed Elman networks (PEX model). This algorithm is derived from the minimization procedure of nite automata under the correspondence between Elman networks and nite automata. In this algorithm, a network predicts next network state by itself, and this prediction plays an important role in re ning the network. In addition, we examined behavior of this model toward noise and found the network gets robust state-transition against noise. Moreover, we discuss the possibility to deal with CFG by an experiment of sub-grammar learning.
The effects of social skills training on a student with attention deficit hyperactivity disorder

THE EFFECTS OF SOCIAL SKILLS TRAINING ON A STUDENT WITH ATTENTION DEFICIT HYPERACTIVITY DISORDER In recent years, pressure for students to achieve high test scores in academics has limited the provision of social skills training. To examine the effectiveness of social skills training in increasing time on-task and improving the initiation of peer interaction for students diagnosed with ADHD, an elementary student attending public school and receiving special education services was given social skills training. Social skills training was provided in the form of direct lessons, role-play activities, and guided practice. The student with identified social skills deficits significantly increased his time on-task behavior from an average of 44% at baseline to an average of 78% after training and showed an increase in initiation of peer interaction from zero times per day for 9 days to one time per day for 12 out of 20 days. Ella Shasky May 2010 THE EFFECTS OF SOCIAL SKILLS TRAINING ON A STUDENT WITH ATTENTION DEFICIT HYPERACTIVITY DISORDER
Probing the cosmological variation of the fine - structure constant: Results based on VLT - UVES sample

Development of fundamental physics relies on the constancy of various fundamental quantities such as the fine- structure constant. Detecting or constraining the possible time variations of these fundamental physical quantities is an im- portant step toward a complete understanding of basic physics. High-quality absorption lines seen in the spectra of distant QSOs allow one to probe time variations of several of these quantities. Here we present the results from a detailed many- multiplet analysis, to detect the possible variation of the fine-structure constant, performed using high signal-to-noise ratio, (∼70 per pixel), high spectral resolution (R ≥ 45 000) observations of 23 Mg  systems detected toward 18 QSOs in the redshift range 0.4 ≤ z ≤ 2.3 obtained using UVES at the VLT. We validate our procedure and define the selection criteria that will avoid possible systematics using a detailed analysis of a simulated data set. The spectra of Mg  doublets and Fe  multiplets are generated considering variations in α and specifications identical to that of our UVES spectra. We show that our Voigt profile fitting code recovers the variation in α very accurately when we use single component systems and multiple-component systems that are not heavily blended. Spurious detections are frequently seen when we use heavily blended systems or systems with very weak lines. Thus we avoided these system while analysing the UVES data. To make the analysis transparent and accessible to the community for critical scrutiny, all the steps involved in the analysis are presented in detail. The weighted mean value of the variation in α obtained from our analysis over the redshift range 0.4 ≤ z ≤ 2. 3i s∆α/α = (−0.06 ± 0.06) × 10 −5 . The median redshift of our sample is 1.55 and corresponds to a look-back time of 9.7 Gyr in the most favored cosmological model today. The 3σ upper limit on the time variation of α is −2.5 × 10 −16 yr −1 ≤ (∆α/α∆t) ≤ +1.2 × 10 −16 yr −1 . To our knowledge this is the strongest constraint from quasar absorption line studies to till date.
SAR image classification based on texture feature fusion

This paper presents a method for feature extraction and classification of synthetic aperture radar (SAR) images. This proposed method consists of three steps. First, two kinds of texture features are extracted for SAR image, which are the gray level co-occurrence matrix (GLCM) and Gabor filters (GFs). Second, these two kinds of extracted feature vectors from the first step were fused using the canonical correlation analysis (CCA) to reduce the dimensionality of the feature spaces. Third, the SAR images are classified with the support vector machine (SVM) in the fused feature space. The experimental results demonstrate that the proposed SAR classification method obtains good classification performance and the dimensionality reduction of CCA leads to high efficiency.
Automatic Design of Locally Adaptive Filters for Pre-processing of Images Subject to Further Interpretation

Locally adaptive filters are widely used in image processing applications. However, their design commonly requires sufficient efforts and does not take into consideration some important aspects of further processing (interpreting and/or classification) of images. This paper puts forward a novel approach to automatic design of locally adaptive filters subject to further interpretation, namely, detection and localization of small size objects. Design is based on learning with clustering for a test image corrupted by a noise with statistical characteristics observed in real life images to which the obtained filter intend to be further applied. Quantitative data confirming the designed filter efficiency are presented
Tackling Food Marketing to Children in a Digital World: Trans-disciplinary Perspectives

This publication provides up-to-date information on the marketing of foods and non-alcoholic beverages to children and the changes that have occurred in recent years, focusing in particular on the major shift to digital marketing. It examines trends n media use among children, marketing methods in the new digital media landscape and children’s engagement with such marketing. It also considers the impact on children and their ability to counter marketing as well as the implications for children’s digital privacy. Finally the report discusses the policy implications and some of the recent policy action by WHO
A New Skin Detection Approach for Adult Image Identification

With rapid proliferation of adult content on the internet, development of software to detect and filter this content is gaining more and more attention. In this study, a new method for adult image identification is proposed. Accurate human skin detection and extraction of relative features are the bottlenecks in this regard. In the proposed skin detection method, first, Hue color information is utilized to examine whether skin is present in the image, and if so, using dynamic thresholding an estimate of the skin region is obtained. In the second step, for images that contain skin, an exponential function is fitted to the histogram of the estimated skin area. Based on the parameters of the fitted function, the final skin map of the image is extracted. Area, texture, and a new shape feature are extracted from each region and used for image classification using neural networks. 95% accuracy for skin detection and 93.7% accuracy of adult image detection, compared to other methods showed the significance of the proposed method.
Privacy Preserving Clustering on Distorted data

In designing various security and privacy related data mining applications, privacy preserving has become a major concern. Protecting sensitive or confidential information in data mining is an important long term goal. An increased data disclosure risks may encounter when it is released. Various data distortion techniques are widely used to protect sensitive data; these approaches protect data by adding noise or by different matrix decomposition methods. In this paper we primarily focus, data distortion methods such as singular value decomposition (SVD) and sparsified singular value decomposition (SSVD). Various privacy metrics have been proved to measure the difference between original dataset and distorted dataset and degree of privacy protection. The data mining utility k-means clustering is used on these distorted datasets. Our experimental results use a real world dataset. An efficient solution is achieved using sparsified singular value decomposition and singular value decomposition, meeting privacy requirements. The accuracy while using the distorted data is almost equal to that of the original dataset. Keywords- Privacy Preserving, Data Distortion, Singular Value Decomposition (SVD), Sparsified Singular Value Decomposition (SSVD), k--means clustering.
Improved risk predictions via sparse imputation of patient conditions in electronic medical records

Electronic Medical Records (EMR) are increasingly used for risk prediction. EMR analysis is complicated by missing entries. There are two reasons - the “primary reason for admission” is included in EMR, but the co-morbidities (other chronic diseases) are left uncoded, and, many zero values in the data are accurate, reflecting that a patient has not accessed medical facilities. A key challenge is to deal with the peculiarities of this data - unlike many other datasets, EMR is sparse, reflecting the fact that patients have some, but not all diseases. We propose a novel model to fill-in these missing values, and use the new representation for prediction of key hospital events. To “fill-in” missing values, we represent the feature-patient matrix as a product of two low rank factors, preserving the sparsity property in the product. Intuitively, the product regularization allows sparse imputation of patient conditions reflecting common comorbidities across patients. We develop a scalable optimization algorithm based on Block coordinate descent method to find an optimal solution. We evaluate the proposed framework on two real world EMR cohorts: Cancer (7000 admissions) and Acute Myocardial Infarction (2652 admissions). Our result shows that the AUC for 3 months admission prediction is improved significantly from (0.741 to 0.786) for Cancer data and (0.678 to 0.724) for AMI data. We also extend the proposed method to a supervised model for predicting of multiple related risk outcomes (e.g. emergency presentations and admissions in hospital over 3, 6 and 12 months period) in an integrated framework. For this model, the AUC averaged over outcomes is improved significantly from (0.768 to 0.806) for Cancer data and (0.685 to 0.748) for AMI data.
Determining the Geometry and the Cosmological Parameters of the Universe through Sunyaev-Zeldovich Effect Cluster Counts

We study Sunyaev-Zeldovich effect (SZE) cluster counts in different cosmologies. We find that even without full knowledge of the redshift distribution of SZE clusters, one can still readily distinguish a flat universe with a cosmological constant from an open universe. We divide clusters into a low-redshift group (with redshift z ≤ 0.5) and a high-redshift group (with z ≥ 1), and compute the ratio of r = N(z ≤ 0.5)/N(z ≥ 1), where N(z ≤ 0.5) is the number of flux-limited (S) SZE clusters with z ≤ 0.5, and N(z ≥ 1) is the number of flux-limited SZE clusters with z ≥ 1. With about the same total number of SZE clusters N(z ≥ 0), the r values for a flat universe with a nonzero cosmological constant and for an open universe occupy different regions in the S-r plot for the most likely cosmological parameters 0.25 ≤ Ω0 ≤ 0.35 and 0.2 ≤ Γ ≤ 0.3, where Ω0 is the matter density parameter of the universe and Γ is the shape parameter of the power spectrum of linear density fluctuations. Thus, with a deep SZE cluster survey, the ratio r can reveal independently of the normalization of the power spectrum whether we are living in a low-density flat universe or in an open universe. Within the flat universe scenario, the SZE cluster-normalized σ8 is studied, where σ8 is the rms density fluctuation within the top-hat scale 8 Mpc h-1, where h is the Hubble constant in units of 100 km s-1 Mpc-1. A functional relation of σ8 ∝ Ω is found. Combined with the X-ray cluster-normalized σ8 ∝ Ω, one can put constraints on both Ω0 and σ8 simultaneously.
TreeMatrix: A Hybrid Visualization of Compound Graphs

We present a hybrid visualization technique for compound graphs (i.e. networks with a hierarchical clustering defined on the nodes) that combines the use of adjacency matrices, node‐link and arc diagrams to show the graph, and also combines the use of nested inclusion and icicle diagrams to show the hierarchical clustering. The graph visualized with our technique may have edges that are weighted and/or directed. We first explore the design space of visualizations of compound graphs and present a taxonomy of hybrid visualization techniques. We then present our prototype, which allows clusters (i.e. subtrees) of nodes to be grouped into matrices or split apart using a radial menu. We also demonstrate how our prototype can be used in the software engineering domain, and compare it to the commercial matrix‐based visualization tool Lattix using a qualitative user study.
A peer to peer based approach to collaborative product engineering

In this paper we present key elements of a peer to peer (P2P) based software architecture for integrated and collaborative product engineering. We argue that distributed and cross-enterprise product lifecycle management can benefit from the availability of decentrally managed product model repositories. We present the overall system architecture of a platform called Peer-to-Peer Product Collaboration Platform (PCP). Our focus in this paper is on the design of a decentral product model metadata repository and registry based on a peer-to-peer resource management platform, and on a prototype implementation of an Eclipsebased tool for editing and browsing the metadata repository.
A self-regulating adaptive system

The viability of providing adaptive user interfaces has been demonstrated ([3], [5]). Such systems identify differences between users in order to provide purposeful change at the user interface. Thus, adaptive systems have objectives, as indicated by the term 'purposeful. ' The research reported here takes an important step forward by demonstrating that adaptive systems can be built that regulate their own behaviour by assessing whether their adaptations are being successful in meeting these objectives.
Direction-based spatial skylines

Traditional location-based services recommend nearest objects to the user by considering their spatial proximity. However, an object not only has its distance but also has its direction which originates from the user to it. In this paper, we study direction-based spatial skyline queries (DSS queries) which retrieve nearest objects around the user from different directions. The closer object is better than or dominates the further object if they are in the same direction. The objects that cannot be dominated by any other object are included in the direction-based spatial skyline (DSS). We propose algorithms to answer snapshot queries which find objects on the DSS according to the user's current position. We also develop algorithms to support continuous queries which retrieve objects on the DSS while the user is moving linearly. Extensive experiments verify the performance of our proposed algorithms using both real and synthetic datasets.
Provenance for Natural Language Queries

Multiple lines of research have developed Natural Language (NL) interfaces for formulating database queries. We build upon this work, but focus on presenting a highly detailed form of the answers in NL. The answers that we present are importantly based on the provenance of tuples in the query result, detailing not only the results but also their explanations. We develop a novel method for transforming provenance information to NL, by leveraging the original NL query structure. Furthermore, since provenance information is typically large and complex, we present two solutions for its effective presentation as NL text: one that is based on provenance factorization, with novel desiderata relevant to the NL case, and one that is based on summarization. We have implemented our solution in an end-to-end system supporting questions, answers and provenance, all expressed in NL. Our experiments, including a user study, indicate the quality of our solution and its scalability.
On the Estimation of Heritability with Family-Based and Population-Based Samples

For a family-based sample, the phenotypic variance-covariance matrix can be parameterized to include the variance of a polygenic effect that has then been estimated using a variance component analysis. However, with the advent of large-scale genomic data, the genetic relationship matrix (GRM) can be estimated and can be utilized to parameterize the variance of a polygenic effect for population-based samples. Therefore narrow sense heritability, which is both population and trait specific, can be estimated with both population- and family-based samples. In this study we estimate heritability from both family-based and population-based samples, collected in Korea, and the heritability estimates from the pooled samples were, for height, 0.60; body mass index (BMI), 0.32; log-transformed triglycerides (log TG), 0.24; total cholesterol (TCHL), 0.30; high-density lipoprotein (HDL), 0.38; low-density lipoprotein (LDL), 0.29; systolic blood pressure (SBP), 0.23; and diastolic blood pressure (DBP), 0.24. Furthermore, we found differences in how heritability is estimated—in particular the amount of variance attributable to common environment in twins can be substantial—which indicates heritability estimates should be interpreted with caution.
Order, Disorder, and Everything in Between

In addition to the “traditional” proteins characterized by the unique crystal-like structures needed for unique functions, it is increasingly recognized that many proteins or protein regions (collectively known as intrinsically disordered proteins (IDPs) and intrinsically disordered protein regions (IDPRs)), being biologically active, do not have a specific 3D-structure in their unbound states under physiological conditions. There are also subtler categories of disorder, such as conditional (or dormant) disorder and partial disorder. Both the ability of a protein/region to fold into a well-ordered functional unit or to stay intrinsically disordered but functional are encoded in the amino acid sequence. Structurally, IDPs/IDPRs are characterized by high spatiotemporal heterogeneity and exist as dynamic structural ensembles. It is important to remember, however, that although structure and disorder are often treated as binary states, they actually sit on a structural continuum.
Homomorphic Autocomplete

With the rapid progress in fully homomorpic encryption (FHE) and somewhat homomorphic encryption (SHE) schemes, we are witnessing renewed efforts to revisit privacy preserving protocols. Several works have already appeared in the literature that provide solutions to these problems by employing FHE or SHE techniques. These applications range from cloud computing to computation over confidential patient data to several machine learning problems such as classifying privatized data. One application where privacy is a major concern is web search – a task carried out on a daily basis by billions of users around the world. In this work, we focus on a more surmountable yet essential version of the search problem, i.e. autocomplete. By utilizing a SHE scheme we propose concrete solutions to a homomorphic autocomplete problem. To investigate the real-life viability, we tackle a number of problems in the way towards a practical implementation such as communication and computational efficiency.
Establishment of In Vivo Brain Imaging Method in Conscious Mice

In vivo imaging, such as PET, requires restriction of body movements and is generally conducted under sedation by anesthetic agents in studies using laboratory animals. Because anesthetics reduce neural activity and metabolism, physiologic neural functions are difficult to assess in animal PET studies. Therefore, use of an appropriate method in conscious animals is important and is a practical requirement for physiologic in vivo brain imaging studies. Here, we established an in vivo imaging system for conscious mice to reveal the physiologic regional cerebral glucose metabolic rate (rCMRglu) with 18F-FDG PET. Methods: We first developed a head holder to enable brain PET of a conscious mouse. To obtain optimal rCMRglu, we examined the effects of physical and psychologic stresses caused by ambient temperature, intravenous injection, and acclimation to the apparatus and immobile state. Finally, quantitative kinetic analysis was performed for rCMRglu based on a 2-tissue-compartment model with an input function of arterial blood sampling under both conscious and anesthetized (1.5% isoflurane) conditions. Results: Increasing the ambient temperature increased uptake of 18F-FDG in the brain significantly while reducing the uptake in skeletal muscle and brown adipose tissue that was caused by shivering. The reduction of brain 18F-FDG uptake caused by tail holding and manual injection was significantly ameliorated by the use of an automated slow injection. Although brain uptake of 18F-FDG varied at the first session of PET, uptake at the second and subsequent sessions was stable, even after long-term acclimation. After these beneficial changes, brain uptake of 18F-FDG improved significantly, to approximately 260% above the preconditioned state, which is comparable with that obtained in mice that have been allowed to move freely about their home cages. Quantitative kinetic analyses revealed that isoflurane anesthesia lowered rCMRglu in the cerebral cortex, striatum, thalamus, and cerebellum by 66%, 59%, 62%, and 22%, respectively, mainly by reducing the k3 value, a rate constant for phosphorylation by hexokinase. Conclusion: To our knowledge, this is the first study to report quantitative kinetic analysis of rCMRglu in mice that have been conscious throughout PET. This investigation will open avenues for research into in vivo functional brain molecular imaging in both normal and genetically manipulated mice.
Vascular Endothelial Growth Factors Enhance the Permeability of the Mouse Blood-brain Barrier

The blood-brain barrier (BBB) impedes entry of many drugs into the brain, limiting clinical efficacy. A safe and efficient method for reversibly increasing BBB permeability would greatly facilitate central nervous system (CNS) drug delivery and expand the range of possible therapeutics to include water soluble compounds, proteins, nucleotides, and other large molecules. We examined the effect of vascular endothelial growth factor (VEGF) on BBB permeability in Kunming (KM) mice. Human VEGF165 was administered to treatment groups at two concentrations (1.6 or 3.0 µg/mouse), while controls received equal-volume saline. Changes in BBB permeability were measured by parenchymal accumulation of the contrast agent Gd-DTPA as assessed by 7 T magnetic resonance imaging (MRI). Mice were then injected with Evans blue, sacrificed 0.5 h later, and perfused transcardially. Brains were removed, fixed, and sectioned for histological study. Both VEGF groups exhibited a significantly greater signal intensity from the cerebral cortex and basal ganglia than controls (P<0.001). Evans blue fluorescence intensity was higher in the parenchyma and lower in the cerebrovasculature of VEGF-treated animals compared to controls. No significant brain edema was observed by diffusion weighted MRI (DWI) or histological staining. Exogenous application of VEGF can increase the permeability of the BBB without causing brain edema. Pretreatment with VEGF may be a feasible method to facilitate drug delivery into the CNS.