Miracle-dz/UniFAR-FTUs huggingface dataset:
Dual Neuroprotective Signaling Mediated by Downregulating Two Distinct Phosphatase Activities of PTEN

The tumor suppressor PTEN (phosphatase and tensin homolog deleted on chromosome 10) is a lipid and protein phosphatase. We report here that PTEN physically associates with the NR1 and NR2B subunits of NMDA receptors (NMDARs) in rat hippocampus. Downregulating the protein expression of PTEN inhibits the function of extrasynaptic NMDARs and decreases NMDAR surface expression, suggesting a crucial role for endogenous PTEN in the modulation of NMDAR-mediated neuronal function. Reducing PTEN expression also enhances Akt/Bad phosphorylation in hippocampal neurons. Importantly, suppressing lipid and protein phosphatase activity of PTEN, respectively, activates Akt and inhibits extrasynaptic NMDAR activity and thereby protects against ischemic neuronal death in vitro and in vivo. Thus, our study reveals a dual neuroprotective mechanism by which Akt/Bad and extrasynaptic NMDARs are regulated via downregulation of two distinct PTEN phosphatase activities and present the possibility of PTEN as a potential therapeutic target for stroke treatment.
A Grid and Fractal Dimension-Based Data Stream Clustering Algorithm

The data stream problem has been studied extensively in recent years. This is because the great in collection of the nature of data stream. The nature of stream data makes it essential to use algorithms which require only one pass over the data. And single-scan, stream analysis methods have been proposed in this context. However, clustering is still a challenging task since many published algorithms fail to do well in scaling with the size of the data stream sets and the number of dimensions that describe the point, or in finding arbitrary shapes of clusters, or dealing effectively with the presence of noise. In this paper, we propose a new data stream clustering approach, called GFDStream (a grid and fractal dimension-based data stream clustering). The method incorporates a grid method, and the fractal clustering methodology. This clustering idea in which divide the clustering process into an online component which periodically stores detailed summary statistics and an offline component which uses only this summary statistics and concepts of a pyramidal time frame in conjunction with a micro-clustering approach. The idea uses the fractal dimension in the grids as a parameter, and deals with the data space by gridding, which can improve the processing speed of the algorithm. Since points in the same cluster have a great degree of self-similarity among them. (and much less self-similarity with respect to points in other clusters), it can distinct the data better. We show via experiments that GFDStream effectively deals with data stream and is capable of recognizing clusters of arbitrary shape.
Imaging and integral‐field spectroscopy of shocked H2 around G25.65+1.05

Near-infrared imaging of the emission from molecular hydrogen is a powerful method for discovering outflows in star-forming regions. We present new near-infrared images, and long-slit and integral-field spectroscopy of the ultracompact HII region G25.65+1.05. These new observations reveal shocked H 2 emission associated with a bipolar outflow from a young high-mass star at the centre of the source. The physical parameters of the outflow are discussed and compared with those of outflows from lower mass stars.
Hybrid Fuzzy SVM Model Using CART and MARS for Credit Scoring

Credit scoring model development became a very important issue as the credit industry has many competitions. Therefore, most credit scoring models have been widely studied in the areas of statistics to improve the accuracy of credit scoring models during the past few years. This study used three strategies to construct the hybrid FSVM-based credit scoring models to evaluate the applicant’s credit score from the applicant’s input features. (1) using CART to select input features, (2) using MARS to select input features, (3) using GA to optimize model parameters. Two credit datasets in UCI database are selected as the experimental data to demonstrate the accuracy of the hybrid FSVM-based model not only has the best classification, but also has the lower Type II error.
A Lightweight Videogame Dialogue Manager

©2016 Authors. Personal and educational classroom use of this paper is allowed, commercial use requires specific permission from the author. ABSTRACT We present a fully procedural alternative to branching dialogue that is influenced by theories from linguistic pragmatics and technical work in the field of dialogue systems. Specifically, this is a dialogue manager that extends the Talk of the Town framework, in which non-player characters (NPCs) develop and propagate subjective knowledge of the gameworld. While previously knowledge exchange in this framework could only be expressed symbolically, such exchanges may now be rendered as naturalistic conversations between characters. The larger conversation engine currently lacks a player interface, so in this paper we demonstrate our dialogue manager through conversations between NPCs. From an evaluation task, we find that our system produces conversations that flow far more naturally than randomly assembled ones. As a design objective, we have endeavored to make this dialogue manager lightweight and agnostic to its particular application in Talk of the Town; it is our hope that interested readers will consider porting its straightforward design to their own game engines.
Scalar calibration of vector magnetometers

The calibration parameters of a vector magnetometer are estimated only by the use of a scalar reference magnetometer. The method presented in this paper differs from those previously reported in its linearized parametrization. This allows the determination of three offsets or signals in the absence of a magnetic field, three scale factors for normalization of the axes and three non-orthogonality angles which build up an orthogonal system intrinsically in the sensor. The advantage of this method compared with others lies in its linear least squares estimator, which finds independently and uniquely the parameters for a given data set. Therefore, a magnetometer may be characterized inexpensively in the Earth's magnetic-field environment. This procedure has been used successfully in the pre-flight calibration of the state-of-the-art magnetometers on board the magnetic mapping satellites Orsted, Astrid-2, CHAMP and SAC-C. By using this method, full-Earth-field-range magnetometers (± 65536.0 nT) can be characterized down to an absolute precision of 0.5 nT, non-orthogonality of only 2 arcsec and a resolution of 0.2 nT.
THE PERSISTENCE OF POVERTY IN THE CONTEXT OF FINANCIAL INSTABILITY: A BEHAVIORAL PERSPECTIVE

We review recent findings regarding the psychology of decisionmaking in contexts of poverty, and consider their application to public policy. Of particular interest are the oft‐neglected psychological and behavioral consequences of economic scarcity coupled with financial instability. The novel framework highlights the psychological costs of low and unstable incomes, and how these can transform small and momentary financial hurdles into long‐lasting poverty traps. Financial instability, we suggest, not only has obvious economic ramifications for well‐being, but it also creates the need for constant focus and attention, and can distract from the very opportunities otherwise designed to alleviate the effects of poverty. We describe a variety of public policy strategies that emerge from this perspective that are not readily apparent in conventional theories that permeate the design of social programs.
A delta-sampling verification theorem for discrete-time, possibly discontinuous systems

This paper considers the problem of safety verification for discrete-time, possibly discontinuous dynamical systems. Typical solutions rely on finding invariant sets or Lyapunov functions and require solving optimization problems, which suffer from scalability and numerical solvers issues. Recently, a δ-sampling method for verifying invariance for Lipschitz continuous dynamics was proposed, which does not make use of optimization. In this work we present a δ-sampling verification theorem that extends the previous result to general discrete-time, possibly discontinuous dynamics. This opens up the application of δ-sampling verification to hybrid systems. Moreover, this paper proposes verification of stability on a set by jointly verifying (finite-step) Lyapunov type functions on an annulus with a (finite-step) Lyapunov function on the inner hole. We further indicate that δ-sampling can also be used to verify Lyapunov conditions on the annulus. Lastly, we employ finite-step invariant sets and finite-step Lyapunov functions, respectively, together with δ-sampling to achieve more practical safety verification methods.
University-industry relations and the evolution of knowledge governance. the italian evidence in the first part of the xx century

This paper contributes the large literature on the university industry relations providing unique historic evidence on the positive effects of academic spillovers as proxied by chairs, distinguished by disciplinary field, on total factor productivity growth. The analysis impinges upon an original data-base on the evolution of the size and the disciplinary composition of the stock of academic chairs in Italy in the years 1900-1959. The results confirm the contribution of academic knowledge to economic growth and the positive effects of the public support to the academic system. At the same time they shed new light on the differentiated impact of the different disciplines on economic growth. The increase in the number of chairs in engineering and chemistry contributed to total factor productivity growth more than any other discipline. This is consistent with the historic context characterized by the radical transformation of a backward agricultural economy into a highly industrialized and rich one. The results of this cliometric analysis of a case where the corporate mode of knowledge governance had not yet been introduced confirm the viability of the academic mode of knowledge governance.
Experiments With A Self-Supervised Adaptive Classification Strategy In On-Line Recognition Of Isolat

Results on a comparison of recognition techniques for on-line recognition of handwritten Latin alphabets are presented. The emphasis is on an adaptive classii-cation strategy introduced in this paper. The classiication strategy is based on compressing or distilling a large database of handwritten characters to a small set of character prototypes. The distillation is performed as a clustering procedure to which the number of clusters are given for each character class. Each user's personal characters are then added to this set as they become available. The classiication decision uses the 1-Nearest Neighbor (1-NN) rule for the distances between the unknown character and the stored prototypes. The distances are calculated using Dynamic Time Warping (DTW). The beneets of the proposed method include the automatic unsupervised learning from user input simultaneously with the normal mode of operation. The presented experiments show that the recognition system is able to adapt to the user's writing style with only a very few { say some tens of { handwritten characters.
A Lightweight Framework for Exchanging Web Data

We propose a lightweight framework for data exchange that is suitable for non-expert and casual users sharing data on the Web and/or through peer-to-peer systems. Unlike previous work, we consider a minimalistic data model and schema formalism that are suitable for describing online data and propose algorithms for mapping such schemas as well as for translating the corresponding instances. Also our solution requires minimal overhead and setup costs (e.g., we consider data stored in tables, XML or CSV files) comparing to existing data exchange systems, making it very attractive in our setting. We report experimental results indicating that our method works well with real Web data from various domains. General Terms ALGORITHMS, EXPERIMENTATION.
Hierarchical multi-rate control design for constrained linear systems

This paper proposes a hierarchical multi-rate control design approach to linear systems subject to linear constraints on input and output variables. At the lower level, a linear controller stabilizes the open-loop process without considering the constraints. A higher-level controller commands reference signals at a lower sampling frequency so as to enforce linear constraints on the variables of the process. By optimally constraining the magnitude and the rate of variation of the reference signals applied to the lower control layer, we provide quantitative criteria for selecting the ratio between the sampling rates of the upper and lower layers to preserve closed-loop stability without violating the prescribed constraints.
Simultaneous determination of levocetirizine and pseudoephedrine in dog plasma by liquid chromatography-mass spectrometry in the presence of dextrocetirizine.

PURPOSE This study describes the development of a rapid and sensitive LC-ESI-MS assay for simultaneous enantioselective determination of levocetirizine and pseudoephedrine in dog plasma in the presence of dextrocetirizine. METHODS Separations were achieved on an Ultron ES-OVM chiral column using the mobile phase consisting of 10 mM aqueous NH4OAc (pH 6.6) and acetonitrile (9:1 v/v). RESULTS The retention times of pseudoephedrine, dextrocetirizine, levocetirizine and diazepam (internal standard) were 5.2, 8.3, 9.6 and 11.6 min, respectively, and the total run time was less than 15 min. The assay was validated to demonstrate the linearity, accuracy and precision, recovery and stability. The calibration curves were linear over the concentration range from 1 - 200 ng/mL for levocetirizine and from 5 - 1000 ng/mL for pseudoephedrine. CONCLUSIONS The developed assay was successfully applied to a pharmacokinetic study after oral administration of the racemic cetirizine (0.5 mg/kg, or 0.25 mg/kg as levocetirizine) and pseudoephedrine (12 mg/kg) in the dog. This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.
Did Behavioral Mutual Funds Exploit Market Inefficiencies During or after the Financial Crisis?

This study examines the performance of mutual funds that employ investment strategies based on the principles of behavioral finance, collectively known as “behavioral mutual funds”. A series of performance measures is employed in order to test whether behavioral mutual funds outperform the stock market, their benchmarks or passively managed index funds, using monthly data for the period January 2007-March 2013. Results from the full sample and subperiod analysis show that behavioral mutual funds actually exhibited poor performance, both during the recent financial crisis and in its aftermath, rejecting the conjecture that the crisis period would provide an ideal environment for their strategies to be profitable by exploiting market inefficiencies and investors behavioral biases.
Hedging strategies with a put option and their failure rates

The problem of stock hedging is reconsidered in this paper, where a put option is chosen from a set of available put options to hedge the market risk of a stock. A formula is proposed to determine the probability that the potential loss exceeds a predetermined level of Value-at-Risk, which is used to find the optimal strike price and optimal hedge ratio. The assumptions that the chosen put option finishes in-the-money and the constraint of hedging budget is binding are relaxed in this paper. A hypothesis test is proposed to determine whether the failure rate of hedging strategy is greater than the predetermined level of risk. The performances of the proposed method and the method with those two assumptions are compared through simulations. The results of simulated investigations indicate that the proposed method is much more prudent than the method with those two assumptions.
NCBI at 2013 ShARe/CLEF eHealth Shared Task: Disorder Normalization in Clinical Notes with Dnorm

We describe an application of DNorm - a mathematically principled and high performing methodology for disease recognition and normalization, even in the presence of term variation - to clinical notes. DNorm consists of a text processing pipeline, including the BANNER named entity recognizer to lo- cate diseases in the text, and a novel machine learning approach based on pair- wise learning to rank to normalize the recognized mentions to concepts within a controlled lexicon. DNorm achieved the second highest performance in Task 1a (named entity recognition) and the highest performance (strict accuracy) in Task 1b (normalization). A web-based demonstration of DNorm is available at http://www.ncbi. nlm.nih. gov/CBBresearch/Lu/Demo/DNorm/
Proximity-Aware Calculation of Cable Series Impedance for Systems of Solid and Hollow Conductors

Wideband cable models for the prediction of electromagnetic transients in power systems require an accurate calculation of the cable series impedance as a function of frequency. A surface current approach was recently proposed for systems of round solid conductors, with the inclusion of skin and proximity effects. In this paper, we extend the approach to include tubular conductors, allowing to model realistic cables with tubular sheaths, armors, and pipes. We also include the effect of a lossy ground. A noteworthy feature of the proposed technique is the accurate prediction of proximity effects, which can be of major importance in three-phase, pipe-type, and closely packed single-core cables. The new approach is highly efficient compared to finite elements. In the case of a cross-bonded cable system featuring three-phase conductors and three screens, the proposed technique computes the required 120 frequency samples in only six seconds of CPU time.
Multiple target tracking in the fully adaptive radar framework

The fully adaptive radar framework aims to use some known information, or cognition, about the environment in which the system is deployed to obtain some improvement in its performance, typically either reducing the uncertainty of the obtained results or optimizing the use of the available resources. In this paper, the extension of the fully adaptive radar framework for the case of multiple target tracking is introduced. In order to illustrate the proposed framework use, a simulation is carried out in an scenario comprising multiple targets and a sensor network with resource constraints. Results show a remarkable performance improvement when the proposed fully adaptive radar approach is used.
Macroeconomics: Economic Growth, Fluctuations, and Policy

The Sixth Edition incorporates the most current data and theory into the text's signature single-model approach, clearly demonstrating the real-world applications of macroeconomic theory. Additionally, the Sixth Edition has been revised to include updated and expanded coverage of long-run economic growth, the Taylor Rule, and international economics. A revised study guide and a new online edition of Macrosolve, the innovative multimedia learning resource, are fully integrated with the text.
Bayesian numerical inference for Markovian models -- Application to tropical forest dynamics

Bayesian modelling is fluently employed to assess natural ressources. It is associated with Monte Carlo Markov Chains (MCMC) to get an approximation of the distribution law of interest. Hence in such situations it is important to be able to propose N independent realiza- tions of this distribution law. We propose a strategy for making N parallel Monte Carlo Markov Chains interact in order to get an approximation of an independent N -sample of a given target law. In this method each individual chain proposes candidates for all other chains. We prove that the set of interacting chains is itself a MCMC method for the product of N target measures. Compared to independent parallel chains this method is more time consuming, but we show through example that it possesses many advantages. This approach will be applied to a forest dynamic model.
Distributed computing in IoT: System-on-a-chip for smart cameras as an example

There are four major components in application systems with internet-of-things (IoT): sensors, communications, computation and service, where large amount of data are acquired for ultra-big data analysis to discover the context information and knowledge behind signals. To support such large-scale data size and computation tasks, it is not feasible to employ centralized solutions on cloud servers. Thanks for the advances of silicon technology, the cost of computation become lower, and it is possible to distribute computation on every node in IoT. In this paper, we take video sensing network as an example to show the idea of distributed computing in IoT. Existing related works are reviewed and the architecture of a system-on-a-chip solution for distributed smart cameras is proposed with coarse-grained reconfigurable image stream processing architecture. It can accelerate various computer vision algorithms for distributed smart cameras in IoT.
Benevolent Colluders? the Effects of Antitrust Action on College Financial Aid and Tuition

The Department of Justice's (DOJ's) investigation of private colleges for price-fixing caused the Overlap' group of colleges to discontinue their meetings. DOJ alleged that the meetings enabled the colleges to collude on higher tuition and increase their tuition revenue. The colleges claimed that they needed the meeting to implement their policies of basing aid on need and fully covering need. This paper investigates whether the cessation of the meeting caused a break-down of need-based aid policies or whether, as DOJ argued, the meeting was unnecessary for such policies. I also attempt to determine whether the cessation of the meeting affected tuition or tuition revenue. Finally, I examine the question of whether need-based aid is simply redistribution or a method of internalizing externalities among students. Many students would like colleges to maintain policies of need-based aid for others while making exceptions for them, awarding them grants for which they would not qualify based on need. Yet, the same students might prefer a regime of need-based aid, knowing that it would apply them, because basing aid on need affects colleges' selectivity and diversity.