Miracle-dz/UniFAR-FTUs huggingface dataset:
MONETARY SHOCKS AND STOCK RETURNS IN AN EMERGING MARKET: A VAR ANALYSIS

The literature on financial economics contains a considerable number of empirical analyses that investigate the interactions and dynamic linkages between stock returns and macroeconomic variables. Among various macroeconomic variables, money supply has perhaps received the most attention. Theoretically, the influences of a macroeconomic variable such as money supply may be motivated using such models as the standard stock valuation model or standard macroeconomic textbook models. According to the stock valuation model, stock prices reflect discounted expected future cash flows earned by owners of the stocks. This means that any changes in economic variables that have a bearing on the future cash flows or the discount factor affect the stock prices. The standard aggregate demand and aggregate supply (AD/AS) framework also allows for the roles of equity markets in the transmissions of monetary policy shocks (Mishkin 1998, p. 646). In specific, the transmission mechanisms of monetary policy include not only the normal interest rate channel (traditional liquidity effect) but also other channels, one of which involves equity prices via Tobin's q theory, wealth effects and household liquidity effects. These theoretical foundations, unfortunately, do not provide unambiguous testable implications on the directional influences of money supply on stock prices. The argument from the monetary transmission mechanisms highlights positive effects of money supply on stock prices and, subsequently, on other macroeconomic variables. Arguing through the stock valuation model, Mukherjee and Naka (1995) note that the relation between the two variables is either positive or negative. The expansionary effects of the increase in money supply on real economic activity, and thus expected future cash flows, suggest a positive relation between them. Conversely, if the increase in money supply generates inflation or inflation uncertainty and, consequently, raises the discount factor, then monetary expansion may exert a negative influence on equity markets. To complicate the matter, money supply itself may well be endogenous responding to movements in other variables including stock prices (Park and Ratti, 2000). Accordingly, for better understanding of the nature of money supply - equity market interactions, an empirical analysis seems to be called for. The present paper has a main focus on investigating the dynamic linkages between money supply and stock returns for the case of an emerging market, Malaysia. The rapid growth of emerging equity markets including Malaysia has attracted increasing attention from both investors and financial economists. However,
An Application of Approximated Entropy Measures in Decision Tree Induction

The main task in decision tree construction algorithms is to find the ”best partition” of set of objects. We consider the problem of searching for optimal binary partition of continuous attribute domain for large data sets stored in relational data bases (RDB). The straightforward approach to optimal partition selection with respect to entropy measure (which evaluates the quality of a partition) needs O(N) simple queries, where N is the number of pre-assumed partitions. We present new approximated entropy measures that allow to construct the partition very close to optimal, using only O(logN) simple queries.
Efficiency evaluation of parametric failure mitigation techniques for reliable SRAM operation

The efficiency of different assist techniques for SRAM cell functionality improvement under the influence of random process variation is studied in this paper. The sensitivity of an SRAM cell functionality metrics when using control voltage level assist techniques is analyzed in read and write operation modes. The efficiency of the assist techniques is estimated by means of parametric analysis. The purpose is to find the degree of functionality metric improvement in each operation mode. The Acceptance Region concept is used for parametric analysis of SRAM cell functionality under random threshold voltage variations. In order to increase the reliability of the SRAM several assist techniques, chosen among the most efficient ones for each operation mode, are considered. This analysis offers a qualitative indication of the cell's functionality improvement by means of the efficient computation of a metric in parameter domain analysis. The results are proven to have high correlation with the ones obtained by means of the classical Monte Carlo simulations with significant savings in comparing different assist techniques.
Event identification in social media through latent dirichlet allocation and named entity recognition

Nowadays people use social networks such as Facebook, Twitter, Orkut for sharing personal information and also significant events that occurs all over the world. Online news broadcast the most momentous events among users. Then the users discuss those events and post their reviews in micro blogs or in blogosphere. Since the web seems to be too huge and also the information available on the web is constantly updating, the task of identifying such events that are accessible on the web and the comments that are discussed related to those events in social networks is much daunting. Usually an event is temporal in nature, they change in time. Due to the temporal nature of events, the identification of event becomes even more difficult. Existing visual text analysis system extracts temporal themes from the large document stream and with that information; it provides temporal views of changes that occur on the event. Consequently, events that cause the changes can't be identified. In this paper, an Event Identification System is proposed to identify the most important events that occur and to identify the user discussion and also to rate their reviews. The proposed methodology make use of topic clustering, named entity recognition, latent dirichlet allocation, topic modeling to identify the significant events that are available on the web.
Microbial diversity and activity in the Nematostella vectensis holobiont : insights from 16 S rRNA gene sequencing , isolate genomes , and a pilot-scale survey of Citation

Citation Har, Jia Y. , Tim Helbig, Ju H. Lim, Samodha C. Fernando, Adam M. Reitzel, Kevin Penn, and Janelle R. Thompson. “Microbial Diversity and Activity in the Nematostella Vectensis Holobiont: Insights from 16S rRNA Gene Sequencing, Isolate Genomes, and a Pilot-Scale Survey of Gene Expression. ” Frontiers in Microbiology 6 (September 2, 2015). As Published http://dx.doi. org/10.3389/fmicb.2015. 00818 Publisher Frontiers Research Foundation
Subspace Clustering for High Dimensional Data using Density Notion

Clustering is a crucial task of data mining, which supports in abstracting huge amount of data. Instead of searching for clusters in full feature space, researchers are discovering novel approaches for achieving clusters in subspaces. As a result of which, subspace clustering has put on an ample attention. In this paper we had proposed an algorithmSUBDENCLU (SUBspace DENsity CLUstering) for subspace clustering using density based approach. Proposed algorithm is an extension of DBSCAN and SUBCLU algorithm. It aims to minimize subspace search by effectively pruning irrelevant subspaces. Algorithm minimizes expensive region query associated with density based clustering. Experimental evaluation shows that we succeed to improve quality of clusters produced and clustering result contains meaningful clusters.
A Multiple Time Scale , Aggregate Source Model for Edge Traffic in the Next Generation Network

Edge or core-ingress traffic in the Next Generation Network (NGN) is generated by the hierarchical aggregation of multiple, heterogeneous client flows. We model the edge traffic arrival process for an aggregated multi-service user group. The model quantifies the relationship between traffic shaping factors: user group behaviour and service properties, multiplexing buffering and scheduling architecture, and the resulting multiple-time-scale burst characteristics of the aggregated, edge traffic. An initial model analysis shows significant impact due to Class-based queueing (CBQ) at the voice burst time-scale, and a predictable relationship between changing user-group traffic mix and aggregate traffic burstiness. Further usage of modelling includes determining quantifiable impacts of additional architectural variations and the performance implications of multi-timescale burstiness in queueing systems.
Models for Semantically Annotated Document Collections

Increasingly, web document collections such as PubMed and DBPedia, but also social bookmarking systems, are annotated with semantic meta data. Given that the number of semantically annotated document collections is expected to increase in the near future, it is of interest to analyze if topic models might be able to play a larger role. Since most of the time, annotations are noisy and even human experts annotate inconsistently, a probabilistic view, as provided by topic models, is appropriate. Besides a number of interesting knowledge discovery tasks, representing topics by meta data has an additional advantage: if the concepts refer to real-world objects, the readability of the topics is greatly improved. In this paper, we present several suitable strategies to model this type of data and show experiments on two large semantically annotated document collections.
Effect of Microneedle Design on Pain in Human Volunteers

ObjectivesTo design microneedles that minimize pain, this study tested the hypothesis that microneedles cause significantly less pain than a 26-gauge hypodermic needle, and that decreasing microneedle length and the number of microneedles reduces pain in normal human volunteers. MethodsSingle microneedles with lengths ranging from 480 to 1450 μm, widths from 160 to 465 μm, thicknesses from 30 to 100 μm, and tip angles from 20 to 90 degrees; and arrays containing 5 or 50 microneedles were inserted into the volar forearms of 10 healthy, human volunteers in a double-blinded, randomized study. Visual analog scale pain scores were recorded and compared with each other and to the pain from a 26-gauge hypodermic needle. ResultsAll microneedles investigated were significantly less painful than the hypodermic needle with microneedle pain scores varying from 5% to 40% of the hypodermic needle. Microneedle length had the strongest effect on pain, where a 3-fold increase in length increased the pain score by 7-fold. The number of microneedles also affected the pain score, where a 10-fold increase in the number of microneedles increased pain just over 2-fold. Microneedle tip angle, thickness, and width did not significantly influence pain. DiscussionMicroneedles are significantly less painful than a 26-gauge hypodermic needle over the range of dimensions investigated. Decreasing microneedle length and number of microneedles reduces pain.
Fast Frequency-Domain Modeling of Return Stroke Including Influence of Lossy Ground

A constitutive integral equation (CIE) formulation is proposed to model the lightning return stroke. From the CIE, the electromagnetic field radiated by the return stroke considering the lossy ground is determined by using the moment method (MM). To implement a fast numerical and rigorous approach, an exact image theory is first employed to accelerate the computation of Sommerfeld integral, and a translational mesh is then constructed under the thin-wire approximation. Such grid representation and the convolution properties of the integral kernel equip the fast Fourier transform to have the ability to efficiently solve the MM matrix equations. The feasibility and merits of the proposed method are positively confirmed by the comparison of its performances with those of an existing one on a case study.
THE GALEX ARECIBO SDSS SURVEY. VII. THE BIVARIATE NEUTRAL HYDROGEN–STELLAR MASS FUNCTION FOR MASSIVE GALAXIES

We present the bivariate neutral atomic hydrogen (H i)–stellar mass function (HISMF) for massive (log M*/M☉ \gt 10) galaxies derived from a sample of 480 local (0.025 < z < 0.050) galaxies observed in H i at Arecibo as part of the GALEX Arecibo SDSS Survey. We fit six different models to the HISMF and find that a Schechter function that extends down to a 1% H i gas fraction, with an additional fractional contribution below that limit, is the best parameterization of the HISMF. We calculate and find that massive galaxies contribute 41% of the H i density in the local universe. In addition to the binned HISMF, we derive a continuous bivariate fit, which reveals that the Schechter parameters only vary weakly with stellar mass: , the characteristic H i mass, scales as ; α, the slope of the HISMF at moderate H i masses, scales as ; and f, the fraction of galaxies with H i gas fraction greater than 1%, scales as . The variation of f with stellar mass should be a strong constraint for numerical simulations. To understand the physical mechanisms that produce the shape of the HISMF, we redefine the parameters of the Schechter function as explicit functions of stellar mass and star formation rate (SFR) to produce a trivariate fit. This analysis reveals strong trends with SFR. While varies weakly with stellar mass and SFR ( ∝ , ∝ SFR−0.03), α is a stronger function of both stellar mass and especially SFR (α ∝ , α ∝ SFR0.95). The HISMF is a crucial tool that can be used to constrain cosmological galaxy simulations, test observational predictions of the H i content of populations of galaxies, and identify galaxies whose properties deviate from average trends.
Action Recognition with Joint Attention on Multi-Level Deep Features

We propose a novel deep supervised neural network for the task of action recognition in videos, which implicitly takes advantage of visual tracking and shares the robustness of both deep Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). In our method, a multi-branch model is proposed to suppress noise from background jitters. Specifically, we firstly extract multi-level deep features from deep CNNs and feed them into 3d-convolutional network. After that we feed those feature cubes into our novel joint LSTM module to predict labels and to generate attention regularization. We evaluate our model on two challenging datasets: UCF101 and HMDB51. The results show that our model achieves the state-of-art by only using convolutional features.
Detecting software vulnerabilities in android using static analysis

Now a day's mobile devices like Smartphone, tablets and Personal Digital Assistants etc. were playing most essential part in our daily lives. A high-end mobile device performs the same functionality as computers. Android based smart phone has become more vulnerable, because of an open source operating system. Anyone can develop a new application and post it into android market. These types of applications were not verified by authorized company. So it may include malevolent applications it may be virus, spyware, worms, etc. which can cause system failure, wasting memory resources, corrupting data, stealing personal information and also increases the maintenance cost. Due to these reasons, the mobile phone security or mobile security is very essential one in mobile computing. In the existing system is not able to detect new viruses, due to the limitation of updated signatures. The proposed system aims to motivate static code analysis based malware detection using search based machine learning algorithm which is called N-gram analysis and it detects the unnoticed malicious characteristics or vulnerabilities in the mobile applications.
When students become customers; the changing relationship between the student and the academic: a case study from social sciences in turkey

The definition of higher education is going through a rapid transformation where higher education is seen as a strategic commodity with a high profit potential. Actors involved in an increasingly commodified higher education arena are taking different positions in this changing context. Neoliberal ideology is taken as the basis of the transformation of higher education. Academics taking the role of service providers are losing their professional positions where students are being regarded as customers in the ‘Neoliberal University’. This implies a change in the relationship between institutions, academics and students which will be analyzed throughout this paper. The paper takes the point of view of academics, bringing in results of a qualitative PhD study conducted with 28 academics working in public and foundation (private) universities located in the cities of Istanbul and Izmir. The case presented in this study will be analyzed as part of the discussion on the changing nature of higher education in which academics are increasingly being controlled through their relationship with their students and the effects of the increasing demands of the students taking the role of customers. This analysis is a part of a broader discussion on how the academic profession is changing under the context of the ‘Neoliberal University’.
On the Origin of Three Seismic Sources in the Proton-rich Flare of 2003 October 28

The three seismic sources, S1, S2, and S3, detected from MDI Dopplergrams using the time-distance (TD) diagram technique are presented with the locations, areas, and vertical and horizontal velocities of the visible wave displacements. Within the data cube of 120 Mm, the horizontal velocities and the wave propagation times vary slightly from source to source. The momenta and start times measured from the TD diagrams in sources S1-S3 are compared with those delivered to the photosphere by different kinds of high-energy particles with the parameters deduced from hard X-ray and γ-ray emission, as well as by the hydrodynamic shocks caused by these particles. The energetic protons (power laws combined with quasi-thermal ones, or jets) are shown to deliver momentum high enough and to form the hydrodynamic shocks deep enough in a flaring atmosphere to allow them to be delivered to the photosphere through much shorter distances and times. Then the seismic waves observed in the sources S2 and S3 can be explained by the momenta produced by hydrodynamic shocks, which are caused by mixed proton beams and jets occurring nearly simultaneously with the third burst of hard X-ray and γ-ray emission in the loops with footpoints in the locations of these sources. The seismic wave in source S1, delayed by 4 and 2 minutes from the first and second hard X-ray bursts, respectively, is likely to be associated with a hydrodynamic shock occurring in this loop from precipitation of a very powerful and hard electron beam with higher energy cutoff mixed with quasi-thermal protons generated by either of these two bursts.
Cross-Ethnic Measurement Equivalence of the Children’s Depression Inventory Among Youth in Foster Care

Previous research has indicated differences in the presentation and measurement of depressive symptoms across ethnic groups. In the current study, we examined the factor structure of the Children’s Depression Inventory in an ethnically diverse sample of adolescents in foster care (n = 346; Mage = 13.13 years, SD = 2.18 years) and examined the configural invariance of the measure across ethnic groups. Findings indicated a two-factor structure in the total sample. Furthermore, configural invariance was not supported, in that unique factor structures emerged for African American, White, and Hispanic subsamples. Findings hold implications for research and practice involving the assessment of depressive symptoms among youth in out-of-home care and among ethnic minority adolescents.
A biometric verification system based on the fusion of palmprint and face features

This paper presents a bimodal biometric verification system for physical access control based on the features of the palmprint and the face. The system tries to improve the verification results of unimodal biometric systems based on palmprint or facial features by integrating them using fusion at the matching-score level. The verification process consists of image acquisition using a scanner and a camera, palmprint recognition based on the principal lines, face recognition with eigenfaces, fusion of the unimodal results at the matching-score level, and finally, a decision based on thresholding. The experimental results show that fusion improves the equal error rate by 0.74% and the minimum total error rate by 1.72%.
Oligometastatic state predicts a favorable outcome for renal cell carcinoma patients with bone metastasis under the treatment of sunitinib

Background The aim of the study was to investigate whether RCC patients with oligometastatic state of bone metastasis treated with sunitinib had a favorable clinical outcome. Results 22 patients were classified into oligometastatic state of bone metastasis with a median OS of 30.1 months (95%CI: 26.3 to 33.8 months). The 45 patients with non-oligometastatic state had a median OS of 12.7 months (95%CI: 9.43 to 16.0 months). Kaplan-Meier analysis showed significant difference between them (Log Rank test p<0.001). When we set patients with only multiple bone (at least 5 sites) metastases as a single group, there was still significant difference between oligometastatic state group and non-oligometastatic state groups. In multivariate Cox proportion hazard ratio analysis, metastatic states (p=0.012), MSKCC score (p=0.002), ECOG (p=0.001) and lymph nodes metastasis (p=0.000) were significantly associated with prognosis. The integration of metastatic state into the MSKCC risk model improved the c-index from 0.651 to 0.752 Method 67 patients from Fudan University Shanghai Cancer Center with bone metastatic RCC were divided into 2 metastatic states. One included those with oligometastatic state of bone metastasis with less than 5 sites of bone metastasis. The other involved those patients with multiple bone metastases (at least 5 sites) or together with other sites of metastasis. Then patients with only multiple bone (at least 5 sites) metastases were set into a single group. Conclusion RCC patients with oligometastatic state of bone metastasis treated with sunitinib had a favorable clinical outcome.
Buddy SM

A modern general-purpose graphics processing unit (GPGPU) usually consists of multiple streaming multiprocessors (SMs), each having a pipeline that incorporates a group of threads executing a common instruction flow. Although SMs are designed to work independently, we observe that they tend to exhibit very similar behavior for many workloads. If multiple SMs can be grouped and work in the lock-step manner, it is possible to save energy by sharing the front-end units among multiple SMs, including the instruction fetch, decode, and schedule components. However, such sharing brings architectural challenges and sometime causes performance degradation. In this article, we show our design, implementation, and evaluation for such an architecture, which we call Buddy SM. Specifically, multiple SMs can be opportunistically grouped into a buddy cluster. One SM becomes the master, and the rest become the slaves. The front-end unit of the master works actively for itself as well as for the slaves, whereas the front-end logics of the slaves are power gated. For efficient flow control and program correctness, the proposed architecture can identify unfavorable conditions and ungroup the buddy cluster when necessary. We analyze various techniques to improve the performance and energy efficiency of Buddy SM. Detailed experiments manifest that 37.2% front-end and 7.5% total GPU energy reduction can be achieved.