Miracle-dz/UniFAR-FTUs huggingface dataset:
A Feature Terms based Method for Improving Text Summarization with Supervised POS Tagging

Text summarization is the process of distilling the most important information from a source to produce an abridged version for a particular user and task. When this is done by means of a computer, i.e. automatically, it calls as Automatic Text Summarization. Summarization can be classified into two approaches: extraction and abstraction. Extraction based summaries are produced by concatenating several sentences taken exactly as they appear in the texts being summarized. Abstraction based summaries are written to convey the main information in the input and may reuse phrases or clauses from it. This paper focuses on extraction approach. The goal of text summarization based on extraction approach is sentences selection. One of the methods to obtain the sentences is to assign some feature terms of sentences for the summary called ranking sentences and then select the best ones. The first step in summarization by extraction is the identification of important features. In our approach 1000 computer science related research papers are used as test documents. Each document is prepared by preprocessing process: sentence segmentation, tokenization, stop word removal, case folding, lemmatization, and stemming. Then, using important features, sentence filtering features, data compression features and finally calculating score for each sentence. The proposed text summarization is based on HMM tagger to improve the quality of the summary. Here, comparing our results with the existing summarizers which are Copernicus summarizer, Great summarizer and Microsoft Word 2007 summarizers etc. The proposed system is also tested with four types‘ similarities: Cosine, Jaccard, Jarowinkler and Sorenson similarities. The results show that the best quality for the summaries was obtained by feature terms method. General Terms Text Mining, Information Extraction, Automatic Text Summarization, Natural Language Processing, POS Tagging.
Altered Dendritic Morphology of Purkinje cells in Dyt1 ΔGAG Knock-In and Purkinje Cell-Specific Dyt1 Conditional Knockout Mice

Background DYT1 early-onset generalized dystonia is a neurological movement disorder characterized by involuntary muscle contractions. It is caused by a trinucleotide deletion of a GAG (ΔGAG) in the DYT1 (TOR1A) gene encoding torsinA; the mouse homolog of this gene is Dyt1 (Tor1a). Although structural and functional alterations in the cerebellum have been reported in DYT1 dystonia, neuronal morphology has not been examined in vivo. Methodology/Principal Findings In this study, we examined the morphology of the cerebellum in Dyt1 ΔGAG knock-in (KI) mice. Golgi staining of the cerebellum revealed a reduction in the length of primary dendrites and a decrease in the number of spines on the distal dendrites of Purkinje cells. To determine if this phenomenon was cell autonomous and mediated by a loss of torsinA function in Purkinje cells, we created a knockout of the Dyt1 gene only in Purkinje cells of mice. We found the Purkinje-cell specific Dyt1 conditional knockout (Dyt1 pKO) mice have similar alterations in Purkinje cell morphology, with shortened primary dendrites and decreased spines on the distal dendrites. Conclusion/Significance These results suggest that the torsinA is important for the proper development of the cerebellum and a loss of this function in the Purkinje cells results in an alteration in dendritic structure.
Steerable dyadic wavelet transform and interval wavelets for enhancement of digital mammography

This paper describes two approaches for accomplishing interactive feature analysis by overcomplete multiresolution representations. We show quantitatively that transform coefficients, modified by an adaptive non-linear operator, can make more obvious unseen or barely seen features of mammography without requiring additional radiation. Our results are compared with traditional image enhancement techniques by measuring the local contrast of known mammographic features. We design a filter bank representing a steerable dyadic wavelet transform that can be used for multiresolution analysis along arbitrary orientations. Digital mammograms are enhanced by orientation analysis performed by a steerable dyadic wavelet transform. Arbitrary regions of interest (ROI) are enhanced by Deslauriers-Dubuc interpolation representations on an interval. We demonstrate that our methods can provide radiologists with an interactive capability to support localized processing of selected (suspicion) areas (lesions). Features extracted from multiscale representations can provide an adaptive mechanism for accomplishing local contrast enhancement. By improving the visualization of breast pathology can improve changes of early detection while requiring less time to evaluate mammograms for most patients.
The Accessibility Determinants of Internet Financial Reporting of Manufacture Company Listed in Indonesia Stock Exchange

This research is aimed to examine an association between the accessibility of IFR in the company website using Accessibility Index Value and the determinant variables named as company size, profitability, liquidity, and leverage which explain the choice to provide IFR in the company website. The population is the manufacture companies listed in Indonesia Stock Exchange. The sample is selected in certain parts of the population with the criteria. They are first, a company listed on the Indonesia Stock Exchange, second, the company’s website is active and not in a condition under maintenance, third, it must have a company’s financial statements in 2013, forth, and the availability of other data for the observed variables. The results of this research concluded that only profitability has positive association with the accessibility of the financial statements. This results discussion will expand the theories and models that have been developed from voluntary reporting through traditional media towards IFR.
Modified two-dimensional compressed sensing scheme for massive MIMO channel estimation

The problem of compressed sensing (CS) is considered in two-dimensional (2D) sparse decomposition measurement model. Correspondingly, a novel recovery algorithm - modified 2D subspace pursuit (M-2DSP) algorithm is proposed with the available prior support and chunk sparse structure. The massive multi-input multi-out (MIMO) system show a concealed sparse structure and temporal correlation in the user (UE) channel matrix in virtue of the shared local scatterers in the physical propagation environment. Thereby the proposed scheme can be applied to sparse channel estimation in massive MIMO systems with temporal correlation. Furthermore its effectiveness is proved, both theoretical analysis and experiment simulations testify the usefulness and advantages of the new algorithm in recovery performance, especially in smaller overhead training pilot quantity and lower transmit signal noise ratio (SNR).
Twinning rates and survival of twins in rural Nepal.

BACKGROUND Twin pregnancies are common but there are few data on rates of twinning or survival of liveborn twin infants in developing countries. METHODS The rates of multiple births were calculated in a population-based cohort of married women of childbearing age who were enrolled in a randomized community trial to assess the impact of vitamin A or beta-carotene on maternal and infant health and survival. RESULTS The rate of twinning was 16.1 per 1000 pregnancies (7.4 if only twin pregnancies resulting in two liveborn infants were used). The rate for triplets and quadruplets was 0.19 and 0.06 per 1000 pregnancies. Twinning rates were higher among women of higher parity, but were not associated with maternal age. Twinning rates among twins where at least one was live born (or increased in utero survival) were 30% (95% CI : -1%, 71%) and 44% (95% CI : 9%, 89%) higher among women receiving vitamin A and beta-carotene supplements than placebo, after adjusting for maternal age, gestational age, and parity. The perinatal mortality rate was 8.54 times higher for twins than singletons, 7.32 higher for neonatal mortality, and 5.84 higher for cumulative 24-week mortality. This difference was reduced but not erased by adjusting for gestational age. No difference in survival of liveborn twin infants was seen by supplement group. A higher mortality rate among male twins was largely explained by gestational age. CONCLUSIONS Multiple births are relatively common occurrences in rural Nepal, and carry a much higher mortality risk for the infants than for singletons. Vitamin A or beta-carotene supplementation appeared to increase the rate of twinning, or improve the survival of twins in utero, but did not increase twin survival after birth.
Glass Imprint Templates by Spark Assisted Chemical Engraving for Microfabrication by Hot Embossing

As the field of microelectromechanical systems (MEMS) matures, new demands are being placed on the microfabrication of complex architectures in robust materials, such as hard plastics. Iterative design optimization in a timely manner—rapid prototyping—places challenges on template fabrication, for methods such as injection moulding and hot embossing. In this paper, we demonstrate the possibility of using spark assisted chemical engraving (SACE) to produce micro patterned glass templates. The direct, write-based approach enabled the facile fabrication of smooth microfeatures with variations in all three-dimensions, which could be replicated by hot embossing different thermoplastics. As a proof of principle, we demonstrated the technique for a high glass transition temperature polycarbonate. Good fidelity over more than 10 cycles provides evidence that the approach is viable for rapid prototyping and has the potential to satisfy commercial-grade production at medium-level output volumes. Glass imprint templates showed no degradation after use, but care must be taken due to brittleness. The technique has the potential to advance microfabrication needs in academia and could be used by MEMS product developers.
Cross-Calibration of GE Healthcare Lunar Prodigy and iDXA Dual-Energy X-Ray Densitometers for Bone Mineral Measurements

In long-term prospective studies, dual-energy X-ray absorptiometry (DXA) devices need to be inevitably changed. It is essential to assess whether systematic differences will exist between measurements with the new and old device. A group of female volunteers (21–72 years) underwent anteroposterior lumbar spine L2–L4 (n = 72), proximal femur (n = 72), and total body (n = 62) measurements with the Prodigy and the iDXA scanners at the same visit. The bone mineral density (BMD) measurements with these two scanners showed a high linear association at all tested sites (r = 0.962–0.995; p < 0.0001). The average iDXA BMD values were 1.5%, 0.5%, and 0.9% higher than those of Prodigy for lumbar spine (L2–L4) (p < 0.0001), femoral neck (p = 0.048), and total hip (p < 0.0001), respectively. Total body BMD values measured with the iDXA were −1.3% lower (p < 0.0001) than those measured with the Prodigy. For total body, lumbar spine, and femoral neck, the BMD differences as measured with these two devices were independent of subject height and weight. Linear correction equations were developed to ensure comparability of BMD measurements obtained with both DXA scanners. Importantly, use of equations from previous studies would have increased the discrepancy between these particular DXA scanners, especially at hip and at spine.
Mild hyperthermia with magnetic resonance-guided high-intensity focused ultrasound for applications in drug delivery

Purpose: Mild hyperthermia (40–45°C) is a proven adjuvant for radiotherapy and chemotherapy. Magnetic resonance guided high intensity focused ultrasound (MR-HIFU) can non-invasively heat solid tumours under image guidance. Low temperature-sensitive liposomes (LTSLs) release their drug cargo in response to heat (>40°C) and may improve drug delivery to solid tumours when combined with mild hyperthermia. The objective of this study was to develop and implement a clinically relevant MR-HIFU mild hyperthermia heating algorithm for combination with LTSLs. Materials and methods: Sonications were performed with a clinical MR-HIFU platform in a phantom and rabbits bearing VX2 tumours (target = 4–16 mm). A binary control algorithm was used for real-time mild hyperthermia feedback control (target = 40–41°C). Drug delivery with LTSLs was measured with HPLC. Data were compared to simulation results and analysed for spatial targeting accuracy (offset), temperature accuracy (mean), homogeneity of heating (standard deviation (SD), T10 and T90), and thermal dose (CEM43). Results: Sonications in a phantom resulted in better temperature control than in vivo. Sonications in VX2 tumours resulted in mean temperatures between 40.4°C and 41.3°C with a SD of 1.0–1.5°C (T10 = 41.7–43.7°C, T90 = 39.0–39.6°C), in agreement with simulations. 3D spatial offset was 0.1–3.2 mm in vitro and 0.6–4.8 mm in vivo. Combination of MR-HIFU hyperthermia and LTSLs demonstrated heterogeneous delivery to a partially heated VX2 tumour, as expected. Conclusions: An MR-HIFU mild hyperthermia heating algorithm was developed, resulting in accurate and homogeneous heating within the targeted region in vitro and in vivo, which is suitable for applications in drug delivery.
Balance of Recurrece Order in Time-Inhomogenous Markov Chains with Application to Simulated Annealing

We define a notion of order of recurrence for the states and transitions of a general class of time-inhomogeneous Markov chains with transition probabilities proportional to powers of a small vanishing parameter. These orders are shown to satisfy a balance equation across every edge cut in the associated graph. The resulting order balance equations allow computation of the orders of recurrence of the states, and thereby the determination of the asymptotic behavior of the Markov chain. The method of optimization by simulated annealing is a special case of such Markov processes, and can therefore be treated by means of these balance equations. In particular, in this special situation we show that there holds a detailed balance of order of recurrence across every edge in the graph. Moreover, the sum of the order of recurrence of a state and its cost is shown to be a constant in each connected set of recurrent states. By this approach, we determine the necessary and sufficient condition on the “rate of cooling” to guarantee that a minimum of the optimization problem is hit with probability one. Moreover, the rates of convergence of the probabilities can be deduced from the orders of recurrence.
On the Condition of Orthogonal Polynomials via Modified Moments

We consider the condition of orthogonal polynomials, encoded by the coefficients of their three-term recurrence relation, if the measure is given by modified moments (i.e. integrals of certain polynomials forming a basis). The results concerning various polynomial bases are illustrated by simple examples of generating (possibly shifted) Chebyshev polynomials of first and second kind.
On the Eeectiveness of Memory-based Methods in Machine Learning 5

Many memory-based methods for learning use some form of nearest neighbor inference. By memory-based, we mean methods that localize data in the training sample to make inferences about novel feature values. The conventional wisdom about nearest neighbor methods is that they are subject to various curses of dimensionality and so become in-feasible in high dimensional feature spaces. However, recent results such as those by Barron and Jones suggest that these dimensionality problems can be overcome in the case of parametric models such as sigmoidal neural networks which are patently nonlocal. This creates a paradox because memory-based methods have been shown to perform well in a number of applications. They are often competative with parametric methods in terms of prediction error and actually superior in terms of training time. In this paper, we study the unreasonable eeectiveness of memory-based methods. We analyze their performance in terms of new metrics that take into consideration the interaction between the function being estimated and the underlying probability distribution generating the samples. Extensions of this analysis method might serve as the basis for a new foundation for more general memory-based methods that could explain their observed performance on real problems.
Designing concurrent objects

The me too method of software design has many similarities with the Vienna Development Method (VDM) [3] , but is simpler and leads to executable specifications . It has three steps . In the model step an informal model of a real-world situation is built by identifying abstract objects and their associated operations. In the specify step , each object in the model is given a formal representation in terms of sets, relations, finite functions, tuples an d sequences . Operations on each object are defined in terms of the mathematical operations available on the object' s formal representation .
A study of two dimensional linear discriminants for ASR

We study the information in the joint time-frequency domain using 1515 dimensional-15 spectral energies and temporal span of 1s-block of spectrogram as features. In this feature space, we first derive 20 joint linear discriminants (JLDs) using linear discriminant analysis (LDA). Using principal component analysis (PCA), we conclude that information in this block of the spectrogram can be analyzed independently across the time and frequency domains. Under this assumption, we propose a sequential design of two dimensional discriminants (CLDs), i.e., spectral discriminants followed by temporal discriminants. We show that these CLDs are similar to first few JLDs and the discriminant features derived from the CLDs outperform those obtained from JLDs in the continuous-digit recognition task.
Snap-To-It: A User-Inspired Platform for Opportunistic Device Interactions

The ability to quickly interact with any nearby appliance from a mobile device would allow people to perform a wide range of one-time tasks (e.g., printing a document in an unfamiliar office location). However, users currently lack this capability, and must instead manually configure their devices for each appliance they want to use. To address this problem, we created Snap-To-It, a system that allows users to opportunistically interact with any appliance simply by taking a picture of it. Snap-To-It shares the image of the appliance a user wants to interact with over a local area network. Appliances then analyze this image (along with the user's location and device orientation) to see if they are being "selected," and deliver the corresponding control interface to the user's mobile device. Snap-To-It's design was informed by two technology probes that explored how users would like to select and interact with appliances using their mobile phone. These studies highlighted the need to be able to select hardware and software via a camera, and identified several novel use cases not supported by existing systems (e.g., interacting with disconnected objects, transferring settings between appliances). In this paper, we show how Snap-To-It's design is informed by our probes and how developers can utilize our system. We then show that Snap-To-It can identify appliances with over 95.3% accuracy, and demonstrate through a two-month deployment that our approach is robust to gradual changes to the environment.
A closed form approach to valuing and hedging basket options

We develop a new approach to valuing and hedging basket options. We consider baskets of assets with potentially negative portfolio weights (spread options are a subclass of such basket options). The basket distribution is approximated using a generalized family of log-normal distributions. This approximation copes with negative basket values as well as negative skewness of the basket distribution, and it provides closed formulae for the option price and greeks. Numerical simulations show our approach provides a very close approximation of the option price, and performs remarkably well in terms of the hedging error. We analyze the option price sensitivities with respect to the assets' volatilities and correlations; and explain the seemingly paradoxical phenomenon of negative volatility vegas
Be a good neighbour: Characterizing performance interference of virtual machines under xen virtualization environments

With the rapid development of virtualization techniques, modern data centers move into a new era of cloud in recent years. Despite numerous advantages such as high resource utilization and rapid service scalability, current virtualization techniques don't guarantee perfect performance isolation among virtual machines sharing the physical machine, which may lead to unstable and unpredictable user-perceived application performance in clouds. Therefore, understanding and modeling performance interference among collocated applications is of utmost importance. However, the hypervisor and guest OSes usually run independent resource schedulers and are invisible into each other, thereby making accurately characterizing performance interference a non-trivial work. In this paper, we first present a comprehensive experimental study on performance interference of different combinations of benchmarks, observing that virtual CPU floating overhead between multiple physical CPUs, and VMEXITs, i.e., the control transitions between the hypervisor and VMs, constitute the key source of performance interference. In order to characterize the performance interference effects, we measure both the application-level and VM-level characteristics from the collocated applications and then build a novel interference prediction framework based on kernel canonical correlation analysis. Our evaluations first show the practicability of KCCA in finding reliable correlation, and further confirm the high accuracy and great applicability of our interference model with a low prediction error of no more than 7.9%.
Disease State Differentiation and Identification of Tuberculosis Biomarkers via Native Antigen Array Profiling*S

A critical element of tuberculosis control is early and sensitive diagnosis of infection and disease. Our laboratories recently showed that different stages of disease were distinguishable via two-dimensional Western blot analyses of Mycobacterium tuberculosis culture filtrate proteins. However, this methodology is not suitable for high throughput testing. Advances in protein microarray technology provide a realistic mechanism to screen a large number of serum samples against thousands of proteins to identify biomarkers of disease states. Techniques were established for separation of native M. tuberculosis cytosol and culture filtrate proteins, resulting in 960 unique protein fractions that were used to generate protein microarrays. Evaluation of serological reactivity from 42 patients in three tuberculosis disease states and healthy purified protein derivative-positive individuals demonstrated that human immunodeficiency virus (HIV)-negative cavitary and noncavitary tuberculosis (TB) patients’ sera recognized 126 and 59 fractions, respectively. Sera from HIV patients coinfected with TB recognized 20 fractions of which five overlapped with those recognized by non-HIV TB patients’ sera and 15 were unique to the HIV+TB+ disease state. Identification of antigens within the reactive fractions yielded 11 products recognized by both cavitary and noncavitary TB patients’ sera and four proteins (HspX, MPT64, PstS1, and TrxC) specific to cavitary TB patients. Moreover four novel B cell antigens (BfrB, LppZ, SodC, and TrxC) of human tuberculosis were identified.