Miracle-dz/UniFAR-FTUs huggingface dataset:
Almost-linear-time algorithms for Markov chains and new spectral primitives for directed graphs

In this paper, we begin to address the longstanding algorithmic gap between general and reversible Markov chains. We develop directed analogues of several spectral graph-theoretic tools that had previously been available only in the undirected setting, and for which it was not clear that directed versions even existed. In particular, we provide a notion of approximation for directed graphs, prove sparsifiers under this notion always exist, and show how to construct them in almost linear time. Using this notion of approximation, we design the first almost-linear-time directed Laplacian system solver, and, by leveraging the recent framework of [Cohen-Kelner-Peebles-Peng-Sidford-Vladu, FOCS '16], we also obtain almost-linear-time algorithms for computing the stationary distribution of a Markov chain, computing expected commute times in a directed graph, and more. For each problem, our algorithms improve the previous best running times of O((nm3/4 + n2/3 m) logO(1) (n κ ε-1)) to O((m + n2O(√lognloglogn)) logO(1) (n κε-1)) where n is the number of vertices in the graph, m is the number of edges, κ is a natural condition number associated with the problem, and ε is the desired accuracy. We hope these results open the door for further studies into directed spectral graph theory, and that they will serve as a stepping stone for designing a new generation of fast algorithms for directed graphs.
Review of bosentan in the management of pulmonary arterial hypertension

The dual endothelin receptor antagonist, bosentan, is an orally active therapy, which is effective in the treatment of pulmonary arterial hypertension (PAH). This review critically appraises the evidence for the efficacy of bosentan in idiopathic and familial PAH, in PAH associated with connective tissue disease and in PAH which may develop in association with other conditions. Data from the pivotal placebo controlled studies and their open labeled extensions as well as long term survival and quality of life data is presented. Data is also presented on the potential benefit of bosentan in patients with inoperable chronic thromboembolic pulmonary hypertension. The safety and tolerability of bosentan as well as drug interactions are discussed. Dosage recommendations in adults and pediatrics are presented. An algorithm is provided to guide the reader in monitoring potential increases in alanine and aspartate transaminase levels that may occur with bosentan use and the dose adjustments that are recommended as a result of any increase in the levels of these enzymes are shown. Finally, the role of bosentan as part of combination therapy in PAH is examined.
Pseudospectra of the Convection-Diffusion Operator

The spectrum of the simplest 1D convection-diffusion operator is a discrete subset of the negative real axis, but the pseudospectra are regions in the complex plane that approximate parabolas. Put another way, the norm of the resolvent is exponentially large as a function of the Peclet number throughout a certain parabolic region. These observations have a simple physical basis and suggest that conventional spectral analysis for convection-diffusion operators may be of limited value in some applications.
A demo of Coco : a compiler for monadic coercions in ML Nataliya Guts

Combining monadic computations may induce a significant syntactic overhead. To allow monadic programming in direct style, we have developed Coco, a type-based tool that automatically rewrites ML code inserting necessary binds, unit, and morphisms between monads. This tool demonstration will show how to take advantage of Coco to facilitate using monadic libraries in practice, and will discuss possible future development of Coco to fit the actual needs of programmers.
Development of an endoscopic manipulator control system with intention recognition based on pupil movement

The coordination between the operator and laparoscope holder has a major effect on the process and results of laparoscopic surgery. The most intuitive method is for surgeons to manipulate the laparoscope by themselves. Here we demonstrate an endoscopic manipulator control system based on pupil movement that uses an intention recognition system to improve manipulation performance. An experimental task was designed to verify the effects of the intention recognition system. The intention recognition system is installed on endoscopic manipulator. The two core elements of the intention recognition system are an intention judge and direction judge. A comparable experiment was designed to verify the effects of the intention recognition system. The results of the experiment show that control performance was improved using the intention judge element: the task time was much shorter and the motion trail was more stable.
Prediction of Refrigerant Mass Flow Rates through Capillary Tubes Using Adaptive Neuro-fuzzy Inference System

A capillary tube is a common expansion device widely used in small-scale refrigeration and air conditioning systems. Generalized correlation method for refrigerant flow rate through adiabatic capillary tubes is developed by combining dimensional analysis and adaptive neuron-fuzzy inference system (ANFIS). Dimensional analysis is utilized to provide the generalized dimensionless parameters and reduce the number of input parameters, while a five-layer feedforward ANFIS is served as a universal approximator of the nonlinear multi-input and single output function. For ANFIS training and test,measured data for R134a, R22, R290, R407C, R410A,and R600a in the open literature are employed. The most suitable membership function and number of membership functions are found as Gauss and two,respectively, for the ANFIS correlation. The statistical data can be considered as very promising. This paper shows the appropriateness of ANFIS for the prediction of refrigerant mass flow rates through capillary tubes.
Breast cancer survival in Canada and the USA: meta-analytic evidence of a Canadian advantage in low-income areas.

BACKGROUND This study tested the hypothesis that relatively poor Canadian women with breast cancer have a survival advantage over their counterparts in the USA. METHODS Seventy-eight independent retrospective cohort (incidence between 1984 and 2000, followed until 2006) outcomes were synthesized. Fixed effects meta-regression models compared women with breast cancer in low-income areas of Canada and the USA. RESULTS Low-income Canadian women were advantaged on survival [rate ratio (RR) = 1.14; 95% confidence interval (CI) 1.13-1.15] and their advantage was even larger among women <65 years of age who are not yet eligible for Medicare coverage in the USA (RR = 1.21, 95% CI 1.18-1.24). Canadian advantages were also larger for node positive breast cancer, which may present with greater clinical and managerial discretion (RR = 1.40, 95% CI 1.30-1.50), and smaller when Hawaii, the state providing the most Canadian-like access, was the US comparator (RR = 1.12, 95% CI 1.01-1.20). CONCLUSIONS More inclusive health care insurance coverage in Canada vs the USA, particularly among each country's relatively poor people, seems the most plausible explanation for such Canadian advantages. Provision of health care for all Americans would likely prevent countless early deaths, particularly among the relatively poor.
Performance and power analysis of time-multiplexed execution on dynamically reconfigurable processor

Dynamically reconfigurable processor (DRP) developed by NEC Electronics is a coarse grain reconfigurable processor that selects a datapath called a context from the on-chip repository of sixteen circuit configurations at runtime. The time-multiplexed execution based on the multi-context functionality is expected to drastically improve area and power efficiency. To demonstrate the impact of the time-multiplexed execution, we have implemented several stream applications on DRP with various context sizes. Throughout the evaluation based on real application designs, we analyzed the impact of the time-multiplexed execution on performance and power dissipation quantitatively.
Implicit first class genericity , or Dynamic Object Instantiation

While the software systems are growing bigger, and the market demands shorter software production cycles, IT companies need more and more extensive reuse of existing components. Additionally, sophisticated reuse scenarios demand flexible components which are able to adapt to different needs. As a result, it is required that all the components are as customizable as possible. In object-oriented (oo) programming languages, the base tool of reuse and customization is inheritance, which is widely known and exploited, however it shows its limitations when it comes to reuse complicated components. There exist several techniques which allow a stronger and more flexible customization of components. Unfortunately, most of those techniques require additional work to be performed by the programmer in advance, or have other limitations. As a result , those techniques are not widely used, and there is still research to be done in this area. In this paper we present a new proposal that offers a feature for replacing a class with a compatible subclass within a library without the need of modifying its sources. As a result, it increases the possible reuse and the customization scenarios of existing components, with little additional coding required. Our idea is presented as an extension of an arbitrary oo language, however it is studied in more details as an extension of Java called ImpliJava.
Performance analysis of edge detection algorithms with THEOS satellite images

The goal of this research is to find a suitable edge detection algorithm with 4 bands, B1, B2, B3, and B4 of different types of satellite image. In this paper, the dataset is derived from raw satellite images, namely THEOS, of the seashore in the Samut Prakan province, a fruit garden in the Chantaburi province and river line in Ayuthaya provinces. Edge detection performance algorithms are Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), and edge detection processing time, as well as qualitative human visual perception. Our result shows that Canny algorithm and Laplacian of Gaussian algorithm were the best edge detection algorithm for both qualitatively and quantitatively. Moreover, the different kinds of bands in satellite images illustrated the same result. All of the algorithms consumed similar times.
On Independence and Separability between Points and Marks of Marked Point Processes

An important problem in statistical methods for marked point processes (MPPs) is to evaluate the relationship between points and marks, which can be developed under either the concept of independence or the concept of separability. Although both have been used, the connection between these two concepts is still unclear in the literature. The present article provides a way to evaluate such a connection, concluding that the concept of independence and the concept of separability are equivalent if the Kolmogorov consistency condition is satisfied, but not otherwise. We also provide a testing method to assess first-order independence between points and marks, where first-order independence is concluded if the test statistic is insignificant and first-order dependence is concluded if the test statistic is significant. The performance of the testing method is evaluated under simulation and case studies.
SpVM Acceleration with Latency Masking Threads on FPGAs

Author(s): Halstead, RJ; Najjar, WA; Huseini, O | Abstract: Long memory latencies are mitigated through the use of large cache hierarchies in multi-core architectures, SIMD execution in GPU architectures and streaming of data in FPGA-based accelerators. However, none of these approaches benefits irregular applications that exhibit no locality and rely on extensive pointer de-referencing for data accesses. By masking the memory latency, multi-threaded execution has been demonstrated to deal effectively with such applications. In the MT-FPGA model a multi-threaded engine is implemented on the FPGA accelerator specifically for the masking on the memory latency in the execution of irregular applications: following a memory access, the execution is switched to a ready thread while the suspended threads wait for the return of the requested data value from memory. The multi-threaded engine is automatically generated, from C code, by the CHAT compilation tool and is customized to the specific application. In this paper we use the Sparse Vector Matrix application to evaluate the performance of the MT-FPGA execution and compare it to the latest GPU architectures over a wide range of benchmarks.
Equity dimensions of road traffic injuries in low- and middle-income countries

Globally, poorer population groups bear a disproportionate burden of avoidable morbidity and mortality from road traffic injuries. The distribution of road traffic injuries is generally influenced by socioeconomic factors. Poor countries bear a disproportionate burden of injuries and fatalities, and within countries, poor people account for a disproportionate portion of the ill health due to road traffic injuries. The main source of data for this paper was the road traffic injury database of the WHO World Health Report for 1999 supplemented by the WHO Global Burden of Disease Study 2000 report, and published and unpublished works. Fatality rates for 0-4 and 5-14 year olds in low- and middle-income regions, measured as deaths per 100,000 population, were six times the rates for high-income regions, while within low- and middle-income regions the rates varied widely. Within poor countries, poor people – represented by pedestrians, passengers in buses and trucks, and cyclists – suffer a higher burden of morbidity and mortality from traffic injuries. In rich countries, children from poor socioeconomic classes suffer more injuries and deaths from road crashes than their counterparts from highincome groups. The disproportionate burden of morbidity and mortality in low- and middle-income countries, and among low socioeconomic groups in those countries, illustrates problems of global inequities in health. The problems can be addressed through policies that focus on the road safety of vulnerable groups.
Web Text Clustering for Personalized E-learning Based on Maximal Frequent Itemsets

With the rapid development of the network technique and the prevalence of the Internet, e-learning has become the major trend of the development of international education since 1980s, and the important access for the internationalization and the information of education. To meet the personalized needs of learners in e-learning, a new Web text clustering method for personalized e-learning based on maximal frequent itemsets is proposed. Firstly, the Web documents are represented by vector space model. Then, maximal frequent word sets are discovered. Finally, based on a new similarity measure of itemsets, maximal itemsets are used for clustering documents. Experimental results show that the proposed method is effective.
Second-Order Asymptotics for Source Coding, Dense Coding, and Pure-State Entanglement Conversions

We introduce two variants of the information spectrum relative entropy defined by Tomamichel and Hayashi, which have the particular advantage of satisfying the data-processing inequality, i.e., monotonicity under quantum operations. This property allows us to obtain one-shot bounds for various information-processing tasks in terms of these quantities. Moreover, these relative entropies have a second-order asymptotic expansion, which in turn yields tight second-order asymptotics for optimal rates of these tasks in the independent and identically distributed setting. The tasks studied in this paper are fixed-length quantum source coding, noisy dense coding, entanglement concentration, pure-state entanglement dilution, and transmission of information through a classical-quantum channel. In the latter case, we retrieve the second-order asymptotics obtained by Tomamichel and Tan. Our results also yield the known second-order asymptotics of fixed-length classical source coding derived by Hayashi. The second-order asymptotics of entanglement concentration and dilution provide a refinement of the inefficiency of these protocols-a quantity which, in the case of entanglement dilution, was studied by Harrow and Lo. We prove how the discrepancy between the optimal rates of these two processes in the second-order implies the irreversibility of entanglement concentration established by Kumagai and Hayashi. In addition, the spectral divergence rates of the information spectrum approach (ISA) can be retrieved from our relative entropies in the asymptotic limit. This enables us to directly obtain the more general results of the ISA from our one-shot bounds.
Bayesian Method with Spatial Constraint for Retinal Vessel Segmentation

A Bayesian method with spatial constraint is proposed for vessel segmentation in retinal images. The proposed model makes the assumption that the posterior probability of each pixel is dependent on posterior probabilities of their neighboring pixels. An energy function is defined for the proposed model. By applying the modified level set approach to minimize the proposed energy function, we can identify blood vessels in the retinal image. Evaluation of the developed method is done on real retinal images which are from the DRIVE database and the STARE database. The performance is analyzed and compared to other published methods using a number of measures which include accuracy, sensitivity, and specificity. The proposed approach is proved to be effective on these two databases. The average accuracy, sensitivity, and specificity on the DRIVE database are 0.9529, 0.7513, and 0.9792, respectively, and for the STARE database 0.9476, 0.7147, and 0.9735, respectively. The performance is better than that of other vessel segmentation methods.
A Labeling Algorithm for the Earliest and Latest Time-Varying Maximum Flow Problems

The time-varying maximum flow problem is to find the maximum flow in a time-varying network. A time-varying network is the network which the transit time and the capacity of an arc are functions of the departure time at the beginning node of an arc. The earliest (or the latest) maximum flow is the maximum flow could be sent from the source node to the sink node in the time-varying network within the given time duration with the earliest (or the latest) arrival time. We assume that the transit time and the capacity of an arc are positive integers, and waiting at any node is prohibited except the source node. The problem is known to be NP-complete. A labeling algorithms is proposed for solving problems in pseudo polynomial time.
Alternative Energy Sources for Surgical Treatment of Atrial Fibrillation in Patients Undergoing Mitral Valve Surgery: Microwave Ablation vs Cryoablation

The study aim was to compare maze outcomes using microwave ablation or cryoablation in patients with mitral disease and atrial fibrillation (AF). Between 1999 and 2005, 340 patients underwent mitral valve surgery and concomitant maze procedure involving either microwave ablation (n=96, MW group) or cryoablation (n=244, Cryo group). Mean age at operation was 50.0±12.5 yr. Follow-up period was 46.1±28.2 months. The Cryo group showed a longer aortic clamping time than the MW group (P=0.005). There were no differences in operative mortality and morbidity rates. The unadjusted 5-yr AF free rate was 61.3±1.2% in the MW group and 79.9±3.2% in the Cryo group (P=0.089). After adjustment, the MW group only showed a tendency toward more frequent AF recurrence than the Cryo group (Hazard ration 1.66, 95% confidence interval 0.89 to 3.07). Multivariate analysis revealed that older patient age (P<0.001) and greater left atrial size (P<0.001) were independent risk factors for AF recurrence. Although the use of microwave ablation results in shorter aortic clamping time, it has a tendency toward more frequent late AF recurrence than with cryoablation.
Effective and Diverse Adaptive Game AI

Adaptive techniques tend to converge to a single optimum. For adaptive game AI, such convergence is often undesirable, as repetitive game AI is considered to be uninteresting for players. In this paper, we propose a method for automatically learning diverse but effective macros that can be used as components of adaptive game AI scripts. Macros are learned by a cross-entropy method (CEM). This is a selection-based optimization method that, in our experiments, maximizes an interestingness measure. We demonstrate the approach in a computer role-playing game (CRPG) simulation with two duelling wizards, one of which is controlled by an adaptive game AI technique called ldquodynamic scripting.rdquo Our results show that the macros that we learned manage to increase both adaptivity and diversity of the scripts generated by dynamic scripting, while retaining playing strength.
Impact of Experimental Hookworm Infection on the Human Gut Microbiota

The interactions between gastrointestinal parasitic helminths and commensal bacteria are likely to play a pivotal role in the establishment of host-parasite cross-talk, ultimately shaping the development of the intestinal immune system. However, little information is available on the impact of infections by gastrointestinal helminths on the bacterial communities inhabiting the human gut. We used 16S rRNA gene amplification and pyrosequencing to characterize, for the first time to our knowledge, the differences in composition and relative abundance of fecal microbial communities in human subjects prior to and following experimental infection with the blood-feeding intestinal hookworm, Necator americanus. Our data show that, although hookworm infection leads to a minor increase in microbial species richness, no detectable effect is observed on community structure, diversity or relative abundance of individual bacterial species.
Approaches For Capturing Time-Varying Functional Network Connectivity With Application to Normative Development and Mental Illness

Since the beginning of medical science, the human brain has remained an unsolved puzzle; an illusive organ that controls everythingfrom breathing to heartbeats, from emotion to anger, and more. With the power of advanced neuroimaging techniques, scientists have now started to solve this nearly impossible puzzle, piece by piece. Over the past decade, various in vivo techniques, including functional magnetic resonance imaging (fMRI), have been increasingly used to understand brain functions. fMRI is extensively being used to facilitate the identi cation of various neuropsychological disorders such as schizophrenia (SZ), bipolar disorder (BP) and autism spectrum disorder (ASD). These disorders are currently diagnosed based on patients' self-reported experiences, and observed symptoms and behaviors over the course of the illnesses. Therefore, e cient identi cation of biological-based markers (biomarkers) can lead to early diagnosis of these mental disorders, and provide a trajectory for disease progression. By applying advanced machine learning techniques on fMRI data, signi cant di erences in brain function among patients with mental disorders and healthy controls can be identi ed. Moreover, by jointly estimating information from multiple modalities, such as, functional brain data and genetic factors, we can now investigate the relationship between brain function and genes. Functional connectivity (FC) has become a very common measure to characterize brain functions, where FC is de ned as the temporal covariance of neural signals between multiple spatially distinct brain regions. Recently, researchers are studying the FC among functionally specialized brain networks which can be de ned as