Miracle-dz/UniFAR-FTUs huggingface dataset:
Balancing Local and Global Parallelism for Single-Thread Applications in a Composable Multi-core System

One way to improve the performance of a singlethreaded application is to run regions of the code speculatively on a multi-core system. In a composable multicore system, resources such as the L1 cache, register file, and instruction window are distributed among the cores. In such a system, there are many ways to map instructions to the cores. At one extreme, instructions in each code region are distributed evenly across all of the cores, favoring local parallelism within the region, but also increasing local communication. At the other extreme, instructions in each region are assigned to a single core. This strategy minimizes local communication within the code region, but at the cost of local parallelism. We propose a hybrid strategy that uses a compile time critical path analysis to determine the available concurrency in the region of code. A block mapper uses this concurrency value to decide the number of cores to which the region of code should be mapped. Our results show that this method improves performance significantly and reduces communication among cores.
Autoconvolution and panorama: Augmenting second-order signal analysis

The autocorrelation does not differentiate between deterministic and stochastic signals, as phase information is not maintained. This paper introduces the autoconvolution for both deterministic and stochastic signals. The autoconvolution with the autocorrelation provides a second-order description that discriminates between deterministic and stochastic signals - even those with identical power spectra. We also introduce the panorama as the Fourier transform of the autoconvolution. The power spectrum and panorama admit a two-dimensional spectral representation that has unique and powerful properties, such as detecting deterministic sinusoidal components in correlated stochastic noise without knowledge of the sinusoidal frequencies or amplitudes. Additional extensions are indicated.
Long tours and short superstrings

This paper considers weight-maximizing variants of the classical symmetric and asymmetric traveling-salesman problems. Like their weight-minimizing counterparts, these variants are MAX SNP-hard. We present the first nontrivial approximation algorithms for these problems. Our algorithm for directed graphs finds a tour whose weight is at least 38/63/spl ap/0.603 times the weight of a maximum-weight tour, and our algorithm for undirected graphs finds a tour whose weight is at least 5/7/spl ap/0.714 times optimal. These bounds compare favorably with the 1/2 and 2/3 bounds that can be obtained for undirected and directed graphs, respectively, by simply deleting the minimum-weight edge from each cycle of a maximum-weight cycle cover. Our algorithm for directed graphs can be used to improve several recent approximation results for the shortest-superstring problem. <<ETX>>
Prognostic Physiology: Modeling Patient Severity in Intensive Care Units Using Radial Domain Folding

Real-time scalable predictive algorithms that can mine big health data as the care is happening can become the new "medical tests" in critical care. This work describes a new unsupervised learning approach, radial domain folding, to scale and summarize the enormous amount of data collected and to visualize the degradations or improvements in multiple organ systems in real time. Our proposed system is based on learning multi-layer lower dimensional abstractions from routinely generated patient data in modern Intensive Care Units (ICUs), and is dramatically different from most of the current work being done in ICU data mining that rely on building supervised predictive models using commonly measured clinical observations. We demonstrate that our system discovers abstract patient states that summarize a patient's physiology. Further, we show that a logistic regression model trained exclusively on our learned layer outperforms a customized SAPS II score on the mortality prediction task.
A Knowledge-Based Answer Engine for Open-Domain Questions

This paper describes a knowledge-based methodology of mining textual answers from large collections of texts. We present an answer engine capable of providing answers to any question posed in natural language, regardless of the domain of interest. The answers are produced by combining semantic data from question taxonomies with information abductively inferred from texts. The high precision of the retrieved answers is due to novel, efficient knowledge processing techniques.
Development of biosignal processing algorithm for pulse wave velocity calculation

Thispaper proposes abiosignal processingalgorithm to calculate Pulse Wave Velocity. The Pulse Wave Velocity (PWV) is well known to be an indicator of arterial stiffness, and has been regarded as a marker reflecting vascular damages. In this paper a modified algorithmof derivative test is presented, as a mean to calculate PWV.
Combinatorial Vector Fields for Piecewise Affine Control Systems

Abstract This paper is intended to be a continuation of Habets and van Schuppen [2004] and Habets et al. [2006], which address the control problem for piecewise-affine systems on an arbitrary polytope or a family of these. Our work deals with the underlying combinatorics of the underlying discrete system. Motivated by Forman [1998], on the triangulated state space we define a combinatorial vector field, which indicates for a given face the future simplex. In the suggested definition we allow nondeterminacy in form of splitting and merging of solution trajectories. The combinatorial vector field gives rise to combinatorial counterparts of the flow lines. The main result is then an algorithm for synthesis of supervisory control.
Cs + ADC in rat brain decreases markedly at death

Spectroscopic resolution of intracellular and extracellular compartments can be used to probe the kinetic environment of those spaces and the compartment‐specific changes that occur following injury. This is important for understanding the biophysical mechanisms that underlie the remarkable diffusion‐weighted MRI contrast of injured central nervous system (CNS) tissue. Cesium‐133 is a physiologic analog of potassium that is actively taken up by cells and resides primarily in the intracellular space. The 133Cs+ signal can, thus, be exploited to probe the kinetic environment of the intracellular space. Two principal 133Cs+ resonances were observed at 11.74 T. These resonances arise separately from 133Cs+ in brain and temporalis muscle. The apparent diffusion coefficient (ADC) of Cs+ in brain decreased from 1.0 ± 0.2 μm2/ms in healthy tissue to 0.24 ± 0.04 μm2/ms following global ischemia (average ADC ± average uncertainty), while there was no significant change in the ADC of Cs+ in temporalis muscle after injury. This finding underscores the tissue‐specific nature of the decrease in ADC that accompanies brain injury. Further, as the Cs+ ADC should reflect water ADC in the intracellular space, these results strongly support the hypothesis that the decrease in water ADC associated with CNS injury arises largely from kinetic changes taking place in the intracellular space. Magn Reson Med 59:65–72, 2008. © 2007 Wiley‐Liss, Inc.
Linear Dimensionality Reduction for Multi-label Classification

Dimensionality reduction is an essential step in high-dimensional data analysis. Many dimensionality reduction algorithms have been applied successfully to multi-class and multi-label problems. They are commonly applied as a separate data preprocessing step before classification algorithms. In this paper, we study a joint learning framework in which we perform dimensionality reduction and multi-label classification simultaneously. We show that when the least squares loss is used in classification, this joint learning decouples into two separate components, i.e., dimensionality reduction followed by multi-label classification. This analysis partially justifies the current practice of a separate application of dimensionality reduction for classification problems. We extend our analysis using other loss functions, including the hinge loss and the squared hinge loss. We further extend the formulation to the more general case where the input data for different class labels may differ, overcoming the limitation of traditional dimensionality reduction algorithms. Experiments on benchmark data sets have been conducted to evaluate the proposed joint formulations.
Targeted Expression of Placental Lactogen in the Beta Cells of Transgenic Mice Results in Beta Cell Proliferation, Islet Mass Augmentation, and Hypoglycemia*

The factors that regulate pancreatic beta cell proliferation are not well defined. In order to explore the role of murine placental lactogen (PL)-I (mPL-I) in islet mass regulationin vivo, we developed transgenic mice in which mPL-I is targeted to the beta cell using the rat insulin II promoter. Rat insulin II-mPL-I mice displayed both fasting and postprandial hypoglycemia (71 and 105 mg/dl, respectively) as compared with normal mice (92 and 129 mg/dl; p < 0.00005 for both). Plasma insulin concentrations were inappropriately elevated, and insulin content in the pancreas was increased 2-fold. Glucose-stimulated insulin secretion by perifused islets was indistinguishable from controls at 7.5, 15, and 20 mm glucose. Beta cell proliferation rates were twice normal (p = 0.0005). This hyperplasia, together with a 20% increase in beta cell size, resulted in a 2-fold increase in islet mass (p = 0.0005) and a 1.45-fold increase in islet number (p = 0.0012). In mice, murine PL-I is a potent islet mitogen, is capable of increasing islet mass, and is associated with hypoglycemia over the long term. It can be targeted to the beta cell using standard gene targeting techniques. Potential exists for beta cell engineering using this strategy.
Segments in Web Search Queries : What ? How ? Why ?

This paper, through rigorous analysis of query logs and Web documents, proves the usefulness of query segmentation in enhancing search performance, especially with relatively rare queries. It shows that a comprehensive IR-based performance evaluation, rather than human annotation, is necessary to establish the effectiveness of query segmentation schemes. Further, it has been proposed that any well-formed query is made up of two distinct classes of segments, which we name as heads and modifiers. A new query segmentation scheme has been presented that can satisfactorily discover segments that align with these classes. It has been shown that our segmentation procedure, together with the labeling framework, significantly outperforms the state-of-the-art in the IR-based performance evaluation scheme. We also obtain various insights like the differences in people’s perceptions of relevance and the search engine’s methodology in defining the goodness of the pages retrieved as search results.
Semantic similarity for sequenced shingles

In this investigation, we introduce new kinds of sentence similarity, called Euclid similarity and Levenshtein similarity, to capture both word sequences and semantic aspects. This is especially useful for Semantic Textual Similarity (STS) so that we could retrieve SNS texts, short sentences or something including collocations. We show the usefulness of our approach by some experimental results.
Trend and Acceleration: A Multi-model Approach to Key West Sea Level Rise

OF THE THESIS TREND AND ACCELERATION: A MULTI-MODEL APPROACH TO KEY WEST SEA LEVEL RISE by John S. Tenenholtz Florida International University, 2017 Miami, Florida Professor Sneh Gulati, Major Professor Sea level rise (SLR) varies depending on location. It is therefore important to local residents, businesses and government to analyze SLR locally. Further, because of increasing ice melt and other effects of climate change, rates of SLR may change. It is therefore also important to evaluate rates of change of SLR, which we call sea level acceleration (SLA) or deceleration. The present thesis will review the annual average sea level data compiled at the Key West tidal gauge in Key West, Florida. We use a multi-model approach that compares the results of various models on that data set. The goal is to determine if there is a consistent result that can be ascertained from the various models. Generally, all the models reveal a clear upward trend of SLR. Further, the models provide evidence that the trend has increased over the last 8-10 years, i.e., that there is SLA.
Evaluation of kernel function modification in text classification using SVMs

In this study, we propose a method to modify the RBF kernel function by incorporating category related information into it in text classification. We use two approaches to measure the relative power of a term in discriminating a category from others. Such information is incorporated in mapping from input space into feature space by modifying the RBF kernel function. This allows the use of both labeled and unlabeled documents in calculating TFIDF. We apply the kernel modification methods on different sizes of training sets from Reuter-12578. We also compare our methods with a Chi-square feature selection method. The new kernel modification methods give at least as good results as the Chisquare feature selection.
Diagnosis of Lymphangitic Carcinoma to Lung by Sputum Cytology: Case Report

Documenting lymphangitic metastases to the lungs may be therapeutically important. Several techniques have been described to this end. Sputum cytology, the least invasive and expensive, is the most available. It has a role that may be overlooked but that should be emphasized, since abundant malignant cells in sputum may be associated with this pattern of tumor spread. This case is the first, to the authors' knowledge, in which the pathologic findings associated with malignant cells in lymphangitic carcinomatosis were demonstrated in sputum in the absence of tumor mass invasion of large airways.
Characterizing intrusion tolerant systems using a state transition model

Intrusion detection and response research has so far mostly concentrated on known and well-defined attacks. We believe that this narrow focus of attacks accounts for both the successes and limitation of commercial intrusion detection systems (IDS). Intrusion tolerance, on the other hand, is inherently tied to functions and services that require protection. This paper presents a state transition model to describe the dynamic behavior of intrusion-tolerant systems. This model provides a framework from which we can define the vulnerability and the threat set to be addressed. We also show how this model helps us to describe both known and unknown security exploits by focusing on impacts rather than specific attack procedures. By going through the exercise of mapping known vulnerabilities to this transition model, we identify a reasonably complete fault space that should be considered in a general intrusion-tolerant system.
A Low-Complexity Detector for BPPM Systems Under Additive Gaussian Mixture Noise

We analyze the performance of the single-threshold detector (STD) for binary pulse position modulation in the presence of symmetric Gaussian mixture noise (GMN). We first derive a general closed-form expression for the average error probability that is later used to investigate the optimality of the STD with zero threshold, which is the simplest form of STD. We also establish conditions sufficient to guarantee minimum error rates with the low-complexity STD for a wide range of signal-to-noise ratio. The theoretical results are verified through simulations of GMN with various noise parameters.
Building Ventilation as an Effective Disease Intervention Strategy in a Dense Indoor Contact Network in an Ideal City

Emerging diseases may spread rapidly through dense and large urban contact networks, especially they are transmitted by the airborne route, before new vaccines can be made available. Airborne diseases may spread rapidly as people visit different indoor environments and are in frequent contact with others. We constructed a simple indoor contact model for an ideal city with 7 million people and 3 million indoor spaces, and estimated the probability and duration of contact between any two individuals during one day. To do this, we used data from actual censuses, social behavior surveys, building surveys, and ventilation measurements in Hong Kong to define eight population groups and seven indoor location groups. Our indoor contact model was integrated with an existing epidemiological Susceptible, Exposed, Infectious, and Recovered (SEIR) model to estimate disease spread and with the Wells-Riley equation to calculate local infection risks, resulting in an integrated indoor transmission network model. This model was used to estimate the probability of an infected individual infecting others in the city and to study the disease transmission dynamics. We predicted the infection probability of each sub-population under different ventilation systems in each location type in the case of a hypothetical airborne disease outbreak, which is assumed to have the same natural history and infectiousness as smallpox. We compared the effectiveness of controlling ventilation in each location type with other intervention strategies. We conclude that increasing building ventilation rates using methods such as natural ventilation in classrooms, offices, and homes is a relatively effective strategy for airborne diseases in a large city.
Review of Psychodynamic Psychotherapy Neuroimaging Studies

The clinical efficacy of psychodynamic psychotherapy (PDT) has undergone extensive study and review. Recently, researchers have studied the effects of this treatment on brain metabolic or synaptic activity, but the collective findings have never been reviewed. The objective of this review was to describe the findings of all neuroimaging studies of any form of PDT treatment. An extensive literature search through databases along with surveying of research groups were undertaken to acquire all available published studies. Eleven series were included in the final sample, consisting of 2 randomized controlled trials, 5 controlled trials and 4 case series, altogether involving 210 people: 94 healthy controls and 116 people with mood disorders, panic disorder, somatoform disorders and borderline personality disorder. A variety of neuroimaging techniques were used to examine regional metabolic activity and synaptic neurotransmission before and after treatment. The common finding was normalization of synaptic or metabolic activity in limbic, midbrain and prefrontal regions, occurring in association with improved clinical outcomes. PDT has demonstrable effects on brain function in diverse clinical populations as evidenced by a modest group of mixed neuroimaging studies.
Inactivation of Factor VIIa by Antithrombin In Vitro, Ex Vivo and In Vivo: Role of Tissue Factor and Endothelial Cell Protein C Receptor

Recent studies have suggested that antithrombin (AT) could act as a significant physiologic regulator of FVIIa. However, in vitro studies showed that AT could inhibit FVIIa effectively only when it was bound to tissue factor (TF). Circulating blood is known to contain only traces of TF, at best. FVIIa also binds endothelial cell protein C receptor (EPCR), but the role of EPCR on FVIIa inactivation by AT is unknown. The present study was designed to investigate the role of TF and EPCR in inactivation of FVIIa by AT in vivo. Low human TF mice (low TF, ∼1% expression of the mouse TF level) and high human TF mice (HTF, ∼100% of the mouse TF level) were injected with human rFVIIa (120 µg kg−1 body weight) via the tail vein. At varying time intervals following rFVIIa administration, blood was collected to measure FVIIa-AT complex and rFVIIa antigen levels in the plasma. Despite the large difference in TF expression in the mice, HTF mice generated only 40–50% more of FVIIa-AT complex as compared to low TF mice. Increasing the concentration of TF in vivo in HTF mice by LPS injection increased the levels of FVIIa-AT complexes by about 25%. No significant differences were found in FVIIa-AT levels among wild-type, EPCR-deficient, and EPCR-overexpressing mice. The levels of FVIIa-AT complex formed in vitro and ex vivo were much lower than that was found in vivo. In summary, our results suggest that traces of TF that may be present in circulating blood or extravascular TF that is transiently exposed during normal vessel damage contributes to inactivation of FVIIa by AT in circulation. However, TF’s role in AT inactivation of FVIIa appears to be minor and other factor(s) present in plasma, on blood cells or vascular endothelium may play a predominant role in this process.
Chinese Syntactic Reordering for Statistical Machine Translation

Syntactic reordering approaches are an effective method for handling word-order differences between source and target languages in statistical machine translation (SMT) systems. This paper introduces a reordering approach for translation from Chinese to English. We describe a set of syntactic reordering rules that exploit systematic differences between Chinese and English word order. The resulting system is used as a preprocessor for both training and test sentences, transforming Chinese sentences to be much closer to English in terms of their word order. We evaluated the reordering approach within the MOSES phrase-based SMT system (Koehn et al., 2007). The reordering approach improved the BLEU score for the MOSES system from 28.52 to 30.86 on the NIST 2006 evaluation data. We also conducted a series of experiments to analyze the accuracy and impact of different types of reordering rules.
Local climates of Vardar, Struma and Mesta valleys (Balkan peninsula) according to the modified Köppen climate classification

The Köppen classification is commonly used and helpful for characteristics of different climate types and their changes. Available maps based on this classification are dealing with continental and worldwide zones. This is the reason mountainous territory of the Balkans to be defined as zones with cold or high mountain climate without further details. The main aim of the study is detail classification of the territory of Vardar, Struma and Mesta in the view of the Köppen system and establishing the inner zones variability trough time. In order to achieve this goal we use the SAT (surface air temperature) and precipitation data from meteorological stations located in key areas of the valleys. We build 30 year moving average to present climate fluctuations in these areas according to the modified Köppen classification. Results show that index changing is variable through the different periods but there has a tendency for general reduction of the annual precipitation in the whole period of observation.
Extracting Hot Events from News Feeds , Visualization , and Insights

Our aim is a real time news extraction system capable of identifying up-to-date " hot news " from large amounts of news reports on the internet. We show two important characteristics of hotness: short-term burst and long-term historical variation. Based on these factors, we define our problem: given all news articles on the current day p and previous news archives, identify the hot news on day p. Our novel system consists of four parts. First, all entities (atomic elements in text such as the names of persons, organizations, locations, etc) which occurred on the current day are extracted. Second, hot terms on day p are identified with our novel term weighting scheme for hotness. Third, each news article on the current day will be represented as a vector of hot terms with frequency. The last step is to cluster all new articles on day p and rank the result clusters with their hotness score. The scheme is also adaptive since the hot terms extracted are different from day to day, and reflect the hot events on each day. The focus of this paper is to illustrate the visualization and the insight of the hot-news through time, not the underlying algorithm details we developed. Our experiments conducted on a Yahoo! News dataset indicate that our term weighting scheme incorporating the two important features of hotness evaluate the significance of each extracted entity properly.