Miracle-dz/UniFAR-FTUs huggingface dataset:
Risk of over-diagnosis of COPD in asymptomatic elderly never-smokers

The Global Initiative for Chronic Obstructive Lung Disease (GOLD) has defined stage 1 chronic obstructive pulmonary disease (COPD) as forced expiratory volume in one second/forced vital capacity (FEV1/FVC)% <70% and a FEV1% predicted of >80%. Stage 2 has been defined as FEV1/FVC <70% and a FEV1% pred of <80%. The authors examined the extent of COPD misdiagnosis using this definition in healthy, never-smoker, asymptomatic adults aged >70 yrs in Bergen, Norway. A respiratory questionnaire was mailed to a random sample of 2,871 persons aged>70 yrs. In a random, well-defined subgroup of 208 never-smoker respondents with no current respiratory disease and significant dyspnoea or heart disease/hypertension complicated with dyspnoea, 71 were able to perform an acceptable spirometry. Approximately 35% of these healthy, elderly never-smokers had an FEV1/FVC% of <70% and would be classified as having at least a stage 1 COPD. This percentage increased with age and in those aged >80 yrs ∼50% would be classified as having COPD and approximately one-third would have an FEV1 of <80% pred (stage 2 COPD). The estimated 5th percentile of FEV1 was consistently <80% pred. The Global Initiative for Chronic Obstructive Lung Disease criteria will probably lead to a significant degree of over-diagnosis of chronic obstructive pulmonary disease in those aged >70 yrs. The criteria used to define the various stages of chronic obstructive pulmonary disease need to be age-specific.
A renewed image annotation baseline by image embedding and tag correlation

This paper presents a renewed image annotation baseline method under the nearest neighbor tag transfer framework. Two key problems are considered in this paper: (1) which images are determined as the neighbors; (2) how their keywords are transferred. Firstly, a soft neighbor selection scheme is designed by image embedding technique, with which we can provide more power to the crucial neighbors in decision making. Next, diffused tag propagation is introduced to allow one tag be propagated to other relevant tags. Besides this, the above two measures are formulated into an optimization framework to further improve the prediction performance. Experimental results on standard database show that the proposed approaches outperform the current state-of-the-art methods.
Image-Based Material Restyling with Fast Non-local Means Filtering

This paper presents a new GPU-based implementation of fast non-local means (NLM) filtering for material restyling. Our fast NLM filtering algorithm is able to achive realtime feedback of interactive image editing. Furthermore a novel material editing method based on our fast NLM filtering is proposed to change the material appearance of image-based objects. Given an input image, an alpha matte is created to differentiate the object from its background. After the automatic matting process, the object is removed from the background, and the 3D shape of the object is recovered. We use the fast non-local means (NLM) filtering to process the luminance channel of an image and obtain a pseudo-depth map that is sufficient for altering the material appearance of the observed object. We recover the gradient luminance maps for the region to be material-restyled; and change the material property in the region-of-interest by solving the Poisson equation. In this way, the new material is mapped onto the 3D shape, resulting in an object which appears to be made of a different material. Our new NLM filtering-based material editing is easy to implement in parallel with graphics hardware. The experimental results have demonstrated the satisfactory performance of our method.
A multilayer nanophotonic interconnection network for on-chip many-core communications

Multi-core chips or chip multiprocessors (CMPs) are becoming the de facto architecture for scaling up performance and taking advantage of the increasing transistor count on the chip within reasonable power consumption levels. The projected increase in the number of cores in future CMPs is putting stringent demands on the design of the on-chip network (or network-on-chip, NOC). Nanophotonic interconnects have recently emerged as a viable alternate technology solution for the design of NOC because of their higher communication bandwidth, much reduced power consumption and wiring simplification. Several photonic NOC approaches have recently been proposed. A common feature of almost all of these approaches is the integration of the entire optical network onto a single silicon waveguide layer. However, keeping the entire network on a single layer has a serious implication for power losses and design complexity due to the large amount of waveguide crossings. In this paper, we propose MPNOC: a multilayer photonic networks-on-chip. MPNOC combines the recent advances in silicon photonics and three-dimensional (3D) stacking technology with architectural innovations in an integrated architecture that provides ample bandwidth, low latency, and energy efficient on-chip communications for future CMPs. Simulation results show MPNOC can achieve 81.92 TFLOP/s peak bandwidth and an energy savings up to 23% compared to other proposed planar photonic NOC architectures.
On the relation of aspects and monads

The relation between aspects and monads is a recurring topic in discussions in the programming language community, although it has never been elaborated whether their resemblences are only superficial, and if not, where they are rooted. The aim of this paper is to contrast both mechanisms w.r. t. their capabilities and their effects on modularity, first by looking at monads as a way to express tangling concerns in functional programming and by discussing whether they can be regarded as a form of AOP, then by taking the view that monads express concerns of computations and by analyzing the extent to which aspects are able to handle those concerns. Our results are mostly negative: monads are not capable of quantifying over points in the program execution in a declarative way, whereas aspects are not very useful in abstracting over computational capabilities.
A robust electro-mechanical interface for cooperating heterogeneous multi-robot teams

This paper presents the mechanical development and testing of a docking device for a highly heterogeneous self-reconfigurable multi-module/multi-robot system. The overall system is meant to emulate a robotic lunar exploration mission. The docking device, more precisely the electro-mechanical interface (EMI), is an advancement of the reliable electromechanical connection of the project RIMRES. Since possible combinations and roles of modules in the multi-robot system are defined before a mission, a gender-principle approach with one active and one passive face to be mated was chosen. The experiments in this paper are conducted to compare the improved mechanical design with the previous design. With the new design, docking is successfully tested under loads up to 800 N. The experiments presented include attaching and detaching in different EMI orientations with various loads, exceeding those expected for the application scenario. In further experiments operations under heavy dust/small particle contamination are presented.
A reconfigurable accelerator for quantum computations

This paper presents a new architecture to accelerate quantum computations, using reconfigurable computing structures. It was designed post place and route, validated with standard benchmarks, and its performance has been evaluated vs. a high end computer running the same benchmarks with highly optimized code. The acceleration of arbitrary quantum circuits is very promising because it extends the kind of quantum computing problems that can be solved with reconfigurable architectures.
Effects of intraoperative low dose ketamine on remifentanil-induced hyperalgesia in gynecologic surgery with sevoflurane anesthesia

Background Remifentanil is useful during general anesthesia because of its rapid onset and short acting time. However, some studies report that due to opioid-induced hyperalgesia (OIH) and tolerance, remifentanil also increases early postoperative pain. The occurrence of OIH and opioid-induced tolerance is mainly thought to be due to central sensitization by the activation of NMDA receptors. Therefore, we investigated the effects of continuous infusion of ketamine, an NMDA receptor antagonist, on postoperative pain and the quantity of opioids used. Methods 40 patients scheduled to undergo laparoscopic gynecologic surgery were randomly allocated into two groups. Anesthesia was equally maintained with sevoflurane and 4 ng/ml of remifentanil in all patients. Ketamine (0.3 mg/kg) was injected and followed with a continuous dosage of 3 µl/kg/min in the ketamine group (n = 20) while the control group was injected and infused with an equal amount of normal saline. We compared postoperative VAS up to 7 hours and morphine demand through PCA. Results Postoperative VAS and morphine demand was significantly lower in the ketamine group 2 and 3 hours after surgery, respectively. Conclusions When general anesthesia is maintained with sevoflurane and remifentanil in patients undergoing laparoscopic gynecologic surgery, continuous infusion of low dose ketamine decreased early postoperative pain and the quantity of opioids used.
Low-Rank Kernel Matrix Factorization for Large-Scale Evolutionary Clustering

Traditional clustering techniques are inapplicable to problems where the relationships between data points evolve over time. Not only is it important for the clustering algorithm to adapt to the recent changes in the evolving data, but it also needs to take the historical relationship between the data points into consideration. In this paper, we propose ECKF, a general framework for evolutionary clustering large-scale data based on low-rank kernel matrix factorization. To the best of our knowledge, this is the first work that clusters large evolutionary data sets by the amalgamation of low-rank matrix approximation methods and matrix factorization-based clustering. Since the low-rank approximation provides a compact representation of the original matrix, and especially, the near-optimal low-rank approximation can preserve the sparsity of the original data, ECKF gains computational efficiency and hence is applicable to large evolutionary data sets. Moreover, matrix factorization-based methods have been shown to effectively cluster high-dimensional data in text mining and multimedia data analysis. From a theoretical standpoint, we mathematically prove the convergence and correctness of ECKF, and provide detailed analysis of its computational efficiency (both time and space). Through extensive experiments performed on synthetic and real data sets, we show that ECKF outperforms the existing methods in evolutionary clustering.
MS: Multiple Segments with Combinatorial Approach for Mining Frequent Itemsets Over Data Streams

Mining frequent itemsets in data stream applications is beneficial for a number of purposes such as knowledge discovery, trend learning, fraud detection, transaction prediction and estimation. In data streams, new data are continuously coming as time advances. It is costly even impossible to store all streaming data received so far due to the memory constraint. It is assumed that the stream can only be scanned once and hence if an item is passed, it can not be revisited, unless it is stored in main memory. Storing large parts of the stream, however, is not possible because the amount of data passing by is typically huge. In this paper, we study the problem of finding frequent items in a continuous stream of items. A new frequency measure is introduced, based on a variable window length. We study the properties of the new method, and propose an incremental algorithm that allows producing the frequent itemsets immediately at any time. In our method, we used multiple s egments for handling different size of windows. By storing these segments in a data structure, the usage of memory can be optimized. Our experiments show that our algorithm performs much better in optimizing memory usage and mining only the most recent patterns in very less time.
An algorithm for fast REM construction

The Radio Environment Map (REM) stores radio environmental information that can be used to enhance cognitive radio resource management in wireless networks. In this paper, we propose an iterative REM building process based on Kriging interpolation technique that builds the REM using geolocated measurements performed by mobile terminals. As terminal measurements are costly in terms of signalling and battery consumption, we propose an algorithm that chooses the most appropriate measurements to be requested to the mobiles. We compare the performance of this algorithm with a random choice of measurements and show that our algorithm reduces the squared error of the power map by 16%. The proposed algorithm has also the merit of being fast enough to be implemented in an online fashion.
BluLab: Temporal Information Extraction for the 2015 Clinical TempEval Challenge

The 2015 Clinical TempEval Challenge addressed the problem of temporal reasoning in the clinical domain by providing an annotated corpus of pathology and clinical notes related to colon cancer patients. The challenge consisted of six subtasks: TIMEX3 and event span detection, TIMEX3 and event attribute classification, document relation time and narrative container relation classification. Our BluLab team participated in all six subtasks. For the TIMEX3 and event subtasks, we developed a ClearTK support vector machine pipeline using mainly simple lexical features along with information from rule-based systems. For the relation subtasks, we employed a conditional random fields classification approach, with input from a rule-based system for the narrative container relation subtask. Our team ranked first for all TIMEX3 and event subtasks, as well as for the document relation subtask.
Comparison of a fungal (family I) and bacterial (family II) cellulose-binding domain

A family II cellulose-binding domain (CBD) of an exoglucanase/xylanase (Cex) from the bacterium Cellulomonas fimi was replaced with the family I CBD of cellobiohydrolase I (CbhI) from the fungus Trichoderma reesei. Expression of the hybrid gene in Escherichia coli yielded up to 50 mg of the hybrid protein, CexCBDCbhI, per liter of culture supernatant. The hybrid was purified to homogeneity by affinity chromatography on cellulose. The relative association constants (Kr) for the binding of Cex, CexCBDCbhI, the catalytic domain of Cex (p33), and CbhI to bacterial microcrystalline cellulose (BMCC) were 14.9, 7.8, 0.8, and 10.6 liters g-1, respectively. Cex and CexCBDCbhI had similar substrate specificities and similar activities on crystalline and amorphous cellulose. Both released predominantly cellobiose and cellotriose from amorphous cellulose. CexCBDCbhI was two to three times less active than Cex on BMCC, but significantly more active than Cex on soluble cellulose and on xylan. Unlike Cex, the hybrid protein neither bound to alpha-chitin nor released small particles from dewaxed cotton fibers.
A fast and spatialized urban weather generator for long-term urban studies at the city-scale

In order to make urban climate predictions at the city-scale and on long term experiment accessible to communities such as building engineers or urban planner, a method to calculate meteorological forcing for surface models is presented. This method is computed with weather data files from an operational measurement station outside of the city. The model, called the spatialized urban weather generator (SUWG) calculates the temperature field above the urban canopy level with an energy budget for each volume of the boundary layer considered and 2D lagrangian advection model in order to take wind advection into account.This method has multiple advantages. First, the files from operational weather stations can easily be found for a lot of cities, in most case in airports. Second, the calculated urban heat island (UHI) can be influenced by urban planning scenarios. The method has been validated with an operational weather station network giving temperatures over the region of Paris and by comparing the SUWG simulations to a complete high resolution atmospheric simulation (MesoNH model) done over the Paris region at 2km of resolution during years 2010 and 2011. The full atmospheric model and the SUWG give comparable results with comparison to the data over the period studied for each urban operational station.
Origination, extinction, and mass depletions of marine diversity

Abstract In post-Cambrian time, five events—the end-Ordovician, end-Frasnian in the Late Devonian, end-Permian, end-Triassic, and end-Cretaceous—are commonly grouped as the “big five” global intervals of mass extinction. Plotted by magnitude, extinction intensities for all Phanerozoic substages show a continuous distribution, with the five traditionally recognized mass extinctions located in the upper tail. Plotted by time, however, proportional extinctions clearly divide the Phanerozoic Eon into six stratigraphically coherent intervals of alternating high and low extinction intensity. These stratigraphic neighborhoods provide a temporal context for evaluating the intensity of extinction during the “big five” events. Compared with other stages and substages in the same neighborhood, only the end-Ordovician, end-Permian, and end-Cretaceous extinction intensities appear as outliers. Moreover, when origination and extinction are considered together, only these three of the “big five” events appear to have been generated exclusively by elevated extinction. Low origination contributed more than high extinction to the marked loss of diversity in the late Frasnian and at the end of the Triassic. Therefore, whereas the “big five” events are clearly times when diversity suffered mass depletion, only those at the end of the Ordovician, Permian, and Cretaceous periods unequivocally qualify as globally distinct mass extinctions. Each of the three has a unique pattern of extinction, and the diversity dynamics of these events differ, as well, from the other two major diversity depletions. As mass depletions of diversity have no common effect, common causation seems unlikely.
Flashbulb memories? The effects of when the initial memory report was obtained

Why have some researchers found reports of flashbulb memories to be stable, while others have observed inconsistencies? Paradoxically, it appears that relatively long delays between event and initial documentation have produced greater consistency of participants' reports. To investigate this directly, we collected the initial documentation of hearing about O.J. Simpson's acquittal either five hours or one week after the acquittal was read. Observed consistency of memories varied as a function of documentation time; following an eight-week retention, the delayed reports were more consistent. The delayed group also reported fewer propositions in their initial documentation. We proposed a consolidation model to explain these results: during the days immediately following a newsworthy event, the narrative structure of these memories changes in that some details are forgotten. After this consolidation period, the memories may solidify. Thus, it may have been easier for the delayed group to provide consistent memories at the two intervals.
HybridSim: A Modeling and Co-simulation Toolchain for Cyber-physical Systems

Cyber-physical systems (CPS) involve communication networks, computation algorithms, control systems and physical systems. Many CPS, such as Smart Buildings, are subject to very expensive deployment costs and complex network interactions. Thus comprehensive modeling and simulation of such systems are crucial to ensure that they function as intended before deployment. Given the multi-domain nature of CPS, it is more appropriate to use a heterogeneous simulation environment to study system dynamics. In this paper, we design and implement an integrated modeling and co-simulation tool chain, called HybridSim, for the design and simulation of CPS. Firstly, HybridSim can transform and import existing system components from multi-domains into SysML, which enables systems engineers to design CPS with only these imported SysML blocks. Secondly, HybridSim can generate Functional Mock-up Units (FMUs) and configuration scripts directly from SysML designs. Finally, HybridSim can co-simulate these FMUs according to the Functional Mock-up Interface standard to synchronize their corresponding simulators and exchange information between them. We demonstrate the convenience and efficiency of HybridSim using a comprehensive hydronic heating system model for Smart Buildings as the case study to investigate the impact of packet loss and sampling rate introduced by the communication network.
A Timing-Driven Global Router for Symmetrical Array Based FPGAs

In this paper, we present a timing-driven global router for symmetrical array-based architecture FPGAs. The routing resources in symmetrical array based FPGAs consist of segments of various lengths. The timing constraints are speciied as delay bounds on source-sink pairs of nets. The algorithm proceeds in a hierarchical top-down manner and is able to utilize various routing segments with global consideration in order to meet the timing constraints. Furthermore , the algorithm can be extended to perform detailed routing simultaneously with global routing. Experimental results on real circuits show that the algorithm is promising in satisfying the timing constraints compared with the conventional global router.
Logic programming in the LF logical framework

3 A Meta-Logic for Unification 4 3.1 A First-Order Unification Logic with Quantifier Dependencies . 5 3.2 Transformations for First-Order Unification . 6 3.3 A Unification Logic for LF . 7 3.4 A Generalization of Lλ Unification to LF . 7 3.5 Precompleteness of the Transformations for Unification . 11
Fault Management in Distributed Systems WPE-II Written Report

In the past decade, distributed systems have rapidly evolved, from simple client/server applications in local area networks, to Internet-scale peer-to-peer networks and large-scale cloud platforms deployed on tens of thousands of nodes across multiple administrative domains and geographical areas. Despite of the growing popularity and interests, designing and implementing distributed systems remains challenging, due to their everincreasing scales and the complexity and unpredictability of the system executions. Fault management strengthens the robustness and security of distributed systems, by detecting malfunctions or violations of desired properties, diagnosing the root causes and maintaining verif iable evidences to demonstrate the diagnosis results. While its importance is well recognized, fault management in distributed systems, on the other hand, is notoriously difficult. To address the problem, various mechanisms and systems have been proposed in the past few years. In this report, we present a survey of these mechanisms and systems, and taxonomize them according to the techniques adopted and their application domains. Based on four representative systems (Pip, Friday, PeerReview and TrInc), we discuss various aspects of fault management, including fault detection, fault diagnosis and evidence generation. Their strength, limitation and application domains are evaluated and compared in detail. Comments University of Pennsylvania Department of Computer and Information Science Technical Report No. MSCIS-10-03. This technical report is available at ScholarlyCommons: http://repository.upenn. edu/cis_reports/914 Fault Management in Distributed