Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and optimization of DBSCAN Algorithm based on CUDA

DBSCAN is a very classic algorithm for data clus- tering, which is widely used in many fields. However, with the data scale growing much more bigger than before, the traditional serial algorithm can not meet the performance requirement. Recently, parallel computing based on CUDA has developed very fast and has great advantage on big data. This paper summarizes the algorithms proposed before and improves the performance of the old DBSCAN algorithm by using CUDA and parallel computing. The algorithm uses shared memory as much as possible compared with other algorithms and it has very good scalability. A data set is tested on the new version of DBSCAN. Finally, we analyze the results and give a conclusion that our algorithm is approximately 97 times faster than the serial version.
Simultaneous Measurements of Cytosolic and Mitochondrial Ca2+ Transients in HT29 Cells*

Loading of HT29 cells with the Ca2+ dye fura-2/AM resulted in an nonhomogeneous intracellular distribution of the dye. Cellular compartments with high fura-2 concentrations were identified by correlation with mitochondrial markers, cellular autofluorescence induced by UV, and dynamic measurement of autofluorescence after inhibition of oxidative phosphorylation. Stimulation with carbachol (10−4mol/liter) increased cytosolic, nuclear, and mitochondrial Ca2+ activity ([Ca2+] c , [Ca2+] n , and [Ca2+] m , respectively) measured by UV confocal and conventional imaging. Similar results were obtained with a prototype two-photon microscope (Zeiss, Jena, Germany) allowing for fura-2 excitation. The increase of [Ca2+] m lagged behind that of [Ca2+] c and [Ca2+] n by 10–20 s, and after removing the agonist, [Ca2+] m also decreased with a delay. A strong increase of [Ca2+] m occurred only when a certain threshold of [Ca2+] c (around 1 μmol/liter) was exceeded. In a very similar way, ATP, neurotensin, and thapsigargin increased [Ca2+] c and [Ca2+] m . Carbonyl cyanide p-trifluoromethoxyphenylhyrdrazone reversibly reduced the increase of [Ca2+] m . The source of the mitochondrial Ca2+ increase had intra- and extracellular components, as revealed by experiments in low extracellular Ca2+. We conclude that agonist-induced Ca2+ signals are transduced into mitochondria. 1) Mitochondria could serve as a Ca2+ sink, 2) mitochondria could allow the modulation of [Ca2+] c and [Ca2+] n signals, and 3) [Ca2+] m may serve as a stimulatory metabolic signal when a cell is highly stimulated.
Deliberate Self-Indulgence vs. Involuntary Loss of Self-Control: Exploring the Influence of Culture on Consumer Impulsiveness Trait

Impulse buying is considered an important and widespread phenomenon by consumer researchers as well as marketing practitioners in the US and other western countries and it has been considered largely universal in nature (Beatty and Ferrell 1998; Hausman 2000; Rook and Fisher 1995). However, there is no consensus among researchers on the exact structure or nature of this complex psychological construct and extant literature is replete with its several significantly different interpretations and conceptualizations e.g. buying impulsiveness (Rook and Fisher 1995), consumer impulsiveness (Puri 1996), impulse buying tendency (Weun et al. 1998) and consumer buying impulsivity (Youn and Faber 2002).
Fast analog layout prototyping for nanometer design migration

This paper presents an analog layout migration methodology to quickly provide multiple layouts while keeping similar or better circuit performance. Unlike previous works that often generate a single layout that has exactly the same topology with the original layout, this new migration algorithm is able to provide results with different aspect ratios. First, various placement constraints, including topology, matching, and symmetry, are extracted from the original layout. The extracted constraints are hierarchically stored into a topology slicing tree. Placement is performed from the bottom tree nodes to the root tree node. In each tree node, multiple placements for the subtree are recorded. All possible placements under the constraints are recorded in the root node. This algorithm has been successfully applied to a variable gain amplifier and a folded cascode operational amplifier migrating from UMC 90nm to UMC 65nm. The experimental results validate that our approach can provide reasonable layouts, even a better result almost in no time.
The Dynamics of Merger and Acquisition Waves

The past two decades have witnessed the largest merger and acquisition (M&A) waves in history. Yet, the empirical evidence suggests that the perceived financial benefits of M&As often are not realized for corporate acquirers. We build a three-stage conceptual framework to illuminate the dynamics of this mergers and acquisitions wave activity and to help explain the frenzied persistence of this phenomenon despite poor performance results. From this framework, we show key considerations that are often missed in M&A assessments, particularly during a merger wave, and offer advice to ensure that managers make more enlightened M&A decisions.
Oral therapy for type 1 diabetes mellitus using a novel immunomodulator, FTY720 (fingolimod), in combination with sitagliptin, a dipeptidyl peptidase‐4 inhibitor, examined in non‐obese diabetic mice

Aims/Introduction: The therapeutic effectiveness against type 1 diabetes mellitus of a novel immunomodulator, FTY720 (fingolimod), in combination with sitagliptin, a dipeptidyl peptidase‐4 inhibitor, was examined in the non‐obese diabetic (NOD) mouse model.
Durable Therapeutic Efficacy Utilizing Combinatorial Blockade against IDO, CTLA-4, and PD-L1 in Mice with Brain Tumors

Purpose: Glioblastoma (GBM) is the most common form of malignant glioma in adults. Although protected by both the blood–brain and blood–tumor barriers, GBMs are actively infiltrated by T cells. Previous work has shown that IDO, CTLA-4, and PD-L1 are dominant molecular participants in the suppression of GBM immunity. This includes IDO-mediated regulatory T-cell (Treg; CD4+CD25+FoxP3+) accumulation, the interaction of T-cell–expressed, CTLA-4, with dendritic cell-expressed, CD80, as well as the interaction of tumor- and/or macrophage-expressed, PD-L1, with T-cell–expressed, PD-1. The individual inhibition of each pathway has been shown to increase survival in the context of experimental GBM. However, the impact of simultaneously targeting all three pathways in brain tumors has been left unanswered. Experimental Design and Results: In this report, we demonstrate that, when dually challenged, IDO-deficient tumors provide a selectively competitive survival advantage against IDO-competent tumors. Next, we provide novel observations regarding tryptophan catabolic enzyme expression, before showing that the therapeutic inhibition of IDO, CTLA-4, and PD-L1 in a mouse model of well-established glioma maximally decreases tumor-infiltrating Tregs, coincident with a significant increase in T-cell–mediated long-term survival. In fact, 100% of mice bearing intracranial tumors were long-term survivors following triple combination therapy. The expression and/or frequency of T cell expressed CD44, CTLA-4, PD-1, and IFN-γ depended on timing after immunotherapeutic administration. Conclusions: Collectively, these data provide strong preclinical evidence that combinatorially targeting immunosuppression in malignant glioma is a strategy that has high potential value for future clinical trials in patients with GBM. Clin Cancer Res; 20(20); 5290–301. ©2014 AACR.
Effects of Improper Spectral Delay on Speech Recognition with Cochlear Implants: A Simulation Study

Actual pulse rate in common cochlear implant systems is an essential parameter in terms of recognition performance and preference of patients. Finding the ideal pulse rate seems to be an evergreen topic independently of the used encoding strategy and architecture of the electrode array. At the same time, implantees react sensitively to rate changes, which makes the design of such trials difficult. We give a simplified model of spectral delays caused by a given total pulse rate, and compare these delays to natural cochlear delays. The comparison is carried out by feeding a hidden Markov model based speech recognizer with an adequately preprocessed speech corpus. Simulation outcomes give an insight why both too low and too high total pulse rates manifest in poor performance, and give a prediction on the recognition capabilities of each phone at various pulse rates. The experiments included in this study emphasize the importance of biotic cochlear delays and urge their application in future cochlear implant speech processing strategies.
Composite medium with simultaneously negative permeability and permittivity

We demonstrate a composite medium, based on a periodic array of interspaced conducting nonmagnetic split ring resonators and continuous wires, that exhibits a frequency region in the microwave regime with simultaneously negative values of effective permeability &mgr;(eff)(omega) and permittivity varepsilon(eff)(omega). This structure forms a "left-handed" medium, for which it has been predicted that such phenomena as the Doppler effect, Cherenkov radiation, and even Snell's law are inverted. It is now possible through microwave experiments to test for these effects using this new metamaterial.
Balance Sheet Strength and Bank Lending: Evidence from the Global Financial Crisis

We use the 2007-2008 financial crisis as a lens to study the link between banks’ financial health and the strength of transmission of financial sector shocks to the real economy. We find that banks with ex-ante stronger balance sheets, in particular higher levels of common equity, were better able to maintain credit supply when faced with liquidity shocks during the crisis. Bank recapitalizations mitigated the lending gap between high and low capital banks, but only in countries with strong sovereigns. These findings support the view that strong financial intermediary balance sheets are key for the recovery of credit and economic performance after large financial sector shocks.
Genomic Prediction in Animals and Plants: Simulation of Data, Validation, Reporting, and Benchmarking

The genomic prediction of phenotypes and breeding values in animals and plants has developed rapidly into its own research field. Results of genomic prediction studies are often difficult to compare because data simulation varies, real or simulated data are not fully described, and not all relevant results are reported. In addition, some new methods have been compared only in limited genetic architectures, leading to potentially misleading conclusions. In this article we review simulation procedures, discuss validation and reporting of results, and apply benchmark procedures for a variety of genomic prediction methods in simulated and real example data. Plant and animal breeding programs are being transformed by the use of genomic data, which are becoming widely available and cost-effective to predict genetic merit. A large number of genomic prediction studies have been published using both simulated and real data. The relative novelty of this area of research has made the development of scientific conventions difficult with regard to description of the real data, simulation of genomes, validation and reporting of results, and forward in time methods. In this review article we discuss the generation of simulated genotype and phenotype data, using approaches such as the coalescent and forward in time simulation. We outline ways to validate simulated data and genomic prediction results, including cross-validation. The accuracy and bias of genomic prediction are highlighted as performance indicators that should be reported. We suggest that a measure of relatedness between the reference and validation individuals be reported, as its impact on the accuracy of genomic prediction is substantial. A large number of methods were compared in example simulated and real (pine and wheat) data sets, all of which are publicly available. In our limited simulations, most methods performed similarly in traits with a large number of quantitative trait loci (QTL), whereas in traits with fewer QTL variable selection did have some advantages. In the real data sets examined here all methods had very similar accuracies. We conclude that no single method can serve as a benchmark for genomic prediction. We recommend comparing accuracy and bias of new methods to results from genomic best linear prediction and a variable selection approach (e.g., BayesB), because, together, these methods are appropriate for a range of genetic architectures. An accompanying article in this issue provides a comprehensive review of genomic prediction methods and discusses a selection of topics related to application of genomic prediction in plants and animals.
Improving Environmental Performance Assessment: A Comparative Analysis of Weighting Methods Used to Evaluate Chemical Release Inventories

Managers, management scholars, regulators, nonprofit organizations, and the media are increasingly using emissions inventory data to measure organizations' environmental performance. Whereas some analysts use total mass emitted, others have applied one or more of the growing number of toxicity‐weighting databases aimed at predicting the environmental and health impacts of emissions. Little research is available to guide analysts in selecting among these databases. This article compares 13 methods in terms of their sophistication, complexity, and comprehensiveness. Seven of these methods are then evaluated as to their usefulness in weighting emissions data from the U.S. Environmental Protection Agency's (U.S. EPA's) toxic release inventory, and three pair‐wise comparisons are conducted. We recommend the U.S. EPA's Risk Screening Environmental Indicators for estimating impacts to human health. We recommend the Tool for the Reduction and Assessment of Chemical Impacts for estimating impacts to human health and the environment.
Predicting Dissemination of News Content in Social Media

Social media are an emerging news source, but questions remain regarding how citizens engage news content in this environment. This study focuses on social media news reception and friending a journalist/news organization as predictors of social media news dissemination. Secondary analysis of 2010 Pew data (N = 1,264) reveals reception and friending to be positive predictors of dissemination, and a reception-by-friending interaction is also evident. Partisanship moderates these relationships such that reception is a stronger predictor of dissemination among partisans, while the friending-dissemination link is evident for nonpartisans only. These results provide novel insights into citizens’ social media news experiences.
Etanercept Restores the Antinociceptive Effect of Morphine and Suppresses Spinal Neuroinflammation in Morphine-Tolerant Rats

BACKGROUND:In the present study we examined the effect of the tumor necrosis factor (TNF)-&agr; antagonist etanercept on the antinociceptive effect of morphine in morphine-tolerant rats. METHODS:Male Wistar rats were implanted with 2 intrathecal catheters, and 1 was connected to a mini-osmotic pump for either morphine (15 &mgr;g/h) or saline (1 &mgr;L/h) infusion for 5 days. On day 5, either etanercept (5 &mgr;g, 25 &mgr;g, and 50 &mgr;g/10 &mgr;L) or saline (10 &mgr;L) was injected via the other catheter after morphine infusion was discontinued. Three hours later, morphine (15 &mgr;g/10 &mgr;L, intrathecally) was given and tail-flick latency was measured to evaluate the antinociceptive effect of morphine. Rats were then killed and their spinal cords were removed for quantitative real-time polymerase chain reaction and immunohistochemistry to measure proinflammatory cytokines expression. RESULTS:We found that acute etanercept (50 &mgr;g) treatment preserved a significant antinociceptive effect of morphine in morphine-tolerant rats. In addition, the expression of TNF&agr; mRNA was increased by 2.5-fold, interleukin (IL)-1&bgr; mRNA increased by 13-fold and IL-6 mRNA by 111-fold in the dorsal spinal cord of morphine-tolerant rats. The increase in TNF&agr;, IL-1&bgr;, and IL-6 mRNA expression was blocked by 50 &mgr;g etanercept pretreatment. The immunohistochemistry analysis revealed that 50 &mgr;g etanercept suppressed proinflammatory cytokines expression and neuroinflammation in the microglia. CONCLUSIONS:The present study demonstrates that etanercept restores the antinoceptive effect of morphine in morphine-tolerant rats by inhibition of proinflammatory cytokine TNF-&agr;, IL-1&bgr;, and IL-6 expression and spinal neuroinflammation. The results suggest that etanercept could also be an adjuvant therapy for morphine tolerance, which extends the effectiveness of opioids in clinical pain management.
External Quality Assessment of Platelet Disorder Investigations: Results of International Surveys on Diagnostic Tests for Dense Granule Deficiency and Platelet Aggregometry Interpretation

Abstract The quality of platelet aggregation and dense granule deficiency testing is important for diagnosing platelet function disorders. After a successful pilot exercise on diagnosing platelet dense granule deficiency by electron microscopy (EM), the North American Specialized Coagulation Laboratory Association (NASCOLA) has launched regular external quality assurance (EQA) for dense granule EM, as well as for the interpretation of platelet aggregation findings. EQA records were analyzed to assess performance. For EM EQA, between 2009 and 2011, there was excellent performance in distinguishing normal from dense granule-deficient samples and good (>70%) agreement on classifying most electron dense structures in platelets. For aggregation EQA, some normal variants were misclassified and overall case interpretations were more acceptable for rare disorders than for common findings. NASCOLA experiences with these EQAs indicate that there is a need to improve the quality of platelet disorder evaluations. For aggregometry interpretations, deficits in performance could be addressed by translating guideline recommendations into practice.
A geometric modeling and animation system for virtual reality

V IRTUAL Reality (VR) immerses the user in a three-dimensional (3D) environment that can be actively interacted with and explored. Users are free to move about this environment under their own control, and to interact with any objects they find using natural hand gestures and other motions. While virtual reality holds great promise as a useful interaction style for 3D applications, the hardware and software technologies supporting it have not matured enough to realize this promise. One of the main software challenges is reducing the effort required to create good virtual environments. This involves designing both the geometry and behavior of objects appearing in the environment, plus the composition of environments based on these objects. The problem of virtual environment design is complicated by the fact that the user can freely interact with the objects, therefore, objects must be prepared to respond to these actions. An environment where the user can merely move amongst the objects, like a ghost, and not interact with them in any form is not very interesting. When the objects react, they must take into account the user’s actions, and the other objects in the environment. For example, when the user kicks a ball, the ball must bounce off the other objects in the environment, and not just sail through them. Thus, objects can’t have canned behaviors triggered by user actions, but must be able to respond to a wide range of events and circumstances. A wide variety of tools and techniques are currently used in the construction of virtual environments, but except for the simplest environments, some form of programming is required. This increases the cost of environment construction, since every group producing a virtual environment must include a programmer who may not be familiar with the application domain. The cost of environment construction can be greatly reduced Mark Green and Sean Halliday
Inference of transcriptional regulation using multi-objective optimization with Gaussian Process

For transcriptional kinetic mechanism research, raising the accuracy of identifying model parameters is an important work. Usually, the training data we obtained are noisy and sparse. This paper uses the ordinary differential equation to model transcriptional regulation. Gaussian prior distribution is adopted to describe the unobserved transcriptional factors activity. The approximation of gene expression profiles together with their time derivatives are considered during the inference. Gaussian Process helps to smooth and denoise experimental data and get their derivatives. Cultural genetic algorithm is utilized to optimize kinetic parameters in the model and hyper-parameters in kernel function. Experimental results on simulated data and real data show that the proposed method can improve the accuracy of identification and make the model fit data better.
What Can the Ontology Describe? Visualizing Local Coverage in PURO Modeler

Ontologies and vocabularies written in OWL are a crucial part of the semantic web. OWL however allows to model the same part of reality using different combinations of constructs, constituting ‘modeling styles’. Comparing how different ontologies from a similar domain cover a specific part of reality might be more difficult when each ontology uses a different style. PURO, a language for ontological background models, can serve as mediator, as it allows to create models that are directly mappable to OWL but overcome its unnatural modeling limits dictated by description logic. By highlighting the parts of the PURO model covered by particular ontologies, the ontology coverage comparison can be visualized. We demonstrate this approach using a simple graphical tool.
Proceedings of the NAACL HLT 2010 Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk

The NAACL-2010Workshop on Creating Speech and Language DataWith Amazon's Mechanical Turk explores applications of crowdsourcing technologies for the creation and study of language data. Recent work has evaluated the effectiveness of using crowdsourcing platforms, such as Amazon's Mechanical Turk, to create annotated data for natural language processing applications. This workshop further explores this area and these proceedings contain 34 papers and an overview paper that each experiment with applications of Mechanical Turk. The diversity of applications showcases the new possibilities for annotating speech and text, and has the potential to dramatically change how we create data for human language technologies. Papers in the workshop also looked at best practices in creating data using Mechanical Turk. Experiments evaluated how to design Human Intelligence Tasks (HITs), how to attract users to the task, how to price annotation tasks, and how to ensure data quality. Applications include the creation of data sets for standard NLP tasks, developing entirely new tasks, and investigating new ways of integrating user feedback in the learning process. The workshop featured an open-ended shared task in which 35 teams were awarded $100 of credit on Amazon Mechanical Turk to spend on an annotation task of their choosing. Results of the shared task are described in short papers and all collected data is publicly available. Shared task participants focused on data collection questions, such as how to convey complex tasks to non-experts, how to evaluate and ensure quality and annotation cost and speed.