Miracle-dz/UniFAR-FTUs huggingface dataset:
Chance and necessity revisited : A reassessment of path dependence , and its implications for organization studies *

This paper has three objectives. First, to provid e a clear and distinctive definition of path depend ce as well as to unpack its antecedents and implicatio ns n order to outline its strong theoretical inter est. Second, to reassess the empirical scope of path dependence in the light of recent controversies, by delineating t he epistemological limitations that prevent the theory from fruitfully informing the empirical sphere. In particular, three issues are identified that preclude path depe ndence from consensually connecting theory and obse rvations: i) its reliance on the contingency assumption, ii) its claim that contingent events matter “in the lon g run” and iii) its ambiguous relationship to suboptimality. The th ird objective of this paper is to suggest methodolo gical ways to overcome these limitations and to present a simp le simulation that illustrates how path dependence can be integrated in organization and management research in a useful and epistemologically sustainable manne r. Notably, the simulation shows that path dependence can help scholars to generate new hypotheses about the evolution of organizational capabilities.
Clinical and immunological aspects of pretransplant blood transfusions

Blood transfusions can lead to immunization or tolerance in the recipient. The latter is characterized by an improved transplant outcome after pretransplant blood transfusions. First observations of improved kidney graft outcome after blood transfusion date 35 years back, however no exclusive immunological mechanism has been found yet. At the Leiden University Medical Center, patients on the waiting list for simultaneous pancreas-kidney transplantation (SPKT) received a non-leukocyte depleted, HLA-DR shared blood transfusion before transplantation. We showed that a pretransplant blood transfusion was able to diminish the severity of acute rejection episodes after SPKT. The need for HLA-DR sharing suggests a role for the indirect allorecognition. Our in vitro experiments focusing on the indirect allorecognition and on several subsets of recipient immune cells, showed that a pretransplant blood transfusion activates the recipient’s immune system. It is still not clear if and how this activation state can lead to transplantation tolerance. Additionally, there may be a role for microchimeric donor cells in the immunological mechanism leading to tolerance. Pretransplant blood transfusions may still be of clinical benefit in these times of potent immunosuppressive drug use, but requires the support of well-designed RCTs as well as more insight into the immunological mechanism.
GOBOOT : TESTING SET ENRICHMENT ANALYSIS ROBUSTNESS TO BACKGROUND SELECTION

Set enrichment analysis identifies enriched biological categories/terms by evaluating the proportion of differentially expressed genes against a background reference (BR) in highthroughput experiments. However, results will depend on BR choice. Currently there is no way to analyze enrichment robustness to BR choice. Bootstrapped versions of a given BR were carried out to provide robustness of enriched terms to BR selection. Thus, terms with a high stability are candidates to by explored leaving spurious enrichment out of the analysis. Results showed that stable terms were found to validate main experimental hypothesis and also new terms emerged providing new biological insight.
Study of pipeline signal de-nosing based on wavelet transform

Pipeline transport as the most important mode of transportation of oil and gas energy is widely used, But as time goes by, Pipeline would inevitably leak oil because of aging, corrosion and even stiletto steal and other reasons, So it's crucial to equip the pipeline with a set of real-time stability leak detection and location system. In view of the negative pressure wave test system has the advantage such as simple structure, small workload, cost-effective, high efficiency, high precision and sensitivity, this method has been gradually popularized. In consideration of the influence of opening and stop the pump working condition adjustment, the vibration of the oil transfer pump, the road and environment during the process of detection, the collected signal will be a lot of noise, which seriously affect the information transfer and leak positioning. So, the noise reduction processing of collected signal determines the system is good or bad, and the ultimate position precision.
A Roadmap for Privacy Preserving Speech Processing

This paper presents an overview of a strategy for enabling speech recognition to be performed in the cloud whilst preserving the privacy of users. The strategy advocates a demarcation of responsibilities between the client and server-side components for performing the speech recognition task. On the client-side resides the acoustic model, which symbolically encodes the audio and encrypts the data before uploading to the server. The server-side then employs searchable encryption-based language modelling to perform the speech recognition task. The paper details the proposed client-side acoustic model components, and the proposed server-side searchable encryption which will be the basis of the language modelling. Some preliminary results are presented, and potential problems and their solutions regarding the encrypted communication between client and server are discussed. Preliminary benchmarking results with acceleration of the client and server operations with GPGPU computing are also presented.
Variational Inference from Ranked Samples with Features

In many supervised learning settings, elicited labels comprise pairwise comparisons or rankings of samples. We propose a Bayesian inference model for ranking datasets, allowing us to take a probabilistic approach to ranking inference. Our probabilistic assumptions are motivated by, and consistent with, the so-called Plackett-Luce model. We propose a variational inference method to extract a closed-form Gaussian posterior distribution. We show experimentally that the resulting posterior yields more reliable ranking predictions compared to predictions via point estimates
ON THE USE OF NS-2 IN SIMULATIONS OF INTERNET-BASED DISTRIBUTED EMBEDDED SYSTEMS

The paper discusses the application of Network Simulator (NS-2) in simulation of Internet-based distributed embedded systems. It envisions the pros and cons of NS-2 as a simulation tool for distributed embedded systems and suggests some design techniques to implement simulation scenarios. At the end, an example of time-delay simulation of switched Ethernet as a communication media for distributed embedded systems is presented.
Creation of synthetic microdata in 2021 Census Transformation Programme ( proof of concept )

The Census Transformation Programme of the Office for National Statistics in England and Wales is investigating the use of synthetic data for a number of potential uses. One application is the creation of a household microdata sample. Due to concerns about preserving the confidentiality of individual respondents, we have not been able to provide a 2011 Census household microdata sample accessible outside secure research environments, with sufficient utility for users, using standard disclosure control techniques. The method being tested to create a household microdata file punches holes in a microdata sample, and uses the edit and imputation process from 2011 Census (using CANCEIS) to fill in the holes. This attempts to preserve the relationships between variables within households and individuals, but introduces sufficient uncertainty to mitigate disclosure risk. Methods are being investigated to test utility and risk in the resultant data. This presentation will demonstrate issues we have to overcome, and the methods we are investigating to come up with a solution to provide useful, non-disclosive, microdata for a wider range of users, and how we are measuring risk and utility. Given these issues, this is just a proof of concept at this stage, to test whether the approach is feasible.
Lead iodide optical detectors for gamma ray spectroscopy

This paper describes the research performed in developing low noise, high quantum efficiency lead iodide photodetectors for use with scintillators. These photodetectors operate with very low leakage current and show high quantum efficiency (>60% in 350 to 500 mm region). Successful scintillation studies have been performed at room temperature as well as elevated temperatures (100/spl deg/C) using PbI/sub 2/ photodetectors with LSO and CsI(Na) scintillators. Detailed analysis of noise has also been performed and potential applications are discussed.
Well-being in public transport : an empirical approach of the crowding effect

We investigate the influence of in-vehicle crowding on the comfort satisfaction experienced during a public transport journey. Moreover, we describe the anatomy of the crowding effect by testing various nuisance factors (Smell, Noise, Standing.) as channels through which crowding may decrease the comfort satisfaction. The analysis takes the responses to a travel satisfaction question as a measure for user subjective well-being, and the responses to questions about dis-satisfaction related to various aspects of comfort when the in-vehicle density is very high as measures for nuisance factors. The data originates from a survey collected on 1,000 Paris subway users. We find a clear crowding effect: on average, an extra-user per square meter decreases by one the expected 0 to 10 scale individual well-being. We do not find any empirical evidence of this effect being intensified by the travel time. However, the crowding effect increases with the income of users. Over-closeness, standing situation, noise and wasted time are the main nuisances caused by over-crowding. These features of discomfort matter more for women and wealthy individuals.
PHMon: A Programmable Hardware Monitor and Its Security Use Cases

There has been a resurgent trend in the industry to enforce a variety of security policies in hardware. The current trend for developing dedicated hardware security extensions is an imperfect, lengthy, and costly process. In contrast to this trend, a ﬂexible hardware monitor can efﬁciently enforce and enhance a variety of security policies as security threats evolve. Existing hardware monitors typically suffer from one (or more) of the following drawbacks: a restricted set of monitoring actions, considerable performance and power overheads, or an invasive design. In this paper, we propose a minimally-invasive and efﬁcient implementation of a Programmable Hardware Monitor (PHMon) with expressive monitoring rules and ﬂexible ﬁne-grained actions. PHMon can enforce a variety of security policies and can also assist with detecting software bugs and security vulnerabilities. Our prototype of PHMon on an FPGA includes the hardware monitor and its interface with a RISC-V Rocket processor as well as a complete Linux software stack. We demonstrate the versatility of PHMon and its ease of adoption through four different use cases: a shadow stack, a hardware-accelerated fuzzing engine, an information leak prevention mechanism, and a hardware-accelerated debugger. Our prototype implementation of PHMon incurs 0.9% performance overhead on average, while the hardware-accelerated fuzzing engine improves fuzzing performance on average by 16 × over the state-of-the art software-based implementation. Our ASIC implementation of PHMon only incurs a 5% power overhead and a 13.5% area overhead.
Compilation of OpenCL Programs for Stream Processing Architectures

The stream architecture and the stream execution model optimize memory usage while minimizing processor idle time by exploring the multiple types of locality in stream applications: temporal, spatial, and instruction locality. These are the main focus of this work. Heterogeneous systems can contain various computing devices that implement different architectures, and it can an arduous task to take the architectural details of each device into account when developing applications for these systems. This work proposes StreamOCL, an implementation of the OpenCL framework, and its goal is to provide a programming environment for heterogeneous systems, targeting stream devices. The proposed approach also enables applications to explore the advantages of stream computing, without requiring them to be specifically implemented for stream devices. The polyhedral model is used to analyse memory access patterns of OpenCL kernels and capture the locality of OpenCL applications. The results validate the approach since the current implementation was able to capture the access patterns of a baseline stream application. A 2× speedup was also achieved in kernel execution time on the current testing platform by scheduling kernels according to the stream execution model. In spite of the positive results, the overhead of computing memory dependences is currently not compensated by the performance gain in kernel execution.
Differences in First Semester GPAs by Dual Credit Enrollment Status for Texas Community College Students : A Multiyear Investigation

In this study, the degree to which differences were present in first semester GPAs by dual credit enrollment status for Texas community college students were examined for the 2012-2013 through the 2014-2015 academic years. Inferential statistical analyses revealed the presence of statistically significant differences in first semester GPAs for all students, separately by gender, and then by ethnicity/race (i.e., Asian, White, Hispanic, and Black). In all instances, students who completed a dual credit course while enrolled in high school earned statistically significantly higher average first semester GPAs in community college than did students who did not complete a dual credit course while enrolled in high school. Implications and recommendations for future research were discussed.
Web Course on Computer Aided Geometric Design

In this contribution we show a web-oriented course for learning Computer Aided Geometric Design at university level. The topics that are covered are Bézier, rational and spline curves and surfaces. The contents and notation are mainly grounded on reference [1]. To create this course an application package for Maple has been written, which allows symbolic calculation of most common procedures in CAGD. This has enabled us to enclose animated graphics in our course. Interactive surface graphics have also been included by using JavaviewLib (http://www.javaview.de). Finally interactive applets in Java can also be found in the book. They depict simple examples of spline, rational and Bezier curves. They have been programmed with the interactive geometry program Cinderella (http://www.cinderella.de). More details about the course can be found at http://debin.etsin. upm.es/ ̃leonardo.
Aalborg Universitet Harmonic Stability in Power Electronic Based Power Systems Concept , Modeling , and Analysis

The large-scale integration of power electronicbased systems poses new challenges to the stability and power quality of modern power grids. The wide timescale and frequency-coupling dynamics of electronic power converters tend to bring in harmonic instability in the form of resonances or abnormal harmonics in a wide frequency range. This paper provides a systematic analysis of harmonic stability in the future power-electronic-based power systems. The basic concept and phenomena of harmonic stability are elaborated first. It is pointed out that the harmonic stability is a breed of small-signal stability problems, featuring the waveform distortions at the frequencies above and below the fundamental frequency of the system. The linearized models of converters and system analysis methods are then discussed. It reveals that the linearized models of ac–dc converters can be generalized to the harmonic transfer function, which is mathematically derived from linear time-periodic system theory. Lastly, future challenges on the system modeling and analysis of harmonic stability in large-scale power electronic based power grids are summarized.
Convolutional Neural Network with Semantically Meaningful Activations for Speech Analysis

Semantic analysis of speech is more prospective compared to analysis of text since speech contains more information that is important for understanding. The most important distinguishing feature of speech is intonation, which is inaccessible in the text analysis. For successful semantic analysis of speech it is necessary to transform the speech signal into features with semantic interpretation. The mathematical apparatus of convolutional neural networks (CNN) seems suitable to implement this kind of transformation. However there is a scalability problem that makes it hard to combine many CNN’s in a single solution. To overcome this we propose to develop a CNN model with semantically meaningful activations i.e. the model that is capable of semantic interpretation of its internal states. The ultimate goal of the transform is to extract all semantically meaningful information, however the present work is confined to voice activity detection (VAD) and intonation extraction. Unlike other VADs based on artificial neural networks, the proposed model does not require a lot of computing resources and has a comparable or even better performance. Keywords—semantic speech analysis, voice activity detection, convolution neural network, VAD, CNN.
Expectations-Driven Cycles in the Housing Market Mendicino , Caterina and Lambertini , Luisa and Punzi

This paper analyzes housing market boom-bust cycles driven by changes in households’ expectations. We explore the role of expectations not only on productivity but on several other shocks that originate in the housing market, the credit market and the conduct of monetary policy. We find that, in the presence of nominal rigidities, expectations on both the conduct of monetary policy and future productivity can generate housing market boom-bust cycles in accordance with the empirical findings. Moreover, expectations of either a future reduction in the policy rate or a temporary increase in the central bank’s inflation target that are not fulfilled generate a macroeconomic recession. Increased access to credit generates a boom-bust cycle in most variables only if it is expected to be reversed in the near future. The opinions expressed in this article are the sole responsibility of the authors and do not necessarily reflect the position of the Banco de Portugal or the Eurosystem. We are grateful to seminar participants at the Banco de España, the Banco de Portugal, Universidade Nova de Lisboa, Catholic University of Louvain and the University of Surrey for useful feedbacks on this project. We also thank Nobuhiro Kiyotaki, Isabel Correia, Joao Sousa and Eva Ortega for valuable comments and suggestions. EPFL, College of Management. Email: luisa.lambertini@epfl.ch Departamento Estudos Economicos, Banco de Portugal. Email: cmendicino@bportugal.pt. Banco de Portugal and University of Alicante. Email: punzi@bc.edu. 1
Chapter 5 Recurrence rates of cutaneous squamous cell carcinoma of the head and neck after Mohs micrographic surgery vs . standard excision : a retrospective cohort study

Background: Recurrent cutaneous squamous cell carcinoma (cSCC) has been associated with an increased risk of local functional and aesthetic comorbidity, metastasis and mortality. Objectives: To compare the risk of recurrence between Mohs micrographic surgery (MMS) and standard excision (SE) for cSCC of the head and neck. Methods: This was a retrospective cohort study of all patients with a cSCC treated with MMS or SE at the departments of dermatology of a secondary or tertiary care hospital in the Netherlands between 2003 and 2012. To detect all recurrences, patients were linked to the Dutch pathology registry. To compare the risk of recurrence between MMS and SE, hazard ratios (HRs) were used adjusted for clinical tumour size > 2 cm and deep tumour invasion. Results: A total of 579 patients with 672 cSCCs were included: 380 cSCCs were treated with MMS and 292 with SE. The risk of recurrence was 8% (22/292) after SE during a median follow-up of 5.7 years (IQR 3.5-7.8), which was higher than the 3% (12/380) after MMS during a median follow-up of 4.9 years (IQR 2.3-6.0). The cumulative incidence of recurrence was higher for SE than for MMS during the entire follow-up period of 8.6 years. Carcinomas treated with MMS were at a three times lower risk of recurrence than those treated with SE when adjusted for tumour size and deep tumour invasion (adjusted HR 0.31, 95% confidence interval 0.12-0.66). Conclusion: MMS might be superior to SE for cSCCs of the head and neck because of a lower rate of recurrence. 2 Erasmus Medical Center Rotterdam
Improving Interaction Quality Estimation with BiLSTMs and the Impact on Dialogue Policy Learning

Learning suitable and well-performing dialogue behaviour in statistical spoken dialogue systems has been in the focus of research for many years. While most work which is based on reinforcement learning employs an objective measure like task success for modelling the reward signal, we use a reward based on user satisfaction estimation. We propose a novel estimator and show that it outperforms all previous estimators while learning temporal dependencies implicitly. Furthermore, we apply this novel user satisfaction estimation model live in simulated experiments where the satisfaction estimation model is trained on one domain and applied in many other domains which cover a similar task. We show that applying this model results in higher estimated satisfaction, similar task success rates and a higher robustness to noise.
Aalborg Universitet R-FCN Object Detection Ensemble based on Object Resolution and Image Quality

Object detection can be difficult due to challenges such as variations in objects both interand intra-class. Additionally, variations can also be present between images. Based on this, research was conducted into creating an ensemble of Region-based Fully Convolutional Networks (R-FCN) object detectors. Ensemble strategies explored were firstly data sampling and selection and secondly combination strategies. Data sampling and selection aimed to create different subsets of data with respect to object size and image quality such that expert R-FCN ensemble members could be trained. Two combination strategies were explored for combining the individual member detections into an ensemble result, namely average and a weighted average. R-FCNs were trained and tested on the PASCAL VOC benchmark object detection dataset. Results proved positive with an increase in Average Precision (AP), compared to state-of-the-art similar systems, when ensemble members were combined appropriately.