Miracle-dz/UniFAR-FTUs huggingface dataset:
Trends and complementarities in the Canadian Automobile Industry

Three issues have deeply influenced the Canadian automobile industry in the last decades. Proliferation of models and vehicle-types has reduced the average sales per vehicle with important consequences on the manufacturing side. Manufacturers have responded by adopting flexible production technology in their assembly plants. One of the most important features of the flexible technology is the ability to build a variety of different models on the same assembly line. Many firms also reorganized their supply chain. Increased outsourcing of components, even sharing the burden of research and development with preferred (tier 1) suppliers, and just-in-time inventory management are the most visible exponents of this transformation. These three trends are discussed with an eye to their interrelatedness, the impact on industrial performance, and policy options available to Canada. The importance of each trend independently and taken together is evaluated using plant and firm-level data.
MOIRCS Deep Survey. I : DRG Number Counts

We use very deep near-infrared imaging data taken with Multi-Object InfraRed Camera and Spectrograph (MOIRCS) on the Subaru Telescope to investigate the number counts of Distant Red Galaxies (DRGs). We have observed a 4x7 arcmin^2 field in the Great Observatories Origins Deep Survey North (GOODS-N), and our data reach J=24.6 and K=23.2 (5sigma, Vega magnitude). The surface density of DRGs selected by J-K>2.3 is 2.35+-0.31 arcmin^-2 at K 22 is smaller than that expected from the number counts at the brighter magnitude. The result indicates that while there are many bright galaxies at 2 22 suggest that the mass-dependent color distribution, where most of low-mass galaxies are blue while more massive galaxies tend to have redder colors, had already been established at that epoch.
Sperm Chromatin Dispersion Test before Sperm Preparation Is Predictive of Clinical Pregnancy in Cases of Unexplained Infertility Treated with Intrauterine Insemination and Induction with Clomiphene Citrate

Background/aims A large proportion of men with normal sperm results as analyzed using conventional techniques have fragmented DNA in their spermatozoa. We performed a prospective study to examine the incidence of DNA fragmentation in sperm in cases of couples with previously unexplained infertility and treated with intrauterine insemination. We evaluated whether there was any predictive value of DNA fragmentation for pregnancy outcome in such couples. Methods The percentage of DNA fragmentation and all classical variables to evaluate sperm before and after sperm treatment were determined. We studied the probable association between these results and pregnancy outcome in terms of clinical and ongoing pregnancy rate per started first cycle. We also assessed the optimal threshold level to diagnose DNA fragmentation in our center. Results When using threshold levels of 20, 25, and 30%, the occurrence of DNA fragmentation was 42.9, 33.3, and 28.6%, respectively. Receiver operating characteristic (ROC) analysis of all cases revealed an area under the curve of 80% to predict the clinical pregnancy rate per cycle from testing the sperm motility (a + b) before treatment. We failed to generate an ROC curve to estimate pregnancy outcome from the amount of DNA fragmentation before treatment. However, when selecting only those men with a pretreatment DNA fragmentation of at least 20%, the pretreatment result was statistically different between couples who achieved a clinical pregnancy and those who did not. Conclusion DNA fragmentation is often diagnosed in couples with unexplained infertility. Each center should evaluate the type of test it uses to detect DNA fragmentation in sperm and determine its own threshold values.
User Trust Dynamics: An Investigation Driven by Differences in System Performance

Trust is a key factor affecting the way people rely on automated systems. On the other hand, system performance has comprehensive implications on a user's trust variations. This paper examines systems of varied levels of accuracy, in order to reveal the relationship between system performance, a user's trust and reliance on the system. In particular, it is identified that system failures have a stronger effect on trust than system successes. We also describe how patterns of trust change according to a number of consecutive system failures or successes. Importantly, we show that increasing user familiarity with the system decreases the rate of trust change, which provides new insights on the development of user trust. Finally, our analysis established a correlation between a user's reliance on a system and their trust level. Combining all these findings can have important implications in general system design and implementation, by predicting how trust builds and when it stabilizes, as well as allowing for indirectly reading a user's trust in real time based on system reliance.
Topic Detection and Tracking interface with named entities approach

Topic Detection and Tracking (TDT) refer to the technologies and techniques in analysing and handling the vast amount of information arriving continuously from the news stream. Some issues in news monitoring regarding interface design are considered. In this paper we present the techniques and works done in TDT domain. We discuss the named entities approach used in document representation and in user interface. Finally, we present the suggested approach to improve users' performance and to facilitate them to perform an effective TDT task.
Detection of unknown malicious script code using a conceptual graph and SVM

There are a lot of malicious codes on the internet and many research studies methods for detection of them. Generally, detection methods of malicious codes compare source codes through definition and analysis pattern of malicious codes. In this paper, proposed method is a malicious code detection using relations and concepts between codes pattern based on semantics. Also, this method is detection of malicious script code through token conceptualization for extraction of relations and concepts in source codes because conceptual graph and regularization pattern matching between malicious behaviors in codes. In experiment, we test a malicious behavior distinction based on SVM(Support Vector Machine) training and the result is indicated adequate rate of malicious code detection.
Nonnegative Spectral Clustering with Discriminative Regularization

Clustering is a fundamental research topic in the field of data mining. Optimizing the objective functions of clustering algorithms, e.g. normalized cut and k-means, is an NP-hard optimization problem. Existing algorithms usually relax the elements of cluster indicator matrix from discrete values to continuous ones. Eigenvalue decomposition is then performed to obtain a relaxed continuous solution, which must be discretized. The main problem is that the signs of the relaxed continuous solution are mixed. Such results may deviate severely from the true solution, making it a nontrivial task to get the cluster labels. To address the problem, we impose an explicit nonnegative constraint for a more accurate solution during the relaxation. Besides, we additionally introduce a discriminative regularization into the objective to avoid overfitting. A new iterative approach is proposed to optimize the objective. We show that the algorithm is a general one which naturally leads to other extensions. Experiments demonstrate the effectiveness of our algorithm.
A Content Providing System with Privacy Protection

With the development of Internet technology, people can conveniently exchange and share image/video without the limit of time and space. In one image/video, some parts can be open to the public and some cannot or only can be open to specific people because of privacy issue. How to protect privacy information in the image/video has become more and more important. This paper proposes a content (image/video) providing system with privacy protection, which is supposed to make the most of the data and provide varying degrees of privacy/security. We apply fuzzy homomorphic encryption to protect privacy-sensitive area and realize hierarchical access to the data based on Chinese Remainder Theorem. Finally, we conduct the simulation experiments and evaluate the system feasibility.
Computational Modelling Approaches on Epigenetic Factors in Neurodegenerative and Autoimmune Diseases and Their Mechanistic Analysis

Neurodegenerative as well as autoimmune diseases have unclear aetiologies, but an increasing number of evidences report for a combination of genetic and epigenetic alterations that predispose for the development of disease. This review examines the major milestones in epigenetics research in the context of diseases and various computational approaches developed in the last decades to unravel new epigenetic modifications. However, there are limited studies that systematically link genetic and epigenetic alterations of DNA to the aetiology of diseases. In this work, we demonstrate how disease-related epigenetic knowledge can be systematically captured and integrated with heterogeneous information into a functional context using Biological Expression Language (BEL). This novel methodology, based on BEL, enables us to integrate epigenetic modifications such as DNA methylation or acetylation of histones into a specific disease network. As an example, we depict the integration of epigenetic and genetic factors in a functional context specific to Parkinson's disease (PD) and Multiple Sclerosis (MS).
Distance Matters

One of the untested assumptions within the prison visitation literature is that inmates receive fewer visits when visitors must travel long distances to prisons. Measuring distance by comparing the addresses of the prisons where offenders were confined with the residential addresses of those who visited them, we tested this hypothesis by estimating the effects of distance on the number of times Minnesota prisoners were visited. We estimated the effects of distance by performing multilevel repeated measure analyses, measuring the frequency of visitation across the different facilities at which inmates were housed, the different neighborhoods from which they received visits, and between-inmate differences in visitation frequency. Besides distance, we also estimated the effects of social disorganization on the frequency of visitation. Finding that distance does indeed decrease the frequency of prison visitation, as does concentrated disadvantage in neighborhoods, we discuss the implications of this research for prison administrative policies.
Ű HELYTANULMÁNYOK DISCUSSION PAPERS 2005 / 9 IMPORTS AND PRODUCTIVITY

What is the effect of imports on productivity? To answer this question, we estimate a structural model of producers using product-level import data for a panel of Hungarian manufacturing firms from 1992 to 2001. In our model with heterogenous firms, producers choose to import or purchase domestically varieties of intermediate inputs. Imports affect firm productivity through expanding variety as well as improved input quality. The model leads to a production function where the total factor productivity of a firm depends on the share of inputs imported. To estimate this import-augmented production function, we extend the Olley and Pakes (1996) procedure for a setting with an additional state variable, the number of input varieties imported. Our results suggest that the role of imports is both statistically and economically significant. Imports are responsible for 30% of the growth in aggregate total factor productivity in Hungary during the 1990s. About 50% of this effect is through imports advancing firm level productivity, while the remaining 50% comes from the reallocation of capital and labor to importers.
All-digital impulse radio with multiuser detection for wireless cellular systems

Impulse radio is an ultrawideband system with attractive features for baseband asynchronous multiple-access, multimedia services, and tactical wireless communications. Implemented with analog components, the continuous-time impulse radio multiple-access model utilizes pulse-position modulation and random time-hopping codes to alleviate multipath effects and suppress multiuser interference. We introduce a novel continuous-time impulse radio transmitter model and deduce from it an approximate one with lower complexity. We also develop a time-division duplex access protocol along with orthogonal user codes to enable impulse radio as a radio link for wireless cellular systems. Relying on this protocol, we then derive a multiple-input/multiple-output equivalent model for full continuous-time model and a single-input/single-output model, for the approximate one. Based on these models, we finally develop design composite linear/nonlinear receivers for the downlink. The linear step eliminates multiuser interference deterministically and accounts for frequency-selective multipath while a maximum-likelihood receiver performs symbol detection. Simulations are provided to compare performance of the different receivers.
Bandwidth Aggregation with Path Interleaving Forward Error Correction Mechanism for Delay-Sensitive Video Streaming in Wireless Multipath Environments

Multipath transmission which uses multiple paths for data transfer has been used in wireless networks to improve the performance of end-to-end transmission. However, wireless networks suffer from high packet loss rate, variations in end-to-end delay and available bandwidth. Hence, the Forward Error Correction (FEC) mechanism has been proposed to recover lost packets by adapting to the changing conditions in the network. Legacy multipath transmission with the FEC mechanism is used not only to improve the transmission performance by relying on bandwidth aggregation but also to reduce packet losses by relying on path selection. However, bandwidth aggregation of legacy multipath transmission with the FEC mechanism cannot select the appropriate transmission rate on each path as this needs more FEC redundancy to protect lost packets. Moreover, because the larger end-to-end delay leads to the video frame not being playable on-time at the receiver end, legacy multipath transmission with the FEC mechanism cannot be used in delay-sensitive video streaming when the FEC block length is so long that may exceed the end-to-end delay. This paper proposes the Bandwidth Aggregation with Path Interleaving FEC (BAPI-FEC) mechanism for delay-sensitive video streaming in a wireless multipath environment. The BAPI-FEC mechanism involves a mathematical analytical model with which the appropriate transmission rate, FEC block length and FEC redundancy on each path in a wireless multipath environment can be determined. Moreover, in order to overcome the burst packet loss problem, the BAPI-FEC mechanism relies on path interleaving technology to disperse burst packet losses to different FEC blocks.
Population pharmacokinetic and pharmacodynamic modeling of acetazolamide in peritoneal dialysis patients and healthy volunteers.

PURPOSE To characterize the pharmacokinetics (PK) and pharmacodynamics (PD) of acetazolamide (ACTZ) in peritoneal dialysis patients, ACTZ 500 mg was administered intravenously to 7 healthy subjects (HV) and 8 peritoneal dialysis patients (CAPD). METHODS Population PK/PD modeling was performed with ACTZ serum (total and unbound), urine and dialysate concentrations, intra-ocular pressure (IOP) and covariates. A multi-compartment PK model (accounting for non-linear protein binding) and an inhibitory Emax (maximal change in IOP) PD model were selected. RESULTS As expected, renal clearance (which almost equals total body clearance) was severely decreased in CAPD (1.2 vs 80.3 L/h) and the elimination half-life of total ACTZ was prolonged (20.6 vs 3.4 hours). The protein binding was significantly altered with a mean free fraction 4.2% in HV and 8.6% in CAPD. Moreover protein binding of ACTZ was concentration dependent in both HV and CAPD. Despite a higher free fraction of ACTZ, the Emax was lower in CAPD: 4.4±1.4 vs 7.4±2.8 mmHg. CONCLUSION Both PK and PD are significantly altered in dialysis patients.
Providing lexicographers with corpus evidence for fine-grained syntactic descriptions: adjectives taking subject and complement clauses

This article deals with techniques for lexical acquisition which allow lexicographers to extract evidence for fine-grained syntactic descriptions of words from corpora. The extraction tools are applied to partially parsed text corpora, and aim to provide the lexicographer with easy to use syntactically pre-classified evidence. As an example we extracted German adjectives taking subject and complement clauses.
Stability analysis for cognitive radio with multi-access primary transmission

This letter analyzes the impact, from a network-layer perspective, of having a single cognitive radio transmitter-receiver pair share the spectrum with multiple primary users wishing to communicate to a single receiver in a multi-access channel (MAC). In contrast to previous work which assumes a time division multi-access strategy, here, we assume the set of primary users simultaneously access the channel to deliver their packets to a common destination. We derive the symmetric stable throughput regions, consisting of maximal arrival rates for primary and secondary (or cognitive radio) users under two investigated protocols. The first protocol is a conventional MAC scheme where the primary and secondary nodes operate independenly. The second protocol corresponds to a multi-access relay channel (MARC) which exploits user cooperation between primary and secondary nodes. We prove that cooperation is beneficial in the considered MARC as it enables higher throughputs for both primary and secondary users.
Joint Epipolar Tracking (JET): Simultaneous Optimization of Epipolar Geometry and Feature Correspondences

Traditionally, pose estimation is considered as a two step problem. First, feature correspondences are determined by direct comparison of image patches, or by associating feature descriptors. In a second step, the relative pose and the coordinates of corresponding points are estimated, most often by minimizing the reprojection error (RPE). RPE optimization is based on a loss function that is merely aware of the feature pixel positions but not of the underlying image intensities. In this paper, we propose a sparse direct method which introduces a loss function that allows to simultaneously optimize the unscaled relative pose, as well as the set of feature correspondences directly considering the image intensity values. Furthermore, we show how to integrate statistical prior information on the motion into the optimization process. This constructive inclusion of a Bayesian bias term is particularly efficient in application cases with a strongly predictable (short term) dynamic, e.g. in a driving scenario. In our experiments, we demonstrate that the 'JET' algorithm we propose outperforms the classical reprojection error optimization on two synthetic datasets and on the KITTI dataset. The JET algorithm runs in real-time on a single CPU thread.
Influence of Holding Smart Phone for Acceleration-Based Gait Authentication

In most previous researches on acceleration-based gait authentication, dedicated accelerometers are mounted on hip or ankle, or smart phones are in the pocket in fixed manners. However, when a user walks, smart phone is not only put in the pocket but also used calling or touching on the screen. In these situations, the direction of the phone should be taken into account. In this study, we first improve the application developed in our previous study to record more information such as the rotation of the phone around the 3 axes. And then we perform preliminary experiments using the improved application for 4 subjects to collect the user-generated acceleration data on walk when the phone is held calling and touching as well as in the pocket. The authentication results show that although 1.30% false acceptance rate (FAR) at 2.34% false rejection rate (FRR) is obtained when the phone is in the pocket, the FRR is extremely bad as touching on the screen.
Adjusted Light and Dark Cycles Can Optimize Photosynthetic Efficiency in Algae Growing in Photobioreactors

Biofuels from algae are highly interesting as renewable energy sources to replace, at least partially, fossil fuels, but great research efforts are still needed to optimize growth parameters to develop competitive large-scale cultivation systems. One factor with a seminal influence on productivity is light availability. Light energy fully supports algal growth, but it leads to oxidative stress if illumination is in excess. In this work, the influence of light intensity on the growth and lipid productivity of Nannochloropsis salina was investigated in a flat-bed photobioreactor designed to minimize cells self-shading. The influence of various light intensities was studied with both continuous illumination and alternation of light and dark cycles at various frequencies, which mimic illumination variations in a photobioreactor due to mixing. Results show that Nannochloropsis can efficiently exploit even very intense light, provided that dark cycles occur to allow for re-oxidation of the electron transporters of the photosynthetic apparatus. If alternation of light and dark is not optimal, algae undergo radiation damage and photosynthetic productivity is greatly reduced. Our results demonstrate that, in a photobioreactor for the cultivation of algae, optimizing mixing is essential in order to ensure that the algae exploit light energy efficiently.
Neural Network Techniques for the Estimation of Ozone Vertical Distributions from GOME Data (451a)

The Global Ozone Monitoring Experiment (GOME) aboard ESAs ERS-2 satellite measures the reflected and backscattered radiation from the Earth in the UV/VIS spectral range at moderate spectral resolution. In this paper a neural network based technique for atmospheric ozone profiles retrieval from radiance spectra measured by GOME is presented. Depending on the choice of the spectral interval used for the inversion, two different neural algorithms have been designed and their retrieval performance has been tested. The effectiveness of the retrieval algorithms has been evaluated comparing their results to that yielded by other instruments and inversion techniques.
Activity Recognition with Fuzzy Finite Automata

Offering context aware services to users is one of the main objectives of pervasive computing. A context aware system needs to know the activities being performed by the user. Deciding what a user is doing at a given time poses a number of challenges. One significant challenge is dealing with the variation in the number, order and duration of the constituent steps of an activity. There happens to be considerable variation in these parameters even if the same user is performing the same activity at different times. Though fuzzy finite automata have been used by researchers to overcome this challenge, manual construction of the automata for daily life activities becomes onerous. This paper illustrates how finite automata can be constructed automatically and fuzziness incorporated into it, to recognize user activities in a smart environment. The proposed method is tested with a publicly available dataset and is found to give promising results.