Miracle-dz/UniFAR-FTUs huggingface dataset:
Between Attrition and Acquisition: the Dynamics between Two Languages in Adult Migrants

In the context of language use of Romanian migrants living in Canada, this thesis examines the linguistic changes and the contributing social, personal, and affective factors involved in the maintenance of a migrant’s first language (L1) as she or he simultaneously acquires a second language (L2) in an L2-dominant environment. Specifically, it investigates the incidence of L1 attrition in the bilingual group, the existence of a possible systematic relationship between the levels of L1 and L2 proficiency, and the influence of sociolinguistic and cognitive factors on L1 attrition and L2 acquisition in bilingual contexts. L1 attrition is defined here as both the reduction of L1 language skills and the restructuring of the L1 according to L2 patterns to the point where communication in the L1 is impaired. The present study involves extensive elicitation of written and oral data in both Romanian and English. First, an assessment of the participants’ history of language acquisition, their attitudes, their patterns of language use, and the nature of their social networks was conducted via sociolinguistic questionnaires and individual interviews. The language aptitude tests used in this project examined phonetic memory, vocabulary learning, and grammatical inferencing abilities. As an indicator of overall language proficiency, formal tests (a C-test and a verbal fluency task) and samples of spontaneous speech in the form of film commenting tasks were analyzed for fluency and grammatical complexity. The results showed that L1 attrition is a possible, but not obligatory, outcome in the first generation of migrants. With the exception of two participants, the majority of the bilinguals in this study, while not perfect matches for the L1 monolinguals, remained within the L1 native range even after a long residence in the L2 country. While proficiency trade-off patterns are not observable at the group level, they were evidenced in the cases of the two attriters identified in this study. With respect to cognitive predictor variables, L1 maintenance does not appear to be a direct function of linguistic aptitude. At the same time, levels of L1 attrition and L2 achievement are dependent on a combination of attitudinal and personal background variables.
Telerehabilitation: An Adjunct Service Delivery Model For Early Intervention Services

Early Intervention (EI) services for children birth through two years of age are mandated by Part C of the Individuals with Disabilities Education Act (IDEA); however, personnel shortages, particularly in rural areas, limit access for children who qualify. Telerehabilitation has the potential to build capacity among caregivers and local providers as well as promote family-centered services through remote consultation. This article provides an overview of research related to telerehabilitation and early intervention services; discusses the feasibility of telerehabilitation within traditional EI service delivery models; examines telecommunications technology associated with telerehabilitation; and provides hypothetical case examples designed to illustrate potential applications of telerehabilitation in early intervention.
Quality checking and mining nephrology biopsy data

The Charité hospital in Berlin possesses one of the most secure records of hundreds of kidney transplants, most of which originate from the electronic patient record TBase©. One of the grave problems after kidney transplantation is the recipient body's immune rejection of the transplanted organ. T-cells and antibodies attack the organ, which in the worst case can lead to graft failure. Biopsy is an important diagnostic tool to evaluate a rejection episode. TBase© includes a biopsy protocol that can easily be filled out by the physician, which helped to collect and store 1447 biopsy cases in the TBase© database. With respect to different kinds of rejections, there exist some basic rules for the entered data that are enforced during completion of the protocol. Nevertheless, because so much biopsy data was entered in by hand, it was necessary to check the quality and plausibility of existing data with respect to more complex rules that were not enforced during protocol completion. In this paper, we present the process of checking the biopsy data for consistency with complex rules provided by an expert, as well as mining of new rules using interpretable rule-based classification methods. We discovered interesting rules and relationships between features with respect to T-cell mediated rejection (TCMR), antibody-mediated rejection (AMR), interstitial fibrosis and tubular atrophy (IF/TA), and polyoma virus (BKV) nephropathy, with negative results concerning acute tubular necrosis (ATN). The discovered rules further support the quality and plausibility of the data, and open avenues for further research.
Hidden Markov Models for Human/Computer Interface Modeling

Automated modeling of human behaviors is useful in the computer security domain of anomaly detection. In the user modeling facet of the anomaly detection domain, the task is to develop a model or pro(cid:12)le of the normal working state of a computer system user and to detect anomalous conditions as deviations from expected behavior patterns. In this paper, we examine the use of hidden Markov models (HMMs) as user pro(cid:12)les for the anomaly detection task. We formulate a user identity clas-si(cid:12)cation system based on the posterior likelihood of the model parameters and present an approximation that allows this quantity to be quickly estimated to a high degree of accuracy for subsequences of the total sequence of observed data. We give an empirical analysis of the HMM anomaly detection sensor. We examine performance across a range of model sizes (i.e. number of hidden states). We demonstrate that, for most of our user population, a single-state model is inferior to the multi-state models, and that, within multi-state models, those with more states tend to model the pro(cid:12)led user more e(cid:11)ectively but imposters less e(cid:11)ectively than do smaller models. These observations are consistent with the interpretation that larger models are necessary to capture high degrees of user behavioral complexity. We describe extensions of these techniques to other tasks and domains.
The Minimum Risk Principle That Underlies the Criteria of Bounded Component Analysis

This paper studies the problem of the blind extraction of a subset of bounded component signals from the observations of a linear mixture. In the first part of this paper, we analyze the geometric assumptions of the observations that characterize the problem, and their implications on the mixing matrix and latent sources. In the second part, we solve the problem by adopting the principle of minimizing the risk, which refers to the encoding complexity of the observations in the worst admissible situation. This principle provides an underlying justification of several bounded component analysis (BCA) criteria, including the minimum normalized volume criterion of the estimated sources or the maximum negentropy-likelihood criterion with a uniform reference model for the estimated sources. This unifying framework can explain the differences between the criteria in accordance with their considered hypotheses for the model of the observations. This paper is first presented for the case of the extraction of a complex and multidimensional source, and later is particularized for the case of the extraction of subsets of 1-D complex sources. The results also hold true in the case of real signals, where the obtained criteria for the extraction of a set of 1-D sources usually coincide with the existing BCA criteria.
The Computational Structure of Spike Trains

Neurons perform computations, and convey the results of those computations through the statistical structure of their output spike trains. Here we present a practical method, grounded in the information-theoretic analysis of prediction, for inferring a minimal representation of that structure and for characterizing its complexity. Starting from spike trains, our approach finds their causal state models (CSMs), the minimal hidden Markov models or stochastic automata capable of generating statistically identical time series. We then use these CSMs to objectively quantify both the generalizable structure and the idiosyncratic randomness of the spike train. Specifically, we show that the expected algorithmic information content (the information needed to describe the spike train exactly) can be split into three parts describing (1) the time-invariant structure (complexity) of the minimal spike-generating process, which describes the spike train statistically; (2) the randomness (internal entropy rate) of the minimal spike-generating process; and (3) a residual pure noise term not described by the minimal spike-generating process. We use CSMs to approximate each of these quantities. The CSMs are inferred nonparametrically from the data, making only mild regularity assumptions, via the causal state splitting reconstruction algorithm. The methods presented here complement more traditional spike train analyses by describing not only spiking probability and spike train entropy, but also the complexity of a spike train's structure. We demonstrate our approach using both simulated spike trains and experimental data recorded in rat barrel cortex during vibrissa stimulation.
Using Attribute-Based Access Control to Enable Attribute-Based Messaging

Attribute based messaging (ABM) enables message senders to dynamically create a list of recipients based on their attributes as inferred from an enterprise database. Such targeted messaging can reduce unnecessary communications and enhance privacy, but faces challenges in access control. In this paper, we explore an approach to ABM based on deriving access control information from the same attribute database exploited by the addressing scheme. We show how to address three key challenges. First, we demonstrate a manageable access control system based on attributes. Second we show how this can be used with existing messaging systems to provide a practical deployment strategy. Third, we show that such a system can be efficient enough to support ABM for mid-size enterprises. Our implementation can dispatch ABM messages approved by XACML review for an enterprise of at least 60,000 users with only seconds of latency
Chronic Functional Constipation in Children: Adherence and Factors Associated With Drug Treatment

Objective: The aim of the present study was to evaluate the treatment adherence of children with chronic functional constipation. Methods: The present study is a prospective and longitudinal study realized at a pediatric gastroenterology clinic of a Brazilian University Hospital, between August 2009 and October 2011. Rome III criteria and the Bristol Stool Scale were used to define constipation and to characterize feces, respectively. Drug treatment was prescribed for patients according to the protocols previously standardized in the clinic. Specific questionnaires, containing questions related to 1 dependent variable and independent variables were completed in the first and sixth months of the treatment. Independent variables related to the patients, their caregivers, the disease itself, and the therapeutic plan were analyzed and compared with the dependent variable (adherence to the treatment). Adherence was considered when the patient returned with >75% of the prescribed medicine containers empty. Results: Fifty children participated in both the first and sixth months of treatment. The mean age of the sample was 77.6 ± 43.8 months and the mean age of the onset of symptoms was 18.8 ± 27.9 months. The adherence rate was 38% in the first month and 30% in the sixth month. Patients who were treated with polyethylene glycol had greater adherence than patients who were prescribed other laxatives, with statistical significance in the second moment of the study (P = 0.19 and P = 0.04, respectively). Conclusions: The study showed low adherence rates to drug treatment of constipation in children. It is necessary to seek new strategies to increase treatment adherence, while avoiding complications and reducing costs.
A standby-sparing technique with low energy-overhead for fault-tolerant hard real-time systems

Time redundancy (rollback-recovery) and hardware redundancy are commonly used in real-time systems to achieve fault tolerance. From an energy consumption point of view, time redundancy is generally more preferable than hardware redundancy. However, hard real-time systems often use hardware redundancy to meet high reliability requirements of safety-critical applications. In this paper we propose a hardware-redundancy technique with low energy-overhead for hard real-time systems. The proposed technique is based on standby-sparing, where the system is composed of a primary unit and a spare. Through analytical models, we have developed an online energy-management method which uses a slack reclamation scheme to reduce the energy consumption of both the primary and spare units. In this method, dynamic voltage scaling (DVS) is used for the primary unit and dynamic power management (DPM) is used for the spare. We conducted several experiments to compare the proposed system with a fault-tolerant real-time system which uses time redundancy for fault tolerance and DVS with slack reclamation for low energy consumption. The results show that for relaxed time constraints, the proposed system provides up to 24% energy saving as compared to the time-redundancy system. For tight deadlines when the time-redundancy system can tolerate no faults, the proposed system preserves its fault-tolerance but with about 32% more energy consumption.
On the Fairness-Efficiency Tradeoff for Packet Processing with Multiple Resources

Middleboxes are widely deployed in today's networks. They apply a variety of complex network functions to transform, filter, and optimize incoming traffic based on the payload of packets. These functions require the support of multiple types of resources, such as CPU and link bandwidth, for processing incoming packets. Hence, a multi-resource packet scheduling algorithm is needed to allow flows to share these resources fairly and efficiently. However, unlike traditional fair queueing where bandwidth is the only concern, we show in this paper that fairness and efficiency are conflicting objectives that cannot be achieved simultaneously in the presence of multiple resources. Ideally, a scheduling algorithm should allow network operators to flexibly specify their fairness and efficiency requirements, so as to meet the Quality of Service demands while keeping the system at a high utilization level. Yet, existing multi-resource scheduling algorithms focus on fairness only, and may lead to poor resource utilization. In this paper, we propose a new scheduling algorithm to achieve a flexible tradeoff between fairness and efficiency for packet processing, consuming both CPU and link bandwidth. Experimental results based on both real-world implementation and trace-driven simulation suggest that trading off a modest level of fairness can potentially improve the efficiency to the point where the system capacity is almost saturated.
A quotient basis kernel for the prediction of mortality in severe sepsis patients

In this paper, we describe a novel kernel for multinomial distributions, namely the Quotient Basis Kernel (QBK), which is based on a suitable reparametrization of the input space through algebraic geometry and statistics. The QBK is used here for data transformation prior to classification in a medical problem concerning the prediction of mortality in patients suffering severe sepsis. This is a common clinical syndrome, often treated at the Intensive Care Unit (ICU) in a time-critical context. Mortality prediction results with Support Vector Machines using QBK compare favorably with those obtained using alternative kernels and standard clinical procedures.
Distributional Assumptions in Voxel-Based Morphometry

In this paper we address the assumptions about the distribution of errors made by voxel-based morphometry. Voxel-based morphometry (VBM) uses the general linear model to construct parametric statistical tests. In order for these statistics to be valid, a small number of assumptions must hold. A key assumption is that the model's error terms are normally distributed. This is usually ensured through the Central Limit Theorem by smoothing the data. However, there is increasing interest in using minimal smoothing (in order to sensitize the analysis to regional differences at a small spatial scale). The validity of such analyses is investigated. In brief, our results indicate that nonnormality in the error terms can be an issue in VBM. However, in balanced designs, provided the data are smoothed with a 4-mm FWHM kernel, nonnormality is sufficiently attenuated to render the tests valid. Unbalanced designs appear to be less robust to violations of normality: a significant number of false positives arise at a smoothing of 4 and 8 mm when comparing a single subject to a group. This is despite the fact that conventional group comparisons appear to be robust, remaining valid even with no smoothing. The implications of the results for researchers using voxel-based morphometry are discussed.
Substrate Binding to the Molecular Chaperone Hsp104 and Its Regulation by Nucleotides*

The Hsp104 protein from Saccharomyces cerevisiae is a member of the Hsp100/Clp family of molecular chaperones. It mediates the solubilization of aggregated proteins in an ATP-dependent process assisted by the Hsp70/40 system. Although the principal function of Hsp104 is well established, the mechanistic details of this catalyzed disaggregation are poorly understood. In this work, we have investigated the interaction of Hsp104 with reduced, carboxymethylated α-lactalbumin (RCMLa), a permanently unfolded model substrate. Our results demonstrate that the affinity of Hsp104 toward polypeptides is regulated by nucleotides. In the presence of ATP or adenosine-5′ -O-(3-thiotriphosphate), the chaperone formed complexes with RCMLa, whereas no binding was observed in the presence of ADP. In particular, the occupation of the N-terminally located nucleotide-binding domain with ATP seems to be crucial for substrate interaction. When ATP binding to this domain was impaired by mutation, Hsp104 lost its ability to interact with RCMLa. Our results also indicate that upon association with a polypeptide, a conformational change occurs within Hsp104 that strongly reduces the dynamics of nucleotide exchange and commits the bound polypeptide to ATP hydrolysis.
Joint detection and recognition of human actions in wireless surveillance camera networks

Automatic recognition of human actions in video has been a highly addressed problem in robotics and computer vision. Majority of the recent work in literature has focused on classifying pre-segmented video clips, and some progress has also been made on joint detection and recognition of actions in complex video sequences. These methods, however, are not designed for wireless camera networks where the sensors have limited internal processing and communication capabilities. In this paper we present an efficient system for the joint detection and recognition of human actions using a network of wireless smart cameras. The foundation of our work is based on Deformable Part Models (DPMs) for detecting objects in static images. We have extended this framework to the single-view and multi-view video setting to jointly detect and recognize actions. We call this the Deformable Keyframe Model (DKM) and tightly integrate it within a centralized video analysis system. In our system, feature extraction is locally performed on-board wireless smart cameras, and the classification is performed at a base station with higher processing power. Our analysis demonstrates that this decoupling of the the recognition pipeline can significantly minimize the power and bandwidth consumed by the wireless cameras. We experimentally validate our DKMs on two data sets. We first demonstrate the competitiveness of our algorithm by comparing its performance against other state-of-the-art methods, on a publicly available dataset. Then, we extensively validate our system on a novel dataset called the Bosch Multiview Complex Action (BMCA) dataset. Our dataset consists of 11 actions continuously performed by 20 different subjects while being captured by cameras located at 4 different vantage points. In our experiments, we demonstrate that the presence of multiple-views improves the performance of action detection and recognition by about 15% over the single-view setting.
Self-Configuration of Waypoints for Docking Maneuvers of Flexible Automated Guided Vehicles

We study the problem of automatic configuration of the initial position, the so-called waypoint, from which to initiate a robust and accurate docking maneuver using nonholonomic (car-like) robotic forklifts in the context of automated manufacturing. The proper selection of these positions is of paramount importance to operate with the industrial grade of accuracy, repeatability, and reliability required by load transfer operations in industrial settings. An unconstrained optimization method coupled with probabilistic techniques is proposed to solve this problem. The proposed method permits to increase significantly the flexibility and adaptability of the autonomous robotic forklifts.
Timed and probabilistic automata for automatic animal Call Recognition

Automatic Call Recognition is vital for environmental monitoring. Patten recognition has been applied in automatic species recognition for years. However, few studies have applied formal syntactic methods to species call structure analysis. This paper introduces a novel method to adopt timed and probabilistic automata in automatic species recognition based upon acoustic components as the primitives. We demonstrate this through one kind of birds in Australia: Eastern Yellow Robin.
Machine-Learning Approaches for Classifying Haplogroup from Y Chromosome STR Data

Genetic variation on the non-recombining portion of the Y chromosome contains information about the ancestry of male lineages. Because of their low rate of mutation, single nucleotide polymorphisms (SNPs) are the markers of choice for unambiguously classifying Y chromosomes into related sets of lineages known as haplogroups, which tend to show geographic structure in many parts of the world. However, performing the large number of SNP genotyping tests needed to properly infer haplogroup status is expensive and time consuming. A novel alternative for assigning a sampled Y chromosome to a haplogroup is presented here. We show that by applying modern machine-learning algorithms we can infer with high accuracy the proper Y chromosome haplogroup of a sample by scoring a relatively small number of Y-linked short tandem repeats (STRs). Learning is based on a diverse ground-truth data set comprising pairs of SNP test results (haplogroup) and corresponding STR scores. We apply several independent machine-learning methods in tandem to learn formal classification functions. The result is an integrated high-throughput analysis system that automatically classifies large numbers of samples into haplogroups in a cost-effective and accurate manner.