Miracle-dz/UniFAR-FTUs huggingface dataset:
Synthetic Data for Neural Machine Translation of Spoken-Dialects

In this paper, we introduce a novel approach to generate synthetic data for training Neural Machine Translation systems. The proposed approach supports language variants and dialects with very limited parallel training data. This is achieved using a seed data to project words from a closely-related resource-rich language to an under-resourced language variant via word embedding representations. The proposed approach is based on localized embedding projection of distributed representations which utilizes monolingual embeddings and approximate nearest neighbors queries to transform parallel data across language variants. Our approach is language independent and can be used to generate data for any variant of the source language such as slang or spoken dialect or even for a different language that is related to the source language. We report experimental results on Levantine to English translation using Neural Machine Translation. We show that the synthetic data can provide significant improvements over a very large scale system by more than 2.8 Bleu points and it can be used to provide a reliable translation system for a spoken dialect which does not have sufficient parallel data.
A 15-GHz 7-channel SiGe:C PLL for 60-GHz WPAN Application

In this paper, we present the design of 15-GHz frequency synthesizer for 60-GHz WPAN. The PLL generates 7 channels of output signals with 250 MHz step by using the highspeed programmable divider operating up to 10 GHz. A double cross-coupled LC VCO is used for achieving higher oscillation frequency and shows about 20 % tuning range. The PLL represents phase noise of -88 dBc/Hz at 1 MHz offset from 15.75 GHz and consumes 115 mA at 2.5 V supply voltage. The PLL occupies 0.7 times 0.8 mm2 area and is fabricated using 0.25 mum SiGe:C BiCMOS process technology.
A Tool for Model-Based Language Specification

Formal languages let us define the textual representation of data with precision. Formal grammars, typically in the form of BNF-like productions, describe the language syntax, which is then annotated for syntax-directed translation and completed with semantic actions. When, apart from the textual representation of data, an explicit representation of the corresponding data structure is required, the language designer has to devise the mapping between the suitable data model and its proper language specification, and then develop the conversion procedure from the parse tree to the data model instance. Unfortunately, whenever the format of the textual representation has to be modified, changes have to propagated throughout the entire language processor tool chain. These updates are time-consuming, tedious, and error-prone. Besides, in case different applications use the same language, several copies of the same language specification have to be maintained. In this paper, we introduce a model-based parser generator that decouples language specification from language processing, hence avoiding many of the problems caused by grammar-driven parsers and parser generators.
Parametrization of the Local Scattering Function Estimator for Vehicular-to-Vehicular Channels

Non wide-sense stationary (WSS) uncorrelated scatterering (US) fading processes are observed in vehicular communications. To estimate such a process under additive white Gaussian noise we use the local scattering function (LSF). In this paper we present an optimal parametrization of the multitaper-based LSF estimator. We do this by quantizing the mean square error (MSE). For that purpose we use the structure of a twodimensional Wiener filter and optimize the parameters of the estimator to obtain the minimum MSE (MMSE). We split the observed fading process in WSS regions and analyze the influence of the estimator parameters and the length of the stationarity regions on the MMSE. The analysis is performed considering three different scenarios representing different scattering properties. We show that there is an optimal combination of estimator parameters and length of stationarity region which provides a minimum MMSE. (Less)
A naturalistic study of the determinants of health related quality of life improvement in osteoarthritic patients treated with non-specific non-steroidal anti-inflammatory drugs

Objectives: To capture changes in the quality of life (QoL) occurring in patients with osteoarthritis (OA) during treatment with non-specific non-steroidal anti-inflammatory drugs (NSAIDs) and to identify factors that predict such changes. Methods: A naturalistic, prospective follow up of 783 patients with OA in whom primary care physicians decided to start treatment with non-selective NSAIDs. Short Form-36 (SF-36) and the Western Ontario and McMaster Universities OA index (WOMAC) were assessed at baseline and after 3 months. Baseline results were compared with QoL values in 4800 subjects randomly selected from the general population. Multiple regression analysis was performed to identify determinants of QoL at baseline and measures influencing changes in SF-36 or WOMAC during follow up. Results: All QoL dimensions were significantly (p<0.01) decreased in patients with OA compared with controls. Significant improvement (p<0.05) in four dimensions of the SF-36 (vitality, role emotional, role physical, bodily pain) and in all components of the WOMAC was seen between baseline and month 3. Older age, female sex, longer duration of OA, and a higher number of comorbidities were the major determinants of a poor QoL at baseline. Maximal benefit from non-specific NSAIDs was seen in patients with the most severe impairment in QoL and the shortest duration of OA. Conclusion: OA negatively impacts all dimensions of the QoL. Non-specific NSAIDs improve the QoL in patients with OA treated in a “real life setting”. The profile of patients receiving maximal benefit from such treatment may be of interest for health providers, enabling them to decide who should preferentially be given cytoprotective treatments or coxibs.
Congenital amegakaryocytic thrombocytopenia: clinical and biological consequences of five novel mutations

Background and Objectives Congenital amegakaryocytic thrombocytopenia (CAMT) is a rare, autosomal recessive disorder induced by mutations of the gene coding for thrombopoietin (TPO) receptor (c-MPL). Patients initially present with isolated thrombocytopenia that subsequently progresses into pancytopenia. Although the mechanisms leading to aplasia are unknown, the age of onset has been reported to depend on the severity of the c-MPL functional defect. To improve our knowledge in this field, we studied clinical and biological features of five new patients. Design and Methods We diagnosed five CAMT patients, identified c-MPL mutations, including five novel alterations and investigated relationships between mutations and their clinical-biological consequences. Results In all cases, platelet c-MPL and bone marrow colonies were reduced, while serum TPO levels were elevated. We also documented that the percentage of bone marrow cells expressing tumor necrosis factor-α and interferon-γ was increased during pancytopenia as compared to controls, suggesting that, as in other bone marrow failure diseases, these inhibitory cytokines contributed to the pancytopenia. Contrary to previously published data, we found no evidence of correlations between different types of mutations and the clinical course. Interpretation and Conclusions These results suggest that therapies, such as hematopoietic stem cell transplantation, which are potentially curative although associated with a risk of treatment-related mortality, should not be postponed even in those CAMT patients whose c-MPL mutations might predict residual activity of the TPO receptor.
A secure kernelized architecture for multilevel object-oriented databases

The authors present a secure kernelized architecture for multilevel object-oriented database management systems. The architecture is based on the notion of a message filter. It builds upon the typical architecture of current object-oriented database management systems. Since the operations mediated by the message filter are arbitrarily complex operations (as opposed to primitive reads and writes), a secure message filter requires careful attention to potential timing covert channels. Although the overall computation is logically a sequential one, to be secure one must actually execute pieces of the computation concurrently. This raises a synchronization problem for which they give a secure multiversion protocol. The fundamental problem solved is how to securely and correctly 'write up' in terms of abstract operations. <<ETX>>
Impact of groups on academic entrepreneurs in Sri Lanka

The intention of this paper is to explore the types of groups that academics involve in and their impact towards the entrepreneurialism of individual faculty members in Sri Lanka. Plenty of prior researches were conducted to analyze the academic entrepreneurship phenomenon based on developed countries, but not many studies were in developing countries like Sri Lanka. Primary data were collected through semistructured, face-to-face interviews with thirty faculty members from selected three universities in Sri Lanka. The results were analyzed through qualitative approach and findings depicted a variance in group impact according to the demographics of the universities such as age and proximity to the highly industrialized urban area of the country and the years of working experience of individual university lecturers. Finally, seven research propositions were posited to shed light on future research of academic entrepreneurs in developing countries with inadequate university-industry linkages.
Journal of Neuroengineering and Rehabilitation Open Access Robotic Neurorehabilitation: a Computational Motor Learning Perspective

Conventional neurorehabilitation appears to have little impact on impairment over and above that of spontaneous biological recovery. Robotic neurorehabilitation has the potential for a greater impact on impairment due to easy deployment, its applicability across of a wide range of motor impairment, its high measurement reliability, and the capacity to deliver high dosage and high intensity training protocols. We first describe current knowledge of the natural history of arm recovery after stroke and of outcome prediction in individual patients. Rehabilitation strategies and outcome measures for impairment versus function are compared. The topics of dosage, intensity, and time of rehabilitation are then discussed. Robots are particularly suitable for both rigorous testing and application of motor learning principles to neurorehabilitation. Computational motor control and learning principles derived from studies in healthy subjects are introduced in the context of robotic neurorehabilitation. Particular attention is paid to the idea of context, task generalization and training schedule. The assumptions that underlie the choice of both movement trajectory programmed into the robot and the degree of active participation required by subjects are examined. We consider rehabilitation as a general learning problem, and examine it from the perspective of theoretical learning frameworks such as supervised and unsupervised learning. We discuss the limitations of current robotic neurorehabilitation paradigms and suggest new research directions from the perspective of computational motor learning.
A Multiscale Moving Boundary Model Arising in Cancer Invasion

Cancer invasion of tissue is a key aspect of the growth and spread of cancer and is crucial in the process of metastatic spread, i.e., the growth of secondary cancers. Invasion consists in cancer cells secreting various matrix degrading enzymes (MDEs) which destroy the surrounding tissue or extracellular matrix (ECM). Through a combination of proliferation and migration, the cancer cells then actively spread locally into the surrounding tissue. Thus processes occurring at the level of individual cells eventually give rise to processes occurring at the tissue level. In this paper we introduce a new type of multiscale model describing the process of cancer invasion of tissue. Our multiscale model is a two-scale model which focuses on the macroscopic dynamics of the distributions of cancer cells and of the surrounding extracellular matrix, and on the microscale dynamics of the MDEs, produced at the level of the individual cancer cells. These microscale dynamics take place at the interface of the cancer cells.
Modified Newtonian Dynamics and Induced gravity

Modified Newtonian dynamics, a successful alternative to the cosmic dark matter model, proposes that gravitational field deviates from the Newtonian law when the field strength $g$ is weaker than a critical value $g_0$. We will show that the dynamics of MOND can be derived from an induced gravity model. New dynamics is shown to be compatible with the spatial deformation of scalar fields coupled to the system. Approximate solutions are shown explicitly for a simple toy model.
A Dynamic Model of Polyelectrolyte Gels

We derive a model of the coupled mechanical and electrochemical effects of polyelectrolyte gels. We assume that the gel, which is immersed in a fluid domain, is an immiscible and incompressible mixture of a solid polymeric component and the fluid. As the gel swells and deswells, the gel-fluid interface can move. Our model consists of a system of partial differential equations for mass and linear momentum balance of the polymer and fluid components of the gel, the Navier--Stokes equations in the surrounding fluid domain, and the Poisson--Nernst--Planck equations for the ionic concentrations on the whole domain. These are supplemented by a novel and general class of boundary conditions expressing mass and linear momentum balance across the moving gel-fluid interface. Our boundary conditions include the permeability boundary conditions proposed in earlier studies. A salient feature of our model is that it satisfies a free energy dissipation identity, in accordance with the second law of thermodynamics. We al.
Image-Filtering Techniques for meteorological radar

The images collected by meteorological satellites and radars are often infected by noise due to the environment, the data acquisition devices and the transmission system used. These images carry information about weather, which must be cleared up. In general, the filtering techniques applied to scrambled images aim at giving them a better visual aspect. In the case of meteorological radar images, such techniques are usefully employed to separate the atmospheric echoes from the ground clutter. Most radars equipping the meteorological stations are non coherent and then especially designed to estimate the amount of precipitations falling in the observed region. In this paper, the images collected by 5.6 GHz non coherent radar in the region of Setif (Algeria), are processed so as to keep the rainfall echoes unchanged and eliminate the ground ones. For this purpose, two filtering techniques are utilized, which respectively consist in computing the textural features of these images and masking the ground echoes. The textural approach is implemented by using an analysis window of 7times7 pixels. The radar echoes are then filtered by using either energy or homogeneity. The masking method is based on the selection of clear sky images among a sequence of radar data collected during a sufficiently long period of time. The template of the relief of the region under study is built by averaging all the clear sky images. To get only the rainfall echoes, the resulting mask is applied to each of the original radar images and the filtering is improved by removing the residual clutter with the help of median filter and shape detection. All these filters are found to be accurate enough for meteorological uses.
Multi-class Protein Classification Using Adaptive Codes

Predicting a protein's structural class from its amino acid sequence is a fundamental problem in computational biology. Recent machine learning work in this domain has focused on developing new input space representations for protein sequences, that is, string kernels, some of which give state-of-the-art performance for the binary prediction task of discriminating between one class and all the others. However, the underlying protein classification problem is in fact a huge multi-class problem, with over 1000 protein folds and even more structural subcategories organized into a hierarchy. To handle this challenging many-class problem while taking advantage of progress on the binary problem, we introduce an adaptive code approach in the output space of one-vs-the-rest prediction scores. Specifically, we use a ranking perceptron algorithm to learn a weighting of binary classifiers that improves multi-class prediction with respect to a fixed set of output codes. We use a cross-validation set-up to generate output vectors for training, and we define codes that capture information about the protein structural hierarchy. Our code weighting approach significantly improves on the standard one-vs-all method for two difficult multi-class protein classification problems: remote homology detection and fold recognition. Our algorithm also outperforms a previous code learning approach due to Crammer and Singer, trained here using a perceptron, when the dimension of the code vectors is high and the number of classes is large. Finally, we compare against PSI-BLAST, one of the most widely used methods in protein sequence analysis, and find that our method strongly outperforms it on every structure classification problem that we consider. Supplementary data and source code are available at http://www.cs. columbia.edu/compbio/adaptive .
Action-status access control

We introduce a generalization of Role-based Access Control that we call the Action-Status Access Control (ASAC) model. The ASAC model addresses certain shortcomings with RBAC models when applied in distributed computing contexts. The ASAC model is based on the notion of status, and a nonmonotonic theory of access control that is founded upon the notions of events, actions and times. The approach allows automatic changes to be made to policy requirements and agent authorizations that may be based, in part, on an agent's intentional behaviors.
Operation of WDM networks with different wavelength conversion capabilities

We study the impact of wavelength conversion capability on wavelength routing WDM networks with fixed shortest-path routing. We propose a method for implementing wavelength routing in a WDM network with partial wavelength conversion capability. Simulation results show that such partial wavelength conversion networks provide a performance in between that of wavelength continuous networks and those with full conversion capability. In addition, it can be seen that only limited wavelength conversion capability is enough to provide a performance close to that of a network with full conversion. Analytical and simulation bounding results for the full and no conversion cases have also been provided.
An Efficient Semi-Supervised Classifier Based on Block-Polynomial Mapping

In this paper, we propose a block-polynomial mapping for image feature learning, which can be efficiently represented by the matrix Khatri-Rao product. The block-polynomial mapping not only captures the local discriminative information within the image structure, but is also much more efficient than the traditional kernel mapping. Moreover, we embed the proposed mapping into the manifold regularization framework for semi-supervised image classification. Experimental results demonstrate that, while maintaining a comparable classification accuracy, the proposed algorithm performs much more efficient than the state-of-the-art methods.
Dual Adaptation Supports a Parallel Architecture of Motor Memory

Although our understanding of the mechanisms underlying motor adaptation has greatly benefited from previous computational models, the architecture of motor memory is still uncertain. On one hand, two-state models that contain both a fast-learning–fast-forgetting process and a slow-learning–slow-forgetting process explain a wide range of data on motor adaptation, but cannot differentiate whether the fast and slow processes are arranged serially or in parallel and cannot account for learning multiple tasks simultaneously. On the other hand, multiple parallel-state models learn multiple tasks simultaneously but cannot account for a number of motor adaptation data. Here, we investigated the architecture of human motor memory by systematically testing possible architectures via a combination of simulations and a dual visuomotor adaptation experimental paradigm. We found that only one parsimonious model can account for both previous motor adaptation data and our dual-task adaptation data: a fast process that contains a single state is arranged in parallel with a slow process that contains multiple states switched via contextual cues. Our result suggests that during motor adaptation, fast and slow processes are updated simultaneously from the same motor learning errors.
Systolic Time Intervals and Left Ventricular Function in Acute Myocardial Infarction

Systolic time intervals (STI) were measured simultaneously with left ventricular pressure, right atrial pressure, and cardiac output in patients with acute myocardial infarction (AMI) within 24 hours of the onset of symptoms. Electromechanical systole (QS2) and left ventricular ejection time (LVET) were measured and preejection period (PEP) calculated and corrected for heart rate. LVET showed significant correlation with stroke volume (r = 0.62), confirming previous observations. Patients with significantly elevated left ventricular end-diastolic pressure (LVEDP) and clinical signs of congestive heart failure (CHF) usually exhibited a prolonged PEP when compared with normal subjects or patients with AMI who did not have signs of CHF. The normal PEP in the latter group might be due to adrenergic stimulation offsetting the effect of myocardial damage, although measurements of circulating catecholamines did not conclusively support this hypothesis. We conclude that STI are not reliable indices of left ventricular performance in AMI.