Miracle-dz/UniFAR-FTUs huggingface dataset:
Discriminative Methods for Transliteration

We present two discriminative methods for name transliteration. The methods correspond to local and global modeling approaches in modeling structured output spaces. Both methods do not require alignment of names in different languages -- their features are computed directly from the names themselves. We perform an experimental evaluation of the methods for name transliteration from three languages (Arabic, Korean, and Russian) into English, and compare the methods experimentally to a state-of-the-art joint probabilistic modeling approach. We find that the discriminative methods outperform probabilistic modeling, with the global discriminative modeling approach achieving the best performance in all languages.
Kernel-Based Extraction of Slow Features: Complex Cells Learn Disparity and Translation Invariance from Natural Images

In Slow Feature Analysis (SFA [1]), it has been demonstrated that, high-order invariant properties can be extracted by projecting inputs into a nonlinear space and computing the slowest changing features in this space; this has been proposed as a simple general model for learning nonlinear invariances in the visual system. However, this method is highly constrained by the curse of dimensionality which limits it to simple theoretical simulations. This paper demonstrates that by using a different but closely-related objective function for extracting slowly varying features {[2, 3]), and then exploiting the kernel trick, this curse can be avoided. Using this new method we show that both the complex cell properties of translation invariance and disparity coding can be learnt simultaneously from natural images when complex cells are driven by simple cells also learnt from the image.
Disc segmentation and BMO-MRW measurement from SD-OCT image using graph search and tracing of three bench mark reference layers of retina

Spectral Domain Optical Coherence Tomography (SD-OCT) imaging is a robust diagnostic tool for visualizing pathology in the retina for glaucoma and other diseases. Bruch's Membrane Opening minimum rim width (BMO-MRW) of retina is a good measurable marker for distinguishing glaucoma and normal individuals. In this paper, we have proposed a novel automatic method to compute BMO-MRW from segmenting the Inner limiting membrane (ILM) and Bruch's Membrane Opening (BMO). Our technique uses the location tracking of three bench mark reference (TBMR) layers of the retina that reduce search space with improving accuracy. This also allows locating the boundary of the optic disc. The accuracy was tested against manually detected disc boundary for 13 subjects. The precision, recall and F1 score shows more than 95% for disc area and unsigned mean error rate of BMO-MRW is 58.62 db 43.12 (Mean ± SD) μm.
A novel approach for slicing of object oriented programs

This paper extends the graph-less technique proposed by Beszedes for slicing Object Oriented Programs. The proposed approach computes the dynamic slices of the OOPs, especially, in case of polymorphism. The approach generates the defined-used (d-u) chains of the objects and variables used in the program and computes the slice using the generated d-u chains; it then debugs the program by detecting the various possible bugs and generates messages suggesting that a bug may be present. A GUI tool has been developed to compute and display the computed slices. The tool allows the user to browse the program and see the generated advice.
Multicontrast High-Resolution Vessel Wall Magnetic Resonance Imaging and Its Value in Differentiating Intracranial Vasculopathic Processes

Background and Purpose— Although studies have attempted to differentiate intracranial vascular disease using vessel wall magnetic resonance imaging (VWI), none have incorporated multicontrast imaging. This study uses T1- and T2-weighted VWI to differentiate intracranial vasculopathies. Methods— We retrospectively reviewed patients with clinically defined intracranial vasculopathies causing luminal stenosis/irregularity who underwent VWI studies. Two blinded experts evaluated T1 precontrast and postcontrast and T2-weighted VWI characteristics, including the pattern of wall thickening; presence, pattern, and intensity of postcontrast enhancement; and T2 signal characteristics. Results— Twenty-one cases of atherosclerosis (intracranial atherosclerotic disease [ICAD]), 4 of reversible cerebral vasoconstriction syndrome, and 4 of vasculitis were identified, with a total of 118 stenotic lesions (81 ICAD, 22 reversible cerebral vasoconstriction syndrome, and 15 vasculitic lesions). There was substantial to excellent inter-reader agreement for the assessment of lesional T2 hyperintensity (&kgr;=0.80), pattern of wall thickening (&kgr;=0.87), presence (&kgr;=0.90), pattern (&kgr;=0.73), and intensity (&kgr;=0.77) of enhancement. ICAD lesions were significantly more likely to have eccentric wall involvement (90.1%) than reversible cerebral vasoconstriction syndrome (8.2%; P<0.001) and vasculitic lesions (6.7%; P<0.001) and were also more likely to have T2 hyperintensity present than the other 2 vasculopathies (79% versus 0%; P<0.001). There were also significant differences in the presence, intensity, and pattern of enhancement between all lesion types. Combining T1 and T2 VWI increased the sensitivity of VWI in differentiating ICAD from other vasculopathies from 90.1% to 96.3%. Conclusions— Multicontrast VWI can be a complementary tool for intracranial vasculopathy differentiation, which often leads to more invasive workups when reversible cerebral vasoconstriction syndrome and vasculitis are in the differential diagnosis.
Data quality and integration in collaborative environments

The trend to merge medical practices into cooperatively operating networks and organizational units like Medical Supply Centers generates new challenges for an adequate IT support. In particular, new use cases for common economic planning, controlling and treatment coordination arise. This requires consolidation of data originating from heterogeneous and autonomous software systems. Heterogeneity and autonomy are core reasons for low data quality. The intuitive approach of initially integrating heterogeneous systems into a federated system creates a very high upfront effort before the system can become operable and does not adequately consider the fact that data quality requirements might change over time. To remedy this, we propose an approach for continuous data quality improvement which enables a demand driven step by step system integration. By adapting the generic Total Data Quality Management process to healthcare specific use cases, we are developing an extended model for continuous data quality management in cooperative healthcare settings. The IT tools which are needed to provide the information that drives this process are currently in development within a government supported project involving both industry and academia.
Detecting Viral Propagations Using Email Behavior Profiles

The Email Mining Toolkit (EMT) is a data mining system that computes behavior profiles or models of user email accounts. These models may be used for a variety of forensic analyses and detection tasks. In this paper we focus on the application of these models to detect the early onset of a viral propagation without "contentbased" (or signature-based) analysis in common use in virus scanners. We present several experiments using real email from 15 users with injected simulated viral emails and describe how the combination of different behavior models improves overall detection rates. The performance results vary depending upon parameter settings, approaching 99% true positive(TP) (percentage of viral emails caught) in general cases and with 0.38% false positive(FP) (percentage of emails with attachments that are mislabeled as viral). The models used for this study are based upon volume and velocity statistics of a user’s email rate and an analysis of the user’s (social) cliques revealed in their email behavior. We show by way of simulation that virus propagations are detectable since viruses may emit emails at rates different than human behavior suggests is normal, and email is directed to groups of recipients that violates the user’s typical communication with their social groups.
7 N ov 2 01 4 Al 20 + does melt , albeit above the bulk melting temperature of aluminium

Employing first principles parallel tempering molecular dynamics in the microcanonical ensemble, we report the presence of a clear solid-liquid-like melting transition in Al20 + clusters, not found in experiments. The phase transition temperature obtained from the multiple histogram method is 993 K, 60 K above the melting point of aluminium. Root mean squared bond length fluctuation, the velocity auto-correlation function and the corresponding power spectrum further confirm the phase transition from a solid-like to liquid-like phase. Atoms-In-Molecules analysis shows a strong charge segregation between the internal and surface atoms, with negatively charged internal atoms and positive charge at the surface. Analysis of the calculated diffusion coefficients indicates different mobilities of the internal and surface atoms in the solid-like phase, and the differences between the environment of the internal atoms in these clusters with that of the bulk atoms suggest a physical picture for the origin of greater-than-bulk melting temperatures.
A neural decision forest scheme with application to EMG gesture classification

Herein presents a novel neural decision forest by bootstrap aggregating randomized neural decision trees with the purpose of untangling signal data representations and performing heirarchical classification. The individual trees in a neural decision forest embed randomized neural networks (rNet) with 1-hidden layer acting as bottlenecks and hard gating functions. The rNets are trained using a multi-class exchange method with l2-regularization to sparsify the networks. By applying a projection map prior to each rNet input layer, a novel method to randomly window a vectorized tensor of electromyogram (EMG) signals recorded from hand gestures is attained. These random windows show the promise of greatly simplifying and generalizing the process of feature extraction. Neural decision forests prevent overfitting and the inability of single trees to generalize to new variations by bootstrap sampling the training set, controling the rNet convergence rates, and increasing the forest size. The key findings in three gesture classification experiments are: i) neural decision forests with random windows outperform the random forest algorithm in terms of accuracy measures in personalized gestures by as much as 20% with equal tree sizes, ii) neural decision forests outperform random forests in intra-subject classification discriminating 27 gestures on 4 out of 5 people, and iii) neural decision forests are competitive with random forests in discriminating 7 gestures recorded with a low sampling rate.
Joint channel estimation algorithm based on structured compressed sensing for FDD multi-user massive MIMO

Accurate channel state information (CSI) at transmitter is of importance to sufficiently exploit the merits of massive multiple input multiple output (MIMO). Because of the large amount of antennas at base station (BS), the pilot overhead becomes unaffordable, especially in frequency-division duplexing (FDD) massive MIMO systems. To alleviate the overwhelming pilot overhead, a novel channel estimation algorithm for multi-user massive MIMO system employing structured compressed sensing (CS) theory is proposed. Firstly, the angular domain channel representation of massive MIMO is analyzed. Then, due to the practical scattering environment, the common sparsity and private sparsity structure of channel matrix exist in multi-user massive MIMO system. Finally, basing on the statistical information of multi-user channel matrix, a structured joint subspace matching pursuit (SJSMP) algorithm is proposed, which is to estimate channels with limited pilot jointly at the BS. Particularly, the common support and private support of multi-user channel matrix are separately estimated to reduce the pilot overhead with improved CSI estimation quality in terms of MSE.
Optimization of Collision Detection Algorithm Based on OBB

In this paper, an optimization algorithm is proposed based on the Oriented Bounding Box (OBB), due to comparatively large cost generated in the overlap test among OBBs (B-B). A simpler bounding box tree is established by researching the collision detection between the Bounding Box and primitive (B-P). It uses an improved tree structure to reduce storage space, thus effectively increasing operating efficiency of the collision detection algorithm.
A Reconfiguration Algorithm for Power-Aware Parallel Applications

In current computing systems, many applications require guarantees on their maximum power consumption to not exceed the available power budget. On the other hand, for some applications, it could be possible to decrease their performance, yet maintain an acceptable level, in order to reduce their power consumption. To provide such guarantees, a possible solution consists in changing the number of cores assigned to the application, their clock frequency, and the placement of application threads over the cores. However, power consumption and performance have different trends depending on the application considered and on its input. Finding a configuration of resources satisfying user requirements is, in the general case, a challenging task. In this article, we propose Nornir, an algorithm to automatically derive, without relying on historical data about previous executions, performance and power consumption models of an application in different configurations. By using these models, we are able to select a close-to-optimal configuration for the given user requirement, either performance or power consumption. The configuration of the application will be changed on-the-fly throughout the execution to adapt to workload fluctuations, external interferences, and/or application’s phase changes. We validate the algorithm by simulating it over the applications of the Parsec benchmark suit. Then, we implement our algorithm and we analyse its accuracy and overhead over some of these applications on a real execution environment. Eventually, we compare the quality of our proposal with that of the optimal algorithm and of some state-of-the-art solutions.
A Novel Image Correlation Matching Approach

In this paper we present a novel approach which is com bined local invariant feature descriptor named ARPIH (Angular Radial Partitioning Intensity Histogram) with histogram-based similar distance (HSD). The approach succeeds the ARPIH descriptor’s distinctive advantage and provides higher robustness in deformation image matching, such as rotation image, illumination changing image and perspective image, etc. Based on the MCD algorithm, we present the HSD algorithm . This algorithm transforms the image matching into the histogram matching by calculat ing the number of the similar points between template histogram and target histogram in order to decrease the calculation complicacy and improve the matching efficiency. A large amount groups of images are used in testing the approach presented in this paper. The matching results presented here indicate that the presented algorithm is efficient to figure out both the geometric deformation image matching and the illumination changing image matching. Contrast with the traditional matching algorithm, the approach presented in this paper has the obvious advantage of high matching precision, robustness and performance efficiency.
HLA-Cw Allele Frequency in Definite Meniere’s Disease Compared to Probable Meniere’s Disease and Healthy Controls in an Iranian Sample

Introduction Several lines of evidence support the contribution of autoimmune mechanisms in the pathogenesis of Meniere’s disease. The aim of this study was determining the association between HLA-Cw Alleles in patients with definite Meniere’s disease and patients with probable Meniere’s disease and a control group. Materials and Methods: HLA-Cw genotyping was performed in 23 patients with definite Meniere’s disease, 24 with probable Meniere’s disease, and 91 healthy normal subjects, using sequence specific primers polymerase chain reaction technique. The statistical analysis was performed using stata 8 software. Results: There was a significant association between HLA-Cw*04 and HLA-Cw*16 in both definite and probable Meniere’s disease compared to normal healthy controls. We observed a significant difference in HLA-Cw*12 frequencies between patients with definite Meniere’s disease compared to patients with probable Meniere’s disease (P=0.04). The frequency of HLA-Cw*18 is significantly higher in healthy controls (P=0.002). Conclusion: Our findings support the rule of HLA-Cw Alleles in both definite and probable Meniere’s disease. In addition, differences in HLA-Cw*12 frequency in definite and probable Meniere’s disease in our study’s population might indicate distinct immune and inflammatory mechanisms involved in each condition.
Speeding up Bureaucrats by Greasing Them: An Experimental Study

We explore experimentally how active and passive bribing behaviour relates to bureaucratic discretion. The limited and asymmetric power of bureau- crats is captured via an ultimatum game with one proposer and two responders. Both responders have veto power, but only one has the additional option of delay- ing the agreement with a cost for the proposer. We speak of active (passive) 'greasing' when, in order to avoid delay, the responder who has more discretion is offered (demanding) a higher reward. Our findings suggest that greasing bureau- crats is moderately efficient in speeding them up.
The Growing Importance of Family and Community: An Analysis of Changes in the Sibling Correlation in Earnings

This study presents evidence that the correlation in brothers’ earnings has risen in recent decades. We use two distinct cohorts of young men from the National Longitudinal Surveys and estimate that the correlation in earnings between brothers rose from 0.26 to 0.45. This suggests that family and community influences shared by siblings have become increasingly important in determining economic outcomes. We find that neither the correlation in years of schooling nor the rising return to schooling accounts for this increase. We also argue that the PSID is not an appropriate dataset for analyzing changes over time because of its sampling design, small sample of siblings, and high attrition rate.
Soluble Interleukin-6 Receptor Induces Motor Stereotypies and Co-Localizes with Gp130 in Regions Linked to Cortico-Striato-Thalamo-Cortical Circuits

Soluble cytokine receptors are normal constituents of body fluids that regulate peripheral cytokine and lymphoid activity and whose levels are increased in states of immune activation. Soluble interleukin-6 receptor (sIL-6R) levels positively correlate with disease progression in some autoimmune conditions and psychiatric disorders. Particularly strong links between levels of sIL-6R and the severity of psychotic symptoms occur in schizophrenia, raising the possibility that sIL-6R is involved in this disease. However, there is no evidence that peripheral sIL-6R induces relevant behavioral disturbances. We showed that single subcutaneous injections of sIL-6R (0–1 µg), stimulated novelty stress-induced exploratory motor behaviors in male Balb/c mice within 20–40-min of injection. A progressive increase in vertical stereotypies was observed 40–80 min post injection, persisting for the remainder of the test session. Paralleling these stimulant-like effects, sIL-6R pre-treatment significantly enhanced stereotypy scores following challenge with GBR 12909. We found that peripherally administered sIL-6R crossed the blood-brain barrier, localizing in brain regions associated with cortico-striatal-thalamo-cortical (CSTC) circuits, which are putative neuroanatomical substrates of disorders associated with repetitive stereotypies. Peripherally administered sIL-6R co-localized with gp130, a transmembrane protein involved in IL-6 trans-signaling, in the nucleus accumbens, caudate-putamen, motor and infralimbic cortices, and thalamic nuclei, but not with gp130 in the ventral tegmental area, substantia nigra, or sensorimotor cortex,. The results suggest that peripheral sIL-6R can act as a neuroimmune messenger, crossing the blood brain barrier (BBB) to selectively target CSTC circuits rich in IL-6 trans-signaling protein, and inducing repetitive stereotypies. As such sIL-6R may represent a novel therapeutic agent for relevant psychiatric disorders.
D1.1.1 Requirements Analysis and Assessment of Relevant Methodologies Keyword List: Web Services, Semantic Web, Multi-agent Systems, Semantic Web Services Wp1 Semantic Web Services Bootstrapping Methodology Report / Pu Contractual Date of Delivery:31 Tao Consortium Executive Summary

EU-IST Specific targeted research project (STREP) IST-2004-026460 TAO Deliverable D1.1 First Draft (WP 1.1) The evolution of Service Oriented Computing, emerging from such fields as Object-Oriented software engineering and Multi-Agent systems, is re-defining the way functional components are being built and used by Application engineers. However, before embarking on the development of a methodology for transitioning existing or legacy systems into reusable, semantically described services, one must first understand the characteristics of the distributed service-oriented computing paradigm, and its origins both within Multi-Agent systems and Web Services. Semantic Web Service frameworks, such as OWL-S and WSMO, provide knowledge-based annotations for service descriptions, but differ in the approach taken to not only annotate services, but more importantly, facilitate orchestration and choreography. We examine the field of distributed computing and service oriented computing from a knowledge-requirement perspective, whereby services are described, provisioned, and managed using knowledge-based methods. Notions of Web Services, Multi-Agent Systems and Semantic Web Services are reviewed, before considering the business perspective of such technologies. It then proposes a number of issues that may need consideration to facilitate the migration of legacy systems into fully deployable Semantic Web Services. Keyword list: Web Services, Semantic Web, Multi-Agent Systems, Semantic Web Services WP1 Semantic Web Services Bootstrapping Methodology Report / PU Contractual date of delivery:31 Aug 2006 Actual date of delivery:31 Sept 2006 D1.1.1 / Requirements Analysis and Assessment of Relevant Methodologies
Fractal-based approach in analysis of intracranial pressure (ICP) in severe head injury

Physiologic monitoring is a key to guiding severe head injury therapy in the neurological intensive care unit (ICU), of which intracranial pressure (ICP) plays a critical role. Currently, decision-making with respect to escalating therapy in the stepwise protocol for refractory raised ICP is made by interpreting absolute values of the physiologic parameters coupled with the doctor clinician knowledge and experience. In this paper, we proposed a fractal dimension-based method for processing and quantification of time-series ICP data for neurological monitoring to predict the transition from maximal medical therapy to decompressive craniectomy. The fractal analysis results in a quantitative measure, known as a fractal dimension (FD), describing the self-similar patterns observed in time-series data. In this paper, we proposed fractal based method to analyse ICP data to predict changes in the patient state. We processed ICP data of 9 patients before and after decompression with well known Box-counting and Higuchi algorithms. Our results suggest that FD values could be used as a valuable additional parameter to indicate the need for surgical decompression. The FD based method could potentially be implemented as a software tool in intensive care units.