Miracle-dz/UniFAR-FTUs huggingface dataset:
Cyclic Task Scheduling with Storage Requirement Minimisation under Specific Architectural Constraints : Case of Buffers and Rotating Storage Facilities

In this report, we study the exact and an approximate formulation of the general problem of onedimensional periodic task scheduling under storage requirement, irrespective of machine constraints. We rely on the SIRA theoretical framework that allows an optimisation of periodic storage requirement [18]. SIRA is based on inserting some storage dependence arcs (storage reuse arcs) labeled with reuse distances directly on the data dependence graph. In this new graph, we are able to bound the storage requirement measured as the exact number of necessary storage locations. The determination of storage and distance reuse is parametrised by the desired minimal scheduling period (respectively maximal execution throughput) as well as by the storage requirement constraints either can be minimised while the other one is bounded, or alternatively, both are bounded [6, 14]. This report recalls our fundamental results on this problem, and proposes new experimental heuristics. We typically show how we can deal with some specific storage architectural constraints such as buffers and rotating storage facilities. Key-words: Task scheduling, Storage requirement, Periodic scheduling, Task parallelism ∗ Sid.Touati@uvsq.fr in ria -0 04 40 44 6, v er si on 1 11 D ec 2 00 9 Ordonnancement périodique de tâches avec minimisation du besoin de stockage sous contraintes architecturales spécifiques: cas des tampons et des mécanismes de stockage rotatifs Résumé : Dans ce rapport, nous étudions la formulation exacte et simplifiée du problème d’ordonnancement périodique uni-dimensionnel de tâches, avec minimisation du besoin de stockage, sans considération des contraintes de ressources. Nous nous appuyons sur la plateforme théorique appelée SIRA [18]. SIRA se base sur l’insertion d’arcs valués dans le graphes de tâches afin de controller le besoin périodique en stockage. Dans le graphe étendu, nous pouvons borner le besoin en stockage en fonction de la période souhaitée pour l’ordonnancement [6, 14]. Ce rapport rappelle nos résultats et apporte de nouvelles heuristiques expérimentales sur le problème d’ordonnancement sous contraintes de stockage. Typiquement, nous étudions les résultats dans des cas particuliers d’architectures de stockage avec des tampons (files d’attente) ou avec des mécanismes de stockage rotatifs. Mots-clés : Ordonnancement de tâches, besoin en stockage, ordonnancement périodique, parallélisme de tâches in ria -0 04 40 44 6, v er si on 1 11 D ec 2 00 9 Cyclic Task Scheduling Heuristics with Storage Requirement Minimisation 3
Growth and Volatility Revisited

This paper revisits the relation between growth and volatility in disaggregated data. Across countries, the link is confirmed to be significantly negative, but the paper shows that across sectors, the relation becomes positive. This reversal has a natural interpretation. The macroeconomic component of aggregate volatility, common across sectors, correlates negatively with growth, whereas sector-specific volatility correlates positively with growth, as if risky investments commanded high returns. This latter effect is absent from standard cross-country studies, which focus de facto on the macroeconomic component of volatility. Furthermore, while investment and volatility are unrelated in the aggregate, sectoral investment is more intense in volatile activities, as if resources were reallocated from safe low-yield activities to risky high-yield ones. Thus, aggregation is key in determining the empirical answer to the growth volatility question, suggesting competing theories may all be supported by the data, but at different aggregation levels.
ONP-MF: An Orthogonal Nonnegative Matrix Factorization Algorithm with Application to Clustering

Given a nonnegative matrix M , the orthogonal nonnegative matrix factorization (ONMF) problem consists in finding a nonnegative matrix U and an orthogonal nonnegative matrix V such that the product UV is as close as possible to M . The importance of ONMF comes from its tight connection with data clustering. In this paper, we propose a new ONMF method, called ONP-MF, and we show that it performs in average better than other ONMF algorithms in terms of accuracy on several datasets in text clustering and hyperspectral unmixing.
Active Learning for Mention Detection: A Comparison of Sentence Selection Strategies

We propose and compare various sentence selection strategies for active learning for the task of detecting mentions of entities. The best strategy employs the sum of confidences of two statistical classifiers trained on different views of the data. Our experimental results show that, compared to the random selection strategy, this strategy reduces the amount of required labeled training data by over 50% while achieving the same performance. The effect is even more significant when only named mentions are considered: the system achieves the same performance by using only 42% of the training data required by the random selection strategy.
Analysis of Potato virus Y Coat Protein Epitopes Recognized by Three Commercial Monoclonal Antibodies

Background Potato virus Y (PVY, genus Potyvirus) causes substantial economic losses in solanaceous plants. Routine screening for PVY is an essential part of seed potato certification, and serological assays are often used. The commercial, commonly used monoclonal antibodies, MAb1128, MAb1129, and MAb1130, recognize the viral coat protein (CP) of PVY and distinguish PVYN strains from PVYO and PVYC strains, or detect all PVY strains, respectively. However, the minimal epitopes recognized by these antibodies have not been identified. Methodology/Principal Findings SPOT peptide array was used to map the epitopes in CP recognized by MAb1128, MAb1129, and MAb1130. Then alanine replacement as well as N- and C-terminal deletion analysis of the identified peptide epitopes was done to determine critical amino acids for antibody recognition and the respective minimal epitopes. The epitopes of all antibodies were located within the 30 N-terminal-most residues. The minimal epitope of MAb1128 was 25NLNKEK30. Replacement of 25N or 27N with alanine weakened the recognition by MAb1128, and replacement of 26L, 29E, or 30K nearly precluded recognition. The minimal epitope for MAb1129 was 16RPEQGSIQSNP26 and the most critical residues for recognition were 22I and 23Q. The epitope of MAb1130 was defined by residues 5IDAGGS10. Mutation of residue 6D abrogated and mutation of 9G strongly reduced recognition of the peptide by MAb1130. Amino acid sequence alignment demonstrated that these epitopes are relatively conserved among PVY strains. Finally, recombinant CPs were produced to demonstrate that mutations in the variable positions of the epitope regions can affect detection with the MAbs. Conclusions/Significance The epitope data acquired can be compared with data on PVY CP-encoding sequences produced by laboratories worldwide and utilized to monitor how widely the new variants of PVY can be detected with current seed potato certification schemes or during the inspection of imported seed potatoes as conducted with these MAbs.
Humanoid walking pattern generation based on the ground reaction force features of human walking

This paper presents a humanoid pattern generation method based on the ground reaction force features of human walking. A human can walk with high power efficiency and compliant landing, which have a close relationship to the ground reaction force. By investigating the ground reaction force of human walking, some features of the ground reaction force are obtained. According to these features, a novel method to design humanoid ZMP trajectories is proposed to mimic human ZMP trajectories. This method can vary ZMP trajectories easily to generate waking patterns with less fluctuations of walking velocity. In addition, a humanoid robot model is presented for alternating support leg smoothly to avoid the contact impact. This model covers the dynamics of both single support phase and double support phase. Finally, the reliability of the proposed methods is verified by dynamic simulation and walk experiment on a real humanoid robot.
Unraveling the Evolution of the Atlantic Cod’s (Gadus morhua L.) Alternative Immune Strategy

Genes encoding the major histocompatibility complex (MHC) have been thought to play a vital role in the adaptive immune system in all vertebrates. The discovery that Atlantic cod (Gadus morhua) has lost important components of the MHC II pathway, accompanied by an unusually high number of MHC I genes, shed new light on the evolution and plasticity of the immune system of teleosts as well as in higher vertebrates. The overall aim of this study was to further investigate the highly expanded repertoire of MHC I genes using a cDNA approach to obtain sequence information of both the binding domains and the sorting signaling potential in the cytoplasmic tail. Here we report a novel combination of two endosomal sorting motifs, one tyrosine-based associated with exogenous peptide presentation by cross-presenting MHCI molecules, and one dileucine-based associated with normal MHC II functionality. The two signal motifs were identified in the cytoplasmic tail in a subset of the genes. This indicates that these genes have evolved MHC II-like functionality, allowing a more versatile use of MHC I through cross-presentation. Such an alternative immune strategy may have arisen through adaptive radiation and acquisition of new gene function as a response to changes in the habitat of its ancestral lineage.
Customer Segmentation in Foreign Trade based on Clustering Algorithms Case Study: Trade Promotion Organization of Iran

— The goal of this paper is to segment the countries based on the value of export from Iran during 14 years ending at 2005. To measure the dissimilarity among export baskets of different countries, we define Dissimilarity Export Basket (DEB) function and use this distance function in K-means algorithm. The DEB function is defined based on the concepts of the association rules and the value of export group-commodities. In this paper, clustering quality function and clusters intraclass inertia are defined to, respectively, calculate the optimum number of clusters and to compare the functionality of DEB versus Euclidean distance. We have also study the effects of importance weight in DEB function to improve clustering quality. Lastly when segmentation is completed, a designated RFM model is used to analyze the relative profitability of each cluster.
FIW – Working Paper Does Outward Foreign Direct Investment affect domestic real wages ? An investigation using French microdata

This paper investigates to which extent outward foreign direct investment (FDI) affects domestic wages. We are first interested in the raw wage differential between multinational and domestic firms. Results reveal that multinational companies pay a wage premium to their employees, even within precise skill-groups (blue-collar workers, intermediate occupations and managers). The wage premium is increasing within the wage distribution. In a second step, we use spell of workers within a firm in a fixed effect model to analyze the effect of outward FDI within job-spells. Results suggest that outward FDI raises wages for managers and reduces wages for workers performing offshorable tasks. The positive effect of FDI on managers’ wages is mainly driven by the intensive margin of outward FDI, that is by large firms already established abroad. This result is observed even after controlling for endogenous workers’ mobility.
Geometric Approaches in Sensor Networks : A Survey ( 788 Course Project )

The geometric and topological features of a sensor network are critical in the design of protocols and applications for it because sensor networks are tightly associated with the physical areas where they are deployed. One fundamental problem is to discover and represent the global topological features of a sensor network efficiently. However, classical geometrical algorithms can not be easily applied to the resource constrained sensor networks with distributed and discrete nature. Many research works have been done recently to address the gap between two. In this survey paper, the focus is put on the discovery of topological information of sensor networks, and its applications to geographic routing and data storage and retrieval.
Interplay of Filaggrin Loss-of-Function Variants, Allergic Sensitization, and Eczema in a Longitudinal Study Covering Infancy to 18 Years of Age

Background Immune specific genes as well as genes regulating the formation of skin barrier are major determinants for eczema manifestation. There is a debate as to whether allergic sensitization and filaggrin gene (FLG) variants lead to eczema or FLG variants and eczema increase the risk of allergic sensitization. To investigate the time-order between eczema and allergic sensitization with respect to FLG variants, data from a large prospective study covering infancy to late adolescence were analyzed. Methodology/Principal Findings Repeated measurements of eczema and allergic sensitization (documented by skin prick tests) at ages 1, 2, 4, 10, and 18 years were ascertained in the Isle of Wight birth cohort (n = 1,456). Three transition periods were analyzed: age 1-or-2 to 4, 4 to 10, and 10 to 18 years. FLG variants were genotyped in 1,150 participants. Over the three transition periods, in temporal sequence analyses of initially eczema-free participants, the combined effect of FLG variants and allergic sensitization showed a 2.92-fold (95% CI: 1.47–5.77) increased risk ratio (RR) of eczema in subsequent examinations. This overall risk was more pronounced at a younger age (transition period 1-or-2 to 4, RR = 6.47, 95% CI: 1.96–21.33). In contrast, FLG variants in combination with eczema showed a weaker, but significant, risk ratio for subsequent allergic sensitization only up to 10 years of age. Conclusions/Significance Taking the time order into account, this prospective study demonstrates for the first time, that a combination of FLG variants and allergic sensitization increased the risk of eczema in subsequent years. Also FLG variants interacted with eczema and increased the risk of subsequent allergic sensitization, which, was limited to the younger age. Hence, early restoration of defective skin barrier could prevent allergic sensitization and subsequently reduce the risk of eczema development.
A Framework for Sensemaking in Collaborative Information Seeking

An important aspect of collaborative information seeking (CIS) is making sense of the information found, i.e., collaborative sensemaking. We conducted an ethnographic study of the CIS activities of healthcare providers in a hospital emergency department to gain a conceptual understanding of why and how sensemaking occurs during CIS activities. Based on these findings, we discuss a framework of collaborative sensemaking during CIS activities and design implications for supporting sensemaking in collaborative information retrieval tools.
Visualizing User Model in Exploratory Search Tasks

We present our ongoing work on an interactive exploratory information retrieval system designed to explain to the user the reasons for its predictions and help the user to build a mental model to predict how the system will behave. Users indicate which documents interest them and reinforcement learning is used to model the user by allowing the system to trade off between exploration and exploitation.
Adapting Hash Joins For Modern Processors

Hash join algorithms are crucial to the performance of modern database systems. Conventional hash joins exhibit poor memory system performance on modern processors because their key data structure, the bucket-chain hash table, is ill-suited for the performance characteristics of out-of-order processors with large cache hierarchies. Whereas prior research has considered a variety of optimizations to reduce join latency, these studies fall short of asking whether the bucket-chain hash table is the right data structure for modern systems. We propose a set of mechanisms that redesign the hash table to better fit modern CPU architectures for primarykey/foreign-key hash joins. First, we present the collision cache, a data structure that densely packs hash join elements into an L1 cache line. Second, we examine the performance of the collision cache in partitioned single-stage, two-stage and concurrent hash-join workloads. Third, we propose an extension to the collision cache that embeds additional attributes from the SELECT clause within the collision cache to reduce the output relation’s materialization cost. Finally, we show that the collision cache improves hash join performance without requiring special hardware features and reduces hash join sensitivity to partition size.
Common Gate Transformer Feedback LNA in a High IIP3 Current Mode RF CMOS Front-End

A new topology of transformer based low noise amplifier is presented. The structure realizes a low noise input match and a current gain greater than one by a current to current positive feedback closed around a common gate stage. The amplifier is inserted in a high linearity current mode RF front-end receiver working between 4.15-4.4GHz with a NF of 4.2dB, a gain of 24.2dB and an IIP3 of -2dBm
The Surgical Treatment for Spinal Intradural Extramedullary Tumors

Background We wanted to investigate the results of surgical treatment and analyze the factors that have an influence on the neurologic symptoms and prognosis of spinal intradural extramedullary (IDEM) tumors. Methods The spinal IDEM tumor patients (11 cases) who had been treated by surgical excision and who were followed up more than 1 year were retrospectively analyzed. Pain was evaluated by the visual analogue scale (VAS) and the neurologic function was assessed by Nurick's grade. The pathological diagnosis, the preoperative symptom duration, the tumor location on the sagittal and axial planes and the percentage of tumor occupying the intradural space were investigated. In addition, all these factors were analyzed in relation to the degree of the preoperative symptoms and the prognosis. On the last follow-up, the MRI was checked to evaluate whether or not the tumor had recurred. Results The most common diagnosis was schwannomas (73%), followed by meningiomas (18%). The percentage of tumor occupying the intradural space was 82.9 ± 9.4%. The VAS score was reduced in all cases from 8.0 ± 1.2 to 1.2 ± 0.8 (p = 0.003) and the Nurick's grade was improved in all cases from 3.0 ± 1.3 to 1.0 ± 0.0 (p = 0.005). The preoperative symptoms were correlated with only the percentage of tumor occupying the intradural space (VAS; r2 = 0.75, p = 0.010, Nurick's grade; r2 = 0.69, p = 0.019). One case of schwannoma recurred. Conclusions The degree of neurologic symptoms was correlated with the percentage of tumor occupying the intradural space. All the tumors were able to be excised through the posterior approach. The postoperative neurologic recovery was excellent in all the cases regardless of any condition. Therefore, aggressive surgical excision is recommended even for cases with a long duration of symptoms or a severe neurologic deficit.
A fuzzy modelling of decision support system for crop selection

The impact of climate change is expected to impact on high productivity and crop patterns agricultural sector in Malaysia. In this study, in order to handle the attribute of incomplete information, we use fuzzy modelling approach to overcome the uncertainty during the development of an agriculture web-based decision support system. Since data integration in the progeny test requires resolution of conflicts arising from the heterogeneity of data, we generalize decision algorithms classifier from incomplete information to provide simpler and more understandable information during user interaction. The analytical data about the rainfall pattern, soil structure of the area will partition data by taking full advantage of the incomplete information to achieve better performance. The contributions of this study are newly developed algorithms and analyses for planting material classification. The researcher reports the empirical results that may provide high return in planting material breeders in agriculture industry through effective policies of decision making.
Distributed Classifier Training for Large Scale OCR

OCRopus (www.ocropus.org) is a new open source OCR system targeted at large books scanning and digital library applications, sponsored by Google for use in the Google Book system. Development started in 2007, with a beta release planned for April 2008. It is based on an earlier handwriting recognition system for U.S. Census forms . OCRopus currently contains two character recognizers (experimental HMM, shape-based, and scanning neural network recognizers are also being developed but will not be discussed here). The first is based on the Tesseract OCR engine and works by nearest neighbor classification based on a carefully constructed shape matching algorithm in a two-stage hierarchical search . Tesseract returns similarity scores but no posterior probabilities. The second character recognizer is based on oversegmentation followed by estimation of both a segmentation cost P (S) and posterior probabilities P (c|x). OCRopus inherited a simple single hidden layer neural network classifiers whose input consists of a manually constructed feature map. When trained correctly, such networks give an estimate of P (S)P (c|x), that is, a product of the segmentation cost and the posterior probability. Although this simple architecture has been sufficient for handwriting recognition, it does not scale up to the large number of classes and training examples found in OCR applications. A recent 1000 book data release by Google contains upwards of 5×10 training samples, and book scanning projects are aiming for upwards of 20 million books. The requirements for a character classifier for the OCRopus OCR system are the following:
ArtWork recognition in 360-degree image using 32-hedron based rectilinear projection and scale invariant feature transform

Recently, with the increased generalization of 360-degree multimedia, the copyrighted art works are photographed without permissions. To prevent the infringements, we propose a method of art work recognition in 360-degree image using a 32-hedron based rectilinear projection and the well-known scale invariant feature transform (SIFT). The method transforms an equirectangular projected 360-degree image to a 3-dimensional sphere and generates a 32-hedron composed of 12 pentagons and 20 hexagons to wrap the sphere. It finds the nearest points on the sphere from vertices of the 32-hedron. The distances between these points are used to define the height and width of the rectilinear projection range. It also finds the nearest points on the sphere from the centers of 32 polygons. The locations of these points are used to define the viewpoint directions of rectilinear projection. With those parameters, the equirectangular projected 360-degree image is transformed to several rectilinear projected images. The recognition is implemented by matching the SIFT features extracted from the transformed images to those of original art work. The experimental results show some comparisons of matching results between before and after applying the proposed method of 32-hedron based rectilinear projection. With the proposed method, the features are well matched. Otherwise, the features can't be matched because of the seriously distorted equirectangular projection.