Miracle-dz/UniFAR-FTUs huggingface dataset:
ULT: a Java threads model for platform independent execution

Java is known to be a valuable technology for building platform independent applications, based on an independent execution environment provided by a virtual machine (JVM, Java Virtual Machine) and an API formed by a set of classes. The Java platform was conceived as a solution for application transportation between heterogeneous platforms without the need of adapting and recompiling the source code. Some previous analyses of Sun JVM implementation (Java 2 SDK 1.2.2-006) establish that the HPI (Host Porting Interface) layer does not abstract enough the concurrency offered by the underlying platform.Consequently, the execution of the same Java application over distinct HPI platform implementations offers different behavior, being a possible cause of incorrect program execution. We consider that it's necessary a new HPI layer that can really abstract the concurrency supplied by the platform, giving a set of operations whose semantic does not vary between diverse platform implementations. With the new HPI, the JVM will be capable to perform a uniform behavior independently of the operating system. The new JVM with ULT offers an acceptable performance on multiprocessor architectures increasing the throughput and it implements a synchronization mechanism that achieves better results than the original JVM on SMP architectures.
A performance maximization algorithm to design ASIPs under the constraint of chip area including RAM and ROM sizes

In designing ASIPs (Application Specific Integrated Processors) the papers investigated so far have almost focused on the optimization of the CPU core and did not pay enough attention to the optimization of the RAM and ROM size together. This paper overcomes this limitation and proposes an optimization algorithm to define the best tradeoff between the CPU core, RAM and ROM of an ASIP chip to achieve the highest performance while satisfying design constraints on the chip area. The partitioning problem is formalized as a combinatorial optimization problem that partitions the operations into hardware and software so that the performance of the designed ASIP is maximized under given chip area constraint, where the chip area includes the HW cost of the register file for a given application program with the associated input data set. The optimization problem is parameterized so that it can be applied with different technologies to synthesize CPU cores, RAMs or ROMs. The experimental results show that the proposed algorithm is found to be effective and efficient.
Time handling for real-time progressive activity recognition

In a dense sensor-based smart home (SH), a significant challenge is to segment the sensor data stream in real-time to continuously support progressive activity recognition (AR). In this paper, we evaluate an approach that supports the segmentation of the sensor data stream used for knowledge-driven AR. The approach is based on the notion of dynamically varied sliding time windows, where data segments are formally modeled as time windows and ontological reasoning used to infer the ongoing activities of daily living (ADLs). We then present an algorithm that supports perpetual, real-time activity recognition and provide an implementation of both the proposed approach and the ADL ontology it uses. For evaluation, we developed a synthetic data generator and generated a set of synthetic ADLs. In addition, we implemented a real-time activity recognition system and a simple simulator that plays back a synthetic ADL as if the sensors are activated in real-time. We evaluated the real-time activity recognition algorithm, and obtained 99.2% average recognition accuracy on the synthetic ADLs tested. The ability of the algorithm to discriminate sensors that are activated in error was evaluated for selected ADLs and impressive results obtained.
Learning Stochastic Lexicalized Tree Grammars from Hpsg

We 1 present a method for automatically extracting a Stochastic Lexicalized Tree Grammar (SLTG) from an HPSG source grammar and a given corpus. Processing of a SLTG is performed by a specialized fast parser. The approach has been tested on a large English grammar and has been shown to achieve a speed-up by a factor of better than 10 compared to parsing with a highly tuned HPSG parser. Our approach is simple and transparent, and comes with no magic tuning strategies. The extracted grammars are declaratively represented and have a high degree of practical applicability.
Accelerated Online Learning for Collaborative Filtering and Recommender Systems

Collaborative filtering (CF) is one of the major approaches to building recommender systems. Traditional batch-trained algorithms for CF suffer from some drawbacks, and online learning algorithms for CF, is a promising tool for attacking the large-scale dynamic problems. However, the low time complexity of online algorithm often be accompanied by low convergence rate, and the convergence rate of current dual-averaging online algorithm is only O(1/√T) up to T-th iteration. In order to tackle this problem, we propose a novel accelerated online learning framework for CF. Our algorithm has a accelerated capability, and its theoretical convergence rate bound is O(1/T2). Moreover, the proposed algorithm has low time and memory complexity, and scales linearly with the number of observed ratings. The experimental results on real-world datasets demonstrate the merits of the proposed online learning algorithm for large-scale dynamic collaborative filtering problems.
INTERFACES AND ADAPTERS TO DATABASES USING STANDARD TEMPLATE LIBRARY

This paper presents a set of interfaces and cooperating adapters that greatly simplify the access to datasets, which are the result of a query to a database system in applications developed in the C++ programming language. These interfaces isolate the implementation of the database system from the client code and the adapters allow accessing the records of the dataset, in a more natural way, as native C++ structures possibly using the algorithms from the C++ Standard template library (STL).
Fast and Accurate Singular Point Extraction of Fingerprint

In order to maximize the robustness of fingerprint identification and to minimize the identification time, it is better to align the fingerprint first by referring the singular points of fingerprint. In this paper, we proposed a new algorithm to localize the singular points of fingerprint. The proposed approach is less computational complexity and higher precision. This paper shows how, from the x-gradient component of orientation filed and its sine component, very accurate extraction of singular points can be obtained. This localization can be used for accurate registration of two fingerprints
Four Decades of Educational Inequalities in Hospitalization and Mortality among Older Swedes

Background The inverse association between education and mortality has grown stronger the last decades in many countries. During the same period, gains in life expectancy have been concentrated to older ages; still, old-age mortality is seldom the focus of attention when analyzing trends in the education-mortality gradient. It is further unknown if increased educational inequalities in mortality are preceded by increased inequalities in morbidity of which hospitalization may be a proxy. Methods Using administrative population registers from 1971 and onwards, education-specific annual changes in the risk of death and hospital admission were estimated with complimentary log-log models. These risk changes were supplemented by estimations of the ages at which 25, 50, and 75% of the population had been hospitalized or died (after age 60). Results The mortality decline among older people increasingly benefitted the well-educated over the less well-educated. This inequality increase was larger for the younger old, and among men. Educational inequalities in the age of a first hospital admission generally followed the development of growing gaps, but at a slower pace than mortality and inequalities did not increase among the oldest individuals. Conclusions Education continues to be a significant predictor of health and longevity into old age. That the increase in educational inequalities is greater for mortality than for hospital admissions (our proxy of overall morbidity) may reflect that well-educated individuals gradually have obtained more possibilities or resources to survive a disease than less well-educated individuals have the last four decades.
The three-dimensional structure of the nemaline rod Z-band

In nemaline myopathy and some cardiac muscles, the Z-band becomes greatly enlarged and contains multiple layers of a zigzag structure similar to that seen in normal muscle. Because of the additional periodicity in the direction of the filament axis, these structures are particularly favorable for three-dimensional analysis since it becomes possible to average the data in all three dimensions and thus improve the reliability of the reconstruction. Individual views of the structure corresponding to tilted longitudinal and transverse sections were combined by matching the phases of common reflections. Examination of the tilted views strongly suggested that to the available resolution, the structure possesses fourfold screw symmetry along the actin filament axes. This symmetry could be used both in establishing the correct alignment for the combination of individual tilted views and to generate additional views not readily accessible in a single tilt series. The reconstruction shows actin filaments from one sarcomere surrounded by an array of four actin filaments with opposite polarity from the adjacent sacormere. The actin filaments show a right- handed twist and are connected by a structure that links adjacent filaments with the same polarity at the same axial level, then runs parallel to the filaments, and finally forms a link between two actin filaments whose polarity is opposite to that of the first pair. The connecting structure is probably composed of alpha-actinin which is located in Z-bands and cross-links actin filaments. The connecting structure may consist of two alpha-actinin molecules linking actin filaments of opposite polarity.
A novel structural model for regulation of clathrin function

The distinctive triskelion shape of clathrin allows assembly into polyhedral lattices during the process of clathrin‐coated vesicle formation. We have used random and site‐directed mutagenesis of the yeast clathrin heavy chain gene (CHC1) to characterize regions which determine Chc trimerization and binding to the clathrin light chain (Clc) subunit. Analysis of the mutants indicates that mutations in the trimerization domain at the triskelion vertex, as well as mutations in the adjacent leg domain, frequently influence Clc binding. Strikingly, one mutation in the trimerization domain enhances the association of Clc with Chc. Additional mutations in the trimerization domain, in combination with mutations in the adjacent leg domain, exhibit severe defects in Clc binding while maintaining near normal trimerization properties. The position of these trimerization domain mutations on one face of a putative α‐helix defines a region on the trimer surface that interacts directly with Clc. These results suggest that Clc extends into the Chc trimerization domain from the adjacent leg, thereby bridging the two domains. On the basis of this conclusion, we propose a new model for the organization of the triskelion vertex which provides a structural basis for regulatory effects of Clc on clathrin function.
A study on the role of selective attention, vigilance and ambivalence over emotional expressiveness for predicting pain severity in patients with chronic musculoskeletal pains

Introduction: When there is a pain requiring focused clinical attention and is associated with emotional and functional disorders, it is called pain disorder. The present study aims to study the role of selective attention, vigilance and ambivalence over emotional expressiveness for predicting pain severity in patients with chronic musculoskeletal pains. Methods: The study population included 180 patients with chronic musculoskeletal entered the study by accessible method sampling from 2 hospitals in Tehran (Iran): Imam Khomeini and Atyeh. They completed Selective Attention Test, questionnaires for Vigilance and Ambivalence over emotional expressiveness and Pain severity Scale. Results: Step-by-step multivariate regression analysis showed that ambivalence can significantly predict emotional expressiveness in patients with selective attention and vigilance were not significant. Conclusion: In musculoskeletal chronic pain, ambivalence can be considered as an emotional determinant influencing pain severity.
An Energy-Efficient Transmission Scheme for Cooperative MIMO Wireless Networks

Cooperative Multiple-Input Multiple Output (MIMO) schemes are recently shown to be able to reduce the transmission energy in distributed wireless sensor networks (WSNs). Given the merits of MIMO, in this work we aim to develop a joint routing, scheduling and stream control solution that can minimize energy consumption while satisfying a given end-to-end (ETE) traffic demand in schedulingbased multihop WSNs. To this end, we present a cross-layer formulation that can incorporate various power and rate adaptation schemes, and take into account an antenna beam pattern model and a signal-to-interference-and-noise (SINR) constraint at the receiver side. Specifically, we propose a fast column generation algorithm along with a transmission mode generating algorithm to get rid of the complexity of having to enumerate all possible sets of scheduling links. The formulation is verified by simulation experiments, and the results show its efficiency on the energy consumption with a low time-complexity.
Pattern recognition with “materials that compute”

We design a self-powered materials system that transduces chemical, mechanical, and electrical energy to perform computation. Driven by advances in materials and computer science, researchers are attempting to design systems where the computer and material are one and the same entity. Using theoretical and computational modeling, we design a hybrid material system that can autonomously transduce chemical, mechanical, and electrical energy to perform a computational task in a self-organized manner, without the need for external electrical power sources. Each unit in this system integrates a self-oscillating gel, which undergoes the Belousov-Zhabotinsky (BZ) reaction, with an overlaying piezoelectric (PZ) cantilever. The chemomechanical oscillations of the BZ gels deflect the PZ layer, which consequently generates a voltage across the material. When these BZ-PZ units are connected in series by electrical wires, the oscillations of these units become synchronized across the network, where the mode of synchronization depends on the polarity of the PZ. We show that the network of coupled, synchronizing BZ-PZ oscillators can perform pattern recognition. The “stored” patterns are set of polarities of the individual BZ-PZ units, and the “input” patterns are coded through the initial phase of the oscillations imposed on these units. The results of the modeling show that the input pattern closest to the stored pattern exhibits the fastest convergence time to stable synchronization behavior. In this way, networks of coupled BZ-PZ oscillators achieve pattern recognition. Further, we show that the convergence time to stable synchronization provides a robust measure of the degree of match between the input and stored patterns. Through these studies, we establish experimentally realizable design rules for creating “materials that compute. ”
Presence of Multiparasite Infections Within Individual Colonies of Leaf-Cutter Ants

ABSTRACT Host—parasite dynamics can be altered when a host is infected by multiple parasite genotypes. The different strains of parasite are expected to compete for the limited host resources, potentially affecting the survival and reproduction of the host as well as the infecting parasites. Fungus-growing ants, including the well-known leaf-cutters, are an emerging model system for studying the evolution and ecology of symbiosis and host-parasite dynamics. We examine whether the fungus gardens of leaf-cutter ants can be simultaneously infected by multiple strains of the fungal pathogen Escovopsis. Intensive sampling of Escovopsis was conducted from individual gardens, as well as between different garden chambers within individual colonies of leaf-cutting ants. Isolates obtained were genotyped by DNA sequencing. We found that, minimally, 67% of the individual colonies of the leaf-cutter ant genera Atta and Acromyrmex and 50% of the At. colombica garden chambers studied were simultaneously infected by multiple distinct Escovopsis strains. Experimental challenges showed that different Escovopsis strains do not exhibit obvious antagonism toward each other, suggesting that coinfecting strains of the parasite do not engage in interference competition, although interactions were not studied at the cellular level. Further research is needed to understand interparasite interactions between coinfecting Escovopsis strains and to understand the impact of multiparasite infections on the survival of leaf-cutter ant gardens.
STYLES AND STRATEGIES OF LEARNING

Summary. Previous results are reviewed and two series of experiments on learning are described, one carried out in the laboratory, and the other in educational institutions. Both series use ‘conversational’ systems which allow mental activities to be described in terms of dialogue and behaviour. Several types of result are reported: (a) the significance of understanding; (b) the existence of, and variations in, learning strategies; (c) the effect of matching and mismatching a teaching strategy to an individual's learning strategy; (d) the nature and classification of cognitive style; and (e) transfer effects and ‘learning to learn. ’ A theoretical basis is developed for a classification of learning styles.
Morbidity Costs of Vehicular Air Pollution: Examining Dhaka City in Bangladesh

This study estimates the morbidity costs of reduction in air pollution in Dhaka, the capital of Bangladesh, using the Cost-of-Illness (COI) approach. COI is defined as the sum of lost earnings due to workdays lost or restricted activity days and the mitigation expenditure borne due to illness. The data for the research comes from seasonal household surveys using health diaries. We use a random-effects Zero Inflated Poisson regression model to estimate the equation for lost earnings and use a random-effects Tobit Regression to estimate the equation for mitigation expenditure. We find that the annual savings from reducing air pollution to meet national safety standards is Taka 131.37 (USD 1.88) per person from reductions in lost earnings and Taka 150.49 (USD 2.15) per person from reductions in medical expenditure. The annual saving to the population of Dhaka is Taka 2.39 billion or USD 34.09 million. Our estimates, which are based on primary data, provide significantly lower estimates of the benefits of reducing air pollution in Dhaka relative to previous analyses that has relied on the benefit-transfer approach. [SANDEE Working Paper No. 47-10]
Fiscal Policy Without a State in EMU?: Germany, the Stability and Growth Pact and Policy Coordination

PART I: GERMANY, THE STABILITY AND GROWTH PACT AND POLICY COORDINATION ON THE POLITICAL AND ECONOMIC AGENDA IN EUROPE The Europeanisation of Fiscal Policy Coordination PART II: FISCAL POLICY COORDINATION UNDER THE STABILITY AND GROWTH PACT IN ECONOMIC AND MONETARY UNION The Institutional System of Fiscal Policy Coordination The German Dynamics of Uploading the Stability Pact Downloading the Stability Pact in Germany Reuploading Fiscal Policy Coordination in EMU PART III: GERMANY AND THE STABILITY AND GROWTH PACT: INSTITUTIONAL CAPACITY FOR EFFECTIVE FISCAL POLICY COORDINATION IN EMU Reviewing the Institutional System of Fiscal Policy Coordination Challenging the Existing Understanding of Europeanisation in the Study of Germany, the Stability and Growth Pact and Policy Coordination
Spectral partitioning and fusion techniques for hyperspectral data classification and unmixing

Hyperspectral images are characterized by their large contiguous set of wavelengths. Therefore, it is possible to benefit from this `hyper' spectral information in order to reduce the classification and unmixing errors. For this reason, we propose new classification and unmixing techniques that take into account the correlation between successive spectral bands, by dividing the spectrum into non-overlapping subsets of correlated bands. Afterwards, classification and unmixing are performed on each subset separately, such as to yield several labels per pixel in the classification case, or abundances in the unmixing case. Then, several fusion techniques are proposed to obtain the final decision. Results show that spectral partitioning and appropriate fusion allow a significant gain in performance compared to previous classification and unmixing techniques.
Learning Agents in Quake III

This paper shows the results of applying Reinforcement Learning () to train combat movement behaviour for a Q  A [3] bot. Combat movement of a Q  bot is the part that steers the bot while in battle with an enemy bot or human player. The aim of this research is to train a bot to perform better than the standard Q  bot it is based on, by only changing its combat movement behaviour. We extended a standard bot with a Q-learning algorithm that trains a Neural Network to map a game state vector to Q-values, one for each possible action. This  bot (to which we refer as NeurioN) is trained in a reduced Q  environment, to decrease noise and to make the training phase more effective. The training consists of one-to-one combat with its non-learning counterpart, in runs up to 100.000 kills (frags). Reward is given for avoiding damage, thereby letting the bot learn to avoid getting hit. We found it is possible to improve a Q  bot by using  to train combat movement. The trained bot outperforms its non-learning counterparts, but does not appear smarter in combat with human opponents.
High power factor vernier permanent magnet machines

Vernier permanent magnet (VPM) machines are well known for high torque density but low power factor. This paper deals with the low power factor of VPM machines. The goal is not obtained by reducing the electrical loading or adjusting current advance angle but proposing a novel vernier topology-dual-stator, spoke-array (DSSA) VPM topology. In this paper, the characteristics, such as active part, auxiliary mechanical structure and rotor anisotropy, of the DSSA VPM topology are analyzed in detail. Performances, including power factor, torque density, and cogging torque etc., are evaluated based on finite element analysis (FEA). The analysis results show that the DSSA VPM topology exhibits high power factor viz.,~0.9 and significantly high torque capability. Finally, the prototype machine has been designed, built, and is under testing. The verification of the mechanical structure scheme is done in this paper, and the experimental results will be presented in the near future.