Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementation and evaluation of consistent online backup in transactional file systems

A consistent backup which preserves data integrity across files in a file system is of utmost importance for the purpose of correctness and minimizing system downtime during the process of data recovery. With present day demand for continuous access to data, backup has to be taken of an active file system, putting the consistency of the backup copy at risk. In order to address this issue, we propose a scheme which is referred to as mutual serializability assuming that the file system support transactions. Mutual serializability captures a consistent backup of an active file system by ensuring that the backup transaction is mutually serializable with every other transaction individually. This mutually serializable relationship is established considering an extended set of conflicting operations which include read-read conflicts. User transactions serialize within themselves using some standard concurrency control protocol such as the Strict 2PL and a set of conflicting operations that only include the traditional read-write, write-write and write-read conflicts. The proposed scheme has been implemented on top of a transactional file system and workloads exhibiting a wide range of access patterns were used as inputs to conduct experiments in two scenarios, one with the mutual serializability protocol enabled (thus capturing a consistent backup) and one without (thus capturing an inconsistent backup). The results obtained from the two scenarios were then compared to determine the overhead incurred while capturing a consistent backup. The performance evaluation shows that for workloads resembling most present day real workloads exhibiting low intertransactional sharing and actively accessing only a small percentage of the entire file system space, the proposed scheme has very little overhead (a 5.7% increase in backup time and a user transaction throughput reduction of 3.68%). Noticeable performance improvement is recorded when using performance enhancing heuristics which involve diversion of the backup transaction to currently “colder” regions of the file system hierarchy on detecting conflicts with user transactions.
Multimodal robust speech classifier

In this paper we present an alternative in building robust speech classification systems, biologically inspired from human perception as a multimodal process, in order to improve the machine understanding in the spoken man-machine interaction. The difference between this approach and the unimodal one is the use of features obtained from more than one source to characterize the patterns to be classified. The processing of data obtained on this way will enhance the possibility of the system to have a robust behavior under degraded input conditions, in order to ensure better performance in the real world, under spoken dialog conditions. Such a classification system will have small variations of classification rates when the features from one source or more sources are corrupted with unwanted components. Multimodal classifiers The multimodal systems act in two major steps like each unimodal classification system. In the first step feature extraction is performed, where are determined only the important characteristics of the signal, in the second, the recognition is realized, where based on a classification algorithm is made a decision. There are two main strategies to build multimodal systems. The first method is to apply decision fusion and means having for each source of information a decision and combine them to make the final decision. The most common way to implement the decision fusion algorithm is using neural networks or Markov models where the entries of the network are the output of each classifier from each source. The other method to construct a multimodal system is using the feature fusion. This means that after feature extraction from each source a combined feature set is realized as basis for multimodal models and then applying any classification method we make the decision. The main disadvantage of this second method is that we have to synchronize the signals from the different sources. For each source we can use different parameterization methods depending on the signals. Depending on the application, the signals can be images, audio signals and others. And also for each type of models we can use different classification algorithms depending on models characteristics.
Is Emerging Market Money Smart ?

Neoclassical theory predicts that capital should flow from countries that are capitalabundant to countries that are capital-scarce. Contrary to this prediction, a burgeoning trend that is becoming apparent is outbound foreign direct investment flows from emerging economies to the developed world. Since little is known about the consequences of this new M&A trend, this paper attempts to fill this gap by undertaking the first systematic analysis of cross-border M&A transactions by firms in developing countries. Combining transaction-specific M&A data with firm-level accounting data, this paper evaluates the pre-and post acquisition performance of publicly traded US target firms that have been acquired by emerging market firms. To control for the endogeneity of the foreign acquisition decision, a difference-in-differences approach is combined with propensity score matching. Several alternative measures of performance are explored before and after the acquisition year. The results suggest that emerging market acquirers tend to choose targets that are larger in size (measured as sales, total assets and employment), relative to matched non-acquired firms before the acquisition year. In the years following the acquisition, the performance of foreign acquired target firms tend to improve in terms of operating income. Additionally, we provide evidence indicating that increasing performance is accompanied by changes in investment outlays and employment, suggesting significant restructuring of the target firm following the acquisition.
UNKNOWN DEAD–ZONE COMPENSATION FOR NONLINEAR SYSTEMS USING ADAPTIVE H∞ CONTROL METHOD

This paper deals with the adaptive dead–zone compensation strategy based on notion of H∞ optimality. It is assumed that the dead–zone model can be divided into unknown parameters term and bounded disturbance term, an adaptive H∞ control method is given. Proposed control strategy does not include the discontinuous function, therefore, it is effective for the practical applications. Moreover, in the closed–loop control system, the L2 gains from the disturbance to generalized outputs are made less than prescribed positive constants. The effectiveness of the proposed method is demonstrated by numerical simulations.
Generating recognition, acceptance and social inclusion in marginalised youth populations: the potential of sports-based interventions

ABSTRACT In recent years sport-based interventions have been implemented as a mechanism via which to target marginalised youth in relation to the development of social inclusion. Much of the political rhetoric surrounding social inclusion programmes highlights engagement with education, employment, or training, as key metrics. This has led some scholars to observe that conceptualising social inclusion in this way can act to further marginalise young people who fail to engage with these metrics. In contrast, this paper seeks to employ an alternative understanding of social inclusion, which uses the concepts of recognition and acceptance, to infer how participation in sports-based programmes may enable marginalised youth to meet mainstream societal expectations and aid with social assimilation. Drawing upon findings from two small-scale studies of sport-based interventions located in three UK cities, this paper places participant accounts at the centre of the analysis to explore broader notions of pro-social development in relation to recognition and interpersonal acceptance. The paper concludes by suggesting that within contexts in which young people are able to generate strong interpersonal relationships with key personnel (such as coaches), and which are built upon trust, recognition and developing self-worth, there is clear potential for sport-based programmes to incubate social assimilation.
Passing the California High School Exit Exam: Have Recent Policies Improved Student Performance?.

The California High School Exit Examination (CAHSEE) plays an important role in California’s public school accountability program. Beginning in grade 10, students have multiple chances to pass the mathematics and English Language Arts components of this exam. If they do not pass both components by the end of grade 12, they will not receive a high school diploma. Statewide analysis has shown that many students struggle with this exam and, despite modest improvements in recent years, about 1 in 16 fails to pass the exam before the end of grade 12. To determine which types of interventions might be most effective, we considered two statewide policies and one local San Diego program designed to help struggling students pass the exam.
The winged-helix transcription factor Trident is expressed in cycling cells.

We describe the cloning and characterization of Trident , a novel member of the fork head/winged-helix family, from murine thymus. In the mouse embryo, the gene was expressed in all tissues, whereas in adult mice expression was only detected in the thymus. Further analysis revealed that Trident expression strictly correlated with cell cycling, independent of cell type. Timing of [3H]thymidine incorporation showed that mRNA and protein expression were strongly upregulated upon entry into the S phase of the cell cycle. Moreover, the protein was phosphorylated in M phase. PCR-mediated selection of optimal binding sites yielded a consensus motif resembling that of other family members. These results identify Trident as a transcription factor, which is likely involved in cell cycle-specific gene regulation.
Safe Composition of Transformations

Model transformations are at the heart of Model-Driven Software Development (MDSD) and, once composed in transformation chains to MDSD processes, allow for the development of complex systems and their automated derivation. While there already exist various approaches to specify and execute such MDSD processes, only few of them draw focus on ensuring the validity of the transformation chains, and thus, safe composition of transformations. In this paper, we present the TraCo composition system, which overcomes these limitations and evaluate and discuss the approach based on two case studies.
Computationally efficient sparse Bayesian learning via generalized approximate message passing

The sparse Bayesian learning (also referred to as Bayesian compressed sensing) algorithm is a popular approach for sparse signal recovery, and has demonstrated superior performance in several experiments. Nevertheless, the sparse Bayesian learning algorithm has a computational complexity that grows rapidly with the dimension of the signal, which hinders its application to many practical problems even with moderately large data sets. To address this issue, in this paper, we propose a computationally efficient sparse Bayesian learning method by integrating the generalized approximate message passing (GAMP) technique. Specifically, the algorithm is developed within an expectation-maximization (EM) framework, using the GAMP to efficiently compute an approximation of the posterior distribution of hidden variables. The hyperparameters associated with the hierarchical Gaussian prior are learned by iteratively maximizing the Q-function which is calculated based on the posterior approximation obtained from the GAMP. Numerical results are provided to illustrate the computational efficiency and the effectiveness of the proposed algorithm.
Synchronization and control of spatiotemporal chaos using time-series data from local regions.

In this paper we show that the analysis of the dynamics in localized regions, i.e., sub-systems can be used to characterize the chaotic dynamics and the synchronization ability of the spatiotemporal systems. Using noisy scalar time-series data for driving along with simultaneous self-adaptation of the control parameter representative control goals like suppressing spatiotemporal chaos and synchronization of spatiotemporally chaotic dynamics have been discussed. (c) 1998 American Institute of Physics.
A Hypertextual Augmentation of the Amazon E-Commerce Service ( ECS ) 4 . 0

In this paper, we define an ontology to describe the relationships between merchandise available for purchase on Amazon.com. After gathering raw XML data from their public API, we apply a Python script to meaningfully parse the data according to the ontology as encoded in the RDF Schema language. Our ontology is made as widely comprehensible as possible by extending the Dublin Core Vocabulary. Visualization tools can browse the ontology and the data sets it describes, offering perspectives into Amazon products previously unavailable to the user. The volume and variety of the information we can thus make machine-readable provides evidence that certain promises of the semantic web are feasible, within specific contextual constraints. In particular, Amazon’s products become available to a federated knowledge/data-base semantic web through the use of meaningful, well-defined XML structures.
A Webmiddleware architecture for dynamic customization of Web content for non-traditional clients

We present a new Web middleware architecture that allows users to customize their view of the Web for optimal interaction and system operation when using non-traditional client machines such as wireless palmtops. Our Web Stream Customizers (WSC) are dynamically deployable and can be strategically located to achieve improvements in performance, reliabili ty, or secur ity. An impor tant design feature is that Customizers provide two points of control in the communication path between client and server, suppor ting adaptive system-based and content-based customization. Our architecture exploits HTTP's proxy capabili ties, allowing Customizers to be seamlessly integrated with the basic Web transaction model. We describe the WSC architecture and implementation, and ill ustrate its use with three non-tr ivial, adaptive Customizer applications that we have buil t. The overhead in our implementation which we present is small and tolerable, and we believe is outweighed by the benefits that Customizers provide.
Decidual Macrophages Are Significantly Increased in Spontaneous Miscarriages and Over-Express FasL: A Potential Role for Macrophages in Trophoblast Apoptosis

Decidual macrophages (DM) are the second most abundant population in the fetal-maternal interface. Their role has been so far identified as being local immuno-modulators favoring the maternal tolerance to the fetus. Herein we investigated tissue samples from 11 cases of spontaneous miscarriages and from 9 cases of elective terminations of pregnancy. Using immunohistochemistry and dual immunofluorescence we have demonstrated that in spontaneous miscarriages the DM are significantly increased. Additionally, we noted a significant up-regulation of macrophage FasL expression. Our results further support a dual role for DM during pregnancy and miscarriages. We hypothesize that the baseline DM population in normal pregnancy is in line with an M2 phenotype supporting the ongoing gestation. In contrast, during spontaneous miscarriages, the increased FasL-expressing population could be a part of an M1 phenotype participating in Fas/FasL-related apoptosis. Our results highlight a new aspect of macrophage biology in pregnancy physiology and pathophysiology. Further studies with larger samples are needed to verify the current results and evaluate their clinical impact.
Low-Dose Lipopolysaccharide Pretreatment Suppresses Choroidal Neovascularization via IL-10 Induction

Recent studies have suggested that some kinds of microbial infection may have a crucial role in the development of many diseases such as autoimmune diseases and certain types of cancer. It has been reported that some chronic infections, such as Chlamydia pneumoniae, and immunological dysfunctions are associated with age-related macular degeneration (AMD), a leading cause of blindness. To evaluate the association between systemic low-level inflammation induced by infection and AMD pathogenesis, we investigated whether intraperitoneal injection of lipopolysaccharide (LPS) can modulate the development of laser-induced choroidal neovascularization (CNV), a key feature of AMD. Contrary to our expectations, the sizes of CNV in mice with LPS pretreatment were approximately 65% smaller than those of the control mice. After LPS pretreatment, serum IL-10 concentration and IL-10 gene expression in peritoneal macrophages and in the posterior part of the eye increased. Peritoneal injection of anti-IL10 antibody reduced CNV suppression by LPS pretreatment. Moreover, adoptive transfer of the resident peritoneal macrophages from LPS-treated mice into control littermates resulted in an approximately 26% reduction in the size of CNV compared with PBS-treated mice. We concluded that CNV formation was suppressed by low-dose LPS pretreatment via IL-10 production by macrophages.
Low complexity subspace approach for direction finding in bistatic MIMO radar

Joint estimation on direction of departure (DOD) and direction of arrival (DOA) is a key problem in bistatic multiple-input multiple-output (MIMO) radar. The conventional singular value decomposition (SVD) or eigenvalue decomposition (EVD) based subspace algorithms have heavy computational burden when the large transmit/receive array size and snapshots are required. In this paper, a low complexity subspace approach is proposed for joint DOD and DOA estimation. Firstly, the received data can be tr into a real-valued one by exploiting the centro-symmetric property of the virtual array. Then the subspace for direction finding is changed into real-valued by the Nystöm method, which avoids the estimation and decomposition of the covariance matrix. Combined with the classical subspace based Unitary ESPRIT algorithm, the proposed method provides the comparable angle estimation performance with lower computational complexity compared with the conventional SVD/EVD-based subspace algorithms. Some simulation results verify the performance of the proposed method.
Quality of high resolution synthesised images: Is there a simple criterion ?

Methods exist which are synthesizing high spatial resolution high spectral content images from a set of low spatial resolution high spectral content images and high spatial resolution low spectral content images. There is a need to quantify the quality of these synthesized images for both the producers and customers of such fused products. Some protocols are discussed. The main concern is on the definition of a parameter characterizing the quality. The requirements on this parameter are discussed. A review of published parameters is made and a new one is proposed. From published works, it is shown that a good quality is achieved when the parameter is less than 3.
A hybrid power model for RTL power estimation

We propose a hybrid power model for estimating the power dissipation of a design at the RT-level. This new model combines the advantages of both RT-level and gate-level approaches. We investigate the relationship between steady-state transition power and overall power dissipation. We observe that, statistically, two input sequences causing similar amount of steady-state transitions will exhibit similar overall power dissipation for an RTL module. Based on this observation, we propose a method to construct a hybrid power model for RTL modules. We further propose a hierarchical power estimation method for estimating the power dissipation of data-path consisting of RTL modules. Experimental results show that, for full-chip power estimation, the estimation time of the technique based on our power models is on average 275 times faster than directly running a commercial transistor-level power simulator, and the errors are less than 6% as compared to the transistor-level power simulation results.
Process Discovery from Dependence-Complete Event Logs

Process mining, especially process discovery, has been utilized to extract process models from event logs. One challenge faced by process discovery is to identify concurrency effectively. State-of-the-art approaches employ activity orders in traces to undertake process discovery and they require stringent completeness notions of event logs. Thus, they may fail to extract appropriate processes when event logs cannot meet the completeness criteria. To address this problem, we propose in this paper a novel technique which leverages activity dependences in traces. Based on the observation that activities with no dependencies can be executed in parallel, our technique is in a position to discover processes with concurrencies even if the logs fail to meet the completeness criteria. That is, our technique calls for a weaker notion of completeness. We evaluate our technique through experiments on both real-world and synthetic event logs, and the conformance checking results demonstrate the effectiveness of our technique and its relative advantages compared with state-of-the-art approaches.
Towards scalable interfaces using spatial cues for document management

Document management is an unavoidable task. Previous research evaluated the effect of building document management systems exploiting spatial memory. It was proven that spatial memory enhances retrieval tasks for systems dealing with a small number of data items. We investigate the effect of spatial cues with scalable systems. We present EDM (Enhanced Data Mountain), a scalable picture management system based on the Data Mountain by Robertson, Tesle, Tversky & Mullet (1991). Our system exploits the use of spatial cues and spatial memory in retrieving images. This report explains the design methodology behind EDM, in addition to an evaluation that compares EDM with Microsoft’s My Pictures (MP) system for managing pictures. Results show that our subjects were faster at retrieving pictures in the display when using the MP interface, but not significantly so. As expected, the more time subjects spent browsing through the picture groups, the faster they became at retrieval. Subjective satisfaction rating showed an overall preference favouring the MP system over the EDM system. A conclusive statement can not be made regarding the exploitation of spatial memory in scalable systems.
Estimating the Market Share Attraction Model using Support Vector Regressions

We propose to estimate the parameters of the Market Share Attraction Model (Cooper and Nakanishi, 1988; Fok and Franses, 2004) in a novel way by using a nonparametric technique for function estimation called Support Vector Regressions (SVR) (Smola, 1996; Vapnik, 1995). Traditionally, the parameters of the Market Share Attraction Model are estimated via a Maximum Likelihood (ML) procedure, assuming that the data are drawn from a conditional Gaussian distribution. However, if the distribution is unknown, Ordinary Least Squares (OLS) estimation may seriously fail (Vapnik, 1982). One way to tackle this problem is to introduce a linear loss function over the errors and a penalty on the magnitude of model coefficients. This leads to qualities such as robustness to outliers and avoidance of the problem of overfitting. This kind of estimation forms the basis of the SVR technique, which, as we will argue, makes it a good candidate for estimating the Market Share Attraction Model. We test the SVR approach to predict (the evolution of) the market shares of 36 car brands simultaneously and report promising results.