Miracle-dz/UniFAR-FTUs huggingface dataset:
Incorporating Manifold Ranking with Active Learning in Relevance Feedback for Image Retrieval

Combining manifold ranking with active learning (MRAL for short) is one popular and successful technique for relevance feedback in content-based image retrieval (CBIR). Despite the success, conventional MRAL has two main drawbacks. First, the performance of manifold ranking is very sensitive to the scale parameter used for calculating the Laplacian matrix. Second, conventional MRAL does not take into account the redundancy among examples and thus could select multiple examples that are similar to each other. In this work, a novel MRAL framework is presented to address the drawbacks. Concretely, we first propose a self-tuning manifold ranking algorithm that can adaptively calculate the Laplacian matrix via a local scaling mechanism, and then develop a hybrid active learning algorithm by integrating three well-known selective sampling criteria, which is able to effectively and efficiently identify the most informative and diversified examples for the user to label. Experiments on 10,000 Corel images show that the proposed method is significantly more effective than some existing approaches.
Extra Wide Input Voltage Range and High Efficiency DC-DC Converter Using Hybrid Modulation

In this paper an interleaving phase shift modulating (PSM) control strategy for DC-DC converter is proposed based on two conventional PWM half bridge converters. A new rectifier structure suitable to the interleaving technique is proposed. With the proposed hybrid control method (PSM and PWM) and the proposed rectifier structure the DC transfer gain of the converter can be improved. Therefore, it can be employed in a wide input voltage application to reduce the current stress and conduction loss in primary. According to the proposed control strategy, a new rectifier structure is proposed to reduce the voltage stress of the diode and the filter requirement. Based on the proposed converter, a cell structure concept is proposed. Using this cell structure a multi-phase converter can be constructed with any wide input voltage range. A two phase prototype (100k/500W) is built to verify the theoretical analysis
OPTAS: Optimal Data Placement in MapReduce

The data placement strategy greatly affects the efficiency of MapReduce. The current strategy only takes the map phase into account to optimize the map time. But the ignored shuffle phase may increase the total running time significantly in many jobs. We propose a new data placement strategy, named OPTAS, which optimizes both the map and shuffle phases to reduce their total time. However, the huge search space makes it difficult to find out an optimal data placement instance (DPI) rapidly. To address this problem, an algorithm is proposed which can prune most of the search space and find out an optimal result quickly. The search space firstly is segmented in ascending order according to the potential map time. Within each segment, we propose an efficient method to construct a local optimal DPI with the minimal total time of both the map and shuffle phases. To find the global optimal DPI, we scan the local optimal DPIs in order. We have proven that the global optimal DPI can be found as the first local optimal DPI whose total time stops decreasing, thus further pruning the search space. In practice, we find that at most fourteen local optimal DPIs are scanned in tens of thousands of segments with the pruning strategy. Extensive experiments with real trace data verify not only the theoretic analysis of our pruning strategy and construction method but also the optimality of OPTAS. The best improvements obtained in our experiments can be over 40% compared with the existing strategy used by MapReduce.
Machine learning-based detection of chemical risk

Chemical risk appears with chemical substances that are dangerous for human or animal health, or for environment, such as with Bisphenol A and phtalates. Chemical risk causes several severe health disorders and is particularly dangerous for human health. Specific agencies are involved in the verification of the suitability of products and goods to be marketed. For this, a large amount of scientific and institutional literature is manually analyzed to study the current knowledge on the associated chemical risk. We propose to use machine learning and dedicated classification for the automatic detection of chemical risk statements. We test several algorithms and features and obtain between 0.60 and 0.95 F-measure.
B Cell Receptor-Stimulated Mitochondrial Phospholipase A2 Activation and Resultant Disruption of Mitochondrial Membrane Potential Correlate with the Induction of Apoptosis in WEHI-231 B Cells1

Cross-linking of the Ag receptors on the immature B cell lymphoma, WEHI-231, leads to growth arrest and apoptosis. We now show that although commitment to such B cell receptor (BCR)-mediated apoptosis correlates with mitochondrial phospholipase A2 activation, disruption of mitochondrial function, and ATP depletion, it is executed independently of caspase activation. First, we demonstrate a pivotal role for mitochondrial function in determining B cell fate by showing up-regulation of cytosolic phospholipase A2 expression, induction of mitochondrial phospholipase A2 activity, arachidonic acid-mediated collapse of mitochondrial transmembrane inner potential (Δψm), and depletion of cellular ATP under conditions of apoptotic, but not proliferative, signaling via the BCR. Importantly, disruption of Δψm, ATP depletion, and apoptosis can be prevented by rescue signals via CD40 or by Δψm stabilizers such as antimycin or oligomycin. Second, we show that commitment and postmitochondrial execution of BCR-mediated apoptosis are not dependent on caspase activation by demonstrating that such apoptotic signaling does not induce release of cytochrome c from the mitochondria or activation of effector caspases, as evidenced by poly(ADP-ribose) polymerase or Bcl-xL cleavage. Indeed, apoptotic signaling via the BCR in WEHI-231 B cells does not stimulate the activation of caspase-3 and, consistent with this, BCR-mediated disruption of Δψm and commitment to apoptosis take place in the presence of caspase inhibitors. In contrast, BCR signaling induces the postmitochondrial activation of cathepsin B, and resultant apoptosis is blocked by the cathepsin B inhibitor, (23,35)trans-epoxysuccinyl-l-leucylamindo-3-methylbutane ethyl ester (EST) suggesting a key role for this executioner protease in Ag receptor-driven apoptosis of WEHI-231 immature B cells.
A Methodology for the Deployment of Multi-Agent Systems on Wireless Sensor Networks

Recent technological advances in wireless networking, IC fabrication and sensor technology have lead to the emergence of millimeter scale devices that collectively form a Wireless Sensor Network (WSN). The cost of production for a single node has been reduced to less than 1 dollar, paving the way for large scale deployments (millions of nodes per network) of such devices. It would seem that agent technology should be useful for these highly distributed networks in terms of intelligent network management and data harvesting for example. Indeed this has been shown to be the case, however multi-agent systems for WSNs are scarce. One reason for this is the difﬁculty in the deployment, testing and debugging of a distributed application for these devices due to the minimal (if any) user interfaces they possess. In this paper we will propose a methodology for the rapid development of a MAS for WSNs that allows for comprehensive testing and debugging, a luxury not available on current WSN devices. We also instantiate this methodology in a case study for evaluation purposes.
Human computing games for knowledge acquisition

Automatic information extraction techniques for knowledge acquisition are known to produce noise, incomplete or incorrect facts from textual sources. Human computing offers a natural alternative to expand and complement the output of automated information extraction methods, thereby enabling us to build high-quality knowledge bases. However, relying solely on human inputs for extraction can be prohibitively expensive in practice. We demonstrate human computing games for knowledge acquisition that employ human computing to overcome the limitations in automated fact acquisition methods. We provide a combined approach that tightly integrates automated extraction techniques with human computing for effective gathering of facts. The methods we provide gather facts in the form of relationships between entities. The games we demonstrate are specifically designed to capture hard-to-extract relations between entities in narrative text -- a task that automated systems find challenging.
Excess Yields in Bond Hedging

It has been indicated by Litterman and Scheinkman (1991) that the term structure of interest rates is reliably modeled by an affine three factor model attained by principal component analysis. Such a model is inconsistent with no arbitrage: it allows portfolios with zero instantaneous variance to have returns that are different from the risk free rate. Such an excess return is called convenience yield. We derive an explicit formula for the convenience yield, use historical data of US treasuries to estimate its parameters, and find that the convenience yield predicted by our formula is surprisingly large. We then compare the predicted convenience yield to the one obtained in practice and find an excellent fit between the two.
Altered cingulum bundle microstructure in autism spectrum disorder

Objective Here, we examined the cingulum bundle, a long-range white matter tract mediating dorsal limbic connectivity, using diffusion tensor imaging (DTI) tractography, in children and adolescents with autism spectrum disorder (ASD) versus controls. We hypothesised that cingulum bundle microstructure would be altered in ASD, based on evidence implicating abnormal white matter connectivity in this disorder. Methods DTI data were acquired for 19 ASD participants (IQ ⩾ 70; 7–18 years; mean = 12.4 ± 3.1) and 16 age-matched controls (7–18 years; mean = 12.3 ± 3.6) on a 3 T magnetic resonance imaging system. Deterministic tractography was used to isolate the cingulum bundle. Left and right cingulum bundles were examined for differences in several DTI metrics in ASD children/adolescents versus controls, including: fractional anisotropy (FA), mean, axial, and radial diffusivity. Results Significant age × group interaction effects were found for all DTI metrics (mean diffusivity: F1,28 = 9.5, p = 0.005, radial diffusivity: F1,28 = 7.8, p = 0.009, axial diffusivity: F1,28 = 5.2, p = 0.03, FA: F1,28 = 4.4, p = 0.04). Interaction effects were driven by increases in cingulum bundle diffusivity (mean, radial, and axial diffusivity), and decreased FA, in younger ASD participants within our sample versus controls. Conclusion Our results point to immature microstructural organisation of the cingulum bundle in ASD, particularly during the early years of life, with implications for limbic network synchronisation and complex socio-emotional performance.
Preparation and SPE applications of silica-based monoliths

The monolithic column is a single piece of a continuous rigid porous polymer that possesses an interconnected skeletal structure and contains pores. According to their components, monolithic materials can be divided into organic polymer-based monoliths and inorganic silica-based monoliths. This review focuses on silica-based monolith with special emphasis on the preparation. Moreover, this review attempts to give an overview covering the modification of the surface of the inorganic monolithic materials. The applications of the silica-based monoliths as a sorbent in different applications are included in this review.
Incidental learning of novel words in adult Spanish speaking learners of English as a second language : measures of lexical configuration and lexical engagement and the effects of learners' individual differences

Studies on second language (L2) incidental vocabulary learning through reading have mainly focused on learning and testing factual static knowledge of recently learned words, that is, their form and meaning, often in isolation. Thus, little research has explored how through incidental reading newly learned words lexically engage (or interact) with other lexical levels (e.g. semantic and syntactic) and items in the bilingual mental lexicon. Moreover, the effects of learners’ individual differences in incidental vocabulary learning and lexical engagement remain unexplored. The present work, therefore, aimed to contribute to the existing literature by examining the extent of L2 incidental vocabulary learning, not only through measures of lexical configuration (e.g. recognition and recall) but also via measures of lexical engagement (e.g. predicting upcoming linguistic material, making lexical decisions following semantic priming, and parsing temporarily ambiguous (garden-path) sentences) in adult Spanish speaking learners of English in comparison to a monolingual English speaking control group. The effects of phonological working memory (PWM), language aptitude, vocabulary size, and verbal fluency on lexical configuration and lexical engagement of recently learned pseudowords were also explored. Two offline studies on lexical configuration knowledge (e.g. recognition and recall vocabulary post-tests), and three online studies on lexical engagement of spoken form (e.g. visual-world eye-tracking paradigm), meaning (e.g. lexical decision task with priming), and grammatical use (e.g. eye-tracking study with text) of recently learned pseudowords were designed. A training/learning phase preceded the studies where participants read meaningful English sentences containing the target items. The results confirmed that L2 incidental vocabulary learning from reading reaches lexical engagement of form, meaning, and use in recently learned pseudowords. In addition, it corroborated that learners’ individual differences have an effect on lexical configuration and lexical engagement of novel words. The findings contribute to existing theories on L2 vocabulary learning by demonstrating that incidental learning from reading can lead to lexical engagement, and thus to deeper understanding of word knowledge beyond factual memory.
Partial eigenvalue assignment problem of linear control systems using orthogonality relations

Problém priradenia parciálnych charakteristických hodnôt lineárnych riadiacich systémov použitím ortogonálnych závislostí The partial eigenvalue assignment is the problem of reassigning a part of the open-loop spectrum of a linear system by a feedback control, leaving the rest of the spectrum invariant. In the paper, we propose a novel solution to the partial eigenvalue assignment problem of linear control system using orthogonality relations between eigenvectors of the state matrix . Our solution can be implemented with only a partial knowledge of the spectrum and the corresponding eigenvectors of the linear system. We show that, the number of eigenvalues and eigenvectors that need to remain unchanged will not be affected by the feedback. We prove this, the feedback vector must be of real form. A numerical example is given to illustrate the proposed method. A
daQ, an Ontology for Dataset Quality Information

Data quality is commonly defined as fitness for use. The problem of identifying the quality of data is faced by many data consumers. To make the task of finding good quality datasets more efficient, we introduce the Dataset Quality Ontology (daQ). The daQ is a lightweight, extensible vocabulary for attaching the results of quality benchmarking of a linked open dataset to that dataset. We discuss the design considerations, give examples for extending daQ by custom quality metrics, and present use cases such as browsing datasets by quality. We also discuss how tools can use the daQ to enable consumers find the right dataset for use.
An Implementation of AES Algorithm with the Multiple Spaces Random Key Pre-Distribution Scheme on MOTE-KIT 5040

Wireless sensor networks introduce a new way for us to interact with the physical environment. Since wireless sensor networks use radio frequency as the communication medium, chances for the information retrieved by unexpected people are becoming easier and easier. In wireless sensor networks the resources are limited; it seems that AES is a good solution for this communication problem. Beside, a good key pre-distribution scheme is needed to solve the key agreement problem in wireless sensor networks. The main goal of this work is to implement an AES algorithm with the key pre-distribution scheme proposed by Du et al. on MOTE-KIT 5040 produced by crossbow technology. Under our implementation, the actual time of computing a 64-bit secret key is shorter than Du et al.'s and the performance of AES algorithm is better than the AES proposal
A Morphometric Screen Identifies Specific Roles for Microtubule-Regulating Genes in Neuronal Development of P19 Stem Cells

The first morphological change after neuronal differentiation is the microtubule-dependent initiation of thin cell protrusions called neurites. Here we performed a siRNA-based morphometric screen in P19 stem cells to evaluate the role of 408 microtubule-regulating genes during this early neuromorphogenesis step. This screen uncovered several novel regulatory factors, including specific complex subunits of the microtubule motor dynein involved in neurite initiation and a novel role for the microtubule end-binding protein EB2 in attenuation of neurite outgrowth. Epistasis analysis suggests that competition between EB1 and EB2 regulates neurite length, which links its expression to neurite outgrowth. We propose a model that explains how microtubule regulators can mediate cellular morphogenesis during the early steps of neuronal development by controlling microtubule stabilization and organizing dynein-generated forces.
Better Re-Use of Public Sector Information: Evaluating the Proposal for a Government Data Mashing Lab

The report aims to isolate and examine the socio-institutional barriers to better re-use of Public Sector Information (PSI). It assesses the draft proposal for a Data Mashing Lab (DML) (see: Appendix 2) and is intended for a specialized audience of government policy makers. The findings are informed by key informant interviews. The interviews were conducted on a non-attributable basis in the period 14 September to 6 October 2006 and are supported by the literature review outlined in the opening two chapters, and the insights of four workshops also attended in May-September 2006. We thank the workshop and interview participants for their full and frank contributions to this research project. The stakeholders interviewed included experts from: academia, Non-Departmental Public Bodies (NDPBs), Trading Funds, private entrepreneurs, corporations, standards bodies, and government policy officials with both domestic and international responsibilities. The schedule of interviews and workshop programmes are contained in Appendix 1.
Temi Di Discussione Bank Risk and Monetary Policy Bank Risk and Monetary Policy Bank Risk and Monetary Policy

We find evidence of a bank lending channel for the euro area operating via bank risk. Financial innovation and the new ways to transfer credit risk have tended to diminish the informational content of standard bank balance-sheet indicators. We show that bank risk conditions, as perceived by financial market investors, need to be considered, together with the other indicators (i.e. size, liquidity and capitalization), traditionally used in the bank lending channel literature to assess a bank’s ability and willingness to supply new loans. Using a large sample of European banks, we find that banks characterized by lower expected default frequency are able to offer a larger amount of credit and to better insulate their loan supply from monetary policy changes. JEL classification: E44, E55.
Liouville field theory on a pseudosphere

Liouville field theory is considered with boundary conditions corresponding to a quantization of the classical Lobachevskiy plane (i.e. euclidean version of $AdS_2$). We solve the bootstrap equations for the out-vacuum wave function and find an infinite set of solutions. This solutions are in one to one correspondence with the degenerate representations of the Virasoro algebra. Consistency of these solutions is verified by both boundary and modular bootstrap techniques. Perturbative calculations lead to the conclusion that only the ``basic'' solution corresponding to the identity operator provides a ``natural'' quantization of the Lobachevskiy plane.
Nonparametric Regression!

Among the various nonparametric regression methods, weighted local polynomial fitting is the one which is gaining increasing popularity. This is due to the attractive minimax efficiency of the method and to some further desirable properties such as the automatic incorporation of boundary treatment. In this paper previous results are extended in two directions: in the one-dimensional case, not only local linear fitting is considered but also polynomials of other orders and estimating derivatives. In addition to deriving minimax properties, optimal weighting schemes are derived and the solution obtained at the boundary is discussed in some detail. An equivalent. kernel formulation serves as a tool to derive many of these properties. In the higher dimensional case local linear fitting is considered. Properties in terms of minimax efficiency are derived and optimal weighting ISupported by the Deutsche Forschungsgemeinschaft. 2Supported by NSF grant DMS-9203135 We would like to thank Burkhardt Seifert for intellectual support, in particular in the improvement of section 3.
Auditing Services in Cloud Computing For Achieving Data Access Control

Cloud computing has a great tendancy of providing robust computational power to the society at reduced cost. The wide adoption of this promising computation model is prevented by security which is the primary obstacle especially for customers when their confidential data are consumed and produced during the computation. The data stored in the cloud may be frequently updated by the users. It has been reported by many authors on auditing methods to efficiently audit the cloud data storage without challenging the local copy of data, and introduce no additional on-line load to the cloud user. This paper is a survey of different algorithms used in different auditing mechanisms that have been considered to support the storage correctness of data in cloud environment. General Terms: TPA, HLA, PDP, FAME
Ambiguous Novel Compounds and Models of Morphological Parsing

This paper reports on two experiments that investigated the activation of morphemes in English novel compounds. All experiments employed stimuli that we have called "ambiguous novel compounds. " These words (e.g., clamprod) have two interpretable parses (e.g., clam + prod or clamp + rod) and thus offer an opportunity to investigate which parses are preferred, whether both possible parses are computed, and whether parsing procedures "divide" words into their morphological constituents or "extract" constituent representations. The results suggest that morphological parsing does not simply divide a word into its constituents, but rather generates multiple representations that are subsequently evaluated.