Miracle-dz/UniFAR-FTUs huggingface dataset:
AN INFORMATION RETRIEVAL LOGIC MODEL: IMPLEMENTATION AND EXPERIMENTS*

Two methods defining a logic model for Information Retrieval are introduced. The first one is based on the conditional logic as suggested by van Rijsbergen, while the second is based on the Nie's variations of the uncertainty principle of van Rijsbergen. A Hypercard based system has been also designed and implemented to support small-scale experiments. It has been used to evaluate the performance of the new model in comparison with the standard retrieval methods. Though we imposed some simplifying assumptions to the model in order to have a workable system, the experimental results allow us to claim that the new methods are comparable to the already existing ones.
Does Education Lead to Pacification? A Systematic Review of Statistical Studies on Education and Political Violence

Does more education lead to less political violence, and may education thus be a tool for peace? This article provides the first systematic review of the existing quantitative literature on education and political violence. Looking at arguments pertaining to levels, expansion, inequality, and content of education, we identify 42 quantitative studies from the time period 1996 to 2016 that test the relationship between various measures of education and political violence. An emerging scholarly consensus seems to be that education has a general pacifying effect. However, this general conclusion is challenged by recent evidence showing above-average levels of education among terrorists and genocide perpetrators. This, as well as other findings, underscore that the relationship between education and political violence is complex and multidimensional, depending on type of political violence, mediating factors, and level of analysis. We conclude with policy implications from our findings and discuss directions for future analysis.
A Convex Combination of Models for Predicting Road Traffic

This paper describes an approach to the road traffic prediction problem in Warsaw in the context of a data mining competition that is part of the IEEE ICDM 2010. A solution based on a convex combination of models mining different wells of information within the data is described. Such convex combination allows the final model compensate highly uncorrelated errors from the different underlying models and to achieve higher prediction accuracy.
Timbral modeling for music artist recognition using i-vectors

Music artist (i.e., singer) recognition is a challenging task in Music Information Retrieval (MIR). The presence of different musical instruments, the diversity of music genres and singing techniques make the retrieval of artist-relevant information from a song difficult. Many authors tried to address this problem by using complex features or hybrid systems. In this paper, we propose new song-level timbre-related features that are built from frame-level MFCCs via so-called i-vectors. We report artist recognition results with multiple classifiers such as K-nearest neighbor, Discriminant Analysis and Naive Bayes using these new features. Our approach yields considerable improvements and outperforms existing methods. We could achieve an 84.31% accuracy using MFCC features on a 20-classes artist recognition task.
Cellular automata simulation of coal combustion

Simulation of coal combustion in the kinetic regime was developed using the formal support of cellular automata. The phenomenon was described as a process based on local interactions, with discrete time and space, at a mesoscopic scale level. In order to validate the method, simulation results were checked against both experimental data and traditional numerical models. Materials tested included Spherocarb, marine char and pure graphite. Satisfactory agreement was found.
Validation of Contrast-Enhanced Magnetic Resonance Imaging to Monitor Regenerative Efficacy After Cell Therapy in a Porcine Model of Convalescent Myocardial Infarction

Background— Magnetic resonance imaging (MRI) in the CArdiosphere-Derived aUtologous stem CElls to reverse ventricUlar dySfunction (CADUCEUS) trial revealed that cardiosphere-derived cells (CDCs) decrease scar size and increase viable myocardium after myocardial infarction (MI), but MRI has not been validated as an index of regeneration after cell therapy. We tested the validity of contrast-enhanced MRI in quantifying scarred and viable myocardium after cell therapy in a porcine model of convalescent MI. Methods and Results— Yucatan minipigs underwent induction of MI and 2–3 weeks later were randomized to receive intracoronary infusion of 12.5×106 mismatched allogeneic CDCs or vehicle. Allogeneic CDCs induced mild local mononuclear infiltration but no systemic immunogenicity. MRI revealed that allogeneic CDCs attenuated remodeling, improved global and regional function, decreased scar size, and increased viable myocardium compared with placebo 2 months post-treatment. Extensive histological analysis validated quantitatively the MRI measurements of scar size, scar mass, and viable mass. CDCs neither altered gadolinium contrast myocardial kinetics nor induced changes in vascular density or architecture in viable and scarred myocardium. Histology demonstrated that CDCs lead to cardiomyocyte hyperplasia in the border zone, consistent with the observed stimulation of endogenous regenerative mechanisms (cardiomyocyte cycling, upregulation of endogenous progenitors, angiogenesis). Conclusions— Contrast-enhanced MRI accurately measures scarred and viable myocardium after cell therapy in a porcine model of convalescent MI. MRI represents a useful tool for assessing dynamic changes in the infarct and monitoring regenerative efficacy.
Internal-State Policy-Gradient Algorithms for Partially Observable Markov Decision Processes

Policy-gradient algorithms are attractive as a scalable approach to learning approximate policies for controlling partially observable Markov decision processes (POMDPs). POMDPs can be used to model a wide variety of learning problems, from robot navigation to speech recognition to stock trading. The downside of this generality is that exact algorithms are computationally intractable, motivating approximate methods. Existing policy-gradient methods have worked well for problems admitting good memory-less solutions, but have failed to scale to large problems requiring memory. This paper develops novel algorithms for learning policies with memory. We present a new policy-gradient algorithm that uses an explicit model of the POMDP to estimate gradients, and demonstrate its effectiveness on problems with tens of thousands of states. We also describe three new Monte-Carlo algorithms that learn by interacting with their environment. We compare these algorithms on non-trivial POMDPs, including noisy robot navigation and multi-agent settings.
Stochastic Power Adaptation with Multiagent Reinforcement Learning for Cognitive Wireless Mesh Networks

As the scarce spectrum resource is becoming overcrowded, cognitive radio indicates great flexibility to improve the spectrum efficiency by opportunistically accessing the authorized frequency bands. One of the critical challenges for operating such radios in a network is how to efficiently allocate transmission powers and frequency resource among the secondary users (SUs) while satisfying the quality-of-service constraints of the primary users. In this paper, we focus on the noncooperative power allocation problem in cognitive wireless mesh networks formed by a number of clusters with the consideration of energy efficiency. Due to the SUs' dynamic and spontaneous properties, the problem is modeled as a stochastic learning process. We first extend the single-agent Q-learning to a multiuser context, and then propose a conjecture-based multiagent Q-learning algorithm to achieve the optimal transmission strategies with only private and incomplete information. An intelligent SU performs Q-function updates based on the conjecture over the other SUs' stochastic behaviors. This learning algorithm provably converges given certain restrictions that arise during the learning procedure. Simulation experiments are used to verify the performance of our algorithm and demonstrate its effectiveness of improving the energy efficiency.
Semantic Image Retrieval based on Ontology and Relevance Model - A Preliminary Study

Modern Web search engines index hundreds of millions of images. To search these images is a daunting task for the user who can, realistically, only visually inspect a handful. In general, the way the user responds to an information need depends on the task at hand. Further, some tasks will require browsing, while others are targeted and require a more directed approach. In this paper, we present the preliminary results of research to develop a framework for applying semantics to enhance image retrieval. We consider this problem on two separate levels. First, we consider the application of an Ontology to define the semantic query space for image search and navigation, as well as to approximate the users context for the search. Secondly, in order to further improve upon the search results we apply the Relevance model, using data from the web to train the model. The role of the Relevance Model is to rank images from the search engine. The study also investigates how application of an ontology affects the quantity and quality the retrieved images and also the effects to the exp erience of the user in image search. We then contrast the results with those obtained by the Relevance Model for exactly similar search terms. The Relevance Model is based on a probabilistic model, which applies user definable language models to the text linking to the image. In our Relevance Model, the relevance of a HTML document linking to an image is evaluated and assigned with respect to highly ranked textual documents from the web. The ranking of the HTML document, is also assigned to ranking of the respective image. The main advantage here is that the Relevance Model can be learnt from the Web without any preparation of training data and is independent of the underlying algorithm of the image search engines. We show that navigation is indeed a very powerful tool for image browsing and that using the ontology dramatically enhances recall for specialized terms. Relevance feedback mainly improves precision by effective re-ranking. Keyword: Web Image Search, Language Model, Relevance Model, Wordnet, Ontology
Declaration Effect of Cash & Stock Dividends on Share Price: An Empirical Study on Dhaka Stock Exchange

This study analyzes the impact of different types of dividend declaration, namely stock and cash dividends. Using event study method, MAAR and CAAR, this study found no evidence of abnormal returns on the declaration day for either of the types of dividends. However, significant negative returns are reported on days prior to the declaration day for stock dividend, indicating speculative nature of the investors. Furthermore, it provides positive returns during the post-announcement period as the investors realize the chance of potential gains. However, as far as cash dividend is concerned companies listed under DSE do not provide any significant abnormal returns during the 60 days event window. For CAAR, no significant return is reported for cash dividend, while stock dividend provided a maximum 5.6% abnormal returns during the post announcement period.
A novel vision-based multi-hand tracking algorithm for human computer interface

A novel multi-hand tracking algorithm for human computer interface (HCI) is proposed, to solve the problem caused by the incompletion of tracking, the overlap of multi-object, the lost of the object and the confusion from background. For tracking the hand completely and effectively, we propose a new region-connection-based tracking method. And a hand motion model is established for tracking failure detection, with the utility of Bayesian classifier. Furthermore, to recover tracking after failure, a monitoring windows method is designed.
Beyond online aggregation: parallel and incremental data mining with online Map-Reduce

There are only few data mining algorithms that work in a massively parallel and yet online (i.e. incremental) fashion. A combination of both features is essential for mining of large data streams and adds scalability to the concept of Online Aggregation introduced by J. M. Hellerstein et al. in 1997. We show how an online version of the Map-Reduce programming model can be used to implement such algorithms, and propose a solution for the "hardest" class of these algorithms - those requiring multiple Map-Reduce phases. An experimental evaluation confirms that the proposed methods can substantially accelerate interactive analysis of large data sets and facilitate scalable stream mining.
A MODEL FOR PRODUCT , PROCESS AND SUPPLY CHAIN CONFIGURATION IN A BUILD-TO-ORDER ENVIRONMENT

A key challenge for companies is to combine the high flexibility required to operate according to customer needs and wishes with the high level of efficiency required to stay competitive in terms of costs. Recent research on supply chain management suggests that companies in highly dynamic industries need not only to constantly develop their supply chain, but also simultaneously develop their product structure and their manufacturing process. In this paper we explore ways in which a company can create competitive advantage in a mature industry through a Build to Order Supply Chain. We carried out an in-depth case study covering (i) a global company producing and selling heavy industrial equipment, (ii) three of its industrial customers, each one representing different customer segments, and (iii) its two-level supply network. Based on the case study analysis, we propose that companies in a build-to-order environment can differentiate in two dimensions of performance: delivery time and the degree of product customization. There is a trade-off between the two dimensions: if the customer requires a highly customized product, it takes longer to engineer and make it, and vice versa. In order to deal with this trade-off in a way that benefits both the customer and the supplier, we propose a model to support the supplier’s decision making. The crucial elements of the model are early supplier involvement, product standardization, and a downstream order penetration point. Early involvement in product specification makes it possible to compete effectively in a time-sensitive customer segment and to develop product standardization. Product standardization makes it possible to move the order penetration point downstream. Further, downstream order penetration makes it possible to effectively differentiate by providing fast, flexible deliveries to customers. Product standardization allows simultaneously driving the process costs down.
Analysis and Minimization of Flicker Noise Up-Conversion in Voltage-Biased Oscillators

Traditional design of voltage-controlled oscillators (VCOs) can lead to unacceptable degradation of the <formula formulatype="inline"><tex Notation="TeX">$1/f^3$</tex> </formula> phase-noise component when a broad tuning range is required. In this paper, the flicker noise up-conversion mechanisms in a voltage-biased topology are deeply investigated, providing general design guidelines for its mitigation. An improved VCO architecture is then introduced, where a segmented transconductor tailors the excess gain depending on the operating range to ensure that <formula formulatype="inline"><tex Notation="TeX">$1/f$</tex> </formula> noise up-conversion remains minimal over the tuning range. The solution covers both fourth-generation and WiMAX 2.5-GHz bands and leads to a 10-dB reduction of the <formula formulatype="inline"><tex Notation="TeX">$1/f^3$</tex> </formula> phase noise with respect to a traditional design without impairing the <formula formulatype="inline"><tex Notation="TeX">$1/f^2$</tex></formula> phase-noise performance.
The Search for Aspect-Oriented Refactorings Must Go On ( Position paper )

In this paper, we argue that current research in aspect-oriented refactoring can benefit from specific efforts to develop a new coding style appropriate for aspect-orientation. This notion can be captured through catalogues of refactorings and code smells, after which it can provide a basis for tool developers. We successfully derived useful insights from the study of toy examples and think their potential has not been fully explored yet. We will continue to include toy examples in our next research work, hoping to obtain further insights, which will then be tested and developed with larger code bases.
Characterization and Provenance of Archaeological Obsidian from Pirozza-Spalmatore, a Site of Neolithic Colonization on the Island of Ustica (Sicily)

Abstract This article presents the first archaeometric research carried out so far on a group of obsidian artifacts collected in the promontory of Pirozza-Spalmatore, the site of a Neolithic settlement on the small island of Ustica (Sicily, Italy). The obsidian assemblage, consisting of 28 artifacts, was subjected to optical, typological and geochemical analyses, the latter carried out by means of a portable pXRF, in order to determine their provenance. The results indicate that 89% of the fragments come from geologic obsidian outcrops on the island of Lipari, and 11% from the island of Pantelleria. It is thus ascertained that, during the Neolithic, Ustica used two different sources of obsidian, located 150–250 km away from the site. These obsidian sources were typical of some western Sicily prehistoric villages, while eastern Sicilian villages seem to have imported obsidian exclusively from Lipari. The analyses indicate that all the obsidian artifacts attributed to Lipari come from the geological deposit of Gabellotto, while those attributed to Pantelleria come from the two distinct deposits of Lago di Venere and Balata dei Turchi.
A Novel FLT3 Inhibitor FI-700 Selectively Suppresses the Growth of Leukemia Cells with FLT3 Mutations

Purpose: The aim of this study was to evaluate the antileukemia activity of a novel FLT3 kinase inhibitor, FI-700. Experimental Design: The antileukemia activity of FI-700 was evaluated in human leukemia cell lines, mutant or wild-type (Wt)-FLT3–expressing mouse myeloid precursor cell line, 32D and primary acute myeloid leukemia cells, and in xenograft or syngeneic mouse leukemia models. Results: FI-700 showed a potent IC50 value against FLT3 kinase at 20 nmol/L in an in vitro kinase assay. FI-700 showed selective growth inhibition against mutant FLT3-expressing leukemia cell lines and primary acute myeloid leukemia cells, whereas it did not affect the FLT3 ligand (FL)–driven growth of Wt-FLT3–expressing cells. These antileukemia activities were induced by the significant dephosphorylations of mutant FLT3 and STAT5, which resulted in G1 arrest of the cell cycle. Oral administration of FI-700 induced the regression of tumors in a s.c. tumor xenograft model and increased the survival of mice in an i.v. transplanted model. Furthermore, FI-700 treatment eradicated FLT3/ITD-expressing leukemia cells, both in the peripheral blood and in the bone marrow. In this experiment, the depletion of FLT3/ITD-expressing cells by FI-700 was more significant than that of Ara-C, whereas bone marrow suppression by FI-700 was lower than that by Ara-C. Conclusions: FI-700 is a novel and potent FLT3 inhibitor with promising antileukemia activity.
Disk striping strategies for large video-on-demand servers

The storage structure of videos on disks affects the number of concurrent users a videoon-demand system can support and hence the average waiting time. In this paper, we propose a phase-based striping method which has the desirable characteristics that it guarantees the maximum waiting time. We also develop a data replication scheme to further reduce the average waiting time of the phase-based method. These two schemes, together with the conventional sequential striping scheme, are then employed to optimize the waiting time of videos based on their access pattern. Simulations were conducted using a video server containing 36 videos under different loading and hardware configurations. The results show that such optimization can reduce the waiting time significantly. The use of replication method could further improve the server performance by over 25%. The overall results indicate that with limited resources, the use of phase-based striping method with replication is preferable under heavy loads.
‘Other spaces’ for lesbian, gay, bisexual, transgendered and questioning (LGBTQ) students: positioning LGBTQ-affirming schools as sites of resistance within inclusive education

Abstract This article explores the growing interest in schools which are aimed at children and young people who are lesbian, gay, bisexual, transgendered and questioning (LGBTQ), schools described as LGBTQ-affirming. Schools which target specific groups of students are sometimes viewed as being anti-inclusive because they assign labels to students and separate them from one another. This is based on a notion of inclusive education as a single ‘school for all’; a comprehensive, common school which is suitable for all children in a particular locality. Using academic literature alongside original data from an in-depth qualitative case study of an LGBTQ-affirming school in Atlanta, this article addresses the question of whether there is a place for LGBTQ-affirming schools within inclusive education systems. It argues that the word ‘segregated’ is not an accurate description of these schools, positing that segregated spaces are not the same as separate spaces. It argues that the separateness of LGBTQ-affirming schools is important to their role in inclusive education, specifically when they are positioned as examples of Foucault’s heterotopias. Viewing them through this theoretical lens enables them to be seen as ‘other spaces’, as a form of ‘resistance’ and ‘protest’ which may ‘unstitch’ the utopian vision of inclusive education.
Thread affinity mapping for irregular data access on shared Cache GPGPU

Memory Coalescing and on-chip shared Cache are two effective techniques to alleviate the memory bottleneck in modern GPGPUs. These two techniques are very useful on applications with regular memory accesses. However, they become ineffective on concurrent threads with large numbers of uncoordinated accesses and the potential performance benefit could be significantly degraded. This paper proposes a thread affinity mapping methodology to coordinate the irregular data accesses on shared cache GPGPUs. Based on the proposed affinity metrics, threads are congregated into execution groups which are able to fully exploit the memory coalescing and data sharing within an application. An average of 3.5x runtime speedup is achieved on a Fermi GPGPU. The speedup scales with the sizes of test cases, which makes the proposed methodology an effective and promising solution for the continually increasing complexities of applications in the future many-core systems.
Acute Lung Injury: How Macrophages Orchestrate Resolution of Inflammation and Tissue Repair

Lung macrophages are long living cells with broad differentiation potential, which reside in the lung interstitium and alveoli or are organ-recruited upon inflammatory stimuli. A role of resident and recruited macrophages in initiating and maintaining pulmonary inflammation in lung infection or injury has been convincingly demonstrated. More recent reports suggest that lung macrophages are main orchestrators of termination and resolution of inflammation. They are also initiators of parenchymal repair processes that are essential for return to homeostasis with normal gas exchange. In this review we will discuss cellular cross-talk mechanisms and molecular pathways of macrophage plasticity which define their role in inflammation resolution and in initiation of lung barrier repair following lung injury.