Miracle-dz/UniFAR-FTUs huggingface dataset:
Brain Activity in Self- and Value-Related Regions in Response to Online Antismoking Messages Predicts Behavior Change

In this study, we combined approaches from media psychology and neuroscience to ask whether brain activity in response to online antismoking messages can predict smoking behavior change. In particular, we examined activity in subregions of the medial prefrontal cortex linked to self- and value-related processing, to test whether these neurocognitive processes play a role in message-consistent behavior change. We observed significant relationships between activity in both brain regions of interest and behavior change (such that higher activity predicted a larger reduction in smoking). Furthermore, activity in these brain regions predicted variance independent of traditional, theory-driven self-report metrics such as intention, self-efficacy, and risk perceptions. We propose that valuation is an additional cognitive process that should be investigated further as we search for a mechanistic explanation of the relationship between brain activity and media effects relevant to health behavior change.
Water Balance Methodology for indirect assessment and prediction of basin water yield under human-induced land use changes

The lastest versions of the Heat and Water Balance Models (HWBMs) are presented and their applicability to predict evapotranspiration losses from watersheds under changed land use patterns in rain-fed agriculture (crop allocation, crop yield, fertilizer application), and to predict soil moisture availability dynamics during the growing period (for plant productivity esti- mation) are discussed. The versions are based on the integrated "index" water balance approach which presupposes construction of nonlinear relationships between heat and water budget components under different natural and perturbed conditions (soil type, groundwater table position and salinity, plant species and productivity, etc.) using the multidimensional cubic spline technique. The examples on application of the methodology to different conditions are cited for several regions (Middle and Lower Volga River basin, piedmont and lowland parts of the North Caucassian economic region). A comparison is made of the models with several mechanistic heat and water balance models, and combination techniques. The advantages and shortcomings of the models under analysis in respect of their accuracy and practical applicability are highlighted. BACKGROUND With respect to the scale-dependent prediction of man-induced changes in basin water yield, there is a strong demand for studies, not only into river runoff itself, but into runoff generation processes and catchment modelling, since both quantity and quality of surface and groundwater resources depend largely on interaction between surface runoff, soil and groundwater. During the last decades, much has been learned about runoff generation processes and flows along different pathways, especially at hillslope to small catchments scales. Innovation using remotely sensed data, tracer and new hydrometric approaches, and GIS-based computation techniques greatly contributed to a more realistic representation of the underlying mechanisms within process-oriented rainfall-runoff models at the meso and macroscale, and to providing descriptions of the models and their updating procedures in a standard format for disseminating them to all concerned parties. Modern hydrological models of the distributed system type subdivide drainage basins into a lot of area elements of different scales: the regional climatological scale and different hydrological scales—the choric ones (drainage basin or sub-basin, polygon, runoff forming complex) and the topic ones (sites, plots, hydropedotopes,
Online Learning Based Congestion Control for Adaptive Multimedia Transmission

The increase of Internet application requirements, such as throughput and delay, has spurred the need for transport protocols with flexible transmission control. Current TCP congestion control adopts an Additive Increase Multiplicative Decrease (AIMD) algorithm that linearly increases or exponentially decreases the congestion window based on transmission acknowledgments. In this paper, we propose an AIMD-like media-aware congestion control that determines the optimal congestion window updating policy for multimedia transmission. The media-aware congestion control problem is formulated as a Partially Observable Markov Decision Process (POMDP), which maximizes the long-term expected quality of the received multimedia application. The solution of this POMDP problem gives a policy adapted to multimedia applications' characteristics (i.e., distortion impacts and delay deadlines of multimedia packets). Note that to obtain the optimal congestion policy, the sender requires the complete statistical knowledge of both multimedia traffic and the network environment, which may not be available in practice. Hence, an online reinforcement learning in the POMDP-based solution provides a powerful tool to accurately estimate the environment and to adapt the source to network variations on the fly. Simulation results show that the proposed online learning approach can significantly improve the received video quality while maintaining the responsiveness and TCP-friendliness of the congestion control in various network scenarios.
Identifying the Common Component of International Economic Fluctuations: A New Approach

This paper develops an aggregation procedure using time-varying weights for constructing the common component of international economic fluctuations. The methodology for deriving time-varying weights is based on some stylized features of the data documented in the paper. The model allows for a unified treatment of cyclical and seasonal fluctuations and also captures the dynamic propagation of shocks across countries. Correlations of individual country fluctuations with the common component provide evidence of a "world business cycle" and a distinct European common component. The results suggest that macroeconomic fluctuations have become more closely linked across industrial economies in the post-Bretton Woods period.
Identification of genes and signaling pathways associated with squamous cell carcinoma by bioinformatics analysis

The present study aimed to investigate the genes and signaling pathways associated with squamous cell carcinoma (SCC) by bioinformatics analysis. For this purpose, the GSE2503 was downloaded from the Gene Expression Omnibus database, and the differentially expressed genes (DEGs) between 6 normal skin and 5 SCC samples were analyzed using the Linear Models for Microarray Data package. Gene Ontology (GO) and pathway enrichment analysis of DEGs were performed, followed by functional annotation and construction of a protein-protein interaction (PPI) network. Subnetwork modules were subsequently identified and analyzed. A total of 181 DEGs, including 95 upregulated and 86 downregulated DEGs, were identified, in addition to 20 GO biological processes terms enriched by upregulated DEGs and 14 enriched by downregulated DEGs. The upregulated DEGs were enriched in 18 pathways, and the downregulated DEGs were enriched in 7 pathways. Following functional annotation, three upregulated transcription factors (TFs), including hypoxia inducible factor 1, alpha subunit (HIF1A), and six downregulated TFs were identified. In the PPI network and subnetwork, matrix metallopeptidase 1 (MMP1), also known as interstitial collagenase, and interleukin 8 (IL8) were the hub genes with the highest degree of connectivity (degree =8). Integrin alpha (ITGA)6 and 2 were enriched in several pathways, including focal adhesion and extracellular matrix-receptor interaction. DEGs of SCC were primarily enriched in pathways associated with cancer and cell adhesion. Therefore, DEGs such as IL8, MMP1, HIF1A, ITGA6 and ITGA2 may be potential targets for the diagnosis and treatment of SCC.
Oracles with Costs

While powerful tools have been developed to analyze quantum query complexity, there are still many natural problems that do not fit neatly into the black box model of oracles. We create a new model that allows multiple oracles with differing costs. This model captures more of the difficulty of certain natural problems. We test this model on a simple problem, Search with Two Oracles, for which we create a quantum algorithm that we prove is asymptotically optimal. We further give some evidence, using a geometric picture of Grover's algorithm, that our algorithm is exactly optimal.
Is There a "Case for Property" All the Time?

The “case for property” in the mixed-asset portfolio is a topic of continuing interest to practitioners and academics. Such an analysis typically is performed over a fixed period of time and the optimum allocation to property inferred from the weight assigned to property through the use of mean-variance analysis. It is well known, however, that the parameters used in the portfolio analysis problem are unstable through time. Thus, the weight proposed for property in one period is unlikely to be that found in another. Consequently, in order to assess the case for property more thoroughly, the impact of property in the mixed-asset portfolio is evaluated on a rolling basis over a long period of time. In this way we test whether the inclusion of property significantly improves the performance of an existing equity/bond portfolio all of the time. The main findings are that the inclusion of direct property into an existing equity/bond portfolio leads to increase or decreases in return, depending on the relative performance of property compared with the other asset classes. However, including property in the mixed-asset portfolio always leads to reductions in portfolio risk. Consequently, adding property into an equity/bond portfolio can lead to significant increases in risk-adjusted performance. Thus, if the decision to include direct property in the mixed-asset portfolio is based upon its diversification benefits the answer is yes, there is a “case for property” all the time!
Understanding Sources of the Change in International Business Cycles

Macroeconomic activity has become less volatile over the past three decades in most G7 economies. Current literature focuses on the characterization of the volatility reduction and explanations for this so called "moderation" in each G7 economy separately. In opposed to individual country analysis and individual variable analysis, this paper focuses on common characteristics of the reduction and common explanations for the moderation in G7 countries. In particular, we study three explanations: structural changes in the economy, changes in common international shocks and changes in domestic shocks. We study these explanations in a unified model structure. To this end, we propose a Bayesian factor structural vector autoregressive model. Using the proposed model, we investigate whether we can find common explanations for all G7 economies when information is pooled from multiple domestic and international sources. Our empirical analysis suggests that volatility reductions can largely be attributed to the decline in the magnitudes of the shocks in most G7 countries while only for the U.K., the U.S. and Italy they can partially be attributed to structural changes in the economy. Analyzing the components of the volatility, we also find that domestic shocks rather than common international shocks can account for a large part of the volatility reduction in most of the G7 countries. Finally, we find that after mid-1980s the structure of the economy changes substantially in five of the G7 countries: Germany, Italy, Japan, the U.K. and the U.S.
Simplified Walking: A New Way to Generate Flexible Biped Patterns

In this paper, we present a new way to generate biped patterns online. Linear Inverted Pendulum and Zero Moment Point are employed as the dynamic basics. The simplified walking concept is introduced to describe the complete biped motions with the unit of a walking step, instead of a time step of the robot’s working time in usual methods. With this concept, the complex flexible motion planning can be realized with a low computation cost. We also propose the approach to design periodic biped motions and connect these motions together. The validity of our approach is demonstrated by experiments on simulated and real Nao robots.
- 2010 Chunk-Based EBMT

Corpus driven machine translation approaches such as Phrase-Based Statistical Machine Translation and Example-Based Machine Translation have been successful by using word alignment to find translation fragments for matched source parts in a bilingual training corpus. However, they still cannot properly deal with systematic translation for insertion or deletion words between two distant languages. In this work, we used syntactic chunks as translation units to alleviate this problem, improve alignments and show improvement in BLEU for Korean to English and Chinese to English translation tasks.
A review of Kolb’s learning styles in the context of emerging interactive learning environments

The influential work of David A. Kolb in the 1980’s ranged the learning styles of each individual across two related approaches to experiencing the world (Concrete Experience and Abstract Conceptualisation) and two related approaches to converting experience into understanding (Reflective Observation and Active Experimentation). Whilst there have been many critiques of Kolb’s and related work on learning styles, the conceptualisation remains compelling and is still widely applied. However, what happens to Kolb’s experiential learning theory when the learning context for concrete experience and active experimentation is a virtual one? How is the learning experience affected by technologies that adopt particular abstract conceptualisations and promote specific reflective observations? These are two of the critical potential impacts of emerging interactive learning environments (such as serious video games) on the learning style models derived from Kolb’s work. This paper reviews and critiques Kolb’s experiential learning theory, and the principal models of learning it has spawned, from a primarily learning contexts perspective. Learning contexts represent the predominant consideration when considering the design and development of an interactive learning environment.
Workload Known VMM Scheduler for Server Consolidation for Enterprise Cloud Data Center

This study proposes a novel adaptive meta-heuristics based scheduling policies for provisioning the VCPU resources among competing VM service domains in a cloud. Such provisioning guarantees to Service Level Agreement for each domain, with respect to the diverse workloads on-the-fly. The framework is built on CSIM models and tools, making it easy to understand and configure various virtualization setups. The study demonstrates the usefulness of the framework by evaluating proactive, reactive and adaptive VCPU scheduling algorithms. The paper evaluates how periodic/aperiodic execution of control actions can affect policy performance and speed of convergence. The periodic reactive resource allocation is used as the baseline for analysis and the average response time is the performance metric. Simulation based experiments using variety of real-world arrival traces and synthetic workloads results that the proposed provisioning technique detects changes in arrival pattern and resource demands and allocates resources accordingly to achieve application SLA targets. The proposed model improves CPU utilization and makes the best tradeoff between resource utilization and performance from 2 to 6% comparing with the default VMM scheduler configurations for diverse workloads. In addition, the results of the experiments show that the proposed Weighed Moving Average algorithm combined with the aperiodic policy significantly outperforms other dynamic VM consolidation algorithms in all cases, in regard to the SLA metric due to a substantially reduced level of response time violations and the frequency of algorithm invocation.
A scheduling mechanism for multiple MapReduce jobs in a workflow application (position paper)

MapReduce is currently an attractive model for data intensive application due to easy interface of programming, high scalability and fault tolerance capability. It is well suited for applications requiring processing large data with distributed processing resources such as web data analysis, bio informatics, and high performance computing area. There are many studies of job scheduling mechanism in shared cluster for MapReduce. However there is a need for scheduling workflow service composed of multiple MapReduce tasks with precedence dependency in multiple processing nodes. The contribution of this paper is proposing a scheduling mechanism for a workflow service containing multiple MapReduce jobs. The workflow application has precedence dependency constraints among multiple tasks, represented as directed acyclic graph (DAG). Also, for less data transfer cost in limited bisection bandwidth, data dependency criterion should be considered for scheduling multiple map-reduce jobs in a workflow. The proposed scheduling mechanism provides 1) scheduling MapReduce tasks regarding precedence constraints and 2) pre-data placement method considering data dependency constraints for saving data transfer cost over network.
XML Routing in Data Dissemination Networks

This paper addresses the XML/XPath content-based routing problem. More specifically, this paper focuses on the problem of efficiently routing an XML document emitted from a data producers at one point in the network to a set of data consumers located anywhere throughout the network. Prior to receiving XML documents, consumers must have expressed interest in receiving XML documents by registering XPEs with the network. This problem statement is akin to the well-known publish/subscribe matching problem. However, the main difference here is that in the case of data dissemination networks there exists no one single centralized publish/subscribe system, but a network of content-based routers (i.e., a network or federation of publish/subscribe systems).
A confusion network based confidence measure for active learning in speech recognition

Speech recognition systems are usually trained using tremendous transcribed utterances, and training data preparation is intensively time-consuming and costly. Aiming at reducing the number of training examples to be labeled, active learning is used in acoustic modeling of speech recognition, this learning scheme iteratively inspects the unlabeled samples, selects the most informative samples corresponding to a certain criterion, then annotates them, and adds the newly transcribed samples to the training set to update the acoustic model. Concerning about the importance of the criterion to select the most informative samples, we proposed a confidence measure computed by confusion network, and used this measure as the criterion for sample selection to improve the efficiency of active learning in acoustic modeling. Our experiments show that active learning, which adopts the proposed confidence measure, can achieve 31% maximum reduction of labeled data compared with random selection method.
Pierre Bourdieu in Management and Organization Studies – A Systematic Literature Review

The work of the French sociologist Pierre Bourdieu has received increased attention in management and organization studies (MOS). However, the full potential of his work has so far rarely been exploited. This paper aims to pinpoint the contributions of Bourdieu’s work to research in MOS. I conducted a citation context analysis of nine leading journals to investigate how citations to Bourdieu’s work have developed over time, which contents from Bourdieu’s work are cited and how comprehensively researchers have so far engaged with Bourdieu. Based on these findings, I discuss how Bourdieu’s work may contribute to research in MOS, particularly to a micro-foundation of new institutional theory and to the reflection of academic practice in MOS.
Fast Adaptation and Ca2+ Sensitivity of the Mechanotransducer Require Myosin-XVa in Inner But Not Outer Cochlear Hair Cells

In inner ear hair cells, activation of mechanotransduction channels is followed by extremely rapid deactivation that depends on the influx of Ca2+ through these channels. Although the molecular mechanisms of this “fast” adaptation are largely unknown, the predominant models assume Ca2+ sensitivity as an intrinsic property of yet unidentified mechanotransduction channels. Here, we examined mechanotransduction in the hair cells of young postnatal shaker 2 mice (Myo15 sh2/sh2 ). These mice have no functional myosin-XVa, which is critical for normal growth of mechanosensory stereocilia of hair cells. Although stereocilia of both inner and outer hair cells of Myo15 sh2/sh2 mice lack myosin-XVa and are abnormally short, these cells have dramatically different hair bundle morphology. Myo15 sh2/sh2 outer hair cells retain a staircase arrangement of the abnormally short stereocilia and prominent tip links. Myo15 sh2/sh2 inner hair cells do not have obliquely oriented tip links, and their mechanosensitivity is mediated exclusively by “top-to-top” links between equally short stereocilia. In both inner and outer hair cells of Myo15 sh2/sh2 mice, we found mechanotransduction responses with a normal “wild-type” amplitude and speed of activation. Surprisingly, only outer hair cells exhibit fast adaptation and sensitivity to extracellular Ca2+. In Myo15 sh2/sh2 inner hair cells, fast adaptation is disrupted and the transduction current is insensitive to extracellular Ca2+. We conclude that the Ca2+ sensitivity of the mechanotransduction channels and the fast adaptation require a structural environment that is dependent on myosin-XVa and is disrupted in Myo15 sh2/sh2 inner hair cells, but not in Myo15 sh2/sh2 outer hair cells.
A persistent RNA-DNA hybrid is formed during transcription at a phylogenetically conserved mitochondrial DNA sequence

Critical features of the mitochondrial leading-strand DNA replication origin are conserved from Saccharomyces cerevisiae to humans. These include a promoter and a downstream GC-rich sequence block (CSBII) that encodes rGs within the primer RNA. During in vitro transcription at yeast mitochondrial replication origins, there is stable and persistent RNA-DNA hybrid formation that begins at the 5' end of the rG region. The short rG-dC sequence is the necessary and sufficient nucleic acid element for establishing stable hybrids, and the presence of rGs within the RNA strand of the RNA-DNA hybrid is required. The efficiency of hybrid formation depends on the length of RNA synthesized 5' to CSBII and the type of RNA polymerase employed. Once made, the RNA strand of an RNA-DNA hybrid can serve as an effective primer for mitochondrial DNA polymerase. These results reveal a new mechanism for persistent RNA-DNA hybrid formation and suggest a step in priming mitochondrial DNA replication that requires both mitochondrial RNA polymerase and an rG-dC sequence-specific event to form an extensive RNA-DNA hybrid.
2IBGSOM: interior and irregular boundaries growing self-organizing maps

In this paper, we introduce a new variant of growing self-organizing maps (GSOM) based on Alahakoon's algorithm for SOM training; so called 2IBGSOM (interior and irregular boundaries growing self-organizing maps). It's dynamically evolving structure for SOM, which allocates map size and shape during the unsupervised training process. 2IBGSOM starts with a small number of initial nodes and generates new nodes from the boundary and the interior of the network. 2IBGSOM represents the structure of the training data as accurately as possible. Our proposed method was tested on real world databases and showed better performance than the classical SOM and the growing grid (GG) algorithms. Three criteria were used to compare the above algorithms with our proposed method; the quantization error; the topological error and the labeling error to have more accuracy on the produced structure. Results report that 2IBGSOM shows a very good capacity of estimation for the training data based on the three tested factors.