Miracle-dz/UniFAR-FTUs huggingface dataset:
Go Pro Bono: Prosocial Language as a Success Factor in Crowdfunding

Crowdfunding enables fundraising of various ventures by collecting money from several donors. We argue that the inclusion of prosocial language contributes to success in this new domain of resource acquisition. In Study 1, we analyzed 164,056 projects from the online crowdfunding platform Kickstarter and found that the higher the percentage of prosocial words employed in a project’s description, the larger the number of investors and the greater the chances of reaching a funding goal. In Study 2 (N = 234), an experimental study, we documented that the use of prosocial words increases the support people thought they would give to a project. Our results indicate that people want to invest their financial resources in ventures that contribute to prosocial goals.
Prosodic modeling with rich syntactic context in HMM-based Mandarin speech synthesis

To further explore the relevance between prosody and syntax information, we propose a novel approach of prosodic modeling with rich syntactic context instead of prosodic structure in HMM-based Mandarin speech synthesis. Considering the characteristics of Mandarin itself, word-based and character-based syntactic parsings are investigated in this study respectively. This method can not only avoid the existing cascade error in conventional way of prosodic parameter prediction but also not rely on the manually annotated corpora of prosodic structure. Experimental results show that even though automatic syntactic parsing has limited precision, prosodic modeling with rich syntactic context could still achieve significant better performance than the way of the manually annotated prosodic corpora, especially in duration evaluation.
Kinetic Theory and Mesoscopic Noise

In the theory of noise processes for mesoscopic conductors, the relationship between shot noise and hot-electron noise is absolutely fundamental to understanding the underlying microscopic fluctuations. From the vantage point of orthodox microscopics and kinetics, their relation is a long way from being settled. Its resolution calls for the tools of many-body theory. We motivate the many-body approach to noise, review the analysis of conductance and current noise within linear diffusive theories, and discuss the smooth crossover between thermal noise and shot noise. We outline a kinetic theory of nonequilibrium fluctuations, and show how this completely orthodox approach directly negates drift-diffusive explanations of the crossover.
Foreign Trade Regimes and Import Demand Function: Evidence from Sri Lanka

Time series data for Sri Lanka span periods of pervasive trade and exchange restrictions along with periods of liberalized trade. This paper implements a structural econometric model of aggregate imports which incorporates the implications of the shifts in the policy regime. The results demonstrate that the model outperforms the existing alternatives both on statistical and economic grounds. The estimated elasticities, in contrast to the available evidence, have correct signs, high statistical significance, and plausible magnitudes. The implications for policy analysis like calculation of equilibrium exchange rate are discussed. Special Note: If you are looking for the paper titled "Import Demand Under Trade and Exchange Rate Restrictions: A Structural Econometric Approach with an Application to India", it has been withdrwan temporarily. A revised version will be posted in near future.
Extraction with SPME and Synthesis of 2-Methyl-6-vinylpyrazine by a ‘One Pot’ Reaction Using Microwaves

A synthesis of 2-methyl-6-vinylpyrazine was carried out by way of a ‘one pot’ reaction. In order to establish the efficiency of this synthesis the extraction of the volatiles released by male papaya fruit flies was performed by SPME (solid phase micro-extraction). The compound was separated and identified using GC/MSD (gas chromatography/mass spectrometry detector).
PR2 looking at things — Ensemble learning for unstructured information processing with Markov logic networks

We investigate the perception and reasoning task of answering queries about realistic scenes with objects of daily use perceived by a robot. A key problem implied by the task is the variety of perceivable properties of objects, such as their shape, texture, color, size, text pieces and logos, that go beyond the capabilities of individual state-of-the-art perception methods. A promising alternative is to employ combinations of more specialized perception methods. In this paper we propose a novel combination method, which structures perception in a two-step process, and apply this method in our object perception system. In a first step, specialized methods annotate detected object hypotheses with symbolic information pieces. In the second step, the given query Q is answered by inferring the conditional probability P(Q | E), where E are the symbolic information pieces considered as evidence for the conditional probability. In this setting Q and E are part of a probabilistic model of scenes, objects and their annotations, which the perception method has beforehand learned a joint probability distribution of. Our proposed method has substantial advantages over alternative methods in terms of the generality of queries that can be answered, the generation of information that can actively guide perception, the ease of extension, the possibility of including additional kinds of evidences, and its potential for the realization of self-improving and - specializing perception systems. We show for object categorization, which is a subclass of the probabilistic inferences, that impressive categorization performance can be achieved combining the employed expert perception methods in a synergistic manner.
How Master Teachers Conceptualize Student Engagement: A Comparison of Theoretical and Practitioner Perspectives

ix Chapter One: Introduction . 1 Statement of Problem and Rationale for Study.1 Research Background . 3 Research Questions . 8 Research Design and Methodology . 9 Statement of Purpose . 9 Definition of Terms.11 Chapter Two: Literature Review . 13 Conceptualizing Student Engagement . 15 Approaching Student Engagement from Outside Constructs . 17 Specific Theories of Engagement . 22 The ABC Model as a Pragmatic Solution in Engagement Research . 42 The Potential for Master Teachers as Participants in Engagement Research . 44 Expert Teachers . 46 NBCTs . 48 How Teachers Develop Expertise . 52
Opportunistic Music

While mixed reality has inspired the development of many new musical instruments, few approaches explore the potential of mobile setups. We present a new musical interaction concept, called opportunistic music. It allows musicians to recreate a hardware musical controller using any objects of their immediate environment. This approach benefits from the physical attributes of real objects for controlling music. Our prototype is based on a stereo-vision tracking system associated with FSR sensors. It allows musicians to define and to interact with opportunistic tangible widgets. Linking these widgets with sound processes allows the interactive creation of musical pieces, where musicians get inspiration from the surrounding environment.
Predictors of success for community-driven water quality management—Lessons from three catchments in New Zealand

ABSTRACT Three community engagement projects on the South Island of New Zealand are enacting education and communication initiatives to improve the uptake of best management practices on farms regarding nutrient management for improving water quality. Understanding the enablers and barriers to effective community-based catchment management is fundamental to planning, implementing and evaluating these initiatives. This article investigates some key predictors of success or determinants of effective community engagement. Six factors were identified: access to science, training, and information; confidence in regulatory policies; leadership; farm planning and monitoring; trust in the public; and concern for water quality.
The effect of packing hydrophilization on bacterial attachment and the relationship with the performance of biotrickling filters

Many bioprocesses depend on the effective formation of a biofilm on a solid support. In the present study, three different surface treatments (sandblasting, pure‐O2 plasma, and He–O2 plasma treatments) were conducted on polypropylene (PP) Pall rings used as a support in biotrickling filters for air pollution control. The intent was to modify the ring surface and/or electrochemical properties in order to possibly improve cell adhesion, wetting properties, and possibly reduce the start‐up time and increase the performance of the biotrickling filters. The surface treatments were found to generally increase the hydrophilicity and the zeta potential of the surfaces. However, the startup and performance of lab‐scale biotrickling filters packed with treated Pall rings were not significantly different than the control with untreated rings. Cell and colloid deposition experiments conducted in flow cells showed that the treated surfaces and the hydrodynamic conditions were not favorable for cell deposition indicating that there could be significant opportunities for improving packings used in environmental bioprocess applications. Biotechnol. Bioeng. 2009;103: 1060–1067. © 2009 Wiley Periodicals, Inc.
Endogenous Origins of HIV-1 G-to-A Hypermutation and Restriction in the Nonpermissive T Cell Line CEM2n

The DNA deaminase APOBEC3G converts cytosines to uracils in retroviral cDNA, which are immortalized as genomic strand G-to-A hypermutations by reverse transcription. A single round of APOBEC3G-dependent mutagenesis can be catastrophic, but evidence suggests that sublethal levels contribute to viral genetic diversity and the associated problems of drug resistance and immune escape. APOBEC3G exhibits an intrinsic preference for the second cytosine in a 5′CC dinucleotide motif leading to 5′GG-to-AG mutations. However, an additional hypermutation signature is commonly observed in proviral sequences from HIV-1 infected patients, 5′GA-to-AA, and it has been attributed controversially to one or more of the six other APOBEC3 deaminases. An unambiguous resolution of this problem has been difficult to achieve, in part due to dominant effects of protein over-expression. Here, we employ gene targeting to dissect the endogenous APOBEC3 contribution to Vif-deficient HIV-1 restriction and hypermutation in a nonpermissive T cell line CEM2n. We report that APOBEC3G-null cells, as predicted from previous studies, lose the capacity to inflict 5′GG-to-AG mutations. In contrast, APOBEC3F-null cells produced viruses with near-normal mutational patterns. Systematic knockdown of other APOBEC3 genes in an APOBEC3F-null background revealed a significant contribution from APOBEC3D in promoting 5′GA-to-AA hypermutations. Furthermore, Vif-deficient HIV-1 restriction was strong in parental CEM2n and APOBEC3D-knockdown cells, partially alleviated in APOBEC3G- or APOBEC3F-null cells, further alleviated in APOBEC3F-null/APOBEC3D-knockdown cells, and alleviated to the greatest extent in APOBEC3F-null/APOBEC3G-knockdown cells revealing clear redundancy in the HIV-1 restriction mechanism. We conclude that endogenous levels of APOBEC3D, APOBEC3F, and APOBEC3G combine to restrict Vif-deficient HIV-1 and cause the hallmark dinucleotide hypermutation patterns in CEM2n. Primary T lymphocytes express a similar set of APOBEC3 genes suggesting that the same repertoire may be important in vivo.
Diversity and coding gain evolution in graph codes

This work is a first attempt to analyze and understand the coding gain of full-diversity (MDS) low-density parity-check (LDPC) codes. A diversity population evolution is derived. The steady-state distribution of energy coefficients is found. Based on this new analysis, we show new full-diversity LDPC codes with improved performance. Finally, we describe a family of graph codes where all variable nodes, including parity nodes, achieve full diversity after a large number of decoding iterations. Full-diversity MDS low-density codes are not only useful for digital data transmission over wireless channels, some other applications can be found in situations where coding for a finite number of states is required.
Spectro-temporal features for noise-robust speech recognition using power-law nonlinearity and power-bias subtraction

Previous work has demonstrated that spectro-temporal Gabor features reduced word error rates for automatic speech recognition under noisy conditions. However, the features based on mel spectra were easily corrupted in the presence of noise or channel distortion. We have exploited an algorithm for power normalized cepstral coefficients (PNCCs) to generate a more robust spectro-temporal representation. We refer to it as power normalized spectrum (PNS), and to the corresponding output processed by Gabor filters and MLP nonlinear weighting as PNS-Gabor. We show that the proposed feature outperforms state-of-the-art noise-robust features, ETSI-AFE and PNCC for both Aurora2 and a noisy version of the Wall Street Jounal (WSJ) corpus. A comparison of the individual processing steps of mel spectra and PNS shows that power bias subtraction is the most important aspect of PNS-Gabor features to provide an improvement over Mel-Gabor features. The result indicates that Gabor processing compensates the limitation of PNCC for channels with frequency-shift characteristic. Overall, PNS-Gabor features decrease the word error rate by 32% relative to MFCC and 13% relative to PNCC in Aurora2. For noisy WSJ, they decrease the word error rate by 30.9% relative to MFCC and 24.7% relative to PNCC.
Observing entrainment in Indian music performance : Video-based observational analysis of tanpura playing and beat marking

Entrainment has been suggested as an important phenomenon underlying aspects of musical behaviour, and is attracting increasing attention in music psychology (see e.g. Large and Jones 1999, Large 2000), and in ethnomusicology (Clayton, Sager and Will 2004) as well as in other scientific fields (see e.g. Strogatz 1994, 2003). Approaches to its study in ethnomusicology must address a significant methodological problem: how to study entrainment phenomena in an ecologically valid manner, and to integrate this process into a programme of ethnographic research. Video recordings contain important data regarding the physical movements of participants in musical events (as well as their audible results), and through the application of observational analysis software these recordings can form the basis of studies of entrainment between different quasi-periodic musical processes as manifested in movement patterns. For the present study a short video clip of an Indian raga performance was selected (taken from a performance of Shree Rag by the singer Veena Sahasrabuddhe). Observational analysis was carried out using The Observer Video-Pro software, configured to record the plucking of tanpura strings and performers’ beat markers (hand or finger taps). Time series data thus generated were analysed using plots of phase relationships, revealing several instances of both selfand interpersonal entrainment (the stated intention of the performers is, on the contrary, that the tanpura rhythms should each proceed independently). Entrainment between these behaviours points to a complex, but unintended form of emergent order. This unexpected result demonstrates the usefulness of this method in revealing otherwise unnoticed phenomena in musical performance, and raises important questions for future research.
Application of Advanced Process Control techniques for a cement rotary kiln

In this work, the control optimization of a rotary kiln located in a cement plant is described. The need to enhance the efficiency level, to increase the profitability as well as the commitment to meet precise production standards has motivated the adoption of Model Predictive Control techniques. The adopted system architecture is composed of two different optimization layers. A suitable interaction policy has been developed so as to improve control performances and to meet possibly variable economic goals. The advantages of the proposed architecture are shown by significant simulations. The developed predictive control system has been implemented on real plant and its overall performances are compared to the performances of the previous standard PID control system. Very satisfactory system performances have been achieved been able to attain both economic benefits and minimization of environmental noxious emissions.
Design of an Internet of Things-based smart home system

With more and more applications of Internet of Things in many domains, it also steps into Smart Homes. In this paper, we propose an Internet of Things-based smart home system for home comfort, leisure and safety. This system uses embedded system, 3G, and ZIGBEE technologies to overcome the drawbacks of current smart home systems such as discrete functions, poor portability, weak updating capability, and personal computer dependence. Moreover, the system architecture is presented, and the design of its gateway is shown in detail from hardware to software.
Correction: Selective Targeting of CTNNB1-, KRAS- or MYC-Driven Cell Growth by Combinations of Existing Drugs

The aim of combination drug treatment in cancer therapy is to improve response rate and to decrease the probability of the development of drug resistance. Preferably, drug combinations are synergistic rather than additive, and, ideally, drug combinations work synergistically only in cancer cells and not in non-malignant cells. We have developed a workflow to identify such targeted synergies, and applied this approach to selectively inhibit the proliferation of cell lines with mutations in genes that are difficult to modulate with small molecules. The approach is based on curve shift analysis, which we demonstrate is a more robust method of determining synergy than combination matrix screening with Bliss-scoring. We show that the MEK inhibitor trametinib is more synergistic in combination with the BRAF inhibitor dabrafenib than with vemurafenib, another BRAF inhibitor. In addition, we show that the combination of MEK and BRAF inhibitors is synergistic in BRAF-mutant melanoma cells, and additive or antagonistic in, respectively, BRAF-wild type melanoma cells and non-malignant fibroblasts. This combination exemplifies that synergistic action of drugs can depend on cancer genotype. Next, we used curve shift analysis to identify new drug combinations that specifically inhibit cancer cell proliferation driven by difficult-to-drug cancer genes. Combination studies were performed with compounds that as single agents showed preference for inhibition of cancer cells with mutations in either the CTNNB1 gene (coding for β-catenin), KRAS, or cancer cells expressing increased copy numbers of MYC. We demonstrate that the Wnt-pathway inhibitor ICG-001 and trametinib acted synergistically in Wnt-pathway-mutant cell lines. The ERBB2 inhibitor TAK-165 was synergistic with trametinib in KRAS-mutant cell lines. The EGFR/ERBB2 inhibitor neratinib acted synergistically with the spindle poison docetaxel and with the Aurora kinase inhibitor GSK-1070916 in cell lines with MYC amplification. Our approach can therefore efficiently discover novel drug combinations that selectively target cancer genes.
Towards Credible Driver Behavior Modeling

In order to predict the driver's behaviour, to adapt different mechanisms of the car or to tune up the traffic controls, one has to create a reliable driver's model. Such approaches have already been made, based on the assumption, that certain similarity must be observed for one driver doing the same maneuver (e.g. braking) or even in certain situations for different drivers. The research presented in this paper shows clearly that such assumptions are wrong in general, based on a number of real-life experiments using a real car (equipped with necessary sensors) in real traffic conditions. The experiments show that the same driver performs his/hers maneuvers differently, even though they could have been assumed to be the same or at least very similar. Thus necessity of further looking for reliable models of driver's behaviour is proven.
Fuzzy based Modified SHL algorithm for Spiking Neural Networks

Spiking neural network is the 3 generation neural network. In this paper, we derive spiking neural network‘s topology and the fuzzy reasoning by restricting to the usage of biological components. Input encodes information in the timing of spike train. Fuzzy reasoning is used on biological components such as dynamic synapse, receptive field, inhibitory and excitatory neurons. The enrichment of the flow of information is done by dynamic synapse and the neuron selection by using receptive field. Modeling the dynamics of the limited synaptic resources makes neurons selective to particular spike frequencies. The receptive field behaves like fuzzy membership function which enables the individual neuron respond at certain spike train frequency. The network is supervised and learning occurs at the output layer of the network. Various issues arise while learning with supervised method takes place, namely convergence and continuous updating of weights after the goal is achieved. These issues are discussed in detail and are resolved. The modified SHL algorithm is used for learning. The classification problem of XOR is solved. The implementation is done on MATLAB.
Gabor spectrogram based risk of aliasing

Resampling is a part of many image processing tasks. Traditional linear resampling techniques must compromise between edge-preservation and anti-aliasing. This paper presents a method to locally estimate the risk of aliasing based on a space-frequency representation by means of the Gabor spectrogram. The proposed approach can be applied to general lattice arrangements of the source and the target lattice. To show the feasibility of this new method, results of a practical application are shown, i.e., resampling an image for gravure printing reproduction by a non-linear edge-preserving resampling technique based on the risk of aliasing. Key-Words: Gabor spectrogram, joint time-frequency analysis, aliasing, non-linear resampling
Hashing Functions Performance in Packet Classification

Packet classification remains an important aspect of network processing as it encompasses increasingly more functionality due to newly introduced services. Essentially, it entails the matching of incoming packets against a database of rules performing the operation that is associated with the matching rule with the highest priority. Within the hashingbased packet classification algorithms, the tuple space search algorithm is gaining much interest. Using tuple spaces, the rules can now be subdivided into sets and in turn these sets can be searched in parallel. Within each set, the hashing functions determine the location of storing the rules. However, depending on the chosen collection of hashing functions, rules (within a set) can be mapped to the same location (containing multiple buckets to store the rules) resulting in a collision. A side-effect of such collisions is that more memory accesses are needed to resolve the collision resulting in degraded performance. In this paper, we compare and evaluate different hashing functions taking from the H3 class of hashing functions using which collisions can be reduced and in turn reduce the average bucket sizes. Our results show that when using the H3 class of hashing functions, we were able to reduce the number of collisions by at least 7% and by at most 49% when compared to other hashing functions.