Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Transformations of Musical Material using Gated Autoencoders

Music is usually highly structured and it is still an open question how to design models which can successfully learn to recognize and represent musical structure. A fundamental problem is that structurally related patterns can have very distinct appearances, because the structural relationships are often based on transformations of musical material, like chromatic or diatonic transposition, inversion, retrograde, or rhythm change. In this preliminary work, we study the potential of two unsupervised learning techniques—Restricted Boltzmann Machines (RBMs) and Gated Autoencoders (GAEs)—to capture pre-defined transformations from constructed data pairs. We evaluate the models by using the learned representations as inputs in a discriminative task where for a given type of transformation (e.g. diatonic transposition), the specific relation between two musical patterns must be recognized (e.g. an upward transposition of diatonic steps). Furthermore, we measure the reconstruction error of models when reconstructing musical transformed patterns. Lastly, we test the models in an analogy-making task. We find that it is difficult to learn musical transformations with the RBM and that the GAE is much more adequate for this task, since it is able to learn representations of specific transformations that are largely content-invariant. We believe these results show that models such as GAEs may provide the basis for more encompassing music analysis systems, by endowing them with a better understanding of the structures underlying music.
Entity Linking within a Social Media Platform: A Case Study on Yelp

In this paper, we study a new entity linking problem where both the entity mentions and the target entities are within a same social media platform. Compared with traditional entity linking problems that link mentions to a knowledge base, this new problem have less information about the target entities. However, if we can successfully link mentions to entities within a social media platform, we can improve a lot of applications such as comparative study in business intelligence and opinion leader finding. To study this problem, we constructed a dataset called Yelp-EL, where the business mentions in Yelp reviews are linked to their corresponding businesses on the platform. We conducted comprehensive experiments and analysis on this dataset with a learning to rank model that takes different types of features as input, as well as a few state-of-the-art entity linking approaches. Our experimental results show that two types of features that are not available in traditional entity linking: social features and location features, can be very helpful for this task.
Assessment of Microbial load of raw meat Samples sold in the Open Markets of city of Kolkata Keshab

The present investigation was undertaken to assess the microbial load of raw meat samples collected from open markets of the city of Kolkata. In the present investigation 200 raw meat samples (100 chickens and 100 muttons) were utilized. In this study, the pathogenic microorganisms were isolated in different percentages viz. E. coli (98%), Enterococcus faecalis (90%), Staphylocoocus aureus (20%), Staphylococcus epidermidis (20%), Pseudomonas spp. (10%), Salmonella spp. (2%), Bordetella (1%). Other organisms that were isolated in this study were Klebsiella pneumoniae (98%), Enterococcus aerogenes (90%), Micrococcus spp. (69%), Citrobactor spp. (52%), Proteus spp. (50%), Klebsiella oxytoca (35%) and Providencia spp. (22%). This has a definite implication from the Public Health point of view. The findings indicate substantial presence of microbial contaminants in chicken and mutton meat in open markets of Kolkata where dearth of proper sanitation in the market places prevails.
Improving Markov switching models using realized variance

This paper proposes a class of models that jointly model returns and ex-post variance measures under a Markov switching framework. Both univariate and multivariate return versions of the model are introduced. Bayesian estimation can be conducted under a fixed dimension state space or an infinite one. The proposed models can be seen as nonlinear common factor models subject to Markov switching and are able to exploit the information content in both returns and ex-post volatility measures. Applications to U.S. equity returns and foreign exchange rates compare the proposed models to existing alternatives. The empirical results show that the joint models improve density forecasts for returns and point predictions of return variance. The joint Markov switching models can increase the precision of parameter estimates and sharpen the inference of the latent state variable.
ICP etching of InP and its applications in photonic circuits

Ridge waveguides with smooth and vertical sidewalls are essential in photonic circuits. We have investigated waveguide realization with reactive ion etching of InP and InP-based structures using a SiNx in a Cl2/H2/CH4 chemistry in an ICP plasma. Depending on ICP power and RF power, etching rates can be obtained from 200 nm/min up to > 2μm/min. A maximum etching selectivity of InP vs SiNx of 12 was obtained at 2000 W of ICP power. Deep etched waveguides, fabricated in an InP/InGaAsP double heterostructure, show typical losses of 2 dB/cm. This low value shows the potential of ICP technique in the fabrication of photonic circuits.
Probabilistic Power Distribution Planning Using Multi-Objective Harmony Search Algorithm

-In this paper, power distribution planning (PDP) considering distributed generators (DGs) is investigated as a dynamic multi-objective optimization problem. Moreover, Monte Carlo simulation (MCS) is applied to handle the uncertainty in electricity price and load demand. In the proposed model, investment and operation costs, losses and purchased power from the main grid are incorporated in the first objective function, while pollution emission due to DGs and the grid is considered in the second objective function. One of the important advantages of the proposed objective function is a feeder and substation expansion in addition to an optimal placement of DGs. The resulted model is a mixed-integer non-linear one, which is solved using a non-dominated sorting improved harmony search algorithm (NSIHSA). As multi-objective optimization problems do not have a unique solution, to obtain the final optimum solution, fuzzy decision making analysis tagged with planner criteria is applied. To show the effectiveness of the proposed model and its solution, it is applied to a 9-node distribution system.
Self-Harm and its Link to Peer and Dating Violence among Adolescents in a High-Risk Urban Community

Associations between self-harm and peer and dating violence perpetration and victimization among youth in a high-risk community are examined. Cross-sectional logistic regression analyses are based on data from the Youth Violence Survey, conducted in 2004, and administered to over 80% of public school students in grades 7, 9, 11, and 12 (N=4,131) in an urban school district. Findings show that 20.3% of students reported engaging in self-harm during the previous 12 months. Moreover, self-harm was associated with peer violence perpetration and victimization for both boys and girls, and also with dating violence perpetration among boys and girls. Future research and prevention programs should consider the developmental and behavioral characteristics of adolescents who engage in both self-harm and interpersonal violence. Copyrights belong to the Author(s). Suicidology Online (SOL) is a peer-reviewed open-access journal publishing under the Creative Commons License 3.0.
DEVELOPMENT OF EFFECTIVE BALANCING PROCEDURE FOR CT SCANNER

Since the beginning of medical organizations, engineering departments have introduced new innovative ideas and designs to advance image quality. In the case of a CT scanner, one of the largest and most cost effective ways to achieve superior image quality is through a well balanced rotating array. The focus of this thesis is the development of a balancing procedure which will effectively balance a CT scanner rotating array. Rigid rotor (dynamic) balancing techniques are utilized in order to balance the Philips CT scanner rotor. Upon completion of the dynamic balancing of the CT scanner rotor a new an innovative way to balancing CT scanners is developed. This effective balancing procedure for the CT scanner rotor is proven to be efficient and cost effective when balancing in a high volume manufacturing environment.
Opportunistic Cooperative QoS Guarantee Protocol Based on GOP-length and Video Frame-diversity for Wireless Multimedia Sensor Networks

In wireless multimedia sensor networks, there are some problems, such as how to guarantee stability of multimedia sensors, how to maintain the reliable multimedia stream transmission, as well as how to establish the cooperative route of multimedia data with multimedia sensors. To address the above issues, we researched the characteristics of multimedia sensors based on throughput, packet error rate and average delay, building upon which we proposed the opportunistic cooperative QoS analysis model based on the threshold values such as the GOP length, UDP datagram size, relay sensor scale and video Frame density. Then the opportunistic cooperative QoS guarantee protocol based on GOP-length and Video Frame-diversity (OCQ-GF) was proposed. More importantly, the sender and relay sensors would do the QPSK modulation scheme with 12 frequency division to further improve the performance of the proposed OCQ-GF mechanism. The mathematical and simulation results show the proposed OCQ-GF could perform better in terms of throughput, packet error rate and average delay, as well as Peak Signal to Noise Ratio (PSNR), compared with the traditional cooperative QoS guarantee approach.
Transiting to depth disrupts overall dynamic body acceleration and oxygen consumption rate in freely diving Steller sea lions

Previous research has presented contradictory evidence on the ability of overall dynamic body acceleration (ODBA) to predict mass-corrected oxygen consumption (sVO2) in airbreathing diving vertebrates. We investigated a potential source of these discrepancies by partitioning the ODBA-sVO2 relationship over 3 phases of the dive cycle (transiting to and from depth, bottom time, and post-dive surface interval). Trained Steller sea lions Eumetopias jubatus executed 4 types of dives to 40 m (single dives, long-duration dive bouts of 4-6 dives, short-duration dive bouts of 10 or 12 dives, and transit dives with minimal bottom duration). Partitioning single dives by dive phase showed differing patterns in the ODBA-sVO2 relationship among dive phases, but no significant linear relationships were observed. The proportion of the dive cycle spent tran siting to and from the surface was a significant predictive factor in the ODBA-sVO2 relationship, while bottom duration or post-dive surface interval had no effect. ODBA only predicted sVO2 for dives when the proportion of time spent transiting was small. The apparent inability of ODBA to reliably predict sVO2 reflects differences in the inherent relationships between ODBA and sVO2 during different phases of the dive. These results support the growing body of evidence that ODBA on its own is not a reliable field predictor of energy expenditure at the level of the single dive or dive bout in air-breathing diving vertebrates likely because ODBA (a physical measure) cannot account for physiological changes in sVO2 that occur during the different phases of a dive cycle.
Altered EEG resting state networks in Parkinson ’ s disease

Background: Parkinson’s disease (PD) affects primarily the motor system with the nonmotor symptoms gaining more attention recently. In PD global brain networks are altered, not only during motor tasks, but also at rest. We therefore tested PD patients and healthy controls with electroencephalography (EEG) and extracted resting state networks (RSNs). In particular, we hypothesized the sensorimotor network in PD to be spatially decreased, as reported in a previous functional Magnetic Resonance Imaging (fMRI) study [1]. Furthermore we aimed at identifying frequency specific oscillatory network changes.
MBeacon: Privacy-Preserving Beacons for DNA Methylation Data

The advancement of molecular profiling techniques fuels biomedical research with a deluge of data. To facilitate data sharing, the Global Alliance for Genomics and Health established the Beacon system, a search engine designed to help researchers find datasets of interest. While the current Beacon system only supports genomic data, other types of biomedical data, such as DNA methylation, are also essential for advancing our understanding in the field. In this paper, we propose the first Beacon system for DNA methylation data sharing: MBeacon. As the current genomic Beacon is vulnerable to privacy attacks, such as membership inference, and DNA methylation data is highly sensitive, we take a privacy-by-design approach to construct MBeacon. First, we demonstrate the privacy threat, by proposing a membership inference attack tailored specifically to unprotected methylation Beacons. Our experimental results show that 100 queries are sufficient to achieve a successful attack with AUC (area under the ROC curve) above 0.9. To remedy this situation, we propose a novel differential privacy mechanism, namely SVT2 , which is the core component of MBeacon. Extensive experiments over multiple datasets show that SVT2 can successfully mitigate membership privacy risks without significantly harming utility. We further implement a fully functional prototype of MBeacon which we make available to the research community
Testing conceptual and physically based soil hydrology schemes against observations for the Amazon Basin

Abstract. This study analyzes the performance of the two soil hydrology schemes of the land surface model ORCHIDEE in estimating Amazonian hydrology and phenology for five major sub-basins (Xingu, Tapajos, Madeira, Solimoes and Negro), during the 29-year period 1980–2008. A simple 2-layer scheme with a bucket topped by an evaporative layer is compared to an 11-layer diffusion scheme. The soil schemes are coupled with a river routing module and a process model of plant physiology, phenology and carbon dynamics. The simulated water budget and vegetation functioning components are compared with several data sets at sub-basin scale. The use of the 11-layer soil diffusion scheme does not significantly change the Amazonian water budget simulation when compared to the 2-layer soil scheme (+3.1 and −3.0% in evapotranspiration and river discharge, respectively). However, the higher water-holding capacity of the soil and the physically based representation of runoff and drainage in the 11-layer soil diffusion scheme result in more dynamic soil water storage variation and improved simulation of the total terrestrial water storage when compared to GRACE satellite estimates. The greater soil water storage within the 11-layer scheme also results in increased dry-season evapotranspiration (+0.5 mm d−1, +17%) and improves river discharge simulation in the southeastern sub-basins such as the Xingu. Evapotranspiration over this sub-basin is sustained during the whole dry season with the 11-layer soil diffusion scheme, whereas the 2-layer scheme limits it after only 2 dry months. Lower plant drought stress simulated by the 11-layer soil diffusion scheme leads to better simulation of the seasonal cycle of photosynthesis (GPP) when compared to a GPP data-driven model based on eddy covariance and satellite greenness measurements. A dry-season length between 4 and 7 months over the entire Amazon Basin is found to be critical in distinguishing differences in hydrological feedbacks between the soil and the vegetation cover simulated by the two soil schemes. On average, the multilayer soil diffusion scheme provides little improvement in simulated hydrology over the wet tropical Amazonian sub-basins, but a more significant improvement is found over the drier sub-basins. The use of a multilayer soil diffusion scheme might become critical for assessments of future hydrological changes, especially in southern regions of the Amazon Basin where longer dry seasons and more severe droughts are expected in the next century.
Implicit Government Guarantee and the CDS Spreads

It is commonly agreed that the government is more likely to step in and rescue some troubled companies labeled as “too big to fail” or “too interconnected to fail. ” Since there is no formal contract between these companies and the government, this potential intervention is referred to as an implicit government guarantee. We propose a new approach of assessing and estimating the implicit government guarantee and analyze whether it is reflected in the CDS spreads. We define the implicit government guarantee for a given company as the probability that the government will bail it out in case of a default. Although the company’s size affects the likelihood of government intervention, we find that financial industry membership is a more important factor. Furthermore, we find that the implicit government guarantee is priced into the CDS spreads. The government guarantee for large companies reduces the CDS spread by 16.11 bps and for small companies only by 3.73 bps. Similarly, for the financial industry, we find that the government guarantee reduces the CDS spread by 76.29 bps and for the nonfinancial industry only by 7.50 bps.
Chemical Permeation Enhancers for Transdermal Delivery of Thiocolchicoside: Assessment of Ex-vivo Skin Flux and In-vivo Pharmacokinetics

Present investigation comprises the study of ex-vivo skin flux and in-vivo pharmacokinetics of Thiocolchicoside (THC) from transdermal films. The films were fabricated by solvent casting technique employing combination of hydrophilic and hydrophobic polymers. A flux of 18.08 µg/cm2h and 13. 37µg/cm2h was achieved for optimized formulations containing 1, 8-cineole and oleic acid respectively as permeation enhancers. The observed flux values were higher when compared to passive control (8.66 µg/cm2h). Highest skin permeation was observed when 1,8-cineole was used as chemical permeation enhancer and it considerably (2-2.5 fold) improved the THC transport across the rat skin. In vivo studies were performed in rabbits and samples were analysed by LC-MS-MS. The mean area under the curve (AUC) values of transdermal film showed about 2.35 times statistically significant (p<0.05) improvement in bioavailability when compared with the oral administration of THC solution. The developed transdermal therapeutic systems using chemical permeation enhancers were suitable for drugs like THC in effective management of muscular pain.
Multi-Port Test for Quasi-Static Lumped Element Stand-Alone Model

The stand-alone package modeling scheme is aimed at estimating the high frequency electromagnetic coupling between different types of packages and external structures, such as printed circuit board (PCB) traces and power/ground planes, or other stacked packages, by limiting at the same time the disclosure of internal details of the package itself. The package is at first modeled in stand-alone conditions, that is without any PCB, by using all the detailed information. In the second stage the interaction with a particular PCB is taken into account with a simplified model, which can be open to the public and also reduces the complexity of the calculation. The quasi-static lumped element stand-alone model is one of the possible implementation of this modeling scheme. In the present paper this model is used to calculate the interaction of a scaled quad flat package (QFP) model with two PCBs having different configurations. The results are compared with the measurement results obtained with a 4port network analyzer.
Single nucleotide polymorphisms in BMPR-IB and STAT 5 B genes and their association with growth and reproductive traits in chicken

The aim of the current study was to investigate the association of G4533815A SNP in STAT5B and A287G SNP in BMPR-IB genes with growth and reproduction related traits in chicken. A sample of 205 individuals from breeding station of Mazandaran native chicken population was selected randomly. All of the individuals were genotyped for both SNPs using PCR-RFLP technique. Marker-trait association analyses were performed using estimated breeding value of the traits as dependent variable in GLM procedure of SAS 9.1. Results suggested that breeding value least square means for genotypes of G4533815A SNP is significantly differed from each other for traits of body weight at 8 and 12 weeks (P<0.01). In the case of BMPR-IB gene, no significant difference was found. In conclusion, STAT5B gene may be associated with body growth in chicken and may be considered in Marker Assisted Selection program to improve chicken growth performance.
11 CHAPTER Parametric Estimation

This chapter considers the topic of parametric estimation, which is an important engineering concept that is often used for modeling signals and systems. When a complex signal or system is encountered, it is often desirable to construct a model for the signal or system in question such that it can be readily analyzed. For an input-output system, this may be to understand its behavior, its potential weaknesses, or its stability to bounded inputs. For signals of interest, this may be to understand the content of the signal, such as to determine the words that were spoken in an acoustic signal containing a recording of human speech. In many such cases, models for the signals or systems of interest are sought that can be practically analyzed without excess computation. As in Occam’s razor, a parsimonious model is better than a needlessly complicated one. A good model would be one that is sufficiently rich to enable insights into the salient characteristics of the signals or systems under study, but not any more so than necessary. By using parametric models, whose behavior is uniquely determined by a finite set of parameters, the complexity of the model can often be controlled by limiting the number of parameters. In this chapter, we focus on the estimation of such model parameters under a variety of scenarios. Using both statistical and deterministic formulations, rational models (linear models with a finite number of poles and zeros) are considered in both the batch and online recursive formulations. Such autoregressive and moving average models form the basis for our initial investigation, after which we explore advanced topics, such as spectrum estimation, prediction, and filtering, as well as a number of nonlinear parametric modeling techniques. The background assumed in this chapter is a working knowledge of discrete-time signals and systems, some basic probability theory including knowledge of stationary random processes, and the mathematical skills of a senior level undergraduate student in electrical engineering or a similar discipline. An exemplary treatment of these topics includes the texts by Oppenheim et al. [1], Rabiner and Gold [2], and Rabiner and Schafer [3]. These texts cover discrete-time signal and system theory, with the latter covering some random processes. Additional material on probability and stochastic processes as applied to the problems in this chapter can be found in [4,5].