Miracle-dz/UniFAR-FTUs huggingface dataset:
Popular Music Retrieval by Independent Component Analysis

2. OUR APPROACH In published literature, usually, acoustic input singing or humming is pitch-tracked and segmented into notes or converted into three or five level melody contour, melody contour is also extracted from music in database, our approach (Figure 1) does not try to segment notes at all, we employ statistic model to extract singing from popular music, user’s input singing and extracted singing are converted to self-similarity sequence, which is a curve in 2D space, music retrieval is equivalent to comparison of these curves. Indices on music database are the weights of recurrent neural network; similarity of query key with music in database is represented by their correlation degree.
TouchDevelop: programming cloud-connected mobile devices via touchscreen

The world is experiencing a technology shift. In 2011, more touchscreen-based mobile devices like smartphones and tablets will be sold than desktops, laptops, and netbooks combined. In fact, in many cases incredibly powerful and easy-to-use smart phones are going to be the first and, in less developed countries, possibly the only computing devices which virtually all people will own, and carry with them at all times. Furthermore, mobile devices do not only have touchscreens, but they are also equipped with a multitude of sensors, such as location information and acceleration, and they are always connected to the cloud. TouchDevelop is a novel application creation environment for anyone to script their smartphones anywhere -- you do not need a separate PC. TouchDevelop allows you to develop mobile device applications that can access your data, your media, your sensors and allows using cloud services including storage, computing, and social networks. TouchDevelop targets students, and hobbyists, not necessarily the professional developer. Typical TouchDevelop applications are written for fun, or for personalizing the phone. TouchDevelop's typed, structured programming language is built around the idea of only using a touchscreen as the input device to author code. It has built-in primitives which make it easy to access the rich sensor data available on a mobile device. In our vision, the state of the program is automatically distributed between mobile clients and the cloud, with automatic synchronization of data and execution between clients and cloud, liberating the programmer from worrying (or even having to know about) the details. We report on our experience with our first prototype implementation for the Windows Phone 7 platform, which already realizes a large portion of our vision. It is available on the Windows Phone Marketplace.
Signal processing in receivers for communication over MIMO ISI channels

Digital transmission over dispersive channels which suffer from intersymbol interference and multiuser interference is reviewed. In particular, the digital signal processing required at the receiver for performing combined spatial/temporal decision-feedback equalization is studied and the aims of processing the received signal are discussed. The calculation of the optimum filters by solving a factorization problem and an practical algorithm for its solution are reviewed. Finally, the optimality of DFE with respect to channel capacity is shown.
Apoptosis of Prostate Cancer Cells Gene Induces Growth Inhibition α Acetyl-CoA-Carboxylase-Mediated Silencing of the − RNA Interference

Overexpression of lipogenic enzymes is a common characteristic of many cancers. Thus far, studies aimed at the exploration of lipogenic enzymes as targets for cancer intervention have focused on fatty acid synthase (FAS), the enzyme catalyzing the terminal steps in fatty acid synthesis. Chemical inhibition or RNA interference (RNAi)–mediated knockdown of FAS consistently inhibits the growth and induces death of cancer cells. Accumulation of the FAS substrate malonyl-CoA has been implicated in the mechanism of cytotoxicity of FAS inhibition. Here, using RNAi technology, we have knocked down the expression of acetyl-CoA carboxylase-A (ACC-A), the enzyme providing the malonylCoA substrate. Silencing of the ACC-a gene resulted in a similar inhibition of cell proliferation and induction of caspase-mediated apoptosis of highly lipogenic LNCaP prostate cancer cells as observed after FAS RNAi. In nonmalignant cells with low lipogenic activity, no cytotoxic effects of knockdown of ACC-A or FAS were observed. These findings indicate that accumulation of malonyl-CoA is not a prerequisite for cytotoxicity induced by inhibition of tumorassociated lipogenesis and suggest that in addition to FAS, ACC-A is a potential target for cancer intervention. (Cancer Res 2005; 65(15): 6719-25)
Availability-based Task Monitoring and Adaptation Mechanism in Desktop Grid System

Compliance with the time constraint of the QoS requirement is challenging in the desktop grid environment. Measurements of node behavior indicate the availability of node is cyclic. It is helpful to estimate the execution time of a submitted task on the allocated resource. This paper proposes a novel approach based on available time model to monitor the task execution process and provide running-time adaptation mechanism. Based on this model, a new notion, i.e. Emergency Degree, is proposed to quantitatively indicate the risk of whether the node can complete the task in time. The approach has been implemented and validated on a desktop grid platform - XtremWeb. The evaluation shows the model and mechanism are practical and effective to guarantee the time constraint of the task.
Gas Chromatography-Mass Spectrometry-Based Metabolic Profiling of Cerebrospinal Fluid from Epileptic Dogs

ABSTRACT Epilepsy is a common neurological disorder with seizures, but diagnostic approaches in veterinary clinics remain limited. Cerebrospinal fluid (CSF) is a body fluid used for diagnosis in veterinary medicine. In this study, we explored canine epilepsy diagnostic biomarkers using gas chromatography-mass spectrometry (GC-MS)-based metabolic profiling of CSF and multivariate data analysis. Profiles for subjects with idiopathic epilepsy differed significantly from those of healthy controls and subjects with symptomatic epilepsy. Among 60 identified metabolites, the levels of 20 differed significantly among the three groups. Glutamic acid was significantly increased in idiopathic epilepsy, and some metabolites including ascorbic acid were changed in both forms of epilepsy. These findings show that metabolic profiles of CSF differ between idiopathic and symptomatic epilepsy and that metabolites including glutamic acid and ascorbic acid in CSF may be useful for diagnosis of canine epilepsy.
Usefulness of Faecal Calprotectin Measurement in Children with Various Types of Inflammatory Bowel Disease

Introduction. The aim of the study was to assess the usefulness of the FC measurement in children with various types of IBD and relation to the disease activity. Patients and Methods. 91 patients (49 boys: 53.85% and 42 girls: 46.15%, mean age: 13.38 years, range 6–18 years) were included in the analysis. Patients were divided into the groups: B1—24 children with CD, B2—16 patients with UC, and a group comprising 31 children with other types of colitis; the control group (K) comprised 20 healthy children. FC was assayed by ELISA method, using Phical test (Calpro). Results. The mean faecal calprotectin concentrations were higher in children with CD and UC as compared to healthy controls, patients with eosinophilic, lymphocytic, and nonspecific colitis. A positive correlation was observed between FC concentrations and the disease activity (the PCDAI scale, the Truelove-Witts Scale, and the endoscopic Rachmilewitz Index). Conclusion. It seems that the FC concentrations can be a useful, safe, and noninvasive test in children suspected for IBD, since FC concentration is higher in children with CD and UC than in patients with other inflammatory diseases.
Movement patterns in tag football: Influence of playing position, representative selection and fatigue

This study investigated the physical demands of male and female tag football players, stratified by playing position and representative selection. Global positioning systems provided data on 98 playing appearances during the 2013 Queensland State Cup. Players covered 97 m/min (men’s) and 91 m/min (women’s) over ~23 min of on-field playing time. High-speed running (HSR) accounted for 15% (men’s) and 13% (women’s) of total distance and there were regular changes in running tempo in men’s and women’s matches (1 acceleration >1 m. s-2 every 10-11 s). Selected players covered greater distance (men ES=0.72, p=0.145; women ES=1.06, p=0.002), performed more HSR efforts (men ES=0.73, p=0.085; women ES=1.20, p=0.001) and reached higher running speeds (men ES=0.65, p=0.033; women ES=0.53, p=0.092) than non-selected players. Positional groups showed meaningful differences for very high-intensity running (VHI) distance (men ES=0.56-1.26; women ES=0.53-0.83), frequency of VHI efforts (men ES=0.45-0.97; women ES=0.51-0.98) and maximum running speeds (men ES=0.54-1.35; women ES=0.08-0.69). This study found repeated high-intensity running, change of direction and sprinting performance to be important physical qualities of successful tag football players. Importantly however, the high-intensity movement demands of tag football were found to be dependent on playing position and training should be individualised accordingly.
The Power Spectrum of the CfA/SSRS UZC Galaxy Redshift Survey

The combined CfA2/SSRS redshift catalog has recently been improved and made public. We compute the redshift-space power spectrum of this catalog using the regions at -2.5° ≤ δ1950 ≤ 50° north (8h ≤ α1950 ≤ 17h) and south (20h ≤ α1950 ≤ 3h,b ≤ -13°) of the Galactic plane, where it is 98% complete down to Zwicky magnitude 15.5 and contains 13,681 galaxies. Our analysis uses Heavens-Taylor mode expansion, Karhunen-Loève data compression, and the Fisher matrix technique to compute quadratic band power estimates. This allows an exact calculation of window functions, including the integral constraint, in addition to the production of a power spectrum with uncorrelated error bars. Our results with this larger data set agree well with those of previous studies. We analyze 101 h-1 Mpc and 130 h-1 Mpc volume-limited subsets in addition to the full magnitude-limited sample, and our results are well fitted by, e.g., ΛCDM models with bias b = 1.2, 1.4, and 1.4, respectively. We estimate the effect of extinction using the Schlegel, Finkbeiner, & Davis dust map. Our results are exclusively for the redshift space galaxy power spectrum, so they can only be compared with theoretical predictions if appropriate corrections are made for biasing and redshift space distortions.
Integrated Vehicle Routing and Crew Scheduling ( IVRCS ) in Waste Management Part II

Waste management involves an efficient planing of collectionvehicles and corresponding crews. We present an optimization model and algorithm for the crew scheduling problem which is the second part of an integrative solution approach. We differentiate between a network based duty generation phase and a duty optimization phase where an optimal set of annual duties for all employees is selected.
Blind extraction of sparse sources

In this paper we propose a new algorithm for solving the blind source extraction (BSE) problem when the desired source signals are sparse. Previous approaches for solving this problem are based on the independent component analysis (ICA ) technique, that extracts a source signal by finding a separating vector that maximizes the non-Gaussianity of the extracted source signal. These algorithms are general purpose algorithms and are not designed specifically for extracting sparse signals. In this paper we propose a new algorithm for extracting sparse source signals. The proposed algorithm is based on finding a separating vector that maximizes the sparsity of the extracted source signal. In the proposed algorithm, a nonconvex objective function that measures the sparsity of the separated signal is locally replaced by a quadratic convex function. This results in an iterative algorithm in which a new estimate of the separating vector is obtained by solving an eigenvalue decomposition problem. A numerical example is presented to investigate the superiority of the proposed algorithm in comparison with one of the well known ICA algorithm for extracting sparse sources.
Multifactor Efficiency, State Variables and Bayesian Inference

A water-proof outdoor tap distributor of cable TV network system electrical signals wherein the electro-pin of a tap connector is designed with two different size sheets, a shorter one being assembled in the middle of a longer one and the longer one being stretched up to the end side as a transmission point; the exposed electro-pin of the connector being covered by a super design glue and every connector being also covered with a second glue to prevent water leaks into the inside of the PCB and internal cavity.
Collective Classification in Semantic Mapping with a Probabilistic Description Logic

Sensor data classi cation is very dependent on which features represent primitives. We consider line segments extracted from laser points as primitives, and focus on their collective classi cation into door or wall objects, so as to build semantic maps. Because features may have non-trivial characteristics, and sensor primitives may be inter-related in complex ways, we represent features of spatial relationships using a probabilistic description logic.
Spatial interpolation of high-frequency monitoring data

Climate modelers generally require meteorological information on regular grids, but monitoring stations are, in practice, sited irregularly. Thus, there is a need to produce public data records that interpolate available data to a high density grid, which can then be used to generate meteorological maps at a broad range of spatial and temporal scales. In addition to point predictions, quantifications of uncertainty are also needed. One way to accomplish this is to provide multiple simulations of the relevant meteorological quantities conditional on the observed data taking into account the various uncertainties in predicting a space-time process at locations with no monitoring data. Using a high-quality dataset of minute-by-minute measurements of atmospheric pressure in north-central Oklahoma, this work describes a statistical approach to carrying out these conditional simulations. Based on observations at 11 stations, conditional simulations were produced at two other sites with monitoring stations. The resulting point predictions are very accurate and the multiple simulations produce well-calibrated prediction uncertainties for temporal changes in atmospheric pressure but are substantially overconservative for the uncertainties in the predictions of (undifferenced) pressure.
A Rational Model of the Effect of Information Scent on the Exploration of Menus

This paper describes a computational model based on rational analysis which is compared to experimental data and enables us to explain the number and order of assessments that participants make on the items on a web-page. The model provides both a good fit to the scan paths of the participants and an explanation for why a particular item is chosen. The strength of this research is that it provides a qualitative account of why particular patterns of scent result in particular scan patterns. The research provides further support for the rational framework as an explanation of exploratory behaviour.
PhD Program in Mathematics UC | UP Partial Differential Equations Spring 2011 ; MON and WED , 09 : 15 – 11 : 00

COURSE DESCRIPTION The course is an introduction to the study of partial differential equations (PDEs) using functional analysis and energy methods. Questions of existence, uniqueness and regularity for weak solutions to linear elliptic and parabolic PDEs will be emphasized. Various nonlinear PDEs will also be studied, using a variety of different approaches, like variational and monotonicity methods, fixed-point theorems or intrinsic scaling.
Synergistic Activation of Dopamine D1 and TrkB Receptors Mediate Gain Control of Synaptic Plasticity in the Basolateral Amygdala

Fear memory formation is thought to require dopamine, brain-derived neurotrophic factor (BDNF) and zinc release in the basolateral amygdala (BLA), as well as the induction of long term potentiation (LTP) in BLA principal neurons. However, no study to date has shown any relationship between these processes in the BLA. Here, we have used in vitro whole-cell patch clamp recording from BLA principal neurons to investigate how dopamine, BDNF, and zinc release may interact to modulate the LTP induction in the BLA. LTP was induced by either theta burst stimulation (TBS) protocol or spaced 5 times high frequency stimulation (5xHFS). Significantly, both TBS and 5xHFS induced LTP was fully blocked by the dopamine D1 receptor antagonist, SCH23390. LTP induction was also blocked by the BDNF scavenger, TrkB-FC, the zinc chelator, DETC, as well as by an inhibitor of matrix metalloproteinases (MMPs), gallardin. Conversely, prior application of the dopamine reuptake inhibitor, GBR12783, or the D1 receptor agonist, SKF39393, induced robust and stable LTP in response to a sub-threshold HFS protocol (2xHFS), which does not normally induce LTP. Similarly, prior activation of TrkB receptors with either a TrkB receptor agonist, or BDNF, also reduced the threshold for LTP-induction, an effect that was blocked by the MEK inhibitor, but not by zinc chelation. Intriguingly, the TrkB receptor agonist-induced reduction of LTP threshold was fully blocked by prior application of SCH23390, and the reduction of LTP threshold induced by GBR12783 was blocked by prior application of TrkB-FC. Together, our results suggest a cellular mechanism whereby the threshold for LTP induction in BLA principal neurons is critically dependent on the level of dopamine in the extracellular milieu and the synergistic activation of postsynaptic D1 and TrkB receptors. Moreover, activation of TrkB receptors appears to be dependent on concurrent release of zinc and activation of MMPs.
Impact of the Genetic Background on the Composition of the Chicken Plasma MiRNome in Response to a Stress

Circulating extra-cellular microRNAs (miRNAs) have emerged as promising minimally invasive markers in human medicine. We evaluated miRNAs isolated from total plasma as biomarker candidates of a response to an abiotic stress (feed deprivation) in a livestock species. Two chicken lines selected for high (R+) and low (R−) residual feed intake were chosen as an experimental model because of their extreme divergence in feed intake and energy metabolism. Adult R+ and R− cocks were sampled after 16 hours of feed deprivation and again four hours after re-feeding. More than 292 million sequence reads were generated by small RNA-seq of total plasma RNA. A total of 649 mature miRNAs were identified; after quality filtering, 148 miRNAs were retained for further analyses. We identified 23 and 19 differentially abundant miRNAs between feeding conditions and between lines respectively, with only two miRNAs identified in both comparisons. We validated a panel of six differentially abundant miRNAs by RT-qPCR on a larger number of plasma samples and checked their response to feed deprivation in liver. Finally, we evaluated the conservation and tissue distribution of differentially abundant miRNAs in plasma across a variety of red jungle fowl tissues. We show that the chicken plasma miRNome reacts promptly to the alteration of the animal physiological condition driven by a feed deprivation stress. The plasma content of stress-responsive miRNAs is strongly influenced by the genetic background, with differences reflecting the phenotypic divergence acquired through long-term selection, as evidenced by the profiles of conserved miRNAs with a regulatory role in energy metabolism (gga-miR-204, gga-miR-let-7f-5p and gga-miR-122-5p). These results reinforce the emerging view in human medicine that even small genetic differences can have a considerable impact on the resolution of biomarker studies, and provide support for the emerging interest in miRNAs as potential novel and minimally invasive biomarkers for livestock species.
Estimation of the most effective rhythm for human identification using EEG signal

Human identification using a special biological feature has become a promising field for the purpose of security system. Electroencephalogram (EEG) signals are the signature of human mind and can be used confidently as a strong biometric identifier. As EEG signals are consist of five different frequency bands, this paper represents a general methodology to determine the most effective rhythm for human identification. Using the different features from different rhythms in time and frequency domain, four neural networks are developed for the classification approach. Comparison of the designed neural networks shows that beta rhythm gives the best performance with a very low mean square error whereas delta rhythm gives the worst performance with comparative higher mean square error for identifying a person. It is concluded that beta rhythm is the most effective frequency band for human identification using EEG in resting and problem solving condition.
Serviceability and Durability of Construction Materials

A microplane model for the inelastic behavior of soils is presented. The micro-3Copic constitutive equations are defined independentely for a set of microplanes covering all possible orientations, with a kinematic micro-macro constraint. The stresses on each microplane are defined as explicit functions of the volu-metric and deviatoric normal and shear components of the macroscopic strain tensor. The model can reproduce drained as well as undrained behavior, with an uncoupled formulation between the stress-strain and the pore water pressure terms. The model is calibrated and verified by comparisons with several test data both drained and undrained, and good agreement is attained for most of the basic features of the material behavior. The model involves nine material parameters but four can be fixed constant and five (or four in some cases) have to be determined by data fitting. The fact that the stress is given as an explicit function of strain makes the model suitable for finite elements applications.
FEATURE SELECTION METHODS AND ALGORITHMS

Feature selection is an important topic in data mining, especially for high dimensional datasets. Feature selection (also known as subset selection) is a process commonly used in machine learning, wherein subsets of the features available from the data are selected for application of a learning algorithm. The best subset contains the least number of dimensions that most contribute to accuracy; we discard the remaining, unimportant dimensions. This is an important stage of preprocessing and is one of two ways of avoiding the curse of dimensionality (the other is feature extraction). There are two approaches in Feature selection known as Forward selection and backward selection. Feature selection has been an active research area in pattern recognition, statistics, and data mining communities. The main idea of feature selection is to choose a subset of input variables by eliminating features with little or no predictive information. Feature selection methods can be decomposed into three broad classes. One is Filter methods and another one is Wrapper method and the third one is Embedded method. This paper presents an empirical comparison of feature selection methods and its algorithms. In view of the substantial number of existing feature selection algorithms, the need arises to count on criteria that enable to adequately decide which algorithm to use in certain situations. This work reviews several fundamental algorithms found in the literature and assesses their performance in a controlled scenario.