Miracle-dz/UniFAR-FTUs huggingface dataset:
A Software Framework for Musical Data Augmentation

Predictive models for music annotation tasks are practically limited by a paucity of well-annotated training data. In the broader context of large-scale machine learning, the concept of “data augmentation” — supplementing a training set with carefully perturbed samples — has emerged as an important component of robust systems. In this work, we develop a general software framework for augmenting annotated musical datasets, which will allow practitioners to easily expand training sets with musically motivated perturbations of both audio and annotations. As a proof of concept, we investigate the effects of data augmentation on the task of recognizing instruments in mixed signals.
Auto-encoder based modeling of combustion system for circulating fluidized bed boiler

Deep learning attract the interests of many researchers. Multidimensional algorithms require large data storage space. This paper proposes a modeling of the combustion system used for Circulating Fluidized Bed Boiler (CFBB), which is based on the method of auto-encoder of deep learning. The 20 dimensional input samples set is the input layer, and then the units of hidden layer are calculated. The data dimension is reduced through the auto-encoder, further, these data are as input of the RBF network. The modeling is carried out by the Radical Basis Function (RBF) neutral network. Compared with traditional methods, the auto-encoder is suitable for modeling. The samples are greatly reduced for the subsequent work. Numerical results provided in this paper validate the proposed model and method, as well as the validity of the conversion from the auto-encoder strategy.
Mitigating Android Software Misuse Before It Happens

Mobile phones running open operating systems such as Google Android will soon be the norm in cellular networks. These systems expose previously unavailable phone and network resources to application developers. However, with increased exposure comes increased risk. Poorly or maliciously designed applications can compromise the phone and network. While Android defines a base set of permissions to protect phone resources and core applications, it does not define what a secure phone is, relying on the applications to act together securely. In this paper, we develop the Kirin security framework to enforce policy that transcends applications, called policy invariants, and provides an “at installation” self-certification process to ensure only policy compliant applications will be installed. We begin by describing the Google Android security model and formally model its existing policy. Using relatively simple policy invariants describing realistic security requirements, Kirin identified insecure policy configurations within Android leading to vulnerabilities in core phone services, thereby motivating additional security framework defining system-wide policy.
JOINT CHANNEL-SOURCE CODING FOR ROBUST VECTOR QUANTIZATION : APPLICATION TO SPEECH CODING

Distortion of parameters of a vector quantization system can be alleviated by using an index assignment channel-source coding technique. To carry out this approach, we have developed an iteratif algorithm based on the principle of the simulated Annealing. The goal of this algorithm is to find globally optimal channel-codes, destined for the implicit protection of a 4.8 KBPS CELP speech coder codebook-indices. Such Non-redundant joint channel-source codes can be obtained by minimizing an appropriate distorsion criterion. Also, an improvement has been introduced in the simulated Annealing algorithm to palliate to a drawback of this last. Thus, we can obtain the best optimal result during the total progress of the improved version of the simulated Annealing algorithm.
Acute precipitants of congestive heart failure exacerbations.

BACKGROUND Few studies have prospectively and systematically explored the factors that acutely precipitate exacerbation of congestive heart failure (CHF) in patients with left ventricular dysfunction. Knowledge of such factors is important in designing measures to prevent deterioration of clinical status. The objective of this study was to prospectively describe the precipitants associated with exacerbation of CHF status in patients enrolled in the Randomized Evaluation of Strategies for Left Ventricular Dysfunction Pilot Study. METHODS We conducted a 2-stage, multicenter, randomized trial in 768 patients with CHF who had an ejection fraction of less than 40%. Patients were randomly assigned to receive enalapril maleate, candesartan cilexetil, or both for 17 weeks, followed by randomization to receive metoprolol succinate or placebo for 26 weeks. Investigators systematically documented information on clinical presentation, management, and factors associated with the exacerbation for any episode of acute CHF during follow-up. RESULTS A total of 323 episodes of worsening of CHF occurred in 180 patients during 43 weeks of follow-up; 143 patients required hospitalization, and 5 died. Factors implicated in worsening of CHF status included noncompliance with salt restriction (22%); other noncardiac causes (20%), notably pulmonary infectious processes; study medications (15%); use of antiarrhythmic agents in the past 48 hours (15%); arrhythmias (13%); calcium channel blockers (13%); and inappropriate reductions in CHF therapy (10%). CONCLUSIONS A variety of factors, many of which are avoidable, are associated with exacerbation of CHF. Attention to these factors and patient education are important in the prevention of CHF deterioration.
Distributed Stochastic Variance Reduced Gradient Methods

We study the problem of minimizing the average of $N$ convex functions using $m$ machines with each machine being able to access $n=\frac{N}{m}$ functions. We design a distributed stochastic variance reduced gradient (DSVRG) algorithm which, when the condition number $\kappa$ of the problem is less than $n^{0.9}$ and when the data is assumed to be randomly partitioned onto the machines, simultaneously achieves the optimal parallel runtime, communication and rounds of communication among all first order methods up to constant factors. DSVRG and its extension DPPASVRG also outperform existing distributed algorithms in terms of the rounds of communication as long as $\kappa$ is not too large compared to $n$. For the regime when $n$ is relatively small, we propose a distributed accelerated SVRG algorithm which further reduces the parallel runtime.
Thresholding for Making Classifiers Cost-sensitive

In this paper we propose a very simple, yet general and effective method to make any cost-insensitive classifiers (that can produce probability estimates) cost-sensitive. The method, called Thresholding, selects a proper threshold from training instances according to the misclassification cost. Similar to other cost-sensitive meta-learning methods, Thresholding can convert any existing (and future) costinsensitive learning algorithms and techniques into costsensitive ones. However, by comparing with the existing cost sensitive meta-learning methods and the direct use of the theoretical threshold, Thresholding almost always produces the lowest misclassification cost. Experiments also show that Thresholding has the least sensitivity on the misclassification cost ratio. Thus, it is recommended to use when the difference on misclassification costs is large.
Investigating web search strategies and forum use to support diet and weight loss

Healthcare is shifting from being reactive to preventive, with a focus on maintaining general wellness through positive decisions on diet, exercise, and lifestyle. In this paper, we investigate search behavior as people navigate the Web and find support for dietary and weight loss plans. Inspecting the Web search logs of nearly 2,000 users, we show that people progressively narrow their searches to support their progress through these plans. Interestingly, people that visit online health forums seem to progress through the plans' phases more quickly. Based on these results, we conducted a survey to further explore the roles and importance of online forums in supporting dieting and weight loss.
Long-Range Balanced Electron- and Hole-Transport Lengths in Organic-Inorganic CH3NH3PbI3

Unrestricted Travel in Solar Cells In the past 2 years, organolead halide perovskites have emerged as a promising class of light-harvesting media in experimental solar cells, but the physical basis for their efficiency has been unclear (see the Perspective by Hodes). Two studies now show, using a variety of time-resolved absorption and emission spectroscopic techniques, that these materials manifest relatively long diffusion paths for charge carriers energized by light absorption. Xing et al. (p. 344) independently assessed (negative) electron and (positive) hole diffusion lengths and found them well-matched to one another to the ~100-nanometer optical absorption depth. Stranks et al. (p. 341) uncovered a 10-fold greater diffusion length in a chloride-doped material, which correlates with the material's particularly efficient overall performance. Both studies highlight effective carrier diffusion as a fruitful parameter for further optimization. Spectroscopy pinpoints facile carrier diffusion as a key factor in the efficiency of perovskite solar cells. [Also see Perspective by Hodes] Low-temperature solution-processed photovoltaics suffer from low efficiencies because of poor exciton or electron-hole diffusion lengths (typically about 10 nanometers). Recent reports of highly efficient CH3NH3PbI3-based solar cells in a broad range of configurations raise a compelling case for understanding the fundamental photophysical mechanisms in these materials. By applying femtosecond transient optical spectroscopy to bilayers that interface this perovskite with either selective-electron or selective-hole extraction materials, we have uncovered concrete evidence of balanced long-range electron-hole diffusion lengths of at least 100 nanometers in solution-processed CH3NH3PbI3. The high photoconversion efficiencies of these systems stem from the comparable optical absorption length and charge-carrier diffusion lengths, transcending the traditional constraints of solution-processed semiconductors.
The graphical brain: Belief propagation and active inference

This paper considers functional integration in the brain from a computational perspective. We ask what sort of neuronal message passing is mandated by active inference—and what implications this has for context-sensitive connectivity at microscopic and macroscopic levels. In particular, we formulate neuronal processing as belief propagation under deep generative models. Crucially, these models can entertain both discrete and continuous states, leading to distinct schemes for belief updating that play out on the same (neuronal) architecture. Technically, we use Forney (normal) factor graphs to elucidate the requisite message passing in terms of its form and scheduling. To accommodate mixed generative models (of discrete and continuous states), one also has to consider link nodes or factors that enable discrete and continuous representations to talk to each other. When mapping the implicit computational architecture onto neuronal connectivity, several interesting features emerge. For example, Bayesian model averaging and comparison, which link discrete and continuous states, may be implemented in thalamocortical loops. These and other considerations speak to a computational connectome that is inherently state dependent and self-organizing in ways that yield to a principled (variational) account. We conclude with simulations of reading that illustrate the implicit neuronal message passing, with a special focus on how discrete (semantic) representations inform, and are informed by, continuous (visual) sampling of the sensorium. Author Summary This paper considers functional integration in the brain from a computational perspective. We ask what sort of neuronal message passing is mandated by active inference—and what implications this has for context-sensitive connectivity at microscopic and macroscopic levels. In particular, we formulate neuronal processing as belief propagation under deep generative models that can entertain both discrete and continuous states. This leads to distinct schemes for belief updating that play out on the same (neuronal) architecture. Technically, we use Forney (normal) factor graphs to characterize the requisite message passing, and link this formal characterization to canonical microcircuits and extrinsic connectivity in the brain.
Real-Time Mach: Towards a Predictable Real-Time System

Distributed real-time systems play a very important role in our modern society. They are used in aircraft control, communication systems, military command and control systems, factory automation, and robotics. However, satisfying the rigid timing requirements of various real-time activities in distributed real-time systems often requires ad hoc methods to tune the system’s runtime behavior The objective of Real-Time Mach is to develop a real-time version of the Mach kernel which provides users with a predictable and reliable distributed real-time computing environment. In this paper, we describe a real-time thread model, real-time synchronization, and the ITDS scheduler in RealTime Mach. We also discuss the implementation issues, a real-time toolset, and the current status of the system.
An Approach to Determine Non-filer's of Property Tax using Clustering and Classification

Data mining use everywhere successively, it is the knowledge extraction from large data. In this research, we are using clustering and classification methods to mine the data by using hybrid algorithm. We use K-MEANS algorithm from clustering and CART algorithm from decision tree. Clustering method will use for make the clusters of similar groups to extract the easily features or properties and decision tree method will use for choose to decide the optimal decision to extract the valuable information. This paper reports the development of an approach for property tax that works for the administrator as well as local persons. Using this approach administrator can determine the non-filer of property tax and improve the quality of decisions making.
A Comment on the Viscous Stability of Relativistic Keplerian Accretion Disks

Recently, Ghosh reported a new regime of instability in Keplerian accretion disks that is caused by relativistic effects. This instability appears in the gas pressure-dominated region when all relativistic corrections to the disk structure equations are taken into account. We show that he uses the stability criterion incorrectly, leading to inappropriate conclusions. We perform a standard stability analysis to show that no unstable region exists when the relativistic disk is gas pressure dominated.
Separation of Membrane Trafficking and Actin Remodeling Functions of ARF6 with an Effector Domain Mutant

ABSTRACT The ADP-ribosylation factor 6 (ARF6) GTPase has a dual function in cells, regulating membrane traffic and organizing cortical actin. ARF6 activation is required for recycling of the endosomal membrane back to the plasma membrane (PM) and also for ruffling at the PM induced by Rac. Additionally, ARF6 at the PM induces the formation of actin-containing protrusions. To identify sequences in ARF6 that are necessary for these distinct functions, we examined the behavior of a chimeric protein of ARF1 and ARF6. The 1-6 chimera (with the amino half of ARF1 and the carboxyl half of ARF6) localized like ARF6 in HeLa cells and moved between the endosome and PM, but it did not form protrusions, an ARF6 effector function. Two residues in the amino-terminal half of ARF6, Q37 and S38, when substituted into the 1-6 chimera allowed protrusion formation, whereas removal of these residues from ARF6 resulted in an inability to form protrusions. Interestingly, expression of 1-6 in cells selectively inhibited protrusions induced by wild-type ARF6 but had no effect on ARF6-regulated membrane movement or Rac-induced ruffling. Thus, we have uncoupled two functions of ARF6, one involved in membrane trafficking, which is necessary for Rac ruffling, and another involved in protrusion formation.
A National and International Analysis of Changing Forest Density

Like cities, forests grow by spreading out or by growing denser. Both inventories taken steadily by a single nation and other inventories gathered recently from many nations by the United Nations confirm the asynchronous effects of changing area and of density or volume per hectare. United States forests spread little after 1953, while growing density per hectare increased national volume and thus sequestered carbon. The 2010 United Nations appraisal of global forests during the briefer span of two decades after 1990 reveals a similar pattern: A slowing decline of area with growing volume means growing density in 68 nations encompassing 72% of reported global forest land and 68% of reported global carbon mass. To summarize, the nations were placed in 5 regions named for continents. During 1990–2010 national density grew unevenly, but nevertheless grew in all regions. Growing density was responsible for substantially increasing sequestered carbon in the European and North American regions, despite smaller changes in area. Density nudged upward in the African and South American regions as area loss outstripped the loss of carbon. For the Asian region, density grew in the first decade and fell slightly in the second as forest area expanded. The different courses of area and density disqualify area as a proxy for volume and carbon. Applying forestry methods traditionally used to measure timber volumes still offers a necessary route to measuring carbon stocks. With little expansion of forest area, managing for timber growth and density offered a way to increase carbon stocks.
Electrophoresis-Enhanced Detection of Deoxyribonucleic Acids on a Membrane-Based Lateral Flow Strip Using Avian Influenza H5 Genetic Sequence as the Model

This study reports a simple strategy to detect a deoxyribonucleic acid (DNA) on a membrane-based lateral flow (MBLF) strip without tedious gel preparation, gel electrophoresis, and EtBr-staining processes. The method also enhances the detection signal of the genetic sample. A direct electric field was applied over two ends of the MBLF strips to induce an electrophoresis of DNAs through the strips. The signal enhancement was demonstrated by the detection of the H5 subtype of avian influenza virus (H5 AIV). This approach showed an excellent selectivity of H5 AIV from other two control species, Arabidopsis thaliana and human PSMA5. It also showed an effective signal repeatability and sensitivity over a series of analyte concentrations. Its detection limit could be enhanced, from 40 ng to 0.1 ng by applying 12 V. The nano-gold particles for the color development were labeled on the capture antibody, and UV-VIS and TEM were used to check if the labeling was successful. This detection strategy could be further developed to apply on the detection of drug-allergic genes at clinics or detection of infectious substances at incident sites by a simple manipulation with an aid of a mini-PCR machine and auxiliary kits.
Radiative forcing by aerosols as derived from the AeroCom present-day and pre-industrial simulations

Nine different global models with detailed aerosol modules have independently produced instantaneous direct radiative forcing due to anthropogenic aerosols. The an- thropogenic impact is derived from the difference of two model simulations with prescribed aerosol emissions, one for present-day and one for pre-industrial conditions. The differ- ence in the solar energy budget at the top of the atmosphere (ToA) yields a new harmonized estimate for the aerosol di- rect radiative forcing (RF) under all-sky conditions. On a global annual basis RF is 0.22Wm 2 , ranging from +0.04 to 0.41Wm 2 , with a standard deviation of ±0.16Wm 2 . Anthropogenic nitrate and dust are not included in this esti- mate. No model shows a significant positive all-sky RF. The corresponding clear-sky RF is 0.68Wm 2 . The cloud-sky RF was derived based on all-sky and clear-sky RF and mod- elled cloud cover. It was significantly different from zero and ranged between 0.16 and +0.34Wm 2 . A sensitivity anal- ysis shows that the total aerosol RF is influenced by consid- erable diversity in simulated residence times, mass extinction coefficients and most importantly forcing efficiencies (forc- ing per unit optical depth). The clear-sky forcing efficiency (forcing per unit optical depth) has diversity comparable to that for the all-sky/ clear-sky forcing ratio. While the di- versity in clear-sky forcing efficiency is impacted by factors such as aerosol absorption, size, and surface albedo, we can show that the all-sky/clear-sky forcing ratio is important be- cause all-sky forcing estimates require proper representation of cloud fields and the correct relative altitude placement be- tween absorbing aerosol and clouds. The analysis of the sul- phate RF shows that long sulphate residence times are com- pensated by low mass extinction coefficients and vice versa. This is explained by more sulphate particle humidity growth and thus higher extinction in those models where short-lived sulphate is present at lower altitude and vice versa. Solar atmospheric forcing within the atmospheric column is esti- mated at +0.82±0.17Wm 2 . The local annual average max- ima of atmospheric forcing exceed +5Wm 2 confirming the regional character of aerosol impacts on climate. The annual average surface forcing is 1.02±0.23Wm 2 . With the cur- rent uncertainties in the modelling of the radiative forcing due to the direct aerosol effect we show here that an estimate from one model is not sufficient but a combination of several model estimates is necessary to provide a mean and to ex- plore the uncertainty.
Impact of the switching activity on the aging of delay-PUFs

Physically Unclonable Functions (PUFs) are mainly used for generating unique keys to identify electronic devices. The reliability of PUFs needs to be assured under a wide variety of environmental conditions and aging mechanisms. In this paper, we evaluate the impact of NBTI and HCI aging on two types of delay-PUFs (arbiter-PUFs and loop-PUFs). The results show that the switching activity has a limited impact on delay chains and a significant impact on the arbiter (RS latch) of the arbiter-PUF.
Magnetic Resonance Dispersion Imaging for Localization of Angiogenesis and Cancer Growth

PurposeCancer angiogenesis can be imaged by using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Pharmacokinetic modeling can be used to assess vascular perfusion and permeability, but the assessment of angiogenic changes in the microvascular architecture remains challenging. This article presents 2 models enabling the characterization of the microvascular architecture by DCE-MRI. TheoryThe microvascular architecture is reflected in the dispersion coefficient according to the convective dispersion equation. A solution of this equation, combined with the Tofts model, permits defining a dispersion model for magnetic resonance imaging. A reduced dispersion model is also presented. MethodsThe proposed models were evaluated for prostate cancer diagnosis. Dynamic contrast-enhanced magnetic resonance imaging was performed, and concentration-time curves were calculated in each voxel. The simultaneous generation of parametric maps related to permeability and dispersion was obtained through model fitting. A preliminary validation was carried out through comparison with the histology in 15 patients referred for radical prostatectomy. ResultsCancer localization was accurate with both dispersion models, with an area under the receiver operating characteristic curve greater than 0.8. None of the compared parameters, aimed at assessing vascular permeability and perfusion, showed better results. ConclusionsA new DCE-MRI method is proposed to characterize the microvascular architecture through the assessment of intravascular dispersion, without the need for separate arterial-input-function estimation. The results are promising and encourage further research.