Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic classification of electronic music and speech/music audio content

Automatic audio categorization has great potential for application in the maintenance and usage of large and constantly growing media databases; accordingly, much research has been done to demonstrate the feasibility of such methods. A popular topic is that of automatic genre classification, accomplished by training machine learning algorithms. However, " electronic " or " techno " music is often misrepresented in prior work, especially given the recent rapid evolution of the genre and subsequent splintering into distinctive subgenres. As such, features are extracted from electronic music samples in an experiment to categorize song samples into three subgenres: deep house, dubstep, and progressive house. An overall classification performance of 80.67% accuracy is achieved, comparable to prior work. Similarly, many past studies have been conducted on speech/music discrimination due to the potential applications for broadcast and other media, but it remains possible to expand the experimental scope to include samples of speech with varying amounts of background music. The development and evaluation of two measures of the ratio between speech energy and music energy are explored: a reference measure called speech-to-music ratio (SMR) and a feature which is an imprecise estimate of SMR called estimated voice-to-music ratio (eVMR). eVMR is an objective signal measure computed by taking advantage of broadcast mixing techniques in which vocals, unlike most instruments, are typically placed at stereo center. Conversely, SMR is a hidden variable defined by the relationship between the powers of portions of audio attributed to speech and music. It is shown that eVMR is predictive of SMR and can be combined with state-of-the-art features in order to improve performance. For evaluation, this new metric is applied in speech/music (binary) classification, speech/music/mixed (trinary) classification, and iii a new speech-to-music ratio estimation problem. Promising results are achieved, including 93.06% accuracy for trinary classification and 3.86 dB RMSE estimation of the SMR. iv Acknowledgments
Deliverable D3.2 Report Describing Initial Version of Deep Layered Models

The most common approach to integrating linguistic and textual contexts into acoustic modelling involves constructing highly context-dependent models of phone-sized units, then solving the resulting data sparsity problem by sharing parameters between related models, using decision tree-based state-tying. Decision trees may not be the best choice for this task, because of the factorial nature of context-dependency. This report describes initial work on deep layered models for TTS, which offer an alternative way to integrate linguistic and textual contexts into acoustic modelling. Work on three different approaches to overcoming the problems of the established approach is described. Version 1 (10/11/2012) 2 page 2 of 9 FP7-287678 SIMPLE4ALL deliverable D3.2
Impossible Measurements on Quantum Fields

It is shown that the attempt to extend the notion of ideal measurement to quantum field theory leads to a conflict with locality, because (for most observables) the state vector reduction associated with an ideal measurement acts to transmit information faster than light. Two examples of such information-transfer are given, first in the quantum mechanics of a pair of coupled subsystems, and then for the free scalar field in flat spacetime. It is argued that this problem leaves the Hilbert space formulation of quantum field theory with no definite measurement theory, removing whatever advantages it may have seemed to possess vis a vis the sum-over-histories approach, and reinforcing the view that a sum-over-histories framework is the most promising one for quantum gravity. 1. INTRODUCTION: IDEAL MEASUREMENTS AND QUANTUM FIELD THEORY Whatever may be its philosophical limitations, the textbook interpretation of nonrelativistic quantum mechanics is probably adequate to provide the quantum formalism with all the predictive power required for laboratory applications. It is also self-consistent in the sense that there exist idealized models of measurements which allow the system-observer boundary to be displaced arbitrarily far in the direction of the observer. And the associated “transformation theory” possesses a certain formal beauty, seemingly realizing the “complementarity principle” in terms of the unitary equivalence of all orthonormal bases. It is therefore natural to try to generalize this semantic framework to relativistic quantum field theory in the hope of learning something new, either from the success or failure of the attempt.
X-RAY AND OPTICAL FLUX RATIO ANOMALIES IN QUADRUPLY LENSED QUASARS. II. MAPPING THE DARK MATTER CONTENT IN ELLIPTICAL GALAXIES

We present a microlensing analysis of 61 Chandra observations of 14 quadruply lensed quasars. X-ray flux measurements of the individual quasar images give a clean determination of the microlensing effects in the lensing galaxy and thus offer a direct assessment of the local fraction of stellar matter making up the total integrated mass along the lines of sight through the lensing galaxy. A Bayesian analysis of the ensemble of lensing galaxies gives a most likely local stellar fraction of 7%, with the other 93% in a smooth, dark matter component, at a mean impact parameter Rc of 6.6 kpc from the center of the lensing galaxy. We divide the systems into smaller ensembles based on Rc and find that the most likely local stellar fraction varies qualitatively and quantitatively as expected, decreasing as a function of Rc.
New analogue of cyclolinopeptide B modified by amphiphilic residue of-hydroxymethylmethionine

In order to evaluate the role and influence of the methionine residue on the biological activity of cyclolinopeptide B, an analogue with methionine residue in position 7 replaced by the amphiphilic (S)-hydroxymethylmethionine residue was synthesized. This peptide exhibits high immunosuppressive activity in the cellular, and to a lesser degree in the humoral immune response, comparable to that of CsA. In addition, the peptide was devoid of toxicity, even at high doses.
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Information technology is increasingly an integral part of the competitive strategies for many organizations. As this trend continues, it is not surprising that there is an increasing emphasis placed on the ability oforganizations to plan, design and implement critical information systems. A major strategy to improve the effectiveness of these processes is to utilize computer-based planning and design aids. And yet, there is little empirical evidence that demonstrates a significant performance impact of this technology. One factor limiting research on the impact of technology on planning and design is the manner in which this technology has been conceptualized in order to provide measures of usage behavior. This research develops a functional model of I/S planning and design support technology that distinguishes among three general functional dimensions: Production Technology, Coordination Technology and Infrastructure Technology. An empirical analysis is used to test the robustness of the proposed model and its ability to discriminate between current design aids in a meaningful way. Implications for the use of this model in the study of I/S planning and design processes are discussed.
A Second-Order Method for Compressed Sensing Problems with Coherent and Redundant Dictionaries

In this paper we are interested in the solution of Compressed Sensing (CS) problems where the signals to be recovered are sparse in coherent and redundant dictionaries. CS problems of this type are convex with non-smooth and non-separable regularization term, therefore a specialized solver is required. We propose a primal-dual Newton Conjugate Gradients (pdNCG) method. We prove global convergence and fast local rate of convergence for pdNCG. Moreover, well-known properties of CS problems are exploited for the development of provably effective preconditioning techniques that speed-up the approximate solution of linear systems which arise. Numerical results are presented on CS problems which demonstrate the performance of pdNCG compared to a state-of-the-art existing solver.
An algebraic algorithm for weighted linear matroid intersection

We present a new algebraic algorithm for the classical problem of weighted matroid intersection. This problem generalizes numerous well-known problems, such as bipartite matching, network flow, etc. Our algorithm has running time Õ(<i>nr</i><sup>ω-1</sup><i>W</i>1+ε) for linear matroids with <i>n</i> elements and rank <i>r</i>, where ω is the matrix multiplication exponent, and <i>W</i> denotes the maximum weight of any element. This algorithm is the fastest known when <i>W</i> is small. Our approach builds on the recent work of Sankowski (2006) for Weighted Bipartite Matching and Harvey (2006) for Unweighted Linear Matroid Intersection.
INTERIOR-POINT METHODS FOR NONCONVEX NONLINEAR PROGRAMMING: PRIMAL-DUAL METHODS AND CUBIC REGULARIZATION

In this paper, we present a primal-dual interior-point method for solving nonlinear programming problems. It employs a Levenberg-Marquardt (LM) perturbation to the Karush-Kuhn-Tucker (KKT) matrix to handle indefinite Hessians and a line search to obtain sufficient descent at each iteration. We show that the LM perturbation is equivalent to replacing the Newton step by a cubic regularization step with an appropriately chosen regularization parameter. This equivalence allows us to use the favorable theoretical results of [15], [19], [5], and [6], but its application at every iteration of the algorithm, as proposed by these papers, is computationally expensive. We propose a hybrid method: use a Newton direction with a line search on iterations with positive definite Hessians and a cubic step, found using a sufficiently large LM perturbation to guarantee a steplength of 1 otherwise. Numerical results are provided on a large library of problems to illustrate the robustness and efficiency of the proposed approach on both unconstrained and constrained problems. This paper extends our previous work [2] from purely primal barrier methods to the primal-dual interior-point method framework.
TWO-DIMENSIONAL PROPER RATIONAL MATRICES AND CAUSAL INPUT/OUTPUT REPRESENTATIONS OF TWO-DIMENSIONAL BEHAVIORAL SYSTEMS∗

The concept of proper rational matrix is strictly connected with the representation of causal transfer matrices. In the two-dimensional (2D) case there is much freedom in defining proper rational matrices. This freedom is connected to the fact that past and future in the 2D case can be determined by a 2D cone. In this way the concept of rational matrix which is proper with respect to a cone can be introduced. Moreover, an algorithm that checks the properness of a rational matrix is proposed. Finally, this algorithm is used for determining all possible causal input/output (I/O) representations of a behavior given by a kernel representation.
HIGHER CRITICISM FOR DETECTING SPARSE HETEROGENEOUS MIXTURES1 BY DAVID DONOHO

Higher criticism, or second-level significance testing , is a multiplecomparisons concept mentioned in passing by Tukey. It concerns a situation where there are many independent tests of significance and one is interested in rejecting the joint null hypothesis. Tukey suggested comparing the fraction of observed significances at a given α-level to the expected fraction under the joint null. In fact, he suggested standardizing the difference of the two quantities and forming az-score; the resultingz-score tests the significance of the body of significance tests . We consider a generalization, where we maximize this z-score over a range of significance levels 0 < α ≤ α0. We are able to show that the resultinghigher criticism statisticis effective at resolving a very subtle testing problem: testing whether n normal means are all zero versus the alternative that a small fraction is nonzero. The subtlety of this “sparse normal means” testing problem can be seen from work of Ingster and Jin, who studied such problems in great detail. In their studies, they identified an interesting range of cases where the small fraction of nonzero means is so small that the alternative hypothesis exhibits little noticeable effect on the distribution of the p-values either for the bulk of the tests or for the few most highly significant tests. In this range, when the amplitude of nonzero means is calibrated with the fraction of nonzero means, the likelihood ratio test for a pr ecisely specified alternative would still succeed in separating the two hypotheses. We show that the higher criticism is s uccessful throughout the same region of amplitude sparsity where the likelihood ratio test would succeed. Since it does not require a specification of the alternative, this shows that higher criticism is in a sense optimally adap tive to unknown sparsity and size of the nonnull effects. While our theoretical work is largely asymptotic, we provide simulations in finite samples and suggest some possible applications. We also show that higher critcism works well over a range of non-Gaussian cases.
Total Dietary Fiber

In the 1970’s, a paradigm shift occurred in the thinking of medical and nutrition researchers that profoundly impacts their respective scientific communities to the present day. No longer was fiber nonnutritive and unimportant; now fiber played a significant physiological role, just lowering fat intake was insufficient for a well rounded healthy diet. Research on dietary fiber (DF) spilled over into many other areas, including a significant rethinking of the dietary regimen for diabetics to help reduce the symptoms of that disease. The research effort continues with indications that not only low fat and high fiber are good for well being, but a complex of carbohydrates, including those with slow digestibility, are important.
The Role of Regulatory Focus in Preference Construction

The authors examine how consumers' regulatory focus affects their information search behavior and decision-making process. The results show that uninvolved research participants rely on their regulatory focus as a filter to process information selectively. Participants paid more attention to and based their product evaluation on product information that is relevant to their regulatory concerns, but only when they were not motivated to process information. This regulatory fit effect on evaluation seems to be driven by the perceptual salience of fit versus nonfit information and is more consistent with heuristic than with systematic processing of information.
Deep Reinforcement Learning for Accelerating the Convergence Rate

In this paper, we propose a principled deep reinforcement learning (RL) approach that is able to accelerate the convergence rate of general deep neural networks (DNNs). With our approach, a deep RL agent (synonym for optimizer in this work) is used to automatically learn policies about how to schedule learning rates during the optimization of a DNN. The state features of the agent are learned from the weight statistics of the optimizee during training. The reward function of this agent is designed to learn policies that minimize the optimizee’s training time given a certain performance goal. The actions of the agent correspond to changing the learning rate for the optimizee during training. As far as we know, this is the first attempt to use deep RL to learn how to optimize a large-sized DNN. We perform extensive experiments on a standard benchmark dataset and demonstrate the effectiveness of the policies learned by our approach. All source code for reproducing the experiments can be downloaded from https://github.com/bigaidreamprojects/qan .
Supernova Light Curves Approximation based on Neural Network Models

Photometric data-driven classification of supernovae becomes a challenge due to the appearance of real-time processing of big data in astronomy. Recent studies have demonstrated the superior quality of solutions based on various machine learning models. These models learn to classify supernova types using their light curves as inputs. Preprocessing these curves is a crucial step that significantly affects the final quality. In this talk, we study the application of multilayer perceptron (MLP), bayesian neural network (BNN), and normalizing flows (NF) to approximate observations for a single light curve. We use these approximations as inputs for supernovae classification models and demonstrate that the proposed methods outperform the state-of-the-art based on Gaussian processes applying to the Zwicky Transient Facility Bright Transient Survey light curves. MLP demonstrates similar quality as Gaussian processes and speed increase. Normalizing Flows exceeds Gaussian processes in terms of approximation quality as well.
A QUASISTATIC ELECTRO-ELASTIC CONTACT PROBLEM WITH NORMAL COMPLIANCE, FRICTION AND ADHESION

In this article we consider a mathematical model which describes the contact between a piezoelectric body and a deformable foundation. The constitutive law is assumed linear electro-elastic and the process is quasistatic. The contact is adhesive and frictional and is modelled with a version of normal compliance condition and the associated Coulomb's law of dry friction. The evolution of the bonding eld is described by a rst order dierential equation. We derive a variational formulation for the model, in the form of a coupled system for the displacements, the electric potential and the bonding eld. Un- der a smallness assumption on the coecient of friction, we prove an existence result of the weak solution of the model. The proofs are based on arguments of time-dependent variational inequalities, dierential equations and Banach xed point theorem.
An automatic registration method for SAR and optical images based on line extraction and control points selection

Comprehensive utilization of SAR image and optical satellite photograph is a vital yet challenging task in remote sensing detection field. For this purpose, multisensory image registration is a conceivable approach. This paper presents an automatic registration method for SAR and optical images based on line extraction and control points selection. To begin with, line extraction is implemented with Gaussian-gamma-shaped (GGS) bi-windows and Line Segment Detection (LSD) methods, followed by coarse and fine registration. The coarse registration provides matched control point candidates for the subsequent refined registration to improve precision. Adequately considering individual features of SAR and optical images and advantages of processing algorithms corresponding to them, this method can provide enough and unambiguous information for comprehensive utilization of SAR and optical images.
On new methods of dynamic ensemble selection based on randomized reference classifier

In the paper two dynamic ensemble selection (DES) s ystems are proposed. Both systems are based on a probabilistic model and utilize the concept of Rand omized Reference Classifier (RRC) to determine the competence function of base classifiers. In the first system i n the selection procedure of base classifiers the d ynamic threshold of competence is applied. In the second DES system, se lected classifiers are combined using weighted majo rity voting rule with continuous-valued outputs, where the weights a re equal to the class-dependent competences. The p erformance of proposed MCSs were tested and compared against DES system with better-than-random selection rule using eleven databases taken from the UCI Machine Learning Repos itory. The experimental results clearly show the ef f ctiveness of the proposed methods.
A. Preparation of Whispering-gallery-mode Resonators and Fiber Tapers

A1. Fabrication of Erbium-doped silica microtoroid. The PT-symmetric optical resonator system investigated here is formed by two coupled WGM microtoroids. The passive (lossy) microtoroid is made from silica, whereas the active (with gain) resonator is made from erbium-ion-doped silica film, which is formed using sol-gel synthesis. The flow chart for the preparation of the sol-gel silica film is as follows (Fig. S1):
Arithmetic and Geometry: A survey of applications of the circle method to rational points

of adelic points under the product topology? Can one count the points in X(k) of bounded height? In favourable circumstances, the Hardy–Littlewood circle method can systematically provide answers to all of these questions. The focus of this survey will be upon the first and most basic of these questions: when is X(k) 6= ∅ for a given variety X defined over k? In considering what the circle method has to say about this we shall usually restrict our attention to varieties X defined over k that are geometrically integral, non-singular and projective. Recall that such a family is said to satisfy the Hasse principle if any variety in the family has a k-point as soon as it has a point in every completion of k. Sometimes we will drop the assumption on non-singularity, saying that the smooth Hasse principle holds for a family of such varieties when the Hasse principle holds for the smooth locus of X. Quadrics are among the first examples of families satisfying the Hasse principle. Beyond this simple setting, it is quite rare to find varieties which satisfy the Hasse principle. In dimension 2 we have the following counter-example.
Vladimir Nabokov's Lolita: Text, Paratext, and Translation

Postprint This is the accepted version of a paper published in Translation and Interpreting Studies. This paper has been peer-reviewed but does not include the final publisher proof-corrections or journal pagination. Access to the published version may require subscription. This article addresses the relationship between text and paratext in the publication history of Vladimir Nabokov's novel Lolita. Such paratexts include Nabokov's own afterword to the 1958 American edition and his postscript (published in 1967) to his own translation of Lolita into Russian, as well as various introductions and afterwords, both in English-language editions and in translations of Lolita into Russian and other languages. A particularly interesting type of paratext is constituted by annotations to the main text, and the analysis focuses on parallel examples published in annotated editions of Lolita in English, Russian, Polish, German, Ukrainian, and French. The analysis shows that the most detailed annotations concerning the totality of the English and Russian Lolita text and paratexts can be found in editions published in languages other than English and Russian, whereas most English or Russian editions seem to focus on the respective language version. There is still no complete, annotated edition of the bilingual text containing all the authorial paratexts.
A Wideband MIMO Channel Model Based on Structured Vector Modes Using Orthogonalization

This paper proposes a wideband Multiple Input - Multiple Output (MIMO) channel model and its implementation algorithm suitable for channel emulators. The model is based on a structured vector modes construction scheme, and assumes separability at space and time domains. It uses the Karhunen-Loeve Expansion (KLE) basis for the orthogonalization process both on the time delay and space domains, and is able to make channel realizations based on specific statistics with a predefined Mean Square Error (MSE).