Miracle-dz/UniFAR-FTUs huggingface dataset:
Far-Field Speech Enhancement Using Heteroscedastic Autoencoder for Improved Speech Recognition

Automatic speech recognition (ASR) systems trained on clean speech do not perform well in far-field scenario. Degradation in word error rate (WER) can be as large as 40% in this mismatched scenerio. Typically, speech enhancement is applied to map speech from far-field condition to clean condition using a neural network, commonly known as denoising autoencoder (DA). Such speech enhancement technique has shown significant improvement in ASR accuracy. It is a common pratice to use mean-square error (MSE) loss to train DA which is based on regression model with residual noise modeled by zero-mean and constant co-variance Gaussian distribution. However, both these assumptions are not optimal, especially in highly nonstationary noisy and far-field scenario. Here, we propose a more generalized loss based on non-zero mean and heteroscedastic co-variance distribution for the residual variables. On the top, we present several novel DA architectures that are more suitable for the heteroscedastic loss. It is shown that the proposed methods outperform the conventional DA and MSE loss by a large margin. We observe relative improvement of 7.31% in WER compared to conventional DA and overall, a relative improvement of 14.4% compared to mismatched train and test scenerio.
Tuning to Multiple Statistics: Second Language Processing of Multiword Sequences Across Registers

A substantial body of research has demonstrated that children and adults (both native and non-native speakers) are sensitive to the statistics of multiword sequences (MWS) and rely on knowledge of such statistics to facilitate their language processing and boost their acquisition. However, this research was primarily aimed at determining whether and to what extent speakers can develop sensitivity to MWS statistics of a single type of linguistic input: that of spoken language. Recently, there has been a growing awareness of the key role of written input in the development of linguistic knowledge, as it provides a source of substantial change in the statistics of an individual’s language experience. The present study reports on a series of experiments designed to determine whether second language learners of English are able to develop sensitivity to distributional statistics of MWS inherent in different (registerspecific) input types.
Extraction Socket Preservation with or without Membranes, Soft Tissue Influence on Post Extraction Alveolar Ridge Preservation: a Systematic Review

ABSTRACT Objectives The purpose of this systematic review was to assess quantitatively and qualitatively the influence of two different factors: membranes and soft tissue graft influence for the extraction socket preservation. Material and Methods A wide-ranging electronic search was performed in six databases up to 30 of November 2018 in order to identify all the clinical and randomized clinical trials performed in humans published with no data restriction. The inclusion criteria were extraction socket preservation with and without membranes or a soft tissue graft in a intact socket with at least six months of follow-up, have more than 12 patients or treat more than 12 sites per group and evaluated at least one of the primary outcomes measures (radiographic measures histological assessment, clinical measures). Results From an initial search of 1524 studies only 6 papers fulfil the inclusion and exclusion criterions. All the six selected papers, presented a wide heterogeneity of treatments used, evaluated variables and observation period that made impossible to recommend any specific techniques and/or material to achieve better results. The limited data found suggest that the used of membrane reveals to achieve better results. It wasn’t possible to observe in any clinical trial that compares the used of soft tissue graft. Conclusions New trials need to be performed in order to identify what specific techniques and/or materials are better to decrease the reabsorption of the socket after tooth extraction. Clinical trials designed to understand when/how the soft tissues grafts influence at the socket preservation is needed.
Usage of the gradient schemes for numerical solution of non-linear parabolic equations

In this thesis we deal with numerical methods used for solving non-linear time dependent partial differential equations, where the biggest challenge is numerical approximation of the gradient. We apply this approach in two fields, therefore is the thesis divided into two parts. In the first one we present finite volume scheme based on the EHM (Eymard-Handlovicova-Mikula) approach and apply it in the image segmentation process. We introduce new model, so-called regularised Riemannian mean curvature flow equation. For this new model we prove the necessary theoretical aspects as uniqueness of the numerical solution, stability estimates for the numerical solution and the convergence of the numerical scheme to the solution. Regularised Riemannian mean curvature flow equation is tested on the benchmark examples to prove its suitability. In the end of the section two different approaches of the approximation of the non-linear smoothing term are discussed and compared. The second part of the thesis is dedicated to the introduction of the discrete duality finite volume (DDFV) method and the application of this method for modelling of the development of the financial derivatives’ price in time. So-called Heston model and its regularized version are studied. For the regularised model the stability estimates on the numerical solution and the convergence of the numerical scheme to the solution are proven. The last part is dedicated to the numerical experiments for the DDFV method for the regularised Heston model.
Contribution to the study of the distribution of the uranium from the phosphoric acid and gypsum in the presence of cadmium , lanthanum and clay

This article is devoted to the study of the distribution of uranium from phosphoric acid and gypsum in the presence of several elements in order to determine the amount of uranium that goes into phosphoric acid. To achieve this, we have added a few elements such as CdCl2, LaCl2 and clay by varying their contents from 1 to 5 weight percent. The purpose of this research is to minimize the amount of radioactive uranium in the phosphoric acid because the presence of this element is likely to obstruct the further use of phosphoric acid.
Slot Filling with Weighted Multi-Encoders for Out-of-Domain Values

This paper proposes a new method for slot filling of out-ofdomain (OOD) slot values, which are not included in the training data, in spoken dialogue systems. Word embeddings have been proposed to estimate the OOD slot values included in the word embedding model from keyword information. At the same time, context information is an important clue for estimation because the values in a given slot tend to appear in similar contexts. The proper use of either or both keyword and context information depends on the sentence. Conventional methods input a whole sentence into an encoder and extract important clues by the attention mechanism. However, it is difficult to properly distinguish context and keyword information from the encoder outputs because these two features are already mixed. Our proposed method uses two encoders, which distinctly encode contexts and keywords, respectively. The model calculates weights for the two encoders based on a user utterance and estimates a slot with weighted outputs from the two encoders. Experimental results show that the proposed method achieves a 50% relative improvement in F1 score compared with a baseline model, which detects slot values from user utterances and estimates slots at once with a single encoder.
The “ mini-constructicon ” – a first step towards designing a Reference Constructicon of English

It is one of the central convictions of most usage-based grammarians that any sharp distinction between lexis and grammar is inappropriate (e.g. Langacker 2008), yet this insight has not yet been put into practice in terms of reference works, where the traditional distinction between grammars (in the sense of grammar books) and (various types of) dictionaries is still maintained. The reference constructicon we have in mind has to be imagined as an all-comprehensive electronic reference tool containing all the information we imagine speakers of a language to have, i.e. a network of constructions ranging from very small constructions such as words or morphemes to very abstract ones like the passive construction or argument structure constructions, recognizing the central role of collocations or constructions such as the the X-er the Y-er construction (Fillmore, Kay & O’Connor 1988). To the best of our knowledge, no such all-comprehensive reference tool exists – neither for English nor for any other language. All present constructica and constructicon projects seem to focus on particular spheres of language description (often the ones that have fallen between the stools of traditional grammars and dictionaries). For very obvious (financial) reasons, our project is no exception, but intended as a first step towards a more comprehensive reference tool. Building on the Valency Dictionary of English (Herbst et al. 2004) and the Valency Patternbank, (Herbst & Uhrig 2009) one focus of the present project will lie on the description of argument structure constructions of verbs (Goldberg 1995, 2006), adjectives and nouns. One of the aims of the project is to determine what extent the items identified as occurring in particular valency patterns can be easily subsumed under generalized constructions (compare Herbst 2014). In this paper, we wish to outline the basic principles of this “mini-constructicon”. In particular, it will be shown how the description of more abstract constructions will entail a collo-profile, i.e. a frequencyranked list of words occurring in the construction. We will also outline how our approach differs from FrameNet (which in its present form probably comes closest to the ideal of a comprehensive reference constructicon) – namely that we put a stronger focus on the way that the meaning of an utterance (and its parts) is negotiated between the meanings of constructions. Thus we would consider the differences in meaning between (1) Her pilot flew her to Paris, (2) We flew to Atlanta and (3) Lufthansa flies to Tokyo as the result of different constellations of the semantic roles or frame and not to different senses of the verb fly as in FrameNet, where fly is attributed to five different frames with five different senses. For this purpose, a systematic comparison between FrameNet and the Valency Dictionary of English will be presented.
A Brief Analysis of U-Net and Mask R-CNN for Skin Lesion Segmentation

A brief analysis on the use of two deep neural architectures, the U-Net and Mask R-CNN for the segmentation of skin lesions in dermoscopic images is presented. The two systems were adapted to use the dataset provided by the International Skin Imaging Collaboration (ISIC) for its 2017 challenge and different experiments were carried out. Results showed that the Mask-R-CNN obtained better performance than U-Net, also with lower computation times, being a feasible architecture to further analysis and application also to skin lesion classification.
Author ' s personal copy

We report the single-case study of a brain-damaged individual, JJG, presenting with a conceptual deficit and whose knowledge of living things, man-made objects, and actions was assessed. The aim was to seek for empirical evidence pertaining to the issue of how conceptual knowledge of objects, both living things and man-made objects, is related to conceptual knowledge of actions at the functional level. We first found that JJG’s conceptual knowledge of both man-made objects and actions was similarly impaired while his conceptual knowledge of living things was spared as well as his knowledge of unique entities. We then examined whether this pattern of association of a conceptual deficit for both man-made objects and actions could be accounted for, first, by the “sensory/functional” and, second, the “manipulability” account for category-specific conceptual impairments advocated within the Feature-Based-Organization theory of conceptual knowledge organization, by assessing, first, patient’s knowledge of sensory compared to functional features, second, his knowledge of manipulation compared to functional features and, third, his knowledge of manipulable compared to non-manipulable objects and actions. The later assessment also allowed us to evaluate an account for the deficits in terms of failures of simulating the hand movements implied by manipulable objects and manual actions. The findings showed that, contrary to the predictions made by the “sensory/functional”, the “manipulability”, and the “failure-of-simulating” accounts for category-specific conceptual impairments, the patient’s association of deficits for both manmade objects and actions was not associated with a disproportionate impairment of functional compared to sensory knowledge or of manipulation compared to functional knowledge; manipulable items were not more impaired than non-manipulable items either. In the general discussion, we propose to account for the patient’s association of deficits by the hypothesis that concepts whose core property is that of being a mean of achieving a goal – like the concepts of man-made objects and of actions – are learned, represented and processed by a common domain-specific conceptual system, which would have evolved to allow human beings to quickly and efficiently design and understand means to achieve goals and purposes. © 2011 Elsevier Ltd. All rights reserved.
Contextual Recovery of Out-of-Lattice Named Entities in Automatic Speech Recognition

As voice-driven intelligent assistants become commonplace, adaptation to user context becomes critical for Automatic Speech Recognition (ASR) systems. For example, ASR systems may be expected to recognize a user’s contact names containing improbable or out-of-vocabulary (OOV) words. We introduce a method to identify contextual cues in a firstpass ASR system’s output and to recover out-of-lattice hypotheses that are contextually relevant. Our proposed module is agnostic to the architecture of the underlying recognizer, provided it generates a word lattice of hypotheses; it is sufficiently compact for use on device. The module identifies subgraphs in the lattice likely to contain named entities (NEs), recovers phoneme hypotheses over corresponding time spans, and inserts NEs that are phonetically close to those hypotheses. We measure a decrease in the mean word error rate (WER) of word lattices from 11.5% to 4.9% on a test set of NEs.
End-to-End Spoken Language Understanding: Bootstrapping in Low Resource Scenarios

End-to-end Spoken Language Understanding (SLU) systems, without speech-to-text conversion, are more promising in low resource scenarios. They can be more effective when there is not enough labeled data to train reliable speech recognition and language understanding systems, or where running SLU on edge is preferred over cloud based services. In this paper, we present an approach for bootstrapping end-to-end SLU in low resource scenarios. We show that incorporating layers extracted from pre-trained acoustic models, instead of using the typical Mel filter bank features, lead to better performing SLU models. Moreover, the layers extracted from a model pre-trained on one language perform well even for (a) SLU tasks on a different language and also (b) on utterances from speakers with speech disorder.
Comparison of Data-Driven Modelling Techniques for Predicting River Flow in an Arid Region

Three data-driven modelling techniques (Artificial Neural Networks (ANN), Adaptive Neuro Fuzzy Inference Systems (ANFIS), and Autoregressive Integrated Moving Average (ARIMA)) were used for predicting of Euphrates River flow for one month ahead in Thi Qar City at Southeastern Iraq. The monthly average discharges of Euphrates River for the period (1995-2015) were used in this research. Three layers feedforward network with sigmoid hidden neurons and linear output neurons are used in this research, the ANN is trained with Levenberg-Marquradt back-propagation algorithm. The total number of observations data was equal to 248 samples; divided into three statistical groups (148 samples as a training group, 50 samples as a validation group, and 50 samples as a testing group). Two preceding discharge values (Qt-1, Qt-2) were used for predicting the discharge value (Qt). The clustering radius is an important parameter to determine the number of clusters and fuzzy if-then rules in ANFIS model. The best value of clustering radius (0.8) was associated with lowest value of checking root mean squared error (33.716). According to statistical evaluation criteria, ANFIS model was better than ARIMA model, and slightly better than ANN model. The superiority of the ANFIS technique to the ANN and ARIMA models due to the fuzzy clustering of the input space and for creating a rule-base to generate the output. ANFIS model can be used by the Iraqi Ministry of Water Resources to extract results close to reality and give an approximate estimate of the expected flow values in the Euphrates River, better than using other high-cost models such as conceptual models or physically based models.
Large-eddy simulation and modeling of Taylor-Couette flow with an outer stationary cylinder

We present wall-resolved large-eddy simulations (LES) of the incompressible NavierStokes equations together with empirical modeling for turbulent Taylor-Couette (TC) flow where the inner cylinder is rotating with angular velocity Ωi and the outer cylinder is stationary. With Ri, Ro the inner and outer radii respectively, the radius ratio is η = 0.909. The subgrid-scale (SGS) stresses are represented using the stretched-vortex subgrid-scale model while the flow is resolved close to the wall. LES is implemented in the range Rei = 10 5 − 3 × 10 where Rei = ΩiRi d/ν and d = Ro − Ri is the cylinder gap. It is shown that the LES can capture the salient features of the TC flow, including the quantitative behavior of span-wise Taylor rolls, the log-variation in the mean velocity profile and the angular momentum redistribution due to the presence of Taylor rolls. A simple empirical model of the turbulent, TC flow is developed consisting of near-wall, loglike turbulent wall layers separated by an annulus of constant angular momentum. The model is closed by a proposed scaling relation concerning the thickness of the wall layer on the inner cylinder. Model results include the Nusselt number Nu (torque required to maintain the flow) and various measures of the wall-layer thickness as a function of both the Taylor number Ta and η. These agree reasonably with experimental measurements, direct numerical simulation (DNS) and the present LES over a range of both Ta and η. In particular, the model shows that, at fixed η < 1, Nu grows like Ta divided by the square of the Lambert, (or Product-Log) function of a variable proportional to Ta. This cannot be represented by a power law dependence on Ta. At the same time the wall-layer thicknesses reduce slowly in relation to the cylinder gap. This suggests an asymptotic, very large Ta state consisting of constant angular momentum in the cylinder gap with uθ = 0.5 ΩiR 2 i /r, where r is the radius, with vanishingly thin turbulent wall layers at the cylinder surfaces. An extension of the model to rough-wall turbulent wall flow at the inner cylinder surface is described. This shows an asymptotic, fully rough-wall state where the torque is independent of Rei/Ta, and where Nu ∼ Ta.
Time series analysis and visualization of midwater zooplankton ecology : Automating long-term studies of the VARS dataset

This study uses multi-decadal time series from the Monterey Bay Aquarium Research Institute’s unique VARS database to illustrate spatio-temporal patterns of abundance in mesopelagic zooplankton. In situ observations from approximately 900 ROV dives over 25 years reveal both long and short-term fluctuations in abundance and diversity of these organisms, which heretofore have been largely understudied. Correlations between normalized data for midwater siphonophores, krill, and ctenophores indicated potential ecological relationships between taxa as well as sensitivity to oceanographic parameters like temperature, salinity, spiciness, and oxygen. We discuss evidence of niche partitioning between Praya dubia and Resomia ornicephala, as well as evidence of a bimodal distribution for the ctenophore Bolinopsis infundibulum. We also present trends in seasonality and periodicity within the context of environmental shift, and a method of reproduction for similar studies is set forth.
MOLECULAR STUDIES ON THE SPECIES COMPLEX OF TRICHOSIROCALUS HORRIDUS IN THE BIOLOGICAL CONTROL OF CARDUINAE WEEDS

1 Dipartimento di Biologia e Biotecnologie Charles Darwin, Università di Roma La Sapienza, Viale dell’Università 32, 00185 Rome, Italy 2 Via delle Giunchiglie 56, 00172, Rome, Italy 3 ENEA C.R. Casaccia UTAGRI-ECO, Via Anguillarese 301, 00123 S. Maria di Galeria (Rome), Italy 4 BBCA-onlus, via del Bosco 10, 00060, Sacrofano (Rome), Italy 5 USDA-ARS, 800 Buchanan Street, Albany, CA 94710, USA * Correspondence: alessio.debiase@uniroma1.it Lettura tenuta durante la Tavola rotonda “Gestione sostenibile di piante aliene invasive mediante i loro nemici naturali”. Seduta pubblica dell’Accademia – Firenze, 21 febbraio 2014.
On the use of ultrasound in speech communication systems

Traditionally, speech communication systems employ one or multiple air-conduction (AC) microphones to capture and process speech signals. These, however, are highly susceptible to background noise and reverberation. The performance of the corresponding systems therefore strongly depends on the acoustic characteristics of the environment in which they are being used. While numerous approaches to deal these problems have been investigated in the past [1, 2], a general solution does not exist and most solutions only work within certain constraints. This is especially the case for challenging environments low signal-to-noise ratio (SNR), highly non-stationary noise or high reverberation. For this reason, researchers have started looking into sensors which are more robust to noise in so-called multimodal speech communication systems. Such systems combine AC microphones with sensors like ultrasound, bone-conduction microphones, video cameras or electromyographs. In this work we use ultrasound as the additional modality. An advantage of ultrasound is that it is non-intrusive as opposed to boneconduction microphones and electromyographs [3–6], since no skin contact is required. Also, ultrasound does not raise any privacy concerns unlike video [7, 8]. In the past, ultrasound sensors have been used successfully for voice activity detection [9,10] as well as speaker and speech recognition [11–13]. Ultrasound-based speech communication systems are mostly set up so that the user is seated facing the sensor, which consists of an ultrasound receiver and an ultrasound transmitter. The transmitter emits an ultrasound signal which is reflected off the user’s face and recorded again by the receiver. This reflection contains information about the articulatory movements during speech production. Previously, ultrasound sensors in multi-modal speech communication systems were evaluated under the assumption that only articulatory movements are captured. In real-world applications, however, this assumption is unrealistic. In this work we investigate this and other practical considerations relevant for ensuring high performance of an ultrasound-based speech communication system. This work builds up on the methods described in [14].