dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGEMENTS We thank an anonymous referee for a number of helpful comments, including pertinent references. SR thanks  Christophe Pichon  and  Jean-Baptiste Fouvry  for many interesting discussions, and all authors are grateful to them for commenting on an early draft. This work has made use of the  Horizon Cluster hosted by Institut d'Astrophysique de Paris. All authors thank  Stephane Rouberol  for running smoothly this cluster. We are grateful to  S. Law  and his IT staff in the  Mathematics Department at Edinburgh University , for very helpful management of the GPU hardware. Many simulations were also carried out with the ECDF cluster at the University. All authors thank the  Leverhulme Trust  for financial support from grant  RPG-2015-408. ALV acknowledges support from a  UKRI   Future Leaders Fellowship  ( MR/S018859/1 ) and is grateful to the  Department of Astronomy at the University of Tokyo , where part of this work was conducted, thanks to a  JSPS International Fellowship with Grant-in-Aid  ( KAKENHI-18F18787 ).
The present paper discusses the climatic effects of humidity and temperature on cochlear implant functioning and the quality of the electrical sound signal. MATLAB Simulink simulations were prepared, offering insights into signal behavior under such climatic parameter changes. A simulation setup of the cochlear implant was developed, where a source type selection was used to change between a voice recording and a "chirp" sound. In addition, a DC blocking filter was applied to the input signal. A simulation code, with the application of the climatic influence via the air attenuation function, was developed. Thereby, the attenuation of temperature and humidity in the sound atmospheric circulation of the input signal, at T = 0 • C and RH = 0% and at T = 36 • C and RH = 40% was graphically represented.
(Sydney, Australia), and Advanced Bionics (Santa Clarita, CA, USA). In 2019, the global market value for hearing aids was USD 8. 99 billion (with a CAGR of 8. 2%) and USD 1. 67 billion for cochlear implants (with a CAGR of 10. 6%)  [21]. The majority of hearing aids are sold in developed markets, with the highest percentage found in Europe (41%), closely followed by North America (29%), and Asia and the Pacific (21%). The lowest percentages are found in South America (7%) and Africa (2%)  [22]. The objective of this paper was to study humidity and temperature effects on the quality of sound within cochlear implants via a MATLAB Simulink complete simulation approach. Both humidity and temperature sensors work together to provide a correct and complete response to the sound signal influence of climatic changes.
Materials and Methods This section is intended to present the materials used in setting up our analysis and problem solving. We start by giving the main scientific arguments for our work, then we present the cochlear implant and make an important analysis of the electrical signal in the cochlear implant, and its behavior based on temperature and humidity, via MATLAB Simulink simulations. Previous works analyzed temperature effects on CMOS (complementary metal oxide semiconductor) Hall sensors  [26]  and proposed a pulses generator and its behavior  [27]. We apply in this paper a similar technique to the sound wave as the one applied to the light wavefront aberration correction via programmed computer code in paper  [28]. A precise three-dimensional model of a cochlear implant, together with extensive simulations, were previously performed in Ansoft Ansys ® HFSS software, which is used for electro-magnetic simulations.
Simulation of Cochlear Implant Using MATLAB Simulink In this section, the simulation of the cochlear implant using MATLAB Simulink is detailed, including system blocks, block parameters, some associated code, and the model hierarchy used to evaluate the electrical behavior of the cochlear implant. This tool allows us to rapidly and correctly investigate the device under discussion, as well as to assess the quality of the electrical radio signals. This paper discusses, for the first time in literature, the effect of humidity and temperature on the quality of processed sound. The calibration of sound was carried out on the real temperature and on the existing humidity in the sound emission space. Prior to this, these considerations were not taken into account when designing cochlear implants.
The responses of the sensors to the change of temperature (temperature effect) and to the change of the environmental conditions regarding the humidity (effect of humidity) were highlighted by numerical simulation. These evidently marked the alteration of sound quality depending on the change of environmental conditions, namely the change of temperature and ambient humidity. The designed and developed conceptual blocks, such as the cochlear implant signal source block and cochlear implant signal from Workspace block, were both represented in MATLAB Simulink. The first concept, made to work like source type selection, was used to change between a voice recording and a "chirp" sound, and a DC blocking filter was applied at the input signal. In addition, the cochlear implant filter bank block was represented in the MATLAB Simulink so that signal processing of the input source would use two filter types: FIR (finite impulse response) and IIR (infinite impulse response).
• Run massive simulations in parallel on a multicore desktop, computer cluster, or the cloud, without writing large amounts of code; • Share simulations as standalone executables, web apps, and Functional Mockup Units (FMUs). Industry-specific applications can also be designed in Simulink using prebuilt blocks, so we do not have to create them ourselves. For example, aircraft propulsion systems can be designed with Aerospace Blockset™, or audio and video systems with Signal Processing tools, as in our case. As for the math behind the application Simulink, we will refer to the simulation applications for making graphics. Standalone executables can be complete simulation apps that use MATLAB graphics and UIs designed with the MATLAB App Designer. To provide browser-based access to a deployed simulation, it will create a web app and host it with the MATLAB Web App Server™.
Standalone Functional Mockup Units (FMU) are binaries that adhere to the Functional Mockup Interface (FMI) standard and can be used in an external simulation environment. A standalone executable is a program that does not depend an Objective Caml installation to run. This facilitates the distribution of binary applications and robustness against runtime library changes across Objective Caml versions. The Objective Caml native compiler produces standalone executables by default. Without the custom option, however, the byte code compiler produces an executable which requires the byte code interpreter ocamlrun. For example, imagine the file example. ml is as follows: Let f (x) = x + 1; Print_int (f, 18); Print_newline (. ); Then the following command produces the (approximately 8 k) file example.
e. , the speech bands were arranged around the central frequencies of the five-channel implants, operating in four different frequency ranges (see Table  1 ). The cochlear implant block representation was effectuated in MATLAB Simulink, and contained an input source, cochlear speech processor, filter bank signal processing with designer filter bank, FIR and IIR filter bank response, an optional denoise stage, and electrical pulses with a pulse generation by 8 channels. Thereby, the cochlear implant signal source block, (represented in MATLAB Simulink) was introduced, where source type selection was used to change between a voice recording and a "chirp" sound, and again on the DC blocking filter, which was applied to the input signal. Evidently, a major focus on detailing software was associated with the block structure of the input signal source, when the FIR filter was applied in cascade to the 8 frequency bands (arranged on the 8 channels, one on each channel).
In the following, the most important functional blocks of the electrical model of the cochlear implant will be described. Among the most relevant was the cochlear implant signal from the Workspace block representation in MATLAB Simulink. In this description we will discuss the sampling of the input signal, which was done at a frequency of sampling set at the fixed value of fs = 22,050 Hz. Two more of the modules used included the cascade FIR bank block representation, with three successive cascaded high band/low band blocks, and the IIR filter bank block representation, with the IIR filter bank (with 8 outputs), the rectifier (with 8 inputs), and the supplementary block of 8 biquads applications.
Further on, we applied the FIR filter. Figure  4  presents the magnitude response in dB (on the OY axis) of the eight FIR filtered banks, plotted against the normalized frequency multiplied by π rad/sample (on the OX axis) that also displayed the banks separation (the filter was applied in a cascade to each frequency bank). The obtained magnitude spanned from -80 to 0 dB. In the following, we selected the speech input source for the simulation and ran the MATLAB simulation with various parameters, selecting from a range of filters, noise sources, and denoise functions. Figures  5 6 7 display the results (signal amplitude against time) obtained corresponding to the scenarios presented in Tables  2 3 4 , respectively, according to precise parameters (input source, filter, noise, and denoise) selection.
Denoise Stage of Temperature and Humidity Influence An additional correction step can be applied using a denoise function that corrects the temperature and humidity influence on the system, as in Figure  18. At the beginning of the processing chain, where there is an external microphone, the sound waves arrive already affected by their way through the air and the distance from the source to the ear, which will then have to be processed electronically to reach the optimal standard values of the cochlear implant type used. A Simulink diagram of the complete simulation featuring the microphone input, the temperature and humidity sensor, as well as the additional filters and denoise blocks, is displayed in Figure  19. In this way, we can see how the cochlear speech processor module was optimized by the authors through the addition of temperature and humidity sensors.
Discussion The simulation of a cochlear implant using MATLAB Simulink started by organizing a simulation setup of the cochlear implant, where a source type selection was used to change between a voice recording and a "chirp" sound. In addition, a DC blocking filter to the input signal was applied. In a natural sequence, simulation stages were presented in detail, among which the most important ones included the cochlear implant signal source block representation in MATLAB Simulink, the denoise stage block representation, and, finally, the stimulation pulse generator block. Thus, source type selection was used to change between a voice recording and a "chirp" sound, while a DC blocking filter was applied to the input signal. Special attention was paid to simulation code with the application of the temperature and humidity influence via the air attenuation function.
Conclusions and Future Directions To obtain the results in this project, a thorough understanding of MATLAB Simulink software was necessary. MATLAB Simulink was used to characterize the electrical behavior of the cochlear implant and to assess the audio signal and quantify the atmospheric perturbation impact on the signal quality. A simulation setup of the cochlear implant, where a source type selection was used to change between a voice recording and a "chirp" sound, was used. In addition, a DC blocking filter was applied to the input signal. Taking the initial intake data into account, the cochlear implant signal from the Workspace block representation in MATLAB Simulink, when the input sound was sampled, was included. A simulation code with the application of the climatic influence, via the air attenuation function, was developed.
This paper tackles GAN optimization and stability issues in the context of voice conversion. First, to simplify the conversion task, we propose to use spectral envelopes as inputs. Second we propose two adversarial weight training paradigms, the generalized weighted GAN and the generator impact GAN, both aim at reducing the impact of the generator on the discriminator, so both can learn more gradually and efficiently during training. Applying an energy constraint to the cycleGAN paradigm considerably improved conversion quality. A subjective experiment conducted on a voice conversion task on the voice conversion challenge 2018 dataset shows first that despite a significantly reduced network complexity, the proposed method achieves state-of-the-art results, and second that the proposed weighted GAN methods outperform a previously proposed one.
A. Related works Voice identity conversion (VC) consists in modifying the voice of a source speaker so as to be perceived as the one of a target speaker. Over the past few years, VC has largely gained in popularity and in quality  [1] ,  [2] , in particular with the development of neural voice conversion algorithms  [3] ,  [4]. VC consists in learning a conversion function between the acoustic space of a source and a target speaker. This conversion function is generally learned from a pre-aligned database (parallel VC) in which the source and the target speakers pronounce the same set of sentences, so that a direct correspondence between the frames of the source and target speakers can be established. Unfortunately, this constraint reduces the amount of available recordings of source and target speakers.
Modern VC is mainly based on neural architectures with the particular objective to extend the VC from parallel to nonparallel speech databases. The main advantage of non-parallel VC is that it provides the flexibility to learn the conversion from "on-the-fly" speech databases, which can more easily handle large amount of data and accommodate multiple speakers. These architectures includes Variational Autoencoders (VAEs)  [5] -  [7] , Generative Adversarial Networks (GANs)  [8] -  [12] , Phonetic PosteriorGrams (PPGs)  [13]  and sampleRNNs The research in this paper has been funded by the ANR project TheVoice : ANR-17-CE23-0025  [14]  among others. The use of GAN architectures  [15]  for VC is inspired by advances conducted in the fields of image generation and manipulation. The cycleGAN is a particular configuration of GANs which has been specifically formulated to learn transformations between two different domains or between unaligned or unpaired datasets with application to image-to-image translation  [16].
B. Cycle Generative Adversarial Networks In the cycleGAN architecture  [16] ,  [23] , a generator G X→Y reads data from a dataset X and learns to map it into its respective position in a dataset Y, and vice versa for a generator G Y →X. If X and Y represent languages, this system should be analogous to two translators. To train these generators, the cycleGAN framework uses two adversarially trained discriminators to discriminate respectively any x ∈ X in relation to G Y →X (y) for any y ∈ Y and any y ∈ Y in relation to G X→Y (x) for any x ∈ X. Since G Y →X (G X→Y (x)) should be equal to x, and G X→Y (G Y →X (y)) should be equal to y, a loss named cycle-consistent loss is added to enforce this constraint. In the following equations we state the total objective of the cycleGAN, where E y∼P Data (y) represents the expected value for the distribution Y and E Y ∼P Data (X) represents the expected value for the distribution X.
IV. EXPERIMENT The proposed CycleGAN architectures have been trained and evaluated, using the VCC2018 database  [2]. The VCC2018 training corpus contains 80 short sentences per speaker, sampled at 16 kHz and quantified on 16 bits. For the evaluation set, we used the first 5 sentences, whose length was superior to 2s. Our architecture was inspired by the DCGAN and by the cycleGAN-VC  [10] ,  [11]. For both generators, we used a two-layer encoder with convolutions, followed by a two-layer bottleneck with convolutions and then a two-layer decoder with transposed convolutions. For both the encoder and the decoder, we applied a kernel size 2 and a stride size 2, so as to avoid the checkerboard effect  [25] , noticing that consistently better results were obtained when this undesirable effect was avoided.
In this way, we can reduce the problem of computing h S • sol R (φ) to the problem of computing h B • sol R + (φ ) for some existentially quantified h B -mixed system φ that we can make h B -exact based on our main Theorem 5. Application to Program Analysis. We show how to apply the computation of the sign abstraction of linear equation systems in order to improve the analysis of functional programs with arithmetics. For finding program errors there it can be useful to know about the possible signs of the values of program variables. We elaborate an example in the final Section 10. Implementation. We implemented the h B -exact rewriting algorithm for h B -mixed systems from the main Theorem 5 in Python and plan to freely publish our tool soon. For this we used the cddlib tool from computational geometry  [16]  for computing elementary modes.
Let E be a matrix of elementary modes of A from Theorem 2. The theorem states that Ay • = 0 is R + -equivalent to ∃x. Ex • = y for some vector of fresh variables x. So let emr(φ) be ∃x. φ and φ be Ex • = y. Since all entries of E are positive, the variables in y are pairwise distinct, and the variables in x are chosen freshly, it follows that φ is both quasi-positive and strongly-triangular. We have implemented the elementary mode rewriting in Python based on the cddlib tool  [16]  and plan to freely publish our tool soon. An example input is the system of linear Σ bool -equations φ 0 given in Fig. 4. The corresponding integer matrix equation system is given there too. The elementary modes of the matrix of this system are the vectors (1, 0, 1, 1) and (1, 1, 0, 0). When putting these vectors in the columns of a new matrix, our tool returns the elementary mode rewriting emr(φ 0 ) in Fig.
Nevertheless, it will be beneficial as we will illustrate below. By John's theorem, the sign abstraction h S • sol R (φ I ) can be overapproximated by the abstract interpretation sol S (φ I ). Since S is a finite structure, this abstract interpretation can be computed by finite domain constraint programming. For this, we implemented a solver for first-order formulas over the structure S with Minizinc  [17]. When applied to φ I it returns the set of abstract solutions sol S (φ I ) given in Table  1. This set contains the 6 unjustified abstract solutions 2, 4, 10, 13, 15, 18 outside h S • sol R (φ I ). In the table they are distinguished by gray background color. We also note that the last three solutions 17, 18, 19 could be ruled out when using a more precise abstract program interpretation, taking into account that no recursive call is possible when an exception is thrown.
Results obtained from nonlinear transformation based observer Similarly, the proposed nonlinear transformation based observer for autonomous vehicle is implemented in MATLAB and with the help of the inverse transformation (  27 ), states of vehicle model are computed. The Fig. 4  shows the estimation of state vectors x 1 and x 2 and it has high accuracy which helps to validate the transformation based observer method. However, in the case of estimation of ψ, the proposed nonlinear transformation based observer fails. The Fig. 5  is elaborating the estimation of ψ vector, which shows that the estimated ψ from inverse transformation is not the same as ψ of nonlinear autonomous vehicle model. Thus, the method of estimation using nonlinear transformation fails in case of the motion tracking of autonomous vehicle model.
Besides, we add an automatic tuning of the sparsity parameters using the famous glmnet package. We also proposed three criteria to help the user to choose the number of latent variables. PIntMF was compared to other state-of-the-art integrative methods including feature selection techniques in both synthetic and real data. PIntMF succeeds in finding relevant clusters as well as variables in two types of simulated data (correlated and uncorrelated). Then, PIntMF was applied to two real datasets (Diet and cancer), and it reveals interpretable clusters linked to available clinical data. Our method outperforms the existing ones on two criteria (clustering and variable selection). We show that PIntMF is an easy, fast, and powerful tool to extract patterns and cluster samples from multi-omics data.
, 2016). Indeed, the statistical distributions of omics data are very heterogeneous. For instance, mutations can be modeled by a binary distribution, while RNAseq data can be modeled by a Negative Binomial distribution and metabolomic data by a Gaussian distribution. Besides, the omic block sizes could vary from one hundred to one billion variables. Furthermore, collecting several omics for a single sample could be difficult due to the cost and access to the biological material. To identify potential biomarkers and new classifications in complex diseases, since the last decade, unsupervised integrative methods have been developed to analyze the multi-omics datasets  (Tini et al. , 2017; Huang et al. , 2017; Chauvel et al. , 2019; Pierre-Jean et al. , 2019; Cantini et al. , 2020). Blocks of omics data can be seen as matrices, and relevant information can be extracted using dimension reduction methods, particularly, matrix factorization (MF) methods  (Sastry et al.
, 2014) , MoCluster  (Meng et al. , 2015) , CIMLR  (Ramazzotti et al. , 2018) , and iClusterPlus  (Mo and Shen, 2018). Then, we applied the model on a murine liver dataset  (Williams et al. , 2016)  and glioblastoma cancer data from TCGA already used in  (Shen et al. , 2012).
Solve on W In this step, H k is fixed and the problem 3 is solved on W. min W K k=1 X k -WH k 2 F + n i=1 µ i w i• 1 st. W ≥ 0 (3) All individuals are independent for the weights W when H k are fixed. The problem for an individual i can be written as follows: min wi• K k=1 x k i• -w i• H k 2 + µ i w i• 1 st. w i• ≥ 0 (4) Equation 4 is equivalent to min wi• K k=1 J k j=1 (x k ij -w i• h k •j ) 2 + µ i w i• 1 st. w i• ≥ 0 (5) The optimization problem described by 5 is a classical lasso problem with a positivity constraint. It can be easily and fastly solved by glmnet R package  (Jerome et al. , 2010).
Solve on H k When W is fixed, each H k can be solved independently. In this section, to be more readable, the index k is removed from the equations. min H Q(H) = min H X -WH 2 F + λ P p=1 J j=1 |h pj | (6) Q(H) = Trace (X -WH)(X -WH) T + λ P p=1 J j=1 |h pj | = vec(X -WH) T vec(X -WH)+ λ P p=1 J j=1 |h pj | We denote h = vec(H) =             H 11. H P 1. H 1J. H P J             and x = vec(X) =             X 11. X n1. X 1J. X nJ            . Q(H) = (x -vec(WH)) T (x -vec(WH)) + λ h 1 = (x -(I J ⊗ W)vec(H)) T (x -(I J ⊗ W)vec(H)) +λ h 1 = (x -Wh) T (x -Wh) + λ h 1 where I J is the identity matrix of size J and W = I J ⊗ W We can reformulate the problem as follows: Q(H) = x -Wh 2 + λ h 1 λ will be optimized for each block k = 1,. , K. As for W, we used the glmnet package to solve this problem.
Automatic tuning of sparsity parameters For each block X k , we need to calibrate the sparsity parameter λ k and µ i. The main advantage of glmnet package is the speed (see Supplementary Materials Fig. S9 ). Besides, glmnet implements a cross validation technique to choose the best λ or µ. PIntMF takes advantage of glmnet to calibrate the penalty on each block. Therefore the only parameter that the user needs to tune is the number of latent variables P.
Mean square error The number of latent variables can be optimized by looking at the curve of the Mean Square Error (MSE). In this context, the mean square error (MSE) for each dataset k is defined by: M SE k P = X k -WH k 2 F n × J k (8) Then, the total MSE is then defined by averaging the different M SE k P : M SE P = k M SE k P /K (9).