dataesr/scientific-paragraphs-categorization huggingface dataset:
It was validated through theory and simulations that the mantra algorithm performs almost as well as the original learning algorithm. The main advantage of the mantra algorithm is its finer grain of parallelism, which allows it to be used in hardware with a very large number of processors without compromising the properties of the algorithm. Manolakos et al. [12]  designed a modular SOM systolic architecture, described as a soft IP core in a synthesizable VHDL. The network size, the vectors dimension, the weight and data element bit width precision, and so on are all tunable parameters. The proposed array is made of two types of PEs: the recall mode PE (PER) and the weights update PE (PEU). An SOM module control unit (MCU) generates all the necessary control signals for both array columns.
D. Modular Architecture Problem of the distributed architecture is its expandability. In order to increase the number of neurons, the whole system must be redesigned. The modular architecture shown in Fig. 8(d ) divides the whole SOM hardware into modules. Each module is usually the distributed architecture SOM (see Section IV-B), and the number of neurons can be increased easily by adding new modules. Thus, this approach provides the expandability to the HW SOM architecture. Lachmair et al. [15]  proposed hardware SOM based on the modular architecture, called gNBXe. This work is based on the principles of NBX and gNBX, previously introduced in  [6]  and  [10] , respectively. It consists of a global controller (GC) and the PEs that are the hardware units performing the calculations for simulating the neurons.
e. , wormhole routing and XY routing algorithm). The architecture can define its communication links dynamically by programming the packets. Its significant feature is that the NoC based architecture can perform different applications in a time-sharing manner by dynamically defining the use of neurons and their interconnections  [76]. The proposed hardware SOM architecture also employs the systolic way of data exchange through the NoC, which provides high flexibility and scalability. Recently, Hikawa  [26] ,  [69]  proposed a hierarchical architecture, called nested architecture. In this layered architecture, the top module is made of four submodules placed in the second layer, and each submodule is made of smaller subsubmodules in the third layer. Each module in the bottom layer is made of four neuron units.
F. Vector Representation Researchers have been developing hardware SOMs and their components using digital or analog signals and techniques to represent the vector element values. Some HW SOM components are designed by using different analog techniques (i. e. , current mode circuits  [51]  and WTA classifiers  [79] ). On the other hand, for a complete HW SOM implementation, all of the hardware implementations found in the literature so far employed digital techniques, as summarized in Table  III. This implementation preference can be explained by the fact that digital implementation can take advantage of the benefits of the state-of-the-art VLSI and ULSI techniques  [80]  and powerful design environment tools and methods, such as the hardware description languages (HDL) and the easy accessibility of FPGAs.
The CMOS implementation is superior to the FPGA in terms of all these design issues. In contrast, big advantage of the FPGA technology is its reconfigurability and smaller nonrecurring engineering (NRE) costs. Use of the FPGA can be viewed as an intermediate solution between the ASIC and the software approach. In terms of the area, speed, and power consumption, the recent families of the major FPGA vendors have been improved significantly, which has caused a huge increase in their use in the various hardware designs. Motivated by the high speed and the low cost of parallel computation, numerous studies have employed the FPGA technology as the hardware platform to implement the SOM. As it has been shown in Fig. 11 , almost two thirds of the HW SOM implementations have been implemented by using the FPGA technology.
Moreover, this trend is especially valid in the last five years. In digital design techniques, fixed-point binary representation is usually used to reduce the computing cost and memory usage, instead of floating-point representation. The crucial issue of the binary representation is the data size affecting the obtained precision. In addition, the reduction of the number of bits has an influence on the circuit size of the computing components, which significantly affects the number of neurons that can be synthesized on a single FPGA device. Dlugosz et al. [81]  studied the allowable reduction of the number of bits of some internal signals in an HW SOM implementation that does not deteriorate the SOM network behavior. To determine the influence of the bit lengths of signals on the quality of learning process, a series of simulations was completed using the accurate software model of the SOM implemented on an FPGA device.
The WTA unit, implemented on the FPGA, carries out the WTA computation and returns the winning neuron index to the HPS. Then, software running on the HPS performs the weight update of all neurons. In this way, the SOM learning is carried out within a combined HW/SW codesign. As another pulse-based SOM architecture, Hikawa  [16]  proposed a hardware SOM that uses the phase-modulated pulse signal to represent the weight value of neurons. The elements of the input and weight vectors are given as the phase of the input and internal carrier signals, and a digital phaselocked loop (DPLL) is employed as a computing element because the operation of the DPLL is very similar to that of the SOM's computation. The vector distance is computed as a phase difference of the two signals, and the winner is found by the binary-tree type BMU search circuit.
G. Off-Chip Learning Architecture The works listed in Table III can be divided into two categories: the first category is called on-chip learning where the learning algorithm is also implemented in hardware; the second one, called off-chip learning, where the HW implementations belonging to this category perform only the recall operation. The recall operation, as shown in Algorithm 1, requires only the BMU search without the weight vector update operation. Thus, the vector distance calculation and the argmin function are only implemented in hardware. In the offchip learning approach, weight vectors are trained beforehand, often by a software running on a computer, and then are loaded into the hardware to perform the recall operation. Li et al. [89]  developed an SOM-based positioning scheme for continuous crystal-based PET detector.
A. Implementation Platforms To demonstrate or investigate the proposed HW SOM architectures or their computing components, authors use various methods and tools, including simulation and/or physical experimental validations. Some of the works presented in Fig. 11  and Table  III  were investigated by simulations (almost one fifth of the proposed HW SOM implementations). Among simulation validation tools, we can find examples of VHDL simulation  [87] ,  [88] , HSpice simulation  [65] , and MATLAB simulation  [71]. The simulation verification is very flexible and useful approach in order to validate the algorithms or circuits before their physical implementation. On the other hand, physical experimental verification is the common way of validating HW SOM implementations, where CMOS and/or FPGA technologies are targeted (almost 80% of the reported works in Fig.
B. Datasets/Target Applications Table  III  summarizes the used datasets and target applications for validation of the proposed HW SOM architectures: artificial datasets, image coding and compression experiments, and classification tasks on publicly available datasets (MNIST, IRIS, and so on). From Table  III , it can be noticed that most of the proposed HW SOM architectures were validated by using in-lab artificial datasets, which often represents a simple dataset to confirm the SOM's basic functions, such as the topology-preserving nature. Many HW SOMs are applied to image coding or image compression applications. The main reason for this type of validation is that the obtained results are easily observable. Indeed, in these applications, the weight vectors are often represented as colors from the input dataset, which are the used images.
This palette simply represents the SOM weights of the trained map. To produce the compressed image, each pixel of the initial image is replaced with the index of the most similar color from the SOM palette (obtained through the SOM recall operation). Thus, instead of using 24 bits to code a pixel, each pixel is coded with the number of bits necessary to uniquely identify each neuron in the map (i. e. , 8 bits for a 16 × 16 map). An example of the color compression with an SOM is shown in Fig. 12   [17]. The original image, the color palette consisting of 256 colors and obtained after training a 16 × 16 SOM network, and image reconstructed by using the palette colors are shown. In addition, instead of using only one pixel as the input vector of the SOM map, B × B blocks of pixels can be presented as input vectors and quantized by the SOM in the same way as previously presented.
The HW SOMs in  [13] ,  [17] ,  [18] ,  [20] ,  [28] ,  [40] ,  [67] ,  [68] ,  [70] ,  [71] ,  [76] ,  [91]  were applied to the real-time color/image compression. Another popular application for validation of HW SOMs is various classification tasks. In  [4] , a speech coding benchmark provided by EPFL was used to measure the training performance of the proposed SOM. Appiah et al. [52]  applied their HW SOM to recognize handwritten digits. The authors used the Modified National Institute of Standards and Technology (MNIST) database  [92] , which is a database of handwritten digit images. In this database, each image is a 28 × 28 greyscale image, which can be considered as an input vector whose dimension is 28 × 28 = 784. An example of the MNIST data clustering  [69]  is shown in Fig. 13 , where 784-D vectors were mapped onto a lower two-dimensional 16 × 16 SOM.
It can be seen that due to the SOM topologypreserving nature, the same digit characters are assigned to adjacent neurons. Note also that clusters representing different digits but similar shape are assigned to neighboring neurons. In this way, the SOM can be used to visualize relations among input vectors. Other examples of the use of the MNIST dataset also for recognition or clustering applications can be found in  [53] ,  [69] ,  [71]. Another example of classification tasks used to validate an HW SOM architecture is the hand sign recognition system proposed in  [90]. In the proposed system, preprocessed input images were applied to the off-line trained SOM used in the recall mode allowing to achieve real-time hand sign recognition (60 frames/s with a recognition accuracy of 97. 1%). Moreover, the IRIS dataset has also been used for many statistical classification techniques in machine learning algorithms and HW SOM validation as well.
The IRIS dataset consists of 50 samples from each of three species of Iris  (Iris setosa, Iris virginica, and Iris versicolor). Each sample consists of four features: the length and the width of the sepals and petals, in centimeters. Classification performances of the works in  [85] ,  [87]  were demonstrated by using the IRIS dataset. Finally, the mixed-mode classifier proposed by Shah et al. [79]  was trained to identify the sound source, whether it is a generator, truck, or car. Another data clustering application based on the SOM's vector quantization capability is presented in  [15]. Lachmair et al. [15]  used hyperspectral image data of the lunar crater volcanic field (LCVF) in Nevada, taken by NASA's AVIRIS sensor for clustering target. Since the LCVF dataset is fairly large with the high-dimensional vectors (in the hyperspectral image) and it has complex cluster structure, the clustering of these hyperspectral images is seen as a quite challenging and nontrivial task.
We analyze our data jointly with publicly available datasets of other ancient Native Americans outside of Mexico and present-day Indigenous populations from Mexico. This large compendium of ancient genomic data allows us to study the pre-Hispanic genetic structure in the territory occupied by Mexico, answering longstanding questions regarding population dynamics (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint this version posted June 20, 2022. ; https://doi. org/10. 1101/2022. 06. 19. 496730 doi: bioRxiv preprint at the NFM, disentangling the complex structure of central Mexico populations, and revealing a previously unknown ancient contribution of an unsampled genetic linage to Mexican populations.
Furthermore, we detected unreported variants in the mtDNA PhyloTree  (40)  for seven individuals assigned to the subhaplogroups A2d (n=4), B2c (n=2), and B2l (n=1). The absence of these variants could reflect a poor sampling of Native American mitochondrial genomes in the PhyloTree database or a loss of these haplotypes as a consequence of drift that includes the effect of a population decline following European colonization as reported in other Native American populations  (41, 42). Accordingly, we propose adding these new unreported variants to the PhyloTree database (Table  S5 ). Notably, in SG we found the sub-haplogroup A2d in six out of 13 pre-Hispanic individuals, including the oldest in our dataset, P_CCM_b (320 BCE) (Table  S1 ). Out of this six A2d individuals, four belong to the pre-drought period according to dating based either on C14 or the archaeological context, and two to the drought periods.
A haplotype median-joining network for the haplogroup A2 shows a clustering of the SG individuals carrying the sub-haplogroup A2d despite a time transect of 1,480 years (Fig. 2A ). This observation supports a population continuity, at least of the female population despite the severe droughts in 900 -1,300 CE in this region of the NFM. For the other haplogroups B, C, and D, when individuals from the same site share the same haplogroup their sub-haplogroups tend to cluster together (Fig. S5 ). To estimate the past female effective population size, we merged our dataset of pre-Hispanic mtDNA sequences (n=16, selected after alignments, see methods) with 164 publicly available ancient (n=9) and modern (n=155) mtDNA sequences from Mexico (Table  S4  and S6 ). We generated an extended Bayesian Skyline Plot (EBSP) for all present-day haplogroups, for all pre-Hispanic haplogroups, for each haplogroup (present-day + pre-Hispanic), and for all haplogroups together merging present-day and pre-Hispanic (Fig.
Haplotype networks were constructed with both ancient (from this study and previously published) (n=25) and present-day (n=78) mtDNA of individuals in the Mexican territory (Table  S4  and S6 ). All present-day samples were retrieved from the PhyloTree (version 17) database  (40, 91). Table  S4  reports the ancient individuals for which the mtDNA sequences were used. Only sequences that were reported as being of the same sub haplogroup or neighboring sub-haplogroup as our ancient samples were chosen for sequence comparison. Extended Bayesian skyline. Past female effective population sizes were reconstructed using a Bayesian skyline approach (104) in BEAST 2  (105). The ancient mtDNA sequences, mentioned above were merged with present-day mtDNA sequences collected from published public databases (Table  S4  and S6 ).
Genotyping data was manipulated with Plink version 1. 90 beta  (112). The dataset was converted from Plink format to Eigenstrat's geno format with the command convertf from Eigensoft version 6. 0. 1. 1  (52). Missing genotypes were imputed with the snmf function from the LEA package, using the parameters K = 2, entropy = TRUE, and repetitions = 5. The runs with the lowest cross-entropy value were considered for posterior steps. Genotypes were corrected according to the autosomal coverage depth of each sample. We assigned the average depth from the HGDP dataset mentioned in the publication: 35X (50). We assigned the respective coverage read depth for all 11 individuals from SGDP. Modern samples genotyped qpGraph. We utilized the qpGraph tool of the ADMIXTOOLS v5. 0 software package (65) to construct admixture graphs and test which various models of demographic history best fit the data.
Acknowledgments Acknowledgments: We greatly acknowledge the IT support of  Luis Alberto Aguilar Bautista ,  Alejandro de León Cuevas ,  Carlos Sair Flores Bautista  and  Jair Garcia Sotelo  of the  Laboratorio Nacional de Visualización Científica Avanzada (LAVIS UNAM ). We also thank  Alejandra Castillo Carbajal  and  Carina Uribe Díaz  from  LIIGH-UNAM  for wetlab technical support during the project. We thank  Alexandra Sockell  for technical support at the  Bustamante Lab. We thank  Diego Ortega-Del Vecchyo  and  Angélica González Oliver  for enriching comments throughout the project on earlier versions of the manuscripts. Funding:  Viridiana Villa-Islas  is a doctoral student from the  Programa de Doctorado en Ciencias Biomédicas ,  Universidad Nacional Autónoma de México (UNAM)  and received  CONACyT  fellowship (Número de becaria: 282188).
M. C. A. -A. received support from  UNAM-PAPIIT  (Project numbers:  IA201219  and  IA203821 ) and the  Wellcome Trust Seed Award in Science  (grant number:  208934/Z/17/Z ), and the  International Centre for Genetic Engineering and Biotechnology  (project number:  CRP/MEX10-03 ). M. C. A. -A. , E. H. -S. , and F. J. receive support from the  Human Frontiers Science Program  (grant number:  RGY0075/201 ). R. F. was supported by a grant (PALEUCOL;  PGC2018-094101-A-I00 ) from  FEDER/Ministerio de Ciencia e Innovación-Agencia Estatal de Investigación  and from  CajaCanarias  and  La Caixa Banking  foundations (GENPAC; 2018PATRI16). Data generation for the Sierra Tarahumara Individuals was funded with a  Trainee Research Grant  to M. C. A. -A. by  Stanford Center for Computational, Evolutionary and Human Genomics.
Speech Emotions Recognition (SER) has gained significant attention in the fields of humancomputer interaction and speech processing. In this article, we present a novel approach to improve SER performance by interpreting the Mel Frequency Cepstral Coefficients (MFCC) as a multivariate functional data object, which accelerates learning while maintaining high accuracy. To treat MFCCs as functional data, we preprocess them as images and apply resizing techniques. By representing MFCCs as functional data, we leverage the temporal dynamics of speech, capturing essential emotional cues more effectively. Consequently, this enhancement significantly contributes to the learning process of SER methods without compromising performance. Subsequently, we employ a supervised learning model, specifically a functional Support Vector Machine (SVM), directly on the MFCC represented as functional data.
This enables the utilization of the full functional information, allowing for more accurate emotion recognition. The proposed approach is rigorously evaluated on two distinct databases, EMO-DB and IEMOCAP, serving as benchmarks for SER evaluation. Our method demonstrates competitive results in terms of accuracy, showcasing its effectiveness in emotion recognition. Furthermore, our approach significantly reduces the learning time, making it computationally efficient and practical for real-world applications. In conclusion, our novel approach of treating MFCCs as multivariate functional data objects exhibits superior performance in SER tasks, delivering both improved accuracy and substantial time savings during the learning process. This advancement holds great potential for enhancing human-computer interaction and enabling more sophisticated emotion-aware applications.
The entertainment industry can also benefit from SER with low learning time processes. By analyzing speech emotions in real-time, content creators can develop immersive and interactive experiences that adapt to users' emotional responses. Emotion-aware video games, virtual reality experiences, and interactive storytelling platforms can dynamically adjust their narratives, characters, and gameplay elements based on users' emotional states. This creates a more engaging and personalized entertainment experience, allowing users to become active participants in the content creation process. In the subsequent sections, we provide a detailed description of our proposed method. We evaluate its performance on two databases, EMO-DB and IEMOCAP, and present comparative results with other approaches. Finally, we conclude and discuss the outcomes in the closing section.
Functional Features Let {M l |l = 1,. , n} be the resized MFCC of shape (n MFCC , m) where n is the sample size of the dataset, n MFCC is the number of coefficients of the Discrete Cosine Transform of the spectral envelope (the number of MFCCs) and m is the number of discretization points of time, which is constant over the dataset thanks to the resizing method. Define M i l , for l ∈ {1,. , n} and i ∈ {1, n MFCC }, the evolution of the i-th coefficient of the l-th data across time. In practice, the evolution is sufficiently smooth to consider M i l as a smooth function. Then, for each raw audio signal of index l, we have a multivariate functional data M i l (t) observed at m regular discretization points (t 1 ,. , t m ), where the number of covariates is n MFCC. Then, during the registration step of the functional data, we operate a soft smoothing of the function in order to convert raw discrete data points into a smoothly varying function.
Classification Figure  3  shows a schematic of the proposed method with its different parts. First, we compute the MFCCs. Secondly, we resize them to a desired shape. Finally, we can use a SVM to classify the associated emotion. We refer the reader to the paper of  [19] , which presents an extension to functional data features of the classical SVM algorithm. We choose a radial basis for the kernel K of the SVM. K(u, v) = exp(-γ|u -v| 2 ). The tuned parameters of the SVM are the cost (the C constant of the regularization term in the Lagrange formulation) and the γ parameter used in the radial basis. We optimize the method on a grid with a cost of 5 and γ = 0. 001. We use these parameters in the two databases of the following section. In order to incorporate functional features as inputs into the SVM, it is necessary to adapt the SVM algorithm.
Databases EMO-DB (Berlin Database of Emotional Speech)  [20]  is a publicly available German speech database containing speech recordings of seven emotions: sadness, anger, happiness, fear, disgust, boredom and neutrality. The entire dataset contains 535 speeches in german. The database is created by the Institute of Communication Science, Technical University, Berlin, Germany. Ten professional speakers (five males and five females) participated in data recording. The dataset IEMOCAP (Interactive Emotional Dyadic Motion Capture)  [21]  contains approximately 12 h of speech from 10 speakers. The literature selects a total of 5531 utterances that are labeled with one of five categories: Happy, Angry, Neutral, Sad, and Excited. This set is reduced to four categories by merging Excited and Happy into a single category.
Both IEMOCAP and EMO-DB have significantly contributed to advancing the field of speech emotion recognition. They have been widely used as benchmark datasets for developing and evaluating various emotion recognition algorithms and models. The availability of these databases has facilitated comparative studies, the exploration of new methodologies, and the development of robust and accurate emotion recognition systems. It is imperative to emphasize that solely emotions were employed during both the learning and testing phases. Consequently, metadata elements such as language, gender, actor identity, among others, were not incorporated into the analysis.