Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving X-Vector and PLDA for Text-Dependent Speaker Verification

Recently, the pipeline consisting of an x-vector speaker embedding front-end and a Probabilistic Linear Discriminant Analysis (PLDA) back-end has achieved state-of-the-art results in text-independent speaker veriﬁcation. In this paper, we further improve the performance of x-vector and PLDA based system for text-dependent speaker veriﬁcation by exploring the choice of layer to produce embedding and modifying the back-end training strategies. In particular, we probe that x-vector based embeddings, speciﬁcally the standard deviation statistics in the pooling layer, contain the information related to both speaker characteristics and spoken content. Accordingly, we modify the back-end training labels by utilizing both of the speaker-id and phrase-id. A correlation-alignment-based PLDA adaptation is also adopted to make use of the text-independent labeled data during back-end training. Experimental results on the SDSVC 2020 dataset show that our proposed methods achieve signiﬁ-cant performance improvement compared with the x-vector and HMM based i-vector baselines.
Speaker Code Based Speaker Adaptive Training Using Model Agnostic Meta-Learning

The performance of automatic speech recognition systems can be improved by speaker adaptive training (SAT), which adapts an acoustic model to compensate for the mismatch between training and testing conditions. Speaker code learning is one of the useful ways for speaker adaptive training. It learns a set of speaker dependent codes together with speaker independent acoustic model in order to remove speaker variation. Conventionally, speaker dependent codes and speaker independent acoustic model are jointly optimized. However, this could make it difﬁcult to decouple the speaker code from the acoustic model. In this paper, we take the speaker code based SAT as a meta-learning task. The acoustic model is considered as meta-knowledge, while speaker code is considered as task spe-ciﬁc knowledge. Experiments on the Switchboard task show that our method can not only learn a good speaker code, but also improve the performance of the acoustic model even without speaker code.
SdSV Challenge 2020: Large-Scale Evaluation of Short-Duration Speaker Verification

Modern approaches to speaker veriﬁcation represent speech utterances as ﬁxed-length embeddings. With these approaches, we implicitly assume that speaker characteristics are independent of the spoken content. Such an assumption generally holds when sufﬁciently long utterances are given. In this context, speaker embeddings, like i-vector and x-vector, have shown to be extremely effective. For speech utterances of short duration (in the order of a few seconds), speaker embeddings have shown signiﬁcant dependency on the phonetic content. In this regard, the SdSV Challenge 2020 was organized with a broad focus on systematic benchmark and analysis on varying degrees of phonetic variability on short-duration speaker veriﬁcation (SdSV). In addition to text-dependent and text-independent tasks, the challenge features an unusual and difﬁcult task of cross-lingual speaker veriﬁcation (English vs. Persian). This paper describes the dataset and tasks, the evaluation rules and protocols, the performance metric, baseline systems, and challenge results. We also present insights gained from the evaluation and future research directions.
Multi-Stream Attention-Based BLSTM with Feature Segmentation for Speech Emotion Recognition

This paper proposes a speech emotion recognition technique that considers the suprasegmental characteristics and temporal change of individual speech parameters. In recent years, speech emotion recognition using Bidirectional LSTM (BLSTM) has been studied actively because the model can focus on a particular temporal region that contains strong emotional characteristics. One of the model’s weaknesses is that it cannot consider the statistics of speech features, which are known to be effective for speech emotion recognition. Besides, this method cannot train individual attention parameters for different descriptors because it handles the input sequence by a single BLSTM. In this paper, we introduce feature segmentation and multi-stream processing into attention-based BLSTM to solve these problems. In addition, we employed data augmentation based on emotional speech synthesis in a training step. The classiﬁcation experiments between four emotions (i.e., anger, joy, neutral, and sadness) using the Japanese Twitter-based Emotional Speech corpus (JTES) showed that the proposed method obtained a recognition accuracy of 73.4%, which is comparable to human evaluation (75.5%).
Trained-feature specific offline learning in an orientation detection task

It has been suggested that sleep provides additional enhancement of visual perceptual learning (VPL) acquired before sleep, termed offline performance gains. A majority of the studies that found offline performance gains of VPL used discrimination tasks including the texture discrimination task (TDT). This makes it questionable whether offline performance gains on VPL are generalized to other visual tasks. The present study examined whether a Gabor orientation detection task, which is a standard task in VPL, shows offline performance gains. In Experiment 1, we investigated whether sleep leads to offline performance gains on the task. Subjects were trained with the Gabor orientation detection task, and re-tested it after a 12-hr interval that included either nightly sleep or only wakefulness. We found that performance on the task improved to a significantly greater degree after the interval that included sleep and wakefulness than the interval including wakefulness alone. In addition, offline performance gains were specific to the trained orientation. In Experiment 2, we tested whether offline performance gains occur by a nap. Also, we tested whether spontaneous sigma activity in early visual areas during non-rapid eye movement (NREM) sleep, previously implicated in offline performance gains of TDT, was associated with offline performance gains of the task. A different group of subjects had a nap with polysomnography. The subjects were trained with the task before the nap and re-tested after the nap. The performance of the task improved significantly after the nap only on the trained orientation. Sigma activity in the trained region of early visual areas during NREM sleep was significantly larger than in the untrained region, in correlation with offline performance gains. These aspects were also found with VPL of TDT. The results of the present study demonstrate that offline performance gains are not specific to a discrimination task such as TDT, and can be generalized to other forms of VPL tasks, along with trained-feature specificity. Moreover, the present results also suggest that sigma activity in the trained region of early visual areas plays an important role in offline performance gains of VPL of detection as well as discrimination tasks.
End-to-End Task-Oriented Dialog System Through Template Slot Value Generation

To overcome the limitations of conventional pipeline-based task-oriented dialog systems, an end-to-end approach has been introduced. To date, many end-to-end task-oriented dialog systems have been proposed and these have shown good performance in various domains. However, those have some limitations such as the need for dialog state annotations. And there is also room for improvement for those systems. In this paper, we examine the issues of recent end-to-end task-oriented dialog systems and present a model that can handle these issues. The proposed model classiﬁes a system utterance template in a retrieval-based manner and then generates the slot values in the template through a decoder. Also, we propose an unsupervised learning based template generation method that allows model training even in a domain where the templates are not given and the dialog information is not tagged. Our model obtains new state-of-the-art results on a restaurant search domain.
Temporal Consistent Motion Parallax for Omnidirectional Stereo Panorama Video

We present a new pipeline to enable head-motion parallax in omnidirectional stereo (ODS) panorama video rendering using a neural depth decoder. While recent ODS panorama cameras record short-baseline horizontal stereo parallax to offer the impression of binocular depth, they do not support the necessary translational degrees-of-freedom (DoF) to also provide for head-motion parallax in virtual reality (VR) applications. To overcome this limitation, we propose a pipeline that enhances the classical ODS panorama format with 6 DoF free-viewpoint rendering by decomposing the scene into a multi-layer mesh representation. Given a spherical stereo panorama video, we use the horizontal disparity to store explicit depth information for both eyes in a simple neural decoder architecture. While this approach produces reasonable results for individual frames, video rendering usually suffers from temporal depth inconsistencies. Thus, we perform successive optimization to improve temporal consistency by fine-tuning our depth decoder for both temporal and spatial smoothness. Using a consumer-grade ODS camera, we evaluate our approach on a number of real-world scene recordings and demonstrate the versatility and robustness of the proposed pipeline.
SWiSSSE: System-Wide Security for Searchable Symmetric Encryption

This paper initiates a new direction in the design and analysis of searchable symmetric encryption (SSE) schemes. We provide the first comprehensive security model and definition for SSE that takes into account leakage from the entirety of the SSE system, including not only from access to encrypted indices but also from access to the encrypted database documents themselves. Such system-wide leakage is intrinsic in end-to-end SSE systems, and can be used to break almost all state-of-the-art SSE schemes (Gui et al., IEEE S&P 2023). We then provide a static SSE construction meeting our new security notion. The proposed SSE scheme involves a combination of novel techniques: bucketization to hide volumes of responses to queries, and delayed, pseudorandom write-backs to disrupt access pattern. Our implementation and analysis of the proposed scheme demonstrates that it offers very strong security against general classes of (system-wide) leakage-abuse attacks with moderate overhead. Our scheme scales smoothly to databases containing hundreds of thousand of documents and millions of keyword-document pairs. To the best of our knowledge, this is the first end-to-end SSE scheme that effectively suppresses system-wide leakage while maintaining practical efficiency.
Cirugía de zona epileptogénica localizada en un área elocuente del lóbulo frontal en un adolescente con epilepsia

OBJETIVO: Describir los resultados de la evaluacion prequirurgica, transquirurgica y postquirurgica, en un adolescente con epilepsia farmacorresistente con zona epileptogenica estimada en un area elocuente del lobulo frontal izquierdo. CASO CLINICO: Paciente masculino, de 18 anos, con epilepsia frontal izquierda farmacorresistente desde los tres anos, con una frecuencia de crisis de desconexion, atonicas y, en ocasiones, tonico clonico bilateral entre 30 y 40 diarias, previo a la cirugia. La evaluacion prequirurgica identifico la zona de inicio ictal frontal izquierda, no lesional por imagen de resonancia magnetica de 3 tesla. Se indico tomografia computarizada por emision de foton unico interictal / ictal corregistrada con resonancia magnetica, donde se identifico hiperperfusion frontal izquierda. Se realizo la cirugia con el uso de potenciales evocados somatosensoriales para identificar el surco central, la estimulacion cortical directa para mapear el area motora primaria, y la electrocorticografia transoperatoria para delimitar la zona de reseccion. Se empleo la tecnica combinada desconectiva (callosotomia anterior) y resectiva del giro frontal superior y medio izquierdos, con modificacion del patron electrico en la electrocorticografia posterior a la cirugia. El resultado anatomopatologico fue displasia cortical focal tipo IA. En el seguimiento postquirurgico, el paciente presenta solo entre 2-3 crisis semanales. CONCLUSIONES: La cirugia de epilepsia extratemporal no lesional y con zona epileptogenica que incluye areas elocuentes del lobulo frontal es factible de realizar en nuestro pais con minima invasividad y buenos resultados.
Cyanorak v2.1: a scalable information system dedicated to the visualization and expert curation of marine and brackish picocyanobacteria genomes

Abstract Cyanorak v2.1 (http://www.sb-roscoff.fr/cyanorak) is an information system dedicated to visualizing, comparing and curating the genomes of Prochlorococcus, Synechococcus and Cyanobium, the most abundant photosynthetic microorganisms on Earth. The database encompasses sequences from 97 genomes, covering most of the wide genetic diversity known so far within these groups, and which were split into 25,834 clusters of likely orthologous groups (CLOGs). The user interface gives access to genomic characteristics, accession numbers as well as an interactive map showing strain isolation sites. The main entry to the database is through search for a term (gene name, product, etc.), resulting in a list of CLOGs and individual genes. Each CLOG benefits from a rich functional annotation including EggNOG, EC/K numbers, GO terms, TIGR Roles, custom-designed Cyanorak Roles as well as several protein motif predictions. Cyanorak also displays a phyletic profile, indicating the genotype and pigment type for each CLOG, and a genome viewer (Jbrowse) to visualize additional data on each genome such as predicted operons, genomic islands or transcriptomic data, when available. This information system also includes a BLAST search tool, comparative genomic context as well as various data export options. Altogether, Cyanorak v2.1 constitutes an invaluable, scalable tool for comparative genomics of ecologically relevant marine microorganisms.
Speaker Diarization System Based on DPCA Algorithm for Fearless Steps Challenge Phase-2

This paper describes the ASRGroup team speaker diarization systems submitted to the TRACK 2 of the Fearless Steps Challenge Phase-2. In this system, the similarity matrix among all segments of an audio recording was measured by Sequential Bidirectional Long Short-term Memory Networks (Bi-LSTM), and a clustering scheme based on Density Peak Cluster Algorithm (DPCA) was proposed to clustering the segments. The system was compared with the Kaldi Toolkit diarization sys-tem (x-vector based on TDNN with PLDA scoring model) and the Spectral system (similarity based on Bi-LSTM with Spectral clustering algorithm). Experiments show that our system is signiﬁcantly outperforms above systems and achieves a Diariza-tion Error Rate (DER) of 42.75% and 39.52% respectively on the Dev dataset and Eval dataset of TRACK 2 (Fearless Steps Challenge Phase-2). Compared with the baseline Kaldi Toolkit diarization system and Spectral Clustering algorithm with Bi-LSTM similarity models, the DER of our system is absolutely reduced 4.64%, 1.84% and 8.85%, 7.57% respectively on the two datasets.
Uncovering Network Architecture Using an Exact Statistical Input-Output Relation of a Neuron Model

Identifying network architecture from recorded neural activity is one of the major challenges in neuroscience. A key requirement for this challenge is the knowledge of the statistical input-output relation of single neurons operating under in vivo conditions. However, lack of analytical representation even for a standard neuron model prohibited theoretical investigations. Recently we provided an exact analytical solution of spike-timing for a leaky integrate-and-fire (LIF) neuron responding to synaptic inputs under noisy background input balanced near the thresh-old [Shomali et. al., J Comput Neurosci 44, (2018): 147]. Using that solution, we construct a unified framework that links synaptic inputs, nonlinearity of spiking, network architecture, and statistics of population activity. With this approach, we theoretically predict correlated population activity of in vivo neurons that receive common inputs under different network architectures. It allows us to present a quantitative method to unveil the network architecture behind the sparse population activity ubiquitously observed in visual and hippocampal neurons. We show that two motifs can disclose this activity: Inhibitory common inputs to three (or more) neurons or excitatory common inputs to pairs of neurons. By comparing model prediction with empirical observation of monkey V1 neurons [Ohiorhenuan et. al., Nature 466, (2010): 617], we quantitatively rule out the scenario that common inhibition causes the observed sparse activity characterized by negative triple-wise interactions among the neurons. Instead, we show that an alternative architecture with common excitatory inputs to pairs of neurons well explains it. The proposed analytical approach that connects neural mechanisms with neural interactions could serve as a useful tool to probe network architecture, using recordings of population activity. Significance Statement Inference of the network architecture from neural data is a daunting task. Here, we introduce a quantitative method that lets us reveal the underlying architecture. Using an exact analytic solution for input-output relation of a neuron model, we developed a mathematical framework that connects biophysical properties of individual neurons with neural population activity. With this method, we quantitatively compare two possible architectures in a particular experiment and rule out one hence suggest the other as the possible neuronal architecture for sparse population activity observed in visual neurons. This unified framework allows us to explore biophysical mechanisms of neural information processing from their population activity.
In Vivo Glucosylated LpA-I Subfraction

Abstract This study compared the structural and functional properties of glucosylated and non-glucosylated LpA-I particle subfractions (GLpA-I and NGLpA-I, respectively) isolated from patients with poorly controlled type 1 (insulin-dependent) diabetes. Compared with NGLpA-I, GLpA-I showed an enrichment in triglycerides (P<.05) and a depletion in phospholipid (P<.05) content. Moreover, the triglycerides-to-cholesteryl esters ratio was increased (P<.05), suggesting an increased cholesteryl ester transfer protein activity and a possible transport defect that accelerates atherogenesis. The surface-to-core constituents ratio, an indirect estimate of particles size, is lower in GLpA-I (P<.01) than in NGLpA-I, correlating well with a larger median size (P<.05) as seen by electron microscopy. The apolipoprotein (apo) A-I conformation was evaluated through determination of the immunological accessibility of three different domains defining specific epitopes for anti-apo A-I monoclonal antibodies. We observed a mar.
A Noise-Aware Memory-Attention Network Architecture for Regression-Based Speech Enhancement

We propose a novel noise-aware memory-attention network (NAMAN) for regression-based speech enhancement, aiming at improving quality of enhanced speech in unseen noise conditions. The NAMAN architecture consists of three parts, a main regression network, a memory block and an attention block. First, a long short-term memory recurrent neural network (LSTM-RNN) is adopted as the main network to well model the acoustic context of neighboring frames. Next, the memory block is built with an extensive set of noise feature vectors as the prior noise bases. Finally, the attention block serves as an auxiliary network to improve the noise awareness of the main network by encoding the dynamic noise information at frame level through additional features obtained by weighing the existing noise basis vectors in the memory block. Our experiments show that the proposed NAMAN framework is compact and outperforms the state-of-the-art dynamic noise-aware training approaches in low SNR conditions.
The Status of Soil Microbiome as Affected by the Application of Phosphorus Biofertilizer: Fertilizer Enriched with Beneficial Bacterial Strains

Regarding the unfavourable changes in agroecosystems resulting from the excessive application of mineral fertilizers, biopreparations containing live microorganisms are gaining increasing attention. We assumed that the application of phosphorus mineral fertilizer enriched with strains of beneficial microorganisms contribute to favourable changes in enzymatic activity and in the genetic and functional diversity of microbial populations inhabiting degraded soils. Therefore, in field experiments conditions, the effects of phosphorus fertilizer enriched with bacterial strains on the status of soil microbiome in two chemically degraded soil types (Brunic Arenosol—BA and Abruptic Luvisol—AL) were investigated. The field experiments included treatments with an optimal dose of phosphorus fertilizer (without microorganisms—FC), optimal dose of phosphorus fertilizer enriched with microorganisms including Paenibacillus polymyxa strain CHT114AB, Bacillus amyloliquefaciens strain AF75BB and Bacillus sp. strain CZP4/4 (FA100) and a dose of phosphorus fertilizer reduced by 40% and enriched with the above-mentioned bacteria (FA60). The analyzes performed included: the determination of the activity of the soil enzymes (protease, urease, acid phosphomonoesterase, β-glucosidase), the assessment of the functional diversity of microorganisms with the application of BIOLOGTM plates and the characterization of the genetic diversity of bacteria, archaea and fungi with multiplex terminal restriction fragment length polymorphism and next generation sequencing. The obtained results indicated that the application of phosphorus fertilizer enriched with microorganisms improved enzymatic activity, and the genetic and functional diversity of the soil microbial communities, however these effects were dependent on the soil type.