Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Lingual Multi-Task Speech Emotion Recognition Using wav2vec 2.0

Speech Emotion Recognition (SER) has several use cases for Digital Entertainment Content (DEC) in Over-the-top (OTT) services, emotive Text-to-Speech (TTS) engines and voice assistants. In this work, we present a Multi-Lingual (MLi) and Multi-Task Learning (MTL) audio only SER system based on the multi-lingual pre-trained wav2vec 2.0 model. The model is fine-tuned on 25 open source datasets in 13 locales across 7 emotion categories. We show that, a) Our wav2vec 2.0 single task based model outperforms Pre-trained Audio Neural Network (PANN) based single task pre-trained model by 7.2% (relative), b) The best MTL model outperforms the PANN based and wav2vec 2.0 based single task models by 8.6% and 1.7% (relative) respectively, c) The MTL based system outperforms pre-trained single task wav2vec 2.0 model in 9 out of 13 locales in terms of weighted F1 scores, and d) The MTL-MLi wav2vec 2.0 outperforms the state-of-the-art for the languages contained in the pre-training corpora.
DynSNN: A Dynamic Approach to Reduce Redundancy in Spiking Neural Networks

Current Internet of Things (IoT) embedded applications use machine learning algorithms to process the collected data. However, the computational complexity and storage requirements of existing deep learning methods hinder the wide availability of embedded applications. Spiking Neural Networks (SNN) is a brain-inspired learning methodology that emerged from theoretical neuroscience, as an alternative computing paradigm for enabling low-power computation. Since these IoT devices are usually resource-constrained, compression techniques are crucial in the practical application of SNNs. Most existing methods directly apply pruning methods from artificial neural networks (ANNs) to SNNs, while ignoring the distinction between ANNs and SNNs, thus inhibiting the potential of pruning methods on SNNs. In this paper, inspired by the topology of neuronal co-activity in the neural system, we propose a dynamic pruning framework (dubbed DynSNN) for SNNs, enabling us to seamlessly optimize network topology on the fly almost without accuracy loss. Experimental results on a wide range of classification applications show that the proposed method achieves almost lossless for SNN on MNIST, CIFAR-10, and ImageNet datasets. Moreover, it reaches a ∼0.3% accuracy loss under 34% compression rate on CIFAR and ImageNet, and achieves 60% compression rate with no accuracy loss on MNIST, which reveals remarkable structure refining capability in SNNs.
HOQRI: Higher-Order QR Iteration for Scalable Tucker Decomposition

We propose a new algorithm called higher-order QR iteration (HO-QRI) for computing the Tucker decomposition of large and sparse tensors. Compared to the celebrated higher-order orthogonal iterations (HOOI), HOQRI relies on a simple orthogonalization step in each iteration rather than a more sophisticated singular value de-composition step as in HOOI. More importantly, when dealing with extremely large and sparse data tensors, HOQRI completely eliminates the intermediate memory explosion by defining a new sparse tensor operation called TTMcTC. Furthermore, HOQRI is shown to monotonically improve the objective function, thus enjoying the same convergence guarantee as that of HOOI. Numerical experiments on synthetic and real data showcase the effectiveness of HOQRI.
FRE-GAN 2: Fast and Efficient Frequency-Consistent Audio Synthesis

Although recent advances in neural vocoder have shown significant improvement, most of these models have a trade-off between audio quality and computational complexity. Since the large model has a limitation on the low-resource devices, a more efficient neural vocoder should synthesize high-quality audio for practical applicability. In this paper, we present Fre-GAN 2, a fast and efficient high-quality audio synthesis model. For fast synthesis, Fre-GAN 2 only synthesizes low and high-frequency parts of the audio, and we leverage the inverse discrete wavelet transform to reproduce the target-resolution audio in the generator. Additionally, we also introduce adversarial periodic feature distillation, which makes the model synthesize high-quality audio with only a small parameter. The experimental results show the superiority of Fre-GAN 2 in audio quality. Furthermore, Fre-GAN 2 has a 10.91× generation acceleration, and the parameters are compressed by 21.23× than Fre-GAN.
End-To-End Multi-Modal Speech Recognition with Air and Bone Conducted Speech

Improving the performance of automatic speech recognition (ASR) in adverse acoustic environments is a long-term tough task. Although many robust ASR systems based on conventional microphones have been developed, their performance with air-conducted (AC) speech is still far from satisfactory in low signal-to-noise-ratio (SNR) environments. Bone-conducted (BC) speech is relatively insensitive to ambient noise, and has a potential of promoting the ASR performance at such low SNR environments as an auxiliary source. In this paper, we propose a conformer-based multi-modal speech recognition system. It uses a conformer encoder and a transformer-based truncated decoder to extract the semantic information from AC and BC channels respectively. The semantic information of the two channels are re-weighted and integrated by a novel multi-modal transducer. Experimental results show the effectiveness of the proposed method. For example, given a 0 dB SNR environment, it yields a character error rate of over 59.0% lower than a noise-robust baseline conducted on AC channel only, and over 12.7% lower than a multi-modal baseline that takes the concatenated features of AC and BC speech as the input.
Learning Subject-Invariant Representations from Speech-Evoked EEG Using Variational Autoencoders

The electroencephalogram (EEG) is a powerful method to understand how the brain processes speech. Linear models have recently been replaced for this purpose with deep neural networks and yield promising results. In related EEG classification fields, it is shown that explicitly modeling subject-invariant features improves generalization of models across subjects and benefits classification accuracy. In this work, we adapt factorized hierarchical variational autoencoders to exploit parallel EEG recordings of the same stimuli. We model EEG into two disentangled latent spaces. Subject accuracy reaches 98.96% and 1.60% on respectively the subject and content latent space, whereas binary content classification experiments reach an accuracy of 51.51% and 62.91% on respectively the subject and content latent space.
Difficulty-Aware Neural Band-to-Piano Score Arrangement based on Note- and Statistic-Level Criteria

This paper describes a neural music arrangement method that converts a given band score into a piano score with an elementary or advanced level. The major challenge of this task lies in its ill-posed nature, i.e., various piano arrangements are plausible for a band score. In this paper, we take a score reduction approach based on supervised training of a mask estimation network (U-Net) with note- and statistic-level criteria. Based on statistical analysis of existing piano arrangements, a reasonable piano score is assumed to be obtained by reducing an augmented band score obtained by up- and downshifting an original band score by one octave. This effectively narrows down a solution space. At the heart of our approach is to train a U-Net conditioned by a given difficulty level such that a piano score obtained by masking an augmented band score is close to the ground-truth piano score not only at a note level but also at a statistic level. We focus on three kinds of note statistics, i.e., a distribution of the numbers of concurrent notes, that of the intervals between the highest and lowest pitches, and that of the per-measure numbers of notes. The experimental results show the importance of both the instance- and meta-level criteria for supervised training.
11 Glaucoma in High Myopia

Key Points • In all highly myopic eyes, glaucomatous optic neuropathy may specifically be ruled out, in attempt not to miss the diagnosis • High axial myopia is a major risk factor for glaucomatous or glaucomalike optic neuropathy, even at a normal intraocular pressure. • Morphological risk factors for glaucoma in high myopia may be an enlarged optic disc and enlarged parapapillary delta zone. • Histological hallmarks of the highly myopic optic nerve head are an elongated and thinned lamina cribrosa, steepened trans-lamina cribrosa pressure gradient, enlarged optic disc, elongated peripapillary scleral flange (as equivalent of parapapillary delta zone), and a shallowing of the physiological optic cup.
Introducing the Electronic Knowledge Framework into the Traditional Automotive Suppliers’ Industry: From Mechanical Engineering to Mechatronics

The automotive sector is undergoing radical changes. New trends such as electrification, autonomous driving, connectivity, and car-sharing—to name a few—are disturbing the carmakers, which must satisfy their clients while meeting the increasingly strict environmental regulations. This pressure also falls on automotive parts suppliers, which now are asked to manufacture high-added-value integral systems, while struggling to keep a very adjusted price. As traditional automotive components evolve into electronic systems, suppliers must gain digital mastery to remain competitive. This paper presents different ways of introducing e-skills in a company and illustrates this with some examples from the Basque automotive industry. The aim is to encourage corporations to take the step towards digitalization, providing different options for them to choose the one that best suits their current scenario. For this study we have analyzed the literature and the press releases of the component manufacturers and interviewed staff from some of them. This research seeks to provide solutions so that the automotive sector remains competitive, as it is a strategic sector for the economy and employment.
The Mechanisms Underlying Salt Resistance Mediated by Exogenous Application of 24-Epibrassinolide in Peanut

Peanut is one of the most important oil crops in the world, the growth and productivity of which are severely affected by salt stress. 24-epibrassinolide (EBL) plays an important role in stress resistances. However, the roles of exogenous EBL on the salt tolerance of peanut remain unclear. In this study, peanut seedlings treated with 150 mM NaCl and with or without EBL spray were performed to investigate the roles of EBL on salt resistance. Under 150 mM NaCl conditions, foliar application of 0.1 µM EBL increased the activity of catalase and thereby could eliminate reactive oxygen species (ROS). Similarly, EBL application promoted the accumulation of proline and soluble sugar, thus maintaining osmotic balance. Furthermore, foliar EBL spray enhanced the total chlorophyll content and high photosynthesis capacity. Transcriptome analysis showed that under NaCl stress, EBL treatment up-regulated expression levels of genes encoding peroxisomal nicotinamide adenine dinucleotide carrier (PMP34), probable sucrose-phosphate synthase 2 (SPS2) beta-fructofuranosidase (BFRUCT1) and Na+/H+ antiporters (NHX7 and NHX8), while down-regulated proline dehydrogenase 2 (PRODH). These findings provide valuable resources for salt resistance study in peanut and lay the foundation for using BR to enhance salt tolerance during peanut production.
TorSH: Obfuscating consumer Internet-of-Things traffic with a collaborative smart-home router network

When consumers install Internet-connected “smart devices” in their homes, metadata arising from the communications between these devices and their cloud-based service providers enables adversaries privy to this traffic to profile users, even when adequate encryption is used. Internet service providers (ISPs) are one potential adversary privy to users’ incoming and outgoing Internet traffic and either currently use this insight to assemble and sell consumer advertising profiles or may in the future do so. With existing defenses against such profiling falling short of meeting user preferences and abilities, there is a need for a novel solution that empowers consumers to defend themselves against profiling by ISP-like actors and that is more in tune with their wishes. In this thesis, we present The Onion Router for Smart Homes (TorSH), a network of smart-home routers working collaboratively to defend smart-device traffic from analysis by ISP-like adversaries. We demonstrate that TorSH succeeds in deterring such profiling while preserving smart-device experiences and without encumbering latency-sensitive, non-smart-device experiences like web browsing.
Promoting exogenous repair in multiple sclerosis: myelin regeneration

Purpose of the review Despite the significant progress in the development of disease-modifying treatments for multiple sclerosis (MS), repair of existing damage is still poorly addressed. Current research focuses on stem cell-based therapies as a suitable alternative or complement to current drug therapies. Recent findings Myelin damage is a hallmark of multiple sclerosis, and novel approaches leading to remyelination represent a promising tool to prevent neurodegeneration of the underlying axon. With increasing evidence of diminishing remyelination capacity of the MS brain with ageing and disease progression, exogenous cell transplantation is a promising therapeutic approach for restoration of oligodendrocyte precursor cell pool reserve and myelin regeneration. Summary The present review summarizes recent developments of remyelinating therapies in multiple sclerosis, focusing on exogenous cell-based strategies and discussing related scientific, practical, and ethical concerns.
Learning Quantile Function without Quantile Crossing for Distribution-free Time Series Forecasting

Quantile regression is an effective technique to quantify uncertainty, and ﬁt challenging underlying distributions. Generating full probabilistic predictions requires multiple quantile regressions over multiple quantile levels. As a result, quantile crossing is a common drawback to these approaches since it violates the desirable monotone property of the conditional quantile function. In this work, we propose the incremental quantile function (IQF), a general distribution-free quantile estimation framework with a simple neural network layer that resolves the issue of quantile crossing. Moreover, IQF is able to extrapolate to other quantile levels, resulting in predictions on arbitrary quantile levels that differ from the underlying training quantiles. We apply IQF to the NN-based times series forecasting use case, where this is particularly appealing because it saves the expensive re-training cost for non-trained quantile levels. We provide generalization error analysis with our proposed approach under the sequence-to-sequence forecasting setting. Experimental re-sults demonstrate the effectiveness and resulting accuracy improvement of our method.
Real-time camera analytics for enhancing traffic intersection safety

Crowded metropolises present unique challenges to the potential deployment of autonomous vehicles. Safety of pedestrians cannot be compromised and personal privacy must be preserved. Smart city intersections will be at the core of Artificial Intelligence (AI)-powered citizen-friendly traffic management systems for such metropolises. Hence, the main objective of this work is to develop an experimentation framework for designing applications in support of secure and efficient traffic intersections in urban areas. We integrated a camera and a programmable edge computing node, deployed within the COSMOS testbed in New York City, with an Eclipse sensiNact data platform provided by Kentyou. We use this pipeline to collect and analyze video streams in real-time to support smart city applications. In this demo, we present a video analytics pipeline that analyzes the video stream from a COSMOS' street-level camera to extract traffic/crowd-related information and sends it to a dedicated dashboard for real-time visualization and further assessment. This is done without sending the raw video, in order to avoid violating pedestrians' privacy.
Correlation-guided Network Integration (CoNI), an R package for integrating numerical omics data that allows multiform graph representations to study molecular interaction networks

Abstract Summary Today’s immense growth in complex biological data demands effective and flexible tools for integration, analysis and extraction of valuable insights. Here, we present CoNI, a practical R package for the unsupervised integration of numerical omics datasets. Our tool is based on partial correlations to identify putative confounding variables for a set of paired dependent variables. CoNI combines two omics datasets in an integrated, complex hypergraph-like network, represented as a weighted undirected graph, a bipartite graph, or a hypergraph structure. These network representations form a basis for multiple further analyses, such as identifying priority candidates of biological importance or comparing network structures dependent on different conditions. Availability and implementation The R package CoNI is available on the Comprehensive R Archive Network (https://cran.r-project.org/web/packages/CoNI/) and GitLab (https://gitlab.com/computational-discovery-research/coni). It is distributed under the GNU General Public License (version 3). Supplementary information Supplementary data are available at Bioinformatics Advances online.
GLOBAL TRENDS IN ZOOPLANKTON RESEARCH OF FRESHWATER ECOSYSTEMS DURING 1991-2020: A BIBLIOMETRIC ANALYSIS

Zooplanktons play an important role in the whole freshwater ecosystem, which are the main quality food for almost all fish in their juvenile stage and are the main basic component of the high trophic levels in the aquatic food web. In the context of global environmental change, freshwater zooplankton is experiencing rapid loss of biodiversity and ecosystem integrity. However, our understanding of the geographical distribution pattern of freshwater zooplankton and the diversity-affected ecological factors is still very limited. In order to investigate the research status and future development trend of zooplankton in global freshwater, 26176 publications about freshwater zooplankton/zooplankter indexed by Web of Science from 1991 to 2020 were analyzed with a bibliometric approach in our study. The research contents mainly included the output trend of publications related to the freshwater zooplankton/zooplankter filed over time, the journals with greatest number of zooplankton publications, the countries from which the authors came from, the co-authors and cooperation across countries, the associated keywords in the research areas and so on. This study was searched on a scientific website for scientific study that have limited scientific relevance for the future researches on the freshwater zooplankton. keywords "zooplankton or zooplankter"
Novel Pharmacological Treatment Options in Pediatric Glioblastoma—A Systematic Review

Simple Summary Childhood glioblastoma is an aggressive brain tumor in children that has a very poor prognosis. Standard therapy includes surgery, irradiation and chemotherapy with temozolomide. So far, there is no effective drug treatment for pediatric glioblastoma patients. This systematic review aims to outline currently available data on novel pharmacological treatment options. None of the included phase II studies showed any benefit regarding overall survival or a prolongation of stable disease. New genomic technologies discovered the biologic heterogeneity of these tumors, demanding more individualized immunotherapeutic and targeted approaches. Autoimmune modulated therapies and further targeting of tumor-specific receptors provide promising preclinical results. Clinical trials aligned to the tumor characteristics are needed to establish effective new therapeutic approaches. Abstract Background: Pediatric glioblastoma (GBM) is an aggressive central nervous system tumor in children that has dismal prognosis. Standard of care is surgery with subsequent irradiation and temozolomide. We aimed to outline currently available data on novel pharmacological treatments for pediatric GBM. Methods: We conducted a systematic literature search in PubMed and Embase, including reports published in English from 2010 to 2021. We included randomized trials, cohort studies and case series. Phase I trials were not analyzed. We followed PRISMA guidelines, assessed the quality of the eligible reports using the Newcastle-Ottawa scale (NOS) and the RoB-2 tool and registered the protocol on PROSPERO. Results: We included 6 out of 1122 screened reports. All six selected reports were prospective, multicenter phase II trials (five single-arm and one randomized controlled trial). None of the investigated novel treatment modalities showed any benefit regarding overall or progression free survival. Conclusions: To date, the role of pharmacological approaches regarding pediatric GBM remains unclear, since no novel treatment approach could provide a significant impact on overall or progression free survival. Further research should aim to combine different treatment strategies in large international multicenter trials with central comprehensive diagnostics regarding subgrouping. These novel treatment approaches should include targeted and immunotherapeutic treatments, potentially leading to a more successful outcome.
ANO1: More Than Just Calcium-Activated Chloride Channel in Cancer

ANO1, a calcium-activated chloride channel (CACC), is also known as transmembrane protein 16A (TMEM16A). It plays a vital role in the occurrence, development, metastasis, proliferation, and apoptosis of various malignant tumors. This article reviews the mechanism of ANO1 involved in the replication, proliferation, invasion and apoptosis of various malignant tumors. Various molecules and Stimuli control the expression of ANO1, and the regulatory mechanism of ANO1 is different in tumor cells. To explore the mechanism of ANO1 overexpression and activation of tumor cells by studying the different effects of ANO1. Current studies have shown that ANO1 expression is controlled by 11q13 gene amplification and may also exert cell-specific effects through its interconnected protein network, phosphorylation of different kinases, and signaling pathways. At the same time, ANO1 also resists tumor apoptosis and promotes tumor immune escape. ANO1 can be used as a promising biomarker for detecting certain malignant tumors. Further studies on the channels and the mechanism of protein activity of ANO1 are needed. Finally, the latest inhibitors of ANO1 are summarized, which provides the research direction for the tumor-promoting mechanism of ANO1.