Miracle-dz/UniFAR-FTUs huggingface dataset:
Characteristics of Invasive Fungal Infections among HIV Individuals from an Indigenous Origin in Mexico

In individuals with HIV/AIDS, 47% of the deaths are attributed to invasive fungal infections (IFIs), despite antiretroviral (ARV) therapy. This is a retrospective study carried out in the Hospital Regional de Alta Especialidad Oaxaca (HRAEO), southwest Mexico, where IFIs that occurred during 2016–2017 are described. A total of 55 individuals were included. Histoplasmosis (36%) and possible-IFIs in neutropenic fever (20%) were the most frequent cases, followed by cryptococcosis (14%). The HIV/AIDS subpopulation corresponded with 26 cases (47%), all from an indigenous origin. The incidence of IFIs among them was 24% (95% CI = 15–33%). The CD4+ T cells median was 35 cells/mL (IQR 12–58). Four cases (15%) of unmasking IRIS were identified, three of histoplasmosis and one coccidioidomycosis. Co-infections were found in 52% (12/23), and tuberculosis in 50% (6/12) was the most frequent. The mortality rate was 48%. The general characteristics of the HIV individuals who died were atypical pneumonia (70% vs. 9%, p = 0.01), acute kidney injury, (70% vs. 9%, p = 0.008) and ICU stay (80% vs. 9%, p = 0.002). In conclusion, IFIs are diagnosed in one out of four individuals with HIV/AIDS along with other complicated infectious conditions, leading to major complications and a high mortality rate.
Transcription Correction for Indian Languages Using Acoustic Signatures

Accurate phonetic transcription of the speech corpus has a signiﬁcant impact on the performance of speech processing applications especially for low resource languages. Mismatches between the transcriptions and their utterances occur often at phoneme level due to insertion/deletion/substitution errors. This is very common in Indian languages owing to schwa deletion in the context of vowels, and agglutination in the context of consonants. Anattempt is made in this paper to use acoustic cues at the syllable level to remove vowels from the transcription when they are poorly articulated or absent. Hidden Markov model (HMM) based forced Viterbi alignment (FVA) and group delay (GD) based signal processing are employed in tandem to achieve this task. Disagreement between FVA (which produces vowel boundaries based on transcription) and GD boundaries (which uses signal processing cues for syllables) are used to correct the transcription. An increase in likelihood of 0.3% is observed across 3 Indian languages, namely, Gujarati, Telugu and Tamil.
Analysis of Language Dependent Front-End for Speaker Recognition

In Deep Neural Network (DNN) i-vector based speaker recognition systems, acoustic models trained for Automatic Speech Recognition are employed to estimate sufﬁcient statistics for i-vector modeling. The DNN based acoustic model is typically trained on a well-resourced language like English. In evaluation conditions where enrollment and test data are not in English, as in the NIST SRE 2016 dataset, a DNN acoustic model generalizes poorly. In such conditions, a conventional Universal Background Model/Gaussian Mixture Model (UBM/GMM) based i-vector extractor performs better than the DNN based i-vector system. In this paper, we address the scenario in which one can develop a Automatic Speech Recognizer with limited resources for a language present in the evaluation condition, thus enabling the use of a DNN acoustic model instead of UBM/GMM. Experiments are performed on the Tagalog subset of the NIST SRE 2016 dataset assuming an open training condition. With a DNN i-vector system trained for Tagalog, a relative improvement of 12.1% is obtained over a baseline system trained for English.
Preference-Learning with Qualitative Agreement for Sentence Level Emotional Annotations

The perceptual evaluation of emotional attributes is noisy due to inconsistencies between annotators. The low inter-evaluator agreement arises due to the complex nature of emotions. Conventional approaches average scores provided by multiple annotators. While this approach reduces the influence of dissident annotations, previous studies have showed the value of considering individual evaluations to better capture the underlying ground-truth. One of these approaches is the qualitative agreement (QA) method, which provides an alternative framework that captures the inherent trends amongst the annotators. While previous studies have focused on using the QA method for time-continuous annotations from a fixed number of annotators, most emotional databases are annotated with attributes at the sentence-level (e.g., one global score per sentence). This study proposes a novel formulation based on the QA framework to estimate reliable sentence-level annotations for preferencelearning. The proposed relative labels between pairs of sentences capture consistent trends across evaluators. The experimental evaluation shows that preference-learning methods to rank-order emotional attributes trained with the proposed QAbased labels achieve significantly better performance than the same algorithms trained with relative scores obtained by averaging absolute scores across annotators. These results show the benefits of QA-based labels for preference-learning using sentence-level annotations.
Varicella-zoster virus CNS vasculitis and RNA polymerase III gene mutation in identical twins

Objective Deficiency in the cytosolic DNA sensor RNA Polymerase III (POL III) was recently described in children with severe varicella-zoster virus (VZV) infection in the CNS or lungs. Here, we describe a pair of monozygotic female twins, who both experienced severe recurrent CNS vasculitis caused by VZV reactivation. The clinical presentation and findings included recurrent episodes of headache, dizziness, and neurologic deficits, CSF with pleocytosis and intrathecal VZV antibody production, and MRI of the brain showing ischemic lesions. Methods We performed whole-exome sequencing and identified a rare mutation in the POL III subunit POLR3F. Subsequently, antiviral responses in patient peripheral blood mononuclear cells (PBMCs) were examined and compared with healthy controls. Results The identified R50W POLR3F mutation is predicted by bioinformatics to be damaging, and when tested in functional assays, patient PBMCs exhibited impaired antiviral and inflammatory responses to the POL III agonist poly(dA:dT) and increased viral replication compared with controls. Conclusions Altogether, these cases add genetic and immunologic evidence to the novel association between defects in sensing of AT-rich DNA present in the VZV genome and increased susceptibility to severe manifestations of VZV infection in the CNS in humans.
Detection of Replay-Spoofing Attacks Using Frequency Modulation Features

Prevention of malicious spoofing attacks is currently acknowledged as a priority area of investigation for the deployment of automatic speaker verification systems. Various features of speech signals have been used to fight counterfeit attacks. Among the different spoofing attack variants, replay attacks pose a significant threat as they do not require any expert knowledge and are difficult to detect. This paper proposes the use of a spectral centroid based frequency modulation (FM) features that we term spectral centroid deviation (SCD) for replay attack detection. Spectral centroid frequency (SCF) and spectral centroid magnitude coefficient (SCMC) features extracted from the same front-end as SCD are also investigated as complementary features. Evaluations on the ASVspoof 2017 dataset indicate that the proposed SCD features with a Gaussian Mixture Model (GMM) back-end is highly capable of discriminating genuine from replay spoofed speech, providing an equal error rate improvement greater than 60% relative to the CQCC baseline system from the ASVspoof 2017 challenge. Interestingly, experiments also reveal that the proposed SCD features exhibit an increased variance for replay spoofed speech relative to genuine speech, particularly for the lowest and highest frequency subbands.
Speech Source Separation Using ICA in Constant Q Transform Domain

In order to separate individual sources from convoluted speech mixtures, complex-domain independent component analysis (ICA) is employed on the individual frequency bins of time-frequency representations of the speech mixtures, obtained using short term Fourier transform (STFT). The frequency components computed using STFT are separated by constant frequency di�erence with a constant frequency resolution. However, it is well known that the human auditory mechanism o�ers better resolution at lower frequencies. Hence, the perceptual quality of the extracted sources critically depends on the separation achieved in the lower frequency components. A method has been proposed to perform source separation on the time-frequency representation computed though constant Q transform, which o�ers non uniform logarithmic binning in the frequency domain. Complex-domain ICA is performed on the individual bins of the CQT in order to get separated components in each frequency bin which are suitably scaled and permuted to obtain separated sources in the CQT domain. The estimated sources are obtained by applying inverse Q transform to the scaled and permuted sources. In comparison with the STFT based frequency domain ICA methods, there has been a consistent improvement of 3dB or more in the Signal to Interference Ratios of the extracted sources. vi
Compact Feedforward Sequential Memory Networks for Small-footprint Keyword Spotting

Due to limited resource on devices and complicated scenarios, a compact model with high precision, low computational cost and latency is expected for small-footprint keyword spotting tasks. To fulﬁll these requirements, in this paper, compact Feed-forward Sequential Memory Network (cFSMN) which combines low-rank matrix factorization with conventional FSMN is investigated for a far-ﬁeld keyword spotting task. The effect of its architecture parameters is analyzed. Towards achieving lower computational cost, multiframe prediction (MFP) is applied to cFSMN. For enhancing the modeling capacity, an advanced MFP is attempted by inserting small DNN layers before output layers. The performance is measured by area under the curve (AUC) for detection error tradeoff (DET) curves. The experiments show that compared with a well-tuned long short-term memory (LSTM) which needs the same latency and twofold computational cost, the cFSMN achieves 18.11% and 29.21% AUC relative decreases on the test sets which are recorded in quiet and noisy environment respectively. After applying advanced MFP, the system gets 0.48% and 20.04% AUC relative decrease over conventional cFSMN on the quiet and noisy test sets respectively, while the computational cost relatively reduces 46.58%.
Reconstructed Densenets for Image Super-Resolution

Deep learning has been successfully applied to single image super-resolution problem due to its high data fitting ability. However, the trending of deeper layers and wider receptive field to acquire better performance brings high computation complexity and serious information vanishing. To address this problem, we proposed a new Reconstructed DenseNets model for super-resolution. The basic idea behind Reconstructed DenseNets is to improve the recent DenseNets model by modifying the two core modules, dense blocks and transition blocks, so that the Reconstructed DenseNets can emphasize the quality of data reconstruction. Specifically, on the one hand, the batch normalization layers in dense blocks is ignored to overcome the data shift risk. One the other hand, the pooling layers in transition blocks is also ignored to ensure the ability to reconstruct. Based on the above two improvements, the new DenseNets is named as Reconstructed DenseNets. Extensive experiments evaluate the effectiveness of our model, showing the outperforming of the state-of-the-art approaches.
An Attention Pooling Based Representation Learning Method for Speech Emotion Recognition

This paper proposes an attention pooling based representation learning method for speech emotion recognition (SER). The emotional representation is learned in an end-to-end fashion by applying a deep convolutional neural network (CNN) directly to spectrograms extracted from speech utterances. Motivated by the success of GoogleNet, two groups of filters with different shapes are designed to capture both temporal and frequency domain context information from the input spectrogram. The learned features are concatenated and fed into the subsequent convolutional layers. To learn the final emotional representation, a novel attention pooling method is further proposed. Compared with the existing pooling methods, such as max-pooling and average-pooling, the proposed attention pooling can effectively incorporate class-agnostic bottom-up, and class-specific top-down, attention maps. We conduct extensive evaluations on benchmark IEMOCAP data to assess the effectiveness of the proposed representation. Results demonstrate a recognition performance of 71.8% weighted accuracy (WA) and 68% unweighted accuracy (UA) over four emotions, which outperforms the state-of-the-art method by about 3% absolute for WA and 4% for UA.
Automatic Optic Disk and Cup Segmentation of Fundus Images Using Deep Learning

Automatic segmentation of optic disk (OD) and cup regions in fundus images is essential in deriving clinical parameters, such as, cup-to-disk ratio (CDR), to assist glaucoma diagnosis. This paper presents a deep learning system using fully convolutional neural networks (FCN) to perform such segmentation, discusses various strategies on how to leverage multiple doctor annotations and prioritize pixels belonging to different regions while training the neural network. Experimental evaluations on Drishti-GS dataset demonstrate that the presented method achieves comparable and superior F-score to prior work on optic disk and cup segmentation, respectively.
The Cellular DExD/H-Box RNA Helicase UAP56 Co-localizes With the Influenza A Virus NS1 Protein

UAP56, a member of the DExD/H-box RNA helicase family, is essential for pre-mRNA splicing and mRNA export in eukaryotic cells. In influenza A virus-infected cells, UAP56 mediates viral mRNA nuclear export, facilitates viral ribonucleoprotein complex formation through direct interaction with the viral nucleoprotein, and may indirectly affect antiviral host responses by binding to and/or facilitating the activation of the antiviral host factors MxA and PKR. Here, we demonstrate that UAP56 also co-localizes with the influenza A viral NS1 protein, which counteracts host cell innate immune responses stimulated by virus infection. The UAP56–NS1 association relies on the RNA-binding residues R38 and K41 in NS1 and may be mediated by single-stranded RNA. UAP56 association with NS1 does not affect the NS1-mediated downregulation of cellular innate immune pathways in reporter gene assays, leaving in question the exact biological role and relevance of the UAP56–NS1 association.
Training Recurrent Neural Network through Moment Matching for NLP Applications

Recurrent neural network (RNN) is conventionally trained in the supervised mode but used in the free-running mode for inferences on testing samples. The supervised mode takes ground truth token values as RNN inputs but the free-running mode can only use self-predicted token values as surrogating inputs. Such inconsistency inevitably results in poor generalizations of RNN on out-of-sample data. We propose a moment matching (MM) training strategy to alleviate such inconsistency by simultaneously taking these two distinct modes and their corresponding dynamics into consideration. Our MM-RNN shows signiﬁ-cant performance improvements over existing approaches when tested on practical NLP applications including logic form generation and image captioning.
Improving Time-of-Flight Sensor for Specular Surfaces with Shape from Polarization

Time-of-Flight (ToF) sensors can obtain depth values for diffuse objects. However, the essential problem is that the sensor can not receive active light from specular surfaces due to specular reflections. In this paper, we propose a new depth reconstruction framework for specular objects that combines ToF cues and Shape from Polarization (SfP). To overcome the ill-posedness of SfP with a single view, we integrate superpixel segmentation with planarity constraints for every superpixel. Experimental results demonstrate the effectiveness of the depth reconstruction algorithm for both controlled environment data and real vehicle data in a parking area.
Imbalance Learning-based Framework for Fear Recognition in the MediaEval Emotional Impact of Movies Task

Fear recognition, which aims at predicting whether a movie segment can induce fear or not, is a promising area in movie emotion recognition. Research in this area, however, has reached a bottleneck. Difficulties may partly result from the imbalanced database. In this paper, we propose an imbalance learning-based framework for movie fear recognition. A data rebalance module is adopted before classification. Several sampling methods, including the proposed softsampling and hardsampling which combine the merits of both undersampling and oversampling, are explored in this module. Experiments are conducted on the MediaEval 2017 Emotional Impact of Movies Task. Compared with the current state-of-the-art, we achieve an improvement of 8.94% on F 1 , proving the effectiveness of proposed framework.
Forkhead box protein A1 confers resistance to transforming growth factor‐β‐induced apoptosis in breast cancer cells through inhibition of Smad3 nuclear translocation

Transforming growth factor‐β (TGF‐β) induces apoptosis of normal epithelial cells, such as mammary epithelium. Although breast cancer progression associates with acquisition of resistance to TGF‐β‐induced apoptosis, the molecular mechanisms underlying this resistance are largely unknown. Here, we show that forkhead box protein A1 (FOXA1), which is known as a pioneer transcription factor, suppresses TGF‐β‐induced apoptosis of estrogen receptor–positive breast cancer cells. FOXA1 is found to inhibit nuclear translocation of Smad3, a key transcription factor downstream of TGF‐β signaling, through suppression of the binding of Smad3 to the nuclear import receptor importin7. Furthermore, RNA sequencing analyses show that knockdown of FOXA1 upregulates Smad3‐mediated proapoptotic gene expression. These results demonstrate that FOXA1 as a potent survival factor that suppresses TGF‐β‐induced apoptosis by inhibiting Smad3 signaling in estrogen receptor–positive breast cancer cells. Thus, we provide evidence for the first time that FOXA1 localizing to the cytoplasm negatively regulates Smad3‐induced apoptosis in TGF‐β‐mediated signal transduction.
Texture Characterization via Projections onto a Klein Bottle Topology

This work presents a new approach for texture based image characterization through topological projections onto the Klein bottle of small high-contrast regions (patches) extracted from the images. Several configurations of patch sizes were analyzed in order to reduce the vector size of features and to increase accuracy. Experiments using the proposed method for texture classification, on several established datasets, show that the proposed method not only manages to reduce feature vector size, but also improves correct classification rates when compared to other state-of-the-art methods.
Unsupervised Trajectory Modeling Based on Discrete Descriptors for Classifying Moving Objects in Video Sequences

This paper focuses on modeling and classifying trajectories from video sequences. Location, velocity and time of appearance are considered as features for recognizing and modeling motions of objects. In a training phase, a discretization of the proposed features is performed by using a self-organizing map approach such that a set of clusters (feature vocabulary) is created for describing trajectories. A cluster dissimilarity measure based on a weighted fusion of features facilitates the recognition of trajectory classes in an incremental way. As a result, an unsupervised method for encoding observed motion information and identifying trajectory patterns is proposed in this article. The method is evaluated with real and simulated data. Additionally’ comparisons with previous works show the benefits of our method when encoding and identifying motion patterns in video sequences.
Audiovisual Speech Activity Detection with Advanced Long Short-Term Memory

Speech activity detection (SAD) is a key pre-processing step for a speech-based system. The performance of conventional audio-only SAD (A-SAD) systems is impaired by acoustic noise when they are used in practical applications. An alternative approach to address this problem is to include visual information, creating audiovisual speech activity detection (AV-SAD) solutions. In our previous work, we proposed to build an AV-SAD system using bimodal recurrent neural network (BRNN). This framework was able to capture the task-related characteristics in the audio and visual inputs, and model the temporal information within and across modalities. The approach relied on long short-term memory (LSTM). Although LSTM can model longer temporal dependencies with the cells, the effective memory of the units is limited to a few frames, since the recurrent connection only considers the previous frame. For SAD systems, it is important to model longer temporal dependencies to capture the semi-periodic nature of speech conveyed in acoustic and orofacial features. This study proposes to implement a BRNN-based AV-SAD system with advanced LSTMs (A-LSTMs), which overcomes this limitation by including multiple connections to frames in the past. The results show that the proposed framework can signiﬁcantly outperform the BRNN system trained with the original LSTM layers.
Neural Response Development During Distributional Learning

We investigated online electrophysiological components of distributional learning, specifically of tones by listeners of a non-tonal language. German listeners were presented with a bimodal distribution of syllables with lexical tones from a synthesized continuum based on Cantonese level tones. Tones were presented in sets of four standards (within-category tokens) followed by a deviant (across-category token). Mismatch negativity (MMN) was measured. Earlier behavioral data showed that exposure to this bimodal distribution improved both categorical perception and perceptual acuity for level tones [1]. In the present study we present analyses of the electrophysiological response recorded during this exposure, i.e. the development of the MMN response during distributional learning. This development over time is analyzed using Generalized Additive Mixed Models and results showed that the MMN amplitude increased for both within- and across-category tokens, reflecting higher perceptual acuity accompanying category formation. This is evidence that learners zooming in on phonological categories undergo neural changes associated with more accurate phonetic perception.