Miracle-dz/UniFAR-FTUs huggingface dataset:
Resveratrol and Pterostilbene, Two Analogue Phenolic Compounds, Affect Aquaglyceroporin Expression in a Different Manner in Adipose Tissue

Aquaglyceroporins (AQPs) are transmembrane channels that mediate glycerol release and glycerol uptake. They are involved in fat metabolism, with implications in obesity. The aim was to determine whether the administration of resveratrol and pterostilbene during the six weeks of the experimental period would modify AQPs expression in white and brown adipose tissues from Wistar rats fed an obesogenic diet, and to establish a potential relationship with the delipidating properties of these compounds. Consequently, thirty-six rats were divided into four groups: (a) group fed a standard diet; and three more groups fed a high-fat high-sucrose diet: (b) high-fat high-sucrose group: (c) pterostilbene-treated group (30 mg/kg/d): (d) resveratrol-treated group (30 mg/kg/d). Epididymal, subcutaneous white adipose tissues and interscapular brown adipose tissue were dissected. AQPs gene expression (RT-PCR) and protein expression (western-blot) were measured. In white adipose tissue, pterostilbene reduced subcutaneous adipose tissue weight and prevented the decrease in AQP9 induced by obesogenic feeding, and thus glycerol uptake for triglyceride accumulation. Resveratrol reduced epididymal adipose tissue weight and avoided the decrease in AQPs related to glycerol release induced by high-fat high-sucrose feeding, suggesting the involvement of lipolysis in its body-fat lowering effect. Regarding brown adipose tissue, AQP7 seemed not to be involved in the previously reported thermogenic activity of both phenolic compounds.
Engagement Recognition in Spoken Dialogue via Neural Network by Aggregating Different Annotators' Models

This paper addresses engagement recognition based on four multimodal listener behaviors - backchannels, laughing, eye-gaze, and head nodding. Engagement is an indicator of how much a user is interested in the current dialogue. Multiple third-party annotators give ground truth labels of engagement in a human-robot interaction corpus. Since perception of engagement is subjective, the annotations are sometimes different between individual annotators. Conventional methods directly use integrated labels, such as those generated through simple majority voting, and do not consider each annotator’s recognition. We propose a two-step engagement recognition where each annotator’s recognition is modeled and the different anno-tators’ models are aggregated to recognize the integrated label. The proposed neural network consists of two parts. The ﬁrst part corresponds to each annotator’s model which is trained with the corresponding labels independently. The second part aggregates the different annotators’ models to obtain one integrated label. After each part is pre-trained, the whole network is ﬁne-tuned through back-propagation of prediction errors. Experimental results show that the proposed network outperforms baseline models which directly recognize the integrated label without considering differing annotations.
Search Area Reduction Fast-RCNN for Fast Vehicle Detection in Large Aerial Imagery

Accurate detection of objects in aerial imagery is a crucial image processing step for many applications, such as traffic monitoring, surveillance, reconnaissance and rescue tasks. Recently, conventional methods for vehicle detection in aerial imagery are outperformed by deep learning based detection frameworks like Faster R-CNN. To allow the detection of small vehicles in the range of 10x20 pixels, only shallow layers of standard models like VGG-16 provide a sufficiently high spatial resolution. However, this adaptation to the characteristics of aerial imagery results in poor inference time as proposals or detections are predicted at each feature map location. In this paper, we propose an adaptive model which reduces the input region for the detection module and consequently reduces inference time. For this, we extend Faster R-CNN by an additional Search Area Reduction module which divides the input image into regions and predicts a confidence score of how likely a region contains at least one object. Many image regions, particularly in rural areas, do not contain any vehicles and are filtered out by our approach. This significantly reduces inference time of later stages. Our proposed framework achieves state-of-the-art detection results on a publicly available dataset while the inference time of the object detection stage is reduced by more than 75%. 11Our custom layers and network architecture have been made available at: https://github.com/vehicledetect/sar-frcnn.
Efficient Algorithm for Convolutional Dictionary Learning via Accelerated Proximal Gradient Consensus

Convolutional sparse representations are receiving an increase attention as a better alternative to the standard patch-based formulation for multiple image processing tasks. Several different algorithms based on ADMM, ADMM consensus and APG (Accelerated Proximal Gradient) have been proposed to efficiently solve the convolutional dictionary learning problem. Among them, ADMM consensus is considered as one of the fastest methods implemented in parallel due to its separable structure. However, its usage on large sets of images is computationally restricted by the dictionary update stage. In the present work, we propose a novel method to address this stage based on an APG consensus approach. This method considers particular strategies of the ADMM consensus and APG frameworks to develop a less complex solution decoupled across the training images. We show in our experimental results that the proposed method is significantly faster than the state-of-the-art consensus method implemented in serial and parallel while maintaining comparable performance in terms of reconstruction and sparsity metrics in denoising and inpainting tasks.
Evaluation of anti-inflammatory effect of chorion membrane in periodontal pocket therapy: A clinical and biochemical study

Background: The aim of the study was to evaluate the anti-inflammatory effect of chorion as a barrier membrane in periodontal pocket therapy by assessing interleukin-11 (IL-11) level in gingival crevicular fluid (GCF). Materials and Methods: The study was a randomized, controlled, single-blind clinical trial with split-mouth design. In each patient, two sites in two quadrants were selected and randomly allocated in Group 1 (flap surgery) and Group 2 (flap surgery + chorion membrane placement). After Phase 1 therapy, clinical and biochemical parameters were recorded at baseline and at 4 weeks. Plaque index (PI), sulcus bleeding index (SBI), probing pocket depth (PPD), clinical attachment level (CAL), and IL-11 in GCF were assessed. GCF was collected using microcapillary tubes. IL-11 was assayed in GCF with ELISA kit (Genxbio®). Results: At baseline, the clinical and the biochemical parameters were similar in both the groups. Intragroup comparison revealed statistically significant decrease in all clinical and biochemical parameters in Group 1 (P < 0.001) and Group 2 (P < 0.001). Intergroup comparison showed statistically significant decrease in SBI (P = 0.004), PPD (P = 0.004), CAL (P = 0.013), and IL-11 (P = 0.006) in Group 2 compared to Group 1 at 4 weeks. However, PI did not show any statistically significant difference on intergroup comparison. Conclusion: Adjunctive use of chorion membrane in flap surgery provides an additive anti-inflammatory effect along with improvement in clinical outcomes enhancing the long-term prognosis.
BUT System for DIHARD Speech Diarization Challenge 2018

This paper presents the approach developed by the BUT team for the ﬁrst DIHARD speech diarization challenge, which is based on our Bayesian Hidden Markov Model with eigenvoice priors system. Besides the description of the approach, we provide a brief analysis of different techniques and data processing methods tested on the development set. We also introduce a simple attempt for overlapped speech detection that we used for attaining cleaner speaker models and reassigning overlapped speech to multiple speakers. Finally, we present results obtained on the evaluation set and discuss ﬁndings we made during the development phase and with the help of the DIHARD leader-board feedback.
Subspace Segmentation Based Metric Learning

Distance Metric Learning (DML) has been successfully applied in a variety of computer vision and image processing tasks. Laplacian Regularized Metric Learning (LRML) computes a distance metric by satisfying given sets of pairwise similarity and dissimilarity constraints while preserving the topological structure of the given data via a Laplacian regularizer which is dependent on an affinity matrix. This paper addresses the problem of semi-supervised DML using LRML for image data sampled from a union of low-dimensional subspaces by computing the affinity matrix using a self-representation based graph instead of traditional graph used in LRML, resulting in two variants of LRML called as L-NNLRS and L-NLSP.
Photomontage for Robust HDR Imaging with Hand-Held Cameras

This paper studies the image fusion from multiple images taken by hand-held cameras with different exposures. The existing methods often generate unsatisfactory results, such as the blurring/ghosting artifacts due to the problematic handling of camera motions, dynamic contents, and inappropriate fusion of local regions (e.g., over or under exposed). They often require high quality image registration before fusion. However, the accurate alignment is hard to obtain in many scenarios, such as scenes with large depth variations and dynamic textures. Besides, high quality alignment is also time consuming. In this paper, we only enable a rough registration by a single homography and combine the inputs seamlessly to hide any possible misalignment. Specifically, we propose to use a Markov Random Filed (MRF) function for the labelling of all pixels, which assigns different labels to different aligned input images. During the labelling, we choose well-exposured regions and skip moving objects simultaneously. Then, we combine a Laplace image according to the labels and construct the fusion result by solving the Poisson equation. We present various challenging examples to demonstrate the effectiveness and practicability of our approach.
Latent Factor Analysis of Deep Bottleneck Features for Speaker Verification with Random Digit Strings

Speaker veriﬁcation with prompted random digit strings has been a challenging task due to very short test utterance. This work investigates how to combine methods from deep bottleneck features (DBF) and latent factor analysis (LFA) to result in a new state-of-the-art approach for such task. In order to provide a wider temporal context, a stacked DBF is extracted to replace the traditional MFCC feature in the derivation of the supervector representations and leads to a signiﬁcant improvement for the speaker veriﬁcation. The LFA is used to model these stacked DBFs in both digit and utterance scales. Based on this learned LFA model, two kinds of supervector representations are extracted for utterance and local digits respectively. Since the strengths of DBF and LFA appear complementary, the combination signiﬁcantly outperforms either of its components. Experiments have been conducted on the public RSR2015 part III data corpus, the results showed that our approach can achieve 1.40% EER and 1.55% EER on male and female respectively.
Restoration of Unevenly Illuminated Images

In this paper, we tackle the problem of restoring unevenly illuminated images. Generally, there exist three kinds of exposure conditions in these images: under-, normal-, and over-exposures. Thus, a three-component generalized Gaussian mixture model (3GGMM) is used to fit the histogram of the illuminance image, and probabilistically characterize the three exposure states. Based on the 3GGMM, separate optimal tone mapping functions are designed to enhance under- and overexposed regions by maximizing expected contrast of these regions. The output illumination can be obtained by fusing the restoration results in different exposure states. Experimental results validate the effectiveness of the proposed image restoration approach.
Learning rate adaptation for differentially private stochastic gradient descent

Differentially private learning has recently emerged as the leading approach for privacy-preserving machine learning. Differential privacy can complicate learning procedures because each access to the data needs to be carefully designed and carries a privacy cost. For example, standard parameter tuning with a validation set cannot be easily applied. In this paper, we propose a differentially private algorithm for the adaptation of the learning rate for differentially private stochastic gradient descent (SGD) that avoids the need for validation set use. The idea for the adaptiveness comes from the technique of extrapolation in classical numerical analysis: to get an estimate for the error against the gradient flow which underlies SGD, we compare the result obtained by one full step and two half-steps. We prove the privacy of the method using the moments accountant mechanism. This allows us to compute tight privacy bounds. Empirically we show that our method is competitive with manually tuned commonly used optimisation methods for training deep neural networks and differentially private variational inference.
Analysis of Breathiness in Contextual Vowel of Voiceless Nasals in Mizo

This study analyses the source characteristics of voiced and voiceless nasals in Mizo, a Tibeto-Burman language spoken in North-East India. Mizo is one of the few languages that has voiced and voiceless nasals in its phoneme inventory. This analysis is motivated by the interaction between breathiness and nasality reported in a number of speech perception studies using synthetic stimuli. However, there are no studies examining this interaction in vowels after voiced and voiceless nasals. Existing research has also documented the interaction between breathy phonation and vowel height. The current study is an acoustic analysis of breathiness in high and low vowels following voiced and voiceless nasals in Mizo. The acoustic parameter measures are: H1H2 ratio, spectral balance (SB), strength of excitation (SoE), and waveform peak factor (WPF). The values obtained for all the four acoustic measures suggest that vowels following voiceless nasals exhibit stronger acoustic characteristics associated with breathy phonation than vowels following voiced nasals. In addition, the degree of acoustic breathiness is affected by vowel height.
Speaker Adaptive Training and Mixup Regularization for Neural Network Acoustic Models in Automatic Speech Recognition

This work investigates speaker adaptation and regularization techniques for deep neural network acoustic models (AMs) in automatic speech recognition (ASR) systems. In previous works, GMM-derived (GMMD) features have been shown to be an efﬁcient technique for neural network AM adaptation. In this paper, we propose and investigate a novel way to improve speaker adaptive training (SAT) for neural network AMs using GMMD features. The idea is based on using inaccurate transcriptions from ASR for adaptation during neural network training, while keeping the exact transcriptions for targets of neural networks. In addition, we apply a mixup technique, recently proposed for classiﬁcation tasks, to acoustic models for ASR and investigate the impact of this technique on speaker adapted acoustic models. Experimental results on the TED-LIUM corpus show that the proposed approaches provide an additional gain in speech recognition performance in comparison with the speaker adapted AMs.
Improved Epoch Extraction from Telephonic Speech Using Chebfun and Zero Frequency Filtering

Epoch in speech, represent the instant where maximum excitation at the vocal tract is obtained. Existing epoch extraction algorithms are capable of accurately extracting epoch information from clean speech signals. However, epoch extraction of band limited signals such as telephonic speech is challenging due to the attenuation of the fundamental frequency components. The present work is focused on improving the performance of epoch extraction from telephonic speech signals by exploiting the properties of Chebyshev polynomial interpolation and by reinforcing the frequency components around the fundamental frequency through the Hilbert envelope (HE). The proposed al-gorithm brings a reﬁnement of the existing Zero Frequency Filtering (ZFF) method by incorporating Chebyshev interpolation. The proposed reﬁnements to the ZFF algorithm conﬁrmed to provide improved epoch identiﬁcation rate, identiﬁcation accuracy, reduced miss rate and false alarm rate. The epoch identiﬁ-cation rate of the proposed method is observed to be better than existing methods like Dynamic Programming Phase Slope Algorithm (DYPSA), Speech Event Detection using the Residual Excitation And a Mean-based Signal (SEDREAMS), Dynamic Plosion Index (DPI) and Single Pole Filtering (SPF) methods for telephonic speech quality.
Towards Camera Identification from Cropped Query Images

PRNU (Photo Response Non-Uniformity)-based camera fingerprints are useful for identifying the source camera of an anonymous image. As the query image has to be correlated with each candidate camera fingerprint, one key concern of this approach is the high run time overhead when using a large camera database. Clever techniques have been proposed to reduce the computation and I/O time either by reducing the size of the fingerprint or by group testing where multiple candidate fingerprints can be eliminated by a single correlation operation. However, these techniques assume that the query images have not been scaled or cropped. In practice this may often not be the case, especially when query images are taken from social media sites. This paper presents a simple scaling-based approach for source camera identification when the query image is of full resolution or if it is cropped from an unknown location (of the original image). The proposed approach can also be easily combined with other known approaches for PRNU matching of scaled images. Experiments using 250 cameras showed that the run time overhead can decrease by a factor 13 when the query is a cropped image.
Self-Assessed Affect Recognition Using Fusion of Attentional BLSTM and Static Acoustic Features

In this study, we present a computational framework to participate in the Self-Assessed Affect Sub-Challenge in the INTER-SPEECH 2018 Computation Paralinguistics Challenge. The goal of this sub-challenge is to classify the valence scores given by the speaker themselves into three different levels, i.e., low, medium, and high. We explore fusion of Bi-directional LSTM with baseline SVM models to improve the recognition accuracy. In speciﬁcs, we extract frame-level acoustic LLDs as input to the BLSTM with a modiﬁed attention mechanism, and separate SVMs are trained using the standard ComParE 16 baseline feature sets with minority class upsampling. These diverse prediction results are then further fused using a decision-level score fusion scheme to integrate all of the developed models. Our proposed approach achieves a 62.94% and 67.04% unweighted average recall (UAR), which is an 6.24% and 1.04% absolute improvement over the best baseline provided by the challenge organizer. We further provide a detailed comparison analysis between different models.
Whole Slide Image Classification via Iterative Patch Labelling

Brain tumor can be a fatal disease in the world. With the aim of improving survival rates, many computerized algorithms have been proposed to assist the pathologists to make a diagnosis' using Whole Slide Pathology Images (WSI). Most methods focus on performing patch-level classification and aggregating the patch-level results to obtain the image classification. Since not all patches carry diagnostic information, it is thus important for our algorithm to recognize discriminative and non-discriminative patches. In this study, we propose an iterative patch labelling algorithm based on the Convolutional Neural Network (CNN), with a well-designed thresholding scheme, a training policy and a novel discriminative model architecture, to distinguish patches and use the discriminative ones to achieve WSI -classification. Our method is evaluated on the MICCAI 2015 Challenge Dataset, and shows a large improvement over the baseline approaches.
A Joint Multi-Task CNN for Cross-Age Face Recognition

Cross-age face recognition (CAFR) has received more and more attention in real applications, but it is a challenging task due to complex facial aging process. One popular way is modeling CAFR as a traditional face classification problem. However, most of them suffer from one main difficulty: how to effectively extract identity sensitive features that are age insensitive. In this paper, we propose a joint multi-task convolutional neural network (JMCNN) framework. JMCNN consists of two tasks: one for face recognition to learn identity sensitive features (i.e., age-invariant features), the other for age classification to learn age sensitive features, meanwhile, two tasks enhance each other by enforcing a regularization term on two kinds of features. The experimental results on two well-known cross-age datasets (Morph Album 2, CACD) have shown JMCNN is superior to the existing methods.
Temporal Transformer Networks for Acoustic Scene Classification

Neural networks have been proven to be powerful models for acoustic scene classiﬁcation tasks, but are still limited by the lack of ability to be temporally invariant to the audio data. In this paper, a novel temporal transformer module is proposed to allow the temporal manipulation of data in neural networks. This module is composed of a Fourier transform layer for feature maps and a learnable feature reduction layer, and can be inserted into existing convolutional neural network (CNN) and Long short-term memory (LSTM) models. Experiments on LITIS Rouen dataset and DCASE2016 dataset show that the proposed method leads to a signiﬁcant improvement when compared with the existing neural networks. Our approach is able to perform signiﬁcantly better than the state-of-the-art result on LITIS Rouen dataset, obtaining a relative reduction of 23.6% on classiﬁcation error.
Dual-label Deep LSTM Dereverberation For Speaker Verification

In this paper, we present a reverberation removal approach for speaker verification, utilizing dual-label deep neural networks (DNNs). The networks perform feature mapping between the spectral features of reverberant and clean speech. Long short term memory recurrent neural networks (LSTMs) are trained to map corrupted Mel filterbank (MFB) features to two sets of labels: i) the clean MFB features, and ii) either estimated pitch tracks or the fast Fourier transform (FFT) spectrogram of clean speech. The performance of reverberation removal is evaluated by equal error rates (EERs) of speaker verification experiments.
Optimum Encoding Approaches on Video Resolution Changes: A Comparative Study

Video resolution changes in an HTTP adaptive streaming session may negatively affect the viewer's quality of experience. Our goal is, through encoding, to make such resolution changes less noticeable for the viewers. This can be achieved by taking video complexity features into account during the encoding process. In this paper, we compare Constant Bitrate (CBR) versus Constrained Constant Rate Factor (CRF) coding approaches and their effects on the noticeability of video resolution changes. To this end, we conducted a dedicated subjective study with 20 subjects in a quasi -lab environment. Our results suggest that choices for fixed-bitrate encoding have to be improved by deeper analysis of video complexity and resolution change patterns.