Miracle-dz/UniFAR-FTUs huggingface dataset:
Single Image Dehazing Via a Joint Deep Modeling

Recently, image dehazing has received extensive attention from researchers in vision society. Previous dehazing methods usually estimate transmissions and haze-free images in a separate way, which leads to poor image dehazing results if transmissions are incorrectly estimated. On the other hand, though some CNN-based deep networks have been developed to remove haze, their transmission estimations heavily rely on white balance. In this paper, we propose a residual type CN-N for transmission refinement rather than estimation. Benefit from its residual learning ability, we plug the network in solving an optimization problem, which is able to improve the refinement results through jointly estimating transmissions and clean images in a single framework. Experimental results of synthetic and real-world images demonstrate the superiority and efficiency of our proposed framework, compared to many state-of-the-art methods.
Precise time-matching in chimpanzee allogrooming does not occur after a short delay

Allogrooming is a key aspect of chimpanzee sociality and many studies have investigated the role of reciprocity in a biological market. One theoretical form of reciprocity is time-matching, where payback consists of an equal duration of effort (e.g. twenty seconds of grooming repaid with twenty seconds of grooming). Here, we report a study of allogrooming in a group of twenty-six captive chimpanzees (Chester Zoo, UK), based on more than 150 hours of data. For analysis, we introduce a methodological innovation called the “Delta scale”, which unidimensionally measures the accuracy of time-matching according to the extent of delay after the cessation of grooming. Delta is positive when reciprocation occurs after any non-zero delay (e.g. A grooms B and then B grooms A after a five second break) and it is negative when reciprocation begins whilst the original grooming has not yet ceased. Using a generalized linear mixed-method, we found evidence for time-matched reciprocation. However, this was true only for immediate reciprocation (Delta less than zero). If there was a temporal break in grooming between two members of a dyad, then there was no evidence that chimpanzees were using new bouts to retroactively correct for time-matching imbalances from previous bouts. Our results have implications for some of the cognitive constraints that differentiate real-life reciprocation from abstract theoretical models. Furthermore, we suggest that some apparent patterns of time-matched reciprocity may arise merely due to the law of large numbers, and we introduce a statistical test which takes this into account when aggregating grooming durations over a window of time.
A Two-Stage Method for Skin Lesion Analysis

This report summarises our method and validation results for the ISIC Challenge 2018 Skin Lesion Analysis Towards Melanoma Detection Task 1: Lesion Segmentation. We present a two-stage method for lesion segmentation with optimised training method and ensemble post-process. Our method achieves state-of-the-art performance on lesion segmentation and we win the first place in ISIC 2018 task1.
SOX9/FXYD3/Src Axis Is Critical for ER+ Breast Cancer Stem Cell Function

The presence of cancer stem cells (CSC), which possess the ability of self-renewal and cancer initiation, is correlated with poor prognosis and drug resistance of breast cancer patients. But the molecular regulatory networks for maintenance of CSC function still remain unclear. Here, we identified that an estrogen-inducible gene FXYD3, whose expression is significantly upregulated in ER+ breast CSCs, is a critical player for regulating ER+ breast CSC function. FXYD3 amplification is crucial in mediating tamoxifen resistance in ER+ breast cancer cells. Interestingly, we also find that stem cell–related transcription factor SOX9 directly promotes FXYD3 expression, and FXYD3 is indispensable for SOX9 nucleus localization, thus forming a positive regulatory feedback loop for FXYD3 amplification and function. In terms of mechanism, FXYD3 interacts with Src and ERα to form an activated complex and triggers Src to transduce nongenomic estrogen signaling for facilitating ER+ breast CSCs. Collectively, these results establish a critical role for SOX9/FXYD3/Src axis in boosting nongenomic estrogen signaling and SOX9 nucleus entry, which is required for maintenance of ER+ breast CSCs and endocrine resistance. Targeting FXYD3-mediated pathway might be a promising therapeutic strategy for hormone therapy–refractory ER+ breast cancer. Implications: SOX9/FXYD3/Src axis is critical for promoting CSC function and tamoxifen resistance in ER+ breast cancer.
Intensity-Guided Depth Upsampling Using Edge Sparsity and Weighted $L_{0}$ Gradient Minimization

Although depth cameras acquire depth in dynamic scenes, the captured depth images are often noisy and of low resolution. In this paper, we propose intensity-guided depth upsampling using edge sparsity and weighted $L_{0}$ gradient minimization. We exploit the edge sparsity in the gradient domain to enhance the resolution and quality of depth images. First, we get mutual structure between intensity and depth using joint mutual-structure filtering. Second, we generate weights for $L_{0}$ gradient minimization based on gradient and entropy of the intensity image, and upsample the depth image using weighted $L_{0}$ gradient minimization. Finally, we refine the depth image using adaptive fast weighted median filtering. Experiments on Middlebury 2005 and real-world scene datasets confirm that the proposed method produces edge-preserving depth upsampling results and outperforms state-of-the-art methods in terms of accuracy.
Joint Learning Using Denoising Variational Autoencoders for Voice Activity Detection

Voice activity detection (VAD) is a challenging task in very low signal-to-noise ratio (SNR) environments. To address this issue, a promising approach is to map noisy speech features to corresponding clean features and to perform VAD using the generated clean features. This can be implemented by concatenating a speech enhancement (SE) and a VAD network, whose parameters are jointly updated. In this paper, we propose denoising variational autoencoder-based (DVAE) speech enhancement in the joint learning framework. Moreover, we feed not only the enhanced feature but also the latent code from the DVAE into the VAD network. We show that the proposed joint learning approach outperforms conventional denoising autoencoder-based joint learning approach.
Learning Discriminative Features for Speaker Identification and Verification

The success of any Text Independent Speaker Identiﬁcation and/or Veriﬁcation system relies upon the system’s capability to learn discriminative features. In this paper we propose a Convolutional Neural Network (CNN) Architecture based on the popular Very Deep VGG [1] CNNs, with key modiﬁcations to accommodate variable length spectrogram inputs, reduce the model disk space requirements and reduce the number of parameters, resulting in signiﬁcant reduction in training times. We also propose a uniﬁed deep learning system for both Text-Independent Speaker Recognition and Speaker Veriﬁcation, by training the proposed network architecture under the joint supervision of Softmax loss and Center loss [2] to obtain highly discriminative deep features that are suited for both Speaker Identiﬁcation and Veriﬁcation Tasks. We use the recently released VoxCeleb dataset [3], which contains hundreds of thousands of real world utterances of over 1200 celebrities belonging to various ethnicities, for benchmarking our approach. Our best CNN model achieved a Top-1 accuracy of 84.6%, a 4% absolute improvement over Vox-Celeb’s approach, whereas training in conjunction with Center Loss improved the Top-1 accuracy to 89.5%, a 9% absolute improvement over Voxceleb’s approach.
Amift: Affine-Mirror Invariant Feature Transform

In this paper, we propose a descriptor for image matching under multiple mirror reflections. Indeed, existing adaptations of SIFT for the mirror transformation are not successful when object and mirrors orientations are not constrained. Hence, we propose to combine MIFT and Affine-SIFT descriptors as the Affine Mirror Invariant Feature Transform (AMIFT). The experimental results and given evaluation show that our proposed descriptor outperforms MIFT and ASIFT on both synthetic and real images datasets.
Integrating Spectral and Spatial Features for Multi-Channel Speaker Separation

This paper tightly integrates spectral and spatial information for deep learning based multi-channel speaker separation. The key idea is to localize individual speakers so that an enhancement network can be used to separate the speaker from an estimated direction and with specific spectral characteristics. To determine the direction of the speaker of interest, we identify time-frequency (T-F) units dominated by that speaker and only use them for direction of arrival (DOA) estimation. The speaker dominance at each T-F unit is determined by a two-channel permutation invariant training network, which combines spectral and interchannel phase patterns at the input feature level. In addition, beamforming is tightly integrated in the proposed system by exploiting the magnitudes and phase produced by T-F masking based beamforming. Strong separation performance has been observed on a spatialized reverberant version of the wsj0-2mix corpus.
How much should you ask? On the question structure in QA systems.

Datasets that boosted state-of-the-art solutions for Question Answering (QA) systems prove that it is possible to ask questions in natural language manner. However, users are still used to query-like systems where they type in keywords to search for answer. In this study we validate which parts of questions are essential for obtaining valid answer. In order to conclude that, we take advantage of LIME - a framework that explains prediction by local approximation. We find that grammar and natural language is disregarded by QA. State-of-the-art model can answer properly even if ’asked’ only with a few words with high coefficients calculated with LIME. According to our knowledge, it is the first time that QA model is being explained by LIME.
Convolutional Neural Network for Blind Mesh Visual Quality Assessment Using 3D Visual Saliency

In this work, we propose a convolutional neural network (CNN) framework to estimate the perceived visual quality of 3D meshes without having access to the reference. The proposed CNN architecture is fed by small patches selected carefully according to their level of saliency. To do so, the visual saliency of the 3D mesh is computed, then we render 2D projections from the 3D mesh and its corresponding 3D saliency map. Afterward, the obtained views are split to obtain 2D small patches that pass through a saliency filter to select the most relevant patches. Experiments are conducted on two MVQ assessment databases, and the results show that the trained CNN achieves good rates in terms of correlation with human judgment.
Optimal duration of prone positioning in patients with acute respiratory distress syndrome: a protocol for a systematic review and meta-regression analysis

Introduction Several systematic reviews and meta-analyses have demonstrated that prolonged (≥16 hours) prone positioning can reduce the mortality associated with acute respiratory distress syndrome (ARDS). However, the effectiveness and optimal duration of prone positioning was not fully evaluated. To fill these gaps, we will first investigate the effectiveness of prone positioning compared with the conventional management of patients with ARDS, regarding outcomes using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system. Second, if statistical heterogeneity in effectiveness with regard to short-term mortality (intensive care unit death or ≤30-day mortality) is shown, we will conduct a meta-regression analysis to explore the association between duration and effectiveness, and determine the optimal duration of prone positioning. Method and analysis Relevant studies are collected using PubMed/MEDLINE, Embase, Cochrane Central Register of Controlled Trials and the WHO International Clinical Trials Platform Search Portal. Randomised controlled trials comparing prone and supine positioning in adults with ARDS will be included in the meta-analysis. Two independent investigators will screen trials obtained by search eligibility and extract data from selected studies to standardised data recording forms. For each selected trial, the risk of bias and quality of evidence will be evaluated using the GRADE system. Meta-regression analyses will be performed to identify the most important factors associated with short-term mortality, and subgroup analysis will be used to analyse the following: duration of mechanical ventilation in the prone position per day, patient severity, tidal volume and cause of ARDS. If heterogeneity or inconsistency among the studies is detected, subgroup analysis will be conducted on factors that may cause heterogeneity. Ethics and dissemination This study requires no ethical approval. The results obtained from this systematic review and meta-analysis will be disseminated through international conference presentations and publication in a peer-reviewed journal. PROSPERO registration number CRD42017078340.
Dengue modeling in rural Cambodia: statistical performance versus epidemiological relevance

Dengue dynamics are shaped by the complex interplay between several factors, including vector seasonality, interaction between four virus serotypes, and inapparent infections. However, paucity or quality of data do not allow for all of these to be taken into account in mathematical models. In order to explore separately the importance of these factors in models, we combined surveillance data with a local-scale cluster study in the rural province of Kampong Cham (Cambodia), in which serotypes and asymptomatic infections were documented. We formulate several mechanistic models, each one relying on a different set of hypotheses, such as explicit vector dynamics, transmission via asymptomatic infections and coexistence of several virus serotypes. Models are confronted with the observed time series using Bayesian inference, through Markov chain Monte Carlo. Model selection is then performed using statistical information criteria, but also by studying the coherence of epidemiological characteristics (reproduction numbers, incidence proportion, dynamics of the susceptible class) in each model. Considering the available data, our analyses on transmission dynamics in a rural endemic setting highlight both the importance of using two-strain models with interacting effects and the lack of added value of incorporating vector and explicit asymptomatic components.
Efficient Multimedia Similarity Measurement Using Similar Elements

Online social networking techniques and large-scale multimedia retrieval are developing rapidly, which not only has brought great convenience to our daily life, but generated, collected, and stored large-scale multimedia data as well. This trend has put forward higher requirements and greater challenges on massive multimedia retrieval. In this paper, we investigate the problem of image similarity measurement, which is one of the key problems of multimedia retrieval. Firstly, the definition of similarity measurement of images and the related notions are proposed. Then, an efficient similarity measurement framework is proposed. Besides, we present a novel basic method of similarity measurement named SMIN. To improve the performance of similarity measurement, we carefully design a novel indexing structure called SMI Temp Index (SMII for short). Moreover, we establish an index of potential similar visual words off-line to solve to problem that the index cannot be reused. Experimental evaluations on two real image datasets demonstrate that the proposed approach outperforms state-of-the-arts.
A Unified Framework for the Generation of Glottal Signals in Deep Learning-based Parametric Speech Synthesis Systems

In this paper, we propose a uniﬁed training framework for the generation of glottal signals in deep learning (DL)-based parametric speech synthesis systems. The glottal vocoding-based speech synthesis system, especially the modeling-by-generation (MbG) structure that we proposed recently, signiﬁcantly improves the naturalness of synthesized speech by faithfully representing the noise component of the glottal excitation with an additional DL structure. Because the MbG method introduces a multistage processing pipeline, however, its training process is complicated and inefﬁcient. To alleviate this problem, we pro-pose a uniﬁed training approach that directly generates speech parameters by merging all the required models, such as acoustic, glottal, and noise models, into a single uniﬁed network. Considering the fact that noise analysis should be performed after training the glottal model, we also propose a stochastic noise analysis method that enables noise modeling to be included in the uniﬁed training process by iteratively analyzing the noise component in every epoch. Both objective and subjective test results verify the superiority of the proposed algorithm compared to conventional methods.
Efficient Voice Trigger Detection for Low Resource Hardware

We describe the architecture of an always-on keyword spot-ting (KWS) system for battery-powered mobile devices used to initiate an interaction with the device. An always-available voice assistant needs a carefully designed voice keyword detector to satisfy the power and computational constraints of battery powered devices. We employ a multi-stage system that uses a low-power primary stage to decide when to run a more accurate (but more power-hungry) secondary detector. We describe a straightforward primary detector and explore variations that result in very useful reductions in computation (or increased accuracy for the same computation). By reducing the set of target labels from three to one per phone, and reducing the rate at which the acoustic model is operated, the compute rate can be reduced by a factor of six while maintaining the same accuracy.
Deep Noise Tracking Network: A Hybrid Signal Processing/Deep Learning Approach to Speech Enhancement

Noise statistics and speech spectrum characteristics are the essential information for the single channel speech enhancement. The signal processing-based methods mainly rely on noise statistics estimation. They perform very well for stationary noise, but have remained difﬁcult to cope with non-stationary noise. While the deep learning-based methods mainly focus on the perception on the spectrum characteristics of speech and have a capacity in dealing with non-stationary noise. However, the performance would degrade dramatically for the unseen noise types, which could be due to the over-reliance on data and the ignorance to domain knowledge of signal process. Obviously, the hybrid signal processing/deep learning scheme may be a smart alternative. In this paper, we incorporate the powerful perceptual capabilities of deep learning in the conventional speech enhancement framework. Deep learning is used to estimate the speech presence probability and the update factor of noise statistics, which are then integrated into the Wiener ﬁlter-based speech enhancement structure to enhance the desired speech. All components are jointly optimized by a spectrum approximation objective. Systematic experiments on CHiME-4 and NOISEX-92 demonstrate the proposed hybrid signal processing/deep learning approach to noise suppression in noise-unmatched and noise-matched conditions.
Exploration of Compressed ILPR Features for Replay Attack Detection

This paper deals with the problem of detecting replay attacks on speaker veriﬁcation systems. In literature, apart from the acoustic features, source features have also been successfully used for this task. In existing source features, only the information around glottal closure instants (GCIs) have been utilized. We hypothesize that the feature derived by capturing the temporal dynamics between two GCIs would be more discriminative for such task. Motivated by that, in this work we explore the use of discrete cosine transform compressed integrated linear prediction residual (ILPR) features for discriminating be-tween genuine and replayed signals. A spoof detection system is built using the compressed ILPR feature and a Gaussian mixture model (GMM) classiﬁer. A baseline system is also built using constant-Q cepstral coefﬁcient feature with GMM back-end. These systems are tested on the ASVSpoof 2017 Version 2.0 database. On fusing the systems developed using acoustic and proposed source features an equal error rate of 9 . 41% is achieved on the evaluation set.
Revealing Spatiotemporal Brain Dynamics of Speech Production Based on EEG and Eye Movement

To understand the neural circuitry associated with speech production in oral reading, it is essential to describe the whole-range spatiotemporal brain dynamics in the processes including visual word recognition, orthography-phonology mapping, semantic accessing, speech planning, articulation, self-monitoring, etc. This has turned out to be extremely difficult because of demanding resolution in both spatial and temporal domains and advanced algorithms to eliminate severe contamination by articulatory movements. To tackle this hard target, we recruited 16 subjects in a sentence reading task and measured multimodal signals of electroencephalography (EEG), eye movement, and speech simultaneously. The onset/offset of gazing and utterance were used for segmenting brain activation stages. Cortical modeling of causal interactions among anatomical regions was conducted on EEG signals through (i) independent component analysis (ICA) to identify cortical regions of interest (ROIs); (ii) multivariate autoregressive (MVAR) modeling of representative cortical activity from each ROI; and (iii) quantification of the dynamic causal interactions among ROIs using the Short-time direct Directed Transfer function (SdDTF). The resulting brain dynamic model reveals a widely connected bilateral organization with left-lateralized semantic, orthographic and phonological sub-networks, right-lateralized prosody and motor sequencing sub-networks, and bi-lateralized auditory and multisensory integration sub-networks that cooperate along interlaced and paralleled temporal stages for speech processing.
Learning and Modeling Unit Embeddings for Improving HMM-based Unit Selection Speech Synthesis

This paper presents a method of learning and modeling unit embeddings using deep neutral networks (DNNs) to improve the performance of HMM-based unit selection speech synthesis. First, a DNN with an embedding layer is built to learn a ﬁxed-length embedding vector for each phone-sized candidate unit in the corpus from scratch. Then, another two DNNs are constructed to map linguistic features toward the extracted unit vector of each phone. One of them employs the unit vectors of preceding phones as model input. At synthesis time, the L 2 distances between the unit vectors predicted by these two DNNs and the ones derived from candidate units are integrated into the target cost and the concatenation cost of HMM-based unit selection speech synthesis respectively. Experimental results demonstrate that the unit vectors estimated using only acoustic features display phone-dependent clustering properties. Furthermore, integrating unit vector distances into cost functions, especially the concatenation cost, improves the naturalness of HMM-based unit selection speech synthesis in our experiments.