Miracle-dz/UniFAR-FTUs huggingface dataset:
Searching for meaning: A grounded theory of family resilience in adult ICU.

AIM To explore families' experiences of their interactions in an Australian adult intensive care unit (ICU) to develop a grounded theory that can be used by critical care nurses to improve patient- and family-centred care (PFCC). BACKGROUND Families in ICU play an important role in the patient's recovery and outcomes. However, families are at risk of significant psychological morbidity due to their experiences in ICU. Although many ICU patients can make their own decisions, a large proportion are unconscious or chemically sedated and unable to contribute to decisions about their care, leaving the decision-making role to the family. Therefore, the families' psychosocial and emotional well-being must be supported by implementing evidence-based interventions that align with a PFCC approach. This study describes the findings of a grounded theory of family resilience in ICU, of which the core category is Regaining control. The focus of this paper is on the major category: Searching for meaning. METHODS We adopted a constructivist grounded theory method. Twenty-five adult family members (n = 25) of 21 patients admitted unexpectedly to an ICU in metropolitan Australia were recruited. In-depth interviews were used to collect the data, and the analytical processes of constructivist grounded theory underpinned the development of a core category and related subcategories. RESULTS When adult family members experience the unexpected admission of a relative to ICU, they move towards a state of being beyond emotional adversity and regaining control when facilitated to search for meaning in their situation. When families were able to make sense of their situation and find a purpose by contributing to their relative's recovery, it encouraged them to cope and be resilient. CONCLUSIONS Our findings can be used to promote PFCC in ICU, which considers a collaborative approach to meet the patient's needs while providing emotional and psychosocial support to their families.
Trends for Facial Injectable Therapies in Medical Aesthetics

Aesthetic dermatology has evolved and progressed during the last two decades. The term “Medical Aesthetics” is now coined here as the horizons for corrections and restoration by scientific rational are expanding and are beyond cosmetics only. The injectable used before the phase involved only intralesional steroids used for cystic acne. With the advent of minimally invasive procedures, the injectable techniques and options have reached a long way, empowering the dermatologist with the ability to correct facial texture; volumize, contour, lift, and alter muscle tone; and achieve focal fat reduction by simply using injectable therapy.
Bidirectional Long-Short Term Memory Network-based Estimation of Reliable Spectral Component Locations

An accurate Ideal Binary Mask (IBM) estimate is essential for Missing Feature Theory (MFT)-based speaker identifica-tion, as incorrectly labelled spectral components (where a com-ponent is either reliable or unreliable) will degrade the perfor-mance of an Automatic Speaker Identification (ASI) system ad-versely in the presence of noise. In this work a Bidirectional Re-current Neural Network (BRNN) with Long-Short Term Mem-ory (LSTM) cells is proposed for improved IBM estimation. The proposed system had an average IBM estimate accuracy improvement of 4.5% and an average MFT-based speaker iden-tification accuracy improvement of 3.1% over all tested SNRdB levels, when compared to the previously proposed Multilayer Perceptron (MLP)-IBM estimator. When used for speech en-hancement the proposed system had an average MOS-LQO (ob-jective quality measure) improvement of 0.32 and an average QSTI (objective intelligibility measure) improvement of 0.01 over all tested SNRdB levels, when compared to the MLP-IBM estimator. The results presented in this work highlight the effec-tiveness of the proposed BRNN-IBM estimator for MFT-based speaker identification and IBM-based speech enhancement.
Multi-Modal Feature Fusion Network for Ghost Imaging Object Detection

Ghost imaging is a new imaging technology with the advantage of anti-interference and environmental adaptability, but it's far from the practical application in the field of object detection for ghost data. The challenge is that ghost data only contains the depth information with the low resolution and visibility, in the lack of textural features. In this paper, we propose a multi-modal feature fusion network which adapts recent convolutional neural networks (CNNs) based detectors. We also generate a synthetic ghost imaging dataset. To fully exploit the complete characteristics of ghost data, we encode the original data into new feature maps. Our architecture is divided into two streams, one for ghost data and one for encoded maps, which utilizes multi-modal features by mid-level fusion. We obtained an average 4.2% improvement over ghost data baseline and also achieved competitive accuracy on the NYUD2 dataset. Our research is a relatively novel field with significant application value and potential demands.
Expectation-Maximization Algorithms for Itakura-Saito Nonnegative Matrix Factorization

This paper presents novel expectation-maximization (EM) algorithms for estimating the nonnegative matrix factorization model with Itakura-Saito divergence. Indeed, the common EM-based approach exploits the space-alternating generalized EM (SAGE) variant of EM but it usually performs worse than the conventional multiplicative algorithm. We propose to explore more exhaustively those algorithms, in particular the choice of the methodology (standard EM or SAGE variant) and the latent variable set (full or reduced). We then derive four EM-based algorithms, among which three are novel. Speech separation experiments show that one of those novel algorithms using a standard EM methodology and a reduced set of latent variables outperforms its SAGE variants and competes with the conventional multiplicative algorithm.
Long-Term Video Generation with Evolving Residual Video Frames

In this paper, we propose a novel long-term video generation algorithm, motivated by the recent developments of unsupervised deep learning techniques. The proposed technique learns two ingredients of internal video representation, i.e., video textures and motions to reproduce realistic pixels in the future video frames. To this aim, the proposed technique uses two encoders comprising convolutional neural networks (CNN) to extract spatiotemporal features from the original video frame and a residual video frame, respectively. The use of the residual frame facilitates the learning with fewer parameters as there are high spatiotemporal correlations in a video. Moreover, the residual frames are efficiently used for evolving pixel differences in the future frame. In a decoder, the future frame is generated by transforming the combination of two feature vectors into the original video size. Experimental results demonstrate that the proposed technique provides more robust and accurate results of long-term video generation than conventional techniques.
Disability and Its Influencing Factors among the Elderly in a County, Guangxi Province, China

Objectives: This study aims to understand the disability status of the elderly residents of a County (Guangxi Province, China) and explore its influencing factors. Methods: Respondents consisted of 2300 elderly people aged 60 and above from three townships in the county we studied. The Activities of Daily Living (ADL) Scale was used to assess the disability of the elderly sample. Chi-square test was applied to compare the disability rate among the elderly with different demographic characteristics. The graph showed the disability rates of ADL, six items of Physical Activities of Daily Living (PADL) and eight items of Instrumental Activities of Daily Living (IADL) at different ages. Binary logistic regression was used to analyze the influencing factors of disability rate among the elderly. Results: The disability rates of ADL, PADL, and IADL in the elderly were 43.4%, 11.6%, and 42.4%, respectively. As with the increase in age, the disability rates of ADL, IADL, PADL, and their 14 items gradually increased (p < 0.05), with walking, using the telephone, and using public vehicles having higher disability rates than other items. The influencing factors of ADL disability were gender (OR = 0.579, 95%CI = 0.441–0.759), age (OR = 2.270, 95%CI = 1.867–2.759; OR = 4.719, 95%CI = 2.998–7.429; OR = 6.249, 95%CI = 3.667–10.648), educational level (OR = 2.844, 95%CI = 2.076–3.897; OR = 1.677, 95%CI = 1.246–2.230), and having metabolic syndrome (MetS) (OR = 1.298, 95%CI = 1.044–1.613). Compared with ADL, the influencing factor of PADL disability was gender, whereas that of IADL disability was whether someone had MetS. Conclusions: With age, the possibility of ADL, PADL, and IADL damage in the elderly is higher. Gender, age, educational level, the number of chronic diseases, and whether someone has MetS might be the influencing factors of disability. Interventions should be taken from a variety of sources specific to the content of each entry.
A Two-Step Learning Method for Detecting Landmarks on Faces from Different Domains

The detection of fiducial points on faces has significantly been favored by the rapid progress in the field of machine learning, in particular in the convolution networks. However, the accuracy of most of the detectors strongly depends on an enormous amount of annotated data. In this work, we present a domain adaptation approach based on a two-step learning to detect fiducial points on human and animal faces. We evaluate our method on three different datasets composed of different animal faces (cats, dogs, and horses). The experiments show that our method performs better than state of the art and can use few annotated data to leverage the detection of landmarks reducing the demand for large volume of annotated data.
Speech Enhancement Using Deep Mixture of Experts Based on Hard Expectation Maximization

We consider the problem of deep mixture of experts based speech enhancement. The deep mixture of experts, where experts are considered as deep neural network (DNN), is difﬁcult to train due to the network structure. In this work, we propose a pre-training method for individual DNN in deep mixture of experts. We use hard expectation maximization (EM) to pre-train the individual DNNs. After pre-training, we take a weighted combination of outputs of individual DNN experts and jointly train the whole system. We compare the proposed method with single DNN based speech enhancement scheme. Speech enhancement experiments, in four SNR conditions, show the superiority of the proposed method over the baseline scheme. The average improvements obtained for four seen noise cases over single DNN scheme are 0.08, 0.59 dB and 0.015 in terms of objective measures viz perceptual evaluation of speech quality (PESQ), segmental signal to noise ratio (seg SNR) and short time objective intelligibility (STOI) respectively.
Label Propagation on Facial Images Using Similarity and Dissimilarity Labelling Constraints

In this paper, a novel multimedia data (specifically facial images) label propagation method is presented that is based on the inclusion of labelling constraints in the objective function of the MLPP-CLP state of the art algorithm. The proposed method can incorporate pairwise facial image similarity and dissimilarity constraints into the objective function of the aforementioned method. Experiments which have been conducted on facial image labelling in three stereoscopic movies, confirm the increased labelling accuracy of the proposed method.
Spanish Statistical Parametric Speech Synthesis Using a Neural Vocoder

During the 2000s decade, unit-selection based text-to-speech was the dominant commercial technology. Meanwhile, the TTS research community has made a big effort to push statistical-parametric speech synthesis to get similar quality and more ﬂex-ibility on the synthetically generated voice. During last years, deep learning advances applied to speech synthesis have ﬁlled the gap, specially when neural vocoders substitute traditional signal-processing based vocoders. In this paper we propose to substitute the waveform generation vocoder of MUSA, our Spanish TTS, with SampleRNN, a neural vocoder which was recently proposed as a deep autoregressive raw waveform generation model. MUSA uses recurrent neural networks to predict vocoder parameters (MFCC and logF0) from linguistic features. Then, the Ahocoder vocoder is used to recover the speech waveform out of the predicted parameters. In the ﬁrst system SampleRNN is extended to generate speech conditioned on the Ahocoder generated parameters (mfcc and logF0), where two conﬁgurations have been considered to train the system. First, the parameters derived from the signal using Ahocoder are used. Secondly, the system is trained with the parameters predicted by MUSA, where SampleRNN and MUSA are jointly optimized. The subjective evaluation shows that the second system outperforms both the original Ahocoder and SampleRNN as an independent neural vocoder.
Multi-Object Tracking Using Online Metric Learning with Long Short-Term Memory

The capacity to model temporal dependency by Recurrent Neural Networks (RNNs) makes it a plausible selection for the multi-object tracking (MOT) problem. Due to the nonlinear transformations and the unique memory mechanism, Long Short-Term Memory (LSTM) can consider a window of history when learning discriminative features, which suggests that the LSTM is suitable for state estimation of target objects as they move around. This paper focuses on association based MOT, and we propose a novel Siamese LSTM Network to interpret both temporal and spatial components nonlinearly by learning the feature of trajectories, and outputs the similarity score of two trajectories for data association. In addition, we also introduce an online metric learning scheme to update the state estimation of each trajectory dynamically. Experimental evaluation on MOT16 benchmark shows that the proposed method achieves competitive performance compared with other state-of-the-art works.
Catheter Detection in 3D Ultrasound Using Triplanar-Based Convolutional Neural Networks

3D Ultrasound (US) image-based catheter detection can potentially decrease the cost on extra equipment and training. Meanwhile, accurate catheter detection enables to decrease the operation duration and improves its outcome. In this paper, we propose a catheter detection method based on convolutional neural networks (CNNs) in 3D US. Voxels in US images are classified as catheter (or not) using triplanar-based CNNs. Our proposed CNN employs two-stage training with weighted loss function, which can cope with highly imbalanced training data and improves classification accuracy. When compared to state-of-the-art handcrafted features on ex-vivo datasets, our proposed method improves the F2-score with at least 31%. Based on classified volumes, the catheters are localized with an average position error of smaller than 3 voxels in the examined datasets, indicating that catheters are always detected in noisy and low-resolution images.
An Active Action Proposal Method Based on Reinforcement Learning

Detecting human activities in untrimmed video is a significant yet challenging task. Existing methods usually generate temporal action proposals via searching extensively at multiple preset scales or combining a bunch of short video snippets. However, we argue that the localization of action instances should be a process of observation, refinement and determination: observe the attended temporal window, refine its position and scale, then determine whether a true action region has been accurately found. To this end, we formulate temporal action localization task as a Markov Decision Process, and propose an active temporal action proposal model based on reinforcement learning. Our model learns to localize actions in videos by automatically adjusting the position and span of temporal window via a sequence of transformations. We train an action/non-action binary classifier to determine whether a temporal window contains an action instance. Validation results on THUMOS'14 dataset show that our proposed method achieves competitive performance both in accuracy and efficiency compared with some state-of-the-art methods, while using much less proposals.
Simultaneous Facial Landmark and 3D Action Estimation Based on Probabilistic Random Forest

Random forest is effective and efficient for detecting facial landmark from visual images. It has achieved the state-of-the-art performance, both in accuracy and speed, by regressing local binary features (LBF). This paper aims to increase the detection accuracy of random forest for facial landmarks and extends it to facial action estimation. First, probabilistic features with the improved regression measure are designed to overcome the weaknesses of LBF, namely feature sparseness and tracking jitter, for initial detection. Second, the initial detected facial landmarks and 3D facial actions are jointly refined and estimated. Specifically, a deformable facial model is registered to input images based on an optimized iterative closest point framework, in which an optimal weight is additionally assigned to each vertex with the point to plane constraints. Experiments show that the proposed methods significantly outperform the state-of-the-art ones in terms of accuracy, as well as achieve the excellent tracking stability and real-time ability at about 80 fps for estimating landmarks+actions on an ordinary PC.
DeepSSH: Deep Semantic Structured Hashing for Explainable Person Re-Identification

For large collections of gallery images captured by sparsely distributed cameras, we often employ hashing based approaches to enhance the efficiency of person re-identification (re-id). However, these hashing based approaches fail to provide semantically explainable encoding in solving the re-id problem, which makes it infeasible to identify the correct matches in the collection by just using a semantic query. To overcome this limitation, we propose a new deep hashing network called Deep Semantic Structured Hashing (DeepSSH) to obtain the semantic structured representation of human. In the proposed DeepSSH framework, both the mid-level human attributes and the high-level ID labels are used to learn a deep hashing network. Then, based on the obtained semantic structured hash code and the attribute labels, we learn a decoder to find the partial hash code corresponding to the specified attributes. Finally, a new grain scalable re-id framework is constructed to support semantic query of a person by providing partial or full semantic description of a person instead of the whole photo. Experimental results show that DeepSSH is comparable with state-of-the-art hashing-based person re-id approaches, and the experiment in semantic analysis shows that our hash code owns semantic meaning indeed.
Dense Scene Reconstruction from Spherical Light Fields

Spherical images are suitable for reconstruction of large, outdoor scenes. We present a method for dense 3D reconstruction based on spherical light fields (SLFs). In contrast to light fields generated from perspective cameras, SLFs are intrinsically distorted. Our approach is the first to exploit the internal structure of SLFs while properly handling distortion. Additionally, we adopt a variational formulation and our algorithm produces high quality, locally smooth reconstructions.
A Variational Formulation for Hyperspectral and Multispectral Image Fusion

We propose a new convex variational model for hyperspectral and multispectral image fusion. Our approach introduces nonlocal regularization conditioned to the geometry of the multispectral image and incorporates a constraint forcing the fusion product and the multispectral data to share modulated high frequencies. The proposed method is compared with state-of-the-art fusion techniques, showing competitive results for several quality metrics on different data.
On the Usefulness of the Speech Phase Spectrum for Pitch Extraction

© 2018 International Speech Communication Association. All rights reserved. Most frequency domain techniques for pitch extraction such as cepstrum, harmonic product spectrum (HPS) and summation residual harmonics (SRH) operate on the magnitude spectrum and turn it into a function in which the fundamental frequency emerges as argmax. In this paper, we investigate the extension of these three techniques to the phase and group delay (GD) domains. Our extensions exploit the observation that the bin at which F(magnitude) becomes maximum, for some monotonically increasing function F, is equivalent to bin at which F(phase) has maximum negative slope and F(groupdelay) has the maximum value. To extract the pitch track from speech phase spectrum, these techniques were coupled with the source-filter model in the phase domain that we proposed in earlier publications and a novel voicing detection algorithm proposed here. The accuracy and robustness of the phase-based pitch extraction techniques are illustrated and compared with their magnitude-based counterparts using six pitch evaluation metrics. On average, it is observed that the phase spectrum can be successfully employed in pitch tracking with comparable accuracy and robustness to the speech magnitude spectrum.
Temporal Pyramid Relation Network for Video-Based Gesture Recognition

Gesture recognition in video is an important application of computer vision. However, there are few works talked about the temporal order or relation of the frames in video, which is important for model gestures. In this paper, we propose Temporal Pyramid Relation Network (TPRN) which can model the temporal relation of video frames effectively and efficiently. First, we use Temporal Pyramid Pooling (TPP) layer to get temporal feature sequences of multiple scale pyramids. Then, a Temporal Relation Network (TRN) is stacked on the feature sequence of each scale respectively to model the temporal relations of video frames at multiple scales. At last, representations of all scales are aggregated to get the final prediction. TPRN can take video clips of various length as input and is scalable for video length. We evaluate TPRN on a recently released very large video-based gesture recognition dataset - 20BN-Jester dataset v1, and TPRN achieves competitive performance.