Miracle-dz/UniFAR-FTUs huggingface dataset:
Mining Multimodal Repositories for Speech Affecting Diseases

The motivation for this work is to contribute to the collection of large in-the-wild multimodal datasets in which the speech of the subject is affected by certain medical conditions. Our mining effort is focused on video blogs (vlogs), and as a proof-of-concept we have selected three target diseases: Depression, Parkinson’s disease, and cold. Given the large scale nature of the online repositories, we take advantage of existing retrieval algorithms to narrow the pool of candidate videos for a given query related with the disease (e.g. depression vlog), and on top of that we apply several ﬁltering techniques. These techniques explore both audio, video, text and metadata cues, in order to retrieve vlogs that include a single speaker which, at some point, admits that he/she is currently affected by a given disease. The use of straight-forward NLP techniques on the automatically transcribed data showed that distinguishing between narratives of present and past experiences is harder than distinguishing between narratives of self experiences and of someone else’s. The three resulting speech datasets were tested with neural networks trained with speech data collected in controlled conditions, yielding results only slightly below the ones achieved with the original test datasets.
Relating Articulatory Motions in Different Speaking Rates

Movements of articulators (e.g., tongue, lips and jaw) in different speaking rates are related in a complex manner. In this work, we examine the underlying function to transform articulatory movements involved in producing speech at a neutral speaking rate into those at fast and slow speaking rates (N2F and N2S). For this we use articulatory movement data collected from ﬁve subjects using an Electromagnetic articulograph at neutral, fast and slow speaking rates. As candidate transformation functions (TF), we use afﬁne transformations with a diagonal matrix and a full matrix and a nonlinear function modeled by a deep neural network (DNN). Since the duration of an utterance in different speaking rates would typically be unequal, it is required to time align the articulatory movement trajectories, which, in turn, affects the TF learnt. Therefore, we propose an iterative algo-rithm to alternately optimize for the TF and the time alignments. Subject speciﬁc experiments reveal that while N2F transformation can be well described by an afﬁne transformation with a full matrix, N2S transformation is better represented by a more complex nonlinear function modeled by a DNN. This could be because subjects exhibit gross articulatory movements during fast speech and hyper-articulate while producing slow speech.
Role of Regularization in the Prediction of Valence from Speech

Regularization plays a key role in improving the prediction of emotions using attributes such as arousal, valence and dominance. Regularization is particularly important with deep neural networks (DNNs), which have millions of parameters. While previous studies have reported competitive performance for arousal and dominance, the prediction results for valence using acoustic features are signiﬁcantly lower. We hypothesize that higher regularization can lead to better results for valence. This study focuses on exploring the role of dropout as a form of regularization for valence, suggesting the need for higher regularization. We analyze the performance of regression models for valence, arousal and dominance as a function of the dropout probability. We observe that the optimum dropout rates are consistent for arousal and dominance. However, the optimum dropout rate for valence is higher. To understand the need for higher regularization for valence, we perform an empirical analysis to explore the nature of emotional cues conveyed in speech. We compare regression models with speaker-dependent and speaker-independent partitions for training and testing. The experimental evaluation suggests stronger speaker dependent traits for valence. We conclude that higher regularization is needed for valence to force the network to learn global patterns that generalize across speakers.
LF-CAE: Context-Adaptive Encoding for Lenslet Light Fields Using HEVC

Light fields can outperform the representability of current imaging technologies by considering the angle of light-rays striking in the camera in addition to the color information. The additional information brings a set of challenges related to the amount of data required to represent light fields, arising the need for efficient compression schemes. Thiswork proposes a novel and efficient scheme to encode lenslet light fields, called Light Fields Context-Adaptive Encoding(LF-CAE). LF -CAE is a video-based compression solution that defines a flexible and dynamic scheme to create intermediate video sequences from light fields in order to efficiently exploit the HEVC inter-frame encoder structure. This scheme reduces the inter-frame prediction residue leading to compression efficiency gains in light-field coding. LF -CAE reaches an average compression rate of 99.56% in relation to the uncompressed light field, with an average PSNR of 37.61dB. LF-CAE surpasses all related works that decompose the light field into an intermediate video sequence, reaching thehighest PSNR and with BD-Rate gains ranging from 6.07% up to 20.50%.
Impact of IL-27p28 (rs153109) and TNF-α (rs1800629) Genetic Polymorphisms on the Progression of HCV Infection in Egyptian Patients

ABSTRACT Background: Hepatitis C virus (HCV) is a universal health problem. HCV infection may proceed to liver fibrosis, cirrhosis, and eventually hepatocellular carcinoma (HCC). The latest is the third leading global cause of cancer-related mortality. Cytokines including IL-27 and TNF-α play a major role as a link between innate and adaptive immunity which in turn deduct the outcome of HCV infection. Aim: The present study examined the role of both (-964 A/G) single-nucleotide polymorphism (SNP) of IL-27p28 rs153109 and (-308 G/A) SNP of TNF-α rs1800629 on the progression of HCV infection in genotype 4a infected patients. Patients and Methods: The patients enrolled in the study were divided into three main groups group I: 38 fibrotic patients, group II: 51 cirrhotic patients, and finally group III: 29 HCC patients. Sixteen healthy volunteers were used as controls. IL-27p28 rs153109 and TNF-α rs1800629 genotyping were performed using polymerase chain reaction-restriction fragment length polymorphism assay. Results: There was no statistically significant difference between the studied groups regarding the IL-27p28 genotypes. However, TNF-α (-308) studied polymorphism showed a significant difference between the HCC and fibrosis group (p = 0.00), and also between the cirrhosis and fibrosis group (p = 0.031) revealing that AA genotype is the genotype of risk. Furthermore, the association found between allele frequencies of two studied SNPs and the four studied groups were non-significant. Conclusion: TNF-α rs1800629 polymorphism is a potential genetic-susceptibility factor for HCV related cirrhosis and HCC progression.
Deep Metric Learning and Image Classification with Nearest Neighbour Gaussian Kernels

We present a Gaussian kernel loss function and training algorithm for convolutional neural networks that can be directly applied to both distance metric learning and image classification problems. Our method treats all training features from a deep neural network as Gaussian kernel centres and computes loss by summing the influence of a feature's nearby centres in the feature embedding space. Our approach is made scalable by treating it as an approximate nearest neighbour search problem. We show how to make end-to-end learning feasible, resulting in a well formed embedding space, in which semantically related instances are likely to be located near one another, regardless of whether or not the network was trained on those classes. Our approach outperforms state-of-the-art deep metric learning approaches on embedding learning challenges, as well as conventional softmax classification on several datasets.
Dual PAK4-NAMPT Inhibition Impacts Growth and Survival, and Increases Sensitivity to DNA-Damaging Agents in Waldenström Macroglobulinemia

Purpose: p21-activated kinase 4 (PAK4) plays a significant biological and functional role in a number of malignancies, including multiple myeloma (MM). On the basis of our promising findings in MM, we here characterize PAK4 expression and role in WM cells, as well effect of dual PAK4-NAMPT inhibitor (KPT-9274) against WM cell growth and viability. Experimental Design: We have analyzed mRNA and protein expression levels of PAK4 in WM cells, and used loss-of-function approach to investigate its contribution to WM cell viability. We have further tested the in vitro and in vivo effect of KPT-9274 against WM cell growth and viability. Results: We report here high-level expression and functional role of PAK4 in WM, as demonstrated by shRNA-mediated knockdown; and significant impact of KPT-9274 on WM cell growth and viability. The growth inhibitory effect of KPT-9274 was associated with decreased PAK4 expression and NAMPT activity, as well as induction of apoptosis. Interestingly, in WM cell lines treated with KPT-9274, we detected a significant impact on DNA damage and repair genes. Moreover, we observed that apart from inducing DNA damage, KPT-9274 specifically decreased RAD51 and the double-strand break repair by the homologous recombination pathway. As a result, when combined with a DNA alkylating agents bendamustine and melphalan, KPT-9274 provided a synergistic inhibition of cell viability in WM cell lines and primary patient WM cells in vitro and in vivo. Conclusions: These results support the clinical investigation of KPT-9274 in combination with DNA-damaging agent for treatment of WM.
Histone deacetylase 2 is essential for LPS‐induced inflammatory responses in macrophages

The role of specific histone deacetylase (HDAC) proteins in regulating the lipopolysaccharide (LPS)‐induced inflammatory response and its underlying mechanisms are unclear. Here, HDAC2, a class I HDAC family protein, is essential for the LPS‐triggered inflammatory response in macrophages. LPS stimulation increases HDAC2 expression in macrophages. Knockdown of HDAC2 decreases the expression of proinflammatory genes, such as IL‐12, TNF‐α and iNOS following stimulation with LPS. The adoptive transfer of HDAC2 knockdown macrophages attenuates the LPS‐triggered innate inflammatory response in vivo, and these mice are less sensitive to endotoxin shock and Escherichia coli‐induced sepsis. Mechanistically, the c‐Jun protein is the main target of HDAC2‐mediated LPS‐induced production of proinflammatory cytokines. Moreover, HDAC2 knockdown increases the expression of c‐Jun, which directly binds the promoters of proinflammatory genes and forms nuclear receptor corepressor complexes to inhibit the transcription of proinflammatory genes in macrophages. These effects are rescued by c‐Jun expression. According to the chromatin immunoprecipitation analysis, HDAC2 also selectively suppresses c‐Jun expression by directly binding to its promoter and modifying histone acetylation after LPS stimulation. Our findings define a new function and mechanism of the HDAC2/c‐Jun signaling network that regulates the LPS‐induced immune response in macrophages.
A Real-time Robotic Grasp Approach with Oriented Anchor Box

Grasp is an essential skill for robots to interact with humans and the environment. In this paper, we build a vision-based, robust and real-time robotic grasp approach with fully convolutional neural network. The main component of our approach is a grasp detection network with oriented anchor boxes as detection priors. Because the orientation of detected grasps is significant, which determines the rotation angle configuration of the gripper, we propose the Orientation Anchor Box Mechanism to regress grasp angle based on predefined assumption instead of classification or regression without any priors. With oriented anchor boxes, the grasps can be predicted more accurately and efficiently. Besides, to accelerate the network training and further improve the performance of angle regression, Angle Matching is proposed during training instead of Jaccard Index Matching. Five-fold cross validation results demonstrate that our proposed algorithm achieves an accuracy of 98.8% and 97.8% in image-wise split and object-wise split respectively, and the speed of our detection algorithm is 67 FPS with GTX 1080Ti, outperforming all the current state-of-the-art grasp detection algorithms on Cornell Dataset both in speed and accuracy. Robotic experiments demonstrate the robustness and generalization ability in unseen objects and real-world environment, with the average success rate of 90.0% and 84.2% of familiar things and unseen things respectively on Baxter robot platform.
Improving Cross-Lingual Knowledge Transferability Using Multilingual TDNN-BLSTM with Language-Dependent Pre-Final Layer

Multilingual acoustic modeling for improved automatic speech recognition (ASR) has been extensively researched. It’s widely acknowledged that the shared-hidden-layer multilingual deep neural network (SHL-MDNN) acoustic model (AM) could out-perform the conventional monolingual AM, due to its effectiveness in cross-lingual knowledge transfer. In this work, two research aspects are investigated, with the goal of improving multilingual acoustic modeling. Firstly, in the SHL-MDNN architecture, the shared hidden layer conﬁguration is replaced by a combined TDNN-BLSTM structure. Secondly, the improvement of cross-lingual knowledge transferability is achieved through adding the proposed language-dependent pre-ﬁnal layer under each network output. The pre-ﬁnal layer, rarely adopted in past works, is expected to increase nonlinear modeling capability between universal transformed features generated by shared hidden layers and language-speciﬁc outputs. Experiments are carried out with CUSENT, WSJ and RASC-863 corpora, covering Cantonese, English and Mandarin. A Can-tonese ASR task is chosen for evaluation. Experimental re-sults show that SHL-MTDNN-BLSTM achieves the best performance. The proposed additional language-dependent pre-ﬁnal layer brings moderate while consistent performance gains in various multilingual training corpora settings, thus demonstrates its effectiveness in improving cross-lingual knowledge transferability.
Motion Inpainting by an Image-Based Geodesic AMLE Method

This work presents an automatic method for optical flow inpainting. Given a video, each frame domain is endowed with a Riemannian metric based on the video pixel values. The missing optical flow is recovered by solving the Absolutely Minimizing Lipschitz Extension (AMLE) partial differential equation on the Riemannian manifold. An efficient numerical algorithm is proposed using eikonal operators for nonlinear elliptic partial differential equations on a finite graph. The choice of the metric is discussed and the method is applied to optical flow inpainting and sparse-to-dense optical flow estimation, achieving top-tier performance in terms of End-Point-Error (EPE).
Improved Pairwise Pixel-Value-Ordering for High-Fidelity Reversible Data Hiding

Pixel-value-ordering (PVO) appears as an efficient technique for high-fidelity reversible data hiding. This paper proposes a reversible data hiding scheme based on the pairwise PVO framework with improved difference equations. Both the pixel pair selection and the embedding algorithms are also streamlined. The proposed scheme uses a block classification approach based on a local complexity metric. Uniform blocks are processed using the proposed improved pairwise PVO algorithm. Slightly noisy blocks are embedded using a classic PVO scheme and noisy blocks are kept unchanged. The optimal embedding parameters for a given capacity are determined by linear programming. The proposed pairwise PVO approach outperforms other state-of-the-art schemes.
Aprovechamiento de residuos agroindustriales en Colombia

En Colombia, el Departamento Nacional de Planeacion proyecto para el 2018, el 20% en aprovechamiento de residuos que van a los rellenos e incentivar el reciclaje, sin embargo no ha logrado superar el 17%, por lo tanto es prioritario incluir mecanismos de articulacion del componente productivo y de investigacion cientifica y tecnologica. En este articulo se relacionan importantes fuentes de residuos agroindustriales en Colombia, presentando algunas alternativas de subproductos en las que se aprovechan sus caracteristicas y propiedades para obtener materiales que pueden ser utilizados en otros procesos como la industria de la construccion, biocombustibles, productos farmaceuticos, cosmeticos y nutricionales; es el caso del glicerol, residuos de papa y cafe tanto de cultivo como procesados, arroz y cana de azucar en el grupo de las gramineas, y residuos de frutas y verduras. Se concluye que Colombia debe implementar el desarrollo de diversos procesos y/o productos que sean competitivos y cumplan con las respectivas normas de calidad para incursionar en los mercados del mundo
Keypoint Matching for Non-Rigid Object via Locally Consistent Visual Pattern Mining

Keypoint matching is challenging especially when objects are non-rigid because underlying deformation is unknown and its complexity is varying. Finding visually similar (less deformed) regions between an image pair is an intuitive and promising approach, but this observation has not been fully exploited in existing methods. In this work, we propose a novel keypoint matching method for non-rigid objects via discovering such visual patterns shared in the image pair. We solve this problem by recursively finding clusters over a graph where each node represents a tentative match and each edge encodes geometric consistency between matches. Our method exploits the local property of target patterns to efficiently build the graph, and extends a local clustering algorithm to take 1-to-l keypoint matching constraints into account. Experimental results on public datasets show the flexibility and the efficiency of our approach to various deformation patterns.
Foreground Detection in Surveillance Video with Fully Convolutional Semantic Network

Foreground detection is an important part of surveillance video analysis, and also has challenges. For example, the classical methods are difficult to distinguish the foreground, which is similar to the background. In recent years, Convolutional Neural Networks (CNNs) have been widely used in image processing and achieved better performance. In this paper, we proposed an efficient deep Fully Convolutional Semantic Networks (FCSN) model for foreground detection in surveillance video. Our model aimed at learning the global differences between the video frame and the background image, and the semantic information by utilizing the pre-trained weights on semantic segmentation. In the experiment, unlike other related work, we proposed a reasonable method, which is able to avoid overfitting results to construct training data with 20 videos and test data with 6 videos on the dataset of 2014 ChangeDetection.net (CDnet 2014). Experimental results verified that our model outperforms the state-of-the-art methods in the foreground detection of surveillance video.
Transfer Learning with Neural AutoML

We reduce the computational cost of Neural AutoML with transfer learning. AutoML relieves human effort by automating the design of ML algorithms. Neural AutoML has become popular for the design of deep learning architectures, however, this method has a high computation cost. To address this we propose Transfer Neural AutoML that uses knowledge from prior tasks to speed up network design. We extend RL-based architecture search methods to support parallel training on multiple tasks and then transfer the search strategy to new tasks. On language and image classification data, Transfer Neural AutoML reduces convergence time over single-task training by over an order of magnitude on many tasks.
Deep Learning in Paralinguistic Recognition Tasks: Are Hand-crafted Features Still Relevant?

In the past, the performance of machine learning algorithms depended heavily on the representation of the data. Well-designed features therefore played a key role in speech and paralinguis-tic recognition tasks. Consequently, engineers have put a great deal of work into manually designing large and complex acoustic feature sets. With the emergence of Deep Neural Networks (DNNs), however, it is now possible to automatically infer higher abstractions from simple spectral representations or even learn directly from raw waveforms. This raises the question if (complex) hand-crafted features will still be needed in the future. We take this year’s INTERSPEECH Computational Par-alinguistic Challenge as an opportunity to approach this issue by means of two corpora – Atypical Affect and Crying. At ﬁrst, we train a Recurrent Neural Network (RNN) to evaluate the performance of several hand-crafted feature sets of varying complexity. Afterwards, we make the network do the feature engineering all on its own by preﬁxing a stack of convolutional layers. Our results show that there is no clear winner (yet). This creates room to discuss chances and limits of either approach.
Low Case Finding Among Men and Poor Viral Load Suppression Among Adolescents Are Impeding Namibia’s Ability to Achieve UNAIDS 90-90-90 Targets

Abstract Background In 2015, Namibia implemented an Acceleration Plan to address the high burden of HIV (13.0% adult prevalence and 216 311 people living with HIV [PLHIV]) and achieve the UNAIDS 90-90-90 targets by 2020. We provide an update on Namibia’s overall progress toward achieving these targets and estimate the percent reduction in HIV incidence since 2010. Methods Data sources include the 2013 Namibia Demographic and Health Survey (2013 NDHS), the national electronic patient monitoring system, and laboratory data from the Namibian Institute of Pathology. These sources were used to estimate (1) the percentage of PLHIV who know their HIV status, (2) the percentage of PLHIV on antiretroviral therapy (ART), (3) the percentage of patients on ART with suppressed viral loads, and (4) the percent reduction in HIV incidence. Results In the 2013 NDHS, knowledge of HIV status was higher among HIV-positive women 91.8% (95% confidence interval [CI], 89.4%–93.7%) than HIV-positive men 82.5% (95% CI, 78.1%–86.1%). At the end of 2016, an estimated 88.3% (95% CI, 86.3%–90.1%) of PLHIV knew their status, and 165 939 (76.7%) PLHIV were active on ART. The viral load suppression rate among those on ART was 87%, and it was highest among ≥20-year-olds (90%) and lowest among 15–19-year-olds (68%). HIV incidence has declined by 21% since 2010. Conclusions With 76.7% of PLHIV on ART and 87% of those on ART virally suppressed, Namibia is on track to achieve UNAIDS 90-90-90 targets by 2020. Innovative strategies are needed to improve HIV case identification among men and adherence to ART among youth.