Miracle-dz/UniFAR-FTUs huggingface dataset:
Single Image Super Resolution Based on Deep Residual Network via Lateral Modules

Recently, convolutional neural networks have demonstrated high-quality reconstruction for single image super resolution (SISR). In this paper, we propose a Deep Residual Network via lateral modules (DRNLM), DRNLM is the structure with lateral modules, progressive and symmetric residual blocks (convolutional residual blocks and deconvolutional residual blocks). First, DRNLM introduces lateral modules, which are used to transmit low-level features (coarse residue) into high-level features (fine residue) effectively, thus finer residue can be obtained for better image reconstruction. Second, considering more channels can stack more details, progressive channels that vary from 64 to 256 are utilized in DRNLM through residual blocks. Third, symmetric residual blocks have same dimensions of input and output, which can ensure the gradient ranging within certain limits when the network goes deeper. Extensive experiments demonstrate that the proposed method outperforms the existing methods in accuracy and visual impression.
A Fast Intra Coding Algorithm for Spatial Scalability in SHVC

Scalable High Efficiency Video Coding (SHVC) provides high compression efficiency at the expense of considerable computational complexity. In this paper, a fast algorithm is proposed to reduce the computational complexity of SHVC intra coding. The coding depth information, texture complexity, and spatio-temporal correlation are jointly used to achieve a faster depth decision process. The mode dependency between the base layer and the enhancement layer is combined with the temporal correlation to simplify the mode decision process. Experimental results demonstrate that the proposed scheme saves encoding time by up to 59% compared with the SHM12.0 encoder with negligible degradation in Rate Distortion (RD).
Biotoxicological Analyses of Trimeroside from Baccharis trimera Using a Battery of In Vitro Test Systems

The use in folk medicine of Baccharis trimera and recent studies on DNA damage by oxidative stress mechanisms have motivated this study. We investigated the biotoxicological effects of trimeroside from this plant. Aqueous extract from aerial parts of B. trimera was fractioned by flash chromatography for further isolation by thin-layer chromatography. The novel nor-monoterpene glycoside, trimeroside, and three flavonoids, cirsimaritin, luteolin and quercetin, were isolated. The genotoxic and mutagenic potential of trimeroside was determined by Salmonella/microsome (TA98 and TA100), comet assay, and cytokinesis-block micronucleus cytome assay (CBMN-cyt) in HepG2 cells. We also screened trimeroside into different human tumoral cell lines by sulforhodamine B (SRB) assay. Mutagenicity was detected in TA100 strain with metabolic activation. Genotoxic effects were not observed in HepG2 by comet assay. However, a decrease in the nuclear index division in the 2.0 mg·mL−1 concentration and an increase of nucleoplasmic bridges in the 1.5 mg·mL−1 concentration were detected by CBMN-cyt assay indicating cytotoxic and mutagenic effects. In SRB assay, trimeroside showed weak antiproliferative activity against the cell lines.
Automatic Speech Recognition with Articulatory Information and a Unified Dictionary for Hindi, Marathi, Bengali and Oriya

Despite the continuous progress of Automatic Speech recognition (ASR) technologies, these systems for Indian languages are still in infancy stage due to a multitude of challenges involved, including resource deﬁciency. This paper addressed this challenge with four Indian languages, Hindi, Marathi, Bengali, and Oriya by integrating articulatory information into acoustic features, thereby compensating the low resource property of these languages. Articulatory movements were recorded during speech production using an electromagnetic articulograph and trained together with acoustic features to build automatic speech recognizers for these languages. Both speaker-dependent and - independent recognition experiments were conducted by adopting three ASR models: Gaussian Mixture Model (GMM)- Hidden Markov Model (HMM), Deep Neural Network (DNN)- HMM, and Long Short Term Memory recurrent neural network (LSTM)-HMM. A cross-language similarity was discerned in both acoustic and articulatory domains in the pairs of Oriya-Bengali and Hindi-Marathi. Based on these observations, a multi-lingual, multi-modal speech recognizer was built by constructing a uniﬁed dictionary consisting of common and unique phonemes of all the four languages, which signiﬁcantly reduced the phoneme error rates.
Semi-Supervised End-to-End Speech Recognition

We propose a novel semi-supervised method for end-to-end automatic speech recognition (ASR). It can exploit large unpaired speech and text datasets, which require much less human effort to create paired speech-to-text datasets. Our semi-supervised method targets the extraction of an intermediate representation between speech and text data using a shared encoder network. Autoencoding of text data with this shared encoder improves the feature extraction of text data as well as that of speech data when the intermediate representations of speech and text are similar to each other as an inter-domain feature. In other words, by combining speech-to-text and text-to-text mappings through the shared network, we can improve speech-to-text mapping by learning to reconstruct the unpaired text data in a semi-supervised end-to-end manner. We investigate how to design suitable inter-domain loss, which minimizes the dissimilarity between the encoded speech and text sequences, which originally belong to quite different domains. The experimental re-sults we obtained with our proposed semi-supervised training shows a larger character error rate reduction from 15.8% to 14.4% than a conventional language model integration on the Wall Street Journal dataset.
Blind Color Deconvolution of Histopathological Images Using a Variational Bayesian Approach

Whole-slide histological images are routinely used by medical doctors in diagnosis. Most of these images are stained with the very common and inexpensive hematoxylin and eosin dyes. Slide stain separation and color normalization are crucial steps within the digital pathology workflow which require a previous color deconvolution step. This image processing task is not easy, especially when working with images taken from different microscopes and slides stained in different laboratories. In this paper, based on Variational Bayes inference, an efficient new blind color deconvolution method is proposed. The new model takes into account both spatial relations among image pixels and similarity to a given reference color-vector matrix. A comparison with classical and current state-of-the-art color deconvolution algorithms, using real images with known ground truth hematoxylin and eosin values, has been carried out. This comparison has demonstrated the superiority of the proposed approach.
Unsupervised Learning for Forecasting Action Representations

Most of previous works on future forecasting require a mass of videos with frame-level labels which would probably limit their application, since labelling video frame requires much tremendous efforts. In this paper, we present a unsupervised learning framework to anticipate the future representation by utilizing temporal historical information and train the anticipating capacity only using unlabelled videos. Compared to existing methods that predict the future representation from a static image, our proposed model presents a novel temporal context learning model for estimating the temporal evolution tendency by compacting outputs of all time steps in a LST-M. We evaluate the proposed model on two different activity datasets, TV Human Interaction dataset and THUMOS Validation and Test sets. We have demonstrated the effectiveness of our model in anticipating future representation task.
Characteristics of Prescription-Opioid-Impaired and Other Substance-Impaired Drivers in Rural Appalachian Kentucky

ABSTRACT Previous studies have highlighted the prescription opioid epidemic in rural Appalachia and its associated risk behaviors; however, no studies have examined prescription-opioid-impaired driving as a consequence of this epidemic. The purpose of the present study was to describe prescription-opioid-impaired drivers in rural Appalachian Kentucky and examine how they are similar to and different from other substance-impaired drivers from the region. A sample of convicted DUI offenders from rural Appalachian Kentucky completed a confidential research interview focused on their substance use, mental health, and criminal activity. Prescription-opioid-impaired drivers (n = 33) were compared to other drug-impaired drivers (n = 29) and to alcohol-only-impaired drivers (n = 44). Overall, prescription-opioid-impaired drivers had a similar prevalence of illicit substance use and criminal activity, including impaired driving frequency, to other drug-impaired drivers, but had a higher prevalence of illicit substance use and more frequent impaired driving when compared to alcohol-only-impaired drivers. Study implications include the importance of comprehensive substance abuse assessment and treatment for DUI offenders and the need for tailored interventions for prescription-opioid-impaired and other drug-impaired drivers.
Contemporary surgical and non-surgical management of Peyronie’s disease

Numerous treatments have been proposed for Peyronie’s disease (PD). As the evidence base has expanded, the field of operative and non-operative options for patients has narrowed. Collagenase clostridium hystolyticum (CCH) injection now comprises the medical option, and surgical possibilities entail penile plication, plaque incision/excision and grafting, and prosthesis implantation. Still, questions abound regarding the optimal approach and indication for each of these treatments. We conducted a review of literature exploring the contemporary management of PD with a particular focus on work since the last American Urologic Association’s (AUA) guidelines update for PD. Recent results and discussion indicate trends toward minimal invasiveness, toward a more holistic approach to the PD patient, and away from algorithmic management, galvanized, in part, by data challenging long-held beliefs.
Rapid Style Adaptation Using Residual Error Embedding for Expressive Speech Synthesis

Synthesizing expressive speech with appropriate prosodic variations, e.g., various styles, still has much room for improvement. Previous methods have explored to use manual annotations as conditioning attributes to provide variation information. However, the related training data are expensive to obtain and the annotated style codes can be ambiguous and unreliable. In this paper, we explore utilizing the residual error as conditioning attributes. The residual error is the difference between the prediction of a trained average model and the ground truth. We encode the residual error into a style embedding via a neural network-based error encoder. The style embedding is then fed to the target synthesis model to provide information for modeling various style distributions more accurately. The average model and the error encoder are jointly optimized with the target synthesis model. Our proposed method has two advantages: 1) the embedding is automatically learned with no need of manual style annotations, which helps overcome data sparsity and ambiguity limitations; 2) For any unseen audio utterance, the style embedding can be efﬁciently generated. This enables rapid adaptation to the desired style to be achieved with only a single adaptation utterance. Experimental results show that our proposed method outperforms the baseline model in both speech quality and style similarity
Depth Estimation Network for Dual Defocused Images with Different Depth-of-Field

In this work, we propose an algorithm to estimate the depth map of a scene using defocused images. In particular, the depth map is estimated using two defocused images with different depth-of-field for the same scene. Similar to the approach of the general depth from defocus (DFD), the proposed algorithm obtains the depth information from the blurredness of the object. Moreover, our proposed algorithm dramatically improves the accuracy by using both the shallow and deep depth-of-field images, simultaneously. Especially, we propose a novel depth estimation network for dual defocused images using convolutional neural network (CNN). We evaluate our proposed network on the NYU-v2 dataset and show superior performance compared to the existing techniques.
Rapid palatal expansion (RPE): Meta-analysis of long-term effects.

OBJECTS Rapid palatal expansion (RPE) is a widespread orthopaedic procedure used in several clinical situations. The aim of the study was to provide a systematic review and meta-analysis of transverse intercanine and intermolar width changes after RPE and long-term effects. SETTING AND SAMPLE POPULATION A systematic literature search was conducted until January 2018 on original studies with RPE treatment. MATERIAL AND METHODS Intercanine and intermolar distance were measured at the start of RPE (T1, median age 9.2 years) and at the end of treatment (T2). The long-term evaluation was conducted on a subset of articles with 5-year follow-up (T3). RESULTS Eighteen studies T1-T2 and 5 studies T3 were selected. Regarding the intercanine distance, the RPE resulted in a gain of space T1-T2 of 3.73 mm with a significant heterogeneity (P < 0.0001; I2 = 94.21%) and a relapse T2-T3 of -0.81 mm with a significant heterogeneity (P < 0.0001; I2 = 95.8%); the overall gain of space was 2.91 mm. Regarding the intermolar distance, the RPE resulted in a gain of space T1-T2 of 4.85 mm with a significant heterogeneity (P < 0.0001; I2 = 92.41%) and a relapse T2-T3 of -0.47 mm with a significant heterogeneity (P < 0.0001; I2 = 87.5%); the overall gain of space was 4.38 mm. Meta-regression analysis showed that at younger age, the amount of anterior expansion is similar to posterior. With increasing age, the anterior opening diminishes significantly more than the posterior, being half of the posterior at 15 years. CONCLUSION Because long-term results were obtained with simultaneously fixed appliances and long upper retention, RPE should be carefully considered before using.
Semantic Lattice Processing in Contextual Automatic Speech Recognition for Google Assistant

Recent interest in intelligent assistants has increased demand for Automatic Speech Recognition (ASR) systems that can utilize contextual information to adapt to the user’s preferences or the current device state. For example, a user might be more likely to refer to their favorite songs when giving a “music playing” command or request to watch a movie starring a particular favorite actor when giving a “movie playing” command. Similarly, when a device is in a “music playing” state, a user is more likely to give volume control commands. In this paper, we explore using semantic information inside the ASR word lattice by employing Named Entity Recognition (NER) to identify and boost contextually relevant paths in order to improve speech recognition accuracy. We use broad semantic classes comprising millions of entities, such as songs and musical artists, to tag relevant semantic entities in the lattice. We show that our method reduces Word Error Rate (WER) by 12.0% relative on a Google Assistant “media playing” commands test set, while not affecting WER on a test set containing commands unrelated to media.
Design, Simulation, and Testing of a Flexible Actuated Spine for Quadruped Robots

Walking quadruped robots face challenges in positioning their feet and lifting their legs during gait cycles over uneven terrain. The robot Laika is under development as a quadruped with a flexible, actuated spine designed to assist with foot movement and balance during these gaits. This paper presents the first set of hardware designs for the spine of Laika, a physical prototype of those designs, and tests in both hardware and simulations that show the prototype's capabilities. Laika's spine is a tensegrity structure, used for its advantages with weight and force distribution, and represents the first working prototype of a tensegrity spine for a quadruped robot. The spine bends by adjusting the lengths of the cables that separate its vertebrae, and twists using an actuated rotating vertebra at its center. The current prototype of Laika has stiff legs attached to the spine, and is used as a test setup for evaluation of the spine itself. This work shows the advantages of Laika's spine by demonstrating the spine lifting each of the robot's four feet, both as a form of balancing and as a precursor for a walking gait. These foot motions, using specific combinations of bending and rotation movements of the spine, are measured in both simulation and hardware experiments. Hardware data are used to calibrate the simulations, such that the simulations can be used for control of balancing or gait cycles in the future. Future work will attach actuated legs to Laika's spine, and examine balancing and gait cycles when combined with leg movements.
Fast Derivation of Cross-lingual Document Vectors from Self-attentive Neural Machine Translation Model

A universal cross-lingual representation of documents, which can capture the underlying semantics is very useful in many natural language processing tasks. In this paper, we develop a new document vectorization method which effectively selects the most salient sequential patterns from the inputs to create document vectors via a self-attention mechanism using a neural machine translation (NMT) model. The model used by our method can be trained with parallel corpora that are unrelated to the task at hand. During testing, our method will take a monolingual document and convert it into a “ N eural machine T ranslation framework based cross-lingual D ocument V ector” (NTDV). NTDV has two comparative advantages. Firstly, the NTDV can be produced by the forward-pass of the encoder in the NMT, and the process is very fast and does not require any training/optimization. Secondly, our model can be conveniently adapted from a pair of existing attention-based NMT models, and the training requirement on parallel corpus can be reduced signiﬁcantly. In a cross-lingual document classiﬁcation task, our NTDV embeddings surpass the previous state-of-the-art performance in the English-to-German classiﬁcation test, and, to our best knowledge, it also achieves the best performance among the fast decoding methods in the German-to-English classiﬁcation test.
Acoustic Modeling Using Adversarially Trained Variational Recurrent Neural Network for Speech Synthesis

In this paper, we propose a variational recurrent neural network (VRNN) based method for modeling and generating speech parameter sequences. In recent years, the performance of speech synthesis systems has been improved over conventional techniques thanks to deep learning-based acoustic models. Among the popular deep learning techniques, recurrent neural networks (RNNs) has been successful in modeling time-dependent sequential data efﬁciently. However, due to the deterministic nature of RNNs prediction, such models do not reﬂect the full complexity of highly structured data, like natural speech. In this regard, we propose adversarially trained variational recurrent neural network (AdVRNN) which use VRNN to better represent the variability of natural speech for acoustic modeling in speech synthesis. Also, we apply adversarial learning scheme in training AdVRNN to overcome oversmoothing problem. We conducted comparative experiments for the proposed VRNN with the conventional gated recurrent unit which is one of RNNs, for speech synthesis system. It is shown that the proposed AdVRNN based method performed better than the conventional GRU technique.
Features for medically serious suicide attempters who do not have a strong intent to die: a cross-sectional study in rural China

Objective Previous studies have implied that there were many Chinese suicide attempters who did not want to die by suicide. In the current study, we explored the factors which were associated with low levels of suicide intent. We also examined features for medically serious suicide attempters who do not have a strong intent to die in rural china. Design Cross-sectional study. Setting The interviews occurred between May 2012 and July 2013 in 13 rural counties in Shandong and Hunan provinces, China. Participants Subjects were 791 medically serious suicide attempters whose injury and wounds were so serious they required hospitalisation or immediate medical care. Results The results supported that less years of education (β=−0.11, p=0.037), religious beliefs (β=1.20, p=0.005), living alone (β=1.92, p=0.017), negative life events (β=0.29, p=0.003), low levels of impulsivity (β=−0.10, p=0.013) and mental disorders (β=2.82, p<0.001) were associated with higher levels of suicide intent. Conclusion Results imply that there are some medically serious suicide attempters with a higher education and/or exhibit impulsivity who do not want to die by suicide. These findings can inform practice to prevent suicide in rural China.
Spoken Keyword Detection Using Joint DTW-CNN

A method to detect spoken keywords in a given speech utterance is proposed, called as joint Dynamic Time Warping (DTW)- Convolution Neural Network (CNN). It is a combination of DTW approach with a strong classiﬁer like CNN. Both these methods have independently shown signiﬁcant results in solving problems related to optimal sequence alignment and object recognition, respectively. The proposed method modiﬁes the original DTW formulation and converts the warping matrix into a gray scale image. A CNN is trained on these images to classify the presence or absence of keyword by identifying the texture of warping matrix. The TIMIT corpus has been used for conducting experiments and our method shows signiﬁcant improvement over other existing techniques.
Deciphering the absorption profile and interaction of multi-components of Zhi-Zi-Da-Huang decoction based on in vitro–in silico–in vivo integrated strategy

Abstract Zhi-Zi-Da-Huang decoction (ZZDHD) has been acknowledged with striking therapeutic effects for hepatobiliary disorders in the history of China. As decoctions are usually administrated orally, intestinal absorption, the prerequisite task of exerting therapeutic effects, is of utmost significance for screening potential active compounds and understanding the mechanism of drug action. In this work, an in vitro–in silico–in vivo strategy based on HPLC-DAD-ESI-TOF/MS was adopted for precisely profiling the intestinal absorption of ZZDHD, which integrated information obtained from rat everted gut sac model, octanol–water partition model, in silico prediction and in vivo experimental data. Besides, 34 main absorbed ingredients were selected as chemical markers to investigate the compatible interaction of the decoction on absorption level using rat everted gut sac experiment. In total, 106 compounds of ZZDHD were speculated as potential absorptive. Among them, 90 constituents predicted absorbable in at least two experimental models were finally recognized as intestinal absorbable ingredients. In addition, the absorption level of iridoids, terpenoids and flavonoid glycosides were found improved and the absorption of catechins and anthraquinones were inhibited after prescription compatibility. Taken together, this study presents a reliable strategy for evaluating intestinal absorption of herbal medicines and offers a reference for the rationality of herbal compatibility and the modernization of traditional Chinese medicine (TCM).