Miracle-dz/UniFAR-FTUs huggingface dataset:
Supplementary Text: On Hallucinating Context and Background Pixels from a Face Mask using Multi-scale GANs

In this section, we list the layers of each generator block of our model. For both the cascaded and progressively growing (ProGAN) [8] versions of our model, the architectures of the generator block remain the same. For the cascaded model however, we use a set of four pixel shuffling [15] blocks to upscale the hallucination of a block 2x before feeding it as input to the next generator block. The architecture of each upscaling pixel shuffling blocks remains the same. The detailed layers of ‘block 8’, ‘block 16’, ‘block 32’, ‘block 64’, and ‘block 128’ layers are listed in Tables 1, 2, 3, 4, and 5 respectively. The convolution layers, residual blocks and pixel shuffling layers are indicated as ‘conv’, ’RB’, and ‘PS’ respectively in the tables. For each of these layers in the generator, we used leaky ReLU with slope of 0.1 as the activation, except for the last ‘conv’ layer where a tanh activation is used [13, 14].
Predictive and Semantic Layout Estimation for Robotic Applications in Manhattan Worlds

This paper describes an approach to automatically extracting floor plans from the kinds of incomplete measurements that could be acquired by an autonomous mobile robot. The approach proceeds by reasoning about extended structural layout surfaces which are automatically extracted from the available data. The scheme can be run in an online manner to build water tight representations of the environment. The system effectively speculates about room boundaries and free space regions which provides useful guidance to subsequent motion planning systems. Experimental results are presented on multiple data sets.
HaaS(Human Activity Analytics as a Service) Using Sensor Data of Smart Devices

recognizing human activities with sensor data of smart device is challenging task due to issues, such as data privacy, lack of dataset size, and appropriate preprocessing techniques to eliminating noise of time-series data. In this paper, we present a human activity recognition analytical models using time-series formed sensor data of smart devices such as smartphone and wearable devices. In order to understand user’s activities within an environment existing various devices, it needs to collect data generated in that specific environment and analyze it with guaranteeing high performance and accuracy for classifier.This paper makes two specific contributions: first, we present a novel system architecture for recognizing human activities. This architecture comprises of a data collection protocol, which carries sensor data of smartphones and wearable devices into a server platform; second, we show experiment results comparing with three kinds of analytical models and describe its meaning
Isolation and Functional Analysis of PISTILLATA Homolog From Magnolia wufengensis

PISTILLATA (PI) homologs are crucial regulators of flower development in angiosperms. In this study, we isolated the MAwuPI homolog from Magnolia wufengensis, a basal angiosperm belonging to the Magnoliaceae. Molecular phylogenetic analysis suggested that MAwuPI was grouped into the PI/GLO lineages of B-class MADS-box gene with the distinctive PI motif. Further expression profiling analysis showed that MAwuPI was expressed in tepals and stamens but not in juvenile leaves and carpels, similar to the spatial expression pattern of AtPI in Arabidopsis. Interestingly, MAwuPI had higher expression level in inner-tepals than in outer-tepals, whereas the M. wufengensis flower is homochlamydeous. Moreover, ectopic expression of MAwuPI in Arabidopsis pi-1 mutant emerged filament-like structures but had no obvious petals, suggesting a partial phenotypic recovery of pi-1 mutant. The features of MAwuPI in the expression pattern and gene function improved our acknowledgment of B-class genes in M. wufengensis, and contributed to the clarification of M. wufengensis evolution status and relations with other sibling species in molecular perspective.
Natalizumab may control immune checkpoint inhibitor–induced limbic encephalitis

In recent years, new therapeutic approaches that restore the ability of the immune system to attack cancer cells have dramatically improved the outcome of various malignant tumors, including melanoma and lung cancer. Ipilimumab, a monoclonal antibody that binds and inhibits cytotoxic T-lymphocyte–associated antigen 4, and nivolumab, a monoclonal antibody that blocks programmed cell-death protein 1, have been approved. As these treatments, respectively, block T-cell inhibition and potentiate the activation of T cells, they may trigger a number of neurologic immune-related adverse events,1–9 as illustrated here.
Learning to Predict Ischemic Stroke Growth on Acute CT Perfusion Data by Interpolating Low-Dimensional Shape Representations

Cerebrovascular diseases, in particular ischemic stroke, are one of the leading global causes of death in developed countries. Perfusion CT and/or MRI are ideal imaging modalities for characterizing affected ischemic tissue in the hyper-acute phase. If infarct growth over time could be predicted accurately from functional acute imaging protocols together with advanced machine-learning based image analysis, the expected benefits of treatment options could be better weighted against potential risks. The quality of the outcome prediction by convolutional neural networks (CNNs) is so far limited, which indicates that even highly complex deep learning algorithms are not fully capable of directly learning physiological principles of tissue salvation through weak supervision due to a lack of data (e.g., follow-up segmentation). In this work, we address these current shortcomings by explicitly taking into account clinical expert knowledge in the form of segmentations of the core and its surrounding penumbra in acute CT perfusion images (CTP), that are trained to be represented in a low-dimensional non-linear shape space. Employing a multi-scale CNN (U-Net) together with a convolutional auto-encoder, we predict lesion tissue probabilities for new patients. The predictions are physiologically constrained to a shape embedding that encodes a continuous progression between the core and penumbra extents. The comparison to a simple interpolation in the original voxel space and an unconstrained CNN shows that the use of such a shape space can be advantageous to predict time-dependent growth of stroke lesions on acute perfusion data, yielding a Dice score overlap of 0.46 for predictions from expert segmentations of core and penumbra. Our interpolation method models monotone infarct growth robustly on a linear time scale to automatically predict clinically plausible tissue outcomes that may serve as a basis for more clinical measures such as the expected lesion volume increase and can support the decision making on treatment options and triage.
Insomnia Patients With Subjective Short Total Sleep Time Have a Boosted Response to Cognitive Behavioral Therapy for Insomnia Despite Residual Symptoms

ABSTRACT Background: Two distinct insomnia disorder (ID) phenotypes have been proposed, distinguished on the basis of an objective total sleep time less or more than 6 hr. In particular, it has been recently reported that patients with objective short sleep duration have a blunted response to cognitive behavioral therapy for insomnia (CBT-I). The aim of this study was to investigate the differences of CBT-I response in two groups of ID patients subdivided according to total sleep time. Methods: Two hundred forty-six ID patients were subdivided into two groups, depending on their reported total sleep time (TST) assessed by sleep diaries. Patients with a TST greater than 6 hr were classified as “normal sleepers” (NS), while those with a total sleep time less than 6 hr were classified as “short sleepers” (SS). Results: The delta between Insomnia Severity Index scores and sleep efficiency at the beginning as compared to the end of the treatment was significantly higher for SS in comparison to NS, even if they still exhibit more insomnia symptoms. No difference was found between groups in terms of remitters; however, more responders were observed in the SS group in comparison to the NS group. Conclusions: Our results demonstrate that ID patients with reported short total sleep time had a beneficial response to CBT-I of greater magnitude in comparison to NS. However, these patients may still experience the presence of residual insomnia symptoms after treatment.
Adversarial Autoencoders for Generating 3D Point Clouds

Deep generative architectures provide a way to model not only images, but also complex, 3-dimensional objects, such as point clouds. In this work, we present a novel method to obtain meaningful representations of 3D shapes that can be used for clustering and reconstruction. Contrary to existing methods for 3D point cloud generation that train separate decoupled models for representation learning and generation, our approach is the first end-to-end solution that allows to simultaneously learn a latent space of representation and generate 3D shape out of it. To achieve this goal, we extend a deep Adversarial Autoencoder model (AAE) to accept 3D input and create 3D output. Thanks to our end-to-end training regime, the resulting method called 3D Adversarial Autoencoder (3dAAE) obtains either binary or continuous latent space that covers much wider portion of training data distribution, hence allowing smooth interpolation between the shapes. Finally, our extensive quantitative evaluation shows that 3dAAE provides state-of-the-art results on a set of benchmark tasks.
Importance-aware SDN Control Mechanism for Real-time Data Distribution Services

Digital twin technology is applied to various industrial domains. It provides diverse and dynamic services to support user’s demands. A large number of virtual and physical objects are connected to the network to exchange data to provide various services. Each data of digital twin services has different communication features, which may not meet all of them in a limited network. If high important services fail to operate normally due to communication problem, they can cause fatal errors. We propose a novel SDN control mechanism that can dynamically configure a network by considering the importance and communication quality of services of data. It recognizes the importance and real-time of data from OMG DDS as a communication middleware and allocates network resources to satisfy the requirements. As a result, it can show the good performance in terms of real-time, reliability and stability for various application using DDS.
Interference Analysis of Guardband NB-IoT System

Massive connectivity is one of the major goals of 5G mobile communications. The NB-IoT system achieves this goal using only a very narrow bandwidth. The NB-IoT system can also work together without affecting existing LTE systems. In this paper, we analyze the interferences of guardband NB-IoT system from the LTE system when they coexist.
Endothelial Microsomal Prostaglandin E Synthetase-1 Upregulates Vascularity and Endothelial Interleukin-1β in Deteriorative Progression of Experimental Autoimmune Encephalomyelitis

Microsomal prostaglandin E synthetase-1 (mPGES-1) is an inducible terminal enzyme for the production of prostaglandin E2 (PGE2). In experimental autoimmune encephalomyelitis (EAE), an animal model of multiple sclerosis, mPGES-1 is induced in vascular endothelial cells (VECs) around inflammatory foci and facilitates inflammation, demyelination, and paralysis. Therefore, we investigated the role of CD31-positive VECs in mPGES-1-mediated EAE aggravation using immunohistochemical analysis and imaging of wild-type (wt) and mPGES-1-deficient (mPGES-1−/−) mice. We demonstrated that EAE induction facilitated vascularity in inflammatory lesions in the spinal cord, and this was significantly higher in wt mice than in mPGES-1−/− mice. In addition, endothelial interleukin-1β (IL-1β) production was significantly higher in wt mice than in mPGES-1−/− mice. Moreover, endothelial PGE2 receptors (E-prostanoid (EP) receptors EP1–4) were expressed after EAE induction, and IL-1β was induced in EP receptor-positive VECs. Furthermore, IL-1 receptor 1 expression on VECs was increased upon EAE induction. Thus, increased vascularity is one mechanism involved in EAE aggravation induced by mPGES-1. Furthermore, mPGES-1 facilitated the autocrine function of VECs upon EP receptor induction and IL-1β production, modulating mPGES-1 induction in EAE.
Movement and Gesture Recognition Using Deep Learning and Wearable-sensor Technology

Pattern recognition of time-series signals for movement and gesture analysis plays an important role in many fields as diverse as healthcare, astronomy, industry and entertainment. As a new technique in recent years, Deep Learning (DL) has made tremendous progress in computer vision and Natural Language Processing (NLP), but largely unexplored on its performance for movement and gesture recognition from noisy multi-channel sensor signals. To tackle this problem, this study was undertaken to classify diverse movements and gestures using four developed DL models: a 1-D Convolutional neural network (1-D CNN), a Recurrent neural network model with Long Short Term Memory (LSTM), a basic hybrid model containing one convolutional layer and one recurrent layer (C-RNN), and an advanced hybrid model containing three convolutional layers and three recurrent layers (3+3 C-RNN). The models will be applied on three different databases (DB) where the performances of models were compared. DB1 is the HCL dataset which includes 6 human daily activities of 30 subjects based on accelerometer and gyroscope signals. DB2 and DB3 are both based on the surface electromyography (sEMG) signal for 17 diverse movements. The evaluation and discussion for the improvements and limitations of the models were made according to the result.
The impact of a multilevel childhood obesity prevention intervention on healthful food acquisition, preparation, and fruit and vegetable consumption on African-American adult caregivers

Abstract Objective To evaluate the secondary impact of a multilevel, child-focused, obesity intervention on food-related behaviours (acquisition, preparation, fruit and vegetable (FV) consumption) on youths’ primary caregivers. Design B’More Healthy Communities for Kids (BHCK) group-randomized controlled trial promoted access to healthy foods and food-related behaviours through wholesaler and small store strategies, peer mentor-led nutrition education aimed at youths, and social media and text messaging targeting their adult caregivers. Measures included caregivers’ (n 516) self-reported household food acquisition frequency for FV, snacks and grocery items over 30 d, and usual FV consumption in a sub-sample of 226 caregivers via the NCI FV Screener. Hierarchical models assessed average treatment effects (ATE). Treatment-on-the-treated-effect (TTE) analyses evaluated correlation between behavioural change and exposure to BHCK. Exposure scores at post-assessment were based on self-reported viewing of BHCK materials and participating in activities. Setting Thirty Baltimore City low-income neighbourhoods, USA. Participants Adult caregivers of youths aged 9–15 years. Results Of caregivers, 90·89 % were female; mean age 39·31 (sd 9·31) years. Baseline mean (sd) intake (servings/d) was 1·30 (1·69) fruits and 1·35 (1·05) vegetables. In ATE, no significant intervention effect was found on caregivers’ food-related behaviours. In TTE, each point increase in BHCK exposure score (range: 0–6·9) increased caregivers’ daily fruit consumption by 0·2 servings (0·24 (se 0·11); 95 % CI 0·04, 0·47). Caregivers reporting greater social media exposure tripled their daily fruit intake (3·16 (se 0·92); 95 % CI 1·33, 4·99) and increased their frequency of unhealthy food purchasing v. baseline. Conclusions Child-focused community-based nutrition interventions may also benefit family members’ fruit intake. Child-focused interventions should involve adult caregivers and intervention effects on family members should be assessed. Future multilevel studies should consider using social media to improve reach and engage caregiver participants.
The Effectiveness of Home Services in Taiwan: A People-Centered Approach

Among available home and community-based services in Taiwan, there should be a focus on providing people with people-centered, value-based services. This study aims to follow up the people-centered health outcomes of care recipients and to examine the factors associated with to feedback for policy and practice in long-term care. A total of 9889 persons from the long-term care dataset were followed up for two years (T0–T4). The Cox Proportional Hazard Regression analyses to analyze mortality and the mixed effect models for health outcomes were used. Three classes among the care recipients were identified. Health profiles (HR = 1.46 and 2.56 for FI and FD compared with RI, p < 0.001), subsidy gap (HR = 1.01), and living status (HR = 0.88 for those living with spouse only) had a significant impact on mortality. The overall dropout rate was 52.3% at two years, and the health profiles at baseline significantly influenced the health outcome’s change. The health heterogeneity matters and influences subsequent outcomes. To reach the goal of the HCBS, regular and exact monitoring of care recipients is crucial, while feedback regarding health outcomes and a greater focus on providing person-centered and responsive services in the community are also required.
Artificial Intelligence in 5G Technology: A Survey

A fully operative and efficient 5G network cannot be complete without the inclusion of artificial intelligence (AI) routines. Existing 4G networks with all-IP (Internet Protocol) broadband connectivity are based on a reactive conception, leading to a poorly efficiency of the spectrum. AI and its subcategories like machine learning and deep learning have been evolving as a discipline, to the point that nowadays this mechanism allows fifth-generation (5G) wireless networks to be predictive and proactive, which is essential in making the 5G vision conceivable. This paper is motivated by the vision of intelligent base stations making decisions by themselves, mobile devices creating dynamically-adaptable clusters based on learned data rather than pre-established and fixed rules, that will take us to a improve in the efficiency, latency, and reliability of the current and real-time network applications in general. An exploration of the potential of AI-based solution approaches in the context of 5G mobile and wireless communications technology is presented, evaluating the different challenges and open issues for future research.
CONSTANS delays Arabidopsis flowering under short days

Long days (LD) promote flowering of Arabidopsis thaliana compared with short days (SD) by activating the photoperiodic pathway. Here we show that growth under very-SD (3 h) or darkness (on sucrose) also accelerates flowering on a biological scale, indicating that SD actively repress flowering compared with very-SD. CONSTANS (CO) repressed flowering under SD, and the early flowering of co under SD required FLOWERING LOCUS T (FT). FT was expressed at a basal level in the leaves under SD, but these levels were not enhanced in co. This indicates that the action of CO in A. thaliana is not the mirror image of the action of its homologue in rice. In the apex, CO enhanced the expression of TERMINAL FLOWER 1 (TFL1) around the time when FT expression is important to promote flowering. Under SD, the tfl1 mutation was epistatic to co and in turn ft was epistatic to tfl1. These observations are consistent with the long-standing but not demonstrated model where CO can inhibit FT induction of flowering by affecting TFL1 expression.
Nitroxide-radicals–modified gold nanorods for in vivo CT/MRI-guided photothermal cancer therapy

Purpose This article presents a report of the synthesis, characterization, and biomedical application of nitroxide-radicals–modified gold nanorods (Au-TEMPO NRs) for imaging-guided photothermal cancer therapy. Patients and methods Au nanorods were synthesized through seed-mediated growth method, 4-Amino-TEMPO was added and the reaction proceeded under magnetic stirring. Results With a mean length of 39.2 nm and an average aspect ratio of approximately 3.85, Au-TEMPO NRs showed good photothermal ability when they were irradiated by 808-nm laser. Au-TEMPO NRs could be stored in PBS for more than 1 month, showed no cytotoxicity against both tumor and normal cells at a concentration of up to 3 mg/mL, and functioned as a dual-mode contrast agent for CT/magnetic resonance (MR) imaging in vitro and in vivo, due to their high X-ray attenuation of Au and good r1 relaxivity of nitroxide radicals. Further, they had a long retention time (~4 hours) in the main organs, which enabled a long CT/MR imaging time window for diagnosis. Bio-distribution results revealed that these Au-TEMPO NRs passively aggregated in the liver and spleen. After irradiation by 808-nm laser, Au-TEMPO NRs could ablate the solid tumor in 4T1 tumor-bearing mice, which implied they were a potential theranostic agent for dual-mode imaging and photothermal cancer therapy. Conclusion This type of Au-TEMPO NRs with the abilities of CT/MR imaging and photothermal therapy, can play an active role in imaging-guided photothermal cancer therapy.
DeepConsensus: using the consensus of features from multiple layers to attain robust image classification

We consider a classifier whose test set is exposed to various perturbations that are not present in the training set. These test samples still contain enough features to map them to the same class as their unperturbed counterpart. Current architectures exhibit rapid degradation of accuracy when trained on standard datasets but then used to classify perturbed samples of that data. To address this, we present a novel architecture named DeepConsensus that significantly improves generalization to these test-time perturbations. Our key insight is that deep neural networks should directly consider summaries of low and high level features when making classifications. Existing convolutional neural networks can be augmented with DeepConsensus, leading to improved resistance against large and small perturbations on MNIST, EMNIST, FashionMNIST, CIFAR10 and SVHN datasets.
On Human Robot Interaction using Multiple Modes

Humanoid robots have apparently similar body structure like human beings. Due to their technical design, they are sharing the same workspace with humans. They are placed to clean things, to assist old age people, to entertain us and most importantly to serve us. To be acceptable in the household, they must have higher level of intelligence than industrial robots and they must be social and capable of interacting people around it, who are not supposed to be robot specialist. All these come under the field of human robot interaction (HRI). There are various modes like speech, gesture, behavior etc. through which human can interact with robots. To solve all these challenges, a multimodel technique has been introduced where gesture as well as speech is used as a mode of interaction.