Miracle-dz/UniFAR-FTUs huggingface dataset:
LookinGood

Motivated by augmented and virtual reality applications such as telepresence, there has been a recent focus in real-time performance capture of humans under motion. However, given the real-time constraint, these systems often suffer from artifacts in geometry and texture such as holes and noise in the final rendering, poor lighting, and low-resolution textures. We take the novel approach to augment such real-time performance capture systems with a deep architecture that takes a rendering from an arbitrary viewpoint, and jointly performs completion, super resolution, and denoising of the imagery in real-time. We call this approach neural (re-)rendering, and our live system "LookinGood". Our deep architecture is trained to produce high resolution and high quality images from a coarse rendering in real-time. First, we propose a self-supervised training method that does not require manual ground-truth annotation. We contribute a specialized reconstruction error that uses semantic information to focus on relevant parts of the subject, e.g. the face. We also introduce a salient reweighing scheme of the loss function that is able to discard outliers. We specifically design the system for virtual and augmented reality headsets where the consistency between the left and right eye plays a crucial role in the final user experience. Finally, we generate temporally stable results by explicitly minimizing the difference between two consecutive frames. We tested the proposed system in two different scenarios: one involving a single RGB-D sensor, and upper body reconstruction of an actor, the second consisting of full body 360° capture. Through extensive experimentation, we demonstrate how our system generalizes across unseen sequences and subjects.
Evaluation of the internal fixation effect of nano-calcium-deficient hydroxyapatite/poly-amino acid composite screws for intraarticular fractures in rabbits

Objective To evaluate the internal fixation effect of nano-calcium-deficient hydroxyapatite/poly-amino acid (n-CDHA/PAA) composite screws in the intraarticular fracture model. Materials and methods A total of 35 New Zealand White rabbits were used in a bilateral femoral intercondylar fracture model and randomly divided into two groups. n-CDHA/PAA screws were used in the experimental group, and medical metal screws were used in the control group. The fracture condition, range of motion, and the screw push-out strength were assessed, and an arthroscopic examination of knee joint was performed at 4, 8, and 12 weeks after surgery. The biodegradation of the n-CDHA/PAA screws in vivo was tested through weighing, and changes in screw structure were assessed by X-ray diffraction at 12 weeks after surgery. Results The general situation of all animals was good and showed no incision infection and dehiscence after surgery. X-ray scanning showed that significant callus growth was present in both groups at 4 weeks after surgery, and there was no significant difference (P>0.05) in the Lane-Sandhu score between the experimental and control groups at all time points after surgery. There were no statistically significant differences (P>0.05) in the range of motion and Oswestry Arthroscopy Score of arthroscopic examination of the knee joints between the two groups. The screw push-out strength of the control group was stronger than that of the experimental group at 4 weeks after surgery (P<0.05), but after that, there was no significant difference between the groups (P>0.05). The degradation tests showed that the n-CDHA/PAA screws degraded gradually after implantation, and the weight loss rate was approximately 16% at 12 weeks after surgery. The X-ray diffraction results showed that the crystal structure of the outer surface of the n-CDHA/PAA screw has changed at 12 weeks after surgery. Conclusion The n-CDHA/PAA screw is an effective and safe implant as a potential internal fixation device for an intercondylar fracture of the femur, and its internal fixation effect was similar to that of medical metal screw.
Absorption and distribution of lupeol in CD-1 mice evaluated by UPLC-APCI+ -MS/MS.

Lupeol is a dietary triterpene that shows limited water solubility, which affects its bioavailability. It is well known that poor oral bioavailability is one of the major causes of therapeutic variability. Lupeol has been reported to have multiple biological activities; however, there are no reports about its bioavailability. Therefore, the objective of this research was to evaluate the systemic bioavailability of lupeol. An experimental strategy with three groups of female CD-1 strain mice was proposed (control, olive oil and lupeol in olive oil), at six experimental times (0.5, 2, 4, 8, 12 and 24 h) with four animals per experimental point. Mice were sacrificed for organs, urine, feces and blood collection. Lupeol was extracted from samples and analyzed by UPLC-APCI+ -MS/MS, obtaining the pharmacokinetics parameters time to peak concentration 6.444 ± 0.851 h and peak concentration 8.071 ± 2.930 μg/mL. Study of direct digestion and absorption in various organs showed important concentrations of lupeol at earlier post-administration times (stomach, 137.25 ± 19.94 ng/mg and small intestine, 99.00 ± 12.99 ng/mg). The main excretion route was fecal, with a peak at 12 h post-administration (163.28 ± 9.83 μg/mg). Absorption of lupeol by the animals was better than expected despite its nonpolar nature (extent of absorption F = 0.645 ± 0.0581).
Benralizumab, an add-on treatment for severe eosinophilic asthma: evaluation of exacerbations, emergency department visits, lung function, and oral corticosteroid use

There are now multiple monoclonal antibodies targeting different inflammatory pathways of severe asthma. Benralizumab is a recently approved monoclonal antibody indicated for the treatment of severe eosinophilic asthma by targeting a subunit of the IL-5 receptor. Treatment with benralizumab results in significant reductions of blood and tissue eosinophils. Early studies report that this therapy has an adequate safety profile, and this was confirmed in later trials. Phase III studies have shown that benralizumab is effective in reducing the rate of exacerbations and improving asthma symptoms and quality of life in patients with severe eosinophilic asthma. Additionally, treatment with benralizumab has resulted in important reductions in the use of chronic oral corticosteroids. In this review, we evaluate the evidence up to date on the efficacy of benralizumab in severe eosinophilic asthma and explore the implications of this therapy in the ever-growing landscape of therapies for severe asthma.
Fast HARQ Over Finite Blocklength Codes: A Technique for Low-Latency Reliable Communication

This paper studies the performance of delay-constrained hybrid automatic repeat request (HARQ) protocols. Particularly, we propose a fast HARQ protocol where, to increase the end-to-end throughput, some HARQ feedback signals and successive message decodings are omitted. Considering quasi-static channels and a bursty communication model, we derive closed-form expressions for the message decoding probabilities as well as the throughput, the expected delay, and the error probability of the HARQ setups. The analysis is based on the recent results on the achievable rates of finite-length codes and shows the effect of the codeword length on the system performance. Moreover, we evaluate the effect of various parameters, such as imperfect channel estimation and hardware on the system performance. As demonstrated, the proposed fast HARQ protocol reduces the packet transmission delay considerably compared with the state-of-the-art HARQ schemes. For example, with typical message decoding delay profiles and a maximum of 2,…, 5 transmission rounds, the proposed fast HARQ protocol can improve the expected delay compared with standard HARQ by 27%, 42%, 52%, and 60%, respectively, independently of the code rate/fading model.
Factors affecting Observers’ Accuracy when Assessing Credibility: The Effect of the Interaction between Media, Senders’ Competence and Veracity

Abstract The present experiment examined how the interaction between senders’ communicative competence, veracity and the medium through which judgments were made affected observers’ accuracy. Stimuli were obtained from a previous study. Observers (N = 220) judged the truthfulness of statements provided by a good truth teller, a good liar, a bad truth teller, and a bad liar presented either via an audio-only, video-only, audio-video, or transcript format. Log-linear analyses showed that the data were best explained via the saturated model, therefore indicating that all the four variables interacted, G2(0) = 0, p = 1, Q2 = 1. Follow-up analyses showed that the good liar and bad liar were best evaluated via the transcript (z = 2.5) and the audio-only medium (z = 3.9), respectively. Both the good truth teller and the bad truth teller were best assessed through the audio-video medium (z = 2.1, good truth teller, z = 3.4, bad truth teller). Results indicated that all the factors interacted and played a joint role on observers’ accuracy. Difficulties and suggestions for choosing the right medium are presented.
ImageNet/ResNet-50 Training in 224 Seconds

Scaling the distributed deep learning to a massive GPU cluster level is challenging due to the instability of the large mini-batch training and the overhead of the gradient synchronization. We address the instability of the large mini-batch training with batch size control. We address the overhead of the gradient synchronization with 2D-Torus all-reduce. Specifically, 2D-Torus all-reduce arranges GPUs in a logical 2D grid and performs a series of collective operation in different orientations. These two techniques are implemented with Neural Network Libraries (NNL). We have successfully trained ImageNet/ResNet-50 in 224 seconds without significant accuracy loss on ABCI cluster.
MMALFM

Personalized rating prediction is an important research problem in recommender systems. Although the latent factor model (e.g., matrix factorization) achieves good accuracy in rating prediction, it suffers from many problems including cold-start, non-transparency, and suboptimal results for individual user-item pairs. In this article, we exploit textual reviews and item images together with ratings to tackle these limitations. Specifically, we first apply a proposed multi-modal aspect-aware topic model (MATM) on text reviews and item images to model users’ preferences and items’ features from different aspects, and also estimate the aspect importance of a user toward an item. Then, the aspect importance is integrated into a novel aspect-aware latent factor model (ALFM), which learns user’s and item’s latent factors based on ratings. In particular, ALFM introduces a weight matrix to associate those latent factors with the same set of aspects in MATM, such that the latent factors could be used to estimate aspect ratings. Finally, the overall rating is computed via a linear combination of the aspect ratings, which are weighted by the corresponding aspect importance. To this end, our model could alleviate the data sparsity problem and gain good interpretability for recommendation. Besides, every aspect rating is weighted by its aspect importance, which is dependent on the targeted user’s preferences and the targeted item’s features. Therefore, it is expected that the proposed method can model a user’s preferences on an item more accurately for each user-item pair. Comprehensive experimental studies have been conducted on the Yelp 2017 Challenge dataset and Amazon product datasets. Results show that (1) our method achieves significant improvement compared to strong baseline methods, especially for users with only few ratings; (2) item visual features can improve the prediction performance—the effects of item image features on improving the prediction results depend on the importance of the visual features for the items; and (3) our model can explicitly interpret the predicted results in great detail.
Topic and Prosodic Modeling for Interruption Management in Multi-User Multitasking Communication Interactions

When to send system-mediated interruptions within collaborative multi-human-machine environments has been widely debated in the development of interruption management systems. Unfortunately, these studies do not address when to send interruptions in multi-user, multitasking scenarios or predictors of interruptibility within communication tasks. This paper addresses the issue of predicting interruptibility within these interactions with special attention to which users are engaged in which tasks or task engagement and where users are within a current task or task structure as predictors of interruptibility. Using natural human speech from these interactions, we attempt to model task engagement and task structure to predict candidate points of interruptions. The motivation for these models and their performance in a multi-user, multitasking environment are discussed as proposals in developing communication interruption management systems. To model task structure, a task breakpoint model is proposed which performs with a 90% accuracy within a multi-user, multitasking dataset. Integrating this task breakpoint model into a real-time interaction results in an average accuracy of 93% using the proposed task breakpoint model and a rule-based model. To determine the current task in which users are engaged or task engagement, a proposed task topic model performs with an accuracy between 76-88% depending on the topic within the dataset. Closely examining task structure and task engagement as predictors of interruptibility sheds new light on a rarely explored area for system-mediated interruption timings within multi-user, multitasking communication tasks.
Comprehensive analysis of the mitochondrial DNA diversity in Chinese cattle.

Complete mitochondrial DNA D-loop sequences of 1105 individuals were used to assess the diversity of maternal lineages of cattle populations in China. In total, 250 taurine and 88 zebu haplotypes were identified. Five main haplogroups-T1a, T2, T3, T4 and T5-were identified in Bos taurus, whereas Bos indicus harbored two haplogroups-I1 and I2. Our results suggest that the distribution of T1a in Asia was concentrated mainly in the northeast region (northeast China, Korea and Japan); haplogroups T2, T3 and T4 were predominant in Chinese cattle; and T5 was sporadically detected in Mongolian and Pingwu cattle. In contrast to the widespread presence of I1, I2 was distributed only in southwestern China (Yunnan-Guizhou Plateau and the Tibet Autonomous Region) and Xinjiang Uygur Autonomous Region. This is the first time that all five taurine haplogroups and two zebu haplogroups have been found in Mongolian cattle. In addition, eight individuals in Tibetan cattle carried the Bos grunniens mtDNA type. The high mtDNA diversity (H = 0.904 ± 0.008) and the weak genetic structure among the 57 Chinese cattle breeds/populations are consistent with their complex historical background, migration route and ecological environment.
Intraspectrum Discrimination and Interspectrum Correlation Analysis Deep Network for Multispectral Face Recognition

Multispectral images contain rich recognition information since the multispectral camera can reveal information that is not visible to the human eye or to the conventional RGB camera. Due to this characteristic of multispectral images, multispectral face recognition has attracted lots of research interest. Although some multispectral face recognition methods have been presented in the last decade, how to fully and effectively explore the intraspectrum discriminant information and the useful interspectrum correlation information in multispectral face images for recognition has not been well studied. To boost the performance of multispectral face recognition, we propose an intraspectrum discrimination and interspectrum correlation analysis deep network (IDICN) approach. Multiple spectra are divided into several spectrum-sets, with each containing a group of spectra within a small spectral range. The IDICN network contains a set of spectrum-set-specific deep convolutional neural networks attempting to extract spectrum-set-specific features, followed by a spectrum pooling layer, whose target is to select a group of spectra with favorable discriminative abilities adaptively. IDICN jointly learns the nonlinear representations of the selected spectra, such that the intraspectrum Fisher loss and the interspectrum discriminant correlation are minimized. Experiments on the well-known Hong Kong Polytechnic University, Carnegie Mellon University, and the University of Western Australia multispectral face datasets demonstrate the superior performance of the proposed approach over several state-of-the-art methods.
A novel near-infrared triggered dual-targeted nanoplatform for mitochondrial combined photothermal-chemotherapy of cancer in vitro

A combination of photothermal-chemotherapy has received widespread attention in drug delivery systems for cancer treatment. However, the combination therapy operated in subcellular organelles, such as mitochondria, has been rarely reported. Herein, we designed a novel near-infrared (NIR) triggered dual-targeted nanoplatform (FA/TPP-DINPs) based on mitochondrial combined photothermal-chemotherapy by co-loading FDA-approved NIR dye indocyanine green (ICG) and anticancer drug doxorubicin (DOX). The resulting nanoparticles showed a monodispersed sphere and excellent colloidal stability. Specially, the simultaneous introduction of targeted ligands folic acid (FA) and triphenylphosphine (TPP) to nanoparticles significantly promoted the cellular internalization and mitochondrial co-localization of nanoparticles. Moreover, the encapsulated dye could convert NIR light into heat with high efficiency, which makes the FA/TPP-DINPs an effective platform for mitochondrial combination therapy with chemotherapy drug DOX. Meanwhile, the thermal expansion in response to the change of temperature after sustained 808 nm laser irradiation could cause the disintegration of nanoparticles, which triggered the rapid release of DOX from nanoparticles. As expected, the prepared FA/TPP-DINPs exhibited evidently enhanced cytotoxicity and preeminent combination therapy efficiency on MCF-7 cells. Thus, the NIR triggered dual-targeted nanoplatform provides a new drug delivery strategy for mitochondrial combined photothermal-chemotherapy of cancer.
Predicting tuberculosis relapse in patients treated with the standard 6-month regimen: an individual patient data meta-analysis

Background Relapse continues to place significant burden on patients and tuberculosis (TB) programmes worldwide. We aimed to determine clinical and microbiological factors associated with relapse in patients treated with the WHO standard 6-month regimen and then evaluate the accuracy of each factor at predicting an outcome of relapse. Methods A systematic review was performed to identify randomised controlled trials reporting treatment outcomes on patients receiving the standard regimen. Authors were contacted and invited to share patient-level data (IPD). A one-step IPD meta-analysis, using random intercept logistic regression models and receiver operating characteristic curves, was performed to evaluate the predictive performance of variables of interest. Results Individual patient data were obtained from 3 of the 12 identified studies. Of the 1189 patients with confirmed pulmonary TB who completed therapy, 67 (5.6%) relapsed. In multipredictor analysis, the presence of baseline cavitary disease with positive smear at 2 months was associated with an increased odds of relapse (OR 2.3(95% CI 1.3 to 4.2)) and a relapse risk of 10%. When area under the curve for each multipredictor model was compared, discrimination between low-risk and higher-risk patients was modest and similar to that of the reference model which accounted for age, sex and HIV status. Conclusion Despite its poor predictive value, our results indicate that the combined presence of cavitary disease and 2-month positive smear status may be the best currently available marker for identifying individuals at an increased risk of relapse, particularly in resource-limited setting. Further investigation is required to assess whether this combined factor can be used to indicate different treatment requirements in clinical practice.
Liposomal nanoparticle-based conserved peptide influenza vaccine and monosodium urate crystal adjuvant elicit protective immune response in pigs

Background Influenza (flu) is a constant threat to humans and animals, and vaccination is one of the most effective ways to mitigate the disease. Due to incomplete protection induced by current flu vaccines, development of novel flu vaccine candidates is warranted to achieve greater efficacy against constantly evolving flu viruses. Methods In the present study, we used liposome nanoparticle (<200 nm diameter)-based subunit flu vaccine containing ten encapsulated highly conserved B and T cell epitope peptides to induce protective immune response against a zoonotic swine influenza A virus (SwIAV) H1N1 challenge infection in a pig model. Furthermore, we used monosodium urate (MSU) crystals as an adjuvant and co-administered the vaccine formulation as an intranasal mist to flu-free nursery pigs, twice at 3-week intervals. Results Liposome peptides flu vaccine delivered with MSU adjuvant improved the hemagglutination inhibition antibody titer and mucosal IgA response against the SwIAV challenge and also against two other highly genetically variant IAVs. Liposomal vaccines also enhanced the frequency of peptides and virus-specific T-helper/memory cells and IFN-γ response. The improved specific cellular and mucosal humoral immune responses in adjuvanted liposomal peptides flu vaccine partially protected pigs from flu-induced fever and pneumonic lesions, and reduced the nasal virus shedding and viral load in the lungs. Conclusion Overall, our study shows great promise for using liposome and MSU adjuvant- based subunit flu vaccine through the intranasal route, and provides scope for future, pre-clinical investigations in a pig model for developing potent human intranasal subunit flu vaccines.
A Systematic Evaluation of Transient Execution Attacks and Defenses

Research on transient execution attacks including Spectre and Meltdown showed that exception or branch misprediction events might leave secret-dependent traces in the CPU's microarchitectural state. This observation led to a proliferation of new Spectre and Meltdown attack variants and even more ad-hoc defenses (e.g., microcode and software patches). Both the industry and academia are now focusing on finding effective defenses for known issues. However, we only have limited insight on residual attack surface and the completeness of the proposed defenses. In this paper, we present a systematization of transient execution attacks. Our systematization uncovers 6 (new) transient execution attacks that have been overlooked and not been investigated so far: 2 new exploitable Meltdown effects: Meltdown-PK (Protection Key Bypass) on Intel, and Meltdown-BND (Bounds Check Bypass) on Intel and AMD; and 4 new Spectre mistraining strategies. We evaluate the attacks in our classification tree through proof-of-concept implementations on 3 major CPU vendors (Intel, AMD, ARM). Our systematization yields a more complete picture of the attack surface and allows for a more systematic evaluation of defenses. Through this systematic evaluation, we discover that most defenses, including deployed ones, cannot fully mitigate all attack variants.