Miracle-dz/UniFAR-FTUs huggingface dataset:
Per Garment Capture and Synthesis for Real-time Virtual Try-on

Virtual try-on is a promising application of computer graphics and human computer interaction that can have a profound real-world impact especially during this pandemic. Existing image-based works try to synthesize a try-on image from a single image of a target garment, but it inherently limits the ability to react to possible interactions. It is difficult to reproduce the change of wrinkles caused by pose and body size change, as well as pulling and stretching of the garment by hand. In this paper, we propose an alternative per garment capture and synthesis workflow to handle such rich interactions by training the model with many systematically captured images. Our workflow is composed of two parts: garment capturing and clothed person image synthesis. We designed an actuated mannequin and an efficient capturing process that collects the detailed deformations of the target garments under diverse body sizes and poses. Furthermore, we proposed to use a custom-designed measurement garment, and we captured paired images of the measurement garment and the target garments. We then learn a mapping between the measurement garment and the target garments using deep image-to-image translation. The customer can then try on the target garments interactively during online shopping. The proposed workflow requires certain manual labor, but we believe that the cost is acceptable given that the retailers are already paying significant costs for hiring professional photographers and models, stylists, and editors to take photographs for promotion. Our method can remove the need of hiring these costly professionals. We evaluated the effectiveness of the proposed system with ablation studies and quality comparison with previous virtual try-on methods. We perform a user study to show our promising virtual try-on performances. Moreover, we also demonstrate that we use our method for changing virtual costumes in video conferences. Finally, we provide the collected dataset as the cloth dataset parameterized by various viewing angles, body poses, and sizes.
BERT, mBERT, or BiBERT? A Study on Contextualized Embeddings for Neural Machine Translation

The success of bidirectional encoders using masked language models, such as BERT, on numerous natural language processing tasks has prompted researchers to attempt to incorporate these pre-trained models into neural machine translation (NMT) systems. However, proposed methods for incorporating pre-trained models are non-trivial and mainly focus on BERT, which lacks a comparison of the impact that other pre-trained models may have on translation performance. In this paper, we demonstrate that simply using the output (contextualized embeddings) of a tailored and suitable bilingual pre-trained language model (dubbed BiBERT) as the input of the NMT encoder achieves state-of-the-art translation performance. Moreover, we also propose a stochastic layer selection approach and a concept of a dual-directional translation model to ensure the sufficient utilization of contextualized embeddings. In the case of without using back translation, our best models achieve BLEU scores of 30.45 for En→De and 38.61 for De→En on the IWSLT’14 dataset, and 31.26 for En→De and 34.94 for De→En on the WMT’14 dataset, which exceeds all published numbers.
Foreseeing the Benefits of Incidental Supervision

Real-world applications often require improved models by leveraging *a range of cheap incidental supervision signals*. These could include partial labels, noisy labels, knowledge-based constraints, and cross-domain or cross-task annotations – all having statistical associations with gold annotations but not exactly the same. However, we currently lack a principled way to measure the benefits of these signals to a given target task, and the common practice of evaluating these benefits is through exhaustive experiments with various models and hyperparameters. This paper studies whether we can, *in a single framework, quantify the benefits of various types of incidental signals for a given target task without going through combinatorial experiments*. We propose a unified PAC-Bayesian motivated informativeness measure, PABI, that characterizes the uncertainty reduction provided by incidental supervision signals. We demonstrate PABI’s effectiveness by quantifying the value added by various types of incidental signals to sequence tagging tasks. Experiments on named entity recognition (NER) and question answering (QA) show that PABI’s predictions correlate well with learning performance, providing a promising way to determine, ahead of learning, which supervision signals would be beneficial.
Implicit-Explicit Multirate Infinitesimal GARK Methods

This work focuses on the development of a new class of high-order accurate methods for multirate time integration of systems of ordinary diﬀerential equations. Unlike other recent work in this area, the proposed methods support mixed implicit-explicit (IMEX) treatment of the slow time scale. In addition to allowing this slow time scale ﬂexibility, the proposed methods utilize a so-called ‘inﬁnitesimal’ formulation for the fast time scale through deﬁnition of a sequence of modiﬁed ‘fast’ initial-value problems, that may be solved using any viable algorithm. We name the proposed class as implicit-explicit multirate inﬁnitesimal generalized-structure additive Runge–Kutta (IMEX- MRI-GARK) methods. In addition to deﬁning these methods, we prove that they may be viewed as speciﬁc instances of GARK methods, and derive a set of order conditions on the IMEX-MRI-GARK coeﬃcients to guarantee both third and fourth order accuracy for the overall multirate method. Additionally, we provide three speciﬁc IMEX-MRI-GARK methods, two of order three and one of order four. We conclude with numerical simulations on two multirate test problems, demonstrating the methods’ predicted convergence rates and comparing their eﬃciency against both legacy IMEX multirate schemes and recent third and fourth order implicit MRI-GARK methods.
Effects of Parameter Norm Growth During Transformer Training: Inductive Bias from Gradient Descent

The capacity of neural networks like the widely adopted transformer is known to be very high. Evidence is emerging that they learn successfully due to inductive bias in the training routine, typically a variant of gradient descent (GD). To better understand this bias, we study the tendency for transformer parameters to grow in magnitude (\ell_2 norm) during training, and its implications for the emergent representations within self attention layers. Empirically, we document norm growth in the training of transformer language models, including T5 during its pretraining. As the parameters grow in magnitude, we prove that the network approximates a discretized network with saturated activation functions. Such “saturated” networks are known to have a reduced capacity compared to the full network family that can be described in terms of formal languages and automata. Our results suggest saturation is a new characterization of an inductive bias implicit in GD of particular interest for NLP. We leverage the emergent discrete structure in a saturated transformer to analyze the role of different attention heads, finding that some focus locally on a small number of positions, while other heads compute global averages, allowing counting. We believe understanding the interplay between these two capabilities may shed further light on the structure of computation within large transformers.
Interests, Difficulties, Sentiments, and Tool Usages of Concurrency Developers: A Large-Scale Study on Stack Overflow

Context: Software developers are increasingly facing the challenges of writing code that is not only concurrent but also correct. Objective: To help these developers, it is necessary to understand concurrency topics they are interested in, their difficulty in finding answers for questions in these topics, their sentiment for these topics, and how they use concurrency tools and techniques to guarantee correctness. Method: We conduct a large-scale study on the entirety of Stack Overflow to understand interests, difficulties, sentiment, and tool usages of concurrency developers. We discuss the implications of our findings for the practice, research, and education of concurrent software development, and investigate the relation of our findings with the findings of the previous work. Results: A few findings of our study are: (1) questions that concurrency developers ask can be grouped into a hierarchy with 27 concurrency topics under 8 major categories, (2) thread safety is among the most popular concurrency topics and client-server concurrency is among the least popular, (3) irreproducible behavior is among the most difficult topics and memory consistency is among the least difficult, (4) data scraping is among the most positive concurrency topics and irreproducible behavior is among the most negative, (5) root cause identification has the most number of questions for usage of data race tools and alternative use has the least. Conclusion: The results of our study can not only help concurrency developers but also concurrency educators and researchers to better decide where to focus their efforts, by trading off one concurrency topic against another.
Less is More: Pre-train a Strong Text Encoder for Dense Retrieval Using a Weak Decoder

Dense retrieval requires high-quality text sequence embeddings to support effective search in the representation space. Autoencoder-based language models are appealing in dense retrieval as they train the encoder to output high-quality embedding that can reconstruct the input texts. However, in this paper, we provide theoretical analyses and show empirically that an autoencoder language model with a low reconstruction loss may not provide good sequence representations because the decoder may take shortcuts by exploiting language patterns. To address this, we propose a new self-learning method that pre-trains the autoencoder using a \textit{weak} decoder, with restricted capacity and attention flexibility to push the encoder to provide better text representations. Our experiments on web search, news recommendation, and open domain question answering show that our pre-trained model significantly boosts the effectiveness and few-shot ability of dense retrieval models. Our code is available at https://github.com/microsoft/SEED-Encoder/.
In situ neutron diffraction study of the reduction of New Zealand ironsands in dilute hydrogen mixtures

ABSTRACT The reduction of New Zealand titanomagnetite ironsand in a dilute hydrogen–nitrogen gas mixture was studied in situ using neutron diffraction. Neutron diffraction allowed in situ observation of large samples during reduction at high temperatures. Australian hematite ore, studied as a comparison, reduced much more quickly than the pre-oxidised ironsand, which in turn reduced more quickly than raw ironsand. The ironsand was predominantly titanomagnetite with small amounts of titanohematite. The rate of wüstite formation increased and metallic iron was formed only after the reduction of titanohematite. Experimental results confirmed the expected reduction pathway for initial reduction of titanomagnetite ore was described well by a three-interface shrinking core model. The rate controlling step in the reduction reactions studied was the mass transport of water vapour in the bulk gas. At higher temperatures, slow removal of water vapour meant that the pH2O increased, thus preventing reduction of wüstite to metallic iron.
A Novel Smart City-Based Framework on Perspectives for Application of Machine Learning in Combating COVID-19

The spread of COVID-19 worldwide continues despite multidimensional efforts to curtail its spread and provide treatment. Efforts to contain the COVID-19 pandemic have triggered partial or full lockdowns across the globe. This paper presents a novel framework that intelligently combines machine learning models and the Internet of Things (IoT) technology specifically to combat COVID-19 in smart cities. The purpose of the study is to promote the interoperability of machine learning algorithms with IoT technology by interacting with a population and its environment to curtail the COVID-19 pandemic. Furthermore, the study also investigates and discusses some solution frameworks, which can generate, capture, store, and analyze data using machine learning algorithms. These algorithms can detect, prevent, and trace the spread of COVID-19 and provide a better understanding of the disease in smart cities. Similarly, the study outlined case studies on the application of machine learning to help fight against COVID-19 in hospitals worldwide. The framework proposed in the study is a comprehensive presentation on the major components needed to integrate the machine learning approach with other AI-based solutions. Finally, the machine learning framework presented in this study has the potential to help national healthcare systems in curtailing the COVID-19 pandemic in smart cities. In addition, the proposed framework is poised as a pointer for generating research interests that would yield outcomes capable of been integrated to form an improved framework.
Effects of the fluorinated liquid crystal molecules on the electro-optical properties of polymer dispersed liquid crystal films

ABSTRACT The different fluorinated liquid crystal (LC) molecules doped to E8 were used as LC component to prepare polymer dispersed liquid crystal (PDLC) films. The mass fraction of the LC mixture is fixed 50.0 wt%. Results indicate that doping 8.0 wt% fluorinated LC molecule ME3CP to E8 significantly reduced the driving voltage of the PDLC films, and the driving voltage reduced with the rise of mass fraction of ME3CP. Besides, the terminal flexible chain length of the fluorinated LC molecule influenced the LC mixture properties based on E8, such as the dielectric anisotropy, birefringence and viscosity of the LC mixture, and the morphology and the electro-optical properties of PDLC films were controlled not only by the physical properties of the LC mixture, but also by the terminal flexible chain length of the fluorinated LC molecule . GRAPHICAL ABSTRACT
Simultaneous quantification of aqueous peroxide, nitrate, and nitrite during the plasma–liquid interactions by derivative absorption spectrophotometry

A derivative absorption spectroscopic method is used in situ to simultaneously trace and quantify the aqueous peroxide (H2O2), nitrate (NO3−) and nitrite (NO2−) generated during plasma–liquid interactions. The results indicate that the time evolutions of H2O2, NO3− and NO2− generated from the plasma–liquid interactions strongly depend on the solution’s pH value, which varies with the plasma treatment. The concentrations of aqueous H2O2, NO3− and NO2− increase independently from each other during the plasma treatment when the solution’s pH value is higher than 3.0. However, when the solution’s pH value is less than 3.0, most of the aqueous NO2− (~71.5%) will exist in the form of molecular nitrous acid since the pKa of nitrous acid is 3.4, the aqueous NO3− is mainly formed from the reaction between H2O2 and NO2− as well as the decomposition of molecular HNO2, which leads to a continuous increase of NO3− concentration and an appearance of the maximum concentrations of H2O2 and NO2− as the pH value of the solution reaches 3.0.
Deferoxamine accelerates endothelial progenitor cell senescence and compromises angiogenesis

Senescence reduces the circulating number and angiogenic activity of endothelial progenitor cells (EPCs), and is associated with aging-related vascular diseases. However, it is very time-consuming to obtain aged cells (~1 month of repeated replication) or animals (~2 years) for senescence studies. Here, we established an accelerated senescence model by treating EPCs with deferoxamine (DFO), an FDA-approved iron chelator. Four days of low-dose (3 μM) DFO induced senescent phenotypes in EPCs, including a senescent pattern of protein expression, impaired mitochondrial bioenergetics, altered mitochondrial protein levels and compromised angiogenic activity. DFO-treated early EPCs from young and old donors (< 35 vs. > 70 years old) displayed similar senescent phenotypes, including elevated senescence-associated β-galactosidase activity and reduced relative telomere lengths, colony-forming units and adenosine triphosphate levels. To validate this accelerated senescence model in vivo, we intraperitoneally injected Sprague-Dawley rats with DFO for 4 weeks. Early EPCs from DFO-treated rats displayed profoundly senescent phenotypes compared to those from control rats. Additionally, in hind-limb ischemic mice, DFO pretreatment compromised EPC angiogenesis by reducing both blood perfusion and capillary density. DFO thus accelerates EPC senescence and appears to hasten model development for cellular senescence studies.
Selective heterogeneous hydrodeoxygenation of acetophenone over monometallic and bimetallic Pt–Co catalyst

The hydrodeoxygenation (HDO) of acetophenone was evaluated in liquid phase and gas phase over monometallic Pt/SiO2, Co/SiO2 and bimetallic Pt–Co/SiO2 catalysts. The influence of reaction time and loading of the catalyst were analysed by following the conversion and products selectivity. Phenylethanol, cyclohexylethanone and cyclohexylethanol are the main products of reaction using the Pt/SiO2 catalyst. By contrast, ethylbenzene and phenylethanol are the only products formed on the Co/SiO2 and Pt–Co/SiO2 catalysts. The bimetallic catalyst is more stable as a function of time and more active towards the HDO process than the monometallic systems. The presence of an organic solvent showed only minor changes in product yields with no effect on the product speciation. Periodic density functional theory analysis indicates a stronger interaction between the carbonyl group of acetophenone with Co than with Pt sites of the mono and bimetallic systems, indicating a key activity of oxophilic sites towards improved selectivity to deoxygenated products. This article is part of the theme issue ‘Bio-derived and bioinspired sustainable advanced materials for emerging technologies (part 2)’.
Toxicological priority index framework for prioritizing pesticide products into human exposure and health risk assessments

Abstract Pesticide exposure assessment in humans involves selecting a set of representative products, performing a detailed examination of all the label permitted uses, and developing relevant exposure scenarios. Due to the potentially large number of pesticide application methods and use sites involved, this process is time-consuming and often requires professional, but subjective, judgment to triage the information. In this proof-of-concept study, an objective method was developed to systematically rank pesticide products for human exposure assessment based on a set of pre-determined criteria. Information employed in this study was obtained from an open access database for 66 cyfluthrin or β-cyfluthrin containing products. Exposure characterization of these pesticide products is based on five normalized product profile indices designed to capture the exposure potential of products, within known exposure settings, through activities exhibited by different population subgroups. Based on the five normalized indices, Toxicity Priority Index (ToxPi) software was used to visually rank the exposure potential of the cyfluthrin/β-cyfluthrin containing products. The ToxPi-based toxicity predictions were assessed using Pesticide Illness Surveillance Program database. The utility of the methodology was discussed in terms of ranking exposure potential of new pesticide products, assisting with future exposure mitigations and applications to pesticides with a common mode-of-action.
Microstructure and photocatalytic properties of polyimide/heterostructured NiO–Fe2O3–ZnO nanocomposite films via an ion-exchange technique

A series of NiO–Fe2O3–ZnO layers on double surfaces of polyimide (PI) film have been fabricated by alkaline-induced chemical modification and ion-exchange reaction. The X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS) and Fourier transform infrared spectroscopy (FT-IR) analyses confirmed the successful formation of the heterostructured NiO–Fe2O3–ZnO layers on PI surfaces. Scanning electron microscopy (SEM) and atomic force microscopy (AFM) showed that the NiO–Fe2O3–ZnO nanoparticles were dispersed uniformly on the PI surface and exhibited an interesting mesh-shaped morphology. The UV-vis absorption results demonstrated that the PI/heterostructured NiO–Fe2O3–ZnO nanocomposite exhibited wide visible-light photo absorption in the 400–740 nm range. Moreover, the PI/heterostructured NiO–Fe2O3–ZnO nanocomposite films possessed excellent mechanical properties and acceptable adhesion, and they exhibited superior photocatalytic activity compared with PI/ZnO nanocomposite film toward the degradation of dyes under solar irradiation. The degradation rate of methyl orange over PZNF-3 after 240 min simulated solar light irradiation reached an optimum value 96.2%. The improved photocatalytic activity of the PI/heterostructured NiO–Fe2O3–ZnO could be mainly attributed to the efficient separation of photoinduced electrons and holes of the photocatalysts, caused by the vectorial transfer of electrons and holes among ZnO, NiO and Fe2O3. This study introduces a new class of promising sunlight-driven photocatalytic nanocomposites with outstanding comprehensive performance.
Enhancing Linkage to Hepatitis C Virus Treatment Following Pregnancy in Women Identified During Perinatal Care

Amid the current US opioid crisis, hepatitis C virus (HCV) infection rates continue to rise in young adults, including among pregnant women, yet few studies describe linkage to care and treatment in pregnant or postpartum women with HCV infection. We used electronic health record data to estimate HCV treatment rates for postpartum women before (January 2014‐September 2016) and during (October 2016‐March 2018) implementation of a maternal–infant HCV linkage program in combination with a multidisciplinary clinic to colocate mother and infant care. Using Poisson regression models, we compared HCV treatment initiation rates, adjusting for demographics, substance use, and treatment. From January 2014 through March 2018, 343 women who were HCV seropositive delivered at our institution. Of these, 95% completed HCV nucleic acid testing and 255 women had chronic HCV infection. Mean age was 30 years, 96% were publicly insured, and 94% had documented substance use. HCV treatment initiation increased from 28/164 (17.1%) women with chronic HCV infection in the preintervention period to 16/66 (24.2%) with the linkage‐only intervention and 13/25 (52.0%) with the linkage intervention and colocated care. Adjusted analyses demonstrated that women delivering during the intervention period initiated HCV treatment at 2.40 times (95% confidence interval [CI], 1.10‐5.25; linkage only) and 3.36 times (95% CI, 1.57‐7.17; linkage and colocated care) the rate of women delivering preintervention. Women on buprenorphine had higher HCV treatment initiation rates compared with those on methadone (rate ratio, 2.10; 95% CI, 1.05‐4.21). Conclusion: HCV linkage to care and treatment rates improved in the setting of mother–infant linkage and colocated care interventions. Perinatal care may represent a critical venue to identify, link, and treat women for HCV infection to improve their own health and prevent transmission to subsequent pregnancies.