Miracle-dz/UniFAR-FTUs huggingface dataset:
Object-Occluded Human Shape and Pose Estimation From a Single Color Image

Occlusions between human and objects, especially for the activities of human-object interactions, are very common in practical applications. However, most of the existing approaches for 3D human shape and pose estimation require human bodies are well captured without occlusions or with minor self-occlusions. In this paper, we focus on the problem of directly estimating the object-occluded human shape and pose from single color images. Our key idea is to utilize a partial UV map to represent an object-occluded human body, and the full 3D human shape estimation is ultimately converted as an image inpainting problem. We propose a novel two-branch network architecture to train an end-to-end regressor via the latent feature supervision, which also includes a novel saliency map sub-net to extract the human information from object-occluded color images. To supervise the network training, we further build a novel dataset named as 3DOH50K. Several experiments are conducted to reveal the effectiveness of the proposed method. Experimental results demonstrate that the proposed method achieves the state-of-the-art comparing with previous methods. The dataset, codes are publicly available at https://www.yangangwang. com.
Learning Knowledge Bases with Parameters for Task-Oriented Dialogue Systems

Task-oriented dialogue systems are either modularized with separate dialogue state tracking (DST) and management steps or end-to-end trainable. In either case, the knowledge base (KB) plays an essential role in fulfilling user requests. Modularized systems rely on DST to interact with the KB, which is expensive in terms of annotation and inference time. End-to-end systems, instead, use the KB directly as input, but they cannot scale when the KB is larger than a few hundred entries. In this paper, we propose a method to embed the KB, of any size, directly into the model parameters. The resulting model does not require any DST or template responses, nor the KB as input, and it can dynamically update its KB via fine-tuning. We evaluate our solution in five task-oriented dialogue datasets with small, medium, and large KB size. Our experiments show that end-to-end models can effectively embed knowledge bases in their parameters and achieve competitive performance in all evaluated datasets.
Rapid diagnosis of α0‐thalassemia using the relative quantitative PCR and the dissociation curve analysis

: A method for diagnosis of alpha(o)-thalassemia was developed based on detection of accumulated PCR product using SYBR Green I, a double-stranded DNA binding dye, and a fluorescence-detecting thermocycler. Primers were designed to specifically amplify - -SEA and - -Thai deletions of alpha(o)-thalassemia. Albumin was selected as the reference gene. The comparative CT method was used to quantitate the target gene relative to the albumin gene. Dissociation curve analysis was used as a qualitative tool to distinguish different types of alpha(o)-thalassemia. In this study, the melting temperatures of the - -Thai and - -SEA deletions were 83 and 86 degrees C, respectively. Application of the assay for the diagnosis of alpha(o)-thalassemia heterozygotes and homozygotes is reported. The assay is highly robust and amenable to high throughput. It is thus a useful tool for the rapid detection of alpha(o)-thalassemia in populations with a high frequency of alpha(o)-thalassemia, - -SEA and - -Thai deletions.
Video Super-Resolution With Temporal Group Attention

Video super-resolution, which aims at producing a high-resolution video from its corresponding low-resolution version, has recently drawn increasing attention. In this work, we propose a novel method that can effectively incorporate temporal information in a hierarchical way. The input sequence is divided into several groups, with each one corresponding to a kind of frame rate. These groups provide complementary information to recover missing details in the reference frame, which is further integrated with an attention module and a deep intra-group fusion module. In addition, a fast spatial alignment is proposed to handle videos with large motion. Extensive results demonstrate the capability of the proposed model in handling videos with various motion. It achieves favorable performance against state-of-the-art methods on several benchmark datasets.
Learning to Cartoonize Using White-Box Cartoon Representations

This paper presents an approach for image cartoonization. By observing the cartoon painting behavior and consulting artists, we propose to separately identify three white-box representations from images: the surface representation that contains smooth surface of cartoon images, the structure representation that refers to the sparse color-blocks and flatten global content in the celluloid style workflow, and the texture representation that reflects high-frequency texture, contours and details in cartoon images. A Generative Adversarial Network (GAN) framework is used to learn the extracted representations and to cartoonize images. The learning objectives of our method are separately based on each extracted representations, making our framework controllable and adjustable. This enables our approach to meet artists' requirements in different styles and diverse use cases. Qualitative comparisons and quantitative analyses, as well as user studies, have been conducted to validate the effectiveness of this approach, and our method outperforms previous methods in all comparisons. Finally, the ablation study demonstrates the influence of each component in our framework.
Portal: Design and Fabrication of Incidence-Driven Screens

We introduce "Portal", an optically-illusive screen panel with a grid of mirrors serving as its pixels. The RGB values of each mirror are obtained from its environment using the law of reflection. Specifically, we employ two images: one target image for the mirror panel, and another which serves as a palette to lend mirrors its RGB values. Our methodology uniquely orients each mirror in the grid to reflect a particular region of the palette image to a specified viewpoint. The holistic image from the mirror grid composes the target image. Within the process, we need to satisfy a set of rules to secure the maximum approximation of the result with the intended images. We also propose a methodology to fabricate Portal using laser cuttings. As a proof of concept, we created a Portal equipped with 540 mirrors. Based on the physical and digital simulation results, we speculate possible applications of Portal in a range of disciplines, including computer graphics, art, and architecture. Figure 1: Portal is a structure composed of cellular mirrors that creates an image by reflecting colors from another image that acts as a palette. Here, the Mona Lisa (left, in each panel) is our palette image that is used to produce other artworks.
Broad Polyphyly in Pleurospermum s. l. (Umbelliferae-Apioideae) as Inferred from nrDNA ITS and Chloroplast Sequences

Abstract Traditionally the genus Pleurospermum and the related genera Aulacospermum, Hymenidium, Hymenolaena, Physospermopsis, Pseudotrachydium, Pterocyclus, and Trachydium, have been problematic taxa with regard to their circumscription and composition. Pleurospermum s. s. includes one or two closely related boreal species but more than 40 other species, distributed mainly in the Sino-Himalayan floristic area, have been attributed to it by different authors in various classifications. Relationships in this taxonomic group are unclear. A molecular phylogenetic analysis of nuclear (nrITS) and cpDNA (psbA-trnH and trnL-trnF) sequences of representative species of Pleurospermum s. l. and closely related Hymenidium, Aulacospermum, Trachydium, Physospermopsis, Pseudotrachydium, Sinolimprichtia, Pterocyclus, and Hymenolaena (55 species in all) was conducted and compared with an analysis of morphological characters. Only two traditional genera were supported as monophyletic groups in the molecular trees, namely Aulacospermum (including Pseudotrachydium) and Hymenolaena. Two stable groups were revealed within Hymenidium. One group included H. chloroleucum, H. heterosciadium, H. hookeri, and H. delavayi. The second included H. lindleyanum, H. stellatum, H. huzhihaoi, H. wilsonii, and Trachydium roylei. Various species of Hymenidium and Trachydium were scattered throughout the tree. The molecular data did not confirm an early divergence between northern and Sino-Himalayan species of Pleurospermum. This indicates that Pleurospermum s. l. and most of the other genera included in this study are polyphyletic.
Predicting Influencer Virality on Twitter

The ability to successfully predict virality on Twitter holds great potential as a resource for Twitter inﬂuencers, enabling the development of more sophisticated strategies for audience engagement, audience monetization, and information sharing. To our knowledge, focusing exclusively on tweets posted by inﬂuencers is a novel context for studying Twitter virality. We ﬁnd, among feature categories traditionally considered in the literature, that combining categories covering a range of information performs better than models only incorporating individual feature categories. Moreover, our general predictive model, encompassing a range of feature categories, achieves a prediction accuracy of 68% for inﬂuencer virality. We also investigate the role of inﬂuencer audiences in predicting virality, a topic we believe to be understud-ied in the literature. We suspect that incorporating audience information will allow us to better discriminate between virality classes, thus leading to better predictions. We pursue two diﬀerent approaches, resulting in 10 diﬀerent predictive models that leverage inﬂuencer audience information in addition to traditional feature categories. Both of our attempts to incorporate audience information plateau at an accuracy of approximately 61%, roughly a 7% decrease in performance compared to our general predictive model. We conclude that we are unable to ﬁnd experimental evidence to support our claim that incorporating inﬂuencer audience information will improve vi-rality predictions. Nonetheless, the performance of our general model holds promise for the deployment of a tool that allows inﬂuencers to reap the beneﬁts of virality prediction. As stronger performance from the underlying model would make this tool more useful in practice to inﬂuencers, improving the predictive performance of our general model is a cornerstone of future work.
Discovering Divergence: A Framework for Finding Unexpected Behavior Using Directed Exploration

Systems that are written to achieve the same high level specifications can vary in subtle ways. Depending on a programmer's objective, using one variant of a program or algorithm over another may be beneficial, and this objective may change over time. However we do not have sufficient techniques to compare two different system variants side-by-side to find specific behavioral differences, particularly in the absence of source code. Assuming two system implementations take the same inputs and produce the same outputs or exhibit the same behavior under most conditions, we want to find input instances where the behavior diverges for a given objective. In this paper we present a framework called UDivE to fill this gap. UDivE accepts a model of the input space and system constraints, as well as an objective measure for the output behavior that is of interest. It then uses a genetic algorithm to explore the input space of two implementations, guiding the search towards divergent behavior. We have implemented a prototype of UDivE and evaluate it on three different software case studies, each with different input spaces and objectives. In all three cases we find 'unexpected' divergent behavior. In addition, we take a first-step towards applying UDivE to a cyber-physical system by providing a feasibility study in which UDivE interacts with a simulation of an unmanned aerial vehicle (UAV), the results of which are validated on the UAV itself. We show that UDivE can produce promising results, even in the presence of a simplistic simulator. iii ACKNOWLEDGMENTS First and foremost, I would like to thank my advisors, Myra Cohen and Sebastian Elbaum. Over the past two years, they have helped transform me from an eager new graduate student into a researcher capable of asking insightful questions and evaluating complex choices. I am excited to continue learning about the field of computer science (a journey on which I have only just begun), and they have provided me with the first step that will guide me throughout my career. Another professor that has had a large impact on my graduate education is Carrick Detweiler. I would like to thank him for all that he has taught me, and for serving on my thesis committee. Further, being part of both the ESQuaReD Lab and Nimbus Lab during my time as a graduate student has been a tremendous advantage. Not only have I gained a diverse skill-set, I have been …
The influence of planktivorous fish on zooplankton resting-stage communities in riverine slackwater regions

Planktivorous fish are important in shaping zooplankton communities in many freshwater ecosystems, and there is an intrinsic link between the active and resting forms of many zooplankton taxa. However, few field studies have considered the influence of planktivorous fish on zooplankton resting-stage communities, and none have considered this phenomenon in rivers. In two separate but concurrent 12-day experiments, we investigated the effects of Eastern Gambusia (Gambusia holbrooki Girard) and carp gudgeon (Hypseleotris spp.) on zooplankton resting-stage communities in slackwaters of an Australian floodplain river. This was achieved by setting coconut fibre mats on the bottom of the experimental enclosures and later incubating the resting stages captured. A high biomass (2.5 g m -2 ) of Gambusia suppressed the total abundance and diversity of resting-stage microcrustaceans between days 6 and 12, and the abundance of resting-stage Asplanchna spp. throughout the entire 12-day period. Furthermore, a high biomass of Hypseleotris suppressed the total diversity of resting-stage rotifers between days 0 and 6. These response patterns generally reflected changes in active populations according to the fish biomass treatment, rather than changes in dormancy induction by active individuals. This suggests that a high biomass of planktivorous fish can potentially influence zooplankton resting-stage communities in riverine slackwaters in addition to active zooplankton communities due to the inherent association between resting stage and active forms of zooplankton taxa.
Eddy Current Solver for Nondestructive Testing using an Integral- FEM Approach and Zero-Thickness Flaw Model

EVERAL methods were proposed for fast and accurate solution of eddy-currents in conductive media in view of inversion of probe signals for extraction of defect geometry in nondestructive testing: both FEM-BEM [1][2] or FEM [3] based. One significant limitation of all these methods appears when one need to model defects with very thin geometry, to simulate the natural cracks. Zero-thickness cracks may be approximated using volume elements with very small dimension and in this case mesh refinement will increase very much the problem size, the solution being therefore unpractical for calculation of large datasets required for signal inversion. Very thin crack geometry, approximated as zero-thickness cracks are treated in [4]. Zero-thickness cracks are also modeled using a formulation based on the circulation of magnetic vector potential along primal edges and on timeintegrated electric scalar potential in primal nodes of conducting region [5].
Formation of Distal Bone Elements in Amputated Neonatal Mouse Forelimbs

Mammalian limbs show little regeneration potency. Mouse and human limbs regenerate only finger tips. However, some Bone Morphogenetic Proteins (BMPs) stimulate the formation of a new radius and ulna on amputated distal elements of neonatal mouse forearms. In this article, we describe the formation of more distal bone elements, carpi and metacarpi-like structures, other than the radius and ulna, after forearm amputation and BMP-7 and hedgehog agonist (Hh-Ag 1.8) treatment. However, since skin and soft connective tissues at the hand level remained in the stumps in these experiments, the small bones may be formed from these hand tissues by BMP and Hh-Ag. Thus, amputation was carried out at the proximal level of the wrist. With BMP and Hh-Ag treatment, the distal ends of the radius and ulna showed bifurcated and segmented bone structures. These results suggest the formation of distal bone elements, carpi and metacarpi, from forearm tissues.
Enhancing the Farm Bill's Conservation Potential Through Land Prioritization

Abstract The Farm Bill conservation programs serve as the primary tools for the creation and improvement of wildlife habitat on working lands. Wildlife conservation would benefit from a working land-prioritization system that integrates these programs. We developed a Geographic Information System (GIS)–based system to prioritize land for inclusion in Farm Bill conservation programs. We designed the system to be applicable throughout the United States, to minimize potential conflicts of interest, and to facilitate simple implementation. We designated high conservation value (HCV) lands using habitats of greatest conservation need. We placed priority zones around HCV lands to determine high- and low-priority working lands. Nationwide implementation of this system would require gathering and manipulating data from multiple sources, as well as creation of a GIS layer denoting locations of working lands currently in conservation programs. This system would allow funding to be maximized through the ability to select participation based on property location and size, and to target landowners for participation. The wide-ranging potential benefits of this system make it well-suited for serving as the backbone to conservation on working lands.
Supplementary materials: PointRend: Image Segmentation as Rendering

DeeplabV3 [3]: We use SGD with 0.9 momentum with 16 images per mini-batch cropped to a fixed 768×768 size; the training schedule is 90k updates with a poly learning rate [13] update strategy, starting from 0.01; a linear learning rate warmup [6] over 1000 updates starting from a learning rate of 0.001 is applied; the learning rate for ASPP and the prediction convolution are multiplied by 10; weight decay of 0.0001 is applied; random horizontal flipping and scaling of 0.5× to 2.0× with a 32 pixel step is used as training data augmentation; BN is applied to 16 images minibatches; no test-time augmentation is used;
Automating Cloud Deployment for Deep Learning Inference of Real-time Online Services

Real-time online services using pre-trained deep neural network (DNN) models, e.g., Siri and Instagram, require low-latency and cost-efficiency for quality-of-service and commercial competitiveness. When deployed in a cloud environment, such services call for an appropriate selection of cloud configurations (i.e., specific types of VM instances), as well as a considerate device placement plan that places the operations of a DNN model to multiple computation devices like GPUs and CPUs. Currently, the deployment mainly relies on service providers’ manual efforts, which is not only onerous but also far from satisfactory oftentimes (for a same service, a poor deployment can incur significantly more costs by tens of times). In this paper, we attempt to automate the cloud deployment for real-time online DNN inference with minimum costs under the constraint of acceptably low latency. This attempt is enabled by jointly leveraging the Bayesian Optimization and Deep Reinforcement Learning to adaptively unearth the (nearly) optimal cloud configuration and device placement with limited search time. We implement a prototype system of our solution based on TensorFlow and conduct extensive experiments on top of Microsoft Azure. The results show that our solution essentially outperforms the nontrivial baselines in terms of inference speed and cost-efficiency.
Diversity and Succession of Actinobacteria in the Forelands of the Tianshan Glacier, China

ABSTRACT Actinobacteria are typically soil bacteria that have important roles in soil development and biogeochemical cycling. However, little is known about the occurrence or the succession of communities of Actinobacteria in new habitats. In this study, we investigated the diversity and succession of the actinobacterial communities that inhabited the forelands of the Tianshan Glacier (China), which ranged in successional age from 0 to 100 years since the forefield was deglaciated. Actinobacteria was one of the dominant phyla in the glacier foreland and included the orders Acidimicrobiales, Actinomycetales, Rubrobacteriales and Solirubrobacteriales. Actinomycetales was the dominant order, but its relative abundance decreased through the chronosequence. Acidimicrobiales and Solirubrobacteriales were more abundant in the late stages of succession than in the early ones. The abundance of Rubrobacteriales was only high at 74a. The dominant genera Nocardioides and Arthrobacter were widely distributed and were found in each stage of succession. With nonparametric and rarefaction estimated analyses, we found that the phylotype richness of Actinobacteria was significantly correlated with time (r = 0.886, p = 0.019). The succession of actinobacterial communities was divided into 3 stages: the early stage (6a), the intermediate stage (10a and 20a) and the late stage (60a, 74a, and 100a). Based on the canonical correspondence analysis, the actinobacterial communities were affected significantly by soil pH (r = −0.834, p = 0.039) and somewhat by the C/N ratio (r = 0.783, p = 0.066). The nonmetric multidimensional scaling analysis showed that the effect of geographical isolation on the actinobacterial communities was greater than that of the soils in the development of the chronosequence.
A GUIDE TO REMOTE WORKING IN AUDIOLOGY SERVICES DURING COVID-19 AND BEYOND Evidence based practice: what works and why?

Introduction. 1 1.1. Audiometric evaluation for adults . 2 1.2 Hearing aid programming and fine tuning . 3 1.3 Cochlear implant programming . 3 1.4 Paediatric care . 4 1.5 Tinnitus management . 4 1.6 Attitudes towards teleaudiology. 4 1.7 Remote risk assessment . 5 1.8 Mobile health and other interventions. 6 References . 7
Precast Concrete Deck-to-Girder Mechanical Connection for Accelerated Bridge Construction

■ Experimental results show 25% higher interface shear resistance results for the new mechanical connection than for the conventional connection. Full-depth precast concrete bridge deck systems have been increasingly used in new construction and as a replacement for deteriorating cast-in-place concrete decks because of their high quality, durability, and ease/ speed of construction. Full-depth precast concrete deck systems were originally used in the 1960s as noncomposite with the supporting girders, and their first use in composite construction was in 1973. Composite full-depth precast concrete deck systems provide economical design because they satisfy strength and serviceability requirements using smaller and shallower girder sections than are used in noncomposite systems. Precast concrete deck panels are usually made composite with the supporting girders via shear connectors, such as shear studs, bent bars, or threaded rods, projecting from the girder top flange and embedded in either discrete shear pockets or continuous longitudinal troughs (channels) in the deck panels. A summary of different deckto-girder connection details can be found in Tawadrous.