bluuebunny/crossref_metadata_2025_split huggingface dataset:
Semi‐Supervised 3D Shape Recognition via Multimodal Deep Co‐training
Authors: Song Mofei , Liu Yu , Liu Xiao Fan 
Published: November 2020

Abstract3D shape recognition has been actively investigated in the field of computer graphics. With the rapid development of deep learning, various deep models have been introduced and achieved remarkable results. Most 3D shape recognition methods are supervised and learn only from the large amount of labeled shapes. However, it is expensive and time consuming to obtain such a large training set. In contrast to these methods, this paper studies a semi‐supervised learning framework to train a deep model for 3D shape recognition by using both labeled and unlabeled shapes. Inspired by the co‐training algorithm, our method iterates between model training and pseudo‐label generation phases. In the model training phase, we train two deep networks based on the point cloud and multi‐view representation simultaneously. In the pseudo‐label generation phase, we generate the pseudo‐labels of the unlabeled shapes using the joint prediction of two networks, which augments the labeled set for the next iteration. To extract more reliable consensus information from multiple representations, we propose an uncertainty‐aware consistency loss function to combine the two networks into a multimodal network. This not only encourages the two networks to give similar predictions on the unlabeled set, but also eliminates the negative influence of the large performance gap between the two networks. Experiments on the benchmark ModelNet40 demonstrate that, with only 10% labeled training data, our approach achieves competitive performance to the results reported by supervised methods.
FAKIR: An algorithm for revealing the anatomy and pose of statues from raw point sets
Authors: Fu Tong , Chaine Raphaëlle , Digne Julie 
Published: November 2020

Abstract3D acquisition of archaeological artefacts has become an essential part of cultural heritage research for preservation or restoration purpose. Statues, in particular, have been at the center of many projects. In this paper, we introduce a way to improve the understanding of acquired statues representing real or imaginary creatures by registering a simple and pliable articulated model to the raw point set data. Our approach performs a Forward And bacKward Iterative Registration (FAKIR) which proceeds joint by joint, needing only a few iterations to converge. We are thus able to detect the pose and elementary anatomy of sculptures, with possibly non realistic body proportions. By adapting our simple skeleton, our method can work on animals and imaginary creatures.
An Occlusion‐aware Edge‐Based Method for Monocular 3D Object Tracking using Edge Confidence
Authors: Huang Hong , Zhong Fan , Sun Yuqing , Qin Xueying 
Published: November 2020

AbstractWe propose an edge‐based method for 6DOF pose tracking of rigid objects using a monocular RGB camera. One of the critical problem for edge‐based methods is to search the object contour points in the image corresponding to the known 3D model points. However, previous methods often produce false object contour points in case of cluttered backgrounds and partial occlusions. In this paper, we propose a novel edge‐based 3D objects tracking method to tackle this problem. To search the object contour points, foreground and background clutter points are first filtered out using edge color cue, then object contour points are searched by maximizing their edge confidence which combines edge color and distance cues. Furthermore, the edge confidence is integrated into the edge‐based energy function to reduce the influence of false contour points caused by cluttered backgrounds and partial occlusions. We also extend our method to multi‐object tracking which can handle mutual occlusions. We compare our method with the recent state‐of‐art methods on challenging public datasets. Experiments demonstrate that our method improves robustness and accuracy against cluttered backgrounds and partial occlusions.
Coarse to Fine: Weak Feature Boosting Network for Salient Object Detection
Authors: Zhang Chenhao , Gao Shanshan , Pan Xiao , Wang Yuting , Zhou Yuanfeng 
Published: November 2020

AbstractSalient object detection is to identify objects or regions with maximum visual recognition in an image, which brings significant help and improvement to many computer visual processing tasks. Although lots of methods have occurred for salient object detection, the problem is still not perfectly solved especially when the background scene is complex or the salient object is small. In this paper, we propose a novel Weak Feature Boosting Network (WFBNet) for the salient object detection task. In the WFBNet, we extract the unpredictable regions (low confidence regions) of the image via a polynomial function and enhance the features of these regions through a well‐designed weak feature boosting module (WFBM). Starting from a coarse saliency map, we gradually refine it according to the boosted features to obtain the final saliency map, and our network does not need any post‐processing step. We conduct extensive experiments on five benchmark datasets using comprehensive evaluation metrics. The results show that our algorithm has considerable advantages over the existing state‐of‐the‐art methods.
Generating High‐quality Superpixels in Textured Images
Authors: Zhang Zhe , Xu Panpan , Chang Jian , Wang Wencheng , Zhao Chong , Zhang Jian Jun 
Published: November 2020

AbstractSuperpixel segmentation is important for promoting various image processing tasks. However, existing methods still have difficulties in generating high‐quality superpixels in textured images, because they cannot separate textures from structures well. Though texture filtering can be adopted for smoothing textures before superpixel segmentation, the filtering would also smooth the object boundaries, and thus weaken the quality of generated superpixels. In this paper, we propose to use the adaptive scale box smoothing instead of the texture filtering to obtain more high‐quality texture and boundary information. Based on this, we design a novel distance metric to measure the distance between different pixels, which considers boundary, color and Euclidean distance simultaneously. As a result, our method can achieve high‐quality superpixel segmentation in textured images without texture filtering. The experimental results demonstrate the superiority of our method over existing methods, even the learning‐based methods. Benefited from using boundaries to guide superpixel segmentation, our method can also suppress noise to generate high‐quality superpixels in non‐textured images.
Not All Areas Are Equal: A Novel Separation‐Restoration‐Fusion Network for Image Raindrop Removal
Authors: Ren Dongdong , Li Jinbao , Han Meng , Shu Minglei 
Published: November 2020

AbstractDetecting and removing raindrops from an image while keeping the high quality of image details has attracted tremendous studies, but remains a challenging task due to the inhomogeneity of the degraded region and the complexity of the degraded intensity. In this paper, we get rid of the dependence of deep learning on image‐to‐image translation and propose a separation‐restoration‐fusion network for raindrops removal. Our key idea is to recover regions of different damage levels individually, so that each region achieves the optimal recovery result, and finally fuse the recovered areas. In the region restoration module, to complete the restoration of a specific area, we propose a multi‐scale feature fusion global information aggregation attention network to achieve global to local information aggregation. Besides, we also design an inside and outside dense connection dilated network, to ensure the fusion of the separated regions and the fine restoration of the image. The qualitatively and quantitatively evaluations are conducted to evaluate our method with the latest existing methods. The result demonstrates that our method outperforms state‐of‐the‐art methods by a large margin on the benchmark datasets in extensive experiments.
SCGA‐Net: Skip Connections Global Attention Network for Image Restoration
Authors: Ren Dongdong , Li Jinbao , Han Meng , Shu Minglei 
Published: November 2020

AbstractDeep convolutional neural networks (DCNN) have shown their advantages in the image restoration tasks. But most existing DCNN‐based methods still suffer from the residual corruptions and coarse textures. In this paper, we propose a general framework “Skip Connections Global Attention Network” to focus on the semantics delivery from shallow layers to deep layers for low‐level vision tasks including image dehazing, image denoising, and low‐light image enhancement. First of all, by applying dense dilated convolution and multi‐scale feature fusion mechanism, we establish a novel encoder‐decoder network framework to aggregate large‐scale spatial context and enhance feature reuse. Secondly, the solution we proposed for skipping connection uses attention mechanism to constraint information, thereby enhancing the high‐frequency details of feature maps and suppressing the output of corruptions. Finally, we also present a novel attention module dubbed global constraint attention, which could effectively captures the relationship between pixels on the entire feature maps, to obtain the subtle differences among pixels and produce an overall optimal 3D attention maps. Extensive experiments demonstrate that the proposed method achieves significant improvements over the state‐of‐the‐art methods in image dehazing, image denoising, and low‐light image enhancement.
A Multi‐Person Selfie System via Augmented Reality
Authors: Lin Jie , Yang Chuan‐Kai 
Published: November 2020

AbstractThe limited length of a selfie stick always poses the problem of distortion in a selfie, in spite of the prevalence of selfie stick in recent years. We propose a technique, based on modifying existing augmented reality technology, to support the selfie of multiple persons, through properly aligning different photographing processes. It can be shown that our technique helps avoiding the common distortion drawback of using a selfie stick, and facilitates the composition process of a group photo. It can also be used to create some special effects, including creating an illusion of having multiple appearances of a person.
StyleProp: Real‐time Example‐based Stylization of 3D Models
Authors: Hauptfleisch F. , Texler O. , Texler A. , Krivánek J. , Sýkora D. 
Published: November 2020

AbstractWe present a novel approach to the real‐time non‐photorealistic rendering of 3D models in which a single hand‐drawn exemplar specifies its appearance. We employ guided patch‐based synthesis to achieve high visual quality as well as temporal coherence. However, unlike previous techniques that maintain consistency in one dimension (temporal domain), in our approach, multiple dimensions are taken into account to cover all degrees of freedom given by the available space of interactions (e.g., camera rotations). To enable interactive experience, we precalculate a sparse latent representation of the entire interaction space, which allows rendering of a stylized image in real‐time, even on a mobile device. To the best of our knowledge, the proposed system is the first that enables interactive example‐based stylization of 3D models with full temporal coherence in predefined interaction space.
RadEx: Integrated Visual Exploration of Multiparametric Studies for Radiomic Tumor Profiling
Authors: Mörth E. , Wagner‐Larsen K. , Hodneland E. , Krakstad C. , Haldorsen I. S. , Bruckner S. , Smit N. N. 
Published: November 2020

AbstractBetter understanding of the complex processes driving tumor growth and metastases is critical for developing targeted treatment strategies in cancer. Radiomics extracts large amounts of features from medical images which enables radiomic tumor profiling in combination with clinical markers. However, analyzing complex imaging data in combination with clinical data is not trivial and supporting tools aiding in these exploratory analyses are presently missing. In this paper, we present an approach that aims to enable the analysis of multiparametric medical imaging data in combination with numerical, ordinal, and categorical clinical parameters to validate established and unravel novel biomarkers. We propose a hybrid approach where dimensionality reduction to a single axis is combined with multiple linked views allowing clinical experts to formulate hypotheses based on all available imaging data and clinical parameters. This may help to reveal novel tumor characteristics in relation to molecular targets for treatment, thus providing better tools for enabling more personalized targeted treatment strategies. To confirm the utility of our approach, we closely collaborate with experts from the field of gynecological cancer imaging and conducted an evaluation with six experts in this field.
Childhood innocence and experience: Memory, discourse and practice
Authors: Garlen Julie C. , Chang‐Kredl Sandra , Farley Lisa , Sonu Debbie 
Published: November 2020

AbstractThis article examines how childhood innocence is taken up in (92) memories of undergraduate students across four sites in the US and Canada. Drawing from Foucault's theory of discourse, we examine how three themes—innocence as not knowing, innocence as being provided for, and loss of innocence as exposure to adversity—construct childhood as the absence of conflict, which perpetuates the myth of an innocence/experience binary and encourages a deficit perspective of childhood. These findings contribute to teacher education and childhood studies by highlighting the importance of interrogating adult memories in order to disrupt normative assumptions about children.
Coasting along to a wider range: niche conservatism in the recent range expansion of the Tawny Coster, Acraea terpsicore (Lepidoptera: Nymphalidae)
Authors: Chowdhury Shawan , Braby Michael F. , Fuller Richard A. , Zalucki Myron P. 
Published: November 2020

AbstractAimThe Tawny Coster Acraea terpsicore is a highly mobile butterfly that has recently expanded its spatial distribution from South Asia to South‐East Asia and Australia. Here, we determine if the realized climatic niche has changed during the expansion and analyse the geographic pattern of spread in Australia.LocationAsia and Australia.MethodsWe collated occurrence records, divided the geographic range into three spatio‐temporal phases (pre‐expansion, early‐expansion and late‐expansion) and then developed ecological niche models for each phase. To determine whether the realized niche has changed during the range expansion, we performed a principal component analyses and niche overlap analysis. Finally, we calculated the annual rate of range expansion to estimate the speed and pattern of geographic spread.ResultsThe climatic niche of A. terpsicore differs only slightly in the pre‐expansion and late‐expansion ranges and was most distinct in the early‐expansion range. The species' range in Australia expanded at an average rate of ~ 135 km/year (range: 34–359 km/year). Female‐biased migration occurred in north‐eastern Queensland at the leading edge of the range, the first documentation of this phenomenon in butterflies.Main ConclusionsAcraea terpsicore represents one of the fastest documented geographic range expansions of any species, highlighting how rapidly butterflies can colonize new areas, even where environmental conditions are substantially different to those in their original distribution. However, we found little evidence of climatic niche shift, and only a minor niche shift is apparent in the early‐expansion and late‐expansion ranges. It remains unclear what triggered the sudden expansion, but it has been hypothesized that tropical deforestation provided conditions that initiated local range expansion, and further work on the possible mechanisms involved is required.
Characterization of impressions created by turfgrass arthropods on clay models
Authors: Khan Fawad Z. A. , Joseph Shimat V. 
Published: November 2020

AbstractLepidopteran pests such as fall armyworm, Spodoptera frugiperda (JE Smith), and black cutworm, Agrotis ipsilon Hufnagel (both Noctuidae), are serious problems of turfgrass (Poaceae) in the USA. Several predators in turfgrass systems attack lepidopteran larvae; however, their predaceous activity is rarely recorded, as they leave hardly any evidence. Predaceous activity can be studied using clay models, an approach still rarely employed in turfgrass. Thus, assays were conducted to determine the types of impressions that common turfgrass arthropods leave on clay models simulating lepidopteran larvae. Nine impression types were characterized after exposing 16 arthropod taxa in enclosed arenas in the laboratory and one taxon in the field to two sizes of clay models, including scratches, paired marks, pricks, deep distortions, disturbed surfaces, detached segments, granulated surfaces, dents, and elongated scratches. Most arthropods produced scratches and paired marks, whereas granulated surfaces and elongated scratches were produced by only a few of the selected arthropods. To ensure that the impressions were correctly identified, non‐expert volunteers reviewed the impressions, and they were accurately identified in >85% of the responses. Video recordings of arthropod interactions were captured to determine the behavior of the arthropods during their first interaction with the clay models. Most arthropods first interacted with the terminal rather than the middle regions of the models. There were no differences between their interactions with the large or small models, and they used their mouthparts as often as their legs. Knowledge of the impression types caused by common arthropods will improve the utilization of the clay model technique for field studies in turfgrass. This could contribute to improved management of natural enemies as part of an integrated pest management approach.
Characterization and prognostic implication of delayed complete response in AL amyloidosis
Authors: Muchtar Eli , Gertz Morie A. , Kumar Shaji K. , Lacy Martha Q. , Leung Nelson , Buadi Francis K. , Dingli David , Hayman Suzanne R. , Go Ronald S. , Kapoor Prashant , Gonsalves Wilson , Kourelis Taxiarchis V. , Warsame Rahma , Lisa Hwa Yi , Fonder Amie , Hobbs Miriam , Russell Stephen , Lust John A. , Siddiqui Mustaqueem , Vincent Rajkumar S. , Kyle Robert A. , Dispenzieri Angela 
Published: November 2020

AbstractIntroductionLittle is known on continued response following completion of therapy in light chain (AL) amyloidosis.MethodsWe studied 373 AL amyloidosis patients who achieved complete response (CR) or very good partial response (VGPR) to first‐line therapy.ResultsBy end of therapy (EOT), 46% of patients achieved a CR and 54% a VGPR. With no further therapy, 17.5% of patients were upstaged from VGPR to CR (delayed CR), with a median of 9 months. Compared with CR and VGPR at EOT, patients with a delayed CR were characterized by higher proportion of t(11;14) and lower rate of trisomies. Autologous stem cell transplant was more frequent in the delayed CR group. Patients with a delayed CR were characterized by minimal residual disease negativity and organ response rates similar to patients with CR at EOT and higher than patients achieving VGPR at EOT. Patients with a delayed CR had a longer PFS/OS compared to patients with CR or VGPR by EOT (median PFS 149 vs 92 vs 52 months, P < .001; 10‐year OS 87% vs 71% vs 56%, P < .001).ConclusionsThis study characterizes delayed CR in AL amyloidosis, highlights its prognostic impact which is at least similar to those who achieved CR at EOT, and underlines another aspect of response monitoring.
Human immunodeficiency virus–related peripheral neuropathy: A systematic review and meta‐analysis
Authors: Julian Thomas , Rekatsina Martina , Shafique Faiza , Zis Panagiotis 
Published: November 2020

AbstractBackground and purposeHuman immunodeficiency virus (HIV)‐associated neurological syndromes occur in affected individuals as a consequence of primary HIV infection, opportunistic infections, inflammation and as an adverse effect of some forms of antiretroviral treatment (ART). The aim of this systematic review was to establish the epidemiological characteristics, clinical features, pathogenetic mechanisms and risk factors of HIV‐related peripheral neuropathy (PN).MethodsA systematic, computer‐based search was conducted using the PubMed database. Data regarding the above parameters were extracted. Ninety‐four articles were included in this review.ResultsThe most commonly described clinical presentation of HIV neuropathy is the distal predominantly sensory polyneuropathy. The primary pathology in HIVPN appears to be axonal rather than demyelinating. Age and treatment with medications belonging in the nucleoside analogue reverse transcriptase class are risk factors for developing HIV‐related neuropathy. The pooled prevalence of PN in patients naïve to ARTs was established to be 29% (95% CI: 9%–62%) and increased to 38% (95% confidence interval [CI]: 29%–48%) when looking into patients at various stages of their disease. More than half of patients with HIV‐related neuropathy are symptomatic (53%, 95% CI: 41%–63%). Management of HIV‐related neuropathy is mainly symptomatic, although there is evidence that discontinuation of some types of ART, such as didanosine, can improve or resolve symptoms.ConclusionsHuman immunodeficiency virus–related neuropathy is common and represents a significant burden in patients’ lives. Our understanding of the disease has grown over the last years, but there are unexplored areas requiring further study.
Inhibitory effects of climate change on the growth and extracellular enzyme activities of a widespread Antarctic soil fungus
Authors: Misiak Marta , Goodall‐Copestake William P. , Sparks Tim H. , Worland M. Roger , Boddy Lynne , Magan Naresh , Convey Peter , Hopkins David W. , Newsham Kevin K. 
Published: November 2020

AbstractTemperatures approaching or exceeding 20°C have been measured during summer in polar regions at the surfaces of barren fellfield soils under cloudless skies around solar noon. However, despite the upper temperature limit for the growth of cold‐adapted microbes—which are abundant in polar soils and have pivotal roles in nutrient cycling—typically being close to this temperature, previous studies have not addressed the consequences of climate change for the metabolism of these organisms in the natural environment. Here in a 5‐year field experiment on Alexander Island in the southern maritime Antarctic, we show that the abundance ofPseudogymnoascus roseus, the most widespread decomposer fungus in maritime Antarctic fellfield soils, is reduced by 1–2 orders of magnitude when irrigated and nutrient‐amended soils are warmed to >20°C during summer. Laboratory experiments under conditions mimicking those during midsummer in the natural environment indicated that the hyphal extension rates ofP. roseusisolates and the activities of five extracellular enzymes are reduced by 54%–96% at high water availability after exposure to temperatures cycling daily from 2 to 21°C and 2 to 24°C, relative to temperatures cycling from 2 to 18°C. Given that the temperatures of surface soils at the study site already reach 19°C during midsummer, the observations reported here suggest that, at predicted rates of warming arising from moderate greenhouse gas emissions, inhibitory effects of climate change on the metabolism ofP. roseuscould manifest themselves within the next few decades. Furthermore, with peak temperatures at the surfaces of fellfield soils at other maritime Antarctic locations and in High Arctic and alpine regions already exceeding 20°C during summer, the observations suggest that climate warming has the potential to inhibit the growth of other cold‐adapted microbes, with negative effects on soils as the Earth's climate continues to warm.