ActiveNeRF: Learning where to See with Uncertainty Estimation

Recently, Neural Radiance Fields (NeRF) has shown promising performances on reconstructing 3D scenes and synthesizing novel views from a sparse set of 2D images. Albeit effective, the performance of NeRF is highly influenced by the quality of training samples. With limited posed images from the scene, NeRF fails to generalize well to novel views and may collapse to trivial solutions in unobserved regions. This makes NeRF impractical under resource-constrained scenarios. In this paper, we present a novel learning framework, ActiveNeRF, aiming to model a 3D scene with a constrained input budget. Specifically, we first incorporate uncertainty estimation into a NeRF model, which ensures robustness under few observations and provides an interpretation of how NeRF understands the scene. On this basis, we propose to supplement the existing training set with newly captured samples based on an active learning scheme. By evaluating the reduction of uncertainty given new inputs, we select the samples that bring the most information gain. In this way, the quality of novel view synthesis can be improved with minimal additional resources. Extensive experiments validate the performance of our model on both realistic and synthetic scenes, especially with scarcer training data. Code will be released at \url{https://github.com/LeapLabTHU/ActiveNeRF}.Comment: Accepted by ECCV202

Rearrange Indoor Scenes for Human-Robot Co-Activity

We present an optimization-based framework for rearranging indoor furniture to accommodate human-robot co-activities better. The rearrangement aims to afford sufficient accessible space for robot activities without compromising everyday human activities. To retain human activities, our algorithm preserves the functional relations among furniture by integrating spatial and semantic co-occurrence extracted from SUNCG and ConceptNet, respectively. By defining the robot's accessible space by the amount of open space it can traverse and the number of objects it can reach, we formulate the rearrangement for human-robot co-activity as an optimization problem, solved by adaptive simulated annealing (ASA) and covariance matrix adaptation evolution strategy (CMA-ES). Our experiments on the SUNCG dataset quantitatively show that rearranged scenes provide an average of 14% more accessible space and 30% more objects to interact with. The quality of the rearranged scenes is qualitatively validated by a human study, indicating the efficacy of the proposed strategy.Comment: 7 pages, 7 figures; Accepted by ICRA 202

OmniAvatar: Geometry-Guided Controllable 3D Head Synthesis

We present OmniAvatar, a novel geometry-guided 3D head synthesis model trained from in-the-wild unstructured images that is capable of synthesizing diverse identity-preserved 3D heads with compelling dynamic details under full disentangled control over camera poses, facial expressions, head shapes, articulated neck and jaw poses. To achieve such high level of disentangled control, we first explicitly define a novel semantic signed distance function (SDF) around a head geometry (FLAME) conditioned on the control parameters. This semantic SDF allows us to build a differentiable volumetric correspondence map from the observation space to a disentangled canonical space from all the control parameters. We then leverage the 3D-aware GAN framework (EG3D) to synthesize detailed shape and appearance of 3D full heads in the canonical space, followed by a volume rendering step guided by the volumetric correspondence map to output into the observation space. To ensure the control accuracy on the synthesized head shapes and expressions, we introduce a geometry prior loss to conform to head SDF and a control loss to conform to the expression code. Further, we enhance the temporal realism with dynamic details conditioned upon varying expressions and joint poses. Our model can synthesize more preferable identity-preserved 3D heads with compelling dynamic details compared to the state-of-the-art methods both qualitatively and quantitatively. We also provide an ablation study to justify many of our system design choices

Learning to Weight Samples for Dynamic Early-exiting Networks

Early exiting is an effective paradigm for improving the inference efficiency of deep networks. By constructing classifiers with varying resource demands (the exits), such networks allow easy samples to be output at early exits, removing the need for executing deeper layers. While existing works mainly focus on the architectural design of multi-exit networks, the training strategies for such models are largely left unexplored. The current state-of-the-art models treat all samples the same during training. However, the early-exiting behavior during testing has been ignored, leading to a gap between training and testing. In this paper, we propose to bridge this gap by sample weighting. Intuitively, easy samples, which generally exit early in the network during inference, should contribute more to training early classifiers. The training of hard samples (mostly exit from deeper layers), however, should be emphasized by the late classifiers. Our work proposes to adopt a weight prediction network to weight the loss of different training samples at each exit. This weight prediction network and the backbone model are jointly optimized under a meta-learning framework with a novel optimization objective. By bringing the adaptive behavior during inference into the training phase, we show that the proposed weighting mechanism consistently improves the trade-off between classification accuracy and inference efficiency. Code is available at https://github.com/LeapLabTHU/L2W-DEN.Comment: ECCV 202

Sulfur signaling pathway in cardiovascular disease

Hydrogen sulfide (H2S) and sulfur dioxide (SO2), recognized as endogenous sulfur-containing gas signaling molecules, were the third and fourth molecules to be identified subsequent to nitric oxide and carbon monoxide (CO), and exerted diverse biological effects on the cardiovascular system. However, the exact mechanisms underlying the actions of H2S and SO2 have remained elusive until now. Recently, novel post-translational modifications known as S-sulfhydration and S-sulfenylation, induced by H2S and SO2 respectively, have been proposed. These modifications involve the chemical alteration of specific cysteine residues in target proteins through S-sulfhydration and S-sulfenylation, respectively. H2S induced S-sulfhydrylation can have a significant impact on various cellular processes such as cell survival, apoptosis, cell proliferation, metabolism, mitochondrial function, endoplasmic reticulum stress, vasodilation, anti-inflammatory response and oxidative stress in the cardiovascular system. Alternatively, S-sulfenylation caused by SO2 serves primarily to maintain vascular homeostasis. Additional research is warranted to explore the physiological function of proteins with specific cysteine sites, despite the considerable advancements in comprehending the role of H2S-induced S-sulfhydration and SO2-induced S-sulfenylation in the cardiovascular system. The primary objective of this review is to present a comprehensive examination of the function and potential mechanism of S-sulfhydration and S-sulfenylation in the cardiovascular system. Proteins that undergo S-sulfhydration and S-sulfenylation may serve as promising targets for therapeutic intervention and drug development in the cardiovascular system. This could potentially expedite the future development and utilization of drugs related to H2S and SO2

Analysis of the current status and influencing factors of cross-regional hospitalization services utilization by basic medical insurance participants in China − taking a central province as an example

BackgroundThe geographically uneven distribution of healthcare resources has resulted in a dramatic increase of cross-regional hospitalization services in China. The over-use of cross-regional hospitalization services may hinder the utilization and improvement of local hospitalization services. It is of great practical significance to study the utilization of cross-regional hospitalization services and its influencing factors in order to effectively allocate medical resources and guide patients to seek medical treatment rationally. Therefore, this study aims to analyze the current situation and influencing factors of the utilization of cross-regional hospitalization services by patients insured by basic medical insurance in China.MethodsA total of 3,291 cross-provincial inpatients were randomly selected in a central province of China in 2020. The level of medical institutions, hospitalization expenses and actual reimbursement rate were selected as indicators of hospitalization service utilization. Exploratory factor analysis was used to assess the dimensionality of influencing factors and reduce the number of variables, and binomial logistic regression analysis and multiple linear regression analysis to explore the influencing factors of the utilization of cross-regional hospitalization services.ResultsThe proportion of cross-provincial inpatients choosing tertiary hospitals was the highest with average hospitalization expenses of 24,662 yuan and an actual reimbursement rate of 51.0% on average. Patients insured by Urban Employees’ Basic Medical Insurance (UEBMI) were more frequently (92.9% vs. 88.5%) to choose tertiary hospitals than those insured by Urban and Rural Residents’ Basic Medical Insurance (URRBMI), and their average hospitalization expenses (30,727 yuan) and actual reimbursement rate (68.2%) were relatively higher (p < 0.001). The factor “income and security,” “convenience of medical treatment” and “disease severity” had significant effects on inpatients’ selection of medical institution level, hospitalization expenses and actual reimbursement rate, while the factor “demographic characteristics” only had significant effects on hospitalization expenses and actual reimbursement rate.ConclusionCross-provincial inpatients choose tertiary hospitals more frequently, and their financial burdens of medical treatment are heavy. A variety of factors jointly affect the utilization of cross-provincial hospitalization services for insured patients. It is necessary to narrow down the gap of medical treatment between UEBMI and URRBMI patients, and make full use of high-quality medical resources across regions

Biological control of Fusarium crown rot of wheat with Chaetomium globosum 12XP1-2-3 and its effects on rhizosphere microorganisms

Chaetomium globosum is a common plant endophytic fungi that exhibits great biocontrol potential in plant disease. Fusarium crown rot (FCR) is an important disease in wheat that seriously threatens wheat production worldwide. The control effect of C. globosum against wheat FCR remains unclear. In this study, we introduced an identified C. globosum 12XP1-2-3 and tested its biological control potential against wheat FCR. The hypha and fermentation broth exhibited an antagonistic effect against Fusarium pseudograminearum. Results from indoor experiments showed that C. globosum 12XP1-2-3 might delay the onset of symptoms of brown stem base and significantly reduced the disease index (37.3%). Field trials showed that wheat seeds coated with a spore suspension of 12XP1-2-3 grew better than the control seeds, had control effects of 25.9–73.1% on FCR disease, and increased wheat yield by 3.2–11.9%. Analysis of rhizosphere microorganisms revealed that seeds coated with C. globosum (‘Cg’ treatment) had a greater effect on fungal rather than on bacterial alpha diversity and may improve the health state of rhizosphere microorganisms, as reflected by the significantly increased fungal Shannon index at Feekes 11 and the increased complexity of the bacterial co-occurrence network but decreased complexity of the fungal network. Moreover, the accumulation of beneficial bacteria such as Bacillus and Rhizobium at Feekes 3, and Sphingomonas at Feekes 7 in the ‘Cg’ treatment may be the important contributions to healthier wheat growth state, significantly reduced relative abundance of Fusarium at Feekes 11, and reduced occurrence of FCR disease. These results provide a basis for further research on the mechanism of action of C. globosum and its application in the biological control of FCR in the field

Manipulation of ionized impurity scattering for achieving high thermoelectric performance in n-type Mg

Achieving higher carrier mobility plays a pivotal role for obtaining potentially high thermoelectric performance. In principle, the carrier mobility is governed by the band structure as well as by the carrier scattering mechanism. Here, we demonstrate that by manipulating the carrier scattering mechanism in n-type Mg[subscript 3]Sb[subscript 2 ]-based materials, a substantial improvement in carrier mobility, and hence the power factor, can be achieved. In this work, Fe, Co, Hf, and Ta are doped on the Mg site of Mg[subscript 3.2]Sb[subscript 1.5]Bi[subscript 0.49]Te [subscript 0.01], where the ionized impurity scattering crosses over to mixed ionized impurity and acoustic phonon scattering. A significant improvement in Hall mobility from ∼16 to ∼81 cm 2 ·V[superscript −1]·s[superscript − 1] is obtained, thus leading to a notably enhanced power factor of ∼13 μW·cm [superscript −1]·K [superscript −2] from ∼5 μW·cm[superscript −1]·K[superscript −2]. A simultaneous reduction in thermal conductivity is also achieved. Collectively, a figure of merit (ZT) of ∼1.7 is obtained at 773 K in Mg[subscript 3.1]Co[subscript 0.1]Sb[subscript 1.5]Bi[subscript 0.49]Te [subscript 0.01]. The concept of manipulating the carrier scattering mechanism to improve the mobility should also be applicable to other material systems. Keywords: thermoelectric; carrier scattering mechanism; ionized impurity scattering; n-type; Mg[subscript 3]Sb[subscript 2]; defect

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe