What Matters for 3D Scene Flow Network

3D scene flow estimation from point clouds is a low-level 3D motion perception task in computer vision. Flow embedding is a commonly used technique in scene flow estimation, and it encodes the point motion between two consecutive frames. Thus, it is critical for the flow embeddings to capture the correct overall direction of the motion. However, previous works only search locally to determine a soft correspondence, ignoring the distant points that turn out to be the actual matching ones. In addition, the estimated correspondence is usually from the forward direction of the adjacent point clouds, and may not be consistent with the estimated correspondence acquired from the backward direction. To tackle these problems, we propose a novel all-to-all flow embedding layer with backward reliability validation during the initial scene flow estimation. Besides, we investigate and compare several design choices in key components of the 3D scene flow network, including the point similarity calculation, input elements of predictor, and predictor & refinement level design. After carefully choosing the most effective designs, we are able to present a model that achieves the state-of-the-art performance on FlyingThings3D and KITTI Scene Flow datasets. Our proposed model surpasses all existing methods by at least 38.2% on FlyingThings3D dataset and 24.7% on KITTI Scene Flow dataset for EPE3D metric. We release our codes at https://github.com/IRMVLab/3DFlow.Comment: Accepted by ECCV 202

Crowdsourcing geospatial data for Earth and human observations: a review

The transformation from authoritative to user-generated data landscapes has garnered considerable attention, notably with the proliferation of crowdsourced geospatial data. Facilitated by advancements in digital technology and high-speed communication, this paradigm shift has democratized data collection, obliterating traditional barriers between data producers and users. While previous literature has compartmentalized this subject into distinct platforms and application domains, this review offers a holistic examination of crowdsourced geospatial data. Employing a narrative review approach due to the interdisciplinary nature of the topic, we investigate both human and Earth observations through crowdsourced initiatives. This review categorizes the diverse applications of these data and rigorously examines specific platforms and paradigms pertinent to data collection. Furthermore, it addresses salient challenges, encompassing data quality, inherent biases, and ethical dimensions. We contend that this thorough analysis will serve as an invaluable scholarly resource, encapsulating the current state-of-the-art in crowdsourced geospatial data, and offering strategic directions for future interdisciplinary research and applications across various sectors

Promoting Sino-UK Collaboration on Developing Low Carbon and Sustainable Methodologies for Brownfields and Marginal Land Re-use in China

The authors are grateful to all partners of the SPF project which include a wide team of collaborators and advisors across China and UK for their useful discussions and contribution during the project. Ming Liu and Chris He (Department of Science, Technology & Innovation, British Consulate-General Guangzhou), Rongxia Liu and Xia Yang (Administrative Centre for China’s Agenda21), Kate Canning (Arup) and David Middleton (Department for Environment and Rural Affairs, UK) helped discussion and revision of the report. We acknowledge the financial support from the Foreign Common Office’s Prosperity Fund programme. We also are grateful to the contribution of the University of Brighton and the Land Trust who supported the PSRP case study development project and shared its findings with this project. This report is one of the outputs of the China Prosperity Strategic Programme Fund (SPF) on “Promoting Sino-UK collaboration on developing low carbon and sustainable methodologies for Brownfields and marginal land re-use in China” (project 16AG15)Rapid urbanisation and changes in land use resulting from industrial change has left a legacy of vast polluted industrial and commercial areas (also called brownfields) and marginal land areas. Recent evidence from the UK, EU and USA indicate that these land areas may have considerable potential for renewables production, for example from solar, wind or biomass. In parallel there are opportunities for carbon storage in rehabilitated soil, as well as substitution by the production of renewables. The UK is also leading the understanding in the wider parallel benefits that can be achieved from ecosystem services and public health benefits from improved provision of green space. These multiple services can be provided together, in synergy, from soft re-uses of post-industrial sites, and in this way the post-industrial regeneration areas in China should be seen as a major opportunity for new enterprise, society and the wider environment. The improving bankability of renewable energy projects, and the possibility of creating a voluntary carbon offset business, means that revenue streams may be sufficient to pay for ongoing land management over time as a profit generating activity. In terms of fastest benefit to UK PLC and China, the likelihood is that combination of renewable energies with “dual use” for habitat will provide both more readily commercial brownfield re-use opportunities for cities in China in the short term, and also create better carbon management opportunities, as well as a variety of wider sustainability benefits. Thus this type of re-uses will create a platform for rapid commercial exchange and development between Chinese and UK companies. Considering that China is preparing an action plan for managing soil pollution and remediation across the country estimated to be RMB 7tn which is equivalent to one-third of the national exchange reserves, this report on developing low carbon and sustainable methodologies for brownfields and marginal land re-use in China provides timely information that will support the decision making for sustainable remediation opportunities in China. The report is intended to serve as a tool and resource guide to stakeholders involved in land remediation willing to engage in sustainable remediation implementation for renewable energy and carbon management applications. It is intended to inform remediation stakeholders unfamiliar with sustainable remediation about the concept, practices, and available resources. The report capitalises on UK leadership positions on the sustainable rehabilitation of brownfields land (SURF-UK), the soft re-use of brownfields (e.g. for energy or amenity rather than buildings); effective end-use directed risk management for contaminated land, and sustainable remediation.Foreign Common Office’s Prosperity Fund programme SPF project 16AG1

Zinc finger and SCAN domain containing 1, ZSCAN1, is a novel stemness-related tumor suppressor and transcriptional repressor in breast cancer targeting TAZ

IntroductionCancer stem cells (CSCs) targeted therapy holds the potential for improving cancer management; identification of stemness-related genes in CSCs is necessary for its development.MethodsThe Cancer Genome Atlas (TCGA) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) datasets were used for survival analysis. ZSCAN1 correlated genes was identified by Spearman correlation analysis. Breast cancer stem-like cells (BCSLCs) were isolated by sorting CD44+CD24- cells from suspension cultured breast cancer (BC) spheroids. The sphere-forming capacity and sphere- and tumor-initiating capacities were determined by sphere formation and limiting dilution assays. The relative gene expression was determined by qRT-PCR, western blot. Lentivirus system was used for gene manipulation. Nuclear run-on assay was employed to examine the levels of nascent mRNAs. DNA pull-down and Chromatin immunoprecipitation (ChIP) assays were used for determining the interaction between protein and target DNA fragments. Luciferase reporter assay was used for evaluating the activity of the promoter.Results and discussionZSCAN1 is aberrantly suppressed in BC, and this suppression indicates a bad prognosis. Ectopic expression of ZSCAN1 inhibited the proliferation, clonogenicity, and tumorigenicity of BC cells. ZSCAN1-overexpressing BCSLCs exhibited weakened stemness properties. Normal human mammary epithelial (HMLE) cells with ZSCAN1 depletion exhibited enhanced stemness properties. Mechanistic studies showed that ZSCAN1 directly binds to -951 ~ -925bp region of WWTR1 (encodes TAZ) promoter, inhibits WWTR1 transcription, thereby inhibiting the stemness of BCSCs. Our work thus revealed ZSCAN1 as a novel stemness-related tumor suppressor and transcriptional repressor in BC

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Recent advances in small incision lenticule extraction (SMILE)-derived refractive lenticule preservation and clinical reuse

Small incision lenticule extraction (SMILE) has become one of the mainstream refractive surgeries in recent years, with satisfactory efficacy, safety, and predictability. SMILE-derived refractive lenticule, the byproduct of the surgery, holds great potential in clinical practice given its easy access and good biocompatibility. Numerous studies have been published to describe its applications in refractive correction, corneal ectasia diseases, and corneal defects. The feasibility and safety were validated in both animal models and clinical studies. Moreover, the preservation method is also crucial for its further promotion and application. Novel techniques are also evaluated and applied in lenticule preservation. We covered the recent advances in the preservation of corneal stromal lenticules and their clinical reuse in this review

Development of a molecular feature-based survival prediction model of ovarian cancer using the deep neural network

202304 bcwwVersion of RecordSelf-fundedEarly releaseCC, In Press, Corrected Proof, need replace Vo