Chinese Real Estate Market Risk Measure by Value-at-Risk: an Empirical Study on Real Estate Market of Beijing and Chongqing

Purpose – The purpose of this article is introducing a quantified risk assessment use in real estate for forecasting mark risks from a macro view, and applies it in real market to give suggestions for investors and government. Early studies of real estate investment market are focus on finance productions, such as property trust, real estate stocks. This article tries to outline the whole real estate industry’s risk level. Design/methodology/approach – The article presents an idea borrow from Value at Risk (VaR). VaR is a classic quantified risk measurement. This design will find the relation between property industry’s developments and macro factors, such as GDP, population, as risk factors. Through Monte Carol simulation, gain the volatility of each risk factor and forecast real estate industry’s loss-VaR. Empirical research findings – The empirical research is compare the property market risk between Chinese first-tier cities (Beijing as sample) and second-tier cities (Chongqing as sample) through the VaR method in real estate market. The result show both real estate markets of first-tier cities (Beijing) and second-tier cities (Chongqing) are safety for investor. Practical implications – VaR as a risk measurement of finance production, it is also can apply in other industries. Keywords: Real estate, Market risk, Va

SAM3D: Segment Anything in 3D Scenes

In this work, we propose SAM3D, a novel framework that is able to predict masks in 3D point clouds by leveraging the Segment-Anything Model (SAM) in RGB images without further training or finetuning. For a point cloud of a 3D scene with posed RGB images, we first predict segmentation masks of RGB images with SAM, and then project the 2D masks into the 3D points. Later, we merge the 3D masks iteratively with a bottom-up merging approach. At each step, we merge the point cloud masks of two adjacent frames with the bidirectional merging approach. In this way, the 3D masks predicted from different frames are gradually merged into the 3D masks of the whole 3D scene. Finally, we can optionally ensemble the result from our SAM3D with the over-segmentation results based on the geometric information of the 3D scenes. Our approach is experimented with ScanNet dataset and qualitative results demonstrate that our SAM3D achieves reasonable and fine-grained 3D segmentation results without any training or finetuning of SAM.Comment: Technical Report. The code is released at https://github.com/Pointcept/SegmentAnything3

CLIP2Point: Transfer CLIP to Point Cloud Classification with Image-Depth Pre-training

Pre-training across 3D vision and language remains under development because of limited training data. Recent works attempt to transfer vision-language pre-training models to 3D vision. PointCLIP converts point cloud data to multi-view depth maps, adopting CLIP for shape classification. However, its performance is restricted by the domain gap between rendered depth maps and images, as well as the diversity of depth distributions. To address this issue, we propose CLIP2Point, an image-depth pre-training method by contrastive learning to transfer CLIP to the 3D domain, and adapt it to point cloud classification. We introduce a new depth rendering setting that forms a better visual effect, and then render 52,460 pairs of images and depth maps from ShapeNet for pre-training. The pre-training scheme of CLIP2Point combines cross-modality learning to enforce the depth features for capturing expressive visual and textual features and intra-modality learning to enhance the invariance of depth aggregation. Additionally, we propose a novel Dual-Path Adapter (DPA) module, i.e., a dual-path structure with simplified adapters for few-shot learning. The dual-path structure allows the joint use of CLIP and CLIP2Point, and the simplified adapter can well fit few-shot tasks without post-search. Experimental results show that CLIP2Point is effective in transferring CLIP knowledge to 3D vision. Our CLIP2Point outperforms PointCLIP and other self-supervised 3D networks, achieving state-of-the-art results on zero-shot and few-shot classification

Data-Driven Network Neuroscience: On Data Collection and Benchmark

This paper presents a comprehensive and quality collection of functional human brain network data for potential research in the intersection of neuroscience, machine learning, and graph analytics. Anatomical and functional MRI images of the brain have been used to understand the functional connectivity of the human brain and are particularly important in identifying underlying neurodegenerative conditions such as Alzheimer's, Parkinson's, and Autism. Recently, the study of the brain in the form of brain networks using machine learning and graph analytics has become increasingly popular, especially to predict the early onset of these conditions. A brain network, represented as a graph, retains richer structural and positional information that traditional examination methods are unable to capture. However, the lack of brain network data transformed from functional MRI images prevents researchers from data-driven explorations. One of the main difficulties lies in the complicated domain-specific preprocessing steps and the exhaustive computation required to convert data from MRI images into brain networks. We bridge this gap by collecting a large amount of available MRI images from existing studies, working with domain experts to make sensible design choices, and preprocessing the MRI images to produce a collection of brain network datasets. The datasets originate from 5 different sources, cover 3 neurodegenerative conditions, and consist of a total of 2,642 subjects. We test our graph datasets on 5 machine learning models commonly used in neuroscience and on a recent graph-based analysis model to validate the data quality and to provide domain baselines. To lower the barrier to entry and promote the research in this interdisciplinary field, we release our brain network data https://doi.org/10.17608/k6.auckland.21397377 and complete preprocessing details including codes

Observation of the Knot Topology of Non-Hermitian Systems in a Single Spin

The non-Hermiticity of the system gives rise to distinct knot topology that has no Hermitian counterpart. Here, we report a comprehensive study of the knot topology in gapped non-Hermitian systems based on the universal dilation method with a long coherence time nitrogen-vacancy center in a $^{\text{12}}$C isotope purified diamond. Both the braiding patterns of energy bands and the eigenstate topology are revealed. Furthermore, the global biorthogonal Berry phase related to the eigenstate topology has been successfully observed, which identifies the topological invariance for the non-Hermitian system. Our method paves the way for further exploration of the interplay among band braiding, eigenstate topology and symmetries in non-Hermitian quantum systems

Third-order exceptional line in a nitrogen-vacancy spin system

The exceptional points (EPs) aroused from the non-Hermiticity bring rich phenomena, such as exceptional nodal topologies, unidirectional invisibility, single-mode lasing, sensitivity enhancement and energy harvesting. Isolated high-order EPs have been observed to exhibit richer topological characteristics and better performance in sensing over 2nd-order EPs. Recently, high-order EP geometries, such as lines or rings formed entirely by high order EPs, are predicted to provide richer phenomena and advantages over stand-alone high-order EPs. However, experimental exploration of high-order EP geometries is hitherto beyond reach due to the demand of more degrees of freedom in the Hamiltonian's parameter space or a higher level of symmetries. Here we report the observation of the third-order exceptional line (EL) at the atomic scale. By introducing multiple symmetries, the emergence of the third-order EL has been successfully realized with a single electron spin of nitrogen-vacancy center in diamond. Furthermore, the behaviors of the EP structure under different symmetries are systematically investigated. The symmetries are shown to play essential roles in the occurrence of high-order EPs and the related EP geometries. Our work opens a new avenue to explore high-order EP-related topological physics at the atomic scale and to the potential applications of high-order EPs in quantum technologies

Eye movements as predictor of cognitive improvement after cognitive remediation therapy in patients with schizophrenia

AimBaseline cognitive functions of patients predicted the efficacy of cognitive remediation therapy (CRT), but results are mixed. Eye movement is a more objective and advanced assessment of cognitive functions than neuropsychological testing. We aimed to investigate the applicability of eye movements in predicting cognitive improvement after patients with schizophrenia were treated with CRT.MethodsWe recruited 79 patients with schizophrenia to complete 8 weeks of CRT and assessed their cognitive improvement outcomes. Eye movements were assessed by prosaccades, antisaccades, and free-viewing tasks at baseline, and neuropsychological tests in four cognitive domains were assessed before and after treatment to calculate treatment outcomes. Predictors of demographic information, clinical characteristics, and eye movement measures at baseline on cognitive improvement outcomes were analyzed using logistic regression analysis. We further compared the predictive performance between eye movement measurements and neuropsychological test regarding the effect of CRT on cognitive improvement, and explored factors that could be affect the treatment outcomes in different cognitive domains.ResultsAs operationally defined, 33 patients showed improved in cognition (improved group) and 46 patients did not (non-improved group) after CRT. Patients with schizophrenia being employed, lower directional error rate in antisaccade task, and lower the gap effect (i.e., the difference in saccadic latency between the gap condition and overlap condition) in prosaccade task at baseline predicted cognitive improvement in CRT. However, performance in the free-viewing task not associated with cognitive improvement in patients in CRT. Our results show that eye-movement prediction model predicted the effect of CRT on cognitive improvement in patients with schizophrenia better than neuropsychological prediction model in CRT. In addition, baseline eye-movements, cognitive reserve, antipsychotic medication dose, anticholinergic cognitive burden change, and number of training sessions were associated with improvements in four cognitive domains.ConclusionEye movements as a non-invasiveness, objective, and sensitive method of evaluating cognitive function, and combined saccadic measurements in pro- and anti-saccades tasks could be more beneficial than free-viewing task in predicting the effect of CRT on cognitive improvement in patients with schizophrenia

Chinese Real Estate Market Risk Measure by Value-at-Risk: an Empirical Study on Real Estate Market of Beijing and Chongqing

Purpose – The purpose of this article is introducing a quantified risk assessment use in real estate for forecasting mark risks from a macro view, and applies it in real market to give suggestions for investors and government. Early studies of real estate investment market are focus on finance productions, such as property trust, real estate stocks. This article tries to outline the whole real estate industry’s risk level. Design/methodology/approach – The article presents an idea borrow from Value at Risk (VaR). VaR is a classic quantified risk measurement. This design will find the relation between property industry’s developments and macro factors, such as GDP, population, as risk factors. Through Monte Carol simulation, gain the volatility of each risk factor and forecast real estate industry’s loss-VaR. Empirical research findings – The empirical research is compare the property market risk between Chinese first-tier cities (Beijing as sample) and second-tier cities (Chongqing as sample) through the VaR method in real estate market. The result show both real estate markets of first-tier cities (Beijing) and second-tier cities (Chongqing) are safety for investor. Practical implications – VaR as a risk measurement of finance production, it is also can apply in other industries. Keywords: Real estate, Market risk, Va

Impact of TGEV infection on the pig small intestine

Abstract Background Pig diarrhea causes high mortality and large economic losses in the swine industry. Transmissible gastroenteritis virus (TGEV) causes pig diarrhea, with 100% mortality in piglets less than 2 weeks old. No investigation has yet been made of the small intestine of piglets that survived infection by TGEV. Methods In this study, we evaluated the impact of TGEV infection on the small intestine of recovered pigs. Results Histological analyses showed that TGEV infection led to villi atrophy, and reduced villous height and crypt depth. The number of SIgA positive cells, CD3+T cells, and dendritic cells (DCs) in jejunum decreased after TGEV infection in vivo. In contrast, microfold cell (M cell) numbers and cell proliferation increased in infected pigs. TGEV infection also significantly enhanced the mRNA expression levels of cytokine IL-1β, IL-6, TNF-α, IL-10, and TGF-β. Additionally, lower gene copy numbers of Lactobacillus, and higher numbers of Enterobacteriaceae, were detected in mucosal scraping samples from TGEV-infected pigs. Conclusions TGEV infection damages the small intestine, impairs immune functions, and increases pathogenic bacterial loading, all of which may facilitate secondary infections by other pathogens. These findings help quantify the impact of TGEV infection and clarify the pathogenic mechanisms underlying its effects in pigs