Stock market trading volumes and economic uncertainty dependence: before and during Sino-U.S. trade friction

This article mainly studies the interaction between the economic uncertainty and stock market trading volumes changes before and during Sino-U.S. trade friction using multifractal detrended fluctuation analysis (M.F.-D.F.A.) and multifractal detrended crosscorrelation analysis (M.F.-D.C.C.A.). Our research aims to reveal whether the economic uncertainty increased by Sino-U.S. trade friction affects stock market trading volume more susceptible, as well as how policymaker strengthen risk management and maintain financial stability. The results show that the dynamic volatility linkages between economic uncertainty and stock market trading volumes changes are multifractal, and the cross-correlation of volatility linkages are anti-persistent. Through the rolling-windows analysis, we also find that the economic uncertainty and trading volumes are anti-persistent dynamic cross-correlated. This means that while economic uncertainty increases, trading volume decreases. Besides, Sino-U.S. trade friction has impact on the cross-correlated behaviour significantly, suggesting that stock markets’ risks are relatively large and trading volumes changes are more susceptible by economic uncertainty during Sino-U.S. trade friction in the U.S. Our study complements existing literature about the stock markets trading volumes and economic uncertainty dependence relationship by multifractal theory’s methods. The overall findings imply that the increased economic uncertainty caused by Sino-U.S. trade friction exacerbates financial risks, which are useful for policymakers and investors

Uncertainty quantification for nonconvex tensor completion: Confidence intervals, heteroscedasticity and optimality

We study the distribution and uncertainty of nonconvex optimization for noisy tensor completion -- the problem of estimating a low-rank tensor given incomplete and corrupted observations of its entries. Focusing on a two-stage estimation algorithm proposed by Cai et al. (2019), we characterize the distribution of this nonconvex estimator down to fine scales. This distributional theory in turn allows one to construct valid and short confidence intervals for both the unseen tensor entries and the unknown tensor factors. The proposed inferential procedure enjoys several important features: (1) it is fully adaptive to noise heteroscedasticity, and (2) it is data-driven and automatically adapts to unknown noise distributions. Furthermore, our findings unveil the statistical optimality of nonconvex tensor completion: it attains un-improvable $\ell_{2}$ accuracy -- including both the rates and the pre-constants -- when estimating both the unknown tensor and the underlying tensor factors.Comment: Accepted in part to ICML 202

Nonconvex Low-Rank Tensor Completion from Noisy Data

We study a noisy tensor completion problem of broad practical interest, namely, the reconstruction of a low-rank tensor from highly incomplete and randomly corrupted observations of its entries. While a variety of prior work has been dedicated to this problem, prior algorithms either are computationally too expensive for large-scale applications, or come with sub-optimal statistical guarantees. Focusing on "incoherent" and well-conditioned tensors of a constant CP rank, we propose a two-stage nonconvex algorithm -- (vanilla) gradient descent following a rough initialization -- that achieves the best of both worlds. Specifically, the proposed nonconvex algorithm faithfully completes the tensor and retrieves all individual tensor factors within nearly linear time, while at the same time enjoying near-optimal statistical guarantees (i.e. minimal sample complexity and optimal estimation accuracy). The estimation errors are evenly spread out across all entries, thus achieving optimal $\ell_{\infty}$ statistical accuracy. We have also discussed how to extend our approach to accommodate asymmetric tensors. The insight conveyed through our analysis of nonconvex optimization might have implications for other tensor estimation problems.Comment: Accepted to Operations Researc

Minimax Estimation of Linear Functions of Eigenvectors in the Face of Small Eigen-Gaps

Eigenvector perturbation analysis plays a vital role in various data science applications. A large body of prior works, however, focused on establishing $\ell_{2}$ eigenvector perturbation bounds, which are often highly inadequate in addressing tasks that rely on fine-grained behavior of an eigenvector. This paper makes progress on this by studying the perturbation of linear functions of an unknown eigenvector. Focusing on two fundamental problems -- matrix denoising and principal component analysis -- in the presence of Gaussian noise, we develop a suite of statistical theory that characterizes the perturbation of arbitrary linear functions of an unknown eigenvector. In order to mitigate a non-negligible bias issue inherent to the natural ``plug-in'' estimator, we develop de-biased estimators that (1) achieve minimax lower bounds for a family of scenarios (modulo some logarithmic factor), and (2) can be computed in a data-driven manner without sample splitting. Noteworthily, the proposed estimators are nearly minimax optimal even when the associated eigen-gap is {\em substantially smaller} than what is required in prior statistical theory

Binarized Spectral Compressive Imaging

Existing deep learning models for hyperspectral image (HSI) reconstruction achieve good performance but require powerful hardwares with enormous memory and computational resources. Consequently, these methods can hardly be deployed on resource-limited mobile devices. In this paper, we propose a novel method, Binarized Spectral-Redistribution Network (BiSRNet), for efficient and practical HSI restoration from compressed measurement in snapshot compressive imaging (SCI) systems. Firstly, we redesign a compact and easy-to-deploy base model to be binarized. Then we present the basic unit, Binarized Spectral-Redistribution Convolution (BiSR-Conv). BiSR-Conv can adaptively redistribute the HSI representations before binarizing activation and uses a scalable hyperbolic tangent function to closer approximate the Sign function in backpropagation. Based on our BiSR-Conv, we customize four binarized convolutional modules to address the dimension mismatch and propagate full-precision information throughout the whole network. Finally, our BiSRNet is derived by using the proposed techniques to binarize the base model. Comprehensive quantitative and qualitative experiments manifest that our proposed BiSRNet outperforms state-of-the-art binarization methods and achieves comparable performance with full-precision algorithms. Code and models are publicly available at https://github.com/caiyuanhao1998/BiSCI and https://github.com/caiyuanhao1998/MSTComment: NeurIPS 2023; The first work to study binarized spectral compressive imaging reconstruction proble

Increased expression of Gremlin1 promotes proliferation and epithelial mesenchymal transition in gastric cancer cells and correlates with poor prognosis of patients with gastric cancer

Background/Aim: Gremlin1 (GREM1) plays an important role in certain malignancies by antagonising bone morphogenetic proteins and regulating angiogenesis directly/indirectly. The present study aimed to investigate the role of Gremlin1 in the development and progression of gastric cancer (GC). Materials and Methods: Expression of GREM1 in GCs was examined using quantitative real time PCR and The Cancer Genomic Atlas (TCGA) data. Influence on cellular functions was determined in both Gremlin1 knockdown and overexpression cell line models. Results: GREM1 expression was up-regulated in GCs, which was correlated with poorer survival. Increased GREM1 expression was significantly correlated with tumour growth/invasion and lymphatic metastasis. Gremlin1 promoted proliferation and tumourigenic capacity of GC cells in vitro. GREM1 expression was associated with epithelial mesenchymal transition (EMT), angiogenesis and lymphangiogenesis in GC. Conclusion: Increased GREM1 expression in GCs is associated with disease progression and poor prognosis in which EMT, angiogenesis and lymphangiogenesis are likely involved

Persistent organic pollutants in ocean sediments from the North Pacific to the Arctic Ocean

Concentrations of polychlorinated biphenyls (PCBs), organochlorine pesticides (OC pesticides), and polybrominated diphenyl ethers (PBDEs) are reported in surficial sediments sampled along cruise transects from the Bering Sea to the central Arctic Ocean. OCs and PCBs all had significantly higher concentrations in the relatively shallow water (500 m) of the Bering Sea and Arctic Ocean (e.g., Canada Basin ΣPCB 149 ± 102 pg g−1 dw). Concentrations were similar to, or slightly lower than, studies from the 1990s, indicating a lack of a declining trend. PBDEs (excluding BDE-209) displayed very low concentrations (e.g., range of median values, 3.5–6.6 pg/g dw). In the shelf areas, the sediments comprised similar proportions of silt and clay, whereas the deep basin sediments were dominated by clay, with a lower total organic carbon (TOC) content. While significant positive correlations were observed between persistent organic pollutant (POP) concentrations and TOC (Pearson correlation, r = 0.66–0.75, p <0.05), the lack of strong correlations, combined with differing chemical profiles between the sediments and technical formulations (and/or marine surface waters), indicate substantial chemical processing during transfer to the benthic environment. Marked differences in sedimentation rates between the shallow and deeper water regions are apparent (the ∼5 cm-depth grab samples collected here representing ∼100 years of accumulation for the shelf sediments and ∼1000 years for the deeper ocean regions), which may bias any comparisons. Nonetheless, the sediments of the shallower coastal arctic seas appear to serve as significant repositories for POPs deposited from surface waters

Multiple tumor suppressors regulate a HIF-dependent negative feedback loop via ISGF3 in human clear cell renal cancer.

Whereas VHL inactivation is a primary event in clear cell renal cell carcinoma (ccRCC), the precise mechanism(s) of how this interacts with the secondary mutations in tumor suppressor genes, including PBRM1, KDM5C/JARID1C, SETD2, and/or BAP1, remains unclear. Gene expression analyses reveal that VHL, PBRM1, or KDM5C share a common regulation of interferon response expression signature. Loss of HIF2α, PBRM1, or KDM5C in VHL-/-cells reduces the expression of interferon stimulated gene factor 3 (ISGF3), a transcription factor that regulates the interferon signature. Moreover, loss of SETD2 or BAP1 also reduces the ISGF3 level. Finally, ISGF3 is strongly tumor-suppressive in a xenograft model as its loss significantly enhances tumor growth. Conversely, reactivation of ISGF3 retards tumor growth by PBRM1-deficient ccRCC cells. Thus after VHL inactivation, HIF induces ISGF3, which is reversed by the loss of secondary tumor suppressors, suggesting that this is a key negative feedback loop in ccRCC. © 2018, Liao et al