Delayed self-feedback echo state network for long-term dynamics of hyperchaotic systems

Analyzing the long-term behavior of hyperchaotic systems poses formidable challenges in the field of nonlinear science. This paper proposes a data-driven model called the delayed self-feedback echo state network (self-ESN) specifically designed for the evolution behavior of hyperchaotic systems. Self-ESN incorporates a delayed self-feedback term into the dynamic equation of a reservoir to reflect the finite transmission speed of neuron signals. Delayed self-feedback establishes a connection between the current and previous time steps of the reservoir state and provides an effective means to capture the dynamic characteristics of the system, thereby significantly improving memory performance. In addition, the concept of local echo state property (ESP) is introduced to relax the conventional ESP condition, and theoretical analysis is conducted on guiding the selection of feedback delay and gain to ensure the local ESP. The judicious selection of feedback gain and delay in self-ESN improves prediction accuracy and overcomes the challenges associated with obtaining optimal parameters of the reservoir in conventional ESN models. Numerical experiments are conducted to assess the long-term prediction capabilities of the self-ESN across various scenarios, including a 4D hyperchaotic system, a hyperchaotic network, and an infinite-dimensional delayed chaotic system. The experiments involve reconstructing bifurcation diagrams, predicting the chaotic synchronization, examining spatiotemporal evolution patterns, and uncovering the hidden attractors. The results underscore the capability of the proposed self-ESN as a strategy for long-term prediction and analysis of the complex systems

Delayed self-feedback echo state network for long-term dynamics of hyperchaotic systems

Analyzing the long-term behavior of hyperchaotic systems poses formidable challenges in the field of nonlinear science. This paper proposes a data-driven model called the delayed self-feedback echo state network (self-ESN) specifically designed for the evolution behavior of hyperchaotic systems. Self-ESN incorporates a delayed self-feedback term into the dynamic equation of a reservoir to reflect the finite transmission speed of neuron signals. Delayed self-feedback establishes a connection between the current and previous time steps of the reservoir state and provides an effective means to capture the dynamic characteristics of the system, thereby significantly improving memory performance. In addition, the concept of local echo state property (ESP) is introduced to relax the conventional ESP condition, and theoretical analysis is conducted on guiding the selection of feedback delay and gain to ensure the local ESP. The judicious selection of feedback gain and delay in self-ESN improves prediction accuracy and overcomes the challenges associated with obtaining optimal parameters of the reservoir in conventional ESN models. Numerical experiments are conducted to assess the long-term prediction capabilities of the self-ESN across various scenarios, including a 4D hyperchaotic system, a hyperchaotic network, and an infinite-dimensional delayed chaotic system. The experiments involve reconstructing bifurcation diagrams, predicting the chaotic synchronization, examining spatiotemporal evolution patterns, and uncovering the hidden attractors. The results underscore the capability of the proposed self-ESN as a strategy for long-term prediction and analysis of the complex systems

Short sellers’ accusations against Chinese reverse mergers: Information analytics or guilt by association?

AbstractThis paper studies short sellers’ trading strategies and their effects on the financial market by examining their accusations of fraud against Chinese reverse merger firms (CRMs) in the US. We find that short sellers rely on firms’ fundamental information, especially relative financial indicators, to locate their “prey.” Specifically, they compare a target firm’s financial indicators (e.g., growth and receivables) with both the industry average and the firm’s history. We find no evidence that short sellers accuse CRMs simply because of their reverse merger label. Additionally, we test the accuracy of short sellers’ accusations in the long run and find that accused firms are more likely to delist and less likely to recover from price plunges. Our results also indicate that CRMs’ high exposure to short sellers’ accusations stem from adverse selection problems: firms with high litigation risk are more likely to choose reverse mergers to access the US capital market. Overall, our results support the view that short sellers are sophisticated investors and shed some light on their decision processes

Effects of Shell on Bore center Annular Shaped Charges Formation and Penetrating into Steel Targets

Annular shaped charge can efficiently create large penetration diameter, which can solve the problem of small penetration diameter of a traditional shaped charge, and thus meeting the requirements of large penetration diameter in some specific situations. In this paper, the influence of five kinds shell structures, i.e. no shell, aluminum shell with thickness of 2.0 mm and steel shell with thickness of 2.0 mm, 3.0 mm and 4.0 mm, on bore-center annular shaped charges (BCASCs) formation and penetrating steel targets was investigated by numerical simulations and experiments. The numerical simulation results are in good agreement with the experimental results. The results showed that, from no shell to aluminum shell of 2.0 mm and then to steel shell of 2.0 mm, 3.0 mm and 4.0 mm for BCASCs, the diameter and radial velocity of projectile head decrease, the axial velocity of BCASC projectiles increases gradually, the penetration diameter of the targets decreases, and the penetration depth increases. The penetration diameter caused by the BCASC with no shell is the largest, being 116.0 mm (1.16D), D is the charge diameter. The penetration depth caused by the BCASC with steel shell of 4.0 mm thickness is the deepest, being 76.4 mm (0.76D)

The Role of SRC-1 in Murine Prostate Carcinogenesis Is Nonessential due to a Possible Compensation of SRC-3/AIB1 Overexpression

The androgen and androgen receptor (AR)-regulated gene expression plays important roles in normal prostate and prostate cancer development, and AR transcriptional control of genes is mediated by transcriptional coactivators, including the three members of the steroid receptor coactivator (SRC) family, SRC-1 (NCOA1), SRC-2 (TIF2/GRIP1/NCOA2) and SRC-3 (AIB1, ACTR/RAC3/NCOA3). SRC-1 and SRC-3 are overexpressed in multiple human endocrine cancers and knockdown of either one of them in prostate cancer cell lines impedes cellular proliferation. Knockout of SRC-3 in mice suppresses the progression of spontaneous prostate carcinogenesis. In this study, we investigated SRC-1 contribution to prostate cancer in vivo by deleting the SRC-1 gene in TRAMP mice, which contain the probasin promoter-driven SV40 T/t antigen transgene. In assessing tumor mass of mice at various ages, we found that initiation and progression of prostate cancer induced by SV40 T/t antigens were unaltered in SRC-1(-/-) mice versus WT mice. Primary tumor histology and metastasis to distant lymph nodes were also similar in these mice at all time points assessed. These results demonstrate that the role of SRC-1 in mouse prostate carcinogenesis is nonessential and different from the essential contribution of SRC-3 that is required for prostate cancer progression and metastasis in mice. Interestingly, we observed that during prostate tumorigenesis SRC-1 expression was relatively constant, while SRC-3 expression was significantly elevated. Therefore, the loss of SRC-1 function may be compensated by SRC-3 overexpression during prostate tumorigenesis in SRC-1(-/-) mice