Horizon-unbiased Investment with Ambiguity

In the presence of ambiguity on the driving force of market randomness, we consider the dynamic portfolio choice without any predetermined investment horizon. The investment criteria is formulated as a robust forward performance process, reflecting an investor's dynamic preference. We show that the market risk premium and the utility risk premium jointly determine the investors' trading direction and the worst-case scenarios of the risky asset's mean return and volatility. The closed-form formulas for the optimal investment strategies are given in the special settings of the CRRA preference

Exploring Target Representations for Masked Autoencoders

Masked autoencoders have become popular training paradigms for self-supervised visual representation learning. These models randomly mask a portion of the input and reconstruct the masked portion according to the target representations. In this paper, we first show that a careful choice of the target representation is unnecessary for learning good representations, since different targets tend to derive similarly behaved models. Driven by this observation, we propose a multi-stage masked distillation pipeline and use a randomly initialized model as the teacher, enabling us to effectively train high-capacity models without any efforts to carefully design target representations. Interestingly, we further explore using teachers of larger capacity, obtaining distilled students with remarkable transferring ability. On different tasks of classification, transfer learning, object detection, and semantic segmentation, the proposed method to perform masked knowledge distillation with bootstrapped teachers (dBOT) outperforms previous self-supervised methods by nontrivial margins. We hope our findings, as well as the proposed method, could motivate people to rethink the roles of target representations in pre-training masked autoencoders.The code and pre-trained models are publicly available at https://github.com/liuxingbin/dbot.Comment: The first two authors contributed equall

A review for solar panel fire accident prevention in large-scale PV applications

Due to the wide applications of solar photovoltaic (PV) technology, safe operation and maintenance of the installed solar panels become more critical as there are potential menaces such as hot spot effects and DC arcs, which may cause fire accidents to the solar panels. In order to minimize the risks of fire accidents in large scale applications of solar panels, this review focuses on the latest techniques for reducing hot spot effects and DC arcs. The risk mitigation solutions mainly focus on two aspects: structure reconfiguration and faulty diagnosis algorithm. The first is to reduce the hot spot effect by adjusting the space between two PV modules in a PV array or relocate some PV modules. The second is to detect the DC arc fault before it causes fire. There are three types of arc detection techniques, including physical analysis, neural network analysis, and wavelet detection analysis. Through these detection methods, the faulty PV cells can be found in a timely manner thereby reducing the risk of PV fire. Based on the review, some precautions to prevent solar panel related fire accidents in large-scale solar PV plants that are located adjacent to residential and commercial areas

Evidence supported by Mendelian randomization: impact on inflammatory factors in knee osteoarthritis

BackgroundPrior investigations have indicated associations between Knee Osteoarthritis (KOA) and certain inflammatory cytokines, such as the interleukin series and tumor necrosis factor-alpha (TNFα). To further elaborate on these findings, our investigation utilizes Mendelian randomization to explore the causal relationships between KOA and 91 inflammatory cytokines.MethodsThis two-sample Mendelian randomization utilized genetic variations associated with KOA from a large, publicly accessible Genome-Wide Association Study (GWAS), comprising 2,227 cases and 454,121 controls of European descent. The genetic data for inflammatory cytokines were obtained from a GWAS summary involving 14,824 individuals of European ancestry. Causal relationships between exposures and outcomes were primarily investigated using the inverse variance weighted method. To enhance the robustness of the research results, other methods were combined to assist, such as weighted median, weighted model and so on. Multiple sensitivity analysis, including MR-Egger, MR-PRESSO and leave one out, was also carried out. These different analytical methods are used to enhance the validity and reliability of the final results.ResultsThe results of Mendelian randomization indicated that Adenosine Deaminase (ADA), Fibroblast Growth Factor 5(FGF5), and Hepatocyte growth factor (HFG) proteins are protective factors for KOA (IVWADA: OR = 0.862, 95% CI: 0.771–0.963, p = 0.008; IVWFGF5: OR = 0.850, 95% CI: 0.764–0.946, p = 0.003; IVWHFG: OR = 0.798, 95% CI: 0.642–0.991, p = 0.042), while Tumor necrosis factor (TNFα), Colony-stimulating factor 1(CSF1), and Tumor necrosis factor ligand superfamily member 12(TWEAK) proteins are risk factors for KOA. (IVWTNFα: OR = 1.319, 95% CI: 1.067–1.631, p = 0.011; IVWCSF1: OR = 1.389, 95% CI: 1.125–1.714, p = 0.002; IVWTWEAK: OR = 1.206, 95% CI: 1.016–1.431, p = 0.032).ConclusionThe six proteins identified in this study demonstrate a close association with the onset of KOA, offering valuable insights for future therapeutic interventions. These findings contribute to the growing understanding of KOA at the microscopic protein level, paving the way for potential targeted therapeutic approaches

X-ray emission for 424 MeV/u C ions impacting on selected targets

In inertial Confinement Fusion (ICF), X-ray radiation drives the implosion requiring not only sufficient conversion efficiency of the drive energy to the X-ray but also the highly spatial symmetry..

Combinational effect of mutational bias and translational selection for translation efficiency in tomato (Solanum lycopersicum) cv. Micro-Tom

AbstractWe conducted a comprehensive analysis of codon usage bias (CUB) based on the available non-redundant full-length cDNA (nrFLcDNA) and expressed sequence tags (ESTs) data of cultivar Micro-Tom and evaluated the associations of observed CUB and measurements of transcriptional and translational effectiveness. The analysis presented in our study suggests a correlation, which is negative but highly correlated between Axis 1 and GC3s (r=−0.827, P<0.01), indicating that mutational bias has a significant and dominant repressive role to the choices of GC3. We also observed a strong positive correlation between codon adaptation index (CAI) and translational adaptation index (tAIg) (0.407, P<0.01), which demonstrates the facilitation of efficient translation by the optimal codon usage patterns of the highly expressed genes. We believe that the complete set of optimal codon usage patterns detected in this study will serve as a model to enhance the transgenesis in the studied cultivar of Solanum lycopersicum

PHOTOMETRIC OBSERVATION OF 3024 HAINAN, 3920 AUBIGNAN, AND 5951 ALICEMONET

Three minor planets were measured photometrically between 2012 September 4 and 21 using the SARA (Southeastern Association for Research in Astronomy) South telescope, located in Cerro Tololo Inter-American Observatory. The following synodic periods were found: 3024 Hainan, P = 11.785 ± 0.005 h; 3920 Aubignan, P = 4.4762 ± 0.0005 h; and 5951 Alicemonet, P = 3.8871 ± 0.0005 h

PHOTOMETRIC OBSERVATIONS OF 782 MONTEFIORE, 3842 HARLANSMITH, 5542 MOFFATT, 6720 GIFU, AND (19979) 1989 VJ

Five solar system minor planets were measured photometrically between 2012 October and December using the SARA (Southeastern Association for Research in Astronomy) telescopes located in Kitt Peak National Observatory in USA and Cerro Tololo Inter-American Observatory in Chile. The following synodic periods were found: 782 Montefiore P = 4.0728 ± 0.0006 h; 3842 Harlansmith, P = 2.7938 ± 0.0005 h; 5542 Moffatt P = 5.187 ± 0.001 h; 6720 Gifu, P = 4.231 ± 0.001 h; and (19979) 1989 VJ, P = 7.568 ± 0.005 h

Association of gestational hypertriglyceridemia, diabetes with serum ferritin levels in early pregnancy: a retrospective cohort study

AimsPrevious studies showed conflicting results linking body iron stores to the risk of gestational diabetes mellitus (GDM) and dyslipidemia. We aim to investigate the relationship between serum ferritin, and the prevalence of GDM, insulin resistance (IR) and hypertriglyceridemia.MethodsA total of 781 singleton pregnant women of gestation in Shanghai General Hospital took part in the retrospective cohort study conducted. The participants were divided into four groups by quartiles of serum ferritin levels (Q1–4). Binary logistic regressions were used to examine the strength of association between the different traits and the serum ferritin (sFer) quartiles separately, where Q1 (lowest ferritin quartile) was taken as the base reference. One-way ANOVA was adopted to compare the averages of the different variables across Sfer quartiles.ResultsCompared with the lowest serum ferritin quartile (Q1), the ORs for Q3, and Q4 in our population were 1.79 (1.01–2.646), and 2.07 (1.089-2.562) respectively and this trend persisted even after adjusted for age and pre-BMI. Women with higher serum ferritin quartile including Q3 (OR=2.182, 95%CI=1.729-5.527, P=0.003) and Q4(OR=3.137, 95%CI=3.137-8.523, P&lt;0.01)are prone to develop insulin resistance disorders. No significant difference was observed between sFer concentrations and gestational hypertriglyceridemia(GTG) in the comparison among these 4 groups across logistic regressions but TG was found positively correlated with increased ferritin values in the second trimester.ConclusionsIncreased concentrations of plasma ferritin in early pregnancy are significantly and positively associated with insulin resistance and incidence of GDM but not gestational dyslipidemia. Further clinical studies are warranted to determine whether it is necessary to encourage pregnant women to take iron supplement as a part of routine antenatal care