RAGIC: Risk-Aware Generative Adversarial Model for Stock Interval Construction

Efforts to predict stock market outcomes have yielded limited success due to the inherently stochastic nature of the market, influenced by numerous unpredictable factors. Many existing prediction approaches focus on single-point predictions, lacking the depth needed for effective decision-making and often overlooking market risk. To bridge this gap, we propose a novel model, RAGIC, which introduces sequence generation for stock interval prediction to quantify uncertainty more effectively. Our approach leverages a Generative Adversarial Network (GAN) to produce future price sequences infused with randomness inherent in financial markets. RAGIC's generator includes a risk module, capturing the risk perception of informed investors, and a temporal module, accounting for historical price trends and seasonality. This multi-faceted generator informs the creation of risk-sensitive intervals through statistical inference, incorporating horizon-wise insights. The interval's width is carefully adjusted to reflect market volatility. Importantly, our approach relies solely on publicly available data and incurs only low computational overhead. RAGIC's evaluation across globally recognized broad-based indices demonstrates its balanced performance, offering both accuracy and informativeness. Achieving a consistent 95% coverage, RAGIC maintains a narrow interval width. This promising outcome suggests that our approach effectively addresses the challenges of stock market prediction while incorporating vital risk considerations

SafeLight: A Reinforcement Learning Method toward Collision-free Traffic Signal Control

Traffic signal control is safety-critical for our daily life. Roughly one-quarter of road accidents in the U.S. happen at intersections due to problematic signal timing, urging the development of safety-oriented intersection control. However, existing studies on adaptive traffic signal control using reinforcement learning technologies have focused mainly on minimizing traffic delay but neglecting the potential exposure to unsafe conditions. We, for the first time, incorporate road safety standards as enforcement to ensure the safety of existing reinforcement learning methods, aiming toward operating intersections with zero collisions. We have proposed a safety-enhanced residual reinforcement learning method (SafeLight) and employed multiple optimization techniques, such as multi-objective loss function and reward shaping for better knowledge integration. Extensive experiments are conducted using both synthetic and real-world benchmark datasets. Results show that our method can significantly reduce collisions while increasing traffic mobility.Comment: Accepted by AAAI 2023, appendix included. 9 pages + 5 pages appendix, 12 figures, in Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI'23), Feb 202

Design of double band Beidou navigation antenna with wide axial ratio beam

ObjectivesIn order to effectively broaden the beam width of the 3 dB axis ratio of a Beidou antenna, this paper proposes a slotted laminated microstrip patch antenna loaded with parasitic elements. MethodsThe Beidou dual frequency band feature is realized through a two-layer substrate structure. The bandwidth is widened by slotting. Circular polarized radiation is achieved by cutting corners at the edge of the patch. At the same time, L-shaped parasitic elements are loaded around the patch to broaden the 3 dB axial ratio beam width of the antenna.ResultsThe measured and simulated results are in good agreement. The relative bandwidths of the antenna in the Beidou B1 and B2 bands are 3% and 7.5%. The beam width of the 3 dB axis ratio can reach 180° and 176°, and the maximum gain can reach 6 dBi. In the Beidou receiver test, the number of antenna satellites can reach more than 17, and the positioning accuracy is within meters. ConclusionsThis paper proposes and validates a high-precision positioning dual band Beidou navigation antenna with wide axis ratio beam width. The results can provide references for the design of small Beidou navigation antennas

Integrative Analysis of Genome and Expression Profile Data Reveals the Genetic Mechanism of the Diabetic Pathogenesis in Goto Kakizaki (GK) Rats

The Goto Kakizaki (GK) rats which can spontaneously develop type 2 diabetes (T2D), are generated by repeated inbreeding of Wistar rats with glucose intolerance. The glucose intolerance in GK rat is mainly attributed to the impairment in glucose-stimulated insulin secretion (GSIS). In addition, GK rat display a decrease in beta cell mass, and a change in insulin action. However, the genetic mechanism of these features remain unclear. In the present study, we analyzed the population variants of GK rats and control Wistar rats by whole genome sequencing and identified 1,839 and 1,333 specific amino acid changed (SAAC) genes in GK and Wistar rats, respectively. We also detected the putative artificial selective sweeps (PASS) regions in GK rat which were enriched with GK fixed variants and were under selected in the initial diabetic-driven derivation by homogeneity test with the fixed and polymorphic sites between GK and Wistar populations. Finally, we integrated the SAAC genes, PASS region genes and differentially expressed genes in GK pancreatic beta cells to reveal the genetic mechanism of the impairment in GSIS, a decrease in beta cell mass, and a change in insulin action in GK rat. The results showed that Slc2a2 gene was related to impaired glucose transport and Adcy3, Cacna1f, Bmp4, Fam3b, and Ptprn2 genes were related to Ca2+ channel dysfunction which may responsible for the impaired GSIS. The genes Hnf4g, Bmp4, and Bad were associated with beta cell development and may be responsible for a decrease in beta cell mass while genes Ide, Ppp1r3c, Hdac9, Ghsr, and Gckr may be responsible for the change in insulin action in GK rats. The overexpression or inhibition of Bmp4, Fam3b, Ptprn2, Ide, Hnf4g, and Bad has been reported to change the glucose tolerance in rodents. However, the genes Bmp4, Fam3b, and Ptprn2 were found to be associated with diabetes in GK rats for the first time in the present study. Our findings provide a comprehensive genetic map of the abnormalities in GK genome which will be helpful in understand the underlying genetic mechanism of pathogenesis of diabetes in GK rats

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

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

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

Safety in Traffic Management Systems: A Comprehensive Survey

Traffic management systems play a vital role in ensuring safe and efficient transportation on roads. However, the use of advanced technologies in traffic management systems has introduced new safety challenges. Therefore, it is important to ensure the safety of these systems to prevent accidents and minimize their impact on road users. In this survey, we provide a comprehensive review of the literature on safety in traffic management systems. Specifically, we discuss the different safety issues that arise in traffic management systems, the current state of research on safety in these systems, and the techniques and methods proposed to ensure the safety of these systems. We also identify the limitations of the existing research and suggest future research directions

Effects of lipocalin-2 on brain endothelial adhesion and permeability.

Lipocalin-2 (LCN2) is a stress protein, and can be hyper-produced by many kinds of cells after exposure to injury or disease conditions. In this study, we asked whether LCN2 may play a protective role in cerebral endothelium. After focal cerebral ischemia in rats, plasma levels of LCN2 were significantly elevated at 6, 12, and 24 hrs, and persisted until 3 days post-stroke. To assess the vascular mechanisms of LCN2, we used brain endothelial cell cultures to investigate its effects on neutrophil adhesion and endothelial barrier integrity. LCN2 did not affect neutrophil adhesion to endothelial cells either under normal conditions or after TNFα stimulation. TNFα significantly increased endothelial permeability, and LCN2 rescued endothelial permeability. Concomitantly, LCN2 restored the membrane distribution of the tight junction protein ZO-1 and the adherens junction protein VE-cadherin. Our findings suggest that elevated LCN2 in the blood after ischemic stroke might affect endothelial function, in part by reducing damage to endothelial junctional proteins and maintain blood-brain barrier integrity

Transcriptome Changes of Skeletal Muscle RNA-Seq Speculates the Mechanism of Postprandial Hyperglycemia in Diabetic Goto-Kakizaki Rats During the Early Stage of T2D

To address how skeletal muscle contributes to postprandial hyperglycemia, we performed skeletal muscle transcriptome analysis of diabetic Goto-Kakizaki (GK) and control Wistar rats by RNA sequencing (RNA-Seq). We obtained 600 and 1785 differentially expressed genes in GK rats compared to those Wistar rats at three and four weeks of age, respectively. Specifically, Tbc1d4, involved in glucose uptake, was significantly downregulated in the skeletal muscle of GK aged both three and four weeks compared to those of age-matched Wistar rats. Pdk4, related to glucose uptake and oxidation, was significantly upregulated in the skeletal muscle of GK aged both three and four weeks compared to that of age-matched Wistar rats. Genes (Acadl, Acsl1 and Fabp4) implicated in fatty acid oxidation were significantly upregulated in the skeletal muscle of GK aged four weeks compared to those of age-matched Wistar rats. The overexpression or knockout of Tbc1d4, Pdk4, Acadl, Acsl1 and Fabp4 has been reported to change glucose uptake and fatty acid oxidation directly in rodents. By taking the results of previous studies into consideration, we speculated that dysregulation of key dysregulated genes (Tbc1d4, Pdk4, Acadl, Acsl1 and Fabp4) may lead to a decrease in glucose uptake and oxidation, and an increase in fatty acid oxidation in GK skeletal muscle at three and four weeks, which may, in turn, contribute to postprandial hyperglycemia. Our research revealed transcriptome changes in GK skeletal muscle at three and four weeks. Tbc1d4, Acadl, Acsl1 and Fabp4 were found to be associated with early diabetes in GK rats for the first time, which may provide a new scope for pathogenesis of postprandial hyperglycemia