Asset Allocation under the Basel Accord Risk Measures

Financial institutions are currently required to meet more stringent capital requirements than they were before the recent financial crisis; in particular, the capital requirement for a large bank's trading book under the Basel 2.5 Accord more than doubles that under the Basel II Accord. The significant increase in capital requirements renders it necessary for banks to take into account the constraint of capital requirement when they make asset allocation decisions. In this paper, we propose a new asset allocation model that incorporates the regulatory capital requirements under both the Basel 2.5 Accord, which is currently in effect, and the Basel III Accord, which was recently proposed and is currently under discussion. We propose an unified algorithm based on the alternating direction augmented Lagrangian method to solve the model; we also establish the first-order optimality of the limit points of the sequence generated by the algorithm under some mild conditions. The algorithm is simple and easy to implement; each step of the algorithm consists of solving convex quadratic programming or one-dimensional subproblems. Numerical experiments on simulated and real market data show that the algorithm compares favorably with other existing methods, especially in cases in which the model is non-convex

Laser Intensity Noise Suppression for Preparing Audio-Frequency 795 nm Squeezed Vacuum State of Light at Rubidium D1 Line

Laser intensity noise suppression has essential effects on preparation and characterization of the audio-frequency squeezed vacuum state of light based on a sub-threshold optical parametric oscillator (OPO).We have implemented two feedback loops by using relevant acousto-optical modulators (AOM) to stabilize the intensity of 795-nm near infrared (NIR) fundamental laser and 397.5-nm ultraviolet (UV) laser generated by cavity-enhanced frequency doubling.Typical peak-to-peak laser intensity fluctuation with a bandwidth of $\sim10$ kHz in a half hour has been improved from $\pm7.45$$\%$ to $\pm0.06$$\%$ for 795-nm NIR laser beam, and from $\pm9.04$$\%$ to $\pm0.05$$\%$ for 397.5-nm UV laser beam, respectively. The squeezing level of the squeezed vacuum state at 795 nm prepared by the sub-threshold OPO with a PPKTP crystal has been improved from -3.3 to -4.0 dB around 3$\sim$9 kHz of audio analysis frequency range.Comment: 5 pages, 4 figure

Living in a Simulation? An Empirical Investigation of a Smart Driving-Simulation Testing System

The internet of things (IoT) generally refers to the embedding of computing and communication devices in various types of physical objects (e.g., automobiles) used in people’s daily lives. This paper draws on feedback intervention theory to investigate the impact of IoT-enabled immediate feedback interventions on individual task performance. Our research context is a smart test-simulation service based on internet-of-vehicles (IoV) technology that was implemented by a large driver-training service provider in China. This system captures and analyzes data streams from onboard sensors and cameras installed in vehicles in real time and immediately provides individual students with information about errors made during simulation tests. We postulate that the focal smart service functions as a feedback intervention (FI) that can improve task performance. We also hypothesize that student training schedules moderate this effect and propose an interaction effect on student performance based on feedback timing and the number of FI cues. We collected data about students’ demographics, their training session records, and information about their simulation test(s) and/or their official driving skills field tests and used a quasi-experimental method along with propensity score matching to empirically validate our research model. Difference-in-difference analysis and multiple regression results support the significant impact of the simulation test as an FI on student performance on the official driving skills field test. Our results also supported the interaction effect between feedback timing and the number of corrective FI cues on official test performance. This paper concludes with a discussion of the theoretical contributions and practical significance of our research

Characterizing and Predicting Early Reviewers for Effective Product Marketing on E-Commerce Websites

Online reviews have become an important source of information for users before making an informed purchase decision. Early reviews of a product tend to have a high impact on the subsequent product sales. In this paper, we take the initiative to study the behavior characteristics of early reviewers through their posted reviews on two real-world large e-commerce platforms, i.e., Amazon and Yelp. In specific, we divide product lifetime into three consecutive stages, namely early, majority and laggards. A user who has posted a review in the early stage is considered as an early reviewer. We quantitatively characterize early reviewers based on their rating behaviors, the helpfulness scores received from others and the correlation of their reviews with product popularity. We have found that (1) an early reviewer tends to assign a higher average rating score; and (2) an early reviewer tends to post more helpful reviews. Our analysis of product reviews also indicates that early reviewers' ratings and their received helpfulness scores are likely to influence product popularity. By viewing review posting process as a multiplayer competition game, we propose a novel margin-based embedding model for early reviewer prediction. Extensive experiments on two different e-commerce datasets have shown that our proposed approach outperforms a number of competitive baselines

EulerNet: Adaptive Feature Interaction Learning via Euler's Formula for CTR Prediction

Learning effective high-order feature interactions is very crucial in the CTR prediction task. However, it is very time-consuming to calculate high-order feature interactions with massive features in online e-commerce platforms. Most existing methods manually design a maximal order and further filter out the useless interactions from them. Although they reduce the high computational costs caused by the exponential growth of high-order feature combinations, they still suffer from the degradation of model capability due to the suboptimal learning of the restricted feature orders. The solution to maintain the model capability and meanwhile keep it efficient is a technical challenge, which has not been adequately addressed. To address this issue, we propose an adaptive feature interaction learning model, named as EulerNet, in which the feature interactions are learned in a complex vector space by conducting space mapping according to Euler's formula. EulerNet converts the exponential powers of feature interactions into simple linear combinations of the modulus and phase of the complex features, making it possible to adaptively learn the high-order feature interactions in an efficient way. Furthermore, EulerNet incorporates the implicit and explicit feature interactions into a unified architecture, which achieves the mutual enhancement and largely boosts the model capabilities. Such a network can be fully learned from data, with no need of pre-designed form or order for feature interactions. Extensive experiments conducted on three public datasets have demonstrated the effectiveness and efficiency of our approach. Our code is available at: https://github.com/RUCAIBox/EulerNet.Comment: 10 pages, 7 figures, accepted for publication in SIGIR'2

Astrocytes in intracerebral hemorrhage: impact and therapeutic objectives

Intracerebral hemorrhage (ICH) manifests precipitously and profoundly impairs the neurological function in patients who are affected. The etiology of subsequent injury post-ICH is multifaceted, characterized by the intricate interplay of various factors, rendering therapeutic interventions challenging. Astrocytes, a distinct class of glial cells, interact with neurons and microglia, and are implicated in a series of pathophysiological alterations following ICH. A comprehensive examination of the functions and mechanisms associated with astrocytic proteins may shed light on the role of astrocytes in ICH pathology and proffer innovative therapeutic avenues for ICH management

Enhanced surface acceleration of fast electrons by using sub-wavelength grating targets

Surface acceleration of fast electrons in intense laser-plasma interaction is improved by using sub-wavelength grating targets. The fast electron beam emitted along the target surface was enhanced by more than three times relative to that by using planar target. The total number of the fast electrons ejected from the front side of target was also increased by about one time. The method to enhance the surface acceleration of fast electron is effective for various targets with sub-wavelength structured surface, and can be applied widely in the cone-guided fast ignition, energetic ion acceleration, plasma device, and other high energy density physics experiments.Comment: 14 pages, 4figure

Meta-analysis of quantitative diffusion-weighted MR imaging in the differential diagnosis of breast lesions

BACKGROUND: To determine, in a meta-analysis, the diagnostic performance of quantitative diffusion-weighted (DW) MR imaging in patients with breast lesions. METHODS: English and Chinese studies published prior to June 2009 to assess the diagnostic performance of quantitative DWI in patients with breast lesions were reviewed and summarized with reference to the inclusion and exclusion criteria. Methodological quality was assessed by using the quality assessment of diagnostic studies (QUADAS) instrument. Publication bias analysis was performed by using Comprehensive Meta-analysis version 2. Meta-Disc version 1.4 was used to describe primary results and explore homogeneity by Chi-square test and inconsistency index; to explore threshold effect by receiver operator characteristic (ROC) space and Spearman correlation coefficient; and to pool weighted sensitivity and specificity by fixed or random effect model. A summary ROC (sROC) curve was constructed to calculate the area under the curve (AUC). RESULTS: Of 65 eligible studies, 13 with 615 malignant and 349 benign lesions were included in the original meta-analysis, among which heterogeneity arising from factors other than threshold effect and publication bias was explored. Methodological quality was moderate. The pooled weighted sensitivity and specificity with corresponding 95% confidence interval (CI) in one homogenous subgroup of studies using maximum b = 1000 s/mm(2 )were 0.84 (0.80, 0.87) and 0.84 (0.79, 0.88) respectively. AUC of sROC was 0.9085. Sensitivity analysis demonstrated that the pooled estimates were stable and reliable. CONCLUSIONS: Quantitative DWI has a higher specificity to differentiate between benign and malignant breast lesions compared to that of contrast-enhanced MRI. However, large scale randomized control trials (RCTs) are necessary to assess its clinical value because of disunified diffusion gradient factor b and diagnosis threshold