Robust portfolio selection involving options under a “ marginal+joint ” ellipsoidal uncertainty set

AbstractIn typical robust portfolio selection problems, one mainly finds portfolios with the worst-case return under a given uncertainty set, in which asset returns can be realized. A too large uncertainty set will lead to a too conservative robust portfolio. However, if the given uncertainty set is not large enough, the realized returns of resulting portfolios will be outside of the uncertainty set when an extreme event such as market crash or a large shock of asset returns occurs. The goal of this paper is to propose robust portfolio selection models under so-called “ marginal+joint” ellipsoidal uncertainty set and to test the performance of the proposed models. A robust portfolio selection model under a “marginal + joint” ellipsoidal uncertainty set is proposed at first. The model has the advantages of models under the separable uncertainty set and the joint ellipsoidal uncertainty set, and relaxes the requirements on the uncertainty set. Then, one more robust portfolio selection model with option protection is presented by combining options into the proposed robust portfolio selection model. Convex programming approximations with second-order cone and linear matrix inequalities constraints to both models are derived. The proposed robust portfolio selection model with options can hedge risks and generates robust portfolios with well wealth growth rate when an extreme event occurs. Tests on real data of the Chinese stock market and simulated options confirm the property of both the models. Test results show that (1) under the “ marginal+joint” uncertainty set, the wealth growth rate and diversification of robust portfolios generated from the first proposed robust portfolio model (without options) are better and greater than those generated from Goldfarb and Iyengar’s model, and (2) the robust portfolio selection model with options outperforms the robust portfolio selection model without options when some extreme event occurs

Automatic Distractor Generation for Multiple Choice Questions in Standard Tests

To assess the knowledge proficiency of a learner, multiple choice question is an efficient and widespread form in standard tests. However, the composition of the multiple choice question, especially the construction of distractors is quite challenging. The distractors are required to both incorrect and plausible enough to confuse the learners who did not master the knowledge. Currently, the distractors are generated by domain experts which are both expensive and time-consuming. This urges the emergence of automatic distractor generation, which can benefit various standard tests in a wide range of domains. In this paper, we propose a question and answer guided distractor generation (EDGE) framework to automate distractor generation. EDGE consists of three major modules: (1) the Reforming Question Module and the Reforming Passage Module apply gate layers to guarantee the inherent incorrectness of the generated distractors; (2) the Distractor Generator Module applies attention mechanism to control the level of plausibility. Experimental results on a large-scale public dataset demonstrate that our model significantly outperforms existing models and achieves a new state-of-the-art.Comment: accepted by COLING202

Analyzing the Dependency of {ConvNets} on Spatial Information

Intuitively, image classification should profit from using spatial information. Recent work, however, suggests that this might be overrated in standard CNNs. In this paper, we are pushing the envelope and aim to further investigate the reliance on spatial information. We propose spatial shuffling and GAP+FC to destroy spatial information during both training and testing phases. Interestingly, we observe that spatial information can be deleted from later layers with small performance drops, which indicates spatial information at later layers is not necessary for good performance. For example, test accuracy of VGG-16 only drops by 0.03% and 2.66% with spatial information completely removed from the last 30% and 53% layers on CIFAR100, respectively. Evaluation on several object recognition datasets (CIFAR100, Small-ImageNet, ImageNet) with a wide range of CNN architectures (VGG16, ResNet50, ResNet152) shows an overall consistent pattern

Treatment of diabetic peripheral neuropathy with engineered mesenchymal stromal cell-derived exosomes enriched with microRNA-146a provide amplified therapeutic efficacy.

Diabetic peripheral neuropathy (DPN) is one of the most prevalent chronic complications of diabetes mellitus with no effective treatment. We recently demonstrated that mesenchymal stromal cell (MSC)-derived exosomes (exo-naïve) alleviate neurovascular dysfunction and improve functional recovery. MicroRNA (miRNA), one of the exosomal cargos, downregulates inflammation-related genes, resulting in suppression of pro-inflammatory gene activation. In the present study, we developed engineered MSC-exosomes loaded with miR-146a (exo-146a) and compared the therapeutic effects of exo-146a with exo-naïve in diabetic (db/db) mice with DPN. Exo-146a possesses a high loading capacity, robust ability to accumulate in peripheral nerve tissues upon systemic administration, and evokes substantially enhanced therapeutic efficacy on neurological recovery compared with exo-naïve. Treatment of DPN in diabetic mice with exo-146a for two weeks significantly increased and decreased nerve conduction velocity, and thermal and mechanical stimuli threshold, respectively, whereas it took four weeks of exo-naive treatment to achieve these improvements. Compared with exo-naïve, exo-146a significantly suppressed the peripheral blood inflammatory monocytes and the activation of endothelial cells via inhibiting Toll-like receptor (TLR)-4/NF-κB signaling pathway. These data provide a proof-of-concept about both the feasibility and efficacy of the exosome-based gene therapy for DPN. The translation of this approach to the clinic has the potential to improve the prospects for people who suffer from DPN

Versatile soliton emission from a WS2 mode-locked fiber laser

Recently, few-layer tungsten disulfide (WS2), as a shining 2D material, has been discovered to possess both the saturable absorption ability and large nonlinear refractive index. Here, we demonstrate versatile soliton pulses in a passively mode-locked fiber laser with a WS2-deposited microfiber. The few-layer WS2 is prepared by the liquid-phase exfoliation method and transferred onto a microfiber by the optical deposition method. Study found, the WS2-deposited microfiber can operate simultaneously as a mode-locker and a high-nonlinear device. In experiment, by further inserting the WS2 device into the fiber laser, besides the dual-wavelength soliton, noise-like soliton pulse, conventional soliton and its harmonic form are obtained by properly adjusting the pump strength and the polarization states. For the dual-wavelength soliton pulses and noise-like pulse, the maximum output power of 14.2 mW and pulse energy of 4.74 nJ is obtained, respectively. In addition, we also achieve the maximum harmonic number (135) of conventional soliton, corresponding to a repetition rate of ∼497.5 MHz. Our study shows clearly that WS2-deposited microfiber can be as a high-nonlinear photonic device for studying a plenty of nonlinear soliton phenomena