Statistical Learning for Individualized Asset Allocation

We establish a high-dimensional statistical learning framework for individualized asset allocation. Our proposed methodology addresses continuous-action decision-making with a large number of characteristics. We develop a discretization approach to model the effect of continuous actions and allow the discretization frequency to be large and diverge with the number of observations. The value function of continuous-action is estimated using penalized regression with our proposed generalized penalties that are imposed on linear transformations of the model coefficients. We show that our proposed Discretization and Regression with generalized fOlded concaVe penalty on Effect discontinuity (DROVE) approach enjoys desirable theoretical properties and allows for statistical inference of the optimal value associated with optimal decision-making. Empirically, the proposed framework is exercised with the Health and Retirement Study data in finding individualized optimal asset allocation. The results show that our individualized optimal strategy improves the population financial well-being

Joint unmixing-deconvolution algorithms for hyperspectral images

International audienceThis paper combines supervised linear unmixing and deconvolution problems to increase the resolution of the abundance maps for industrial imaging systems. The joint unmixing-deconvolution (JUD) algorithm is introduced based on the Tikhonov regularization criterion for offline processing. In order to meet the needs of industrial applications, the proposed JUD algorithm is then extended for online processing by using a block Tikhonov criterion. The performance of JUD is increased by adding a non-negativity constraint which is implemented in a fast way using the quadratic penalty method and fast Fourier transform. The proposed algorithm is then assessed using both simulated and real hyperspectral images

The Physiological Mechanism of Improved Formaldehyde Resistance in Petunia hybrida Harboring a Mammalian cyp2e1 Gene

AbstractCytochrome P450 CYP2E1 is mainly present in hepatocytes in the livers of mammals, where it plays an important role in the metabolism of xenobiotic organic substances. Previous studies showed that transgenic petunia (Petunia hybrid) plants harboring a mammalian cyp2e1 gene (designated cyp2e1-transgenic petunia) exhibited increased resistance to formaldehyde stress. In this study, we used cyp2e1-transgenic petunia plants to analyze physiological indexes related to formaldehyde stress responses. The results indicated that under formaldehyde stress, the malondialdehyde content in cyp2e1-transgenic petunia plants was lower than in β-glucuronidase gene (gus)-transgenic and wild-type petunia plants. The activities of both superoxide dismutase and peroxidase in the cyp2e1-transgenic plants were higher than in gus-transgenic and wild-type plants. The alcohol dehydrogenase activity was slightly increased and more glutathione was consumed. Additionally, under formaldehyde stress, the levels of plant hormones including indole-3-acetic acid, zeatin and abscisic acid in cyp2e1-transgenic petunia plants displayed decreasing trends, whereas the level of gibberellic acid displayed an increasing trend. In contrast, the indole-3-acetic acid, zeatin and abscisic acid levels in gus-transgenic and wild-type petunia plants displayed increasing trends, whereas the gibberellic acid level displayed a decreasing trend. At 72h after incubation of 0.5g of cyp2e1-transgenic petunia plants in 40mL of treatment solution containing formaldehyde at 50mg · L−1, the formaldehyde content remaining in the treatment solution was close to zero while approximately half of original formaldehyde remained in the treatment solutions containing gus-transgenic and wild-type petunia plants

Multidimensional Resource Fragmentation-Aware Virtual Network Embedding in MEC Systems Interconnected by Metro Optical Networks

The increasing demand for diverse emerging applications has resulted in the interconnection of multi-access edge computing (MEC) systems via metro optical networks. To cater to these diverse applications, network slicing has become a popular tool for creating specialized virtual networks. However, resource fragmentation caused by uneven utilization of multidimensional resources can lead to reduced utilization of limited edge resources. To tackle this issue, this paper focuses on addressing the multidimensional resource fragmentation problem in virtual network embedding (VNE) in MEC systems with the aim of maximizing the profit of an infrastructure provider (InP). The VNE problem in MEC systems is transformed into a bilevel optimization problem, taking into account the interdependence between virtual node embedding (VNoE) and virtual link embedding (VLiE). To solve this problem, we propose a nested bilevel optimization approach named BiVNE. The VNoE is solved using the ant colony system (ACS) in the upper level, while the VLiE is solved using a combination of a shortest path algorithm and an exact-fit spectrum slot allocation method in the lower level. Evaluation results show that the BiVNE algorithm can effectively enhance the profit of the InP by increasing the acceptance ratio and avoiding resource fragmentation simultaneously

LATFormer: Locality-Aware Point-View Fusion Transformer for 3D Shape Recognition

Recently, 3D shape understanding has achieved significant progress due to the advances of deep learning models on various data formats like images, voxels, and point clouds. Among them, point clouds and multi-view images are two complementary modalities of 3D objects and learning representations by fusing both of them has been proven to be fairly effective. While prior works typically focus on exploiting global features of the two modalities, herein we argue that more discriminative features can be derived by modeling ``where to fuse''. To investigate this, we propose a novel Locality-Aware Point-View Fusion Transformer (LATFormer) for 3D shape retrieval and classification. The core component of LATFormer is a module named Locality-Aware Fusion (LAF) which integrates the local features of correlated regions across the two modalities based on the co-occurrence scores. We further propose to filter out scores with low values to obtain salient local co-occurring regions, which reduces redundancy for the fusion process. In our LATFormer, we utilize the LAF module to fuse the multi-scale features of the two modalities both bidirectionally and hierarchically to obtain more informative features. Comprehensive experiments on four popular 3D shape benchmarks covering 3D object retrieval and classification validate its effectiveness

UniFuzz: Optimizing Distributed Fuzzing via Dynamic Centralized Task Scheduling

Fuzzing is one of the most efficient technology for vulnerability detection. Since the fuzzing process is computing-intensive and the performance improved by algorithm optimization is limited, recent research seeks to improve fuzzing performance by utilizing parallel computing. However, parallel fuzzing has to overcome challenges such as task conflicts, scalability in a distributed environment, synchronization overhead, and workload imbalance. In this paper, we design and implement UniFuzz, a distributed fuzzing optimization based on a dynamic centralized task scheduling. UniFuzz evaluates and distributes seeds in a centralized manner to avoid task conflicts. It uses a "request-response" scheme to dynamically distribute fuzzing tasks, which avoids workload imbalance. Besides, UniFuzz can adaptively switch the role of computing cores between evaluating, and fuzzing, which avoids the potential bottleneck of seed evaluation. To improve synchronization efficiency, UniFuzz shares different fuzzing information in a different way according to their characteristics, and the average overhead of synchronization is only about 0.4\%. We evaluated UniFuzz with real-world programs, and the results show that UniFuzz outperforms state-of-the-art tools, such as AFL, PAFL and EnFuzz. Most importantly, the experiment reveals a counter-intuitive result that parallel fuzzing can achieve a super-linear acceleration to the single-core fuzzing. We made a detailed explanation and proved it with additional experiments. UniFuzz also discovered 16 real-world vulnerabilities.Comment: 14 pages, 4 figure