THE EFFECT OF PORTFOLIO MANAGERS' PAST EXPERIENCE ON THEIR PERFORMANCE

Understanding where the mutual fund returns come from may be advantageous to construct a portfolio of managers for a fund. This paper uses mutual fund performance data in China market from 2001 to 2018 to study whether differences in manager’s former working experience affect their performance. After regressions and bootstrap tests, there come some conclusions: managers who starting careers as buy-side industry and sell-side macro analysts generate significant excess returns of 0.4% and 0.6%. What’s more, there is no evidence that portfolio managers have market-timing abilities. Last but not least, industry groups show larger portions of return from small-cap stocks and growth stocks. Specifically, buy-side industry group shows superior ability in picking up promising small-cap stocks, and sell-side industry group tends to pay most attention to and invest more in growth stocks

Power spectral density of a single Brownian trajectory: what one can and cannot learn from it

The power spectral density (PSD) of any time-dependent stochastic processXt is ameaningful feature of its spectral content. In its text-book definition, the PSD is the Fourier transform of the covariance function of Xt over an infinitely large observation timeT, that is, it is defined as an ensemble-averaged property taken in the limitT  ¥.Alegitimate question iswhat information on the PSDcan be reliably obtained from single-trajectory experiments, if one goes beyond the standard definition and analyzes thePSD of a single trajectory recorded for a finite observation timeT. In quest for this answer, for a d-dimensionalBrownian motion (BM) we calculate the probability density function of a single-trajectory PSDfor arbitrary frequency f, finite observation timeTand arbitrary number k of projections of the trajectory on different axes.We show analytically that the scaling exponent for the frequency-dependence of the PSDspecific to an ensemble ofBMtrajectories can be already obtained from a single trajectory, while the numerical amplitude in the relation between the ensemble-averaged and single-trajectory PSDs is afluctuating property which varies from realization to realization. The distribution of this amplitude is calculated exactly and is discussed in detail.Our results are confirmed by numerical simulations and single-particle tracking experiments, with remarkably good agreement. In addition we consider a truncatedWiener representation ofBM, and the case of a discrete-time lattice randomwalk.Wehighlight some differences in the behavior of a single-trajectory PSDforBMand for the two latter situations.The framework developed herein will allow formeaningful physical analysis of experimental stochastic trajectories

Simple spatial scaling rules behind complex cities

Although most of wealth and innovation have been the result of human interaction and cooperation, we are not yet able to quantitatively predict the spatial distributions of three main elements of cities: population, roads, and socioeconomic interactions. By a simple model mainly based on spatial attraction and matching growth mechanisms, we reveal that the spatial scaling rules of these three elements are in a consistent framework, which allows us to use any single observation to infer the others. All numerical and theoretical results are consistent with empirical data from ten representative cities. In addition, our model can also provide a general explanation of the origins of the universal super- and sub-linear aggregate scaling laws and accurately predict kilometre-level socioeconomic activity. Our work opens a new avenue for uncovering the evolution of cities in terms of the interplay among urban elements, and it has a broad range of applications.This work is supported by the National Natural Science Foundation of China under Grant Nos. 61673070, 61773069, 71731002 and the Fundamental Research Funds for the Central Universities with the Grant No. 2015KJJCB13, and also partially supported by NSF Grants PHY-1505000, CMMI-1125290, CHE-1213217, DTRA Grant HDTRA1-14-1-0017, DOE Grant DE-AC07-05Id14517. J.Z. acknowledges discussions with Prof. Bettencourt of the Santa Fe Institute, Dr. Lingfei Wu of Arizona State University, and Profs. Yougui Wang and Qinghua Chen of Beijing Normal University. R.L. acknowledges helpful discussions with and comments from Dr. Remi Louf in CASA, University College London, Dr. Longfeng Zhao from Huazhong (Central China) Normal University, and selfless help from Prof. Yougui Wang. R.L. is also supported by the Chinese Scholarship Council. (61673070 - National Natural Science Foundation of China; 61773069 - National Natural Science Foundation of China; 71731002 - National Natural Science Foundation of China; 2015KJJCB13 - Fundamental Research Funds for the Central Universities; PHY-1505000 - NSF; CMMI-1125290 - NSF; CHE-1213217 - NSF; HDTRA1-14-1-0017 - DTRA Grant; DE-AC07-05Id14517 - DOE; Chinese Scholarship Council)Published versio

A Lightweight Approach for Network Intrusion Detection based on Self-Knowledge Distillation

Network Intrusion Detection (NID) works as a kernel technology for the security network environment, obtaining extensive research and application. Despite enormous efforts by researchers, NID still faces challenges in deploying on resource-constrained devices. To improve detection accuracy while reducing computational costs and model storage simultaneously, we propose a lightweight intrusion detection approach based on self-knowledge distillation, namely LNet-SKD, which achieves the trade-off between accuracy and efficiency. Specifically, we carefully design the DeepMax block to extract compact representation efficiently and construct the LNet by stacking DeepMax blocks. Furthermore, considering compensating for performance degradation caused by the lightweight network, we adopt batch-wise self-knowledge distillation to provide the regularization of training consistency. Experiments on benchmark datasets demonstrate the effectiveness of our proposed LNet-SKD, which outperforms existing state-of-the-art techniques with fewer parameters and lower computation loads.Comment: Accepted to IEEE ICC 202

A Robust Method for Speech Emotion Recognition Based on Infinite Student’s t

Speech emotion classification method, proposed in this paper, is based on Student’s t-mixture model with infinite component number (iSMM) and can directly conduct effective recognition for various kinds of speech emotion samples. Compared with the traditional GMM (Gaussian mixture model), speech emotion model based on Student’s t-mixture can effectively handle speech sample outliers that exist in the emotion feature space. Moreover, t-mixture model could keep robust to atypical emotion test data. In allusion to the high data complexity caused by high-dimensional space and the problem of insufficient training samples, a global latent space is joined to emotion model. Such an approach makes the number of components divided infinite and forms an iSMM emotion model, which can automatically determine the best number of components with lower complexity to complete various kinds of emotion characteristics data classification. Conducted over one spontaneous (FAU Aibo Emotion Corpus) and two acting (DES and EMO-DB) universal speech emotion databases which have high-dimensional feature samples and diversiform data distributions, the iSMM maintains better recognition performance than the comparisons. Thus, the effectiveness and generalization to the high-dimensional data and the outliers are verified. Hereby, the iSMM emotion model is verified as a robust method with the validity and generalization to outliers and high-dimensional emotion characters

Planar carbon nanotube-graphene hybrid films for high-performance broadband photodetectors

Graphene has emerged as a promising material for photonic applications fuelled by its superior electronic and optical properties. However, the photoresponsivity is limited by the low absorption cross section and ultrafast recombination rates of photoexcited carriers. Here we demonstrate a photoconductive gain of $\sim$ 10$^5$ electrons per photon in a carbon nanotube-graphene one dimensional-two dimensional hybrid due to efficient photocarriers generation and transport within the nanostructure. A broadband photodetector (covering 400 nm to 1550 nm) based on such hybrid films is fabricated with a high photoresponsivity of more than 100 AW$^{-1}$ and a fast response time of approximately 100 {\mu}s. The combination of ultra-broad bandwidth, high responsivities and fast operating speeds affords new opportunities for facile and scalable fabrication of all-carbon optoelectronic devices.Comment: 21 pages, 3 figure

Towards General Low-Light Raw Noise Synthesis and Modeling

Modeling and synthesizing low-light raw noise is a fundamental problem for computational photography and image processing applications. Although most recent works have adopted physics-based models to synthesize noise, the signal-independent noise in low-light conditions is far more complicated and varies dramatically across camera sensors, which is beyond the description of these models. To address this issue, we introduce a new perspective to synthesize the signal-independent noise by a generative model. Specifically, we synthesize the signal-dependent and signal-independent noise in a physics- and learning-based manner, respectively. In this way, our method can be considered as a general model, that is, it can simultaneously learn different noise characteristics for different ISO levels and generalize to various sensors. Subsequently, we present an effective multi-scale discriminator termed Fourier transformer discriminator (FTD) to distinguish the noise distribution accurately. Additionally, we collect a new low-light raw denoising (LRD) dataset for training and benchmarking. Qualitative validation shows that the noise generated by our proposed noise model can be highly similar to the real noise in terms of distribution. Furthermore, extensive denoising experiments demonstrate that our method performs favorably against state-of-the-art methods on different sensors.Comment: 11 pages, 7 figures. Accepted by ICCV 202

Research of Surfactant-polymer Flooding Response Characteristic and Mobility Optimization of J Oilfield in Bohai Bay

Surfactant-polymer flooding technology which used in J oilfield is still the first time in Bohai bay, the reference materials are very seldom for its response characteristic and project optimization. Since there’s no blank water flooding stage between polymer flooding and surfactant-polymer flooding in J oilfield, it’s difficult to accurately judge the response characteristic of production wells and injection wells by the conventional method; on the other side, as surfactant-polymer flooding gradually entered the end stage, the effect of decrease water and increase oil became worse, there’s urgently need to improve the effect of chemical flooding. Thus, the research of response characteristic and mobility optimization are conducted in this article. The water cut funnel method is used for the first time to recognize the response of the production wells in J oilfield, and to use the Hall curve method to recognize the response of the injection wells. Meanwhile, based on the idea of mobility control, the minimum polymer concentration which is needed to control the mobility of surfactant-polymer flooding is studied, and establish the mobility control template, the effect of the surfactant-polymer flooding is improved effectively by use of the template to guide the optimization of the polymer concentration, and daily production increase about 15% of J oilfield. The research can be used to guide and refer to other similar offshore oilfield development

Chinese Medicine in the Battle Against Obesity and Metabolic Diseases

Obesity is a multi-factor chronic disease caused by the mixed influence of genetics, environments and an imbalance of energy intake and expenditure. Due to lifestyle changes, modern society sees a rapid increase in obesity occurrence along with an aggravated risk of metabolic syndromes in the general population, including diabetes, hepatic steatosis, cardiovascular diseases and certain types of cancer. Although obesity has become a serious worldwide public health hazard, effective and safe drugs treating obesity are still missing. Traditional Chinese medicine (TCM) has been implicated in practical use in China for thousands of years and has accumulated substantial front line experience in treating various diseases. Compared to western medicine that features defined composition and clear molecular mechanisms, TCM is consisted with complex ingredients from plants and animals and prescribed based on overall symptoms and collective experience. Because of their fundamental differences, TCM and western medicine were once considered irreconcilable. However, nowadays, sophisticated isolation technologies and deepened molecular understanding of the active ingredients of TCM are gradually bridging the gap between the two, enabling the identification of active TCM components for drug development under the western-style paradigms. Thus, studies on TCM open a new therapeutic avenue and show great potential in the combat against obesity, though challenges exist. In this review, we highlight six key candidate substances derived from TCM, including artemisinin, curcumin, celastrol, capsaicin, berberine and ginsenosides, to review their recent discoveries in the metabolic field, with special focus on their therapeutic efficacy and molecular mechanisms in treating obesity and metabolic diseases. In addition, we discuss the translational challenges and perspectives in implementing modern Chinese medicine into the western pharmaceutical industry