Fund family tournament and performance consequences: evidence from the UK fund industry

By applying tournament analysis to the UK Unit Trusts data, the results support significant risk shifting in the family tournament; i.e. interim winning managers tend to increase their level of risk exposure more than losing managers. It also shows that the risk-adjusted returns of the winners outperform those of the losers following the risk taking, which implies that risk altering can be regarded as an indication of managers’ superior ability. However, the tournament behaviour can still be a costly strategy for investors, since winners can be seen to beat losers in the observed returns due to the deterioration in the performance of their major portfolio holdings

How long is enough to detect terrestrial animals? Estimating the minimum trapping effort on camera traps

Camera traps is an important wildlife inventory tool for estimating species diversity at a site. Knowing what minimum trapping effort is needed to detect target species is also important to designing efficient studies, considering both the number of camera locations, and survey length. Here, we take advantage of a two-year camera trapping dataset from a small (24-ha) study plot in Gutianshan National Nature Reserve, eastern China to estimate the minimum trapping effort actually needed to sample the wildlife community. We also evaluated the relative value of adding new camera sites or running cameras for a longer period at one site. The full dataset includes 1727 independent photographs captured during 13,824 camera days, documenting 10 resident terrestrial species of birds and mammals. Our rarefaction analysis shows that a minimum of 931 camera days would be needed to detect the resident species sufficiently in the plot, and c. 8700 camera days to detect all 10 resident species. In terms of detecting a diversity of species, the optimal sampling period for one camera site was c. 40, or long enough to record about 20 independent photographs. Our analysis of evaluating the increasing number of additional camera sites shows that rotating cameras to new sites would be more efficient for measuring species richness than leaving cameras at fewer sites for a longer period

Daylight-driven rechargeable antibacterial and antiviral nanofibrous membranes for bioprotective applications.

Emerging infectious diseases (EIDs) are a significant burden on global economies and public health. Most present personal protective equipment used to prevent EID transmission and infections is typically devoid of antimicrobial activity. We report on green bioprotective nanofibrous membranes (RNMs) with rechargeable antibacterial and antiviral activities that can effectively produce biocidal reactive oxygen species (ROS) solely driven by the daylight. The premise of the design is that the photoactive RNMs can store the biocidal activity under light irradiation and readily release ROS under dim light or dark conditions, making the biocidal function "always online." The resulting RNMs exhibit integrated properties of fast ROS production, ease of activity storing, long-term durability, robust breathability, interception of fine particles (>99%), and high bactericidal (>99.9999%) and virucidal (>99.999%) efficacy, which enabled to serve as a scalable biocidal layer for protective equipment by providing contact killing against pathogens either in aerosol or in liquid forms. The successful synthesis of these fascinating materials may provide new insights into the development of protection materials in a sustainable, self-recharging, and structurally adaptive form

Determinants of Air Quality in Building Environments: A Multi-Regression Analysis and Implications for Open Teaching Practices

In the ever-evolving educational milieu, the integration of innovative teaching methodologies is increasingly crucial to meet the changing needs of modern learners. This research meticulously explores the application of open teaching practices in the fields of building environment and energy application engineering. Through an in-depth examination of multi-regression data pertaining to various environmental factors, this study reveals significant correlations and patterns that are relevant to both educators and environmental specialists. Emphasis is placed on the student-centric ethos of this approach, combining the dual concepts of environmental science and pedagogical progression. The relationship between environmental variables, such as PM2.5, PM10, temperature, and humidity, and the air quality index (AQI) is rigorously analyzed. Such analysis underscores the educational improvements brought about by open teaching strategies. The presented findings not only offer nuanced insights into how the aforementioned variables influence air quality but also highlight the benefits and potential of open teaching methodologies in creating a more interactive and enlightening academic environment

Learning to Rank for Active Learning via Multi-Task Bilevel Optimization

Active learning is a promising paradigm to reduce the labeling cost by strategically requesting labels to improve model performance. However, existing active learning methods often rely on expensive acquisition function to compute, extensive modeling retraining and multiple rounds of interaction with annotators. To address these limitations, we propose a novel approach for active learning, which aims to select batches of unlabeled instances through a learned surrogate model for data acquisition. A key challenge in this approach is developing an acquisition function that generalizes well, as the history of data, which forms part of the utility function's input, grows over time. Our novel algorithmic contribution is a bilevel multi-task bilevel optimization framework that predicts the relative utility -- measured by the validation accuracy -- of different training sets, and ensures the learned acquisition function generalizes effectively. For cases where validation accuracy is expensive to evaluate, we introduce efficient interpolation-based surrogate models to estimate the utility function, reducing the evaluation cost. We demonstrate the performance of our approach through extensive experiments on standard active classification benchmarks. By employing our learned utility function, we show significant improvements over traditional techniques, paving the way for more efficient and effective utility maximization in active learning applications

Synthesis and Characterization of YAG Nanoparticles by Ultrasound-Assisted and Ultrasound-Microwave-Assisted Alkoxide Hydrolysis Precipitation Methods

Yttrium aluminum garnet (YAG, Y3Al5O12) nanoparticles were synthesized by ultrasound-assisted and ultrasound-microwave-assisted alkoxide hydrolysis precipitation methods. The effect of reaction parameters including pH value, ultrasonic radiation time, and calcination temperature on the composition of the products was investigated. The YAG nanoparticles and their precursor were characterized by X-ray powder diffraction (XRD), differential thermal analysis (DTA), Fourier-transform infrared spectroscopy (FT-IR), and high-resolution transmission electron microscopy (HRTEM). The results show that the single ultrasound-assisted method to synthesize YAG phase often contains intermediate phases of YAM (Y4Al2O9) and YAP (YAlO3); pure YAG phase can form only at special conditions and as single crystal. The pure phase YAG powders can be obtained at each experimental condition when using ultrasound-microwave-assisted synthesis and the grain is polycrystalline. This is due to the microwave radiation which promotes atomic diffusion and forms a lot of crystal nuclei of YAG in the precursor. The YAG nanoparticles with a grain size of 18 nm can be obtained at a calcination temperature of 900°C when using ultrasound-microwave-assisted method

BPJDet: Extended Object Representation for Generic Body-Part Joint Detection

Detection of human body and its parts (e.g., head or hands) has been intensively studied. However, most of these CNNs-based detectors are trained independently, making it difficult to associate detected parts with body. In this paper, we focus on the joint detection of human body and its corresponding parts. Specifically, we propose a novel extended object representation integrating center-offsets of body parts, and construct a dense one-stage generic Body-Part Joint Detector (BPJDet). In this way, body-part associations are neatly embedded in a unified object representation containing both semantic and geometric contents. Therefore, we can perform multi-loss optimizations to tackle multi-tasks synergistically. BPJDet does not suffer from error-prone post matching, and keeps a better trade-off between speed and accuracy. Furthermore, BPJDet can be generalized to detect any one or more body parts. To verify the superiority of BPJDet, we conduct experiments on three body-part datasets (CityPersons, CrowdHuman and BodyHands) and one body-parts dataset COCOHumanParts. While keeping high detection accuracy, BPJDet achieves state-of-the-art association performance on all datasets comparing with its counterparts. Besides, we show benefits of advanced body-part association capability by improving performance of two representative downstream applications: accurate crowd head detection and hand contact estimation. Code is released in https://github.com/hnuzhy/BPJDet.Comment: 15 pages. arXiv admin note: text overlap with arXiv:2212.0765

DeepE: a deep neural network for knowledge graph embedding

Recently, neural network based methods have shown their power in learning more expressive features on the task of knowledge graph embedding (KGE). However, the performance of deep methods often falls behind the shallow ones on simple graphs. One possible reason is that deep models are difficult to train, while shallow models might suffice for accurately representing the structure of the simple KGs. In this paper, we propose a neural network based model, named DeepE, to address the problem, which stacks multiple building blocks to predict the tail entity based on the head entity and the relation. Each building block is an addition of a linear and a non-linear function. The stacked building blocks are equivalent to a group of learning functions with different non-linear depth. Hence, DeepE allows deep functions to learn deep features, and shallow functions to learn shallow features. Through extensive experiments, we find DeepE outperforms other state-of-the-art baseline methods. A major advantage of DeepE is the robustness. DeepE achieves a Mean Rank (MR) score that is 6%, 30%, 65% lower than the best baseline methods on FB15k-237, WN18RR and YAGO3-10. Our design makes it possible to train much deeper networks on KGE, e.g. 40 layers on FB15k-237, and without scarifying precision on simple relations.Comment: 10 pages, 5 figures, 7 table