Randomized low-rank Dynamic Mode Decomposition for motion detection

N. Benjamin Erichson acknowledges support from the UK Engineering and Physical Sciences Research Council (EPSRC).This paper introduces a fast algorithm for randomized computation of a low-rank Dynamic Mode Decomposition (DMD) of a matrix. Here we consider this matrix to represent the development of a spatial grid through time e.g. data from a static video source. DMD was originally introduced in the fluid mechanics community, but is also suitable for motion detection in video streams and its use for background subtraction has received little previous investigation. In this study we present a comprehensive evaluation of background subtraction, using the randomized DMD and compare the results with leading robust principal component analysis algorithms. The results are convincing and show the random DMD is an efficient and powerful approach for background modeling, allowing processing of high resolution videos in real-time. Supplementary materials include implementations of the algorithms in Python.PostprintPeer reviewe

Forecasting overhead distribution line failures using weather data and gradient-boosted location, scale, and shape models

Overhead distribution lines play a vital role in distributing electricity, however, their freestanding nature makes them vulnerable to extreme weather conditions and resultant disruption of supply. The current UK regulation of power networks means preemptive mitigation of disruptions avoids financial penalties for distribution companies, making accurate fault predictions of direct financial importance. Here we present predictive models developed for a UK network based on gradient-boosted location, scale, and shape models, providing spatio-temporal predictions of faults based on forecast weather conditions. The models presented are based on (a) tree base learners or (b) penalised smooth and linear base learners -- leading to a Generalised Additive Model (GAM) structure, with the latter category of models providing best performance in terms of out-of-sample log-likelihood. The models are fitted to fifteen years of fault and weather data and are shown to provide good accuracy over multi-day forecast windows, giving tangible support to power restoration.Comment: 25 pages, 7 figures, based on the MSc dissertation of the primary author submitted for the MSc degree in Applied Statistics and Datamining at the University of St Andrews in 2021 -- under the supervision of the co-autho

Chinese Communist Materials at the Bureau of Investigation Archives, Taiwan

During the long years of civil strife in China the Nationalist authorities amassed extensive materials on their Communist adversaries. Now stored in government institutions on Taiwan, these materials are an excellent source for the study of the Chinese Communist movement. Among them is the Bureau of Investigation Collection (BIC), which holds over 300,000 volumes of primary documents on the Chinese Communist movement. The purpose of Chinese Communist Materials is, without any attempt at comprehensive listing of the Bureau’s holdings, to give scholars a representative description of the collection, to point out its implications for research, and suggest new areas for research at the Bureau in the fields of political science and history [1, 4]

Semi-Supervised Crowd Counting with Contextual Modeling: Facilitating Holistic Understanding of Crowd Scenes

To alleviate the heavy annotation burden for training a reliable crowd counting model and thus make the model more practicable and accurate by being able to benefit from more data, this paper presents a new semi-supervised method based on the mean teacher framework. When there is a scarcity of labeled data available, the model is prone to overfit local patches. Within such contexts, the conventional approach of solely improving the accuracy of local patch predictions through unlabeled data proves inadequate. Consequently, we propose a more nuanced approach: fostering the model's intrinsic 'subitizing' capability. This ability allows the model to accurately estimate the count in regions by leveraging its understanding of the crowd scenes, mirroring the human cognitive process. To achieve this goal, we apply masking on unlabeled data, guiding the model to make predictions for these masked patches based on the holistic cues. Furthermore, to help with feature learning, herein we incorporate a fine-grained density classification task. Our method is general and applicable to most existing crowd counting methods as it doesn't have strict structural or loss constraints. In addition, we observe that the model trained with our framework exhibits a 'subitizing'-like behavior. It accurately predicts low-density regions with only a 'glance', while incorporating local details to predict high-density regions. Our method achieves the state-of-the-art performance, surpassing previous approaches by a large margin on challenging benchmarks such as ShanghaiTech A and UCF-QNRF. The code is available at: https://github.com/cha15yq/MRC-Crowd

A simulation approach to assessing environmental risk of sound exposure to marine mammals

Intense underwater sounds caused by military sonar, seismic surveys, and pile driving can harm acoustically sensitive marine mammals. Many jurisdictions require such activities to undergo marine mammal impact assessments to guide mitigation. However, the ability to assess impacts in a rigorous, quantitative way is hindered by large knowledge gaps concerning hearing ability, sensitivity, and behavioral responses to noise exposure. We describe a simulation-based framework, called SAFESIMM (Statistical Algorithms For Estimating the Sonar Influence on Marine Megafauna), that can be used to calculate the numbers of agents (animals) likely to be affected by intense underwater sounds. We illustrate the simulation framework using two species that are likely to be affected by marine renewable energy developments in UK waters: gray seal (Halichoerus grypus) and harbor porpoise (Phocoena phocoena). We investigate three sources of uncertainty: How sound energy is perceived by agents with differing hearing abilities; how agents move in response to noise (i.e., the strength and directionality of their evasive movements); and the way in which these responses may interact with longer term constraints on agent movement. The estimate of received sound exposure level (SEL) is influenced most strongly by the weighting function used to account for the specie's presumed hearing ability. Strongly directional movement away from the sound source can cause modest reductions (~5 dB) in SEL over the short term (periods of less than 10 days). Beyond 10 days, the way in which agents respond to noise exposure has little or no effect on SEL, unless their movements are constrained by natural boundaries. Most experimental studies of noise impacts have been short-term. However, data are needed on long-term effects because uncertainty about predicted SELs accumulates over time. Synthesis and applications. Simulation frameworks offer a powerful way to explore, understand, and estimate effects of cumulative sound exposure on marine mammals and to quantify associated levels of uncertainty. However, they can often require subjective decisions that have important consequences for management recommendations, and the basis for these decisions must be clearly described.Publisher PDFPeer reviewe

Structurally-related (−)-epicatechin metabolites in humans: assessment using de novo chemically synthesized authentic standards

Accumulating data suggest that diets rich in flavanols and procyanidins are beneficial for human health. In this context, there has been a great interest in elucidating the systemic levels and metabolic profiles at which these compounds occur in humans. While recent progress has been made, there still exist considerable differences and various disagreements with regard to the mammalian metabolites of these compounds, which in turn is largely a consequence of the lack of availability of authentic standards that would allow for the directed development and validation of expedient analytical methodologies. In the present study, we developed a method for the analysis of structurally-related flavanol metabolites using a wide range of authentic standards. Applying this method in the context of a human dietary intervention study using comprehensively characterized and standardized flavanol- and procyanidin-containing cocoa, we were able to identify the structurally-related (−)-epicatechin metabolites (SREM) postprandially extant in the systemic circulation of humans. Our results demonstrate that (−)-epicatechin-3′-β-D-glucuronide, (−)-epicatechin-3′-sulfate, and a 3′-O-methyl(−)-epicatechin-5/7-sulfate are the predominant SREM in humans, and further confirm the relevance of the stereochemical configuration in the context of flavanol metabolism. In addition, we also identified plausible causes for the previously reported discrepancies regarding flavanol metabolism, consisting to a significant extent of inter-laboratory differences in sample preparation (enzymatic treatment and sample conditioning for HPLC analysis) and detection systems. Thus, these findings may also aid in the establishment of consensus on this topic

Identification of a serum biomarker signature associated with metastatic prostate cancer

Purpose: Improved early diagnosis and determination of aggressiveness of prostate cancer (PC) is important to select suitable treatment options and to decrease over-treatment. The conventional marker is total prostate specific antigen (PSA) levels in blood, but lacks specificity and ability to accurately discriminate indolent from aggressive disease. Experimental design: In this study, we sought to identify a serum biomarker signature associated with metastatic PC. We measured 157 analytes in 363 serum samples from healthy subjects, patients with non-metastatic PC and patients with metastatic PC, using a recombinant antibody microarray. Results: A signature consisting of 69 proteins differentiating metastatic PC patients from healthy controls was identified. Conclusions and clinical relevance: The clinical value of this biomarker signature requires validation in larger independent patient cohorts before providing a new prospect for detection of metastatic PC