Recognition of Visual Dynamical Processes: Theory, Kernels, and Experimental Evaluation

Over the past few years, several papers have used Linear Dynamical Systems (LDS)s for modeling, registration, segmentation, and recognition of visual dynamical processes, such as human gaits, dynamic textures and lip articulations. The recognition framework involves identifying the parameters of the LDSs from features extracted from a training set of videos, using metrics on the space of dynamical systems to compare them, and combining these metrics with different classification methods. Usually, each paper makes an ad-hoc choice for every step, and tests the recognition framework on small data sets often involving only one application. We present a detailed evaluation of the LDS-based recognition pipeline; comparing identification methods, metrics, and classification techniques. We propose new metrics that have certain invariance properties and explore a number of variations to the existing metrics. We perform experimental evaluations on well-known data sets of human gaits, dynamic textures, and lip articulations and provide benchmark recognition results. We also analyze the robustness of the recognition pipeline with respect to changes in observation and experimental conditions. Overall, this work represents the most extensive to-date evaluation of the LDS-based recognition framework.This work was partially supported by startup funds from JHU, by grants ONR N00014-05-10836, NSF CAREER 0447739, NSF EHS-0509101, and by contract JHU APL-934652

A Novel Energy-Efficient Reservation System for Edge Computing in 6G Vehicular Ad Hoc Network

The roadside unit (RSU) is one of the fundamental components in a vehicular ad hoc network (VANET), where a vehicle communicates in infrastructure mode. The RSU has multiple functions, including the sharing of emergency messages and the updating of vehicles about the traffic situation. Deploying and managing a static RSU (sRSU) requires considerable capital and operating expenditures (CAPEX and OPEX), leading to RSUs that are sparsely distributed, continuous handovers amongst RSUs, and, more importantly, frequent RSU interruptions. At present, researchers remain focused on multiple parameters in the sRSU to improve the vehicle-to-infrastructure (V2I) communication; however, in this research, the mobile RSU (mRSU), an emerging concept for sixth-generation (6G) edge computing vehicular ad hoc networks (VANETs), is proposed to improve the connectivity and efficiency of communication among V2I. In addition to this, the mRSU can serve as a computing resource for edge computing applications. This paper proposes a novel energy-efficient reservation technique for edge computing in 6G VANETs that provides an energy-efficient, reservation-based, cost-effective solution by introducing the concept of the mRSU. The simulation outcomes demonstrate that the mRSU exhibits superior performance compared to the sRSU in multiple aspects. The mRSU surpasses the sRSU with a packet delivery ratio improvement of 7.7%, a throughput increase of 5.1%, a reduction in end-to-end delay by 4.4%, and a decrease in hop count by 8.7%. The results are generated across diverse propagation models, employing realistic urban scenarios with varying packet sizes and numbers of vehicles. However, it is important to note that the enhanced performance parameters and improved connectivity with more nodes lead to a significant increase in energy consumption by 2%

Bio-inspired Dynamic 3D Discriminative Skeletal Features for Human Action Recognition

Over the last few years, with the immense popularity of the Kinect, there has been renewed interest in developing methods for human gesture and action recognition from 3D data. A number of approaches have been proposed that ex-tract representative features from 3D depth data, a recon-structed 3D surface mesh or more commonly from the re-covered estimate of the human skeleton. Recent advances in neuroscience have discovered a neural encoding of static 3D shapes in primate infero-temporal cortex that can be represented as a hierarchy of medial axis and surface fea-tures. We hypothesize a similar neural encoding might also exist for 3D shapes in motion and propose a hierarchy of dynamic medial axis structures at several spatio-temporal scales that can be modeled using a set of Linear Dynami-cal Systems (LDSs). We then propose novel discriminative metrics for comparing these sets of LDSs for the task of hu-man activity recognition. Combined with simple classifica-tion frameworks, our proposed features and corresponding hierarchical dynamical models provide the highest human activity recognition rates as compared to state-of-the-art methods on several skeletal datasets. 1

Endoscopic Endonasal Excision of Pituitary Tumors Using a Mono-nostril Technique

Objectives: The purpose of this study is to assess the effectiveness and advantage of endoscopic mono-nostril approach to the pituitary tumors.Materials and Methods: We analyzed 70 patients undergoing transsphenoidal mono-nostril excision of pituitary tumors from September, 2016 to March, 2018.Results: We operated 70 patients, out of which 51 were males and 19 were females; the age of the patients ranged from 15 years to 65 years.In our study, out of 70 patients, 61 (87.1%) patients had excellent results with total tumor resection, marked visual improvement, early discharge on the second post-operative day, resuming their daily activities within two weeks and recurrence free interval of 1 year. Nine (12.8%) of our patients had a partial excision of the tumor, whereby there was improvement of headaches in all of them while visual status remained at the pre-operative status. Five (7.1%) of our patients had a post-operative cerebral spinal fluid (CSF) rhinorrhea, 4 (5.7%) in total excision group and 1 (1.4%) in partial excision group. These patients of CSF leak were retained in hospital and their mean stay in hospital was 12 ? 4. Conclusion: We consider that endoscopic mono-nostril excision of the pituitary tumor is a relatively safer, effective, minimally invasive procedure; whereby there is a fast recovery, early discharge and good cosmetic results

Tail risk, systemic risk and spillover risk of crude oil and precious metals

The relationship between oil prices and metal prices has been extensively investigated. However, the tail risk, systemic risk and spillover risk of oil prices have not been investigated via extreme value theory (EVT). We use this novel approach to determine the tail risk of oil, precious metals, how much risk they pose to the financial system and to what extent a shock in oil prices spill over to other precious metals as well as from the financial system. We use long time series of daily data from 1st January 1987 to 31st December 2021 as long time series is required for the EVT. The data is based on the total return index (RI) of four precious metals including gold, platinum, palladium and silver. Our results show that the tail risk of these metals is lower during the crisis period except the Covid-19 pandemic crisis. Most importantly, gold is a safer asset due to the lowest tail risk among four precious metals, indicating the claim that gold is a precious asset to mitigate the returns during market downturns and acts as a ‘safe haven’. Moreover, we also find that extreme systemic risk (tail-β) for crude oil and selected precious metals reduces during crisis period. This is also recognising the fact that these commodities act as a prospective asset for portfolio diversification to hedge against financial assets' volatility. Finally, the spillover risk among crude oil and selected precious metals varies over time, especially during the crisis period and crude oil is an important stimulator of the spillover risk for precious metals. By using our findings, financial market investors can improve their investment planning to attain the maximum advantage of portfolio diversification. Financial managers can further apply these results in forecasting to estimate future global oil market trends for improving their hedging skills and portfolio performance

DAWM: cost-aware asset claim analysis approach on big data analytic computation model for cloud data centre.

The heterogeneous resource-required application tasks increase the cloud service provider (CSP) energy cost and revenue by providing demand resources. Enhancing CSP profit and preserving energy cost is a challenging task. Most of the existing approaches consider task deadline violation rate rather than performance cost and server size ratio during profit estimation, which impacts CSP revenue and causes high service cost. To address this issue, we develop two algorithms for profit maximization and adequate service reliability. First, a belief propagation-influenced cost-aware asset scheduling approach is derived based on the data analytic weight measurement (DAWM) model for effective performance and server size optimization. Second, the multiobjective heuristic user service demand (MHUSD) approach is formulated based on the CPS profit estimation model and the user service demand (USD) model with dynamic acyclic graph (DAG) phenomena for adequate service reliability. The DAWM model classifies prominent servers to preserve the server resource usage and cost during an effective resource slicing process by considering each machine execution factor (remaining energy, energy and service cost, workload execution rate, service deadline violation rate, cloud server configuration (CSC), service requirement rate, and service level agreement violation (SLAV) penalty rate). The MHUSD algorithm measures the user demand service rate and cost based on the USD and CSP profit estimation models by considering service demand weight, tenant cost, and energy cost. The simulation results show that the proposed system has accomplished the average revenue gain of 35%, cost of 51%, and profit of 39% than the state-of-the-art approaches

Risk modelling of ESG (environmental, social, and governance), healthcare, and financial sectors

Climate change poses enormous ecological, socio-economic, health, and financial challenges. A novel extreme value theory is employed in this study to model the risk to environmental, social, and governance (ESG), healthcare, and financial sectors and assess their downside risk, extreme systemic risk, and extreme spillover risk. We use a rich set of global daily data of exchange-traded funds (ETFs) from 1 July 1999 to 30 June 2022 in the case of healthcare and financial sectors and from 1 July 2007 to 30 June 2022 in the case of ESG sector. We find that the financial sector is the riskiest when we consider the tail index, tail quantile, and tail expected shortfall. However, the ESG sector exhibits the highest tail risk in the extreme environment when we consider a shock in the form of an ETF drop of 25% or 50%. The ESG sector poses the highest extreme systemic risk when a shock comes from China. Finally, we find that ESG and healthcare sectors have lower extreme spillover risk (contagion risk) compared to the financial sector. Our study seeks to provide valuable insights for developing sustainable economic, business, and financial strategies. To achieve this, we conduct a comprehensive risk assessment of the ESG, healthcare, and financial sectors, employing an innovative approach to risk modelling in response to ecological challenges

Prediction of bend pressure losses in horizontal lean phase pneumatic conveying

EThOS - Electronic Theses Online ServiceGBUnited Kingdo