Universal Large Deviations for the Tagged Particle in Single File Motion

We consider a gas of point particles moving in a one-dimensional channel with a hard-core inter-particle interaction that prevents particle crossings --- this is called single-file motion. Starting from equilibrium initial conditions we observe the motion of a tagged particle. It is well known that if the individual particle dynamics is diffusive, then the tagged particle motion is sub-diffusive, while for ballistic particle dynamics, the tagged particle motion is diffusive. Here we compute exactly the large deviation function for the tagged particle displacement and show that this is universal, independent of the individual dynamics.Comment: 6 pages including supplementary material, 3 figures, accepted for publication in Phys. Rev. Let

A Feasibility Study on Indoor Localization and Multi-person Tracking Using Sparsely Distributed Camera Network with Edge Computing

Camera-based activity monitoring systems are becoming an attractive solution for smart building applications with the advances in computer vision and edge computing technologies. In this paper, we present a feasibility study and systematic analysis of a camera-based indoor localization and multi-person tracking system implemented on edge computing devices within a large indoor space. To this end, we deployed an end-to-end edge computing pipeline that utilizes multiple cameras to achieve localization, body orientation estimation and tracking of multiple individuals within a large therapeutic space spanning $1700m^2$, all while maintaining a strong focus on preserving privacy. Our pipeline consists of 39 edge computing camera systems equipped with Tensor Processing Units (TPUs) placed in the indoor space's ceiling. To ensure the privacy of individuals, a real-time multi-person pose estimation algorithm runs on the TPU of the computing camera system. This algorithm extracts poses and bounding boxes, which are utilized for indoor localization, body orientation estimation, and multi-person tracking. Our pipeline demonstrated an average localization error of 1.41 meters, a multiple-object tracking accuracy score of 88.6\%, and a mean absolute body orientation error of 29\degree. These results shows that localization and tracking of individuals in a large indoor space is feasible even with the privacy constrains

1st Workshop on Maritime Computer Vision (MaCVi) 2023: Challenge Results

The 1$^{\text{st}}$ Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Detection, (ii) UAV-based Maritime Object Tracking, (iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime Obstacle Detection. The subchallenges were based on the SeaDronesSee and MODS benchmarks. This report summarizes the main findings of the individual subchallenges and introduces a new benchmark, called SeaDronesSee Object Detection v2, which extends the previous benchmark by including more classes and footage. We provide statistical and qualitative analyses, and assess trends in the best-performing methodologies of over 130 submissions. The methods are summarized in the appendix. The datasets, evaluation code and the leaderboard are publicly available at https://seadronessee.cs.uni-tuebingen.de/macvi.Comment: MaCVi 2023 was part of WACV 2023. This report (38 pages) discusses the competition as part of MaCV

High-Risk genotypes associated with poor response to controlled ovarian stimulation in Indian women

Background: Infertility is a global burden and has become exceedingly common in the preceding years; controlled ovarian stimulation (COS) is a pre-requisite for couples opting to conceive via in vitro fertilisation (IVF). Based on the number of oocytes retrieved upon COS, a patient may be classified as a good responder or poor responder. The genetic aspect of response to COS has not been elucidated in the Indian population. Aims: This study aimed to establish a genomic basis for COS in IVF in the Indian population and to understand its predictive value. Settings and Design: The patient samples were collected at both Hegde Fertility Centre and GeneTech laboratory. The test was carried out at GeneTech, a diagnostic research laboratory based in Hyderabad, India. Patients with infertility without any history of polycystic ovary syndrome and hypogonadotropic hypogonadism were included in the study. Detailed clinical, medical and family history was obtained from patients. The controls had no history of secondary infertility or pregnancy losses. Materials and Methods: A total of 312 females were included in the study comprising 212 women with infertility and 100 controls. Next-generation sequencing technology was employed to sequence multiple genes associated with response to COS. Statistical Analysis Used: Statistical analysis using odds ratio was carried out to understand the significance of the results obtained. Results: Strong association of c.146G>T of AMH, c.622-6C>T of AMHR2, c.453-397T>C and c.975G>C of ESR1, c.2039G>A of FSHR and c.161+4491T>C of LHCGR with infertility and response to COS was established. Further, combined risk analysis was carried out to establish a predictive risk factor for patients with a combination of the genotypes of interest and biochemical parameters commonly considered during IVF procedures. Conclusion: This study has enabled the identification of potential markers pertaining to response to COS in the Indian population

An Edge Computing and Ambient Data Capture System for Clinical and Home Environments

The non-contact patient monitoring paradigm moves patient care into their homes and enables long-term patient studies. The challenge, however, is to make the system non-intrusive, privacy-preserving, and low-cost. To this end, we describe an open-source edge computing and ambient data capture system, developed using low-cost and readily available hardware. We describe five applications of our ambient data capture system. Namely: (1) Estimating occupancy and human activity phenotyping; (2) Medical equipment alarm classification; (3) Geolocation of humans in a built environment; (4) Ambient light logging; and (5) Ambient temperature and humidity logging. We obtained an accuracy of 94% for estimating occupancy from video. We stress-tested the alarm note classification in the absence and presence of speech and obtained micro averaged F1 scores of 0.98 and 0.93, respectively. The geolocation tracking provided a room-level accuracy of 98.7%. The root mean square error in the temperature sensor validation task was 0.3°C and for the humidity sensor, it was 1% Relative Humidity. The low-cost edge computing system presented here demonstrated the ability to capture and analyze a wide range of activities in a privacy-preserving manner in clinical and home environments and is able to provide key insights into the healthcare practices and patient behaviors

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