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. 83.50.Ha, 87.16.dp, 05.60.CdThe motion of particles in narrow channels where the particles cannot overtake each other is referred to as single-file motion [see Fig. (1)]. This concept was introduced by Hodgkin and Keynes [1] to describe ion transport in biological channels. The motion of a tagged particle in such a single-file system has been of great interest since the classic papers by Jepsen [2] and Harris [3]. These papers showed that, in a gas of hard rods evolving with Hamiltonian dynamics, a tagged particle moves diffusively [2] with the mean square displacement (MSD) growing linearly with time t, whereas for a gas of Brownian particles, the tagged particle shows subdiffusion [3] with the MSD growing as √ t. There has been a revival of interest in tagged particle diffusion as several experiments are now able to observe this in single-file systems in both colloidal and atomic single-file systems [4][5][6][7][8][9], and some of the theoretical predictions have been verified.There have been a number of studies to understand tagged particle motion in systems with deterministic as well as stochastic dynamics [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]. Attempts have been made to obtain the full probability density function (PDF) for the tagged particle displacement. The N-particle propagator has been obtained using the "reflection principle" [16] and Bethe Ansatz [18], and from this the tagged particle distribution has been obtained by integrating out all other particles. However, the resulting form of the distribution is complicated and not very illuminating. An approximate scheme relying on Jepsen's mapping to non-interacting particles has been used in [19,22]. A recent work [28] has used macroscopic fluctuation theory [29] to compute the cumulant generating function (CGF) corresponding to the tagged particle PDF.In this Letter, we show that it is possible to exactly compute the large time asymptotic form of the PDF of tagged particle displacement. Our method is applicable to deterministic as well as stochastic systems that are initially in equilibrium. This leads to a universal form for the PDF. We consider a collection of hard-point identical particles distributed with an uniform density ρ on the one dimensional line from −∞ to ∞. Each particle moves independently using the same dynamics, except that the hard-core repulsion prevents crossing of part...

With the recent COVID-19 pandemic, healthcare systems all over the world are struggling to manage the massive increase in emergency department (ED) visits. This has put an enormous demand on medical professionals. Increased wait times in the ED increases the risk of infection transmission. In this work we present an open-source, low cost, off-body system to assist in the automatic triage of patients in the ED based on widely available hardware. The system initially focuses on two symptoms of the infection -fever and cyanosis. The use of visible and far-infrared cameras allows for rapid assessment at a 1m distance, thus reducing the load on medical staff and lowering the risk of spreading the infection within hospitals. Its utility can be extended to a general clinical setting in non-emergency times as well to reduce wait time, channel the time and effort of healthcare professionals to more critical tasks and also prioritize severe cases.Our system consists of a Raspberry Pi 4, a Google Coral USB accelerator, a Raspberry Pi Camera v2 and a FLIR Lepton 3.5 Radiometry Long-Wave Infrared Camera with an associated IO module. Algorithms running in real-time detect the presence and body parts of individual(s) in view, and segments out the forehead and lip regions using PoseNet. The temperature of the forehead-eye area is estimated from the infrared camera image and cyanosis is assessed from the image of the lips in the visible spectrum. In our preliminary experiments, an accuracy of 97% was achieved for detecting fever and 77% for the detection of cyanosis, with a sensitivity of 91% and area under the receiver operating characteristic curve of 0.91. Heart rate and respiratory effort are also estimated from the visible camera.Although preliminary results are promising, we note that the entire system needs to be optimized before use and assessed for efficacy. The use of low-cost instrumentation will not produce temperature readings and identification of cyanosis that is acceptable in many situations. For this reason, we are releasing the full code stack and system design to allow others to rapidly iterate and improve the system. This may be of particular benefit in low-resource settings, and low-to-middle income countries in particular, which are just beginning to be affected by COVID-19.

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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: (a) Estimating occupancy and human activity phenotyping; (b) Medical equipment alarm classification; (c) Geolocation of humans in a built environment; (d) Ambient light logging; and (e) 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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