The Manifestation of Stopping Sets and Absorbing Sets as Deviations on the Computation Trees of LDPC Codes

The error mechanisms of iterative message-passing decoders for low-density parity-check codes are studied. A tutorial review is given of the various graphical structures, including trapping sets, stopping sets, and absorbing sets that are frequently used to characterize the errors observed in simulations of iterative decoding of low-density parity-check codes. The connections between trapping sets and deviations on computation trees are explored in depth using the notion of problematic trapping sets in order to bridge the experimental and analytic approaches to these error mechanisms. A new iterative algorithm for finding low-weight problematic trapping sets is presented and shown to be capable of identifying many trapping sets that are frequently observed during iterative decoding of low-density parity-check codes on the additive white Gaussian noise channel. Finally, a new method is given for characterizing the weight of deviations that result from problematic trapping sets

Iterative Construction of Regular LDPC Codes from Independent Tree-Based Minimum Distance Bounds

An independent tree-based method for lower bounding the minimum distance of low-density parity-check (LDPC) codes is presented. This lower-bound is then used as the decision criterion during the iterative construction of regular LDPC codes. The new construction algorithm results in LDPC codes with greater girth and improved minimum-distance bounds when compared to regular LDPC codes constructed using the progressive edge-growth (PEG) construction and the approximate cycle extrinsic message degree (ACE)-constrained PEG construction. Simulation results of codes constructed with the new method show improved performance on the additive white Gaussian noise channel at moderate signal-to-noise ratios

Multi-Pig Part Detection and Association with a Fully-Convolutional Network

Computer vision systems have the potential to provide automated, non-invasive monitoring of livestock animals, however, the lack of public datasets with well-defined targets and evaluation metrics presents a significant challenge for researchers. Consequently, existing solutions often focus on achieving task-specific objectives using relatively small, private datasets. This work introduces a new dataset and method for instance-level detection of multiple pigs in group-housed environments. The method uses a single fully-convolutional neural network to detect the location and orientation of each animal, where both body part locations and pairwise associations are represented in the image space. Accompanying this method is a new dataset containing 2000 annotated images with 24,842 individually annotated pigs from 17 different locations. The proposed method achieves over 99% precision and over 96% recall when detecting pigs in environments previously seen by the network during training. To evaluate the robustness of the trained network, it is also tested on environments and lighting conditions unseen in the training set, where it achieves 91% precision and 67% recall. The dataset is publicly available for download

Using Visual and Digital Imagery to Quantify Horn Fly (Diptera: Muscidae) Densities

The horn fly, Haematobia irritans L. (Diptera: Muscidae), is a persistent pest of cattle globally. A threshold of 200 flies per animal is considered the standard management goal; however, determining when that threshold has been exceeded is difficult using visual estimates that tend to overestimate the actual fly densities and are, at best, subjective. As a result, a more reliable and durable method of determining horn fly densities is needed. Here, we describe the methods commonly used to quantify horn fly densities including visual estimates and digital photography, and provide examples of quantification software and the prospect for computer automation methods

A Universal Theory of Pseudocodewords

Three types of pseudocodewords for LDPC codes are found in the literature: graph cover pseudocodewords, linear programming pseudocodewords, and computation tree pseudocodewords. In this paper we first review these three notions and known connections between them. We then propose a new decoding rule — universal cover decoding — for LDPC codes. This new decoding rule also has a notion of pseudocodeword attached, and this fourth notion provides a framework in which we can better understand the other three

End-Effector Contact and Force Detection for Miniature Autonomous Robots Performing Lunar and Expeditionary Surgery

Introduction: The U.S. Space Force was stood up on December 20, 2019 as an independent branch under the Air Force consisting of about 16,000 active duty and civilian personnel focused singularly on space. In addition to the Space Force, the plans by NASA and private industry for exploration-class long-duration missions to the moon, near-earth asteroids, and Mars makes semi-independent medical capability in space a priority. Current practice for space-based medicine is limited and relies on a “life-raft” scenario for emergencies. Discussions by working groups on military space-based medicine include placing a Role III equivalent facility in a lunar surface station. Surgical capability is a key requirement for that facility. Materials and Methods: To prepare for the eventuality of surgery in space, it is necessary to develop low-mass, low power, mini-surgical robots, which could serve as a celestial replacement for existing terrestrial robots. The current study focused on developing semi-autonomous capability in surgical robotics, specifically related to task automation. Two categories for end-effector tissue interaction were developed: Visual feedback from the robot to detect tissue contact, and motor current waveform measurements to detect contact force. Results: Using a pixel-to-pixel deep neural network to train, we were able to achieve an accuracy of nearly 90% for contact/nocontact detection. Large torques were predicted well by a trained long short-term memory recursive network, but the technique did not predict small torques well. Conclusion: Surgical capability on long-duration missions will require human/machine teaming with semi-autonomous surgical robots. Our existing small, lightweight, low-power miniature robots perform multiple essential tasks in one design including hemostasis, fluid management, suturing for traumatic wounds, and are fully insertable for internal surgical procedures. To prepare for the inevitable eventuality of an emergency surgery in space, it is essential that automated surgical robot capabilities be developed

Evaluation of a novel computer vision-based livestock monitoring system to identify and track specific behaviors of individual nursery pigs within a group-housed environment

Animal behavior is indicative of health status and changes in behavior can indicate health issues (i.e., illness, stress, or injury). Currently, human observation (HO) is the only method for detecting behavior changes that may indicate problems in group-housed pigs. While HO is effective, limitations exist. Limitations include HO being time consuming, HO obfuscates natural behaviors, and it is not possible to maintain continuous HO. To address these limitations, a computer vision platform (NUtrack) was developed to identify (ID) and continuously monitor specific behaviors of group-housed pigs on an individual basis. The objectives of this study were to evaluate the capabilities of the NUtrack system and evaluate changes in behavior patterns over time of group-housed nursery pigs. The NUtrack system was installed above four nursery pens to monitor the behavior of 28 newly weaned pigs during a 42-d nursery period. Pigs were stratified by sex, litter, and randomly assigned to one of two pens (14 pigs/pen) for the first 22 d. On day 23, pigs were split into four pens (7 pigs/pen). To evaluate the NUtrack system’s capabilities, 800 video frames containing 11,200 individual observations were randomly selected across the nursery period. Each frame was visually evaluated to verify the NUtrack system’s accuracy for ID and classification of behavior. The NUtrack system achieved an overall accuracy for ID of 95.6%. This accuracy for ID was 93.5% during the first 22 d and increased (P \u3c 0.001) to 98.2% for the final 20 d. Of the ID errors, 72.2% were due to mislabeled ID and 27.8% were due to loss of ID. The NUtrack system classified lying, standing, walking, at the feeder (ATF), and at the waterer (ATW) behaviors accurately at a rate of 98.7%, 89.7%, 88.5%, 95.6%, and 79.9%, respectively. Behavior data indicated that the time budget for lying, standing, and walking in nursery pigs was 77.7% ± 1.6%, 8.5% ± 1.1%, and 2.9% ± 0.4%, respectively. In addition, behavior data indicated that nursery pigs spent 9.9% ± 1.7% and 1.0% ± 0.3% time ATF and ATW, respectively. Results suggest that the NUtrack system can detect, identify, maintain ID, and classify specific behavior of group-housed nursery pigs for the duration of the 42-d nursery period. Overall, results suggest that, with continued research, the NUtrack system may provide a viable real-time precision livestock tool with the ability to assist producers in monitoring behaviors and potential changes in the behavior of group-housed pigs

Evaluation of Precision Livestock Technology and Human Scoring of Nursery Pigs in a Controlled Immune Challenge Experiment

The objectives were to determine the sensitivity, specificity, and cutoff values of a visual-based precision livestock technology (NUtrack), and determine the sensitivity and specificity of sickness score data collected with the live observation by trained human observers. At weaning, pigs (n = 192; gilts and barrows) were randomly assigned to one of twelve pens (16/pen) and treatments were randomly assigned to pens. Sham-pen pigs all received subcutaneous saline (3 mL). For LPS-pen pigs, all pigs received subcutaneous lipopolysaccharide (LPS; 300 µg/kg BW; E. coli O111:B4; in 3 mL of saline). For the last treatment, eight pigs were randomly assigned to receive LPS, and the other eight were sham (same methods as above; half-and-half pens). Human data from the day of the challenge presented high true positive and low false positive rates (88.5% sensitivity; 85.4% specificity; 0.871 Area Under Curve, AUC), however, these values declined when half-and-half pigs were scored (75% sensitivity; 65.5% specificity; 0.703 AUC). Precision technology measures had excellent AUC, sensitivity, and specificity for the first 72 h after treatment and AUC values were \u3e0.970, regardless of pen treatment. These results indicate that precision technology has a greater potential for identifying pigs during a natural infectious disease event than trained professionals using timepoint sampling

Suicide trends in the early months of the COVID-19 pandemic: an interrupted time-series analysis of preliminary data from 21 countries

BackgroundThe COVID-19 pandemic is having profound mental health consequences for many people. Concerns have been expressed that at its most extreme, this may manifest itself in increased suicide rates.MethodsWe sourced real-time suicide data from around the world via a systematic internet search and recourse to our networks and the published literature. We used interrupted time series analysis to model the trend in monthly suicides prior to COVID-19 in each country/area-within-country, comparing the expected number of suicides derived from the model with the observed number of suicides in the early months of the pandemic. Countries/areas-within countries contributed data from at least 1 January 2019 to 31 July 2020 and potentially from as far back as 1 January 2016 until as recently as 31 October 2020. We conducted a primary analysis in which we treated 1 April to 31 July 2020 as the COVID-19 period, and two sensitivity analyses in which we varied its start and end dates (for those countries/areas-within-countries with data beyond July 2020).OutcomesWe sourced data from 21 countries (high income [n=16], upper-middle income [n=5]; whole country [n=10], area(s)-within-the-country [n=11]). In general, there does not appear to have been a significant increase in suicides since the pandemic began in the countries for which we had data. In fact, in a number of countries/areas-within-countries there appears to have been a decrease.InterpretationThis is the first study to examine suicides occurring in the context of the COVID-19 pandemic in multiple countries. It offers a consistent picture, albeit from high- and upper-middle income countries, of suicide numbers largely remaining unchanged or declining in the early months of the pandemic. We need to remain vigilant and be poised to respond if the situation changes as the longer-term mental health and economic impacts of the pandemic unfold

Prevalence of HIV, hepatitis B, and hepatitis C in people with severe mental illness: A systematic review and meta-analysis

BackgroundAlthough people with serious mental illnesses have a high risk of contracting blood-borne viral infections, sexual health has largely been neglected by researchers and policy makers involved in mental health. Failure to address this shortcoming could increase morbidity and mortality as a result of undetected and untreated infection. We did a systematic review and meta-analysis to estimate the prevalence of blood-borne viral infection in people with serious mental illness.MethodWe searched the Cochrane Library, Medline, Embase, PsycInfo, CINAHL, and DARE for studies of the prevalence of HIV, hepatitis B virus, and hepatitis C virus in people with serious mental illness, published between Jan 1, 1980, and Jan 1, 2015. We group prevalence data by region and by virus and estimated pooled prevalence. We did a sensitivity analysis of the effect of study quality on prevalence.FindingsAfter removal of duplicates, we found 373 abstracts, 91 of which met our eligibility criteria. The prevalences of blood-borne viral infections in people with serious mental illness were higher than in the general population in places with low prevalence of blood-borne viruses, such as the USA and Europe, and on par with the general population in regions with high prevalence of blood-borne viruses (Africa for HIV and southeast Asia for hepatitis B virus and hepatitis C virus). Pooled prevalence of HIV in people with serious mental illness in the USA was 6·0% (95% CI 4·3–8·3). Sensitivity analysis showed that quality scores did not significantly affect prevalence.InterpretationPeople with serious mental illness are at risk of blood-borne viral infections. However, because of methodological limitations of the studies the prevalence might be overestimated. Serious mental illness is unlikely to be a sole risk factor and risk of blood-borne viral infection is probably multifactorial and associated with low socioeconomic status, drug and alcohol misuse, ethnic origin, and sex. Health providers should routinely discuss sexual health and risks for blood-borne viruses (including risks related to drug misuse) with people who have serious mental illness, as well as offering testing and treatment for those at risk