Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantifying the nutritional and income loss caused by crop raiding in a rural African subsistence farming community in South Africa

Globally, crop damage by wildlife contributes to food insecurity through the direct loss of food and income. We investigated the calories lost and the potential economic impact of crop raiding at subsistence homesteads abutting the Hluhluwe Game Reserve, and assessed mitigation measures to combat crop raiding. We quantified the seasonal loss of calories (kJ/g) of four common crops, namely, beetroot, common bean, maize, and spinach, and determined the seasonal potential income loss. We used a stratified sampling approach to sample the homesteads. We found that season, crop type and the interaction between season and crop type predicted relative calorie loss and potential income loss, with the highest income loss recorded for spinach in the dry season. Significant differences were found for the potential income loss for all crop types in the wet season, and for the interaction between the crop types (maize, spinach) and the wet season. Farm slope was also a significant predictor of the relative calorie loss. Crop raiding animals, crops raided and distance of farms from the reserve all had a significant effect on the choice of mitigation measures of farmers. The highest relative calorie loss was for maize during the dry season, which could affect the subsistence farmers by reducing their daily calorie intake. This has an impact on their food security, especially during the dry season. Moreover, the most preferred mitigation measure used by farmers can have opportunity costs. These results have important implications for food security policies and practices.
Periprocedural complications and one-year outcomes after catheter ablation for treatment of atrial fibrillation in elderly patients: a nationwide Danish cohort study

OBJECTIVES To investigate complications within 30-days following first-time ablation for atrial fibrillation (AF), including a composite of cardiac tamponade, hematoma requiring intervention, stroke or death, in patients ≥ 75 years of age, compared to patients aged 65−74 years. In addition, one-year all-cause mortality and AF relapse were compared. METHODS & RESULTS All patients receiving their first catheter ablation for AF between 2012 and 2016 were identified using Danish nationwide registries. Patients aged 65−74 years served as the reference group for patients ≥ 75 years. Relapse of AF within one year was defined as cardioversion following a three-month blanking period, re-ablation or confirmed relapse within follow-up. The composite complication outcome did not differ between the two age groups, with 39/1554 (2.8%) in patients 65−74 years of age, versus 5/199 (2.5%) in older patients (adjusted HR = 0.94), 95% CI: 0.37−2.39, P = 0.896). Patients ≥ 75 years or older had no increased hazard of death within 30 days after the procedure, with an incidence of 3/1554 (0.2%) in younger patients and 2/199 (1.0%) in patients ≥ 75 years of age (adjusted HR = 4.71, 95% CI: 0.78−28.40, P = 0.091). There was no difference in relapse of AF after one year between age groups (≥ 75 years adjusted HR = 1.00, 95% CI: 0.78-1.26, P = 0.969). CONCLUSION In patients ≥ 75 years of age selected for catheter ablation for AF, the incidence of periprocedural complications, as well as one-year freedom from AF showed no statistical difference, when compared to patients 65−74 years of age.
Solving Inverse Problems with NerfGANs

We introduce a novel framework for solving inverse problems using NeRF-style generative models. We are interested in the problem of 3-D scene reconstruction given a single 2-D image and known camera parameters. We show that naively optimizing the latent space leads to artifacts and poor novel view rendering. We attribute this problem to volume obstructions that are clear in the 3-D geometry and become visible in the renderings of novel views. We propose a novel radiance field regularization method to obtain better 3-D surfaces and improved novel views given single view observations. Our method naturally extends to general inverse problems including inpainting where one observes only partially a single view. We experimentally evaluate our method, achieving visual improvements and performance boosts over the baselines in a wide range of tasks. Our method achieves $30-40\%$ MSE reduction and $15-25\%$ reduction in LPIPS loss compared to the previous state of the art.
Optimal location of battery electric vehicle charging stations in urban areas: A new approach

ABSTRACT Battery electric vehicles (BEV) are increasingly seen as one of the most suitable alternatives to internal combustion engine vehicles. An important issue involved in the dissemination of BEV relates with the deployment of the respective charging network and, in particular, with the locations of charging stations. Typically, the locations selected correspond to popular places such as city centers, shopping areas, train stations, and university campuses. Although these places are highly visible, the low parking times and high rotation rates often observed there could deliver an inadequate solution for the daily charging needs of users. In this article, a new approach to determine the location of BEV charging stations in urban areas is proposed. The approach relies on a detailed analysis of BEV charging needs, charging coverage, and adoption potential, as well as on an innovative type of location-allocation model. The usefulness of the approach is demonstrated for a set of hypothetical instances replicating the essential ingredients of real-world charging station location problems.
Seasonal Evolution of Mixed Layers in the Red Sea and the Relative Contribution of Atmospheric Buoyancy and Momentum Forcing

The seasonal and spatial evolution of mixed layers (MLs) in the Red Sea (RS) is analyzed for the 2001-2015 period using the results of a high resolution (~1km horizontal, 50 vertical layers) ocean circulation model forced by a novel regional high resolution (5 km) atmospheric reanalysis dataset. The simulation reproduces the main features of the near-surface stratification, as described by the available observations. The seasonal evolution of the modeled mixed layer depths (MLDs) in the RS is predominantly driven by atmospheric buoyancy forcing, especially its heat flux component. Everywhere in the basin the model MLs are deepest in January and February. The deepest MLDs develop in the northern parts of the Gulf of Aqaba and in the western parts of the north RS. The MLDs gradually shoal towards the south, reflecting the meridional gradient of wintertime surface buoyancy loss. In spring and summer, the surface ocean heat gain increases the stratification and the MLs are becoming shallow everywhere in the basin. During this season wind may have a significant local impact on the MLD. Particularly important are strong winds channeled by topography, such as in the vicinity of the Strait of Bab-Al-Mandeb and the straits connecting the two gulfs in the north, and lateral jets blowing through mountain gaps, such as the Tokar jet in the central RS. The MLD distribution further suggests influence by the general and mesoscale circulation. The complex patterns of air-sea buoyancy flux, wind forcing, and the thermohaline and mesoscale circulation, are all strongly imprinted on the MLD distribution.
CDRec: Cayley-Dickson Recommender

In this paper, we propose a recommendation framework named Cayley-Dickson Recommender. We introduce Cayley-Dickson construction which uses a recursive process to define hypercomplex algebras and their mathematical operations. We also design a graph convolution operator to learn representations in the hypercomplex space. To the best of our knowledge, it is the first time that Cayley-Dickson construction and graph convolution techniques have been used in hypercomplex recommendation. Compared with the state-of-the-art recommendation methods, our method achieves superior performance on real-world datasets.
Assessing the reliability of automatic sentiment analysis tools on rating the sentiment of reviews of NHS dental practices in England

Background Online reviews may act as a rich source of data to assess the quality of dental practices. Assessing the content and sentiment of reviews on a large scale is time consuming and expensive. Automation of the process of assigning sentiment to big data samples of reviews may allow for reviews to be used as Patient Reported Experience Measures for primary care dentistry. Aim To assess the reliability of three different online sentiment analysis tools (Amazon Comprehend DetectSentiment API (ACDAPI), Google and Monkeylearn) at assessing the sentiment of reviews of dental practices working on National Health Service contracts in the United Kingdom. Methods A Python 3 script was used to mine 15800 reviews from 4803 unique dental practices on the NHS.uk websites between April 2018 –March 2019. A random sample of 270 reviews were rated by the three sentiment analysis tools. These reviews were rated by 3 blinded independent human reviewers and a pooled sentiment score was assigned. Kappa statistics and polychoric evalutaiton were used to assess the level of agreement. Disagreements between the automated and human reviewers were qualitatively assessed. Results There was good agreement between the sentiment assigned to reviews by the human reviews and ACDAPI (k = 0.660). The Google (k = 0.706) and Monkeylearn (k = 0.728) showed slightly better agreement at the expense of usability on a massive dataset. There were 33 disagreements in rating between ACDAPI and human reviewers, of which n = 16 were due to syntax errors, n = 10 were due to misappropriation of the strength of conflicting emotions and n = 7 were due to a lack of overtly emotive language in the text. Conclusions There is good agreement between the sentiment of an online review assigned by a group of humans and by cloud-based sentiment analysis. This may allow the use of automated sentiment analysis for quality assessment of dental service provision in the NHS.
The Impact of TV Advertising on Website Traffic

Abstract: We propose a modeling procedure for estimating immediate responses to TV ads and evaluating the factors influencing their size. First, we capture diurnal and seasonal patterns of website visits using the kernel smoothing method. Second, we estimate a gradual increase in website visits after an ad using the maximum likelihood method. Third, we analyze the non-linear dependence of the estimated increase in website visits on characteristics of the ads using the random forest method. The proposed methodology is applied to a dataset containing minute-by-minute organic website visits and detailed characteristics of TV ads for an e-commerce company in 2019. The results show that people are indeed willing to switch between screens and multitask. Moreover, the time of the day, the TV channel, and the advertising motive play a great role in the impact of the ads. Based on these results, marketers can quantify the return on a single ad spot, evaluate the extra-paid ad options (such as a premium position), and optimize the buying process.
Diabetic retinopathy service delivery and integration into the health system in Pakistan—Findings from a multicentre qualitative study

This paper is based on qualitative research carried out in a diabetic retinopathy (DR) programme in three districts of Pakistan. It analyses the organisation and delivery of DR services and the extent to which the interventions resulted in a fully functioning integrated approach to DR care and treatment. Between January and April 2019, we conducted 14 focus group discussions and 37 in-depth interviews with 144 purposively selected participants: patients, lady health workers (LHWs) and health professionals. Findings suggest that integration of services was helpful in the prevention and management of DR. Through the efforts of LHWs and general practitioners, diabetic patients in the community became aware of the eye health issues related to uncontrolled diabetes. However, a number of systemic pressure points in the continuum of care seem to have limited the impact of the integration. Some components of the intervention, such as a patient tracking system and reinforced interdepartmental links, show great promise and need to be sustained. The results of this study point to the need for action to ensure inclusion of DR on the list of local health departments’ priority conditions, greater provision of closer-to-community services, such as mobile clinics. Future interventions will need to consider the complexity of adding diabetic retinopathy to an already heavy workload for the LHWs.
Determinants of retention in HIV antiretroviral treatment (ART) in the Cameroon International epidemiology Database to Evaluate AIDS (IeDEA) study clinics: the context of the HIV treat all strategy in Cameroon

Introduction retaining patients in antiretroviral treatment (ART) is essential for successful outcomes. Unfortunately, Cameroon continues to report suboptimal ART retention. This study focused on identifying determinants of ART retention in three HIV clinics in Cameroon within the HIV treat all context. Methods a medical chart review of 423 subjects who initiated ART between July and September 2016 in the Limbe, Bamenda and Jamot Hospitals. Patients' sociodemographic and clinical characteristics and ART retention data were abstracted using structured paper forms. Chi square test was used to assess bivariate associations. Logistic regression was used to adjust for confounders. P-value was set at <0.05 at 95% confidence interval. Results the mean age was 40±11 years, and 65.1% were females. Antiretroviral treatment retention after 24 months was 309/392 (78.83%) and 30/423 (7.1%) were transferred-out, 11/423 (2.6%) reported dead and 73/423 (17.3%) lost to follow-up. HIV status disclosure (AOR 0.16 95% CI: 0.05-0.51, p<0.01) and age group 31-50 years (AOR 3.63, 95% CI: 1.04-12.59, P= 0.04) were associated with lower and higher ART retention respectively. Conclusion about a quarter of the participants were not retained in ART after 24 months. Patient-level factors determined ART retention. These factors should be considered in designing strategies to improve ART retention. More research is needed to identify other determinants of ART retention under the HIV treat all strategy.
CLIN-X: pre-trained language models and a study on cross-task transfer for concept extraction in the clinical domain

MOTIVATION The field of natural language processing (NLP) has recently seen a large change towards using pre-trained language models for solving almost any task. Despite showing great improvements in benchmark datasets for various tasks, these models often perform sub-optimal in non-standard domains like the clinical domain where a large gap between pre-training documents and target documents is observed. In this paper, we aim at closing this gap with domain-specific training of the language model and we investigate its effect on a diverse set of downstream tasks and settings. RESULTS We introduce the pre-trained CLIN-X (Clinical XLM-R) language models and show how CLIN-X outperforms other pre-trained transformer models by a large margin for ten clinical concept extraction tasks from two languages. In addition, we demonstrate how the transformer model can be further improved with our proposed task- and language-agnostic model architecture based on ensembles over random splits and cross-sentence context. Our studies in low-resource and transfer settings reveal stable model performance despite a lack of annotated data with improvements of up to 47 F1 points when only 250 labeled sentences are available. Our results highlight the importance of specialized language models, such as CLIN-X, for concept extraction in non-standard domains, but also show that our task-agnostic model architecture is robust across the tested tasks and languages so that domain- or task-specific adaptations are not required. AVAILABILITY The CLIN-X language models and source code for fine-tuning and transferring the model are publicly available at https://github.com/boschresearch/clin_x/ and the huggingface model hub.
NOVEL INSIGHTS ON LOWER LIMB MUSCULOSKELETAL HEALTH AND PERFORMANCE IN PRE-ADOLESCENT AND ADOLESCENT GYMNASTS

New analyses are presented from the data of Bradshaw and Le Rossignol (2004) to examine the relationship between musculoskeletal health, and physical and performance qualities, with measures of leg stiffness in a group of female gymnasts. A gymnasts leg stiffness can be assessed through continuous jump (straight & bent legged) series tests, as well as from rebound jumps. Although the observations are retrospective, the study revealed a potential safe zone for ankle extensor stiffness. The gymnast with low stiffness had previously suffered a landing ankle injury; whilst four gymnasts with high stiffness had histories of take-off ankle injuries. Aside from profiling physical performance qualities, biomechanical (kinetic) testing has the potential to also aid in the assessment and management of the athlete’s functional musculoskeletal health (rehabilitation, injury risk).
Lower extremity leg amputation: An advantage in running?

The goal in the rehabilitation of amputees is full reintegration into society. The ability to engage in sports activities is part of the rehabilitation. The prosthetic leg is individually fitted and specialized for one specific type of competition. The discussions regarding bilateral trans‐tibial amputee, Oscar Pistorius, focus on the advantages of wearing prosthetic feet. With a holistic view on prosthetic applications, such as the challenges of selection, fitting, alignment, the adaptation of the socket, and the compensatory strategies of the amputee, it is hard to imagine that a bilateral amputee has advantages compared to an able‐bodied athlete. This article describes the clinical view of fitting an amputee athlete and describes the disadvantages of amputees compared to able‐bodied sprinters with a focus on the prosthetic fitting and the interface.
Dynamic capital allocation rules via BSDEs: an axiomatic approach

In this paper, we study capital allocation for dynamic risk measures, with an axiomatic approach but also by exploiting the relation between risk measures and BSDEs. Although there is a wide literature on capital allocation rules in a static setting and on dynamic risk measures, only a few recent papers on capital allocation work in a dynamic setting and, moreover, those papers mainly focus on the gradient approach. To ﬁll this gap, we then discuss new perspectives to the capital allocation problem going beyond those already existing in the literature. In particular, we introduce and investigate a general axiomatic approach to dynamic capital allocations as well as an approach suitable for risk measures induced by g -expectations under weaker assumptions than Gateaux diﬀerentiability.
Decentralized Mean Field Games

Multiagent reinforcement learning algorithms have not been widely adopted in large scale environments with many agents as they often scale poorly with the number of agents. Using mean field theory to aggregate agents has been proposed as a solution to this problem. However, almost all previous methods in this area make a strong assumption of a centralized system where all the agents in the environment learn the same policy and are effectively indistinguishable from each other. In this paper, we relax this assumption about indistinguishable agents and propose a new mean field system known as Decentralized Mean Field Games, where each agent can be quite different from others. All agents learn independent policies in a decentralized fashion, based on their local observations. We define a theoretical solution concept for this system and provide a fixed point guarantee for a Q-learning based algorithm in this system. A practical consequence of our approach is that we can address a `chicken-and-egg' problem in empirical mean field reinforcement learning algorithms. Further, we provide Q-learning and actor-critic algorithms that use the decentralized mean field learning approach and give stronger performances compared to common baselines in this area. In our setting, agents do not need to be clones of each other and learn in a fully decentralized fashion. Hence, for the first time, we show the application of mean field learning methods in fully competitive environments, large-scale continuous action space environments, and other environments with heterogeneous agents. Importantly, we also apply the mean field method in a ride-sharing problem using a real-world dataset. We propose a decentralized solution to this problem, which is more practical than existing centralized training methods.
Efficient simulation of dynamics in two-dimensional quantum spin systems with isometric tensor networks

We investigate the computational power of the recently introduced class of isometric tensor network states (isoTNSs), which generalizes the isometric conditions of the canonical form of one-dimensional matrix-product states to tensor networks in higher dimensions. We discuss several technical details regarding the implementation of isoTNSs-based algorithms and compare diﬀerent disentanglers—which are essential for an eﬃcient handling of isoTNSs. We then revisit the time evolving block decimation for isoTNSs (TEBD 2 ) and explore its power for real time evolution of two-dimensional (2D) lattice systems. Moreover, we introduce a density matrix renormalization group algorithm for isoTNSs (DMRG 2 ) that allows to variationally ﬁnd ground states of 2D lattice systems. As a demonstration and benchmark, we compute the dynamical spin structure factor of 2D quantum spin systems for two paradigmatic models: First, we compare our results for the transverse ﬁeld Ising model on a square lattice with the prediction of the spin-wave theory. Second, we consider the Kitaev model on the honeycomb lattice and compare it to the result from the exact solution.
The torsional stiffness of involute spur gears

Abstract This paper presents the results of a detailed analysis of torsional stiffness of a pair of involute spur gears in mesh using finite element methods. Adaptive meshing has been employed within a commercial finite element program to reveal the detailed behaviour in the change over region from single- to double-tooth contact zones and vice versa. Analysis of past gear tooth stiffness models is presented including single- and multitooth models of the individual and combined torsional mesh stiffness. The gear body stiffness has been shown to be a major component of the total mesh stiffness, and a revised method for predicting the combined torsional mesh stiffness is presented. It is further shown tha the mesh stiffness and load sharing ratios will be a function of applied load.