Machine learning classification of entrepreneurs in British historical census data

This paper presents a binary classification of entrepreneurs in British historical data based on the recent availability of big data from the I-CeM dataset. The main task of the paper is to attribute an employment status to individuals that did not fully report entrepreneur status in earlier censuses (1851-1881). The paper assesses the accuracy of different classifiers and machine learning algorithms, including Deep Learning, for this classification problem. We first adopt a ground-truth dataset from the later censuses to train the computer with a Logistic Regression (which is standard in the literature for this kind of binary classification) to recognize entrepreneurs distinct from non-entrepreneurs (i.e. workers). Our initial accuracy for this base-line method is 0.74. We compare the Logistic Regression with ten optimized machine learning algorithms: Nearest Neighbors, Linear and Radial Support Vector Machine, Gaussian Process, Decision Tree, Random Forest, Neural Network, AdaBoost, Naive Bayes, and Quadratic Discriminant Analysis. The best results are boosting and ensemble methods. AdaBoost achieves an accuracy of 0.95. Deep-Learning, as a standalone category of algorithms, further improves accuracy to 0.96 without using the rich text-data that characterizes the OccString feature, a string of up to 500 characters with the full occupational statement of each individual collected in the earlier censuses. Finally, and now using this OccString feature, we implement both shallow (bag-of-words algorithm) learning and Deep Learning (Recurrent Neural Network with a Long Short-Term Memory layer) algorithms. These methods all achieve accuracies above 0.99 with Deep Learning Recurrent Neural Network as the best model with an accuracy of 0.9978. The results show that standard algorithms for classification can be outperformed by machine learning algorithms. This confirms the value of extending the techniques traditionally used in the literature for this type of classification problem.ESRC Leverhulme Trust Isaac Newton Trus

Ising model for distribution networks

An elementary Ising spin model is proposed for demonstrating cascading failures (break-downs, blackouts, collapses, avalanches, ...) that can occur in realistic networks for distribution and delivery by suppliers to consumers. A ferromagnetic Hamiltonian with quenched random fields results from policies that maximize the gap between demand and delivery. Such policies can arise in a competitive market where firms artificially create new demand, or in a solidary environment where too high a demand cannot reasonably be met. Network failure in the context of a policy of solidarity is possible when an initially active state becomes metastable and decays to a stable inactive state. We explore the characteristics of the demand and delivery, as well as the topological properties, which make the distribution network susceptible of failure. An effective temperature is defined, which governs the strength of the activity fluctuations which can induce a collapse. Numerical results, obtained by Monte Carlo simulations of the model on (mainly) scale-free networks, are supplemented with analytic mean-field approximations to the geometrical random field fluctuations and the thermal spin fluctuations. The role of hubs versus poorly connected nodes in initiating the breakdown of network activity is illustrated and related to model parameters

Uteroplacental bleeding disorders during pregnancy: do missing paternal characteristics influence risk?

BACKGROUND: Several studies have assessed the risks of uteroplacental bleeding disorders in relation to maternal characteristics. The association between uteroplacental bleeding disorders and paternal characteristics, however, has received considerably less attention. Data on paternal demographics, notably race and age, from birth certificate data are becoming increasingly incomplete in recent years. This pattern of increasingly underreporting of paternal demographic data led us to speculate that pregnancies for which paternal characteristics are partially or completely missing may be associated with increased risk for uteroplacental bleeding disorders. The objective of this study is to examine the association between placenta previa and placental abruption and missing paternal age and race. METHODS: A retrospective cohort study using U.S. linked birth/infant death data from 1995 through 2001 (n = 26,336,549) was performed. Risks of placenta previa and placental abruption among: (i) pregnancies with complete paternal age and race data; (ii) paternal age only missing; (iii) paternal race only missing; and (iv) both paternal age and race missing, were evaluated. Relative risk (RR) with 95% confidence interval (CI) for placenta previa and placental abruption by missing paternal characteristics were derived after adjusting for confounders. RESULTS: Adjusted RR for placental abruption were 1.30 (95% CI 1.24, 1.37), 1.00 (95% CI 0.95, 1.05), and 1.08 (95% CI 1.06, 1.10) among pregnancies with "paternal age only", "paternal race only", and "both paternal age and race" missing, respectively. The increased risk of placental abruption among the "paternal age only missing" category is partly explained by increased risks among whites aged 20–29 years, and among blacks aged ≥30 years. However, no clear patterns in the associations between missing paternal characteristics and placenta previa were evident. CONCLUSION: Missing paternal characteristics are associated with increased risk of placental abruption, likely mediated through low socio-economic conditions

AI enhanced collaborative human-machine interactions for home-based telerehabilitation

The use of robots in a telerehabilitation paradigm could facilitate the delivery of rehabilitation on demand while reducing transportation time and cost. As a result, it helps to motivate patients to exercise frequently in a more comfortable home environment. However, for such a paradigm to work, it is essential that the robustness of the system is not compromised due to network latency, jitter, and delay of the internet. This paper proposes a solution to data loss compensation to maintain the quality of the interaction between the user and the system. Data collected from a well-defined collaborative task using a virtual reality (VR) environment was used to train a robotic system to adapt to the users' behaviour. The proposed approach uses nonlinear autoregressive models with exogenous input (NARX) and long-short term memory (LSTM) neural networks to smooth out the interaction between the user and the predicted movements generated from the system. LSTM neural networks are shown to learn to act like an actual human. The results from this paper have shown that, with an appropriate training method, the artificial predictor can perform very well by allowing the predictor to complete the task within 25 s versus 23 s when executed by the human

The hand of Homo naledi

A nearly complete right hand of an adult hominin was recovered from the Rising Star cave system, South Africa. Based on associated hominin material, the bones of this hand are attributed to Homo naledi. This hand reveals a long, robust thumb and derived wrist morphology that is shared with Neandertals and modern humans, and considered adaptive for intensified manual manipulation. However, the finger bones are longer and more curved than in most australopiths, indicating frequent use of the hand during life for strong grasping during locomotor climbing and suspension. These markedly curved digits in combination with an otherwise human-like wrist and palm indicate a significant degree of climbing, despite the derived nature of many aspects of the hand and other regions of the postcranial skeleton in H. naledi

Demograpics of Patients Admitted with Traumatic Intracranial Bleeds in Kenyatta National Hospital in Nairobi, Kenya

Background: This study was designed to describe the demographics of patients presenting with traumatic intracranial bleeds at the Kenyatta National Hospital (KNH).Methods: A descriptive cross sectional analysis of consecutive patients who had traumatic intracranial bleeds, and admitted at the KNH between December 2010 and March 2011 was performed. A total of 51 patients with traumatic intracranial bleeds were recruited in the study with a male: female ratio of 24:1.Results: The age of patients ranged from 4-82 years with a mean of 34.3 (+/- 18.5). Ninety six point one (96.1) percent of the patients were males, with a male to female ratio of 24:1. Majority of the patients only had primary school education, 56.9%, while a few had tertiary level education, 3.9%. Eleven point eight (11.8%) percent of the patients did not have any form of education. Most of the patients were in some form of employment, 47.1%, while 7.8% of patients had no employment. A clear majority of these patients were married, 51%, while 47.1% were single. Thirty five point three (35.3%) percent of these patients were alcohol consumers, while 21.6% were cigarette smokers. A number of these patients had other co-morbidities. Only 7.8% of the patients were hypertensive while 2% had HIV infection.Conclusion: From the foregoing, the population greatly affected by traumatic brain injury involves the young and productive segment of the population. Specific interventions by policy makers and clinicians, based on findings of patient demographics can help prevent some of these  preventable causes of traumatic brain injury

Patterns of Traumatic Intracranial Bleeds at Kenyatta National Hospital in Nairobi, Kenya

Background: This study was designed to describe the pattern of traumatic intracranial bleeds at the Kenyatta National Hospital (KNH).Methods: A descriptive cross sectional analysis of consecutive patients who had traumatic intracranial bleeds, and admitted at the KNH between December 2010 and March 2011 was performed. A total of 51 patients with traumatic intracranial bleeds were recruited in the study with a male: female ratio of 24.5:1.Results: Subdural (29.4%) and Intra-cerebral (29.4%) hematomas were the commonest among these patients. Intra-ventricular bleeds (2%) were the least common. On the basis of chronicity, Acute Subdural hematomas (64.7%) were the commonest, while subacute subdural hematomas(5.9%) were the least common. Assaults (33.3%) and Road Traffic Accidents (27.5%) were the leading causes among aetiology, while bomb blasts (2%) were the least.Conclusion: Acute subdural hematomas are the commonest traumatic intracranial bleeds. Further, assaults and road traffic accidents account for the leading causes of traumatic intracranial bleeds. Specific interventions based on findings of this study will guide clinicians in the care of these patients and form entry points for further clinical studies.Key words: Patterns, Trauma, Intracranial, Blee

An assistive tabletop keyboard for stroke rehabilitation

We propose a tabletop keyboard that assists stroke patients in using computers. Using computers for purposes such as paying bills, managing bank accounts, sending emails, etc., which all include typing, is part of Activities of Daily Living (ADL) that stroke patients wish to recover. To date, stroke rehabilitation research has greatly focused on using computer-assisted technology for rehabilitation. However, working with computers as a skill that patients need to recover has been neglected. The conventional human computer interfaces are mouse and keyboard. Using keyboard stays the main challenge for hemiplegic stroke patients because typing is usually a bimanual task. Therefore, we propose an assistive tabletop keyboard which is not only a novel computer interface that is specially designed to facilitate patient-computer interaction but also a rehab medium through which patients practice the desired arm/hand functions. © 2013 Authors

Pathfinder cells provide a novel therapeutic intervention for acute kidney injury

Pathfinder cells (PCs) are a novel class of adult-derived cells that facilitate functional repair of host tissue. We used rat PCs to demonstrate that they enable the functional mitigation of ischemia reperfusion (I/R) injury in a mouse model of renal damage. Female C57BL/6 mice were subjected to 30 min of renal ischemia and treated with intravenous (i.v.) injection of saline (control) or male rat pancreas-derived PCs in blinded experimentation. Kidney function was assessed 14 days after treatment by measuring serum creatinine (SC) levels. Kidney tissue was assessed by immunohistochemistry (IHC) for markers of cellular damage, proliferation, and senescence (TUNEL, Ki67, p16ink4a, p21). Fluorescence in situ hybridization (FISH) was performed to determine the presence of any rat (i.e., pathfinder) cells in the mouse tissue. PC-treated animals demonstrated superior renal function at day 14 post-I/R, in comparison to saline-treated controls, as measured by SC levels (0.13 mg/dL vs. 0.23 mg/dL, p<0.001). PC-treated kidney tissue expressed significantly lower levels of p16ink4a in comparison to the control group (p=0.009). FISH analysis demonstrated that the overwhelming majority of repaired kidney tissue was mouse in origin. Rat PCs were only detected at a frequency of 0.02%. These data confirm that PCs have the ability to mitigate functional damage to kidney tissue following I/R injury. Kidneys of PC-treated animals showed evidence of improved function and reduced expression of damage markers. The PCs appear to act in a paracrine fashion, stimulating the host tissue to recover functionally, rather than by differentiating into renal cells. This study demonstrates that pancreatic-derived PCs from the adult rat can enable functional repair of renal damage in mice. It validates the use of PCs to regenerate damaged tissues and also offers a novel therapeutic intervention for repair of solid organ damage in situ