Neural computation of arithmetic functions

A neuron is modeled as a linear threshold gate, and the network architecture considered is the layered feedforward network. It is shown how common arithmetic functions such as multiplication and sorting can be efficiently computed in a shallow neural network. Some known results are improved by showing that the product of two n-bit numbers and sorting of n n-bit numbers can be computed by a polynomial-size neural network using only four and five unit delays, respectively. Moreover, the weights of each threshold element in the neural networks require O(log n)-bit (instead of n -bit) accuracy. These results can be extended to more complicated functions such as multiple products, division, rational functions, and approximation of analytic functions

Fast arithmetic computing with neural networks

The authors introduce a restricted model of a neuron which is more practical as a model of computation then the classical model of a neuron. The authors define a model of neural networks as a feedforward network of such neurons. Whereas any logic circuit of polynomial size (in n) that computes the product of two n-bit numbers requires unbounded delay, such computations can be done in a neural network with constant delay. The authors improve some known results by showing that the product of two n-bit numbers and sorting of n n-bit numbers can both be computed by a polynomial size neural network using only four unit delays, independent of n . Moreover, the weights of each threshold element in the neural networks require only O(log n)-bit (instead of n-bit) accuracy

On the Power of Threshold Circuits with Small Weights

Linear threshold elements (LTEs) are the basic processing elements in artificial neural networks. An LTE computes a function that is a sign of a weighted sum of the input variables. The weights are arbitrary integers; actually they can be very big integers-exponential in the number of input variables. However, in practice, it is very difficult to implement big weights. So the natural question that one can ask is whether there is an efficient way to simulate a network of LTEs with big weights by a network of LTEs with small weights. We prove the following results: 1) every LTE with big weights can be simulated by a depth-3, polynomial size network of LTEs with small weights, 2) every depth-d polynomial size network of LTEs with big weights can be simulated by a depth-(2d+1), polynomial size network of LTEs with small weights. To prove these results, we use tools from harmonic analysis of Boolean functions. Our technique is quite general, it provides insights to some other problems. For example, we were able to improve the best known results on the depth of a network of threshold elements that computes the COMPARISON, ADDITION and PRODUCT of two n-bits numbers, and the MAXIMUM and the SORTING of n n-bit numbers

Roles of the CHADS2 and CHA2DS2-VASc scores in post-myocardial infarction patients: Risk of new occurrence of atrial fibrillation and ischemic stroke

Background: Patients with myocardial infarction (MI) are at risk of the development of atrial fibrillation (AF) and ischemic stroke. We sought to evaluate the prognostic performance of the CHADS2 and CHA2DS2-VASc scores in predicting new AF and/or ischemic stroke in post-ST segment elevation MI (STEMI) patients. Six hundred and seven consecutive post-STEMI patients with no previously documented AF were studied.Methods and Results: After a follow-up of 63 months (3,184 patient-years), 83 (13.7%) patients developed new AF (2.8% per year). Patients with a high CHADS2 and/or CHA2DS2-VASc score were more likely to develop new AF. The annual incidence of new AF was 1.18%, 2.10%, 4.52%, and 7.03% in patients with CHADS2 of 0, 1, 2, and ≥ 3; and 0.39%, 1.72%, 1.83%, and 5.83% in patients with a CHA2DS2-VASc score of 1, 2, 3 and ≥ 4. The CHA2DS2-VASc score (C-statistic = 0.676) was superior to the CHADS2 (C-statistic = 0.632) for discriminating new AF. Ischemic stroke occurred in 29 patients (0.9% per year), the incidence increasing in line with the CHADS2 (0.41%, 1.02%, 1.11%, and 1.95% with score of 0, 1, 2, and ≥ 3) and CHA2DS2-VASc scores (0.39%, 0.49%, 1.02%, and 1.48% with score of 1, 2, 3 and ≥ 4). The C-statistic of the CHA2DS2-VASc score as a predictor of ischemic stroke was 0.601, superior to that of CHADS2 score (0.573). CHADS2 and CHA2DS2-VASc scores can identify post-STEMI patients at high risk of AF and stroke.Conclusions: The CHADS2 and CHA2DS2-VASc scores can identify post-STEMI patients at high risk of AF and ischemic stroke. This enables close surveillance and prompt anticoagulation for stroke prevention

Differential actions of glycodelin-A on Th-1 and Th-2 cells: A paracrine mechanism that could produce the Th-2 dominant environment during pregnancy

Background: The maternalfetal interface has a unique immunological response towards the implanting placenta. It is generally accepted that a T-helper type-2 (Th-2) cytokine prevailing environment is important in pregnancy. The proportion of Th-2 cells in the peripheral blood and decidua is significantly higher in pregnant women in the first trimester than in non-pregnant women. Glycodelin-A (GdA) is a major endocrine-regulated decidual glycoprotein thought to be related to fetomaternal defence. Yet the relationship between its immunoregulatory activities and the shift towards Th-2 cytokine profile during pregnancy is unclear. Methods GdA was immunoaffinity purified from human amniotic fluid. T-helper, T-helper type-1 (Th-1) and Th-2 cells were isolated from the peripheral blood. The viability of these cells was studied by XTT assay. Immunophenotyping of CD4/CD294, cell death and GdA-binding were determined by flow cytometry. The mRNA expression, surface expression and secretion of Fas/Fas ligand (FasL) were determined by quantitative polymerase chain reaction, flow cytometry and ELISA, respectively. The activities of caspase-3, -8 and -9 were measured. The phosphorylation of extracellular signal-regulated kinases (ERK), p38 and, c-Jun N-terminal kinase was determined by western blotting. Results Although GdA bound to both Th-1 and Th-2 cells, it had differential actions on the two cell-types. GdA induced cell death of the Th-1 cells but not the Th-2 cells. The cell death was mediated through activation of caspase -3, -8 and -9 activities. GdA up-regulated the expression of Fas and inhibited ERK activation in the Th-1 cells, which might enhance the vulnerability of the cells to cell death caused by a trophoblast-derived FasL. Conclusions The data suggest that GdA could be an endometrial factor that contributes to the Th-2/Th-1 shift during pregnancy. © 2011 The Author.postprin

Towards a global partnership model in interprofessional education for cross-sector problem-solving

Objectives A partnership model in interprofessional education (IPE) is important in promoting a sense of global citizenship while preparing students for cross-sector problem-solving. However, the literature remains scant in providing useful guidance for the development of an IPE programme co-implemented by external partners. In this pioneering study, we describe the processes of forging global partnerships in co-implementing IPE and evaluate the programme in light of the preliminary data available. Methods This study is generally quantitative. We collected data from a total of 747 health and social care students from four higher education institutions. We utilized a descriptive narrative format and a quantitative design to present our experiences of running IPE with external partners and performed independent t-tests and analysis of variance to examine pretest and posttest mean differences in students’ data. Results We identified factors in establishing a cross-institutional IPE programme. These factors include complementarity of expertise, mutual benefits, internet connectivity, interactivity of design, and time difference. We found significant pretest–posttest differences in students’ readiness for interprofessional learning (teamwork and collaboration, positive professional identity, roles, and responsibilities). We also found a significant decrease in students’ social interaction anxiety after the IPE simulation. Conclusions The narrative of our experiences described in this manuscript could be considered by higher education institutions seeking to forge meaningful external partnerships in their effort to establish interprofessional global health education

A synthesis of evidence for policy from behavioural science during COVID-19

Scientific evidence regularly guides policy decisions1, with behavioural science increasingly part of this process2. In April 2020, an influential paper3 proposed 19 policy recommendations (‘claims’) detailing how evidence from behavioural science could contribute to efforts to reduce impacts and end the COVID-19 pandemic. Here we assess 747 pandemic-related research articles that empirically investigated those claims. We report the scale of evidence and whether evidence supports them to indicate applicability for policymaking. Two independent teams, involving 72 reviewers, found evidence for 18 of 19 claims, with both teams finding evidence supporting 16 (89%) of those 18 claims. The strongest evidence supported claims that anticipated culture, polarization and misinformation would be associated with policy effectiveness. Claims suggesting trusted leaders and positive social norms increased adherence to behavioural interventions also had strong empirical support, as did appealing to social consensus or bipartisan agreement. Targeted language in messaging yielded mixed effects and there were no effects for highlighting individual benefits or protecting others. No available evidence existed to assess any distinct differences in effects between using the terms ‘physical distancing’ and ‘social distancing’. Analysis of 463 papers containing data showed generally large samples; 418 involved human participants with a mean of 16,848 (median of 1,699). That statistical power underscored improved suitability of behavioural science research for informing policy decisions. Furthermore, by implementing a standardized approach to evidence selection and synthesis, we amplify broader implications for advancing scientific evidence in policy formulation and prioritization

A synthesis of evidence for policy from behavioural science during COVID-19

Scientific evidence regularly guides policy decisions 1, with behavioural science increasingly part of this process 2. In April 2020, an influential paper 3 proposed 19 policy recommendations (‘claims’) detailing how evidence from behavioural science could contribute to efforts to reduce impacts and end the COVID-19 pandemic. Here we assess 747 pandemic-related research articles that empirically investigated those claims. We report the scale of evidence and whether evidence supports them to indicate applicability for policymaking. Two independent teams, involving 72 reviewers, found evidence for 18 of 19 claims, with both teams finding evidence supporting 16 (89%) of those 18 claims. The strongest evidence supported claims that anticipated culture, polarization and misinformation would be associated with policy effectiveness. Claims suggesting trusted leaders and positive social norms increased adherence to behavioural interventions also had strong empirical support, as did appealing to social consensus or bipartisan agreement. Targeted language in messaging yielded mixed effects and there were no effects for highlighting individual benefits or protecting others. No available evidence existed to assess any distinct differences in effects between using the terms ‘physical distancing’ and ‘social distancing’. Analysis of 463 papers containing data showed generally large samples; 418 involved human participants with a mean of 16,848 (median of 1,699). That statistical power underscored improved suitability of behavioural science research for informing policy decisions. Furthermore, by implementing a standardized approach to evidence selection and synthesis, we amplify broader implications for advancing scientific evidence in policy formulation and prioritization