A translation mechanism for recommendations

An important class of distributed Trust-based solutions is based on the information sharing. A basic requirement of such systems is the ability of participating agents to effectively communicate, receiving and sending messages that can be interpreted correctly. Unfortunately, in open systems it is not possible to postulate a common agreement about the representation of a rating, its semantic meaning and cognitive and computational mechanisms behind a trust-rating formation. Social scientists agree to consider unqualified trust values not transferable, but a more pragmatic approach would conclude that qualified trust judgments are worth being transferred as far as decisions taken considering others’ opinion are better than the ones taken in isolation. In this paper we investigate the problem of trust transferability in open distributed environments, proposing a translation mechanism able to make information exchanged from one agent to another more accurate and useful. Our strategy implies that the parties involved disclose some elements of their trust models in order to understand how compatible the two systems are. This degree of compatibility is used to weight exchanged trust judgements. If agents are not compatible enough, transmitted values can be discarded. We define a complete simulation environment where agents are modelled with characteristics that may differ. We show how agents’ differences deteriorate the value of recommendations so that agents obtain better predictions on their own. We then show how different translation mechanisms based on the degree of compatibility improve drastically the quality of recommendations

Auditing and Generating Synthetic Data with Controllable Trust Trade-offs

Data collected from the real world tends to be biased, unbalanced, and at risk of exposing sensitive and private information. This reality has given rise to the idea of creating synthetic datasets to alleviate risk, bias, harm, and privacy concerns inherent in the real data. This concept relies on Generative AI models to produce unbiased, privacy-preserving synthetic data while being true to the real data. In this new paradigm, how can we tell if this approach delivers on its promises? We present an auditing framework that offers a holistic assessment of synthetic datasets and AI models trained on them, centered around bias and discrimination prevention, fidelity to the real data, utility, robustness, and privacy preservation. We showcase our framework by auditing multiple generative models on diverse use cases, including education, healthcare, banking, human resources, and across different modalities, from tabular, to time-series, to natural language. Our use cases demonstrate the importance of a holistic assessment in order to ensure compliance with socio-technical safeguards that regulators and policymakers are increasingly enforcing. For this purpose, we introduce the trust index that ranks multiple synthetic datasets based on their prescribed safeguards and their desired trade-offs. Moreover, we devise a trust-index-driven model selection and cross-validation procedure via auditing in the training loop that we showcase on a class of transformer models that we dub TrustFormers, across different modalities. This trust-driven model selection allows for controllable trust trade-offs in the resulting synthetic data. We instrument our auditing framework with workflows that connect different stakeholders from model development to audit and certification via a synthetic data auditing report.Comment: 49 pages; submitte

Protests and Beliefs in Social Coordination in Africa

Leaders' misbehaviors may durably undermine the credibility of the state. Using individual level survey in the aftermath of geo-localized social protests in Africa, we find that trust in monitoring institutions and beliefs in social coordination strongly evolve after riots, together with trust in leaders. As no signs of social unrest can be recorded before, the social conflict can be interpreted as a sudden signal sent on a leader's action from which citizens extract information on the country's institutions. Our interpretation is the following. Agents lend their taxes to a leader with imperfect information on the leader's type and the underlying capacity of institutions to monitor her. A misbehavior is then interpreted as a failure of institutions to secure taxes given by citizens and makes agents (i) reluctant to contribute to the state effort, (ii) skeptical about the contributions of others

Quality of life in patients receiving platinum based chemotherapy for advanced non-small cell lung cancer.

Quality of life in patients receiving platinum based chemotherapy for advanced non-small cell lung cancer. Lung cancer is the cause of 34,000 deaths in the UK each year, with a five year survival rate of only 7.5%. The current treatment for advanced Non Small Cell Lung Cancer is combination chemotherapy but this confers only a small survival advantage. Quality of Life is often proposed as a secondary outcome to most chemotherapy studies as chemotherapy remains palliative. Quality of life is measured using a series of tools, such as the EORTC QLQ 30 that although established and tested for validity are functionally based or focus on physical symptoms. The aim of this study is to explore the meaning of quality of life in this group of patients. The study utilises use comparative methods (interview n=50, QLQ EORTC 30 data, clinical observation/field notes, medical notes, nursing notes and mapping) to examine the meaning of quality of life in this patient group. This is essentially a collaboration of medical and nursing practice with the aim of understanding what quality of life means to these patients, improving the experience of patients undergoing treatment and offering appropriate psycho-social support. Content analysis has generated a core theme of patient experience as having an impact on quality of life (negotiation of the treatment calendar, value of treatment broker and interactions with professionals) the overlapping themes are Lens of diagnosis (viewed as atrocity stories), The worth of treatment (despite physical side effects and poor life expectancy, chemotherapy is a focus of hope and allows for adjustment to poor prognosis) and Suffering (psychological and social, for example exclusion from social activities and loss of independence). This study has impacted on the service to cancer patients at a central London NHS Foundation Trust

Measurement and reporting of climate-smart agriculture: technical guidance for a countrycentric process

Given the extent of climate-smart agriculture (CSA) initiatives at project, national, regional and global levels, there is increasing interest in tracking progress in implementing CSA at national level. CSA is also expected to contribute to higher-level goals (e.g., the Paris Agreement, Africa Union’s Vision 25x25, and the Sustainable Development Goals [SDGs], etc.). Measurement and reporting of climate-smart agriculture (MR of CSA) provides intelligence on necessary the status, effectiveness, efficiency and impacts of interventions, which is critical for meeting stakeholders’ diverse management and reporting needs. In this paper, we build the case for a stakeholder-driven, country-centric framework for MR of CSA, which aims to increase coordination and coherence across stakeholders’ MR activities, while also aligning national reporting with reporting on international commitments. We present practical guidance on how to develop an integrated MR framework, drawing on findings from a multi-country assessment of needs, opportunities and capacities for national MR of CSA. The content of a unified MR framework is determined by stakeholders’ activities (how they promote CSA), needs (why MR is useful to them) and current capacities to conduct periodic monitoring, evaluation and reporting (how ready are institutions, staff and finances). Our analysis found that explicit demand for integration of data systems and active engagement of stakeholders throughout the entire process are key ingredients for building a MR system that is relevant, useful and acted upon. Based on these lessons, we identify a seven-step framework for stakeholders to develop a comprehensive information system for MR of progress in implementing CSA

Fifteen new risk loci for coronary artery disease highlight arterial-wall-specific mechanisms

Coronary artery disease (CAD) is a leading cause of morbidity and mortality worldwide. Although 58 genomic regions have been associated with CAD thus far, most of the heritability is unexplained, indicating that additional susceptibility loci await identification. An efficient discovery strategy may be larger-scale evaluation of promising associations suggested by genome-wide association studies (GWAS). Hence, we genotyped 56,309 participants using a targeted gene array derived from earlier GWAS results and performed meta-analysis of results with 194,427 participants previously genotyped, totaling 88,192 CAD cases and 162,544 controls. We identified 25 new SNP-CAD associations (P < 5 × 10(-8), in fixed-effects meta-analysis) from 15 genomic regions, including SNPs in or near genes involved in cellular adhesion, leukocyte migration and atherosclerosis (PECAM1, rs1867624), coagulation and inflammation (PROCR, rs867186 (p.Ser219Gly)) and vascular smooth muscle cell differentiation (LMOD1, rs2820315). Correlation of these regions with cell-type-specific gene expression and plasma protein levels sheds light on potential disease mechanisms

Mixed-Integer Projections for Automated Data Correction of EMRs Improve Predictions of Sepsis among Hospitalized Patients

Machine learning (ML) models are increasingly pivotal in automating clinical decisions. Yet, a glaring oversight in prior research has been the lack of proper processing of Electronic Medical Record (EMR) data in the clinical context for errors and outliers. Addressing this oversight, we introduce an innovative projections-based method that seamlessly integrates clinical expertise as domain constraints, generating important meta-data that can be used in ML workflows. In particular, by using high-dimensional mixed-integer programs that capture physiological and biological constraints on patient vitals and lab values, we can harness the power of mathematical "projections" for the EMR data to correct patient data. Consequently, we measure the distance of corrected data from the constraints defining a healthy range of patient data, resulting in a unique predictive metric we term as "trust-scores". These scores provide insight into the patient's health status and significantly boost the performance of ML classifiers in real-life clinical settings. We validate the impact of our framework in the context of early detection of sepsis using ML. We show an AUROC of 0.865 and a precision of 0.922, that surpasses conventional ML models without such projections

‘Local and local organic food in schools and hospitals – contributing to the health of our nation’

Procurement of food within the public sector cannot be divorced from the industry that supplies it or the public demand that consumes it. The contents of this Report reflect the various perspectives of the partners who have produced it as well as outlining the wide range of issues involved in making sure that local food, organic where available, is served in our schools and hospitals. All these perspectives and issues need to be brought together in order to deliver the potentially huge and positive outcomes that this work has identified. These outcomes, as the Report demonstrates, work across the economic, social and environmental agendas of Health and Education as well as Agriculture and Rural Development. The key conclusions of the Powys Public Procurement Partnership are that sustainability has to be at the heart of ‘Best Value’ and the ‘Wales Improvement Programme’; that leadership at ministerial level is needed to co-ordinate a package of measures and to promote a cross–cutting approach to creative and sustainable public procurement of food; and that the same cross-cutting approach is needed at the local level to achieve real chang

Grey-matter texture abnormalities and reduced hippocampal volume are distinguishing features of schizophrenia

Neurodevelopmental processes are widely believed to underlie schizophrenia. Analysis of brain texture from conventional magnetic resonance imaging (MRI) can detect disturbance in brain cytoarchitecture. We tested the hypothesis that patients with schizophrenia manifest quantitative differences in brain texture that, alongside discrete volumetric changes, may serve as an endophenotypic biomarker. Texture analysis (TA) of grey matter distribution and voxel-based morphometry (VBM) of regional brain volumes were applied to MRI scans of 27 patients with schizophrenia and 24 controls. Texture parameters (uniformity and entropy) were also used as covariates in VBM analyses to test for correspondence with regional brain volume. Linear discriminant analysis tested if texture and volumetric data predicted diagnostic group membership (schizophrenia or control). We found that uniformity and entropy of grey matter differed significantly between individuals with schizophrenia and controls at the fine spatial scale (filter width below 2 mm). Within the schizophrenia group, these texture parameters correlated with volumes of the left hippocampus, right amygdala and cerebellum. The best predictor of diagnostic group membership was the combination of fine texture heterogeneity and left hippocampal size. This study highlights the presence of distributed grey-matter abnormalities in schizophrenia, and their relation to focal structural abnormality of the hippocampus. The conjunction of these features has potential as a neuroimaging endophenotype of schizophrenia