On the Interpretation of World Values Survey Trust Question: Global Expectations vs. Local Beliefs

How should we interpret the World Values Survey (WVS) trust question? We conduct an experiment in India, a low trust country, to correlate the WVS trust question with trust decisions in an incentivized Trust Game. Evidence supports findings from one strand of the fractured literature – the WVS trust question captures expectations about others' trustworthiness, though not always. We show that WVS trust question correlates with globally determined stable expectations but does not correlate with short term locally determined fluctuations in beliefs about trustworthiness. One implication of our study is that survey based methods may not be used to measure contextualized beliefs

Student and Teacher Attendance: The Role of Shared Goods in Reducing Absenteeism

A theoretical model is advanced that demonstrates that, if teacher and student attendance generate a shared good, then teacher and student attendance will be mutually reinforcing.� Using data from the Northwest Frontier Province of Pakistan, empirical evidence supporting that proposition is advanced.� Controlling for the endogeneity of teacher and student attendance, the most powerful factor raising teacher attendance is the attendance of the children in the school, and the most important factor influencing child attendance is the presence of the teacher.� The results suggest that one important avenue to be explored in developing policies to reduce teacher absenteeism is to focus on raising the attendance of children.Absenteeism; teacher attendance; student attendance; shared good; Northwest Frontier Province; Pakistan

Maximizing all margins: Pushing face recognition with Kernel Plurality

We present two theses in this paper: First, performance of most existing face recognition algorithms improves if instead of the whole image, smaller patches are individually classified followed by label aggregation using voting. Second, weighted plurality voting outperforms other popular voting methods if the weights are set such that they maximize the victory margin for the winner with respect to each of the losers. Moreover, this can be done while taking higher order relationships among patches into account using kernels. We call this scheme Kernel Plurality. We verify our proposals with detailed experimental results and show that our framework with Kernel Plurality improves the performance of various face recognition algorithms beyond what has been previously reported in the literature. Furthermore, on five different benchmark datasets - Yale A, CMU PIE, MERL Dome, Extended Yale B and Multi-PIE, we show that Kernel Plurality in conjunction with recent face recognition algorithms can provide state-of-the-art results in terms of face recognition rates.Engineering and Applied Science

Using social recognition to address the gender difference in volunteering for low-promotability tasks

Research shows that women volunteer significantly more for tasks that people prefer others to complete. Such tasks carry little monetary incentives because of their very nature. We use a modified version of the volunteer's dilemma game to examine if non-monetary interventions, particularly, social recognition can be used to change the gender norms associated with such tasks. We design three treatments, where a) a volunteer receives positive social recognition, b) a non-volunteer receives negative social recognition, and c) a volunteer receives positive, but a non-volunteer receives negative social recognition. Our results indicate that competition for social recognition increases the overall likelihood that someone in a group has volunteered. Positive social recognition closes the gender gap observed in the baseline treatment, so does the combination of positive and negative social recognition. Our results, consistent with the prior literature on gender differences in competition, suggest that public recognition of volunteering can change the default gender norms in organizations and increase efficiency at the same time.Comment: 20 pages with references. Additional appendi

Knowledge Extraction from Diverse Biomedical Corpora with Applications in Healthcare: Bridging the Translational Gap

A wealth of knowledge in the biomedical domain is available in unstructured or semi-structured data repositories as natural language narratives. Much of this knowledge can provide immediate and tangible benefits in patient welfare and the healthcare industry. Extracting relevant knowledge from these natural language sources and providing them as structured information suitable for immediate real-time consumption in clinical settings is, however, a manual process restricted to human domain experts. As a result, it is expensive and time-consuming. A very real consequence of this is that the journey made by medical knowledge nuggets from research publications to patient care settings like hospitals often take several years. Even so, the knowledge still gets presented to clinicians in natural language -- unsuitable for machine consumption, and an impediment to the pace of work often demanded of clinicians (e.g. | in emergency rooms). Automatic extraction of this knowledge is a challenging task. Biomedical research literature is replete with language constructs that are highly specific to not just the domain, but internal sub-domains. The linguistic semantics used in discussions of, say, diabetes, are very different from the semantics used to discuss diseases like malaria that are caused by external agents. Moreover, being research literature, authors typically write for readers with a fair amount of encyclopaedic domain knowledge. Consequently, important information can often only be gleaned by identifying causal relations that are implicit. Standard information extraction methods that depend on identifying causality in text usually require explicit discourse connectives like because , since , etc. Additionally, they manage to extract only those relations that are expressed within the span of a single sentence. This proposal presents a novel relation learning methodology for biomedical natural language that is able to infer relations where (a) the relation is implicit, and (b) the related entities do not co-occur within the span of a single sentence. We show that our technique outperforms a sentence-level supervised classification approach. Further, as a human-in-the-loop (HITL) model, it is capable of augmenting biomedical knowledge bases quickly and accurately. Finally, we contribute two novel applications that demonstrate the use of such relational knowledge in providing real-time clinical decision support. | 75 page

Modelling Einstein cluster using Einasto profile

We demonstrate a general relativistic approach to model dark matter halos using the Einstein cluster, with the matter stress-energy generated by collisionless particles moving on circular geodesics in all possible angular directions and orbital radii. Such matter, as is known, allows an anisotropic pressure profile with non-zero tangential but zero radial pressure. We use the Einasto density profile for the Einstein cluster. Analytical studies on its properties (metric functions) and stability issues are investigated. Further, to establish this model (with the Einasto profile) as one for a dark matter halo, we use the SPARC galactic rotation curve data and estimate the best-fit values for the model parameters. General relativistic features (beyond the Keplerian velocities) such as the tangential pressure profile, are quantitatively explored. Thus, Einstein clusters with the Einasto profile, which tally well with observations, may be considered as a viable model for dark matter halos.Comment: 18 pages, 6 figure

Exponential-growth prediction bias and compliance with safety measures in the times of COVID-19

We conduct a unique, Amazon MTurk-based global experiment to investigate the importance of an exponential-growth prediction bias (EGPB) in understanding why the COVID-19 outbreak has exploded. The scientific basis for our inquiry is the well-established fact that disease spread, especially in the initial stages, follows an exponential function meaning few positive cases can explode into a widespread pandemic if the disease is sufficiently transmittable. We define prediction bias as the systematic error arising from faulty prediction of the number of cases x-weeks hence when presented with y-weeks of prior, actual data on the same. Our design permits us to identify the root of this under-prediction as an EGPB arising from the general tendency to underestimate the speed at which exponential processes unfold. Our data reveals that the "degree of convexity" reflected in the predicted path of the disease is significantly and substantially lower than the actual path. The bias is significantly higher for respondents from countries at a later stage relative to those at an early stage of disease progression. We find that individuals who exhibit EGPB are also more likely to reveal markedly reduced compliance with the WHO-recommended safety measures, find general violations of safety protocols less alarming, and show greater faith in their government's actions. A simple behavioral nudge which shows prior data in terms of raw numbers, as opposed to a graph, causally reduces EGPB. Clear communication of risk via raw numbers could increase accuracy of risk perception, in turn facilitating compliance with suggested protective behaviors

The financial crisis: impact on BRIC and policy response

The paper looks at the transmission channels by which the financial crisis affected the four emerging economies- Brazil, Russia, India and China, the degree and extent of the impact of the crisis, the subsequent policy interventions which enabled recovery and an assessment of how successful recovery has been in these economies. We conclude by noting that in the long term global recovery will necessitate a rebalancing of the world economy which in turn means that the hub of global consumption has to shift from the west to the global south, particularly to BRICs

The financial crisis: impact on BRIC and policy response

The paper looks at the transmission channels by which the financial crisis affected the four emerging economies- Brazil, Russia, India and China, the degree and extent of the impact of the crisis, the subsequent policy interventions which enabled recovery and an assessment of how successful recovery has been in these economies. We conclude by noting that in the long term global recovery will necessitate a rebalancing of the world economy which in turn means that the hub of global consumption has to shift from the west to the global south, particularly to BRICs