Since 2012, in a case-study in Bucaramanga-Colombia, 179 pedestrians died in car accidents, and another 2873 pedestrians were injured. Each day, at least one passerby is involved in a tragedy. Knowing the causes to decrease accidents is crucial, and using system-dynamics to reproduce the collisions' events is critical to prevent further accidents. This work implements simulations to save lives by reducing the city's accidental rate and suggesting new safety policies to implement. Simulation's inputs are video recordings in some areas of the city. Deep Learning analysis of the images results in the segmentation of the different objects in the scene, and an interaction model identifies the primary reasons which prevail in the pedestrians or vehicles' behaviours. The first and most efficient safety policy to implement - validated by our simulations - would be to build speed bumps in specific places before the crossings reducing the accident rate by 80%.

Distributed networks and real-time systems are becoming the most important components for the new computer age, the Internet of Things (IoT), with huge data streams or data sets generated from sensors and data generated from existing legacy systems. The data generated offers the ability to measure, infer and understand environmental indicators, from delicate ecologies and natural resources to urban environments. This can be achieved through the analysis of the heterogeneous data sources (structured and unstructured). In this paper, we propose a distributed framework Event STream Processing Engine for Environmental Monitoring Domain (ESTemd) for the application of stream processing on heterogeneous environmental data. Our work in this area demonstrates the useful role big data techniques can play in an environmental decision support system, early warning and forecasting systems. The proposed framework addresses the challenges of data heterogeneity from heterogeneous systems and real time processing of huge environmental datasets through a publish/subscribe method via a unified data pipeline with the application of Apache Kafka for real time analytics.

The global expansion of schooling in the past three decades is unprecedented: Primary school enrollment is near-universal, expected years of schooling have risen rapidly, and the number of children out of school has fallen sharply. Yet the greatest challenge for the global education system, a “learning crisis” per the World Bank, is that these gains in schooling are not translating into commensurate gains in learning outcomes. This crisis is well exemplified by India, which has the largest education system in the world. Over 95% of children aged 6 to 14 years are in school, but nearly half of students in grade 5 in rural areas cannot read at a grade 2 level, and less than one-third can do basic division ([ 1 ][1]). India's new National Education Policy (NEP) of 2020 (the first major revision since 1986) recognizes the centrality of achieving universal foundational literacy and numeracy. Whether India succeeds in this goal matters intrinsically through its impact on over 200 million children and will also have lessons for other low- and middle-income countries. We review the NEP's discussion of school education in light of accumulated research evidence that may be relevant to successfully implementing this ambitious goal. India has made tremendous progress on access to schooling since the 1990s. Yet multiple nationally representative datasets suggest that learning levels have remained largely flat over the past 15 years. A large body of evidence has shown that increasing “business as usual” expenditure on education is only weakly correlated with improvement in learning ([ 2 ][2]). Two key constraints that limit the translation of spending (of time and money) into outcomes are weaknesses in governance and pedagogy. Governance challenges are exemplified by high rates of teacher absence in public schools, with nearly one in four teachers absent at the time of surprise visits ([ 3 ][3]). Even when teachers are present, instructional time is low for a variety of reasons, including large amounts of administrative paperwork. Further, teacher recognition for performance and sanctions for nonperformance are low. Studies in India and elsewhere have shown that even modest amounts of performance-linked bonus pay for teachers can improve student learning in a cost-effective way ([ 4 ][4]). By contrast, unconditional increases in teacher pay (the largest component of education budgets) have no impact on student learning ([ 4 ][4], [ 5 ][5]). Overall, improving governance and management in public schools may be a much more cost-effective way of improving student learning than simply expanding education spending along default patterns. An even greater challenge in translating school attendance into learning outcomes may be weaknesses in pedagogy. Even motivated teachers primarily focus on completing the textbook, without recognizing the mismatch between the academic standards of the textbook and student learning levels. The rapid expansion of school enrollment has brought tens of millions of first-generation learners into the formal education system who lack instructional support at home and often fall behind grade-appropriate curricular standards. The mismatch is clearly illustrated in the figure, which presents the levels and dispersion of student achievement in mathematics in a sample of students from public middle schools in Delhi ([ 6 ][6]). There are three points to note about this figure: (i) The vast majority of students are below curricular standards (represented by the blue line of equality), with the average grade 6 student 2.5 years behind; (ii) the average rate of learning progress is much flatter than that envisaged by the curricular standards, resulting in widening learning gaps at higher grades; (iii) there is enormous variation in learning levels of students in the same grade, spanning five to six grade levels in all grades. The figure captures many features that we think are central to understanding the Indian education system. It suggests a curriculum that targets the top of the achievement distribution and moves much faster than the actual achievement level of students. Coupled with social promotion—grade retention is forbidden by law until grade 8—this leads to student achievement being widely dispersed within the same grade and most students receiving instruction that they are not academically prepared for. Similar patterns likely exist in many other developing countries ([ 6 ][6]). The figure may also help explain why increased expenditures on items such as teacher salaries and school infrastructure may have little impact on learning. Students, having fallen so far behind the curriculum, may not gain much from the default of textbook-linked instruction. By contrast, pedagogical interventions that target instruction at the level of students' academic preparation can be highly effective ([ 6 ][6]–[ 8 ][7]). The figure also highlights the stark inequality in Indian education. The true inequality is likely even greater because the figure does not reflect the large number of students in private schools. A comparison of data from two Indian states to countries included in an international learning assessment found that learning inequality in India is second only to South Africa ([ 9 ][8]). Thus, although the academically strongest Indian students are internationally competitive, with many ultimately achieving world-renowned success, most Indian children fail to acquire even basic skills at the end of their schooling. To better understand the Indian education system, it is useful to recognize that education systems have historically served two very different purposes: (i) to impart knowledge and skills (a “human development” role) and (ii) to assess, classify, and select students for higher education and skill-intensive occupations (a “sorting and selection” role). The Indian education system primarily serves as a “sorting and selection” or a “filtration” system rather than a “human development” system. The system focuses primarily on setting high standards for competitive exams to identify those who are talented enough to meet those standards, but it ends up neglecting the vast majority of students who do not. Thus, a fundamental challenge for Indian education policy is to reorient the education system from one focused on sorting and identifying talented students to one that is focused on human development that can improve learning for all. The NEP, released in 2020, does an excellent job of reflecting key insights from research. Three points are especially noteworthy. First, and most important, is the centrality accorded to universal foundational literacy and numeracy, which the NEP calls an “urgent and necessary prerequisite for learning.” This represents a substantial shift in the definition of education “quality” from inputs and expenditure to actual learning outcomes. Relatedly, the NEP recognizes the importance of early childhood care and education and brings preschool education into the scope of national education policy alongside school education. The NEP's focus on stronger and universal preschool education is consistent with global recognition of the importance of “the early years” in developing cognitive and socioemotional skills. Second, consistent with the evidence, the NEP aims to strengthen teacher effectiveness through a combination of improving their skills, reducing extraneous demands on their time, and rewarding performance. Notably, the NEP highlights the need for “a robust merit-based structure of tenure, promotion, and salary structure.” This is a meaningful departure from the status quo that does not reward good performance. If implemented well, improving teacher motivation and effort can be a force multiplier for the effectiveness of other input-based spending. School inputs on their own do not seem to translate into learning gains ([ 2 ][2]), but inputs can be highly effective when teachers and principals are motivated to improve learning outcomes ([ 10 ][9]). Third, the NEP recognizes that improving school effectiveness may require changes to how schools are organized and managed. Large-scale school construction in the 1990s played an important role in promoting universal school access by providing a school in every habitation. However, as of 2016, over 417,000 government primary schools (∼40% of schools) had fewer than 50 students across grades 1 to 5 ([ 11 ][10]). Small and spread-out schools present challenges for governance (by making supervision difficult), pedagogy (by requiring teachers to simultaneously teach students in multiple grades), and infrastructure quality (by being too small for libraries and computer laboratories), as well as cost-effectiveness. The NEP, therefore, recommends investing in larger school complexes and also recognizes the importance of school management, emphasizing the need for customized school development plans to anchor a process of continuous school improvement. Given large improvements in rural road construction, it will be viable to provide buses or other transport to ensure universal school access for all children while also obtaining the benefits of larger-scale schools. ![Figure][11] Achievement versus curricular standards The estimated level of student achievement (determined by a computer-aided instruction program) in mathematics in public middle schools in Delhi is plotted against the grade in which students are actually enrolled. See ([ 6 ][6]) for details and data. Most students are below curricular standards (line of equality), average progress in learning is flatter than curricular standards, and there is substantial variation in achievement. GRAPHIC: ADAPTED FROM ([ 6 ][6]) BY H. BISHOP/ SCIENCE ; © AMERICAN ECONOMIC ASSOCIATION; REPRODUCED WITH PERMISSION OF THE AMERICAN ECONOMIC REVIEW Although the NEP is an excellent document that reflects research and evidence, delivering on its promise will require sustained attention to implementation. The glaring gaps between the high quality of policy and program design on one hand, and the low quality of implementation on the other, are widely recognized in India across many dimensions of public policy. Preliminary findings from two of our recent projects illustrate this challenge in relation to policy recommendations in the NEP. First, in a large-scale randomized controlled trial covering over 5000 schools in the state of Madhya Pradesh, we found no notable effects on school functioning or student achievement of an ambitious reform that aimed to improve school management, largely through the type of school development plans that are recommended in the NEP ([ 12 ][12]). Yet, this model is perceived to be successful and has been scaled up to over 600,000 schools nationally (and aims to reach 1.6 million schools). Our work suggests that this perception is based primarily on completion of paperwork (such as school assessments and improvement plans), even though there was no change in management, pedagogy, or learning outcomes. The second example illustrates how even measuring learning outcomes accurately is challenging. The state of Madhya Pradesh administers an annual state-level standardized assessment to all children in public schools from grades 1 to 8. This has been declared a national “best practice” and the NEP recommends a similar assessment for students in all schools in grades 3, 5, and 8. Yet, an independent audit that administered the same test questions to the same students a few weeks after the official tests showed that levels of student achievement are severely overstated in official data ([ 13 ][13]). The audit found that a large fraction of students did not possess even basic skills even though most of these students were shown as having passed the test. In light of such challenges, we highlight three key principles that may increase the likelihood of success. The first is measurement. India's success in achieving universal enrollment shows that the system is capable of delivering on well-defined goals that are easily measured. A similar approach needs to be implemented for delivering universal foundational literacy and numeracy. Although the challenge of data integrity is real, one reason for optimism is that there is evidence that using technology-based independent testing sharply reduced the extent to which data on learning was inflated ([ 13 ][13]). Thus, investing in independent ongoing measurement of learning outcomes in representative samples to set goals and monitor progress will be a foundational investment. The second key principle is ongoing evaluations of policy and program effectiveness. An important lesson from the past two decades of research on education is that many commonly advocated interventions for improving education (such as increasing teacher salaries, providing school grants, or giving out free textbooks) may have very little impact on learning outcomes, whereas other interventions (such as teaching at the right level) may be highly effective. Even in the same class of policies, different interventions may have widely varying effectiveness; for instance, in the case of education technology, the impact of providing hardware alone is zero or even negative, but personalized adaptive learning programs have been found to be highly effective ([ 6 ][6], [ 7 ][14]). Yet, use of rigorous, experimental evidence in education policy-making remains more an exception than the rule. Disciplining interventions under the NEP with high-quality evaluations can accelerate the scaling up of effective programs as well as course corrections of ineffective ones. The third key principle is cost-effectiveness. Evidence has shown pronounced variation in the cost-effectiveness of education interventions, with many expensive policies having no impact and inexpensive ones being very effective. Given limited resources and competing demands on them, cost-effectiveness is not only an economic consideration but also a moral one. The World Bank and the UK Foreign and Commonwealth Development Office recently synthesized a large body of evidence on the most cost-effective education interventions ([ 14 ][15]). India would do well to heed these recommendations (suitably modified to its context) when allocating scarce public resources. Education has been sharply disrupted around India and the world by the COVID-19 shock. Public schools in India have been mostly closed and are likely to remain so for the entire academic year. This presents one major threat and two opportunities. The threat is that the learning crisis will worsen. Children who have missed a year of school—especially those without educated parents—are likely to have regressed in their learning and suffer long-term learning losses. Thus, the challenges (see the figure) are likely to have worsened, making it imperative to provide high-quality supplementary instruction when schools reopen, including perhaps through reducing holidays and vacation days. Yet, there may also be two important longer-term opportunities. The first is the rapid acceleration in the use of education technology by both households and the government. Given evidence of strong positive effects of personalized instruction, the widespread adoption of education technology may help accelerate the NEP's stated goal of reducing the digital divide and leveraging potential benefits of technology for education, such as opportunities to increase student engagement and personalize instruction to individual student needs. The second is increasing engagement with parents and families. Households play a critical role in education. Yet, education policy has mostly focused on school-based interventions, reflecting a belief that it is more feasible to improve schools than to intervene in households at scale. The COVID-19 crisis and the resulting growth in the use of mobile phones for engaging children have sharply increased educators' engagement with parents, with approaches ranging from text-message reminders to check their child's homework to parent groups for peer coaching and motivation. Work is under way to evaluate the impacts of these promising approaches. The benefits of increased parental engagement may persist even after schools reopen. Effective reform will require a confluence of ideas, interests, institutions, and implementation. Our focus has been on the ideas of the NEP and the extent to which they are supported, or may be refined by, research evidence. The NEP also pays attention to institutional infrastructure needed to deliver on this vision and acknowledges the centrality of implementation. However, both the NEP and our discussion are silent on the interests, specifically on political and bureaucratic constraints. We remain optimistic that substantial improvements are possible. In particular, backing the intent of the NEP with a commitment to regular independent measurement and reporting of learning outcomes in a representative sample of all children—as envisaged by the NEP in setting up a quasi-independent national testing agency—may help to provide an institutionalized focus on learning to both political and bureaucratic leadership. The NEP's proposal to provide such information to parents directly, if implemented in easily accessible formats, may catalyze improvements in both public and private schools. Such reforms are particularly urgent given India's demographic transition. In many states, especially in South India, total fertility rates are already below replacement levels, and cohort sizes in primary schooling are shrinking. Thus, much of the country has already passed the peak of potential demographic dividend without having solved the learning crisis. Some large populous states in Northern India, such as Uttar Pradesh and Bihar, still have a window for intervention, but this window is shrinking. The one silver lining is that declining cohort sizes may increase resources per student in coming years, thus freeing up fiscal space for cost-effective investments. There is nothing inevitable about low learning levels in Indian schools. Other developing countries, such as Vietnam, have been able to achieve substantially superior learning outcomes at very similar levels of per capita incomes. Research suggests that a key explanation is the greater productivity of Vietnam's schooling system, which focuses attention on ensuring that even the weakest students reach minimum standards of learning ([ 15 ][16]). The NEP provides an important opportunity to move Indian education from “sorting and selection” to “human development,” enabling every student to develop to their maximum potential. India, and the world, will be better off if this vision is realized in practice. 1. [↵][17]Pratham, Annual Status of Education Report 2018, Pratham, New Delhi, 2019. 2. [↵][18]1. P. Glewwe, 2. K. Muralidharan , “Improving education outcomes in developing countries: Evidence, knowledge gaps, and policy implications” in Handbook of the Economics of Education (Elsevier, 2016), vol. 5, pp. 653–743. [OpenUrl][19] 3. [↵][20]1. K. Muralidharan, 2. J. Das, 3. A. Holla, 4. A. Mohpal , J. Public Econ. 145, 116 (2017). [OpenUrl][21][CrossRef][22][Web of Science][23] 4. [↵][24]1. K. Muralidharan, 2. V. Sundararaman , J. Polit. Econ. 119, 39 (2011). [OpenUrl][25] 5. [↵][26]1. J. de Ree, 2. K. Muralidharan, 3. M. Pradhan, 4. H. Rogers , Q. J. Econ. 133, 993 (2018). [OpenUrl][27] 6. [↵][28]1. K. Muralidharan, 2. A. Singh, 3. A. Ganimian , Am. Econ. Rev. 109, 1426 (2019). [OpenUrl][29][CrossRef][30][Web of Science][31] 7. [↵][32]1. A. V. Banerjee, 2. S. Cole, 3. E. Duflo, 4. L. Linden , Q. J. Econ. 122, 1235 (2007). [OpenUrl][33] 8. [↵][34]1. A. Banerjee et al ., J. Econ. Perspect. 31, 73 (2017). [OpenUrl][35] 9. [↵][36]1. J. Das, 2. T. Zajonc , J. Dev. Econ. 92, 175 (2010). [OpenUrl][37] 10. [↵][38]1. I. Mbiti et al ., Q. J. Econ. 134, 1627 (2019). [OpenUrl][39] 11. [↵][40]1. G. G. Kingdon , J. Dev. Stud. 56, 1795 (2020). [OpenUrl][41] 12. [↵][42]1. K. Muralidharan, 2. A. Singh , “Improving Public Sector Management at Scale: Experimental Evidence on School Governance in India,” NBER Working Paper, 2020. 13. [↵][43]1. A. Singh , “Myths of Official Measurement: Auditing and Improving Administrative Data in Developing Countries,” Tech. Rep., RISE Programme, Oxford, 2020. 14. [↵][44]Global Education Evidence Advisory Panel, “Cost Effective Approaches to Improve Global Learning: What does recent evidence tell us are “Smart Buys” for improving learning in low- and middle-income countries?” World Bank, Washington, DC, 2020. 15. [↵][45]1. A. Singh , J. Eur. Econ. Assoc. 18, 1770 (2020). [OpenUrl][46] [1]: #ref-1 [2]: #ref-2 [3]: #ref-3 [4]: #ref-4 [5]: #ref-5 [6]: #ref-6 [7]: #ref-8 [8]: #ref-9 [9]: #ref-10 [10]: #ref-11 [11]: pending:yes [12]: #ref-12 [13]: #ref-13 [14]: #ref-7 [15]: #ref-14 [16]: #ref-15 [17]: #xref-ref-1-1 "View reference 1 in text" [18]: #xref-ref-2-1 "View reference 2 in text" [19]: {openurl}?query=rft.jtitle%253DHandbook%2Bof%2Bthe%2BEconomics%2Bof%2BEducation%26rft.volume%253D145%26rft.spage%253D116%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [20]: #xref-ref-3-1 "View reference 3 in text" [21]: {openurl}?query=rft.jtitle%253DJ.%2BPublic%2BEcon.%26rft.volume%253D119%26rft.spage%253D39%26rft_id%253Dinfo%253Adoi%252F10.1086%252F659655%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [22]: /lookup/external-ref?access_num=10.1086/659655&link_type=DOI [23]: /lookup/external-ref?access_num=000289847500002&link_type=ISI [24]: #xref-ref-4-1 "View reference 4 in text" [25]: {openurl}?query=rft.jtitle%253DJ.%2BPolit.%2BEcon.%26rft.volume%253D133%26rft.spage%253D993%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [26]: #xref-ref-5-1 "View reference 5 in text" [27]: {openurl}?query=rft.jtitle%253DQ.%2BJ.%2BEcon.%26rft.volume%253D109%26rft.spage%253D1426%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [28]: #xref-ref-6-1 "View reference 6 in text" [29]: {openurl}?query=rft.jtitle%253DAm.%2BEcon.%2BRev.%26rft_id%253Dinfo%253Adoi%252F10.1162%252Fqjec.122.3.1235%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [30]: /lookup/external-ref?access_num=10.1162/qjec.122.3.1235&link_type=DOI [31]: /lookup/external-ref?access_num=000248618600009&link_type=ISI [32]: #xref-ref-7-1 "View reference 7 in text" [33]: {openurl}?query=rft.jtitle%253DQ.%2BJ.%2BEcon.%26rft.volume%253D31%26rft.spage%253D73%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [34]: #xref-ref-8-1 "View reference 8 in text" [35]: {openurl}?query=rft.jtitle%253DJ.%2BEcon.%2BPerspect.%26rft.volume%253D92%26rft.spage%253D175%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [36]: #xref-ref-9-1 "View reference 9 in text" [37]: {openurl}?query=rft.jtitle%253DJ.%2BDev.%2BEcon.%26rft.volume%253D134%26rft.spage%253D1627%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [38]: #xref-ref-10-1 "View reference 10 in text" [39]: {openurl}?query=rft.jtitle%253DQ.%2BJ.%2BEcon.%26rft.volume%253D56%26rft.spage%253D1795%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [40]: #xref-ref-11-1 "View reference 11 in text" [41]: {openurl}?query=rft.jtitle%253DJ.%2BDev.%2BStud.%26rft.volume%253D56%26rft.spage%253D1795%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [42]: #xref-ref-12-1 "View reference 12 in text" [43]: #xref-ref-13-1 "View reference 13 in text" [44]: #xref-ref-14-1 "View reference 14 in text" [45]: #xref-ref-15-1 "View reference 15 in text" [46]: {openurl}?query=rft.jtitle%253DJ.%2BEur.%2BEcon.%2BAssoc.%26rft.volume%253D18%26rft.spage%253D1770%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx

SCI COMMUN### Astrophysics The team that in 2019 used a global network of radio telescopes to reveal the first image of a black hole has offered a new twist on that iconic view: the same black hole in polarized light. The thin lines spiraling in toward the black hole's shadow (above) show areas of light that differ in their polarization—the direction in which the light waves vibrate. The light, from plasma near the black hole's edge, was polarized by magnetic fields, and so the new image, described last week in The Astrophysical Journal by the Event Horizon Telescope team, indicates their structure. Researchers hope to learn how the fields help accreting black holes funnel matter and energy into jets emanating from their poles. 69% —Percentage of postdoctoral researchers surveyed in October 2020 by the U.S. National Institutes of Health who anticipate the COVID-19 pandemic will negatively affect their careers. For researchers at all levels, the figure was 55%. ### Conservation Despite the antienvironmental policies of its current leadership, Brazil has become the 130th country to ratify the Nagoya Protocol, a part of the Convention on Biological Diversity that lays out measures to protect countries' biodiversity claims, the CBD announced last week. The ratification, first proposed by a previous administration in 2012, had languished until 2019, when rampant deforestation led pro-environment leaders to push for approval. The current government is seen as having consented because the protocol allows nations to impose rules on the international trade in its plant and animal products; by legitimizing the sales, the regulations are expected to increase exports and tax revenues. For example, money from sales of native plants such as açai ( Euterpe oleracea ) and Brazil nut ( Bertholletia excelsa ) could be returned to help Indigenous communities that use and harvest them. Observers question whether the ratification alone will protect Brazil's biodiversity, perhaps the world's greatest—but hailed the step as helpful. ### Public health The United States and 13 other countries this week criticized a report by a World Health Organization panel that had visited China to investigate how the COVID-19 pandemic started. The 300-page document says the most likely cause was a bat coronavirus that infected another, unidentified animal and then moved to humans, but it recommends further research. The report's most definitive conclusion is also its most controversial: that it is “extremely unlikely” that SARS-CoV-2 came out of a Chinese laboratory. Scientists from China made up half of the 34-member international panel. A joint statement by other countries complained that the investigation was “significantly delayed and lacked access to complete, original data, and samples.” It called for a transparent, “rapid, independent, expert-led, and unimpeded evaluation of the origins.” ### Funding The science committee in the U.S. House of Representatives wants to more than double the budget of the National Science Foundation (NSF) in the next 5 years, from $8.5 billion to $18.3 billion. A sizable chunk of the extra money—$5 billion by 2026—would go to a new directorate, Science and Engineering Solutions, that would accelerate the conversion of basic research into new technologies and products. Last year, Senate Majority Leader Chuck Schumer (D–NY) proposed growing NSF to $100 billion over 5 years, with roughly one-third of that money going to a new technology directorate. Schumer's vision for NSF is part of still-evolving draft legislation affecting many federal agencies that pinpoints key technologies needed to address economic and security threats posed by China's growing technological prowess. In contrast, the House bill is limited to NSF's programs and is aimed at strengthening basic research across all disciplines that NSF supports. The House and Senate would need to agree on a vision for NSF, and other legislation would be needed to appropriate the money. ### Astronomy Light pollution from space junk and satellites may have already robbed the entire Earth of the dark skies best for sensitive astronomical observations, an analysis has found. Researchers estimated the size and shininess of tens of thousands of objects in orbit as of 2020, before an onslaught of thousands more satellites that companies plan to launch in the coming years. Even at Earth's darkest sites, the sky glows from natural sources such as ionized particles; but the existing orbiting objects reflect and scatter about 10% more of this diffuse light back into the atmosphere, the research team calculates in a paper accepted this week by the Monthly Notices of the Royal Astronomical Society . That extra amount violates an International Astronomical Union standard for observing sites and could compromise observations of the dimmest galaxies, which scientists study for clues about the physics of galaxy formation and the nature of dark matter. To gather such data, astronomers already need long exposures on the biggest telescopes at the darkest available sites. ### Ethics Harvard University last week penalized quantitative biologist Martin Nowak for his connections with disgraced financier Jeffrey Epstein. Epstein had donated $6.5 million for Nowak's research in 2003; after being convicted in 2008 of soliciting prostitution from a minor, Epstein introduced Nowak to donors who provided an additional $7.5 million. Nowak's actions after 2008—repeatedly hosting Epstein on campus, promoting Epstein on his program's web page, and providing false information about Epstein's support in a grant application—violated Harvard policies, and other actions showed “blameworthy negligence and unprofessional behavior,” Claudine Gay, dean of arts and sciences, wrote in an email last week to faculty members. Nowak will continue at Harvard as a math professor, but his Program for Evolutionary Dynamics will be shut down and he will be barred for at least 2 years from serving as a principal investigator on grants. “I regret the connection I was part of fostering between Harvard and Jeffrey Epstein,” Nowak said in a statement last week. Epstein died by suicide in 2019. ### Archaeology Chinese archaeologists last week reported unearthing more than 500 artifacts, including gold ornaments, bronze heads, ivory and jade tools, and a gold mask dating back about 3000 years at the Sanxingdui archaeological site in southwestern Sichuan province. Sanxingdui, then ruled by the Shu kingdom, has already yielded thousands of bronze relics unlike anything found elsewhere in China, including at sites of the contemporaneous Shang dynasty in the Yellow River region. The new finds, retrieved from what are thought to be sacrificial pits, may shed light on how the Shu kingdom contributed to Chinese civilization. VACCINE LEADER FIRED Moncef Slaoui, who headed COVID-19 vaccine development during the Trump administration, has been fired as chairman of a medical research firm controlled by manufacturer GlaxoSmithKline after he was accused of sexual harassment. The company said an outside investigation substantiated the allegation by a female employee about Slaoui's behavior several years ago when he worked there. Slaoui also stepped down from leadership roles at two other pharmaceutical companies and issued a statement in which he apologized to the woman and his family. RETURNING LOOTED ART Museums in Germany have pledged to return hundreds of artifacts, including bronze statues, looted during the colonial era from the kingdom of Benin in what is now Nigeria. The British Museum and others face growing pressure to join them. PARDON SOUGHT The Australian Academy of Science issued a statement saying a court ignored new genetic evidence when it denied last week an appeal by a woman convicted of killing her four young children. Tests point to a natural cause of the deaths: Two of the children carried a mutation in the CALM2 gene that is associated with sudden death by cardiac failure in infants and children. Prosecutors had accused Kathleen Folbigg of smothering the children but have not presented medical evidence that supports that position. Academy members have signed a petition asking New South Wales's governor to pardon her. AI IN MEDICINE The Broad Institute has received $300 million to study how machine learning can improve the prevention and treatment of disease. Half the sum is coming from a foundation of Wendy and Eric Schmidt, a member of Broad's board and former CEO of Google, and the rest from the Broad Foundation. R&D SPENDING RISE The United States spent more than 3% of gross domestic product on R&D in 2019 for the first time. The 3.07% share is a record and met a goal set by former President Barack Obama a decade ago. Israel led globally with 4.9%, the Organisation for Economic Co-operation and Development said. Total U.S. spending was more than any other country's.

The generation of plausible and controllable 3D human motion animations is a long-standing problem that often requires a manual intervention of skilled artists. Existing machine learning approaches try to semi-automate this process by allowing the user to input partial information about the future movement. However, they are limited in two significant ways: they either base their pose prediction on past prior frames with no additional control over the future poses or allow the user to input only a single trajectory that precludes fine-grained control over the output. To mitigate these two issues, we reformulate the problem of future pose prediction into pose completion in space and time where trajectories are represented as poses with missing joints. We show that such a framework can generalize to other neural networks designed for future pose prediction. Once trained in this framework, a model is capable of predicting sequences from any number of trajectories. To leverage this notion, we propose a novel transformer-like architecture, TrajeVAE, that provides a versatile framework for 3D human animation. We demonstrate that TrajeVAE outperforms trajectory-based reference approaches and methods that base their predictions on past poses in terms of accuracy. We also show that it can predict reasonable future poses even if provided only with an initial pose.

Out of a hundred trials, how many errors does your speaker verifier make? For the user this is an important, practical question, but researchers and vendors typically sidestep it and supply instead the conditional error-rates that are given by the ROC/DET curve. We posit that the user's question is answered by the Bayes error-rate. We present a tutorial to show how to compute the error-rate that results when making Bayes decisions with calibrated likelihood ratios, supplied by the verifier, and an hypothesis prior, supplied by the user. For perfect calibration, the Bayes error-rate is upper bounded by min(EER,P,1-P), where EER is the equal-error-rate and P, 1-P are the prior probabilities of the competing hypotheses. The EER represents the accuracy of the verifier, while min(P,1-P) represents the hardness of the classification problem. We further show how the Bayes error-rate can be computed also for non-perfect calibration and how to generalize from error-rate to expected cost. We offer some criticism of decisions made by direct score thresholding. Finally, we demonstrate by analyzing error-rates of the recently published DCA-PLDA speaker verifier.

This work supports further development of language technology for the languages of Africa by providing a Wikidata-derived resource of name lists corresponding to common entity types (person, location, and organization). While we are not the first to mine Wikidata for name lists, our approach emphasizes scalability and replicability and addresses data quality issues for languages that do not use Latin scripts. We produce lists containing approximately 1.9 million names across 28 African languages. We describe the data, the process used to produce it, and its limitations, and provide the software and data for public use. Finally, we discuss the ethical considerations of producing this resource and others of its kind.

Deep generative models, such as Variational Autoencoders (VAEs), have been employed widely in computational creativity research. However, such models discourage out-of-distribution generation to avoid spurious sample generation, limiting their creativity. Thus, incorporating research on human creativity into generative deep learning techniques presents an opportunity to make their outputs more compelling and human-like. As we see the emergence of generative models directed to creativity research, a need for machine learning-based surrogate metrics to characterize creative output from these models is imperative. We propose group-based subset scanning to quantify, detect, and characterize creative processes by detecting a subset of anomalous node-activations in the hidden layers of generative models. Our experiments on original, typically decoded, and "creatively decoded" (Das et al 2020) image datasets reveal that the proposed subset scores distribution is more useful for detecting creative processes in the activation space rather than the pixel space. Further, we found that creative samples generate larger subsets of anomalies than normal or non-creative samples across datasets. The node activations highlighted during the creative decoding process are different from those responsible for normal sample generation.

We present a parallel machine translation training corpus for English and Akuapem Twi of 25,421 sentence pairs. We used a transformer-based translator to generate initial translations in Akuapem Twi, which were later verified and corrected where necessary by native speakers to eliminate any occurrence of translationese. In addition, 697 higher quality crowd-sourced sentences are provided for use as an evaluation set for downstream Natural Language Processing (NLP) tasks. The typical use case for the larger human-verified dataset is for further training of machine translation models in Akuapem Twi. The higher quality 697 crowd-sourced dataset is recommended as a testing dataset for machine translation of English to Twi and Twi to English models. Furthermore, the Twi part of the crowd-sourced data may also be used for other tasks, such as representation learning, classification, etc. We fine-tune the transformer translation model on the training corpus and report benchmarks on the crowd-sourced test set.

In the much-applauded interventions by Google The advancement in machine learning computational and Microsoft through their translation services, power coupled with the recent investment quite a number of African languages have been within the domain by technological companies integrated, but Ghanaian languages are excluded has stimulated considerable interest and (Google, 2020; Microsoft, 2021). A historic move brought about a legion of applications in natural worth mentioning is Baidu Translate's incorporation language digitisation in developed countries, of the Twi language in their translation service.