Weerapan Khovidhunkit
Jul 1, 2004; 45:1169-1196
Thematic Reviews

DL Brasaemle
Nov 1, 1997; 38:2249-2263
Articles

RK Tangirala
Nov 1, 1995; 36:2320-2328
Articles

Counter-terrorism measures and sanctions: How to avoid negative consequences for humanitarian action? 9 September 2021 — 2:00PM TO 3:30PM Anonymous (not verified) 21 July 2021 Online

Exploring current endeavours to address the tensions between counter-terrorism measures, sanctions and humanitarian action.

Counter-terrorism measures  address broad forms of support to terrorist acts. Their expansion, internationally and domestically, has given rise to new points of friction with international humanitarian law. Unless the measures include adequate safeguards, they  can impede humanitarian action. Country-specific sanctions imposed for other objectives, such as ending conflicts or protecting civilians, raise similar challenges for humanitarian action. 

These problems are not new, but solutions at international and national level remain elusive. 

At this panel event, which marks the launch of a new Chatham House research paper, panellists explore current endeavours to address the tensions between counter-terrorism measures, sanctions and humanitarian action.

• What are the current dynamics and developments at Security Council level?  
• What are the opportunities now that the UK is developing its independent sanctions strategy? 
• What challenges do counter-terrorism requirements in funding agreements for humanitarian action  pose? 
• What is necessary to make progress?

Elizabeth Wilmshurst CMG appointed Honorary Queen’s Counsel News release jon.wallace 14 January 2022

Founder of the International Law Programme at Chatham House recognized for her major contribution to the law of England and Wales.

Elizabeth Wilmshurst CMG, distinguished fellow of Chatham House’s International Law Programme, has been awarded the title of Honorary Queen’s Counsel (QC Honoris Causa), recognizing her major contribution to the law of England and Wales, outside practice in the courts. The Lord Chancellor will preside over an appointment ceremony at Westminster Hall on 21 March 2022.

Elizabeth founded the International Law Programme at Chatham House and is an academic expert member of Doughty Street Chambers. She was a legal adviser in the United Kingdom diplomatic service between 1974 and 2003. Between 1994 and 1997 she was the Legal Adviser to the United Kingdom mission to the United Nations in New York. She also took part in the negotiations for the establishment of the International Criminal Court.

Throughout her career, Elizabeth has worked to strengthen the role of international law in reducing global tensions, addressing cross-border challenges and promoting individual liberty, including through influential publications at the Institute such as The Chatham House Principles of International Law on the Use of Force in Self-Defence. 

Robin Niblett CMG, Director and Chief Executive of Chatham House said:

‘We are delighted by this award which recognizes Elizabeth’s outstanding contribution to the field of international law, both in government and – on a continuing basis – through the International Law Programme at Chatham House.’

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RecQ family helicases are highly conserved from bacteria to humans and have essential roles in maintaining genome stability. Mutations in three human RecQ helicases cause severe diseases with the main features of premature aging and cancer predisposition. Most RecQ helicases shared a conserved domain arrangement which comprises a helicase core, an RecQ C-terminal domain, and an auxiliary element helicase and RNaseD C-terminal (HRDC) domain, the functions of which are poorly understood. In this study, we systematically characterized the roles of the HRDC domain in E. coli RecQ in various DNA transactions by single-molecule FRET. We found that RecQ repetitively unwinds the 3'-partial duplex and fork DNA with a moderate processivity and periodically patrols on the ssDNA in the 5'-partial duplex by translocation. The HRDC domain significantly suppresses RecQ activities in the above transactions. In sharp contrast, the HRDC domain is essential for the deep and long-time unfolding of the G4 DNA structure by RecQ. Based on the observations that the HRDC domain dynamically switches between RecA core- and ssDNA-binding modes after RecQ association with DNA, we proposed a model to explain the modulation mechanism of the HRDC domain. Our findings not only provide new insights into the activities of RecQ on different substrates but also highlight the novel functions of the HRDC domain in DNA metabolisms.

Rhodopsin is a canonical class A photosensitive G protein–coupled receptor (GPCR), yet relatively few pharmaceutical agents targeting this visual receptor have been identified, in part due to the unique characteristics of its light-sensitive, covalently bound retinal ligands. Rhodopsin becomes activated when light isomerizes 11-cis-retinal into an agonist, all-trans-retinal (ATR), which enables the receptor to activate its G protein. We have previously demonstrated that, despite being covalently bound, ATR can display properties of equilibrium binding, yet how this is accomplished is unknown. Here, we describe a new approach for both identifying compounds that can activate and attenuate rhodopsin and testing the hypothesis that opsin binds retinal in equilibrium. Our method uses opsin-based fluorescent sensors, which directly report the formation of active receptor conformations by detecting the binding of G protein or arrestin fragments that have been fused onto the receptor's C terminus. We show that these biosensors can be used to monitor equilibrium binding of the agonist, ATR, as well as the noncovalent binding of β-ionone, an antagonist for G protein activation. Finally, we use these novel biosensors to observe ATR release from an activated, unlabeled receptor and its subsequent transfer to the sensor in real time. Taken together, these data support the retinal equilibrium binding hypothesis. The approach we describe should prove directly translatable to other GPCRs, providing a new tool for ligand discovery and mutant characterization.

Erythromycin-resistance methyltransferases are SAM dependent Rossmann fold methyltransferases that convert A2058 of 23S rRNA to m6 2A2058. This modification sterically blocks binding of several classes of antibiotics to 23S rRNA, resulting in a multidrug-resistant phenotype in bacteria expressing the enzyme. ErmC is an erythromycin resistance methyltransferase found in many Gram-positive pathogens, whereas ErmE is found in the soil bacterium that biosynthesizes erythromycin. Whether ErmC and ErmE, which possess only 24% sequence identity, use similar structural elements for rRNA substrate recognition and positioning is not known. To investigate this question, we used structural data from related proteins to guide site-saturation mutagenesis of key residues and characterized selected variants by antibiotic susceptibility testing, single turnover kinetics, and RNA affinity–binding assays. We demonstrate that residues in α4, α5, and the α5-α6 linker are essential for methyltransferase function, including an aromatic residue on α4 that likely forms stacking interactions with the substrate adenosine and basic residues in α5 and the α5-α6 linker that likely mediate conformational rearrangements in the protein and cognate rRNA upon interaction. The functional studies led us to a new structural model for the ErmC or ErmE-rRNA complex.

A. Kh. Khachatryan, Kh. A. Khachatryan and A. Zh. Narimanyan
Trans. Moscow Math. Soc. 83 (), 183-200.
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A. V. Silantyev
Trans. Moscow Math. Soc. 83 (), 75-149.
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Climate action and gender equality: Can we close the gap on one without the other? 8 March 2021 — 2:30PM TO 4:00PM Anonymous (not verified) 15 February 2021 Online

In partnership with the COP26 presidency, policymakers and experts examine the interrelationship between gender equality and climate action, and highlight innovative examples of policy and practice from around the world.

Marking International Women’s Day, this event organized by the COP26 presidency in partnership with Chatham House, will explore how gender equality and climate action go hand-in-hand. The agreement of the Gender Action Plan (GAP) at COP25 sent the message that the time for gender-responsive climate action is now.

This will be a unique opportunity to hear from policymakers and civil society leaders discussing whether enough is being done, as well as highlighting cutting-edge work around the world and suggesting what the future could hold.

The event will be livestreamed on this event page.

Prioritizing equity and justice in climate action 30 June 2021 — 11:00AM TO 12:00PM Anonymous (not verified) 10 June 2021 Online

London Climate Action Week event: Why understanding equity and justice is essential to the ability to meaningfully inform climate politics.

Citizen-led climate activism is demonstrating the need to think about climate change ‘not just as a problem for science to solve’ but also as a problem of equity, human rights and justice.

The disproportionate impacts of climate change on the poor and the marginalized across the world means that understanding equity and justice is essential for the ability to meaningfully inform climate politics.

Excluding these issues risks ignoring, or intentionally omitting, the consequences of policies, tools and frameworks on those who are most likely to face the severe costs of any climate action or inaction.

In a pivotal year for climate decision-making, this event explores the necessity of equity and justice in climate action and how the world can move the political conversation to one that is more inclusive.

The speakers explore how communities themselves articulate the justice dimensions of climate change and how fairness can create a greener future for current and future generations.

This event is being hosted as a part of Strengthening Climate Diplomacy, a series of events from Chatham House during London Climate Action Week 2021.

Artificial Intelligence Apps Risk Entrenching India’s Socio-economic Inequities Expert comment sysadmin 14 March 2018

Artificial intelligence applications will not be a panacea for addressing India’s grand challenges. Data bias and unequal access to technology gains will entrench existing socio-economic fissures.

Artificial intelligence (AI) is high on the Indian government’s agenda. Some days ago, Prime Minister Narendra Modi inaugurated the Wadhwani Institute for Artificial Intelligence, reportedly India’s first research institute focused on AI solutions for social good. In the same week, Niti Aayog CEO Amitabh Kant argued that AI could potentially add $957 billion to the economy and outlined ways in which AI could be a ‘game changer’.

During his budget speech, Finance Minister Arun Jaitley announced that Niti Aayog would spearhead a national programme on AI; with the near doubling of the Digital India budget, the IT ministry also announced the setting up of four committees for AI-related research. An industrial policy for AI is also in the pipeline, expected to provide incentives to businesses for creating a globally competitive Indian AI industry.

Narratives on the emerging digital economy often suffer from technological determinism — assuming that the march of technological transformation has an inner logic, independent of social choice and capable of automatically delivering positive social change. However, technological trajectories can and must be steered by social choice and aligned with societal objectives. Modi’s address hit all the right notes, as he argued that the ‘road ahead for AI depends on and will be driven by human intentions’. Emphasising the need to direct AI technologies towards solutions for the poor, he called upon students and teachers to identify ‘the grand challenges facing India’ – to ‘Make AI in India and for India’.

To do so, will undoubtedly require substantial investments in R&D, digital infrastructure and education and re-skilling. But, two other critical issues must be simultaneously addressed: data bias and access to technology gains.

While computers have been mimicking human intelligence for some decades now, a massive increase in computational power and the quantity of available data are enabling a process of ‘machine learning.’ Instead of coding software with specific instructions to accomplish a set task, machine learning involves training an algorithm on large quantities of data to enable it to self-learn; refining and improving its results through multiple iterations of the same task. The quality of data sets used to train machines is thus a critical concern in building AI applications.

Much recent research shows that applications based on machine learning reflect existing social biases and prejudice. Such bias can occur if the data set the algorithm is trained on is unrepresentative of the reality it seeks to represent. If for example, a system is trained on photos of people that are predominantly white, it will have a harder time recognizing non-white people. This is what led a recent Google application to tag black people as gorillas.

Alternatively, bias can also occur if the data set itself reflects existing discriminatory or exclusionary practices. A recent study by ProPublica found for example that software that was being used to assess the risk of recidivism in criminals in the United States was twice as likely to mistakenly flag black defendants as being at higher risk of committing future crimes.

The impact of such data bias can be seriously damaging in India, particularly at a time of growing social fragmentation. It can contribute to the entrenchment of social bias and discriminatory practices, while rendering both invisible and pervasive the processes through which discrimination occurs. Women are 34 per cent less likely to own a mobile phone than men – manifested in only 14 per cent of women in rural India owning a mobile phone, while only 30 per cent of India’s internet users are women.

Women’s participation in the labour force, currently at around 27 per cent, is also declining, and is one of the lowest in South Asia. Data sets used for machine learning are thus likely to have a marked gender bias. The same observations are likely to hold true for other marginalized groups as well.

Accorded to a 2014 report, Muslims, Dalits and tribals make up 53 per cent of all prisoners in India; National Crime Records Bureau data from 2016 shows in some states, the percentage of Muslims in the incarcerated population was almost three times the percentage of Muslims in the overall population. If AI applications for law and order are built on this data, it is not unlikely that it will be prejudiced against these groups.

(It is worth pointing out that the recently set-up national AI task force is comprised of mostly Hindu men – only two women are on the task force, and no Muslims or Christians. A recent article in the New York Times talked about AI’s ‘white guy problem’; will India suffer from a ‘Hindu male bias’?)

Yet, improving the quality, or diversity, of data sets may not be able to solve the problem. The processes of machine learning and reasoning involve a quagmire of mathematical functions, variables and permutations, the logic of which are not readily traceable or predictable. The dazzle of AI-enabled efficiency gains must not blind us to the fact that while AI systems are being integrated into key socio-economic systems, their accuracy and logic of reasoning have not been fully understood or studied.

The other big challenge stems from the distribution of AI-led technology gains. Even if estimates of AI contribution to GDP are correct, the adoption of these technologies is likely to be in niches within the organized sector. These industries are likely to be capital- rather than labour-intensive, and thus unlikely to contribute to large-scale job creation.

At the same time, AI applications can most readily replace low- to medium-skilled jobs within the organized sector. This is already being witnessed in the outsourcing sector – where basic call and chat tasks are now automated. Re-skilling will be important, but it is unlikely that those who lose their jobs will also be those who are being re-skilled – the long arch of technological change and societal adaptation is longer than that of people’s lives. The contractualization of work, already on the rise, is likely to further increase as large industries prefer to have a flexible workforce to adapt to technological change. A shift from formal employment to contractual work can imply a loss of access to formal social protection mechanisms, increasing the precariousness of work for workers.

The adoption of AI technologies is also unlikely in the short- to medium-term in the unorganized sector, which engages more than 80 per cent of India’s labor force. The cost of developing and deploying AI applications, particularly in relation to the cost of labour, will inhibit adoption. Moreover, most enterprises within the unorganized sector still have limited access to basic, older technologies – two-thirds of the workforce are employed in enterprises without electricity. Eco-system upgrades will be important but incremental. Given the high costs of developing AI-based applications, most start-ups are unlikely to be working towards creating bottom-of-the-pyramid solutions.

Access to AI-led technology gains is thus likely to be heavily differentiated – a few high-growth industries can be expected, but these will not necessarily result in the welfare of labour. Studies show that labour share of national income, especially routine labour, has been declining steadily across developing countries.

We should be clear that new technological applications themselves are not going to transform or disrupt this trend – rather, without adequate policy steering, these trends will be exacerbated.

Policy debates about AI applications in India need to take these two issues seriously. AI applications will not be a panacea for addressing ‘India’s grand challenges’. Data bias and unequal access to technology gains will entrench existing socio-economic fissures, even making them technologically binding.

In addition to developing AI applications and creating a skilled workforce, the government needs to prioritize research that examines the complex social, ethical and governance challenges associated with the spread of AI-driven technologies. Blind technological optimism might entrench rather than alleviate the grand Indian challenge of inequity and growth.

This article was originally published in the Indian Express.

Gender and equality

Exploring empowerment in areas such as women’s rights, equality and discrimination, the sharing of unpaid work, and the role of gender in achieving the SDGs.

Many women are still excluded from economic decision-making within their own household, receive lower salaries than men, and work longer hours. They are also often excluded from the labour market, do not have access to finance, and are denied property rights.

Wider areas of interest include examining the potential for foreign policies that prioritize gender equality, the physical, cultural and practical challenges to integrating women into close combat roles in the military, and the future of women in peace and security.

Equality and diversity research work also examines why people from black and minority ethnic groups still do less well in terms of education and the jobs they do than people from other groups, and considers the rights of workers across the world. It is strongly linked with promoting human rights and freedoms, based on principles such as dignity and respect.

Martin Ehler and Karlheinz Gröchenig
Math. Comp. 93 (), 2885-2919.
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Ran Gu and Bing Gao
Math. Comp. 93 (), 2837-2860.
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Ole Løseth Elvetun and Bjørn Fredrik Nielsen
Math. Comp. 93 (), 2811-2836.
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Katherine Baker, Lehel Banjai and Mariya Ptashnyk
Math. Comp. 93 (), 2711-2743.
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Constantin Carle and Marlis Hochbruck
Math. Comp. 93 (), 2611-2641.
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David M. Williams and Qingguo Hong
Math. Comp. 93 (), 2587-2609.
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Buyang Li, Zongze Yang and Zhi Zhou
Math. Comp. 93 (), 2557-2586.
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Ricardo Bermudez-Otero and Tobias Galla discuss the mathematics describing the evolution of human languages. The sounds and structures of the world's approximately 7,000 languages never stop changing. Just compare the English in Romeo and Juliet or the Spanish in Don Quixote to the modern forms. But historical records give an incomplete view of language evolution. Increasingly, linguists draw upon mathematical models to figure out which features of a language change often and which ones change more rarely over the course of thousands of years. A new model inspired by physics assigns a "temperature" to many sounds and grammatical structures. Features with higher temperatures are less stable, so they change more often as time goes on. The linguistic thermometer will help researchers reconstruct how our languages came to be, and how they might change in future generations.

Angela Robinson explains the math behind the next generation of cryptographic algorithms. Whenever you log in to a website, send an email, or make an online purchase, you're counting on your data being sent securely, without hackers being able to crack the code. Our standard cryptographic systems hinge on mathematical problems that stump present-day computers, like finding the prime factors of a very large number. But in the coming decades, powerful quantum computers are expected to be able to rapidly solve some such problems, threatening the security of our online communications. To develop new methods that can withstand even the most sophisticated quantum computer, cryptographers are using a wide range of mathematical tools, many of which were originally developed without any real-life applications in mind.

Dr. Stan Wagon of Macalester College discusses the mathematics behind rolling a square smoothly. In 1997, inspired by a square wheel exhibit at The Exploratorium museum in San Francsico, Dr. Stan Wagon enlisted his neighbor Loren Kellen in building a square-wheeled tricycle and accompanying catenary track. For years, you could ride the tricycle at Macalester College in St. Paul, Minnesota. The National Museum of Mathematics in New York now also has square-wheeled tricycles that can be ridden around a circular track. And more recently, the impressive Cody Dock Rolling Bridge was built using rolling square mathematics by Thomas Randall-Page in London.

Tsz-Kiu Aaron Chow and Yangyang Li
Trans. Amer. Math. Soc. 377 (), 5993-6020.
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Yinji Li, Zhiwei Wang and Xiangyu Zhou
Trans. Amer. Math. Soc. 377 (), 5947-5992.
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Ning Jiang, Yi-Long Luo and Shaojun Tang
Trans. Amer. Math. Soc. 377 (), 5323-5359.
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Bero Roos
Theor. Probability and Math. Statist. 111 (), 137-151.
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Lutz Mattner
Theor. Probability and Math. Statist. 111 (), 45-122.
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Chris A.J. Klaassen
Theor. Probability and Math. Statist. 111 (), 9-19.
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Abdessamad Dehaj and Mohamed Guessous
Theor. Probability and Math. Statist. 111 (), 1-8.
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Lauri Hitruhin and Sauli Lindberg
Proc. Amer. Math. Soc. 152 (), 5265-5278.
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Anh Tuan Nguyen, Tómas Caraballo and Nguyen Huy Tuan
Proc. Amer. Math. Soc. 152 (), 5175-5189.
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András Kroó
Proc. Amer. Math. Soc. 152 (), 5149-5162.
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Simão Correia
Proc. Amer. Math. Soc. 152 (), 5117-5136.
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E. P. Ushakova and K. E. Ushakova
St. Petersburg Math. J. 35 (), 571-595.
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