Collaborative Learning of Stochastic Bandits over a Social Network

We consider a collaborative online learning paradigm, wherein a group of agents connected through a social network are engaged in playing a stochastic multi-armed bandit game. Each time an agent takes an action, the corresponding reward is instantaneously observed by the agent, as well as its neighbours in the social network. We perform a regret analysis of various policies in this collaborative learning setting. A key finding of this paper is that natural extensions of widely-studied single agent learning policies to the network setting need not perform well in terms of regret. In particular, we identify a class of non-altruistic and individually consistent policies, and argue by deriving regret lower bounds that they are liable to suffer a large regret in the networked setting. We also show that the learning performance can be substantially improved if the agents exploit the structure of the network, and develop a simple learning algorithm based on dominating sets of the network. Specifically, we first consider a star network, which is a common motif in hierarchical social networks, and show analytically that the hub agent can be used as an information sink to expedite learning and improve the overall regret. We also derive networkwide regret bounds for the algorithm applied to general networks. We conduct numerical experiments on a variety of networks to corroborate our analytical results.Comment: 14 Pages, 6 Figure

Three dimensional lithospheric structure of the western continental margin of India constrained from gravity modelling: implication for tectonic evolution

This paper describes a 3-D lithospheric density model of the Western Continental Margin of India (WCMI) based on forward modelling of gravity data derived from satellite altimetry over the ocean and surface measurements on the Indian peninsula. The model covers the north-eastern Arabian Sea and the western part of the Indian Peninsula and incorporates constraints from a wide variety of geophysical and geological information. Salient features of the density model include: (1) the Moho depth varying from 13 km below the oceanic crust to 46 km below the continental interior; (2) the lithosphere–asthenosphere boundary (LAB) located at depths between 70 km in the southwestern corner (under oceanic crust) and about 165 km below the continental region; (3) thickening of the crust under the Chagos–Laccadive and Laxmi Ridges and (4) a revised definition of the continent–ocean boundary. The 3-D density structure of the region enables us to propose an evolutionary model of the WCMI that revisits earlier views of passive rifting. The first stage of continental-scale rifting of Madagascar from India at about 90 Ma is marked by relatively small amounts of magmatism. A second episode of rifting and large-scale magmatism was possibly initiated around 70 Ma with the opening of the Gop Rift. Subsequently at around 68 Ma, the drifting away of the Seychelles and formation of the Laxmi Ridge was a consequence of the down-faulting of the northern margin. During this second episode of rifting, the northern part of the WCMI witnessed massive volcanism attributed to interaction with the Reunion hotspot at around 65 Ma. Subsequent stretching of the transitional crust between about 65 and 62 Ma formed the Laxmi Basin, the southward extension of the failed Gop Rift. As the interaction between plume and lithosphere continued, the Chagos–Laccadive Ridge was emplaced on the edge of the nascent oceanic crust/rifted continental margin in the south as the Indian Plate was moving northwards

The impact of surgical delay on resectability of colorectal cancer: An international prospective cohort study

AIM: The SARS-CoV-2 pandemic has provided a unique opportunity to explore the impact of surgical delays on cancer resectability. This study aimed to compare resectability for colorectal cancer patients undergoing delayed versus non-delayed surgery. METHODS: This was an international prospective cohort study of consecutive colorectal cancer patients with a decision for curative surgery (January-April 2020). Surgical delay was defined as an operation taking place more than 4 weeks after treatment decision, in a patient who did not receive neoadjuvant therapy. A subgroup analysis explored the effects of delay in elective patients only. The impact of longer delays was explored in a sensitivity analysis. The primary outcome was complete resection, defined as curative resection with an R0 margin. RESULTS: Overall, 5453 patients from 304 hospitals in 47 countries were included, of whom 6.6% (358/5453) did not receive their planned operation. Of the 4304 operated patients without neoadjuvant therapy, 40.5% (1744/4304) were delayed beyond 4 weeks. Delayed patients were more likely to be older, men, more comorbid, have higher body mass index and have rectal cancer and early stage disease. Delayed patients had higher unadjusted rates of complete resection (93.7% vs. 91.9%, P = 0.032) and lower rates of emergency surgery (4.5% vs. 22.5%, P < 0.001). After adjustment, delay was not associated with a lower rate of complete resection (OR 1.18, 95% CI 0.90-1.55, P = 0.224), which was consistent in elective patients only (OR 0.94, 95% CI 0.69-1.27, P = 0.672). Longer delays were not associated with poorer outcomes. CONCLUSION: One in 15 colorectal cancer patients did not receive their planned operation during the first wave of COVID-19. Surgical delay did not appear to compromise resectability, raising the hypothesis that any reduction in long-term survival attributable to delays is likely to be due to micro-metastatic disease

Collaborative Learning of Stochastic Bandits Over a Social Network

We consider a collaborative online learning paradigm, wherein a group of agents connected through a social network are engaged in learning a stochastic multi-armed bandit problem. Each time an agent takes an action, the corresponding reward is instantaneously observed by the agent, as well as its neighbors in the social network. We perform a regret analysis of various policies in this collaborative learning setting. A key finding of this paper is that natural extensions of widely studied single agent learning policies to the network setting need not perform well in terms of regret. In particular, we identify a class of non-altruistic and individually consistent policies and argue by deriving regret lower bounds that they are liable to suffer a large regret in the networked setting. We also show that the learning performance can be substantially improved if the agents exploit the structure of the network and develop a simple learning algorithm based on dominating sets of the network. Specifically, we first consider a star network, which is a common motif in hierarchical social networks and show analytically that the hub agent can be used as an information sink to expedite learning and improve the overall regret. We also derive network-wide regret bounds for the algorithm applied to general networks. We conduct numerical experiments on a variety of networks to corroborate our analytical results

The impact of surgical delay on resectability of colorectal cancer: An international prospective cohort study

AimThe SARS-CoV-2 pandemic has provided a unique opportunity to explore the impact of surgical delays on cancer resectability. This study aimed to compare resectability for colorectal cancer patients undergoing delayed versus non-delayed surgery.MethodsThis was an international prospective cohort study of consecutive colorectal cancer patients with a decision for curative surgery (January-April 2020). Surgical delay was defined as an operation taking place more than 4 weeks after treatment decision, in a patient who did not receive neoadjuvant therapy. A subgroup analysis explored the effects of delay in elective patients only. The impact of longer delays was explored in a sensitivity analysis. The primary outcome was complete resection, defined as curative resection with an R0 margin.ResultsOverall, 5453 patients from 304 hospitals in 47 countries were included, of whom 6.6% (358/5453) did not receive their planned operation. Of the 4304 operated patients without neoadjuvant therapy, 40.5% (1744/4304) were delayed beyond 4 weeks. Delayed patients were more likely to be older, men, more comorbid, have higher body mass index and have rectal cancer and early stage disease. Delayed patients had higher unadjusted rates of complete resection (93.7% vs. 91.9%, P = 0.032) and lower rates of emergency surgery (4.5% vs. 22.5%, P ConclusionOne in 15 colorectal cancer patients did not receive their planned operation during the first wave of COVID-19. Surgical delay did not appear to compromise resectability, raising the hypothesis that any reduction in long-term survival attributable to delays is likely to be due to micro-metastatic disease