UNHEARD VOICES OF THE ‘CHARUAS’ : BARRIERS TO EFFECTIVE HEALTH DELIVERY IN THE CHAR AREAS OF ASSAM

Health is a major area concerning development for countries like India which is yet to be able to provide universal and basic health care services to its entire population. Among the major health issues confronting the country are increasing disease burden, maternal health and child health issues etc. With one of the most complex demographic scenarios in the country, the northeastern state of Assam is facing a huge challenge as of today. The geographical terrain, socio-religious beliefs etc. of the state contributes significantly to the variability seen in socio-economic data; accessibility is thus a major issue in implementing various government schemes. There are certain areas which have chronically suffered from lack of adequate health infrastructure – the char areas of the river Brahmaputra may specially be mentioned here. In this article it is argued that the char areas of the Brahamaputra river have been a classic case of neglect by the state government. Even though demographic challenges are present, the poor living condition of people cannot be justified in this day and age; appropriate measures need to be taken to uplift the lives of the charuas

A Combinatorial Approach to Robust PCA

We study the problem of recovering Gaussian data under adversarial corruptions when the noises are low-rank and the corruptions are on the coordinate level. Concretely, we assume that the Gaussian noises lie in an unknown $k$-dimensional subspace $U \subseteq \mathbb{R}^d$, and $s$ randomly chosen coordinates of each data point fall into the control of an adversary. This setting models the scenario of learning from high-dimensional yet structured data that are transmitted through a highly-noisy channel, so that the data points are unlikely to be entirely clean. Our main result is an efficient algorithm that, when $ks^2 = O(d)$, recovers every single data point up to a nearly-optimal $\ell_1$ error of $\tilde O(ks/d)$ in expectation. At the core of our proof is a new analysis of the well-known Basis Pursuit (BP) method for recovering a sparse signal, which is known to succeed under additional assumptions (e.g., incoherence or the restricted isometry property) on the underlying subspace $U$. In contrast, we present a novel approach via studying a natural combinatorial problem and show that, over the randomness in the support of the sparse signal, a high-probability error bound is possible even if the subspace $U$ is arbitrary.Comment: To appear at ITCS 202