Explaining Manipur’s breakdown and Mizoram’s peace: the state and identities in north east India

Material from North East India provides clues to explain both state breakdown as well as its avoidance. They point to the particular historical trajectory of interaction of state-making leaders and other social forces, and the divergent authority structure that took shape, as underpinning this difference. In Manipur, where social forces retained their authority, the state’s autonomy was compromised. This affected its capacity, including that to resolve group conflicts. Here powerful social forces politicized their narrow identities to capture state power, leading to competitive mobilisation and conflicts. State’s poor capacity has facilitated frequent breakdown in Manipur. In Mizoram, where state-making leaders managed to incorporate other social forces within their authority structure, state autonomy was enhanced. This has helped enhance state capacity and its ability to resolve conflicts. Crucial to this dynamic in Mizoram was the role of state-making leaders inventing and mobilising an overarching and inclusive identity to counter entrenched social forces. This has helped with social cohesion

Reconstruction from Breakdown in Northeastern India: Building State Capability

conflict, reconstruction, state-building, capability, legitimacy, rule of law, governance, institutions, development,

Power Imbalance Detection in Smart Grid via Grid Frequency Deviations: A Hidden Markov Model based Approach

We detect the deviation of the grid frequency from the nominal value (i.e., 50 Hz), which itself is an indicator of the power imbalance (i.e., mismatch between power generation and load demand). We first pass the noisy estimates of grid frequency through a hypothesis test which decides whether there is no deviation, positive deviation, or negative deviation from the nominal value. The hypothesis testing incurs miss-classification errors---false alarms (i.e., there is no deviation but we declare a positive/negative deviation), and missed detections (i.e., there is a positive/negative deviation but we declare no deviation). Therefore, to improve further upon the performance of the hypothesis test, we represent the grid frequency's fluctuations over time as a discrete-time hidden Markov model (HMM). We note that the outcomes of the hypothesis test are actually the emitted symbols, which are related to the true states via emission probability matrix. We then estimate the hidden Markov sequence (the true values of the grid frequency) via maximum likelihood method by passing the observed/emitted symbols through the Viterbi decoder. Simulations results show that the mean accuracy of Viterbi algorithm is at least $5$\% greater than that of hypothesis test.Comment: 5 pages, 6 figures, accepted by IEEE VTC conference, Fall 2018 editio

What do Neural Machine Translation Models Learn about Morphology?

Neural machine translation (MT) models obtain state-of-the-art performance while maintaining a simple, end-to-end architecture. However, little is known about what these models learn about source and target languages during the training process. In this work, we analyze the representations learned by neural MT models at various levels of granularity and empirically evaluate the quality of the representations for learning morphology through extrinsic part-of-speech and morphological tagging tasks. We conduct a thorough investigation along several parameters: word-based vs. character-based representations, depth of the encoding layer, the identity of the target language, and encoder vs. decoder representations. Our data-driven, quantitative evaluation sheds light on important aspects in the neural MT system and its ability to capture word structure.Comment: Updated decoder experiment