Krishna Jagannathan

In today's regime of information overload, it is reasonable to model a human executing routine tasks such as responding to emails as a priority queue. Humans typically prioritize task execution based on intrinsic motivators such as interest in the task, as well as extrinsic motivation stemming from the importance of the task to the sender. We view the human priority queue from the perspective of a principal-agent problem and characterize the effect of misalignment between the task sender's and task receiver's priorities. Our model provides insights into how different levels of misalignment affect delays of tasks of varying importance. Further, our approach starts to quantitatively address the effect of human dynamics in routine communication tasks, such as responding to emails. © 2014 IEEE.

This paper focuses on the design and analysis of scheduling policies for multi-class queues, such as those found in wireless networks and high-speed switches. In this context, we study the response time tail under generalized max-weight policies in settings where the traffic flows are highly asymmetric. Specifically, we study an extreme setting with two traffic flows, one heavy-tailed, and one light-tailed. In this setting, we prove that classical max-weight scheduling, which is known to be throughput optimal, results in the light-tailed flow having heavy-tailed response times. However, we show that via a careful design of inter-queue scheduling policy (from the class of generalized max-weight policies) and intra-queue scheduling policies, it is possible to maintain throughput optimality, and guarantee light-tailed delays for the light-tailed flow, without affecting the response time tail for the heavy-tailed flow. © 2013 IEEE.

In this paper, we explore various statistical techniques for anomaly detection in conjunction with the popular Long Short-Term Memory (LSTM) deep learning model for transportation networks. We obtain the prediction errors from an LSTM model, and then apply three statistical models based on (i) the Gaussian distribution, (ii) Extreme Value Theory (EVT), and (iii) the Tukey’s method. Using statistical tests and numerical studies, we find strong evidence against the widely employed Gaussian distribution based detection rule on the prediction errors. Next, motivated by fundamental results from Extreme Value Theory, we propose a detection technique that does not assume any parent distribution on the prediction errors. Through numerical experiments conducted on several real-world traffic data sets, we show that the EVT-based detection rule is superior to other detection rules, and is supported by statistical evidence.

Recent work has shown that adaptive CSMA algorithms can achieve throughput optimality. However, these adaptive CSMA algorithms assume a rather simplistic model for the wireless medium. Specifically, the interference is typically modelled by a conflict graph, and the channels are assumed to be static. In this work, we propose a distributed and adaptive CSMA algorithm under a more realistic signal-to-interference ratio (SIR) based interference model, with time-varying channels. We prove that our algorithm is throughput optimal under this generalized model. Further, we augment our proposed algorithm by using a parallel update technique. Numerical results show that our algorithm outperforms the conflict graph based algorithms, in terms of supportable throughput and the rate of convergence to steady-state.

Bus transportation is the most convenient and cheapest way of public transportation in Indian cities. Due to cost-effectiveness and wide reachability, buses bring people to their destinations every day. Although the bus transportation has numerous advantages over other ways of public transportation, this mode of transportation also poses a serious threat of spreading contagious diseases throughout the city. It is extremely difficult to predict the extent and spread of such an epidemic. Earlier studies have focused on the contagion processes on scale-free network topologies; whereas, real-world networks such as bus networks exhibit a wide-spectrum of network topology. Therefore, we aim in this study to understand this complex dynamical process of epidemic outbreak and information diffusion on the bus networks for six different Indian cities using SI and SIR models. We identify epidemic thresholds for these networks which help us in controlling outbreaks by developing node-based immunization techniques. © 2016 Wiley Periodicals, Inc. Complexity 21: 451–458, 2016.

Time delays play an important role in determining the qualitative dynamical properties of a platoon of self-driven vehicles driving on a straight road. In this paper, we investigate the impact of Delayed Acceleration Feedback (DAF) on the dynamics of the Reduced Classical Car-Following Model (RCCFM). We first derive the Reduced Classical Car-Following Model with Delayed Acceleration Feedback (RCCFM-DAF). Next, we demonstrate that the transition of traffic flow from the locally stable to the unstable regime occurs via a Hopf bifurcation. The analysis also yields the necessary and sufficient condition for local stability. We characterise the type of Hopf bifurcation and the asymptotic orbital stability of the emergent limit cycles for the RCCFM by using Poincaré normal forms and the center manifold theory. We then use this analysis to infer requisite insights into the RCCFM-DAF by means of an appropriately defined linear transformation. The analysis is complemented with a stability chart and a bifurcation diagram. Our work reveals three effects of DAF on the RCCFM: (i) reduction in the stable region, (ii) increase in the frequency of the emergent limit cycles, and (iii) decrease in the amplitude of the emergent limit cycles. This, in turn, has two immediate repercussions: (i) decrease in resilience to the reaction delay, and (ii) an increase in the risk of a collision due to jerky vehicular motion.

We consider a setting where qubits are processed sequentially, and derive fundamental limits on the rate at which classical information can be transmitted using quantum states that decohere in time. Specifically, we model the sequential processing of qubits using a single server queue, and derive explicit expressions for the capacity of such a 'queue-channel.' We also demonstrate a sweet-spot phenomenon with respect to the arrival rate to the queue, i.e., we show that there exists a value of the arrival rate of the qubits at which the rate of information transmission (in bits/sec) through the queue-channel is maximised. Next, we consider a setting where the average rate of processing qubits is fixed, and show that the capacity of the queue-channel is maximised when the processing time is deterministic. We also discuss design implications of these results on quantum information processing systems.

In this paper, we use time-series modeling to forecast taxi travel demand, in the context of a mobile applicationbased taxi hailing service. In particular, we model the passenger demand density at various locations in the city of Bengaluru, India. Using the data, we first shortlist time-series models that suit our application. We then analyse the performance of these models by using Mean Absolute Percentage Error (MAPE) as the performance metric. In order to improve the model performance, we employ a multi-level clustering technique where we aggregate demand over neighboring cells/geohashes. We observe that the improved model based on clustering leads to a forecast accuracy of 80% per km2. In addition, our technique obtains an accuracy of 89% per km2 for the most frequently occurring use case.

In several applications such as clinical trials and financial portfolio optimization, the expected value (or the average reward) does not satisfactorily capture the merits of a drug or a portfolio. In such applications, risk plays a crucial role, and a risk-aware performance measure is preferable, so as to capture losses in the case of adverse events. This survey aims to consolidate and summarise the existing research on risk measures, specifically in the context of multi-armed bandits. We review various risk measures of interest, and comment on their properties. Next, we review existing concentration inequalities for various risk measures. Then, we proceed to defining risk-aware bandit problems, We consider algorithms for the regret minimization setting, where the exploration-exploitation trade-off manifests, as well as the best-arm identification setting, which is a pure exploration problem-both in the context of risk-sensitive measures. We conclude by commenting on persisting challenges and fertile areas for future research.

Several real-time delay-sensitive applications pose varying degrees of freshness demands on the requested content. The performance of cache replacement policies that are agnostic to these demands is likely to be sub-optimal. Motivated by this concern, in this paper, we study caching policies under a request arrival process which incorporates user freshness demands. We consider the performance metric to be the steady-state cache hit probability. We first provide a universal upper bound on the performance of any caching policy. We then analytically obtain the content-wise hit-rates for the Least Popular (LP) policy. Our key contributions are two-fold. Firstly, we develop a modified version of the LP policy which ejects cache redundancies present in the form of stale contents. Secondly, we propose a new policy which outperforms the above policies by explicitly using freshness specifications of user requests to prioritize among the cached contents. We corroborate our analytical insights with extensive simulations.