A Game Theoretic Approach to Modelling Jamming Attacks in Delay Tolerant Networks

Cyberspace plays a prominent role in our social, economic and civic welfare and cyber security issues are of paramount importance today. Growing reliance of the intertwined military and civilian applications on wireless computer networks makes these networks highly vulnerable to attacks of which jamming attacks are a vital and exigent problem. In this paper, we study defence against jamming attacks as game in a delay tolerant network, with two adversarial players: the jammer playing against the transmitter. The transmitters seek to choose an optimal time to schedule his transmission securely, so as to maximize the probability of successful delivery before his session expires, while these transmissions are subject to inference from the jammer, who attempts to minimize this probability . We design strategies for the transmitters that offset transmission period based inference of network traffic by the jammer. We model these interactions and decisions as a game and use simulation as a tool to evaluate the games. Probability distribution functions over finite set of strategies are proposed to compute the expected payoff of both the players. Simulation results are used to evaluate the expected payoff along with the resulting equilibrium in cases where players are biased and unbiased. These results are used to strategically decide on the optimal time for both the players, and evaluate the efficiency of the strategies used by the transmitters against jammer attacks.

Spin Decoherence from Hamiltonian dynamics in Quantum Dots

The dynamics of a spin-1/2 particle coupled to a nuclear spin bath through an isotropic Heisenberg interaction is studied, as a model for the spin decoherence in quantum dots. The time-dependent polarization of the central spin is calculated as a function of the bath-spin distribution and the polarizations of the initial bath state. For short times, the polarization of the central spin shows a gaussian decay, and at later times it revives displaying nonmonotonic time dependence. The decoherence time scale dep ends on moments of the bath-spin distribuition, and also on the polarization strengths in various bath-spin channels. The bath polarizations have a tendency to increase the decoherence time scale. The effective dynamics of the central spin polarization is shown to be describ ed by a master equation with non-markovian features.Comment: 11 pages, 6 figures Accepted for publication in Phys.Rev

PIKS: A Technique to Identify Actionable Trends for Policy-Makers Through Open Healthcare Data

With calls for increasing transparency, governments are releasing greater amounts of data in multiple domains including finance, education and healthcare. The efficient exploratory analysis of healthcare data constitutes a significant challenge. Key concerns in public health include the quick identification and analysis of trends, and the detection of outliers. This allows policies to be rapidly adapted to changing circumstances. We present an efficient outlier detection technique, termed PIKS (Pruned iterative-k means searchlight), which combines an iterative k-means algorithm with a pruned searchlight based scan. We apply this technique to identify outliers in two publicly available healthcare datasets from the New York Statewide Planning and Research Cooperative System, and California's Office of Statewide Health Planning and Development. We provide a comparison of our technique with three other existing outlier detection techniques, consisting of auto-encoders, isolation forests and feature bagging. We identified outliers in conditions including suicide rates, immunity disorders, social admissions, cardiomyopathies, and pregnancy in the third trimester. We demonstrate that the PIKS technique produces results consistent with other techniques such as the auto-encoder. However, the auto-encoder needs to be trained, which requires several parameters to be tuned. In comparison, the PIKS technique has far fewer parameters to tune. This makes it advantageous for fast, "out-of-the-box" data exploration. The PIKS technique is scalable and can readily ingest new datasets. Hence, it can provide valuable, up-to-date insights to citizens, patients and policy-makers. We have made our code open source, and with the availability of open data, other researchers can easily reproduce and extend our work. This will help promote a deeper understanding of healthcare policies and public health issues

A system for exploring big data: an iterative k-means searchlight for outlier detection on open health data

The interactive exploration of large and evolving datasets is challenging as relationships between underlying variables may not be fully understood. There may be hidden trends and patterns in the data that are worthy of further exploration and analysis. We present a system that methodically explores multiple combinations of variables using a searchlight technique and identifies outliers. An iterative k-means clustering algorithm is applied to features derived through a split-apply-combine paradigm used in the database literature. Outliers are identified as singleton or small clusters. This algorithm is swept across the dataset in a searchlight manner. The dimensions that contain outliers are combined in pairs with other dimensions using a susbset scan technique to gain further insight into the outliers. We illustrate this system by anaylzing open health care data released by New York State. We apply our iterative k-means searchlight followed by subset scanning. Several anomalous trends in the data are identified, including cost overruns at specific hospitals, and increases in diagnoses such as suicides. These constitute novel findings in the literature, and are of potential use to regulatory agencies, policy makers and concerned citizens.Comment: 2018 International Joint Conference on Neural Networks (IJCNN

Building predictive models of healthcare costs with open healthcare data

Due to rapidly rising healthcare costs worldwide, there is significant interest in controlling them. An important aspect concerns price transparency, as preliminary efforts have demonstrated that patients will shop for lower costs, driving efficiency. This requires the data to be made available, and models that can predict healthcare costs for a wide range of patient demographics and conditions. We present an approach to this problem by developing a predictive model using machine-learning techniques. We analyzed de-identified patient data from New York State SPARCS (statewide planning and research cooperative system), consisting of 2.3 million records in 2016. We built models to predict costs from patient diagnoses and demographics. We investigated two model classes consisting of sparse regression and decision trees. We obtained the best performance by using a decision tree with depth 10. We obtained an R-square value of 0.76 which is better than the values reported in the literature for similar problems.Comment: 2020 IEEE International Conference on Healthcare Informatics (ICHI

Efficient online computation of core speeds to maximize the throughput of thermally constrained multi-core processors

Abstract—We address the problem of efficient online computation of the speeds of different cores of a multi-core processor to maximize the throughput (which is expressed as a weighted sum of the speeds), subject to an upper bound on the core temperatures. We first compute the solution for steady-state thermal conditions by solving a linear program. We then present two approaches to computing the transient speed curves for each core: (i) a local solution, which involves solving a linear program every time step (of about 10 ms), and (ii) a global solution, which computes the optimal speed curve over a large time window (of about 100 s) by solving a non-linear program. We showed that the local solution is insensitive to the weights assigned in the performance objective (hence the need for the global solution). This is because a reduction in the speed of a core can only reduce the temperature of the other cores over much larger time periods (of the order of several seconds). The local solution is then completely determined by the temperature constraint equations. We show that the constraint matrix exhibits a special property- it can be expressed as the sum of a diagonal matrix and a matrix with identical rows. This allows us to solve the multi-core thermal constraint equations analytically to determine the (temporally) local optimum speeds. Further, we showed that due to this property, the steady-state speed solution selects a set of threads to operate at maximum temperature, and turns off all unused cores. Hence, to ensure that all available threads are scheduled, we impose a “fairness ” constraint. Finally, we show how the open-loop speed control methods proposed above could be used together with a feedback controller to achieve robustness to model uncertainty. I