Learning to detect an oddball target with observations from an exponential family

The problem of detecting an odd arm from a set of K arms of a multi-armed bandit, with fixed confidence, is studied in a sequential decision-making scenario. Each arm's signal follows a distribution from a vector exponential family. All arms have the same parameters except the odd arm. The actual parameters of the odd and non-odd arms are unknown to the decision maker. Further, the decision maker incurs a cost for switching from one arm to another. This is a sequential decision making problem where the decision maker gets only a limited view of the true state of nature at each stage, but can control his view by choosing the arm to observe at each stage. Of interest are policies that satisfy a given constraint on the probability of false detection. An information-theoretic lower bound on the total cost (expected time for a reliable decision plus total switching cost) is first identified, and a variation on a sequential policy based on the generalised likelihood ratio statistic is then studied. Thanks to the vector exponential family assumption, the signal processing in this policy at each stage turns out to be very simple, in that the associated conjugate prior enables easy updates of the posterior distribution of the model parameters. The policy, with a suitable threshold, is shown to satisfy the given constraint on the probability of false detection. Further, the proposed policy is asymptotically optimal in terms of the total cost among all policies that satisfy the constraint on the probability of false detection

Characterization of pseudobasophilia on Sysmex-XT 1800i automated hematology analyser

Background: Pseudobasophilia is a common automation related phenomenon which requires manual peripheral smear study in an era of complete automation. This study has attempted to evaluate the reasons for pseudobasophilia and in-turn suggest measures to eliminate the errors.Methods: A sample size of 207 cases showing pseudobasophilia on automation were studied by manual peripheral examination to categorize the possible cause for its occurrence. Descriptive and inferential statistical analysis was carried out. Results on continuous measurements are presented on Mean SD and results on categorical measurements are presented in Number (%). Significance is assessed at 5% level of significance. Student t test has been used to find the significance of study parameters on continuous scale within each group.Results: Atypical/ reactive lymphocytes were present in 86.5% cases contributing to pseudobasophilia phenomenon on automation, which also showed falsely increased absolute basophil count with more percentage of lymphocytes showing reactive changes. Temperature and storage effects did not contribute to their occurrence in this study. Another finding was an associated pseudomonocytosis with pseudobasophilia on automation which was statistically significant (p<0.001).Conclusions: Pseudobasophilia, and pseudomonocytosis are automation related phenomenon. Atypical/ reactive lymphocytes, which are cytoplasmic strip resistant, contribute to their occurrence. Hence, newer modalities like multicolour flow cytometry coupled with antibody tagging, multiangle polarised scatter separation and volume conductivity scatter may reduce the chances of pseudobasophilia, thereby reducing the overall turnaround time

Sequential Multi-hypothesis Testing in Multi-armed Bandit Problems:An Approach for Asymptotic Optimality

We consider a multi-hypothesis testing problem involving a K-armed bandit. Each arm's signal follows a distribution from a vector exponential family. The actual parameters of the arms are unknown to the decision maker. The decision maker incurs a delay cost for delay until a decision and a switching cost whenever he switches from one arm to another. His goal is to minimise the overall cost until a decision is reached on the true hypothesis. Of interest are policies that satisfy a given constraint on the probability of false detection. This is a sequential decision making problem where the decision maker gets only a limited view of the true state of nature at each stage, but can control his view by choosing the arm to observe at each stage. An information-theoretic lower bound on the total cost (expected time for a reliable decision plus total switching cost) is first identified, and a variation on a sequential policy based on the generalised likelihood ratio statistic is then studied. Due to the vector exponential family assumption, the signal processing at each stage is simple; the associated conjugate prior distribution on the unknown model parameters enables easy updates of the posterior distribution. The proposed policy, with a suitable threshold for stopping, is shown to satisfy the given constraint on the probability of false detection. Under a continuous selection assumption, the policy is also shown to be asymptotically optimal in terms of the total cost among all policies that satisfy the constraint on the probability of false detection

Growth dynamics and forecasting of minor millets in India: A time series analysis

The forecasting behaviour of millet plays a critical role in production planning at the Indian farm level. This study made an effort to forecast the area and production of small millets in India with time series analysis. The performance of the forecasting models was appraised and collated by the Mean Absolute Percentage Error (MAPE), Partial Autocorrelation Function (PACF) and Auto Correlation Function (ACF) criteria. For this analysis, the yearly data of the area and production of small millet from 1950 to 2021 were calculated. Among all Autoregressive Integrated Moving Average (ARIMA) models, ARIMA (0,1,0) was found to be the best fitted for forecasting the area and production of minor millets in India since, principally, this model relies on historical ideals of the sequences in addition to earlier error relations for forecasting minor millets and it does not adopt information of any fundamental model or associations as in some other approaches. The predicted values of minor millet area showed decreased trend from 422.4 thousand hectares in the year 2022 to 409.2 thousand hectares in the year 2026. Likewise, the production under small millets declined from 393.5 thousand tons to 159.5 thousand tons for the corresponding period. Hence, production of these crops can be enhanced by suitable use of inputs and timely application of inputs, high yielding varieties, government interventions like policy support, subsidising through the Public Distribution System and awareness by the way of propaganda and demonstration

Scalable Fixed Point QRD Core Using Dynamic Partial Reconfiguration

A Givens rotation based scalable QRD core which utilizes an efficient pipelined and unfolded 2D multiply and accumulate (MAC) based systolic array architecture with dynamic partial reconfiguration (DPR) capability is proposed. The square root and inverse square root operations in the Givens rotation algorithm are handled using a modified look-up table (LUT) based Newton-Raphson method, thereby reducing the area by 71% and latency by 50% while operating at a frequency 49% higher than the existing boundary cell architectures. The proposed architecture is implemented on Xilinx Virtex-6 FPGA for any real matrices of size m×n, where 4≤n≤8 and m≥n by dynamically inserting or removing the partial modules. The evaluation results demonstrate a significant reduction in latency, area, and power as compared to other existing architectures. The functionality of the proposed core is evaluated for a variable length adaptive equalizer