Quantum states satisfying classical probability constraints

For linear combinations of quantum product averages in an arbitrary bipartite state, we derive new quantum Bell-form and CHSH-form inequalities with the right-hand sides expressed in terms of a bipartite state. This allows us to specify in a general setting bipartite state properties sufficient for the validity of a classical CHSH-form inequality and the perfect correlation form of the original Bell inequality for any bounded quantum observables. We also introduce a new general condition on a bipartite state and quantum observables sufficient for the validity of the original Bell inequality, in its perfect correlation or anticorrelation forms. Under this general sufficient condition, a bipartite quantum state does not necessarily exhibit perfect correlations or anticorrelations.Comment: v.2: 13 pages, reorganized and shortened version (most examples removed); one reference added; the results not change

A modified Next Reaction Method for simulating chemical systems with time dependent propensities and delays

Chemical reaction systems with a low to moderate number of molecules are typically modeled as discrete jump Markov processes. These systems are oftentimes simulated with methods that produce statistically exact sample paths such as the Gillespie Algorithm or the Next Reaction Method. In this paper we make explicit use of the fact that the initiation times of the reactions can be represented as the firing times of independent, unit rate Poisson processes with internal times given by integrated propensity functions. Using this representation we derive a modified Next Reaction Method and, in a way that achieves efficiency over existing approaches for exact simulation, extend it to systems with time dependent propensities as well as to systems with delays.Comment: 25 pages, 1 figure. Some minor changes made to add clarit

Development of a Cost-Effective Database Software for Psychiatric Research: A Study From Tertiary Care Teaching Hospital

Background: Technological progression made drastic changes in health care. Still there is a growing concern about proper utilization of health information within hospitals for various research activities. Huge volumes of such health information in majority of hospitals are redundant due to lack of appropriate and cost-effective technological tools for retrieving relevant health information for research purpose. Objective: To develop a cost-effective and user-friendly computerized medical record database for psychiatry using available technology with the department. Methodology: Study performed at a tertiary care teaching hospital in Udupi district of South India. Various datasets from psychiatry medical records were utilized for the design and creation of database. A computerized database called PsyCase was developed with the help of technology available within the department. A 4612 patient’s data were entered into the PsyCase and subjected to various analyses. Results: Applications of PsyCase in various epidemiological studies were explored through performing numerous analyses with actual data. PsyCase was found effective in supporting psychiatric research as well as routine clinical and administrative activities. Conclusion: This study emphasizes need of appropriate use of technology available within a healthcare system to facilitate medical research in psychiatry and role of health information professional in such initiatives. Healthcare organization must focus on collective utilization of resources within the system to improve the utilization of health information for medical research

A2-RL: Aesthetics Aware Reinforcement Learning for Image Cropping

Image cropping aims at improving the aesthetic quality of images by adjusting their composition. Most weakly supervised cropping methods (without bounding box supervision) rely on the sliding window mechanism. The sliding window mechanism requires fixed aspect ratios and limits the cropping region with arbitrary size. Moreover, the sliding window method usually produces tens of thousands of windows on the input image which is very time-consuming. Motivated by these challenges, we firstly formulate the aesthetic image cropping as a sequential decision-making process and propose a weakly supervised Aesthetics Aware Reinforcement Learning (A2-RL) framework to address this problem. Particularly, the proposed method develops an aesthetics aware reward function which especially benefits image cropping. Similar to human's decision making, we use a comprehensive state representation including both the current observation and the historical experience. We train the agent using the actor-critic architecture in an end-to-end manner. The agent is evaluated on several popular unseen cropping datasets. Experiment results show that our method achieves the state-of-the-art performance with much fewer candidate windows and much less time compared with previous weakly supervised methods.Comment: Accepted by CVPR 201