Collisionless Pattern Discovery in Robot Swarms Using Deep Reinforcement Learning

We present a deep reinforcement learning-based framework for automatically discovering patterns available in any given initial configuration of fat robot swarms. In particular, we model the problem of collision-less gathering and mutual visibility in fat robot swarms and discover patterns for solving them using our framework. We show that by shaping reward signals based on certain constraints like mutual visibility and safe proximity, the robots can discover collision-less trajectories leading to well-formed gathering and visibility patterns

Hanbury-Brown and Twiss Intensity Correlations of Parabosons

This paper shows that in intensity correlation measurements there will be clear and unambiguous signals that new-physics particles are, or aren't, parabosons. For a parabosonic field in a dominant single-mode, there is a diagonal P-representation in the "even and odd coherent states" basis. It is used to analyze zero-time-interval intensity correlations of parabosons in a maximum-entropic state. As the mean number of parabosons decreases, there is a monotonic reduction to (2/p) of the constant bosonic ``factor of two'' proportionality of the second-order versus the squared first-order intensity correlation function.Comment: 16 pages; version 4 to add simple p-independent recursion relatio

Phase classification in the long-range Harper model using machine learning

In this work, we map the phase diagrams of one-dimensional quasiperiodic models using artificial neural networks. We observe that the multi-class classifier precisely distinguishes the various phases, namely the delocalized, multifractal, and localized phases when trained on the eigenstates of the long-range Aubry-Andr\'e Harper (LRH) model. Additionally, when this trained multi-layer perceptron is fed with the eigenstates of the Aubry-Andr\'e Harper (AAH) model, it identifies various phases with reasonable accuracy. We examine the resulting phase diagrams produced using a single disorder realization and demonstrate that they are consistent with those obtained from the conventional method of fractal dimension analysis which employs a large number of disorder samples. Interestingly, when the neural network is trained using the eigenstates of the AAH model, the resulting phase diagrams for the LRH model are less exemplary than those previously obtained. Further, we study binary classification by training the neural network on the probability density corresponding to the delocalized and localized eigenstates of the AAH model. We are able to pinpoint the critical transition point by examining the metric ``accuracy" for the central eigenstate. The effectiveness of the binary classifier in identifying a previously unknown multifractal phase is then evaluated by applying it to the LRH model.Comment: 10 pages, 8 figure

Clinical study to evaluate the efficacy of Shiro Abhyanga in Nidranasha w.s.r. to Insomnia

Nidranasha is one of common disorder which affects the quality of life. Acharya Charaka has mentioned Nidra as one among the Trayo upastambha. which is an essential factor to lead a healthy life. Further he mentions Sukha, Dukha, Pushti, Karshya, Bala, Dourbalya, Purushatva, Klaibyata, Jnaana, Ajnaana, Jeevitha and Mrityu all are depended on proper and improper Sleep. Nidranasha is one among the Vataja Nanatmaja Vyadhi. In Nidranasha Shiroabhyanga is the one of the effective treatment. Abhyanga means the application of Sneha, suitable to one’s constitution, age, season, particular disease and atmosphere. Shiro Abhyanga is a Bahirparimarjana Chikitsa and are also a part of Dinacharya, is told to be beneficial in inducing Nidra. For clinical study total 15 patients were registered from O.P.D. and I.P.D. of K.V.G. Ayurveda Medical College and Hospital, Ambatedka. Result of the study revealed that Shiro Abhyanga effective in reducing the sign & symptoms of Insomnia as well as physical assessment

Deep Meta Q-Learning based Multi-Task Offloading in Edge-Cloud Systems

Resource-Constrained Edge Devices Can Not Efficiently Handle the Explosive Growth of Mobile Data and the Increasing Computational Demand of Modern-Day User Applications. Task Offloading Allows the Migration of Complex Tasks from User Devices to the Remote Edge-Cloud Servers Thereby Reducing their Computational Burden and Energy Consumption While Also Improving the Efficiency of Task Processing. However, Obtaining the Optimal Offloading Strategy in a Multi-Task Offloading Decision-Making Process is an NP-Hard Problem. Existing Deep Learning Techniques with Slow Learning Rates and Weak Adaptability Are Not Suitable for Dynamic Multi-User Scenarios. in This Article, We Propose a Novel Deep Meta-Reinforcement Learning-Based Approach to the Multi-Task Offloading Problem using a Combination of First-Order Meta-Learning and Deep Q-Learning Methods. We Establish the Meta-Generalization Bounds for the Proposed Algorithm and Demonstrate that It Can Reduce the Time and Energy Consumption of IoT Applications by Up to 15%. through Rigorous Simulations, We Show that Our Method Achieves Near-Optimal Offloading Solutions While Also Being Able to Adapt to Dynamic Edge-Cloud Environments

A Chemical Biology Approach to Developing STAT Inhibitors: Molecular Strategies for Accelerating Clinical Translation

STAT transcription factors transduce signals from the cell surface to the nucleus, where they regulate the expression of genes that control proliferation, survival, self-renewal, and other critical cellular functions. Under normal physiological conditions, the activation of STATs is tightly regulated. In cancer, by contrast, STAT proteins, particularly STAT3 and STAT5, become activated constitutively, thereby driving the malignant phenotype of cancer cells. Since these proteins are largely dispensable in the function of normal adult cells, STATs represent a potentially important target for cancer therapy. Although transcription factors have traditionally been viewed as suboptimal targets for pharmacological inhibition, chemical biology approaches have been particularly fruitful in identifying compounds that can modulate this pathway through a variety of mechanisms. STAT inhibitors have notable anti-cancer effects in many tumor systems, show synergy with other therapeutic modalities, and have the potential to eradicate tumor stem cells. Furthermore, STAT inhibitors identified through the screening of chemical libraries can then be employed in large scale analyses such as gene expression profiling, RNA interference screens, or large-scale tumor cell line profiling. Data derived from these studies can then provide key insights into mechanisms of STAT signal transduction, as well as inform the rational design of targeted therapeutic strategies for cancer patients

The State-Vector Space for Two-Mode Parabosons and Charged Parabose Coherent States

The structure of the state-vector space for the two-mode parabose system is investigated and a complete set of state-vectors is constructed. The basis vectors are orthonormal in order $p=2$. In order $p=2$, conserved-charge parabose coherent states are constructed and an explicit completeness relation is obtained.Comment: 13 pages, LaTeX file, no figures and no macro

North Dakota Economic-Demographic Assessment Model (NEDAM): Technical Description

This report describes the logic, structure, data bases, and operational procedures of the North Dakota model.Research Methods/ Statistical Methods,

Eigenstates of Paraparticle Creation Operators

Eigenstates of the parabose and parafermi creation operators are constructed. In the Dirac contour representation, the parabose eigenstates correspond to the dual vectors of the parabose coherent states. In order $p=2$, conserved-charge parabose creation operator eigenstates are also constructed. The contour forms of the associated resolutions of unity are obtained.Comment: 14 pages, LaTex file, no macros, no figure