Abstract

In this paper, we describe the simulation of Ising minimization on a classical machine by executing variational quantum algorithms on our density-matrix simulator. We outline the Ising formulation of the Graph Partitioning problem and the Hamiltonian Cycle problem, and solve the Max-Cut variant of graph partitioning for a weighted square graph Sq2 using the Quantum Approximate Optimization Algorithm. We finally study the effect of errors present in Noisy Intermediate-Scale Quantum processors on the obtained solutions. This paper illustrates the approach to approximately solving hard combinatorial optimization problems using a hybrid quantum-classical scheme and describes the issues in hardware implementation of such schemes. The simulations of NISQ noise models will be useful in understanding the performance and capabilities of such approaches. Index Terms—Non-deterministic Polynomial, Ising Model, Combinatorial Optimization, Variational Quantum Algorithms

Abstract

—Real time air pollution monitoring is vital in densely populated areas to assess the micro level air pollution. Monitoring the air quality can be useful for providing accurate information to the public and to help policy decision makers to take actions that can improve the quality of air. Monitoring of the pollutants using Internet of Things (IoT) is a cost effective solution. This has the capability to provide high spatial and temporal air pollutants levels depending on the density of the IoT network. In this paper, we present an end to end prototype solution for monitoring of air pollutants. low cost LoraWAN CO2, CO and PM2.5 sensors are used in this prototype application. We also implemented a real time monitoring system using web dashboards with the capability to perform real time analysis using open source software elasticsearch and kibana. A prototype web dashboard showing the real time values of the sensor values along with temperature and humidity values is also developed. Index Terms—Air pollutants, Real time, Visualization, Analysis

Abstract

: In this research paper, the problem of indexing physical locations across a planet (like time indexing based on dates/ months/years) is addressed and a solution is provided. Different countries around the world devised various geographical location specification codes (along with state/ province names, district names, city/ town/ village names etc.). But, to this day, there is no addressing scheme to specify various types of geographical locations such as hospitals, hotels etc. around the world. Apart from coarse grain localization of spatial locations using longitude, latitude (on the globe), and fine grain specification of spatial positions is an open research problem. In this research paper, we provide an interesting solution to this problem and its applications. This research paper is an effort in that direction to provide global position addressing. Various interesting applications of such local/global position indexing are provided. It is hoped that such indexing when linked with other addresses such as IPv6 addresses will be very useful.

Abstract

There have been numerous attempts in explaining the general learning behaviours using model-based and modelfree methods. While the model-based control is flexible yet computationally expensive in planning, the model-free control is quick but inflexible. The model-based control is therefore immune from reward devaluation and contingency degradation. Multiple arbitration schemes have been suggested to achieve the data efficiency and computational efficiency of modelbased and model-free control respectively. In this context, we propose a quantitative ’value of information’ based arbitration between both the controllers in order to establish a general computational framework for skill learning. The interacting model-based and model-free reinforcement learning processes are arbitrated using an uncertainty-based value of information. We further show that our algorithm performs better than Qlearning as well as Q-learning with experience replay

Abstract

In urban driving scenarios, forecasting future trajectories of surrounding vehicles is of paramount importance. While several approaches for the problem have been proposed, the best-performing ones tend to require extremely detailed input representations (eg. image sequences). But, such methods do not generalize to datasets they have not been trained on. We propose intermediate representations that are particularly well-suited for future prediction. As opposed to using texture (color) information, we rely on semantics and train an autoregressive model to accurately predict future trajectories of traffic participants (vehicles) (see fig. above). We demonstrate that using semantics provides a significant boost over techniques that operate over raw pixel intensities/disparities. Uncharacteristic of state-of-the-art approaches, our representations and models generalize to completely different datasets, collected across several cities, and also across countries where people drive on opposite sides of the road (left-handed vs right-handed driving). Additionally, we demonstrate an application of our approach in multi-object tracking (data association). To foster further research in transferrable representations and ensure reproducibility, we release all our code and data. 3

Abstract

The expectation of unmanned air vehicles (UAVs) pushes the operation environment to narrow spaces, where the systems may fly very close to an object and perform an interaction. This phase brings the variation in UAV dynamics: thrust and drag coefficient of the propellers might change under different proximity. At the same time, UAVs may need to operate under external disturbances to follow time-based trajectories. Under these challenging conditions, a standard controller approach may not handle all missions with a fixed structure, where there may be a need to adjust its parameters for each different case. With these motivations, practical implementation and evaluation of an autonomous controller applied to a quadrotor UAV are proposed in this work. A self-adaptive controller based on a composite control scheme where a combination of sliding mode control (SMC) and evolving neuro-fuzzy control is used. The parameter vector of the neuro-fuzzy controller is updated adaptively based on the sliding surface of the SMC. The autonomous controller possesses a new elastic structure, where the number of fuzzy rules keeps growing or get pruned based on bias and variance balance. The interaction of the UAV is experimentally evaluated in real time considering the ground effect, ceiling effect and flight through a strong fan-generated wind while following time-based trajectories.

Abstract

We report photo-isomerization of malonaldehyde in its electronic ground state (S0), mediated by coupled 1nπ∗ (S1) - 1ππ∗ 10 (S2) electronic excited states, accomplished with the aid of optimally 11 designed ultraviolet (UV)-laser pulses. In particular, the control of H-transfer from a configuration 12 predominantly located in the left well (say, reactant) to that in the right well (say, product) of the 13 electronic ground S0 potential energy surface is achieved by a pump-dump mechanism including 14 the nonadiabatic interactions between the excited S1 and S2 states. An interplay between the 15 nonadiabatic coupling due to conical intersection of the S1 and S2 states and the laser-molecule 16 interaction is found to be imprinted in the time-dependent electronic population. The latter is also 17 examined by employing optimal fields of varying intensity and frequency of the UV laser pulses. 18 For the purpose of the present study, we constructed a three-states and two-modes coupled diabatic 19 Hamiltonian with the help of adiabatic electronic energies and transition dipole moments calculated 20 by ab initio quantum chemistry methods. The electronic diabatic model is developed using the 21 calculated adiabatic energies of the two excited electronic states (S1 and S2) in order to carry out the 22 dynamics study. The optimal fields for achieving the controlled isomerization are designed within 23 the framework of optimal control theory employing the optimization technique of a multitarget 24 functional using the genetic algorithm. The laser-driven dynamics of the system is treated by 25 numerically solving the time-dependent Schr¨odinger equation within the dipole approximation. 26 Time-averaged yield of the target prod

Abstract

In recent years, Generative Adversarial Networks (GANs) have received significant attention from the research community. With a straightforward implementation and outstanding results, GANs have been used for numerous applications. Despite the success, GANs lack a proper theoretical explanation. These models suffer from issues like mode collapse, non-convergence, and instability during training. To address these issues, researchers have proposed theoretically rigorous frameworks inspired by varied fields of Game theory, Statistical theory, Dynamical systems, etc. In this paper, we propose to give an appropriate structure to study these contributions systematically. We essentially categorize the papers based on the issues they raise and the kind of novelty they introduce to address them. Besides, we provide insight into how each of the discussed articles solves the concerned problems. We compare and contrast different results and put forth a summary of theoretical contributions about GANs with focus on image/visual applications. We expect this summary paper to give a bird’s eye view to a person wishing to understand the theoretical progress in GANs so far.

Abstract

Micropumps are one of the most important parts of a microfluidic system. In particular, for biomedical applications such as Lab-on-Chip systems, micropumps are used to transport and manipulate test fluids in a controlled manner. In this paper, a low-cost, structurally simple, piezoelectrically actuated micropump was simulated and fabricated using poly-dimethylsiloxane (PDMS). The channels in PDMS were fabricated using patterned SU-8 structures. The pump flow rate was measured to be 9.49 μL/min, 14.06 μL/min and 20.87 μL/min for applied voltages of 12 V, 14 V and 16 V respectively. Further, we report finite element analysis (FEA) simulation to confirm the operation of the micropump and compare favorably the experimentally obtained flowrate with the one predicted by simulation. By taking these flow rates as a reference, the chamber pressure was found to be 1.1 to 1.5 kPa from FEA simulations.

Abstract

As the “Internet communications infrastructure” develops to encircle smart devices, it is very much essential for designing suitable methods for secure communications with these smart devices, in the future Internet of Things (IoT) applications context. Due to wireless communication among the IoT smart devices and the gateway node (GWN), several security threats may arise in the IoT environment, including replay,man-in-the-middle, impersonation, malicious devices deployment,and physical devices capture attacks. In this article, to mitigate such security threats, we design a new certificate-based device access control scheme in IoT environment which is not only secure against mentioned attacks, but it also preserves anonymity property. A detailed security analysis using the widely accepted real-or-random (ROR) model-based formal security analysis, informal security analysis, and also formal security verification based on the broadly accepted automated validation of Internet security protocols and applications (AVISPAs) tool has been performed on the proposed scheme to show that it is secure against various known attacks. In addition, a comprehensive comparative analysis among the proposed scheme and other relevant schemes shows that a better trade off among the security and functionality attributes, communication, and computational costs is achieved for the proposed scheme as compared to other schemes.