Abstract

It has long been understood that there is an interplay between affect and cognition((Kort & Reilly, 2003), but this interaction, based on the recent chess studies((Guntz, Crowley, Vaufreydaz, Balzarini, & Dessus, 2018), is much more intertwined than what the established theories postulate. To understand the underlying mechanisms in greater detail we propose an integrated model using Chrest and Nengo. We analyze the results based on simulations with data from previous empirical studies. Keywords: Affect, Cognition , Chess, Nengo , Chrest

Abstract

A method is proposed for time delay estimation (TDE) from mixed source (speaker) signals collected at two spatially separated microphones. The key idea in this proposal is that the crosscorrelation between corresponding segments of the mixed source signals is computed using the outputs of single frequency filtering (SFF) obtained at several frequencies, rather than using the collected waveforms directly. The advantage of the SFF output is that it will have high signal-to-noise ratio regions in both time and frequency domains. Also it gives multiple evidences, one from each of the SFF outputs. These multiple evidences are combined to obtain robustness in the TDE. The estimated time delays can be used to determine the number of speakers present in the mixed signals. The TDE is shown to be robust against different types and levels of degradations. The results are shown for actual mixed signals collected at two spatially separated microphones in a live laboratory environment, where the mixed signals contain speech from several spatially distributed speakers.

Abstract

Ragas are a fundamental concept to the Indian Classical music which provides immense scope for artists to improvise aesthetically. Classification of ragas have been studied since the inception but even the best models in practice have ambiguities in the classification of certain ragas and this has troubled many musicians and musicologists. We have constructed a new model to classify ragas which adheres to all the existing conventions and resolves these ambiguities.

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