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.

Abstract

A large portion of the computation in sparse neural networks comprises of multiplying a sparse matrix with a dense matrix, denoted SDMM in this paper. The SDMM operation with an unstructured sparsity pattern cannot be efficiently processed on modern architectures such as GPUs due to irregularity in compute and memory accesses. However, efficient parallel algorithms on a GPU can be designed for SDMM when the sparsity pattern is more structured. Thus, the run time performance of sparse neural networks on a GPU is dependent on the sparsity pattern present in the underlying matrix.In sparse neural networks obtained using pruning based approaches, the choice of sparsity pattern not only effects the run time, but also effects the accuracy of the task for which the neural network is trained for. Sparsity patterns which have a good run time performance on a GPU may not have a good accuracy and vice-versa. The real challenge then is to given a target architecture, identify a sparsity pattern, a storage format,and an algorithm for SDMM that leads to sparse neural networks which are efficient in both run time and accuracy.In this work, we propose a novel, structured, flexible, and generic sparsity pattern called the RMB (Regularized MultiBlock) sparsity pattern, and an efficient storage format (CRMB),and a fast GPU algorithm for processing RMBMM (SDMMwith the multiplicand having RMB sparsity pattern). Using theRMB sparsity pattern, we achieve better trade-offs between the accuracy, and the run time performance of sparse neural networks on a GPU when compared to commonly used sparsity patterns like unstructured, and block sparsity patterns.

Abstract

In this paper, we study scalable parallel algorithms for estimating the farness-centrality value of the nodes in a given undirected and connected graph. Our algorithms consider approaches that are more suitable for sparse graphs. To this end, we propose four optimization techniques based on removing redundant nodes, removing identical nodes, removing chain nodes, and making use of decomposition based on the biconnected components of the input graph.We test our techniques on a collection of real-world graphs for the time taken and the average error percentage. We further analyze the applicability of our techniques on various classes of real-world graphs. We suggest why certain techniques work better on certain classes of graphs

Abstract

Quality Models play a critical role in assuring quality and have evolved over 40+ years. They provide support for defining quality attributes, building and measuring the quality of the resulting product. Each quality model adopts a critical view on quality in terms of a set of model elements and relationships between them. This study aims to provide an overview of the state-of-the-art research on quality models with a focus on encompassing model elements and their support to architecting quality. The study was conducted using systematic mapping as the research methodology. A total of 238 primary papers were classified based on the type of research, standards usage, and publication trends. We identified that 17% (40) of papers belong to quality models. These 40 models were analyzed for the underlying meta-model elements and their support for a quality architecture using   rence architecture framework. The architecture phase mapping analysis shows that quality planning phase is 100% supported, quality assessment is 75% supported, quality documentation is included in 40% models and quality realization aspect is barely considered in 13% models. Quality realization happens through software processes and patterns, and it is necessary to evolve quality models and software process architectures that correlate quality definitions and quality realization mechanisms. Future research is expected in this direction