Navigating Occluded Intersections with Autonomous Vehicles using Deep Reinforcement Learning

Providing an efficient strategy to navigate safely through unsignaled intersections is a difficult task that requires determining the intent of other drivers. We explore the effectiveness of Deep Reinforcement Learning to handle intersection problems. Using recent advances in Deep RL, we are able to learn policies that surpass the performance of a commonly-used heuristic approach in several metrics including task completion time and goal success rate and have limited ability to generalize. We then explore a system's ability to learn active sensing behaviors to enable navigating safely in the case of occlusions. Our analysis, provides insight into the intersection handling problem, the solutions learned by the network point out several shortcomings of current rule-based methods, and the failures of our current deep reinforcement learning system point to future research directions.Comment: IEEE International Conference on Robotics and Automation (ICRA 2018

The contemporary framework on social media analytics as an emerging tool for behavior informatics, HR analytics and business process

Organizations can use social media analytics as an effective assessment tool from behavioral perspectives, HR as well as business process to collaboratively build competency framework. The present piece of work is an attempt to put forth social media as a contemporary model in the light of the existing literature. Providing literature reviews, this paper also highlights current practices of social media analytics as to how organizations are accessing vast amount of live data from social media in order to understand work-related behavioral aspects of the employees and their employability for both HR process and the business. The analyzed data on customers is also being taken into account to highlight patterns and customers’ sentiments related to the marketing efforts. Furthermore, on the basis of the above presented discussion this paper suggests the road map to how organizations can foster the social media practices. Finally, this paper raises some significant plausible research questions to be empirically researched in order to methodically validate or discard the presented framework on this rapidly emerging phenomenon

Increasing the penetration depth for ultrafast laser tissue ablation using glycerol based optical clearing

Background: Deep tissue ablation is the next challenge in ultrafast laser microsurgery. By focusing ultrafast pulses below the tissue surface one can create an ablation void confined to the focal volume. However, as the ablation depth increases in a scattering tissue, increase in the required power can trigger undesired nonlinear phenomena out of focus that restricts our ability to ablate beyond a maximum ablation depth of few scattering lengths. Optical clearing (OC) might reduce the intensity and increase the maximal ablation depth by lowering the refractive index mismatch, and therefore reducing scattering. Some efforts to ablate deeper showed out of focus damage, while others used brutal mechanical methods for clearing. Our clinical goal is to create voids in the scarred vocal folds and inject a biomaterial to bring back the tissue elasticity and restore phonation. Materials and methods: Fresh porcine vocal folds were excised and applied a biocompatible OC agent (75% glycerol). Collimated transmittance was monitored. The tissue was optically cleared and put under the microscope for ablation threshold measurements at different depths. Results: The time after which the tissue was optically cleared was roughly two hours. Fitting the threshold measurements to an exponential decay graph indicated that the scattering length of the tissue increased to 83±16 μm, which is more than doubling the known scattering length for normal tissue. Conclusion: Optical clearing with Glycerol increases the tissue scattering length and therefore reduces the energy for ablation and increases the maximal ablation depth. This technique can potentially improve clinical microsurgery

Policy Shaping: Integrating Human Feedback with Reinforcement Learning

Copyright© (2013) by Neural Information Processing SystemsPresented at the 27th Annual Conference on Neural Information Processing Systems (NIPS 2013), 5-10 December 2013, Lake Tahoe, Nevada.A long term goal of Interactive Reinforcement Learning is to incorporate non- expert human feedback to solve complex tasks. Some state-of -the-art methods have approached this problem by mapping human information to rewards and values and iterating over them to compute better control policies. In this paper we argue for an alternate, more effective characterization of human feedback: Policy Shaping. We introduce Advise, a Bayesian approach that attempts to maximize the information gained from human feedback by utilizing it as direct policy labels. We compare Advise to state-of-the-art approaches and show that it can outperform them and is robust to infrequent and inconsistent human feedback

Dietary nutrient composition affects digestible energy utilisation for growth: a study on Nile tilapia (Oreochromis niloticus) and a literature comparison across fish species

The effect of the type of non-protein energy (NPE) on energy utilisation in Nile tilapia was studied, focusing on digestible energy utilisation for growth (kgDE). Furthermore, literature data on kgDE across fish species were analysed in order to evaluate the effect of dietary macronutrient composition. A total of twelve groups of fish were assigned in a 2 × 2 factorial design: two diets (‘fat’ v. ‘starch’) and two feeding levels (‘low’ v. ‘high’). In the ‘fat’-diet, 125 g fish oil and in the ‘starch’-diet 300 g maize starch were added to 875 g of an identical basal mixture. Fish were fed restrictively one of two ration levels (‘low’ or ‘high’) for estimating kgDE. Nutrient digestibility, N and energy balances were measured. For estimating kgDE, data of the present study were combined with previous data of Nile tilapia fed similar diets to satiation. The type of NPE affected kgDE (0·561 and 0·663 with the ‘starch’ and ‘fat’-diets, respectively; P <0·001). Across fish species, literature values of kgDE range from 0·31 to 0·82. Variability in kgDE was related to dietary macronutrient composition, the trophic level of the fish species and the composition of growth (fat:protein gain ratio). The across-species comparison suggested that the relationships of kgDE with trophic level and with growth composition were predominantly induced by dietary macronutrient composition. Reported kgDE values increased linearly with increasing dietary fat content and decreasing dietary carbohydrate content. In contrast, kgDE related curvilinearly to dietary crude protein content. In conclusion, energy utilisation for growth is influenced by dietary macronutrient composition

Oxygen Consumption Constrains Food Intake in Fish Fed Diets Varying in Essential Amino Acid Composition

Compromisation of food intake when confronted with diets deficient in essential amino acids is a common response of fish and other animals, but the underlying physiological factors are poorly understood. We hypothesize that oxygen consumption of fish is a possible physiological factor constraining food intake. To verify, we assessed the food intake and oxygen consumption of rainbow trout fed to satiation with diets which differed in essential amino acid (methionine and lysine) compositions: a balanced vs. an imbalanced amino acid diet. Both diets were tested at two water oxygen levels: hypoxia vs. normoxia. Trout consumed 29% less food under hypoxia compared to normoxia (p0.05). This difference in food intake between diets under normoxia together with the identical oxygen consumption supports the hypothesis that food intake in fish can be constrained by a set-point value of oxygen consumption, as seen here on a six-week time scale

Control of voluntary feed intake in fish: a role for dietary oxygen demand in Nile tilapia (Oreochromis niloticus) fed diets with different macronutrient profiles

It has been hypothesised that, at non-limiting water oxygen conditions, voluntary feed intake (FI) in fish is limited by the maximal physiological capacity of oxygen use (i.e. an ‘oxystatic control of FI in fish’). This implies that fish will adjust FI when fed diets differing in oxygen demand, resulting in identical oxygen consumption. Therefore, FI, digestible energy (DE) intake, energy balance and oxygen consumption were monitored at non-limiting water oxygen conditions in Nile tilapia fed diets with contrasting macronutrient composition. Diets were formulated in a 2 × 2 factorial design in order to create contrasts in oxygen demand: two ratios of digestible protein (DP):DE (‘high’ v. ‘low’); and a contrast in the type of non-protein energy source (‘starch’ v. ‘fat’). Triplicate groups of tilapia were fed each diet twice daily to satiation for 48 d. FI (g DM/kg0·8 per d) was significantly lower (9·5 %) in tilapia fed the starch diets relative to the fat diets. The DP:DE ratio affected DE intakes (P <0·05), being 11 % lower with ‘high’ than with ‘low’ DP:DE ratio diets, which was in line with the 11·9 % higher oxygen demand of these diets. Indeed, DE intakes of fish showed an inverse linear relationship with dietary oxygen demand (DOD; R 2 0·81, P <0·001). As hypothesised (‘oxystatic’ theory), oxygen consumption of fish was identical among three out of the four diets. Altogether, these results demonstrate the involvement of metabolic oxygen use and DOD in the control of FI in tilapia

Habituation based synaptic plasticity and organismic learning in a quantum perovskite

A central characteristic of living beings is the ability to learn from and respond to their environment leading to habit formation and decision making. This behavior, known as habituation, is universal among all forms of life with a central nervous system, and is also observed in single-cell organisms that do not possess a brain. Here, we report the discovery of habituation-based plasticity utilizing a perovskite quantum system by dynamical modulation of electron localization. Microscopic mechanisms and pathways that enable this organismic collective charge-lattice interaction are elucidated by first-principles theory, synchrotron investigations, ab initio molecular dynamics simulations, and in situ environmental breathing studies. We implement a learning algorithm inspired by the conductance relaxation behavior of perovskites that naturally incorporates habituation, and demonstrate learning to forget: A key feature of animal and human brains. Incorporating this elementary skill in learning boosts the capability of neural computing in a sequential, dynamic environment.United States. Army Research Office (Grant W911NF-16-1-0289)United States. Air Force Office of Scientific Research (Grant FA9550-16-1-0159)United States. Army Research Office (Grant W911NF-16-1-0042