Global Continuous Optimization with Error Bound and Fast Convergence

This paper considers global optimization with a black-box unknown objective function that can be non-convex and non-differentiable. Such a difficult optimization problem arises in many real-world applications, such as parameter tuning in machine learning, engineering design problem, and planning with a complex physics simulator. This paper proposes a new global optimization algorithm, called Locally Oriented Global Optimization (LOGO), to aim for both fast convergence in practice and finite-time error bound in theory. The advantage and usage of the new algorithm are illustrated via theoretical analysis and an experiment conducted with 11 benchmark test functions. Further, we modify the LOGO algorithm to specifically solve a planning problem via policy search with continuous state/action space and long time horizon while maintaining its finite-time error bound. We apply the proposed planning method to accident management of a nuclear power plant. The result of the application study demonstrates the practical utility of our method

Onsager's missing steps retraced

Onsager's paper on phase transition and phase coexistence in anisotropic colloidal systems is a landmark in the theory of lyotropic liquid crystals. However, an uncompromising scrutiny of Onsager's original derivation reveals that it would be rigorously valid only for ludicrous values of the system's number density (of the order of the reciprocal of the number of particles) Based on Penrose's tree identity and an appropriate variant of the mean-field approach for purely repulsive, hard-core interactions, our theory shows that Onsager's theory is indeed valid for a reasonable range of densities

Dimensional Analysis and the Time Required to Urinate

According to the recently discovered 'Law of Urination', mammals, ranging in size from mice to elephants, take, on the average, 21s to urinate. We attempt to gain insights into the physical processes responsible for this uniformity using simple dimensional analysis. We assume that the biological apparatus for urination in mammals simply scales with linear size, and consider the scenarios where the driving force is gravity or elasticity, and where the response is dominated by inertia or viscosity. We ask how the time required for urination depends on the length scale, and find that for the time to be independent of body size, the dominant driving force must be elasticity, and the dominant response viscosity. Our note demonstrates that dimensional analysis can indeed readily give insights into complex physical and biological processes

Preparation and assessment of ketamine hydrogels for prolonged transdermal anaesthesia

Purpose: To formulate and assess thermoresponsive ketamine hydrogels for prolonged transdermal analgesia/anaesthesia.Methods: Thermoresponsive ketamine hydrogels were prepared from chitosan (CTS) and poloxamer 407. Four different formulations (2 formulations of ketamine with 1 and 2 % w/w CTS and 2 formulations with 10 and 15 % w/w ploxamer 407) were assessed for pH, spreadability, drug content, viscosity, in vitro permeation/diffusion, in vivo skin irritancy, and in vivo analgesia (using the hot plate/writhing method in Wistar rats).Results: The formulations had a high drug content (96.12 ± 1.24 to 98.49 ± 0.07 %) with good spreadability. They showed prolonged drug release/permeation of ketamine across the skin, ranging from 81.23 to 98.28 %, and were non-irritating to the denuded skin of Wistar rats with no erythema or oedema after 24 h. The preparation showed effective analgesia that lasted 24 to 30 h. In the writhing test, CTS hydrogels showed stronger analgesia (60.26 – 58.97 %) than those made with poloxamerbased hydrogels (56.41 and 53.85 %). Compared to the activity shown by the standard, lidocaine (which produced 62.82 % analgesia), the effect of the test formulations seem good for probable therapeutic use. Using the hot plate method, the poloxamer-based hydrogels showed more prolonged analgesia than the CTS-based hydrogels.Conclusion: Ketamine hydrogels of CTS and poloxamer may be useful for prolonged analgesia in neuropathic pain and local anaesthesia in minor surgeries.Keywords: Ketamine, Chitosan, Poloxamer, Thermoresponsive hydrogel, Transdermal, Skin permeatio

Contributions of Repulsive and Attractive Interactions to Nematic Order

Both repulsive and attractive molecular interactions can be used to explain the onset of nematic order. The object of this paper is to combine these two nematogenic molecular interactions in a unified theory. This attempt is not unprecedented; what is perhaps new is the focus on the understanding of nematics in the high density limit. There, the orientational probability distribution is shown to exhibit a unique feature: it has compact support on configuration space. As attractive interactions are turned on, the behavior changes, and at a critical attractive interaction strength, thermotropic behavior of the Maier-Saupe type is attained.Comment: 14 pages, 4 figure

Interaction-aware Kalman Neural Networks for Trajectory Prediction

Forecasting the motion of surrounding obstacles (vehicles, bicycles, pedestrians and etc.) benefits the on-road motion planning for intelligent and autonomous vehicles. Complex scenes always yield great challenges in modeling the patterns of surrounding traffic. For example, one main challenge comes from the intractable interaction effects in a complex traffic system. In this paper, we propose a multi-layer architecture Interaction-aware Kalman Neural Networks (IaKNN) which involves an interaction layer for resolving high-dimensional traffic environmental observations as interaction-aware accelerations, a motion layer for transforming the accelerations to interaction aware trajectories, and a filter layer for estimating future trajectories with a Kalman filter network. Attributed to the multiple traffic data sources, our end-to-end trainable approach technically fuses dynamic and interaction-aware trajectories boosting the prediction performance. Experiments on the NGSIM dataset demonstrate that IaKNN outperforms the state-of-the-art methods in terms of effectiveness for traffic trajectory prediction.Comment: 8 pages, 4 figures, Accepted for IEEE Intelligent Vehicles Symposium (IV) 202