Dynamic Factored Particle Filtering for Context-Specific Correlations

In order to control any system one needs to know the system's current state. In many real-world scenarios the state of the system cannot be determined with certainty due to the sensors being noisy or simply missing. In cases like these one needs to use probabilistic inference techniques to compute the likely states of the system and because such cases are common, there are lots of techniques to choose from in the field of Artificial Intelligence. Formally, we must compute a probability distribution function over all possible states. Doing this exactly is difficult because the number of states is exponential in the number of variables in the system and because the joint PDF may not have a closed form. Many approximation techniques have been developed over the years, but none ideally suited the problem we faced. Particle filtering is a popular scheme that approximates the joint PDF over the variables in the system by a set of weighted samples. It works even when the joint PDF has no closed form and the size of the sample can be adjusted to trade off accuracy for computation time. However, with many variables the size of the sample required for a good approximation can still become prohibitively large. Factored particle filtering uses the structure of variable dependencies to split the problem into many smaller subproblems and scales better if such decomposition is possible. However, our problem was unusual because some normally independent variables would become strongly correlated for short periods of time. This dynamically-changing dependency structure was not handled effectively by existing techniques. Considering variables to be always correlated meant the problem did not scale, considering them to be always independent introduced errors too large to tolerate. It was necessary to develop an approach that would utilize variables' independence whenever possible, but not introduce large errors when variables become correlated. We have developed a new technique for monitoring the state of the system for a class of systems with context-specific correlations. It is based on the idea of caching the context in which correlations arise and otherwise keeping the variables independent. Our evaluation shows that our technique outperforms existing techniques and is the first viable solution for the class of problems we consider

Predictions of Heat Transfer and Flow Circulations in Differentially Heated Liquid Columns With Applications to Low-Pressure Evaporators

Numerical computations are presented for the temperature and velocity distributions of two differentially heated liquid columns with liquor depths of 0.1 m and 2.215 m, respectively. The temperatures in the liquid columns vary considerably with respect to position for pure conduction, free convection, and nucleate boiling cases using one-dimensional (1D) thermal resistance networks. In the thermal resistance networks the solutions are not sensitive to the type of condensing and boiling heat transfer coefficients used. However, these networks are limited and give no indication of velocity distributions occurring within the liquor. To alleviate this issue, two-dimensional (2D) axisymmetric and three-dimensional (3D) computational fluid dynamics (CFD) simulations of the test rigs have been performed. The axisymmetric conditions of the 2D simulations produce unphysical solutions; however, the full 3D simulations do not exhibit these behaviors. There is reasonable agreement for the predicted temperatures, heat fluxes, and heat transfer coefficients when comparing the boiling case of the 1D thermal resistance networks and the CFD simulations

Detection of cannabinoid receptor type 2 in native cells and zebrafish with a highly potent, cell-permeable fluorescent probe.

Despite its essential role in the (patho)physiology of several diseases, CB2R tissue expression profiles and signaling mechanisms are not yet fully understood. We report the development of a highly potent, fluorescent CB2R agonist probe employing structure-based reverse design. It commences with a highly potent, preclinically validated ligand, which is conjugated to a silicon-rhodamine fluorophore, enabling cell permeability. The probe is the first to preserve interspecies affinity and selectivity for both mouse and human CB2R. Extensive cross-validation (FACS, TR-FRET and confocal microscopy) set the stage for CB2R detection in endogenously expressing living cells along with zebrafish larvae. Together, these findings will benefit clinical translatability of CB2R based drugs

Palladium-Catalyzed Cascade Assembly of Tricyclic Spiroethers from Diene-Alcohol Precursors

Palladium-catalyzed carboetherification-Heck reactions to form tricyclic spiroethers, which are frequently observed as scaffold segments of various biochemical compounds, from simple diene-alcohols have been carried out in a cascade fashion. This is the first attempt to link simple alcohols with diverse, medium-sized spiroether architectures. The reported synthetic strategy is short and robust and offers rapid delivery of a broad spectrum of tricyclic spiranoid ethers

Palladium-Catalyzed Cascade Assembly of Tricyclic Spiroethers from Diene-Alcohol Precursors

Palladium-catalyzed carboetherification-Heck reactions to form tricyclic spiroethers, which are frequently observed as scaffold segments of various biochemical compounds, from simple diene-alcohols have been carried out in a cascade fashion. This is the first attempt to link simple alcohols with diverse, medium-sized spiroether architectures. The reported synthetic strategy is short and robust and offers rapid delivery of a broad spectrum of tricyclic spiranoid ethers