Novel aryne chemistry in organic synthesis

Arynes are among the most intensively studied systems in chemistry. However, many aspects of the chemistry of these reactive intermediates are not well understood yet and their use as reagents in synthetic organic chemistry has been somewhat limited, due to the harsh conditions needed to generate arynes and the often uncontrolled reactivity exhibited by these species. Recently, o-silylaryl triflates, which can generate the corresponding arynes under very mild reaction conditions, have been found very useful in organic synthesis. This thesis describes several novel and useful methodologies by employing arynes, which generate from o-silylaryl triflates, in organic synthesis;An efficient, reliable method for the N-arylation of amines, sulfonamides and carbamates, and the O-arylation of phenols and carboxylic acids is described in Chapter 1. Amines, sulfonamides, phenols, and carboxylic acids are good nuclephiles, which can react with arynes generated from o-silylaryl triflates to afford the corresponding N- and O-arylated products in very high yields. The regioselectivity of unsymmetrical arynes has also been studied. A lot of useful, functional groups can tolerate our reaction conditions;Carbazoles and dibenzofurans are important heteroaromatic compounds, which have a variety of biological activities. A variety of substituted carbazoles and dibenzofurans are readily prepared in good to excellent yields starting with the corresponding o-iodoanilines or o-iodophenols and o-silylaryl triflates by a treatment with CsF, followed by a Pd-catalyzed cyclization, which overall provides a one-pot, two-step process. By using this methodology, the carbazole alkaloid mukonine has been concisely synthesized in a very good yield;Insertion of an aryne into a sigma-bond between a nucleophile and an electrophile (Nu-E) should potentially be a very beneficial process from the standpoint of organic synthesis. A variety of substituted ketones and sulfoxides have been synthesized in good yields via the intermolecular C-N sigma-bond addition of amides and S-N sigma-bond addition of sulfinamides to arynes under mild reaction conditions;The indazole moiety is a frequently found subunit in drug substances with important biological activities. Indazole analogues have been readily synthesized under mild reaction conditions by the [3+2] cycloaddition of a variety of diazo compounds with o-silylaryl triflates in the presence of CsF or TBAF;Polycyclic aromatic and heteroaromatic hydrocarbons have been synthesized in high yields by two different processes involving the Pd-catalyzed annulation of arynes. Both processes appear to involve the catalytic, stepwise coupling of two very reactive substrates, an aryne and an organopalladium species, to generate excellent yields of cross-coupled products

Physical Primitive Decomposition

Objects are made of parts, each with distinct geometry, physics, functionality, and affordances. Developing such a distributed, physical, interpretable representation of objects will facilitate intelligent agents to better explore and interact with the world. In this paper, we study physical primitive decomposition---understanding an object through its components, each with physical and geometric attributes. As annotated data for object parts and physics are rare, we propose a novel formulation that learns physical primitives by explaining both an object's appearance and its behaviors in physical events. Our model performs well on block towers and tools in both synthetic and real scenarios; we also demonstrate that visual and physical observations often provide complementary signals. We further present ablation and behavioral studies to better understand our model and contrast it with human performance.Comment: ECCV 2018. Project page: http://ppd.csail.mit.edu

HAQ: Hardware-Aware Automated Quantization with Mixed Precision

Model quantization is a widely used technique to compress and accelerate deep neural network (DNN) inference. Emergent DNN hardware accelerators begin to support mixed precision (1-8 bits) to further improve the computation efficiency, which raises a great challenge to find the optimal bitwidth for each layer: it requires domain experts to explore the vast design space trading off among accuracy, latency, energy, and model size, which is both time-consuming and sub-optimal. Conventional quantization algorithm ignores the different hardware architectures and quantizes all the layers in a uniform way. In this paper, we introduce the Hardware-Aware Automated Quantization (HAQ) framework which leverages the reinforcement learning to automatically determine the quantization policy, and we take the hardware accelerator's feedback in the design loop. Rather than relying on proxy signals such as FLOPs and model size, we employ a hardware simulator to generate direct feedback signals (latency and energy) to the RL agent. Compared with conventional methods, our framework is fully automated and can specialize the quantization policy for different neural network architectures and hardware architectures. Our framework effectively reduced the latency by 1.4-1.95x and the energy consumption by 1.9x with negligible loss of accuracy compared with the fixed bitwidth (8 bits) quantization. Our framework reveals that the optimal policies on different hardware architectures (i.e., edge and cloud architectures) under different resource constraints (i.e., latency, energy and model size) are drastically different. We interpreted the implication of different quantization policies, which offer insights for both neural network architecture design and hardware architecture design.Comment: CVPR 2019. The first three authors contributed equally to this work. Project page: https://hanlab.mit.edu/projects/haq

A Novel Method for the Absolute Pose Problem with Pairwise Constraints

Absolute pose estimation is a fundamental problem in computer vision, and it is a typical parameter estimation problem, meaning that efforts to solve it will always suffer from outlier-contaminated data. Conventionally, for a fixed dimensionality d and the number of measurements N, a robust estimation problem cannot be solved faster than O(N^d). Furthermore, it is almost impossible to remove d from the exponent of the runtime of a globally optimal algorithm. However, absolute pose estimation is a geometric parameter estimation problem, and thus has special constraints. In this paper, we consider pairwise constraints and propose a globally optimal algorithm for solving the absolute pose estimation problem. The proposed algorithm has a linear complexity in the number of correspondences at a given outlier ratio. Concretely, we first decouple the rotation and the translation subproblems by utilizing the pairwise constraints, and then we solve the rotation subproblem using the branch-and-bound algorithm. Lastly, we estimate the translation based on the known rotation by using another branch-and-bound algorithm. The advantages of our method are demonstrated via thorough testing on both synthetic and real-world dataComment: 10 pages, 7figure

Predictive Power of Machine Learning for Optimizing Solar Water Heater Performance: The Potential Application of High-Throughput Screening

Predicting the performance of solar water heater (SWH) is challenging due to the complexity of the system. Fortunately, knowledge-based machine learning can provide a fast and precise prediction method for SWH performance. With the predictive power of machine learning models, we can further solve a more challenging question: how to cost-effectively design a high-performance SWH? Here, we summarize our recent studies and propose a general framework of SWH design using a machine learning-based high-throughput screening (HTS) method. Design of water-in-glass evacuated tube solar water heater (WGET-SWH) is selected as a case study to show the potential application of machine learning-based HTS to the design and optimization of solar energy systems