Can Online Learning Promote Educational Equity? An Empirical Study on the “Digital Divide” in Online Learning of Primary and Secondary Students During the Epidemic

The whole world including China has experienced a large-scale practice of online teaching during the COVID-19 pandemic. As a result, “digital divide” induced by online learning has aroused wide concerns of the public. Recently, a study published in Journal of Schooling Studies sampled 508 students from 15 primary and secondary schools in Henan Province to analyze the influence of urban vs rural, school, and social class differences on online learning of primary and secondary students, using binary logistic regression model

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

Prediction of the Size Distributions of Methanol-Ethanol Clusters Detected in VUV Laser/Time-of-flight Mass Spectrometry

The size distributions and geometries of vapor clusters equilibrated with methanol−ethanol (Me−Et) liquid mixtures were recently studied by vacuum ultraviolet (VUV) laser time-of-flight (TOF) mass spectrometry and density functional theory (DFT) calculations (Liu, Y.; Consta, S.; Ogeer, F.; Shi, Y. J.; Lipson, R. H. Can. J. Chem. 2007, 85, 843−852). On the basis of the mass spectra recorded, it was concluded that the formation of neutral tetramers is particularly prominent. Here we develop grand canonical Monte Carlo (GCMC) and molecular dynamics (MD) frameworks to compute cluster size distributions in vapor mixtures that allow a direct comparison with experimental mass spectra. Using the all-atom optimized potential for liquid simulations (OPLS-AA) force field, we systematically examined the neutral cluster size distributions as functions of pressure and temperature. These neutral cluster distributions were then used to derive ionized cluster distributions to compare directly with the experiments. The simulations suggest that supersaturation at 12 to 16 times the equilibrium vapor pressure at 298 K or supercooling at temperature 240 to 260 K at the equilibrium vapor pressure can lead to the relatively abundant tetramer population observed in the experiments. Our simulations capture the most distinct features observed in the experimental TOF mass spectra: Et3H+ at m/z = 139 in the vapor corresponding to 10:90% Me−Et liquid mixture and Me3H+ at m/z = 97 in the vapors corresponding to 50:50% and 90:10% Me−Et liquid mixtures. The hybrid GCMC scheme developed in this work extends the capability of studying the size distributions of neat clusters to mixed species and provides a useful tool for studying environmentally important systems such as atmospheric aerosols

Learning to Prove Trigonometric Identities

Automatic theorem proving with deep learning methods has attracted attentions recently. In this paper, we construct an automatic proof system for trigonometric identities. We define the normalized form of trigonometric identities, design a set of rules for the proof and put forward a method which can generate theoretically infinite trigonometric identities. Our goal is not only to complete the proof, but to complete the proof in as few steps as possible. For this reason, we design a model to learn proof data generated by random BFS (rBFS), and it is proved theoretically and experimentally that the model can outperform rBFS after a simple imitation learning. After further improvement through reinforcement learning, we get AutoTrig, which can give proof steps for identities in almost as short steps as BFS (theoretically shortest method), with a time cost of only one-thousandth. In addition, AutoTrig also beats Sympy, Matlab and human in the synthetic dataset, and performs well in many generalization tasks