GreenVis: Energy-Saving Color Schemes for Sequential Data Visualization on OLED Displays

The organic light emitting diode (OLED) display has recently become popular in the consumer electronics market. Compared with current LCD display technology, OLED is an emerging display technology that emits light by the pixels themselves and doesn’t need an external back light as the illumination source. In this paper, we offer an approach to reduce power consumption on OLED displays for sequential data visualization. First, we create a multi-objective optimization approach to find the most energy-saving color scheme for given visual perception difference levels. Second, we apply the model in two situations: pre-designed color schemes and auto generated color schemes. Third, our experiment results show that the energy-saving sequential color scheme can reduce power consumption by 17.2% for pre-designed color schemes. For auto-generated color schemes, it can save 21.9% of energy in comparison to the reference color scheme for sequential data

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

Predicting the Configuration of Planetary System: KOI-152 Observed by Kepler

The recent Kepler discovery of KOI-152 reveals a system of three hot super-Earth candidates that are in or near a 4:2:1 mean motion resonance. It is unlikely that they formed in situ, the planets probably underwent orbital migration during the formation and evolution process. The small semimajor axes of the three planets suggest that migration stopped at the inner edge of the primordial gas disk. In this paper we focus on the influence of migration halting mechanisms, including migration "dead zones", and inner truncation by the stellar magnetic field. We show that the stellar accretion rate, stellar magnetic field and the speed of migration in the proto-planetary disk are the main factors affecting the final configuration of KOI-152. Our simulations suggest that three planets may be around a star with low star accretion rate or with high magnetic field. On the other hand, slow type I migration, which decreases to one tenth of the linear analysis results, favors forming the configuration of KOI-152. Under such formation scenario, the planets in the system are not massive enough to open gaps in the gas disk. The upper limit of the planetary masses are estimated to be about $15,~19$, and $24 M_\oplus$, respectively. Our results are also indicative of the near Laplacian configurations that are quite common in planetary systems.Comment: 11 pages, 8 figures, accepted for publication in Ap

The Application of Jet Grouting in Shanghai Foundation Pit Projects

Jet grouting in Shanghai can date back to late 1970s but was seldom applied in constructive projects. It was not until the construction of Metro in Shanghai in 1980s that jet grouting became widely accepted. Now it has been successfully applied in the constructions of two subway lines and basic constructions of some buildings nearby. A lot of experience has been accumulated through practice in terms of suitable conditions and situations. First, it can reduce deformation caused by excavation. When the system of braced framing in a certain geological condition cannot meet the demand of deforming control and bracing path cannot be increased, the method of enhancing resistance of passive zone of enclosing structure should be taken into consideration. Soil body strengthening of passive zone is usually a reasonable method. When problems of piping or artesian water occur, jet grouting of stabilizing foundation pit bottom can be used if method of groundwater lowering is not suitable due to environmental circumstances. Jet grouting is also employed in cut-off wall of foundation pit and entrance in shield working well

Radical-Enhanced Chinese Character Embedding

We present a method to leverage radical for learning Chinese character embedding. Radical is a semantic and phonetic component of Chinese character. It plays an important role as characters with the same radical usually have similar semantic meaning and grammatical usage. However, existing Chinese processing algorithms typically regard word or character as the basic unit but ignore the crucial radical information. In this paper, we fill this gap by leveraging radical for learning continuous representation of Chinese character. We develop a dedicated neural architecture to effectively learn character embedding and apply it on Chinese character similarity judgement and Chinese word segmentation. Experiment results show that our radical-enhanced method outperforms existing embedding learning algorithms on both tasks.Comment: 8 pages, 4 figure