Analyzing Quantization in TVM

There has been many papers in academic literature on quantizing weight tensors in deep learning models to reduce inference latency and memory footprint. TVM also has the ability to quantize weights and support low-bit computations. Although quantization is typically expected to improve inference time, in TVM, the performance of 8-bit quantization does not meet the expectations. Typically, when applying 8-bit quantization to a deep learning model, it is usually expected to achieve around 50% of the full-precision inference time. However, in this particular case, not only does the quantized version fail to achieve the desired performance boost, but it actually performs worse, resulting in an inference time that is about 2 times as slow as the non-quantized version. In this project, we thoroughly investigate the reasons behind the underperformance and assess the compatibility and optimization opportunities of 8-bit quantization in TVM. We discuss the optimization of two different types of tasks: computation-bound and memory-bound, and provide a detailed comparison of various optimization techniques in TVM. Through the identification of performance issues, we have successfully improved quantization by addressing a bug in graph building. Furthermore, we analyze multiple optimization strategies to achieve the optimal quantization result. The best experiment achieves 163.88% improvement compared with the TVM compiled baseline in inference time for the compute-bound task and 194.98% for the memory-bound task

Mastering Complex Control in MOBA Games with Deep Reinforcement Learning

We study the reinforcement learning problem of complex action control in the Multi-player Online Battle Arena (MOBA) 1v1 games. This problem involves far more complicated state and action spaces than those of traditional 1v1 games, such as Go and Atari series, which makes it very difficult to search any policies with human-level performance. In this paper, we present a deep reinforcement learning framework to tackle this problem from the perspectives of both system and algorithm. Our system is of low coupling and high scalability, which enables efficient explorations at large scale. Our algorithm includes several novel strategies, including control dependency decoupling, action mask, target attention, and dual-clip PPO, with which our proposed actor-critic network can be effectively trained in our system. Tested on the MOBA game Honor of Kings, our AI agent, called Tencent Solo, can defeat top professional human players in full 1v1 games.Comment: AAAI 202

Relationships Between Leaf Carbon and Macronutrients Across Woody Species and Forest Ecosystems Highlight How Carbon Is Allocated to Leaf Structural Function

Stoichiometry of leaf macronutrients can provide insight into the tradeoffs between leaf structural and metabolic investments. Structural carbon (C) in cell walls is contained in lignin and polysaccharides (cellulose, hemicellulose, and pectins). Much of leaf calcium (Ca) and a fraction of magnesium (Mg) were further bounded with cell wall pectins. The macronutrients phosphorus (P), potassium (K), and nitrogen (N) are primarily involved in cell metabolic functions. There is limited information on the functional interrelations among leaf C and macronutrients, and the functional dimensions characterizing the leaf structural and metabolic tradeoffs are not widely appreciated. We investigated the relationships between leaf C and macronutrient (N, P, K, Ca, Mg) concentrations in two widespread broad-leaved deciduous woody species Quercus wutaishanica (90 individuals) and Betula platyphylla (47 individuals), and further tested the generality of the observed relationships in 222 woody eudicots from 15 forest ecosystems. In a subsample of 20 broad-leaved species, we also analyzed the relationships among C, Ca, lignin, and pectin concentrations in leaf cell walls. We found a significant leaf C–Ca tradeoff operating within and across species and across ecosystems. This basic relationship was explained by variations in the share of cell wall lignin and pectin investments at the cell scale. The C–Ca tradeoffs were mainly driven by soil pH and mean annual temperature and precipitation, suggesting that leaves were more economically built with less C and more Ca as soil pH increased and at lower temperature and lower precipitation. However, we did not detect consistent patterns among C–N, and C–Mg at different levels of biological organization, suggesting substantial plasticity in N and Mg distribution among cell organelles and cell protoplast and cell wall. We observed two major axes of macronutrient differentiation: the cell-wall structural axis consisting of protein-free C and Ca and the protoplasm metabolic axis consisting of P and K, underscoring the decoupling of structural and metabolic elements inherently linked with cell wall from protoplasm investment strategies. We conclude that the tradeoffs between leaf C and Ca highlight how carbon is allocated to leaf structural function and suggest that this might indicate biogeochemical niche differentiation of species

Security-Constrained Unit Commitment Considering Differentiated Regional Air Pollutant Intensity

Conventional environmental-economic power dispatch methods constrain the total amount of emissions of power plants, and they succeed in reducing emissions from the power sector. However, they fail to address the mismatch between emission reductions and the resulting changes in regional air quality. This paper proposes an ecology- and security-constrained unit commitment (Eco-SCUC) model considering the differentiated impacts of generation-associated emissions on regional air quality. A Gaussian puff dispersion model is applied to capture the temporal-spatial transport of air pollutants. Additionally, an air pollutant intensity (API) index is defined for assessing the impacts of emissions on the air quality in regions with differentiated atmospheric environmental capacities. Then the API constraints are formulated based on air quality forecast and included in SCUC model. Moreover, the stochastic optimization is employed to accommodate wind power uncertainty, and the Benders decomposition technique is used to solve the formulated mixed-integer quadratic programming (MIQP) problem. Case studies demonstrate that the Eco-SCUC can cost-effectively improve air quality for densely-populated regions via shifting generation among units and can significantly reduce the person-hours exposed to severe air pollution. Furthermore, the benefits of wind power for air quality control are investigated

Analysis of Four Types of Anchorage Devices for Prestressed Glulam Beam and Experimental Research

An anchorage device is an integral part of the prestressed Glulam beams. Therefore, its rationality and practicability have significant effects on the mechanical performance of the prestressed beams. To investigate the impact of the anchorage devices on the bearing capacity and stiffness of the prestressed beams, this paper compared and analyzed four kinds of anchors in detail through the finite element software. The results showed that when the initial mid-span deflection was 5 mm, 10 mm, and 15 mm, the bearing capacity of prestressed beams with four anchorage devices was 80.37–177.24%, 93.56–182.51%, and 95.62–194.60% higher than that of ordinary Glulam beam, respectively. When the initial mid-span top prestresses were 1 MPa, 1.5 MPa, and 2 MPa, the bearing capacity of prestressed beams with four anchorage devices was 101.71–172.57%, 105.85–175.88%, and 109.64–180.87% higher than that of ordinary Glulam beam, respectively. In addition, based on the simulation results, the prestressed beam with the external anchorage had the highest bearing capacity and stiffness. The deformation capacity of the beam with boot anchorage was the largest. The stress distribution of the beam installed under beam anchorage was the most uniform, and the beam with slotted anchorage was easy to cause stress concentration at the notch. Finally, based on the outstanding performance of the external anchorage, it was selected to carry out one experiment, and the experimental result showed that the simulation could predict the damage model and load–deflection relationship of the prestressed beams well

Research on Linear Combination Models of BDS Multi-Frequency Observations and Their Characteristics

The linear combination of multi-frequency carrier-phase and pseudorange observations can form the combined observations with special properties. The type and number of combined frequencies will directly affect the characteristics of the combined observations. BDS-2 and BDS-3 broadcast three and five signals, respectively, and the study of their linear combination is of great significance for precision positioning. In this contribution, the linear combination form of multi-frequency carrier-phase observations in cycles and meters is sorted out. Seven frequency combination modes are formed, and some special combinations for positioning are searched. Then, based on the principle of minimum combined noise, a simpler search method for the optimal real coefficients of ionosphere-free (IF) combination based on the least squares (LS) principle is proposed. The general analytical expressions of optimal real coefficients for multi-frequency geometry-based and ionosphere-free (GBIF), geometry-free and ionosphere-free (GFIF), and pseudorange multipath (PMP) combinations with the first-order ionosphere delay taken into account are derived. And the expression derivation process is given when both the first-order and second-order ionospheric delays are eliminated. Based on this, the characteristics of the optimal real coefficient combination in various modes are compared and discussed. The various combinations reflect that the accuracy of the combined observations from dual-frequency (DF) to five-frequency (FF) is gradually improving. The combination coefficient becomes significantly larger after taking the second-order ionospheric delay into account. In addition, the combined accuracy of BDS-3 is better than that of BDS-2. When only the first-order ionosphere is considered, the combination attribute of (B1C, B1I, B2a) is the best among the triple-frequency (TF) combinations of BDS-3. When both the first-order and second-order ionospheric delays are considered, the (B1C, B3I, B2a) combination is recommended

Thermodynamic Modeling and Exergy Analysis of A Combined High-Temperature Proton Exchange Membrane Fuel Cell and ORC System for Automotive Applications

A combined system consisting of a high-temperature proton exchange membrane fuel cell (HT-PEMFC) and an organic Rankine cycle (ORC) is provided for automotive applications in this paper. The combined system uses HT-PEMFC stack cathode exhaust gas to preheat the inlet gas and the ORC to recover the waste heat from the stack. The model of the combined system was developed and the feasibility of the model was verified. In addition, the evaluation index of the proposed system was derived through an energy and exergy analysis. The numerical simulation results show that the HT-PEMFC stack, cathode heat exchanger, and evaporator contributed the most to the total exergy loss of the system. These components should be optimized as a focus of future research to improve system performance. The lower current density increased the ecological function and the system efficiency, but reduced the system’s net out-power. A higher inlet temperature and higher hydrogen pressures of the stack and the lower oxygen pressure helped improve the system performance. Compared to the HT-PEFC system without an ORC subsystem, the output power of the combined system was increased by 12.95%