Stochastic Modeling of Hybrid Cache Systems

In recent years, there is an increasing demand of big memory systems so to perform large scale data analytics. Since DRAM memories are expensive, some researchers are suggesting to use other memory systems such as non-volatile memory (NVM) technology to build large-memory computing systems. However, whether the NVM technology can be a viable alternative (either economically and technically) to DRAM remains an open question. To answer this question, it is important to consider how to design a memory system from a "system perspective", that is, incorporating different performance characteristics and price ratios from hybrid memory devices. This paper presents an analytical model of a "hybrid page cache system" so to understand the diverse design space and performance impact of a hybrid cache system. We consider (1) various architectural choices, (2) design strategies, and (3) configuration of different memory devices. Using this model, we provide guidelines on how to design hybrid page cache to reach a good trade-off between high system throughput (in I/O per sec or IOPS) and fast cache reactivity which is defined by the time to fill the cache. We also show how one can configure the DRAM capacity and NVM capacity under a fixed budget. We pick PCM as an example for NVM and conduct numerical analysis. Our analysis indicates that incorporating PCM in a page cache system significantly improves the system performance, and it also shows larger benefit to allocate more PCM in page cache in some cases. Besides, for the common setting of performance-price ratio of PCM, "flat architecture" offers as a better choice, but "layered architecture" outperforms if PCM write performance can be significantly improved in the future.Comment: 14 pages; mascots 201

Priori Information Based Support Vector Regression and Its Applications

In order to extract the priori information (PI) provided by real monitored values of peak particle velocity (PPV) and increase the prediction accuracy of PPV, PI based support vector regression (SVR) is established. Firstly, to extract the PI provided by monitored data from the aspect of mathematics, the probability density of PPV is estimated with ε-SVR. Secondly, in order to make full use of the PI about fluctuation of PPV between the maximal value and the minimal value in a certain period of time, probability density estimated with ε-SVR is incorporated into training data, and then the dimensionality of training data is increased. Thirdly, using the training data with a higher dimension, a method of predicting PPV called PI-ε-SVR is proposed. Finally, with the collected values of PPV induced by underwater blasting at Dajin Island in Taishan nuclear power station in China, contrastive experiments are made to show the effectiveness of the proposed method

Support Vector Regression Method for Wind Speed Prediction Incorporating Probability Prior Knowledge

Prior knowledge, such as wind speed probability distribution based on historical data and the wind speed fluctuation between the maximal value and the minimal value in a certain period of time, provides much more information about the wind speed, so it is necessary to incorporate it into the wind speed prediction. First, a method of estimating wind speed probability distribution based on historical data is proposed based on Bernoulli’s law of large numbers. Second, in order to describe the wind speed fluctuation between the maximal value and the minimal value in a certain period of time, the probability distribution estimated by the proposed method is incorporated into the training data and the testing data. Third, a support vector regression model for wind speed prediction is proposed based on standard support vector regression. At last, experiments predicting the wind speed in a certain wind farm show that the proposed method is feasible and effective and the model’s running time and prediction errors can meet the needs of wind speed prediction

Decoding trust: A reinforcement learning perspective

Behavioral experiments on the trust game have shown that trust and trustworthiness are universal among human beings, contradicting the prediction by assuming \emph{Homo economicus} in orthodox Economics. This means some mechanism must be at work that favors their emergence. Most previous explanations however need to resort to some factors based upon imitative learning, a simple version of social learning. Here, we turn to the paradigm of reinforcement learning, where individuals update their strategies by evaluating the long-term return through accumulated experience. Specifically, we investigate the trust game with the Q-learning algorithm, where each participant is associated with two evolving Q-tables that guide one's decision making as trustor and trustee respectively. In the pairwise scenario, we reveal that high levels of trust and trustworthiness emerge when individuals appreciate both their historical experience and returns in the future. Mechanistically, the evolution of the Q-tables shows a crossover that resembles human's psychological changes. We also provide the phase diagram for the game parameters, where the boundary analysis is conducted. These findings are robust when the scenario is extended to a latticed population. Our results thus provide a natural explanation for the emergence of trust and trustworthiness without external factors involved. More importantly, the proposed paradigm shows the potential in deciphering many puzzles in human behaviors.Comment: 12 pages, 11 figures. Comments are appreciate

Individual and Combined Effects of Herbicide Tribenuron-Methyl and Fungicide Tebuconazole on Soil Earthworm \u3cem\u3eEisenia fetida\u3c/em\u3e

Earthworms are soil engineers that alter the soil bio-physical properties to favor plant growth whereas pesticides represent a significant threat to their abundance and soil health. Thus, we investigated the toxic effects of tribenuron-methyl (TBM) and tebuconazole (TEB) on the soil earthworm, Eisenia fetida. The TBM demonstrated low toxicity to E. fetida in the contact filter paper and artificial soil tests, with median lethal concentrations (LC50) of 135.6 μg cm−2 at 48 h and 511 mg kg−1 on day 14, respectively. Similarly, TEB also showed low toxicity to E. fetida in the artificial soil test with LC50 of 287 mg kg−1 on day 14. However, TEB was highly toxic to earthworm in the contact filter paper test with LC50 of 5.7 μg cm−2 at 48 h. The mixture of two pesticides had an antagonistic effect on the earthworm. Under 0.1 LC50 of TBM and TEB, either single or combined application of pesticides induced oxidative stress and inhibited cellulase activity in early days of the earthworm exposure. However, both pesticides did not damage the earthworm DNA. Our results suggest that pesticides can negatively affect soil earthworms and provide valuable information regarding the responses of soil biological engineers to the lethal agrochemicals

Blade-Tip Vortex Noise Mitigation Traded-Off against Aerodynamic Design for Propellers of Future Electric Aircraft

We study noise generation at the blade tips of propellers designed for future electric aircraft propulsion and, furthermore, analyze the interrelationship between noise mitigation and aerodynamics improvement in terms of propeller geometric designs. Classical propellers with three or six blades and a conceptual propeller with three joined dual-blades are compared to understand the effects of blade tip vortices on the noise generation and aerodynamics. The dual blade of the conceptual propeller is constructed by joining the tips of two sub-blades. These propellers are designed to operate under the same freestream flow conditions and similar electric power consumption. The Improved Delayed Detached Eddy Simulation (IDDES) is adopted for the flow simulation to identify high-resolution time-dependent noise sources around the blade tips. The acoustic computations use a time-domain method based on the convective Ffowcs Williams–Hawkings (FW-H) equation. The thrust of the 3-blade conceptual propeller is\ua04%\ua0larger than the 3-blade classical propeller and\ua08%\ua0more than the 6-blade one, given that they have similar efficiencies. Blade tip vortices are found emitting broadband noise. Since the classical and conceptual 3-blade propellers have different geometries, especially at the blade tips, they introduce deviations in the vortex development. However, the differences are small regarding the broadband noise generation. As compared to the 6-blade classical propeller, both 3-blade propellers produce much larger noise. The reason is that the increased number of blades leads to the reduced strength of tip vortices. The findings indicate that the noise mitigation through the modification of the blade design and number can be traded-off by the changed aerodynamic performance

Burst pressures of high-density polyethylene pipes considering the notch effect : testing and prediction

This work aims at testing and predicting burst pressures of high-density polyethylene (HDPE) pipes with various groove conditions. Four types of notches, including U-type, V-type, lineartype (L-type), and rectangular-type (R-type) notches, with different depth ratios are discussed. A unified damage model is proposed to predict the damage behaviors of notched HDPE pipes for different notch shapes. Results indicate that the notch shape has an important influence on the burst pressure of HDPE pipes. Generally, the notch types ordered by impact extent from highest to lowest are R-type, U-type, V-type, and L-type, according to the experimental data. The burst pressure obviously decreases with the increment of the notch-depth ratio. However, when the notch depth is less than 0.5 mm in this work, the burst pressure reduces slightly compared with that of the virgin pipe because of its good fracture toughness. Moreover, the predicted damage factors given by the proposed model agree very well with the experimental data under different groove shapes. This work provides important experimental data and damage assessment approaches for various notched HDPE pipes