Experimental Study on Performance of Asphalt Mixture Designed by Different Method

AbstractIn order to explore the Marshall, Superpave and GTM methods, the similarities and differences of the three design methods are detail studied. Experimental study on performance of asphalt mixture AC-13、AC-20 and AC-25 designed by different method were executed in this paper. Test results show that, GTM method gives lower void and lower bitumen aggregate ratio than other two methods, it have very better water stability and high-temperature stability. The DS value is 3256 / mm by GTM design method, is 1.91 times of the Marshall and 1.33 times of the Superpave. Superpave method gives lower bitumen aggregate ratio than Marshall method. The asphalt mixture designed by Superpave method has longer fatigue life than the mixture designed by other two methods. The volume parameters of asphalt mixture designed by Marshall can conform specification very well, but the performance of asphalt mixture seems to be worse than other two methods

HS-CAI: A Hybrid DCOP Algorithm via Combining Search with Context-based Inference

Search and inference are two main strategies for optimally solving Distributed Constraint Optimization Problems (DCOPs). Recently, several algorithms were proposed to combine their advantages. Unfortunately, such algorithms only use an approximated inference as a one-shot preprocessing phase to construct the initial lower bounds which lead to inefficient pruning under the limited memory budget. On the other hand, iterative inference algorithms (e.g., MB-DPOP) perform a context-based complete inference for all possible contexts but suffer from tremendous traffic overheads. In this paper, $(i)$ hybridizing search with context-based inference, we propose a complete algorithm for DCOPs, named {HS-CAI} where the inference utilizes the contexts derived from the search process to establish tight lower bounds while the search uses such bounds for efficient pruning and thereby reduces contexts for the inference. Furthermore, $(ii)$ we introduce a context evaluation mechanism to select the context patterns for the inference to further reduce the overheads incurred by iterative inferences. Finally, $(iii)$ we prove the correctness of our algorithm and the experimental results demonstrate its superiority over the state-of-the-art

Auto Adaptive Identification Algorithm Based on Network Traffic Flow

Traffic identification is a key task for any Internet Service Provider (ISP) or network administrator. Machine learning method is an important researchmethod on traffic identification, while impact of the asymmetry router on the  traffic identification is considered, so this paper analyzes the impact of asymmetry routing on traffic identification, and proposes an effective method to decrease the impact, and experimental results show the auto adaptive algorithm can improve the traffic identification

The Phosphodiesterase-4 Inhibitor Rolipram Attenuates Heroin-Seeking Behavior Induced By Cues Or Heroin Priming In Rats

Inhibition of phosphodiesterase-4 (PDE4), an enzyme that specifically hydrolyzes cyclic adenosine monophosphate (cAMP) increases intracellular cAMP/cAMP-response element binding protein (CREB) signaling. Activation of this signaling is considered as an important compensatory response that decreases motivational properties of drugs of abuse. However, it is not known whether PDE4 is involved in heroin seeking. Self-administration of heroin (5

Phantom Undulations: Remote Physiological Sensing in Abstract Installation Works

Phantom Undulations is a mixed-media work in which an artist’s physiological data is being used remotely to manipulate the sounds and visuals of an abstract artifact in a gallery setting. This work relies heavily on the concept of showing the artist’s presence or liveness in an abstract and remote manner through changes in the harmony, rhythm, and timbre of a loosely structured soundscape as well as the physical appearance of the artifact. We propose a method of utilizing real time physiological sensing data through a custom built sensing wristband and accompanying software. This system reads the physiological data of the artist and sends it to the Internet, where it can be received by the artifact anywhere on Earth. In addition to the artist’s physiological data, we also offer a way for the audience to incorporate their own data into the work via several sensing wristbands which will accompany the artifact. Through this collaborative process, we wish to invite the audience to join the artist in manipulating the sonic and visual characteristics of this artifact and create a contrapuntally fluid and responsive musical experience

Two-Stream Retentive Long Short-Term Memory Network for Dense Action Anticipation

Analyzing and understanding human actions in long-range videos has promising applications, such as video surveillance, automatic driving, and efficient human-computer interaction. Most researches focus on short-range videos that predict a single action in an ongoing video or forecast an action several seconds earlier before it occurs. In this work, a novel method is proposed to forecast a series of actions and their durations after observing a partial video. This method extracts features from both frame sequences and label sequences. A retentive memory module is introduced to richly extract features at salient time steps and pivotal channels. Extensive experiments are conducted on the Breakfast data set and 50 Salads data set. Compared to the state-of-the-art methods, the method achieves comparable performance in most cases