TENSILE: A Tensor granularity dynamic GPU memory scheduling method towards multiple dynamic workloads system

Recently, deep learning has been an area of intense research. However, as a kind of computing-intensive task, deep learning highly relies on the scale of GPU memory, which is usually prohibitive and scarce. Although there are some extensive works have been proposed for dynamic GPU memory management, they are hard to be applied to systems with multiple dynamic workloads, such as in-database machine learning systems. In this paper, we demonstrated TENSILE, a method of managing GPU memory in tensor granularity to reduce the GPU memory peak, considering the multiple dynamic workloads. TENSILE tackled the cold-starting and across-iteration scheduling problem existing in previous works. We implement TENSILE on a deep learning framework built by ourselves and evaluated its performance. The experiment results show that TENSILE can save more GPU memory with less extra time overhead than prior works in both single and multiple dynamic workloads scenarios

Study of Cu-Pt Alloy Catalysis for Unsaturated Aldehyde Hydrogenation

One of the major challenges in heterogeneous catalysis is the preparation of highly selective catalysts. Cu catalysts have been ubiquitous in many applications, including for the promotion of the low temperature water-shift reaction and hydrogenation of carbon monoxide. By combining with a second metal, Cu can also assist in the selective hydrogenation of organic molecules with multiple double and triple bonds. In this work, Pt-Cu alloy catalysts have been studied to obtain better understanding of some fundamental questions, such as what the oxidation state of Cu under reaction conditions is, where the atoms of the two metals are.Majority of this work has been focused on the Pt-Cu bimetallic catalyst, so-called single atom catalyst, where Pt atoms diluted in Cu nanoparticles. Pt is presumed to activate H2 while Cu is believed to aid in the selective addition of the resulting H atoms to C=O bonds. In fact, catalysts with Pt content as low as 0.1% of the total composition have shown improved selectivity. In this catalysis, it is reasonable to expect the active catalyst to be metallic copper. However, in situ x-ray absorption spectroscopy (XAS) and CO infrared (IR) titration experiments have suggested that at low temperatures, below 100C, copper-containing nanoparticles dispersed on silica support develop a thin layer copper oxide film. This was found even in the presence of a hydrogen atmosphere for several different experiment setups. However, our latest experiment from ANL indicated oxidation can be prohibited under hydrogen atmosphere with adequate sealing. At high temperatures, Cu is fully reduced to metallic state, and the interconversion is reversible. Nonetheless, plenty of hydrogenation catalysis is carried out under mild temperature, under which reaction may take place on oxidized copper sites. In addition to the oxidation of Cu surface, several CO IR titration experiments were carried out and suggested that Pt sites are not available on the surface until the Pt content surpasses about 10% of the total metal loading. Where are the Pt atoms during reaction, on the surface or inside Cu nanoparticles, or attached to the oxide support? XAS absorption spectroscopies and in-situ IR have been carried out. Both in vacuum and under gaseous atmospheres, Pt atoms are found to be diffusing within the Cu-Pt nanoparticles. From in situ IR in CO atmosphere, for diluted single atom catalysts, Pt was found to reversibly segregate to the surface and diffuse back into the bulk when ramp up the temperature from 295 K to 495 K and cool back. A comparison of the thermodynamics of the adsorption of CO on the metal surface of a Cu/SBA-15 catalysts was made as well, under two different environments: under vacuum versus in the presence of atmospheric pressures of CO. Changes associated with entropic effects introduced by the presence of CO gas such as adsorbate displacement and adsorbate-assisted adsorption steps need to be introduced to explain the differences seen between the two environments. Also, core-shell Pt@Cu catalysts were prepared successful, and it will be utilized to study the behavior of Pt atoms under reaction condition. Future follow-up work of this project might include designing flow reactor for in-site infrared experiments and optimizing current core-shell Pt@Cu catalysts to minimize the amount of single Cu atom site on the surface and test them with in situ IR and XAS absorption spectroscopies

Synthesizing the High Surface Area g-C3N4 for Greatly Enhanced Hydrogen Production

Adjusting the structure of g-C3N4 to significantly enhance its photocatalytic activity has attracted considerable attention. Herein, a novel, sponge-like g-C3N4 with a porous structure is prepared from the annealing of protonated melamine under N2/H2 atmosphere (PH-CN). Compared to bulk g-C3N4 via calcination of melamine under ambient atmosphere (B-CN), PH-CN displays thinner nanosheets and a higher surface area (150.1 m2/g), which is a benefit for shortening the diffusion distance of photoinduced carriers, providing more active sites, and finally favoring the enhancement of the photocatalytic activity. Moreover, it can be clearly observed from the UV-vis spectrum that PH-CN displays better performance for harvesting light compared to B-CN. Additionally, the PH-CN is prepared with a larger band gap of 2.88 eV with the Fermi level and conduction band potential increased and valence band potential decreased, which could promote the water redox reaction. The application experiment results show that the hydrogen evolution rate on PH-CN was nearly 10 times higher than that of B-CN, which was roughly 4104 μmol h−1 g−1. The method shown in this work provides an effective approach to adjust the structure of g-C3N4 with considerable photocatalytic hydrogen evolution activity

Panniculitis with late onset enthesitis-related arthritis: a case report

Abstract Background Panniculitis, a type of inflammation of subcutaneous fat, is a relatively uncommon condition that usually presents as inflammatory nodules or plaques, with various proposed etiologic factors. The association between panniculitis and enthesitis-related arthritis has not been described previously. Case presentation Herein, we describe a case of a 11-year-old girl who presented with recurrent fever and painful subcutaneous nodules on her extremities and buttocks. Histological examination of the skin biopsy specimen revealed lobular panniculitis. Despite the use of prednisone and mycophenolate mofetil for several months, the patient experienced a relapse of skin lesions and additional symptoms of peripheral joint swelling and inflammatory lumbar pain. She was diagnosed with enthesitis-related arthritis after confirmation by imaging. The panniculitis demonstrated a sustained response when a tumor necrosis factor alpha inhibitor was used for enthesitis-related arthritis. At 2-year follow-up, her skin lesions and arthritis remained stable. Conclusions Although rare, panniculitis can be considered an unusual extra-articular manifestation of enthesitis-related arthritis based on clinical and pathological insights

A Comprehensive Spatio-Temporal Model for Subway Passenger Flow Prediction

Accurate subway passenger flow prediction is crucial to operation management and line scheduling. It can also promote the construction of intelligent transportation systems (ITS). Due to the complex spatial features and time-varying traffic patterns of subway networks, the prediction task is still challenging. Thus, a hybrid neural network model, GCTN (graph convolutional and comprehensive temporal neural network), is proposed. The model combines the Transformer network and long short-term memory (LSTM) network to capture the global and local temporal dependency. Besides, it uses a graph convolutional network (GCN) to capture the spatial features of the subway network. For the sake of the stability and accuracy for long-term passenger flow prediction, we enhance the influence of the station itself and the global station and combine the convolutional neural networks (CNN) and Transformer. The model is verified by the passenger flow data of the Shanghai Subway. Compared with some typical data-driven methods, the results show that the proposed model improves the prediction accuracy in different time intervals and exhibits superiority in prediction stability and robustness. Besides, the model has a better performance in the peak value and the period when passenger flow changes quickly

A Hybrid GLM Model for Predicting Citywide Spatio-Temporal Metro Passenger Flow

Accurate prediction of citywide short-term metro passenger flow is essential to urban management and transport scheduling. Recently, an increasing number of researchers have applied deep learning models to passenger flow prediction. Nevertheless, the task is still challenging due to the complex spatial dependency on the metro network and the time-varying traffic patterns. Therefore, we propose a novel deep learning architecture combining graph attention networks (GAT) with long short-term memory (LSTM) networks, which is called the hybrid GLM (hybrid GAT and LSTM Model). The proposed model captures the spatial dependency via the graph attention layers and learns the temporal dependency via the LSTM layers. Moreover, some external factors are embedded. We tested the hybrid GLM by predicting the metro passenger flow in Shanghai, China. The results are compared with the forecasts from some typical data-driven models. The hybrid GLM gets the smallest root-mean-square error (RMSE) and mean absolute percentage error (MAPE) in different time intervals (TIs), which exhibits the superiority of the proposed model. In particular, in the TI 10 min, the hybrid GLM brings about 6–30% extra improvements in terms of RMSE. We additionally explore the sensitivity of the model to its parameters, which will aid the application of this model

A Comprehensive Spatio-Temporal Model for Subway Passenger Flow Prediction

Accurate subway passenger flow prediction is crucial to operation management and line scheduling. It can also promote the construction of intelligent transportation systems (ITS). Due to the complex spatial features and time-varying traffic patterns of subway networks, the prediction task is still challenging. Thus, a hybrid neural network model, GCTN (graph convolutional and comprehensive temporal neural network), is proposed. The model combines the Transformer network and long short-term memory (LSTM) network to capture the global and local temporal dependency. Besides, it uses a graph convolutional network (GCN) to capture the spatial features of the subway network. For the sake of the stability and accuracy for long-term passenger flow prediction, we enhance the influence of the station itself and the global station and combine the convolutional neural networks (CNN) and Transformer. The model is verified by the passenger flow data of the Shanghai Subway. Compared with some typical data-driven methods, the results show that the proposed model improves the prediction accuracy in different time intervals and exhibits superiority in prediction stability and robustness. Besides, the model has a better performance in the peak value and the period when passenger flow changes quickly

POSTER

In this paper we explore the problem of collecting malicious smartphone advertisements. Most smartphone app contains advertisements and also suffers from vulnerable advertisement libraries. Malicious advertisements exploit the ad library vulnerability and attack victim smartphones. Similar to the traditional honeypots, we need an effective way to capture malicious ads. In this paper, we provide our approach named AdHoneyDroid. We build a crawler to gather apps on the android marketplaces and manually collect ad libraries and their vulnerabilities. Then AdHoneyDroid executes the apps and detects malicious advertisements. In our approach, we adopt the idea of API sandbox and TaintDroid to detect the attack event. We store the malicious advertise-ments in a database for future analysis. Malicious ads can help security analysts have a better understanding of current mobile attacks and also disclose the attack payloads. Copyright is held by the owner/author(s).EI