SASG: Sparsification with Adaptive Stochastic Gradients for Communication-efficient Distributed Learning

Stochastic optimization algorithms implemented on distributed computing architectures are increasingly used to tackle large-scale machine learning applications. A key bottleneck in such distributed systems is the communication overhead for exchanging information such as stochastic gradients between different workers. Sparse communication with memory and the adaptive aggregation methodology are two successful frameworks among the various techniques proposed to address this issue. In this paper, we creatively exploit the advantages of Sparse communication and Adaptive aggregated Stochastic Gradients to design a communication-efficient distributed algorithm named SASG. Specifically, we first determine the workers that need to communicate based on the adaptive aggregation rule and then sparse this transmitted information. Therefore, our algorithm reduces both the overhead of communication rounds and the number of communication bits in the distributed system. We define an auxiliary sequence and give convergence results of the algorithm with the help of Lyapunov function analysis. Experiments on training deep neural networks show that our algorithm can significantly reduce the number of communication rounds and bits compared to the previous methods, with little or no impact on training and testing accuracy.Comment: 12 pages, 5 figure

2-Iodo-3-meth­oxy-6-methyl­pyridine

The title compound, C7H8INO, which crystallizes with three independent mol­ecules in the asymmetric unit, was prepared by the reaction of 3-meth­oxy-6-methyl­pyridine with KI and I2 in tetra­hydro­furan solution. In the crystal structure, the three independent mol­ecules are arranged in a similar orientation with the three polar meth­oxy groups aligned on one side and the three non-polar methyl groups on the other side. The three mol­ecules, excluding methyl H atoms, are essentially planar, with r.m.s. deviations of 0.0141 (1), 0.0081 (1) and 0.0066 (2)Å. The three pyridine rings make dihedral angles of 58.09 (3) 66.64 (4) and 71.5 (3)°. The crystal structure features rather weak inter­molecular C—H⋯O hydrogen bonds, which link two mol­ecules into dimers, and short I⋯N contacts [4.046 (3) Å]

Deep learning-based dynamic forecasting method and application for ultra-deep fractured reservoir production

Addressing the complex challenges in dynamic production forecasting for the deep-ultra-deep fractured carbonate reservoirs in the Tarim Basin’s Tahe Oilfield, characterized by numerous influencing factors, strong temporal variations, high non-linearity, and prediction difficulties, We proposes a prediction method based on Gated Recurrent Unit networks (GRU). Initially, the production data and influencing factors are subjected to dimensionality reduction using Pearson correlation coefficient and principal component analysis methods to obtain multi-attribute time series data. Subsequently, deep learning modeling of time series data is conducted using Gated Recurrent Unit networks. The model is then optimized using the Optuna algorithm and applied to the dynamic production forecasting of the deep-ultra-deep fractured carbonate reservoirs in the Tahe Oilfield. The results demonstrate that the Gated Recurrent Unit network model optimized by Optuna excels in the dynamic production forecasting of the Tahe fractured carbonate reservoirs. Compared with the traditional method, the mean absolute error (MAE), the root mean square error (MSE) and the mean absolute percentage error (MAPE) are reduced by 0.04, 0.1 and 1.1, respectively. This method proves to be more adaptable to the production forecasting challenges of deep fractured reservoirs, providing an effective means to enhance model performance. It holds significant practical value and importance in guiding the development of fractured reservoirs

Towards Understanding the Generalizability of Delayed Stochastic Gradient Descent

Stochastic gradient descent (SGD) performed in an asynchronous manner plays a crucial role in training large-scale machine learning models. However, the generalization performance of asynchronous delayed SGD, which is an essential metric for assessing machine learning algorithms, has rarely been explored. Existing generalization error bounds are rather pessimistic and cannot reveal the correlation between asynchronous delays and generalization. In this paper, we investigate sharper generalization error bound for SGD with asynchronous delay $\tau$. Leveraging the generating function analysis tool, we first establish the average stability of the delayed gradient algorithm. Based on this algorithmic stability, we provide upper bounds on the generalization error of $\tilde{\mathcal{O}}(\frac{T-\tau}{n\tau})$ and $\tilde{\mathcal{O}}(\frac{1}{n})$ for quadratic convex and strongly convex problems, respectively, where $T$ refers to the iteration number and $n$ is the amount of training data. Our theoretical results indicate that asynchronous delays reduce the generalization error of the delayed SGD algorithm. Analogous analysis can be generalized to the random delay setting, and the experimental results validate our theoretical findings

(2R,3R)-2-[(4-Chloro­phen­yl)hydroxy­meth­yl]cyclo­penta­none

The title compound, C12H13ClO2, was prepared by the direct asymmetric inter­molecular aldol reaction of cyclo­penta­none and 4-chloro­benzaldehyde catalysed by l-tryptophan in water. The absolute mol­ecular structure was determined to be a racemic twin with 91% (2R,3R) isomer and 9% of the (2S,3S) form. In the crystal structure, the mol­ecules are connected into a one-dimensional chain along the a axis through the formation of inter­molecular O—H⋯O hydrogen bonds. Further, non-conventional C—H⋯O and C—H⋯π contacts are observed in the structure, which consolidate the crystal packing

1,1-Bis(4-fluoro­phen­yl)-3,4-dihydro-1H-1,3-oxazino[3,4-a]indole

The title compound, C23H17F2NO, which crystallizes with two independent mol­ecules in the asymmetric unit, was prepared by the cyclization of 4-[2-bis­(4-fluoro­phen­yl)methyl­eneamino]but-3-yn-1-ol at room temperature. The mol­ecules display a tripod conformation. The two fluoro­phenyl rings make dihedral angles of 79.26 (2) and 85.87 (1)° [86.53 (1) and 83.67 (2)° in the second mol­ecule] with the indole ring, and the dihedral angles between the fluoro­phenyl rings are 67.74 (2) and 66.33 (2)°, respectively. Furthermore, the indole rings are located on the edge of the respective oxazine half-chair ring systems. Nonconventional C—H⋯π contacts between indole and fluoro­phenyl rings are observed

Sealing of oil-gas reservoir caprock: Destruction of shale caprock by micro-fractures

The sealing ability of caprock is affected by many factors, such as cap thickness, displacement pressure, fracture development, and lithology of caprock. Shale is one of the ideal materials for oil and gas sealing cap formation due to its low porosity and permeability. Microfractures can destroy the sealing property of shale caprock. When buried deep enough, shale will change from toughness to brittleness. In general, the greater the brittleness of shale, the more developed the fractures will be. In areas with high tectonic stress, such as the anticline axis, syncline axis and stratum dip end, the strata stress is high and concentrated, and it is easier to generate fractures. When the stress state of the caprock changes, new micro-cracks are formed or previously closed cracks are re-opened, reducing the displacement pressure of the caprock. These micro-fractures are interconnected to form microleakage spaces, which reduces the sealing capacity of the caprock

TNC-UTM: A Holistic Solution to Secure Enterprise Networks

This paper presents TNC-UTM, a holistic solution to secure enterprise networks from gateway to endpoints. Just as its name suggested, the TNC-UTM solution combines two popular techniques TNC and UTM together by defining an interface between them that integrates their security capacity to provide efficiently network access control and security protection for enterprise network. Not only TNC-UTM provides the features of TNC and UTM, but also it achieves stronger security and higher performance by introducing intelligent configuration decisions and RBAC mechanism. Experiment demonstrated the superior advantages of the TNC-UTM solution

Invisible Backdoor Attack with Dynamic Triggers against Person Re-identification

In recent years, person Re-identification (ReID) has rapidly progressed with wide real-world applications, but also poses significant risks of adversarial attacks. In this paper, we focus on the backdoor attack on deep ReID models. Existing backdoor attack methods follow an all-to-one/all attack scenario, where all the target classes in the test set have already been seen in the training set. However, ReID is a much more complex fine-grained open-set recognition problem, where the identities in the test set are not contained in the training set. Thus, previous backdoor attack methods for classification are not applicable for ReID. To ameliorate this issue, we propose a novel backdoor attack on deep ReID under a new all-to-unknown scenario, called Dynamic Triggers Invisible Backdoor Attack (DT-IBA). Instead of learning fixed triggers for the target classes from the training set, DT-IBA can dynamically generate new triggers for any unknown identities. Specifically, an identity hashing network is proposed to first extract target identity information from a reference image, which is then injected into the benign images by image steganography. We extensively validate the effectiveness and stealthiness of the proposed attack on benchmark datasets, and evaluate the effectiveness of several defense methods against our attack

3-Dimethyl­amino-1-(4-methyl­phen­yl)prop-2-en-1-one

In the title compound, C12H15NO, the C=C and C=O functional groups and the benzene ring are involved in an extended conjugated system. The mol­ecules are essentially planar with a maximal deviation from planarity for the non-H atoms of 0.062 (2) Å