BACKGROUND: Ligand-binding proteins play key roles in many biological processes. Identification of protein-ligand binding residues is important in understanding the biological functions of proteins. Existing computational methods can be roughly categorized as sequence-based or 3D-structure-based methods. All these methods are based on traditional machine learning. In a series of binding residue prediction tasks, 3D-structure-based methods are widely superior to sequence-based methods. However, due to the great number of proteins with known amino acid sequences, sequence-based methods have considerable room for improvement with the development of deep learning. Therefore, prediction of protein-ligand binding residues with deep learning requires study. RESULTS: In this study, we propose a new sequence-based approach called DeepCSeqSite for ab initio protein-ligand binding residue prediction. DeepCSeqSite includes a standard edition and an enhanced edition. The classifier of DeepCSeqSite is based on a deep convolutional neural network. Several convolutional layers are stacked on top of each other to extract hierarchical features. The size of the effective context scope is expanded as the number of convolutional layers increases. The long-distance dependencies between residues can be captured by the large effective context scope, and stacking several layers enables the maximum length of dependencies to be precisely controlled. The extracted features are ultimately combined through one-by-one convolution kernels and softmax to predict whether the residues are binding residues. The state-of-the-art ligand-binding method COACH and some of its submethods are selected as baselines. The methods are tested on a set of 151 nonredundant proteins and three extended test sets. Experiments show that the improvement of the Matthews correlation coefficient (MCC) is no less than 0.05. In addition, a training data augmentation method that slightly improves the performance is discussed in this study. CONCLUSIONS: Without using any templates that include 3D-structure data, DeepCSeqSite significantlyoutperforms existing sequence-based and 3D-structure-based methods, including COACH. Augmentation of the training sets slightly improves the performance. The model, code and datasets are available at https://github.com/yfCuiFaith/DeepCSeqSite .

Understanding citywide transit patterns is important for transportation management, including city planning and route optimization. The wide deployment of automated fare collection (AFC) systems in public transit vehicles has enabled us to collect massive amounts of transit records, which capture passengers’ traveling activities. Based on such transit records, origin–destination associations have been studied extensively in the literature. However, the identification of transit patterns that establish the origin–transfer–destination (OTD) associations, in spite of its importance, is underdeveloped. In this paper, we propose a framework based on transit tensor factorization (TTF) to identify citywide travel patterns. In particular, we create a transit tensor, which summarizes the citywide OTD information of all passenger trips captured in the AFC records. The TTF framework imposes spatial regularization in the formulation to group nearby stations into meaningful regions and uses multitask learning to identify traffic flows among these regions at different times of the day and days of the week. Evaluated with large-scale, real-world data, our results show that the proposed TTF framework can effectively identify meaningful citywide transit patterns. The online supplement is available at https://doi.org/10.1287/ijoc.2018.0824 .

This work concentrates on the problem of vehicular adaptive cruise control (ACC) in complicated driving conditions especially in low-adhesion road situations, and an ACC control scheme based on real-time estimation of road adhesion is proposed. The ACC controller is designed based on the model predictive control (MPC) method and the steady-state inter-vehicle spacing, relative velocity and the acceleration limits are incorporated into the MPC controller as the constraints. Thereby, the MPC problem is converted into the problem of optimal control with constraints and a receding-horizon approach is utilized to compute the desired vehicle acceleration. The real-time estimation of road adhesion coefficient is accomplished by the recursive least square (RLS) method. A four-degree-of-freedom (4DOF) nonlinear vehicle model is established based on the Lagrangian approach to act as the actual vehicle in the ACC system. The proposed ACC scheme is evaluated by simulations respectively in high and low adhesion conditions. The results reveal that collisions may occur in low adhesion condition if a fixed acceleration limit constraint is employed in the MPC control law. While the proposed adhesion-estimation based ACC system can accomplish the desired inter-vehicle distance safely.

In urban transportation systems, transfer stations refer to hubs connecting a variety of bus and subway lines and, thus, are the most important nodes in transportation networks. The pervasive availability of large-scale travel traces of passengers, collected from automated fare collection (AFC) systems, has provided unprecedented opportunities for understanding citywide transfer patterns, which can benefit smart transportation, such as smart route recommendation to avoid crowded lines, and dynamic bus scheduling to enhance transportation efficiency. To this end, in this article, we provide a systematic study of the measurement, patterns, and modeling of spatiotemporal dynamics of passenger transfers. Along this line, we develop a data-driven analytical system for modeling the transfer volumes of each transfer station. More specifically, we first identify and quantify the discriminative patterns of spatiotemporal dynamics of passenger transfers by utilizing heterogeneous sources of transfer related data for each station. Also, we develop a multi-task spatiotemporal learning model for predicting the transfer volumes of a specific station at a specific time period. Moreover, we further leverage the predictive model of passenger transfers to provide crowdedness-aware route recommendations. Finally, we conduct the extensive evaluations with a variety of real-world data. Experimental results demonstrate the effectiveness of our proposed modeling method and its applications for smart transportation.

The omission of nonlinear effects in large-scale 3D ground motion estimation, which are particularly challenging due to memory and scalability issues, can result in costly misguidance for structural design in earthquake-prone regions. We have implemented nonlinearity using a Drucker-Prager yield condition in AWP-ODC and further optimized the CUDA kernels to more efficiently utilize the GPU's memory bandwidth. The application has resulted in a significant increase in the model region and accuracy for state-of-the-art earthquake simulations in a realistic earth structure, which are now able to resolve the wavefield at frequencies relevant for the most vulnerable buildings (> 1 Hz) while maintaining the scalability and efficiency of the method. We successfully run the code on 4,200 Kepler K20X GPUs on NCSA Blue Waters and OLCF Titan to simulate a M 7.7 earthquake on the southern San Andreas fault with a spatial resolution of 25 m for frequencies up to 4 Hz.

In this study, a new fast algorithm for optimal design of block digital filters is proposed based on the skew circulant matrix, the Toeplitz and the skew shift cyclic matrices. The developed skew‐cyclic filter, compared with the cyclic filter, not only achieves the same computational complexity of the design process and the memory requirements, but is more efficient with only approximately half of the computational cost for the real signals.

We have developed a highly scalable and efficient GPU-based finite-difference code (AWP) for earthquake simulation that implements high throughput, memory locality, communication reduction and communication/computation overlap and achieves linear scalability on Cray XK7 Titan at ORNL and NCSA's Blue Waters system. We simulate realistic 0-10 Hz earthquake ground motions relevant to building engineering design using high-performance AWP. Moreover, we show that AWP provides a speedup by a factor of 110 in key strain tensor calculations critical to probabilistic seismic hazard analysis (PSHA). These performance improvements to critical scientific application software, coupled with improved co-scheduling capabilities of our workflow-managed systems, make a statewide hazard model a goal reachable with existing supercomputers. The performance improvements of GPU-based AWP are expected to save millions of core-hours over the next few years as physics-based seismic hazard analysis is developed using heterogeneous petascale supercomputers.

PATUS is a code generation and auto-tuning framework for stencil computations targeting modern multi and many-core processors. The goals of the framework are productivity and portability for achieving high performance on the target platform. Its stencil specification language allows the programmer to express the computation in a concise way independently of hardware architecture-specific details. Thus, it increases the programmer productivity by removing the need for manual low-level tuning. We illustrate the impact of the stencil code generation in seismic applications, for which both weak and strong scaling are important. We evaluate the performance by focusing on a scalable discretization of the wave equation and testing complex simulation types of the AWP-ODC code to aim at excellent parallel efficiency, preparing for petascale 3-D earthquake calculations.

AWM-Olsen is a widely used ground motion simulation code based on a parallel finite difference solution of the 3-D velocity-stress wave equation. This application runs on tens of thousands of cores and consumes several million CPU hours on the TeraGrid Clusters every year. A significant portion of its run-time (37% in a 4,096 process run), is spent in MPI communication routines. Hence, it demands an optimized communication design coupled with a low-latency, high-bandwidth network and an efficient communication subsystem for good performance. In this paper, we analyze the performance bottlenecks of the application with regard to the time spent in MPI communication calls. We find that much of this time can be overlapped with computation using MPI non-blocking calls. We use both two-sided and MPI-2 one-sided communication semantics to re-design the communication in AWM-Olsen. We find that with our new design, using MPI-2 one-sided communication semantics, the entire application can be sped up by 12% at 4K processes and by 10% at 8K processes on a state-of-the-art InfiniBand cluster, Ranger at the Texas Advanced Computing Center (TACC).

Petascale simulations are needed to understand the rupture and wave dynamics of the largest earthquakes at shaking frequencies required to engineer safe structures (> 1 Hz). Toward this goal, we have developed a highly scalable, parallel application (AWP-ODC) that has achieved “M8”: a full dynamical simulation of a magnitude-8 earthquake on the southern San Andreas fault up to 2 Hz. M8 was calculated using a uniform mesh of 436 billion 40-m3cubes to represent the three-dimensional crustal structure of Southern California, in a 800 km by 400 km area, home to over 20 million people. This production run producing 360 sec of wave propagation sustained 220 Tflop/s for 24 hours on NCCS Jaguar using 223,074 cores. As the largest-ever earthquake simulation, M8 opens new territory for earthquake science and engineering - the physics-based modeling of the largest seismic hazards with the goal of reducing their potential for loss of life and property.