Graph database systems (GDBS) have become popular for representing real-world entities and their relationships, and offering convenient query languages based on graph pattern matching. As graphs increase in size and complexity, GDBS need to provide the appropriate support for abstraction for which views have demonstrated to be an effective tool, facilitating query writing and improving query execution time via materialization techniques. This paper explores how views can be defined and used in GDBS. We propose view-based extensions to the widely used graph query language Cypher, explore a wide range of possible view types, and outline several implementation strategies for view materialization. Using a set of micro- and macro-benchmarks, we provide insight into how expressive different view types are and how effective the proposed implementation strategies are for different GDBS. Our results show that views can be a powerful tool for GDBS, offering great flexibility in query expression and providing performance improvements if materialized.

Blind signal recognition (BSR) is a significant research topic in the field of intelligent signal processing. However, existing BSR of space-time block codes (STBC) mainly depends on conventional algorithms, which require priori information and can only identify a relatively limited amount of STBC. Although deep learning (DL) has been widely used in signal recognition, so far there are few studies on BSR of STBC in multiple-input multiple-output (MIMO) systems using DL. In this paper, a blind recognition approach for STBC based on multichannel convolutional neural network (MCNN) is proposed. By leveraging the structure of multiple input channel, the in-phase and quadrature (IQ) channel information of STBC signals can be comprehensively extracted. Simulation results demonstrate that the proposed algorithm extends the recognizable STBC codes to 6, and can also improve the recognition accuracy in comparison to traditional convolutional neural network (CNN). The model proposed in this paper has been validated with two datasets and experimentally proved to be well generalized.

This paper introduces a digital readout integrated circuit (DROIC) for 320×256 target-tracking infrared focal plane arrays(IRFPAs). The circuit is based on a two-stage ADC, which implements 10-bit coarse quantization and 4-bit fine quantization, enabling high frame frequency trace detection. In addition, the locating and independent output of sensitive pixels are realized by a scanning function, which greatly reduces output frequency and power consumption. The proposed ROIC of 15μm pixel-pitch is fabricated using 55nm 1P6M CMOS process. The pixel circuit consumes 379nW. Frame frequency of the target-tracking mode is 1 kHz, the two-stage ADC has a nonlinearity of 0.06%.

Plesiosaurians are an extinct group of highly derived Mesozoic marine reptiles with a global distribution that spans 135 million years from the Early Jurassic to the Late Cretaceous. During their long evolutionary history they maintained a unique body plan with two pairs of large wing-like flippers, but their locomotion has been a topic of debate for almost 200 years. Key areas of controversy have concerned the most efficient biologically possible limb stroke, e.g. whether it consisted of rowing, underwater flight, or modified underwater flight, and how the four limbs moved in relation to each other: did they move in or out of phase? Previous studies have investigated plesiosaur swimming using a variety of methods, including skeletal analysis, human swimmers, and robotics. We adopt a novel approach using a digital, three-dimensional, articulated, free-swimming plesiosaur in a simulated fluid. We generated a large number of simulations under various joint degrees of freedom to investigate how the locomotory repertoire changes under different parameters. Within the biologically possible range of limb motion, the simulated plesiosaur swims primarily with its forelimbs using an unmodified underwater flight stroke, essentially the same as turtles and penguins. In contrast, the hindlimbs provide relatively weak thrust in all simulations. We conclude that plesiosaurs were forelimb-dominated swimmers that used their hind limbs mainly for maneuverability and stability.

We present a general approach for simulating and controlling a human character that is riding a bicycle. The two main components of our system are offline learning and online simulation. We simulate the bicycle and the rider as an articulated rigid body system. The rider is controlled by a policy that is optimized through offline learning. We apply policy search to learn the optimal policies, which are parameterized with splines or neural networks for different bicycle maneuvers. We use Neuroevolution of Augmenting Topology (NEAT) to optimize both the parametrization and the parameters of our policies. The learned controllers are robust enough to withstand large perturbations and allow interactive user control. The rider not only learns to steer and to balance in normal riding situations, but also learns to perform a wide variety of stunts, including wheelie, endo, bunny hop, front wheel pivot and back hop.

We present a general approach to creating realistic swimming behavior for a given articulated creature body. The two main components of our method are creature/fluid simulation and the optimization of the creature motion parameters. We simulate two-way coupling between the fluid and the articulated body by solving a linear system that matches acceleration at fluid/solid boundaries and that also enforces fluid incompressibility. The swimming motion of a given creature is described as a set of periodic functions, one for each joint degree of freedom. We optimize over the space of these functions in order to find a motion that causes the creature to swim straight and stay within a given energy budget. Our creatures can perform path following by first training appropriate turning maneuvers through offline optimization and then selecting between these motions to track the given path. We present results for a clownfish, an eel, a sea turtle, a manta ray and a frog, and in each case the resulting motion is a good match to the real-world animals. We also demonstrate a plausible swimming gait for a fictional creature that has no real-world counterpart.