Simultaneous Learning of Nonlinear Manifold and Dynamical Models for High-dimensional Time Series

The goal of this work is to learn a parsimonious and informative representation for high-dimensional time series. Conceptually, this comprises two distinct yet tightly coupled tasks: learning a low-dimensional manifold and modeling the dynamical process. These two tasks have a complementary relationship as the temporal constraints provide valuable neighborhood information for dimensionality reduction and conversely, the low-dimensional space allows dynamics to be learnt efficiently. Solving these two tasks simultaneously allows important information to be exchanged mutually. If nonlinear models are required to capture the rich complexity of time series, then the learning problem becomes harder as the nonlinearities in both tasks are coupled. The proposed solution approximates the nonlinear manifold and dynamics using piecewise linear models. The interactions among the linear models are captured in a graphical model. By exploiting the model structure, efficient inference and learning algorithms are obtained without oversimplifying the model of the underlying dynamical process. Evaluation of the proposed framework with competing approaches is conducted in three sets of experiments: dimensionality reduction and reconstruction using synthetic time series, video synthesis using a dynamic texture database, and human motion synthesis, classification and tracking on a benchmark data set. In all experiments, the proposed approach provides superior performance.National Science Foundation (IIS 0308213, IIS 0329009, CNS 0202067

Optimizing Irregular Communication with Neighborhood Collectives and Locality-Aware Parallelism

Irregular communication often limits both the performance and scalability of parallel applications. Typically, applications individually implement irregular messages using point-to-point communications, and any optimizations are added directly into the application. As a result, these optimizations lack portability. There is no easy way to optimize point-to-point messages within MPI, as the interface for single messages provides no information on the collection of all communication to be performed. However, the persistent neighbor collective API, released in the MPI 4 standard, provides an interface for portable optimizations of irregular communication within MPI libraries. This paper presents methods for optimizing irregular communication within neighborhood collectives, analyzes the impact of replacing point-to-point communication in existing codebases such as Hypre BoomerAMG with neighborhood collectives, and finally shows an up to 1.32x speedup on sparse matrix-vector multiplication within a BoomerAMG solve through the use of our optimized neighbor collectives. The authors analyze multiple implementations of neighborhood collectives, including a standard implementation, which simply wraps standard point-to-point communication, as well as multiple implementations of locality-aware aggregation. All optimizations are available in an open-source codebase, MPI Advance, which sits on top of MPI, allowing for optimizations to be added into existing codebases regardless of the system MPI install

Stomata at the crossroad of molecular interaction between biotic and abiotic stress responses in plants

Increasing global food production is threatened by harsh environmental conditions along with biotic stresses, requiring massive new research into integrated stress resistance in plants. Stomata play a pivotal role in response to many biotic and abiotic stresses, but their orchestrated interactions at the molecular, physiological, and biochemical levels were less investigated. Here, we reviewed the influence of drought, pathogen, and insect herbivory on stomata to provide a comprehensive overview in the context of stomatal regulation. We also summarized the molecular mechanisms of stomatal response triggered by these stresses. To further investigate the effect of stomata–herbivore interaction at a transcriptional level, integrated transcriptome studies from different plant species attacked by different pests revealed evidence of the crosstalk between abiotic and biotic stress. Comprehensive understanding of the involvement of stomata in some plant– herbivore interactions may be an essential step towards herbivores’ manipulation of plants, which provides insights for the development of integrated pest management strategies. Moreover, we proposed that stomata can function as important modulators of plant response to stress combination, representing an exciting frontier of plant science with a broad and precise view of plant biotic interactions

Reinforcement learning for human-robot shared control

This paper aims at proposing a general framework of shared control for human-robot interaction. Human dynamics are considered in analysis of the coupled human-robot system. Motion intentions of both human and robot are taken into account in the control objective of the robot. Reinforcement learning is developed to achieve the control objective subject to unknown dynamics of human and robot. The closed-loop system performance is discussed through a rigorous proof. Simulations are conducted to demonstrate the learning capability of the proposed method and its feasibility in handling various situations. Compared to existing works, the proposed framework combines motion intentions of both human and robot in a human-robot shared control system, without the requirement of the knowledge of humans and robots dynamics

Synthesis of CdTe quantum dot-conjugated CC49 and their application for in vitro imaging of gastric adenocarcinoma cells

The purpose of this experiment was to investigate the visible imaging of gastric adenocarcinoma cells in vitro by targeting tumor-associated glycoprotein 72 (TAG-72) with near-infrared quantum dots (QDs). QDs with an emission wavelength of about 550 to 780 nm were conjugated to CC49 monoclonal antibodies against TAG-72, resulting in a probe named as CC49-QDs. A gastric adenocarcinoma cell line (MGC80-3) expressing high levels of TAG-72 was cultured for fluorescence imaging, and a gastric epithelial cell line (GES-1) was used for the negative control group. Transmission electron microscopy indicated that the average diameter of CC49-QDs was 0.2 nm higher compared with that of the primary QDs. Also, fluorescence spectrum analysis indicated that the CC49-QDs did not have different optical properties compared to the primary QDs. Immunohistochemical examination and in vitro fluorescence imaging of the tumors showed that the CC49-QDs probe could bind TAG-72 expressed on MGC80-3 cells