Dynamic Inference on Graphs using Structured Transition Models

Enabling robots to perform complex dynamic tasks such as picking up an object in one sweeping motion or pushing off a wall to quickly turn a corner is a challenging problem. The dynamic interactions implicit in these tasks are critical towards the successful execution of such tasks. Graph neural networks (GNNs) provide a principled way of learning the dynamics of interactive systems but can suffer from scaling issues as the number of interactions increases. Furthermore, the problem of using learned GNN-based models for optimal control is insufficiently explored. In this work, we present a method for efficiently learning the dynamics of interacting systems by simultaneously learning a dynamic graph structure and a stable and locally linear forward model of the system. The dynamic graph structure encodes evolving contact modes along a trajectory by making probabilistic predictions over the edges of the graph. Additionally, we introduce a temporal dependence in the learned graph structure which allows us to incorporate contact measurement updates during execution thus enabling more accurate forward predictions. The learned stable and locally linear dynamics enable the use of optimal control algorithms such as iLQR for long-horizon planning and control for complex interactive tasks. Through experiments in simulation and in the real world, we evaluate the performance of our method by using the learned interaction dynamics for control and demonstrate generalization to more objects and interactions not seen during training. We introduce a control scheme that takes advantage of contact measurement updates and hence is robust to prediction inaccuracies during execution

Visual Analytics and Interactive Machine Learning for Human Brain Data

Indiana University-Purdue University Indianapolis (IUPUI)This study mainly focuses on applying visualization techniques on human brain data for data exploration, quality control, and hypothesis discovery. It mainly consists of two parts: multi-modal data visualization and interactive machine learning. For multi-modal data visualization, a major challenge is how to integrate structural, functional and connectivity data to form a comprehensive visual context. We develop a new integrated visualization solution for brain imaging data by combining scientific and information visualization techniques within the context of the same anatomic structure. For interactive machine learning, we propose a new visual analytics approach to interactive machine learning. In this approach, multi-dimensional data visualization techniques are employed to facilitate user interactions with the machine learning process. This allows dynamic user feedback in different forms, such as data selection, data labeling, and data correction, to enhance the efficiency of model building

A spinning top model of formal structure and informal behaviour: dynamics of advice networks in a commercial court

The longitudinal study of advice networks among 240 judges at the Commercial Court of Paris permits the examination of learning as an interactive process. We argue that a spinning top model is a useful heuristic for intra-organizational learning in dynamic advice networks. This model proposes that a stabilized elite preserves accumulated knowledge in a community that overall experiences high turnover and systematic job rotation, and hence runs the danger of inadequately sharing knowledge among its members. We test the model by analyzing the structure and dynamics of advice networks among judges at the Commercial Court of Paris. This dynamic structure reflects the informal homophilous preferences among judges organized in a strong formal system, a high relational turnover in the selection of advisors, and the emergence of an elite of senior advisors that stabilizes the learning process - much like the behavior of a spinning top. This case study also identifies an endogenous process of increasing and then decreasing centralization of this network over time, raising questions about the maintenance of the stability of the pecking order and about the relationship between learning and seniority. Results illustrate the importance of dynamic over static network analysis and call for a renewed attention to formal structure in organizations

A spinning top model of formal structure and informal behaviour: dynamics of advice networks in a commercial court

The longitudinal study of advice networks among 240 judges at the Commercial Court of Paris permits the examination of learning as an interactive process. We argue that a spinning top model is a useful heuristic for intra-organizational learning in dynamic advice networks. This model proposes that a stabilized elite preserves accumulated knowledge in a community that overall experiences high turnover and systematic job rotation, and hence runs the danger of inadequately sharing knowledge among its members. We test the model by analyzing the structure and dynamics of advice networks among judges at the Commercial Court of Paris. This dynamic structure reflects the informal homophilous preferences among judges organized in a strong formal system, a high relational turnover in the selection of advisors, and the emergence of an elite of senior advisors that stabilizes the learning process - much like the behavior of a spinning top. This case study also identifies an endogenous process of increasing and then decreasing centralization of this network over time, raising questions about the maintenance of the stability of the pecking order and about the relationship between learning and seniority. Results illustrate the importance of dynamic over static network analysis and call for a renewed attention to formal structure in organizations

Novel Dynamic Structure Neural Network for Optical Character Recognition

[[abstract]]This paper presents a novel dynamic structure neural network (DSNN) and a learning algorithm for training DSNN. The performance of a neural network system depends on several factors. In that, the architecture of a neural network plays an important role. The objective of the developing DSNN is to avoid trial-and-error process for designing a neural network system. The architecture of DSNN consists of a three-dimensional set of neurons with input/output nodes and connection weights. Designers can define the maximum connection number of each neuron. Moreover, designers can manually deploy neurons in a virtual 3D space, or randomly generate the system structure by the proposed learning algorithm. This work also develops an automatic restructuring algorithm integrated in the proposed learning algorithm to improve the system performance. Due to the novel dynamic structure of DSNN and the restructuring algorithm, the design of DSNN is fast and convenient. Furthermore, DSNN is implemented in C++ with man-machine interactive procedures and tested on many cases with very promising results.[[conferencetype]]國際[[conferencedate]]20041218~20041221[[iscallforpapers]]Y[[conferencelocation]]Rome, Ital

Student Understanding of DNA Structure–Function Relationships Improves from Using 3D Learning Modules with Dynamic 3D Printed Models

Understanding the relationship between molecular structure and function represents an important goal of undergraduate life sciences. Although evidence suggests that handling physical models supports gains in student understanding of structure–function relationships, such models have not been widely implemented in biochemistry classrooms. Three-dimensional (3D) printing represents an emerging cost-effective means of producing molecular models to help students investigate structure–function concepts. We developed three interactive learning modules with dynamic 3D printed models to help biochemistry students visualize biomolecular structures and address particular misconceptions. These modules targeted specific learning objectives related to DNA and RNA structure, transcription factor-DNA interactions, and DNA supercoiling dynamics. We also designed accompanying assessments to gauge student learning. Students responded favorably to the modules and showed normalized learning gains of 49% with respect to their ability to understand and relate molecular structures to biochemical functions. By incorporating accurate 3D printed structures, these modules represent a novel advance in instructional design for biomolecular visualization. We provide instructors with the materials necessary to incorporate each module in the classroom, including instructions for acquiring and distributing the models, activities, and assessments. 9 supplemental files attached (below

Educational Platform SOLL with the IoT

The Internet of Things (IoT) is a network composed of various objects and devices connected to the Internet, which emerge with great potential for education. Thus, in order to verify the potential of IoT in an interdisciplinary approach of the science curriculum in the 3rd Cycle of Basic Education emerges project SOLL: Intelligent Objects Linked to Learning, which is an interactive, dynamic and interdisciplinary learning platform, supported by a set of technologies that collect and store data from a greenhouse for later interdisciplinary analysis. In this article, the platform’s architecture is exposed and, from a mixed methodology - student questionnaires, teacher focus group interviews and continuous observation of participants recorded in the researcher’s diary - the data obtained show that this platform respond to the new learning community structure, by adopting a different learning model, with exploration of interests and enrichment of educational experiences.info:eu-repo/semantics/publishedVersio