A Decomposition Algorithm for Learning Bayesian Networks Based on Scoring Function

Learning Bayesian network (BN) structure from data is a typical NP-hard problem. But almost existing algorithms have the very high complexity when the number of variables is large. In order to solve this problem(s), we present an algorithm that integrates with a decomposition-based approach and a scoring-function-based approach for learning BN structures. Firstly, the proposed algorithm decomposes the moral graph of BN into its maximal prime subgraphs. Then it orientates the local edges in each subgraph by the K2-scoring greedy searching. The last step is combining directed subgraphs to obtain final BN structure. The theoretical and experimental results show that our algorithm can efficiently and accurately identify complex network structures from small data set

FOLT: Fast Multiple Object Tracking from UAV-captured Videos Based on Optical Flow

Multiple object tracking (MOT) has been successfully investigated in computer vision. However, MOT for the videos captured by unmanned aerial vehicles (UAV) is still challenging due to small object size, blurred object appearance, and very large and/or irregular motion in both ground objects and UAV platforms. In this paper, we propose FOLT to mitigate these problems and reach fast and accurate MOT in UAV view. Aiming at speed-accuracy trade-off, FOLT adopts a modern detector and light-weight optical flow extractor to extract object detection features and motion features at a minimum cost. Given the extracted flow, the flow-guided feature augmentation is designed to augment the object detection feature based on its optical flow, which improves the detection of small objects. Then the flow-guided motion prediction is also proposed to predict the object's position in the next frame, which improves the tracking performance of objects with very large displacements between adjacent frames. Finally, the tracker matches the detected objects and predicted objects using a spatially matching scheme to generate tracks for every object. Experiments on Visdrone and UAVDT datasets show that our proposed model can successfully track small objects with large and irregular motion and outperform existing state-of-the-art methods in UAV-MOT tasks.Comment: Accepted by ACM Multi-Media 202

Making the Invisible Visible: Action Recognition Through Walls and Occlusions

Understanding people's actions and interactions typically depends on seeing them. Automating the process of action recognition from visual data has been the topic of much research in the computer vision community. But what if it is too dark, or if the person is occluded or behind a wall? In this paper, we introduce a neural network model that can detect human actions through walls and occlusions, and in poor lighting conditions. Our model takes radio frequency (RF) signals as input, generates 3D human skeletons as an intermediate representation, and recognizes actions and interactions of multiple people over time. By translating the input to an intermediate skeleton-based representation, our model can learn from both vision-based and RF-based datasets, and allow the two tasks to help each other. We show that our model achieves comparable accuracy to vision-based action recognition systems in visible scenarios, yet continues to work accurately when people are not visible, hence addressing scenarios that are beyond the limit of today's vision-based action recognition.Comment: ICCV 2019. The first two authors contributed equally to this pape

Novel Self-Organized Structure of a Ag-S Complex on the Ag(111) Surface below Room Temperature

A well-ordered, self-organized dot-row structure appears after adsorption of S on Ag(111) at 200 K. This dot-row motif, which exhibits fixed spacing between dots within rows, is present over a wide range of coverage. The dots are probably Ag3S3 clusters with adsorbed S in the spaces between dots. Dynamic rearrangements are observed. Small domains of aligned dot-rows form during adsorption and grow quickly after adsorption ends. The domains also exhibit large equilibrium fluctuations after adsorption. The dot-row structure disappears reversibly upon heating above 200 K and transforms reversibly to an elongated island structure upon cooling below 200 K. DFT supports the assignment of the dots as Ag3S3 trimers and also lends insight into the possible origins of other structures observed in this complex system

Learning Bayesian Networks in the Space of Structures by a Hybrid Optimization Algorithm

Bayesian networks (BNs) are one of the most widely used class for machine learning and decision making tasks especially in uncertain domains. However, learning BN structure from data is a typical NP-hard problem. In this paper, we present a novel hybrid algorithm for BN structure learning, called MMABC. It’s based on a recently introduced meta-heuristic, which has been successfully applied to solve a variety of optimization problems: Artificial Bee Colony (ABC). MMABC algorithm consists of three phases: (i) obtain an initial undirected graph by the subroutine MMPC. (ii) Generate the initial population of solutions based on the undirected graph and (iii) perform the ABC algorithm to orient the edges. We describe all the elements necessary to tackle our learning problem, and experimentally compare the performance of our algorithm with two state-of-the-art algorithms reported in the literature. Computational results demonstrate that our algorithm achieves better performance than other two related algorithms

Destabilization of Ag nanoislands on Ag(100) by adsorbed sulfur

Sulfur accelerates coarsening of Ag nanoislands on Ag(100) at 300 K, and this effect is enhanced with increasing sulfur coverage over a range spanning a few hundredths of a monolayer, to nearly 0.25 monolayers. We propose that acceleration of coarsening in this system is tied to the formation of AgS2 clusters primarily at step edges. These clusters can transport Ag more efficiently than can Ag adatoms (due to a lower diffusion barrier and comparable formation energy). The mobility of isolated sulfur on Ag(100) is very low so that formation of the complex is kinetically limited at low sulfur coverages, and thus enhancement is minimal. However, higher sulfur coverages force the population of sites adjacent to step edges, so that formation of the cluster is no longer limited by diffusion of sulfur across terraces. Sulfur exerts a much weaker effect on the rate of coarsening on Ag(100) than it does on Ag(111). This is consistent with theory, which shows that the difference between the total energy barrier for coarsening with and without sulfur is also much smaller on Ag(100) than on Ag(111)

Dashboards for Real-time Monitoring of Winter Operations Activities and After-action Assessment

The Indiana Department of Transportation (INDOT) operates a fleet of nearly 1100 snowplows and spends up to $60M annually on snow removal and de-icing as part of their winter operation maintenance activities. Systematically allocating resources and optimizing material application rates can potentially save revenue that can be reallocated for other roadway maintenance operations. Modern snowplows are beginning to be equipped with a variety of Mobile Road Weather Information Sensors (MARWIS) which can provide a host of analytical data characterizing on-the-ground conditions during periods of wintry precipitation. Traffic speeds fused with road conditions and precipitation data from weather stations provide a uniquely detailed look at the progression of a winter event and the performance of the fleet. This research uses a combination of traffic speeds, MARWIS and North American Land Data Assimilation System (NLDAS) data to develop real-time dashboards characterizing the impact of precipitation and pavement surface temperature on mobility. Twenty heavy snow events were identified for the state of Indiana from November 2018 through April 2019. Two particular instances, that impacted 182 miles and 231 miles of interstate at their peaks occurred in January and March, respectively, and were used as a case study for this paper. The dashboards proposed in this paper may prove to be particularly useful for agencies in tracking fleet activity through a winter storm, helping in resource allocation and scheduling and forecasting resource needs