1,869 research outputs found
Greedy Methods in Plume Detection, Localization and Tracking
Greedy method, as an efficient computing tool, can be applied to various combinatorial or nonlinear optimization problems where finding the global optimum is difficult, if not computationally infeasible. A greedy algorithm has the nature of making the locally optimal choice at each stage and then solving the subproblems that arise later. It iteratively make
Multi-View 3D Object Detection Network for Autonomous Driving
This paper aims at high-accuracy 3D object detection in autonomous driving
scenario. We propose Multi-View 3D networks (MV3D), a sensory-fusion framework
that takes both LIDAR point cloud and RGB images as input and predicts oriented
3D bounding boxes. We encode the sparse 3D point cloud with a compact
multi-view representation. The network is composed of two subnetworks: one for
3D object proposal generation and another for multi-view feature fusion. The
proposal network generates 3D candidate boxes efficiently from the bird's eye
view representation of 3D point cloud. We design a deep fusion scheme to
combine region-wise features from multiple views and enable interactions
between intermediate layers of different paths. Experiments on the challenging
KITTI benchmark show that our approach outperforms the state-of-the-art by
around 25% and 30% AP on the tasks of 3D localization and 3D detection. In
addition, for 2D detection, our approach obtains 10.3% higher AP than the
state-of-the-art on the hard data among the LIDAR-based methods.Comment: To appear in IEEE Conference on Computer Vision and Pattern
Recognition (CVPR) 201
Voice and Action: Sell-Side Analysis and Hedge Fund Activism
Recent literature has shown hedge fund activism to be an important external corporate governance mechanism. Sell-side analysts, however, provide idea generation and analysis to buy-side clients including hedge funds. Using a propensity score matched sample, we examine sell-side analyst activity around hedge fund activism. We find that declining trends in analyst coverage begin in the year before hedge fund intervention and continue afterward. Stock market responses to analyst reports are negative before hedge fund intervention but revert to positive after. We introduce a new textual analysis dictionary to identify the activism objectives and tactics of Brav, Jiang, Partnoy, and Thomas (2008) within analyst reports and show pre-event sell-side reports contain significantly more language related to subsequent activism. Higher activism dictionary content in sell-side reports is correlated with activism-date target stock performance and predicts multiple activist interventions. Our results suggest that critical voice of sell-side analysis reveals coverage firm flaws that influence subsequent hedge fund intervention outcomes
Enhancing Stock Movement Prediction with Adversarial Training
This paper contributes a new machine learning solution for stock movement
prediction, which aims to predict whether the price of a stock will be up or
down in the near future. The key novelty is that we propose to employ
adversarial training to improve the generalization of a neural network
prediction model. The rationality of adversarial training here is that the
input features to stock prediction are typically based on stock price, which is
essentially a stochastic variable and continuously changed with time by nature.
As such, normal training with static price-based features (e.g. the close
price) can easily overfit the data, being insufficient to obtain reliable
models. To address this problem, we propose to add perturbations to simulate
the stochasticity of price variable, and train the model to work well under
small yet intentional perturbations. Extensive experiments on two real-world
stock data show that our method outperforms the state-of-the-art solution with
3.11% relative improvements on average w.r.t. accuracy, validating the
usefulness of adversarial training for stock prediction task.Comment: IJCAI 201
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