389,687 research outputs found
Probing the arrangement of hyperplanes
AbstractIn this paper we investigate the combinatorial complexity of an algorithm to determine the geometry and the topology related to an arrangement of hyperplanes in multi-dimensional Euclidean space from the “probing” on the arrangement. The “probing” by a flat means the operation from which we can obtain the intersection of the flat and the arrangement. For a finite set H of hyperplanes in Ed, we obtain the worst-case number of fixed direction line probes and that of flat probes to determine a generic line of H and H itself. We also mention the bound for the computational complexity of these algorithms based on the efficient line probing algorithm which uses the dual transform to compute a generic line of H.We also consider the problem to approximate arrangements by extending the point probing model, which have connections with computational learning theory such as learning a network of threshold functions, and introduce the vertical probing model and the level probing model. It is shown that the former is closely related to the finger probing for a polyhedron and that the latter depends on the dual graph of the arrangement.The probing for an arrangement can be used to obtain the solution for a given system of algebraic equations by decomposing the μ-resultant into linear factors. It also has interesting applications in robotics such as a motion planning using an ultrasonic device that can detect the distances to obstacles along a specified direction
Online Domain Adaptation for Multi-Object Tracking
Automatically detecting, labeling, and tracking objects in videos depends
first and foremost on accurate category-level object detectors. These might,
however, not always be available in practice, as acquiring high-quality large
scale labeled training datasets is either too costly or impractical for all
possible real-world application scenarios. A scalable solution consists in
re-using object detectors pre-trained on generic datasets. This work is the
first to investigate the problem of on-line domain adaptation of object
detectors for causal multi-object tracking (MOT). We propose to alleviate the
dataset bias by adapting detectors from category to instances, and back: (i) we
jointly learn all target models by adapting them from the pre-trained one, and
(ii) we also adapt the pre-trained model on-line. We introduce an on-line
multi-task learning algorithm to efficiently share parameters and reduce drift,
while gradually improving recall. Our approach is applicable to any linear
object detector, and we evaluate both cheap "mini-Fisher Vectors" and expensive
"off-the-shelf" ConvNet features. We quantitatively measure the benefit of our
domain adaptation strategy on the KITTI tracking benchmark and on a new dataset
(PASCAL-to-KITTI) we introduce to study the domain mismatch problem in MOT.Comment: To appear at BMVC 201
Online Weighted Q-Ensembles for Reduced Hyperparameter Tuning in Reinforcement Learning
Reinforcement learning is a promising paradigm for learning robot control,
allowing complex control policies to be learned without requiring a dynamics
model. However, even state of the art algorithms can be difficult to tune for
optimum performance. We propose employing an ensemble of multiple reinforcement
learning agents, each with a different set of hyperparameters, along with a
mechanism for choosing the best performing set(s) on-line. In the literature,
the ensemble technique is used to improve performance in general, but the
current work specifically addresses decreasing the hyperparameter tuning
effort. Furthermore, our approach targets on-line learning on a single robotic
system, and does not require running multiple simulators in parallel. Although
the idea is generic, the Deep Deterministic Policy Gradient was the model
chosen, being a representative deep learning actor-critic method with good
performance in continuous action settings but known high variance. We compare
our online weighted q-ensemble approach to q-average ensemble strategies
addressed in literature using alternate policy training, as well as online
training, demonstrating the advantage of the new approach in eliminating
hyperparameter tuning. The applicability to real-world systems was validated in
common robotic benchmark environments: the bipedal robot half cheetah and the
swimmer. Online Weighted Q-Ensemble presented overall lower variance and
superior results when compared with q-average ensembles using randomized
parameterizations
Efficient embedding of information and knowledge into CSCL applications
This study aims to explore two crucial aspects of collaborative work and learning: the importance of enabling CSCL applications, on the one hand, to capture and structure the information generated by group activity and, on the other hand, to extract the relevant knowledge in order to provide learners and tutors with efficient awareness and support as regards collaboration. To this end, we first identify and define the main types of information generated in on-line group activity and then propose a process for efficiently embedding this information and the knowledge extracted from it into CSCL applications for awareness and feedback purposes. The conceptual model proposed finally gave rise to the design and implementation of a CSCL generic platform, called the Collaborative Learning Purpose Library (CLPL), which serves as a basis for the systematic development of collaborative learning applications and for providing full support to the mentioned process of knowledge management.Peer ReviewedPostprint (author's final draft
Performance Appraisal Meeting Fundamentals Project Documentation
ARAMARK, a large global organization is undergoing a change in their performance management model. In the current model, a performance appraisal form documenting the employee’s performance over the past 12 months is to be completed annually. In addition, a performance appraisal meeting should take place between the manager and employee to discuss what has been documented on the appraisal form and to establish performance goals for the coming year. This is currently viewed by management as an activity that needs to be completed just to check a box of completion rather than a tool to improve performance. The performance feedback that managers provide on the appraisal form is generic in nature and meetings to discuss performance rarely happen. The company is gradually shifting to a pay for performance model. Once the new model is in place comprehensive written performance appraisals will need to be completed and meetings to discuss performance will be required to take place. Due to the size of the company (12 Lines of Business and approximately 255,000 employees globally), the new pay for performance model will be implemented gradually. The first line of business to use the new model will be K-12 Education. Front line managers within the Education K-12 line of business (LOB), many who are new to management, have not been provided any training on the basics of performance appraisals. Many managers are afraid to facilitate the appraisal meeting not knowing what the employee’s reaction will be to their assessment. To address this gap it is proposed that the front-line managers complete a 45-minute e-learning course, Performance Appraisal Meeting Fundamentals. The e- learning course will be designed to increase the front line manager’s skill level with conducting appraisal meetings by familiarizing them with preparation techniques for the meeting and areas to be covered during the actual meeting. Using mini branching scenarios, it will also address skills and techniques for managing potential emotional responses from the appraisee
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