5,295 research outputs found
Learning and work: Efficacy of university internships for Syrian and Ugandan education students
Internships and related strategies within the diverse family of Work-Related learning pedagogies are widely credited for bridging the gap between the world of academia and world of work. Situated in a qualitative paradigm, this evidenced-based paper shows opportunities and challenges that define the efficacy dimensions of Syrian and Ugandan university internship models for students of Education
Learning and work: Efficacy of university internships for Syrian and Ugandan education students
Internships and related strategies within the diverse family of Work-Related learning pedagogies are widely credited for bridging the gap between the world of academia and world of work. Situated in a qualitative paradigm, this evidenced-based paper shows opportunities and challenges that define the efficacy dimensions of Syrian and Ugandan university internship models for students of Education
Evaluating kernels on Xeon Phi to accelerate Gysela application
This work describes the challenges presented by porting parts ofthe Gysela
code to the Intel Xeon Phi coprocessor, as well as techniques used for
optimization, vectorization and tuning that can be applied to other
applications. We evaluate the performance of somegeneric micro-benchmark on Phi
versus Intel Sandy Bridge. Several interpolation kernels useful for the Gysela
application are analyzed and the performance are shown. Some memory-bound and
compute-bound kernels are accelerated by a factor 2 on the Phi device compared
to Sandy architecture. Nevertheless, it is hard, if not impossible, to reach a
large fraction of the peek performance on the Phi device,especially for
real-life applications as Gysela. A collateral benefit of this optimization and
tuning work is that the execution time of Gysela (using 4D advections) has
decreased on a standard architecture such as Intel Sandy Bridge.Comment: submitted to ESAIM proceedings for CEMRACS 2014 summer school version
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On the adequacy of untuned warmup for adaptive optimization
Adaptive optimization algorithms such as Adam are widely used in deep
learning. The stability of such algorithms is often improved with a warmup
schedule for the learning rate. Motivated by the difficulty of choosing and
tuning warmup schedules, recent work proposes automatic variance rectification
of Adam's adaptive learning rate, claiming that this rectified approach
("RAdam") surpasses the vanilla Adam algorithm and reduces the need for
expensive tuning of Adam with warmup. In this work, we refute this analysis and
provide an alternative explanation for the necessity of warmup based on the
magnitude of the update term, which is of greater relevance to training
stability. We then provide some "rule-of-thumb" warmup schedules, and we
demonstrate that simple untuned warmup of Adam performs more-or-less
identically to RAdam in typical practical settings. We conclude by suggesting
that practitioners stick to linear warmup with Adam, with a sensible default
being linear warmup over training iterations.Comment: AAAI 202
Human-Machine Collaborative Optimization via Apprenticeship Scheduling
Coordinating agents to complete a set of tasks with intercoupled temporal and
resource constraints is computationally challenging, yet human domain experts
can solve these difficult scheduling problems using paradigms learned through
years of apprenticeship. A process for manually codifying this domain knowledge
within a computational framework is necessary to scale beyond the
``single-expert, single-trainee" apprenticeship model. However, human domain
experts often have difficulty describing their decision-making processes,
causing the codification of this knowledge to become laborious. We propose a
new approach for capturing domain-expert heuristics through a pairwise ranking
formulation. Our approach is model-free and does not require enumerating or
iterating through a large state space. We empirically demonstrate that this
approach accurately learns multifaceted heuristics on a synthetic data set
incorporating job-shop scheduling and vehicle routing problems, as well as on
two real-world data sets consisting of demonstrations of experts solving a
weapon-to-target assignment problem and a hospital resource allocation problem.
We also demonstrate that policies learned from human scheduling demonstration
via apprenticeship learning can substantially improve the efficiency of a
branch-and-bound search for an optimal schedule. We employ this human-machine
collaborative optimization technique on a variant of the weapon-to-target
assignment problem. We demonstrate that this technique generates solutions
substantially superior to those produced by human domain experts at a rate up
to 9.5 times faster than an optimization approach and can be applied to
optimally solve problems twice as complex as those solved by a human
demonstrator.Comment: Portions of this paper were published in the Proceedings of the
International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and
in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper
consists of 50 pages with 11 figures and 4 table
Channel Access Management in Data Intensive Sensor Networks
There are considerable challenges for channel access in Data Intensive Sensor Networks - DISN, supporting Data Intensive Applications like Structural Health Monitoring. As the data load increases, considerable degradation of the key performance parameters of such sensor networks is observed. Successful packet delivery ratio drops due to frequent collisions and retransmissions. The data glut results in increased latency and energy consumption overall. With the considerable limitations on sensor node resources like battery power, this implies that excessive transmissions in response to sensor queries can lead to premature network death. After a certain load threshold the performance characteristics of traditional WSNs become unacceptable. Research work indicates that successful packet delivery ratio in 802.15.4 networks can drop from 95% to 55% as the offered network load increases from 1 packet/sec to 10 packets/sec. This result in conjunction with the fact that it is common for sensors in an SHM system to generate 6-8 packets/sec of vibration data makes it important to design appropriate channel access schemes for such data intensive applications.In this work, we address the problem of significant performance degradation in a special-purpose DISN. Our specific focus is on the medium access control layer since it gives a fine-grained control on managing channel access and reducing energy waste. The goal of this dissertation is to design and evaluate a suite of channel access schemes that ensure graceful performance degradation in special-purpose DISNs as the network traffic load increases.First, we present a case study that investigates two distinct MAC proposals based on random access and scheduling access. The results of the case study provide the motivation to develop hybrid access schemes. Next, we introduce novel hybrid channel access protocols for DISNs ranging from a simple randomized transmission scheme that is robust under channel and topology dynamics to one that utilizes limited topological information about neighboring sensors to minimize collisions and energy waste. The protocols combine randomized transmission with heuristic scheduling to alleviate network performance degradation due to excessive collisions and retransmissions. We then propose a grid-based access scheduling protocol for a mobile DISN that is scalable and decentralized. The grid-based protocol efficiently handles sensor mobility with acceptable data loss and limited overhead. Finally, we extend the randomized transmission protocol from the hybrid approaches to develop an adaptable probability-based data transmission method. This work combines probabilistic transmission with heuristics, i.e., Latin Squares and a grid network, to tune transmission probabilities of sensors, thus meeting specific performance objectives in DISNs. We perform analytical evaluations and run simulation-based examinations to test all of the proposed protocols
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