98,349 research outputs found
Method to extract the primary cosmic ray spectrum from very high energy gamma-ray data and its application to SNR RX J1713.7-3946
Supernova remnants are likely to be the accelerators of the galactic cosmic
rays. Assuming the correctness of this hypothesis, we develop a method to
extract the parent cosmic ray spectrum from the VHE gamma ray flux emitted by
supernova remnants (and other gamma transparent sources). Namely, we calculate
semi-analytically the (inverse) operator which relates an arbitrary gamma ray
flux to the parent cosmic ray spectrum, without relying on any theoretical
assumption about the shape of the cosmic ray and/or photon spectrum. We
illustrate the use of this technique by applying it to the young SNR RX
J1713.7-3946 which has been observed by H.E.S.S. experiment during the last
three years. Specific implementations of the method permit to use as an input
either the parameterized VHE gamma ray flux or directly the raw data. The
possibility to detect features in the cosmic rays spectrum and the error in the
determination of the parent cosmic ray spectrum are also discussed.Comment: 20 pages, 6 figures, version accepted for publication in Phys.ReV.
Generation of Policy-Level Explanations for Reinforcement Learning
Though reinforcement learning has greatly benefited from the incorporation of
neural networks, the inability to verify the correctness of such systems limits
their use. Current work in explainable deep learning focuses on explaining only
a single decision in terms of input features, making it unsuitable for
explaining a sequence of decisions. To address this need, we introduce
Abstracted Policy Graphs, which are Markov chains of abstract states. This
representation concisely summarizes a policy so that individual decisions can
be explained in the context of expected future transitions. Additionally, we
propose a method to generate these Abstracted Policy Graphs for deterministic
policies given a learned value function and a set of observed transitions,
potentially off-policy transitions used during training. Since no restrictions
are placed on how the value function is generated, our method is compatible
with many existing reinforcement learning methods. We prove that the worst-case
time complexity of our method is quadratic in the number of features and linear
in the number of provided transitions, . By applying
our method to a family of domains, we show that our method scales well in
practice and produces Abstracted Policy Graphs which reliably capture
relationships within these domains.Comment: Accepted to Proceedings of the Thirty-Third AAAI Conference on
Artificial Intelligence (2019
Packing Sporadic Real-Time Tasks on Identical Multiprocessor Systems
In real-time systems, in addition to the functional correctness recurrent
tasks must fulfill timing constraints to ensure the correct behavior of the
system. Partitioned scheduling is widely used in real-time systems, i.e., the
tasks are statically assigned onto processors while ensuring that all timing
constraints are met. The decision version of the problem, which is to check
whether the deadline constraints of tasks can be satisfied on a given number of
identical processors, has been known -complete in the strong sense.
Several studies on this problem are based on approximations involving resource
augmentation, i.e., speeding up individual processors. This paper studies
another type of resource augmentation by allocating additional processors, a
topic that has not been explored until recently. We provide polynomial-time
algorithms and analysis, in which the approximation factors are dependent upon
the input instances. Specifically, the factors are related to the maximum ratio
of the period to the relative deadline of a task in the given task set. We also
show that these algorithms unfortunately cannot achieve a constant
approximation factor for general cases. Furthermore, we prove that the problem
does not admit any asymptotic polynomial-time approximation scheme (APTAS)
unless when the task set has constrained deadlines, i.e.,
the relative deadline of a task is no more than the period of the task.Comment: Accepted and to appear in ISAAC 2018, Yi-Lan, Taiwa
How Far Can We Go in Compute-less Networking: Computation Correctness and Accuracy
Emerging applications such as augmented reality and tactile Internet are
compute-intensive and latency-sensitive, which hampers their running in
constrained end devices alone or in the distant cloud. The stringent
requirements of such application drove to the realization of Edge computing in
which computation is offloaded near to users. Compute-less networking is an
extension of edge computing that aims at reducing computation and abridging
communication by adopting in-network computing and computation reuse.
Computation reuse aims to cache the result of computations and use them to
perform similar tasks in the future and, therefore, avoid redundant
calculations and optimize the use of resources. In this paper, we focus on the
correctness of the final output produced by computation reuse. Since the input
might not be identical but similar, the reuse of previous computation raises
questions about the accuracy of the final results. To this end, we implement a
proof of concept to study and gauge the effectiveness and efficiency of
computation reuse. We are able to reduce task completion time by up to 80%
while ensuring high correctness. We further discuss open challenges and
highlight future research directions.Comment: Accepted for publication by the IEEE Network Magazin
Evaluating ChatGPT's Decimal Skills and Feedback Generation in a Digital Learning Game
While open-ended self-explanations have been shown to promote robust learning
in multiple studies, they pose significant challenges to automated grading and
feedback in technology-enhanced learning, due to the unconstrained nature of
the students' input. Our work investigates whether recent advances in Large
Language Models, and in particular ChatGPT, can address this issue. Using
decimal exercises and student data from a prior study of the learning game
Decimal Point, with more than 5,000 open-ended self-explanation responses, we
investigate ChatGPT's capability in (1) solving the in-game exercises, (2)
determining the correctness of students' answers, and (3) providing meaningful
feedback to incorrect answers. Our results showed that ChatGPT can respond well
to conceptual questions, but struggled with decimal place values and number
line problems. In addition, it was able to accurately assess the correctness of
75% of the students' answers and generated generally high-quality feedback,
similar to human instructors. We conclude with a discussion of ChatGPT's
strengths and weaknesses and suggest several venues for extending its use cases
in digital teaching and learning.Comment: Be accepted as a Research Paper in 18th European Conference on
Technology Enhanced Learnin
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