115 research outputs found
Counterfactual Optimism: Rate Optimal Regret for Stochastic Contextual MDPs
We present the UCRL algorithm for regret minimization in Stochastic
Contextual MDPs (CMDPs). The algorithm operates under the minimal assumptions
of realizable function class, and access to offline least squares and log loss
regression oracles. Our algorithm is efficient (assuming efficient offline
regression oracles) and enjoys an regret guarantee,
with being the number of episodes, the state space, the action
space, the horizon, and and are finite function
classes, used to approximate the context-dependent dynamics and rewards,
respectively. To the best of our knowledge, our algorithm is the first
efficient and rate-optimal regret minimization algorithm for CMDPs, which
operates under the general offline function approximation setting
Efficient Rate Optimal Regret for Adversarial Contextual MDPs Using Online Function Approximation
We present the OMG-CMDP! algorithm for regret minimization in adversarial
Contextual MDPs. The algorithm operates under the minimal assumptions of
realizable function class and access to online least squares and log loss
regression oracles. Our algorithm is efficient (assuming efficient online
regression oracles), simple and robust to approximation errors. It enjoys an
regret guarantee, with being the number of episodes,
the state space, the action space, the horizon and
is the sum of the
regression oracles' regret, used to approximate the context-dependent rewards
and dynamics, respectively. To the best of our knowledge, our algorithm is the
first efficient rate optimal regret minimization algorithm for adversarial
CMDPs that operates under the minimal standard assumption of online function
approximation
RadArnomaly: Protecting Radar Systems from Data Manipulation Attacks
Radar systems are mainly used for tracking aircraft, missiles, satellites,
and watercraft. In many cases, information regarding the objects detected by
the radar system is sent to, and used by, a peripheral consuming system, such
as a missile system or a graphical user interface used by an operator. Those
systems process the data stream and make real-time, operational decisions based
on the data received. Given this, the reliability and availability of
information provided by radar systems has grown in importance. Although the
field of cyber security has been continuously evolving, no prior research has
focused on anomaly detection in radar systems. In this paper, we present a deep
learning-based method for detecting anomalies in radar system data streams. We
propose a novel technique which learns the correlation between numerical
features and an embedding representation of categorical features in an
unsupervised manner. The proposed technique, which allows the detection of
malicious manipulation of critical fields in the data stream, is complemented
by a timing-interval anomaly detection mechanism proposed for the detection of
message dropping attempts. Real radar system data is used to evaluate the
proposed method. Our experiments demonstrate the method's high detection
accuracy on a variety of data stream manipulation attacks (average detection
rate of 88% with 1.59% false alarms) and message dropping attacks (average
detection rate of 92% with 2.2% false alarms)
In Planta Colonization and Role of T6SS in Two Rice Kosakonia Endophytes.
Endophytes live inside plants and are often beneficial. Kosakonia is a novel bacterial genus that includes many diazotrophic plant-associated isolates. Plant–bacteria studies on two rice endophytic Kosakonia beneficial strains were performed, including comparative genomics, secretome profiling, in planta tests, and a field release trial. The strains are efficient rhizoplane and root endosphere colonizers and localized in the root cortex. Secretomics revealed 144 putative secreted proteins, including type VI secretory system (T6SS) proteins. A Kosakonia T6SS genomic knock-out mutant showed a significant decrease in rhizoplane and endosphere colonization ability. A field trial using rice seed inoculated with Kosakonia spp. showed no effect on plant growth promotion upon nitrogen stress and microbiome studies revealed that Kosakonia spp. were significantly more present in the inoculated rice. Comparative genomics indicated that several protein domains were enriched in plant-associated Kosakonia spp. This study highlights that Kosakonia is an important, recently classified genus involved in plant–bacteria interaction
DropCompute: simple and more robust distributed synchronous training via compute variance reduction
Background: Distributed training is essential for large scale training of
deep neural networks (DNNs). The dominant methods for large scale DNN training
are synchronous (e.g. All-Reduce), but these require waiting for all workers in
each step. Thus, these methods are limited by the delays caused by straggling
workers. Results: We study a typical scenario in which workers are straggling
due to variability in compute time. We find an analytical relation between
compute time properties and scalability limitations, caused by such straggling
workers. With these findings, we propose a simple yet effective decentralized
method to reduce the variation among workers and thus improve the robustness of
synchronous training. This method can be integrated with the widely used
All-Reduce. Our findings are validated on large-scale training tasks using 200
Gaudi Accelerators.Comment: https://github.com/paper-submissions/dropcomput
Links between Main Frequencies of Established Rotating Stall and Rotational Frequencies and/or Blade Passing Frequencies
The ratios between the main frequency of rotating stall and rotational frequency may be considered in the form of exact ratios of small natural numbers if the pressure signals in compressors during rotating stall include the rotor rotation frequency component. During rotating stall in compressors with good rotor balancing (with absence of the rotational frequency component in the frequency characteristics of pressure signals), these ratios between the main frequency of rotating stall and rotational frequency are or are not in the form of ratios of small natural numbers. The experimentally received characteristics of power spectral density of pressure signals also show the presence of components with combinations of blade passing frequency and different harmonics of main rotating stall frequency
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