The Distributed Messaging Systems (DMSs) used in IoT systems require timely and reliable data dissemination, which can be achieved through configurable parameters. However, the high-dimensional configuration space makes it difficult for users to find the best options that maximize application throughput while meeting specific latency constraints. Existing approaches to automatic software profiling have limitations, such as only optimizing throughput, not guaranteeing explicit latency limitations, and resulting in local optima due to discretizing parameter ranges. To overcome these challenges, a novel configuration tuning system called DMSConfig is proposed that uses machine learning and deep reinforcement learning. DMSConfig interacts with a data-driven environment prediction model, avoiding the cost of online interactions with the production environment. DMSConfig employs the deep deterministic policy gradient (DDPG) method and a custom reward mechanism to make configuration decisions based on predicted DMS states and performance. Experiments show that DMSConfig performs significantly better than the default configuration, is highly adaptive to serve tuning requests with different latency boundaries, and has similar throughput to prevalent parameter tuning tools with fewer latency violations.
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Offline estimation of the dynamical model of a Markov Decision Process (MDP) is a non-trivial task that greatly depends on the data available in the learning phase. Sometimes the dynamics of the model is invariant with respect to some transformations of the current state and action. Recent works showed that an expert-guided pipeline relying on Density Estimation methods as Deep Neural Network based Normalizing Flows effectively detects this structure in deterministic environments, both categorical and continuous-valued. The acquired knowledge can be exploited to augment the original data set, leading eventually to a reduction in the distributional shift between the true and the learned model. Such data augmentation technique can be exploited as a preliminary process to be executed before adopting an Offline Reinforcement Learning architecture, increasing its performance. In this work we extend the paradigm to also tackle non-deterministic MDPs, in particular, 1) we propose a detection threshold in categorical environments based on statistical distances, and 2) we show that the former results lead to a performance improvement when solving the learned MDP and then applying the optimized policy in the real environment.
The rapid development of parallel and distributed computing paradigms has brought about great revolution in computing. Thanks to the intrinsic parallelism of evolutionary computation (EC), it is natural to implement EC on parallel and distributed computing systems. On the one hand, the computing power provided by parallel computing systems can significantly improve the efficiency and scalability of EC. On the other hand, data are collected and processed in a distributed manner, which brings a novel development direction and new challenges to EC. In this paper, we intend to give a systematic review on distributed EC (DEC). First, a new taxonomy for DEC is proposed from top design mechanism to bottom implementation mechanism. Based on this taxonomy, existing studies on DEC are reviewed in terms of purpose, parallel structure of the algorithm, parallel model for implementation, and the implementation environment. Second, we clarify two major purposes of DEC, i.e., improving efficiency through parallel processing for centralized optimization and cooperating distributed individuals/sub-populations with partial information to perform distributed optimization. Third, noting that the latter purpose of DEC is an emerging and attractive trend for EC with the booming of spatially distributed paradigms, this paper gives a systematic definition of the distributed optimization and classifies it into dimension distributed-, data distributed-, and objective distributed-optimization problems. Formal formulations for these problems are provided and various DEC studies on these problems are reviewed. We also discuss challenges and potential research directions, aiming to enlighten the design of DEC and pave the way for future developments.
Novel non-volatile memory (NVM) technologies offer high-speed and high-density data storage. In addition, they overcome the von Neumann bottleneck by enabling computing-in-memory (CIM). Various computer architectures have been proposed to integrate CIM blocks in their design, forming a mixed-signal system to combine the computational benefits of CIM with the robustness of conventional CMOS. Novel electronic design automation (EDA) tools are necessary to design and manufacture these so-called neuromorphic systems. Furthermore, EDA tools must consider the impact of security vulnerabilities, as hardware security attacks have increased in recent years. Existing information flow analysis (IFA) frameworks offer an automated tool-suite to uphold the confidentiality property for sensitive data during the design of hardware. However, currently available mixed-signal EDA tools are not capable of analyzing the information flow of neuromorphic systems. To illustrate the shortcomings, we develop information flow protocols for NVMs that can be easily integrated in the already existing tool-suites. We show the limitation of the state-of-the-art by analyzing the flow from sensitive signals through multiple memristive crossbar structures to potential untrusted components and outputs. Finally, we provide a thorough discussion of the merits and flaws of the mixed-signal IFA frameworks on neuromorphic systems.
Large language models (LLMs) are able to do accurate classification with zero or only a few examples (in-context learning). We show a prompting system that enables regression with uncertainty for in-context learning with frozen LLM (GPT-3, GPT-3.5, and GPT-4) models, allowing predictions without features or architecture tuning. By incorporating uncertainty, our approach enables Bayesian optimization for catalyst or molecule optimization using natural language, eliminating the need for training or simulation. Here, we performed the optimization using the synthesis procedure of catalysts to predict properties. Working with natural language mitigates difficulty synthesizability since the literal synthesis procedure is the model's input. We showed that in-context learning could improve past a model context window (maximum number of tokens the model can process at once) as data is gathered via example selection, allowing the model to scale better. Although our method does not outperform all baselines, it requires zero training, feature selection, and minimal computing while maintaining satisfactory performance. We also find Gaussian Process Regression on text embeddings is strong at Bayesian optimization. The code is available in our GitHub repository: //github.com/ur-whitelab/BO-LIFT
In building practical applications of evolutionary computation (EC), two optimizations are essential. First, the parameters of the search method need to be tuned to the domain in order to balance exploration and exploitation effectively. Second, the search method needs to be distributed to take advantage of parallel computing resources. This paper presents BLADE (BLAnket Distributed Evolution) as an approach to achieving both goals simultaneously. BLADE uses blankets (i.e., masks on the genetic representation) to tune the evolutionary operators during the search, and implements the search through hub-and-spoke distribution. In the paper, (1) the blanket method is formalized for the (1 + 1)EA case as a Markov chain process. Its effectiveness is then demonstrated by analyzing dominant and subdominant eigenvalues of stochastic matrices, suggesting a generalizable theory; (2) the fitness-level theory is used to analyze the distribution method; and (3) these insights are verified experimentally on three benchmark problems, showing that both blankets and distribution lead to accelerated evolution. Moreover, a surprising synergy emerges between them: When combined with distribution, the blanket approach achieves more than $n$-fold speedup with $n$ clients in some cases. The work thus highlights the importance and potential of optimizing evolutionary computation in practical applications.
The advances in deep learning have enabled machine learning methods to outperform human beings in various areas, but it remains a great challenge for a well-trained model to quickly adapt to a new task. One promising solution to realize this goal is through meta-learning, also known as learning to learn, which has achieved promising results in few-shot learning. However, current approaches are still enormously different from human beings' learning process, especially in the ability to extract structural and transferable knowledge. This drawback makes current meta-learning frameworks non-interpretable and hard to extend to more complex tasks. We tackle this problem by introducing concept discovery to the few-shot learning problem, where we achieve more effective adaptation by meta-learning the structure among the data features, leading to a composite representation of the data. Our proposed method Concept-Based Model-Agnostic Meta-Learning (COMAML) has been shown to achieve consistent improvements in the structured data for both synthesized datasets and real-world datasets.
Quality-Diversity (QD) algorithms are designed to generate collections of high-performing solutions while maximizing their diversity in a given descriptor space. However, in the presence of unpredictable noise, the fitness and descriptor of the same solution can differ significantly from one evaluation to another, leading to uncertainty in the estimation of such values. Given the elitist nature of QD algorithms, they commonly end up with many degenerate solutions in such noisy settings. In this work, we introduce Archive Reproducibility Improvement Algorithm (ARIA); a plug-and-play approach that improves the reproducibility of the solutions present in an archive. We propose it as a separate optimization module, relying on natural evolution strategies, that can be executed on top of any QD algorithm. Our module mutates solutions to (1) optimize their probability of belonging to their niche, and (2) maximize their fitness. The performance of our method is evaluated on various tasks, including a classical optimization problem and two high-dimensional control tasks in simulated robotic environments. We show that our algorithm enhances the quality and descriptor space coverage of any given archive by at least 50%.
With the breakthrough of AlphaGo, deep reinforcement learning becomes a recognized technique for solving sequential decision-making problems. Despite its reputation, data inefficiency caused by its trial and error learning mechanism makes deep reinforcement learning hard to be practical in a wide range of areas. Plenty of methods have been developed for sample efficient deep reinforcement learning, such as environment modeling, experience transfer, and distributed modifications, amongst which, distributed deep reinforcement learning has shown its potential in various applications, such as human-computer gaming, and intelligent transportation. In this paper, we conclude the state of this exciting field, by comparing the classical distributed deep reinforcement learning methods, and studying important components to achieve efficient distributed learning, covering single player single agent distributed deep reinforcement learning to the most complex multiple players multiple agents distributed deep reinforcement learning. Furthermore, we review recently released toolboxes that help to realize distributed deep reinforcement learning without many modifications of their non-distributed versions. By analyzing their strengths and weaknesses, a multi-player multi-agent distributed deep reinforcement learning toolbox is developed and released, which is further validated on Wargame, a complex environment, showing usability of the proposed toolbox for multiple players and multiple agents distributed deep reinforcement learning under complex games. Finally, we try to point out challenges and future trends, hoping this brief review can provide a guide or a spark for researchers who are interested in distributed deep reinforcement learning.
The combination of Reinforcement Learning (RL) with deep learning has led to a series of impressive feats, with many believing (deep) RL provides a path towards generally capable agents. However, the success of RL agents is often highly sensitive to design choices in the training process, which may require tedious and error-prone manual tuning. This makes it challenging to use RL for new problems, while also limits its full potential. In many other areas of machine learning, AutoML has shown it is possible to automate such design choices and has also yielded promising initial results when applied to RL. However, Automated Reinforcement Learning (AutoRL) involves not only standard applications of AutoML but also includes additional challenges unique to RL, that naturally produce a different set of methods. As such, AutoRL has been emerging as an important area of research in RL, providing promise in a variety of applications from RNA design to playing games such as Go. Given the diversity of methods and environments considered in RL, much of the research has been conducted in distinct subfields, ranging from meta-learning to evolution. In this survey we seek to unify the field of AutoRL, we provide a common taxonomy, discuss each area in detail and pose open problems which would be of interest to researchers going forward.
Deployment of Internet of Things (IoT) devices and Data Fusion techniques have gained popularity in public and government domains. This usually requires capturing and consolidating data from multiple sources. As datasets do not necessarily originate from identical sensors, fused data typically results in a complex data problem. Because military is investigating how heterogeneous IoT devices can aid processes and tasks, we investigate a multi-sensor approach. Moreover, we propose a signal to image encoding approach to transform information (signal) to integrate (fuse) data from IoT wearable devices to an image which is invertible and easier to visualize supporting decision making. Furthermore, we investigate the challenge of enabling an intelligent identification and detection operation and demonstrate the feasibility of the proposed Deep Learning and Anomaly Detection models that can support future application that utilizes hand gesture data from wearable devices.
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