243 research outputs found
Perspective: Composition–structure–property mapping in high-throughput experiments: Turning data into knowledge
With their ability to rapidly elucidate composition-structure-property relationships, high-throughput experimental studies have revolutionized how materials are discovered, optimized, and commercialized. It is now possible to synthesize and characterize high-throughput libraries that systematically address thousands of individual cuts of fabrication parameter space. An unresolved issue remains transforming structural characterization data into phase mappings. This difficulty is related to the complex information present in diffraction and spectroscopic data and its variation with composition and processing. We review the field of automated phase diagram attribution and discuss the impact that emerging computational approaches will have in the generation of phase diagrams and beyond
Human-In-the-Loop for Bayesian Autonomous Materials Phase Mapping
Autonomous experimentation (AE) combines machine learning and research
hardware automation in a closed loop, guiding subsequent experiments toward
user goals. As applied to materials research, AE can accelerate materials
exploration, reducing time and cost compared to traditional Edisonian studies.
Additionally, integrating knowledge from diverse sources including theory,
simulations, literature, and domain experts can boost AE performance. Domain
experts may provide unique knowledge addressing tasks that are difficult to
automate. Here, we present a set of methods for integrating human input into an
autonomous materials exploration campaign for composition-structure phase
mapping. The methods are demonstrated on x-ray diffraction data collected from
a thin film ternary combinatorial library. At any point during the campaign,
the user can choose to provide input by indicating regions-of-interest, likely
phase regions, and likely phase boundaries based on their prior knowledge
(e.g., knowledge of the phase map of a similar material system), along with
quantifying their certainty. The human input is integrated by defining a set of
probabilistic priors over the phase map. Algorithm output is a probabilistic
distribution over potential phase maps, given the data, model, and human input.
We demonstrate a significant improvement in phase mapping performance given
appropriate human input
Edge Learning for 6G-enabled Internet of Things: A Comprehensive Survey of Vulnerabilities, Datasets, and Defenses
The ongoing deployment of the fifth generation (5G) wireless networks
constantly reveals limitations concerning its original concept as a key driver
of Internet of Everything (IoE) applications. These 5G challenges are behind
worldwide efforts to enable future networks, such as sixth generation (6G)
networks, to efficiently support sophisticated applications ranging from
autonomous driving capabilities to the Metaverse. Edge learning is a new and
powerful approach to training models across distributed clients while
protecting the privacy of their data. This approach is expected to be embedded
within future network infrastructures, including 6G, to solve challenging
problems such as resource management and behavior prediction. This survey
article provides a holistic review of the most recent research focused on edge
learning vulnerabilities and defenses for 6G-enabled IoT. We summarize the
existing surveys on machine learning for 6G IoT security and machine
learning-associated threats in three different learning modes: centralized,
federated, and distributed. Then, we provide an overview of enabling emerging
technologies for 6G IoT intelligence. Moreover, we provide a holistic survey of
existing research on attacks against machine learning and classify threat
models into eight categories, including backdoor attacks, adversarial examples,
combined attacks, poisoning attacks, Sybil attacks, byzantine attacks,
inference attacks, and dropping attacks. In addition, we provide a
comprehensive and detailed taxonomy and a side-by-side comparison of the
state-of-the-art defense methods against edge learning vulnerabilities.
Finally, as new attacks and defense technologies are realized, new research and
future overall prospects for 6G-enabled IoT are discussed
Assuring the Machine Learning Lifecycle: Desiderata, Methods, and Challenges
Machine learning has evolved into an enabling technology for a wide range of highly successful applications. The potential for this success to continue and accelerate has placed machine learning (ML) at the top of research, economic and political agendas. Such unprecedented interest is fuelled by a vision of ML applicability extending to healthcare, transportation, defence and other domains of great societal importance. Achieving this vision requires the use of ML in safety-critical applications that demand levels of assurance beyond those needed for current ML applications. Our paper provides a comprehensive survey of the state-of-the-art in the assurance of ML, i.e. in the generation of evidence that ML is sufficiently safe for its intended use. The survey covers the methods capable of providing such evidence at different stages of the machine learning lifecycle, i.e. of the complex, iterative process that starts with the collection of the data used to train an ML component for a system, and ends with the deployment of that component within the system. The paper begins with a systematic presentation of the ML lifecycle and its stages. We then define assurance desiderata for each stage, review existing methods that contribute to achieving these desiderata, and identify open challenges that require further research
DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models
Generative Pre-trained Transformer (GPT) models have exhibited exciting
progress in capabilities, capturing the interest of practitioners and the
public alike. Yet, while the literature on the trustworthiness of GPT models
remains limited, practitioners have proposed employing capable GPT models for
sensitive applications to healthcare and finance - where mistakes can be
costly. To this end, this work proposes a comprehensive trustworthiness
evaluation for large language models with a focus on GPT-4 and GPT-3.5,
considering diverse perspectives - including toxicity, stereotype bias,
adversarial robustness, out-of-distribution robustness, robustness on
adversarial demonstrations, privacy, machine ethics, and fairness. Based on our
evaluations, we discover previously unpublished vulnerabilities to
trustworthiness threats. For instance, we find that GPT models can be easily
misled to generate toxic and biased outputs and leak private information in
both training data and conversation history. We also find that although GPT-4
is usually more trustworthy than GPT-3.5 on standard benchmarks, GPT-4 is more
vulnerable given jailbreaking system or user prompts, potentially due to the
reason that GPT-4 follows the (misleading) instructions more precisely. Our
work illustrates a comprehensive trustworthiness evaluation of GPT models and
sheds light on the trustworthiness gaps. Our benchmark is publicly available at
https://decodingtrust.github.io/
- …