182 research outputs found
DataCI: A Platform for Data-Centric AI on Streaming Data
We introduce DataCI, a comprehensive open-source platform designed
specifically for data-centric AI in dynamic streaming data settings. DataCI
provides 1) an infrastructure with rich APIs for seamless streaming dataset
management, data-centric pipeline development and evaluation on streaming
scenarios, 2) an carefully designed versioning control function to track the
pipeline lineage, and 3) an intuitive graphical interface for a better
interactive user experience. Preliminary studies and demonstrations attest to
the easy-to-use and effectiveness of DataCI, highlighting its potential to
revolutionize the practice of data-centric AI in streaming data contexts.Comment: 3 pages, 4 figure
Relative Policy-Transition Optimization for Fast Policy Transfer
We consider the problem of policy transfer between two Markov Decision
Processes (MDPs). We introduce a lemma based on existing theoretical results in
reinforcement learning to measure the relativity gap between two arbitrary
MDPs, that is the difference between any two cumulative expected returns
defined on different policies and environment dynamics. Based on this lemma, we
propose two new algorithms referred to as Relative Policy Optimization (RPO)
and Relative Transition Optimization (RTO), which offer fast policy transfer
and dynamics modelling, respectively. RPO transfers the policy evaluated in one
environment to maximize the return in another, while RTO updates the
parameterized dynamics model to reduce the gap between the dynamics of the two
environments. Integrating the two algorithms results in the complete Relative
Policy-Transition Optimization (RPTO) algorithm, in which the policy interacts
with the two environments simultaneously, such that data collections from two
environments, policy and transition updates are completed in one closed loop to
form a principled learning framework for policy transfer. We demonstrate the
effectiveness of RPTO on a set of MuJoCo continuous control tasks by creating
policy transfer problems via variant dynamics.Comment: Accepted by AAAI 202
Active-Learning-as-a-Service: An Efficient MLOps System for Data-Centric AI
The success of today's AI applications requires not only model training
(Model-centric) but also data engineering (Data-centric). In data-centric AI,
active learning (AL) plays a vital role, but current AL tools can not perform
AL tasks efficiently. To this end, this paper presents an efficient MLOps
system for AL, named ALaaS (Active-Learning-as-a-Service). Specifically, ALaaS
adopts a server-client architecture to support an AL pipeline and implements
stage-level parallelism for high efficiency. Meanwhile, caching and batching
techniques are employed to further accelerate the AL process. In addition to
efficiency, ALaaS ensures accessibility with the help of the design philosophy
of configuration-as-a-service. It also abstracts an AL process to several
components and provides rich APIs for advanced users to extend the system to
new scenarios. Extensive experiments show that ALaaS outperforms all other
baselines in terms of latency and throughput. Further ablation studies
demonstrate the effectiveness of our design as well as ALaaS's ease to use. Our
code is available at \url{https://github.com/MLSysOps/alaas}.Comment: 8 pages, 7 figure
Flexible generation of structured terahertz fields via programmable exchange-biased spintronic emitters
Structured light, particularly in the terahertz frequency range, holds
considerable potential for a diverse range of applications. However, the
generation and control of structured terahertz radiation pose major challenges.
In this work, we demonstrate a novel programmable spintronic emitter that can
flexibly generate a variety of structured terahertz waves. This is achieved
through the precise and high-resolution programming of the magnetization
pattern on the emitter surface, utilizing laser-assisted local field cooling of
an exchange-biased ferromagnetic heterostructure. Moreover, we outline a
generic design strategy for realizing specific complex structured terahertz
fields in the far field. Our device successfully demonstrates the generation of
terahertz waves with diverse structured polarization states, including
spatially separated circular polarizations, azimuthal or radial polarization
states, and a full Poincare beam. This innovation opens a new avenue for
designing and generating structured terahertz radiations, with potential
applications in terahertz microscopy, communication, quantum information, and
light-matter interactions
Active spintronic-metasurface terahertz emitters with tunable chirality
The ability to manipulate the electric-field vector of broadband terahertz
waves is essential for applications of terahertz technologies in many areas,
and can open up new possibilities for nonlinear terahertz spectroscopy and
coherent control. Here, we propose a novel laser-driven terahertz emitter,
consisting of metasurface-patterned magnetic multilayer heterostructures. Such
hybrid terahertz emitters can combine the advantages of spintronic emitters for
being ultrabroadband, efficient and flexible, as well as those of metasurfaces
for the unique capability to manipulate terahertz waves with high precision and
degree of freedom. Taking a stripe-patterned metasurface as an example, we
demonstrate the generation of broadband terahertz waves with tunable chirality.
Based on experimental and theoretical studies, the interplay between the
laser-induced spintronic-origin currents and the metasurface-induced transient
charges/currents are investigated, revealing the strong influence on the device
functionality originated from both the light-matter interactions in individual
metasurface units and the dynamic coupling between them. Our work not only
offers a flexible, reliable and cost-effective solution for chiral terahertz
wave generation and manipulation, but also opens a new pathway to
metasurface-tailored spintronic devices for efficient vector-control of
electromagnetic waves in the terahertz regime
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