91 research outputs found
Diffusion Approximations for Online Principal Component Estimation and Global Convergence
In this paper, we propose to adopt the diffusion approximation tools to study
the dynamics of Oja's iteration which is an online stochastic gradient descent
method for the principal component analysis. Oja's iteration maintains a
running estimate of the true principal component from streaming data and enjoys
less temporal and spatial complexities. We show that the Oja's iteration for
the top eigenvector generates a continuous-state discrete-time Markov chain
over the unit sphere. We characterize the Oja's iteration in three phases using
diffusion approximation and weak convergence tools. Our three-phase analysis
further provides a finite-sample error bound for the running estimate, which
matches the minimax information lower bound for principal component analysis
under the additional assumption of bounded samples.Comment: Appeared in NIPS 201
Research on Identification of Damage Development Mode of Rural Highway Asphalt Pavement Structure
According to the damage condition of asphalt pavement of rural roads in Liaoning Province, five development modes of asphalt pavement damage were proposed to determine the damage condition of asphalt pavement in the investigation section. The trapezoidal and semi-trapezoidal membership functions are used to construct the membership matrix, and the fuzzy comprehensive evaluation model is established, and the weight vector of each factor is determined. Finally, fuzzy mathematics is used to determine the stage of the general development period of the structural damage of the road segment. The research results have certain guiding significance for the management and maintenance of rural road asphalt pavement in Liaoning Province
Embodied Executable Policy Learning with Language-based Scene Summarization
Large Language models (LLMs) have shown remarkable success in assisting robot
learning tasks, i.e., complex household planning. However, the performance of
pretrained LLMs heavily relies on domain-specific templated text data, which
may be infeasible in real-world robot learning tasks with image-based
observations. Moreover, existing LLMs with text inputs lack the capability to
evolve with non-expert interactions with environments. In this work, we
introduce a novel learning paradigm that generates robots' executable actions
in the form of text, derived solely from visual observations, using
language-based summarization of these observations as the connecting bridge
between both domains. Our proposed paradigm stands apart from previous works,
which utilized either language instructions or a combination of language and
visual data as inputs. Moreover, our method does not require oracle text
summarization of the scene, eliminating the need for human involvement in the
learning loop, which makes it more practical for real-world robot learning
tasks. Our proposed paradigm consists of two modules: the SUM module, which
interprets the environment using visual observations and produces a text
summary of the scene, and the APM module, which generates executable action
policies based on the natural language descriptions provided by the SUM module.
We demonstrate that our proposed method can employ two fine-tuning strategies,
including imitation learning and reinforcement learning approaches, to adapt to
the target test tasks effectively. We conduct extensive experiments involving
various SUM/APM model selections, environments, and tasks across 7 house
layouts in the VirtualHome environment. Our experimental results demonstrate
that our method surpasses existing baselines, confirming the effectiveness of
this novel learning paradigm.Comment: 15 pages. arXiv admin note: text overlap with arXiv:2107.06912 by
other author
Can Brain Signals Reveal Inner Alignment with Human Languages?
Brain Signals, such as Electroencephalography (EEG), and human languages have
been widely explored independently for many downstream tasks, however, the
connection between them has not been well explored. In this study, we explore
the relationship and dependency between EEG and language. To study at the
representation level, we introduced \textbf{MTAM}, a \textbf{M}ultimodal
\textbf{T}ransformer \textbf{A}lignment \textbf{M}odel, to observe coordinated
representations between the two modalities. We used various relationship
alignment-seeking techniques, such as Canonical Correlation Analysis and
Wasserstein Distance, as loss functions to transfigure features. On downstream
applications, sentiment analysis and relation detection, we achieved new
state-of-the-art results on two datasets, ZuCo and K-EmoCon. Our method
achieved an F1-score improvement of 1.7% on K-EmoCon and 9.3% on Zuco datasets
for sentiment analysis, and 7.4% on ZuCo for relation detection. In addition,
we provide interpretations of the performance improvement: (1) feature
distribution shows the effectiveness of the alignment module for discovering
and encoding the relationship between EEG and language; (2) alignment weights
show the influence of different language semantics as well as EEG frequency
features; (3) brain topographical maps provide an intuitive demonstration of
the connectivity in the brain regions. Our code is available at
\url{https://github.com/Jason-Qiu/EEG_Language_Alignment}.Comment: EMNLP 2023 Finding
Transfer Knowledge from Natural Language to Electrocardiography: Can We Detect Cardiovascular Disease Through Language Models?
Recent advancements in Large Language Models (LLMs) have drawn increasing
attention since the learned embeddings pretrained on large-scale datasets have
shown powerful ability in various downstream applications. However, whether the
learned knowledge by LLMs can be transferred to clinical cardiology remains
unknown. In this work, we aim to bridge this gap by transferring the knowledge
of LLMs to clinical Electrocardiography (ECG). We propose an approach for
cardiovascular disease diagnosis and automatic ECG diagnosis report generation.
We also introduce an additional loss function by Optimal Transport (OT) to
align the distribution between ECG and language embedding. The learned
embeddings are evaluated on two downstream tasks: (1) automatic ECG diagnosis
report generation, and (2) zero-shot cardiovascular disease detection. Our
approach is able to generate high-quality cardiac diagnosis reports and also
achieves competitive zero-shot classification performance even compared with
supervised baselines, which proves the feasibility of transferring knowledge
from LLMs to the cardiac domain.Comment: EACL 202
ChipGPT: How far are we from natural language hardware design
As large language models (LLMs) like ChatGPT exhibited unprecedented machine
intelligence, it also shows great performance in assisting hardware engineers
to realize higher-efficiency logic design via natural language interaction. To
estimate the potential of the hardware design process assisted by LLMs, this
work attempts to demonstrate an automated design environment that explores LLMs
to generate hardware logic designs from natural language specifications. To
realize a more accessible and efficient chip development flow, we present a
scalable four-stage zero-code logic design framework based on LLMs without
retraining or finetuning. At first, the demo, ChipGPT, begins by generating
prompts for the LLM, which then produces initial Verilog programs. Second, an
output manager corrects and optimizes these programs before collecting them
into the final design space. Eventually, ChipGPT will search through this space
to select the optimal design under the target metrics. The evaluation sheds
some light on whether LLMs can generate correct and complete hardware logic
designs described by natural language for some specifications. It is shown that
ChipGPT improves programmability, and controllability, and shows broader design
optimization space compared to prior work and native LLMs alone
Complex 3D microfluidic architectures formed by mechanically guided compressive buckling.
Microfluidic technologies have wide-ranging applications in chemical analysis systems, drug delivery platforms, and artificial vascular networks. This latter area is particularly relevant to 3D cell cultures, engineered tissues, and artificial organs, where volumetric capabilities in fluid distribution are essential. Existing schemes for fabricating 3D microfluidic structures are constrained in realizing desired layout designs, producing physiologically relevant microvascular structures, and/or integrating active electronic/optoelectronic/microelectromechanical components for sensing and actuation. This paper presents a guided assembly approach that bypasses these limitations to yield complex 3D microvascular structures from 2D precursors that exploit the full sophistication of 2D fabrication methods. The capabilities extend to feature sizes <5 μm, in extended arrays and with various embedded sensors and actuators, across wide ranges of overall dimensions, in a parallel, high-throughput process. Examples include 3D microvascular networks with sophisticated layouts, deterministically designed and constructed to expand the geometries and operating features of artificial vascular networks
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