20,176 research outputs found
H-TSP: Hierarchically Solving the Large-Scale Travelling Salesman Problem
We propose an end-to-end learning framework based on hierarchical
reinforcement learning, called H-TSP, for addressing the large-scale Travelling
Salesman Problem (TSP). The proposed H-TSP constructs a solution of a TSP
instance starting from the scratch relying on two components: the upper-level
policy chooses a small subset of nodes (up to 200 in our experiment) from all
nodes that are to be traversed, while the lower-level policy takes the chosen
nodes as input and outputs a tour connecting them to the existing partial route
(initially only containing the depot). After jointly training the upper-level
and lower-level policies, our approach can directly generate solutions for the
given TSP instances without relying on any time-consuming search procedures. To
demonstrate effectiveness of the proposed approach, we have conducted extensive
experiments on randomly generated TSP instances with different numbers of
nodes. We show that H-TSP can achieve comparable results (gap 3.42% vs. 7.32%)
as SOTA search-based approaches, and more importantly, we reduce the time
consumption up to two orders of magnitude (3.32s vs. 395.85s). To the best of
our knowledge, H-TSP is the first end-to-end deep reinforcement learning
approach that can scale to TSP instances of up to 10000 nodes. Although there
are still gaps to SOTA results with respect to solution quality, we believe
that H-TSP will be useful for practical applications, particularly those that
are time-sensitive e.g., on-call routing and ride hailing service.Comment: Accepted by AAAI 2023, February 202
Security and Privacy Problems in Voice Assistant Applications: A Survey
Voice assistant applications have become omniscient nowadays. Two models that
provide the two most important functions for real-life applications (i.e.,
Google Home, Amazon Alexa, Siri, etc.) are Automatic Speech Recognition (ASR)
models and Speaker Identification (SI) models. According to recent studies,
security and privacy threats have also emerged with the rapid development of
the Internet of Things (IoT). The security issues researched include attack
techniques toward machine learning models and other hardware components widely
used in voice assistant applications. The privacy issues include technical-wise
information stealing and policy-wise privacy breaches. The voice assistant
application takes a steadily growing market share every year, but their privacy
and security issues never stopped causing huge economic losses and endangering
users' personal sensitive information. Thus, it is important to have a
comprehensive survey to outline the categorization of the current research
regarding the security and privacy problems of voice assistant applications.
This paper concludes and assesses five kinds of security attacks and three
types of privacy threats in the papers published in the top-tier conferences of
cyber security and voice domain.Comment: 5 figure
Towards Advantages of Parameterized Quantum Pulses
The advantages of quantum pulses over quantum gates have attracted increasing
attention from researchers. Quantum pulses offer benefits such as flexibility,
high fidelity, scalability, and real-time tuning. However, while there are
established workflows and processes to evaluate the performance of quantum
gates, there has been limited research on profiling parameterized pulses and
providing guidance for pulse circuit design. To address this gap, our study
proposes a set of design spaces for parameterized pulses, evaluating these
pulses based on metrics such as expressivity, entanglement capability, and
effective parameter dimension. Using these design spaces, we demonstrate the
advantages of parameterized pulses over gate circuits in the aspect of duration
and performance at the same time thus enabling high-performance quantum
computing. Our proposed design space for parameterized pulse circuits has shown
promising results in quantum chemistry benchmarks.Comment: 11 Figures, 4 Table
The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions
The Metaverse offers a second world beyond reality, where boundaries are
non-existent, and possibilities are endless through engagement and immersive
experiences using the virtual reality (VR) technology. Many disciplines can
benefit from the advancement of the Metaverse when accurately developed,
including the fields of technology, gaming, education, art, and culture.
Nevertheless, developing the Metaverse environment to its full potential is an
ambiguous task that needs proper guidance and directions. Existing surveys on
the Metaverse focus only on a specific aspect and discipline of the Metaverse
and lack a holistic view of the entire process. To this end, a more holistic,
multi-disciplinary, in-depth, and academic and industry-oriented review is
required to provide a thorough study of the Metaverse development pipeline. To
address these issues, we present in this survey a novel multi-layered pipeline
ecosystem composed of (1) the Metaverse computing, networking, communications
and hardware infrastructure, (2) environment digitization, and (3) user
interactions. For every layer, we discuss the components that detail the steps
of its development. Also, for each of these components, we examine the impact
of a set of enabling technologies and empowering domains (e.g., Artificial
Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on
its advancement. In addition, we explain the importance of these technologies
to support decentralization, interoperability, user experiences, interactions,
and monetization. Our presented study highlights the existing challenges for
each component, followed by research directions and potential solutions. To the
best of our knowledge, this survey is the most comprehensive and allows users,
scholars, and entrepreneurs to get an in-depth understanding of the Metaverse
ecosystem to find their opportunities and potentials for contribution
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Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
Conditional Adapters: Parameter-efficient Transfer Learning with Fast Inference
We propose Conditional Adapter (CoDA), a parameter-efficient transfer
learning method that also improves inference efficiency. CoDA generalizes
beyond standard adapter approaches to enable a new way of balancing speed and
accuracy using conditional computation. Starting with an existing dense
pretrained model, CoDA adds sparse activation together with a small number of
new parameters and a light-weight training phase. Our experiments demonstrate
that the CoDA approach provides an unexpectedly efficient way to transfer
knowledge. Across a variety of language, vision, and speech tasks, CoDA
achieves a 2x to 8x inference speed-up compared to the state-of-the-art Adapter
approach with moderate to no accuracy loss and the same parameter efficiency
Model Parameter Identification via a Hyperparameter Optimization Scheme for Autonomous Racing Systems
In this letter, we propose a model parameter identification method via a
hyperparameter optimization scheme (MI-HPO). Our method adopts an efficient
explore-exploit strategy to identify the parameters of dynamic models in a
data-driven optimization manner. We utilize our method for model parameter
identification of the AV-21, a full-scaled autonomous race vehicle. We then
incorporate the optimized parameters for the design of model-based planning and
control systems of our platform. In experiments, MI-HPO exhibits more than 13
times faster convergence than traditional parameter identification methods.
Furthermore, the parametric models learned via MI-HPO demonstrate good fitness
to the given datasets and show generalization ability in unseen dynamic
scenarios. We further conduct extensive field tests to validate our model-based
system, demonstrating stable obstacle avoidance and high-speed driving up to
217 km/h at the Indianapolis Motor Speedway and Las Vegas Motor Speedway. The
source code for our work and videos of the tests are available at
https://github.com/hynkis/MI-HPO.Comment: 6 pages, 8 figures. Published in IEEE Control Systems Letters (L-CSS
One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey on ChatGPT in AIGC Era
OpenAI has recently released GPT-4 (a.k.a. ChatGPT plus), which is
demonstrated to be one small step for generative AI (GAI), but one giant leap
for artificial general intelligence (AGI). Since its official release in
November 2022, ChatGPT has quickly attracted numerous users with extensive
media coverage. Such unprecedented attention has also motivated numerous
researchers to investigate ChatGPT from various aspects. According to Google
scholar, there are more than 500 articles with ChatGPT in their titles or
mentioning it in their abstracts. Considering this, a review is urgently
needed, and our work fills this gap. Overall, this work is the first to survey
ChatGPT with a comprehensive review of its underlying technology, applications,
and challenges. Moreover, we present an outlook on how ChatGPT might evolve to
realize general-purpose AIGC (a.k.a. AI-generated content), which will be a
significant milestone for the development of AGI.Comment: A Survey on ChatGPT and GPT-4, 29 pages. Feedback is appreciated
([email protected]
In-situ crack and keyhole pore detection in laser directed energy deposition through acoustic signal and deep learning
Cracks and keyhole pores are detrimental defects in alloys produced by laser
directed energy deposition (LDED). Laser-material interaction sound may hold
information about underlying complex physical events such as crack propagation
and pores formation. However, due to the noisy environment and intricate signal
content, acoustic-based monitoring in LDED has received little attention. This
paper proposes a novel acoustic-based in-situ defect detection strategy in
LDED. The key contribution of this study is to develop an in-situ acoustic
signal denoising, feature extraction, and sound classification pipeline that
incorporates convolutional neural networks (CNN) for online defect prediction.
Microscope images are used to identify locations of the cracks and keyhole
pores within a part. The defect locations are spatiotemporally registered with
acoustic signal. Various acoustic features corresponding to defect-free
regions, cracks, and keyhole pores are extracted and analysed in time-domain,
frequency-domain, and time-frequency representations. The CNN model is trained
to predict defect occurrences using the Mel-Frequency Cepstral Coefficients
(MFCCs) of the lasermaterial interaction sound. The CNN model is compared to
various classic machine learning models trained on the denoised acoustic
dataset and raw acoustic dataset. The validation results shows that the CNN
model trained on the denoised dataset outperforms others with the highest
overall accuracy (89%), keyhole pore prediction accuracy (93%), and AUC-ROC
score (98%). Furthermore, the trained CNN model can be deployed into an
in-house developed software platform for online quality monitoring. The
proposed strategy is the first study to use acoustic signals with deep learning
for insitu defect detection in LDED process.Comment: 36 Pages, 16 Figures, accepted at journal Additive Manufacturin
Self-Supervised Learning to Prove Equivalence Between Straight-Line Programs via Rewrite Rules
We target the problem of automatically synthesizing proofs of semantic
equivalence between two programs made of sequences of statements. We represent
programs using abstract syntax trees (AST), where a given set of
semantics-preserving rewrite rules can be applied on a specific AST pattern to
generate a transformed and semantically equivalent program. In our system, two
programs are equivalent if there exists a sequence of application of these
rewrite rules that leads to rewriting one program into the other. We propose a
neural network architecture based on a transformer model to generate proofs of
equivalence between program pairs. The system outputs a sequence of rewrites,
and the validity of the sequence is simply checked by verifying it can be
applied. If no valid sequence is produced by the neural network, the system
reports the programs as non-equivalent, ensuring by design no programs may be
incorrectly reported as equivalent. Our system is fully implemented for a given
grammar which can represent straight-line programs with function calls and
multiple types. To efficiently train the system to generate such sequences, we
develop an original incremental training technique, named self-supervised
sample selection. We extensively study the effectiveness of this novel training
approach on proofs of increasing complexity and length. Our system, S4Eq,
achieves 97% proof success on a curated dataset of 10,000 pairs of equivalent
programsComment: 30 pages including appendi
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