25,851 research outputs found
Optimizations of Autoencoders for Analysis and Classification of Microscopic In Situ Hybridization Images
Currently, analysis of microscopic In Situ Hybridization images is done
manually by experts. Precise evaluation and classification of such microscopic
images can ease experts' work and reveal further insights about the data. In
this work, we propose a deep-learning framework to detect and classify areas of
microscopic images with similar levels of gene expression. The data we analyze
requires an unsupervised learning model for which we employ a type of
Artificial Neural Network - Deep Learning Autoencoders. The model's performance
is optimized by balancing the latent layers' length and complexity and
fine-tuning hyperparameters. The results are validated by adapting the
mean-squared error (MSE) metric, and comparison to expert's evaluation.Comment: 9 pages; 9 figure
Towards Autonomous Selective Harvesting: A Review of Robot Perception, Robot Design, Motion Planning and Control
This paper provides an overview of the current state-of-the-art in selective
harvesting robots (SHRs) and their potential for addressing the challenges of
global food production. SHRs have the potential to increase productivity,
reduce labour costs, and minimise food waste by selectively harvesting only
ripe fruits and vegetables. The paper discusses the main components of SHRs,
including perception, grasping, cutting, motion planning, and control. It also
highlights the challenges in developing SHR technologies, particularly in the
areas of robot design, motion planning and control. The paper also discusses
the potential benefits of integrating AI and soft robots and data-driven
methods to enhance the performance and robustness of SHR systems. Finally, the
paper identifies several open research questions in the field and highlights
the need for further research and development efforts to advance SHR
technologies to meet the challenges of global food production. Overall, this
paper provides a starting point for researchers and practitioners interested in
developing SHRs and highlights the need for more research in this field.Comment: Preprint: to be appeared in Journal of Field Robotic
Differentially private partitioned variational inference
Learning a privacy-preserving model from sensitive data which are distributed
across multiple devices is an increasingly important problem. The problem is
often formulated in the federated learning context, with the aim of learning a
single global model while keeping the data distributed. Moreover, Bayesian
learning is a popular approach for modelling, since it naturally supports
reliable uncertainty estimates. However, Bayesian learning is generally
intractable even with centralised non-private data and so approximation
techniques such as variational inference are a necessity. Variational inference
has recently been extended to the non-private federated learning setting via
the partitioned variational inference algorithm. For privacy protection, the
current gold standard is called differential privacy. Differential privacy
guarantees privacy in a strong, mathematically clearly defined sense.
In this paper, we present differentially private partitioned variational
inference, the first general framework for learning a variational approximation
to a Bayesian posterior distribution in the federated learning setting while
minimising the number of communication rounds and providing differential
privacy guarantees for data subjects.
We propose three alternative implementations in the general framework, one
based on perturbing local optimisation runs done by individual parties, and two
based on perturbing updates to the global model (one using a version of
federated averaging, the second one adding virtual parties to the protocol),
and compare their properties both theoretically and empirically.Comment: Published in TMLR 04/2023: https://openreview.net/forum?id=55Bcghgic
Satellite Image Based Cross-view Localization for Autonomous Vehicle
Existing spatial localization techniques for autonomous vehicles mostly use a
pre-built 3D-HD map, often constructed using a survey-grade 3D mapping vehicle,
which is not only expensive but also laborious. This paper shows that by using
an off-the-shelf high-definition satellite image as a ready-to-use map, we are
able to achieve cross-view vehicle localization up to a satisfactory accuracy,
providing a cheaper and more practical way for localization. While the
utilization of satellite imagery for cross-view localization is an established
concept, the conventional methodology focuses primarily on image retrieval.
This paper introduces a novel approach to cross-view localization that departs
from the conventional image retrieval method. Specifically, our method develops
(1) a Geometric-align Feature Extractor (GaFE) that leverages measured 3D
points to bridge the geometric gap between ground and overhead views, (2) a
Pose Aware Branch (PAB) adopting a triplet loss to encourage pose-aware feature
extraction, and (3) a Recursive Pose Refine Branch (RPRB) using the
Levenberg-Marquardt (LM) algorithm to align the initial pose towards the true
vehicle pose iteratively. Our method is validated on KITTI and Ford Multi-AV
Seasonal datasets as ground view and Google Maps as the satellite view. The
results demonstrate the superiority of our method in cross-view localization
with median spatial and angular errors within meter and ,
respectively.Comment: Accepted by ICRA202
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
An incremental input-to-state stability condition for a generic class of recurrent neural networks
This paper proposes a novel sufficient condition for the incremental
input-to-state stability of a generic class of recurrent neural networks
(RNNs). The established condition is compared with others available in the
literature, showing to be less conservative. Moreover, it can be applied for
the design of incremental input-to-state stable RNN-based control systems,
resulting in a linear matrix inequality constraint for some specific RNN
architectures. The formulation of nonlinear observers for the considered system
class, as well as the design of control schemes with explicit integral action,
are also investigated. The theoretical results are validated through simulation
on a referenced nonlinear system
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
Manipulating Federated Recommender Systems: Poisoning with Synthetic Users and Its Countermeasures
Federated Recommender Systems (FedRecs) are considered privacy-preserving
techniques to collaboratively learn a recommendation model without sharing user
data. Since all participants can directly influence the systems by uploading
gradients, FedRecs are vulnerable to poisoning attacks of malicious clients.
However, most existing poisoning attacks on FedRecs are either based on some
prior knowledge or with less effectiveness. To reveal the real vulnerability of
FedRecs, in this paper, we present a new poisoning attack method to manipulate
target items' ranks and exposure rates effectively in the top-
recommendation without relying on any prior knowledge. Specifically, our attack
manipulates target items' exposure rate by a group of synthetic malicious users
who upload poisoned gradients considering target items' alternative products.
We conduct extensive experiments with two widely used FedRecs (Fed-NCF and
Fed-LightGCN) on two real-world recommendation datasets. The experimental
results show that our attack can significantly improve the exposure rate of
unpopular target items with extremely fewer malicious users and fewer global
epochs than state-of-the-art attacks. In addition to disclosing the security
hole, we design a novel countermeasure for poisoning attacks on FedRecs.
Specifically, we propose a hierarchical gradient clipping with sparsified
updating to defend against existing poisoning attacks. The empirical results
demonstrate that the proposed defending mechanism improves the robustness of
FedRecs.Comment: This paper has been accepted by SIGIR202
- …