10,409 research outputs found
Deep generative models for network data synthesis and monitoring
Measurement and monitoring are fundamental tasks in all networks, enabling the down-stream management and optimization of the network.
Although networks inherently
have abundant amounts of monitoring data, its access and effective measurement is
another story. The challenges exist in many aspects. First, the inaccessibility of network monitoring data for external users, and it is hard to provide a high-fidelity dataset
without leaking commercial sensitive information. Second, it could be very expensive
to carry out effective data collection to cover a large-scale network system, considering the size of network growing, i.e., cell number of radio network and the number of
flows in the Internet Service Provider (ISP) network. Third, it is difficult to ensure fidelity and efficiency simultaneously in network monitoring, as the available resources
in the network element that can be applied to support the measurement function are
too limited to implement sophisticated mechanisms. Finally, understanding and explaining the behavior of the network becomes challenging due to its size and complex
structure. Various emerging optimization-based solutions (e.g., compressive sensing)
or data-driven solutions (e.g. deep learning) have been proposed for the aforementioned challenges. However, the fidelity and efficiency of existing methods cannot yet
meet the current network requirements.
The contributions made in this thesis significantly advance the state of the art in
the domain of network measurement and monitoring techniques. Overall, we leverage
cutting-edge machine learning technology, deep generative modeling, throughout the
entire thesis. First, we design and realize APPSHOT , an efficient city-scale network
traffic sharing with a conditional generative model, which only requires open-source
contextual data during inference (e.g., land use information and population distribution). Second, we develop an efficient drive testing system — GENDT, based on generative model, which combines graph neural networks, conditional generation, and quantified model uncertainty to enhance the efficiency of mobile drive testing. Third, we
design and implement DISTILGAN, a high-fidelity, efficient, versatile, and real-time
network telemetry system with latent GANs and spectral-temporal networks. Finally,
we propose SPOTLIGHT , an accurate, explainable, and efficient anomaly detection system of the Open RAN (Radio Access Network) system. The lessons learned through
this research are summarized, and interesting topics are discussed for future work in
this domain. All proposed solutions have been evaluated with real-world datasets and
applied to support different applications in real systems
An innovative network intrusion detection system (NIDS): Hierarchical deep learning model based on Unsw-Nb15 dataset
With the increasing prevalence of network intrusions, the development of effective network intrusion detection systems (NIDS) has become crucial. In this study, we propose a novel NIDS approach that combines the power of long short-term memory (LSTM) and attention mechanisms to analyze the spatial and temporal features of network traffic data. We utilize the benchmark UNSW-NB15 dataset, which exhibits a diverse distribution of patterns, including a significant disparity in the size of the training and testing sets. Unlike traditional machine learning techniques like support vector machines (SVM) and k-nearest neighbors (KNN) that often struggle with limited feature sets and lower accuracy, our proposed model overcomes these limitations. Notably, existing models applied to this dataset typically require manual feature selection and extraction, which can be time-consuming and less precise. In contrast, our model achieves superior results in binary classification by leveraging the advantages of LSTM and attention mechanisms. Through extensive experiments and evaluations with state-of-the-art ML/DL models, we demonstrate the effectiveness and superiority of our proposed approach. Our findings highlight the potential of combining LSTM and attention mechanisms for enhanced network intrusion detection
The development of bioinformatics workflows to explore single-cell multi-omics data from T and B lymphocytes
The adaptive immune response is responsible for recognising, containing and eliminating viral infection, and protecting from further reinfection. This antigen-specific response is driven by T and B cells, which recognise antigenic epitopes via highly specific heterodimeric surface receptors, termed T-cell receptors (TCRs) and B cell receptors (BCRs). The theoretical diversity of the receptor repertoire that can be generated via homologous recombination of V, D and J genes is large enough (>1015 unique sequences) that virtually any antigen can be recognised. However, only a subset of these are generated within the human body, and how they succeed in specifically recognising any pathogen(s) and distinguishing these from self-proteins remains largely unresolved.
The recent advances in applying single-cell genomics technologies to simultaneously measure the clonality, surface phenotype and transcriptomic signature of pathogen- specific immune cells have significantly improved understanding of these questions. Single-cell multi-omics permits the accurate identification of clonally expanded populations, their differentiation trajectories, the level of immune receptor repertoire diversity involved in the response and the phenotypic and molecular heterogeneity.
This thesis aims to develop a bioinformatic workflow utilising single-cell multi-omics data to explore, quantify and predict the clonal and transcriptomic signatures of the human T-cell response during and following viral infection. In the first aim, a web application, VDJView, was developed to facilitate the simultaneous analysis and visualisation of clonal, transcriptomic and clinical metadata of T and B cell multi-omics data. The application permits non-bioinformaticians to perform quality control and common analyses of single-cell genomics data integrated with other metadata, thus permitting the identification of biologically and clinically relevant parameters. The second aim pertains to analysing the functional, molecular and immune receptor profiles of CD8+ T cells in the acute phase of primary hepatitis C virus (HCV) infection. This analysis identified a novel population of progenitors of exhausted T cells, and lineage tracing revealed distinct trajectories with multiple fates and evolutionary plasticity. Furthermore, it was observed that high-magnitude IFN-γ CD8+ T-cell response is associated with the increased probability of viral escape and chronic infection. Finally, in the third aim, a novel analysis is presented based on the topological characteristics of a network generated on pathogen-specific, paired-chain, CD8+ TCRs. This analysis revealed how some cross-reactivity between TCRs can be explained via the sequence similarity between TCRs and that this property is not uniformly distributed across all pathogen-specific TCR repertoires. Strong correlations between the topological properties of the network and the biological properties of the TCR sequences were identified and highlighted.
The suite of workflows and methods presented in this thesis are designed to be adaptable to various T and B cell multi-omic datasets. The associated analyses contribute to understanding the role of T and B cells in the adaptive immune response to viral-infection and cancer
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Neural Network-Based Multi-Target Detection within Correlated Heavy-Tailed Clutter
This work addresses the problem of range-Doppler multiple target detection in
a radar system in the presence of slow-time correlated and heavy-tailed
distributed clutter. Conventional target detection algorithms assume
Gaussian-distributed clutter, but their performance is significantly degraded
in the presence of correlated heavy-tailed distributed clutter. Derivation of
optimal detection algorithms with heavy-tailed distributed clutter is
analytically intractable. Furthermore, the clutter distribution is frequently
unknown. This work proposes a deep learning-based approach for multiple target
detection in the range-Doppler domain. The proposed approach is based on a
unified NN model to process the time-domain radar signal for a variety of
signal-to-clutter-plus-noise ratios (SCNRs) and clutter distributions,
simplifying the detector architecture and the neural network training
procedure. The performance of the proposed approach is evaluated in various
experiments using recorded radar echoes, and via simulations, it is shown that
the proposed method outperforms the conventional cell-averaging constant
false-alarm rate (CA-CFAR), the ordered-statistic CFAR (OS-CFAR), and the
adaptive normalized matched-filter (ANMF) detectors in terms of probability of
detection in the majority of tested SCNRs and clutter scenarios.Comment: Accepted to IEEE Transactions on Aerospace and Electronic System
Confidence-Based Feature Imputation for Graphs with Partially Known Features
This paper investigates a missing feature imputation problem for graph
learning tasks. Several methods have previously addressed learning tasks on
graphs with missing features. However, in cases of high rates of missing
features, they were unable to avoid significant performance degradation. To
overcome this limitation, we introduce a novel concept of channel-wise
confidence in a node feature, which is assigned to each imputed channel feature
of a node for reflecting certainty of the imputation. We then design
pseudo-confidence using the channel-wise shortest path distance between a
missing-feature node and its nearest known-feature node to replace unavailable
true confidence in an actual learning process. Based on the pseudo-confidence,
we propose a novel feature imputation scheme that performs channel-wise
inter-node diffusion and node-wise inter-channel propagation. The scheme can
endure even at an exceedingly high missing rate (e.g., 99.5\%) and it achieves
state-of-the-art accuracy for both semi-supervised node classification and link
prediction on various datasets containing a high rate of missing features.
Codes are available at https://github.com/daehoum1/pcfi.Comment: Accepted to ICLR 2023. 28 page
A brief review of contrastive learning applied to astrophysics
Reliable tools to extract patterns from high-dimensionality spaces are
becoming more necessary as astronomical datasets increase both in volume and
complexity. Contrastive Learning is a self-supervised machine learning
algorithm that extracts informative measurements from multi-dimensional
datasets, which has become increasingly popular in the computer vision and
Machine Learning communities in recent years. To do so, it maximizes the
agreement between the information extracted from augmented versions of the same
input data, making the final representation invariant to the applied
transformations. Contrastive Learning is particularly useful in astronomy for
removing known instrumental effects and for performing supervised
classifications and regressions with a limited amount of available labels,
showing a promising avenue towards \emph{Foundation Models}. This short review
paper briefly summarizes the main concepts behind contrastive learning and
reviews the first promising applications to astronomy. We include some
practical recommendations on which applications are particularly attractive for
contrastive learning.Comment: Invited review to be published in RAST
CLC: Cluster Assignment via Contrastive Representation Learning
Clustering remains an important and challenging task of grouping samples into
clusters without manual annotations. Recent works have achieved excellent
results on small datasets by performing clustering on feature representations
learned from self-supervised learning. However, for datasets with a large
number of clusters, such as ImageNet, current methods still can not achieve
high clustering performance. In this paper, we propose Contrastive
Learning-based Clustering (CLC), which uses contrastive learning to directly
learn cluster assignment. We decompose the representation into two parts: one
encodes the categorical information under an equipartition constraint, and the
other captures the instance-wise factors. We propose a contrastive loss using
both parts of the representation. We theoretically analyze the proposed
contrastive loss and reveal that CLC sets different weights for the negative
samples while learning cluster assignments. Further gradient analysis shows
that the larger weights tend to focus more on the hard negative samples.
Therefore, the proposed loss has high expressiveness that enables us to
efficiently learn cluster assignments. Experimental evaluation shows that CLC
achieves overall state-of-the-art or highly competitive clustering performance
on multiple benchmark datasets. In particular, we achieve 53.4% accuracy on the
full ImageNet dataset and outperform existing methods by large margins (+
10.2%).Comment: 10 pages, 7 tables, 4 figure
BASiS: Batch Aligned Spectral Embedding Space
Graph is a highly generic and diverse representation, suitable for almost any
data processing problem. Spectral graph theory has been shown to provide
powerful algorithms, backed by solid linear algebra theory. It thus can be
extremely instrumental to design deep network building blocks with spectral
graph characteristics. For instance, such a network allows the design of
optimal graphs for certain tasks or obtaining a canonical orthogonal
low-dimensional embedding of the data. Recent attempts to solve this problem
were based on minimizing Rayleigh-quotient type losses. We propose a different
approach of directly learning the eigensapce. A severe problem of the direct
approach, applied in batch-learning, is the inconsistent mapping of features to
eigenspace coordinates in different batches. We analyze the degrees of freedom
of learning this task using batches and propose a stable alignment mechanism
that can work both with batch changes and with graph-metric changes. We show
that our learnt spectral embedding is better in terms of NMI, ACC, Grassman
distance, orthogonality and classification accuracy, compared to SOTA. In
addition, the learning is more stable.Comment: 14 pages, 10 figure
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