pwc-archive/papers-with-abstracts huggingface dataset:
Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond
Authors: Loukas Ilias, George Doukas, Vangelis Lamprou, Christos Ntanos, Dimitris Askounis
Date: 2024-12-04 00:00:00
Tasks: Intrusion Detection, Mixture-of-Experts, Network Intrusion Detection

The advent of 6G/NextG networks comes along with a series of benefits, including extreme capacity, reliability, and efficiency. However, these networks may become vulnerable to new security threats. Therefore, 6G/NextG networks must be equipped with advanced Artificial Intelligence algorithms, in order to evade these attacks. Existing studies on the intrusion detection task rely on the train of shallow machine learning classifiers, including Logistic Regression, Decision Trees, and so on, yielding suboptimal performance. Others are based on deep neural networks consisting of static components, which are not conditional on the input. This limits their representation power and efficiency. To resolve these issues, we present the first study integrating Mixture of Experts (MoE) for identifying malicious traffic. Specifically, we use network traffic data and convert the 1D array of features into a 2D matrix. Next, we pass this matrix through convolutional neural network (CNN) layers followed by batch normalization and max pooling layers. After obtaining the representation vector via the CNN layers, a sparsely gated MoE layer is used. This layer consists of a set of experts (dense layers) and a router, where the router assigns weights to the output of each expert. Sparsity is achieved by choosing the most relevant experts of the total ones. Finally, we perform a series of ablation experiments to prove the effectiveness of our proposed model. Experiments are conducted on the 5G-NIDD dataset, a network intrusion detection dataset generated from a real 5G test network. Results show that our introduced approach reaches weighted F1-score up to 99.95% achieving comparable performance to existing approaches. Findings also show that our proposed model achieves multiple advantages over state-of-the-art approaches.
Cluster Specific Representation Learning
Authors: Mahalakshmi Sabanayagam, Omar Al-Dabooni, Pascal Esser
Date: 2024-12-04 00:00:00
Tasks: Clustering, Contrastive Learning, Representation Learning

Representation learning aims to extract meaningful lower-dimensional embeddings from data, known as representations. Despite its widespread application, there is no established definition of a ``good'' representation. Typically, the representation quality is evaluated based on its performance in downstream tasks such as clustering, de-noising, etc. However, this task-specific approach has a limitation where a representation that performs well for one task may not necessarily be effective for another. This highlights the need for a more agnostic formulation, which is the focus of our work. We propose a downstream-agnostic formulation: when inherent clusters exist in the data, the representations should be specific to each cluster. Under this idea, we develop a meta-algorithm that jointly learns cluster-specific representations and cluster assignments. As our approach is easy to integrate with any representation learning framework, we demonstrate its effectiveness in various setups, including Autoencoders, Variational Autoencoders, Contrastive learning models, and Restricted Boltzmann Machines. We qualitatively compare our cluster-specific embeddings to standard embeddings and downstream tasks such as de-noising and clustering. While our method slightly increases runtime and parameters compared to the standard model, the experiments clearly show that it extracts the inherent cluster structures in the data, resulting in improved performance in relevant applications.
Validity and efficiency of the conformal CUSUM procedure
Authors: Vladimir Vovk, Ilia Nouretdinov, Alex Gammerman
Date: 2024-12-04 00:00:00
Tasks: Change Detection

In this paper we study the validity and efficiency of a conformal version of the CUSUM procedure for change detection both experimentally and theoretically.
State Frequency Estimation for Anomaly Detection
Authors: Clinton Cao, Agathe Blaise, Annibale Panichella, Sicco Verwer
Date: 2024-12-04 00:00:00
Tasks: Anomaly Detection, Unsupervised Anomaly Detection

Many works have studied the efficacy of state machines for detecting anomalies within NetFlows. These works typically learn a model from unlabeled data and compute anomaly scores for arbitrary traces based on their likelihood of occurrence or how well they fit within the model. However, these methods do not dynamically adapt their scores based on the traces seen at test time. This becomes a problem when an adversary produces seemingly common traces in their attack, causing the model to miss the detection by assigning low anomaly scores. We propose SEQUENT, a new unsupervised approach that uses the state visit frequency of a state machine to adapt its scoring dynamically for anomaly detection. SEQUENT subsequently uses the scores to generate root causes for anomalies. These allow the grouping of alarms and simplify the analysis of anomalies. We evaluate SEQUENT's effectiveness in detecting network anomalies on three publicly available NetFlow datasets and compare its performance against various existing unsupervised anomaly detection methods. Our evaluation shows promising results for using the state visit frequency of a state machine to detect network anomalies.
Assessing Foundation Models' Transferability to Physiological Signals in Precision Medicine
Authors: Matthias Christenson, Cove Geary, Brian Locke, Pranav Koirala, Warren Woodrich Pettine
Date: 2024-12-04 00:00:00

The success of precision medicine requires computational models that can effectively process and interpret diverse physiological signals across heterogeneous patient populations. While foundation models have demonstrated remarkable transfer capabilities across various domains, their effectiveness in handling individual-specific physiological signals - crucial for precision medicine - remains largely unexplored. This work introduces a systematic pipeline for rapidly and efficiently evaluating foundation models' transfer capabilities in medical contexts. Our pipeline employs a three-stage approach. First, it leverages physiological simulation software to generate diverse, clinically relevant scenarios, particularly focusing on data-scarce medical conditions. This simulation-based approach enables both targeted capability assessment and subsequent model fine-tuning. Second, the pipeline projects these simulated signals through the foundation model to obtain embeddings, which are then evaluated using linear methods. This evaluation quantifies the model's ability to capture three critical aspects: physiological feature independence, temporal dynamics preservation, and medical scenario differentiation. Finally, the pipeline validates these representations through specific downstream medical tasks. Initial testing of our pipeline on the Moirai time series foundation model revealed significant limitations in physiological signal processing, including feature entanglement, temporal dynamics distortion, and reduced scenario discrimination. These findings suggest that current foundation models may require substantial architectural modifications or targeted fine-tuning before deployment in clinical settings.
Deep Operator BSDE: a Numerical Scheme to Approximate the Solution Operators
Authors: Giulia Di Nunno, Pere Díaz Lozano
Date: 2024-12-04 00:00:00

Motivated by dynamic risk measures and conditional $g$-expectations, in this work we propose a numerical method to approximate the solution operator given by a Backward Stochastic Differential Equation (BSDE). The main ingredients for this are the Wiener chaos decomposition and the classical Euler scheme for BSDEs. We show convergence of this scheme under very mild assumptions, and provide a rate of convergence in more restrictive cases. We then implement it using neural networks, and we present several numerical examples where we can check the accuracy of the method.
Can neural operators always be continuously discretized?
Authors: Takashi Furuya, Michael Puthawala, Maarten V. de Hoop, Matti Lassas
Date: 2024-12-04 00:00:00

We consider the problem of discretization of neural operators between Hilbert spaces in a general framework including skip connections. We focus on bijective neural operators through the lens of diffeomorphisms in infinite dimensions. Framed using category theory, we give a no-go theorem that shows that diffeomorphisms between Hilbert spaces or Hilbert manifolds may not admit any continuous approximations by diffeomorphisms on finite-dimensional spaces, even if the approximations are nonlinear. The natural way out is the introduction of strongly monotone diffeomorphisms and layerwise strongly monotone neural operators which have continuous approximations by strongly monotone diffeomorphisms on finite-dimensional spaces. For these, one can guarantee discretization invariance, while ensuring that finite-dimensional approximations converge not only as sequences of functions, but that their representations converge in a suitable sense as well. Finally, we show that bilipschitz neural operators may always be written in the form of an alternating composition of strongly monotone neural operators, plus a simple isometry. Thus we realize a rigorous platform for discretization of a generalization of a neural operator. We also show that neural operators of this type may be approximated through the composition of finite-rank residual neural operators, where each block is strongly monotone, and may be inverted locally via iteration. We conclude by providing a quantitative approximation result for the discretization of general bilipschitz neural operators.
Risk-aware Classification via Uncertainty Quantification
Authors: Murat Sensoy, Lance M. Kaplan, Simon Julier, Maryam Saleki, Federico Cerutti
Date: 2024-12-04 00:00:00
Tasks: Classification, Decision Making, Deep Learning, Uncertainty Quantification, Unity

Autonomous and semi-autonomous systems are using deep learning models to improve decision-making. However, deep classifiers can be overly confident in their incorrect predictions, a major issue especially in safety-critical domains. The present study introduces three foundational desiderata for developing real-world risk-aware classification systems. Expanding upon the previously proposed Evidential Deep Learning (EDL), we demonstrate the unity between these principles and EDL's operational attributes. We then augment EDL empowering autonomous agents to exercise discretion during structured decision-making when uncertainty and risks are inherent. We rigorously examine empirical scenarios to substantiate these theoretical innovations. In contrast to existing risk-aware classifiers, our proposed methodologies consistently exhibit superior performance, underscoring their transformative potential in risk-conscious classification strategies.
Reactive Orchestration for Hierarchical Federated Learning Under a Communication Cost Budget
Authors: Ivan Čilić, Anna Lackinger, Pantelis Frangoudis, Ivana Podnar Žarko, Alireza Furutanpey, Ilir Murturi, Schahram Dustdar
Date: 2024-12-04 00:00:00
Tasks: Federated Learning

Deploying a Hierarchical Federated Learning (HFL) pipeline across the computing continuum (CC) requires careful organization of participants into a hierarchical structure with intermediate aggregation nodes between FL clients and the global FL server. This is challenging to achieve due to (i) cost constraints, (ii) varying data distributions, and (iii) the volatile operating environment of the CC. In response to these challenges, we present a framework for the adaptive orchestration of HFL pipelines, designed to be reactive to client churn and infrastructure-level events, while balancing communication cost and ML model accuracy. Our mechanisms identify and react to events that cause HFL reconfiguration actions at runtime, building on multi-level monitoring information (model accuracy, resource availability, resource cost). Moreover, our framework introduces a generic methodology for estimating reconfiguration costs to continuously re-evaluate the quality of adaptation actions, while being extensible to optimize for various HFL performance criteria. By extending the Kubernetes ecosystem, our framework demonstrates the ability to react promptly and effectively to changes in the operating environment, making the best of the available communication cost budget and effectively balancing costs and ML performance at runtime.
Classical Shadows with Improved Median-of-Means Estimation
Authors: Winston Fu, Dax Enshan Koh, Siong Thye Goh, Jian Feng Kong
Date: 2024-12-04 00:00:00

The classical shadows protocol, introduced by Huang et al. [Nat. Phys. 16, 1050 (2020)], makes use of the median-of-means (MoM) estimator to efficiently estimate the expectation values of $M$ observables with failure probability $\delta$ using only $\mathcal{O}(\log(M/\delta))$ measurements. In their analysis, Huang et al. used loose constants in their asymptotic performance bounds for simplicity. However, the specific values of these constants can significantly affect the number of shots used in practical implementations. To address this, we studied a modified MoM estimator proposed by Minsker [PMLR 195, 5925 (2023)] that uses optimal constants and involves a U-statistic over the data set. For efficient estimation, we implemented two types of incomplete U-statistics estimators, the first based on random sampling and the second based on cyclically permuted sampling. We compared the performance of the original and modified estimators when used with the classical shadows protocol with single-qubit Clifford unitaries (Pauli measurements) for an Ising spin chain, and global Clifford unitaries (Clifford measurements) for the Greenberger-Horne-Zeilinger (GHZ) state. While the original estimator outperformed the modified estimators for Pauli measurements, the modified estimators showed improved performance over the original estimator for Clifford measurements. Our findings highlight the importance of tailoring estimators to specific measurement settings to optimize the performance of the classical shadows protocol in practical applications.
Granular Ball Twin Support Vector Machine with Universum Data
Authors: M. A. Ganaie, Vrushank Ahire
Date: 2024-12-04 00:00:00
Tasks: Classification, Computational Efficiency

Classification with support vector machines (SVM) often suffers from limited performance when relying solely on labeled data from target classes and is sensitive to noise and outliers. Incorporating prior knowledge from Universum data and more robust data representations can enhance accuracy and efficiency. Motivated by these findings, we propose a novel Granular Ball Twin Support Vector Machine with Universum Data (GBU-TSVM) that extends the TSVM framework to leverage both Universum samples and granular ball computing during model training. Unlike existing TSVM methods, the proposed GBU-TSVM represents data instances as hyper-balls rather than points in the feature space. This innovative approach improves the model's robustness and efficiency, particularly in handling noisy and large datasets. By grouping data points into granular balls, the model achieves superior computational efficiency, increased noise resistance, and enhanced interpretability. Additionally, the inclusion of Universum data, which consists of samples that are not strictly from the target classes, further refines the classification boundaries. This integration enriches the model with contextual information, refining classification boundaries and boosting overall accuracy. Experimental results on UCI benchmark datasets demonstrate that the GBU-TSVM outperforms existing TSVM models in both accuracy and computational efficiency. These findings highlight the potential of the GBU-TSVM model in setting a new standard in data representation and classification.
On Approximability of $\ell_2^2$ Min-Sum Clustering
Authors: Karthik C. S., Euiwoong Lee, Yuval Rabani, Chris Schwiegelshohn, Samson Zhou
Date: 2024-12-04 00:00:00
Tasks: Clustering

The $\ell_2^2$ min-sum $k$-clustering problem is to partition an input set into clusters $C_1,\ldots,C_k$ to minimize $\sum_{i=1}^k\sum_{p,q\in C_i}\|p-q\|_2^2$. Although $\ell_2^2$ min-sum $k$-clustering is NP-hard, it is not known whether it is NP-hard to approximate $\ell_2^2$ min-sum $k$-clustering beyond a certain factor. In this paper, we give the first hardness-of-approximation result for the $\ell_2^2$ min-sum $k$-clustering problem. We show that it is NP-hard to approximate the objective to a factor better than $1.056$ and moreover, assuming a balanced variant of the Johnson Coverage Hypothesis, it is NP-hard to approximate the objective to a factor better than 1.327. We then complement our hardness result by giving a nearly linear time parameterized PTAS for $\ell_2^2$ min-sum $k$-clustering running in time $O\left(n^{1+o(1)}d\cdot \exp((k\cdot\varepsilon^{-1})^{O(1)})\right)$, where $d$ is the underlying dimension of the input dataset. Finally, we consider a learning-augmented setting, where the algorithm has access to an oracle that outputs a label $i\in[k]$ for input point, thereby implicitly partitioning the input dataset into $k$ clusters that induce an approximately optimal solution, up to some amount of adversarial error $\alpha\in\left[0,\frac{1}{2}\right)$. We give a polynomial-time algorithm that outputs a $\frac{1+\gamma\alpha}{(1-\alpha)^2}$-approximation to $\ell_2^2$ min-sum $k$-clustering, for a fixed constant $\gamma>0$.
Multi-Action Restless Bandits with Weakly Coupled Constraints: Simultaneous Learning and Control
Authors: Jing Fu, Bill Moran, José Niño-Mora
Date: 2024-12-04 00:00:00

We study a system with finitely many groups of multi-action bandit processes, each of which is a Markov decision process (MDP) with finite state and action spaces and potentially different transition matrices when taking different actions. The bandit processes of the same group share the same state and action spaces and, given the same action that is taken, the same transition matrix. All the bandit processes across various groups are subject to multiple weakly coupled constraints over their state and action variables. Unlike the past studies that focused on the offline case, we consider the online case without assuming full knowledge of transition matrices and reward functions a priori and propose an effective scheme that enables simultaneous learning and control. We prove the convergence of the relevant processes in both the timeline and the number of the bandit processes, referred to as the convergence in the time and the magnitude dimensions. Moreover, we prove that the relevant processes converge exponentially fast in the magnitude dimension, leading to exponentially diminishing performance deviation between the proposed online algorithms and offline optimality.
Scalable Bayesian Tensor Ring Factorization for Multiway Data Analysis
Authors: Zerui Tao, Toshihisa Tanaka, Qibin Zhao
Date: 2024-12-04 00:00:00
Tasks: Variational Inference

Tensor decompositions play a crucial role in numerous applications related to multi-way data analysis. By employing a Bayesian framework with sparsity-inducing priors, Bayesian Tensor Ring (BTR) factorization offers probabilistic estimates and an effective approach for automatically adapting the tensor ring rank during the learning process. However, previous BTR method employs an Automatic Relevance Determination (ARD) prior, which can lead to sub-optimal solutions. Besides, it solely focuses on continuous data, whereas many applications involve discrete data. More importantly, it relies on the Coordinate-Ascent Variational Inference (CAVI) algorithm, which is inadequate for handling large tensors with extensive observations. These limitations greatly limit its application scales and scopes, making it suitable only for small-scale problems, such as image/video completion. To address these issues, we propose a novel BTR model that incorporates a nonparametric Multiplicative Gamma Process (MGP) prior, known for its superior accuracy in identifying latent structures. To handle discrete data, we introduce the P\'olya-Gamma augmentation for closed-form updates. Furthermore, we develop an efficient Gibbs sampler for consistent posterior simulation, which reduces the computational complexity of previous VI algorithm by two orders, and an online EM algorithm that is scalable to extremely large tensors. To showcase the advantages of our model, we conduct extensive experiments on both simulation data and real-world applications.
FlashAttention on a Napkin: A Diagrammatic Approach to Deep Learning IO-Awareness
Authors: Vincent Abbott, Gioele Zardini
Date: 2024-12-04 00:00:00
Tasks: GPU, Quantization

Optimizing deep learning algorithms currently requires slow, manual derivation, potentially leaving much performance untapped. Methods like FlashAttention have achieved a x6 performance improvement over native PyTorch by avoiding unnecessary data transfers, but required three iterations over three years to be developed. Automated compiled methods have consistently lagged behind. This paper extends Neural Circuit Diagrams for deep learning models to consider resource usage and the distribution of tasks across a GPU hierarchy. We show how diagrams can use simple relabellings to derive high-level streaming and tiling optimization strategies along with performance models. We show how this high-level performance model allows the effects of quantization and multi-level GPU hierarchies to be readily considered. We develop a methodology for representing intermediate-level pseudocode with diagrams, allowing hardware-aware algorithms to be derived step-by-step. Finally, we show how our methodology can be used to better understand existing techniques like FlashAttention. This work uses a theoretical framework to link assumptions about GPU behaviour to claims about performance. We aim to lay the groundwork for a scientific approach to GPU optimization where experiments can address clear hypotheses rather than post-hoc rationalizations.
Conveying Emotions to Robots through Touch and Sound
Authors: Qiaoqiao Ren, Remko Proesmans, Frederick Bossuyt, Jan Vanfleteren, Francis wyffels, Tony Belpaeme
Date: 2024-12-04 00:00:00

Human emotions can be conveyed through nuanced touch gestures. However, there is a lack of understanding of how consistently emotions can be conveyed to robots through touch. This study explores the consistency of touch-based emotional expression toward a robot by integrating tactile and auditory sensory reading of affective haptic expressions. We developed a piezoresistive pressure sensor and used a microphone to mimic touch and sound channels, respectively. In a study with 28 participants, each conveyed 10 emotions to a robot using spontaneous touch gestures. Our findings reveal a statistically significant consistency in emotion expression among participants. However, some emotions obtained low intraclass correlation values. Additionally, certain emotions with similar levels of arousal or valence did not exhibit significant differences in the way they were conveyed. We subsequently constructed a multi-modal integrating touch and audio features to decode the 10 emotions. A support vector machine (SVM) model demonstrated the highest accuracy, achieving 40% for 10 classes, with "Attention" being the most accurately conveyed emotion at a balanced accuracy of 87.65%.