pwc-archive/papers-with-abstracts huggingface dataset:
Unsupervised Learning of Distributional Properties can Supplement Human Labeling and Increase Active Learning Efficiency in Anomaly Detection
Authors: Jaturong Kongmanee, Mark Chignell, Khilan Jerath, Abhay Raman
Date: 2023-07-13 00:00:00
Tasks: Active Learning, Anomaly Detection, Unsupervised Anomaly Detection

Exfiltration of data via email is a serious cybersecurity threat for many organizations. Detecting data exfiltration (anomaly) patterns typically requires labeling, most often done by a human annotator, to reduce the high number of false alarms. Active Learning (AL) is a promising approach for labeling data efficiently, but it needs to choose an efficient order in which cases are to be labeled, and there are uncertainties as to what scoring procedure should be used to prioritize cases for labeling, especially when detecting rare cases of interest is crucial. We propose an adaptive AL sampling strategy that leverages the underlying prior data distribution, as well as model uncertainty, to produce batches of cases to be labeled that contain instances of rare anomalies. We show that (1) the classifier benefits from a batch of representative and informative instances of both normal and anomalous examples, (2) unsupervised anomaly detection plays a useful role in building the classifier in the early stages of training when relatively little labeling has been done thus far. Our approach to AL for anomaly detection outperformed existing AL approaches on three highly unbalanced UCI benchmarks and on one real-world redacted email data set.
A Novel Scene Coupling Semantic Mask Network for Remote Sensing Image Segmentation
Authors: Xiaowen Ma, Rongrong Lian, Zhenkai Wu, Renxiang Guan, Tingfeng Hong, Mengjiao Zhao, Mengting Ma, Jiangtao Nie, Zhenhong Du, Siyang Song, Wei zhang
Date: 2025-01-22 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

As a common method in the field of computer vision, spatial attention mechanism has been widely used in semantic segmentation of remote sensing images due to its outstanding long-range dependency modeling capability. However, remote sensing images are usually characterized by complex backgrounds and large intra-class variance that would degrade their analysis performance. While vanilla spatial attention mechanisms are based on dense affine operations, they tend to introduce a large amount of background contextual information and lack of consideration for intrinsic spatial correlation. To deal with such limitations, this paper proposes a novel scene-Coupling semantic mask network, which reconstructs the vanilla attention with scene coupling and local global semantic masks strategies. Specifically, scene coupling module decomposes scene information into global representations and object distributions, which are then embedded in the attention affinity processes. This Strategy effectively utilizes the intrinsic spatial correlation between features so that improve the process of attention modeling. Meanwhile, local global semantic masks module indirectly correlate pixels with the global semantic masks by using the local semantic mask as an intermediate sensory element, which reduces the background contextual interference and mitigates the effect of intra-class variance. By combining the above two strategies, we propose the model SCSM, which not only can efficiently segment various geospatial objects in complex scenarios, but also possesses inter-clean and elegant mathematical representations. Experimental results on four benchmark datasets demonstrate the the effectiveness of the above two strategies for improving the attention modeling of remote sensing images. The dataset and code are available at https://github.com/xwmaxwma/rssegmentation
Lipschitz Safe Bayesian Optimization for Automotive Control
Authors: Johanna Menn, Pietro Pelizzari, Michael Fleps-Dezasse, Sebastian Trimpe
Date: 2025-01-22 00:00:00
Tasks: Bayesian Optimization

Controller tuning is a labor-intensive process that requires human intervention and expert knowledge. Bayesian optimization has been applied successfully in different fields to automate this process. However, when tuning on hardware, such as in automotive applications, strict safety requirements often arise. To obtain safety guarantees, many existing safe Bayesian optimization methods rely on assumptions that are hard to verify in practice. This leads to the use of unjustified heuristics in many applications, which invalidates the theoretical safety guarantees. Furthermore, applications often require multiple safety constraints to be satisfied simultaneously. Building on recently proposed Lipschitz-only safe Bayesian optimization, we develop an algorithm that relies on readily interpretable assumptions and satisfies multiple safety constraints at the same time. We apply this algorithm to the problem of automatically tuning a trajectory-tracking controller of a self-driving car. Results both from simulations and an actual test vehicle underline the algorithm's ability to learn tracking controllers without leaving the track or violating any other safety constraints.
Trajectory tracking model-following control using Lyapunov redesign with output time-derivatives to compensate unmatched uncertainties
Authors: Niclas Tietze, Kai Wulff, Johann Reger
Date: 2025-01-22 00:00:00

We study trajectory tracking for flat nonlinear systems with unmatched uncertainties using the model-following control (MFC) architecture. We apply state feedback linearisation control for the process and propose a simplified implementation of the model control loop which results in a simple model in Brunovsky-form that represents the nominal feedback linearised dynamics of the nonlinear process. To compensate possibly unmatched model uncertainties, we employ Lyapunov redesign with numeric derivatives of the output. It turns out that for a special initialisation of the model, the MFC reduces to a single-loop control design. We illustrate our results by a numerical example.
Orbit-Attitude Predictive Control in the Vicinity of Asteroids with In Situ Gravity Estimation
Authors: Julio C. Sanchez, Rafael Vazquez, James D. Biggs, Franco Bernelli-Zazzera
Date: 2025-01-22 00:00:00

This paper presents an integrated model-learning predictive control scheme for spacecraft orbit-attitude station-keeping in the vicinity of asteroids. The orbiting probe relies on optical and laser navigation while attitude measurements are provided by star trackers and gyroscopes. The asteroid gravity field inhomogeneities are assumed to be unknown a priori. The state and gravity model parameters are estimated simultaneously using an unscented Kalman filter. The proposed gravity model identification enables the application of a learning-based predictive control methodology. The predictive control allows for a high degree of accuracy because the predicted model is progressively identified in situ. Consequently, the tracking errors decrease over time as the model accuracy increases. Finally, a constellation mission concept is analyzed in order to speed up the model identification process. Numerical results are shown and discussed.
Keypoint Detection Empowered Near-Field User Localization and Channel Reconstruction
Authors: Mengyuan Li, Yu Han, Zhizheng Lu, Shi Jin, Yongxu Zhu, Chao-Kai Wen
Date: 2025-01-21 00:00:00
Tasks: compressed sensing, Keypoint Detection, parameter estimation

In the near-field region of an extremely large-scale multiple-input multiple-output (XL MIMO) system, channel reconstruction is typically addressed through sparse parameter estimation based on compressed sensing (CS) algorithms after converting the received pilot signals into the transformed domain. However, the exhaustive search on the codebook in CS algorithms consumes significant computational resources and running time, particularly when a large number of antennas are equipped at the base station (BS). To overcome this challenge, we propose a novel scheme to replace the high-cost exhaustive search procedure. We visualize the sparse channel matrix in the transformed domain as a channel image and design the channel keypoint detection network (CKNet) to locate the user and scatterers in high speed. Subsequently, we use a small-scale newtonized orthogonal matching pursuit (NOMP) based refiner to further enhance the precision. Our method is applicable to both the Cartesian domain and the Polar domain. Additionally, to deal with scenarios with a flexible number of propagation paths, we further design FlexibleCKNet to predict both locations and confidence scores. Our experimental results validate that the CKNet and FlexibleCKNet-empowered channel reconstruction scheme can significantly reduce the computational complexity while maintaining high accuracy in both user and scatterer localization and channel reconstruction tasks.
Few-shot Human Motion Recognition through Multi-Aspect mmWave FMCW Radar Data
Authors: Hao Fan, Lingfeng Chen, Chengbai Xu, Jiadong Zhou, Yongpeng Dai, Panhe Hu
Date: 2025-01-19 00:00:00

Radar human motion recognition methods based on deep learning models has been a heated spot of remote sensing in recent years, yet the existing methods are mostly radial-oriented. In practical application, the test data could be multi-aspect and the sample number of each motion could be very limited, causing model overfitting and reduced recognition accuracy. This paper proposed channel-DN4, a multi-aspect few-shot human motion recognition method. First, local descriptors are introduced for a precise classification metric. Moreover, episodic training strategy was adopted to reduce model overfitting. To utilize the invariant sematic information in multi-aspect conditions, we considered channel attention after the embedding network to obtain precise implicit high-dimensional representation of sematic information. We tested the performance of channel-DN4 and methods for comparison on measured mmWave FMCW radar data. The proposed channel-DN4 produced competitive and convincing results, reaching the highest 87.533% recognition accuracy in 3-way 10-shot condition while other methods suffer from overfitting. Codes are available at: https://github.com/MountainChenCad/channel-DN4
Correlation Neglect in Games
Authors: Florian Mudekereza
Date: 2025-01-22 00:00:00

This paper proposes a simple framework to study the effect of correlation neglect on social learning and welfare in games with social incentives. It examines statistical learners (frequentists, Bayesians, etc.) who make decisions based on their peers' actions but overlook the correlation between the actions they observe. A novel solution concept called correlated sampling equilibrium with statistical inference (CoSESI) reveals that correlation neglect affects strategic behavior through persistent overprecision, which leads to polarization and information cascades. CoSESI always exists and differs from existing concepts. It captures the fact that naive beliefs are overly sensitive to correlations, which causes failures of social learning. Applications of CoSESI in matching markets, monopoly pricing, and financial markets demonstrate that correlation neglect bears significant economic consequences.
Large Language Model-Based Semantic Communication System for Image Transmission
Authors: Soheyb Ribouh, Osama Saleem
Date: 2025-01-22 00:00:00
Tasks: Decoder, Intelligent Communication, Language Modeling, Language Modelling, Large Language Model, Semantic Communication

The remarkable success of Large Language Models (LLMs) in understanding and generating various data types, such as images and text, has demonstrated their ability to process and extract semantic information across diverse domains. This transformative capability lays the foundation for semantic communications, enabling highly efficient and intelligent communication systems. In this work, we present a novel OFDM-based semantic communication framework for image transmission. We propose an innovative semantic encoder design that leverages the ability of LLMs to extract the meaning of transmitted data rather than focusing on its raw representation. On the receiver side, we design an LLM-based semantic decoder capable of comprehending context and generating the most appropriate representation to fit the given context. We evaluate our proposed system under different scenarios, including Urban Macro-cell environments with varying speed ranges. The evaluation metrics demonstrate that our proposed system reduces the data size 4250 times, while achieving a higher data rate compared to conventional communication methods. This approach offers a robust and scalable solution to unlock the full potential of 6G connectivity.
A control system framework for counterfactuals: an optimization based approach
Authors: Pierluigi Francesco De Paola, Jared Miller, Alessandro Borri, Alessia Paglialonga, Fabrizio Dabbene
Date: 2025-01-22 00:00:00

Counterfactuals are a concept inherited from the field of logic and in general attain to the existence of causal relations between sentences or events. In particular, this concept has been introduced also in the context of interpretability in artificial intelligence, where counterfactuals refer to the minimum change to the feature values that changes the prediction of a classification model. The artificial intelligence framework of counterfactuals is mostly focused on machine learning approaches, typically neglecting the physics of the variables that determine a change in class. However, a theoretical formulation of counterfactuals in a control system framework - i.e., able to account for the mechanisms underlying a change in class - is lacking. To fill this gap, in this work we propose an original control system, physics-informed, theoretical foundation for counterfactuals, by means of the formulation of an optimal control problem. We apply the proposed methodology to a general glucose-insulin regulation model and results appear promising and pave the way to the possible integration with artificial intelligence techniques, with the aim of feeding machine learning models with the physics knowledge acquired through the system framework.
Why disentanglement-based speaker anonymization systems fail at preserving emotions?
Authors: Ünal Ege Gaznepoglu, Nils Peters
Date: 2025-01-22 00:00:00
Tasks: Disentanglement, Emotion Recognition, Speaker anonymization

Disentanglement-based speaker anonymization involves decomposing speech into a semantically meaningful representation, altering the speaker embedding, and resynthesizing a waveform using a neural vocoder. State-of-the-art systems of this kind are known to remove emotion information. Possible reasons include mode collapse in GAN-based vocoders, unintended modeling and modification of emotions through speaker embeddings, or excessive sanitization of the intermediate representation. In this paper, we conduct a comprehensive evaluation of a state-of-the-art speaker anonymization system to understand the underlying causes. We conclude that the main reason is the lack of emotion-related information in the intermediate representation. The speaker embeddings also have a high impact, if they are learned in a generative context. The vocoder's out-of-distribution performance has a smaller impact. Additionally, we discovered that synthesis artifacts increase spectral kurtosis, biasing emotion recognition evaluation towards classifying utterances as angry. Therefore, we conclude that reporting unweighted average recall alone for emotion recognition performance is suboptimal.
LLM4WM: Adapting LLM for Wireless Multi-Tasking
Authors: Xuanyu Liu, Shijian Gao, Boxun Liu, Xiang Cheng, Liuqing Yang
Date: 2025-01-22 00:00:00
Tasks: General Knowledge, Language Modeling, Language Modelling, Large Language Model, Mixture-of-Experts, Transfer Learning

The wireless channel is fundamental to communication, encompassing numerous tasks collectively referred to as channel-associated tasks. These tasks can leverage joint learning based on channel characteristics to share representations and enhance system design. To capitalize on this advantage, LLM4WM is proposed--a large language model (LLM) multi-task fine-tuning framework specifically tailored for channel-associated tasks. This framework utilizes a Mixture of Experts with Low-Rank Adaptation (MoE-LoRA) approach for multi-task fine-tuning, enabling the transfer of the pre-trained LLM's general knowledge to these tasks. Given the unique characteristics of wireless channel data, preprocessing modules, adapter modules, and multi-task output layers are designed to align the channel data with the LLM's semantic feature space. Experiments on a channel-associated multi-task dataset demonstrate that LLM4WM outperforms existing methodologies in both full-sample and few-shot evaluations, owing to its robust multi-task joint modeling and transfer learning capabilities.
SoundSpring: Loss-Resilient Audio Transceiver with Dual-Functional Masked Language Modeling
Authors: Shengshi Yao, Jincheng Dai, Xiaoqi Qin, Sixian Wang, Siye Wang, Kai Niu, Ping Zhang
Date: 2025-01-22 00:00:00
Tasks: Audio Compression, Language Modeling, Language Modelling, Masked Language Modeling, Packet Loss Concealment

In this paper, we propose "SoundSpring", a cutting-edge error-resilient audio transceiver that marries the robustness benefits of joint source-channel coding (JSCC) while also being compatible with current digital communication systems. Unlike recent deep JSCC transceivers, which learn to directly map audio signals to analog channel-input symbols via neural networks, our SoundSpring adopts the layered architecture that delineates audio compression from digital coded transmission, but it sufficiently exploits the impressive in-context predictive capabilities of large language (foundation) models. Integrated with the casual-order mask learning strategy, our single model operates on the latent feature domain and serve dual-functionalities: as efficient audio compressors at the transmitter and as effective mechanisms for packet loss concealment at the receiver. By jointly optimizing towards both audio compression efficiency and transmission error resiliency, we show that mask-learned language models are indeed powerful contextual predictors, and our dual-functional compression and concealment framework offers fresh perspectives on the application of foundation language models in audio communication. Through extensive experimental evaluations, we establish that SoundSpring apparently outperforms contemporary audio transmission systems in terms of signal fidelity metrics and perceptual quality scores. These new findings not only advocate for the practical deployment of SoundSpring in learning-based audio communication systems but also inspire the development of future audio semantic transceivers.
The Intrinsic State Variable in Fundamental Lemma and Its Use in Stability Design for Data-based Control
Authors: Yitao Yan, Jie Bao, Biao Huang
Date: 2025-01-22 00:00:00
Tasks: LEMMA

In the data-based setting, analysis and control design of dynamical systems using measured data are typically based on overlapping trajectory segments of the input and output variables. This could lead to complex designs because the system internal dynamics, which is typically reflected by the system state variable, is unavailable. In this paper, we will show that the coefficient vector in a modified version of Willems' fundamental lemma is an intrinsic and observable state variable for the system behavior. This argument evolves from the behavioral framework without the requirement of prior knowledge on the causality among system variables or any predefined representation structure (e.g., a state space representation). Such a view allows for the construction of a state map based on the fundamental lemma, bridging the trajectory space and the state space. The state property of the coefficient vector allows for a simple stability design approach using memoryless quadratic functions of it as Lyapunov functions, from which the control action for each step can be explicitly constructed. Using the coefficient vector as a state variable could see wide applications in the analysis and control design of dynamical systems including directions beyond the discussions in this paper.
Set-point control and local stability for flat nonlinear systems using model-following control
Authors: Julian Willkomm, Kai Wulff, Johann Reger
Date: 2025-01-22 00:00:00

We consider the set-point control problem for nonlinear systems with flat output that are subject to perturbations. The nonlinear dynamics as well as the perturbations are locally Lipschitz. We apply the model-following control (MFC) approach which consists of a model control loop (MCL) for a feedforward generation and a process control loop (PCL) that compensates the perturbations using high-gain feedback. We analyse the resulting closed-loop system and discuss its relation to a standard flatness-based high-gain approach. In particular we analyse the estimated region of attraction provided by a quadratic Lyapunov function. A case study illustrates the approach and quantifies the region of attraction obtained for each control approach. Using the initial condition of the model control loop as tuning parameter for the MFC design, provides that a significantly larger region of attraction can be guaranteed compared to a conventional single-loop high-gain design.
Retrieval-Augmented Neural Field for HRTF Upsampling and Personalization
Authors: Yoshiki Masuyama, Gordon Wichern, François G. Germain, Christopher Ick, Jonathan Le Roux
Date: 2025-01-22 00:00:00
Tasks: Audio Generation, Retrieval, Task 2

Head-related transfer functions (HRTFs) with dense spatial grids are desired for immersive binaural audio generation, but their recording is time-consuming. Although HRTF spatial upsampling has shown remarkable progress with neural fields, spatial upsampling only from a few measured directions, e.g., 3 or 5 measurements, is still challenging. To tackle this problem, we propose a retrieval-augmented neural field (RANF). RANF retrieves a subject whose HRTFs are close to those of the target subject from a dataset. The HRTF of the retrieved subject at the desired direction is fed into the neural field in addition to the sound source direction itself. Furthermore, we present a neural network that can efficiently handle multiple retrieved subjects, inspired by a multi-channel processing technique called transform-average-concatenate. Our experiments confirm the benefits of RANF on the SONICOM dataset, and it is a key component in the winning solution of Task 2 of the listener acoustic personalization challenge 2024.
Shifts of dominant personality and spatial pattern formation due to spatially heterogeneous pollution
Authors: Tianxu Wang, Jiwoon Sim, Hao Wang
Date: 2025-01-22 00:00:00

Personality traits, such as boldness and shyness, play a significant role in shaping the survival strategies of animals. Industrial pollution has long posed serious threats to ecosystems and is typically distributed heterogeneously. However, how animals with different personalities respond to spatially heterogeneous pollution remains largely unexplored. In this study, we introduce a prey-taxis model with nonlinear cross-diffusion to examine population dynamics in such environments. The global existence of classical solutions is established by deriving initial bounds through energy estimates and improving solution regularity via heat kernel properties and a bootstrap process. Our findings reveal that behavior, population structure, and spatial distribution are heavily influenced by pollution. Bold individuals maintain a competitive advantage in pollution-free or very low-toxin environments, whereas shy individuals become dominant in regions with low to moderate toxin levels. In highly polluted areas, no populations can survive. The spatial pattern of the population is also closely tied to the distribution of toxins. Grazers tend to move along toxin gradient and exhibit periodic behavior. As toxin concentrations rise, aggregation behavior becomes increasingly pronounced across all species. Interestingly, the total population in polluted areas may initially increase when toxin levels are low to moderate, but eventually declines, leading to extinction as toxin levels continue to rise.