pwc-archive/papers-with-abstracts huggingface dataset:
Hyperspectral image denoising based on global and non-local low-rank factorizations
Authors: Lina Zhuang, Jose M. Bioucas-Dias
Date: 2021-01-08 00:00:00
Proceeding: IEEE Transactions on Geoscience and Remote Sensing 2021 1
Tasks: Denoising, Hyperspectral Image Denoising, Image Denoising

The ever-increasing spectral resolution of hyperspectral images (HSIs) is often obtained at the cost of a decrease in the signal-to-noise ratio of the measurements, thus calling for effective denoising techniques. HSIs from the real world lie in low-dimensional subspaces and are self-similar. The low dimensionality stems from the high correlation existing among the reflectance vectors, and self-similarity is common in real-world images. In this article, we exploit the above two properties. The low dimensionality is a global property that enables the denoising to be formulated just with respect to the subspace representation coefficients, thus greatly improving the denoising performance and reducing the computational complexity during processing. The self-similarity is exploited via a low-rank tensor factorization of nonlocal similar 3-D patches. The proposed factorization hinges on the optimal shrinkage/thresholding of the singular value decomposition (SVD) singular values of low-rank tensor unfoldings. As a result, the proposed method is user friendly and insensitive to its parameters. Its effectiveness is illustrated in a comparison with state-of-the-art competitors. A MATLAB demo of this work is available at https://github.com/LinaZhuang for the sake of reproducibility.
A Self-Supervised Deep Denoiser for Hyperspectral and Multispectral Image Fusion
Authors: Zhicheng Wang, Michael K. Ng, Joseph Michalski, Lina Zhuang
Date: 2023-08-10 00:00:00
Proceeding: IEEE Transactions on Geoscience and Remote Sensing 2023 8
Tasks: Super-Resolution

The plug-and-play (PnP) technique enables us to plug image priors into an alternating direction method of multipliers (ADMM) framework for solving a regularized optimization problem. Deep image priors have shown their flexibility and robustness in solving several image inverse problems. Hyperspectral image (HSI) super-resolution problem is an ill-posed inverse problem that aims to obtain a high-resolution HSI (HR-HSI) by combining the information of low-resolution HSI (LR-HSI) and HR multispectral image (HR-MSI) simultaneously. This article proposes an HSI and MSI fusion framework termed E2E-Fusion, plugged with a self-supervised deep learning prior called Eigenimage2Eigenimage. First, the spectral low-rank structure of HSIs is exploited via subspace representations of spectra vectors. Meanwhile, benefiting from the high quality of the first eigenimage (i.e., representation coefficients), we design a self-supervised deep eigenimage guidance network image prior, E2E. By using the PnP technique, we plugged the E2E prior into the ADMM fusion framework to update the optimal objective function iteratively. The numerical experimental results both on the simulated datasets and real datasets demonstrate that the proposed method performs better than state-of-the-art fusion methods.
Hyperspectral Mixed Noise Removal By L1-Norm-Based Subspace Representation
Authors: L. Zhuang, Michael K. Ng
Date: 2020-03-18 00:00:00
Proceeding: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020 3
Tasks: Denoising

This article introduces a new hyperspectral image (HSI) denoising method that is able to cope with additive mixed noise, i.e., mixture of Gaussian noise, impulse noise, and stripes, which usually corrupt hyperspectral images in the acquisition process. The proposed method fully exploits a compact and sparse HSI representation based on its low-rank and self-similarity characteristics. In order to deal with mixed noise having a complex statistical distribution, we propose to use the robust ℓ 1 data fidelity instead of using the ℓ 1 data fidelity, which is commonly employed for Gaussian noise removal. In a series of experiments with simulated and real datasets, the proposed method competes with state-of-the-art methods, yielding better results for mixed noise removal.
Hyperspectral Image Mixed Noise Removal Using Subspace Representation and Deep CNN Image Prior
Authors: Lina Zhuang, Michael K. Ng, Xiyou Fu
Date: 2021-10-13 00:00:00
Proceeding: Remote Sensing 2021 10
Tasks: Denoising

The ever-increasing spectral resolution of hyperspectral images (HSIs) is often obtained at the cost of a decrease in the signal-to-noise ratio (SNR) of the measurements. The decreased SNR reduces the reliability of measured features or information extracted from HSIs, thus calling for effective denoising techniques. This work aims to estimate clean HSIs from observations corrupted by mixed noise (containing Gaussian noise, impulse noise, and dead-lines/stripes) by exploiting two main characteristics of hyperspectral data, namely low-rankness in the spectral domain and high correlation in the spatial domain. We take advantage of the spectral low-rankness of HSIs by representing spectral vectors in an orthogonal subspace, which is learned from observed images by a new method. Subspace representation coefficients of HSIs are learned by solving an optimization problem plugged with an image prior extracted from a neural denoising network. The proposed method is evaluated on simulated and real HSIs. An exhaustive array of experiments and comparisons with state-of-the-art denoisers were carried out.
Regularization Parameter Selection in Minimum Volume Hyperspectral Unmixing
Authors: Lina Zhuang, Chia-Hsiang Lin, Mario A. T. Figueiredo, Jose M. Bioucas-Dias
Date: 2019-08-14 00:00:00
Proceeding: IEEE Transactions on Geoscience and Remote Sensing 2019 8
Tasks: Hyperspectral Unmixing

Linear hyperspectral unmixing (HU) aims at factoring the observation matrix into an endmember matrix and an abundance matrix. Linear HU via variational minimum volume (MV) regularization has recently received considerable attention in the remote sensing and machine learning areas, mainly owing to its robustness against the absence of pure pixels. We put some popular linear HU formulations under a unifying framework, which involves a data-fitting term and an MV-based regularization term, and collectively solve it via a nonconvex optimization. As the former and the latter terms tend, respectively, to expand (reducing the data-fitting errors) and to shrink the simplex enclosing the measured spectra, it is critical to strike a balance between those two terms. To the best of our knowledge, the existing methods find such balance by tuning a regularization parameter manually, which has little value in unsupervised scenarios. In this paper, we aim at selecting the regularization parameter automatically by exploiting the fact that a too large parameter overshrinks the volume of the simplex defined by the endmembers, making many data points be left outside of the simplex and hence inducing a large data-fitting error, while a sufficiently small parameter yields a large simplex making data-fitting error very small. Roughly speaking, the transition point happens when the simplex still encloses the data cloud but there are data points on all its facets. These observations are systematically formulated to find the transition point that, in turn, yields a good parameter. The competitiveness of the proposed selection criterion is illustrated with simulated and real data.
Hy-demosaicing: Hyperspectral blind reconstruction from spectral subsampling
Authors: Lina Zhuang, Michael K. Ng, Xiyou Fu, Jose M Bioucas-Dias
Date: 2021-08-13 00:00:00
Proceeding: IEEE Transactions on Geoscience and Remote Sensing 2021 8
Tasks: Demosaicking

This article proposes a smart hyperspectral sensing strategy, implemented in the spectral domain, conceived for spaceborne sensor systems, where physical space, storage resources, and communication bandwidth are extremely scarce and expensive. Smart sensing means faster and hardware-friendly imaging. Instead of acquiring all band samples in the spectral domain, we randomly select a few band samples per spatial pixel location. A periodic structure of spectral band selector array (SBSA) is designed so that we can learn a subspace basis from subsamples, which is essential to the underlying hyperspectral image (HSI) recovery algorithm. This spectral subsampling sensing strategy yields a demosaicing problem. We propose a blind hyperspectral reconstruction technique termed hyperspectral demosaicing (Hy-demosaicing) exploiting spectral low-rankness and spatial correlation of HSIs. It is blind in the sense that the signal subspace is learned from measured spectral subsamples. The subspace basis is data-adaptive and provides a more compact representation than other non-adaptive representations. This adaptiveness leads to improved image recovery as illustrated in experiments with real data.
Mean field equilibrium asset pricing model under partial observation: An exponential quadratic Gaussian approach
Authors: Masashi Sekine
Date: 2024-10-02 00:00:00

This paper studies an asset pricing model in a partially observable market with a large number of heterogeneous agents using the mean field game theory. In this model, we assume that investors can only observe stock prices and must infer the risk premium from these observations when determining trading strategies. We characterize the equilibrium risk premium in such a market through a solution to the mean field backward stochastic differential equation (BSDE). Specifically, the solution to the mean field BSDE can be expressed semi-analytically by employing an exponential quadratic Gaussian framework. We then construct the risk premium process, which cannot be observed directly by investors, endogenously using the Kalman-Bucy filtering theory. In addition, we include a simple numerical simulation to visualize the dynamics of our market model.
Edge-Guided Enhancement Network for Building Change Detection of Remote Sensing Images with a Hybrid CNN-Transformer Architecture
Authors: Kaiwen Jing, Chenhe Wang, Bingyao Li, Yanhan Wang, Jiarui Ban, Junli Yang
Date: 2024-09-05 00:00:00
Proceeding: IEEE International Geoscience and Remote Sensing Symposium 2024 9

The utilization of remote sensing images for building change detection has become a focal point of concern. Many contemporary change detection methodologies primarily focus on extracting more discriminative features while neglecting the use of prior edge information, leading to inaccurate detection results, especially in areas like building boundaries. To address this issue, we propose a method called Edge-Guided Enhancement Network(EGENet) that combines discriminative information and edge features within a unified framework. In particular, as edge represents part of the image that changes drastically and indicates high-frequency information in image, we construct an Edge Enhancement Module (EEM) to enhance high-frequency information and suppress noise. Furthermore, We design the Edge Detection Branch (EDB) to better enhance edge information and employ the detected edge information for subsequent cross attention. Our experiments on two building change detection datasets demonstrate that our approach is proper and achieves advanced performance compared to other methods.
Hedging via Perpetual Derivatives: Trinomial Option Pricing and Implied Parameter Surface Analysis
Authors: Jagdish Gnawali, W. Brent Lindquist, Svetlozar T. Rachev
Date: 2024-10-07 00:00:00

We introduce a fairly general, recombining trinomial tree model in the natural world. Market-completeness is ensured by considering a market consisting of two risky assets, a riskless asset, and a European option. The two risky assets consist of a stock and a perpetual derivative of that stock. The option has the stock and its derivative as its underlying. Using a replicating portfolio, we develop prices for European options and generate the unique relationships between the risk-neutral and real-world parameters of the model. We discuss calibration of the model to empirical data in the cases in which the risky asset returns are treated as either arithmetic or logarithmic. From historical price and call option data for select large cap stocks, we develop implied parameter surfaces for the real-world parameters in the model.
A 9T4R RRAM-Based ACAM for Analogue Template Matching at the Edge
Authors: Georgios Papandroulidakis, Shady Agwa, Ahmet Cirakoglu, Themis Prodromakis
Date: 2024-10-04 00:00:00
Tasks: Edge-computing, Template Matching

The continuous shift of computational bottlenecks to the memory access and data transfer, especially for AI applications, poses the urgent needs of re-engineering the computer architecture fundamentals. Many edge computing applications, like wearable and implantable medical devices, introduce increasingly more challenges to conventional computing systems due to the strict requirements of area and power at the edge. Emerging technologies, like Resistive RAM (RRAM), have shown a promising momentum in developing neuro-inspired analogue computing paradigms capable of achieving high classification capabilities alongside high energy efficiency. In this work, we present a novel RRAM-based Analogue Content Addressable Memory (ACAM) for on-line analogue template matching applications. This ACAM-based template matching architecture aims to achieve energy-efficient classification where low energy is of utmost importance. We are showcasing a highly tuneable novel RRAM-based ACAM pixel implemented using a commercial 180nm CMOS technology and in-house RRAM technology and exhibiting low energy dissipation of approximately 0.036pJ and 0.16pJ for mismatch and match, respectively, at 66MHz with 3V voltage supply. A proof-of-concept system-level implementation based on this novel pixel design is also implemented in 180nm.
On Long-Term Species Coexistence in Five-Species Evolutionary Spatial Cyclic Games with Ablated and Non-Ablated Dominance Networks
Authors: Dave Cliff
Date: 2024-10-04 00:00:00

I present a replication and, to some extent, a refutation of key results published by Zhong, Zhang, Li, Dai, & Yang in their 2022 paper "Species coexistence in spatial cyclic game of five species" (Chaos, Solitons and Fractals, 156: 111806), where ecosystem species coexistence was explored via simulation studies of the evolutionary spatial cyclic game (ESCG) Rock-Paper-Scissors-Lizard-Spock (RPSLS) with certain predator-prey relationships removed from the game's "interaction structure", i.e. with specific arcs ablated in the ESCG's dominance network, and with the ESCG run for 100,000 Monte Carlo Steps (MCS) to identify its asymptotic behaviors. I replicate the results presented by Zhong et al. for interaction structures with one, two, three, and four arcs ablated from the dominance network. I then empirically demonstrate that the dynamics of the RPSLS ESCG have sufficiently long time constants that the true asymptotic outcomes can often only be identified after running the ablated ESCG for 10,000,000MCS or longer, and that the true long-term outcomes can be markedly less diverse than those reported by Zhong et al. as asymptotic. Finally I demonstrate that, when run for sufficiently many MCS, the original unablated RPSLS system exhibits essentially the same asymptotic outcomes as the ablated RPSLS systems, and in this sense the only causal effect of the ablations is to alter the time required for the system to converge to the long-term asymptotic states that the unablated system eventually settles to anyhow.
Cracking the code: Lessons from 15 years of digital health IPOs for the era of AI
Authors: Tamen Jadad-Garcia, Alejandro R. Jadad
Date: 2024-10-03 00:00:00

Introduction: As digital health evolves, identifying factors that drive success is crucial. This study examines how reimbursement billing codes affect the long-term financial performance of digital health companies on U.S. stock markets, addressing the question: What separates the winners from the rest? Methods: We analyzed digital health companies that went public on U.S. stock exchanges between 2010 and 2021, offering products or services aimed at improving personal health or disease management within the U.S. market. A search using Google and existing IPO lists identified eligible companies. They were categorized based on the presence or absence of billing codes at the time of their initial public offering (IPO). Key performance indicators, including Compound Annual Growth Rate (CAGR), relative performance to benchmark indices, and market capitalization change, were compared using Mann-Whitney U and Fisher's Exact tests. Results: Of the 33 companies analyzed, 15 (45.5%) had billing codes at IPO. The median IPO price was $17.00, with no significant difference between groups. Those with billing codes were 25.5 times more likely to achieve a positive CAGR. Their median market capitalization increased 56.3%, compared to a median decline of 80.1% for those without billing codes. All five top performers, in terms of CAGR, had billing codes at IPO, whereas nine of the ten worst performers lacked them. Companies without billing codes were 16 times more likely to experience a drop in market capitalization by the study's end. Conclusion: Founders, investors, developers and analysts may have overestimated consumers' willingness to pay out-of-pocket or underestimated reimbursement complexities. As the sector evolves, especially with AI-driven solutions, stakeholders should prioritize billing codes to ensure sustainable growth, financial stability, and maximized investor returns.
EvoMesh: Adaptive Physical Simulation with Hierarchical Graph Evolutions
Authors: Huayu Deng, Xiangming Zhu, Yunbo Wang, Xiaokang Yang
Date: 2024-10-03 00:00:00

Graph neural networks have been a powerful tool for mesh-based physical simulation. To efficiently model large-scale systems, existing methods mainly employ hierarchical graph structures to capture multi-scale node relations. However, these graph hierarchies are typically manually designed and fixed, limiting their ability to adapt to the evolving dynamics of complex physical systems. We propose EvoMesh, a fully differentiable framework that jointly learns graph hierarchies and physical dynamics, adaptively guided by physical inputs. EvoMesh introduces anisotropic message passing, which enables direction-specific aggregation of dynamic features between nodes within each hierarchy, while simultaneously learning node selection probabilities for the next hierarchical level based on physical context. This design creates more flexible message shortcuts and enhances the model's capacity to capture long-range dependencies. Extensive experiments on five benchmark physical simulation datasets show that EvoMesh outperforms recent fixed-hierarchy message passing networks by large margins. The project page is available at https://hbell99.github.io/evo-mesh/.
Estimating the Unobservable Components of Electricity Demand Response with Inverse Optimization
Authors: Adrian Esteban-Perez, Derek Bunn, Yashar Ghiassi-Farrokhfal
Date: 2024-09-17 00:00:00

Understanding and predicting the electricity demand responses to prices are critical activities for system operators, retailers, and regulators. While conventional machine learning and time series analyses have been adequate for the routine demand patterns that have adapted only slowly over many years, the emergence of active consumers with flexible assets such as solar-plus-storage systems, and electric vehicles, introduces new challenges. These active consumers exhibit more complex consumption patterns, the drivers of which are often unobservable to the retailers and system operators. In practice, system operators and retailers can only monitor the net demand (metered at grid connection points), which reflects the overall energy consumption or production exchanged with the grid. As a result, all "behind-the-meter" activities-such as the use of flexibility-remain hidden from these entities. Such behind-the-meter behavior may be controlled by third party agents or incentivized by tariffs; in either case, the retailer's revenue and the system loads would be impacted by these activities behind the meter, but their details can only be inferred. We define the main components of net demand, as baseload, flexible, and self-generation, each having nonlinear responses to market price signals. As flexible demand response and self generation are increasing, this raises a pressing question of whether existing methods still perform well and, if not, whether there is an alternative way to understand and project the unobserved components of behavior. In response to this practical challenge, we evaluate the potential of a data-driven inverse optimization (IO) methodology. This approach characterizes decomposed consumption patterns without requiring direct observation of behind-the-meter behavior or device-level metering [...]
Hidden in Plain Text: Emergence & Mitigation of Steganographic Collusion in LLMs
Authors: Yohan Mathew, Ollie Matthews, Robert McCarthy, Joan Velja, Christian Schroeder de Witt, Dylan Cope, Nandi Schoots
Date: 2024-10-02 00:00:00
Tasks: In-Context Reinforcement Learning, reinforcement-learning, Reinforcement Learning

The rapid proliferation of frontier model agents promises significant societal advances but also raises concerns about systemic risks arising from unsafe interactions. Collusion to the disadvantage of others has been identified as a central form of undesirable agent cooperation. The use of information hiding (steganography) in agent communications could render collusion practically undetectable. This underscores the need for evaluation frameworks to monitor and mitigate steganographic collusion capabilities. We address a crucial gap in the literature by demonstrating, for the first time, that robust steganographic collusion in LLMs can arise indirectly from optimization pressure. To investigate this problem we design two approaches -- a gradient-based reinforcement learning (GBRL) method and an in-context reinforcement learning (ICRL) method -- for reliably eliciting sophisticated LLM-generated linguistic text steganography. Importantly, we find that emergent steganographic collusion can be robust to both passive steganalytic oversight of model outputs and active mitigation through communication paraphrasing. We contribute a novel model evaluation framework and discuss limitations and future work. Our findings imply that effective risk mitigation from steganographic collusion post-deployment requires innovation in passive and active oversight techniques.
FlipAttack: Jailbreak LLMs via Flipping
Authors: Yue Liu, Xiaoxin He, Miao Xiong, Jinlan Fu, Shumin Deng, Bryan Hooi
Date: 2024-10-02 00:00:00

This paper proposes a simple yet effective jailbreak attack named FlipAttack against black-box LLMs. First, from the autoregressive nature, we reveal that LLMs tend to understand the text from left to right and find that they struggle to comprehend the text when noise is added to the left side. Motivated by these insights, we propose to disguise the harmful prompt by constructing left-side noise merely based on the prompt itself, then generalize this idea to 4 flipping modes. Second, we verify the strong ability of LLMs to perform the text-flipping task, and then develop 4 variants to guide LLMs to denoise, understand, and execute harmful behaviors accurately. These designs keep FlipAttack universal, stealthy, and simple, allowing it to jailbreak black-box LLMs within only 1 query. Experiments on 8 LLMs demonstrate the superiority of FlipAttack. Remarkably, it achieves $\sim$98\% attack success rate on GPT-4o, and $\sim$98\% bypass rate against 5 guardrail models on average. The codes are available at GitHub\footnote{https://github.com/yueliu1999/FlipAttack}.