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How Unique is Whose Web Browser? The role of demographics in browser fingerprinting among US users
No full textBrowser fingerprinting can be used to identify and track users across the Web, even without cookies, by collecting attributes from users\u27 devices to create unique fingerprints . This technique and resulting privacy risks have been studied for over a decade. Yet further research is limited because prior studies used data not publicly available. Additionally, data in prior studies lacked user demographics. Here we provide a first-of-its-kind dataset to enable further research. It includes browser attributes with users\u27 demographics and survey responses, collected with informed consent from 8,400 US study participants. We use this dataset to demonstrate how fingerprinting risks differ across demographic groups. For example, we find lower income users are more at risk, and find that as users\u27 age increases, they are both more likely to be concerned about fingerprinting and at real risk of fingerprinting. Furthermore, we demonstrate an overlooked risk: user demographics, such as gender, age, income level and race, can be inferred from browser attributes commonly used for fingerprinting, and we identify which browser attributes most contribute to this risk. Our data collection process also conducted an experiment to study what impacts users\u27 likelihood to share browser data for open research, in order to inform future data collection efforts, with responses from 12,461 total participants. Female participants were significantly less likely to share their browser data, as were participants who were shown the browser data we asked to collect. Overall, we show the important role of user demographics in the ongoing work that intends to assess fingerprinting risks and improve user privacy, with findings to inform future privacy enhancing browser developments. The dataset and data collection tool we provide can be used to further study research questions not addressed in this work.In Proceedings on Privacy Enhancing Technologies 2025(1
Detection of Undeclared EV Charging Events in a Green Energy Certification Scheme
No full textThe green potential of electric vehicles (EVs) can be fully realized only if their batteries are charged using energy generated from renewable (i.e. green) sources. For logistic or economic reasons, however, EV drivers may be tempted to avoid charging stations certified as providing green energy, instead opting for conventional ones, where only a fraction of the available energy is green. This behaviour may slow down the achievement of decarbonisation targets of the road transport sector. In this paper, we use GPS data to infer whether an undeclared charging event has occurred. Specifically, we construct a Bayesian hypothesis test for the charging behaviour of the EV. Extensive simulations are carried out for an area of London, using the mobility simulator, SUMO, and exploring various operating conditions. Excellent detection rates for undeclared charging events are reported. We explain how the algorithm can serve as the basis for an incentivization scheme, encouraging compliance by drivers with green charging policies.This work has been submitted to the IEEE for possible publicatio
Probing long-lived doubly charged scalar in the Georgi-Machacek model at the LHC and in far detectors
No full textSearching for long-lived particles (LLPs) beyond the Standard Model (SM) is a promising direction in collider experiments. The Georgi-Machacek (GM) model extends the scalar sector in the SM by introducing various new scalar bosons. In this study, we focus on the parameter space that allows the light doubly charged scalar to become long-lived. This light doubly charged scalar is fermophobic and predominantly decays into a pair of on-shell or off-shell same-sign bosons. We investigate three types of signal signatures at the LHC: displaced vertices in the inner tracking detector, displaced showers in the muon system, and heavy stable charged particles. Additionally, we analyze the potential for detecting such doubly charged scalars in far detectors, including ANUBIS, MATHUSLA, FACET, FASER, CODEX-b, MoEDAL-MAPP and AL3X. By combining the LLP searches at the LHC and in far detectors, we project that the limits on the mixing angle, , (between the doublet and triplets) can cover most of the parameter space with for the mass range of long-lived doubly charged scalars between GeV to GeV, assuming the full integrated luminosity at the LHC and HL-LHC.37 pages, 5 tables and 8 figures. v2: add references, more discussions for far detector
Almansi-type decomposition and Fueter-Sce theorem for generalized partial-slice regular functions
No full textVery recently, the concept of generalized partial-slice monogenic (or regular) functions has been introduced to unify the theory of monogenic functions and of slice monogenic functions over Clifford algebras. Inspired by the work of A. Perotti, in this paper we provide two analogous versions of the Almansi decomposition in this new setting. Additionally, two enhancements of the Fueter-Sce theorem have been obtained for generalized partial-slice regular functions.18 page
Unbounded Tellegen Response in Media with Multiple Resonances
No full textTellegen response is a special type of nonreciprocal magneto-electric coupling which long remained elusive in photonics and extremely weak in condensed matter. It is widely accepted that the Tellegen coefficient is restricted by , where and are permittivity and permeability of the material. Here, we demonstrate that this restriction is lifted in the medium with several close resonances, which provides a theoretical foundation for giant Tellegen response
Deep Learning-Based Image Compression for Wireless Communications: Impacts on Reliability,Throughput, and Latency
No full textIn wireless communications, efficient image transmission must balance reliability, throughput, and latency, especially under dynamic channel conditions. This paper presents an adaptive and progressive pipeline for learned image compression (LIC)-based architectures tailored to such environments. We investigate two state-of-the-art learning-based models: the hyperprior model and Vector Quantized Generative Adversarial Network (VQGAN). The hyperprior model achieves superior compression performance through lossless compression in the bottleneck but is susceptible to bit errors, necessitating the use of error correction or retransmission mechanisms. In contrast, the VQGAN decoder demonstrates robust image reconstruction capabilities even in the absence of channel coding, enhancing reliability in challenging transmission scenarios. We propose progressive versions of both models, enabling partial image transmission and decoding under imperfect channel conditions. This progressive approach not only maintains image integrity under poor channel conditions but also significantly reduces latency by allowing immediate partial image availability. We evaluate our pipeline using the Kodak high-resolution image dataset under a Rayleigh fading wireless channel model simulating dynamic conditions. The results indicate that the progressive transmission framework enhances reliability and latency while maintaining or improving throughput compared to non-progressive counterparts across various Signal-to-Noise Ratio (SNR) levels. Specifically, the progressive-hyperprior model consistently outperforms others in latency metrics, particularly in the 99.9th percentile waiting time-a measure indicating the maximum waiting time experienced by 99.9% of transmission instances-across all SNRs, and achieves higher throughput in low SNR scenarios. where Adaptive WebP fails
Light fields in various patches of de Sitter space-time
No full textWe start with the consideration of the loop effects for light fields with non-zero mass in the expanding Poincaré patch of de Sitter space-time. We derive the Dyson-Schwinger equation, which sums up the leading infrared (growing with time) loop corrections in certain limit for small initial perturbations above the Bunch-Davies state. The solution of this equation shows the destiny of the initial state at the future infinity. Then we discuss the case of the contracting Poincaré patch and global de Sitter space-time and briefly the case of different initial conditions in the expanding Poincaré patch.23 pages, 3 figure
Uniqueness of the solution of the filtering equations in spaces of measures
No full textNonlinear filtering is a pivotal problem that has attracted significant attention from mathematicians, statisticians, engineers, and various other scientific disciplines. The solution to this problem is governed by the so-called filtering equations. In this paper, we investigate the uniqueness of solutions to these equations within measure spaces and introduce a novel, generalized framework for this analysis. Our approach provides new insights and extends the applicability of existing theories in the study of nonlinear filtering
Integrated Ising Model with global inhibition for decision making
No full textHumans and other organisms make decisions choosing between different options, with the aim to maximize the reward and minimize the cost. The main theoretical framework for modeling the decision-making process has been based on the highly successful drift-diffusion model, which is a simple tool for explaining many aspects of this process. However, new observations challenge this model. Recently, it was found that inhibitory tone increases during high cognitive load and situations of uncertainty, but the origin of this phenomenon is not understood. Motivated by this observation, we extend a recently developed model for decision making while animals move towards targets in real space. We introduce an integrated Ising-type model, that includes global inhibition, and use it to explore its role in decision-making. This model can explain how the brain may utilize inhibition to improve its decision-making accuracy. Compared to experimental results, this model suggests that the regime of the brain\u27s decision-making activity is in proximity to a critical transition line between the ordered and disordered. Within the model, the critical region near the transition line has the advantageous property of enabling a significant decrease in error with a small increase in inhibition and also exhibits unique properties with respect to learning and memory decay.Main text: 14 pages; SI: 40 page
Feasibility of PET-enabled dual-energy CT imaging: First physical phantom and initial patient results
No full textX-ray computed tomography (CT) in PET/CT is commonly operated with a single energy, resulting in a limitation of lacking tissue composition information. Dual-energy (DE) spectral CT enables material decomposition by using two different x-ray energies and may be combined with PET for improved multimodality imaging, but would either require hardware upgrade or increase radiation dose due to the added second x-ray CT scan. Recently proposed PET-enabled DECT method allows dual-energy spectral imaging using a conventional PET/CT scanner without the need for a second x-ray CT scan. A gamma-ray CT (gCT) image at 511 keV can be generated from the existing time-of-flight PET data with the maximum-likelihood attenuation and activity (MLAA) approach and is then combined with the low-energy x-ray CT image to form dual-energy spectral imaging. To improve the image quality of gCT, a kernel MLAA method was further proposed by incorporating x-ray CT as a priori information. The concept of this PET-enabled DECT has been validated using simulation studies, but not yet with 3D real data. In this work, we developed a general open-source implementation for gCT reconstruction from PET data and use this implementation for the first real data validation with both a physical phantom study and a human subject study on a uEXPLORER total-body PET/CT system. These results have demonstrated the feasibility of this method for spectral imaging and material decomposition.20 pages, 7 figure