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Vehicle Visual Perception Under Low Visibility Road Environments Based on AoP&DoP Multi-Polarization Parameter Characterization
Get PDFVehicle visual perception is essential for safe autonomous driving, especially in challenging low-visibility conditions. Polarimetric imaging has shown enhanced perception by improving target-background contrast and reducing glare. However, current research in polarimetric imaging for autonomous driving largely focuses on singular use of polarimetric features. Exploiting polarimetric information to enhance vehicle visual perception in low-visibility scenarios remains a critical challenge. This paper addresses this challenge by integrating multiple polarimetric parameter characterization with a deep learning model for semantic segmentation visual perception task, which is called TransWNet. The model combines Convolutional Neural Networks (CNN) and Transformer architectures, extracting features and contextual information of both Degree of Polarization (DoP) and Angle of Polarization (AoP) comprehensively during the encoding phase. In the decoding phase, it incorporates skip connections to effectively retain multimodal polarimetric information across deep and shallow features, generating output through feature fusion. To the best of our knowledge, this is the first work that jointly exploits DoP and AoP for vehicle scene understanding in degraded visibility. Experimental results demonstrate that TransWNet, by effectively leveraging multimodal polarimetric information, achieves significantly better performance in semantic segmentation of low-visibility traffic scenes, with marked improvements in mIoU, mPA, and Accuracy over all single-feature baselines. Compared with the baseline method, TransWNet improves the Accuracy by 3.44%
Board Gender Diversity and Carbon Trade Finance: Evidence From Multinational Corporations on the Role of Institutional Quality and Cultural Environment
Get PDFThis study investigates whether board gender diversity influences carbon trade finance and ultimately achieves decarbonisation targets. Using a dataset of 5198 firm-year observations from 336 multinational corporations (MNCs) spanning 42 industries and 32 countries over the period 2006–2022, we employ panel regression analysis to uncover key insights. Our findings reveal that although board gender diversity is positively associated with carbon trade finance, a critical mass of at least four female board directors is necessary to exert significant influence. Our results highlight the critical role of institutional factors, such as high control of corruption, strong voice and accountability, government effectiveness and a strong rule of law, in enhancing the impact of board gender diversity on carbon trade finance. Additionally, cultural environments play a pivotal role in shaping the relationship between board gender diversity and carbon trade finance. Our main conclusions are robust across alternative measures and validated using two-stage least squares and propensity score matching techniques. We contribute to the literature on board gender diversity and carbon trade finance by empirically demonstrating the role of (in)formal institutional factors that influence the effectiveness of female directors in achieving sustainability outcomes. The findings offer valuable policy and practical implications for managers, regulators and stakeholders, shedding light on the interplay among board gender diversity, carbon emissions management, and the governance and cultural contexts at the country level
Joint Beamforming Design for Integrated Sensing and Communication Systems with Hybrid-Colluding Eavesdroppers
Get PDFIn this paper, we consider the physical layer security (PLS) problem for integrated sensing and communication (ISAC) systems in the presence of hybrid-colluding eavesdroppers, where an active eavesdropper (AE) and a passive eavesdropper (PE) collude to intercept the confidential information. To ensure the accuracy of sensing while preventing the eavesdropping, a base station transmits a signal consisting of information symbols and sensing waveform, in which the sensing waveform can be also used as artificial noise to interfere with eavesdroppers. Under this setup, we propose an alternating optimization-based two stage scheme (AO-TSS) for improving the sensing and communication performance. In the first stage, based on the assumptions that the perfect channel state information (CSI) of the AE and statistical CSI of the PE are known, the communication and sensing beamforming problem is formulated with the objective of minimizing the weighted sum of the beampattern matching mean squared error (MSE) and cross-correlation, subject to the secure transmission constraint. To tackle the non-convexity, we propose a semi-definite relaxation (SDR) algorithm and a reduced-complexity zero-forcing (ZF) algorithm. Then, the scenarios are further extended to more general cases with imperfect AE CSI and unknown PE CSI. To further improve the communication performance, the second-stage problem is developed to optimize the secrecy rate threshold under the radar performance constraint. Finally, numerical results demonstrate the superiority of the proposed scheme in terms of sensing and secure communication
Accountability for digital harm under international criminal law
No full textThis thesis is concerned with how international criminal law (ICL) frameworks may be able to account for ‘digital harm’, or harm perpetrated through digital technologies. Technology can be used both as a new method of perpetrating existing international crimes or can inflict novel forms of harm that may only be adequately addressed by the creation of a new offence. This thesis analyses how harm has been understood for core international crimes and how digital harms (such as online hate speech and disinformation, sharing footage of crimes online, digital mass surveillance, and online sexual violence) can be encompassed within definitions of those crimes. It draws upon theories of criminalisation commonly considered for ICL to determine why digital harms should be criminalised, and explores the potential challenges to such criminalisation, such as whether digital harms can meet the gravity threshold under ICL and whether prosecutions of digital harms would be viable. Further, obstacles to prosecuting digital harms at international criminal courts and tribunals (ICCTs) are considered, including jurisdictional challenges, the issues that arise when using digital evidence, and cooperation between ICCTs and States, private companies, and civil society. The thesis concludes with two case studies of different digital harms—the beheading of American journalist James Foley by ISIS, which was circulated online, and the digital surveillance used against the Uyghur population in Xinjiang, China—to illustrate how digital harms may be criminalised and prosecuted at an ICCT in practice. Ultimately, this thesis finds that digital harms are similar in nature to the harms traditionally encompassed by core international crimes, and that these harms can and should be criminalised and prosecuted to provide justice to victims. It emphasises that as technology will only continue to develop and serve as a vehicle for an increasing array of harms, finding ways to account for digital harm should be an issue at the forefront of ICL
The dual effects of job design on knowledge hiding: expanding job demands–resources theory to employee rational-choice behaviour
Get PDFHuman resource management (HRM) literature often uses motivational theories to examine how job design motivates employees to manage newly established employee behaviours such as knowledge-hiding. However, the literature finds that whereas job-design characteristics reduce knowledge hiding, others unexpectedly encourage it. By integrating the cost-benefit analysis framework into the job demands–resources (JD–R) theory, we examine how job demands and job resources as two distinct types of job-design characteristics influence the expected costs and benefits of sharing solicited knowledge to affect knowledge hiding differently. In summary, we find that job demands encourage knowledge hiding, whereas job resources lower it. We contribute that job-design characteristics act as job demands or resources to affect knowledge hiding differently. Further, we explain the unexpected findings concerning why and how job-design characteristics – as job demands – encourage knowledge hiding by stimulating the expected costs but do not motivate employees to produce the expected benefits. In addition, by integrating the cost-benefit analysis framework into the JD–R theory, we contribute that job demands and resources affect the cost-benefit analyses, influencing employees’ rational choice behaviour. This integration considerably expands the JD–R theory’s application scope from employee well-being and performance to rational-choice behaviours
Analyzing Wet-Neuromorphic Computing Using Bacterial Gene Regulatory Neural Networks
Get PDFBiocomputing envisions the development computing paradigms using biological systems, ranging from micron-level components to collections of cells, including organoids. This paradigm shift exploits hidden natural computing properties, to develop miniaturized wet-computing devices that can be deployed in harsh environments, and to explore designs of novel energy-efficient systems. In parallel, we witness the emergence of AI hardware, including neuromorphic processors with the aim of improving computational capacity. This study brings together the concept of biocomputing and neuromorphic systems by focusing on the bacterial gene regulatory networks and their transformation into Gene Regulatory Neural Networks (GRNNs). We explore the intrinsic properties of gene regulations, map this to a gene-perceptron function, and propose an application-specific sub-GRNN search algorithm that maps the network structure to match a computing problem. Focusing on the model organism Escherichia coli, the base-GRNN is initially extracted and validated for accuracy. Subsequently, a comprehensive feasibility analysis of the derived GRNN confirms its computational prowess in classification and regression tasks. Furthermore, we discuss the possibility of performing a well-known digit classification task as a use case. Our analysis and simulation experiments show promising results in the offloading of computation tasks to GRNN in bacterial cells, advancing wet-neuromorphic computing using natural cells
Attention-based Fusion for Stroke Lesion Segmentation on Computed Tomography Perfusion Data
Get PDFIn recent times, stroke has emerged as a significant threat to humans, transforming affected brain tissue into core and penumbra regions. As the penumbra becomes irreversible over time, early core region segmentation is crucial. Automatic segmentation systems offer an efficient alternative to manual segmentation and aid radiologists in stroke lesion segmentation using computed tomography and Computed Tomography Perfusion (CTP) maps that comprise four parameter maps. This automatic segmentation is increasingly used in interactive, multimedia-based systems for diagnostic tools and AI-driven health applications. Top-performing models that follow the patch-processing approach suffer from high inference times. To incorporate effective feature extraction at image-level inferences, which reduces the inference time, we present a hybrid fusion technique that combines early and bottleneck fusion, leveraging two separate encoders for effective feature extraction. Moreover, fusing the information from various fusion methods arbitrarily may not yield optimal results. Consequently, we have introduced two attention modules, i.e., cross-modal attention and cross-fusion attention modules, designed for the effective integration of features derived from diverse modalities and multiple fusion strategies, respectively. The findings highlight a considerable reduction in computational time alongside achieving a comparable Dice score. Additionally, the incorporation of hybrid fusion and attention modules in the baseline notably increased the Dice score from 0.482 to 0.521 in the validation dataset and achieved 0.48 in the test dataset of ISLES 2018. It also demonstrates competitive performance compared to existing models while maintaining efficient prediction times
G-complete reducibility and saturation
Get PDFLet H ⊆ G be connected reductive linear algebraic groups defined over an algebraically closed field of characteristic p > 0. In our first main theorem we show that if a closed subgroup K of H is H-completely reducible, then it is also G-completely reducible in the sense of Serre, under some restrictions on p, generalising the known case for G = GL(V ). Our proof uses R.W. Richardson’s notion of reductive pairs to reduce to the GL(V ) case. We study Serre’s notion of saturation and prove that saturation behaves well with respect to products and regular subgroups. Our second main theorem shows that if K is H-completely reducible, then the saturation of K in G is completely reducible in the saturation of H in G (which is again a connected reductive subgroup of G), under suitable restrictions on p, again generalising the known instance for G = GL(V ). We also study saturation of finite subgroups of Lie type in G. We show that saturation is compatible with standard Frobenius endomorphisms, and we use this to generalise a result due to Nori from 1987 in case G = GL(V )
A psychoanalytic investigation of the hostile environment discourse in Britain: A case study of Suella Braverman
Get PDFWhy does the hostile environment, a set of public policies designed to make life more difficult for immigrants, exist in Britain, a country which relies on immigration culturally and economically? Since the official announcement of the hostile environment by Theresa May in 2012, a feedback loop of increasing hostility has taken over the British large group psyche leading to punitive policies such as the recent Rwanda scheme, and more panic about immigration among a large segment of the public. Following an interdisciplinary approach that combines psychoanalysis, political discourse analysis, and colonial studies, I investigate some of the psychological mechanisms that motivate the official hostility towards immigrants and allows for the existence of the hostile environment. I posit that the current official hostility towards immigration is connected to the repression of the violent and coercive sides of the British empire by imperial representatives, the collective meaning and identity vacuums left in the wake of the end of the British Empire, and to the government’s subsequent disavowal of the connection between modern immigration and the British Empire. Since whatever is repressed always returns in the form of symptoms; I conceive of the hostile environment as a symptom of repressed colonial violence, triggered by the multicultural reality of modern Britain, the ’return of the oppressed’, and by the decline in Britain’s politico-economic relevance over the past decades. Finally, for my case study, using Norman and Isabella Fairclough’s framework of political discourse analysis, I analyze a speech made by former Home Secretary Suella Braverman. I illustrate what an argument for the hostile environment looks like and provide a reconstruction and evaluation of it and contextualize it within my analysis
Temporal changes in the microbial nitrogen cycling communities in intertidal estuarine seagrass ecosystems
Get PDFCoastal vegetated habitats have recently begun to gain attention for their potential to act as greenhouse gas sinks. However, so far, this research has largely focused on the microbial driven carbon dioxide (CO2) flux in saltmarshes, whilst the more potent greenhouse gas (GHG) nitrous oxide (N2O) has often been overlooked. The overarching aim of this study was to measure the temporal changes in the microbial communities driving the cycling of nitrogen (N) in estuarine seagrass (Zostera noltei) meadows on the coast of East Anglia, United Kingdom, and relate to sediment nutrient concentrations and N2O flux. This study found that seagrass meadows harboured higher abundances of archaeal and bacterial 16S rRNA genes compared to non-vegetated sediments. Ammonia-oxidation was largely dominated by ammonia-oxidising bacteria (AOB) rather than archaea (AOA) throughout the year. Specifically, greater AOA abundance was found in the rhizosphere in spring, whereas greater AOB abundance occurred in non-vegetated anoxic sediments in winter. Nitrite (NO2-) reduction was potentially being driven by nirS rather than nirK gene associated communities. Additionally, the nosZ gene was significantly higher than either nirS or nirK regardless of seagrass presence, but significantly lower than the two genes combined. N2O concentration was negligible in winter, but drastically increased in the spring. Furthermore, non-vegetated habitats sunk N2O at a higher rate than seagrass habitats. However, more importantly, there was a decrease in N2O in both habitats over time. Eutrophicating nutrients (e.g. nitrates, ammonium) were also found at higher concentrations in the seagrass sediments. These findings suggest that Zostera noltei seagrass meadows can act as a sink for N2O due to the overall reduction in N2O emissions and may work as a nature-based solution to reduce GHG emissions and estuarine eutrophication as part of the wider estuarine ecosystem function