9,745 research outputs found

    Joint Activity Detection, Channel Estimation, and Data Decoding for Grant-free Massive Random Access

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    In the massive machine-type communication (mMTC) scenario, a large number of devices with sporadic traffic need to access the network on limited radio resources. While grant-free random access has emerged as a promising mechanism for massive access, its potential has not been fully unleashed. In particular, the common sparsity pattern in the received pilot and data signal has been ignored in most existing studies, and auxiliary information of channel decoding has not been utilized for user activity detection. This paper endeavors to develop advanced receivers in a holistic manner for joint activity detection, channel estimation, and data decoding. In particular, a turbo receiver based on the bilinear generalized approximate message passing (BiG-AMP) algorithm is developed. In this receiver, all the received symbols will be utilized to jointly estimate the channel state, user activity, and soft data symbols, which effectively exploits the common sparsity pattern. Meanwhile, the extrinsic information from the channel decoder will assist the joint channel estimation and data detection. To reduce the complexity, a low-cost side information-aided receiver is also proposed, where the channel decoder provides side information to update the estimates on whether a user is active or not. Simulation results show that the turbo receiver is able to reduce the activity detection, channel estimation, and data decoding errors effectively, while the side information-aided receiver notably outperforms the conventional method with a relatively low complexity

    Corporate Social Responsibility: the institutionalization of ESG

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    Understanding the impact of Corporate Social Responsibility (CSR) on firm performance as it relates to industries reliant on technological innovation is a complex and perpetually evolving challenge. To thoroughly investigate this topic, this dissertation will adopt an economics-based structure to address three primary hypotheses. This structure allows for each hypothesis to essentially be a standalone empirical paper, unified by an overall analysis of the nature of impact that ESG has on firm performance. The first hypothesis explores the evolution of CSR to the modern quantified iteration of ESG has led to the institutionalization and standardization of the CSR concept. The second hypothesis fills gaps in existing literature testing the relationship between firm performance and ESG by finding that the relationship is significantly positive in long-term, strategic metrics (ROA and ROIC) and that there is no correlation in short-term metrics (ROE and ROS). Finally, the third hypothesis states that if a firm has a long-term strategic ESG plan, as proxied by the publication of CSR reports, then it is more resilience to damage from controversies. This is supported by the finding that pro-ESG firms consistently fared better than their counterparts in both financial and ESG performance, even in the event of a controversy. However, firms with consistent reporting are also held to a higher standard than their nonreporting peers, suggesting a higher risk and higher reward dynamic. These findings support the theory of good management, in that long-term strategic planning is both immediately economically beneficial and serves as a means of risk management and social impact mitigation. Overall, this contributes to the literature by fillings gaps in the nature of impact that ESG has on firm performance, particularly from a management perspective

    VIVE3D: Viewpoint-Independent Video Editing using 3D-Aware GANs

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    We introduce VIVE3D, a novel approach that extends the capabilities of image-based 3D GANs to video editing and is able to represent the input video in an identity-preserving and temporally consistent way. We propose two new building blocks. First, we introduce a novel GAN inversion technique specifically tailored to 3D GANs by jointly embedding multiple frames and optimizing for the camera parameters. Second, besides traditional semantic face edits (e.g. for age and expression), we are the first to demonstrate edits that show novel views of the head enabled by the inherent properties of 3D GANs and our optical flow-guided compositing technique to combine the head with the background video. Our experiments demonstrate that VIVE3D generates high-fidelity face edits at consistent quality from a range of camera viewpoints which are composited with the original video in a temporally and spatially consistent manner.Comment: CVPR 2023. Project webpage and video available at http://afruehstueck.github.io/vive3

    SViTT: Temporal Learning of Sparse Video-Text Transformers

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    Do video-text transformers learn to model temporal relationships across frames? Despite their immense capacity and the abundance of multimodal training data, recent work has revealed the strong tendency of video-text models towards frame-based spatial representations, while temporal reasoning remains largely unsolved. In this work, we identify several key challenges in temporal learning of video-text transformers: the spatiotemporal trade-off from limited network size; the curse of dimensionality for multi-frame modeling; and the diminishing returns of semantic information by extending clip length. Guided by these findings, we propose SViTT, a sparse video-text architecture that performs multi-frame reasoning with significantly lower cost than naive transformers with dense attention. Analogous to graph-based networks, SViTT employs two forms of sparsity: edge sparsity that limits the query-key communications between tokens in self-attention, and node sparsity that discards uninformative visual tokens. Trained with a curriculum which increases model sparsity with the clip length, SViTT outperforms dense transformer baselines on multiple video-text retrieval and question answering benchmarks, with a fraction of computational cost. Project page: http://svcl.ucsd.edu/projects/svitt.Comment: CVPR 202

    INVESTIGATING THE PERCEPTION OF EXPATRIATES TOWARDS IMMIGRATION SERVICE QUALITY IN SHARJAH, UNITED ARAB EMIRATES THROUGH MIXED METHOD APPROACH

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    The public sectors in UAE are under immense pressure to demonstrate that their services are customer-focused and that continuous performance improvement is being delivered. The United Arab Emirates is a favoured destination for expatriates due to its own citizens form a minority of the population and are barely represented in the private sector workforce. These highly unusual demographics confer high importance on the national immigration services. Recently, increased interest in international migration, specifically within the United Arab Emirates, has been shown both by government agencies and by the governments of industrialised countries. Given the importance of the expatriate labour force to economic stability and growth in the Emirates, this research investigates how immigration services are perceived, with the aim of contributing to their improvement, thus ultimately supporting economic growth. It proposes a service quality perception framework to improve understanding within SID of how to raise levels of service delivered to migrants and other persons directly or indirectly affected by SID services. Qualitative data were collected by means of semi-structured interviews and quantitative data by means of a questionnaire survey based on the abovementioned framework. The survey data, on the variables influencing participants’ experiences and perceptions of SID services, were subjected to statistical analysis. The framework was then used to evaluate quality of service in terms of general impressions, delivery, location, response, SID culture and behaviour. Numerical data were analysed using inferential and descriptive statistics. It was found that service quality positively influenced service behaviour and that this relationship was mediated by SID culture. This research makes an original contribution to knowledge as one of the few studies of immigration to the United Arab Emirates. By examining the workings of one immigration department, it adds to the literature on immigration departments and organisational development in developing countries. It illuminates the mechanics of immigration services and demonstrates their increasing importance to the world economy

    Joint optimization of depth and ego-motion for intelligent autonomous vehicles

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    The three-dimensional (3D) perception of autonomous vehicles is crucial for localization and analysis of the driving environment, while it involves massive computing resources for deep learning, which can't be provided by vehicle-mounted devices. This requires the use of seamless, reliable, and efficient massive connections provided by the 6G network for computing in the cloud. In this paper, we propose a novel deep learning framework with 6G enabled transport system for joint optimization of depth and ego-motion estimation, which is an important task in 3D perception for autonomous driving. A novel loss based on feature map and quadtree is proposed, which uses feature value loss with quadtree coding instead of photometric loss to merge the feature information at the texture-less region. Besides, we also propose a novel multi-level V-shaped residual network to estimate the depths of the image, which combines the advantages of V-shaped network and residual network, and solves the problem of poor feature extraction results that may be caused by the simple fusion of low-level and high-level features. Lastly, to alleviate the influence of image noise on pose estimation, we propose a number of parallel sub-networks that use RGB image and its feature map as the input of the network. Experimental results show that our method significantly improves the quality of the depth map and the localization accuracy and achieves the state-of-the-art performance

    Estudo do IPFS como protocolo de distribuição de conteúdos em redes veiculares

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    Over the last few years, vehicular ad-hoc networks (VANETs) have been the focus of great progress due to the interest in autonomous vehicles and in distributing content not only between vehicles, but also to the Cloud. Performing a download/upload to/from a vehicle typically requires the existence of a cellular connection, but the costs associated with mobile data transfers in hundreds or thousands of vehicles quickly become prohibitive. A VANET allows the costs to be several orders of magnitude lower - while keeping the same large volumes of data - because it is strongly based in the communication between vehicles (nodes of the network) and the infrastructure. The InterPlanetary File System (IPFS) is a protocol for storing and distributing content, where information is addressed by its content, instead of its location. It was created in 2014 and it seeks to connect all computing devices with the same system of files, comparable to a BitTorrent swarm exchanging Git objects. It has been tested and deployed in wired networks, but never in an environment where nodes have intermittent connectivity, such as a VANET. This work focuses on understanding IPFS, how/if it can be applied to the vehicular network context, and comparing it with other content distribution protocols. In this dissertation, IPFS has been tested in a small and controlled network to understand its working applicability to VANETs. Issues such as neighbor discoverability times and poor hashing performance have been addressed. To compare IPFS with other protocols (such as Veniam’s proprietary solution or BitTorrent) in a relevant way and in a large scale, an emulation platform was created. The tests in this emulator were performed in different times of the day, with a variable number of files and file sizes. Emulated results show that IPFS is on par with Veniam’s custom V2V protocol built specifically for V2V, and greatly outperforms BitTorrent regarding neighbor discoverability and data transfers. An analysis of IPFS’ performance in a real scenario was also conducted, using a subset of STCP’s vehicular network in Oporto, with the support of Veniam. Results from these tests show that IPFS can be used as a content dissemination protocol, showing it is up to the challenge provided by a constantly changing network topology, and achieving throughputs up to 2.8 MB/s, values similar or in some cases even better than Veniam’s proprietary solution.Nos últimos anos, as redes veiculares (VANETs) têm sido o foco de grandes avanços devido ao interesse em veículos autónomos e em distribuir conteúdos, não só entre veículos mas também para a "nuvem" (Cloud). Tipicamente, fazer um download/upload de/para um veículo exige a utilização de uma ligação celular (SIM), mas os custos associados a fazer transferências com dados móveis em centenas ou milhares de veículos rapidamente se tornam proibitivos. Uma VANET permite que estes custos sejam consideravelmente inferiores - mantendo o mesmo volume de dados - pois é fortemente baseada na comunicação entre veículos (nós da rede) e a infraestrutura. O InterPlanetary File System (IPFS - "sistema de ficheiros interplanetário") é um protocolo de armazenamento e distribuição de conteúdos, onde a informação é endereçada pelo conteúdo, em vez da sua localização. Foi criado em 2014 e tem como objetivo ligar todos os dispositivos de computação num só sistema de ficheiros, comparável a um swarm BitTorrent a trocar objetos Git. Já foi testado e usado em redes com fios, mas nunca num ambiente onde os nós têm conetividade intermitente, tal como numa VANET. Este trabalho tem como foco perceber o IPFS, como/se pode ser aplicado ao contexto de rede veicular e compará-lo a outros protocolos de distribuição de conteúdos. Numa primeira fase o IPFS foi testado numa pequena rede controlada, de forma a perceber a sua aplicabilidade às VANETs, e resolver os seus primeiros problemas como os tempos elevados de descoberta de vizinhos e o fraco desempenho de hashing. De modo a poder comparar o IPFS com outros protocolos (tais como a solução proprietária da Veniam ou o BitTorrent) de forma relevante e em grande escala, foi criada uma plataforma de emulação. Os testes neste emulador foram efetuados usando registos de mobilidade e conetividade veicular de alturas diferentes de um dia, com um número variável de ficheiros e tamanhos de ficheiros. Os resultados destes testes mostram que o IPFS está a par do protocolo V2V da Veniam (desenvolvido especificamente para V2V e VANETs), e que o IPFS é significativamente melhor que o BitTorrent no que toca ao tempo de descoberta de vizinhos e transferência de informação. Uma análise do desempenho do IPFS em cenário real também foi efetuada, usando um pequeno conjunto de nós da rede veicular da STCP no Porto, com o apoio da Veniam. Os resultados destes testes demonstram que o IPFS pode ser usado como protocolo de disseminação de conteúdos numa VANET, mostrando-se adequado a uma topologia constantemente sob alteração, e alcançando débitos até 2.8 MB/s, valores parecidos ou nalguns casos superiores aos do protocolo proprietário da Veniam.Mestrado em Engenharia de Computadores e Telemátic

    Aristotle: Stratified Causal Discovery for Omics Data

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    Background There has been a simultaneous increase in demand and accessibility across genomics, transcriptomics, proteomics and metabolomics data, known as omics data. This has encouraged widespread application of omics data in life sciences, from personalized medicine to the discovery of underlying pathophysiology of diseases. Causal analysis of omics data may provide important insight into the underlying biological mechanisms. Existing causal analysis methods yield promising results when identifying potential general causes of an observed outcome based on omics data. However, they may fail to discover the causes specific to a particular stratum of individuals and missing from others. Methods To fill this gap, we introduce the problem of stratified causal discovery and propose a method, Aristotle, for solving it. Aristotle addresses the two challenges intrinsic to omics data: high dimensionality and hidden stratification. It employs existing biological knowledge and a state-of-the-art patient stratification method to tackle the above challenges and applies a quasi-experimental design method to each stratum to find stratum-specific potential causes. Results Evaluation based on synthetic data shows better performance for Aristotle in discovering true causes under different conditions compared to existing causal discovery methods. Experiments on a real dataset on Anthracycline Cardiotoxicity indicate that Aristotle’s predictions are consistent with the existing literature. Moreover, Aristotle makes additional predictions that suggest further investigations
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