Evaluating Caching Mechanisms In Future Internet Architectures

MEng thesisThis thesis seeks to test and evaluate the effects of in-­network storage in novel proposed Internet architectures in terms of their performance. In a world where more and more people are mobile and connected to the Internet, we look at how the added variable of user mobility can affect how these architectures perform under different loads. Evaluating the effects of in­-network storage and caching in these novel architectures will provide another facet to understanding how viable of an alternative they would be to the current TCP/IP paradigm of today's Internet. In Named Data Networking, where the storage is used to directly cache content, we see its use of storage impact the locality of where things are, while in MobilityFirst, where storage is used to cache chunks to provide robust delivery, we look at how its different layers work together in a mobility event

Social Media as a Disguise and an Aid: Disabled Women in the Cyber Workforce in China

Existing literature shows that people living with physical impairment are systematically disadvantaged in the workforce and their voices are often silenced. With a perspective of intersectionality, this article looks into how disabled women suffer from multiple forms of discrimination and how social media may emerge as a tool of empowerment for them in both the workforce and their everyday lives. Drawing on five cases of Chinese disabled women in the cyber workforce, the study finds that the booming Internet economy enables more disabled women to financially support themselves. Social media appears as a cover for these women to disguise their disability identity and get more job opportunities. It serves as an aid in many cases to allow these women to increase social participation, to project their voice, and to form alliances. The risks and challenges that disabled women often encounter in the cyber workforce are also discussed

RPEFlow: Multimodal Fusion of RGB-PointCloud-Event for Joint Optical Flow and Scene Flow Estimation

Recently, the RGB images and point clouds fusion methods have been proposed to jointly estimate 2D optical flow and 3D scene flow. However, as both conventional RGB cameras and LiDAR sensors adopt a frame-based data acquisition mechanism, their performance is limited by the fixed low sampling rates, especially in highly-dynamic scenes. By contrast, the event camera can asynchronously capture the intensity changes with a very high temporal resolution, providing complementary dynamic information of the observed scenes. In this paper, we incorporate RGB images, Point clouds and Events for joint optical flow and scene flow estimation with our proposed multi-stage multimodal fusion model, RPEFlow. First, we present an attention fusion module with a cross-attention mechanism to implicitly explore the internal cross-modal correlation for 2D and 3D branches, respectively. Second, we introduce a mutual information regularization term to explicitly model the complementary information of three modalities for effective multimodal feature learning. We also contribute a new synthetic dataset to advocate further research. Experiments on both synthetic and real datasets show that our model outperforms the existing state-of-the-art by a wide margin. Code and dataset is available at https://npucvr.github.io/RPEFlow.Comment: ICCV 2023. Project page: https://npucvr.github.io/RPEFlow Code: https://github.com/danqu130/RPEFlo

Mutual Information Regularization for Weakly-supervised RGB-D Salient Object Detection

In this paper, we present a weakly-supervised RGB-D salient object detection model via scribble supervision. Specifically, as a multimodal learning task, we focus on effective multimodal representation learning via inter-modal mutual information regularization. In particular, following the principle of disentangled representation learning, we introduce a mutual information upper bound with a mutual information minimization regularizer to encourage the disentangled representation of each modality for salient object detection. Based on our multimodal representation learning framework, we introduce an asymmetric feature extractor for our multimodal data, which is proven more effective than the conventional symmetric backbone setting. We also introduce multimodal variational auto-encoder as stochastic prediction refinement techniques, which takes pseudo labels from the first training stage as supervision and generates refined prediction. Experimental results on benchmark RGB-D salient object detection datasets verify both effectiveness of our explicit multimodal disentangled representation learning method and the stochastic prediction refinement strategy, achieving comparable performance with the state-of-the-art fully supervised models. Our code and data are available at: https://github.com/baneitixiaomai/MIRV.Comment: IEEE Transactions on Circuits and Systems for Video Technology 202

Measuring the contribution of the ocean: A comparison of the statistical classification of the marine economy used by China and Canada

Most of the major marine countries share an identical knowledge about marine economy. Ocean-related principle is the primary principles which distinguish the ocean economy from national economy and other economies. The understandings of marine economy from various countries all take into consideration the ocean-relativeness character geographically or industrially. However, there are certain differences in statistical frameworks and specific industrial classifications. In this paper, the statistical classification of marine economy between China and Canada is comparatively studied from the perspectives of the connotation of marine economy, the classification of regional statistics, and the classification of industrial statistics. Moreover, the identification of the statistical calibers of the two countries’ marine economy is further analyzed. This allows for a comparison of the statistical data between the two countries’ marine economy. Several suggestions on enforcing the statistical work for the marine economy are proposed in the end

A genome-wide transcriptome profiling reveals the early molecular events during callus initiation in Arabidopsis multiple organs

AbstractInduction of a pluripotent cell mass termed callus is the first step in an in vitro plant regeneration system, which is required for subsequent regeneration of new organs or whole plants. However, the early molecular mechanism underlying callus initiation is largely elusive. Here, we analyzed the dynamic transcriptome profiling of callus initiation in Arabidopsis aerial and root explants and identified 1342 differentially expressed genes in both explants after incubation on callus-inducing medium. Detailed categorization revealed that the differentially expressed genes were mainly related to hormone homeostasis and signaling, transcriptional and post transcriptional regulations, protein phosphorelay cascades and DNA- or chromatin-modification. Further characterization showed that overexpression of two transcription factors, HB52 or CRF3, resulted in the callus formation in transgenic plants without exogenous auxin. Therefore, our comprehensive analyses provide some insight into the early molecular regulations during callus initiation and are useful for further identification of the regulators governing callus formation

Simultaneous Resonant and Broadband Detection for Dark Sectors

Electromagnetic resonant systems, such as cavities or LC circuits, have emerged as powerful detectors for probing ultralight boson dark matter and high-frequency gravitational waves. However, the limited resonant bandwidth of conventional single-mode resonators, imposed by quantum fluctuations, necessitates numerous scan steps to cover broad unexplored frequency regions. The incorporation of multiple auxiliary modes can realize a broadband detector while maintaining a substantial signal response. The broadened sensitive width can be on the same order as the resonant frequency, encompassing several orders of the source frequency for heterodyne detection, where a background cavity mode transitions into another. Consequently, our approach enables significantly deeper exploration of the parameter space within the same integration time compared to single-mode detection.Comment: 18 pages, 6 figure

Towards Video Anomaly Retrieval from Video Anomaly Detection: New Benchmarks and Model

Video anomaly detection (VAD) has been paid increasing attention due to its potential applications, its current dominant tasks focus on online detecting anomalies% at the frame level, which can be roughly interpreted as the binary or multiple event classification. However, such a setup that builds relationships between complicated anomalous events and single labels, e.g., ``vandalism'', is superficial, since single labels are deficient to characterize anomalous events. In reality, users tend to search a specific video rather than a series of approximate videos. Therefore, retrieving anomalous events using detailed descriptions is practical and positive but few researches focus on this. In this context, we propose a novel task called Video Anomaly Retrieval (VAR), which aims to pragmatically retrieve relevant anomalous videos by cross-modalities, e.g., language descriptions and synchronous audios. Unlike the current video retrieval where videos are assumed to be temporally well-trimmed with short duration, VAR is devised to retrieve long untrimmed videos which may be partially relevant to the given query. To achieve this, we present two large-scale VAR benchmarks, UCFCrime-AR and XDViolence-AR, constructed on top of prevalent anomaly datasets. Meanwhile, we design a model called Anomaly-Led Alignment Network (ALAN) for VAR. In ALAN, we propose an anomaly-led sampling to focus on key segments in long untrimmed videos. Then, we introduce an efficient pretext task to enhance semantic associations between video-text fine-grained representations. Besides, we leverage two complementary alignments to further match cross-modal contents. Experimental results on two benchmarks reveal the challenges of VAR task and also demonstrate the advantages of our tailored method.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Binarized Spectral Compressive Imaging

Existing deep learning models for hyperspectral image (HSI) reconstruction achieve good performance but require powerful hardwares with enormous memory and computational resources. Consequently, these methods can hardly be deployed on resource-limited mobile devices. In this paper, we propose a novel method, Binarized Spectral-Redistribution Network (BiSRNet), for efficient and practical HSI restoration from compressed measurement in snapshot compressive imaging (SCI) systems. Firstly, we redesign a compact and easy-to-deploy base model to be binarized. Then we present the basic unit, Binarized Spectral-Redistribution Convolution (BiSR-Conv). BiSR-Conv can adaptively redistribute the HSI representations before binarizing activation and uses a scalable hyperbolic tangent function to closer approximate the Sign function in backpropagation. Based on our BiSR-Conv, we customize four binarized convolutional modules to address the dimension mismatch and propagate full-precision information throughout the whole network. Finally, our BiSRNet is derived by using the proposed techniques to binarize the base model. Comprehensive quantitative and qualitative experiments manifest that our proposed BiSRNet outperforms state-of-the-art binarization methods and achieves comparable performance with full-precision algorithms. Code and models are publicly available at https://github.com/caiyuanhao1998/BiSCI and https://github.com/caiyuanhao1998/MSTComment: NeurIPS 2023; The first work to study binarized spectral compressive imaging reconstruction proble