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Since 2020, synchrotron radiation facilities in several Asia-Pacific countries have been collaborating in a major project called "SYNAPSE"(Synchrotrons for Neuroscience: an Asia-Pacific Scientific Enterprise). They use x-ray imaging to attack in a coordinated fashion one of the major issues in modern science: the structure of animal and human brains, including neurons and connections. The objective is to develop Google-like maps also including detailed structural and functional information for selected regions of interest. The sheer mass of data needed for the objective poses huge problems for the acquisition, processing, storage and use of images. In order to complete the task within a reasonable time, the key element of the SYNAPSE strategy is the parallel and coordinated work of several facilities on the same specimens. This article reviews different aspects of the enterprise, including the foundations of synchrotron radiation, coherence and of its role in advanced imaging, electron accelerators, x-ray optics and detectors. This will provide the foundation for an extensive presentation of the different components of SYNAPSE, with an overview of results already obtained within the consortium.

Due to the emergence of mobile 3D devices, multi-view 3D videos are expected to play increasingly important roles in providing immersively interactive experiences to users. Compared with traditional single-view videos, it is envisaged that a multi-view 3D video requires larger storage space and bandwidth consumption. Nevertheless, efficient caching for multi-view 3D videos in a mobile proxy has not been explored in the literature. In this paper, therefore, we first observe that the storage space can be effectively reduced by leveraging Depth Image Based Rendering (DIBR) in multi-view 3D videos. We then formulate a new cache management problem, named Adaptive View Selection and Cache Operation (AVSCO), and find the optimal policy based on Markov Decision Process (MDP). Afterward, we propose an approximate MDP to effectively aggregate similar states to reduce the MDP state space and devise an efficient and effective algorithm to support human visual continuity in AVSCO. Simulation results manifest that the proposed algorithms can significantly improve the cache hit rate and reduce the bandwidth consumption for the remote access compared with the existing cache replacement algorithms.

Flow consolidation has been proposed for merging multiple flows from different services into an aggregate flow to remedy the state explosion problem in SDN. However, we observe that the QoS requirements are no longer sustained in aggregate flows since the bandwidth decided by TCP is usually different from the desired rate of each service. Therefore, this paper explores an idea to control the rates of only a few service flows so that the rates of all uncontrolled flows allocated by TCP will meet their QoS requirements. We design a new architecture, called Scalable Per-Flow Rate Allocation (SPFRA), and formulate a new optimization problem, termed Scalable Rate Allocation for Aggregate Flows (SRAF), to find a minimum number of controlled flows to increase the scalability of SDN with diverse service requirements. We prove the NP-hardness and inapproximability of SRAF. To solve the problem, we design an algorithm, named Aggregate Flow Selection and Flow Release (AFSFR), to achieve the tightest bound and extend it to support distributed computation and dynamic traffic for instant services. Simulations and implementation on an SDN testbed manifest that AFSFR performs nearly optimally in real networks, and the number of controlled flows can be effectively reduced by 50%.

The social presence theory in social psychology suggests that computer-mediated online interactions are inferior to face-to-face, in-person interactions. In this paper, we consider the scenarios of organizing in person friend-making social activities via online social networks (OSNs) and formulate a new research problem, namely, Hop-bounded Maximum Group Friending (HMGF), that takes into consideration both existing friendships and the likelihood of new friend making in organization of the targeted in person friend-making social activities. To find a set of attendees for such social activities, HMGF is unique and challenging due to the interplay of the group size, the constraint on existing friendships, and the objective of maximizing the likelihood of friend making. We propose an error-bounded approximation algorithm, MaxGF, to efficiently obtain the solutions very close to the optimal solutions. To boost the performance, we devise two graph-theoretical pruning strategies, namely Neighbor Pruning and Core Pruning, which can effectively avoid redundant graph explorations to improve the performance of HMGF. We conduct a user study to validate our problem formulation and perform extensive experiments on real datasets to demonstrate the efficiency and effectiveness of our proposed algorithm. The experimental results manifest that our proposed algorithms outperform the baselines, including the ILP formulation.

Online social networks have become a crucial medium to disseminate the latest political, commercial, and social information. Users with high visibility are often selected as seeds to spread information and affect their adoption in target groups. We study how gender differences and similarities can impact the information spreading process. Using a large-scale Instagram dataset and a small-scale Facebook dataset, we first conduct a multi-faceted analysis taking the interaction type, directionality and frequency into account. To this end, we explore a variety of existing and new single and multihop centrality measures. Our analysis unveils that males and females interact differently depending on the interaction types, e.g., likes or comments, and they feature different support and promotion patterns. We complement prior work showing that females do not reach top visibility (often referred to as the glass ceiling effect) jointly factoring in the connectivity and interaction intensity, both of which were previously mainly discussed independently. Inspired by these observations, we propose a novel seeding framework, called Disparity Seeding, which aims at maximizing spread while reaching a target user group, e.g., a certain percentage of females - promoting the influence of under-represented groups. Disparity Seeding ranks influential users with two gender-aware measures, the Target HI-index and the Embedding index. Extensive simulations comparing Disparity Seeding with target-agnostic algorithms show that Disparity Seeding meets the target percentage while effectively maximizing the spread. Disparity Seeding can be generalized to counter different types of inequality, e.g., race, and proactively promote minorities in the society.

Since early 2020, the COVID-19 global pandemic has significantly impacted almost every aspect of the human society throughout the world. Until now, middle of 2021, although with all the efforts on pandemic intervention and vaccination, COVID-19 is still hovering around the world, resulting in more than 177 million confirmed cases and 3.8 million deaths.

In late 2019, a new virus was discovered, namely SARS-CoV-2. This strain causes severe acute respiratory syndrome coronavirus 2, defined as COVID-19. The disease soon spread all over the world, thus becoming a pandemic. It has been almost two years since worldwide restrictions on our lives started, and the traditional way people live and work has completely changed.