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Federated learning (FL) is a collaborative technique for training large-scale models while protecting user data privacy. Despite its substantial benefits, the free-riding behavior raises a major challenge for the formation of FL, especially in competitive markets. Our paper explores this under-explored issue on how the free-riding behavior in a competitive market affects firms' incentives to form FL. Competing firms can improve technologies through forming FL to increase the performance of their products, which in turn, affects consumers' product selection and market size. The key complication is whether the free-riding behavior discourages information contribution by participating firms and results in the decomposition of FL, and even free riding does not discourage information contribution, this does not necessarily mean that a firm wants to form FL in a competitive market because free riding may reshape the competition positions of each participating firm and thus forming FL may not be profitable. We build a parsimonious game theoretical model that captures these interactions and our analyses show several new findings. First, even in the presence of the free-riding behavior, competing firms under FL find it optimal to contribute all its available information. Second, the firm with less amount of information always finds it profitable to free ride; whether its rival (with more amount of information) have an incentive to form FL only when the level of competition or when the gap in information volume is not high. Third, when FL is formed, there exists an "All-Win" situation in which all stakeholders (participating firms, consumers, and social planner) benefit. Last, subsidizing by the free-riding firm can align its rival's incentive to form FL only when the level of competition is intermediate.

Nonlinear metasurfaces with subwavelength thickness were recently established as versatile platforms for the enhanced and tailorable generation of entangled photon pairs. The small dimensions and inherent stability of integrated metasurface sources are attractive for free-space applications in quantum communications, sensing, and imaging, yet this remarkable potential remained unexplored. Here, we formulate and experimentally demonstrate the unique benefits and practical potential of nonlinear metasurfaces for quantum imaging at infrared wavelengths, facilitating an efficient protocol combining ghost and all-optical scanning imaging. The metasurface incorporates a subwavelength-scale silica metagrating on a lithium niobate thin film. Its distinguishing feature is the capability to all-optically scan the photon emission angle in the direction across the grating simply by tuning the pump beam wavelength. Simultaneously, the photon emission is broad and anti-correlated along the grating direction, allowing for ghost imaging. Thereby, we reconstruct the images of 2D objects using just a 1D detector array in the idler path and a bucket detector in the signal path, by recording the dependencies of photon coincidences on the pump wavelength. Our results reveal new possibilities for quantum imaging with ultra-large field of view and improved imaging resolution as compared to photon pairs from conventional bulky crystals. The demonstrated concept can be extended to multi-wavelength operation and other applications such as quantum object tracking, paving the way for advancements in quantum technologies using ultra-compact nanostructured metasurfaces., Comment: 20 pages, 5 figures

Different imaging modalities are used to extract the diverse information carried in an optical field. Two prominent modalities include bright field and phase contrast microscopy that can visualize the amplitude and phase features of a sample, respectively. However, capturing both of these images on the same camera typically requires interchanging optical components. Metasurfaces are ultra-thin nanostructures that can merge both of these operations into a single miniaturized device. Here, a silicon-based metasurface that supports a Mie resonance is demonstrated to perform near-infrared phase contrast and bright field multi-modal microscopy that can be tuned by changing the polarization of the illumination. We performed experiments using optical fields with phase variations synthesized by a spatial light modulator and introduced by propagation through semi-transparent samples, including C. elegans, unstained human prostate cancer cells and breast tissue. The results demonstrate the potential of metasurfaces for label-free point-of-care testing., Comment: Main text 18 pages, 5 main figures, Supplementary information 19 pages, 19 supplementary figures, 2 supplementary videos

A comprehensive understanding of interested human-to-human interactions in video streams, such as queuing, handshaking, fighting and chasing, is of immense importance to the surveillance of public security in regions like campuses, squares and parks. Different from conventional human interaction recognition, which uses choreographed videos as inputs, neglects concurrent interactive groups, and performs detection and recognition in separate stages, we introduce a new task named human-to-human interaction detection (HID). HID devotes to detecting subjects, recognizing person-wise actions, and grouping people according to their interactive relations, in one model. First, based on the popular AVA dataset created for action detection, we establish a new HID benchmark, termed AVA-Interaction (AVA-I), by adding annotations on interactive relations in a frame-by-frame manner. AVA-I consists of 85,254 frames and 86,338 interactive groups, and each image includes up to 4 concurrent interactive groups. Second, we present a novel baseline approach SaMFormer for HID, containing a visual feature extractor, a split stage which leverages a Transformer-based model to decode action instances and interactive groups, and a merging stage which reconstructs the relationship between instances and groups. All SaMFormer components are jointly trained in an end-to-end manner. Extensive experiments on AVA-I validate the superiority of SaMFormer over representative methods. The dataset and code will be made public to encourage more follow-up studies.

Compared with the progress made on human activity classification, much less success has been achieved on human interaction understanding (HIU). Apart from the latter task is much more challenging, the main cause is that recent approaches learn human interactive relations via shallow graphical models, which is inadequate to model complicated human interactions. In this paper, we propose a consistency-aware graph network, which combines the representative ability of graph network and the consistency-aware reasoning to facilitate the HIU task. Our network consists of three components, a backbone CNN to extract image features, a factor graph network to learn third-order interactive relations among participants, and a consistency-aware reasoning module to enforce labeling and grouping consistencies. Our key observation is that the consistency-aware-reasoning bias for HIU can be embedded into an energy function, minimizing which delivers consistent predictions. An efficient mean-field inference algorithm is proposed, such that all modules of our network could be trained jointly in an end-to-end manner. Experimental results show that our approach achieves leading performance on three benchmarks.