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Image projection through scattering media has applications ranging from light delivery through multimode fiber to near-eye displays. Conventional methods utilize the transmission matrix (TM) measured by interfering with a reference beam. However, it is noise-sensitive, often resulting in artifacts that degrade the projection quality. Here we propose to characterize the scattering by computationally retrieving TM from intensity-only measurements and solve the projection problem formulated with the retrieved TM by optimization. We experimentally validate the proposed method by projecting through a multimode fiber. Compared to the conventional methods, it projects improved-quality images with resolution near to the diffraction limit, and simplifies the experimental setup by eliminating the reference. It paves the way for applications of high-quality near-diffraction-limited projection through scattering.

Transmission matrix (TM) linearly maps the incident and transmitted complex fields, and has been used widely due to its ability to characterize scattering media. It is computationally demanding to reconstruct the TM from intensity images measured by a reference-less experimental setup. Removing reference beam for interference gains the advantage of simple experimental setup. However, the long computational time still limits its practical application. We propose an efficient reference-less TM retrieval method for multimode fiber (MMF). Our method adopts a data acquisition scheme which employs Fourier transform matrix in the design of the incident fields. We develop a nonlinear optimization algorithm to solve the TM retrieval problem in a parallel manner. The data acquisition scheme allows the algorithm to be implemented with fast Fourier transform (FFT), and hence achieves great efficiency improvement. Further, our method acquires intensity images at a defocus plane and correct the error of relative phase offset of TM recovered from the intensity images measured at one fixed plane. We validate the proposed TM retrieval method with both simulations and experiments. By using FFT, our TM retrieval algorithm achieves 1200x speed-up in computational time, and recovers $2286 \times 8192$ TM of a 0.22 NA and $50 \ \mu m$ diameter MMF with 124.9 seconds by a computer of 32 CPU cores. With the advantages of efficiency and the correction of phase offset, our method paves the way for the application of reference-less TM retrieval in real practice.

Super-resolution microscopy normally requiring complex and cumbersome optics is not applicable for in situ imaging through a narrow channel. Here, we demonstrate single hair-thin multimode fiber (MMF) endoscope (less than 250 ${\mu}m$) for in vivo light-field nano-imaging, which is called spatial-frequency tracking adaptive beacon light-field encoded nano-endoscopy (STABLE nano-endoscopy) that enables three-dimensional (3D) subcellular-scale imaging. Spatial-frequency tracking provides up to $10^3$ Hz disorder tracking that ensures stable imaging in long-haul MMFs (up to 200 m) under various conditions. Full-vector modulation and fluorescence emission difference are combined to enhance the imaging signal-to-noise ratio two times and to improve the resolution to sub-diffraction-limited 250 nm (${\lambda}/3NA$). STABLE nano-endoscopy and white-light endoscopy (WLE) are integrated to achieve cross-scale in vivo imaging inside the lumen. This high-resolution and robust observation in a minimally invasive manner paves the way to gain a deeper understanding of the disease mechanisms and to bridge clinical and biological sciences., Comment: 14 pages, 5 figures

Recent years have witnessed the great accuracy performance of graph-based Collaborative Filtering (CF) models for recommender systems. By taking the user-item interaction behavior as a graph, these graph-based CF models borrow the success of Graph Neural Networks (GNN), and iteratively perform neighborhood aggregation to propagate the collaborative signals. While conventional CF models are known for facing the challenges of the popularity bias that favors popular items, one may wonder "Whether the existing graph-based CF models alleviate or exacerbate popularity bias of recommender systems?" To answer this question, we first investigate the two-fold performances w.r.t. accuracy and novelty for existing graph-based CF methods. The empirical results show that symmetric neighborhood aggregation adopted by most existing graph-based CF models exacerbate the popularity bias and this phenomenon becomes more serious as the depth of graph propagation increases. Further, we theoretically analyze the cause of popularity bias for graph-based CF. Then, we propose a simple yet effective plugin, namely r-AdjNorm, to achieve an accuracy-novelty trade-off by controlling the normalization strength in the neighborhood aggregation process. Meanwhile, r-AdjNorm can be smoothly applied to the existing graph-based CF backbones without additional computation. Finally, experimental results on three benchmark datasets show that our proposed method can improve novelty without sacrificing accuracy under various graph-based CF backbones., Comment: To appear in SIGIR 2022

Reinforcement learning based recommender systems (RL-based RS) aim at learning a good policy from a batch of collected data, by casting recommendations to multi-step decision-making tasks. However, current RL-based RS research commonly has a large reality gap. In this paper, we introduce the first open-source real-world dataset, RL4RS, hoping to replace the artificial datasets and semi-simulated RS datasets previous studies used due to the resource limitation of the RL-based RS domain. Unlike academic RL research, RL-based RS suffers from the difficulties of being well-validated before deployment. We attempt to propose a new systematic evaluation framework, including evaluation of environment simulation, evaluation on environments, counterfactual policy evaluation, and evaluation on environments built from test set. In summary, the RL4RS (Reinforcement Learning for Recommender Systems), a new resource with special concerns on the reality gaps, contains two real-world datasets, data understanding tools, tuned simulation environments, related advanced RL baselines, batch RL baselines, and counterfactual policy evaluation algorithms. The RL4RS suite can be found at https://github.com/fuxiAIlab/RL4RS. In addition to the RL-based recommender systems, we expect the resource to contribute to research in applied reinforcement learning., Comment: 4-th version, SIGIR2023

In business domains, \textit{bundling} is one of the most important marketing strategies to conduct product promotions, which is commonly used in online e-commerce and offline retailers. Existing recommender systems mostly focus on recommending individual items that users may be interested in. In this paper, we target at a practical but less explored recommendation problem named bundle recommendation, which aims to offer a combination of items to users. To tackle this specific recommendation problem in the context of the \emph{virtual mall} in online games, we formalize it as a link prediction problem on a user-item-bundle tripartite graph constructed from the historical interactions, and solve it with a neural network model that can learn directly on the graph-structure data. Extensive experiments on three public datasets and one industrial game dataset demonstrate the effectiveness of the proposed method. Further, the bundle recommendation model has been deployed in production for more than one year in a popular online game developed by Netease Games, and the launch of the model yields more than 60\% improvement on conversion rate of bundles, and a relative improvement of more than 15\% on gross merchandise volume (GMV)., Comment: 8 pages, 10 figures, accepted paper on CIKM 2020

In recent years, there are great interests as well as challenges in applying reinforcement learning (RL) to recommendation systems (RS). In this paper, we summarize three key practical challenges of large-scale RL-based recommender systems: massive state and action spaces, high-variance environment, and the unspecific reward setting in recommendation. All these problems remain largely unexplored in the existing literature and make the application of RL challenging. We develop a model-based reinforcement learning framework, called GoalRec. Inspired by the ideas of world model (model-based), value function estimation (model-free), and goal-based RL, a novel disentangled universal value function designed for item recommendation is proposed. It can generalize to various goals that the recommender may have, and disentangle the stochastic environmental dynamics and high-variance reward signals accordingly. As a part of the value function, free from the sparse and high-variance reward signals, a high-capacity reward-independent world model is trained to simulate complex environmental dynamics under a certain goal. Based on the predicted environmental dynamics, the disentangled universal value function is related to the user's future trajectory instead of a monolithic state and a scalar reward. We demonstrate the superiority of GoalRec over previous approaches in terms of the above three practical challenges in a series of simulations and a real application., Comment: 9 pages, 4 figures, to be published in Proceedings of the AAAI Conference on Artificial Intelligence 2021

We study the potential of the LHCb 13 TeV single $W^{\pm}$ and $Z$ boson pseudo-data for constraining the parton distribution functions (PDFs) of the proton. As an example, we demonstrate the sensitivity of the LHCb 13 TeV data, collected with integrated luminosities of 5 fb$^{-1}$ and 300 fb$^{-1}$, to reducing the PDF uncertainty bands of the CT14HERA2 PDFs, using the error PDF updating package {\sc ePump}. The sensitivities of various experimental observables are compared. Generally, sizable reductions in PDF uncertainties can be observed in the 300 fb$^{-1}$ data sample, particularly in the small-$x$ region. The double-differential cross section measurement of $Z$ boson $p_T$ and rapidity can greatly reduce the uncertainty bands of $u$ and $d$ quarks in almost the whole $x$ range, as compared to various single observable measurements., Comment: 25 pages, 13 figures