An adaptive model checking test for functional linear model

Numerous studies have been devoted to the estimation and inference problems for functional linear models (FLM). However, few works focus on model checking problem that ensures the reliability of results. Limited tests in this area do not have tractable null distributions or asymptotic analysis under alternatives. Also, the functional predictor is usually assumed to be fully observed, which is impractical. To address these problems, we propose an adaptive model checking test for FLM. It combines regular moment-based and conditional moment-based tests, and achieves model adaptivity via the dimension of a residual-based subspace. The advantages of our test are manifold. First, it has a tractable chi-squared null distribution and higher powers under the alternatives than its components. Second, asymptotic properties under different underlying models are developed, including the unvisited local alternatives. Third, the test statistic is constructed upon finite grid points, which incorporates the discrete nature of collected data. We develop the desirable relationship between sample size and number of grid points to maintain the asymptotic properties. Besides, we provide a data-driven approach to estimate the dimension leading to model adaptivity, which is promising in sufficient dimension reduction. We conduct comprehensive numerical experiments to demonstrate the advantages the test inherits from its two simple components

Service Outsourcing Character Oriented Privacy Conflict Detection Method in Cloud Computing

Cloud computing has provided services for users as a software paradigm. However, it is difficult to ensure privacy information security because of its opening, virtualization, and service outsourcing features. Therefore how to protect user privacy information has become a research focus. In this paper, firstly, we model service privacy policy and user privacy preference with description logic. Secondly, we use the pellet reasonor to verify the consistency and satisfiability, so as to detect the privacy conflict between services and user. Thirdly, we present the algorithm of detecting privacy conflict in the process of cloud service composition and prove the correctness and feasibility of this method by case study and experiment analysis. Our method can reduce the risk of user sensitive privacy information being illegally used and propagated by outsourcing services. In the meantime, the method avoids the exception in the process of service composition by the privacy conflict, and improves the trust degree of cloud service providers

A 2D-3D non-contact anthropometric method for daily dressing state-takes young Asian women as example

Taking young Asian women as research object, this paper is to develop a 2D-3D non-contact body measurement and calculation method for daily dressing state used to generate the CWH (chest, waist, and hip) circumferences suitable for garment practical applications. The general research approach is ‘analyzing related curves of human body, finding their fitting functions and length calculating formulas through mathematical analysis and measuring experiment with large number of samples – extracting effective feature points data in daily dressing state by dynamic and static experiments – calculating such dimensions as width and thickness after curve fitting – generating CWH circumferences via formulas and making comparison’. The numerical results validate the 2D-3D method presented in this paper as a useful and effective approach. Being different from other 3D non-contact measuring methods, the authors provide a new way to get the CWH measurements in the condition of daily natural dressing, which not only optimizes the non-contact anthropometrics theory, but also breaks the static measuring mode. Furthermore, the bringing forth of the concept of ‘daily dressing state’ and the laboratory experiments of this paper would be worthy for practical use

Exploring the Training Robustness of Distributional Reinforcement Learning against Noisy State Observations

In real scenarios, state observations that an agent observes may contain measurement errors or adversarial noises, misleading the agent to take suboptimal actions or even collapse while training. In this paper, we study the training robustness of distributional Reinforcement Learning~(RL), a class of state-of-the-art methods that estimate the whole distribution, as opposed to only the expectation, of the total return. Firstly, we validate the contraction of distributional Bellman operators in the State-Noisy Markov Decision Process~(SN-MDP), a typical tabular case that incorporates both random and adversarial state observation noises. In the noisy setting with function approximation, we then analyze the vulnerability of least squared loss in expectation-based RL with either linear or nonlinear function approximation. By contrast, we theoretically characterize the bounded gradient norm of distributional RL loss based on the categorical parameterization equipped with the Kullback-Leibler~(KL) divergence. The resulting stable gradients while the optimization in distributional RL accounts for its better training robustness against state observation noises. Finally, extensive experiments on the suite of environments verified that distributional RL is less vulnerable against both random and adversarial noisy state observations compared with its expectation-based counterpart

Gloss-Free End-to-End Sign Language Translation

In this paper, we tackle the problem of sign language translation (SLT) without gloss annotations. Although intermediate representation like gloss has been proven effective, gloss annotations are hard to acquire, especially in large quantities. This limits the domain coverage of translation datasets, thus handicapping real-world applications. To mitigate this problem, we design the Gloss-Free End-to-end sign language translation framework (GloFE). Our method improves the performance of SLT in the gloss-free setting by exploiting the shared underlying semantics of signs and the corresponding spoken translation. Common concepts are extracted from the text and used as a weak form of intermediate representation. The global embedding of these concepts is used as a query for cross-attention to find the corresponding information within the learned visual features. In a contrastive manner, we encourage the similarity of query results between samples containing such concepts and decrease those that do not. We obtained state-of-the-art results on large-scale datasets, including OpenASL and How2Sign. The code and model will be available at https://github.com/HenryLittle/GloFE.Comment: ACL 2023 Main Conference (Oral

Sclerotherapy for the recurrent granulomatous epulis with pingyangmycin

Relapse of granulomatous epulis is common after surgery because of local irritations, hormonal level in vivo, or incomplete resection. Currently, if recurrence occurs, then extraction of the teeth adjacent to the lesion is commonly performed, which may influence the aesthetics or masticatory function. Thus, a more effective and less aggressive treatment method is urgently demanded, particularly for the recurring lesion. This study investigated the effects of the intralesional pingyangmycin (PYM) injections for the recurrent granulomatous epulis and assessed the complications. A total of 16 patients with recurrent granulomatous epulis underwent intralesional PYM injections, between July 2010 and June 2014. The effects and complications of the treatment were retrospectively reviewed. The total number of injections performed was 48 (for all patients). The median number of injections per patient was three (range, two to four). All cases completely recovered with no recurrence and resorption of the alveolar bone after a follow-up of more than 12 months. The complications included slight bleeding, local swelling and pain following injection. All these symptoms resolved 7 to 10 days after the injection. In summary, intralesional PYM injections may be a preferred option for recurring granulomatous epulis