Multi-Device Task-Oriented Communication via Maximal Coding Rate Reduction

Task-oriented communication offers ample opportunities to alleviate the communication burden in next-generation wireless networks. Most existing work designed the physical-layer communication modules and learning-based codecs with distinct objectives: learning is targeted at accurate execution of specific tasks, while communication aims at optimizing conventional communication metrics, such as throughput maximization, delay minimization, or bit error rate minimization. The inconsistency between the design objectives may hinder the exploitation of the full benefits of task-oriented communications. In this paper, we consider a specific task-oriented communication system for multi-device edge inference over a multiple-input multiple-output (MIMO) multiple-access channel, where the learning (i.e., feature encoding and classification) and communication (i.e., precoding) modules are designed with the same goal of inference accuracy maximization. Instead of end-to-end learning which involves both the task dataset and wireless channel during training, we advocate a separate design of learning and communication to achieve the consistent goal. Specifically, we leverage the maximal coding rate reduction (MCR2) objective as a surrogate to represent the inference accuracy, which allows us to explicitly formulate the precoding optimization problem. We cast valuable insights into this formulation and develop a block coordinate descent (BCD) solution algorithm. Moreover, the MCR2 objective also serves the loss function of the feature encoding network, based on which we characterize the received features as a Gaussian mixture (GM) model, facilitating a maximum a posteriori (MAP) classifier to infer the result. Simulation results on both the synthetic and real-world datasets demonstrate the superior performance of the proposed method compared to various baselines.Comment: submitted to IEEE for possible publicatio

Manage risk of sustainable product–service systems: a case-based operations research approach

© 2018, Springer Science+Business Media, LLC, part of Springer Nature. Sustainable product–service systems (SusPSSs) offer an innovation-driven approach to production based on providing results or functions with minimal material use and emissions. Networks of SusPSSs partners are central to the decision-making of sustainability policies. Evaluations and assessments of network-oriented risks sources are therefore crucial to informing an industrial firm’s reorientation towards SusPSS. Traditionally, these risks beleaguer production and continue to grow in significance with complex production and innovation processes. This article presents a novel operations research application for evaluating network-oriented risks of industrial firms in pursuing SusPSSs. The model conceptualises a framework for network risk metrics and applies a fuzzy-based multi-criteria decision-making technique to evaluate levels of risk associated with reorientations to SusPSS approaches. It takes explicit account of multiple risk sources in aiding decision-making and assists in indicating strategies for improving business sustainability. In addition, it compares and ranks alternative SusPSSs as a system and on an indicator basis, which is a practical and effective decision support tool. A case study of an industrial firm is conducted to verify the effectiveness and applicability of the proposed approach in supporting firms’ decision on SusPSSs

Semantic-Enhanced Image Clustering

Image clustering is an important and open-challenging task in computer vision. Although many methods have been proposed to solve the image clustering task, they only explore images and uncover clusters according to the image features, thus being unable to distinguish visually similar but semantically different images. In this paper, we propose to investigate the task of image clustering with the help of a visual-language pre-training model. Different from the zero-shot setting, in which the class names are known, we only know the number of clusters in this setting. Therefore, how to map images to a proper semantic space and how to cluster images from both image and semantic spaces are two key problems. To solve the above problems, we propose a novel image clustering method guided by the visual-language pre-training model CLIP, named \textbf{Semantic-Enhanced Image Clustering (SIC)}. In this new method, we propose a method to map the given images to a proper semantic space first and efficient methods to generate pseudo-labels according to the relationships between images and semantics. Finally, we propose performing clustering with consistency learning in both image space and semantic space, in a self-supervised learning fashion. The theoretical result of convergence analysis shows that our proposed method can converge at a sublinear speed. Theoretical analysis of expectation risk also shows that we can reduce the expected risk by improving neighborhood consistency, increasing prediction confidence, or reducing neighborhood imbalance. Experimental results on five benchmark datasets clearly show the superiority of our new method

Clinical efficacy and safety of Kanglaite injection, adjuvant cemcitabine and cisplatin chemotherapy for advanced non-small-cell lung cancer: A systematic review and meta-analysis

Purpose: To investigate the effectiveness and safety of the combination of Kanglaite injection (KLTi) and gemcitabine and cisplatin (GP) chemotherapy in the treatment of advanced non-small cell lung cancer (NSCLC).Methods: PubMed, Web of Science, Embase, Cochrane Library, CNKI, Wan-Fang, CBM, and CQVIP were comprehensively searched from January 2010 till November 2020. Randomized controlled trials (RCTs) of KLTi plus GP in the treatment of NSCLC were selected and assessed for inclusion. Review Manager 5.3 software was used for meta-analysis.Results: Twenty-five RCTs on advanced NSCLC examined the inclusion criteria. The meta-analysis showed that compared with GP chemotherapy alone, KLTi plus GP chemotherapy significantly improved objective response rate (ORR) (RR = 1.36, 95% CI 1.23-1.51, p < 0.00001), disease control rate (DCR) (RR = 1.17, 95% CI 1.11 - 1.23, p < 0.00001), and reduced adverse drug reactions(ADRs) such as hair loss (RR = 0.60, 95% CI 0.47 - 0.76, p < 0.0001), gastrointestinal reaction (RR = 0.68, 95% CI 0.62 - 0.75, p < 0.00001), impairment of liver and kidney function (RR = 0.65, 95% CI 0.53 - 0.80, p < 0.001), nervous system damage (RR = 0.42, 95% CI 0.26 - 0.69, p = 0.0005), myelosuppression (I-II phase) (RR = 0.79, 95 % CI 0.66 - 0.95, p = 0.01), myelosuppression (III-IV phase) (RR = 0.44, 95 % CI0.27 - 0.72, p = 0.001), anemia (RR = 0.74, 95 % CI 0.60 - 0.91, p = 0.006), leukopenia (RR = 0.78, 95% CI 0.69, 0.87, p < 0.0001), thrombocytopenia (RR = 0.59, 95 % CI 0.49, 0.72, p < 0.00001), hypochromia (RR = 0.74, 95% CI 0.59, 0.92, p = 0.008).Conclusion: KLTi adjuvant GP chemotherapy reduces adverse effects in patients with advanced NSCLC. Thus, KLTi might be an effective and safe intervention for NSCLC&nbsp

The chasm in percutaneous coronary intervention and in-hospital mortality rates among acute myocardial infarction patients in rural and urban hospitals in China: A mediation analysis

Objectives: To determine to what extent the inequality in the ability to provide percutaneous coronary intervention (PCI) translates into outcomes for AMI patients in China. Methods: We identified 82,677 patients who had primary diagnoses of AMI and were hospitalized in Shanxi Province, China, between 2013 and 2017. We applied logistic regressions with inverse probability weighting based on propensity scores and mediation analyses to examine the association of hospital rurality with in-hospital mortality and the potential mediating effects of PCI. Results: In multivariate models where PCI was not adjusted for, rural hospitals were associated with a significantly higher risk of in-hospital mortality (odds ratio [OR]: 1.19, 95% confidence interval [CI]: 1.03–1.37). However, this association was nullified (OR: 0.94, 95% CI: 0.81–1.08) when PCI was included as a covariate. Mediation analyses revealed that PCI significantly mediated 132.3% (95% CI: 104.1–256.6%) of the effect of hospital rurality on in-hospital mortality. The direct effect of hospital rurality on in-hospital mortality was insignificant. Conclusion: The results highlight the need to improve rural hospitals’ infrastructure and address the inequalities of treatments and outcomes in rural and urban hospitals