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Federated Learning (FL) has emerged as a powerful paradigm in Artificial Intelligence, facilitating the parallel training of Artificial Neural Networks on edge devices while safeguarding data privacy. Nonetheless, to encourage widespread adoption, Federated Learning Frameworks (FLFs) must tackle (i) the power imbalance between a central authority and its participants, and (ii) the challenge of equitably measuring and incentivizing contributions. Existing approaches to decentralize and incentivize FL processes are hindered by (i) computational overhead and (ii) uncertainty in contribution assessment (Witt et al. 2023), limiting FL's scalability beyond use cases where trust between participants and the server is established. This work introduces a cutting-edge, blockchain-enabled federated learning framework that incorporates Federated Knowledge Distillation (FD) with compressed 1-bit soft-labels, aggregated through a smart contract. Furthermore, we present the Peer Truth Serum for Federated Distillation (PTSFD), which cultivates an incentive-compatible ecosystem by rewarding honest participation based on an implicit yet effective comparison of worker contributions. The primary innovation stems from its lightweight architecture that simultaneously promotes decentralization and incentivization, addressing critical challenges in contemporary FL approaches.

Federated Learning (FL) has emerged as a powerful paradigm in Artificial Intelligence, facilitating the parallel training of Artificial Neural Networks on edge devices while safeguarding data privacy. Nonetheless, to encourage widespread adoption, Federated Learning Frameworks (FLFs) must tackle (i) the power imbalance between a central authority and its participants, and (ii) the challenge of equitably measuring and incentivizing contributions. Existing approaches to decentralize and incentivize FL processes are hindered by (i) computational overhead and (ii) uncertainty in contribution assessment (Witt et al. 2023), limiting FL's scalability beyond use cases where trust between participants and the server is established. This work introduces a cutting-edge, blockchain-enabled federated learning framework that incorporates Federated Knowledge Distillation (FD) with compressed 1-bit soft-labels, aggregated through a smart contract. Furthermore, we present the Peer Truth Serum for Federated Distillation (PTSFD), which cultivates an incentive-compatible ecosystem by rewarding honest participation based on an implicit yet effective comparison of worker contributions. The primary innovation stems from its lightweight architecture that simultaneously promotes decentralization and incentivization, addressing critical challenges in contemporary FL approaches.

Federated Learning (FL) has emerged as a powerful paradigm in Artificial Intelligence, facilitating the parallel training of Artificial Neural Networks on edge devices while safeguarding data privacy. Nonetheless, to encourage widespread adoption, Federated Learning Frameworks (FLFs) must tackle (i) the power imbalance between a central authority and its participants, and (ii) the challenge of equitably measuring and incentivizing contributions. Existing approaches to decentralize and incentivize FL processes are hindered by (i) computational overhead and (ii) uncertainty in contribution assessment (Witt et al. 2023), limiting FL's scalability beyond use cases where trust between participants and the server is established. This work introduces a cutting-edge, blockchain-enabled federated learning framework that incorporates Federated Knowledge Distillation (FD) with compressed 1-bit soft-labels, aggregated through a smart contract. Furthermore, we present the Peer Truth Serum for Federated Distillation (PTSFD), which cultivates an incentive-compatible ecosystem by rewarding honest participation based on an implicit yet effective comparison of worker contributions. The primary innovation stems from its lightweight architecture that simultaneously promotes decentralization and incentivization, addressing critical challenges in contemporary FL approaches.

Federated Learning (FL) has emerged as a powerful paradigm in Artificial Intelligence, facilitating the parallel training of Artificial Neural Networks on edge devices while safeguarding data privacy. Nonetheless, to encourage widespread adoption, Federated Learning Frameworks (FLFs) must tackle (i) the power imbalance between a central authority and its participants, and (ii) the challenge of equitably measuring and incentivizing contributions. Existing approaches to decentralize and incentivize FL processes are hindered by (i) computational overhead and (ii) uncertainty in contribution assessment (Witt et al. 2023), limiting FL's scalability beyond use cases where trust between participants and the server is established. This work introduces a cutting-edge, blockchain-enabled federated learning framework that incorporates Federated Knowledge Distillation (FD) with compressed 1-bit soft-labels, aggregated through a smart contract. Furthermore, we present the Peer Truth Serum for Federated Distillation (PTSFD), which cultivates an incentive-compatible ecosystem by rewarding honest participation based on an implicit yet effective comparison of worker contributions. The primary innovation stems from its lightweight architecture that simultaneously promotes decentralization and incentivization, addressing critical challenges in contemporary FL approaches.

Wang J, Liu X, Yan C, et al. Onco Targets Ther. 2017;10:4251–4260. We, the Editors and Publisher of OncoTargets and Therapy, have retracted the following article. Following publication of the article, concerns were raised about the duplication of images from Figure 5 with images from other unrelated articles. Specifically, Images for Figure 5E, Ki-67, have been duplicated with images for Figure 3E from Zhang X, Lian T, Fan W, et al. Long-Noncoding RNA CASC9 Promotes Progression of Non-Small Cell Lung Cancer by Promoting the Expression of CDC6 Through Binding to HuR. Cancer Manag Res. 2020;12:9033–9043. https://doi.org/10.2147/CMAR.S268375. The authors did not respond to our queries and were unable to provide an explanation for the duplicated images or provide data for the study. As verifying the validity of published work is core to the integrity of the scholarly record, we are therefore retracting the article and the authors were notified of this. We have been informed in our decision-making by our editorial policies and COPE guidelines. The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as “Retracted”.

Traditional applications of natural language processing (NLP) in healthcare have predominantly focused on patient-centered services, enhancing patient interactions and care delivery, such as through medical dialogue systems. However, the potential of NLP to benefit inexperienced doctors, particularly in areas such as communicative medical coaching, remains largely unexplored. We introduce ``ChatCoach,'' an integrated human-AI cooperative framework. Within this framework, both a patient agent and a coaching agent collaboratively support medical learners in practicing their medical communication skills during consultations. Unlike traditional dialogue systems, ChatCoach provides a simulated environment where a human doctor can engage in medical dialogue with a patient agent. Simultaneously, a coaching agent provides real-time feedback to the doctor. To construct the ChatCoach system, we developed a dataset and integrated Large Language Models such as ChatGPT and Llama2, aiming to assess their effectiveness in communicative medical coaching tasks. Our comparative analysis demonstrates that instruction-tuned Llama2 significantly outperforms ChatGPT's prompting-based approaches., Comment: NA

Recently proliferated deep learning-based semantic communications (DLSC) focus on how transmitted symbols efficiently convey a desired meaning to the destination. However, the sensitivity of neural models and the openness of wireless channels cause the DLSC system to be extremely fragile to various malicious attacks. This inspires us to ask a question: "Can we further exploit the advantages of transmission efficiency in wireless semantic communications while also alleviating its security disadvantages?". Keeping this in mind, we propose SemEntropy, a novel method that answers the above question by exploring the semantics of data for both adaptive transmission and physical layer encryption. Specifically, we first introduce semantic entropy, which indicates the expectation of various semantic scores regarding the transmission goal of the DLSC. Equipped with such semantic entropy, we can dynamically assign informative semantics to Orthogonal Frequency Division Multiplexing (OFDM) subcarriers with better channel conditions in a fine-grained manner. We also use the entropy to guide semantic key generation to safeguard communications over open wireless channels. By doing so, both transmission efficiency and channel security can be simultaneously improved. Extensive experiments over various benchmarks show the effectiveness of the proposed SemEntropy. We discuss the reason why our proposed method benefits secure transmission of DLSC, and also give some interesting findings, e.g., SemEntropy can keep the semantic accuracy remain 95% with 60% less transmission., Comment: 13 pages, 12 figures

Identification of the core functional microorganisms in food fermentations is necessary to understand the ecological and functional processes for making those foods. Wheat qu, which provides liquefaction and saccharifying power, and affects the flavor quality, is a key ingredient in ancient alcoholic huangjiu fermentation, while core microbiota of them still remains indistinct. In this study, metagenomics, metabolomics, microbial isolation and co-fermentation were used to investigate huangjiu. Although Aspergillus is usually regarded as core microorganism in wheat qu to initiate huangjiu fermentations, our metagenomic analysis showed that bacteria Saccharopolyspora are predominant in wheat qu and responsible for breakdown of starch and cellulose. Metabolic network and correlation analysis showed that Saccharopolyspora rectivirgula, Saccharopolyspora erythraea, and Saccharopolyspora hirsuta made the greatest contributions to huangjius metabolites, consisting of alcohols (phenylethanol, isoamylol and isobutanol), esters, amino acids (Pro, Arg, Glu and Ala) and organic acids (lactate, tartrate, acetate and citrate). S. hirsuta J2 isolated from wheat qu had the highest amylase, glucoamylase and protease activities. Co-fermentations of S. hirsuta J2 with S. cerevisiae HJ resulted in a higher fermentation rate and alcohol content, and huangjiu flavors were more similar to that of traditional huangjiu compared to co-fermentations of Aspergillus or Lactiplantibacillus with S. cerevisiae HJ. Genome of S. hirsuta J2 contained genes encoding biogenic amine degradation enzymes. By S. hirsuta J2 inoculation, biogenic amine content was reduced by 45%, 43% and 62% in huangjiu, sausage and soy sauce, respectively. These findings show the utility of Saccharopolyspora as a key functional organism in fermented food products.

Wei Li,1 Yinan Gong,1 Jingyi Liu,1 Yongming Guo,1– 3 Huiling Tang,1 Siru Qin,1 Yadan Zhao,1 Songtao Wang,1 Zhifang Xu,1– 3 Bo Chen1– 3 1Research Center of Experimental Acupuncture Science, Tianjin University of Traditional Chinese Medicine, Tianjin, People’s Republic of China; 2School of Acupuncture & Moxibustion and Tuina, Tianjin University of Traditional Chinese Medicine, Tianjin, 301617, People’s Republic of China; 3National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, 300381, People’s Republic of ChinaCorrespondence: Zhifang XuSchool of Acupuncture & Moxibustion and Tuina, Tianjin University of Traditional Chinese Medicine, No. 10 Poyang Lake Road, Tuanbo New Town, Jinghai District, Tianjin, 301617, People’s Republic of ChinaTel +86-22-5959-6290Fax +86-22-2748-5189Email xuzhifangmsn@hotmail.comBo ChenSchool of Acupuncture & Moxibustion and Tuina, Tianjin University of Traditional Chinese Medicine, No. 10 Poyang Lake Road, Tuanbo New Town, Jinghai District, Tianjin, 301617, People’s Republic of ChinaTel +86-22-5959-6290Fax +86-22-2748-5189Email tjutcmchenbo@163.comAbstract: Chronic low back pain (CLBP), lasting > 3 months, is the end result of multiple pathogenic factors. Unfortunately, little is known about CLBP pathogenesis, which limits its advancements in clinical therapy and disease management. This paper summarizes the known pathological axes of CLBP, involving both peripheral and central systems. In particular, this paper details injurious nerve stimulation, inflammation-induced peripheral pathway, and central sensitization. Lumbar components, such as intervertebral disc (IVD), facet joints, muscles, fascia, ligaments, and joint capsules, contain pain receptors called nociceptors. Degeneration of the aforementioned lumbar components activates inflammatory pathways, which can directly damage nerves, lower noc

We propose NormalGAN, a fast adversarial learning-based method to reconstruct the complete and detailed 3D human from a single RGB-D image. Given a single front-view RGB-D image, NormalGAN performs two steps: front-view RGB-D rectification and back-view RGBD inference. The final model was then generated by simply combining the front-view and back-view RGB-D information. However, inferring backview RGB-D image with high-quality geometric details and plausible texture is not trivial. Our key observation is: Normal maps generally encode much more information of 3D surface details than RGB and depth images. Therefore, learning geometric details from normal maps is superior than other representations. In NormalGAN, an adversarial learning framework conditioned by normal maps is introduced, which is used to not only improve the front-view depth denoising performance, but also infer the back-view depth image with surprisingly geometric details. Moreover, for texture recovery, we remove shading information from the front-view RGB image based on the refined normal map, which further improves the quality of the back-view color inference. Results and experiments on both testing data set and real captured data demonstrate the superior performance of our approach. Given a consumer RGB-D sensor, NormalGAN can generate the complete and detailed 3D human reconstruction results in 20 fps, which further enables convenient interactive experiences in telepresence, AR/VR and gaming scenarios., Comment: 10 pages, 11 figures, ECCV 2020

Positive Unlabeled (PU) learning is widely used in many applications, where a binary classifier is trained on the datasets consisting of only positive and unlabeled samples. In this paper, we improve PU learning over state-of-the-art from two aspects. Firstly, existing model evaluation methods for PU learning requires ground truth of unlabeled samples, which is unlikely to be obtained in practice. In order to release this restriction, we propose an asymptotic unbiased practical AUL (area under the lift) estimation method, which makes use of raw PU data without prior knowledge of unlabeled samples. Secondly, we propose ProbTagging, a new training method for extremely imbalanced data sets, where the number of unlabeled samples is hundreds or thousands of times that of positive samples. ProbTagging introduces probability into the aggregation method. Specifically, each unlabeled sample is tagged positive or negative with the probability calculated based on the similarity to its positive neighbors. Based on this, multiple data sets are generated to train different models, which are then combined into an ensemble model. Compared to state-of-the-art work, the experimental results show that ProbTagging can increase the AUC by up to 10%, based on three industrial and two artificial PU data sets., Comment: 10 pages, 4 figures

This article analyzes the effect of liquidity risk on the performance of equity hedge fund portfolios. Similarly to Avramov, Kosowski, Naik, and Teo (2007), (2011), we observe that, before accounting for the effect of liquidity risk, hedge fund portfolios that incorporate predictability in managerial skills generate superior performance. This outperformance disappears or weakens substantially for most emerging markets, event-driven, and long/short hedge fund portfolios once we account for liquidity risk. Moreover, we show that the equity market-neutral and long/short hedge fund portfolios' "alphas” also entail rents for their service as liquidity providers. These results hold under various robustness tests

This paper has been retracted. Wang J, Liu X, Yan C, et al. Onco Targets Ther. 2017;10:4251–4260. We, the Editors and Publisher of OncoTargets and Therapy, have retracted the following article. Following publication of the article, concerns were raised about the duplication of images from Figure 5 with images from other unrelated articles. Specifically, Images for Figure 5E, Ki-67, have been duplicated with images for Figure 3E from Zhang X, Lian T, Fan W, et al. Long-Noncoding RNA CASC9 Promotes Progression of Non-Small Cell Lung Cancer by Promoting the Expression of CDC6 Through Binding to HuR. Cancer Manag Res. 2020;12:9033–9043. https://doi.org/10.2147/CMAR.S268375. The authors did not respond to our queries and were unable to provide an explanation for the duplicated images or provide data for the study. As verifying the validity of published work is core to the integrity of the scholarly record, we are therefore retracting the article and the authors were notified of this. We have been informed in our decision-making by our editorial policies and COPE guidelines. The retracted article will remain online to maintain the scholarly record, but it will be digitally watermarked on each page as “Retracted”.

This article analyzes the effect of liquidity risk on the performance of equity hedge fund portfolios. Similarly to Avramov, Kosowski, Naik, and Teo (2007), (2011), we observe that, before accounting for the effect of liquidity risk, hedge fund portfolios that incorporate predictability in managerial skills generate superior performance. This outperformance disappears or weakens substantially for most emerging markets, event-driven, and long/short hedge fund portfolios once we account for liquidity risk. Moreover, we show that the equity market-neutral and long/short hedge fund portfolios' "alphas” also entail rents for their service as liquidity providers. These results hold under various robustness tests

Housing affordability has long been on the agenda of China’s policy makers. Imperfections in the housing system and growing disparities in income and wealth have been central to the problem of affordable housing. Housing reforms in China led to the creation of a market-oriented housing system, but also increased economic and social stratification. This article considers the effect of privatization of public housing on the problem of housing affordability in Beijing. We draw on a household survey to assess affordability for households according to occupants’ family structures, educational attainments and employment status, using a ‘residual income’ approach. The implications for policies designed to improve housing affordability are discussed., QC 20240201

Over the past decade, China's major cities have experienced significant real estate price increase which has been fueled by the sustained growth of the economic fundamentals. It is well believed that deeper integration with the world market is one of the major reasons for such high speed macroeconomic growth. In this paper, we examined the linkage between urban economic openness, the ratio of trade volume as a percentage of GDP, and urban real estate prices basing on the quality of life theory as well as Balassa-Samuelson (B-S) effects. Using panel data of 35 large Chinese cities from the year 1998 to 2006, we empirically find that for every 1% increase in urban economic openness, urban real estate prices will increase significantly by 0.282%, after controlling for other traditional demand-supply factors. Urban economic openness alone accounted for about 15.90% appreciations of Chinese real estate prices during the sample period, which is not neglectable. This result implies that urban real estate price fluctuations are potentially affected by international economic factors. The economic distance to the international market could be deemed as productive and consumption amenity for a city as well. This study enriches the perception of quality of life factors, and furthermore provides a new perspective for understanding the real estate price dynamics in the globalized economic development today., QC 20240130

QC 20240130

This study measures the heterogeneous effects of monetary policy on regional house prices in Sweden 1991-2002. We use a multivariate persistent shock metric to examine the impact of short-term interest rate on the property market. We also segregate the influence of interest rate shocks and influence of local shocks to capture their respective effects on regional price boom. We find significant regional effect of monetary policy on housing markets. Interest rate effects dominate the influence of local price innovations in the core economic regions in Sweden. We further discuss the monetary transmission channels and highlight political implications.

Paper presented in CERC - CCER Conference China in the World Economy in Stockholm School of Economics, November 30-December 2, 2007.

This article analyzes the effect of liquidity risk on the performance of equity hedge fund portfolios. Similarly to Avramov, Kosowski, Naik, and Teo (2007), (2011), we observe that, before accounting for the effect of liquidity risk, hedge fund portfolios that incorporate predictability in managerial skills generate superior performance. This outperformance disappears or weakens substantially for most emerging markets, event-driven, and long/short hedge fund portfolios once we account for liquidity risk. Moreover, we show that the equity market-neutral and long/short hedge fund portfolios' "alphas” also entail rents for their service as liquidity providers. These results hold under various robustness tests