Single-source cross-condition fault diagnosis faces significant challenges due to drastic distribution shifts caused by fluctuating operating conditions. This issue becomes more severe when a single model must simultaneously generalize across global structural shifts and fine-grained class-wise patterns. Existing methods typically rely on a single-stream architecture, which suffers from an inherent optimization conflict between domain invariance and class discriminability, often combined with unreliable pseudolabels. To address these limitations, this study proposes a Dual-Stream Collaborative Regularization Adaptation Network (DS-CRAN). The proposed framework explicitly decouples global marginal alignment and class-conditional alignment into two parallel streams, thereby reducing gradient conflicts. Furthermore, a synergistic regularization mechanism is introduced to enhance representation quality. A mask-based consistency constraint enforces robustness against local signal disturbances, while a class-conditioned reconstruction task prevents feature collapse and preserves semantic information despite label noise. Extensive experiments on bearing and gear datasets, particularly under large speed and load variations, demonstrate that DS-CRAN consistently outperforms state-of-the-art methods. The results validate the effectiveness of the decoupling strategy and collaborative regularization in achieving resilient diagnosis.

Reposting plays an essential role in boosting visibility on online social networks (OSNs). In this paper, we study the problem of designing “reposting service” in an OSN to incentivize “transactions” between requesters (users who seek to enhance visibility) and suppliers (users who are willing to repost if certain incentives are given), and maximize the welfare increase accumulated through a given time horizon. We formulate a mathematical model for reposting which captures various factors like click-through rates (CTRs), requesters’ valuations and suppliers’ costs. We formulate the problem of maximizing the welfare increase via judiciously assigning suppliers to requesters from two aspects: (a) “user-centric” and (b) “platform-centric”. The user-centric aspect deals with situations where requesters and suppliers collaborate and share valuations and costs. To address the challenge of unknown CTRs, we propose an online learning protocol and achieve a sub-linear regret. The platform-centric aspect corresponds to the scenario where users keep their valuations or costs private. To address the challenges of unknown CTRs, valuations and costs, we design an “explore-then-commit” online protocol. We prove the truthfulness of the proposed online protocol, and we also prove that this protocol has a sub-linear regret. Lastly, we conduct extensive experiments on six public datasets to evaluate the effectiveness and scalability of the proposed protocols.

Reposting plays an essential role in visibility boosting in online social networks (OSNs). In this paper, we study the problem of designing “reposting service” in an OSN to incentivize “transactions” between requesters (users who seek to enhance visibility) and suppliers (users who are willing to repost if certain incentives are given), and maximize the welfare increase accumulated through a given time horizon. We formulate a mathematical model for reposting which captures various factors like click through rates (CTRs), requesters' valuations and suppliers' costs. We formulate the problem of maximizing the welfare increase via judiciously assigning suppliers to requesters from two aspects: (a) “user-centric” and (b) “platform-centric”. The user-centric aspect deals with the situation where requesters and suppliers would collaborate and share valuations and costs. To address the challenge of unknown CTRs, we propose an online learning protocol and achieve a sub-linear regret. The platform-centric aspect corresponds to the scenario where users keep their valuations or costs private. To address the challenges of unknown CTR, valuations and costs, we design an “explore-then-commit” online protocol which can be proved to be truthful. Lastly, we conduct extensive experiments to evaluate the efficiency and effectiveness of the proposed protocols.

In online social networks (OSNs), users may want to enhance their social visibility, as it can make their contents, i.e., opinions, videos, pictures, etc., attract attention from more users. Motivated by this, we propose a mechanism, where the OSN operator provides a "social visibility boosting service" to incentivize "transactions" between requesters (users who seek to enhance their social visibility via adding new "neighbors") and suppliers (users who are willing to be added as a new "neighbor" of any requester when certain "rewards" is provided). We design a posted pricing scheme for the OSN provider to charge the requesters who use such boosting service, and reward the suppliers who contribute to such boosting service. The OSN operator keeps a fraction of the payment from requesters and distributes the remaining part to participating suppliers "fairly" via a scheme based on the Shapley value. The objective of the OSN provider is to select the price and supplier set to maximize the revenue under the budget constraint of requesters. We first show that the revenue maximization problem is not simpler than an NP-hard problem. We then decompose it into two subroutines, prove the hardness of each sub-routine, and eventually design computationally efficient approximation algorithms to solve the revenue maximization problem. We conduct extensive experiments to evaluate our proposed algorithms.

Online social network (OSN) is an ideal venue to enhance one's visibility. This paper considers how a user (called requester) in an OSN selects a small number of available users and invites them as new friends/followers so as to maximize his "social visibility". More importantly, the requester has to do this under the anonymity setting, which means he is not allowed to know the neighborhood information of these available users in the OSN. In this paper, we first develop a mathematical model to quantify the social visibility and formulate the problem of visibility maximization with anonymity guarantee, abbreviated as "VisMAX-A". Then we design an algorithmic framework named as "AdaExp", which adaptively expands the requester's visibility in multiple rounds. In each round of the expansion, AdaExp uses a query oracle with anonymity guarantee to select only one available user. By using probabilistic data structures like the k-minimum values (KMV) sketch, we design an efficient query oracle with anonymity guarantees. We also conduct experiments on real-world social networks and validate the effectiveness of our algorithms.

Given the dramatic growth of digital content, new solutions are needed for us to be able to get a quick overview of pertinent information without being inundated by irrelevant details. While there has been ample research on automatic summarization methods, summaries may still be somewhat convoluted and hard to absorb. In this paper, we propose the novel task of structured text summarization, which we address by combining ranking techniques with open information extraction. This method collaborates with linguistics and yields an uncluttered, more easily digestible overview of key insights from a text and performs very well in certain aspect in our experiments.

A 0.9-5.8-GHz receiver RF front-end (RFE) integrating a dual-band low-noise transconductance amplifier (LNTA), a passive harmonic-rejection (HR) down-conversion mixer, and an all-digital frequency synthesizer for software-defined radios are presented. A switchable three-coil transformer acting as the interface between the LNTA and the mixer features current-gain boosting in addition to wideband operation. Automatic local oscillator phase-error detection and calibration circuitry is implemented for the mixers to achieve high HR ratio (HRR). Fabricated in 65-nm CMOS, the RFE measures the noise figure between 2.9 and 3.8 dB, the third-order input intercept point (IIP3) between -1.6 and -12.8 dBm, the third-order HRR of 81 dB, and the fifth-order HRR of 70 dB, while consuming 66-82 mA from a 1.2-V supply and occupying a chip area of 4.2 mm2.