Explainable History Distillation by Marked Temporal Point Process

Explainability of machine learning models is mandatory when researchers introduce these commonly believed black boxes to real-world tasks, especially high-stakes ones. In this paper, we build a machine learning system to automatically generate explanations of happened events from history by \gls{ca} based on the \acrfull{tpp}. Specifically, we propose a new task called \acrfull{ehd}. This task requires a model to distill as few events as possible from observed history. The target is that the event distribution conditioned on left events predicts the observed future noticeably worse. We then regard distilled events as the explanation for the future. To efficiently solve \acrshort{ehd}, we rewrite the task into a \gls{01ip} and directly estimate the solution to the program by a model called \acrfull{model}. This work fills the gap between our task and existing works, which only spot the difference between factual and counterfactual worlds after applying a predefined modification to the environment. Experiment results on Retweet and StackOverflow datasets prove that \acrshort{model} significantly outperforms other \acrshort{ehd} baselines and can reveal the rationale underpinning real-world processes

Online auction-based relay selection for cooperative communication in CR networks

Cognitive radio and cooperative communication are two new network technologies. So, the combination of these two new technologies is a novel solution to solve the problem of spectrum scarcity. Two main challenges exist in the integration of cognitive radio and cooperative communication. First, there is a lack of incentives for the participating wireless devices to serve as relay nodes. Second, there is not an effective relay selection policy. In this paper, we propose an online auction-based relay selection scheme for cooperative communication in cognitive radio (CR) networks. Specifically, we design an auction scheme through adopting stopping theory. The proposed scheme ensures that the primary user (PU) can effectively select a CR relay to transmit its packets in a given time bound. In addition, we have analytically proven the truthfulness and the individual rationality of our online auction scheme. Extensive simulations demonstrate that the proposed relay selection scheme can always successfully and efficiently select a proper relay for a PU and can achieve a higher cooperative communication throughput compared with the conventional schemes

Trustworthy Recommender Systems

Recommender systems (RSs) aim to help users to effectively retrieve items of their interests from a large catalogue. For a quite long period of time, researchers and practitioners have been focusing on developing accurate RSs. Recent years have witnessed an increasing number of threats to RSs, coming from attacks, system and user generated noise, system bias. As a result, it has become clear that a strict focus on RS accuracy is limited and the research must consider other important factors, e.g., trustworthiness. For end users, a trustworthy RS (TRS) should not only be accurate, but also transparent, unbiased and fair as well as robust to noise or attacks. These observations actually led to a paradigm shift of the research on RSs: from accuracy-oriented RSs to TRSs. However, researchers lack a systematic overview and discussion of the literature in this novel and fast developing field of TRSs. To this end, in this paper, we provide an overview of TRSs, including a discussion of the motivation and basic concepts of TRSs, a presentation of the challenges in building TRSs, and a perspective on the future directions in this area. We also provide a novel conceptual framework to support the construction of TRSs

Enabling smartphone-based HD video chats by cooperative transmissions in CRNs

Smartphones have been equipped with the cameras that can shoot HD videos, and the video chat apps such as Skype are becoming popular. We can, therefore, intuitively predict the trend that users are expecting to enjoy HD video chats via utilizing their smartphones. Most of the current Internet services, however, cannot support the live HD video transmissions because of their low uplink rate. In order to overcome this limit, we propose to offload the uplink transmissions to cooperative users via cognitive radio networks. Specifically, we first divide the video stream into several substreams according to the H.264/SVC standard and the cooperative users’ uplink rates. Then, the cooperative users are selected by employing our proposed optimal multiple stopping method. Finally, the substreams are assigned to the selected cooperative users by a 0-1 Knapsack-based allocation algorithm. The simulation results demonstrate that our proposed scheme can successfully support 720P HD video chats

Targeting Cell Division Cycle 25 Homolog B To Regulate Influenza Virus Replication

Influenza virus is a worldwide global health concern causing seasonal morbidity mortality and economic burden. Chemotherapeutics is available; however, rapid emergence of drug-resistant influenza virus strains has reduced its efficacy. Thus, there is a need to discover novel antiviral agents. in this study, RNA interference (RNAi) was used to screen host genes required for influenza virus replication. One pro-influenza virus host gene identified was dual-specificity phosphatase cell division cycle 25 B (CDC25B). RNAi screening of CDC25B resulted in reduced influenza A virus replication, and a CDC25B small-molecule inhibitor (NSC95397) inhibited influenza A virus replication in a dose-dependent fashion. Viral RNA synthesis was reduced by NSC95397 in favor of increased beta interferon (IFN-beta) expression, and NSC95397 was found to interfere with nuclear localization and chromatin association of NS1, an influenza virus protein. As NS1 has been shown to be chromatin associated and to suppress host transcription, it is likely that CDC25B supports NS1 nuclear function to hijack host transcription machinery in favor of viral RNA synthesis, a process that is blocked by NSC95397. Importantly, NSC95397 treatment protects mice against lethal influenza virus challenge. the findings establish CDC25B as a pro-influenza A virus host factor that may be targeted as a novel influenza A therapeutic strategy.National Institutes of Health, National Institute of Allergy and Infectious DiseasesGeorgia Research AllianceUniv Georgia, Coll Vet Med, Dept Infect Dis, Athens, GA 30602 USAUniversidade Federal de São Paulo, UNIFESP, Dept Biol Sci, São Paulo, BrazilUniversidade Federal de São Paulo, UNIFESP, Dept Biol Sci, São Paulo, BrazilNational Institutes of Health, National Institute of Allergy and Infectious Diseases: HHSN266200700006CWeb of Scienc

A Self-Correcting Sequential Recommender

Sequential recommendations aim to capture users' preferences from their historical interactions so as to predict the next item that they will interact with. Sequential recommendation methods usually assume that all items in a user's historical interactions reflect her/his preferences and transition patterns between items. However, real-world interaction data is imperfect in that (i) users might erroneously click on items, i.e., so-called misclicks on irrelevant items, and (ii) users might miss items, i.e., unexposed relevant items due to inaccurate recommendations. To tackle the two issues listed above, we propose STEAM, a Self-correcTing sEquentiAl recoMmender. STEAM first corrects an input item sequence by adjusting the misclicked and/or missed items. It then uses the corrected item sequence to train a recommender and make the next item prediction.We design an item-wise corrector that can adaptively select one type of operation for each item in the sequence. The operation types are 'keep', 'delete' and 'insert.' In order to train the item-wise corrector without requiring additional labeling, we design two self-supervised learning mechanisms: (i) deletion correction (i.e., deleting randomly inserted items), and (ii) insertion correction (i.e., predicting randomly deleted items). We integrate the corrector with the recommender by sharing the encoder and by training them jointly. We conduct extensive experiments on three real-world datasets and the experimental results demonstrate that STEAM outperforms state-of-the-art sequential recommendation baselines. Our in-depth analyses confirm that STEAM benefits from learning to correct the raw item sequences

Hydrothermal Synthesis, Microstructure and Photoluminescence of Eu3+-Doped Mixed Rare Earth Nano-Orthophosphates

Eu3+-doped mixed rare earth orthophosphates (rare earth = La, Y, Gd) have been prepared by hydrothermal technology, whose crystal phase and microstructure both vary with the molar ratio of the mixed rare earth ions. For LaxY1–xPO4: Eu3+, the ion radius distinction between the La3+ and Y3+ is so large that only La0.9Y0.1PO4: Eu3+ shows the pure monoclinic phase. For LaxGd1–xPO4: Eu3+ system, with the increase in the La content, the crystal phase structure of the product changes from the hexagonal phase to the monoclinic phase and the microstructure of them changes from the nanorods to nanowires. Similarly, YxGd1–xPO4: Eu3+, Y0.1Gd0.9PO4: Eu3+ and Y0.5Gd0.5PO4: Eu3+ samples present the pure hexagonal phase and nanorods microstructure, while Y0.9Gd0.1PO4: Eu3+ exhibits the tetragonal phase and nanocubic micromorphology. The photoluminescence behaviors of Eu3+ in these hosts are strongly related to the nature of the host (composition, crystal phase and microstructure)

A Glimpse of Streptococcal Toxic Shock Syndrome from Comparative Genomics of S. suis 2 Chinese Isolates

BACKGROUND: Streptococcus suis serotype 2 (SS2) is an important zoonotic pathogen, causing more than 200 cases of severe human infection worldwide, with the hallmarks of meningitis, septicemia, arthritis, etc. Very recently, SS2 has been recognized as an etiological agent for streptococcal toxic shock syndrome (STSS), which was originally associated with Streptococcus pyogenes (GAS) in Streptococci. However, the molecular mechanisms underlying STSS are poorly understood. METHODS AND FINDINGS: To elucidate the genetic determinants of STSS caused by SS2, whole genome sequencing of 3 different Chinese SS2 strains was undertaken. Comparative genomics accompanied by several lines of experiments, including experimental animal infection, PCR assay, and expression analysis, were utilized to further dissect a candidate pathogenicity island (PAI). Here we show, for the first time, a novel molecular insight into Chinese isolates of highly invasive SS2, which caused two large-scale human STSS outbreaks in China. A candidate PAI of ∼89 kb in length, which is designated 89K and specific for Chinese SS2 virulent isolates, was investigated at the genomic level. It shares the universal properties of PAIs such as distinct GC content, consistent with its pivotal role in STSS and high virulence. CONCLUSIONS: To our knowledge, this is the first PAI candidate from S. suis worldwide. Our finding thus sheds light on STSS triggered by SS2 at the genomic level, facilitates further understanding of its pathogenesis and points to directions of development on some effective strategies to combat highly pathogenic SS2 infections