Miracle-dz/UniFAR-FTUs huggingface dataset:
Tight Complex Formation of the Fumarate Sensing DcuS-DcuR Two-Component System at the Membrane and Target Promoter Search by Free DcuR Diffusion

Complex formation of membrane-bound sensor kinases with the response regulators represents an inherent step of signaling from the membrane to the promoters on the DNA. In the C4-dicarboxylate-sensing DcuS-DcuR two-component system, complex formation is strengthened by activation (phosphorylation) in vitro and in vivo, with trapping of the response regulator DcuR at the membrane. ABSTRACT Signaling of two-component systems by phosphoryl transfer requires interaction of the sensor kinase with the response regulator. Interaction of the C4-dicarboxylate-responsive and membrane-integral sensor kinase DcuS with the response regulator DcuR was studied. In vitro, the cytoplasmic part of DcuS (PASC-Kin) was employed. Stable complexes were formed, when either DcuS or DcuR were phosphorylated (Kd 22 ± 11 and 28 ± 7 nM, respectively). The unphosphorylated proteins produced a more labile complex (Kd 1380 ± 395 nM). Bacterial two-hybrid studies confirm interaction of DcuR with DcuS (and PASC-Kin) in vivo. The absolute contents of DcuR (197-979 pmol mg−1 protein) in the bacteria exceeded those of DcuS by more than 1 order of magnitude. According to the Kd values, DcuS exists in complex, with phosphorylated but also unphosphorylated DcuR. In live cell imaging, the predominantly freely diffusing DcuR becomes markedly less mobile after phosphorylation and activation of DcuS by fumarate. Portions of the low mobility fraction accumulated at the cell poles, the preferred location of DcuS, and other portions within the cell, representing phosphorylated DcuR bound to promoters. In the model, acitvation of DcuS increases the affinity toward DcuR, leading to DcuS-P × DcuR formation and phosphorylation of DcuR. The complex is stable enough for phosphate-transfer, but labile enough to allow exchange between DcuR from the cytosol and DcuR-P of the complex. Released DcuR-P diffuses to target promoters and binds. Uncomplexed DcuR-P in the cytosol binds to nonactivated DcuS and becomes dephosphorylated. The lower affinity between DcuR and DcuS avoids blocking of DcuS and allows rapid exchange of DcuR. IMPORTANCE Complex formation of membrane-bound sensor kinases with the response regulators represents an inherent step of signaling from the membrane to the promoters on the DNA. In the C4-dicarboxylate-sensing DcuS-DcuR two-component system, complex formation is strengthened by activation (phosphorylation) in vitro and in vivo, with trapping of the response regulator DcuR at the membrane. Single-molecule tracking of DcuR in the bacterial cell demonstrates two populations of DcuR with decreased mobility in the bacteria after activation: one at the membrane, but a second in the cytosol, likely representing DNA-bound DcuR. The data suggest a model with binding of DcuR to DcuS-P for phosphorylation, and of DcuR-P to DcuS for dephosphorylation, allowing rapid adaptation of the DcuR phosphorylation state. DcuR-P is released and transferred to DNA by 3D diffusion.
OVS‐Net: An effective feature extraction network for optical coherence tomography angiography vessel segmentation

Optical coherence tomography angiography (OCTA), as a noninvasive imaging modality, has been widely used in clinical ophthalmology. However, the segmentation of retinal vessels in OCTA is under‐studied due to OCTA is a relatively new technology. In this article, an effective feature extraction network, OVS‐Net, is proposed for OCTA vessel segmentation. The OVS‐Net is divided into coarse stage and refine stage which structures are basically the same. In each stage, we utilize OctaveResBlock as the basic block to better extract the hierarchical multifrequency features of OCTA and capture the multiscale semantic features of the vessels. In order to improve the feature characterization, feature enhanced attention block is introduced into the network, which is proved to be more conducive for microvessel segmentation in our experiments. Multiscale feature blocks are introduced into the network to promote the deep integration of semantic features at different scales. Experiments on OCTA‐SS and OCTA‐500 datasets show that our proposed OVS‐Net achieves more competitive segmentation results than the existing methods, especially for microvessel segmentation.
Contacto domiciliar e profissional com cães como fatores de risco para infecção humana por larvas de Toxocara

The contact with dogs at home or place of work has been investigated as factors of risk in the occurrence of the visceral larva migrans syndrome caused by Toxocara, in man. Through the E.L. I.S. A. (enzyme-linked immunosorbent assay) technique, the presence of antibodies to Toxocara was searched in the sera of 79 women who have been raising or had raised dogs at home in the last two years and 123 men, who were municipal public employees in charge of the capture and keeping of stray dogs. The control groups were constituted by 205 sera from women who denied domiciliary contact with dogs, at least in the last two years, and 139 sera from men whose occupation did not urge them to contact with dogs. A significant more elevated frequency of antibodies to Toxocara was observed among women with domiciliary contact with dogs; nevertheless, there was not a significant difference in the positive rates in the case of men with occupational contact with dogs.
A Study of Cross-Session Cross-Device Search Within an Academic Digital Library

Information seeking in an academic digital library is complex in nature, often spanning multiple search sessions. Resuming academic search tasks requires significant cognitive effort as searchers must re-acquaint themselves with previous search session activities and previously discovered documents before resuming their search. Further, some academic searchers may find it convenient to initiate such searches on their mobile devices during short gaps in time (e.g., between classes), and resume them later in a desktop environment when they can use the extra screen space and more convenient document storage capabilities of their computers. To support such searching, we have developed an academic digital library search interface that assists searchers in managing cross-session search tasks even when moving between mobile and desktop environments. Using a controlled laboratory study we compared our approach (Dilex) to a standard academic digital library search interface. We found increased user engagement in both the initial (mobile) and resumed (desktop) search activities, and that participants spent more time on the search results pages and had an increased degree of interaction with information and personalization features during the resumed tasks. These results provide evidence that the participants were able to make effective use of the visualization features in Dilex, which enabled them to readily resume their search tasks and stay engaged in the search activities. This work represents an example of how semi-automatic search task/session management and visualization features can support cross-session search, and how designing for both mobile and desktop use can support cross-device search.
Webformer: Pre-training with Web Pages for Information Retrieval

Pre-trained language models (PLMs) have achieved great success in the area of Information Retrieval. Studies show that applying these models to ad-hoc document ranking can achieve better retrieval effectiveness. However, on the Web, most information is organized in the form of HTML web pages. In addition to the pure text content, the structure of the content organized by HTML tags is also an important part of the information delivered on a web page. Currently, such structured information is totally ignored by pre-trained models which are trained solely based on text content. In this paper, we propose to leverage large-scale web pages and their DOM (Document Object Model) tree structures to pre-train models for information retrieval. We argue that using the hierarchical structure contained in web pages, we can get richer contextual information for training better language models. To exploit this kind of information, we devise four pre-training objectives based on the structure of web pages, then pre-train a Transformer model towards these tasks jointly with traditional masked language model objective. Experimental results on two authoritative ad-hoc retrieval datasets prove that our model can significantly improve ranking performance compared to existing pre-trained models.
Masking and Generation: An Unsupervised Method for Sarcasm Detection

Existing approaches for sarcasm detection are mainly based on supervised learning, in which the promising performance largely depends on a considerable amount of labeled data or extra information. In the real world scenario, however, the abundant labeled data or extra information requires high labor cost, not to mention that sufficient annotated data is unavailable in many low-resource conditions. To alleviate this dilemma, we investigate sarcasm detection from an unsupervised perspective, in which we explore a masking and generation paradigm in the context to extract the context incongruities for learning sarcastic expression. Further, to improve the feature representations of the sentences, we use unsupervised contrastive learning to improve the sentence representation based on the standard dropout. Experimental results on six perceived sarcasm detection benchmark datasets show that our approach outperforms baselines. Simultaneously, our unsupervised method obtains comparative performance with supervised methods for the intended sarcasm dataset.
Competitive Search

The Web is a canonical example of a competitive search setting that includes document authors with ranking incentives: their goal is to promote their documents in rankings induced for queries. The incentives affect some of the corpus dynamics as the authors respond to rankings by applying strategic document manipulations. This well known reality has deep consequences that go well beyond the need to fight spam. As a case in point, researchers showed using game theoretic analysis that the probability ranking principle is not optimal in competitive retrieval settings; specifically, it leads to reduced topical diversity in the corpus. We provide a broad perspective on recent work on competitive retrieval settings, argue that this work is the tip of the iceberg, and pose a suite of novel research directions; for example, a general game theoretic framework for competitive search, methods of learning-to-rank that account for post-ranking effects, approaches to automatic document manipulation, addressing societal aspects and evaluation.
CTnoCVR: A Novelty Auxiliary Task Making the Lower-CTR-Higher-CVR Upper

In recent years, multi-task learning models based on deep learning in recommender systems have attracted increasing attention from researchers in industry and academia. Accurately estimating post-click conversion rate (CVR) is often considered as the primary task of multi-task learning in recommender systems. However, some advertisers may try to get higher click-through rates (CTR) by over-decorating their ads, which may result in excessive exposure to samples with lower CVR. For example, some only eye-catching clickbait have higher CTR, but actually, CVR is very low. As a result, the overall performance of the recommender system will be hurt. In this paper, we introduce a novelty auxiliary task called CTnoCVR, which aims to predict the probability of events with click but no-conversion, in various state-of-the-art multi-task models of recommender systems to promote samples with high CVR but low CTR. Plentiful Experiments on a large-scale dataset gathered from traffic logs of Taobao's recommender system demonstrate that the introduction of CTnoCVR task significantly improves the prediction effect of CVR under various multi-task frameworks. In addition, we conduct the online test and evaluate the effectiveness of our proposed method to make those samples with high CVR and low CTR rank higher.
Effects of Scene Detection, Scene Prediction, and Maneuver Planning Visualizations on Trust, Situation Awareness, and Cognitive Load in Highly Automated Vehicles

The successful introduction of automated vehicles (AVs) depends on the user's acceptance. To gain acceptance, the intended user must trust the technology, which itself relies on an appropriate understanding. Visualizing internal processes could aid in this. For example, the functional hierarchy of autonomous vehicles distinguishes between perception, prediction, and maneuver planning. In each of these stages, visualizations including possible uncertainties (or errors) are possible. Therefore, we report the results of an online study (N=216) comparing visualizations and their combinations on these three levels using a pre-recorded real-world video with visualizations shown on a simulated augmented reality windshield. Effects on trust, cognitive load, situation awareness, and perceived safety were measured. Situation Prediction-related visualizations were perceived as worse than the remaining levels. Based on a negative evaluation of the visualization, the abilities of the AV were also judged worse. In general, the results indicate the presence of overtrust in AVs.
Users and Contemporary SERPs: A (Re-)Investigation

TheSearch Engine Results Page (SERP) has evolved significantly over the last two decades, moving away from the simple ten blue links paradigm to considerably more complex presentations that contain results from multiple verticals and granularities of textual information. Prior works have investigated how user interactions on the SERP are influenced by the presence or absence of heterogeneous content (e.g., images, videos, or news content), the layout of the SERP (\emphlist vs. grid layout), and task complexity. In this paper, we reproduce the user studies conducted in prior works---specifically those of~\citetarguello2012task and~\citetsiu2014first ---to explore to what extent the findings from research conducted five to ten years ago still hold today as the average web user has become accustomed to SERPs with ever-increasing presentational complexity. To this end, we designed and ran a user study with four different SERP interfaces:(i) ~\empha heterogeneous grid ;(ii) ~\empha heterogeneous list ;(iii) ~\empha simple grid ; and(iv) ~\empha simple list. We collected the interactions of $41$ study participants over $12$ search tasks for our analyses. We observed that SERP types and task complexity affect user interactions with search results. We also find evidence to support most (6 out of 8) observations from~\citearguello2012task,siu2014first indicating that user interactions with different interfaces and to solve tasks of different complexity have remained mostly similar over time.
Replacing a 'Striped-Box' with the Normal Approximation

Abstract A simple procedure is presented for obtaining the sample size and acceptance number for a single sample acceptance sampling plan, given the probability of lot acceptance for lots having proportion defective equal to p 1, and the probability of lot rejection for lots having proportion defective equal to p 2. The procedure gives a practical illustration of the use of the normal approximation to the binomial distribution that is appropriate for courses on statistical quality control as well as on introductory statistics.
Digital analytics approach to understanding short video advertising in digital marketing

ABSTRACT This study relied on datasets from global B2C and C2C to investigate the relationship between short video advertising (SV), customer satisfaction, price, quality signals, and sales in digital marketing. Using the web-scraping mining technique, the results from over twenty-three thousand online shops indicate that logistics service quality overrides the relevance of location in digital marketing. SV directly impacts sales and increases the shops’ dynamic scores, including quality of service and customer satisfaction. We identified actual online data to justify why SV adoption is essential in digital marketing and recommend logistic service quality and price fairness to improve sales in e-commerce.
Predicting Parental Mediation of Personalized Advertising and Online Data Collection Practices Targeting Teenagers

ABSTRACT This study tested a model for predicting parental mediation of teenagers’ exposure to personalized advertising and the preceding online data collection. Data was collected through a survey of 354 parents of teenagers and analyzed using structural equation modeling. The model showed that parents are more inclined to engage in parental mediation when they have greater levels of concern and self-efficacy. Parental concerns were higher when parents perceived personalized advertising as inappropriate. While privacy literacy predicted more self-efficacy, perceptions of inappropriateness predicted lower self-efficacy. Disliking of personalized advertising only had a direct negative relationship with active parental mediation.
Understanding and Improving Knowledge Graph Embedding for Entity Alignment

Embedding-based entity alignment (EEA) has recently received great attention. Despite signiﬁcant performance improvement, few efforts have been paid to facilitate understanding of EEA methods. Most existing studies rest on the assumption that a small number of pre-aligned entities can serve as anchors connecting the embedding spaces of two KGs. Nevertheless, no one has investigated the rationality of such an assumption. To ﬁll the research gap, we deﬁne a typical paradigm abstracted from existing EEA methods and analyze how the embedding discrepancy between two potentially aligned entities is implicitly bounded by a predeﬁned margin in the score function. Further, we ﬁnd that such a bound cannot guarantee to be tight enough for alignment learning. We mitigate this problem by proposing a new approach, named NeoEA, to explicitly learn KG-invariant and principled entity embeddings. In this sense, an EEA model not only pursues the closeness of aligned entities based on geometric distance, but also aligns the neural ontologies of two KGs by eliminating the discrepancy in embedding distribution and underlying ontology knowledge. Our experiments demonstrate consistent and signiﬁ-cant performance improvement against the best-performing EEA methods.
On the Convergence of Local Stochastic Compositional Gradient Descent with Momentum

Federated Learning has been actively studied due to its efficiency in numerous real-world applications in the past few years. However, the federated stochastic compositional optimization problem is still underexplored, even though it has widespread applications in machine learning. In this paper, we developed a novel local stochastic compositional gradient descent with momentum method, which facilitates Federated Learning for the stochastic compositional problem. Impor-tantly, we investigated the convergence rate of our proposed method and proved that it can achieve the O (1 /ϵ 4 ) sample complexity, which is better than existing methods. Meanwhile, our communication complexity O (1 /ϵ 3 ) can match existing methods. To the best of our knowledge, this is the first work achieving such favorable sample and communication complexities. Additionally, extensive experimental results demonstrate the superior empirical performance over existing methods, confirming the efficacy of our method.
Fast-Rate PAC-Bayesian Generalization Bounds for Meta-Learning

PAC-Bayesian error bounds provide a theoretical guarantee on the generalization abilities of meta-learning from training tasks to unseen tasks. However, it is still unclear how tight PAC-Bayesian bounds we can achieve for meta-learning. In this work, we propose a general PAC-Bayesian framework to cope with single-task learning and meta-learning uniformly. With this framework, we generalize the two tightest PAC-Bayesian bounds (i.e., kl-bound and Catoni-bound) from single-task learning to standard meta-learning, resulting in fast convergence rates for PAC-Bayesian meta-learners. By minimizing the derived two bounds, we develop two meta-learning algorithms for classiﬁcation problems with deep neural networks. For regression problems, by setting Gibbs optimal posterior for each training task, we obtain the closed-form formula of the minimizer of our Catoni-bound, leading to an efﬁcient Gibbs meta-learning algorithm. Although minimizing our kl-bound can not yield a closed-form solution, we show that it can be extended for analyzing the more challenging meta-learning setting where samples from different training tasks exhibit inter-dependencies. Experiments empirically show that our proposed meta-learning algorithms achieve competitive results with respect to latest works.
On Measuring Causal Contributions via do-interventions

Causal contributions measure the strengths of different causes to a target quantity. Understanding causal contributions is important in empirical sciences and data-driven disciplines since it allows to answer practical questions such as “what are the contributions of each cause to the effect? ” In this paper, we develop a principled method for quantifying causal contributions. First, we provide desiderata in terms of axioms that causal contribution measures should satisfy and propose the do -Shapley values (inspired by do -interventions (Pearl, 2000)) as a method satisfying these properties. Next, we develop a criterion under which the do -Shapley values can be efficiently inferred from non-experimental data. Finally, we introduce do Shapley estimators exhibiting consistency and statistical robustness. Simulation results corroborate with the proposed theory.
ViT-NeT: Interpretable Vision Transformers with Neural Tree Decoder

Vision transformers (ViTs), which have demonstrated a state-of-the-art performance in image classification, can also visualize global interpretations through attention-based contributions. However, the complexity of the model makes it difficult to interpret the decision-making process, and the ambiguity of the attention maps can cause incorrect correlations between image patches. In this study, we propose a new ViT neural tree decoder (ViT-NeT). A ViT acts as a backbone, and to solve its limitations, the output contextual image patches are applied to the proposed NeT. The NeT aims to accurately classify fine-grained objects with similar inter-class correlations and different intra-class correlations. In addition, it describes the decision-making process through a tree structure and prototype and enables a visual interpretation of the results. The proposed ViT-NeT is designed to not only improve the classification performance but also provide a human-friendly interpretation, which is effective in resolving the trade-off between performance and interpretability. We compared the performance of ViT-NeT with other state-of-art methods using widely used fine-grained visual categorization benchmark datasets and experimentally proved that the proposed method is superior in terms of the classification performance and interpretability. The code and models are publicly available at https://github.com/ jumpsnack/ViT-NeT .