InfoNet: Neural Estimation of Mutual Information without Test-Time Optimization

Estimating mutual correlations between random variables or data streams is essential for intelligent behavior and decision-making. As a fundamental quantity for measuring statistical relationships, mutual information has been extensively studied and utilized for its generality and equitability. However, existing methods often lack the efficiency needed for real-time applications, such as test-time optimization of a neural network, or the differentiability required for end-to-end learning, like histograms. We introduce a neural network called InfoNet, which directly outputs mutual information estimations of data streams by leveraging the attention mechanism and the computational efficiency of deep learning infrastructures. By maximizing a dual formulation of mutual information through large-scale simulated training, our approach circumvents time-consuming test-time optimization and offers generalization ability. We evaluate the effectiveness and generalization of our proposed mutual information estimation scheme on various families of distributions and applications. Our results demonstrate that InfoNet and its training process provide a graceful efficiency-accuracy trade-off and order-preserving properties. We will make the code and models available as a comprehensive toolbox to facilitate studies in different fields requiring real-time mutual information estimation

TeachCLIP: Multi-Grained Teaching for Efficient Text-to-Video Retrieval

For text-to-video retrieval (T2VR), which aims to retrieve unlabeled videos by ad-hoc textual queries, CLIP-based methods are dominating. Compared to CLIP4Clip which is efficient and compact, the state-of-the-art models tend to compute video-text similarity by fine-grained cross-modal feature interaction and matching, putting their scalability for large-scale T2VR into doubt. For efficient T2VR, we propose TeachCLIP with multi-grained teaching to let a CLIP4Clip based student network learn from more advanced yet computationally heavy models such as X-CLIP, TS2-Net and X-Pool . To improve the student's learning capability, we add an Attentional frame-Feature Aggregation (AFA) block, which by design adds no extra storage/computation overhead at the retrieval stage. While attentive weights produced by AFA are commonly used for combining frame-level features, we propose a novel use of the weights to let them imitate frame-text relevance estimated by the teacher network. As such, AFA provides a fine-grained learning (teaching) channel for the student (teacher). Extensive experiments on multiple public datasets justify the viability of the proposed method

Renmin University of China at TRECVID 2022: Improving Video Search by Feature Fusion and Negation Understanding

We summarize our TRECVID 2022 Ad-hoc Video Search (AVS) experiments. Our solution is built with two new techniques, namely Lightweight Attentional Feature Fusion (LAFF) for combining diverse visual / textual features and Bidirectional Negation Learning (BNL) for addressing queries that contain negation cues. In particular, LAFF performs feature fusion at both early and late stages and at both text and video ends to exploit diverse (off-the-shelf) features. Compared to multi-head self attention, LAFF is much more compact yet more effective. Its attentional weights can also be used for selecting fewer features, with the retrieval performance mostly preserved. BNL trains a negation-aware video retrieval model by minimizing a bidirectionally constrained loss per triplet, where a triplet consists of a given training video, its original description and a partially negated description. For video feature extraction, we use pre-trained CLIP, BLIP, BEiT, ResNeXt-101 and irCSN. As for text features, we adopt bag-of-words, word2vec, CLIP and BLIP. Our training data consists of MSR-VTT, TGIF and VATEX that were used in our previous participation. In addition, we automatically caption the V3C1 collection for pre-training. The 2022 edition of the TRECVID benchmark has again been a fruitful participation for the RUCMM team. Our best run, with an infAP of 0.262, is ranked at the second place teamwise

Hyperchloremia Is Associated With Poorer Outcome in Critically Ill Stroke Patients

Background and Purpose: This study aims to explore the cause and predictive value of hyperchloremia in critically ill stroke patients.Materials and Methods: We conducted a retrospective study of a prospectively collected database of adult patients with first-ever acute ischemic stroke (AIS) or intracerebral hemorrhage (ICH) admitted to the neurointensive care unit (NICU) of a university-affiliated hospital, between January 2013 and December 2016. Patients were excluded if admitted beyond 72 h from onset, if they required neurocritical care for less than 72 h, and were treated with hypertonic saline within 72 h or had creatinine clearance less than 15 mL/min.Results: Of 405 eligible patients, the prevalence of hyperchloremia ([Cl−] ≥ 110 mmol/L) was 8.6% at NICU admission ([Cl−]0) and 17.0% within 72 h ([Cl−]max). Thirty-eight (9.4%) patients had new-onset hyperchloremia and 110 (27.1%) had moderate increase in chloride (Δ[Cl−] ≥ 5 mmol/L; Δ[Cl−] = [Cl−]max − [Cl−]0) in the first 72 h after admission, which were found to be determined by the sequential organ failure assessment score in multivariate logistic regression analysis. Neither total fluid input nor cumulative fluid balance had significant association with such chloride disturbance. New-onset hyperchloremia and every 5 mmol/L increment in Δ[Cl−] were both associated with increased odds of 30-day mortality and 6-month poor outcome, although no independent significance was found in multivariate models.Conclusion: Hyperchloremia tends to occur in patients more severely affected by AIS and ICH. Although no independent association was found, new-onset hyperchloremia and every 5 mmol/L increment in Δ[Cl−] were related to poorer outcome in critically ill AIS and ICH patients.Subject terms: clinical studies, intracranial hemorrhage, ischemic stroke, mortality/survival, quality and outcomes

Small-Size Target Detection in Remotely Sensed Image Using Improved Multi-Scale Features and Attention Mechanism

Object detection for remote sensing images has problems such as complex backgrounds, multi-scale and difficulty in the detection of small targets. Because of the above problems, an improved object detection algorithm for remote-sensing images is proposed. Firstly, fusing the characteristics of shallow and deep networks, the original feature pyramid structure is reconstructed, the improved adaptive feature fusion structure is introduced before the network prediction, and the location and category information of different feature maps are fused. Secondly, the attention mechanism is introduced into the network to reduce the interference caused by complex backgrounds; Finally, the up-sampling module is improved to expand the receptive field and realize up-sampling based on semantic information. Through experiments, we show that our network achieves 93.9% and 98.5% detection accuracy on Levir and RSOD dataset, respectively, with the detection speed, reaching 98.26fps. Compared with the original network, the accuracy is increasing by 4% and 1.2%, respectively. Experimental results show that the detection accuracy of our algorithm is improved while maintaining the light weights and efficiencies of the original network

Polymerases Leave Fingerprints: Analysis of the Mutational Spectrum in Escherichia coli rpoB To Assess the Role of Polymerase IV in Spontaneous Mutation

We compared the distribution of mutations in rpoB that lead to rifampin resistance in strains with differing levels of polymerase IV (Pol IV), including strains with deletions of the Pol IV-encoding dinB gene, strains with a chromosomal copy of dinB, strains with the F′128 plasmid, and strains with plasmid amplification of either the dinB operon (dinB-yafNOP) or the dinB gene alone. This analysis identifies several hot spots specific to Pol IV which are virtually absent from the normal spontaneous spectrum, indicating that Pol IV does not contribute significantly to mutations occurring during exponential growth in liquid culture

A Modified Xinanjiang Model for Quantifying Streamflow Components in a Typical Watershed in Eastern China

An accurate quantification of flow components and an understanding of water source dynamics are essential for effective water resource and quality management. However, the complexity of hydrological processes and the interference of intensive human activities pose significant challenges in precisely separating water discharge into distinct components such as surface runoff, interflow, and groundwater. The Xinanjiang (XAJ) model, a conceptual watershed hydrological model, has been developed and successfully implemented for rainfall–runoff simulations and hydrograph separations across various Chinese watersheds. While the model framework is robust, it fails to account for agricultural irrigation water withdrawals and the variations in in-stream water travel times across different hydrological regimes, introducing considerable uncertainty in simulating low-flow conditions. This study introduced modifications to the XAJ model by allowing parameter adjustments across different flow regimes and incorporating irrigation withdrawals into the runoff routing process. Utilizing a decade of hydrometeorological data (2013–2022) from the Yongan River watershed in eastern China, the modified model demonstrated improved efficiency metrics in low- and medium-flow regimes compared to the original model, with a Nash–Sutcliffe coefficient improvement from −4.43~−0.49 to 0.40~0.46, R2 from 0.21~0.36 to 0.53~0.63, and BIAS reduction from 7.60~89.08% to 2.06~12.71%. Furthermore, the modified XAJ model provided a more accurate estimation of the spatial and temporal distribution of streamflow components across sub-watersheds. The original model tended to overestimate groundwater contributions (13%) and underestimate interflow (14%), particularly in low-flow conditions. The enhanced XAJ model, thus, offers a more effective tool for identifying streamflow components, providing essential insights into hydrological processes for better management decisions

Public Health Emergency Command System of China

China has formed a national emergency management system consisting of “one contingency plan and three sets of institutions” since the outbreak of the severe acute respiratory syndrome (SARS) in 2003. This system is further improved after the establishment of the Ministry of Emergency Management in 2018 and the great achievements made in the fight against the coronavirus disease 2019 (COVID-19). This study aims to improve China’s public health emergency command system via investigating the history, current status, and challenges of the system. In this article, we summarize the emergency command systems responding to the SARS outbreak, the H1N1 2009 pandemic, and the COVID-19 pandemic using literature research, theoretical analysis, and case comparison. China’s public health emergency command system played a significant role in dealing with these public health emergencies and has been increasingly improved; however, it still faces various challenges. Therefore, we propose several policy suggestions to address these challenges, which involve the aspects of joint prevention and control mechanism, law system for public health emergencies, advisory board, and central–local relations