Semantic-Enhanced Differentiable Search Index Inspired by Learning Strategies

Recently, a new paradigm called Differentiable Search Index (DSI) has been proposed for document retrieval, wherein a sequence-to-sequence model is learned to directly map queries to relevant document identifiers. The key idea behind DSI is to fully parameterize traditional ``index-retrieve'' pipelines within a single neural model, by encoding all documents in the corpus into the model parameters. In essence, DSI needs to resolve two major questions: (1) how to assign an identifier to each document, and (2) how to learn the associations between a document and its identifier. In this work, we propose a Semantic-Enhanced DSI model (SE-DSI) motivated by Learning Strategies in the area of Cognitive Psychology. Our approach advances original DSI in two ways: (1) For the document identifier, we take inspiration from Elaboration Strategies in human learning. Specifically, we assign each document an Elaborative Description based on the query generation technique, which is more meaningful than a string of integers in the original DSI; and (2) For the associations between a document and its identifier, we take inspiration from Rehearsal Strategies in human learning. Specifically, we select fine-grained semantic features from a document as Rehearsal Contents to improve document memorization. Both the offline and online experiments show improved retrieval performance over prevailing baselines.Comment: Accepted by KDD 202

Do we all really know what a fog node is? Current trends towards an open definition

Fog computing has emerged as a promising technology that can bring cloud applications closer to the physical IoT devices at the network edge. While it is widely known what cloud computing is, how data centers can build the cloud infrastructure and how applications can make use of this infrastructure, there is no common picture on what fog computing and particularly a fog node, as its main building block, really is. One of the first attempts to define a fog node was made by Cisco, qualifying a fog computing system as a “mini-cloud” located at the edge of the network and implemented through a variety of edge devices, interconnected by a variety, mostly wireless, communication technologies. Thus, a fog node would be the infrastructure implementing the said mini-cloud. Other proposals have their own definition of what a fog node is, usually in relation to a specific edge device, a specific use case or an application. In this paper, we first survey the state of the art in technologies for fog computing nodes, paying special attention to the contributions that analyze the role edge devices play in the fog node definition. We summarize and compare the concepts, lessons learned from their implementation, and end up showing how a conceptual framework is emerging towards a unifying fog node definition. We focus on core functionalities of a fog node as well as in the accompanying opportunities and challenges towards their practical realization in the near future.Postprint (author's final draft

Stability and sensitivity characteristic analysis for the hydropower unit considering the sloping roof tailrace tunnel and coupling effect of the power grid

This paper focuses on the stability and dynamic characteristics of the coupled system of nonlinear hydraulic turbine regulating system (HTRS) and power grid (PG). By establishing a nonlinear mathematical model considering the downstream surge chamber and sloping roof tailrace tunnel, the coupling effect and influence mechanism between the hydropower station and power grid are revealed. First, with regard to the coupled system, HTRS considering downstream surge chamber and sloping roof tailrace tunnel and PG model is established. Then, dynamic performance of the coupled system is investigated based on the nonlinear mathematical model as well as Hopf bifurcation theory and validated by numerical simulation. Meanwhile, the impact mechanism of HTRS and PG is revealed by investigating dynamic characteristics. In addition, stability is studied by using eigenvalue method according to the Jacobian matrix of the coupled system. Finally, parameter sensitivity is investigated to quantify parameter effects on system performance. The experimental results indicate that bifurcation line divides the whole proportional–integral adjustment coefficient plane into two parts and the region at the bottom of bifurcation line is stability region. HTRS and PG possess a coupling effect on stable domain and dynamic properties of the coupled system. The variation of HTRS parameters is most significant for the coupled system, especially for the inertia time constant of the hydraulic turbine unit and penstock flow inertia time constant

Drivers of vegetation and soil determine natural regeneration of a single plantation at different slope positions

Promoting natural regeneration in artificial forest ecosystems is crucial for sustainable management. Understanding the fundamental mechanisms and drivers of tree regeneration is the prerequisite for promoting it effectively. This study worked with Larix principis-rupprechtii, a species considered difficult to regenerate. Twenty-four sample plots measuring 30 m × 30 m were established, with eight plots at each of the lower, middle, and upper slope positions, respectively. Field investigation and multivariate analysis were performed to uncover the regeneration traits in the plantations with abundant seedlings on the continuous slope. The results revealed that ground diameter and height of the regeneration (RGD and RH) were larger at the lower slope, with significant positive correlations to available nitrogen (contribution rate, CR: 0.858) and slope (CR: 0.652). In contrast, regeneration density (RD), representing the quantity of regeneration, was greater at the middle slope. Its significant impact factors were slope position (CR: −0.648) and herb diversity, represented by Pielou index (CR: 0.961). Stand density had a significant negative effect on regeneration, particularly at the upper slope, with CRs of −0.842 and −0.764 to RGD/RH and RD, respectively. Common contribution was found among the factors, with the largest contribution groups being the topographical and soil factors (CR: 0.358). These findings provide valuable insights into the single species regeneration progress on northern mountainous slopes and offer essential information for developing facilitation methods for the natural regeneration in artificial forests

Modality-based attention and dual-stream multiple instance convolutional neural network for predicting microvascular invasion of hepatocellular carcinoma

Background and purposeThe presence of microvascular invasion (MVI) is a crucial indicator of postoperative recurrence in patients with hepatocellular carcinoma (HCC). Detecting MVI before surgery can improve personalized surgical planning and enhance patient survival. However, existing automatic diagnosis methods for MVI have certain limitations. Some methods only analyze information from a single slice and overlook the context of the entire lesion, while others require high computational resources to process the entire tumor with a three-dimension (3D) convolutional neural network (CNN), which could be challenging to train. To address these limitations, this paper proposes a modality-based attention and dual-stream multiple instance learning(MIL) CNN.Materials and methodsIn this retrospective study, 283 patients with histologically confirmed HCC who underwent surgical resection between April 2017 and September 2019 were included. Five magnetic resonance (MR) modalities including T2-weighted, arterial phase, venous phase, delay phase and apparent diffusion coefficient images were used in image acquisition of each patient. Firstly, Each two-dimension (2D) slice of HCC magnetic resonance image (MRI) was converted into an instance embedding. Secondly, modality attention module was designed to emulates the decision-making process of doctors and helped the model to focus on the important MRI sequences. Thirdly, instance embeddings of 3D scans were aggregated into a bag embedding by a dual-stream MIL aggregator, in which the critical slices were given greater consideration. The dataset was split into a training set and a testing set in a 4:1 ratio, and model performance was evaluated using five-fold cross-validation.ResultsUsing the proposed method, the prediction of MVI achieved an accuracy of 76.43% and an AUC of 74.22%, significantly surpassing the performance of the baseline methods.ConclusionOur modality-based attention and dual-stream MIL CNN can achieve outstanding results for MVI prediction

Identifying Opportunities for Aligning Production and Consumption in the U.S. Fisheries by Considering Seasonality

Seasonality is a natural feature of wild caught fisheries that introduces variation in food supply, and which often is amplified by fisheries management systems. Seasonal timing of landings patterns and linkages to consumption patterns can have a potentially strong impact on income for coastal communities as well as import patterns. This study characterizes the relationship between seasonality in seafood production and consumption in the United States by analyzing monthly domestic fisheries landings and imports and retail sales of farmed and wild seafood from 2017 to 2019. Analyses were conducted for total seafood sales, by product form, by species group, and by region of the United States. The data reveal strong seasonal increases in consumption around December and March. Seasonal increases in consumption in Spring and Summer occurred in parallel with domestic fishing production. Domestic landings vary by region, but most regions have peak fishing seasons between May and October. Alaska has the largest commercial fishery in the United States and seasonal peaks in Alaska (July/August, February/March) strongly influence seasonality in national landings. Misalignment between domestic production and consumption in some seasons and species groups creates opportunities for imports to supplement demand and lost opportunities for domestic producers.publishedVersio