Are changes in semantic and structural information sufficient for oculomotor capture?

The effect of temporal discontinuity on visual search was assessed by presenting a display in which one item had an abrupt onset, while other items were introduced by gradually removing line segments that camouflaged them. We hypothesized that an abrupt onset in a visual display would capture visual attention, giving this item a processing advantage over items lacking an abrupt leading edge. This prediction was confirmed in Experiment 1. We designed a second experiment to ensure that this finding was due to attentional factors rather than to sensory or perceptual ones. Experiment 3 replicated Experiment 1 and demonstrated that the procedure used to avoid abrupt onset--camouflage removal--did not require a gradual waveform. Implications of these findings for theories of attention are discussed.

This article investigates the nature of stimulus-driven attentional capture. Previous studies (e.g., J. Jonides & S. Yantis, 1988) have suggested that only a single visual feature--abrupt visual onset--can elicit stimulus-driven attentional capture. Two experiments are reported that challenge the special status of abrupt onsets in attentional capture. In particular, this evidence suggests that abrupt onsets appear to attenuate display-size effects only in certain experimental contexts that inadvertently increase the ease with which they are perceived. Consequently, the special status attributed to abrupt onsets is argued to derive from their higher visual quality.

With the rapid increase of users, online meeting platforms have accumulated massive meeting transcripts. However, it is still a challenge for users to quickly master the chief information and manage the meetings, despite there are already some useful text summarization models. In this paper, a Knowledge Graph-based Meeting Summarization Framework is proposed to tackle this challenge. First, a two-layers meeting domain Knowledge Graph is developed to integrate more information of meetings. Based on which, an encoder-decoder architecture is utilized to summarize meetings. For encoding meetings, a structural-level and semantic-level embedding strategy is considered, concretely, the Knowledge Graph is embedded to obtain the structural information, an interaction intention recognition model and a two-level transformer mechanism are devised to get the semantic information. Finally, the structural information and semantic information are combined and fed into the decoding network to generate meeting summaries. Extensive experiments on the Chinese meeting dataset show that our summarization framework outperforms other state-of-the-art models.

Young and older adults searched for a target character in a 3-item display. On each trial, both a symbolic cue (arrow at fixation) and a spatial cue (abrupt onset of one item) could indicate the target's position. Participants were told to use the central arrow cue on all trials because it had 75% validity. The onset cue also had 75% validity for half the participants and 25% validity for the other half. Both age groups showed about the same cost and benefit effects for the central arrow cues, but the abrupt onsets had much larger cuing effects for older adults. Young adults were able to suppress at least partially an automatic attentional response to an abrupt onset item when the arrow cue preceded the onset and had a higher validity than the onset cue. Older adults appeared to be less able to inhibit their responses to abrupt onsets and to disengage their attention from invalid onset cues than were the young adults.

A formal executable semantics of a programming language has the necessary information to develop program debugging and reasoning techniques. In this paper we choose such a particular technique called program slicing and we introduce a generic algorithm which extracts a set of side-effects inducing constructs, directly from the formal executable semantics of a programming language. These constructs are further used to infer program slices, for given programs and specified slicing criteria. Our proposed approach improves on the parametrization of the language tools development because changes in the formal semantics are automatically carried out in the slicing procedure. We use the rewriting logic and the Maude system to implement a prototype and to test our technique.

Semantic hashing is a crucial component of content based search and retrieval systems. To achieve an effective semantic hashing for images, it is essential to map them to hash space in a way that preserves the semantic information. Most state-of-the-art deep semantic hashing approaches do not fully take into account the structural information and the inherent hierarchy in the dataset. Also, the distribution of hash codes is primarily driven by semantic information that comes from the supervision labels. We propose a semantic hashing framework which utilizes the hyperbolic metric learning to learn the structural and hierarchical information. This information is leveraged in the form of proxy labels for training the hashing network with the proposed novel Structure-Semantic Disagreement (SSD) loss. SSD enforces the model to learn to hash with semantic as well as structural information, leading to more robust and uniformly distributed hash codes. Tests on multiple public domain datasets establish the effectiveness of the proposed approach. Moreover, the developed SSD loss can also be applied to other classification models to improve the representation by enforcing the model to use the structure information more effectively.

Clustering Web services can improve the quality and efficiency of service discovery and management within a service repository. Nowadays, Web services frequently interact (e.g., composition relation and tag sharing relation) with each other to form a complex and heterogeneous service relationship network. The rich network relations inherently reflect either positive or negative clustering association between Web services, which can be a strong supplement to service semantics for characterizing functional affinities between Web services. In this paper, we propose to cluster Web services by utilizing both description documents and the structural information from the service relationship network. We first learn the content semantic information from service description documents based on the widely used Doc2vec model, and meanwhile, learn the structural semantic information from the service relationship network based on a network representation learning algorithm. Then, we propose to pretrain the content and structural semantic information to obtain the most relevant and unified features through training a service classification model with partially labeled data. Finally, a spectral clustering algorithm is utilized for Web services clustering based on the above unified features with preserved content and structural semantics. Therefore, the proposed services clustering approach takes advantage of both service content semantic and service network structure semantic based similarity between services. Extensive experiments are conducted on a real-world dataset from ProgrammableWeb, composed of 12919 Web API services. Experimental results demonstrate that our approach yields an improvement of 4.78% in precision and 5.4% in recall over the state-of-the-art method.

The current eye-tracking study examined the influence of reward on oculomotor performance, and the extent to which learned stimulus-reward associations interacted with voluntary oculomotor control with a modified paradigm based on the classical antisaccade task. Participants were shown two equally salient stimuli simultaneously: a gray and a colored circle, and they were instructed to make a fast saccade to one of them. During the first phase of the experiment, participants made a fast saccade toward the colored stimulus, and their performance determined a (cash) bonus. During the second, participants made a saccade toward the gray stimulus, with no rewards available. On each trial, one of three colors was presented, each associated with high, low or no reward during the first phase. Results from the first phase showed improved accuracy and shorter saccade latencies on high-reward trials, while those from the second replicated well-known effects typical of the antisaccade task, namely, decreased accuracy and increased latency during phase II, even despite the absence of abrupt asymmetric onsets. Crucially, performance differences between phases revealed longer latencies and less accurate saccades during the second phase for high-reward trials, compared with the low- and no-reward trials. Further analyses indicated that oculomotor capture by reward signals is mainly found for saccades with short latencies, while this automatic capture can be overridden through voluntary control with longer ones. These results highlight the natural flexibility and adaptability of the attentional system, and the role of reward in modulating this plasticity.NEW & NOTEWORTHY Typically, in the antisaccade task, participants need to suppress an automatic orienting reflex toward a suddenly appearing peripheral stimulus. Here, we introduce an alternative antisaccade task without such abrupt onsets. We replicate well-known antisaccade effects (more errors and longer latencies), demonstrating the role of reward in developing selective oculomotor biases. Results highlight how reward and selection history facilitate developing automatic biases from goal-driven behavior, and they suggest that this process responds to individual differences in impulsivity.

Статья посвящена изучению употребления пословиц в огузском эпосе и в азербайджанской письменной литературе. Цель настоящей статьи – выявление функционально-семантических особенностей пословиц. Ставятся следующие задачи: проанализировать материалы азербайджанской художественной литературы «Книга моего Деда Коркута» и тексты «Огузнаме», в т. ч. творчества М. П. Вагифа, С. Вургуна, Б. Вагабзаде и др., выявить пословичные единицы и дать их функционально-семантическое описание по диахроническому направлению.Новизна работы заключается в том, что впервые рассматриваются функционально-семантические особенности пословиц в комплексном подходе в эпической и литературной традиции.Материалом для исследования являются огузский эпос «Книга моего Деда Коркута» и цикл эпических текстов по общему называнию «Огузнаме», материалы азербайджанской художественной литературы.При анализе применены описательный, историко-сравнительный и структурно-семантический методы. Проведен историко-сравнительный и функционально-семантический анализ пословиц, выявленных в фольклорно-эпической и литературной традициях.Анализ подтверждает, что пословицы носят в себе народные традиции и стереотипные поведенческие формулы народной мудрости. Они самостоятельно существуют в фольклорной традиции народа и как духовные ресурсы обогащают как эпическую традицию, так и художественную литературу. Пословицы наблюдаются в диахроническом плане в двух формах. В эпосе они сохраняются в оригинале или в малых изменениях. Поскольку нам известны богатые образцы пословичных изречений, зафиксированных в разные времена, эти списки пословиц нам позволят сравнить их и определить изменения в структурно-семантическом освещении. Исходя из этого диахронического анализа можно определить семантический инвариант пословиц и его разные релевантности в среде художественного текста. Сравнительный анализ пословиц такого рода также помогает для систематизации и описания изменений в функционально-семантической структуре.Перспективы изучения пословиц в фольклорной и литературной традиции видится в том, что это позволит проследить функционально-семантические изменения в развитии художественного облика изречений в двух традициях одного народа.

We proposed an unsupervised keyphrase extraction model that incorporates the structural information and the semantic information of a document. The structural information refers to the directed graph that is composed of keyphrase candidates and topics. The weight between two candidates is computed by their relative distance in the document and the positions of the corresponding sentences. Graph ranking algorithm is then applied to get the structural scores of the candidates. Then, the semantic score is obtained by the similarity between candidate and all sentences. The final score of a candidate is the sum of the structural score and the semantic score. The top N candidates with the highest scores are selected as the recommended keyphrases. The comparison experiments on three widely used datasets show that our model achieves the best results in the long documents and a competitive result in the short document. It indicates that our model is effective and is superior to the state-of-the-art unsupervised models.

Multi-hop question answering (MHQA) requires integrating knowledge scattered across multiple passages to derive the correct answer. Traditional retrieval-augmented generation (RAG) methods primarily focus on coarse-grained textual semantic similarity and ignore structural associations among dispersed knowledge, which limits their effectiveness in MHQA tasks. GraphRAG methods address this by leveraging knowledge graphs (KGs) to capture structural associations, but they tend to overly rely on structural information and fine-grained word- or phrase-level retrieval, resulting in an underutilization of textual semantics. In this paper, we propose a novel RAG approach called HGRAG for MHQA that achieves cross-granularity integration of structural and semantic information via hypergraphs. Structurally, we construct an entity hypergraph where fine-grained entities serve as nodes and coarse-grained passages as hyperedges, and establish knowledge association through shared entities. Semantically, we design a hypergraph retrieval method that integrates fine-grained entity similarity and coarse-grained passage similarity via hypergraph diffusion. Finally, we employ a retrieval enhancement module, which further refines the retrieved results both semantically and structurally, to obtain the most relevant passages as context for answer generation with the LLM. Experimental results on benchmark datasets demonstrate that our approach outperforms state-of-the-art methods in QA performance, and achieves a 6× speedup in retrieval efficiency.

In recent years, deep learning-based image extrapolation has achieved remarkable improvements. Image extrapolation utilizes the structural and semantic information from the known area of an image to extrapolate the unknown area. In addition, these extrapolative parts not only maintain the consistency of spatial information and structural information with the known area, but also achieve a clear, beautiful, natural and harmonious visual effect. In view of the shortcomings of traditional image extrapolation methods, this paper proposes an image extrapolation method which is based on perceptual loss and style loss. In the paper, we use the perceptual loss and style loss to restrain the generation of the texture and style of images, which improves the distorted and fuzzy structure generated by traditional methods. The perceptual loss and style loss capture the semantic information and the overall style of the known area respectively, which is helpful for the network to grasp the texture and style of images. The experiments on the Places2 and Paris StreetView dataset show that our approach could produce better results.

The majority of man-made objects are designed to serve a certain function, and this is often reflected by the geometry of the objects, or the way that they are used or organized in an environment. In recent years, many efforts in shape analysis have developed methods that extract high-level structural and semantic information from geometric shapes and scenes, especially involving man-made objects. One can argue that the ultimate goal of some of these works is to understand the functionality of the objects. Moreover, there have also been works that explicitly model and incorporate functionality into the processing of shapes and scenes. Thus, functionality has been receiving increasingly more attention in shape analysis and geometric modeling, either directly or indirectly, since functionality considerations can aid in applications such as semantic classification, shape editing and synthesis, as well as product design, development, and fabrication.In this course, we discuss recent developments that incorporate functionality aspects into the analysis of 3D shapes and scenes, to provide a summary of the state-of-the-art in this area, including a discussion of key ideas and literature works. More specifically, we first discuss approaches that are precursors in this front, such as structure-aware and data-driven methods that learn relationships between shape parts or objects in scenes. Next, we cover works that more explicitly model the functionality of shapes and scenes, such as agent- and interaction-based methods. The course is structured in the form of talks given by four different speakers, aided by electronic slides that include notes for subsequent consultation.

Semantic change detection (SCD) represents a challenging task in the interpretation of remote sensing images (RSIs), with the goal of identifying change regions and extracting semantic information from bitemporal RSIs simultaneously. The recent integration of deep neural networks leveraging multitask learning has shown promise in enhancing SCD performance. However, there is still a challenge in improving SCD performance, specifically in designing a fine-grained network structure that can handle the two subtasks of change region localization and semantic information recognition in parallel. In this context, a novel multitask Siamese network, termed EGMS-Net, is proposed to boost the performance of SCD, which consists of three core components. First, a coarse-to-fine multitask Siamese network is constructed to obtain semantic information and change information at multiple levels. Second, an adaptive change information enhancement method based on spatial-spectral collaborative attention mechanism is proposed, which can assist the accurate localization of change regions without significantly increasing the model parameters. Third, a change information guidance module is developed to strengthen the interaction between multitask branches and reduce the difficulty of network training. Experiments on three benchmark datasets demonstrate that the proposed EGMS-Net outperforms existing state-of-the-art methods in the SCD community.

The corrugated layer structure bismuth has been successfully tailored into negative thermal expansion along c axis by size effect. Pair distribution function and extended X-ray absorption fine structure are combined to reveal the local structural distortion for nanosized bismuth. The comprehensive method to identify the local structure of nanomaterials can benefit the regulating and controlling of thermal expansion in nanodivices.