Different Pieces Of Information Research Articles

Auxiliary Feature Fusion and Noise Suppression for HOI Detection

In recent years, one-stage HOI (Human–Object Interaction) detection methods tend to divide the original task into multiple sub-tasks by using a multi-branch network structure. However, there is no sufficient attention to information communication between these branches. The inference approach in the cascaded structure is singular, while fully parallel methods will disrupt the associations between different pieces of information. Besides, noise interference may occur during the fusion of different features and thus affect the detection performance. To address these issues, this article proposes a one-stage three-branch parallel HOI detection method, which treats HOI as three separate sub-tasks (human detection, object detection, and interaction detection) and leverages three distinct reasoning relationships to generate richer relational information. Firstly , an auxiliary feature fusion (AFF) module is introduced, which integrates features originally extracted independently to form fused features enriched with supplementary information. This approach strengthens communication between branches in the network while handling the three sub-tasks concurrently, thereby facilitating the exchange of more contextual information. Secondly , to mitigate noise interference generated during the fusion process, a fusion noise suppression (FNS) module is introduced, which effectively suppresses noise and enhances the model’s performance in interaction detection tasks. Finally , experiments are conducted on two major benchmark datasets, and experimental results show that our HOI detection method is superior to previous methods. Also, ablation studies confirm the effectiveness of all the components in our proposed method.

Scalable Multistep Imprinting of Multiplexed Optical Anti-counterfeiting Patterns with Hierarchical Structures.

Multiplexed optical techniques with multichannel patterns provide powerful strategies for high-capacity anti-counterfeiting. However, it is still a big challenge to meet the demands of achieving high encryption levels, excellent readability, and simple preparation simultaneously. Herein, we use a multistep imprinting technique, leveraging surface work-hardening to massively produce multiplexed encrypted patterns with hierarchical structures. These patterns with coupled nano- and microstructures can be instantaneously decoded into different pieces of information at different view angles under white light illumination. By incorporating perpendicular nano- and microgratings, we achieve four-channel encoded patterns, enhancing anti-counterfeiting capacity. This versatile method works on various metal/polymer materials, offering high-density information storage, direct visibility, broad material compatibility, and low-cost mass production. Our high-performance anti-counterfeiting patterns show significant potential in real-world applications.

SuiETAS: Developing and Testing ETAS-Based Earthquake Forecasting Models for Switzerland

ABSTRACT We present the development and testing of multiple epidemic-type aftershock sequence (ETAS)-based earthquake forecasting models for Switzerland, aiming to identify suitable candidate models for operational earthquake forecasting (OEF) at the Swiss Seismological Service. We consider seven model variants: four variants use parameters obtained through fitting the ETAS model to the Swiss earthquake catalog, and three use generic parameters that were fit to Californian seismicity or global seismicity from regions tectonically similar to Switzerland. The model variants use different pieces of information from the current state-of-the-art time-independent earthquake rate forecast underlying the Swiss seismic hazard model (SUIhaz2015), and one is calibrated on a larger local data set that includes smaller earthquakes by allowing a time-dependent estimate of the completeness magnitude. We test all variants using pseudoprospective short-term (7-day) forecasting experiments and retrospective long-term (30-year) consistency tests. Our results suggest that all ETAS-based models outperform the time-independent SUIhaz2015 forecast in the short term, but two of the model variants overestimate event numbers in the long term. ETAS parameters are found not to be universally transferrable across tectonic regimes, and region-specific calibration is found to add value over generic parameters. Finally, we rank all model variants based on six criteria, including the results of the pseudoprospective and retrospective tests, as well as other criteria such as model run time or consistency with the existing long-term model, using a multicriteria decision analysis approach. Based on this ranking, we propose the ETAS model calibrated on the Swiss catalog, and with the spatial background seismicity distribution of SUIhaz2015 as the ideal candidate for the first Swiss OEF system. All procedures and choices involved in the development and testing of the Swiss ETAS model follow recently established expert recommendations and can act as a reference in the establishment of time-variant earthquake forecasting models for other regions.

Integration of computer networks and artificial neural networks for an AI-based network operator

This paper proposes an integrated approach combining computer networks and artificial neural networks to construct an intelligent network operator, functioning as an AI model. State information from computer networks is transformed into embedded vectors, enabling the operator to efficiently recognize different pieces of information and accurately output appropriate operations for the computer network at each step. The operator has undergone comprehensive testing, achieving a 100% accuracy rate, thus eliminating operational risks. Additionally, a simple computer network simulator is created and encapsulated into training and testing environment components, enabling automation of the data collection, training, and testing processes. This abstract outline the core contributions of the paper while highlighting the innovative methodology employed in the development and validation of the AI-based network operator.

Resolving Separation Issues with Computational Methods: What Is the Retention Time, Exactly?

Our attention is typically focused on chromatography in the laboratory, yet ultimately, a liquid chromatograph (LC) or gas chromatograph (GC) only produces a signal—a chromatogram—from which information still must be distilled. Fortunately, software tools can generally help us quite well with our contemporary data sets. However, as separation technology improves, both the challenges and the need for methods to obtain information from the data increase. In this series of articles, we will discuss different aspects of data analysis and learn how to extract different pieces of information from our chromatograms, while also understanding the assumptions and risks used to help us interpret the value of the information. We start with simple, yet important, information bits, which we will tie together in future articles. In this way, we can continue solving separation problems beyond the limits of our chromatographs. Each article contains a reference to a web tutorial where the gained knowledge and skills can be practiced and applied to a real signal.

Multimodal Emotion Recognition via Convolutional Neural Networks: Comparison of different strategies on two multimodal datasets

The aim of this paper is to investigate emotion recognition using a multimodal approach that exploits convolutional neural networks (CNNs) with multiple input. Multimodal approaches allow different modalities to cooperate in order to achieve generally better performances because different features are extracted from different pieces of information. In this work, the facial frames, the optical flow computed from consecutive facial frames, and the Mel Spectrograms (from the word melody) are extracted from videos and combined together in different ways to understand which modality combination works better. Several experiments are run on the models by first considering one modality at a time so that good accuracy results are found on each modality. Afterward, the models are concatenated to create a final model that allows multiple inputs. For the experiments the datasets used are BAUM-1 ((Bahçeşehir University Multimodal Affective Database - 1) and RAVDESS (Ryerson Audio–Visual Database of Emotional Speech and Song), which both collect two distinguished sets of videos based on the different intensity of the expression, that is acted/strong or spontaneous/normal, providing the representations of the following emotional states that will be taken into consideration: angry, disgust, fearful, happy and sad. The performances of the proposed models are shown through accuracy results and some confusion matrices, demonstrating better accuracy than the compared proposals in the literature. The best accuracy achieved on BAUM-1 dataset is about 95%, while on RAVDESS it is about 95.5%.

A new family of aggregation functions for intervals

Aggregation operators are unvaluable tools when different pieces of information have to be taken into account with respect to the same object. They allow to obtain a unique outcome when different evaluations are available for the same element/object. In this contribution we assume that the opinions are not given in form of isolated values, but intervals. We depart from two “classical” aggregation functions and define a new operator for aggregating intervals based on the two original operators. We study under what circumstances this new function is well defined and we provide a general characterization for monotonicity. We also study the behaviour of this operator when the departing functions are the most common aggregation operators. We also provide an illustrative example demonstrating the practical application of the theoretical contribution to ensemble deep learning models.

Structural Color‐Based Smart Liquid Windows Address the Tradeoff Between High Optical Transparency and Brilliant Color

AbstractIt is challenging for traditional smart windows to possess both high optical transparency (90%, 380–780 nm) and brilliant colors since they are usually contradictory to each other. Here, novel liquid thermal‐responsive smart windows (TSWs) exhibiting high optical transparency (94%), bright iridescent forward scattering structural color, and extremely weak reflective structural colors are fabricated by constructing a refractive index (RI)‐matched non‐close‐packing ordered structure with large lattice distances and thus unique light diffraction mechanisms. The transmittance (24‐94%) and dual‐mode structural color saturation (1–55 times) can be dynamically regulated by simply altering temperatures (20–100 °C), which can be attributed to the temperature‐dependent light diffraction and scattering efficiency. A new information encryption‐decryption technique is developed by combining TSWs with outstanding shapeability, recyclability, and flowability. The information is hidden under normal conditions, while 76 different pieces of information can be decrypted in programmable ways under different stimuli, showing their potential in anti‐counterfeiting and information encryption.

Are False Memory and Creative Thinking Mediated by Common Neural Substrates? An fMRI Meta-Analysis

ABSTRACT Episodic retrieval plays a functional-adaptive role in supporting divergent creative thinking, the ability to creatively combine different pieces of information. However, the same constructive memory process that provides this benefit can also lead to memory errors. Prior behavioral work has shown that there is a positive correlation between the false recognition of lure items in the Deese-Roediger-McDermott paradigm, and divergent creative thinking as assessed on the alternate uses task. Here, we conducted a meta-analysis of functional magnetic resonance imaging studies to test for convergence in the neural substrates associated with these cognitive linkages. Our individual meta-analyses of false recognition-related activity as well as divergent thinking-related neural activity replicated prior meta-analyses. However, there was no significant statistical overlap across the neural regions associated with false recognition and divergent creative thinking. These null findings may reflect the operation of distinct generative retrieval processes engaged during divergent thinking relative to false recognition.

How Kindergarten Teachers Support Nascent Understanding of Sustainable Development Among Children – A New Label on an Old Practice?

The purpose of this study is to investigate in what ways kindergarten teachers explain their understanding and practices of education for sustainable development. The study uses the term “green sustainability” to refer to the concept of caring for nature, the environment, and the earth’s climate in ways which meet basic human needs and preserve them for future generations. The data are generated from six focus groups conducted in 2020. The sample is comprised of 23 kindergarten teachers representing six kindergartens. Analyses of the data material revealed the following theme-based categories: 1) digital, multimodal resources, 2) experiences in nature, 3) from farm to fork, 4) sorting garbage and food waste, and 5) avoiding waste. The findings indicate that existing practices are largely in accordance with longstanding traditions within the kindergartens. The study also indicates that there is a need for critical and reflective practices to meet the needs of children in developing a nascent understanding of sustainable development. Currently, 21st century skills that can be applied to sustainability challenges include critically evaluating different pieces of information, connecting knowledge, collaborating creatively, and communicating across disciplines.

Two Sides of the Same Coin: Efficient and Predictive Neural Coding.

Some visual properties are consistent across a wide range of environments, while other properties are more labile. The efficient coding hypothesis states that many of these regularities in the environment can be discarded from neural representations, thus allocating more of the brain's dynamic range to properties that are likely to vary. This paradigm is less clear about how the visual system prioritizes different pieces of information that vary across visual environments. One solution is to prioritize information that can be used to predict future events, particularly those that guide behavior. The relationship between the efficient coding and future prediction paradigms is an area of active investigation. In this review, we argue that these paradigms are complementary and often act on distinct components of the visual input. We also discuss how normative approaches to efficient coding and future prediction can be integrated.

Label-Free Quantification by Liquid Chromatography-Tandem Mass Spectrometry of the Kunitz Inhibitor of Trypsin KTI3 in Soy Products.

The greater awareness of consumers regarding the sustainability of food chains has shifted part of the consumption from animal protein sources to vegetable sources. Among these, of relevance both for human food use and for animal feed, is soy. However, its high protein content is unfortunately accompanied by the presence of antinutritional factors, including Kunitz's trypsin inhibitor (KTI). Now there are few analytical methods available for its direct quantification, as the inhibitory activity against trypsin is generically measured, which however can be given by many other molecules and undergo numerous interferences. Therefore, in this work, a direct label-free liquid chromatography-mass spectrometry (LC-MS) method for the identification and quantification of trypsin Kunitz inhibitor KTI3 in soybean and derivative products has been developed. The method is based on the identification and quantification of a marker peptide, specific for the protein of interest. Quantification is achieved with an external calibration curve in the matrix, and the limit of detection and the limit of quantification of the method are 0.75 and 2.51 μg/g, respectively. The results of the LC-MS method were also compared with trypsin inhibition measured spectrophotometrically, highlighting the complementarity of these two different pieces of information.

When and where did it happen? Systematic differences in recall of core and optional sentence information.

This article reports on four experiments aiming to examine immediate post-sentential recall of core sentence information (conveyed by direct objects), and optional/additional information (conveyed by temporal or locative adjuncts). Participants read simple and unambiguous Czech sentences such as Starší důchodce velmi pečlivě pročetl noviny v neděli v knihovně: "An older retiree read the newspaper very carefully on Sunday in the library." Sentences always appeared as a whole after pressing a space bar. Immediately after the sentence disappeared, an open-ended (free response) question was presented targeting either the direct object (e.g., newspaper), temporal adjunct (e.g., on Sunday), or locative adjunct (e.g., in the library). Altogether, it was found that the core information (conveyed by the direct object) was recalled almost perfectly, whereas additional information, conveyed by temporal and locative adjuncts, was recalled with significantly lower accuracy rates. Information structure also played a role: if the temporal or locative adjunct was focused, it was recalled better than if it was unfocused. The present article thus shows systematic differences in recall success for different pieces of information. These findings suggest the presence of selective attention mechanisms during early stages of sentence processing. Factors such as syntactic function or information structure influence the degree of attention to different pieces of information conveyed by a sentence. In turn, certain pieces of information may not be consciously accessible already after the sentence is processed.

TRANSFERRING LEGAL INFORMATION TO THE ELDERLY: ADDRESSING ACCESSIBILITY TO THEIR RIGHTS AND THE RESTRICTIONS THEY ENCOUNTER

The paper addresses areas such as language and legal knowledge in connection with the elders’ awareness of their rights based on proper legal communication. The elder population encounter a wide array of legal obstacles, many of which prevent them from obtaining up-to-date legal information, thereby unwittingly imposing restrictions on them. The first part of the paper explains the terms related to the barriers the older people face with particular emphasis on ageism. What is more, it provides data which emphasize the lack of adequate information, leading to the purpose of this paper: improving the methods in which older generations obtain legal advice. The second part of the paper recognizes the most common, in our opinion, problems the elderly find difficult to deal with. Primarily, we try to concentrate on the reasons lying at the heart of that daunting situation which respectively would be access to the Internet and know-how to use electronic devices in order to get different pieces of information, stressing the point why state-of-the-art methods are not the best educational choice for the older generation. Subsequently, we put forward the way of sorting the issue, highlighting the significance of well-performed face-to-face communication which according to our research works best for people over the age of 60. The final part provides conclusions drawn from the results obtained from the literature review, as well as the dire need of implementing some innovative solutions that may help the elderly in everyday life.

A comparison of three approaches to covariate effects on latent factors

In educational and psychological research, it is common to use latent factors to represent constructs and then to examine covariate effects on these latent factors. Using empirical data, this study applied three approaches to covariate effects on latent factors: the multiple-indicator multiple-cause (MIMIC) approach, multiple group confirmatory factor analysis (MG-CFA) approach, and the structural equation model trees (SEM Trees) approach. The MIMIC approach directly models covariate effects on latent factors. The MG-CFA approach allows testing of measurement invariance before latent factor means could be compared. The more recently developed SEM Trees approach partitions the sample into homogenous subsets based on the covariate space; model parameters are estimated separately for each subgroup. We applied the three approaches using an empirical dataset extracted from the eighth-grade U.S. data from the Trends in International Mathematics and Science Study 2019 database. All approaches suggested differences among mathematics achievement categories for the latent factor of mathematics self-concept. In addition, language spoken at home did not seem to affect students’ mathematics self-concept. Despite these general findings, the three approaches provided different pieces of information regarding covariate effects. For all models, we appropriately considered the complex data structure and sampling weights following recent recommendations for analyzing large-scale assessment data.

Adherence to Misinformation on Social Media Through Socio-Cognitive and Group-Based Processes

Previous work suggests that people's preference for different kinds of information depends on more than just accuracy. This could happen because the messages contained within different pieces of information may either be well-liked or repulsive. Whereas factual information must often convey uncomfortable truths, misinformation can have little regard for veracity and leverage psychological processes which increase its attractiveness and proliferation on social media. In this review, we argue that when misinformation proliferates, this happens because the social media environment enables adherence to misinformation by reducing, rather than increasing, the psychological cost of doing so. We cover how attention may often be shifted away from accuracy and towards other goals, how social and individual cognition is affected by misinformation and the cases under which debunking it is most effective, and how the formation of online groups affects information consumption patterns, often leading to more polarization and radicalization. Throughout, we make the case that polarization and misinformation adherence are closely tied. We identify ways in which the psychological cost of adhering to misinformation can be increased when designing anti-misinformation interventions or resilient affordances, and we outline open research questions that the CSCW community can take up in further understanding this cost.

Eudaimonic entertainment as new Enlightenment: Critical thinking as a mind-set effect of narratives

We propose to extend the theoretical concept of eudaimonia as a media effect with critical thinking as a mind-set effect. Critical thinking as a mind-set effect means that media narratives can stimulate viewers and readers to think critically in any situation, even outside of the exposure situation and applied to other topics. It denotes a generally heightened critical attitude or “mind-set” to approach issues and situations by taking different pieces of information into account, weighing and analyzing them, developing one's own arguments and drawing conclusions. People with this mind-set try to be well-informed, use credible sources and observations, are alert for alternatives, open-minded, maintain and change views according to evidence, and are motivated to hold accurate beliefs. In this article, we provide a theoretical elaboration of the idea of critical thinking as a mind-set effect, and connect it to traditions of Enlightenment literature as well as theoretical approaches on narrative effects. We also present data from a qualitative pilot study using the film “Don't look up” to explore the potential of such an extension of eudaimonic entertainment. Finally, we discuss implications of introducing critical thinking as a mind-set effect for narrative persuasion.

Research on Co-Channel Interference Cancellation for Underwater Acoustic MIMO Communications

Multiple-input–multiple-output (MIMO) communication systems utilize multiple transmitters to send different pieces of information in parallel. This offers a promising way to communicate at a high data rate over bandwidth-limited underwater acoustic channels. However, underwater acoustic MIMO communication not only suffers from serious inter-symbol interference, but also critical co-channel interference (CoI), both of which degrade the communication performance. In this paper, we propose a new framework for underwater acoustic MIMO communications. The proposed framework consists of a CoI-cancellation-based channel estimation method and channel-estimation-based decision feedback equalizer (CE-DFE) with CoI cancellation functionalities for underwater acoustic MIMO communication. We introduce a new channel estimation model that projects the received signal to a specific subspace where the interference is free; therefore, the CoI is cancelled. We also introduce a CE-DFE with CoI cancellation by appending some filters from traditional CE-DFE. In addition, the traditional direct adaptive decision feedback equalization (DA-DFE) method and the proposed method are compared in terms of communication performance and computational complexity. Finally, the sea trial experiment demonstrates the effectiveness and merits of the proposed method. The proposed method achieves a more than 1 dB of output SNR over traditional DA-DFE, and is less sensitive to parameters. The proposed method provides a new approach to the design of robust underwater acoustic MODEM.

Towards a processing model for argument-verb computations in online sentence comprehension

The current study investigated the processing stages by which the parser incorporates different pieces of information, from clausehood to argument roles, to update predictions about the main verb. Using Mandarin to match word position across relevant conditions, we extend classic ERP findings on the impact of argument role reversals ([The millionaireSUBJECT the servantOBJECTfired] vs. #[The servantSUBJECT the millionaireOBJECTfired]), by investigating cases where one of the nouns is not an argument of the verb ([The millionaireSUBJECT the servantOBJECTfired] vs. #[The millionaire thought [the servantSUBJECTfired…]]). The pattern of N400 responses suggest a three-stage model of argument-verb computation: An initial stage demonstrates sensitivity at the verb to semantic association only. Soon after, responses show partial structure-sensitivity, differentiating whether the noun phrases are arguments of the upcoming verb or not. Only at the last stage do the arguments’ roles (e.g. agent/patient) become available to impact computations at the verb.

A weighted constraint satisfaction approach to human goal-directed decision making.

When we plan for long-range goals, proximal information cannot be exploited in a blindly myopic way, as relevant future information must also be considered. But when a subgoal must be resolved first, irrelevant future information should not interfere with the processing of more proximal, subgoal-relevant information. We explore the idea that decision making in both situations relies on the flexible modulation of the degree to which different pieces of information under consideration are weighted, rather than explicitly decomposing a problem into smaller parts and solving each part independently. We asked participants to find the shortest goal-reaching paths in mazes and modeled their initial path choices as a noisy, weighted information integration process. In a base task where choosing the optimal initial path required weighting starting-point and goal-proximal factors equally, participants did take both constraints into account, with participants who made more accurate choices tending to exhibit more balanced weighting. The base task was then embedded as an initial subtask in a larger maze, where the same two factors constrained the optimal path to a subgoal, and the final goal position was irrelevant to the initial path choice. In this more complex task, participants' choices reflected predominant consideration of the subgoal-relevant constraints, but also some influence of the initially-irrelevant final goal. More accurate participants placed much less weight on the optimality-irrelevant goal and again tended to weight the two initially-relevant constraints more equally. These findings suggest that humans may rely on a graded, task-sensitive weighting of multiple constraints to generate approximately optimal decision outcomes in both hierarchical and non-hierarchical goal-directed tasks.