Memory Structure Research Articles

Igniting a knowledge renaissance: revolutionising entrepreneurial ecosystems with transactive memory systems

PurposePrior research has extensively explored the dynamics of knowledge creation and transfer within entrepreneurial ecosystems (EEs). However, the research on knowledge integration within EEs, particularly by entrepreneurs, remains scant. Garnering and effectively using knowledge in such a dynamic and complex environment can provide entrepreneurs with a valuable asset for gaining a competitive advantage. To address this gap, this study aims to explore how entrepreneurs garner and capitalise on knowledge within the EE environment by using a transactive memory system lens.Design/methodology/approachThis study is based on 26 semi-structured interviews with different actors and members of the same ecosystem – the northern Finnish health tech ecosystem. The data were analysed using the Gioia methodology.FindingsThis study results found that transactive memory processes (i.e. knowledge specialisation, credibility and coordination) and structures (i.e. differentiated-, shared- and meta-knowledge) influence knowledge organising in EEs.Originality/valueThis study provides a conceptual interplay between the EE and the transactive memory system’s processes and structures.

Sparking creativity: Encouraging creative idea generation through automatically generated word recommendations

Creative block is a familiar foe to any who attempt to create and is especially related to “writers block”. While significant effort has been focused on developing methods to break such blocks, it remains an active challenge. Here, we focus on the role of semantic memory structure in driving creative block, by having people get “stuck” in a certain part of their semantic memory network. We directly examine whether we can “pull out” a participant from where they got “stuck” in their semantic memory, breaking their creative impasse. Our Associative Creativity Sparker (ACS) is a cognitive network science-based online tool that aims to spark creative ideas and break creative impasse: Once a participant runs out of ideas in a creative idea generation task, word recommendations are suggested to prime new ideas. These word recommendations are either towards or away from previous ideas, as well as close or far from the target object, based on a conceptual space extracted from the participants responses using online text analysis. In Study 1, 121 participants use the ACS to generate creative alternative uses for five different objects and completed creativity and Gf tasks. In Study 2, we repeat the design of Study 1, but further examine the impact of writing experience on the ACS, by examining 120 novice and 120 experienced writers. Across both studies, our results indicate that the location of word recommendations affects the fluency and originality of one’s ideas, and that novice and experienced writers differently benefit from these word recommendations.

Targeting the Manifestations of Subclinical and Overt Hypothyroidism Within the Hippocampus.

The past decade has witnessed a surge of articles describing the neurocognitive sequelae and associated structural and functional brain abnormalities of patients with overt hypothyroidism (OH) and subclinical hypothyroidism (SCH). Findings show effects primarily within the frontal lobes with usually worse outcomes for OH than SCH. Several recent studies have also indicated hypothyroid patients may have smaller hippocampi, a key structure for memory. The JCEM paper by Zhang and colleagues applies 2 novel approaches for analyzing hippocampal structure and function. One uses an automated processing tool that segments the hippocampus into distinct subregions, and the other performs connectivity analysis to assess the relationships between specific hippocampal subregions and cortical areas. Relatively large samples of OH and SCH patients and healthy controls received a test of global cognitive functioning and underwent structural and functional magnetic resonance imaging. Results showed hypothyroid groups scored significantly below controls on the memory scale and also had smaller hippocampal volumes in selective subregions. Effects were stronger for SCH than OH groups, who also showed different patterns of interconnectivity between hippocampal subregions and specific frontal lobe areas. To make sense of these findings, I explored the rodent and human literatures on thyroid hormone's role in hippocampal functioning and on hippocampal subfields and their purported functions and interconnections. Because current results suggest SCH may represent a distinct clinical entity with unique brain manifestations, I hypothesized 2 explanations for these findings, one involving transporter defects in the brain barriers and the other, differential neurodegeneration of the blood-brain barrier vascular unit.

Photonic next-generation reservoir computer based on distributed feedback in optical fiber.

Reservoir computing (RC) is a machine learning paradigm that excels at dynamical systems analysis. Photonic RCs, which perform implicit computation through optical interactions, have attracted increasing attention due to their potential for low latency predictions. However, most existing photonic RCs rely on a nonlinear physical cavity to implement system memory, limiting control over the memory structure and requiring long warm-up times to eliminate transients. In this work, we resolve these issues by demonstrating a photonic next-generation reservoir computer (NG-RC) using a fiber optic platform. Our photonic NG-RC eliminates the need for a cavity by generating feature vectors directly from nonlinear combinations of the input data with varying delays. Our approach uses Rayleigh backscattering to produce output feature vectors by an unconventional nonlinearity resulting from coherent, interferometric mixing followed by a quadratic readout. Performing linear optimization on these feature vectors, our photonic NG-RC demonstrates state-of-the-art performance for the observer (cross-prediction) task applied to the Rössler, Lorenz, and Kuramoto-Sivashinsky systems. In contrast to digital NG-RC implementations, we show that it is possible to scale to high-dimensional systems while maintaining low latency and low power consumption.

Constructing Posthuman Politics: New Forms of Media Subjectivity (fakes, bots and simulacra)/Konstruowanie polityki posthumanizmu: nowe formy podmiotowości medialnej (dezinformacja, boty, symulakra)

The article is devoted to the emergence of the phenomenon of posthuman politics, in particular, to the study of new forms of media subjectivity. Drawing from the theoretical developments of J. Baudrillard, N. Luhmann, B. Latour, K. Knorr-Cetina, P. Bourdieu, P. Virilio, Z. Bauman and a number of other theorists, the author identifies a number of processes that significantly and ontologically transform the communicative space within the world of media. These processes include the industrialization of communication production, the massification and audiovisualization of image production, the intensification of flows, the increasing role of speed, the standardization of reaction control and the narrowing of the range of these reactions, the elimination of memory structures, the “collapse” of the chronotope to the here-and-now, and the increasing “fluidity” of societal structures. Based on this, the author conceptualizes fundamentally new nodes of relations in posthuman politics: integral network complexes “human-human,” “human-non-human (that is, machine),” “machine-machine” become significant,. The “drone-production of media products” is established, with machines and bots beginning to participate in discussions, imitation and simulation of multiplicity and participation, and non-human subjectivity turns out to be even more subjective than humans. The author argues that all of this leads to significant shifts in “posthuman” politics compared to human ones: human subjects lose confidence in the political act in any of its expressions; the political ceases to be taken “seriously,” and alienation begins to affect even formal communicative acts on the Internet, which gives rise to inactivity Meaningful communication about the political is “noisy”; audiovisual content ceases to be a space of meaning, comprehension, dialogue and communication Political understanding and interaction no longer centre on action and discourse about human lives, pain, suffering, values, hopes; The “silent majority” as a “black hole of the social” absorbs an increasing amount of not only meanings and meaningful statements but also potential opportunities, and an increasing part of the future. The article also outlines the prospects for further research, including empirical research on the topic.

Patch-wise vector quantization for unsupervised medical anomaly detection

Radiography images inherently possess globally consistent structures while exhibiting significant diversity in local anatomical regions, making it challenging to model their normal features through unsupervised anomaly detection. Since unsupervised anomaly detection methods localize anomalies by utilizing discrepancies between learned normal features and input abnormal features, previous studies introduce a memory structure to capture the normal features of radiography images. However, these approaches store extremely localized image segments in their memory, causing the model to represent both normal and pathological features with the stored components. This poses a significant challenge in unsupervised anomaly detection by reducing the disparity between learned features and abnormal features. Furthermore, with the diverse settings in radiography imaging, the above issue is exacerbated: more diversity in the normal images results in stronger representation of pathological features. To resolve the issues above, we propose a novel pathology detection method called Patch-wise Vector Quantization (P-VQ). Unlike the previous methods, P-VQ learns vector-quantized representations of normal “patches” while preserving its spatial information by incorporating vector similarity metric. Furthermore, we introduce a novel method for selecting features in the memory to further enhance the robustness against diverse imaging settings. P-VQ even mitigates the “index collapse” problem of vector quantization by proposing top-k% dropout. Our extensive experiments on the BMAD benchmark demonstrate the superior performance of P-VQ against existing state-of-the-art methods.

Differences in advertising’s effectiveness across age groups

Our research explores differences in cognitive evaluative measures of consumer buying behaviour and advertising’s effectiveness across age groups. We seek to ascertain if older segments show dissimilar memory structures and cognitive processes than younger targets, or if modern advertising approaches are simply lacking reach among older consumers. Our main measure of interest is mental availability—i.e. the ease at which a brand comes to mind in buying situations. We compare the scores of three underlying mental availability metrics across age groups and find differences primarily for associative penetration, a proxy for advertising reach. We then examine differences in other important cognitive evaluative measures of advertising’s effectiveness, such as the purchase funnel (narrowing down of alternatives for purchase) and product category knowledge. We detect additional differences that further illustrate why there are likely limits to advertising’s reach amongst older consumers. Hence, we stress the importance of using these cognitive evaluative measures to appraise the effectiveness of inclusive, cross-age advertising strategies in theory and practice.

Thermodynamic model for memory

A thermodynamic model for memory formation is proposed. Key points include: 1) Any thought or consciousness corresponds to a thermodynamic system of nerve cells. 2) The system concept of nerve cells can only be described by thermodynamics of condensed matter. 3) The memory structure is logically associated with the system structure or the normal structure of biology. 4) The development of our thoughts is processed irreversibly, and numerous states or thoughts can be generated. 5) Memory formation results from the reorganization and change of cellular structures (or memory structures), which are related to nerve cell skeleton and membrane. Their alteration can change the excitability of nerve cells and the pathway of neural impulse conduction. 6) Amnesia results from the loss of thermodynamic stability of the memory structure, which can be achieved by different ways. Some related phenomena and facts are discussed. The analysis shows that thermodynamics can account for the basic properties of memory.

Changes in semantic memory structure support successful problem-solving and analogical transfer

Creative problem-solving is central in daily life, yet its underlying mechanisms remain elusive. Restructuring (i.e., reorganization of problem-related representations) is considered one problem-solving mechanism and may lead to an abstract problem-related representation facilitating the solving of analogous problems. Here, we used network science methodology to estimate participants’ semantic memory networks (SemNets) before and after attempting to solve a riddle. Restructuring was quantified as the difference in SemNets metrics between pre- and post-solving phases. Our results provide initial evidence that problem-related SemNets restructuring may be associated with the successful solving of the riddle and, subsequently, an analogous one. Solution-relevant concepts and semantically remote concepts became more strongly related in solvers. Only changes in semantically remote concepts were instrumental in actively solving the riddle while changes in solution-relevant concepts may reflect a pre-exposure to the solution.

A novel transformer-based neural network under model interpretability for improving wheat yield estimation using remotely sensed multi-variables

Timely and accurate yield prediction before wheat harvest is of great significance for food policy formulation and national economic development. Deep learning method gains importance on crop yield estimation and growth monitoring with the rapid development of deep learning models combined with remotely sensed data. However, the problems of nonlinear parameter optimization and long-term dependence in time series data have restricted the improvement of yield estimation accuracy. Transformer model completely abandons the traditional deep learning architecture and is gradually applied to time series tasks due to the advantage in modeling long-term dependence. This study introduced a novel transformer-based deep learning framework to estimate winter wheat yield in the Guanzhong Plain utilizing remotely sensed multi-variables, namely leaf area index (LAI), fraction of photosynthetically active radiation (FPAR) and vegetation temperature condition index (VTCI). The proposed model, called SSA-LSTM-transformer (SLTF), was developed for dealing with above problems under the automatic optimization capability of the sparrow search algorithm (SSA) and the long-term memory advantage of the long short-term memory (LSTM) structure. The findings demonstrated that the SLTF model achieved better estimation accuracy at the county level (R2 = 0.72, RMSE = 488.68 kg/ha) compared with the single transformer model (R2 = 0.61, RMSE = 573.99 kg/ha). The analysis on SLTF model’s performance at sampling sites over different disasters suggested that the model can effectively learn the effects of diseases, lodge and pests on crop yield estimation, which had good generalization ability at all sampling sites (R2 = 0.45, RMSE = 738.63 kg/ha). The Shapley Additive exPlanations (SHAP) approach was employed to evaluate the relative importance of remotely sensed multi-variables to yield and to understand how each input variable influences the estimated yield for global interpretability and local interpretability of the SLTF model. It was found that FPAR played the largest roles in yield estimation with the highest importance value, and FPAR and LAI from late April to late May and VTCI from late March to mid-April were regarded as important features for the estimated yield with high importance value. In conclusion, our findings indicated the potential of SLTF model for winter wheat yield estimation, which contributes to promoting further application of remotely sensed technology for agricultural production.

Mapping the Memory Structure of High-Knowledge Students: A Longitudinal Semantic Network Analysis.

Standard learning assessments like multiple-choice questions measure what students know but not how their knowledge is organized. Recent advances in cognitive network science provide quantitative tools for modeling the structure of semantic memory, revealing key learning mechanisms. In two studies, we examined the semantic memory networks of undergraduate students enrolled in an introductory psychology course. In Study 1, we administered a cumulative multiple-choice test of psychology knowledge, the Intro Psych Test, at the end of the course. To estimate semantic memory networks, we administered two verbal fluency tasks: domain-specific fluency (naming psychology concepts) and domain-general fluency (naming animals). Based on their performance on the Intro Psych Test, we categorized students into a high-knowledge or low-knowledge group, and compared their semantic memory networks. Study 1 (N = 213) found that the high-knowledge group had semantic memory networks that were more clustered, with shorter distances between concepts-across both the domain-specific (psychology) and domain-general (animal) categories-compared to the low-knowledge group. In Study 2 (N = 145), we replicated and extended these findings in a longitudinal study, collecting data near the start and end of the semester. In addition to replicating Study 1, we found the semantic memory networks of high-knowledge students became more interconnected over time, across both domain-general and domain-specific categories. These findings suggest that successful learners show a distinct semantic memory organization-characterized by high connectivity and short path distances between concepts-highlighting the utility of cognitive network science for studying variation in student learning.

A comprehensive framework to enhance numerical simulations in the spectral-element code Nek5000

A framework is presented for the spectral-element code Nek5000, which has been, and still is, widely used in the computational fluid dynamics (CFD) community to perform high-fidelity numerical simulations of transitional and high Reynolds number flows. Despite the widespread usage, there is a deficiency in having a comprehensive set of tools specifically designed for conducting simulations using Nek5000. To address this issue, we have created a unique framework that allows, inter alia, to perform stability analysis and compute statistics of a turbulent flow. The framework encapsulates modules that provide tools, run-time parameters and memory structures, defining interfaces and performing different tasks. First, the framework architecture is described, showing its non-intrusive approach. Then, the modules are presented, explaining the main tools that have been implemented and describing some of the test cases. The code is open-source and available online, with proper documentation, to-run instructions and related examples.

Enhancing Chatbot Intelligence Through Narrative Memory Structures

Through the use of narrative memory structures—a cutting-edge technique intended to provide chatbots context awareness and conversational coherence—we sought to improve the intelligence of chatbots. We carefully designed our process with the goal of immediately incorporating narrative data into the chatbot's memory architecture. In order to find narrative patterns, this required evaluating and classifying previous exchanges. The narrative patterns were then saved and used to guide the chatbot's answers. The incorporation of narrative memory structures into the chatbot resulted in a significant improvement in conversational coherence and relevance, as per our study's key findings. Through the use of its narrative memory, which stores earlier encounters, the chatbot shown an impressive capacity to sustain the conversation's flow and provide more contextually relevant replies. In addition, the chatbot demonstrated improved understanding of human intentions, correctly identifying underlying motives and preferences. In addition, comments produced by integrating narrative memory structures were highly customized in addition to being accurate. By utilizing the extensive history of previous exchanges, the chatbot was able to customize replies to each user's unique situation, building a closer relationship and rapport with them. All things considered; our study highlights how narrative memory processes can revolutionize chatbot intelligence. We can improve chatbots' conversational capabilities and enable more meaningful human-computer interactions by giving them the capacity to extract narrative information from previous conversations. This opens the door to a new era of chatbot designs that put the needs of users, contextuality, and empathy first, changing the game for conversational AI in the process.

The Role of Knowledge in Creative Thinking

ABSTRACT In this invited paper, I briefly review my past, current, and future lines of research. The associative theory of creativity argues that higher creative individuals have a richer semantic memory structure that facilitates broader associative search processes, that leads to the combination of remote concepts into novel and appropriate ideas. Based on this theory, in my research I investigate the role of knowledge – or semantic memory – in high-level cognition, focusing on creativity, associative thinking, and memory search, in typical and clinical populations. To do so, I apply computational tools from network science, natural language processing, and machine learning, coupled with empirical cognitive and neural research. Such computational tools are enabling the representation and operationalization of the structure of semantic memory and the processes that operate over it. This is critical as it allows us to start quantifying issues that for a very long time were studied very subjectively in creativity research – remoteness of ideas, associative thinking, flexible/richer semantic memory structure, etc. Such work is offering unique, quantitative, ways to directly study classic theories of creativity, propelling forward our understanding of its complexity.

Understanding the structure of autobiographical memories: A study of trauma memories from the 1994 Rwandan genocide.

How do we remember traumatic events, and are these memories different in individuals who experience post-traumatic stress? Some evidence suggests that traumatic events are mnemonically enhanced, or include more episodic detail, relative to other types of memories. Simultaneously, individuals with post-traumatic stress disorder (PTSD) have more non-episodic details in all of their memories, a pattern hypothesized to result from impairment in executive function. Here, we explore these questions in a unique population that experienced severely traumatic events more than 20 years ago - individuals who lived through the 1994 genocide in Rwanda. Participants recalled events from the genocide, negative events unrelated to the genocide, neutral events, and positive events. We used the Autobiographical Interview method to label memory details as episodic or non-episodic. We found that memories from the genocide showed robust mnemonic enhancement, with more episodic than non-episodic details, and contained more details overall than any other memory type. This pattern was not impacted by post-traumatic stress. Overall, this study provides evidence that traumatic events create vivid long-lasting episodic memories, in this case even more than 20 years later.

Adaptive staged remaining useful life prediction of roller in a hot strip mill based on multi-scale LSTM with multi-head attention

Accurate prediction of a roller's remaining useful life (RUL) is significant for a hot strip mill to avoid major safety accidents and substantial economic losses. Since rollers’ degradation processes represent multi-stage characteristics, traditional single-stage models cannot acquire accurate RUL prediction results. Therefore, this paper proposes an adaptive staged RUL prediction method based on multi-scale long short-term memory network with multi-head self-attention (LSTM-MHA). The roller's production data and operation data are fused to construct interpretable health indicators (HI), which can represent the roller's remaining rotatable angle. Then the degradation process is divided into multiple stages and analyzed. The proposed multi-scale LSTM-MHA can adaptively update the model weights based on changes in the roller degradation stages by the multi-scale memory structure. The MHA embedding mechanism filters important temporal information for LSTM units. The trained model can predict the roller's RUL by inputting the current roller's HI. The proposed method is verified on an industrial hot strip mill roller dataset from a well-known steel company. The validation results show that the prediction accuracy of the proposed method is higher than 98.98%. Compared with existing deep learn-based methods, the proposed multi-scale LSTM-MHA method has significant advantages in roller's RUL prediction.

A Method for Identifying the Wear State of Grinding Wheels Based on VMD Denoising and AO-CNN-LSTM

Monitoring the condition of the grinding wheel in real-time during the grinding process is crucial as it directly impacts the precision and quality of the workpiece. Deep learning technology plays a vital role in analyzing the changes in sensor signals and identifying grinding wheel wear during the grinding process. Therefore, this paper innovatively proposes a grinding wheel wear recognition method based on Variational Mode Decomposition (VMD) denoising and Aquila Optimizer—Convolutional Neural Network—Long Short-Term Memory (AO-CNN-LSTM). The paper utilizes Acoustic Emission (AE) signals generated during grinding to identify the condition of the grinding wheel. To address noise interference, the study introduces the VMD algorithm for denoising the sample dataset, enhancing the effectiveness of neural network training. Subsequently, the dataset is fed into the designed Convolutional Neural Network—Long Short-Term Memory (CNN-LSTM) structure with AO-optimized parameters. Experimental results demonstrate that this method achieves high accuracy and performance.

Iterative image rain removal network using consecutive residual long short-term memory

Image rain removal is designed to effectively separate rain streaks from the background image layer. However, rain streaks in real-world scenarios vary in density, shape, and direction, making it difficult to decompose rainy images into clean backgrounds and rain layers. In this study, we introduce an iterative framework for image deraining to progressively enhance rainy images using a residual long short-term memory structure. The overall network comprises a multidomain residue channel, a fusion module, and a consecutive residual long short-term memory. We introduce multidomain residue channels by computing them in both the image and wavelet low-frequency domains. We propose a fusion module to combine the residue channel for guidance with wavelet domain features for rain removal. We also propose a feature extraction module based on successive residual long short-term memory to extract the main features in the wavelet domain. An iterative image restoration framework comprising three primary modules is introduced to progressively enhance rainy images. To evaluate the performance of the proposed approach, we conduct experiments using widely used benchmarks. The results demonstrate that our method outperforms state-of-the-art methods in image rain removal https://github.com/workofsu/.

Spontaneous eye movements reflect the representational geometries of conceptual spaces

Functional neuroimaging studies indicate that the human brain can represent concepts and their relational structure in memory using coding schemes typical of spatial navigation. However, whether we can read out the internal representational geometries of conceptual spaces solely from human behavior remains unclear. Here, we report that the relational structure between concepts in memory might be reflected in spontaneous eye movements during verbal fluency tasks: When we asked participants to randomly generate numbers, their eye movements correlated with distances along the left-to-right one-dimensional geometry of the number space (mental number line), while they scaled with distance along the ring-like two-dimensional geometry of the color space (color wheel) when they randomly generated color names. Moreover, when participants randomly produced animal names, eye movements correlated with low-dimensional similarity in word frequencies. These results suggest that the representational geometries used to internally organize conceptual spaces might be read out from gaze behavior.

Temporal memory for threatening events encoded in a haunted house

ABSTRACT Despite the salient experience of encoding threatening events, these memories are prone to distortions and often non-veridical from encoding to recall. Further, threat has been shown to preferentially disrupt the binding of event details and enhance goal-relevant information. While extensive work has characterised distinctive features of emotional memory, research has not fully explored the influence threat has on temporal memory, a process putatively supported by the binding of event details into a temporal context. Two primary competing hypotheses have been proposed; that threat can impair or enhance temporal memory. We analysed two datasets to assess temporal memory for an in-person haunted house experience. In study 1, we examined the temporal structure of memory by characterising memory contiguity in free recall as a function of individual levels of heart rate as a proxy of threat. In study 2, we replicated marginal findings of threat-related increases in memory contiguity found in study 1. We extended these findings by showing threat-related increases in recency discriminations, an explicit test of temporal memory. Together, these findings demonstrate that threat enhances temporal memory regarding free recall structure and during explicit memory judgments.