Demystifying Tacit Knowledge in Graphic Design: Characteristics, Instances, Approaches, and Guidelines

Despite the growing demand for professional graphic design knowledge, the tacit nature of design inhibits knowledge sharing. However, there is a limited understanding on the characteristics and instances of tacit knowledge in graphic design. In this work, we build a comprehensive set of tacit knowledge characteristics through a literature review. Through interviews with 10 professional graphic designers, we collected 123 tacit knowledge instances and labeled their characteristics. By qualitatively coding the instances, we identified the prominent elements, actions, and purposes of tacit knowledge. To identify which instances have been addressed the least, we conducted a systematic literature review of prior system support to graphic design. By understanding the reasons for the lack of support on these instances based on their characteristics, we propose design guidelines for capturing and applying tacit knowledge in design tools. This work takes a step towards understanding tacit knowledge, and how this knowledge can be communicated

GenQuery: Supporting Expressive Visual Search with Generative Models

Designers rely on visual search to explore and develop ideas in early design stages. However, designers can struggle to identify suitable text queries to initiate a search or to discover images for similarity-based search that can adequately express their intent. We propose GenQuery, a novel system that integrates generative models into the visual search process. GenQuery can automatically elaborate on users' queries and surface concrete search directions when users only have abstract ideas. To support precise expression of search intents, the system enables users to generatively modify images and use these in similarity-based search. In a comparative user study (N=16), designers felt that they could more accurately express their intents and find more satisfactory outcomes with GenQuery compared to a tool without generative features. Furthermore, the unpredictability of generations allowed participants to uncover more diverse outcomes. By supporting both convergence and divergence, GenQuery led to a more creative experience.Comment: 18 pages and 12 figure

Unveiling Disparities in Web Task Handling Between Human and Web Agent

With the advancement of Large-Language Models (LLMs) and Large Vision-Language Models (LVMs), agents have shown significant capabilities in various tasks, such as data analysis, gaming, or code generation. Recently, there has been a surge in research on web agents, capable of performing tasks within the web environment. However, the web poses unforeseeable scenarios, challenging the generalizability of these agents. This study investigates the disparities between human and web agents' performance in web tasks (e.g., information search) by concentrating on planning, action, and reflection aspects during task execution. We conducted a web task study with a think-aloud protocol, revealing distinct cognitive actions and operations on websites employed by humans. Comparative examination of existing agent structures and human behavior with thought processes highlighted differences in knowledge updating and ambiguity handling when performing the task. Humans demonstrated a propensity for exploring and modifying plans based on additional information and investigating reasons for failure. These findings offer insights into designing planning, reflection, and information discovery modules for web agents and designing the capturing method for implicit human knowledge in a web task

Elevated RalA activity in the hippocampus of PI3K gamma knock-out mice lacking NMDAR-dependent long-term depression

Phosphoinositide 3-kinases (PI3Ks) play key roles in synaptic plasticity and cognitive functions in the brain. We recently found that genetic deletion of PI3K gamma, the only known member of class IB PI3Ks, results in impaired N-methyl-D-aspartate receptor-dependent long-term depression (NMDAR-LTD) in the hippocampus. The activity of RalA, a small GTP-binding protein, increases following NMDAR-LTD inducing stimuli, and this increase in RalA activity is essential for inducing NMDAR-LTD. We found that RalA activity increased significantly in PI3K gamma knockout mice. Furthermore, NMDAR-LTD-inducing stimuli did not increase RalA activity in PI3K gamma knockout mice. These results suggest that constitutively increased RalA activity occludes further increases in RalA activity during induction of LTD, causing impaired NMDAR-LTD. We propose that PI3K gamma regulates the activity of RalA, which is one of the molecular mechanisms inducing NMDAR-dependent LTD.open1

Reducing time to discovery : materials and molecular modeling, imaging, informatics, and integration

This work was supported by the KAIST-funded Global Singularity Research Program for 2019 and 2020. J.C.A. acknowledges support from the National Science Foundation under Grant TRIPODS + X:RES-1839234 and the Nano/Human Interfaces Presidential Initiative. S.V.K.’s effort was supported by the U.S. Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES), Materials Sciences and Engineering Division and was performed at the Oak Ridge National Laboratory’s Center for Nanophase Materials Sciences (CNMS), a U.S. Department of Energy, Office of Science User Facility.Multiscale and multimodal imaging of material structures and properties provides solid ground on which materials theory and design can flourish. Recently, KAIST announced 10 flagship research fields, which include KAIST Materials Revolution: Materials and Molecular Modeling, Imaging, Informatics and Integration (M3I3). The M3I3 initiative aims to reduce the time for the discovery, design and development of materials based on elucidating multiscale processing-structure-property relationship and materials hierarchy, which are to be quantified and understood through a combination of machine learning and scientific insights. In this review, we begin by introducing recent progress on related initiatives around the globe, such as the Materials Genome Initiative (U.S.), Materials Informatics (U.S.), the Materials Project (U.S.), the Open Quantum Materials Database (U.S.), Materials Research by Information Integration Initiative (Japan), Novel Materials Discovery (E.U.), the NOMAD repository (E.U.), Materials Scientific Data Sharing Network (China), Vom Materials Zur Innovation (Germany), and Creative Materials Discovery (Korea), and discuss the role of multiscale materials and molecular imaging combined with machine learning in realizing the vision of M3I3. Specifically, microscopies using photons, electrons, and physical probes will be revisited with a focus on the multiscale structural hierarchy, as well as structure-property relationships. Additionally, data mining from the literature combined with machine learning will be shown to be more efficient in finding the future direction of materials structures with improved properties than the classical approach. Examples of materials for applications in energy and information will be reviewed and discussed. A case study on the development of a Ni-Co-Mn cathode materials illustrates M3I3's approach to creating libraries of multiscale structure-property-processing relationships. We end with a future outlook toward recent developments in the field of M3I3.Peer reviewe

Altered presynaptic function and number of mitochondria in the medial prefrontal cortex of adult Cyfip2 heterozygous mice

Variants of the cytoplasmic FMR1-interacting protein (CYFIP) gene family, CYFIP1 and CYFIP2, are associated with numerous neurodevelopmental and neuropsychiatric disorders. According to several studies, CYFIP1 regulates the development and function of both pre- and post-synapses in neurons. Furthermore, various studies have evaluated CYFIP2 functions in the postsynaptic compartment, such as regulating dendritic spine morphology; however, no study has evaluated whether and how CYFIP2 affects presynaptic functions. To address this issue, in this study, we have focused on the presynapses of layer 5 neurons of the medial prefrontal cortex (mPFC) in adult Cyfip2 heterozygous (Cyfip2+/−) mice. Electrophysiological analyses revealed an enhancement in the presynaptic short-term plasticity induced by high-frequency stimuli in Cyfip2+/− neurons compared with wild-type neurons. Since presynaptic mitochondria play an important role in buffering presynaptic Ca2+, which is directly associated with the short-term plasticity, we analyzed presynaptic mitochondria using electron microscopic images of the mPFC. Compared with wild-type mice, the number, but not the volume or cristae density, of mitochondria in both presynaptic boutons and axonal processes in the mPFC layer 5 of Cyfip2+/− mice was reduced. Consistent with an identification of mitochondrial proteins in a previously established CYFIP2 interactome, CYFIP2 was detected in a biochemically enriched mitochondrial fraction of the mouse mPFC. Collectively, these results suggest roles for CYFIP2 in regulating presynaptic functions, which may involve presynaptic mitochondrial changes.This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korea Government Ministry of Science and ICT (NRF-2018R1C1B6001235, NRF-2018M3C7A1024603, NRF-2017M3C7A1048086, and NRF-2020R1A2C3011464) and the KBRI Basic Research Programs (20-BR01-08 and 20-BR-04-01)

Intelligent Data-driven Classification and Forecasting Processes for Complex Engineering and Social Systems

Complex engineering systems such as automobiles and chillers in heating, ventilation, and air-conditioning (HVAC) systems are being equipped with increasingly sophisticated electronic systems. Operational problems associated with degraded components, failed sensors, improper installation, poor maintenance, and improperly implemented controls affect the efficiency, safety, and reliability of the systems. Failure frequency increases with age and leads to loss of comfort, degraded operational efficiency, and increased wear and tear of system components. ^ Out of the research directions of this thesis is to develop a data-driven scheme for fault diagnosis and severity estimation to HVAC systems. Most existing HVAC fault-diagnostic schemes are based on analytical models and knowledge bases. These schemes are adequate for generic systems. However, real-world systems significantly differ from the generic ones and necessitate modifications of models and/or customization of the standard knowledge bases, which can be labor intensive. To overcome such issues, we consider a data-driven approach for fault detection and diagnosis (FDD) of chillers in HVAC systems. The research on the faults of interest in the chiller could enable the building system operators to improve energy efficiency and maintain the desired comfort level at reduced cost.^ Another research direction of this thesis is to develop data reduction techniques for on-board implementation of data-driven classification techniques in memory-constrained electronic control units (ECUs) of automobiles. One of the problems with high-dimensional datasets (caused by multiple modes of system operation and sensor data over time) is that not all the measured variables are important for understanding the underlying phenomena of interest. While certain computationally expensive methods can construct predictive models with high accuracy from high-dimensional data, it is still of interest in many applications to reduce the dimension of the original data prior to any modeling of the data. Data-driven applications on reduced datasets could also be suitable for ECUs, which have memory capacity limitations due to cost constraints. ^ We also develop innovative classifier fusion techniques so as to decrease classification error and reduce variability in diagnostic error. We show that fusing marginal classifiers can increase the diagnostic performance substantially. Furthermore, we could reduce the diagnostic errors by combining traditional fusion techniques (e.g., classifier selection, combining classifier outputs, sampling training data, manipulating classifier outputs, classifier feature selection, etc.) with our novel classifier fusion techniques.^ The data-driven framework can be beneficial not only to the engineering community in the diagnostics and prognostics of complex systems, but is also potentially useful in social science research. What if we apply the approaches to the rise and fall of a nation state (a social system)? The approaches could augment the human cognitive capacity via automated information extraction, as well as analytical capabilities via a generalized framework for instability analysis and forecasting models based on data-driven techniques.^ We apply a classification and forecasting framework to conflict and instability analysis, and the objectives are to: (1) present a generalized data-driven framework for conflict analysis and forecasting, (2) show that state-of-the-art pattern classification techniques provide significant improvements to forecasting accuracy, and (3) introduce classification problems arising in social sciences to the engineering community for further enhancement of analysis techniques. The effort in this thesis will help political decision makers to successfully intervene by identifying and forecasting the relative stability of a state. ^ The final direction of research in this thesis is on integrating disparate diagnostic approaches into a rapid prototyping platform for analyzing engineering and social systems. The current approaches to FDD and forecasting are time-consuming and labor-intensive and are conducted via independent computing platforms. The integrated FDD and forecasting toolbox will provide a unified computing platform to solve diagnostic and forecasting problems, and our diagnostic algorithms (i.e., data preprocessing, data-driven classification, fusion, performance evaluation, and forecasting) in the toolbox will continue to evolve in the future.

THE IMPACT OF TRAFFIC INFORMATION ACCURACY ON ROUTE PLANNING QUALITY

With the development of vehicle navigation, drivers are able to find an efficient route to their destination very easily without paper map. Most drivers use mobile navigation, which provides them with an updated map and real-time traffic information. However, the route planning (RP) quality varies in terms of traffic information accuracy. Even though we use the same navigation application, there are specific times the RP quality is more correct. Thus, we investigated the relationship between traffic information accuracy and RP quality. We found that the time of the day has a moderating effect on the relationship between traffic information accuracy (TIA) and RP. We also found that if there is traffic congestion on roads, the traffic information accuracy and RP quality is lower than usual. As the TIA reduces during rush hours, the RP quality is also lower during this period

Dose gradient curve: A new tool for evaluating dose gradient

<div><p>Purpose</p><p>Stereotactic radiotherapy, which delivers an ablative high radiation dose to a target volume for maximum local tumor control, requires a rapid dose fall-off outside the target volume to prevent extensive damage to nearby normal tissue. Currently, there is no tool to comprehensively evaluate the dose gradient near the target volume. We propose the dose gradient curve (DGC) as a new tool to evaluate the quality of a treatment plan with respect to the dose fall-off characteristics.</p><p>Methods</p><p>The average distance between two isodose surfaces was represented by the dose gradient index (DGI) estimated by a simple equation using the volume and surface area of isodose levels. The surface area was calculated by mesh generation and surface triangulation. The DGC was defined as a plot of the DGI of each dose interval as a function of the dose. Two types of DGCs, <i>differential</i> and <i>cumulative</i>, were generated. The performance of the DGC was evaluated using stereotactic radiosurgery plans for virtual targets.</p><p>Results</p><p>Over the range of dose distributions, the dose gradient of each dose interval was well-characterized by the DGC in an easily understandable graph format. Significant changes in the DGC were observed reflecting the differences in planning situations and various prescription doses.</p><p>Conclusions</p><p>The DGC is a rational method for visualizing the dose gradient as the average distance between two isodose surfaces; the shorter the distance, the steeper the dose gradient. By combining the DGC with the dose-volume histogram (DVH) in a single plot, the DGC can be utilized to evaluate not only the dose gradient but also the target coverage in routine clinical practice.</p></div