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Since the end of the twentieth century, there have been a large number of Africans in Guangzhou occupying multiple emplacements and engaging in diverse activities so that a whole zone of the urban area is designated 'Little Africa.' The article investigates the linguistic landscape in the African living areas of Guangzhou from a multimodality perspective, focusing on how street sign translation becomes an important means of cross-cultural communication and a symbol of 'low-end glocalization' and 'grassroots cosmopolitanism'. The translation landscape in the present study was investigated through an ethnographic process of photographing the translations and examining them within their contexts and spaces. The prevalence of bilingual, multilingual, and monolingual English signs in multifarious modes in the African inhabited area of Guangzhou deviates itself from other places of the city to create a translational and transnational space for people of heterogeneous ethnicities to develop social supporting networks and maintain structures of solidarity. The unusually diversified, multimodal translations, on the one hand, reflect the 'glocalized' nature of this African enclave while, on the other hand, demonstrating a 'transient' sense of belonging for foreigners. [ABSTRACT FROM AUTHOR]

Physical landscapes are often designated and given meaning through linguistic landscapes, markers, street signs and placards. While these are ubiquitous in urban areas, within the natural environment, linguistic landscapes serve to designate parts of nature, give significance, and educate. The dynamic, changeable character of nature means there is not always congruence between the linguistic landscapes and the elements of nature these designate. This is particularly the case with marked, didactic, nature trails. In this paper, we interrogate linguistic landscapes as indices that point to absent objects in nature as an act to communicate the trail to visitors and cultivate an understanding of some parts of nature that are not always visible to humans. Using theoretical concepts of the index from Peirce and the notion of différance from Derrida, we explore the inter-connectivity between linguistic landscapes and absent nature elements. Through deconstruction, we explore the boundaries between nature and culture to highlight the circulation of meaning in and out of the linguistic landscape and in and out of the natural landscape in an ongoing representation-in-relation. We situate our theoretical research in two physical places, an interpretive path in the Tampa Mountain in Brasov, Romania, and a nature walk in the middle of Lincoln Park, Chicago, USA. [ABSTRACT FROM AUTHOR]

In October 2017, Vienna's Leopoldstadt community succeeded in reinstalling a Hebrew street sign in a public space of the second district. This achievement became possible in large part due to the efforts of an active online community that encouraged many people to share their wish to have visible signs of the former historic Jewish quarter in the present urban space. Through vigorous Facebook and other social media activities, the interest that the group generated put pressure on the city, leading to the support of an art project. The placement of the Hebrew street sign marked a hybrid way of constructing Jewish urban spaces. The dialogue between virtual and physical spaces added new layers to the historic Jewish quarter of Vienna; this way of relational space making is, I wish to argue, paradigmatic for today's Europe as it witnesses the heyday of Holocaust tourism and klezmer revivals. In this article, I investigate this space-making process in Jewish public history in present Vienna and examine how the virtual community frames the way urban Jewish spaces are constructed. [ABSTRACT FROM AUTHOR]

Reading is an essential skill integral to daily life, facilitating interactions with texts ranging from street signs to personal messages. The inability to read limits access to information and opportunities, underscoring the importance of effective literacy programs. This study evaluated the impact of the "LEARN TO READ" program on 20 Grade III pupils, including 11 non-readers and nine pupils at the frustration reading level. In the pre-test, all participants scored at the BEGINNING level. After implementing the intervention, the post-test results showed significant improvements: students could produce letter sounds, read consonant-vowel (CV) blends, and read consonant-vowel-consonant (CVC) words spontaneously, achieving the ADVANCED level. Statistical analysis revealed a significant difference between the pre-test and post-test scores, demonstrating the program's success in advancing pupils from non-reading or frustration levels to proficient CVC word readers. Based on these findings, it is recommended that the remediation program be continued into the next grade, focusing on advancing skills to reading phrases, sentences, and simple stories with comprehension. Additionally, the "LEARN TO READ" program is recommended for broader adoption among educators working with non-readers and frustration-level pupils to enhance literacy outcomes in English classes. [ABSTRACT FROM AUTHOR]

Information on the depth of floodwater is crucial for rapid mapping of areas affected by floods. However, previous approaches for estimating floodwater depth, including field surveys, remote sensing, and machine learning techniques, can be time-consuming and resource-intensive. This paper presents an automated and rapid approach for estimating floodwater depth from on-site flood photos. A pre-trained large multimodal model, Generative pre-trained transformers (GPT-4) Vision, was used specifically for estimating floodwater. The input data were flood photos that contained referenced objects, such as street signs, cars, people, and buildings. Using the heights of the common objects as references, the model returned the floodwater depth as the output. Results show that the proposed approach can rapidly provide a consistent and reliable estimation of floodwater depth from flood photos. Such rapid estimation is transformative in flood inundation mapping and assessing the severity of the flood in near-real time, which is essential for effective flood response strategies. [ABSTRACT FROM AUTHOR]

Copyright of Politologija is the property of Vilnius University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Many previous studies have investigated visual distance perception, especially for small to moderate distances. Few experiments, however, have evaluated the perception of large distances (e.g., 100 m or more). The studies that have been conducted have found conflicting results (diametrically opposite conclusions). In the current experiment, the functions relating actual and perceived distance were obtained for sixteen adult observers using the method of equal appearing intervals. These functions relating perceived and actual distance were obtained for outdoor viewing in a typical University environment—the experiment was conducted along a sidewalk adjacent to a typical street where campus buildings, trees, street signs, etc., were visible. The overall results indicated perceptual compression of distances in depth so that the stimulus distance intervals appeared significantly shorter than the actual (physical) distance intervals. It is important to note, however, that there were sizeable individual differences—the judgments of half of the observers were relatively accurate, whereas the judgments of the remaining half were inaccurate to varying degrees. The results of the experiment demonstrate that there is no single function that describes how human observers visually perceive large distance intervals in outdoor environments. [ABSTRACT FROM AUTHOR]

Geographical information systems (GIS) are the systems where spatial data are stored and analyzed. The most important raw material in GIS is spatial data. Thus, it is essential to collect and update these data. On the other hand, exchangeable image file (EXIF) format is a special file format that contains camera direction, date-time information and GPS location provided by a digital camera that captures the images. Transferring the objects in EXIF data sets with absolute coordinates on the earth significantly contributes to GIS. In this study, a new hybrid approach, ODPR, which utilizes object detection (O), distance estimation (D), rotation (R) and projection (P) methods, is proposed to detect street sign objects in EXIF with their locations. The performance of the proposed approach has been examined on the natural EXIF data sets obtained from the Kayseri Metropolitan Municipality. In the proposed approach, a deep learning method detects a street sign object in the EXIF. Then, the object's distance is calculated at the point where the photograph is taken. Finally, the spatial location of the detected object on the earth is calculated using distance, direction and GPS data with rotation and projection methods. In the proposed ODRP approach, the performances of convolutional neural network (CNN)-based Faster R-CNN, YOLO V5, YOLO V6 and transformer-based DETR models as deep learning models for object detection are examined. The F1 score metric is widely used to examine the performance of methods in deep learning models. The performances of the proposed approaches are reviewed according to the F1 score values, and ODRP Faster R-CNN, YOLO V5, YOLO V6 and DETR approaches achieved F1 scores of 0.909, 0.956, 0.948 and 0.922, respectively. In addition, to overcome the variability of light and background mixing problems, an improved supervised learning method (ISL) is proposed. Thanks to ISL, ODRP Faster R-CNN, YOLO V5, YOLO V6, and DETR approaches have reached 0.965, 0.985, 0.969 and 0.942 f1 scores, respectively. The proposed ODRP Faster R-CNN, YOLO V5, YOLO V6 and DETR approaches found the location of the street sign object to be 11434.76, 12818.39, 12454.63 and 9843.57 ms closer to its position on earth than the classical method, which considers the location of the EXIF, respectively. Regarding time cost, the ODRP Faster R-CNN, YOLO V5, YOLO V6 and DETR analyze EXIF data at an average of 0.99, 0.42, 0.41 and 0.53 s, respectively. The run time of the ODRP YOLO V5 and V6 approaches is almost equal to each other, and it works approximately 2.5 times faster than the ODRP Faster R-CNN method. Consequently, ODRP YOLO V5 outperforms ODRP Faster R-CNN, YOLO V6 and DETR for detecting the spatial location of street sign objects in EXIF and the F1 score. [ABSTRACT FROM AUTHOR]

The article discusses the book "Group Life: An Invitation to Local Sociology" by Gary Alan Fine and Tim Hallett. The authors argue that sociology should focus on the familiar and mundane aspects of everyday life, such as small groups and local interactions, rather than solely focusing on the exotic or unique. They emphasize the importance of studying these everyday groups as a way to understand universal social processes. The book provides a theoretical framework and empirical examples to support their perspective, drawing on various sociological traditions such as symbolic interactionism, cultural sociology, and institutional analysis. However, the article also acknowledges some potential weaknesses in their approach, such as the limited focus on power dynamics and the challenge of bridging the gap between micro and macro sociology. Overall, the book is seen as a valuable contribution to the field of sociology, offering a roadmap for studying group life and generating new theoretical insights. [Extracted from the article]

In recent years, access to digital images has been made easy, and the use and distribution of images have increased rapidly. Therefore, undesirable adversarial attacks such as FGSM and PGD can occur during the sharing or distribution of images. On the other hand, object detection, one of the widely used applications of deep learning in real life, is used to scan digital images to locate instances of every object. However, the CNN and transformer-based deep learning models trained using classical data augmentation may be unsatisfactory in obtaining the required object detection accuracy on attacked images. Therefore, to overcome the robustness problem of the deep learning-based object detection methods against attacks, the deep learning approach based on grayscale conversion and discrete wavelet transform is proposed. The proposed approach uses well-known Faster R-CNN, YOLOv5, and DETR as deep learning models. The performance of the proposed approach using grayscale and DWT-based data augmentation has been evaluated on the natural scene dataset containing street signs for object detection on attacked images. Against FGSM attack with 0.10 ϵ , f1 scores of Faster R-CNN, YOLOv5, and DETR models have increased by 0.18, 0.10, and 0.59, respectively. Also, against PGD attack with 0.10 ϵ , f1 scores of models have increased, respectively, by 0.19, 0.23, and 0.63 with the proposed technique. In addition, the performance of the proposed approach has also been evaluated on the open dataset taken from Kaggle. On the other hand, the memory size of the images processed by the models and the run times of deep learning models has decreased. [ABSTRACT FROM AUTHOR]

Copyright of Jurisprudence Faculty Journal / Magallaẗ Kulliyyaẗ Al-fiqh is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Language policies for the purpose of (re)vitalizing a minority language are analyzed as a dynamic cost-effectiveness problem. We focus on policy measures with two types of cost structures: costs largely proportional to the number of beneficiaries (a rival measure) and costs independent of the number of beneficiaries (a non-rival measure). An example of the former is home nursing in the minority language and an example of the latter is street signs in the minority language. Both types of measures are assumed to contribute positively to the vitality of the minority language. We stylize the analysis, letting the rival measure have an immediate direct effect on the vitality and the non-rival one a protracted indirect effect on the language's status. Two problems are addressed. Firstly, we study how the optimal combination of the two types of measure changes as the policy is implemented and the vitality of the minority language increases and show that a policy with fixed budget shares as a rule is sub-optimal. Secondly, we compare the opportune policy of a policy maker planning with a fixed time horizon with the optimal policy as the time horizon approaches infinity. The policy maker has incentives to plan for a sub-optimal policy with a reduction in the protracted measure for the whole planning period and making it equal to zero at the time horizon. All effects are illustrated in numeric examples. [ABSTRACT FROM AUTHOR]

The present study deals with the presence and role of the Sardinian language in the linguistic landscape of sixteen villages in the province of Oristano, Sardinia. Specifically, their entry signs and street name signs were photographed and analysed using both quantitative and qualitative approaches. In the entry signs, Sardinian was found to have a very strong presence, generating a high degree of bilingualism with Italian, as recommended by national and regional language policies. Systematic bilingualism could not, however, be observed in the street name signs, where Italian clearly prevails. Notwithstanding, the local language is visible in around a quarter of all street name signs. Complying with the provisions of national and regional legislations, Sardinian is used in the majority of street signs to recall the historical memory of the communities. Nonetheless, signs could be found where Sardinian is used in parallel bilingual texts to express exactly the same content as Italian, fostering a modern vision of the minority language. The present work shows how top-down language policies can be locally implemented or re-interpreted in the linguistic landscape; moreover, the article sheds light on how street naming can be exploited to influence society's perception of minority languages and convey messages of local or regional/national identity. [ABSTRACT FROM AUTHOR]

Copyright of Revista Universitaria de Historia Militar (RUHM) is the property of Centro de Estudios de la Guerra and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper brings an urban communication lens to bear on the geographies of platformisation in cities. It does so by drawing on three select instances of platformised materialities in Toronto and Vancouver that represent familiar contours of urban platformisation: mobility (bike and car sharing), last‐mile logistics (on‐demand delivery), and labour (gig work). These examples are worked through Aiello and Tosoni's heuristic of cities as constituting the mediums, content, and contexts of urban communication, respectively. As mediums, platformised materialities in the form of street signs designate exclusive uses of public space by mobility platforms, communicating the spatial conditions of platform urbanism. As the contents of communication, stickers and signs advertising on‐demand meal delivery available at a restaurant venue express the platform‐driven transformation of the social relations that make the delivered meal take place. And as context, broader trends of the platformisation of labour render communication by other, non‐platform‐based materialities – such as posters calling on urban gig workers to unionise – meaningful. An urban communication perspective contributes to geographical scholarship on platform urbanism by nuancing our understandings of how platforms and platform technology capital secure and sustain themselves in cities through their material communicative capacities. Platforms have established themselves as visible fixtures of urban built environments. The physical forms that platforms assume in cityscapes constitute the mediums, content, and contexts of urban communication. Engaging platforms as expressing material communicative capacities nuances our understanding of how platforms and platform technology capital secure and sustain themselves in cities. [ABSTRACT FROM AUTHOR]

Pseudowords created by transposing two letters of words (e.g., MOHTER; CHOLOCATE) are highly confusable with their base word; this is known as the transposed-letter similarity effect. In this work, we examined whether transposed-letter effects occur when words span more than one line (e.g., CHOLO- in one line and CATE in another line; note that the transposed letters L and C are in different lines). While this type of presentation is not the canonical format for reading in alphabetic languages, it is widely used in advertising, billboards, and street signs. Transposed-letter pseudowords and their replacement-letter controls were written in the standard one-line format versus a two-line format (Experiments 1–2) or a syllable-per-line format (Experiment 3). While results showed some decrease in the transposed-letter effect in the two-line and syllabic formats, the transposed-letter effect was still substantial in the accuracy of responses. These findings demonstrate that even when the letters being transposed are relatively far apart in space, the transposed-letter effect is still robust. Thus, a major component of letter position coding occurs at an abstract level. [ABSTRACT FROM AUTHOR]

13 Curiously, a character in Modiano's I Fleurs de ruine i has an identity document bearing the address 183, rue Belliard, well west of the end of the Promenade Dora-Bruder. The Promenade Dora-Bruder in Paris is a park-like area situated between two parallel streets - the rue Leibniz to the north and the rue Belliard to the south - in the 18 SP th sp arrondissement. Footnotes 1 See Denis Cosnard, "En hommage à Modiano, bientôt une promenade Dora-Bruder à Paris", I Le Monde i , 20 January 2015 < https://www.lemonde.fr/culture/article/2015/01/20/en-hommage-a-modiano-bientot-une-rue-dora-bruder-a-paris%5f4559438%5f3246.html > [accessed 11 March 2022]. [Extracted from the article]

Copyright of Geography & Geographic Information Science is the property of Geography & Geo-Information Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Henan University of Science & Technology, Natural Science is the property of Editorial Office of Journal of Henan University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The street sign text information from natural scenes usually exists in a complex background environment and is affected by natural light and artificial light. However, most of the current text detection algorithms do not effectively reduce the influence of light and do not make full use of the relationship between high-level semantic information and contextual semantic information in the feature extraction network when extracting features from images, and they are ineffective at detecting text in complex backgrounds. To solve these problems, we first propose a multi-channel MSER (Maximally Stable Extreme Regions) method to fully consider color information in text detection, which separates the text area in the image from the complex background, effectively reducing the influence of the complex background and light on street sign text detection. We also propose an enhanced feature pyramid network text detection method, which includes a feature pyramid route enhancement (FPRE) module and a high-level feature enhancement (HLFE) module. The two modules can make full use of the network's low-level and high-level semantic information to enhance the network's effectiveness in localizing text information and detecting text with different shapes, sizes, and inclined text. Experiments showed that the F-scores obtained by the method proposed in this paper on ICDAR 2015 (International Conference on Document Analysis and Recognition 2015) dataset, ICDAR2017-MLT (International Conference on Document Analysis and Recognition 2017- Competition on Multi-lingual scene text detection) dataset, and the Natural Scene Street Signs (NSSS) dataset constructed in this study are 89.5%, 84.5%, and 73.3%, respectively, which confirmed the performance advantage of the method proposed in street sign text detection. [ABSTRACT FROM AUTHOR]

Digital marketing is on the rise Technologies are constantly evolving and the way people use them is changing not only the way personal information is accessed but also the way they interact on a global scale. The evolution of technology has also changed the way products and services are chosen and purchased. This article examines the promotion techniques used by eMAG (the largest e-commerce platform in Romania). Following the analysis, the authors found that although it is an online platform, eMAG successfully uses, in addition to online marketing techniques, traditional marketing techniques that have the role of attracting more conservative customers. From the very beginning, the eMAG company understood these defining aspects for a good promotion policy, which is also because the ones from eMAG started the business in the online environment, and later they went to open physical points, respectively. showrooms. The digital marketing techniques are SEO, Social Media, and Affiliate Marketing, and the traditional promotion techniques are: price reductions; promotional packages; organizing competitions and street signs. [ABSTRACT FROM AUTHOR]

Current mobile systems are capable of efficiently acquiring dense urban point clouds. Still, operational use of such data is hampered by the lack of efficient object extraction methodology. Notably methodology is lacking for automatically extracting objects that do not belong to the road furniture like street signs and light poles but do belong to the street furniture. As an example, object we consider public garbage bins, that are installed and should be maintained in public areas in every city. However, information about types, locations, and condition of these public garbage bins are rarely updated and only obtained through manual measurements. Therefore, an efficient way of collecting information on such public objects is of interest not only for urban management but also when developing digital twins of a city. This study proposes a new method to automatically extract public garbage bins from large urban mobile laser scanning (MLS) point clouds. The proposed method consists of three main steps: (1) cell-, (2) sub-cell-, and (3) surface-based filtering, in which both spatial information of the point clouds and contextual knowledge of the public garbage bins are incorporated to efficiently remove irrelevant 3D points at an early phase and identify and classify different types of public garbage bins. Contextual knowledge includes shape and dimensions, and the relationship between the public garbage bins and the ground surface. A MLS dataset of the city centre of Rotterdam, the Netherlands, consisting of 2.84 billion points organised in 166 tiles of 50 × 75m, and covering an area of about 750 × 750m was used to test the proposed method. Results show that the method can automatically extract ~90 public garbage bins with an overall detection rate of 89.1%. Moreover, the executing time for the entire dataset was only about 163.6 minutes, which is equivalent to 3.46 seconds per one million points. Although the method was tested here one public garbage bins, it can be easily tuned for the detection of other street furniture objects, like benches, post boxes or bollards. [ABSTRACT FROM AUTHOR]

As road traffic accidents (RTAs) cause enormous economic and human losses, especially in developing countries, numerous research efforts are needed to identify the key risk factors that significantly influence accident and crash severity. Despite that Dhaka city is registering alarming rises in related deaths and severe injuries, Bangladesh has yet to collect significant RTAs data. Thus, this study adopts probit and Heckman selection probit models to investigate RTAs and injury severity levels using original data from an on-field survey collecting 786 participants' responses regarding their socio-economic and demographic characteristics, their knowledge of road traffic systems and rules, the roads and vehicles types, and the road infrastructure conditions. Probit model showed that the major risk factors that increase road accidents causing severe injuries were wrong-way driving, and lack of speed control signs and adequate street lights. RTAs resulting in severe injuries were significantly associated with being married, not having an educational degree, driving on highways and in city areas. Furthermore, the Heckman probit model's selection equation showed that respondents who were unaware of road accident risks, resided in rural areas, and with high household income had higher risks of being directly involved in RTAs. [ABSTRACT FROM AUTHOR]

Copyright of Jerash Journal for Research & Studies is the property of Jerash University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article examines the street name signs of Taipei City in their ideological, linguistic, and semiotic dimensions. These different levels of analysis correlate with different processes of sign development. Combining critical toponymy and linguistic landscape research, it is claimed that in ideological and linguistic terms the post-war period has been one of the fundamental changes that has been arguably unparalleled elsewhere. By contrast, the ideological about-turn after the 1980s has had relatively little influence on the contents of street name signs. Alluding to Western-style modernity, semiotic features like the colour of the signs and the direction of writing are later innovations. It is also argued that in the broader context of their multilingual environment, there is an enormous discrepancy between visible normativity and audible multilingualism. [ABSTRACT FROM AUTHOR]

Object geo-localization from images is crucial to many applications such as land surveying, self-driving, and asset management. Current visual object geo-localization algorithms suffer from hardware limitations and impractical assumptions limiting their usability in real-world applications. Most of the current methods assume object sparsity, the presence of objects in at least two frames, and most importantly they only support a single class of objects. In this paper, we present a novel two-stage technique that detects and geo-localizes dense, multi-class objects such as traffic signs from street videos. Our algorithm is able to handle low frame rate inputs in which objects might be missing in one or more frames. We propose a detector that is not only able to detect objects in images, but also predicts a positional offset for each object relative to the camera GPS location. We also propose a novel tracker algorithm that is able to track a large number of multi-class objects. Many current geo-localization datasets require specialized hardware, suffer from idealized assumptions not representative of reality, and are often not publicly available. In this paper, we propose a public dataset called ARTSv2, which is an extension of ARTS dataset that covers a diverse set of roads in widely varying environments to ensure it is representative of real-world scenarios. Our dataset will both support future research and provide a crucial benchmark for the field. [ABSTRACT FROM AUTHOR]

Two aspects of real-world visual search are typically studied in parallel: category knowledge (e.g., searching for food) and visual patterns (e.g., predicting an upcoming street sign from prior street signs). Previous visual search studies have shown that prior category knowledge hinders search when targets and distractors are from the same category. Other studies have shown that task-irrelevant patterns of non-target objects can enhance search when targets appear in locations that previously contained these irrelevant patterns. Combining EEG (N2pc ERP component, a neural marker of target selection) and behavioral measures, the present study investigated how search efficiency is simultaneously affected by prior knowledge of real-world objects (food and toys) and irrelevant visual patterns (sequences of runic symbols) within the same paradigm. We did not observe behavioral differences between locating items in patterned versus random locations. However, the N2pc components emerged sooner when search items appeared in the patterned location, compared to the random location, with a stronger effect when search items were targets, as opposed to non-targets categorically related to the target. A multivariate pattern analysis revealed that neural responses during search trials in the same time window reflected where the visual patterns appeared. Our finding contributes to our understanding of how knowledge acquired prior to the search task (e.g., category knowledge) interacts with new content within the search task. [ABSTRACT FROM AUTHOR]