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Rough fuzzy clustering algorithms have received extensive attention due to the excellent ability to handle overlapping and uncertainty of data. However, existing rough fuzzy clustering algorithms generally consider single view clustering, which neglects the clustering requirements of multiple views and results in the failure to identify diverse data structures in practical applications. In addition, rough fuzzy clustering algorithms are always sensitive to the initialized cluster centers and easily fall into local optimum. To solve the above problems, the multi-view and transfer learning are introduced into rough fuzzy clustering and a robust multi-view knowledge transfer-based rough fuzzy c-means clustering algorithm (MKT-RFCCA) is proposed in this paper. First, multiple distance metrics are adopted as multiple views to effectively recognize different data structures, and thus positively contribute to clustering. Second, a novel multi-view transfer-based rough fuzzy clustering objective function is constructed by using fuzzy memberships as transfer knowledge. This objective function can fully explore and utilize the potential information between multiple views and characterize the uncertainty information. Then, combining the statistical information of color histograms, an initialized centroids selection strategy is presented for image segmentation to overcome the instability and sensitivity caused by the random distribution of the initialized cluster centers. Finally, to reduce manual intervention, a distance-based adaptive threshold determination mechanism is designed to determine the threshold parameter for dividing the lower approximation and boundary region of rough fuzzy clusters during the iteration process. Experiments on synthetic datasets, real-world datasets, and noise-contaminated Berkeley and Weizmann images show that MKT-RFCCA obtains favorable clustering results. Especially, it provides satisfactory segmentation results on images with different types of noise and preserves more specific detail information of images. [ABSTRACT FROM AUTHOR]

In this paper, wireless sensor network technology is applied to an English-assisted reading system to highly simulate and restore the context and improve the performance of all aspects of the English-assisted reading system to optimize the English-assisted reading system. The product designed in this paper is based on wireless sensor network technology with Linux as the core operating system and supports POSIX (Portable Operating System Interface Standard) standard application development interface; QT is used as the component and framework of the system to support many applications. Based on player open-source multimedia audio and video technology, optimized and tailored for the hardware platform, it well supports multimedia learning and entertainment functions; this paper also adopts open-source database technology based on SQL (Structured Quevy Language) and Berkeley DB, using them as a platform for data storage and access, supporting a million-level thesaurus and high-speed, example sentence search. In this paper, we describe the user's personalized needs by creating interest models for the user, recommending the text content, and reading order that can help with understanding through the interest models and reading articles and expanding the recommended text range by making expansions to the reading content through references and related articles to further help the user understand the text. Based on the above work, this paper implements an assisted reading system; finally, a multihop self-organizing network system is formed through a wireless sensor network to make the rigid and boring English reading easy and interesting. [ABSTRACT FROM AUTHOR]

In addition to the low-temperature nuclear orientation studies discussed in Paper I, David Shirley's research group at Berkeley was renowned for other types of studies of the interaction of probe nuclei with their electromagnetic environment, particularly perturbed angular correlations and the Mössbauer effect. The present paper discusses these other fields of research into hyperfine interactions and gives examples of the contributions of the Shirley group toward elucidating these interactions. [ABSTRACT FROM AUTHOR]

This work explores the effectiveness and robustness of quantum computing by conjoining the principles of quantum computing with the conventional computational paradigm for the automatic clustering of colour images. In order to develop such a computationally efficient algorithm, two population-based meta-heuristic algorithms, viz., Particle Swarm Optimization (PSO) algorithm and Enhanced Particle Swarm Optimization (EPSO) algorithm have been consolidated with the quantum computing framework to yield the Quantum Inspired Particle Swarm Optimization (QIPSO) algorithm and the Quantum Inspired Enhanced Particle Swarm Optimization (QIEPSO) algorithm, respectively. This paper also presents a comparison between the proposed quantum inspired algorithms with their corresponding classical counterparts and also with three other evolutionary algorithms, viz., Artificial Bee Colony (ABC), Differential Evolution (DE) and Covariance Matrix Adaption Evolution Strategies (CMA-ES). In this paper, twenty different sized colour images have been used for conducting the experiments. Among these twenty images, ten are Berkeley images and ten are real life colour images. Three cluster validity indices, viz., PBM, CS-Measure (CSM) and Dunn index (DI) have been used as objective functions for measuring the effectiveness of clustering. In addition, in order to improve the performance of the proposed algorithms, some participating parameters have been adjusted using the Sobol's sensitivity analysis test. Four segmentation evaluation metrics have been used for quantitative evaluation of the proposed algorithms. The effectiveness and efficiency of the proposed quantum inspired algorithms have been established over their conventional counterparts and the three other competitive algorithms with regards to optimal computational time, convergence rate and robustness. [ABSTRACT FROM AUTHOR]

Multilevel thresholding segmentation of color images plays an important role in many fields. The pivotal procedure of this technique is determining the specific threshold of the images. In this paper, an improved mayfly algorithm (IMA)-based color image segmentation method is proposed. Tent mapping initializes the female mayfly population to increase population diversity. Lévy flight is introduced in the wedding dance iterative formulation to make IMA jump from the local optimal solution quickly. Two nonlinear coefficients were designed to speed up the convergence of the algorithm. To better verify the effectiveness, eight benchmark functions are used to test the performance of IMA. The average fitness value, standard deviation, and Wilcoxon rank sum test are used as evaluation metrics. The results show that IMA outperforms the comparison algorithm in terms of search accuracy. Furthermore, Kapur entropy is used as the fitness function of IMA to determine the segmentation threshold. 10 Berkeley images are segmented. The best fitness value, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and other indexes are used to evaluate the effect of segmented images. The results show that the IMA segmentation method improves the segmentation accuracy of color images and obtains higher quality segmented images. [ABSTRACT FROM AUTHOR]

This paper considers Berkeley's use of Plato in Siris. Berkeley's engagement with ancient thinkers in Siris has been a source of puzzlement for many readers. In this paper I focus on Siris § 266. In particular, I consider why Berkeley says of the Platonists that they "distinguished the primary qualities in bodies from the secondary" and why, given his own well-known misgivings about the distinction, he characterizes this as part of a "notion of the true system of the world." I argue that in Siris Berkeley accepts a distinctive form of corpuscularianism, and that he thinks a distinction between primary and secondary qualities follows from this. I further argue that in § 266, and elsewhere in Siris, Berkeley engages in a careful reading of Plato's Timaeus, which he uses to bolster his defense of the compatibility between corpuscularianism and his immaterialist idealism. [ABSTRACT FROM AUTHOR]

Under the leadership of David Shirley, the hyperfine interactions group at Berkeley became one of the world's leading laboratories for its diversity of studies of the interaction between probe nuclei and their environment. One branch of those studies, low-temperature nuclear orientation, concerned the radioactive decays of nuclei whose spins were oriented in a variety of electromagnetic environments at temperatures in the mK range. In the years 1960–1975, this group did pioneering research that produced more than 50 papers in the field, and at least 16 graduate students completed their dissertation research. The present paper gives a brief introduction to the field of low-temperature nuclear orientation, summarizes the main accomplishments of the Shirley group, and discusses examples of the variety of results obtained in the group's research. Paper II deals with other hyperfine interaction studies by the Shirley group. [ABSTRACT FROM AUTHOR]

In order to track the research progress of AEB-related technologies, this paper makes a systematic analysis and research on the impact factors, key technologies, and effect evaluation of AEB. First, the paper deeply analyzes the three levels of factors affecting the performance of AEB, which are vehicle factors, driver factors, and environmental factors. Second, the paper deeply studies the technical status of the three subsystems of environment perception, decision-making, and control execution. Particularly, the performance of Mazda, Honda, NHTSA, Berkeley, and Seungwuk Moon are compared and analyzed based on MATLAB. Third, the paper summarizes the current AEB virtual test methods, closed field test methods, and its test sites. Three classic evaluation methods in the world, including the AEB test evaluation standards of ENCAP, IIHS, and i-Vista are analyzed. Finally, the paper prospects the specific research directions, including the protection of vulnerable road users, target detection method, collision avoidance strategy, complex scenarios application, and application of emerging technologies. [ABSTRACT FROM AUTHOR]

In this paper, machine learning (ML) techniques are applied at an early stage of Image Processing (IP). The learning procedures are usually applied from at least the image segmentation level, whereas, in this paper, this is done from a lower processing level: the edge detection level (ED). The main objective is to solve the edge detection problem through ML techniques. The proposed methodology is based on a classification of edges made pixel by pixel, but the predictors employed for the ML task include information about the pixel neighborhood and structures of connected pixels called edge segments. The Sobel operator is employed as input. Making use of 50 images that belong to the Berkeley Computer Vision data set, the average performance of the validation sets when employing our Neural Networks method reached an F-measure significatively higher than with the Sobel operator. The experiment results show that our post-processing technique is a promising new approach for ED. [ABSTRACT FROM AUTHOR]

This paper evaluates the price and consumption effects of the first municipal soda tax imposed in the United States. Using high-resolution scanner data and data-driven approaches to select comparison units for counterfactual analysis, we estimate the tax has no effect on prices or consumption at drugstores, but increases supermarket prices of some soda products, constituting a minority of soda consumption. We estimate UPC-level pass through rates and find that there is significant heterogeneity across UPCs, much of which is explained by brand and size; average UPC-level pass through estimates in the supermarket range between 19% and 23%. We find limited evidence of reduced supermarket purchases of soda in the taxed jurisdiction. Half of these reduced purchases are substituted to just outside the taxed jurisdiction. Retailers' limited price responses are attributed to the localness of the tax; other research studying the Philadelphia soda tax has demonstrated more substantial pass-through in this much larger jurisdiction. [ABSTRACT FROM AUTHOR]

Berkeley is best known for his immaterialism and the texts that extol it—the Principles of Human Knowledge and Three Dialogues Between Hylas and Philonous. He made his case by treatise, then by dialogue, and this tendency towards stylistic experimentation did not end there; this paper explores an early speculative fiction project that pursued his theological and philosophical agendas. Berkeley used satire to challenge his "freethinking" philosophical opponents in "The Pineal Gland" story published in The Guardian in 1713. Echoing the grand tours Berkeley undertook in subsequent years, Part 1 offers a "gland tour" of some literary motivations, influences and legacies of these essays. Berkeley pursues heroic themes from Homer and Alexander Pope, while lampooning the philosophies of both Descartes and the freethinkers. Armed with the device of a magic snuff that transports him to the pineal glands of his adversaries, Berkeley's protagonist uses it "to distinguish the real from the professed sentiments of all persons of eminence in court, city, town, and country". (Guardian, p. 187) Part 1 examines 'The Pineal Gland' in the context of Berkeley's broader philosophical legacy and the text's significant engagement with the literature of Homer and Pope, concluding that "The Pineal Gland" is an important but overlooked source in the history of early speculative fiction. Part 2 continues this analysis by exploring Berkeley's relationship with an expansive London literary circle, interrogating a line of influence beginning with the writing of Margaret Cavendish. In doing so, Part 2 also examines Berkeley's complex attitudes towards women. [ABSTRACT FROM AUTHOR]

Superpixel-based segmentation has been widely used as a primary prepossessing step to simplify the subsequent image processing tasks. Since determining the number of clusters is subjective and varies based on the type of image, the segmentation algorithm may provide over-segmented or under-segmented superpixels. This paper proposes an image segmentation method to improve the SLIC superpixel by region merging. It aims to improve the segmentation accuracy without defining a precise number of superls. The color difference between superpixels is employed as a homogeneity criterion for the merging process. The Berkeley dataset is used with different quantitative performance metrics to evaluate the proposed model's performance. Results obtained from probabilistic rand index (PRI), boundary recall, and under-segmentation error proved the ability of the proposed algorithm to provide comparable segmentation with a reduced number of clusters. [ABSTRACT FROM AUTHOR]

To solve the problems of image threshold segmentation based on weak continuous constraint theory, the running time is long, and the two parameters need to be selected manually, and therefore a fast single-parameter energy function thresholding for image segmentation based on region information (FSEFTISRI) is proposed in this paper. The proposed FSEFTISRI algorithm uses simple linear iterative clustering (SLIC) technology to pre-block the image, extract the image super-pixels, and then map the image super-pixels to the interval type-2 fuzzy set (IT2FS), so as to construct the single-parameter energy function to search the optimal threshold, and adaptively select the penalty parameters in the energy function through the class uncertainty theory. On a non-destructive testing (NDT) database and Berkeley segmentation datasets and benchmarks (BSDS), the proposed FSEFTISRI is compared with five related algorithms. The average misclassification error (ME) of the proposed FSEFTISRI algorithm on NDT and BSDS are 0.0466 and 0.0039, respectively. The results show that the proposed FSEFTISRI has acquired more satisfactory results in visual effect and evaluation index, and the running time of the proposed FSEFTISRI algorithm is shorter, which shows the effectiveness of the proposed FSEFTISRI. [ABSTRACT FROM AUTHOR]

Image segmentation is an important part of image understanding and one of the most difficult problems in image processing. For image segmentation processing, this paper proposes an image segmentation algorithm for multilevel thresholding based on novelty harmony search algorithm. Firstly, the central harmony and central congestion distance are introduced to reduce local aggregation of initial points and expand the search range. Secondly, the new harmony generation strategy is constructed, which is based on dominant harmony learning experience. Then the search space constraints and parameters adaptive adjustment are adopted to improve the search efficiency. Finally, the harmony memory updating rules are designed to enhance the diversity of population. The image segmentation effect is evaluated by the between-class variance, peak signal-to-noise ratio and mean structural similarity. A series of experiments have been carried out to analyze the segmentation effect of the proposed NHS algorithm based on the Berkeley segmentation database. Compared with the basic harmony search algorithm, improved harmony search algorithm, global best harmony search algorithm, particle swarm optimization algorithm and artificial bee colony algorithm, the experimental results show the effectiveness of the proposed algorithm. In particular the proposed algorithm is superior to other methods when the threshold number increases. The influence of noise and artifact on image segmentation is also discussed and analyzed. It illustrates that the image can be segmented in the Gaussian noise, mixed noise and strip line artifact conditions based on the proposed algorithm. [ABSTRACT FROM AUTHOR]

Segmentation refers to the process of dividing an image into multiple regions based on some criteria such as intensity and color. In recent years, color image segmentation has received considerable attention from the researchers. However, it is still a highly complicated task due to the presence of more attributes or components as compared to monochrome images. Numerous meta-heuristics algorithms are developed to determine the optimal threshold value for segmenting color images efficiently. This paper presents an enhanced sine cosine algorithm (ESCA) to seek threshold for segmenting color images. Sine cosine algorithm (SCA) is a population-based optimization algorithm which has the ability of preventing local minima problem. First an input image is transformed to CIE L*a*b* color reduced space. ESCA is applied to determine the optimal threshold values for segmentation. The performance of the proposed method is tested on color images from Berkeley database, and segmentation results are compared with two metaheuristic algorithms, namely particle swarm optimization (PSO) and standard SCA. Experimental results are validated by measuring peak signal–noise ratio (PSNR), structural similarity index and computation time for all the images investigated. Results revealed that the proposed method outperforms the other methods like PSO and SCA by achieving PSNR of 23 dB and SSIM of 0.93 and also require less time for finding optimal threshold values than PSO and SCA. [ABSTRACT FROM AUTHOR]

Archival work among the Tell en-Naṣbeh materials housed in the Badè Museum in Berkeley, California, turned up evidence of a previously unpublished assemblage of in situ storage jars that was excavated in April, 1932. These jars seem to belong to the Babylonian–Persian periods. While the assemblage was obviously known to the excavators, for some unknown reason the jars were not published or discussed as such in the original 1947 site report. This paper discusses how the archival material was discovered, issues involving the interpretation of these old excavation materials, the unusual context of the jars, parallels for the jars and how they add to our knowledge of the site and its history following the Babylonian destruction of Jerusalem. [ABSTRACT FROM AUTHOR]

The concept of intuitionistic fuzzy set has been found to be highly useful to handle vagueness in data. Based on intuitionistic fuzzy set theory, intuitionistic fuzzy clustering algorithms are proposed and play an important role in image segmentation. However, due to the influence of initialization and the presence of noise in the image, intuitionistic fuzzy clustering algorithm cannot acquire the satisfying performance when applied to segment images corrupted by noise. In order to solve above problems, a robust intuitionistic fuzzy clustering with bias field estimation (RIFCB) is proposed for noisy image segmentation in this paper. Firstly, a noise robust intuitionistic fuzzy set is constructed to represent the image by using the neighboring information of pixels. Then, initial cluster centers in RIFCB are adaptively determined by utilizing the frequency statistics of gray level in the image. In addition, in order to offset the information loss of the image when constructing the intuitionistic fuzzy set of the image, a new objective function incorporating a bias field is designed in RIFCB. Based on the new initialization strategy, the intuitionistic fuzzy set representation, and the incorporation of bias field, the proposed method preserves the image details and is insensitive to noise. Experimental results on some Berkeley images show that the proposed method achieves satisfactory segmentation results on images corrupted by different kinds of noise in contrast to conventional fuzzy clustering algorithms. [ABSTRACT FROM AUTHOR]

The data explosion caused by the Internet and its applications has given researchers immense scope for data analysis. A large amount of data is available in form of images. Image processing is required for better understandability of an image. Various image processing steps are available for improving the image in different application areas. Various applications like medical imaging, face recognition, biometric security, and traffic surveillance, etc. depend only on image and its analysis. This analysis in several applications is highly dependent on the outcome of image segmentation. This paper focuses on good segmentation through multi-level thresholding. In this research, the algorithm includes two modules related to Entropy and variance. The first module is concerned with the modified firefly algorithm (FA) with Kapur's, Tsallis, and Fuzzy Entropy. FA is used to optimize fuzzy parameters for obtaining optimal thresholds. The second module is derived from the principle of variance between two classes known as between variance or inter-cluster variance. The opposition-based the learning method is used for initializing the population of candidate solutions and levying flight and local search is implemented with FA. The various experiments have been performed on Berkeley and benchmark images with distinct threshold (i.e. 2, 3, 4, 5) values. The proposed algorithm has been estimated and compared with known metaheuristic optimization methods like particle swarm optimization (PSO) and electromagnetism optimization (EMO). The results have been assessed quantitatively and qualitatively by using parameters like Peak signal-to-noise ratio (PSNR), structured similarity index metric (SSIM), objective function values, and convergence curve. The algorithm proposed observed better experiment results than PSO, EMO in terms of persistency and quality. [ABSTRACT FROM AUTHOR]

This paper presents the curation of a monitored dataset from an office building constructed in 2015 in Berkeley, California. The dataset includes whole-building and end-use energy consumption, HVAC system operating conditions, indoor and outdoor environmental parameters, as well as occupant counts. The data were collected during a period of three years from more than 300 sensors and meters on two office floors (each 2,325 m2) of the building. A three-step data curation strategy is applied to transform the raw data into research-grade data: (1) cleaning the raw data to detect and adjust the outlier values and fill the data gaps; (2) creating the metadata model of the building systems and data points using the Brick schema; and (3) representing the metadata of the dataset using a semantic JSON schema. This dataset can be used in various applications—building energy benchmarking, load shape analysis, energy prediction, occupancy prediction and analytics, and HVAC controls—to improve the understanding and efficiency of building operations for reducing energy use, energy costs, and carbon emissions. Measurement(s) indoor temperature • Electricity • Indoor occupancy Technology Type(s) temperature sensor • electricity use sensor • occupancy sensor Factor Type(s) building energy management • HVAC operation Sample Characteristic - Environment office building Sample Characteristic - Location United States of America [ABSTRACT FROM AUTHOR]

The need for monitoring the state of a traffic network versus the costly installation and maintenance of roadside sensors constitutes the tough sensor placement problem in designing transportation networks. Placement problems naturally lie in the category of subset selection problems, which are known to be inherently combinatorial, and therefore, finding their exact solution is intractable for large problems. Due to this intractability, numerous heuristics have been proposed in the literature for approximately solving placement problems for traffic networks. Among these approaches, it has been observed that greedy algorithms normally outperform other heuristics. In this paper, we show the mathematics of why greedy algorithms are appropriate proxies for solving these subset selection problems; similar to placement problems for linear systems, placement problems for traffic networks also normally have a submodular structure. In this work, we analyze the problem of road sensor placement for transportation networks under different information structures available: when no vehicle routing information is available, when vehicles' routings are known, and when it is necessary to maximize the number of origin–destination (O/D) traffic flows that are monitored with a set of sensors. We show that in all these cases, the placement problem has a submodular monotone structure. It is well known that the submodularity and monotonicity of discrete optimization problems can be leveraged to derive greedy algorithms that approximate the optimal solution. Consequently, our result is of great practical importance since by exploiting the submodularity and monotonicity of a problem, we show that it is possible to use polynomial-time greedy algorithms to approximate the combinatorial optimization problem with guaranteed optimality bounds for large problems, which are intractable to solve otherwise. Our results shed light upon the success of heuristic greedy algorithms that have been developed in some of the literature for solving placement problems at scale. To demonstrate the applicability of submodular optimization for solving placement problems, we first compare the performance of our polynomial-time approximation algorithm with the true optimum in an example traffic network which is small enough for finding the exact optimal solution with enumerating all possible subsets. Then, we investigate and validate our submodular approach in a case study involving a large-scale traffic network in Berkeley, California, where finding the exact optimal solution is intractable. Submodularity of the placement problem in these scenarios provides a powerful computational tool which can be further extended to other placement problem formulations that can become a reference for solving similar problems in the transportation literature. • Sensor placement problems on large scale networks are intractable. • Three different sensor placement problems are shown to share the submodular feature. • Submodular optimization problems have efficient solutions. • The submodular optimization algorithm is validated on both small and large networks. [ABSTRACT FROM AUTHOR]

Image segmentation is one of the key steps of target recognition. However, the accuracy of image segmentation is still challenging. To solve this problem, an image segmentation algorithm combining Pulse Coupled Neural Network (PCNN) and adaptive glowworm algorithm is proposed. The algorithm retains the advantages of the glowworm algorithm. Introduce the adaptive moving step size and the population optimal value as adjustment factors. Enhance the ability to solve the global optimal value of the fitness function. Take the weighted sum of the cross entropy, information entropy and compactness of the image as the fitness function of the glowworm algorithm. This function can ensure the visual effect of image segmentation and limit the running time while maintaining as much as possible the original information of the image. In order to intuitively evaluate the effect of the segmented image, use a number of segmentation evaluation parameters to quantify the image. Maintain the diversity of image features and improving the accuracy of image segmentation. Experimental results show that compared with other algorithms, the segmented image obtained by this algorithm has better visual effect and the segmentation performance has the best comprehensive performance. For the seven gray-scale images in the Berkeley segmentation dataset, the segmentation effect is improved by 10.85% compared with two-dimensional entropies(TDE), 9.22% compared with Genetic Algorithm(GA), and 22.58% compared with AUTO algorithm. [ABSTRACT FROM AUTHOR]

The early 2020s saw a wave of demands for books to be removed from school and public libraries in Virginia and throughout the United States. A disproportionately high percentage of challenges were aimed at books written by LGBTQ+ authors and authors of color. Involvement by public officials was one of the most striking features of the challenges--especially when Republican gubernatorial candidate Glenn Youngkin chose to make "parental control" of children's education a central feature of his 2021 campaign. However, while these events represent a new and troubling phase in the long history of struggles for control of reading material, race and sexuality have been recurring themes in book censorship throughout Virginia history in periods of backlash to social change. This article surveys episodes in the history of book censorship in Virginia from 1960-present set against the longer arc of Virginia and US history. Books provide exposure to knowledge as well as its representation, ensuring that they will be a focus of cultural and political struggles. Demands to restrict library materials in order to protect children tend to focus on literature giving voice to marginalized communities, and can be followed by demands to restrict adults' reading material as well. [ABSTRACT FROM AUTHOR]

Multilevel thresholding segmentation of color images plays an important role in many fields. The pivotal procedure of this technique is determining the specific threshold of the images. In this paper, a hybrid preaching optimization algorithm (HPOA) for color image segmentation is proposed. Firstly, the evolutionary state strategy is adopted to evaluate the evolutionary factors in each iteration. With the introduction of the evolutionary state, the proposed algorithm has more balanced exploration-exploitation compared with the original POA. Secondly, in order to prevent premature convergence, a randomly occurring time-delay is introduced into HPOA in a distributed manner. The expression of the time-delay is inspired by particle swarm optimization and reflects the history of previous personal optimum and global optimum. To better verify the effectiveness of the proposed method, eight well-known benchmark functions are employed to evaluate HPOA. In the interim, seven state-of-the-art algorithms are utilized to compare with HPOA in the terms of accuracy, convergence, and statistical analysis. On this basis, an excellent multilevel thresholding image segmentation method is proposed in this paper. Finally, to further illustrate the potential, experiments are respectively conducted on three different groups of Berkeley images. The quality of a segmented image is evaluated by an array of metrics including feature similarity index (FSIM), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and Kapur entropy values. The experimental results reveal that the proposed method significantly outperforms other algorithms and has remarkable and promising performance for multilevel thresholding color image segmentation. [ABSTRACT FROM AUTHOR]

Interest toward molecule‐molecule interactions developed during the first half of the 19th century with studies on gas liquefaction. In 1869, Andrews carried out the first accurate study on the effects of temperature and pressure on the behaviour of real gases, and in 1873, van der Waals formulated an equation capable of accounting for critical phenomena of each individual gas The nature of the intermolecular forces responsible for aggregation of gases was investigated in the early 20th century by Keesom and Debye (electrostatic interactions between dipoles) and by London (interaction between instantaneous dipoles, studied by quantum mechanics). The hydrogen bond theory, a particular case of dipolar interaction, originated in the 1920s in Berkeley in the institute directed by G. N. Lewis. Later it was made clear that hydrogen bonding is responsible for the aggregation of most biological systems, from proteins to nucleic acids. This Perspective describes the most significant steps through which the science of intermolecular interactions has progressed over the last two centuries, revisiting classical experiments and theoretical formulations, which led to the complex state of art of today. [ABSTRACT FROM AUTHOR]

Image segmentation, the process of partitioning an image into meaningful regions, is a fundamental step in image processing, crucial for applications like computer vision, medical imaging, and object recognition. Image segmentation is an essential step of image processing that directly affects its success. Among the methods used for image segmentation, histogram-based thresholding is prevalent. Two well-known approaches to histogram-based thresholding are Otsu's and Kapur's methods in gray images that maximize the between-class variance and the entropy measure, respectively. Both techniques were introduced for bi-level thresholding. However, these techniques can be expanded to multilevel image thresholding. For this to occur, a large number of iterations are required to account for exact threshold values. To this end, various optimization techniques have been used to overcome this drawback. Recently, a new optimization algorithm called Battle Royal Optimizer (BRO) has been published, which is shown to solve various optimization tasks effectively. In this study, BRO has been applied to yield optimum threshold values in multilevel image thresholding. Here is also demonstrated the effectiveness of BRO for image segmentation on various images from the standard publicly accessible Berkeley segmentation dataset. We compare the performance of BRO to other state-of-the-art optimization-based methods and show that it outperforms them in terms of fitness value, Peak Signal-to-Noise Ratio, Structural Similarity Index Method, Feature Similarity Index Method (FSIM), Color FSIM (FSIMc), and Standard Deviation. These results underscore the potential of BRO as a promising solution for image segmentation tasks, particularly through its effective implementation of multilevel thresholding. [ABSTRACT FROM AUTHOR]

Image classification and unsupervised image segmentation can be achieved using the Gaussian mixture model. Although the Gaussian mixture model enhances the flexibility of image segmentation, it does not reflect spatial information and is sensitive to the segmentation parameter. In this study, we first present an efficient algorithm that incorporates spatial information into the Gaussian mixture model (GMM) without parameter estimation. The proposed model highlights the residual region with considerable information and constructs color saliency. Second, we incorporate the content-based color saliency as spatial information in the Gaussian mixture model. The segmentation is performed by clustering each pixel into an appropriate component according to the expectation maximization and maximum criteria. Finally, the random color histogram assigns a unique color to each cluster and creates an attractive color by default for segmentation. A random color histogram serves as an effective tool for data visualization and is instrumental in the creation of generative art, facilitating both analytical and aesthetic objectives. For experiments, we have used the Berkeley segmentation dataset BSDS-500 and Microsoft Research in Cambridge dataset. In the study, the proposed model showcases notable advancements in unsupervised image segmentation, with probabilistic rand index (PRI) values reaching 0.80, BDE scores as low as 12.25 and 12.02, compactness variations at 0.59 and 0.7, and variation of information (VI) reduced to 2.0 and 1.49 for the BSDS-500 and MSRC datasets, respectively, outperforming current leading-edge methods and yielding more precise segmentations. [ABSTRACT FROM AUTHOR]

While image segmentation is crucial in various computer vision applications, such as autonomous driving, grasping, and robot navigation, annotating all objects at the pixel-level for training is nearly impossible. Therefore, the study of unsupervised image segmentation methods is essential. In this paper, we present a pixel-level clustering framework for segmenting images into regions without using ground truth annotations. The proposed framework includes feature embedding modules with an attention mechanism, a feature statistics computing module, image reconstruction, and superpixel segmentation to achieve accurate unsupervised segmentation. Additionally, we propose a training strategy that utilizes intra-consistency within each superpixel, inter-similarity/dissimilarity between neighboring superpixels, and structural similarity between images. To avoid potential over-segmentation caused by superpixel-based losses, we also propose a post-processing method. Furthermore, we present an extension of the proposed method for unsupervised semantic segmentation. We conducted experiments on three publicly available datasets (Berkeley segmentation dataset, PASCAL VOC 2012 dataset, and COCO-Stuff dataset) to demonstrate the effectiveness of the proposed framework. The experimental results show that the proposed framework outperforms previous state-of-the-art methods. [Display omitted] [ABSTRACT FROM AUTHOR]

Around 1970, the City of Berkeley briefly became an epicenter of radical experimentation in urban planning and design, directly stemming from the counterculture of the late 1960s. This essay examines the ideological and political emergence of Berkeley's counterculture urbanism, arguing that its experiments left two important legacies in the history of planning. On the level of utopian thought, it articulated a clear alternative to mainstream capitalist urban development, or what Henri Lefebvre called "abstract space." On the level of contemporary planning practices, it opened up still-unresolved conflicts, especially between localized environmental preservation and the abstract, economic demands for affordable housing. [ABSTRACT FROM AUTHOR]

Compound heat wave (CompoundHW) has attracted extensive attention for its prolonged extreme heat from daytime to nighttime during its process. However, the performance of identifying and characterizing CompoundHW across different datasets has not been systematically evaluated. Here, we compared the similarities and differences of the ERA5, Berkeley Earth, CHIRTS and CPC datasets in identifying and characterizing CompoundHW. Results showed that the match of CompoundHW identification between datasets was consistent in both temporal and spatial dimensions, with the highest match observed between the ERA5 and CHIRTS datasets. Match of CompoundHW identification exhibited significant correlation with the density of observation stations, with matching rates above 50% in regions with dense observation networks, but extremely low match in regions with sparse data coverage. The rising trends of the CompoundHW metrics were captured by all datasets, especially in parts of North America, Europe, western Russia and Asia. Despite differences in the amplitude of CompoundHW changes across the four datasets, over 42% of global regions concurred on the changes in CompoundHW frequency, duration, and magnitude, and more than 27% agreed on the changes in the proportion of CompoundHW occurrences. Inconsistencies of CompoundHW changes were predominantly observed in regions with low matching rates, indicating that precise identification of CompoundHW is the basis for characterizing the changes in CompoudHW characteristics accurately. This study highlights the importance of multiple datasets comparison in heat wave research, especially in metrics defined by multiple climate variables and regions with sparse observational data. [ABSTRACT FROM AUTHOR]

The targeted maximum likelihood estimation (TMLE) statistical data analysis framework integrates machine learning, statistical theory, and statistical inference to provide a least biased, efficient, and robust strategy for estimation and inference of a variety of statistical and causal parameters. We describe and evaluate the epidemiological applications that have benefited from recent methodological developments. We conducted a systematic literature review in PubMed for articles that applied any form of TMLE in observational studies. We summarized the epidemiological discipline, geographical location, expertize of the authors, and TMLE methods over time. We used the Roadmap of Targeted Learning and Causal Inference to extract key methodological aspects of the publications. We showcase the contributions to the literature of these TMLE results. Of the 89 publications included, 33% originated from the University of California at Berkeley, where the framework was first developed by Professor Mark van der Laan. By 2022, 59% of the publications originated from outside the United States and explored up to seven different epidemiological disciplines in 2021–2022. Double-robustness, bias reduction, and model misspecification were the main motivations that drew researchers toward the TMLE framework. Through time, a wide variety of methodological, tutorial, and software-specific articles were cited, owing to the constant growth of methodological developments around TMLE. There is a clear dissemination trend of the TMLE framework to various epidemiological disciplines and to increasing numbers of geographical areas. The availability of R packages, publication of tutorial papers, and involvement of methodological experts in applied publications have contributed to an exponential increase in the number of studies that understood the benefits and adoption of TMLE. [ABSTRACT FROM AUTHOR]

Highbush blueberry (Vaccinium corymbosum L.) is a significant specie in terms of economical, nutritional, and medicinal point of view. Beside these attributes it is well known for its high anthocyanin content and antioxidant activity. Therefore, obtaining new valuable blueberry genotypes resilient to climatic changing conditions is a priority for breeders. The genotypes studied were obtained by a classical breeding method, respectively by free pollination, the seeds being prior cold stored and then sown in seedlings trays with acidic peat. Germination lasted even two years for some genotypes. The study presents the first phenotypic results for the obtained genotypes, highlighting differences and similarities regarding the foliar system and health status. Thirteen local (including 'Safir', 'Compact', 'Simultan') and international (Duke, Pink Lemonade, Berkeley, etc.) blueberry cultivars were used as parents. The results enclose twenty hybrids obtained from free pollination. [ABSTRACT FROM AUTHOR]

Architect, builder, and professor Christopher Alexander focused his life's work on trying to understand what makes the physical environment beautiful, and how beautiful environments can be created today. Through careful research, innovative teaching, and unorthodox professional practice, Alexander formulated a unified vision of the physical environment based on a theory of "wholeness." He observed that achieving beauty and wholeness in the built environment - as well as teaching it - requires the integration of processes and considerations that are usually kept separate: integrating form and function, integrating teaching and practice, integrating design and construction, integrating projects of various scales, and integrating all of these within the ongoing search for how beauty and wholeness might be reached, taught, and proliferated. Alexander explored and developed ways of implementing these observations throughout his decades of teaching at the University of California at Berkeley, culminating in the Building Process Area of Emphasis, which he founded with his colleagues in 1990. His former students from this period, together with new partners, established "Building Beauty" in 2017, a post-graduate program in architecture that continues to teach and expand upon Alexander's theories and methods of generating beauty and wholeness in the physical environment. [ABSTRACT FROM AUTHOR]

The Camp Fire was one of California's deadliest and most destructive wildfires, and its widespread smoke threatened human health over a large area in Northern California in November 2018. To analyze the Camp Fire influence on air quality on a 200 km distant site in Berkeley, highly time-resolved total carbon (TC), black carbon (BC), and organic carbon (OC) were measured using the Carbonaceous Aerosol Speciation System (CASS, Aerosol Magee Scientific), comprising two instruments, a Total Carbon Analyzer TCA08 in tandem with an Aethalometer AE33. During the period when the air quality was affected by wildfire smoke, the BC concentrations increased four times above the typical air pollution level presented in Berkeley before and after the event, and the OC increased approximately ten times. High-time-resolution measurements allow us to study the aging of OC and investigate how the characteristics of carbonaceous aerosols evolve over the course of the fire event. A higher fraction of secondary carbonaceous aerosols was observed in the later phase of the fire. At the same time, the amount of light-absorbing organic aerosol (brown carbon) declined with time. [ABSTRACT FROM AUTHOR]

In this paper, we have used the Gaia's Data Release-3 (DR3) data to study an intermediate-age open cluster Berkeley 27 (Be 27). A total of 131 most probable cluster members are picked within the cluster's radius based on the membership probability (> 80 %). The cluster's radius was estimated as 3.74 arcmin. The mean proper motion (PM) of Be 27 was determined to be ( μ α c o s δ , μ δ)= (− 1. 076 ± 0. 008 , 0. 152 ± 0. 007) mas yr−1. The blue straggler stars (BSS) of the cluster were found to be located in the central region. Theoretical isochrones of metallicity Z m e t a l = 0.008 were compared to the color-magnitude diagram (CMD) of Be 27. As a result, a heliocentric distance of 4.8 ± 0.2 kpc and log (age) = 9.36 ± 0.03 were determined for Be 27. The Galactic orbits are derived using the Galactic potential model which demonstrate that Be 27 follows a circular path around the Galactic center. The cluster does not seem to be affected much by the tidal forces from the Galactic thin disk. • Open cluster Berkeley 27 is studied using Gaia data. • The cluster's membership, radius, age and distance are determined. • The orbit of Berkeley 27 in the Galaxy is also being presented. [ABSTRACT FROM AUTHOR]

Many college athletes suffer career-ending injuries that leave them with expensive medical bills and lost scholarship opportunities. California's 2012 student athlete bill of rights mandated that the state's universities continue to care for college athletes by providing access to medical care and equivalent scholarships even if they were injured and could no longer participate in athletics. We analyzed publicly available data from the college athletics financial information database using multiple quasi-experimental approaches, including difference-in-differences with propensity score weights and synthetic control methods. We found evidence that Cal-Berkeley and UCLA increased medical expenditures but not student aid. Our findings were robust across both types of analyses. We discuss implications and offer directions for future research related to policy implementation. [ABSTRACT FROM AUTHOR]

Blue straggler stars (BSS) are peculiar objects which normally appear as a single broad sequence along the extension of the main sequence. Only four globular clusters (GCs) have been observed to have two distinct and parallel BSS sequences. For the first time for any open cluster (OC), we report double BSS sequences in Berkeley 17. Using the machine-learning based membership algorithm ML-MOC on Gaia EDR3 data, we identify 627 cluster members, including 21 BSS candidates out to 15 arcmin from the cluster centre. Both the BSS sequences are almost equally populated and parallel to one another in Gaia as well as in Pan-STARRS colour–magnitude diagram (CMD). We statistically confirm their presence and report that both BSS sequences are highly segregated compared to the reference population out to ∼5.5 arcmin and not segregated thereafter. The lower densities of OCs make BSS formation impossible via the collisional channel. Therefore, mass transfer seems to be the only viable channel for forming candidates of both sequences. The gap between the red and blue BSS sequences, on the other hand, is significant and presents a great opportunity to understand the connection between BSS formation and internal as well as external dynamics of the parent clusters. [ABSTRACT FROM AUTHOR]

Three Black women pose for a photograph mid play, a golf course stretching out behind them. With slight smiles, they squint in the sun at the camera, taking a break from the meditative intensity of the game. Two women wear skirts, or maybe one is sporting a culotte, bobby socks, and at least one of them seems to be wearing a regulation cleated shoe. A breeze blows fabric against legs. Each holds her club atop a golf ball, their bodies and the flagstick casting shadows on the putting green (see Figure 1). They are members of the Par-Links Black Women's Golf Club, formed in California's East Bay in 1958. Advertising for new members in the Oakland Black newspaper, the California Voice , the club held its first tournament the following year at Tilden Park Golf Course in Berkeley. "... challenge or be challenged," the group cheered: "Your place on the ladder depends on your win." [ABSTRACT FROM AUTHOR]

The problem of edge detection plays a crucial role in almost all research areas of image processing. If edges are detected accurately, one can detect the location of objects and the parameters such as shape and area can be measured more precisely. In order to overcome the above problem, a feature-based image registration (FBIR) method in combination with an improved version of canny with fuzzy logic is proposed for accurate detection of edges. The major contributions of the present work are summarized in three steps. In the first step, a restoration-based enhancement algorithm is proposed to get a fine image from a distorted noisy image. In the second step, two versions of input images are registered using a modified FBIR approach. In the third step, to overcome the drawback of canny edge detection algorithm, each step of the algorithm is modified. The output is then fed to a "fuzzy inference system". The "fuzzy rule-based technique", when applied to the problem of "edge detection", is very "efficient" because the thickness of the edges can be controlled by simply changing "rules and output parameters". The domain of the images under consideration is various well-known image databases such as Berkeley and USC-SIPI databases, whereas the proposed method is also suitable for other types of both indoor and outdoor images. The robustness of the proposed method is analysed, compared and evaluated with seven image assessment quality (IAQ) parameters. The performance of the proposed method is compared with some of the state-of-the-art edge detection methods in terms of the seven IAQ parameters. [ABSTRACT FROM AUTHOR]

We evaluate thermal conductivity of the skeleton of porous sandstone from the measurements of the effective thermal conductivities of dry and saturated specimens provided by Woodside and Messmer (Woodside and Messmer in J Appl Phys 32:1688, 1961). Six types of the sandstone of different porosity levels are evaluated—Berkeley (porosity 0.03), St. Peters (porosity 0.11), Tensleep (porosity 0.155), Berea (porosity 0.22), Teapot (porosity 0.29), and Tripolite (porosity 0.59). The approach is based on recently developed concept of equivalent microstructure and solution of the inverse homogenization problem in the framework of Mori–Tanaka–Benveniste micromechanical model. We obtained that skeleton conductivities of all the sandstones except of Tripolite are smaller than the value of λ s = 8.40 W / mK typically used in the literature. The equivalent microstructures that lead to the experimentally measured overall conductivity are obtained as combinations of almost spherical pores and strongly oblate, crack-like pores. The obtained results can be used for modeling of thermal conductivity of saturated sandstone. [ABSTRACT FROM AUTHOR]

• BLS is employed as the surrogate model to assist the evolutionary process. • Fuzzy clustering functions with image region information are constructed. • An adaptively updating strategy for the parameters of BLS is adopted. • A model update mechanism is used to boost the prediction accuracy of BLS. For some real-world problems, the objective function evaluation is time-consuming and computationally expensive in multi-objective evolutionary algorithms. Surrogate-assistance approaches can be used to estimate the objective function and solve the computationally expensive problem. Taking advantages of the flatted structure and incremental learning, broad learning system (BLS) has shown a great improvement of time efficiency on the premise of ensuring the performance on the classification and regression problems. In this paper, BLS is adopted to design a BLS surrogate-assisted evolutionary framework to boost the time efficiency of clustering-based image segmentation. A BLS-assisted multi-objective evolutionary fuzzy clustering algorithm with reference points (BLS-MOEFC) is proposed. Firstly, two fuzzy clustering objective functions integrating image region information are constructed as the optimized fitness functions for obtaining satisfactory segmentation quality. Secondly, in order to improve the optimization efficiency and reduce the computational time cost, a BLS surrogate model-assisted multi-objective evolutionary framework with reference points is designed to optimize the constructed objective functions. Thirdly, an adaptively updating strategy of parameters and a model update mechanism based on the non-dominated sorting are adopted to improve the prediction accuracy of the BLS surrogate model. Finally, a novel fuzzy clustering validity index integrating region information is designed to select an optimal solution from the final set of non-dominated solutions. Experimental results on Berkeley and Weizmann images show that the proposed algorithm behaves well in the segmentation performance and time cost. [ABSTRACT FROM AUTHOR]

A new deep learning (DL)-based parameter extraction method is presented in this brief; 50k training cases are generated by Monte Carlo simulations of these preselected parameters in Berkeley short-channel IGFET model (BSIM)-common multigate (CMG). DL models are trained using backward propagation with ${C} _{\text {gg}} - {V} _{g}$ and ${I} _{d} - {V} _{g}$ as the input and selected BSIM-CMG parameters as the output. A TCAD simulated FinFET device, calibrated to Intel 10-nm node, is used to test the DL models. The DL-based parameters extraction results show an excellent fit to capacitance and drain current data, with 0.16% rms error in ${C} _{\text {gg}} - {V} _{g}$ and 6.1% rms error in ${I} _{d} - {V} _{g}$ (0.69% rms error in above-threshold-voltage ${I} _{d} - {V} _{g}$), respectively. In addition, devices with a 10% variation in gate length and oxide thickness are successfully modeled with the trained DL model. The results show tremendous promise in using the DL-based models for parameter extraction. [ABSTRACT FROM AUTHOR]

The development of modern photoelectron spectroscopy is reviewed with a special focus on the importance of research at Uppsala University and at Berkeley. The influence of two pioneers, Kai Siegbahn and Dave Shirley, is underlined. Early interaction between the two centers helped to kick-start the field. Both laboratories have continued to play an important role in the field, both in terms of creating new experimental capabilities and developing the theoretical understanding of the spectroscopic processes. [ABSTRACT FROM AUTHOR]

Through a case study of the Chinese traditional music world, this study explores how artists in different specializations within an art world working on an indigenous art form make sense of divergent aesthetics. By adopting both Becker's (Art worlds: 25th anniversary edition, updated and expanded, University of California Press, Berkeley, 2008) view of art worlds as substantially existing communities shared by artistic individuals and Bourdieu's (Poetics 12(4–5):311–356, 1983) emphasis on artistic divergences stemming from broader social structures, I build a theoretical framework regarding how implicit aesthetic conflicts coexist with explicit collaborations in an art world. Under the impact of cultural globalization, the Chinese traditional music world's conventions have experienced a historical revolution. Since then, music performers enact frames to defend the aesthetics that they consider "traditional" that emphasize stability in terms of the musical content but that have highly idiosyncratic styles of performance. However, other types of musicians—namely those involved in composing, conducting, theoretical research—are more likely to enact frames defending aesthetics that express a willingness to "Westernize" based on their understandings and emphasize on innovation in terms of musical content and systematic and routinized styles of performance. Their framings shape their different reactions to their art world's conventions. By analyzing this process, I show how local–global dynamics constitute aesthetic conflicts in an art world that is often considered highly local and traditional. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of classification of object and background in unevenly illuminated images using Decision-Theoretic Rough Set (DTRS) framework. The proposed scheme employs adaptive windowing technique to partition the image into different windows. Thereafter, the proposed DTRS based method is applied on each window to find out the optimal threshold that is used for classification of the window. Determination of optimal threshold of a given window is dependent on the optimal granule size used for the window. The problem of determination of optimal granule size and optimal threshold is cast in optimization framework. The optimal threshold obtained for each window is used to classify the window and the classification of the entire image is the union of classifications over all the windows. Manual tuning of parameters is not required to determine the optimal threshold. The proposed scheme is tested on different images considered from Berkeley image database. The performance of the proposed scheme is compared with other granular and non-granular computing based schemes. Evaluation of different quantitative measures demonstrates the improved performance of the proposed schemes over others. • Image classification under uneven illumination conditions. • Adaptive windowing strategy to generate different windows over the image. • Parameterless classification of windows of an image using granular computing. • Automatic determination of optimal granule size. • Simultaneous determination of optimal threshold for image classification. [ABSTRACT FROM AUTHOR]

In this work, we establish a link between the classification of ECM-friendly elliptic curves and Mazur's program B, which consists in parameterizing all the families of elliptic curves with exceptional Galois image. Motivated by Barbulescu et al. [ANTS X–proceedings of the tenth algorithmic number theory symposium, Berkeley, CA, 2013], we say an elliptic curve is ECM-friendly if it does not have complex multiplication and if its Galois image is exceptional for some level. Building upon two recent works which treated the case of congruence subgroups of prime-power level which occur for infinitely many j-invariants, we prove that there are exactly 1525 families of rational elliptic curves with distinct Galois images which are cartesian products of subgroups of prime-power level. This makes a complete list of rational families of ECM-friendly elliptic curves with cartesian Galois images, out of which less than 23 were known in the literature. We furthermore refine a heuristic of Montgomery to compare these families and conclude that the best 4 families which can be put in a = −1 twisted Edwards' form are new. [ABSTRACT FROM AUTHOR]

Frame Semantics includes context as a central aspect of the theory. Frames themselves can be regarded as a representation of the immediate context against which meaning is to be construed. Moreover, the notion of frame invocation includes context as one possible source of information comprehenders use to construe meaning. As the original implementation of Frame Semantics, Berkeley FrameNet is capable of providing computational representations of some aspects of context, but not all of them. In this article, we present FrameNet Brasil: a framenet enriched with qualia relations and capable of taking other semiotic modes as input data, namely pictures and videos. We claim that such an enriched model is capable of addressing other types of contextual information in a framenet, namely sentence-level cotext and commonsense knowledge. We demonstrate how the FrameNet Brasil software infrastructure addresses contextual information in both database construction and corpora annotation. We present the guidelines for the construction of two multimodal datasets whose annotations represent contextual information and also report on two experiments: (i) the identification of frame-evoking lexical units in sentences and (ii) a methodology for domain adaptation in Neural Machine Translation that leverages frames and qualia for representing sentence-level context. Experimental results emphasize the importance of computationally representing contextual information in a principled structured fashion as opposed to trying to derive it from the manipulation of linguistic form alone. [ABSTRACT FROM AUTHOR]

Source preference in which equally distributed risks may be valued differently has been receiving increasing attention. Using subjects recruited in Berkeley, Fox and Tversky (1995) demonstrate a familiarity bias in source preference—betting on a less than even-chance event based on San Francisco temperature is valued more than betting on a better than even-chance event based on Istanbul temperature. Neophobia is associated with the amygdala which is GABA-rich and is known to be modulated by benzodiazepines as anxiolytic agents that enhance the activity of the GABAA receptor in processing anxiety and fear. This leads to our hypothesis that familiarity bias in decision making may be explained by polymorphic variations in this receptor mediated by anxiety regulation in the amygdala. In two companion studies involving Beijing-based subjects, we examine 10 single nucleotide polymorphisms (SNPs) of GABRB2 (coding for GABAA receptor, beta 2 subunit) and find 7 SNPs each showing negative association between familiarity bias—preference for betting on parity of Beijing temperature over Tokyo temperature—and having at least one minor allele (less than 50% prevalence). In an imaging genetics study of a subsample of subjects based on the SNP with the most balanced allelic distribution, we find that subjects' familiarity bias in terms of risk aversion towards bets on the parity of the temperature of 20 Chinese cities is negatively associated with their post-scanning familiarity ratings of the cities only for those with no minor allele in this SNP. Moreover, familiarity bias is positively associated with activation in the right amygdala along with the brain's attention networks. Overall, our findings help discriminate between ambiguity aversion and familiarity bias in source preference and supports our gene–brain–behavior hypothesis of GABAergic modulation of amygdala activation in response to familiarity towards the source of uncertainty. [ABSTRACT FROM AUTHOR]