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In October, 2004, the University of California Libraries entered into a two-year contract with JSTOR (Journal Storage) to establish from its journal runs across all UC campuses a paper repository of approximately fourteen million pages (350+ titles). This "dim archive"- accessible to only UC and JSTOR-would benefit JSTOR by making available a reliable and complete print back file, and UC through cost and space savings from no longer having to maintain several copies of low-use volumes. Since January, 2005, the task of establishing system wide support and collaboration, planning and realizing the actual operation itself, and developing strict but realistic guidelines for finding the best UC copy and then validating it (page by page) has been enormous. This article traces the progress of the project thus far, now approximately three-quarters complete, from the original motivations of UCL (University of California Libraries) and JSTOR, to the preparations by the facility housing the repository (the Southern Regional Library Facility), to the planning and re-planning of the project designer/manager. [ABSTRACT FROM AUTHOR]

This paper focuses on the sacred text-production work of a Nyingma Buddhist group based in Berkeley, California. It unpacks their selective engagement with the tools afforded to them by digitisation and new media. Digitisation projects – appearing in growing numbers – offer a powerful resource for the re-assembly of Tibetan Buddhist textual collections scattered in the political upheaval of recent decades. Yet the meeting place between the digital and the sacred is sometimes contested in this context where sacred text is an embodiment of the Buddha's speech. This paper argues that the choice to print ink-and-paper texts is more than a simple rehearsal of tradition and in fact demands alternative forms of engagement with the potential offered by media tools. It explores how the moral invectives contained within sacred Tibetan texts become reshaped through the prisms of contemporary media and the American sponsorship landscape. [ABSTRACT FROM AUTHOR]

Restoration ecology is a deepening and diversifying field with current research incorporating multiple disciplines and infusing long-standing ideas with fresh perspectives. We present a list of 10 recent pivotal papers exemplifying new directions in ecological restoration that were selected by students in a cross-disciplinary graduate seminar at the University of California, Berkeley. We highlight research that applies ecological theory to improve restoration practice in the context of global change (e.g. climate modeling, evaluation of novel ecosystems) and discuss remaining knowledge gaps. We also discuss papers that recognize the social context of restoration and the coupled nature of social and ecological systems, ranging from the incorporation of cultural values and Traditional Ecological Knowledge into restoration, to the consideration of the broader impacts of markets on restoration practices. In addition, we include perspectives that focus on improving communication between social and natural scientists as well as between scientists and practitioners, developing effective ecological monitoring, and applying more integrated, whole-landscape approaches to restoration. We conclude with insights on recurrent themes in the papers regarding planning restoration in human-modified landscapes, application of ecological theory, improvements to restoration practice, and the social contexts of restoration. We share lessons from our cross-disciplinary endeavor, and invite further discussion on the future directions of restoration ecology through contributions to our seminar blog site . [ABSTRACT FROM AUTHOR]

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]

The article reports on the move of geologist Taylor Perron and his colleagues Jim Kirchner and Bill Dietrich at University of California, Berkeley, to examine how erosional processes create uniform patterns of landscapes. They compare a computational model with precise measurements of various landscapes. Moreover, it reveals that having developed a computational model that accurately predicts the natural patterns in landscapes, geologists can learn how factors have shaped Earth's topography.

The article describes how Jay Keasling, a synthetic chemist at the University of California, Berkeley, and his team have engineered yeast cells to produce an important precursor for a drug to treat malaria. Keasling collaborated with Amyris Biotechnologies and non-profit drug company Institute for OneWorld Health to try to complete the synthesis of artemisinin. The research was funded by the Bill & Melinda Gates Foundation.

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]

The article introduces the special issue of "Industrial and Corporate Change" focusing on developments in telecommunications policy. The papers included in this issue were presented at the "Bridging Digital Technologies and Regulatory Paradigms" conference held at the University of California at Berkeley in June 1997. These papers reveal the importance of convergence as a fundamental feature of the business and policy landscape. They are also concerned with policy issues that arise as the telecommunications industry moves toward a competitive market structure made possible by technical and political advances.

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]

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]

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]

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]

This paper evokes the writings of Jacques Rancière to propose a concept of politics for geographies of architecture that is attentive to the polemical conditions under which more equal ways of composing built environments emerge. Discussing Ed Roberts Campus, a building designed and operated by the disability community in Berkeley, California, it argues for a politics of architecture that does not entail conflicts over power or identity, but revolves around a testing of materials that alters the bodily circumstances built form offers for collective inhabitation. Such testing sets in motion an uncertain process where a building undergoes constant destabilisation by new claimants who verify and expand its equality. The paper then counterpoises this disruptive politics to institutional practices in order to investigate how its fragile after‐effects might be sustained. [ABSTRACT FROM AUTHOR]

No satisfactory theory considering the mutual interaction of tones, on the one hand, and accentuation and intonation on the other, has been elaborated as yet. This paper therefore concentrates on the observation of consistence of four tones contours. It is demonstrated by the oscillograms obtained by the author of this paper in 1969-1970 at Berkeley for three speakers where the tones are either controlled by the speaker or are the automatic result of acoustic phenomena. [ABSTRACT FROM AUTHOR]

Announces professors Daniel McFadden and Kenneth Train of the University of California as the winners of the sixth Richard Stone Prize in Applied Econometrics. Published paper 'Mixed MNL Models for Discrete Response;' Criteria; Previous winners.

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]

This paper summarizes the mathematical aspects of fuzzy and bipolar mathematical morphology and shows how morphological operators can be applied to define spatial relations, and then used for spatial reasoning. Fuzzy sets are useful both at representation level and at reasoning level, and contribute to establish links between various areas of artificial intelligence. This is illustrated on the example of spatial reasoning for image understanding. The work on spatial relations was initiated during a sabbatical stay at Berkeley, and this paper was written in memory of Lotfi Zadeh and the enlightening discussions with him and his team. [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]

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]

Rear-end collision prediction has gained an increasing attention for safety improvement in smart cities. It is urgent to design efficient warning strategies for rear-end collisions which is one of the main causes of traffic accidents. The existing researches have been conducted to predict the collisions. Learning-based methods are proposed to solve this complicated issue, which the traditional methods are difficult to solve. However, due to some limitations in terms of the feature extraction and prediction performance, back-propagation learning methods are facing challenge. In this paper, we proposed a novel Rear-end Collision Prediction Mechanism with deep learning method (RCPM), in which a convolutional neural network model is established. In RCPM, the dataset is smoothed and expanded based on genetic theory to alleviate the class imbalance problem. The preprocessed dataset is divided into training and testing sets as the input to train our convolutional neural network model. The experimental results show that compared with the Honda, Berkeley and multi-layer perception neural-network-based algorithms, RCPM effectively improves performance to predict rear-end collisions. [ABSTRACT FROM AUTHOR]

This paper proposes a color image segmentation method based on region salient color and the fuzzy C-means (FCM) algorithm. The method first uses the convex hull theory based on Harris corner detection to detect the object of the image. Thus, the object and the background can be separated. Then, the quantized color histogram can be studied in the HSV color space. By calculating the number of the peak values of both the object and the background histograms, the quantity of the regional salient colors can be obtained. The quantity is the number of the clustering centroids of FCM algorithm. Finally, the FCM algorithm and the noise correction algorithm can be used in the object and the background, respectively. The obtained segmented image consists of the object and the background segmentation. It proves that the method in this paper is an effective segmentation method based on the experiments made by use of Berkeley segmentation dataset. According to the experimental results, it can be concluded that the proposed algorithm has the highest segmentation accuracy and the shortest computing time among the algorithms mentioned in this paper. The algorithm can achieve high-quality, stable and accurate color image segmentation results. [ABSTRACT FROM AUTHOR]

Segmentation is the act of partitioning an image into different regions by creating boundaries between regions. k-means image segmentation is the simplest prevalent approach. However, the segmentation quality is contingent on the initial parameters (the cluster centers and their number). In this paper, a convolution-based modified adaptive k-means (MAKM) approach is proposed and evaluated using images collected from different sources (MATLAB, Berkeley image database, VOC2012, BGH, MIAS, and MRI). The evaluation shows that the proposed algorithm is superior to k-means++, fuzzy c-means, histogram-based k-means, and subtractive k-means algorithms in terms of image segmentation quality (Q-value), computational cost, and RMSE. The proposed algorithm was also compared to state-of-the-art learning-based methods in terms of IoU and MIoU; it achieved a higher MIoU value. [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]

Considering that the 3D pulse-coupled neural network (3D-PCNN) model has the deficiency of high parameter complexity and low accuracy in color image segmentation, swarm intelligence optimization algorithm is adopted to optimize the image segmentation process. In this paper, whale optimization algorithm (WOA) is adopted to optimize the 3D-PCNN model parameters E and β. The improved product cross entropy (IPCE) is chosen as the fitness function of optimization algorithm. WOA algorithm is used to find the minimum fitness function, and the corresponding optimal parameters are obtained. Through the study of image segmentation in the image segmentation library of University of Berkeley and the actual plant canopy image, the maximum entropy value and the Tsallis entropy value are compared and analyzed. Experimental results illustrate that the proposed algorithm can obtain more accurate image segmentation effect and higher segmentation rate. [ABSTRACT FROM AUTHOR]

Most traditional superpixel segmentation methods used binary logic to generate superpixels for natural images. When these methods are used for images with significantly fuzzy characteristics, the boundary pixels sometimes cannot be correctly classified. In order to solve this problem, this paper proposes a Superpixel Method Based on Fuzzy Theory (SMBFT), which uses fuzzy theory as a guide and traditional fuzzy c -means clustering algorithm as a baseline. This method can make full use of the advantage of the fuzzy clustering algorithm in dealing with the images with the fuzzy characteristics. Boundary pixels which have higher uncertainties can be correctly classified with maximum probability. The superpixel has homogeneous pixels. Meanwhile, the paper also uses the surrounding neighborhood pixels to constrain the spatial information, which effectively alleviates the negative effects of noise. The paper tests on the images from Berkeley database and brain MR images from the Brain web. In addition, this paper proposes a comprehensive criterion to measure the weights of two kinds of criterions in choosing superpixel methods for color images. An evaluation criterion for medical image data sets employs the internal entropy of superpixels which is inspired by the concept of entropy in the information theory. The experimental results show that this method has superiorities than traditional methods both on natural images and medical images. [ABSTRACT FROM AUTHOR]

Sensor node location information is critical in many Wireless Sensor Network (WSN) applications with random sensor node deployment. In such applications, node localization using a faster area exploration mechanism is needed to initiate precise sensing and communication. In this paper, faster area exploration approach for mobility-assisted localization scheme is proposed for mission-critical WSN applications. Each Mobile Anchor (MA) node embedded with a localization module moves in the sensor field in a coordinated manner while localizing the deployed static nodes. The proposed area exploration scheme is implemented using a hybrid of max-gain approach and cost-utility based frontier (HMF) approach. The paper addresses the issue of sparse anchor node condition during the localization process. The simulation results obtained using Cooja simulator shows that the proposed mobility-assisted localization scheme results in accurate localization with minimum delay. The proof-of-concept of the proposed scheme is demonstrated using Berkeley static nodes and custom designed MA nodes. [ABSTRACT FROM AUTHOR]

Introduces research papers presented in the inaugural conference of the University of California Council of Women's Programs and published in the 1989 issue of the 'Women's Studies' journal. Relationship between the study of women and social movements; Zavella's discussion on the effect of the problematic relationship between feminism and the Chicano movement on Chicana studies; Roberta Fernandez's analysis of Helena Maria Viramontes' story, 'The Cariboo Caf,'.

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]

In recent decades, image segmentation has aroused great interest of many researchers, and has become an important part of machine learning, pattern recognition, and computer vision. Among many methods of image segmentation, fuzzy C‐means (FCM) algorithm is undoubtedly a milestone in unsupervised method. With the further study of FCM, various different kinds of FCM algorithms are put forward to deal with the specific problems in image segmentation. Because there exist uncertainties in different regions of the image and similarity in the same region, reducing the uncertainty is still the main problem in image segmentation. Considering that Pythagorean fuzzy set (PFS) is a powerful tool to deal with uncertainty, in this paper, we use PFS to describe the uncertainty of image segmentation, including introducing fuzzification and defuzzification process and Pythagorean fuzzy element to describe the membership degree of pixel, combine the neighborhood information with weights and Pythagorean fuzzy distance, and propose Pythagorean fuzzy C‐means (PFCM) algorithm. Finally, we apply PFCM algorithm in image segmentation, such as different size images and Berkeley Segmentation Data Set to illustrate the effectiveness and applicability of our proposed algorithm. Meanwhile, we do comparison analysis between PFCM, fully convolution network and Deep‐image‐Prior networks, these results show that our proposed PFCM has good intuition and effectiveness. [ABSTRACT FROM AUTHOR]

• In spite of its interesting results, Mean shift (MS) has a computational cost which is prohibited for several applications. • In this paper, a new competitive segmentation algorithm for grayscale images is introduced. • To reduce the computational cost, MS is operated using a representative set of elements with a very limited number of points. • Once obtained the MS results, they are generalized to include the not involved data. • Experimental results confirm that the proposed scheme presents better performance. Multi-feature segmentation has demonstrated its superiority against one-dimensional feature approaches based on only grayscale information. Mean shift (MS) is an algorithm that has been used commonly for multi-feature segmentation. In spite of its interesting results, MS maintains a computational cost that is prohibitive for segmentation scenarios where the feature map consists of multi-dimensional features. In this paper, a new competitive segmentation algorithm for grayscale images is introduced. The proposed approach considers a two-dimensional feature map that includes the grayscale value and the local variance for each pixel in the image. To reduce the computational cost, the Mean shift (MS) algorithm is modified to operate with a very limited number of points from all available data. Under such conditions, two sets of elements are differentiated: involved data (the reduced dataset considered in the MS operation) and not involved data (the rest of the available data). Different from the classical MS, which employs Gaussian functions, in our approach, the process of estimating the feature map is carried out using a more accurate approach such as the Epanechnikov kernel function. Once the MS results are obtained, they are generalized to include the not involved data. Therefore, each unused element is assigned to the same cluster of the closest used data. Finally, clusters with the fewest elements are fused with other neighboring clusters. The proposed segmentation method has been compared with other state-of-art algorithms considering the full number of images from the Berkeley dataset. Experimental results confirm that the proposed scheme produces segmented images with a 50% better quality of visual perception approximately two times (≈ 1.8 − 2) faster than its competitors. [ABSTRACT FROM AUTHOR]

This paper considers the alleged pragmatism of Berkeley's philosophy using the two Sellarsian categories of 'manifest' and 'scientific' images of the world and human beings. The 'manifest' image is regarded as a refinement of the ordinary way of conceiving things, and the scientific image is seen as a theoretical picture of the world provided by science. The paper argues that the so-called Berkeleian pragmatism was an effect of Berkeley's work towards a synthesis of 'manifest' and 'scientific' images through the creation of one unified synoptic vision of the world and was a part of a new conceptual framework within which these two images could be combined. [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]

Purpose - This paper seeks to feature an interview with Brad Eden, Associate University Librarian for Technical Services and Scholarly Communication at the University of California, Santa Barbara. Eden discusses the University of California Libraries' Next Generation Technical Services initiative. Design/methodology/approach - The paper notes changes necessary to transform technical services for the new digital realm. Findings - The task force chaired by Eden recommends several changes to traditional processes, three of which are central to achieving transformative change. Originality/value - The paper offers readers insights into the means by which a large consortium under severe financial constraints is attempting to achieve cost-savings and value-added services through system-wised collaboration. [ABSTRACT FROM AUTHOR]

Multilevel thresholding for image segmentation is one of the crucial techniques in image processing. Even though numerous methods have been proposed in literature, it is still a challenge for the existing methods to produce steady satisfactory thresholds at manageable computational cost in segmenting images with various unknown properties. In this paper, a non-revisiting quantum-behaved particle swarm optimization (NrQPSO) algorithm is proposed to find the optimal multilevel thresholds for gray-level images. The proposed NrQPSO uses the non-revisiting scheme to avoid the re-evaluation of the evaluated solution candidates. To reduce the unnecessary computation cost, the NrQPSO provides an automatic stopping mechanism which is capable of gauging the progress of exploration and stops the algorithm rationally in a natural manner. For further improving the computation efficiency, the NrQPSO employs a meticulous solution search method to overcome the drawback of the existing QPSO algorithms using the original search methods. Performance of the NrQPSO is tested on the Berkeley segmentation data set. The experimental results have demonstrated that the NrQPSO can outperform the other state-of-the-art population-based thresholding methods in terms of efficiency, effectiveness and robustness; thus, the NrQPSO can be applied in real-time massive image processing. [ABSTRACT FROM AUTHOR]

The gray-level co-occurrence matrix (GLCM) can obtain the pixel matrix of the image, and selecting multiple thresholds for the matrix can obtain better segmentation results. However, as the number of threshold increases, the computational complexity of the algorithm will also increase. In order to solve this problem, this paper proposes a multi-threshold image segmentation method based on thermal exchange optimization (TEO) algorithm, and take a novel diagonal class entropy (DCE) as the fitness function. We improve TEO algorithm by using two strategic methods of Levy flight (LF) and opposition-based learning (OBL). In order to verify the segmentation ability of the proposed algorithm, color natural images, satellite images and Berkeley images are taken as experimental objects to analyze the segmentation result graph and image segmentation quality evaluation indexes. Experimental results show that the GLCM-ITEO algorithm has good segmentation capability, less CPU time. [ABSTRACT FROM AUTHOR]