Specific Prior Knowledge Research Articles

Prediction of the Remaining Useful Life of Bearings Through CNN-Bi-LSTM-Based Domain Adaptation Model.

Predicting the remaining useful life (RUL) of mechanical bearings is crucial in the industry. Estimating the RUL enables the assessment of health bearing, maintenance planning, and significant cost reduction, thereby fostering industrial development. Existing models rely on traditional feature engineering with feature changes because operating conditions pose a major challenge to the generalization of RUL prediction models. This study focuses on neural network-based feature engineering and the downstream prediction of the RUL, eliminating the need for specific prior knowledge and simplifying the development and maintenance of models. Initially, a convolutional neural network (CNN) model is employed for feature engineering. Subsequently, a bidirectional long short-term memory network (Bi-LSTM) model is used to capture the time-series degradation characteristics of the engineered features and predict the RUL through regression. Finally, the study examines the influence of operating conditions in the model and integrates domain adaptation to minimize differences in feature distribution, thereby enhancing the model's generalizability for the RUL prediction.

Cross-modal and multimodal data analysis based on functional mapping of spectral descriptors and manifold regularization

Multimodal manifold modeling methods extend spectral geometry-aware data analysis to incorporate learning from multiple related and complementary modalities. However, most methods assume an equal number of homogeneous data samples in each modality and partial correspondences between modalities as prior knowledge. This work introduces two new multimodal modeling methods. The first method introduces a comprehensive framework for handling multimodal information in heterogeneous data without requiring specific prior knowledge. To achieve this, we begin by extracting local descriptors using spectral graph wavelet signatures (SGWS) to identify manifold localities. Then, we propose a manifold regularization framework that incorporates functional mapping between SGWS descriptors (FMBSD) to determine pointwise correspondences. The second method involves manifold regularized multimodal classification based on pointwise correspondences (M2CPC). This method addresses the challenge of multiclass classification in multimodal heterogeneous data by determining correspondences between modalities using the FMBSD method. Experimental results evaluating the FMBSD method on three widely used cross-modal retrieval datasets and evaluating the M2CPC method on three benchmark multimodal multiclass classification datasets demonstrate their effectiveness and superiority over state-of-the-art methods.

Investigating Prior-Level Fusion Approaches for Enriched Semantic Segmentation of Urban LiDAR Point Clouds

Three-dimensional semantic segmentation is the foundation for automatically creating enriched Digital Twin Cities (DTCs) and their updates. For this task, prior-level fusion approaches show more promising results than other fusion levels. This article proposes a new approach by developing and benchmarking three prior-level fusion scenarios to enhance the outcomes of point cloud-enriched semantic segmentation. The latter were compared with a baseline approach that used the point cloud only. In each scenario, specific prior knowledge (geometric features, classified images, or classified geometric information) and aerial images were fused into the neural network’s learning pipeline with the point cloud data. The goal was to identify the one that most profoundly enhanced the neural network’s knowledge. Two deep learning techniques, “RandLaNet” and “KPConv”, were adopted, and their parameters were modified for different scenarios. Efficient feature engineering and selection for the fusion step facilitated the learning process and improved the semantic segmentation results. Our contribution provides a good solution for addressing some challenges, particularly for more accurate extraction of semantically rich objects from the urban environment. The experimental results have demonstrated that Scenario 1 has higher precision (88%) on the SensatUrban dataset compared to the baseline approach (71%), the Scenario 2 approach (85%), and the Scenario 3 approach (84%). Furthermore, the qualitative results obtained by the first scenario are close to the ground truth. Therefore, it was identified as the efficient fusion approach for point cloud-enriched semantic segmentation, which we have named the efficient prior-level fusion (Efficient-PLF) approach.

Unsupervised social event detection via hybrid graph contrastive learning and reinforced incremental clustering

Detecting events from social media data streams is gradually attracting researchers. The innate challenge for detecting events is to extract discriminative information from social media data thereby assigning the data into different events. Due to the excessive diversity and high updating frequency of social data, using supervised approaches to detect events from social messages is hardly achieved. To this end, recent works explore learning discriminative information from social messages by leveraging graph contrastive learning (GCL) and embedding clustering in an unsupervised manner. However, two intrinsic issues exist in benchmark methods: conventional GCL can only roughly explore partial attributes, thereby insufficiently learning the discriminative information of social messages; for benchmark methods, the learned embeddings are clustered in the latent space by taking advantage of certain specific prior knowledge, which conflicts with the principle of unsupervised learning paradigm. In this paper, we propose a novel unsupervised social media event detection method via hybrid graph contrastive learning and reinforced incremental clustering (HCRC), which uses hybrid graph contrastive learning to comprehensively learn semantic and structural discriminative information from social messages and reinforced incremental clustering to perform efficient clustering in a solidly unsupervised manner. We conduct comprehensive experiments to evaluate HCRC on the Twitter and Maven datasets. The experimental results demonstrate that our approach yields consistent significant performance boosts. In traditional incremental setting, semi-supervised incremental setting and solidly unsupervised setting, the model performance has achieved maximum improvements of 53%, 45%, and 37%, respectively.

Cognitive influences on fixational eye movements

We perceive the world based on visual information acquired via oculomotor control,1 an activity intertwined with ongoing cognitive processes.2,3,4 Cognitive influences have been primarily studied in the context of macroscopic movements, like saccades and smooth pursuits. However, our eyes are never still, even during periods of fixation. One of the fixational eye movements, ocular drifts, shifts the stimulus over hundreds of receptors on the retina, a motion that has been argued to enhance the processing of spatial detail by translating spatial into temporal information.5 Despite their apparent randomness, ocular drifts are under neural control.6,7,8 However little is known about the control of drift beyond the brainstem circuitry of the vestibulo-ocular reflex.9,10 Here, we investigated the cognitive control of ocular drifts with a letter discrimination task. The experiment was designed to reveal open-loop effects, i.e., cognitive oculomotor control driven by specific prior knowledge of the task, independent of incoming sensory information. Open-loop influences were isolated by randomly presenting pure noise fields (no letters) while subjects engaged indiscriminating specific letter pairs. Our results show open-loop control of drift direction in human observers.

Implementation and evaluation of an assistance software to support decision-making, design and simulation setup for automated guided vehicle systems

Autonomous guided vehicle systems (AGVS) offer huge potential for optimizing transportation tasks in manufacturing and warehouse logistics. Especially for small and medium enterprises, this potential is left untapped, as the selection of a proper solution requires substantial expertise. Support from external consultancies is often not requested. To address this issue, concepts for expert systems and multi-criteria decision algorithms have been discussed in academia for years but have not found any practical application yet. Therefore, in this paper we present an easy-to-use, holistic planning tool implemented with a user-centered development process, followed by the evaluation of the prototype. The results show its feasibility to realize a comprehensive planning process in such way, that users with little or no specific prior knowledge can perform initial steps for planning AGVS on their own. The user tests indicate good usability and acceptance for the prototype and show improvement potential for further development iterations.

Say My Name: Understanding the Power of Names, Correct Pronunciation, and Personal Narratives.

Names are a reflection of identity and often have personal meaning. The chronic mispronunciation of names can undermine one's identity and be experienced as a microaggression. This workshop aims to provide historical context for names as well as resources for correct name pronunciation. We developed a 60-minute interactive virtual workshop with didactics, small-group sharing of personal experiences, and case discussions. We used an anonymous postworkshop survey to evaluate workshop effectiveness. We presented the workshop at one local academic conference and two local educational conferences to learners of all levels from medical students to faculty. We collected postworkshop survey results from 78 participants of diverse racial and ethnic backgrounds. Participants reported learning historical context, ways to ask about correct name pronunciation, correcting name mispronunciation, documenting pronunciation, and sources for applications to practice. The main barriers to implementing workshop lessons included personal and structural factors. This workshop effectively fills an educational gap by addressing the importance of correct name pronunciation in order to provide a more inclusive environment for clinicians and patients alike.

Online triggers for supernova and pre-supernova neutrino detection with cryogenic detectors

Supernovae (SNe) are among the most energetic events in the universe still far from being fully understood. An early and prompt detection of neutrinos is a one-time opportunity for the realization of the first multi-messenger observation of these events. In this work, we present the prospects of detecting neutrinos produced before (pre-SN) and during a SN while running an advanced cryogenic detector. Recent advances in the cryogenic detector technique and the discovery of coherent elastic neutrino-nucleus scattering offer a wealth of opportunities in neutrino detection. The combination of the excellent energy resolution of this experimental technique, with the high cross section of this detection channel and its equal sensitivity to all neutrino flavors, enables the realization of highly sensitive neutrino telescopes of the size of a few tens of cm, as the newly proposed RES-NOVA experiment. We present a detailed study on the detection promptness of pre-SN and SN neutrino signals, with direct comparisons among different classes of test statistics. While the well-established Poisson test offers in general best performance under optimal conditions, the nonparametric Recursive Product of Spacing statistical test (RPS) is more robust for triggering astrophysical neutrino signals with no specific prior knowledge. Based on our statistical tests the RES-NOVA experiment is able to identify SN neutrino signals at a 15 kpc distance with 95% of success rate, and pre-SN signal as far as 450 pc with a pre-warn time of the order of 10 s. These results demonstrate the potential of RPS for the identification of neutrino signals and the physics reach of the RES-NOVA experiment.

Human Activity Recognition Models in Ontology Networks.

We present Arianna+, a framework to design networks of ontologies for representing knowledge enabling smart homes to perform human activity recognition online. In the network, nodes are ontologies allowing for various data contextualisation, while edges are general-purpose computational procedures elaborating data. Arianna+ provides a flexible interface between the inputs and outputs of procedures and statements, which are atomic representations of ontological knowledge. Arianna+ schedules procedures on the basis of events by employing logic-based reasoning, that is, by checking the classification of certain statements in the ontologies. Each procedure involves input and output statements that are differently contextualized in the ontologies based on specific prior knowledge. Arianna+ allows to design networks that encode data within multiple contexts and, as a reference scenario, we present a modular network based on a spatial context shared among all activities and a temporal context specialized for each activity to be recognized. In the article, we argue that a network of small ontologies is more intelligible and has a reduced computational load than a single ontology encoding the same knowledge. Arianna+ integrates in the same architecture heterogeneous data processing techniques, which may be better suited to different contexts. Thus, we do not propose a new algorithmic approach to activity recognition, instead, we focus on the architectural aspects for accommodating logic-based and data-driven activity models in a context-oriented way. Also, we discuss how to leverage data contextualization and reasoning for activity recognition, and to support an iterative development process driven by domain experts.

Empirical scatter correction: CBCT scatter artifact reduction without prior information.

The image quality of cone beam CT (CBCT) scans severely suffers from scattered radiation if no countermeasures are taken. Scatter artifacts may induce cupping and streak artifacts and lead to a reduced image contrast and wrong CT values of the reconstructed volumes. Established software-based approaches for a correction of scattered radiation typically rely on prior knowledge of the CT system, scan parameters, the scanned object, or all of theaforementioned. This study proposes a simple and effective postprocessing software-based correction method of scatter artifacts in CBCT scans without specific priorknowledge. We propose the empirical scatter correction (ESC), which generates scatter-like basis images from each projection image by convolution operations. A linear combination of these basis images is subtracted from the original projection image. The logarithm is taken and an FDK reconstruction is performed. The coefficients needed for the linear combination are determined automatically by a downhill simplex algorithm such that the resulting reconstructed images show no scatter artifacts. We demonstrate the potential of ESC by correcting simulated volumes with Monte Carlo scatter artifacts, a head phantom scan performed on our table-top CBCT, and a pelvis scan from a Varian Edge CBCTscanner. ESC is able to improve the image quality of CBCT scans, which is shown on the basis of our simulations and on measured data. For a simulated head CT, the CT value difference to the scatter-free reference image was as low as -6 HU after using ESC, whereas the uncorrected data deviated by more than -200 HU from the reference data. Simulations of thorax and abdomen CT scans show that although scatter artifacts are not fully removed, anatomical features which were hard to discover prior to the correction become clearly visible and better segmentable with ESC. Similar results are obtained in the phantom measurement, where a comparison to a slit scan of our head phantom shows only small differences. The CT values in soft tissue are improved in this measurement, as well. In soft tissue areas with severe scatter artifacts, the CT values agree well with those of the slit scan (difference to slit scan: 35HU corrected and -289 HU uncorrected). Scatter artifacts in measured patient data can also be reduced using the proposed ESC. The results are comparable to those achieved with designated correction algorithms installed on the Varian Edge CBCT system. ESC allows to reduce artifacts caused by patient scatter solely based on the projectiondata.

Deep Learning Classification Model for English Translation Styles Introducing Attention Mechanism

Both short-distance association knowledge and long-distance interaction knowledge in the knowledge base contain rich semantics. When learning entity and relation representation in a knowledge base, if we can learn short-distance association knowledge and long-distance interaction knowledge at the same time, we can learn the representation method with rich semantic information and keep the original structure of the knowledge base. Among the knowledge contained in a large number of records, some knowledge reflects individual characteristics and can be called local knowledge; others reflect group characteristics and can be called global knowledge. Using different ways to learn local and global knowledge in the deep learning model will better reflect the difference between the two kinds of knowledge at the model level, and make the model have the ability to understand both individual characteristics and overall characteristics. Through layer-by-layer forward propagation and error back propagation algorithms, the entire network is gradually optimized in an “end-to-end” manner. This “end-to-end” approach leaves some means of introducing prior knowledge flexibly into the model. Although it can reduce the burden on researchers, this “data-driven” approach brings the shortcomings of poor interpretability of learning results and weak generalization ability. Combining the specific prior knowledge implicit in the data with the deep learning algorithm can optimize the algorithm in a targeted manner and avoid blind searching in the solution space, so as to obtain a model with better performance and wider use. To this end, this paper investigates combining prior knowledge with deep learning to design efficient algorithms to address the classification of English translation styles. This paper combines local knowledge with global knowledge and deep learning methods and proposes a memory neural network method combining local knowledge and global knowledge. By recording the local knowledge in the local memory module and simultaneously recording the global knowledge in the global memory module, the method effectively learns the latent information in a large number of records. This paper combines short-distance association knowledge with long-distance interaction knowledge and a distributed representation learning method based on deep learning and proposes a deep learning method combining short-distance association knowledge and long-distance interaction knowledge. On the IWSLT English translation task, experiments show that the method significantly improves translation quality, confirming that grammatical dependencies enhance attention by supplementing dependent grammatical information, resulting in more effective and richer context vectors that more accurately represent contextual situations. Additional experimental analysis showed that the model underwent careful parameter selection and analysis. By mining valuable long-distance interactive knowledge in the knowledge base and using it in the distributed representation learning of the knowledge base, while constraining the short-distance related knowledge and constraining the long-distance interactive knowledge, the learned knowledge can be used to effectively complete the knowledge base distributed representation for discovering new relations.

Seismic Random Noise Attenuation via Self-Supervised Transfer Learning

Random noise attenuation is an important step in seismic data processing. Unfortunately, most conventional denoising methods heavily rely on specific prior knowledge and fine-tuning of the parameters. Therefore, they often fail to suppress random noise. Recent works based on supervised learning techniques for seismic noise suppression show outstanding performance. However, this paradigm needs large-scale labeled training datasets that are not available for seismic field data. Inspired by the self-supervised learning, we propose a promising unsupervised learning scheme that aims at suppressing the random noise with only a noisy shot gather. The method is based on a specific <inline-formula> <tex-math notation="LaTeX">$\mathcal {J}$ </tex-math></inline-formula>-invariant function and an assumption that the noise is statistical independent while the useful signal exhibits some correlation. To further improve the denoising quality, we integrate the transfer learning strategy. We experimentally demonstrate that the proposed framework faithfully recovers the denoised data on both prestack and poststack synthetic and field data although the pretrained network is trained with the prestack synthetic dataset. The preliminary comparisons with traditional and learning-based approaches indicate the effectiveness and robustness as well.

Water Extraction in SAR Images Using Features Analysis and Dual-Threshold Graph Cut Model

Timely identifying and detecting water bodies from SAR images are significant for flood monitoring and water resources management. In recent decades, deep learning has been applied to water extraction but is subject to the large difficulty of acquiring SAR dataset of various water bodies types, as well as heavy labeling work. In addition, the traditional methods mostly occur over the large, open lakes and rivers, rarely focusing on complex areas such as the urban water, and cannot automatically acquire the classification threshold. To address these issues, a novel water extraction method is proposed with high accuracy in this paper. Firstly, a multiscale feature extraction using a Gabor filter is conducted to reduce the noise and roughly identify water feature. Secondly, we apply the Otsu algorithm as well as a voting strategy to initially extract the homogeneous regions and for subsequent Gaussian mixture model (GMM). Finally, the dual threshold is obtained from the fitted Gaussian distribution of water and non-water, which is integrated into the graph cut model to redefine the weights of the edges, then constructing the energy function of the water map. The dual-threshold graph cut (DTGC) model precisely pinpoints the water location by minimizing the energy function. To verify the efficiency and robustness, our method and comparison methods, including the IGC method and IACM method, are tested on six different types of water bodies, by performing the accuracy assessment via comparing outcomes with the manually labeled ground truth. The qualitative and quantitative results show that the overall accuracy of our method for the whole dataset all surpasses 99%, along with an obvious improvement of the Kappa, F1-score, and IoU indicators. Therefore, DTGC method has the absolute advantage of automatically capturing water maps in different scenes of SAR images without specific prior knowledge and can also determine the optimal threshold range.

An efficient Bayesian method for estimating runout distance of region-specific landslides using sparse data

ABSTRACT The runout distance of landslides is a critical factor that influences landslide risk quantification and mitigation designs. Nevertheless, empirical correlation models developed in a region may be biased when it is applied to landslides in another region, partly due to different geological formation process undergone in different regions. Besides, the development of a reliable correlation model for estimating runout distance generally requires a relatively large number of data points, collection of which is difficult and time-consuming. This study proposes a Bayesian method for efficiently developing region-specific correlation models to estimate runout distance. The method integrates systematically sparse data collected in a specific region and prior knowledge embedded in existing correlation models in other regions. The method is illustrated using both real and numerical examples. The results show that the proposed method is efficient and robust in developing region-specific correlation models for estimating runout distance.

Variance lower bound on fluorescence microscopy image denoising.

The signal to noise ratio of high-speed fluorescence microscopy is heavily influenced by photon counting noise and sensor noise due to the expected low photon budget. Denoising algorithms are developed to decrease these noise fluctuations in microscopy data by incorporating additional knowledge or assumptions about imaging systems or biological specimens. One question arises: whether there exists a theoretical precision limit for the performance of a microscopy denoising algorithm. In this paper, combining Cramér-Rao Lower Bound with constraints and the low-pass-filter property of microscope systems, we develop a method to calculate a theoretical variance lower bound of microscopy image denoising. We show that this lower bound is influenced by photon count, readout noise, detection wavelength, effective pixel size and the numerical aperture of the microscope system. We demonstrate our development by comparing multiple state-of-the-art denoising algorithms to this bound. This method establishes a framework to generate theoretical performance limit, under a specific prior knowledge, or assumption, as a reference benchmark for microscopy denoising algorithms.

The moderating effect of prior knowledge on the relationship between intelligence and complex problem solving – Testing the Elshout-Raaheim hypothesis

Although the relation between intelligence and complex problem solving (CPS) has been investigated repeatedly, the moderating effect of prior knowledge on this relation still remains an open question. The Elshout-Raaheim hypothesis (see Leutner, 2002) predicts a higher correlation between intelligence and CPS at a medium level of problem specific prior knowledge and lower correlations at higher as well as lower levels of prior knowledge, thus a curvilinear moderation. We investigated this hypothesis in a sample of N = 495 high school students by using general intelligence (g) and a minimal complex systems approach (MCS) of CPS. Strategic prior knowledge in the sense of the relative frequency of the vary-one-thing-at-a-time strategy (VOTAT; Tschirgi, 1980) increased across MCS tasks in the knowledge acquisition phase of CPS. With increasing prior knowledge, correlations followed the predicted inverted U-shaped pattern in the knowledge acquisition phase and the knowledge application phase of CPS, thus supporting the Elshout-Raaheim hypothesis. The moderating effect of strategic prior knowledge for the intelligence–CPS relation and its relevance are discussed.

Escaping the knowledge corridor: How founder human capital and founder coachability impacts product innovation in new ventures

Innovation is of central importance to entrepreneurship research, as independent entrepreneurs account for the most fundamentally novel product offerings. This study investigates how founder human capital and founder coachability relate to exploiting product innovation in new ventures. Drawing on a dyadic sample of founders and startup coaches, we clarify how general human capital broadens the knowledge corridor to enhance product innovation. Conversely, our study shines a light on the dark side of specific human capital and illustrates how specific prior knowledge constrains the knowledge corridor to limit product innovation. Further, we find that founder coachability—the degree to which an entrepreneur seeks, considers, and integrates feedback—is related to exploiting product innovation. Perhaps most importantly, we advance coachability as a learning mechanism that moderates the relationship between specific human capital and product innovation to overcome knowledge corridor constraints. Our findings have implications for human capital, coachability, and entrepreneurial learning.

Toward an Understanding of Attunement as an Autobiographical Theory of Education

I explore the concept of attunement in this paper. Informed by my own story and other scholarly work, attunement in this paper is employed to describe an autobiographical theory of education. In such an autobiographical theory of education learning becomes situated not only in the school-subject and specific prior knowledge, but also in one’s subjective sense of intellectual labor. Basically, this paper comprises two sections. In the first I elucidate my understanding of the concept of attunement. Attunement, in this paper, refers to learning guided and experienced by oneself subjectively striving for deeper understanding, during which one searches for meaning as well as begets one’s engagement with the world. In the second, I describe that attunement has three elements: contingency, boundary and sensitivity. Contingency emphasizes the particularity of experience, identified from one’s own perspective; boundary refers to the challenges and difficulties experienced by oneself; sensitivity underscores how one can go beyond one’s boundary subjectively, thus being attuned to new forms of understanding or thinking. Key-words: attunement, contingency, sensitivity, awakening inwardness

Image super-resolution based on half quadratic splitting

Model-based optimization methods have been widely used in varies image restoration solutions and achieved some remarkable results. However, finding out a closed mathematical solution for certain priors remains a great challenge. To resolve this problem, this paper presents an improved model-based algorithm for single image super-resolution. Instead of focusing on specific prior knowledge, we exploit the optimization scheme of general image restoration formula. In our approach, the general format of model-optimization problem is transformed into an alternant renewal process through half quadratic splitting. This transform can also separate the optimization into a modular structure and allows us to optimize the fidelity term and regularization term separately. Of which the regular optimization process can be considered as a denoising process. Then the guided filter is taken as a denoiser to realize this optimization, which uses local linear transform to keep the detail and L2 norm constraint to smooth the noise of input image. Experiments with benchmark datasets and our own infrared images show that our method can surpass several famous model-based and data-based methods in PSNR and SSIM.

The Cambridge History of Moral Philosophy ed. by Sacha Golob and Jens Timmermann

Reviewed by: The Cambridge History of Moral Philosophy ed. by Sacha Golob and Jens Timmermann T. H. Irwin Sacha Golob and Jens Timmermann, editors. The Cambridge History of Moral Philosophy. Cambridge: Cambridge University Press, 2017. Pp xii + 751. Cloth, $173.00. Given its scope and the size of many Cambridge Histories, this volume is short. It is 751 pages long. The main text consist of 54 chapters of between 12 and 14 pages each (including bibliographies). For comparison, The Cambridge History of Renaissance Philosophy is 968 pages long. One might ask why the present volume could not be allowed a similar length. 200 more pages could have made for a much more useful book, as I will suggest below. The brevity of the chapters makes room for a large cast of characters, and avoids concentration on the best-known texts in the history of ethics. The chapters cover not only Aristotle, Hume, and Kant, but also medieval Jewish and Islamic philosophers, Descartes, Pascal, Bayle, Freud, and contemporary French philosophers. This wide scope is welcome. The editors say that they have sought to ensure "that all chapters are accessible without specific prior knowledge of the philosopher's terminology or technical apparatus," and they intend the chapters to offer "an overview of each figure or school" (1). An "overview" might be expected to survey an author's main contributions to ethics, and some chapters meet this expectation. Some do not. The chapter on Aristotle says it has avoided the standard tour through the highlights of his theory (52). The chapter on Rousseau barely mentions his Social Contract, even though social contract theory is mentioned earlier and later in the book (e.g. 434, 503, and 719). The chapter entitled "Marxism" confines itself to some disputes about Marx in Anglophone moral philosophy (495). Though this chapter follows a chapter on Hegel (which mentions Marx [432]), it does not discuss Hegel or Hegelians, or any other aspect of Marx's historical context, or Marx's successors. The chapter on Rawls mostly attacks the view that his position is genuinely Kantian; this aspect of Rawls is worth discussing, but it leaves out quite a lot that one might expect from an overview. The volume as a whole is certainly worth reading (perhaps not all at one go), and will be found useful by many types of readers. Most chapters are useful and informative rather than stimulating. In the chapters that have conformed to the editors' intention to provide an overview, this task has used up most of an author's limited space, and not much is left for reflection, or critical discussion, or for comparison with predecessors or successors. In some cases, the demands of brevity conflict with clarity. While one may be able to give an overview in a dozen pages, it is more difficult to give an overview that makes a philosopher's position clear or interesting, and still more difficult to sketch a reasoned case for or against someone's views. If one also tries to trace B's response to A, and B's influence on C, the dozen pages do not leave much room. Despite these constraints, some chapters are informative and thought-provoking. (These include, but are not limited to, Warren on Plato, Inwood on Stoicism, Guyer on Hume, Harcourt on psychoanalysis, and Golob on Heidegger.) Many chapters are about just one philosopher, but, as one might expect, there are some on schools or movements (Neoplatonism, Humanism, Cambridge Platonism, and Pragmatism). Some chapters are diptychs, including those on Albert and Aquinas, Scotus and Ockham, Shaftesbury and Hutcheson, Locke and Butler, and Adam Smith and Bentham. A very rough division into "periods" reveals 9 chapters on ancient philosophy, 5 on medieval, 9 on the sixteenth and seventeenth centuries, 7 on the eighteenth, 11 on the nineteenth, and 13 on the twentieth (including some discussion of the twenty-first). (This division is very rough because some chapters cover more than one century or period.) In other words, a little less than half of the book is devoted to the nineteenth and twentieth centuries, and almost a quarter to the twentieth century. I do not know why the book is biased towards...