Spatial-temporal Location Research Articles

HEARTSVG: a fast and accurate method for identifying spatially variable genes in large-scale spatial transcriptomics

Identifying spatially variable genes (SVGs) is crucial for understanding the spatiotemporal characteristics of diseases and tissue structures, posing a distinctive challenge in spatial transcriptomics research. We propose HEARTSVG, a distribution-free, test-based method for fast and accurately identifying spatially variable genes in large-scale spatial transcriptomic data. Extensive simulations demonstrate that HEARTSVG outperforms state-of-the-art methods with higher F1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${F}_{1}$$\\end{document} scores (average F1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${F}_{1}$$\\end{document} Score=0.948), improved computational efficiency, scalability, and reduced false positives (FPs). Through analysis of twelve real datasets from various spatial transcriptomic technologies, HEARTSVG identifies a greater number of biologically significant SVGs (average AUC = 0.792) than other comparative methods without prespecifying spatial patterns. Furthermore, by clustering SVGs, we uncover two distinct tumor spatial domains characterized by unique spatial expression patterns, spatial-temporal locations, and biological functions in human colorectal cancer data, unraveling the complexity of tumors.

The Application of Husserlian Phenomenology to the Examination of the Translatability of Criminal Law Practices Across American and Italian Culture in the Prosecution and Acquittal of Amanda Knox

AbstractThis Article uses various concepts of Husserlian phenomenology to explain the disparate opinion between the North American and Italian public in response to the prosecution and ultimate acquittal of Amanda Knox. This Article argues that the comparative difference in public opinion is due to an extensive shift in culture that is necessarily accompanied by a shift in spatial-temporal location. In this Article, the Husserlian concepts of intentionality, the Self, the Other-I, and empathy overlay the judicial opinions and media releases critical to shaping North American and Italian public perceptions of Amanda Knox. As applied, these Husserlian concepts function as interpretive lenses, providing the reader with a novel framework for analyzing the cause of interpretive difference across cultures.

DIFFUSION-MODEL-ASSISTED SUPERVISED LEARNING OF GENERATIVE MODELS FOR DENSITY ESTIMATION

We present a supervised learning framework of training generative models for density estimation. Generative models, including generative adversarial networks (GANs), normalizing flows, and variational auto-encoders (VAEs), are usually considered as unsupervised learning models, because labeled data are usually unavailable for training. Despite the success of the generative models, there are several issues with the unsupervised training, e.g., requirement of reversible architectures, vanishing gradients, and training instability. To enable supervised learning in generative models, we utilize the score-based diffusion model to generate labeled data. Unlike existing diffusion models that train neural networks to learn the score function, we develop a training-free score estimation method. This approach uses mini-batch-based Monte Carlo estimators to directly approximate the score function at any spatial-temporal location in solving an ordinary differential equation (ODE), corresponding to the reverse-time stochastic differential equation (SDE). This approach can offer both high accuracy and substantial time savings in neural network training. Once the labeled data are generated, we can train a simple, fully connected neural network to learn the generative model in the supervised manner. Compared with existing normalizing flow models, our method does not require the use of reversible neural networks and avoids the computation of the Jacobian matrix. Compared with existing diffusion models, our method does not need to solve the reverse-time SDE to generate new samples. As a result, the sampling efficiency is significantly improved. We demonstrate the performance of our method by applying it to a set of 2D datasets as well as real data from the University of California Irvine (UCI) repository.

Robust data driven discovery of a seismic wave equation

SUMMARY Despite the fact that our physical observations can often be described by derived physical laws, such as the wave equation, in many cases, we observe data that do not match the laws or have not been described physically yet. Therefore recently, a branch of machine learning has been devoted to the discovery of physical laws from data. We test this approach for discovering the wave equation from the observed spatial-temporal wavefields. The algorithm first pre-trains a neural network (NN) in a supervised fashion to establish the mapping between the spatial-temporal locations (x, y, z, t) and the observation displacement wavefield function u(x, y, z, t). The trained NN serves to generate metadata and provide the time and spatial derivatives of the wavefield (e.g. utt and uxx) by automatic differentiation. Then, a preliminary library of potential terms for the wave equation is optimized from an overcomplete library by using a genetic algorithm. We, then, use a physics-informed information criterion to evaluate the precision and parsimony of potential equations in the preliminary library and determine the best structure of the wave equation. Finally, we train the ‘physics-informed’ neural network to identify the corresponding coefficients of each functional term. Examples in discovering the 2-D acoustic wave equation validate the feasibility and effectiveness of our implementation. We also verify the robustness of this method by testing it on noisy and sparsely acquired wavefield data.

State-space modeling for electrochemical performance of Li-ion batteries with physics-informed deep operator networks

Online estimation of unobservable internal states is significant for safe operation of Li-ion batteries, and it constitutes one of the main functions of battery management system (BMS). The next-generation BMS expects model-based state estimation, especially with electrochemical models, which are accurate but often costly for solving. Therefore, it is required to build more easily executable state-space representation of electrochemical models for online state estimation. However, the traditional numerical methods for time discretization are relatively complicated, and the discretized system is not very flexible in modifying predictive time intervals. To address such issues, we introduce the concept of physics-informed operator learning for state-space modeling. Specifically, we propose an architecture, termed the physics-informed multiple-input operator network (PI-MIONet), to reformulate the state-space representation of the extended single particle (eSP) model. In this work, the PI-MIONet takes the Li-ion concentration of the whole electrode particle and current densities at the current time as the input functions, and predicts Li-ion concentration at any spatial-temporal location, which means that the forward predictions can be realized with user-defined step size. In addition, due to the capability of taking discretized functions as inputs, the PI-MIONet can be used for estimating states in the form of long vectors, and it can be conducted very efficiently, which makes it highly suitable for online applications in BMS. We verify the predictive performance of PI-MIONet through several synthetic experiments, and successfully apply it to the estimation of Li-ion concentration across the full particle with unscented Kalman filter algorithms.

CASTLE: A CONTEXT-AWARE SPATIAL-TEMPORAL LOCATION EMBEDDING PRE-TRAINING MODEL FOR NEXT LOCATION PREDICTION

Abstract. Next location prediction is helpful for service recommendation, public safety, intelligent transportation, and other location-based applications. Existing location prediction methods usually use sparse check-in trajectories and require massive historical data to capture complex spatial-temporal correlations. High spatial-temporal resolution trajectories have rich information. However, obtaining personal trajectories with long time series and high spatio-temporal resolution usually proves challenging. Herein, this paper proposes a two-stage Context-Aware Spatial-Temporal Location Embedding (CASTLE) model, a multi-modal pre-training model for sequence-to- sequence prediction tasks. The method is built in two steps. First, large-scale location datasets, which are sparse but easier to be acquired (i.e., check-in and anomalous navigation data), are used for pre-training location embedding to capture the multi-functional properties under different contexts. After that, the learned contextual embedding is used for downstream location prediction in small-scale but higher spatio-temporal resolution trajectory datasets. Specifically, the CASTLE model combines Bidirectional and Auto-Regressive Transformers to generate contextual embedding vectors rather than a fixed vector for each location. Furthermore, we introduce a location and time-aware encoder to reflect the spatial distances between locations and visit times. Experiments are conducted on two real trajectory datasets. The results show that the CASTLE model can pre-train beneficial location embedding and outperforms the model without pre-training by 4.6–7.1%. The proposed method is expected to improve the next location prediction accuracy without massive historical data, which will greatly drive the use of trajectory data.

Attention Guided Relation Detection Approach for Video Visual Relation Detection

Video Visual Relation Detection (VidVRD) aims at detecting the relation instances between two observed objects in the form of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$&lt; $</tex-math></inline-formula> <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">subject-predicate-object</i> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$&gt;$</tex-math></inline-formula> . Unlike image visual relation detection, due to the introduction of the time dimensions, the various predicates and spatial-temporal locations are both required to be tackled, making the task challenging. To balance these challenges, most existing works perform this task in two phases: first predicting relationships in segmented clips to capture the motions, and then associating them into the relation instances with proper locations in videos. These works detect different relationships by collecting the cues from multi-aspects, but treat them equally without distinction. Furthermore, due to the dynamic scenes and drifting problem in object tracking, the rigid spatial overlap used to determine the association in previous works is insufficient, which leads to missing associations. To address the problems, in this paper, we propose a novel attention guided relation detection approach for VidVRD. In order to model the distinction among different cues and strengthen the salient characteristics, we assign these cues the attention weights for relationship prediction and association decision-making. In addition, to comprehensively measure whether merging the relationships, we put forward a customized network to take both visual appearance and geometric location into account. Extensive experiment results on ImageNet-VidVRD dataset and VidOR dataset demonstrate the effectiveness of our proposed approach. And abundant ablation studies verify the component designed in the approach is essential.

Indirect Validation of Ocean Remote Sensing Data via Numerical Model: An Example of Wave Heights from Altimeter

Using numerical model outputs as a bridge, an indirect validation method for remote sensing data was developed to increase the number of effective collocations between remote sensing data to be validated and reference data. The underlying idea for this method is that the local spatial-temporal variability of specific parameters provided by numerical models can compensate for the representativeness error induced by differences of spatial-temporal locations of the collocated data pair. Using this method, the spatial-temporal window for collocation can be enlarged for a given error tolerance. To test the effectiveness of this indirect validation approach, significant wave height (SWH) data from Envisat were indirectly compared against buoy and Jason-2 SWHs, using the SWH gradient information from a numerical wave hindcast as a bridge. The results indicated that this simple indirect validation method is superior to “direct” validation.

A general location-authentication based secure participant recruitment scheme for vehicular crowdsensing

Vehicular crowdsensing is an emerging paradigm where vehicles perform the sensing task published by the requester so as to earn either monetary or non-monetary incentives. During this process, sensitive information of vehicles (location, identity et al.) are easy to leak. Unfortunately, previous works pay little attention on the privacy-preserving in the participants recruitment process. In this paper, we propose a location authentication based secure participant recruitment (LA-SPR) scheme for vehicular crowdsensing that enables each vehicle to perform (1) location authentication and key transmission simultaneously without any pre-shared secret, and (2) participant recruitment process without revealing the actual location and sensitive information to server or the requester. It’s worth mentioning that, our scheme uses a spatial-temporal location tag and fuzzy extractor to realize location authentication and key transmission simultaneously within a round of communication between vehicles and server. We exploit Trust Authority (TA) to collect credible anchor’s location tag and vehicle candidates. We regard the sensing score, which is uploaded to the server by each vehicle, as a secret and encrypted with RSA to prevent information leakage. We obtain comprehensive results demonstrating the feasibility, security, and efficiency of our scheme by theoretical analysis and performance evaluation.

Genesis and evolution of the mantle melts of the devonian mafic-ultramafic rocks from the Eastern Azov region (Dnieper-Donetsk rift, Ukraine) based on the clinopyroxene geochemistry study

The Devonian magmatic association of the Eastern Azov region, which is part of the Pripyat-Dnieper-Donetsk rift zone, was studied. The association includes gabbroids, peridotites, pyroxenites, and lamprophyre dikes of the Pokrovo-Kireevsky massif (PKM) and picrites, picrobasalts and basalts of the Anton-Taramskaya suite (ATS). The clinopyroxenes of different generations from the micaceous PCM gabbro and the alkaline ATS picrite were studied. It was obtained the information on the mantle source composition and the evolution of melts, who determined the close spatial-temporal location of kimberlites, basites, ultramafic rocks, including alkaline ones, in the Eastern Azov region.&#x0D; Clinopyroxenes from micaceous gabbro are composed of Cpx1 (Mg# = 0.870.88) or Сpx2 (Mg# = 0.800.81) cores and Cpx3 external zones (Mg# = 0.700.76). Clinopyroxenes in alkaline picrite are composed of Сpx2 cores (Mg # = 0.800.84) and external Cpx3 zones (Mg# = 0.710.78). The multielement spectra of clinopyroxenes are generally dome-shaped in nature, with enrichment with LREE, depletion of Ba, Nb, TREE, Zr-Hf negative anomaly, a negative Sr-anomaly appears in Cpx2 and Cpx3 also. The resulting compositions of the model melt for Сpx2 from the micaceous gabbro are very close to the composition of this gabbro, and the compositions of the model melt for Cpx2 from the alkaline picrite coincide with those of this picrite. The high Mg# value and concentrations of Cr in Cpx1 cores indicate that the earliest weakly differentiated composition close to the primary could serve as the equilibrium melt. The presence in the Cpx1 geochemical spectra of a negative Zr-Hf anomaly at ZrPM HfРМ may be evidence of the origin of melts that once contained these clinopyroxenes, due to the melting of metasomatized, possibly carbonated garnet-contained peridotites. Probably, the Cpx1 cores are relics of phenocrysts crystallized from the earliest melt during the formation of the PCM and the ATS.&#x0D; An important feature of the Eastern Azov rocks is a very high content of Ti (up to 7.3 wt.% TiO2) in the high-Mg (Mg# = 0.480.65) and deep (CaO/Al2O3 0.8) melts, which formed picrobasalts and lamprophyres. The geochemical features of the early Cpx1 cores compared with the geochemistry of clinopyroxenes from ilmenite-containing mantle metasomatites are consistent with the assumption that carbonated ilmenite-containing peridotites, possibly also phlogopite-containing (PIC), are the source of ultrahigh-Ti primary melts for the Eastern Azov lamprophyres.

Origin of Fish Biomass in a Diverse Subtropical River: An Allochthonic-Supported Biomass Increase Following Flood Pulses

The origin of resources supporting metazoan biomass in rivers has long been a subject of debate. The river wave concept (RWC) postulates that the energetic basis of food webs varies along its spatial–temporal location with respect to flow pulses. According to the RWC, river flow determines carbon assimilation in food webs, but this may also depend on river geomorphology. However, studies testing this theory are scarce, particularly those from large subtropical rivers. To analyse the origin of fish biomass in areas of differing geomorphology, we combined stable isotope analysis with standardised measurements of biomass of a diverse fish assemblage along the lower Uruguay River. Furthermore, using 14 years of monitoring data, we tested for relationships between the biomass of species dominantly fuelled by allochthonic resources and the river flow. Fish biomass was dominantly allochthonous-derived along most of the studied sites. At all trophic levels, autochthonous-derived fish biomass was the highest in an upstream anabranch functional process zone (FPZ) (fuelling 54% of the total biomass), while allochthonous-derived biomass prevailed downstream, in the widest sections of an unconstrained lowland FPZ (fuelling 64–72% of the total biomass). Moreover, the dominant species that derived most of its biomass from allochthonous resources (Prochilodus lineatus) increased its biomass following flood pulses. This study supports the RWC statements that, at a spatial scale, local river geomorphology affects fuelling sources for food webs (probably by determining contrasting resource availability scenarios) and, at a temporal scale, increases in the allochthonous fraction of biomass are driven by flood pulses.

Towards Automated Real-Time Detection and Location of Large-Scale Landslides through Seismic Waveform Back Projection

Rainfall-triggered landslides are one of the most deadly natural hazards in many regions. Seismic recordings have been used to examine source mechanisms and to develop monitoring systems of landslides. We present a semiautomatic algorithm for detecting and locating landslide events using both broadband and short-period recordings and have successfully applied our system to landslides in Taiwan. Compared to local earthquake recordings, the recordings of landslides usually show longer durations and lack distinctivePandSwave arrivals; therefore, the back projection method is adopted for the landslide detection and location. To identify the potential landslide events, the seismic recordings are band-passed from 1 to 3 Hz and the spectrum pattern in the time-frequency domain is used to distinguish landslides from other types of seismic sources based upon carefully selected empirical criteria. Satellite images before and after the detected and located landslide events are used for final confirmation. Our landslide detection and spatial-temporal location system could potentially benefit the establishment of rainfall-triggered landslide forecast models and provide more reliable constraints for physics-based landslide modeling. The accumulating seismic recordings of landslide events could be used as a training dataset for machine learning techniques, which will allow us to fully automate our system in the near future.

Video Super Resolution via Deep Global-Aware Network

Video super-resolution aims to increase the resolution of videos by exploiting the intra-frame and inter-frame dependencies of the low-resolution video sequences. There are usually two dependent steps in the video super-resolution: the motion compensation and the super-resolution reconstruction. In this paper, we propose a new deep learning framework without the explicit motion estimation by utilizing the self-attention model to exploit the full receptive field of the input video frames. In other words, the proposed deep neural network extracts the local features at all spatial–temporal locations for combining into global features using the self-attention networks in order to reconstruct the high-resolution video frame. The proposed global-aware network outperforms the state-of-the-art deep learning-based image and video super-resolution algorithms in terms of subjective and objective quality with less computational operations, as verified by extensive experiments on public image and video datasets, including Set5, Set14, B100, Urban100, and Vid4.

Accelerating motion estimation by genetic algorithm approach in x265

In the last two decades, in the domain of video coding and compression, researchers have suggested several techniques for computation and time reduction for motion estimation (ME). We present a motion estimation algorithm for x265 video codec, based on a deterministic initial population in the genetic algorithm (GA). GA is known for its adaptive convergence, which is motivated by the biological process of survival of the fittest. The suggested scheme is targeted for the reduction of search points (SP) in a block matching motion estimation algorithm for integer-pel in B and P frames that are set to have three reference frames. The initial population constituted in our approach is a function of pre-encoded coding units at different spatial–temporal locations of the video frames and predefined hexagonal (HEX) locations. We propose a “deterministically starting” GA (GADet), toward deployment in x256 structure. In the framework of x265 code, GADet is found to offer reduction in SP at selected classes of videos considered for experimentation. To demonstrate the effectiveness of the proposed work, results have been compared with the block-based fast-full-search algorithm and the HEX search algorithm from the reference software. Traditional GA with a randomly constituted initial population, labeled as GAStc, is also implemented and an empirical comparison is carried out with GADet. The proposed GADet framework provides reduction in motion estimation time while rendering acceptable peak signal-to-noise ratio loss and an increase in a bit rate.

Supporting Spatial-Temporal Provenance Location Proofs for Ad-Hoc Mobile Users

Supporting Spatial-Temporal Provenance Location Proofs for Ad-Hoc Mobile Users

INFORMATION MINING OF SPATIO-TEMPORAL EVOLUTION OF LAKES BASED ON MULTIPLE DYNAMIC MEASUREMENTS

Abstract. Lakes are important water resources and integral parts of the natural ecosystem, and it is of great significance to study the evolution of lakes. The area of each lake increased and decreased at the same time in natural condition, only but the net change of lakes’ area is the result of the bidirectional evolution of lakes. In this paper, considering the effects of net fragmentation, net attenuation, swap change and spatial invariant part in lake evolution, a comprehensive evaluation indexes of lake dynamic evolution were defined,. Such degree contains three levels of measurement: 1) the swap dynamic degree (SDD) reflects the space activity of lakes in the study period. 2) the attenuation dynamic degree (ADD) reflects the net attenuation of lakes into non-lake areas. 3) the fragmentation dynamic degree (FDD) reflects the trend of lakes to be divided and broken into smaller lakes. Three levels of dynamic measurement constitute the three-dimensional "Swap - attenuation – fragmentation" dynamic evolution measurement system of lakes. To show its effectiveness, the dynamic measurement was applied to lakes in Jianghan Plain, the middle Yangtze region of China for a more detailed analysis of lakes from 1984 to 2014. In combination with spatial-temporal location characteristics of lakes, the hidden information in lake evolution in the past 30 years can be revealed.

Location Based Handshake and Private Proximity Test with Location Tags

A location proximity test service allows mobile users to determine whether they are in close proximity to each other, and has found numerous applications in mobile social networks. Unfortunately, existing solutions usually reveal much of users’ private location information during a proximity test. They are also vulnerable to location cheating where an attacker reports false locations to gain an advantage. Moreover, the initial trust establishment among unfamiliar users in large scale mobile social networks has been a challenging task. In this paper, we propose a novel scheme that enables a user to perform (1) a location based handshake that establishes secure communications among strangers, who do not have a pre-shared secret, and (2) a privacy-preserving proximity test without revealing the user’s actual location to the server or other users not within the proximity. The proposed scheme is based on a novel concept, i.e., spatial-temporal location tags, and we put forward a location tag construction method using environmental signals that provides an unforgeable location proof. We use Bloom filters to efficiently represent users’ location tags and vicinity regions. We exploit fuzzy extractor, a lightweight cryptographic primitive, to extract shared secrets between matching location tags. We conduct extensive analysis, simulation, and real experiments to demonstrate the feasibility, security, and efficiency of our scheme.

Efficient Lane Boundary Detection with Spatial-Temporal Knowledge Filtering.

Lane boundary detection technology has progressed rapidly over the past few decades. However, many challenges that often lead to lane detection unavailability remain to be solved. In this paper, we propose a spatial-temporal knowledge filtering model to detect lane boundaries in videos. To address the challenges of structure variation, large noise and complex illumination, this model incorporates prior spatial-temporal knowledge with lane appearance features to jointly identify lane boundaries. The model first extracts line segments in video frames. Two novel filters—the Crossing Point Filter (CPF) and the Structure Triangle Filter (STF)—are proposed to filter out the noisy line segments. The two filters introduce spatial structure constraints and temporal location constraints into lane detection, which represent the spatial-temporal knowledge about lanes. A straight line or curve model determined by a state machine is used to fit the line segments to finally output the lane boundaries. We collected a challenging realistic traffic scene dataset. The experimental results on this dataset and other standard dataset demonstrate the strength of our method. The proposed method has been successfully applied to our autonomous experimental vehicle.

Towards an Endpoint Cell Motility Assay by a Microfluidic Platform.

In vitro cell motility assays are frequently used in the study of cell migration in response to anti-cancer drug treatment. Microfluidic systems represent a unique tool for the in vitro analysis of cell motility. However, they usually rely on using time-lapse microscopy to record the spatial temporal locations of the individual cells being tested. This has created a bottleneck for microfluidic systems to perform high-throughput experiments due to requirement of a costly time-lapse microscopy system. Here, we describe the development of a portable microfluidic device for endpoint analysis of cell motility. The reported device incorporates a cell alignment feature to position the seeded cells on the same initial location, so that the cells' motilities can be analyzed based on their locations at the end of the experiment after the cells have migrated. We show that the device was able to assess cancer cell motility after treatment with a migration inhibitory drug Indole-3-carbinol on MDA-MB-231 breast cancer cells, demonstrating the applicability of our device in screening anti-cancer drug compounds on cancer cells.

The Early Modern Subject: Self-Consciousness and Personal Identity from Descartes to Hume by Udo Thiel

Reviewed by: The Early Modern Subject: Self-Consciousness and Personal Identity from Descartes to Hume by Udo Thiel Regina Janes Udo Thiel. The Early Modern Subject: Self-Consciousness and Personal Identity from Descartes to Hume. Oxford: Oxford, 2011. Pp. xiv + 483. $110; $65 (paper). A remarkable achievement, Mr. Thiel’s Early Modern Subject traces, as its subtitle announces, “self-consciousness” and “personal identity” from Descartes to Hume, with backward glances to Platonism, Aristotle, Lucretius, and the scholastics. In this meticulous parsing of four of modernity’s principal terms—self, consciousness, person, identity—no vacation from thought is permitted. The “historical contexts” in which he sets his thinkers’ arguments are other thinkers’ arguments on the same topics. No historical events, influential trends, or social changes at work between 1650 and 1750 appear. If self-consciousness, as Hans Gadamer insists, is modernity as flickering, fleeting process, how does clunky, more substantial personal identity fit? Can there be personal identity without self-consciousness, or self-consciousness without personal identity? Why must identity be personal? Is consciousness a reflex act by which one reflects on one’s own thinking, the simple act of thinking, or a motion of the will? How does consciousness, the mind’s awareness of perception, differ from self-consciousness? Does self-consciousness require consciousness of a “self or subject or I”? What then is this self or subject or I that one is conscious of? Can there be any position without an argument in its favor? Arguing that consciousness emerges as a salient topic only in the 1720s, Mr. Thiel addresses a stunning number of early modern thinkers—over 53 French, English, Germans, and Americans appear in chapter or section titles alone: South, La Rochefoucauld, Spinoza, Pufendorf, Collins, Watts, Berkeley, Shaftesbury, Butler, Grove, Norris, Edwards, Tucker, Leibniz, Wolff, Mendelssohn, Reimarus, Herder, Kant, Hume, Kames, and 30 more. The introduction teases out the differences between conscience and consciousness, the histories of each as a term, and the ambiguities created by the recurring French failure to have two words for two things. Insistent that one word serve two concepts, they then create multiple neologisms of uncertain reference (Leibniz’s conscience, consciosité) and leave translators to guess which applies. Both the first treatise to name consciousness as its topic (Charles Mein’s anonymous Essay on Consciousness, 1728) and the first survey of accounts of personal identity (Thomas Wallace, 1827) appear late, written by authors unknown to fame. Locke’s “subjectivist revolution” is impeccably laid out within the enlightenment shift from ontology to epistemology. Scholastic thought identifies the person as a union of soul and body, in which either is incomplete without the other. Souls separated from bodies are not persons, and consciousness [End Page 80] cannot constitute individuality. Cartesians identify the soul with man or person, so that a soul, a “complete, simple, and immaterial substance,” separated from its body is still the person, and consciousness individuates. The debate between South, the schoolman, and Sherlock, the Cartesian, over the Trinity explored these issues, with South convicting Sherlock of circularity over individuation. Locke rejects the scholastic account without comment and extends the Cartesian account into a differentiation between man and person, replacing Platonic and Cartesian concepts of soul as an unchanging, immaterial substance with “unity of consciousness.” Locke distinguishes “person” or consciousness from “man,” understood somewhat dimly as a “vital union of parts in a certain shape,” and from soul, a “thinking substance” about which nothing is known. Individuation becomes existence in a particular spatial-temporal location. Mr. Thiel argues that the Sherlock-South Trinitarian debates did not inspire Locke, but guided him to his distinction between man and person to save himself from Sherlock’s circularity. So deft is Locke’s position that he can remain neutral on materialist or immaterialist accounts of soul and his theory of personal identity can deal with all the issues—except the Trinity, where it began. Attacks and defenses pursued Locke’s subjectivist revolution, and Mr. Thiel hunts them down. Alternate accounts of subjectivity in Clarke, Shaftesbury, Norris, and Berkeley precede Leibniz and Wolff’s apperception and consciousness, ending finally with Hume and his bundle view of the self. Immortality, resurrection, the soul anchor the...