Explore the vast range of titles available.

\"This textbook provides readers with the fundamentals and the intent of environmental regulations so that compliance can be greatly improved and streamlined. Through numerous examples and case studies it explains concepts from how environmental laws are applied and work, to why pollution prevention and sustainability are critical for the future of all life on Earth. It is organized to accommodate different needs for students with different backgrounds and career choices. It is also useful for site safety managers, research technicians, and other young professionals wanting to apply environmental regulations to their facilities and staying up to date on recently changed regulations\"-- Provided by publisher.

Key Points Semantic cognition refers to our ability to use, manipulate and generalize knowledge that is acquired over the lifespan to support innumerable verbal and non-verbal behaviours. Semantic cognition relies on two principal interacting neural systems: representation and control. We refer to this two-system view as the controlled semantic cognition framework. Coherent, generalizable concepts are formed through the hub-and-spoke representational system with the hub localised to the anterior temporal region (bilaterally) and spokes localised in modality-specific association cortices that are distributed across the cortex. Convergent clinical and cognitive neuroscience data show that the anterior temporal lobe hub has graded variations of semantic function that follow its pattern of connectivity. Category-specific differences in semantic function reflect the contributions of different parts of the connectivity-constrained version of the hub-and-spoke framework. Semantic control is implemented within a distributed frontal and temporoparietal neural network. Semantic control supports executive mechanisms that constrain how activation propagates through the network for semantic representation. Our ability to use conceptual knowledge to support various behaviours is termed semantic cognition. In this Review, Lambon Ralph et al . argue that this ability arises from two interacting neural systems, one for representation and one for control. Semantic cognition refers to our ability to use, manipulate and generalize knowledge that is acquired over the lifespan to support innumerable verbal and non-verbal behaviours. This Review summarizes key findings and issues arising from a decade of research into the neurocognitive and neurocomputational underpinnings of this ability, leading to a new framework that we term controlled semantic cognition (CSC). CSC offers solutions to long-standing queries in philosophy and cognitive science, and yields a convergent framework for understanding the neural and computational bases of healthy semantic cognition and its dysfunction in brain disorders.

Originally developed to support video games, graphics processor units (GPUs) are now increasingly used for general-purpose (non-graphics) applications ranging from machine learning to mining of cryptographic currencies. GPUs can achieve improved performance and efficiency versus central processing units (CPUs) by dedicating a larger fraction of hardware resources to computation. In addition, their general-purpose programmability makes contemporary GPUs appealing to software developers in comparison to domain-specific accelerators. This book provides an introduction to those interested in studying the architecture of GPUs that support general-purpose computing. It collects together information currently only found among a wide range of disparate sources. The authors led development of the GPGPU-Sim simulator widely used in academic research on GPU architectures. The first chapter of this book describes the basic hardware structure of GPUs and provides a brief overview of their history. Chapter 2 provides a summary of GPU programming models relevant to the rest of the book. Chapter 3 explores the architecture of GPU compute cores. Chapter 4 explores the architecture of the GPU memory system. After describing the architecture of existing systems, Chapters 3 and 4 provide an overview of related research. Chapter 5 summarizes cross-cutting research impacting both the compute core and memory system. This book should provide a valuable resource for those wishing to understand the architecture of graphics processor units (GPUs) used for acceleration of general-purpose applications and to those who want to obtain an introduction to the rapidly growing body of research exploring how to improve the architecture of these GPUs.

The hippocampus replays experiences during quiet rest periods, and this replay benefits subsequent memory. A critical open question is how memories are prioritized for this replay. We used functional magnetic resonance imaging (fMRI) pattern analysis to track item-level replay in the hippocampus during an awake rest period after participants studied 15 objects and completed a memory test. Objects that were remembered less well were replayed more during the subsequent rest period, suggesting a prioritization process in which weaker memories—memories most vulnerable to forgetting—are selected for replay. In a second session 12 hours later, more replay of an object during a rest period predicted better subsequent memory for that object. Replay predicted memory improvement across sessions only for participants who slept during that interval. Our results provide evidence that replay in the human hippocampus prioritizes weakly learned information, predicts subsequent memory performance, and relates to memory improvement across a delay with sleep. The hippocampus is known to 'replay' experiences and memories during rest periods, but it is unclear how particular memories are prioritized for replay. Here, the authors show that information that is remembered less well is replayed more often, suggesting that weaker memories are selected for replay.

Key Points Semantic memory corresponds to people's general conceptual knowledge about objects and events, including knowledge about their characteristic properties and behaviours, as well as knowledge about the words we use to name and describe objects and events in speech. Whereas episodic memory encompasses memory for specific episodes or situations in one's life, semantic memory encompasses factual knowledge divorced from any specific situational context: “a scallop is an edible sea creature” (semantic) as opposed to “I ate scallops for supper last night” (episodic). Essentially all theories agree that a widely distributed brain network is responsible for our semantic knowledge of modality-specific features (for example, what a scallop looks or tastes like); but the theories differ on whether this network is sufficient for all of the functions of semantic memory. The theory highlighted in this review proposes that conceptual knowledge requires an amodal hub, which itself contains no semantic features but rather represents the semantic similarity among concepts — for example, the semantic similarity between scallops and prawns, despite their differences in virtually every modality-specific attribute. This theory predicts that a lesion of the specific brain region supporting the amodal hub should disrupt all abilities requiring central conceptual knowledge, independent of the modality of input (such as objects, words or sounds) or output (such as speaking, drawing or using objects) and independent of the type of concept (living things, man-made objects and abstract ideas, for example). Patients with semantic dementia, a neurodegenerative syndrome resulting from focal atrophy of the anterior temporal lobes (ATL) bilaterally, show precisely this pattern of semantic degradation across all modalities and all types of conceptual knowledge; therefore, semantic dementia suggests that the ATL supports an amodal hub. Functional neuroimaging studies of semantic processing only sometimes reveal activation in the ATL. The likelihood of activation in this region, however, can be predicted by a combination of the specific imaging techniques employed and the specificity of semantic processing required by the imaging task. Simulations of semantic memory in connectionist models suggest one reason why the semantic network might require a hub: without such an architecture, it is not clear how the system can learn representations that capture semantic similarity relations. Semantic memory is thought to be structured as a widely distributed brain network that contains information regarding modality-specific features. Here, Patterson and colleagues discuss the idea, based on neuropsychological and neuroimaging data and connectionist modelling, that conceptual knowledge also requires an amodal hub. Mr M, a patient with semantic dementia — a neurodegenerative disease that is characterized by the gradual deterioration of semantic memory — was being driven through the countryside to visit a friend and was able to remind his wife where to turn along the not-recently-travelled route. Then, pointing at the sheep in the field, he asked her “What are those things?” Prior to the onset of symptoms in his late 40s, this man had normal semantic memory. What has gone wrong in his brain to produce this dramatic and selective erosion of conceptual knowledge?

Early in life and without special training, human beings discern resemblance between abstract visual stimuli, such as drawings, and the real-world objects they represent. We used this capacity for visual abstraction as a tool for evaluating deep neural networks (DNNs) as models of human visual perception. Contrasting five contemporary DNNs, we evaluated how well each explains human similarity judgments among line drawings of recognizable and novel objects. For object sketches, human judgments were dominated by semantic category information; DNN representations contributed little additional information. In contrast, such features explained significant unique variance perceived similarity of abstract drawings. In both cases, a vision transformer trained to blend representations of images and their natural language descriptions showed the greatest ability to explain human perceptual similarity—an observation consistent with contemporary views of semantic representation and processing in the human mind and brain. Together, the results suggest that the building blocks of visual similarity may arise within systems that learn to use visual information, not for specific classification, but in service of generating semantic representations of objects.

Semantic ambiguity is typically measured by summing the number of senses or dictionary definitions that a word has. Such measures are somewhat subjective and may not adequately capture the full extent of variation in word meaning, particularly for polysemous words that can be used in many different ways, with subtle shifts in meaning. Here, we describe an alternative, computationally derived measure of ambiguity based on the proposal that the meanings of words vary continuously as a function of their contexts. On this view, words that appear in a wide range of contexts on diverse topics are more variable in meaning than those that appear in a restricted set of similar contexts. To quantify this variation, we performed latent semantic analysis on a large text corpus to estimate the semantic similarities of different linguistic contexts. From these estimates, we calculated the degree to which the different contexts associated with a given word vary in their meanings. We term this quantity a word’s semantic diversity (SemD). We suggest that this approach provides an objective way of quantifying the subtle, context-dependent variations in word meaning that are often present in language. We demonstrate that SemD is correlated with other measures of ambiguity and contextual variability, as well as with frequency and imageability. We also show that SemD is a strong predictor of performance in semantic judgments in healthy individuals and in patients with semantic deficits, accounting for unique variance beyond that of other predictors. SemD values for over 30,000 English words are provided as supplementary materials.

Microbial communities in terrestrial geothermal systems often contain chemolithoautotrophs with well-characterized distributions and metabolic capabilities. However, the extent to which organic matter produced by these chemolithoautotrophs supports heterotrophs remains largely unknown. Here we compared the abundance and activity of peptidases and carbohydrate active enzymes (CAZymes) that are predicted to be extracellular identified in metagenomic assemblies from 63 springs in the Central American and the Andean convergent margin (Argentinian backarc of the Central Volcanic Zone), as well as the plume-influenced spreading center in Iceland. All assemblies contain two orders of magnitude more peptidases than CAZymes, suggesting that the microorganisms more often use proteins for their carbon and/or nitrogen acquisition instead of complex sugars. The CAZy families in highest abundance are GH23 and CBM50, and the most abundant peptidase families are M23 and C26, all four of which degrade peptidoglycan found in bacterial cells. This implies that the heterotrophic community relies on autochthonous dead cell biomass, rather than allochthonous plant matter, for organic material. Enzymes involved in the degradation of cyanobacterial- and algal-derived compounds are in lower abundance at every site, with volcanic sites having more enzymes degrading cyanobacterial compounds and non-volcanic sites having more enzymes degrading algal compounds. Activity assays showed that many of these enzyme classes are active in these samples. High temperature sites (> 80°C) had similar extracellular carbon-degrading enzymes regardless of their province, suggesting a less well-developed population of secondary consumers at these sites, possibly connected with the limited extent of the subsurface biosphere in these high temperature sites. We conclude that in < 80°C springs, chemolithoautotrophic production supports heterotrophs capable of degrading a wide range of organic compounds that do not vary by geological province, even though the taxonomic and respiratory repertoire of chemolithoautotrophs and heterotrophs differ greatly across these regions.

Research on event perception has focused on transient elevations in predictive uncertainty or surprise as the primary signal driving event segmentation. Here the authors report behavioral and neuroimaging evidence that suggests that event representations can emerge even in the absence of such cues. They propose that this learning occurs in a manner analogous to the learning of semantic categories. Our experience of the world seems to divide naturally into discrete, temporally extended events, yet the mechanisms underlying the learning and identification of events are poorly understood. Research on event perception has focused on transient elevations in predictive uncertainty or surprise as the primary signal driving event segmentation. We present human behavioral and functional magnetic resonance imaging (fMRI) evidence in favor of a different account, in which event representations coalesce around clusters or 'communities' of mutually predicting stimuli. Through parsing behavior, fMRI adaptation and multivoxel pattern analysis, we demonstrate the emergence of event representations in a domain containing such community structure, but in which transition probabilities (the basis of uncertainty and surprise) are uniform. We present a computational account of how the relevant representations might arise, proposing a direct connection between event learning and the learning of semantic categories.

Subduction of the Cocos and Nazca oceanic plates beneath the Caribbean plate drives the upward movement of deep fluids enriched in carbon, nitrogen, sulfur, and iron along the Central American Volcanic Arc (CAVA). These compounds fuel diverse subsurface microbial communities that in turn alter the distribution, redox state, and isotopic composition of these compounds. Microbial community structure and functions vary according to deep fluid delivery across the arc, but less is known about how microbial communities differ along the axis of a convergent margin as geological features ( e . g ., extent of volcanism and subduction geometry) shift. Here, we investigate changes in bacterial 16S rRNA gene amplicons and geochemical analysis of deeply-sourced seeps along the southern CAVA, where subduction of the Cocos Ridge alters the geological setting. We find shifts in community composition along the convergent margin, with communities in similar geological settings clustering together independently of the proximity of sample sites. Microbial community composition correlates with geological variables such as host rock type, maturity of hydrothermal fluid and slab depth along different segments of the CAVA. This reveals tight coupling between deep Earth processes and subsurface microbial activity, controlling community distribution, structure and composition along a convergent margin.