1,165 research outputs found
Emergent dimensions underlying human understanding of the reachable world
Near-scale, reach-relevant environments, like work desks, restaurant place settings or lab benches, are the interface of our hand-based interactions with the world. How are our conceptual representations of these environments organized? For navigable-scale scenes, global properties such as openness, depth or naturalness have been identified, but the analogous organizing principles for reach-scale environments are not known. To uncover such principles, we obtained 1.25 million odd-one-out behavioral judgments on image triplets assembled from 990 reachspace images. Images were selected to comprehensively sample the variation both between and within reachspace categories. Using data-driven modeling, we generated a 30-dimensional embedding which predicts human similarity judgments among the images. First, examination of the embedding dimensions revealed key properties that distinguish among reachspaces, relating to their structural layout, affordances, visual appearances and functional roles. Second, clustering analyses performed over the embedding revealed four distinct interpretable classes of reachspaces, with separate clusters for spaces related to food, electronics, analog activities, and storage or display. Finally, we found that the similarity structure among reachspace images was better predicted by the function of the spaces than their locations, suggesting that reachspaces are largely conceptualized in terms of the actions they are designed to support. Altogether, these results reveal the behaviorally-relevant principles that that structure our internal representations of reach-relevant environments
Taking Quantitative Genomics into the Wild
A key goal in studies of ecology and evolution is understanding the causes of
phenotypic diversity in nature. Most traits of interest, such as those relating
to morphology, life-history, immunity and behaviour are quantitative, and
phenotypic variation is driven by the cumulative effects of genetic and
environmental variation. The field of quantitative genetics aims to quantify
the additive genetic component of this trait variance (i.e. the
"heritability"), often with the underlying assumption that trait variance is
driven by many loci of infinitesimal effects throughout the genome. This
approach allows us to understand the evolutionary potential of natural
populations and can be extended to examine the genetic covariation with fitness
to predict responses to selection. Therefore, quantitative genetic studies are
fundamental to understanding evolution in the wild. Over the last two decades,
there has been a wealth of studies investigating trait heritabilities and
genetic correlations, but these were initially limited to long-term studies of
pedigreed populations or common-garden experiments. However, genomic
technologies have since allowed quantitative genetic studies in a more diverse
range of wild systems and has increased the opportunities for addressing
outstanding questions in ecology and evolution. In particular, genomic studies
can uncover the genetic basis of fitness-related quantitative traits, allowing
a better understanding of their evolutionary dynamics. We organised this
special issue to highlight new work and review recent advances at the cutting
edge of "Wild Quantitative Genomics". In this Editorial, we will present some
history of wild quantitative genetic and genomic studies, before discussing the
main themes in the papers published in this special issue and highlighting the
future outlook of this dynamic field.Comment: 17 page (plus references) Editorial for a special issue of
Proceedings of the Royal Society B: Biological Sciences. Revised submissio
Anomaly Detection Methods to Improve Supply Chain Data Quality and Operations
Supply chain operations drive the planning, manufacture, and distribution of billions of semiconductors a year, spanning thousands of products across many supply chain configurations. The customizations span from wafer technology to die stacking and chip feature enablement. Data quality drives efficiency in these processes and anomalies in data can be very disruptive, and at times, consequential. Developing preventative measures that automate the detection of anomalies before they reach downstream execution systems would result in significant efficiency gain for the organization. The purpose of this research is to identify an effective, actionable, and computationally efficient approach to highlight anomalies in a sparse and highly variable supply chain data structure. This research highlights the application of ensemble unsupervised learning algorithms for anomaly detection on supply chain demand data. The outlier detection algorithms explored include Angle-Based Outlier Detection, Isolation Forest, Local Outlier Factor and K-Nearest Neighbors. The application of an ensemble technique on unconstrained forecast signal, which is traditionally a consistent demand line, demonstrated a dramatic decrease in false positives. The application of the ensemble technique to the sales-order netted demand forecast, a signal that is irregular in structure, the algorithm identifies true anomalous observations relative to historical observations across time. The research team concluded that assessing an outlier is not limited to the most recent forecast’s observations but must be considered in the context of historical demand patterns across time
Neuropathologic basis of frontotemporal dementia in progressive supranuclear palsy.
BackgroundProgressive supranuclear palsy (PSP) is a neurodegenerative disorder characterized by neuronal loss in the extrapyramidal system with pathologic accumulation of tau in neurons and glia. The most common clinical presentation of PSP, referred to as Richardson syndrome, is that of atypical parkinsonism with vertical gaze palsy, axial rigidity, and frequent falls. Although cognitive deficits in PSP are often ascribed to subcortical dysfunction, a subset of patients has dementia with behavioral features similar to the behavioral variant of frontotemporal dementia. In this study we aimed to identify the clinical and pathological characteristics of PSP presenting with frontotemporal dementia.MethodsIn this study, we compared clinical and pathologic characteristics of 31 patients with PSP with Richardson syndrome with 15 patients with PSP with frontotemporal dementia. For pathological analysis, we used semiquantitative methods to assess neuronal and glial lesions with tau immunohistochemistry, as well image analysis of tau burden using digital microscopic methods.ResultsWe found greater frontal and temporal neocortical neuronal tau pathology in PSP with frontotemporal dementia compared with PSP with Richardson syndrome. White matter tau pathology was also greater in PSP with frontotemporal dementia than PSP with Richardson syndrome. Genetic and demographic factors were not associated with atypical distribution of tau pathology in PSP with frontotemporal dementia.ConclusionsThe results confirm the subset of cognitive-predominant PSP mimicking frontotemporal dementia in PSP. PSP with frontotemporal dementia has distinct clinical features that differ from PSP with Richardson syndrome, as well as differences in distribution and density of tau pathology. © 2019 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society
Progressive Apraxia of Speech: Might There Be Subtypes?
This study examined speech and language characteristics of three groups of individuals with neurodegenerative disease: (1) primary progressive apraxia of speech (AOS) without aphasia (N=18), (2) agrammatic primary progressive aphasia (agPPA) less severe than AOS (N=10), and (3) agPPA more severe than AOS (N=9). Findings indicate that differences in the predominant characteristics of AOS (predominance of articulatory versus prosodic abnormalities) distributed differently among the three groups, independent of AOS severity. Neuroimaging findings also differed among the groups. Results suggest that neurodegenerative AOS may include perceptually distinguishable subtypes that are related to the presence or absence of aphasia and neuroimaging findings
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Melt Rate Improvement for DWPF MB3: Melt Rate Furnace Testing
The Defense Waste Processing Facility (DWPF) would like to increase its canister production rate. The goal of this study is to improve the melt rate in DWPF specifically for Macrobatch 3. However, the knowledge gained may result in improved melting efficiencies translating to future DWPF macrobatches and in higher throughput for other Department of Energy's (DOE) melters. Increased melting efficiencies decrease overall operational costs by reducing the immobilization campaign time for a particular waste stream. For melt rate limited systems, a small increase in melting efficiency translates into significant hard dollar savings by reducing life cycle operational costs
Testing refinements by refining tests
One of the potential benefits of formal methods is that they offer the possibility of reducing the costs of testing. A specification acts as both the benchmark against which any implementation is tested, and also as the means by which tests are generated. There has therefore been interest in developing test generation techniques from formal specifications, and a number of different methods have been derived for state based languages such as Z, B and VDM. However, in addition to deriving tests from a formal specification, we might wish to refine the specification further before its implementation. The purpose of this paper is to explore the relationship between testing and refinement. As our model for test generation we use a DNF partition analysis for operations written in Z, which produces a number of disjoint test cases for each operation. In this paper we discuss how the partition analysis of an operation alters upon refinement, and we develop techniques that allow us to refine abstract tests in order to generate test cases for a refinement. To do so we use (and extend existing) methods for calculating the weakest data refinement of a specification
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