1,192,481 research outputs found
Arithmeticity vs. non-linearity for irreducible lattices
We establish an arithmeticity vs. non-linearity alternative for irreducible
lattices in suitable product groups, such as for instance products of
topologically simple groups. This applies notably to a (large class of)
Kac-Moody groups. The alternative relies on a CAT(0) superrigidity theorem, as
we follow Margulis' reduction of arithmeticity to superrigidity.Comment: 11 page
Navigating to Objects Specified by Images
Images are a convenient way to specify which particular object instance an
embodied agent should navigate to. Solving this task requires semantic visual
reasoning and exploration of unknown environments. We present a system that can
perform this task in both simulation and the real world. Our modular method
solves sub-tasks of exploration, goal instance re-identification, goal
localization, and local navigation. We re-identify the goal instance in
egocentric vision using feature-matching and localize the goal instance by
projecting matched features to a map. Each sub-task is solved using
off-the-shelf components requiring zero fine-tuning. On the HM3D
InstanceImageNav benchmark, this system outperforms a baseline end-to-end RL
policy 7x and a state-of-the-art ImageNav model 2.3x (56% vs 25% success). We
deploy this system to a mobile robot platform and demonstrate effective
real-world performance, achieving an 88% success rate across a home and an
office environment
Box2Poly: Memory-Efficient Polygon Prediction of Arbitrarily Shaped and Rotated Text
Recently, Transformer-based text detection techniques have sought to predict
polygons by encoding the coordinates of individual boundary vertices using
distinct query features. However, this approach incurs a significant memory
overhead and struggles to effectively capture the intricate relationships
between vertices belonging to the same instance. Consequently, irregular text
layouts often lead to the prediction of outlined vertices, diminishing the
quality of results. To address these challenges, we present an innovative
approach rooted in Sparse R-CNN: a cascade decoding pipeline for polygon
prediction. Our method ensures precision by iteratively refining polygon
predictions, considering both the scale and location of preceding results.
Leveraging this stabilized regression pipeline, even employing just a single
feature vector to guide polygon instance regression yields promising detection
results. Simultaneously, the leverage of instance-level feature proposal
substantially enhances memory efficiency (>50% less vs. the state-of-the-art
method DPText-DETR) and reduces inference speed (>40% less vs. DPText-DETR)
with minor performance drop on benchmarks
Probabilistic Value Selection for Space Efficient Model
An alternative to current mainstream preprocessing methods is proposed: Value
Selection (VS). Unlike the existing methods such as feature selection that
removes features and instance selection that eliminates instances, value
selection eliminates the values (with respect to each feature) in the dataset
with two purposes: reducing the model size and preserving its accuracy. Two
probabilistic methods based on information theory's metric are proposed: PVS
and P + VS. Extensive experiments on the benchmark datasets with various sizes
are elaborated. Those results are compared with the existing preprocessing
methods such as feature selection, feature transformation, and instance
selection methods. Experiment results show that value selection can achieve the
balance between accuracy and model size reduction.Comment: Accepted in the 21st IEEE International Conference on Mobile Data
Management (July 2020
Solving seismic wave propagation in elastic media using the matrix exponential approach
Three numerical algorithms are proposed to solve the time-dependent
elastodynamic equations in elastic solids. All algorithms are based on
approximating the solution of the equations, which can be written as a matrix
exponential. By approximating the matrix exponential with a product formula, an
unconditionally stable algorithm is derived that conserves the total elastic
energy density. By expanding the matrix exponential in Chebyshev polynomials
for a specific time instance, a so-called ``one-step'' algorithm is constructed
that is very accurate with respect to the time integration. By formulating the
conventional velocity-stress finite-difference time-domain algorithm (VS-FDTD)
in matrix exponential form, the staggered-in-time nature can be removed by a
small modification, and higher order in time algorithms can be easily derived.
For two different seismic events the accuracy of the algorithms is studied and
compared with the result obtained by using the conventional VS-FDTD algorithm.Comment: 13 pages revtex, 6 figures, 2 table
Malignant Mesothelioma subtyping via sampling driven multiple instance prediction on tissue image and cell morphology data
Malignant Mesothelioma is a difficult to diagnose and highly lethal cancer usually associated with asbestos exposure. It can be broadly classified into three subtypes: Epithelioid, Sarcomatoid, and a hybrid Biphasic subtype in which significant components of both of the previous subtypes are present. Early diagnosis and identification of the subtype informs treatment and can help improve patient outcome. However, the subtyping of malignant mesothelioma, and specifically the recognition of transitional features from routine histology slides has a high level of inter-observer variability. In this work, we propose an end-to-end multiple instance learning (MIL) approach for malignant mesothelioma subtyping. This uses an adaptive instance-based sampling scheme for training deep convolutional neural networks on bags of image patches that allows learning on a wider range of relevant instances compared to max or top-N based MIL approaches. We also investigate augmenting the instance representation to include aggregate cellular morphology features from cell segmentation. The proposed MIL approach enables identification of malignant mesothelial subtypes of specific tissue regions. From this a continuous characterisation of a sample according to predominance of sarcomatoid vs epithelioid regions is possible, thus avoiding the arbitrary and highly subjective categorisation by currently used subtypes. Instance scoring also enables studying tumor heterogeneity and identifying patterns associated with different subtypes. We have evaluated the proposed method on a dataset of 234 tissue micro-array cores with an AUROC of 0.89±0.05 for this task. The dataset and developed methodology is available for the community at: https://github.com/measty/PINS
Malignant Mesothelioma subtyping via sampling driven multiple instance prediction on tissue image and cell morphology data
Malignant Mesothelioma is a difficult to diagnose and highly lethal cancer usually associated with asbestos exposure. It can be broadly classified into three subtypes: Epithelioid, Sarcomatoid, and a hybrid Biphasic subtype in which significant components of both of the previous subtypes are present. Early diagnosis and identification of the subtype informs treatment and can help improve patient outcome. However, the subtyping of malignant mesothelioma, and specifically the recognition of transitional features from routine histology slides has a high level of inter-observer variability.
In this work, we propose an end-to-end multiple instance learning (MIL) approach for malignant mesothelioma subtyping. This uses an adaptive instance-based sampling scheme for training deep convolutional neural networks on bags of image patches that allows learning on a wider range of relevant instances compared to max or top-N based MIL approaches. We also investigate augmenting the instance representation to include aggregate cellular morphology features from cell segmentation. The proposed MIL approach enables identification of malignant mesothelial subtypes of specific tissue regions. From this a continuous characterisation of a sample according to predominance of sarcomatoid vs epithelioid regions is possible, thus avoiding the arbitrary and highly subjective categorisation by currently used subtypes. Instance scoring also enables studying tumor heterogeneity and identifying patterns associated with different subtypes. We have evaluated the proposed method on a dataset of 234 tissue micro-array cores with an AUROC of 0.89 ± 0.05 for this task. The dataset and developed methodology is available for the community at: https://github.com/measty/PINS
Generic Trace Semantics via Coinduction
Trace semantics has been defined for various kinds of state-based systems,
notably with different forms of branching such as non-determinism vs.
probability. In this paper we claim to identify one underlying mathematical
structure behind these "trace semantics," namely coinduction in a Kleisli
category. This claim is based on our technical result that, under a suitably
order-enriched setting, a final coalgebra in a Kleisli category is given by an
initial algebra in the category Sets. Formerly the theory of coalgebras has
been employed mostly in Sets where coinduction yields a finer process semantics
of bisimilarity. Therefore this paper extends the application field of
coalgebras, providing a new instance of the principle "process semantics via
coinduction."Comment: To appear in Logical Methods in Computer Science. 36 page
Data Imputation through the Identification of Local Anomalies
We introduce a comprehensive and statistical framework in a model free
setting for a complete treatment of localized data corruptions due to severe
noise sources, e.g., an occluder in the case of a visual recording. Within this
framework, we propose i) a novel algorithm to efficiently separate, i.e.,
detect and localize, possible corruptions from a given suspicious data instance
and ii) a Maximum A Posteriori (MAP) estimator to impute the corrupted data. As
a generalization to Euclidean distance, we also propose a novel distance
measure, which is based on the ranked deviations among the data attributes and
empirically shown to be superior in separating the corruptions. Our algorithm
first splits the suspicious instance into parts through a binary partitioning
tree in the space of data attributes and iteratively tests those parts to
detect local anomalies using the nominal statistics extracted from an
uncorrupted (clean) reference data set. Once each part is labeled as anomalous
vs normal, the corresponding binary patterns over this tree that characterize
corruptions are identified and the affected attributes are imputed. Under a
certain conditional independency structure assumed for the binary patterns, we
analytically show that the false alarm rate of the introduced algorithm in
detecting the corruptions is independent of the data and can be directly set
without any parameter tuning. The proposed framework is tested over several
well-known machine learning data sets with synthetically generated corruptions;
and experimentally shown to produce remarkable improvements in terms of
classification purposes with strong corruption separation capabilities. Our
experiments also indicate that the proposed algorithms outperform the typical
approaches and are robust to varying training phase conditions
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