Multiple Levels Of Resolution Research Articles

Exploring the possibility of adding DGGS support to the S-100 Universal Hydrographic Data Model

reference systems. A DGGS is a spatial reference system consisting of multiple layers of grids with increasing resolution which partition the globe into polygon shaped cells of equal area. Individual cells can be easily identified and basic Geographic Information System (GIS) operations can be performed on data stored within the cells. Advantages of using DGGS for spatial data include the ability to integrate data layers of different data types in a statistically uniform way, the possibility to avoid distortions caused from projection, and the capability to scale analysis across multiple resolution levels. The benefits of using DGGS for hydrographic data, specifically Electronic Navigational Charts (ENC) are examined. The addition of DGGS to S-100 would provide a uniform gridding system for ENC cells, and would eliminate distortions in the Polar Regions caused when projecting rectangular cells based on latitude and longitude. The Open Geospatial Consortium (OGC) led a pilot project for which one of the participants developed a server that offers S-100 marine protected area data organized according to DGGS. Because of this success and the availability of open-source software for the implementation of DGGS, the BSH (German Federal Maritime and Hydrographic Agency) investigated the feasibility and possible benefits of using Discrete Global Grid Systems for S-100 data. The outcome was successful. However, it is recognized that DGGS as a standard is not yet advanced enough and the available software tools are inadequate for wide implementation. More investigation needs to be done, and additionally the best grid shape, orientation and aperture for a uniform grid for hydrographic data need to be determined. Overall, DGGS should be considered for future versions of S-100, the new universal hydrographic data model, because the benefits for ENCs and hydrographic data are clear and in the future, it is expected that DGGS will revolutionize GIS.

Abstract 1203: Mushroom: a tool for identification of 3D cellular neighborhoods in multi-modal spatial datasets

Abstract A comprehensive understanding of the spatial localization of cellular processes is necessary to fully describe tissue biology. Numerous techniques exist to identify cellular neighborhoods in spatial datasets, however, these techniques are limited in that they either only capture two-dimensional phenomena - and can't describe the full, 3-dimensional tumor volume, or they are restricted to one spatial technology (i.e. IF, 10X Visium, Xenium, MERFISH, etc.). To address this, we developed Mushroom, a tool for identification of three-dimensional cellular neighborhoods in serial-sectioned multi-modal datasets. Mushroom’s methodology consists of two main steps: 1) registration of multi-modal technologies taken in serial sections (such as multiplex imaging, H&E, or spatial transcriptomics), and 2) identification of 3-dimensional cellular neighborhoods comprising the tissue volume that has been serially sectioned. Keypoint registration is performed with BigWarp, after which the resulting dense displacement field (DDF) is used to register neighboring serial sections - including those from different data modalities. We then implement a neural network to identify three dimensional cellular neighborhoods. The network is a variational autoencoder (VAE) with a vision transformer (VIT) backbone that has been adapted to work with multiple spatial data modalities. Features extracted from the VIT backbone are then clustered to identify cellular neighborhoods unique to each input data type. With 3D neighborhoods in hand, biological questions not answerable in traditional 2D approaches can be investigated, such as z-dimensional changes in morphology and cellular interactions. Further, seamless characterization of cellular neighborhoods by simultaneous technologies at multiple levels of spatial and genomic resolution becomes possible. Citation Format: Erik Storrs, Siqi Chen, Chia-Kuei Mo, Andrew Houston, Andrew Shinkle, Austin Southard-Smith, Faria Simin, Andre Targino, Xiang Li, Ateih Abedin, Reyka Jayasinghe, Alla Karpova, Jingxian Liu, John Herndon, David Fenyo, Feng Chen, Ryan Fields, Tao Ju, Ben Raphael, Li Ding. Mushroom: a tool for identification of 3D cellular neighborhoods in multi-modal spatial datasets [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1203.

Modeling the cell-type-specific mesoscale murine connectome with anterograde tracing experiments.

The Allen Mouse Brain Connectivity Atlas consists of anterograde tracing experiments targeting diverse structures and classes of projecting neurons. Beyond regional anterograde tracing done in C57BL/6 wild-type mice, a large fraction of experiments are performed using transgenic Cre-lines. This allows access to cell-class-specific whole-brain connectivity information, with class defined by the transgenic lines. However, even though the number of experiments is large, it does not come close to covering all existing cell classes in every area where they exist. Here, we study how much we can fill in these gaps and estimate the cell-class-specific connectivity function given the simplifying assumptions that nearby voxels have smoothly varying projections, but that these projection tensors can change sharply depending on the region and class of the projecting cells. This paper describes the conversion of Cre-line tracer experiments into class-specific connectivity matrices representing the connection strengths between source and target structures. We introduce and validate a novel statistical model for creation of connectivity matrices. We extend the Nadaraya-Watson kernel learning method that we previously used to fill in spatial gaps to also fill in gaps in cell-class connectivity information. To do this, we construct a "cell-class space" based on class-specific averaged regionalized projections and combine smoothing in 3D space as well as in this abstract space to share information between similar neuron classes. Using this method, we construct a set of connectivity matrices using multiple levels of resolution at which discontinuities in connectivity are assumed. We show that the connectivities obtained from this model display expected cell-type- and structure-specific connectivities. We also show that the wild-type connectivity matrix can be factored using a sparse set of factors, and analyze the informativeness of this latent variable model.

Integrated single-cell chromatin and transcriptomic analyses of human scalp identify gene-regulatory programs and critical cell types for hair and skin diseases.

Genome-wide association studies have identified many loci associated with hair and skin disease, but identification of causal variants requires deciphering of gene-regulatory networks in relevant cell types. We generated matched single-cell chromatin profiles and transcriptomes from scalp tissue from healthy controls and patients with alopecia areata, identifying diverse cell types of the hair follicle niche. By interrogating these datasets at multiple levels of cellular resolution, we infer 50-100% more enhancer-gene links than previous approaches and show that aggregate enhancer accessibility for highly regulated genes predicts expression. We use these gene-regulatory maps to prioritize cell types, genes and causal variants implicated in the pathobiology of androgenetic alopecia (AGA), eczema and other complex traits. AGA genome-wide association studies signals are enriched in dermal papilla regulatory regions, supporting the role of these cells as drivers of AGA pathogenesis. Finally, we train machine learning models to nominate single-nucleotide polymorphisms that affect gene expression through disruption of transcription factor binding, predicting candidate functional single-nucleotide polymorphism for AGA and eczema.

RLogist: Fast Observation Strategy on Whole-Slide Images with Deep Reinforcement Learning

Whole-slide images (WSI) in computational pathology have high resolution with gigapixel size, but are generally with sparse regions of interest, which leads to weak diagnostic relevance and data inefficiency for each area in the slide. Most of the existing methods rely on a multiple instance learning framework that requires densely sampling local patches at high magnification. The limitation is evident in the application stage as the heavy computation for extracting patch-level features is inevitable. In this paper, we develop RLogist, a benchmarking deep reinforcement learning (DRL) method for fast observation strategy on WSIs. Imitating the diagnostic logic of human pathologists, our RL agent learns how to find regions of observation value and obtain representative features across multiple resolution levels, without having to analyze each part of the WSI at the high magnification. We benchmark our method on two whole-slide level classification tasks, including detection of metastases in WSIs of lymph node sections, and subtyping of lung cancer. Experimental results demonstrate that RLogist achieves competitive classification performance compared to typical multiple instance learning algorithms, while having a significantly short observation path. In addition, the observation path given by RLogist provides good decision-making interpretability, and its ability of reading path navigation can potentially be used by pathologists for educational/assistive purposes. Our code is available at: https://github.com/tencent-ailab/RLogist.

Multi-level emulation of tsunami simulations over Cilacap, South Java, Indonesia

Carrying out a Probabilistic Tsunami Hazard Assessment (PTHA) requires a large number of simulations done at a high resolution. Statistical emulation builds a surrogate to replace the simulator and thus reduces computational costs when propagating uncertainties from the earthquake sources to the tsunami inundations. To reduce further these costs, we propose here to build emulators that exploit multiple levels of resolution and a sequential design of computer experiments. By running a few tsunami simulations at high resolution and many more simulations at lower resolutions we are able to provide realistic assessments whereas, for the same budget, using only the high resolution tsunami simulations do not provide a satisfactory outcome. As a result, PTHA can be considered with higher precision using the highest spatial resolutions, and for impacts over larger regions. We provide an illustration to the city of Cilacap in Indonesia that demonstrates the benefit of our approach.

Image quality enhancement using HybridGAN for automated railway track defect recognition

Numerous researchers have explored the potential of artificial intelligence to improve the rail inspection process, which required high quality in terms of resolution and clarity images. However, poor resolution and blurriness are chronic, difficult-to-resolve issues in dynamically acquired railway inspection images. Therefore, a novel HybridGAN is proposed that combines ESRGAN and DeblurGANv2 to improve the quality of dynamically acquired images in terms of resolution and blurriness. To validate the performance, results obtained using a YOLOv4 model trained and tested, respectively, on the original dataset and on an improved dataset were compared. Furthermore, three sensitivity analyses were conducted to test the performance. HybridGAN consistently demonstrated the ability to improve mAP scores that were 8.76–10.83% better across multiple resolution levels and 18.80–28.07% higher on the low-quality dataset. HybridGAN offers an effective solution to the problem of using low-quality image datasets both low resolution and high blurriness in automated railway safety maintenance.

Multi‐Spatiotemporal Probing of Neurochemical Events by Advanced Electrochemical Sensing Methods

AbstractNeurochemical events involving biosignals of different time and space dimensionalities constitute the complex basis of neurological functions and diseases. In view of this fact, electrochemical measurements enabling real‐time quantification of neurochemicals at multiple levels of spatiotemporal resolution can provide informative clues to decode the molecular networks bridging vesicles and brains. This Minireview focuses on how scientific questions regarding the properties of single vesicles, neurotransmitter release kinetics, interstitial neurochemical dynamics, and multisignal interconnections in vivo have driven the design of electrochemical nano/microsensors, sensing interface engineering, and signal/data processing. An outlook for the future frontline in this realm will also be provided.

Multi-Spatiotemporal Probing of Neurochemical Events by Advanced Electrochemical Sensing Methods.

Neurochemical events involving biosignals of different time and space dimensionalities constitute the complex basis of neurological functions and diseases. In view of this fact, electrochemical measurements enabling real-time quantification of neurochemicals at multiple levels of spatiotemporal resolution can provide informative clues to decode the molecular networks bridging vesicles and brains. This Minireview focuses on how scientific questions regarding the properties of single vesicles, neurotransmitter release kinetics, interstitial neurochemical dynamics, and multisignal interconnections in vivo have driven the design of electrochemical nano/microsensors, sensing interface engineering, and signal/data processing. An outlook for the future frontline in this realm will also be provided.

MGTdb: a web service and database for studying the global and local genomic epidemiology of bacterial pathogens.

Multilevel genome typing (MGT) enables the genomic characterization of bacterial isolates and the relationships among them. The MGT system describes an isolate using multiple multilocus sequence typing (MLST) schemes, referred to as levels. Thus, for a new isolate, sequence types (STs) assigned at multiple precisely defined levels can be used to type isolates at multiple resolutions. The MGT designation for isolates is stable, and the assignment is faster than the existing approaches. MGT's utility has been demonstrated in multiple species. This paper presents a publicly accessible web service called MGTdb, which enables the assignment of MGT STs to isolates, along with their storage, retrieval and analysis. The MGTdb web service enables upload of genome data as sequence reads or alleles, which are processed and assigned MGT identifiers. Additionally, any newly sequenced isolates deposited in the National Center for Biotechnology Information's Sequence Read Archive are also regularly retrieved (currently daily), processed, assigned MGT identifiers and made publicly available in MGTdb. Interactive visualization tools are presented to assist analysis, along with capabilities to download publicly available isolates and assignments for use with external software. MGTdb is currently available for Salmonella enterica serovars Typhimurium and Enteritidis and Vibrio cholerae. We demonstrate the usability of MGTdb through three case studies - to study the long-term national surveillance of S. Typhimurium, the local epidemiology and outbreaks of S. Typhimurium, and the global epidemiology of V. cholerae. Thus, MGTdb enables epidemiological and microbiological investigations at multiple levels of resolution for all publicly available isolates of these pathogens. Database URL: https://mgtdb.unsw.edu.au.

Automated Lung Cancer Segmentation Using a Dual-Modality Deep Learning Network with PET and CT Images

<h3>Purpose/Objective(s)</h3> To develop an automated lung cancer segmentation method using dual-modality imaging and deep learning, and to perform clinical evaluation of the method. <h3>Materials/Methods</h3> A 3D neural network with dual inputs from diagnostic PET and simulation CT was constructed based on U-Net. The architecture consisted of two parallel convolution paths for independent feature extractions from PET and CT at multiple resolution levels and a single deconvolution path. At each resolution level, the extracted features from the convolution arms were concatenated and fed into the deconvolution path through skip connections. The network was trained/validated/tested by a 3:1:1 split on a dataset of 290 pairs of PET and CT from lung cancer patients treated at our institution, with manual physician contours as the ground truth. The performance of the 3D dual-modality network was compared against that of a CT-only network. evaluated both the manual and the network-produced tumor contours of a randomly selected subset of 20 cases (10 large and 10 small) in a blinded fashion. <h3>Results</h3> The mean Dice similarity coefficient (DSC), Hausdorff Distance (HD), and bi-directional local distance (BLD) comparing the automatic contours versus the ground truth were 0.77 ± 0.12, 7.6 ± 4.7 mm, and 2.9 ± 1.4 mm, and 0.79 ± 0.10, 5.8 ± 3.2 mm, and 2.8 ± 1.5 mm for dual modality inputs, respectively. The stratification method delivered the best results when the model for the large GTV subset (> 25 ml) was trained with GTVs of all sizes (DSC, HD, BLD of 0.85 ± 0.05, 9.5 ± 3.8 mm, and 3.8 ± 1.8 mm), and that for the small GTV subset (< 25 ml) was trained with small GTVs only (DSC, HD, BLD of 0.82 ± 0.08, 3.7 ± 1.7 mm, and 2.2 ± 1.1 mm). From the stratified results, the best combined overall DSC, HD, and BLD were 0.83 ± 0.07, 5.9 ± 2.5 mm, and 2.8 ± 1.4 mm, respectively. In the multi-observer review, on average 91.25% of manual vs. 88.75% of automatic contours were Accepted or Accepted with Modifications. (50% Accepted, 41.25% Accepted w/ Mods and 6.25% Rejected for manual vs. 18.75%, 70%, and 8.75% for automatic), the modifications on the automatic contours were relatively minor with a mean DSC of 0.92 ± 0.04 between the original and modified, comparable to the mean DSC of 0.90 ± 0.04 between all the modified contours and their manual ground truths. <h3>Conclusion</h3> By utilizing an expansive clinical PET and CT image database and a dual-modality architecture, the proposed 3D network with a novel GTV volume-based stratification strategy was able to generate clinically useful lung cancer contours that were quantitatively similar to the ground truth and highly acceptable in physician review.

Automated Lung Cancer Segmentation Using a PET and CT Dual-Modality Deep Learning Neural Network

To develop an automated lung tumor segmentation method for radiation therapy planning based on deep learning and dual-modality positron emission tomography (PET) and computed tomography (CT) images. A 3-dimensional (3D) convolutional neural network using inputs from diagnostic PETs and simulation CTs was constructed with 2 parallel convolution paths for independent feature extraction at multiple resolution levels and a single deconvolution path. At each resolution level, the extracted features from the convolution arms were concatenated and fed through the skip connections into the deconvolution path that produced the tumor segmentation. Our network was trained/validated/tested by a 3:1:1 split on 290 pairs of PET and CT images from patients with lung cancer treated at our clinic, with manual physician contours as the ground truth. A stratified training strategy based on the magnitude of the gross tumor volume (GTV) was investigated to improve performance, especially for small tumors. Multiple radiation oncologists assessed the clinical acceptability of the network-produced segmentations. The mean Dice similarity coefficient, Hausdorff distance, and bidirectional local distance comparing manual versus automated contours were 0.79 ± 0.10, 5.8 ± 3.2 mm, and 2.8 ± 1.5 mm for the unstratified 3D dual-modality model. Stratification delivered the best results when the model for the large GTVs (>25 mL) was trained with all-size GTVs and the model for the small GTVs (<25 mL) was trained with small GTVs only. The best combined Dice similarity coefficient, Hausdorff distance, and bidirectional local distance from the 2 stratified models on their corresponding test data sets were 0.83 ± 0.07, 5.9 ± 2.5 mm, and 2.8 ± 1.4 mm, respectively. In the multiobserver review, 91.25% manual versus 88.75% automatic contours were accepted or accepted with modifications. By using an expansive clinical PET and CT image database and a dual-modality architecture, the proposed 3D network with a novel GTVbased stratification strategy generated clinically useful lung cancer contours that were highly acceptable on physician review.

Remote Production for Live Holographic Teleportation Applications in 5G Networks

Holographic Teleportation is an emerging media application allowing people or objects to be teleported in a real-time and immersive fashion into the virtual space of the audience side. Compared to the traditional video content, the network requirements for supporting such applications will be much more challenging. In this paper, we present a 5G edge computing framework for enabling remote production functions for live holographic Teleportation applications. The key idea is to offload complex holographic content production functions from end user premises to the 5G mobile edge in order to substantially reduce the cost of running such applications on the user side. We comprehensively evaluated how specific network-oriented and application-oriented factors may affect the performances of remote production operations based on 5G systems. Specifically, we tested the application performance from the following four dimensions: (1) different data rate requirements with multiple content resolution levels, (2) different transport-layer mechanisms over 5G uplink radio, (3) different indoor/outdoor location environments with imperfect 5G connections and (4) different object capturing scenarios including the number of teleported objects and the number of sensor cameras required. Based on these evaluations we derive useful guidelines and policies for future remote production operation for holographic Teleportation through 5G systems.

Conditionally Exponential Prior in Focal Near- and Far-Field EEG Source Localization via Randomized Multiresolution Scanning (RAMUS)

In this paper, we focus on the inverse problem of reconstructing distributional brain activity with cortical and weakly detectable deep components in non-invasive Electroencephalography. We consider a recently introduced hybrid reconstruction strategy combining a hierarchical Bayesian model to incorporate a priori information and the advanced randomized multiresolution scanning (RAMUS) source space decomposition approach to reduce modelling errors, respectively. In particular, we aim to generalize the previously extensively used conditionally Gaussian prior (CGP) formalism to achieve distributional reconstructions with higher focality. For this purpose, we introduce as a hierarchical prior, a general exponential distribution, which we refer to as conditionally exponential prior (CEP). The first-degree CEP corresponds to focality enforcing Laplace prior, but it also suffers from strong depth bias, when applied in numerical modelling, making the deep activity unrecoverable. We sample over multiple resolution levels via RAMUS to reduce this bias as it is known to depend on the resolution of the source space. Moreover, we introduce a procedure based on the physiological a priori knowledge of the brain activity to obtain the shape and scale parameters of the gamma hyperprior that steer the CEP. The posterior estimates are calculated using iterative statistical methods, expectation maximization and iterative alternating sequential algorithm, which we show to be algorithmically similar and to have a close resemblance to the iterative ell _1 and ell _2 reweighting methods. The performance of CEP is compared with the recent sampling-based dipole localization method Sequential semi-analytic Monte Carlo estimation (SESAME) in numerical experiments of simulated somatosensory evoked potentials related to the human median nerve stimulation. Our results obtained using synthetic sources suggest that a hybrid of the first-degree CEP and RAMUS can achieve an accuracy comparable to the second-degree case (CGP) while being more focal. Further, the proposed hybrid is shown to be robust to noise effects and compares well with the dipole reconstructions obtained with SESAME.

Multimodal imaging of synaptic vesicles with a single probe

SummaryA complete understanding of synaptic-vesicle recycling requires the use of multiple microscopy methods to obtain complementary information. However, many currently available probes are limited to a specific microscopy modality, which necessitates the use of multiple probes and labeling paradigms. Given the complexity of vesicle populations and recycling pathways, having new single-vesicle probes that could be used for multiple microscopy techniques would complement existing sets of tools for studying vesicle function. Here, we present a probe based on the membrane-binding C2 domain of cytosolic phospholipase A2 (cPLA2) that fulfills this need. By conjugating the C2 domain with different detectable tags, we demonstrate that a single, modular probe can allow synaptic vesicles to be imaged at multiple levels of spatial and temporal resolution. Moreover, as a general endocytic marker, the C2 domain may also be used to study membrane recycling in many cell types.

PMCDM: Privacy-preserving multiresolution community detection in multiplex networks

Multiplex networks can effectively represent network data with communications generated by sensors across multiple platforms in Internet of Things (IoTs). Multiresolutional community detection (MCD) in multiplex networks is essential to understanding the functional modules of complex systems in IoTs, consequently the studies on MCD have attracted much attention in recent years. However, most existing works require full access to the communications of systems in IoTs, which are sensitive and private. To overcome this requirement, in this paper, we propose a privacy-preserving MCD framework (called PMCDM) for multiplex networks, aiming to detect the community structures at multiple resolution levels while preserving the link and weight privacy of the systems in IoTs. PMCDM first encrypts the sensitive link and weight information of multiplex networks using the differential privacy and homomorphic encryption techniques, respectively, and subsequently devises a multi-greedy algorithm (called DH-Louvain) for MCD on the encrypted networks by optimizing a weighted modularity density. Extensive experiments on both the eleven LFR benchmark and the ten real-world multiple networks show that PMCDM can effectively detect the multiresolutional community structures of these networks while preserving the network privacy.

T Cell Responses to the Microbiota.

The immune system employs recognition tools to communicate with its microbial evolutionary partner. Among all the methods of microbial perception, T cells enable the widest spectrum of microbial recognition resolution, ranging from the crudest detection of whole groups of microbes to the finest detection of specific antigens. The application of this recognition capability to the crucial task of combatting infections has been the focus of classical immunology. We now appreciate that the coevolution of the immune system and the microbiota has led to development of a lush immunological decision tree downstream of microbial recognition, of which an inflammatory response is but one branch. In this review we discuss known T cell-microbe interactions in the gut and place them in the context of an algorithmic framework of recognition, context-dependent interpretation, and response circuits across multiple levels of microbial recognition resolution. The malleability of T cells in response to the microbiota presents an opportunity to edit immune response cellularity, identity, and functionality by utilizing microbiota-controlled pathways to promote human health.

Efficient Algebraic Multigrid Methods for Multilevel Overlapping Coclustering of User-Item Relationships

Various digital data sets that encode user-item relationships contain a multilevel overlapping cluster structure. The user-item relation can be encoded in a weighted bipartite graph and uncovering these overlapping coclusters of users and items at multiple levels in the bipartite graph can play an important role in analyzing user-item data in many applications. For example, for effective online marketing, such as placing online ads or deploying smart online marketing strategies, identifying co-occurring clusters of users and items can lead to accurately targeted advertisements and better marketing outcomes. In this paper, we propose fast algorithms inspired by algebraic multigrid methods for finding multilevel overlapping cocluster structures of feature matrices that encode user-item relations. Starting from the weighted bipartite graph structure of the feature matrix, the algorithms use agglomeration procedures to recursively coarsen the bipartite graphs that represent the relations between the coclusters on increasingly coarser levels. New fast coarsening routines are described that circumvent the bottleneck of all-to-all similarity computations by exploiting measures of direct connection strength between row and column variables in the feature matrix. Providing accurate coclusters at multiple levels in a manner that can scale to large data sets is a challenging task. In this paper, we propose heuristic algorithms that approximately and recursively minimize normalized cuts to obtain coclusters in the aggregated bipartite graphs on multiple levels of resolution. Whereas the main novelty and focus of the paper lies in algorithmic aspects of reducing computational complexity to obtain scalable methods specifically for large rectangular user-item matrices, the algorithmic variants also define several new models for determining multilevel coclusters that we justify intuitively by relating them to principles that underlie collaborative filtering methods for user-item relationships. Experimental results show that the proposed algorithms successfully uncover the multilevel overlapping cluster structure for artificial and real data sets. Summary of Contribution: This paper develops new and efficient computational methods for finding the multilevel overlapping cocluster structure of feature matrices that encode user-item relationships. We base our approach on the use of pairwise similarity measures between features, seeking clusters of points that are similar to each other and dissimilar from the points outside the cluster. We approximately solve the problem of finding optimal overlapping coclusters on multiple levels by employing a framework that is based on efficient multilevel methods that have been used previously to solve sparse linear systems and to cluster graphs. Our main contribution is that we extend these methods in efficient manners to find coclusters in the bipartite graphs that encode common and important user-item relationships or social network relations. The novel methods that we propose are inherently scalable to large problem sizes and are naturally able to uncover overlapping coclusters at multiple levels, whereas existing methods generally only find coclusters at the fine level. We illustrate the algorithm and its performance on some standard test problems from the literature and on a proof-of-concept real-world data set that relates LinkedIn users to their skills and expertise.

Hermite Transform Based Algorithm for Detection and Classification of High Impedance Faults

This work proposes a new algorithm to classify high impedance faults (HIFs) in distribution systems. HIFs can be represented by small current magnitudes with non-linear variations, which complicate their detection in distribution grids. The proposed method uses the Hermite transform (HT) as a signal processing technique that offers several advantages, one of the more important being the ability to analyze the signal at multiple resolution levels and at different frequency bands thanks to their filter functions based on Gaussian derivatives. The Hermite coefficients are used to extract signal features that allow efficient identification of the transient behaviour related to HIFs and other typical faults. In this sense, a multichannel approach based on the Hermite transform is proposed to classify different types of faults and HIFs. This analysis is carried out in a distribution network considering photovoltaic (PV) systems considering three different classifiers, which are also used to compare our results with the discrete wavelet transform (DWT) as signal decomposition model; the comparison suggests that our proposal presents better performance discriminating HIFs from typical faults.

A Multiresolution Approach for Large Real-World Camera Placement Optimization Problems

There have been numerous attempts at solving the optimal camera placement problem across multiple applications. Exact linear programming-based, as well as, heuristic combinatorial optimization methods were shown to be successful in providing optimal or near-optimal solutions to this problem. Working over a discrete space model is the general practice when solving the camera placement problem. However, discretized environments often limit the methods&#x2019; usage only to small-scale datasets due to resource and time constraints that grow exponentially with the number of 3D points collected from the discrete space. We propose a multi-resolution approach that enables the usage of existing optimization algorithms on large real-world problems modelled using high resolution 3D grids. Our method works by grouping together the given discrete set of possible camera locations into clusters of points, multiple times, resulting in multiple resolution levels. Camera placement optimization is repeated for all resolution levels while propagating the optimized solution from low to high resolutions. Our experiments on both simulated and real data with grids of varying sizes show that using our multi-resolution approach, existing camera placement optimization methods can be used even on high resolution grids consisting of hundreds of thousands of points. Our results also show that the strategy of grouping points together by exploiting underlying 3D geometry to optimize camera poses is not only significantly faster than optimizing on the entire set of samples but, it also provides better camera coverage.