Multiple Aggregation Research Articles

Toward Automatic Building Footprint Delineation From Aerial Images Using CNN and Regularization

This study proposes an automatic building footprint extraction framework that consists of a convolutional neural network (CNN)-based segmentation and an empirical polygon regularization that transforms segmentation maps into structured individual building polygons. The framework attempts to replace part of the manual delineation of building footprints that are involved in surveying and mapping field with algorithms. First, we develop a scale robust fully convolutional network (FCN) by introducing multiple scale aggregation of feature pyramids from convolutional layers. Two postprocessing strategies are introduced to refine the segmentation maps from the FCN. The refined segmentation maps are vectorized and polygonized. Then, we propose a polygon regularization algorithm consisting of a coarse and fine adjustment, to translate the initial polygons into structured footprints. Experiments on a large open building data set including 181 000 buildings showed that our algorithm reached a high automation level where at least 50% of individual buildings in the test area could be delineated to replace manual work. Experiments on different data sets demonstrated that our FCN-based segmentation method outperformed several most recent segmentation methods, and our polygon regularization algorithm is robust in challenging situations with different building styles, image resolutions, and even low-quality segmentation.

Cross-temporal aggregation: Improving the forecast accuracy of hierarchical electricity consumption

Achieving high accuracy in energy consumption forecasting is critical for improving energy management and planning. However, this requires the selection of appropriate forecasting models, able to capture the individual characteristics of the series to be predicted, which is a task that involves a lot of uncertainty. When hierarchies of load from different sources are considered together, the uncertainty and complexity increase further. For example, when forecasting both at system and region level, not only the model selection problem is expanded to multiple time series, but we also require aggregation consistency of the forecasts across levels. Although hierarchical forecasting, such as the bottom-up, the top-down, and the optimal reconciliation methods, can address the aggregation consistency concerns, it does not resolve the model selection uncertainty. To address this issue, we rely on Multiple Temporal Aggregation (MTA), which has been shown to mitigate the model selection problem for low-frequency time series. We propose a modification of the Multiple Aggregation Prediction Algorithm, a special implementation of MTA, for high-frequency time series to better handle the undesirable effect of seasonality shrinkage that MTA implies and combine it with conventional cross-sectional hierarchical forecasting. The impact of incorporating temporal aggregation in hierarchical forecasting is empirically assessed using a real data set from five bank branches. We show that the proposed MTA approach, combined with the optimal reconciliation method, demonstrates superior accuracy, aggregation consistency, and reliable automatic forecasting.

Green-Building-Material Supplier Selection with a Rough-Set-Enhanced Quality Function Deployment

Building material supplier evaluation and selection is a significant strategic-decision problem for reducing construction costs and ensuring the quality of a residential product. As people are increasingly concerning about the green level of a residential product and the competition in the housing market is becoming increasingly fierce, it becomes important to select a green customer-oriented material supplier for property developers. Quality function deployment (QFD) has been proven to be an effective quality-control technique to take customer voices into consideration. However, the relationship matrix in the QFD technique, as a key to translate customer requirements into technical attributes, was subjectively given by decision-makers in previous studies, which failed to reflect customer requirements accurately. The aim of this study is to put forward a neighborhood-rough-set-based quality function deployment model for a green-building-material supplier selection. The neighborhood rough set, as a nonparametric and flexible data-mining approach, can effectively and objectively determine the core relationships between a variety of factors. A rough number-based aggregation approach is applied to effectively and objectively aggregate the evaluations given by a group of experts. Then, the classical double normalization-based multiple aggregation method, which considers two types of normalization methods, three aggregation models, and a comprehensive score formula, is extended in rough-number form in order to rank the alternatives. Afterward, an attempt is made to evaluate and rank eleven alternative building-material suppliers for a repute property developer in mainland China, and the corresponding comparative and sensitive analyses verify the effectiveness and robustness of the proposed hybrid model.

Individual vs. Collaborative Methods of Crowdsourced Transcription

While online crowdsourced text transcription projects have proliferated in the last decade, there is a need within the broader field to understand differences in project outcomes as they relate to task design, as well as to experiment with different models of online crowdsourced transcription that have not yet been explored. The experiment discussed in this paper involves the evaluation of newly-built tools on the Zooniverse.org crowdsourcing platform, attempting to answer the research question: "Does the current Zooniverse methodology of multiple independent transcribers and aggregation of results render higher-quality outcomes than allowing volunteers to see previous transcriptions and/or markings by other users? How does each methodology impact the quality and depth of analysis and participation?" To answer these questions, the Zooniverse team ran an A/B experiment on the project Anti-Slavery Manuscripts at the Boston Public Library. This paper will share results of this study, and also describe the process of designing the experiment and the metrics used to evaluate each transcription method. These include the comparison of aggregate transcription results with ground truth data; evaluation of annotation methods; the time it took for volunteers to complete transcribing each dataset; and the level of engagement with other project elements such as posting on the message board or reading supporting documentation. Particular focus will be given to the (at times) competing goals of data quality, efficiency, volunteer engagement, and user retention, all of which are of high importance for projects that focus on data from galleries, libraries, archives and museums. Ultimately, this paper aims to provide a model for impactful, intentional design and study of online crowdsourcing transcription methods, as well as shed light on the associations between project design, methodology and outcomes.

Thermally controllable demand response with multiple load aggregators

Demand response (DR), as an effective way to improve operational efficiency of distribution networks, also imposes challenges on the mechanism design of ancillary services. Thermally controllable load (TCL), e.g. heating, ventilating, air-conditioner, has increased significantly in recent decades, which can be leveraged as a flexible resource owing to its well responsive capability towards the market incentives. Nevertheless, the spatially distributed TCLs are hardly coordinated through traditional DR schemes directly implemented by distribution network operators (DNOs). In this paper, a hierarchical market model is newly proposed to address this issue, where multiple load aggregators (LAs) perform as profit-pursing intermediaries by offering aggregated service to bridge interaction between DNOs and end-users. This model is devised with two levels. At the top level, aggregators’ benefits are determined by Shapley value method, which is in proportion to their contributions to cost minimization in utility operation. At the bottom level, end-users decide the temporally responsive amount by reaching a trade-off between the monetary revenue and resultant discomfort. Case studies illustrate that the proposed model provides an easy access to multiple aggregators to coordinate the widely distributed TCLs, which exhibits a high potential of practical application for system operators.

A Survey of Algorithms for Distributed Charging Control of Electric Vehicles in Smart Grid

Electric vehicles (EVs) are an eco-friendly alternative to vehicles with internal combustion engines. Despite their environmental benefits, the massive electricity demand imposed by the anticipated proliferation of EVs could jeopardize the secure and economic operation of the power grid. Hence, proper strategies for charging coordination will be indispensable to the future power grid. Coordinated EV charging schemes can be implemented as centralized, decentralized, and hierarchical systems, with the last two, referred to as distributed charging control systems. This paper reviews the recent literature of distributed charging control schemes, where the computations are distributed across multiple EVs and/or aggregators. First, we categorize optimization problems for EV charging in terms of operational aspects and cost aspects. Then under each category, we provide a comprehensive discussion on algorithms for distributed EV charge scheduling, considering the perspectives of the grid operator, the aggregator, and the EV user. We also discuss how certain algorithms proposed in the literature cope with various uncertainties inherent to distributed EV charging control problems. Finally, we outline several research directions that require further attention.

On the fusion of multiple multi-criteria aggregation functions with focus on the fusion of OWA aggregations

We discuss the concepts of a fuzzy measure and the Choquet integral with respect to a fuzzy measure. We introduce aggregation functions and discus their role in fusing fuzzy measures. We describe the formulation of multi-criteria decision-making using fuzzy measures and explain how the measure is used to capture the relationship between the criteria. We look at some examples of multi-criteria decision-making using a measure-based approach. Two notable examples are the OWA aggregation of the criteria and a propositional logic expression of the relationship between criteria. We next look at the fusion of multiple multi-criteria decision functions. The major objective of this paper is to focus on the problem of the fusion of multiple OWA aggregation functions. A significant benefit of the methodology developed here is that it is based solely on the use of fuzzy measures, this allows us to represent in a unified fashion many different aggregation imperatives.

An update to the inventory of shore-fishes from the Parque Nacional Sistema Arrecifal Veracruzano, Veracruz, México.

Data on marine and brackish-water fishes recorded in the area of the Parque Nacional Sistema Arrecifal Veracruzano in the southwest Gulf of Mexico were extracted from online aggregators of georeferenced location records, the recent ichthyological literature reviewed, and collections and observations made to provide a more complete faunal inventory for that park. Those actions added 95 species to a comprehensive inventory published in 2013, and brought the total to 472 species, an increase of 22%. Seventy-four percent of the additions came from online aggregators of georeferenced species records, which clearly demonstrates the value of reviewing and incorporating such data into species inventories. However, different aggregators recorded different sets of species, and some of their data were linked to outdated taxonomy or included identification errors. Hence individual records from multiple aggregators need to be obtained and reviewed for such issues when using such data to compile and revise faunal inventories. Existing lists also need to be carefully reviewed to ensure that errors are not perpetuated during updates.

Catalytic Prion-Like Cross-Talk between a Key Alzheimer's Disease Tau-Fragment R3 and the Type 2 Diabetes Peptide IAPP.

The aberrant association of proteins/peptides is implicated in the etiology and pathogenesis of a variety of human diseases. In general, the primary protein component responsible for the formation of aggregates is different in each case and is specific to a particular disease condition. However, there are instances where multiple protein aggregates have been found to coexist in the same or different tissue(s), thereby leading to mixed pathologies and exacerbation of disease symptoms. In this context, a strong link has been established between Alzheimer's disease (AD) and type 2 diabetes (T2D). However, the underlying molecular details still remain elusive. Here, we report the direct interaction of an AD-associated amyloidogenic cytotoxic fragment of Tau (R3:306-336) with islet amyloid polypeptide (IAPP) implicated in T2D. Using ion-mobility mass spectrometry (IM-MS) in conjunction with fluorescence spectroscopy, circular dichroism, and transmission electron microscopy, we have been able to provide critical mechanistic insights into these interactions. Our IM-MS data showed the formation of hetero-oligomers of R3 and IAPP. Additionally, using IM-MS, we found that the amyloidogenic extended beta hairpin conformation of IAPP is favored much more in the R3-IAPP mixture, when compared with IAPP alone. Furthermore, we found that the oligomerization of R3 occurs much faster in the presence of IAPP. We also observed a secondary nucleation step in our kinetics data for the R3-IAPP mixture. We believe that the secondary nucleation step is demonstrative of R3 aggregation which otherwise requires the presence of anionic cofactors. Our results provide the first experimental evidence for direct molecular interaction between Tau and IAPP and highlights the repercussion of possible "prion-like" cross-talk in the proliferation of diseases that are associated with different tissues/organs.

Towards Systems-Oriented Energy Solutions: A Multilevel Analysis of a Low-Income Energy Efficiency Program in Brazil

There is a consensus among scholars and practitioners that energy solutions, such as electricity services and related products and systems, are paramount to the ability of nations to overcome environmental and social issues. As a result, policymakers and problem solvers in emerging economies have shown a keen interest in the transition to sustainable energy systems. Nevertheless, the design of sustainable energy solutions in low-income markets presents many challenges, such as those related to limited financial resources and poor infrastructure. In low-income markets, the adoption of a systems-oriented approach to product–service combinations may represent a promising alternative to traditional design approaches and result in a more socially and environmentally sound path to economic development. Building on design theory grounded in systems theory, this paper analyzes multiple aggregation levels of the sociotechnical system of a low-income energy efficiency program in Brazil. In this study, the authors examined findings from the literature, carried out field observations, and had discussions with practitioners and experts. The study identifies constraints that hinder energy solutions that could achieve higher levels of socioeconomic and environmental benefits in low-income energy markets. Based on the findings, the paper provides insights into sustainable energy transitions and concludes that low-income energy efficiency programs can be improved through design-led policy and stakeholder collaboration.

Experimental analysis of Medicare data using hierarchical grouping mechanism

PurposeAnalyzing medicare data is a role undertaken by the government and commercial companies for accepting the appeals and sanctioning the claims of those insured under Medicare. As the data of medicare is robust and made up of heterogeneous typed columns, traditional approaches consist of a laborious and time-consuming process. The understanding and processing of such data sets and finding the role of each attribute for data analysis are tricky tasks which this research will attempt to ease. The paper aims to discuss these issues.Design/methodology/approachThis paper proposes a Hierarchical Grouping (HG) with an experimental model to handle the complex data and analysis of the categorical data which consist of heterogeneous typed columns. The HG methodology starts with feature subset selection. HG forms a structure by quantitatively estimating the similarities and forms groups of the features for data. This is carried by applying metrics like decomposition; it splits the dataset and helps to analyze thoroughly under different labels with different selected attributes of Medicare data. The method of fixed regression includes metrics of re-indexing and grouping which works well for multiple keys (multi-index) of categorical data. The final stage of structure is applying multiple aggregation function on each attribute for quantitative computation.FindingsThe data are analyzed quantitatively with the HG mechanism. The results shown in this paper took less computation cost and speed, which are usually incurred on the publicly available data sets.Practical implicationsThe motive of this paper is to provide a supportive work for the tasks like outlier detection, prediction, decision making and prescriptive tasks for multi-dimensional data.Originality/valueIt provides a new efficient approach to analyze medicare data sets.

1625 Colonic Schwannoma Identified in Asymptomatic Middle-Aged Female

INTRODUCTION: Schwannomas are typically benign tumors of the neural sheath. Schwannomas of the colon are extremely rare entities; in fact, when schwannomas occur in the gastrointestinal tract, the colon is considered the least likely site. Schwannomas of the colon were previously misdiagnosed as gastrointestinal stromal tumors; however, with advances in immunohistochemical staining, the number of cases of intestinal schwannomas has gradually increased. Colorectal schwannomas can be divided into three clinicopathologic types: spindle cell, epithelioid, and plexiform. Here, we present a unique case of a colonic spindle cell schwannoma encountered in an asymptomatic patient on routine colonoscopy. CASE DESCRIPTION/METHODS: A 50-year-old female with no significant past medical history presented to gastroenterology clinic for routine colorectal cancer screening. The patient denied abdominal pain, nausea, vomiting, diarrhea, constipation, rectal bleeding, or weight loss. She also denied family history of neurofibromatosis. Physical examination was unremarkable. Laboratory studies showed that the patient had microcytic anemia (hemoglobin 10.9 g/dl, MCV 59 fL) but were otherwise unremarkable. A colonoscopy was subsequently performed and revealed a 6 mm sub-epithelial lesion in the sigmoid colon. Biopsy showed colonic mucosa with mild chronic inflammation and multiple spindle cell aggregates confined to the lamina propria (Figure 1). Cytological atypia, mitoses, and necrosis were identified in the biopsied specimen (Figure 2). Immunohistochemical assays demonstrated lack of reactivity to CD68, CD117, and DOG-1. The spindle cells were strongly positive for S100, confirming the diagnosis of mucosal Schwann cell hamartoma. DISCUSSION: Rectal bleeding, colonic obstruction, and abdominal pain are the three most common presenting symptoms for colorectal schwannoma; however, our patient denied any of these symptoms. The management of intestinal schwannomas remains controversial. Surgical resection with adequate margins remains the treatment of choice. Several sources question whether radical resection based on oncologic principles is necessary given the benign nature of these tumors. Our patient was managed conservatively with routine surveillance. Repeat colonoscopy performed approximately 3 months after the initial colonoscopy revealed no signs of recurrence or metastases. This case supports the notion that these rare mesenchymal tumors are commonly benign and aggressive therapeutic interventions are likely unnecessary.

Stability of Transactive Energy Market-Based Power Distribution System Under Data Integrity Attack

Future smart power distribution system with multiple active consumers and aggregators exchanging real-time electricity pricing and electricity consumption information over a communication network is prone to cyberattack. The goal of this paper is to understand the impact of attacks on pricing/load signals on the physical grid within a transactive energy market framework. First, this paper models the interaction between real-time electricity price and total energy demand in the form of a discrete time nonlinear autonomous dynamical system. Second, equilibrium electricity price and energy demand associated with this coupled dynamical system is derived and conditions for bounded input bounded output (BIBO) stability are identified. Third, a BIBO stable algorithm to design real-time electricity pricing scheme from a techno-economic perspective is developed. Finally, the impact of various levels of false data injection (FDI) attack on price of electricity, demand, and distribution system voltage is investigated. The proposed model is analyzed using simulations on the IEEE 69-bus test system and the impact of FDI attack on both electricity price/demand and distribution grid voltage is quantified. This paper shows that impact of FDI attack on electricity prices is more severe than an attack on electricity demand.

Formation mechanism of low-density lipoprotein gel induced by NaCl

Salted eggs, which are a traditional Chinese egg product, are favored by Chinese consumers and have become very popular in other Asian countries due to their unique features such as "fresh, fine, tender, loose, gritty and oily texture." In order to illuminate the forming process of salted egg, the gelation behavior and mechanism of low-density lipoprotein (LDL) induced by NaCl were investigated using marinated model outside the eggshell. Results showed that as the salting proceeded, the moisture content exhibited a decreasing trend. The NaCl content, oil exudation, hardness, and surface hydrophobicity showed constantly increasing trends. In the early stages of salting, the size of the LDL particles, soluble protein content, and T21 increased, whereas T21 (with D2O), T22, and the free sulfhydryl content declined. In the later stages of salting, LDL formed a multiple composite aggregate gel structure with filamentous apoproteins and non-spherical lipid particles intertwined with each other. The soluble protein content and T23 (without D2O) decreased, whereas T21 (with D2O), T22 and the free sulfhydryl content increased. Fourier transform infrared spectroscopy revealed that the fresh LDL mainly consisted of α-helix and β-sheet structures. After the gel becomes hardened, the LDL secondary structure was changed remarkably, characterized by the decrease of α-helix elements and increase of β-sheet elements. The results suggested that the oil exudation of salted LDL gels was mainly due to LDL destruction and the release of components (apoproteins, phospholipids, and neutral lipids) facilitated by increased interactions between apoproteins and lipids.

Recent advances in photofunctional polymorphs of molecular materials

Recently, molecule-based luminescent materials have been drawing extensive attention due to their desirable properties and promising applications in the fields of sensors, lighting display, and cell imaging. Crystalline polymorph is an intriguing phenomenon that the presence of multiple packing and aggregate architectures of the same molecular system. The studies on polymorphs for molecule-based fluorophores provide the opportunities to adjust the mode of molecular packing and photophysical properties, which will help to illustrate the structure-property relationship. In this review, we focus on the recent progress in various feasible methods of molecule-based crystalline polymorphism growth and their adjustable photofunctional properties, which will open up possibilities of variant optical applications. Firstly, several effective ways to prepare and screen polymorphs are sorted out. And then, we discuss the discrepant properties and multifunctional applications (such as sensors, laser, and OFET, etc.). Finally, the development trends and future prospects of these polymorphs are also briefly introduced.

Signaling Packet Aggregation and Compression in SIP Network: Modeling and Performance Evaluation

Recent development in communications have increased the demand of internet protocol (IP)-based multimedia conferencing services. Session initiation protocol (SIP) is a signaling protocol used for providing group communication in IP-based next generation network. The conferencing frameworks proposed by Internet Engineering Task Force working groups has limited scalability due to the centralized management of conference control by a single server. In order to overcome this limitation, we propose multiple SIP packet aggregation and compression of common header part of each aggregated packet process. This mechanism increases the bandwidth utilization and processing capability of conference server. A queueing model is developed to describe the process of packet aggregation (PA) and compression at the edge proxy. Using the proposed queueing model, the steady-state probability distribution of the queue length is derived and the average packet transmission delay is obtained. We show the trade-off between the overhead gain and delay. Based on the analytical results, the maximum frame size is optimized for which the average delay of a packet transmission is minimum. A discrete event simulation is performed using network simulator 2 to implement PA and compression mechanism in a SIP network. Further, performance measures are numerically obtained from the simulation results. The simulation results discussed upon the key performance metrics for signaling protocols defined by the International Telecommunication Union (ITU) Telecommunication Standardization Sector (ITU-T E.721).

A Structural Model of a Multitasking Salesforce: Job Task Allocation and Incentive Plan Design

The paper broadens the focus of empirical research on salesforce management to include multitasking settings with multidimensional incentives, where salespeople have private information about customers. This allows us to ask novel substantive questions around multidimensional incentive design and job design while managing the costs and benefits of private information. To this end, the paper introduces the first structural model of a multitasking salesforce in response to multidimensional incentives. The model also accommodates (i) dynamic intertemporal tradeoffs in effort choice across the tasks and (ii) salesperson’s private information about customers. We apply our model in a rich empirical setting in microfinance and illustrate how to address various identification and estimation challenges. We extend two-step estimation methods used for unidimensional compensation plans by embedding a flexible machine learning (random forest) model in the first-stage multitasking policy function estimation within an iterative procedure that accounts for salesperson heterogeneity and private information. Estimates reveal two latent segments of salespeople- a “hunter” segment that is more efficient in loan acquisition and a “farmer” segment that is more efficient in loan collection. Counterfactuals reveal heterogeneous effects: hunters’ private information hurts the firm as they engage in adverse selection; farmers’ private information helps the firm as they use it to better collect loans. The payoff complementarity induced by multiplicative incentive aggregation softens adverse specialization by hunters relative to additive aggregation, but hurts performance among farmers. Overall, task specialization in job design for hunters (acquisition) and farmers (collection) hurts the firm as adverse selection harm overwhelms efficiency gain.

Distributed Processing of Location-Based Aggregate Queries Using MapReduce

The location-based aggregate queries, consisting of the shortest average distance query (SAvgDQ), the shortest minimal distance query (SMinDQ), the shortest maximal distance query (SMaxDQ), and the shortest sum distance query (SSumDQ) are new types of location-based queries. Such queries can be used to provide the user with useful object information by considering both the spatial closeness of objects to the query object and the neighboring relationship between objects. Due to a large amount of location-based aggregate queries that need to be evaluated concurrently, the centralized processing system would suffer a heavy query load, leading eventually to poor performance. As a result, in this paper, we focus on developing the distributed processing technique to answer multiple location-based aggregate queries, based on the MapReduce platform. We first design a grid structure to manage information of objects by taking into account the storage balance, and then develop a distributed processing algorithm, namely the MapReduce-based aggregate query algorithm (MRAggQ algorithm), to efficiently process the location-based aggregate queries in a distributed manner. Extensive experiments using synthetic and real datasets are conducted to demonstrate the scalability and the efficiency of the proposed processing algorithm.

Temporal hierarchies with autocorrelation for load forecasting

We propose four different estimators that take into account the autocorrelation structure when reconciling forecasts in a temporal hierarchy. Combining forecasts from multiple temporal aggregation levels exploits information differences and mitigates model uncertainty, while reconciliation ensures a unified prediction that supports aligned decisions at different horizons. In previous studies, weights assigned to the forecasts were given by the structure of the hierarchy or the forecast error variances without considering potential autocorrelation in the forecast errors. Our first estimator considers the autocovariance matrix within each aggregation level. Since this can be difficult to estimate, we propose a second estimator that blends autocorrelation and variance information, but only requires estimation of the first-order autocorrelation coefficient at each aggregation level. Our third and fourth estimators facilitate information sharing between aggregation levels using robust estimates of the cross-correlation matrix and its inverse. We compare the proposed estimators in a simulation study and demonstrate their usefulness through an application to short-term electricity load forecasting in four price areas in Sweden. We find that by taking account of auto- and cross-covariances when reconciling forecasts, accuracy can be significantly improved uniformly across all frequencies and areas.

USP10 is a critical factor for Tau-positive stress granule formation in neuronal cells

Tau aggregates in neurons of brain lesions is a hallmark pathology of tauopathies, including Alzheimer’s disease (AD). Recent studies suggest that the RNA-binding protein TIA1 initiates Tau aggregation by inducing the formation of stress granules (SGs) containing Tau. SGs are stress-inducible cytoplasmic protein aggregates containing many RNA-binding proteins that has been implicated as an initial site of multiple pathogenic protein aggregates in several neurodegenerative diseases. In this study, we found that ubiquitin-specific protease 10 (USP10) is a critical factor for the formation of Tau/TIA1/USP10-positive SGs. Proteasome inhibition or TIA1-overexpression in HT22 neuronal cells induced the formation of TIA1/Tau-positive SGs, and the formations were severely attenuated by depletion of USP10. In addition, the overexpression of USP10 without stress stimuli in HT22 cells induced TIA1/Tau/USP10-positive SGs in a deubiquitinase-independent manner. In AD brain lesions, USP10 was colocalized with Tau aggregates in the cell body of neurons. The present findings suggest that USP10 plays a key role in the initiation of pathogenic Tau aggregation in AD through SG formation.