Architectural Constraints Research Articles

Asymmetric Spatiotemporal Online Learning for Deep Spiking Neural Networks

Although the precise symmetric forward and feedback connections between neurons are thought to be impossible in the brain, most existing deep spiking neural networks (SNNs) spatio-temporal learning algorithms still require such a strong architectural constraint to complete tasks. Besides that, as an expected feature for specialized neuromorphic hardware to be deployed, effective online learning of deep SNNs for training spatio-temporal data are still lacking. To address these issues, an effective biologically plausible asymmetric spatio-temporal online learning algorithm called ASTOL is proposed for training deep SNNs in real-time. ASTOL could learn the spatial and temporal features simultaneously in real-time, without the precise weights symmetric backpropagation and the need to know the duration of spatio-temporal data in advance. Experimental results show that the proposed ASTOL algorithm achieves comparable performance in real-time learning on the rate coding MNIST dataset and the temporal coding music MedleyDB dataset and speech TIDIGITS dataset with other state-of-the-art algorithms with simpler learning mechanism way as it avoids transport of synaptic weights. Besides that, the experiment on MNIST datasets also shows that ASTOL can use simpler network structures to achieve good performance faster.

Cybersecurity Implications of Edge Computing in Data Engineering

Edge computing, as an extension of cloud computing, brings computation and data storage closer to the data source. This shift offers significant advantages in terms of latency reduction, bandwidth optimization, and real-time processing capabilities. By minimizing the distance that data needs to travel, edge computing enhances the performance of applications that require rapid data processing and immediate response times. This is particularly beneficial for Internet of Things (IoT) devices, autonomous vehicles, smart grids, and other applications that demand low-latency interactions. However, the decentralization inherent in edge computing introduces a unique set of cybersecurity challenges that are distinct from those faced in traditional centralized cloud environments. The distributed architecture of edge computing creates numerous points of vulnerability, each of which can be exploited by cyber attackers. Additionally, edge devices often operate with limited computational resources and power, which complicates the implementation of robust security measures. This paper explores the cybersecurity implications of edge computing in the context of data engineering. It begins with a comprehensive review of existing literature to establish the current understanding of edge computing security issues. The review identifies the primary vulnerabilities associated with edge computing, including those related to distributed architecture, data transmission, and resource constraints. Following the literature review, the paper delves into specific security threats that edge computing environments face. These include man-in-the-middle (MitM) attacks, distributed denial-of-service (DDoS) attacks, malware and ransomware, and insider threats. Each threat is analyzed to understand its potential impact on edge computing systems and the data they process.

Implementation of 802.11ax and cell-free massive MIMO scenario for 6G wireless network analysis extending OMNeT++ simulator

In an era where ubiquitous connectivity and escalating data demands are altering the landscape of wireless communications, our paper proposes a pioneering enhancement for the OMNeT++ simulator to support the advanced features of IEEE 802.11ax high efficiency (HE) alongside cell-free massive multiple-input multiple-output (MIMO) systems. Traditional wireless networks face daunting challenges in sustaining elevated quality of service (QoS), primarily due to fluctuating user densities and signal quality. Cell-free massive MIMO serves as a compelling answer to this predicament by decentralizing the cellular architecture. It eradicates conventional cell boundaries, furnishing uniform QoS regardless of user locations. However, these advancements come at the expense of complex backhaul networks and articulated joint signal processing. The 802.11ax standard, touted for its robustness and efficiency, remains underexplored in this new paradigm. Our research not only dissects the architectural elements and constraints of both 802.11ax and cell-free massive MIMO but also elaborates on the adaptations required to extend OMNeT++ functionalities for these technologies. By doing so, we bridge a crucial gap, enabling the simulator to provide a more precise, detailed, and scalable evaluation of emerging 6G scenarios and directional communications also taking into account the impact of the most known routing protocols such as dynamic source routing (DSR), ad hoc on-demand distance vector routing (AODV), optimized link state routing (OLSR), and dynamic mobile ad hoc network on-demand (DYMO) that were selected for this comparative study. The proposed extensions promise to revolutionize network simulations and lay the foundation for in-depth analyses of wireless systems in complex and dynamic environments. Through extensive simulations, our study demonstrates that cell-free massive MIMO configurations significantly improve network throughput in high-density mobile ad hoc network (MANET) environments, with results indicating an average throughput gain of up to 30% compared with non-cell-free configurations. This improvement highlights the efficacy of cell-free massive MIMO to take advantage of the spatial and frequency multiplexing capabilities inherent in the 802.11ax standard, making it a promising solution for future wireless systems in densely populated areas.

Genomic determinants, architecture, and constraints in drought-related traits in Corymbia calophylla

BackgroundDrought adaptation is critical to many tree species persisting under climate change, however our knowledge of the genetic basis for trees to adapt to drought is limited. This knowledge gap impedes our fundamental understanding of drought response and application to forest production and conservation. To improve our understanding of the genomic determinants, architecture, and trait constraints, we assembled a reference genome and detected ~ 6.5 M variants in 432 phenotyped individuals for the foundational tree Corymbia calophylla.ResultsWe found 273 genomic variants determining traits with moderate heritability (h2SNP = 0.26–0.64). Significant variants were predominantly in gene regulatory elements distributed among several haplotype blocks across all chromosomes. Furthermore, traits were constrained by frequent epistatic and pleiotropic interactions.ConclusionsOur results on the genetic basis for drought traits in Corymbia calophylla have several implications for the ability to adapt to climate change: (1) drought related traits are controlled by complex genomic architectures with large haplotypes, epistatic, and pleiotropic interactions; (2) the most significant variants determining drought related traits occurred in regulatory regions; and (3) models incorporating epistatic interactions increase trait predictions. Our findings indicate that despite moderate heritability drought traits are likely constrained by complex genomic architecture potentially limiting trees response to climate change.

A POSIX/RTEMS Monitoring Tool and a Benchmark to Detect Real-Time Scheduling Anomalies

This article deals with scheduling anomalies in real-time systems. We present MONANO, a POSIX user-level library allowing applications to dynamically detect a preidentified set of real-time scheduling anomalies. The MONANO library is based on the modelling of architecture and runtime constraints. MONANO monitors during the runtime the timing behavior of the application and deduces properties needed to identify scheduling anomalies. We present also a benchmark to evaluate our approach. The benchmark is composed of several programs implementing the most frequent real-time scheduling anomalies.

Effect of marginal beams on the punching shear capacity of concrete slabs at exterior columns

It is common in concrete flat plate construction to connect the exterior columns with marginal, or edge, beams. In addition to the benefit gained from marginal beams in stiffening the slab edges and supporting the concentrated loads on the slab edge, they aid in resisting punching shear at the exterior columns. However, in most cases the marginal beam dimensions are limited by architectural constraints rendering it very small relative to the adjoining columns. In such cases, especially ones when the column protrudes beyond the beam width, the effect of the beam in resisting punching shear is doubtful. Most design codes do not provide a direct method for checking punching shear taking into account the effect of the beam, which might encourage designers to neglect punching shear design due to beam effect. This paper investigates the effect of marginal beams on the punching shear capacity of slabs at edge and corner columns. To the authors’ knowledge, only the Australian Standards (AS3600) accounts for that case, however; the AS3600 provisions are based on studies conducted on beams with width equal to the adjoining column width, a case which is out of the scope of this paper. The results of eight experimentally tested slabs were used to verify the modeling approach used to generate the parametric study models. The test specimens chosen for verification of the numerical model represented two possible modes of failure, punching shear at the column slab interface and torsion shear failure of the marginal beam. Numerical models of 80 slabs were developed using ABAQUS software package. The study results were compared to three international code provisions, the ACI318, EC2 and ECP-203. The study concluded that the presence of a beam with width less than 80% of the column width does not eliminate the punching shear mode of failure, however; the beam increases the overall capacity of the connection by transferring part of the load directly to the column and reducing demand on two-way shear. Design guidelines are presented to check shear capacity of such connections.

Spectrally Segmented-Enhanced Neural Network for Precise Land Cover Object Classification in Hyperspectral Imagery

The paradigm shift brought by deep learning in land cover object classification in hyperspectral images (HSIs) is undeniable, particularly in addressing the intricate 3D cube structure inherent in HSI data. Leveraging convolutional neural networks (CNNs), despite their architectural constraints, offers a promising solution for precise spectral data classification. However, challenges persist in object classification in hyperspectral imagery or hyperspectral image classification, including the curse of dimensionality, data redundancy, overfitting, and computational costs. To tackle these hurdles, we introduce the spectrally segmented-enhanced neural network (SENN), a novel model integrating segmentation-based, multi-layer CNNs, SVM classification, and spectrally segmented dimensionality reduction. SENN adeptly integrates spectral–spatial data and is particularly crucial for agricultural land classification. By strategically fusing CNNs and support vector machines (SVMs), SENN enhances class differentiation while mitigating overfitting through dropout and early stopping techniques. Our contributions extend to effective dimensionality reduction, precise CNN-based classification, and enhanced performance via CNN-SVM fusion. SENN harnesses spectral information to surmount challenges in “hyperspectral image classification in hyperspectral imagery”, marking a significant advancement in accuracy and efficiency within this domain.

Deep Reinforcement Learning for Mapping Quantum Circuits to 2D Nearest‐Neighbor Architectures

AbstractRecently, quantum computing is considered as a promising future computing paradigm. However, when implementing quantum circuits on a quantum device, it is necessary to ensure that quantum circuits satisfy nearest‐neighbor architecture constraints. When performing nearest‐neighbor architecture mapping for quantum circuits, it is inevitable to introduce SWAP gates, which will increase the overhead and reduce the fidelity. Therefore, it is crucial to complete the mapping with the minimum SWAP. In this paper, a 2D nearest‐neighbor architecture mapping method for quantum circuits is proposed based on deep reinforcement learning. In the initial mapping, an isomorphic graph initial mapping search algorithm is designed to quickly find the isomorphic graph between quantum circuit and architecture constraint graph. For quantum circuits without isomorphic graph mapping, the reordering algorithm of qubit impact factors is designed to obtain the initial placement position of qubits. In the SWAP gate addition, a qubit local reordering algorithm based on dueling deep‐Q‐network is designed to reduce SWAP gates. Experiments on the benchmark set B131 and IBM Q20 Tokyo verify that the proposed method can add fewer SWAP gates. Compared with the state‐of‐the‐art method, the average running time is accelerated by 63.1%, and the average SWAP gates added are reduced by 24.7%.

Pixel Distillation: Cost-flexible Distillation across Image Sizes and Heterogeneous Networks.

Previous knowledge distillation (KD) methods mostly focus on compressing network architectures, which is not thorough enough in deployment as some costs like transmission bandwidth and imaging equipment are related to the image size. Therefore, we propose Pixel Distillation that extends knowledge distillation into the input level while simultaneously breaking architecture constraints. Such a scheme can achieve flexible cost control for deployment, as it allows the system to adjust both network architecture and image quality according to the overall requirement of resources. Specifically, we first propose an input spatial representation distillation (ISRD) mechanism to transfer spatial knowledge from large images to student's input module, which can facilitate stable knowledge transfer between CNN and ViT. Then, a Teacher-Assistant-Student (TAS) framework is further established to disentangle pixel distillation into the model compression stage and input compression stage, which significantly reduces the overall complexity of pixel distillation and the difficulty of distilling intermediate knowledge. Finally, we adapt pixel distillation to object detection via an aligned feature for preservation (AFP) strategy for TAS, which aligns output dimensions of detectors at each stage by manipulating features and anchors of the assistant. Comprehensive experiments on image classification and object detection demonstrate the effectiveness of our method.

Assessment Method of Modern Buildings Constructed in a Historical Area; as a Case Study İMÇ Blocks

The addition of new modern layers to the historic urban settings is a significant issue in conserving the historic area's characteristics. Thus, a method of evaluation for modern buildings built in historical areas is being devel oped. The objective includes assessing the maintenance of the historic area's qualities and designing qualified modern buildings. These two issues were evaluated by assessing the example of İstanbul Drapers and Yard Goods Bazaar, İMÇ blocks. First, historical house settlements in the construction area of İMÇ (1933) and then characteristics of İMÇ blocks (1967) were analyzed. In terms of mass proportions, the position of courtyards, circulation areas, street and square relations, and vistas, site plan organizations from 1933 and 1967 were compared, while the proportions and architectural details of the façades were compared. The assessment criteria for the evaluation of modern buildings and their impact on historical areas were determined based on international preservation standards and charters. The conservation criteria include respecting the qualities, vistas, and landmarks of existing historic structures, being recognizable and reversible, responding to the demands of the area, and providing new views, juxtapositions, and textures. Modern heritage criteria include technical, land use, aesthetic, historical, socioeconomic, intangible, canonical, and reference qualities. As a result, it is seen that the İMÇ blocks were designed in harmony with the environment and increased the spatial quality of the area. Even though the İMÇ blocks are large-scale due to architectural constraints, the orientation of the blocks, the placement of courtyards leading to the Süleymaniye Mosque, and the transverse and longitudinal continuous circulation between the blocks respected historical texture. Despite the size of the masses, their heights and architectural elements are consistent with the traditional house layout.

A Review of the Network Arch Bridge

The network arch bridge (NAB) is a new structural form of arch bridge that was devised 60 years ago by the Norwegian engineer Per Tveit, who is now prof. dr. docent emeritus at the University of Agder, Norway. The network arch is a tied-arch (also known as a bowstring-arch) bridge that combines the benefits of tied-arch bridges and trusses in a single system. While in a classical tied arch, the hangers are vertical, in a network arch, the suspension of the deck to the arch is ensured through a network of inclined hangers that intersect each other at least twice. Thus, the core of the NAB is the hanger arrangement that minimizes the bending moment in the arch to very small values, leading to compression in the arch. Compression with only small bending leads to very slender cross-sections for the elements of the bridge, and deep reductions in terms of materials used and economic and environmental costs. This paper reviews the research into the structural form proposed by Per Tveit and extended by researchers and engineers worldwide. The research methodology included bibliometric literature research, obtained by interrogating the ISI Web of Science (WoS) database and the cited references from the articles on WoS. While the first structural form of a network arch is still in use today and it has proven to be a good idea for spans around 100–120 m, engineers worldwide devised new bridge cross-sections. A brief view of the types of bridge cross-section in use today is given, with details about the bridges chosen as representative. Using analysis of Prof. Tveit’s map, Structurae database and literature review, a database of the network arches around the world was created, emphasizing the development of network arches from the perspectives of continental distribution, opening year, number of structures in different structural forms, and bridge purposes. The structural form was assessed from the perspective of materials used for the arch and the tie, span, purpose and number of lanes, the presence/absence of upper wind-bracings and arch disposition in the vertical plane. In the last part of this review, the newest research into the development of the network arch is discussed. In the past 15 years we have seen an acceleration in network arch development from multiple perspectives: new materials used, such as glulam for the arch or carbon fiber-reinforced plastic for the hangers; span lengths of 250 m and 380 m for large bridge widths; architectural constraints that lead to the outward inclination of the arch, that is pleasing to the eye, but difficult to address from an engineering perspective; the most slender arch bridge in the world, with very slender cross-sections for the arch and the tie.

Seismic rehabilitation of URM heritage-listed Namazi school building using multiple retrofitting techniques

When dealing with retrofitting historical URM buildings, two prime aspects of their weakness against seismic action should be considered. These include: (i) the capacity of structural elements, such as foundations, masonry walls and floors, to resist earthquake forces and (ii) the integrity of the building as a whole under ground shaking. In this paper, different retrofitting techniques adopted to counter the above weaknesses in rehabilitation of the heritage-listed Namazi high school buildings are discussed. Extensive field investigations were first carried out to determine the detailed architectural and structural forms of the buildings, as well as, their material types and properties. It was found that in constructing the historic school buildings, traditional Iranian construction techniques and materials were used alongside some more modern elements such as reinforced concrete and masonry jack arch slab systems. This made the buildings unique in form, hence more challenging to retrofit. Seismic vulnerability studies were then carried out to evaluate the weaknesses of structural elements and the buildings as a whole. Different retrofitting techniques were then selected and applied to overcome the weaknesses. Effectiveness, cost and architectural constraints were the main considerations in selecting the retrofitting techniques for different parts of the historical buildings from a pool of different available methods. The techniques used in enhancing the seismic capacity of structural elements included: reinforced shotcrete overlay for masonry walls, FRP overlay for RC floors and beams, steel confinement and bracing of masonry jack arch floor systems and RC ground-beam confinement of brick and stone masonry foundations. To enhance the overall integrity of the buildings through confinement, vertical and horizontal steel tie elements and reinforced concrete tie elements were used.

End‐to‐end generation of structural topology for complex architectural layouts with graph neural networks

AbstractCurrent automated structural topology design methods can only deal with limited design spaces or simplified architectural layouts for lack of data or a proper representation of structure topology. To address this, the abundant information of manually designed architectural and structural layouts should be exploited to guide the topology design. To achieve automatic generation of structural topologies according to real‐world architectural layouts, this research introduces StrucTopo‐generative adversarial network (GAN), an end‐to‐end generative model with node and edge generation stages based on proper graph representation. Nodes are generated using an image‐to‐image translation model, and edges are generated with a GAN‐based approach. The model is trained and tested on a dataset of 300 complex architectural and structural layouts. Measured against the manually designed topologies, the results indicate that the proposed model can generate reasonable structural topologies, with a recall of 97% and an intersection‐over‐union of 80% in node generation, with a precision of 92% and a recall of 91% in edge generation. Additionally, the joint generation shows a graph similarity of 72%. The proposed model is the first of its kind to consider complex architectural layout constraints in the generation of structural topology, marking a step forward in applying artificial intelligence to practical structural design.

High Performance Star Block Aliphatic Polyester Thermoplastic Elastomers Using PDLA-b-PLLA Stereoblock Hard Domains.

Star block (ABC)4 terpolymers consisting of a rubbery poly(γ-methyl-ε-caprolactone) (PγMCL) (C) core and hard poly(l-lactide) (PLLA) (B) and poly(d-lactide) (PDLA) (A) end-blocks with varying PDLA to PLLA block ratios were explored as high-performance, sustainable, aliphatic polyester thermoplastic elastomers (APTPEs). The stereocomplexation of the PDLA/PLLA blocks within the hard domains provided the APTPEs with enhanced thermal stability and an increased resistance to permanent deformation compared to nonstereocomplex analogs. Variations in the PDLA:PLLA block ratio yielded tunable mechanical properties likely due to differences in the extent and location of stereocomplex crystallite formation as a result of architectural constraints. This work highlights the improvements in mechanical performance due to stereocomplexation within the hard domains of these APTPEs and the tunable nature of the hard domains to significantly impact material properties, furthering the development of sustainable materials that are competitive with current industry standard materials.

Optimising data curation pipelines for population-level analytics in secure data environments: Findings from a phenome-wide analysis in the NHS England Secure Data Environment

ObjectiveSecure data environments (SDE) ensure safe access to large population-level sensitive data. However, computational capacity is limited in these environments, which leads to challenges in the analysis of large population data within the constraints of a complex cloud architecture leveraging multiple software ecosystems. Here we present an efficient pipeline to conduct phenome-wide analyses using electronic health records (EHR) in the NHS England SDE.
 MethodsWe accessed deidentified linked EHR from NHS SDE for around 50 million people in England. The exposure is SARS-CoV-2 infection, with outcomes being a phenome-wide atlas of all diseases recorded in EHR data. For computational efficiency, we created three cohorts tables using PySpark within the Databricks environment and a sampling algorithm with inverse probability weights which adds a flag to the dataset to mark the inclusion of a row in the sample of a specific outcome. We will conduct survival analysis using Cox models in RStudio on the samples while adjusting for potential confounders in the main datasets and 15 subgroups.
 ResultsSampling with inverse probability weighting produced datasets that are statistically equivalent to the original population data. In terms of computational efficiency, the time needed to sample and read the data for modeling one outcome is 2.3 min compared to ~45 min when trying to read the entire dataset, which could fail due to the 4GB memory limits of in Rstudio within the SDE. This is particularly important in our study since we will be running at least 13,296 models for main and subgroup analysis in the three cohorts. By adding a flag to each data row to indicate its inclusion in a sample, the sampling strategy significantly reduced the storage space required for the outcome table of each sample.
 ConclusionPreparing datasets in Databricks and applying sampling can increase the efficiency of big data analysis pipelines within SDE, save storage space, and help in avoiding memory overload caused by using complete datasets for statistical analysis.

Hybrid Internet Architecture and Protocol (HIAP): A Self-Evolving and Transformative Framework for Enabling Seamless Real-Time Applications and Secure Peer-to-Peer File Sharing in the Internet of Everything (IoE)

The Internet of Everything (IoE) is rapidly growing and utilized in various applications. However, the growing mobile traffic and services pose significant challenges in flexibility, movement, accessibility, and safety. The existing internet architecture and protocol must be improved to address the challenges. The article examines the attributes and prerequisites of forthcoming networking applications while emphasizing the constraints of conventional network architecture and protocols in fulfilling these needs. The research presents a new Hybrid Internet Architecture and Protocol (HIAP), the Self-Evolving and Transformative (SET) architecture. This architecture is designed to provide diverse control works and smart configuration options for different applications and networking conditions. This HIAP framework's primary emphasis lies in transport protocols and mechanisms for peer-to-peer file sharing. This study proposes implementing a deadline-aware multipath transport protocol within the framework of the Internet architecture with seamless support of real-time applications requiring strict adherence to latency demands. The HIAP framework incorporates evolvability, adaptable routing, and in-network cache mechanisms to enhance contentdelivery. To conduct a more comprehensive examination and conceptualize the peer-to-peer file-sharing system, the research created a pragmatic blueprint for setting the file transfer protocol on the HIAP. The study presented the groundwork for an Internet architecture better suited to the changing demands of the IoE and its various applications. This HIAP architecture aims to be more adaptable, efficient, and transformative. The present study involves a comparative analysis between the proposed research and current architecture. The findings provide evidence of the progress made by the suggested research in addressing the challenges related to multipath and safety in future architectural designs.

Comparative analysis of outdoor energy harvest of organic and silicon solar modules for applications in BIPV systems

Organic photovoltaics (OPV) have the potential to fill niches that traditional silicon photovoltaics (Si-PV) have left open so far. Building-integrated photovoltaics (BIPV) is one key area where OPV modules can play a vital role due to their functional attributes of semitransparency, flexibility, and lightweight. However, the architectural constraints and physical layout of buildings often do not allow the installation of photovoltaic (PV) modules at their optimal angles. Herein, we report the outdoor energy harvest of commercial OPV, and monocrystalline silicon (m-Si) modules mounted at inclinations of 45° and 90°, using the ISOS-O2 protocol as a test standard. The semitransparent OPV modules incorporating a P3HT:PCBM bulk heterojunction absorbing layer were found to offer daily specific energy yields (YF) that are 10–15% higher than those of the m-Si modules at 45° inclination. For vertical (90°) mounting, the YF of the OPV modules is even 24–30% higher than that of their inorganic counterparts, thus making them ideally suited for façade integrated BIPV. Laboratory investigations reveal that the superior performance of OPV modules in the vertical position is mainly due to a strongly enhanced fill factor (FF) at reduced in-plane irradiance. At a 45° inclination, the positive temperature coefficient of the OPV modules also plays an important role during high-irradiance hours.

Optimizing UAV Resupply Scheduling for Heterogeneous and Persistent Aerial Service

With the current advances in unmanned aerial vehicle (UAV) technologies, aerial vehicles are becoming very attractive for many purposes. However, currently the bottleneck in their adoption is no longer due to architectural and protocol challenges and constraints, but rather to the limited energy that they can rely on. In this article, we design two power resupply schemes under the assumption of a fleet of homogeneous UAVs. Such schemes are designed to minimize the size of the fleet to be devoted to a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">persistent</i> service (i.e., carried out at all times) of a set of aerial locations. First, we consider the case where the aerial locations to be served are equidistant from an energy supply station. In that scenario, we design a simple scheduling scheme, that we name homogeneous rotating resupply ( <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HoRR</small> ), which we prove to be feasible and exact in the sense that it uses the minimum possible number of UAVs to guarantee the permanent coverage of the aerial service locations. Then, we extend that work for the case of nonevenly distributed aerial locations. In this new scenario, we demonstrate that the problem becomes NP-hard, and design a lightweight scheduling scheme, partitioned heterogeneous rotating resupply ( <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PHeRR</small> ), which extends the operation of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HoRR</small> to the heterogeneous case. Through numerical analysis, we show that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PHeRR</small> provides near-exact resupply schedules.

A Hierarchy-driven Multi-label Network with Label Constraints for Post-operative Complication Prediction of Lung Cancer.

Lung cancer is one of the most dangerous cancers all over the world. Surgical resection remains the only potentially curative option for patients with lung cancer. However, this invasive treatment often causes various complications, which seriously endanger patient health. In this study, we proposed a novel multi-label network, namely a hierarchy-driven multi-label network with label constraints (HDMN-LC), to predict the risk of complications of lung cancer patients. In this method, we first divided all complications into pulmonary and cardiovascular complication groups and employed the hierarchical cluster algorithm to analyze the hierarchies between these complications. After that, we employed the hierarchies to drive the network architecture design so that related complications could share more hidden features. And then, we combined all complications and employed an auxiliary task to predict whether any complications would occur to impose the bottom layer to learn general features. Finally, we proposed a regularization term to constrain the relationship between specific and combined complication labels to improve performance. We conducted extensive experiments on real clinical data of 593 patients. Experimental results indicate that the proposed method outperforms the single-label, multi-label baseline methods, with an average AUC value of 0.653. And the results also prove the effectiveness of hierarchy-driven network architecture and label constraints. We conclude that the proposed method can predict complications for lung cancer patients more effectively than the baseline methods.Clinical relevance-This study presents a novel multi-label network that can more accurately predict the risk of specific postoperative complications for lung cancer patients. The method can help clinicians identify high-risk patients more accurately and timely so that interventions can be implemented in advance to ensure patient safety.

A Review on Dropout Regularization Approaches for Deep Neural Networks within the Scholarly Domain

Dropout is one of the most popular regularization methods in the scholarly domain for preventing a neural network model from overfitting in the training phase. Developing an effective dropout regularization technique that complies with the model architecture is crucial in deep learning-related tasks because various neural network architectures have been proposed, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), and they have exhibited reasonable performance in their specialized areas. In this paper, we provide a comprehensive and novel review of the state-of-the-art (SOTA) in dropout regularization. We explain various dropout methods, from standard random dropout to AutoDrop dropout (from the original to the advanced), and also discuss their performance and experimental capabilities. This paper provides a summary of the latest research on various dropout regularization techniques for achieving improved performance through “Internal Structure Changes”, “Data Augmentation”, and “Input Information”. We can see that proper regularization with respect to structural constraints of network architecture is a critical factor to facilitate overfitting avoidance. We discuss the strengths and limitations of the methods presented in this work, which can serve as valuable references for future research and the development of new approaches. We also pay attention to the scholarly domain in the discussion in order to meet the overwhelming increase of scientific research outcomes by providing an analysis of several important academic scholarly issues of neural networks.