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Consider the existence of six identical trees of the same species across a variety of environments. The first tree is in a wild and isolated landscape. The second is in a wilderness park. The third is in a heavily forested “tree plantation” owned by International Paper. The fourth is in the Ramble in Central Park. The fifth is in a suburban yard. The sixth is inside the six-story atrium of a Manhattan skyscraper. This paper begins with the intuition that the identical trees have different values because they exist in different environments and biological-social contexts. To understand the different evaluations of the trees we must think along a spectrum that incorporates both axiology and ontology. This thought experiment is useful in exploring arguments about both the management and the preservation of the natural world. The conclusion is that we must think along a spectrum of natural being and value to understand the dualism between humanity and nature and thereby avoid the domination of the natural environment. [ABSTRACT FROM AUTHOR]

Multi-class geospatial object detection in high-resolution remote sensing images has significant potential in various domains such as industrial production, military warning, disaster monitoring, and urban planning. However, the traditional process of remote sensing object detection involves several time-consuming steps, including image acquisition, image download, ground processing, and object detection. These steps may not be suitable for tasks with shorter timeliness requirements, such as military warning and disaster monitoring. Additionally, the transmission of massive data from satellites to the ground is limited by bandwidth, resulting in time delays and redundant information, such as cloud coverage images. To address these challenges and achieve efficient utilization of information, this paper proposes a comprehensive on-board multi-class geospatial object detection scheme. The proposed scheme consists of several steps. Firstly, the satellite imagery is sliced, and the PID-Net (Proportional-Integral-Derivative Network) method is employed to detect and filter out cloud-covered tiles. Subsequently, our Manhattan Intersection over Union (MIOU) loss-based YOLO (You Only Look Once) v7-Tiny method is used to detect remote-sensing objects in the remaining tiles. Finally, the detection results are mapped back to the original image, and the truncated NMS (Non-Maximum Suppression) method is utilized to filter out repeated and noisy boxes. To validate the reliability of the scheme, this paper creates a new dataset called DOTA-CD (Dataset for Object Detection in Aerial Images-Cloud Detection). Experiments were conducted on both ground and on-board equipment using the AIR-CD dataset, DOTA dataset, and DOTA-CD dataset. The results demonstrate the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Copyright of Urban Studies (Sage Publications, Ltd.) is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This is a paper on experimental studies performed in the 1940s at the University of Notre Dame accelerator laboratory to investigate radiation effects on materials and also to probe the efficiency of atmospheric cooling of the fireball created by a nuclear fission device. This was a critical question to be addressed during the early phases in the development of the Manhattan project, which was again raised in the film Oppenheimer, posing the question whether "a bomb can set the atmosphere on fire." This paper brings forth the formerly classified reaction studies performed at Notre Dame's Nuclear Science Laboratory to answer this question. [ABSTRACT FROM AUTHOR]

With the progressive development of new energy technologies, high-power lithium batteries have been widely used in ship power systems due to their high-power density and low environmental pollution, and they have gradually become one of their main propulsion energy sources. However, the large-scale deployment of lithium batteries has also brought a series of safety problems to ship operations, especially the battery internal short circuit (ISC). Battery ISC faults are very hidden and unpredictable at the initial stage and often fail to be detected in time, ultimately leading to overheating, fire or even an explosion of the ship's power system. Based on this, this paper proposes a fast and accurate method for early-stage ISC fault location and detection of lithium batteries. Initially, voltage variations across the lithium battery packs are quantified using curvilinear Manhattan distances to pinpoint faulty battery units. Subsequently, the localized characteristics of voltage variance among adjacent batteries are leveraged to detect an early-stage ISC fault. Simulation results indicate that the proposed method can quickly and accurately locate the position of 5 Ω, 10 Ω and 15 Ω ISC faulty batteries within the battery pack, as well as detect the abnormal batteries in a timely manner with considerable sensitivity and reliability. [ABSTRACT FROM AUTHOR]

Background: With the rapid development of the medical industry and the gradual increase in people's awareness of their health, the use of the Internet for medical question and answer, to obtain more accurate medical answers. It is necessary to first calculate the similarity of the questions asked by users, which further matches professional medical answers. Improving the efficiency of online medical question and answer sessions will not only reduce the burden on doctors, but also enhance the patient's experience of online medical diagnosis. Method: This paper focuses on building a bidirectional gated recurrent unit(BiGRU) deep learning model based on Siamese network for medical interrogative similarity matching, using Word2Vec word embedding tool for word vector processing of ethnic-medical corpus, and introducing an attention mechanism and convolutional neural network. Bidirectional gated recurrent unit extracts contextual semantic information and long-distance dependency features of interrogative sentences; Similar ethnic medicine interrogatives vary in length and structure, and the key information in the interrogative is crucial to similarity identification. By introducing an attention mechanism higher weight can be given to the keywords in the question, further improving the recognition of similar words in the question. Convolutional neural network takes into account the local information of interrogative sentences and can capture local position invariance, allowing feature extraction for words of different granularity through convolutional operations; By comparing the Euclidean distance, cosine distance and Manhattan distance to calculate the spatial distance of medical interrogatives, the Manhattan distance produced the best similarity result. Result: Based on the ethnic medical question dataset constructed in this paper, the accuracy and F1-score reached 97.24% and 97.98%, which is a significant improvement compared to several other models. Conclusion: By comparing with other models, the model proposed in this paper has better performance and achieve accurate matching of similar semantic question data of ethnic medicine. [ABSTRACT FROM AUTHOR]

In today's digital era, human-computer interaction interfaces evolve and increase together with the needs of the users. However, the existing technologies have their limitations, which can hinder the efficiency of modern input devices like the Kinect sensor or other similar sensors. In this paper we improved our previous algorithm by extending it with two algorithms that aim to help telerehabilitation for individuals with movement disabilities. These two algorithms are based on the k-Nearest Neighbors, the k-Means algorithms. The algorithms are designed to accommodate the needs of the patients by adapting to their gestures based on their previous three. Using these gestures, the algorithms create multiple gesture acceptance domains around each coordinate of the gesture. Consequently, they decide whether the next user-input gesture can be considered the same movement. The accuracy of these algorithms was evaluated in three acceptance domains by comparing gesture descriptors with either the Euclidean or the Manhattan distance calculation methods. The results show that k-Nearest Neighbors algorithm yields better results in larger acceptance domains, while the k-Means algorithm can provide a better gesture acceptance rate in the smaller ones. The results show that both algorithms can be used in the telerehabilitation process, although the k-Means algorithm is more accurate than the k-Nearest Neighbors algorithm. [ABSTRACT FROM AUTHOR]

Digital twins for buildings can significantly reduce building operation costs. However, existing methods for constructing geometric digital twins fail to model the complex geometry of indoor environments. To address this problem, this paper proposes a novel method for digitizing building geometry with arbitrary shapes of spaces by detecting empty regions in point clouds and then expanding them to occupy the entire indoor space. The detected spaces are then used to detect structural objects and transition between spaces, such as doors, without assuming their geometric properties. The method reconstructs the volumetric representation of individual spaces, detects walls, windows and doors between them and splits the point cloud data (PCD) into point clusters of individual spaces from large-scale cluttered PCDs of complex environments. We conduct extensive experiments on Stanford 3D Indoor Spaces data set (S3DIS) and TUMCMS data sets and show that the proposed method outperforms existing methods for digitizing Manhattan-world buildings. In contrast to existing approaches, the method allows digitizing buildings with arbitrarily shaped spaces, including complex layouts, nonflat, nonvertical walls, and nonflat, nonhorizontal floors and ceilings. [ABSTRACT FROM AUTHOR]

As a novel network architecture, network function virtualization (NFV) greatly improves the flexibility and scalability of service provision. Deploying the service function chain (SFC) in an NFV environment needs to coordinate the instantiation of the virtual network function descriptor, network function embedding, and traffic steering, which also improves the complexity of the problem. Although heuristic algorithms are widely used to optimize this problem, due to the lack of complete SFC deployment solution space and digital coding scheme, the time complexity of the algorithm is difficult to meet the requirements. Therefore, this paper studies the digital coding scheme of the heuristic SFC deployment to improve time efficiency without reducing performance. Firstly, we model the SFC deployment as a 0–1 integer linear programming, considering the above factors, and then design a continuous digital solution space construction scheme based on N-Base coding (NBACO-SS) to optimize the above problems. NBACO-SS uses integer size and carry to map the complex SFC deployment to simple digital coding, evaluates the continuity of solution space through the Manhattan distance, and optimizes the continuity through the ant colony algorithm. Based on NBACO-SS, we reconstruct two heuristic algorithms to solve the SFC deployment problem. Experimental results demonstrate that NBACO-SS can improve the time efficiency by 20% without reducing the total network service traffic. [ABSTRACT FROM AUTHOR]

Image retrieval is the process of searching and retrieving images from a datastore based on their visual content and features. Recently, much attention has been directed towards the retrieval of irregular patterns within industrial or healthcare images by extracting features from the images, such as deep features, colour-based features, shape-based features, and local features. This has applications across a spectrum of industries, including fault inspection, disease diagnosis, and maintenance prediction. This paper proposes an image retrieval framework to search for images containing similar irregular patterns by extracting a set of morphological features (DefChars) from images. The datasets employed in this paper contain wind turbine blade images with defects, chest computerised tomography scans with COVID-19 infections, heatsink images with defects, and lake ice images. The proposed framework was evaluated with different feature extraction methods (DefChars, resized raw image, local binary pattern, and scale-invariant feature transforms) and distance metrics to determine the most efficient parameters in terms of retrieval performance across datasets. The retrieval results show that the proposed framework using the DefChars and the Manhattan distance metric achieves a mean average precision of 80% and a low standard deviation of ±0.09 across classes of irregular patterns, outperforming alternative feature–metric combinations across all datasets. Our proposed ImR framework performed better (by 8.71%) than Super Global, a state-of-the-art deep-learning-based image retrieval approach across all datasets. [ABSTRACT FROM AUTHOR]

Understanding whether a property is priced fairly hinders buyers and sellers since they usually do not have an objective viewpoint of the price distribution for the overall market of their interest. Drawing from data collected of all possible available properties for rent in Manhattan as of September 2023, this paper aims to strengthen our understanding of model residuals; specifically on machine learning models which generalize for a majority of the distribution of a well-proportioned dataset. Most models generally perceive deviations from predicted values as mere inaccuracies, however this paper proposes a different vantage point: when generalizing to at least 75% of the dataset, the remaining deviations reveal significant insights. To harness these insights, we introduce the Price Anomaly Score (PAS), a metric capable of capturing boundaries between irregularly predicted prices. By combining relative pricing discrepancies with statistical significance, the Price Anomaly Score (PAS) offers a multifaceted view of rental valuations. This metric allows experts to identify overpriced or underpriced properties within a dataset by aggregating PAS values, then fine-tuning upper and lower boundaries to any threshold to set indicators of choice. [ABSTRACT FROM AUTHOR]

In a previous paper (Justification for 'Scrapping the Sky', Ybl Journal of Built Environment, Vol. 7/1,) I set an overall framework for reviewing the elusive topic from its history to the state-of-theart, with a particular focus on urban design perspectives. While conducting the research, the subject offered many interesting (sometimes even surprising) findings. One of them is the so-called Hungarian connection: two epic protagonists who played a significant role in shaping the early skyline of Manhattan (Emery Roth and his sons) and Shanghai (Laszlo Hudec), respectively. Although their heritage is still striking on the ground, they seem to be not well known, particularly Roth, even among professional architects. Hence the rationale and the title of this paper. [ABSTRACT FROM AUTHOR]

Copyright of Studia Universitatis Babes-Bolyai, Philologia is the property of Babes-Bolyai University, Cluj-Napoca, Romania and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The term 'Bilbao effect' describes the potential of iconic architectural developments (IADs) designed by world famous architects to act as a catalyst for economic revitalization and transformation within neighbourhoods or regions. Despite the ubiquity of this term, its validity is still debated. Furthermore, research on the topic has relied almost exclusively on individual or small-N comparative case studies. This paper builds on this research by testing the Bilbao effect through a quantitative analysis of 142 IADs completed in Canada and the United States between 2000 and 2009. Using fixed effects modelling, a method that controls for selection bias, the analysis examines the effect of IADs on a set of outcome variables that measure neighbourhood-level economic and cultural changes during this period: population, rent, the number of local arts establishments and the number of cultural workers living in the area. The analysis demonstrates that neighbourhoods with IADs generally experienced more economic and cultural growth than non-IAD neighbourhoods during this time period. However, the paper also complicates these findings by examining differences in how this growth manifests itself within emerging versus established neighbourhoods. These differences are illustrated through a comparison of the Dallas Arts District and Lincoln Square, Manhattan. [ABSTRACT FROM AUTHOR]

IoT (Internet-of-Things)-powered devices can be exploited to connect vehicles to smart city infrastructure, allowing vehicles to share their intentions while retrieving contextual information about diverse aspects of urban viability. In this paper, we place ourselves in a transient scenario in which next-generation vehicles that are able to communicate with the surrounding infrastructure coexist with traditional vehicles with limited or absent IoT capabilities. We focus on intersection management, in particular on reusing existing traffic lights empowered by a new management system. We propose an auction-based system in which traffic lights are able to exchange contextual information with vehicles and other nearby traffic lights with the aim of reducing average waiting times at intersections and consequently overall trip times. We use bid propagation to improve standard vehicle trip times while allowing emergency vehicles to free up the way ahead without needing ad hoc system for such vehicle, only an increase in their budget. The proposed system is then tested against two baselines: the classical Fixed Time Control system currently adopted for traffic lights, and an auction strategy that does not exploit traffic light coordination. We performed a large set of experiments using the well known MATSim transport simulator on both a synthetic Manhattan map and on a map we built of an urban area located in Modena, Northern Italy. Our results show that the proposed approach performs better than the classical fixed time control system and the auction strategy that does not exploit coordination among traffic lights. [ABSTRACT FROM AUTHOR]

In conventional point-line visual–inertial odometry systems in indoor environments, consideration of spatial position recovery and line feature classification can improve localization accuracy. In this paper, a monocular visual–inertial odometry based on structured and unstructured line features of vanishing points is proposed. First, the degeneracy phenomenon caused by a special geometric relationship between epipoles and line features is analyzed in the process of triangulation, and a degeneracy detection strategy is designed to determine the location of the epipoles. Then, considering that the vanishing point and the epipole coincide at infinity, the vanishing point feature is introduced to solve the degeneracy and direction vector optimization problem of line features. Finally, threshold constraints are used to categorize straight lines into structural and non-structural features under the Manhattan world assumption, and the vanishing point measurement model is added to the sliding window for joint optimization. Comparative tests on the EuRoC and TUM-VI public datasets validated the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Semantic relatedness is one of the most significant aspects of natural language processing. It has been identified as a critical technology for developing intelligent systems like Siri, Microsoft Ice, Cortana, and Xiaoai. In 2014, SemEval ranked SR as the top task. While many existing studies have focused on analyzing the entailment of single phrases, advancements in deep learning have made it possible to analyze complete sentences or texts. While the natural parallelism of membrane computing has shown promise for data processing, harnessing this potential to advance semantic relatedness remains an open problem yet to be tackled. This paper proposes a novel Siamese Manhattan LSTM-SNP approach (SiMaLSTM-SNP) for the SR problem. The approach uses a collaborative Word2vec and 10-Layer Attention strategy to represent and extract sentence pairs and a Siamese LSTM-SNP structure to calculate the hidden states of sentences. The multi-head self-attention layer identifies text associations and redistributes hidden state weights. The last hidden state is extracted, and the relatedness score is calculated using the Manhattan distance. The experiments demonstrate that SiMaLSTM-SNP outperforms 17 classical SR baselines and 7 novel approaches on the standard datasets SICK and STS in terms of mean square error performance. This indicates that SiMaLSTM-SNP can accurately capture the semantic distinction between two sentences and effectively preserve their semantic information. [ABSTRACT FROM AUTHOR]

In this paper, we investigate a new problem of optimal placement of multiple finite-size rectangular facilities with known dimensions in the presence of existing rectangular facilities. This problem has applications in facility layout (re)design in manufacturing, distribution systems, services, and electronic circuit design. Three types of facility interactions are considered: between new facilities and existing facilities; between pairs of existing facilities; and between pairs of new facilities. All interactions are serviced through a finite number of input/output points located on the facility boundaries. Travel is assumed to occur according to the rectilinear (or Manhattan) metric and travel through facilities is prohibited. The objective is to find the simultaneous and non-overlapping placement of new facilities, which minimises the total weighted distance (Minisum objective) between the interacting facilities. To arrive at a solution, we divide the feasible region into sub-regions and prove that the candidates for optimal placement of the new facilities can be drawn from the sub-region boundaries. Being a continuous generalisation of the quadratic assignment problem, the solution complexity of this procedure is exponential in the number of new facilities. Our main contribution is the rigorous treatment of an important problem that unifies facility location and layout theories with minisum objective and rectilinear metrics. [ABSTRACT FROM AUTHOR]

In this paper, the problem of improving reliability and reducing routing overhead in dynamic scalable Wireless Ad hoc NETworks (WANETs) is considered. Having in mind that the number of mobile network users is constantly growing, dynamic network topology and scalability need to be taken into account when designing or choosing an appropriate routing protocol. In this paper, we have proposed an improvement of Ad hoc On Demand Distance Vector (AODV) protocol, called Neighborhood-Density AODV (ND-AODV) to reduce routing overhead in large scale dynamic WANETs. Instead of hop-count metric, our protocol uses Expected Transmission Count (ETX) based metric, called Power Light Reverse ETX (PLRE), which significantly improves the reliability of AODV protocol. Proposed protocol with metric, ND-AODV-PLRE, is compared to AODV and Probabilistic AODV (PAODV) protocols with PLRE metric in the terms of basic key performance indicators such as throughput, packet loss ratio, overhead, average, initial, and jitter of end to end delay. In order to show that it is suitable for various types of large scale dynamic WANETs, the ND-AODV-PLRE protocol is tested in two scenarios, one using the Manhattan Grid, and the other Random Waypoint mobility model. The results obtained in Network Simulator 3 (NS-3) have shown significant improvements of the proposed protocol in both scenarios. NS-3 implementation code is made publicly available. [ABSTRACT FROM AUTHOR]

Background: People with mental illnesses are disproportionately entangled in the criminal legal system. Historically, this involvement has resulted from minor offending, often accompanied by misdemeanor charges. In recent years, policymakers have worked to reduce the footprint of the criminal legal system. This paper seeks to better understand how misdemeanor systems intervene in the lives of people with mental illnesses. Methods: System mapping exercises were conducted with misdemeanor system stakeholders from the jurisdictions of Atlanta, Chicago, Manhattan, and Philadelphia. Narrative detail on decision-making and case processing, both generally and in relation to specific types of behavior, including trespassing, retail theft/shoplifting, and simple assault, were coded and analyzed for thematic patterns. Based on the qualitative analysis, this paper offers a conceptual diagram of contexts shaping misdemeanor system interventions among people with mental illnesses. Results: All four sites have been engaged in efforts to reduce the use of misdemeanor charges both generally and in relation to people with mental illnesses. Decision-makers across all sites experience contexts that shape how, when, and where they intervene, which are: (1) law and policy environments; (2) location of the behavior; (3) expectations of stakeholders; (4) knowledge of mental illnesses; and (5) access to community resources. Law and policy environments expand or constrain opportunities for diversion. The location of offending is relevant to who has a stake in the behavior, and what demands they have. Clinical, experiential, and system-level knowledge of mental illnesses inform a chain of decisions about what to do. The capacity to address mental health needs is contingent on access to social services, including housing. Conclusion: People making decisions along the criminal legal continuum are critical to illuminating the dynamic, inter-related contexts that facilitate and frustrate attempts to address defendants' mental health needs while balancing considerations of public safety. Multi-sector, scenario-based or case study exercises could help identify concrete ways of improving each of the contexts that surround whole-of-system decisions. [ABSTRACT FROM AUTHOR]

With the continuous improvement and development of various decision-making methods, it has led to the widespread use of fuzzy sets and fuzzy numbers. At the same time, the application of decision-making methods in different fuzzy environments has been very effective in addressing the deficiencies in existing research. At present, triangular fuzzy numbers have been widely used in the evaluation aspects of various decision making methods, and the proposed R-number effectively solve the uncertainty involving problems related to future events, but the existing research based on the TOPSIS method in the R-number environment has not yet been clearly applied to the triangular fuzzy number environment, and the indifference threshold-based attribute ratio analysis (ITARA) method in the fuzzy environment has yet to be extended. Therefore, this paper proposes a fuzzy indifference threshold-based attribute ratio analysis (FITARA) method based on triangular fuzzy numbers for solving the problem of determining attribute weights in the multi-attribute decision-making process. Secondly, the various risks of the decision environment and the impact on future events are considered and R-number are used to solve this puzzle. In addition, the incorporation of risk perception factors in the context of the existing RTOPSIS method considering multiple risk factors and the use of Manhattan distances to optimize the large number of operations in the process of the method resulted in the development of the FITARA-RTOPSIS model. Finally, the proposed FITARA-RTOPSIS method is applied to the problem of siting emergency supplies storage depots, and the effectiveness of the proposed method is verified through comparative analysis. [ABSTRACT FROM AUTHOR]

Multimedia data manipulation and forgery has never been easier than today, thanks to the power of Artificial Intelligence (AI). AI-generated fake content, commonly called Deepfakes, have been raising new issues and concerns, but also new challenges for the research community. The Deepfake detection task has become widely addressed, but unfortunately, approaches in the literature suffer from generalization issues. In this paper, the Face Deepfake Detection and Reconstruction Challenge is described. Two different tasks were proposed to the participants: (i) creating a Deepfake detector capable of working in an "in the wild" scenario; (ii) creating a method capable of reconstructing original images from Deepfakes. Real images from CelebA and FFHQ and Deepfake images created by StarGAN, StarGAN-v2, StyleGAN, StyleGAN2, AttGAN and GDWCT were collected for the competition. The winning teams were chosen with respect to the highest classification accuracy value (Task I) and "minimum average distance to Manhattan" (Task II). Deep Learning algorithms, particularly those based on the EfficientNet architecture, achieved the best results in Task I. No winners were proclaimed for Task II. A detailed discussion of teams' proposed methods with corresponding ranking is presented in this paper. [ABSTRACT FROM AUTHOR]

A well-known theorem in plane geometry states that any set of n non-collinear points in the plane determines at least n lines. Chen and Chvátal asked whether an analogous statement holds within the framework of finite metric spaces, with lines defined using the notion of betweenness. In this paper, we prove that in the plane with the L 1 (also called Manhattan) metric, a non-collinear set of n points induces at least ⌈ n / 2 ⌉ lines. This is an improvement of the previous lower bound of n/37, with substantially different proof. As a consequence, we also get the same lower bound for non-collinear point sets in the plane with the L ∞ metric. [ABSTRACT FROM AUTHOR]

Purpose: Radiation Oncology Learning Health System (RO‐LHS) is a promising approach to improve the quality of care by integrating clinical, dosimetry, treatment delivery, research data in real‐time. This paper describes a novel set of tools to support the development of a RO‐LHS and the current challenges they can address. Methods: We present a knowledge graph‐based approach to map radiotherapy data from clinical databases to an ontology‐based data repository using FAIR concepts. This strategy ensures that the data are easily discoverable, accessible, and can be used by other clinical decision support systems. It allows for visualization, presentation, and data analyses of valuable information to identify trends and patterns in patient outcomes. We designed a search engine that utilizes ontology‐based keyword searching, synonym‐based term matching that leverages the hierarchical nature of ontologies to retrieve patient records based on parent and children classes, connects to the Bioportal database for relevant clinical attributes retrieval. To identify similar patients, a method involving text corpus creation and vector embedding models (Word2Vec, Doc2Vec, GloVe, and FastText) are employed, using cosine similarity and distance metrics. Results: The data pipeline and tool were tested with 1660 patient clinical and dosimetry records resulting in 504 180 RDF (Resource Description Framework) tuples and visualized data relationships using graph‐based representations. Patient similarity analysis using embedding models showed that the Word2Vec model had the highest mean cosine similarity, while the GloVe model exhibited more compact embeddings with lower Euclidean and Manhattan distances. Conclusions: The framework and tools described support the development of a RO‐LHS. By integrating diverse data sources and facilitating data discovery and analysis, they contribute to continuous learning and improvement in patient care. The tools enhance the quality of care by enabling the identification of cohorts, clinical decision support, and the development of clinical studies and machine learning programs in radiation oncology. [ABSTRACT FROM AUTHOR]

With the emergence of new information and communication technology, mobile‐based ridesharing services have received more attention and have improved. They offer timely and convenient service and exert an enormous influence on pollution and congestion by allowing passengers to share overlapping routes. However, ridesharing services have also been discussed and called into question by people with respect to their attractiveness, their comfort, and passenger safety. This paper proposes a ridesharing algorithm with a perceived mechanism by introducing multiple dimensions of perceived value to overall assess passengers' perceptions. The approach involved can continuously track the psychological changes in passengers during travel, capture the dynamic and comprehensive perceptions of passengers, and make real‐time adjustments on this basis. Furthermore, the proposed partition‐based parallel strategy can ensure high‐quality services and further enhance computational efficiency. Finally, numerical experiments are conducted based on the urban transportation network and the actual trip data from Manhattan to evaluate the performance of the proposed algorithm. The results show that it is important to effectively capture the passengers' perception of ridesharing services and apply them to optimization mechanisms, which will contribute to improving the perception of ride experiences and attracting potential passengers. [ABSTRACT FROM AUTHOR]

Conformal Cyclic Cosmology (CCC) is a cyclic model of the universe put forward by Sir Roger Penrose (Cycles of Time: An Extraordinary New View of the Universe, Random House, Manhattan, 2010). A conformal invariance assumption in the neighbourhood of the crossover region between cycles (which Penrose calls aeons) allows successive space-times to be related by a conformal rescaling. A major open problem is how to choose the conformal factor in a unique way, and is a fundamental hurdle to further study. Proposals have been put forward by Newman (Gen Relativ Gravit 46(5):1–13, 2014), Tod (Gen Relativ Gravit 47(3):1–13, 2015) and Nurowski (Class Quantum Gravity 38(14):145004, 2021), but they disagree in one way or another with Penrose's original assumptions as well as each other. In this paper we compare these different models in detail and rule out certain choices for the conformal factor that have been put forward by Penrose. We extend the results of Newman and fix inconsistencies that arose in his calculations. A new class of solutions are put forward which agree with Penrose's assumptions exactly so long as a certain additional relation is satisfied. [ABSTRACT FROM AUTHOR]

Archaeological bodies and their afflictions have multiplied in recent years, along with the specialists who study them. The result is a cascade of data, much of it difficult to reconcile. I argue that variable enactments of disease, rather than reflecting an epistemological disconnect or difference in scale, engender ontological gaps. To pursue these malleable matters, I trace the proliferation of "cancer" from the Spring Street Presbyterian Church burial vaults (1820–1850) in Manhattan. To explore the struggles involved in making many things one, I consider emergent multiplicities of this "disease" within specialists' laboratories, archival records, and the writing process. Rather than force these different cancers to cohere, or make one "win" based on disciplinary domain (science/humanities) or hierarchy of substance (bone/paper), I rely on Stengers's (2018) ecology of partial connects. The outcome is not a rubric of knowledge gained, but a sketchbook of lessons learned with bodies multiple along the way. [ABSTRACT FROM AUTHOR]

Few-shot Learning algorithms can be effectively applied to fields where certain categories have only a small amount of data or a small amount of labeled data, such as medical images, terrorist surveillance, and so on. The Metric Learning in the Few-shot Learning algorithm is classified by measuring the similarity between the classified samples and the unclassified samples. This paper improves the Prototypical Network in the Metric Learning, and changes its core metric function to Manhattan distance. The Convolutional Neural Network of the embedded module is changed, and mechanisms such as average pooling and Dropout are added. Through comparative experiments, it is found that this model can converge in a small number of iterations (below 15,000 episodes), and its performance exceeds algorithms such as MAML. Research shows that replacing Manhattan distance with Euclidean distance can effectively improve the classification effect of the Prototypical Network, and mechanisms such as average pooling and Dropout can also effectively improve the model. [ABSTRACT FROM AUTHOR]

This paper evaluated the congestion reduction benefits over a 6-year period from the implementation of an adaptive traffic control system (ATCS) in a congested area of Manhattan, New York, bounded by 2nd and 6th Avenues and 42nd and 57th Streets—known as the Midtown-in-Motion (MIM) area. A methodology using taxi Global Positioning System (GPS) sensors was used to measure traffic speed. Traffic speeds were calculated for May weekdays before the ATCS was installed (May 2011) and for each subsequent year (May 2012 to May 2016). Within 1 to 2 years after the implementation of the ATCS, the area’s traffic speed increased on avenues and cross streets. This gain, however, could not be sustained in subsequent years because of intervening changes in key factors impacting traffic congestion. These factors included reduction in roadway space/capacity for motor vehicles, lack of effective traffic enforcement to maintain/protect available roadway capacity, increased vehicle miles traveled (VMT) from transportation network company vehicles, and increasing volume of bicycle trips sharing street space with vehicles/pedestrians. This paper’s two key findings are, first, traffic speed gains initially seen in the MIM area after 1 year of ATCS implementation could not be sustained because intervening external factors reduced capacity and increased VMT. However, during the same analysis period, the rest of the Midtown Core area (without ATCS deployment) experienced a greater speed loss than the MIM area, indicating the effectiveness of ATCS deployment in minimizing losses in traffic speed. Second, to protect street network capacity and to minimize VMT growth, midtown Manhattan requires adopting proactive, collaborative, and coordinated strategies by the three key agencies involved with traffic management in New York City (NYC): the NYC Department of Transportation (traffic control system technology), the NYC Police Department (traffic enforcement), and the Mayor’s office (traffic demand mitigation policies). [ABSTRACT FROM AUTHOR]

Attitude estimation is central to a wide range of applications such as robotics, virtual reality and mobile smart devices. With the development of sensor technologies, these application devices are often equipped with gyroscopes and depth sensors. In this paper, we propose a novel method to fuse gyroscope and depth information for drift-free and robust attitude estimation in structured indoor applications. Our method relies on the depth information and the Manhattan world assumption to estimate the absolute orientation, which is then fused to correct the accumulated error of the gyroscope-determined attitude. We first utilize the mean shift algorithm on the unit sphere to align the surface normals from the depth measurements with the orthogonal planar structures of the Manhattan world. Therefore, the orientation estimates are drift-free and absolute with respect to the Manhattan world. We then fuse the orientation estimates with the gyroscope measurements in an error-state Kalman filter manner to further improve the attitude estimation accuracy and robustness. We validate the performance of our method on public datasets, demonstrating the robustness and accuracy of the method for attitude estimation. [ABSTRACT FROM AUTHOR]

One of the main steps in the data lifecycle is to publish it for data analysts to discover hidden patterns. But, data publishing may lead to unwanted disclosure of personal information and cause privacy problems. Data anonymization techniques preserve privacy models to prevent the disclosure of individuals' private information in published data. In this paper, a distributed in-memory method is proposed on the Apache Spark framework to preserve the ℓ-diversity privacy model. This method anonymizes large-scale data in a three-phase process, which includes, seed selection, data clustering for ℓ -diversity, and finalizing phase. In this method, a hierarchical kmeans-based data clustering algorithm has been designed for data anonymization. One of the major challenges of anonymization methods is to establish a better trade-off between data utility and privacy. Therefore, for calculating the distance between records and forming more cohesive ℓdiverse-clusters, the authors have designed two Manhattan-based and Euclidean-based distance functions to satisfy the requirements of the ℓ-diversity model. Given the 100-fold speed of the Spark compared to MapReduce, the proposed method is presented using in-memory RDD programming in Apache Spark, to address the runtime, scalability, and performance in large-scale data anonymization as it exists in the previous MapReduce-based algorithms. Our method provides general knowledge to use parallel in-memory computation of Spark in big data anonymization. In experiments, this method has obtained lower information loss and loses about 1% to 2% accuracy and FMeasure criteria; therefore, it establishes a better trade-off than the state-of-the-art MapReduce-based Mondrian methods [ABSTRACT FROM AUTHOR]

AbstractThis practitioner paper describes an inquiry-based approach applying the College, Career, and Civic Life (C3) framework to facilitate instruction of September 11, 2001 history, and its impact on post-9/11 society. An upper elementary level inquiry teaches the Boatlift Rescue from Manhattan Island, and a secondary level inquiry addresses the concepts of loss and persistence. Enduring issues are humanity and resiliency. The featured sources are children’s and youth literature related to 9/11 history and post-9/11 society. Children’s and youth trade books offer educators a valuable starting point to teach about 9/11 and post-9/11 history. For each inquiry instructional strategies are highlighted that encourage students to make their thinking visible when reading trade books and using primary sources from 9/11 repositories. [ABSTRACT FROM AUTHOR]

The so-called Hamming distance measures the difference between two binary strings A and B. In simplified form, it measures the number of changes in A to get B. This type of distance is very useful in classical computing in applications such as error correction. It is also advantageous in quantum computing, being for example widely used in quantum machine learning. Since current quantum computers have limited resources, this type of distance is particularly attractive because it can be computed using fewer qubits and operations than other distances such as Euclidean or Manhattan distances. In this paper, two circuits for calculating Hamming distances using exclusively Clifford+T gates are presented. The aim of both circuits is to reduce the quantum cost and number of T gates needed to compute the Hamming distance. The T gate is more expensive than the other gates, so this reduction will have a significant impact on the total cost of the circuits. Furthermore, the proposed circuits are implemented using only Clifford+T gates. The circuits implemented exclusively with this group of gates are compatible with proven error detection and correction codes. [ABSTRACT FROM AUTHOR]

This paper presents a new approach for the detection of copy-move image forgery, a commonly employed technique for image manipulation. The proposed method combines a modified version of the Gabor Filter and Centre Symmetric Local Binary Pattern (CS-LBP) for feature extraction, aiming to meet the growing demand for accurate forgery detection. The process involves pre-processing the image and extracting features using the Gabor filter and CS-LBP at varying scales and orientations. Key points are matched using the Manhattan distance to identify forged regions. Classification of the forged images is achieved using Hybrid Neural Networks with Decision Tree (HNN-DT). In order to assess the performance of the presented method, diverse image datasets are used and compared to existing feature extraction techniques. The results demonstrate the effectiveness of the modified Gabor filter with CS-LBP in accurately classifying forged images. Specifically, the HNN-DT method with Gabor filter CS-LBP feature extraction surpasses HNN-DT with SURF and PCA feature extraction in terms of classification accuracy and overall performance. Evaluation on the CoMoFoD database confirms superior results compared to existing techniques, establishing the proposed method as a reliable approach for distinguishing between authentic and forged images. Consequently, it serves as a robust solution for image classification and forgery detection. The presented work focuses on detecting mainly three types of image forgery, namely retouching, splicing, and cloning. It is applicable to various image formats, including JPEG and BMP, and is designed specifically for forensic applications in image forgery detection. [ABSTRACT FROM AUTHOR]

In order to estimate the deficiency of a city in its ability to prevent and control risks, as well as to evaluate the corresponding measures, this paper focuses on multi-attribute decision making based on LINMAP method and Manhattan distance at linguistic q-rung orthopair fuzzy. Manhattan distance is a new product that combines clustering distance with linguistic q-rung orthopair fuzzy to be able to use the data more effectively for measurement. LINMAP method is a decision making method based on ideal points, which can solve the weights as well as provide ideal solutions by linear programming model. The combination of the two can create a new decision-making method, which can effectively evaluate the decision scheme of social public facilities according to the actual needs of decision-makers. The new method has the following advantages: (1) the conditions of linguistic fuzzy numbers can be applied more comprehensively, making the decision more realistic and effective; (2) the Manhattan distance is more in line with the human way of thinking and closer to life; (3) after comparative study, the results produced by this method have certain reliability. [ABSTRACT FROM AUTHOR]

A nonhomogeneous dynamic Bayesian network model, which combines the dynamic Bayesian network and the multi-change point process, solves the limitations of the dynamic Bayesian network in modeling non-stationary gene expression data to a certain extent. However, certain problems persist, such as the low network reconstruction accuracy and poor model convergence. Therefore, we propose an MD-birth move based on the Manhattan distance of the data points to increase the rationality of the multi-change point process. The underlying concept of the MD-birth move is that the direction of movement of the change point is assumed to have a larger Manhattan distance between the variance and the mean of its left and right data points. Considering the data instability characteristics, we propose a Markov chain Monte Carlo sampling method based on node-dependent particle filtering in addition to the multi-change point process. The candidate parent nodes to be sampled, which are close to the real state, are pushed to the high probability area through the particle filter, and the candidate parent node set to be sampled that is far from the real state is pushed to the low probability area and then sampled. In terms of reconstructing the gene regulatory network, the model proposed in this paper (FC-DBN) has better network reconstruction accuracy and model convergence speed than other corresponding models on the Saccharomyces cerevisiae data and RAF data. [ABSTRACT FROM AUTHOR]

The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and Simple Additive Weighting (SAW) are among the most employed approaches for aggregating performances in Multi-Criteria Decision-Making (MCDM). TOPSIS and SAW are two MCDM methods based on the value function approach and are often used in combination with other MCDM methods in order to produce rankings of alternatives. In this paper, first, we analyse some common features of these two MCDM methods with a specific reference to the additive properties of the value function and to the sensitivity of the value function to trade-off weights. Based on such methodological insights, an experimental comparison of the results provided by these two aggregation methods across a computational test is performed. Specifically, similarities in rankings of alternatives produced by TOPSIS and SAW are evaluated under three different Minkowski distances (namely, the Euclidean, Manhattan and Tchebichev ones). Similarities are measured trough a set of statistical indices. Results show that TOPSIS, when used in combination with a Manhattan distance, produces rankings which are extremely similar to the ones resulting from SAW. Similarities are also Experimental results confirm that rankings produced by TOPSIS methods are closer to SAW ones when similar formal properties are satisfied. [ABSTRACT FROM AUTHOR]

The terrorist attacks on the World Trade Center in New York on 11th September, 2001, left behind 2977 dead, an altered Manhattan skyline and a changed world order marked by a formidable upsurge of global discourses pertaining to terrorism, multiculturalism, xenophobia, collective memory, and so forth. Indeed, 9/11 inhabits a discursive field of narratives/counter-narratives defying closure. Taking into cognizance this inevitability of myriad discourses, the present paper engages with the politics of the emergence of the discursively constructed Islamic Other in the post-9/11 national imaginary. Using the Foucauldian ideas of Power/Knowledge and "regime of Truth" along with Said's major premises as are found in the works Orientalism and Covering Islam, the paper attempts to debunk the idea that "innocent", neutral and objective representations in the media have been the norm. It argues that the fanatical, regressive and jehad-driven stereotype of the Islamic Other that gained visibility/ circulation/ legitimisation in the post-9/11 American socio-political culturescape had a Stateist genesis rooted in the reductive, ahistorical, Manichean binary of "us versus them" which essentially constituted the official discourse. It traces the trajectory of the Arab-American experience from initial erasure/ invisibility to hyper-visibility in the post-9/11 years, a time marked by deep fractures in the civil society where xenophobia, racial profiling and jingoistic patriotism became normalised. One way of generating resistance to such workings of power is by launching a counternarrative through the literary text. Consequently, the paper ends with a detailed engagement with two novels, one by Mohsin Hamid and another by Laila Halaby, that resist the official stereotypes/discourses while foregrounding the various registers of Othering in the post-9/11 years. [ABSTRACT FROM AUTHOR]

Superpixel segmentation is a popular image preprocessing technology in image processing. Among the various methods used to calculate uniform superpixel, the performance of linear spectral clustering (LSC) is better than the state-of-the-art superpixel segmentation algorithms. However, this method is slow on images and has low accuracy on non-convex images. In order to improve this problem, we propose a subsampled clustering method that can accelerate LSC. Meanwhile, this paper presents an improved distance measurement method based on non-convex image features and Manhattan distance, which can achieve high accuracy on non-convex images. The proposed method is evaluated on the BSDS500 dataset. The experimental results confirmed that this method runs faster than LSC, and at the same time produces almost the same superpixel segmentation accuracy on the images. In addition, the proposed method improves the accuracy of superpixel segmentation on non-convex images. [ABSTRACT FROM AUTHOR]

Precise wind speed prediction is crucial for the management of the wind power generation systems. However, the stochastic nature of the wind speed makes optimal interval prediction very complicated. In this paper, a hybrid approach consisting of improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), temporal convolutional network with attention mechanism (ATCN), and bidirectional long short-term memory network (Bi-LSTM) is proposed for wind speed interval prediction (WSIP). First, ICEEMDAN is used to pre-process the raw data by decomposing the wind signal to several intrinsic mode functions. ATCN is used to reduce the uncertainty from the denoised data and extract the important temporal and spatial characteristics. Then, Bi-LSTM is used to forecast the high-quality intervals for the wind speed. Existing approaches observe a decline in the forecasting performance when the time ahead increases. As a result, the hybrid approach is evaluated using 5-min, 10-min, and 30-min ahead WSIP. To evaluate the novelty of the proposed approach, an experiment is conducted utilising wind speed data from the Garden City, Manhattan wind farm. The experimental results demonstrate that the proposed framework outperformed the comparison models with percentage improvements of 36%, 47%, and 17% for 5-min, 10-min, and 30-min ahead WSIP. [ABSTRACT FROM AUTHOR]

Recently, with the development of autonomous driving technology, vehicle-to-everything (V2X) communication technology that provides a wireless connection between vehicles, pedestrians, and roadside base stations has gained significant attention. Vehicle-to-vehicle (V2V) communication should provide low-latency and highly reliable services through direct communication between vehicles, improving safety. In particular, as the number of vehicles increases, efficient radio resource management becomes more important. In this paper, we propose a deep reinforcement learning (DRL)-based decentralized resource allocation scheme in the V2X communication network in which the radio resources are shared between the V2V and vehicle-to-infrastructure (V2I) networks. Here, a deep Q-network (DQN) is utilized to find the resource blocks and transmit power of vehicles in the V2V network to maximize the sum rate of the V2I and V2V links while reducing the power consumption and latency of V2V links. The DQN also uses the channel state information, the signal-to-interference-plus-noise ratio (SINR) of V2I and V2V links, and the latency constraints of vehicles to find the optimal resource allocation scheme. The proposed DQN-based resource allocation scheme ensures energy-efficient transmissions that satisfy the latency constraints for V2V links while reducing the interference of the V2V network to the V2I network. We evaluate the performance of the proposed scheme in terms of the sum rate of the V2X network, the average power consumption of V2V links, and the average outage probability of V2V links using a case study in Manhattan with nine blocks of 3GPP TR 36.885. The simulation results show that the proposed scheme greatly reduces the transmit power of V2V links when compared to the conventional reinforcement learning-based resource allocation scheme without sacrificing the sum rate of the V2X network or the outage probability of V2V links. [ABSTRACT FROM AUTHOR]

In this paper, we will study the geometric structure on the Sierpinski networks which are skeleton networks of a connected self-similar Sierpinski carpet. Under some suitable condition, we figure out that the renormalized shortest path distance is comparable to the planar Manhattan distance, and obtain the Hausdorff dimension of Sierpinski networks. [ABSTRACT FROM AUTHOR]

Haloperidol, the first butyrophenone neuroleptic, was created in Europe by Janssen Pharmaceuticals in 1958 and was introduced swiftly throughout the continent with great enthusiasm. On September 15, 1959, at Janssen's headquarters in Belgium, teams from around Europe praised the effectiveness of haloperidol. In the United States, on the contrary, its introduction was a tremendous failure, plagued by accusations of inefficacy and patent disputes. A clinical trial in Manhattan has been blamed for this commercial failure. The results of the Manhattan trial were seen as radically different from the results obtained in continental Europe. This divide would have considerable impact not only with regard to haloperidol's path on both sides of the Atlantic, but also possibly on the practical experience and theoretical construction of psychiatry. This article tries to reconstruct the story of that trial based mainly on published papers and interviews. Exploring how societal changes and issues of gender and race shaped this process, this investigation attempts to understand and contextualize different possible reasons for this Atlantic rift. [ABSTRACT FROM AUTHOR]