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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]

This paper makes the case for the connection between making land-use regulatory changes and the process of destruction and redevelopment. Under the capitalist imperative, buildings that do not fulfil the full potential for profit are likely to be demolished (or refurbished) but demolition and new development are not shaped exclusively by the immutable laws of the market as they are mediated and facilitated by specific institutional contexts. Using the case of East Midtown rezoning in New York City, the paper examines the amendments of two land-use regulatory mechanisms: the enlargement of development rights (rezoning or upzoning) and the relaxation of the spatial limitations on the usage of existing unused rights (transferable development rights). While apparently unconnected, upzoning and transferable development rights are part of the regulatory framework that seeks to secure the ongoing generation of the highest-possible profits for private as well as public interests. By examining the choreography of rezoning and transferable development rights, the paper shows how the mechanics of creative destruction work while substantiating an existing body of knowledge on land use policies and practices in New York City. When used together, rezoning and transferable development rights are instrumental in remaking the built environment. Essentially, the rezoning of a 78-block area in East Midtown Manhattan unlocks captured and latent development rights that otherwise could not come about, and demonstrates the necessity of institutional arrangements to make creative destruction actually work. [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]

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]

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]

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 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]

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]

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 the equivalence of connectedness for the Sierpinski-like sponge and skeleton networks, and find out the relation between the geodesic distance on the sponge and renormalized shortest path distance on the skeleton networks. Furthermore, under some assumption on the IFS, we obtain the comparability of the Manhattan distance and the geodesic distance on the sponge. [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]

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]

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]

This publication examines the history of Project Orion and NERVA nuclear engine program within the Integrated Program Plan, the technical reasons for their study, their progress, and the political reasons for their cancellation. Whether pulsed or thermal, nuclear propulsion offers the best compromise in efficiency and thrust for the propulsion of future manned missions. Project Orion was born in the mind of Stanislaw Ulam, an atomic physicist who had worked on the Manhattan project. Despite theoretical performances superior to anything conceivable even with current technologies, and despite the mobilisation of some of the most brilliant physicists of the time (Freeman Dyson), the project was killed by the Treaty banning nuclear weapon tests in the atmosphere, in outer space and under water (Moscow, 5 August 1963). The Integrated Program Plan was Wernher von Braun's proposal to the Space Task Group (STG), chaired by Vice President Spiro Agnew, for the continuation of the Apollo program. This plan envisaged a lunar base in the 1970s, and a first manned mission to Mars in the early 1980s. All of this would have been powered by nuclear-powered orbital shuttles, using NERVA, a nuclear engine tested and operational since 1968. Here again, even though it performed better than anything else in the world, even today, the project failed politically. This retrospective publication therefore aims to answer the following question: How can nuclear space programs of today and tomorrow be protected from the uncertainties of this influence? The study of propulsion systems is very often left to engineers, and political and public relations issues to politicians. However, every technology is the result of a political system, and has a strategic impact, which is a subject for social and political sciences. That's why this paper adopts an original approach crossing social sciences through the historical study of decision-making dynamics, and engineering through the study of the technical characteristics of the projects involved. This publication is based on declassified technical reports, testimonies of the personalities of the time, and a document from the archives of the John F. Kennedy Presidential Library. History can be the academic bridge between political and technical studies. In a context of resurgence of nuclear propulsion projects throughout the world, such an approach is now necessary both at the historical level and in terms of political decisions making. Indeed, space programs involving nuclear energy are, by nature, at the crossroads of public relations issues very specific to both space and atomic subjects. [ABSTRACT FROM AUTHOR]

The paper presents a study of equilibrium in a two-sided market for network platforms with cross externalities between buyers and sellers. The proposed model is a generalization of Armstrong's (2006) monopoly model for the case of a duopoly in a two-sided market for network platforms located on a plane. The paper solves the problem of optimal pricing and investigates the question of the optimal location of platforms in the market, provided that the heterogeneous utility of agents of both groups (buyers and sellers) is formed taking into account the Hotelling specification with the Manhattan metric. [ABSTRACT FROM AUTHOR]

As a novel and economical transportation way, ride-sharing has attracted more and more passengers and drivers to participate. How to match passengers with drivers efficiently has become a key issue. Specifically, drivers are usually heterogeneous with different costs, and they may behave strategically (e.g. reveal their private cost information untruthfully) in order to make more profits. Drivers' strategic behavior may lead to inefficient matching, which results in the loss of social welfare of ride-sharing platforms and drivers. In this paper, an incentive-compatible and efficient mechanism is proposed to solve this issue, which can match passengers with drivers and determine the payments to drivers in order to maximize social welfare while ensuring drivers reveal their cost information truthfully. Specifically, an order matching algorithm with branch and bound based route planning is designed to accelerate the matching process. Meanwhile, the payments to drivers are computed based on the second-pricing algorithm. In so doing, a second-pricing based ride-sharing mechanism (SPRM) is proposed, which satisfies incentive compatibility, individual rationality, budget balance and computational efficiency. Based on the real Manhattan taxi order data and vehicle fuel consumption data, extensive experiments are conducted to evaluate the proposed mechanism. The experimental results show that SPRM can guarantee drivers' profits and improve the ratio of drivers' participation and the ratio of served orders, and eventually achieve greater social welfare than four typical benchmark approaches, GPri, ND, mT-share and mdp. The research of this paper can provide useful insights for designing efficient ride-sharing system to maximize the social welfare of all participants in the real life. • An efficient mechanism to match orders with drivers is proposed. • A branch and bound based route planning method is used to accelerate the matching. • A second pricing based mechanism is proposed to compute the payments to drivers. • The proposed mechanism can outperform benchmark approaches in the social welfare. [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]

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]

What does it mean for the United Nations General Assembly (UNGA) building to be designed through modernist architecture principles on land acquired through settler colonialism? In 1947, construction began on the United Nations Headquarters (UNHQ) in Manhattan, a name derived from Manna-hata, a site within Lenapehoking, the homeland of Indigenous Lenape peoples violently displaced by waves of Dutch, British, and American settlers starting in the 17th century. This paper analyzes the structural dynamics that is in the literal foundations of the United Nations Headquarters, the post-World War II (WWII) worldmaking project intended to safeguard international order. By marshaling the history of Lenapehoking and analyzing the design principles informing the UNGA building, this paper narrows the claim that the post-WWII worldmaking project was contingent upon settler colonialism. Through a capacious reading of settler colonial theory, architectural history, and International Relations (IR), this paper aims to open up conversations on the ongoing structural and spatial dynamics embedded in the foundations of the UNGA building that are constitutive of the post-WWII international order. [ABSTRACT FROM AUTHOR]

There is a need for a probabilistic linguistic term set model for go/no-go product screening problem for new product development to meet a firm's expectation. This paper develops a novel 3-tuple linguistic distance-based model to evaluate whether an overall respondents perception meets a firm's expectation ("go") for new product development. The respondent's perception is collected by a Kansei-based survey as an interval-linguistic term. Then, an expected distance between the firm's expectation and the respondent's perception is computed by a target-based Manhattan distance measure. The expected distance is compared with a threshold to shows that what product attribute meets the firm's expectation based on customers' perceptions. A real case study of Thai-tea soy milk packaging design is provided. The proposed model is compared to the existing model to show its effectiveness and applicability. Experimental results show that the proposed model can effectively point out the inferior product attributes, which leads to redesign the product until all product concepts meet the target attributes before launching the product to the market. Thus, it can significantly reduce the risk of failure of the product in a real market. This paper has significant contributions in that it allows respondents to provide their opinions with uncertainty by providing an interval linguistic assessment, handles a bias of the heterogeneity of respondents by determining the weight of respondents, and overcomes limitations of existing models by applying target-oriented linguistic terms. [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]

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]

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]

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]

Randomized dimensionality reduction has been recognized as one of the cornerstones in handling high-dimensional data, originating in various foundational works such as the celebrated Johnson-Lindenstrauss Lemma. More specifically, nearest neighbor-preserving embeddings exist for ℓ 2 (Euclidean) and ℓ 1 (Manhattan) metrics, as well as doubling subsets of ℓ 2 , where doubling dimension is today the most effective way of capturing intrinsic dimensionality, as well as input structure in various applications. These randomized embeddings bound the distortion only for distances between the query point and a point set. Motivated by the foundational character of fast Approximate Nearest Neighbor search in ℓ 1 , this paper settles an important missing case, namely that of doubling subsets of ℓ 1. In particular, we introduce a randomized dimensionality reduction by means of a near neighbor-preserving embedding, which is related to the decision-with-witness problem. The input set gets represented with a carefully chosen covering point set; in a second step, the algorithm randomly projects the latter. In order to obtain the covering point sets, we leverage either approximate r -nets or randomly shifted grids, with different tradeoffs between preprocessing time and target dimension. We exploit Cauchy random variables, and derive a concentration bound of independent interest. Our algorithms are rather simple and should therefore be useful in practice. [ABSTRACT FROM AUTHOR]

The indoor scenes usually exhibit strong structural regularity; on the other hand, the local geometric features are scarce and difficult to be distinguished. The properties make the solutions of the existing global registration algorithms ambiguous. In addition, finding global optimal solution is usually computationally expensive. These problems make the global registration of indoor point clouds a challenging task. Based on the Manhattan-world assumption and color information, we propose a fast and effective algorithm for the global registration of indoor colored point clouds in this paper. We introduce the Manhattan-world assumption into the global registration of indoor point cloud to limit the number of all possible rotation solutions. As a result, our algorithm solves the problem very fast. Furthermore, we utilize the color information hierarchically to eliminate the ambiguity induced by the structural models. The limited rotation set is filtered by the global color histograms, and the final solution is evaluated by the local features. The experiments demonstrate that our algorithm can complete the global registration for the point clouds with hundreds of thousands of points in 0.1 second with comparable accuracy with others. [ABSTRACT FROM AUTHOR]

The rise in COVID-19 cases has not only generated anxiety and uncertainty but has also caused an upsurge in anti-Asian racism all over the world. The racially motivated attacks have forced us to rethink the concept of migration, integration, and racism in a global context. In this paper, I look back at Meena Alexander's Manhattan Music and relying on Arjun Appadurai's critical theory in Fear of Small Numbers: An Essay on the Geography of Anger I investigate the dynamics that go into making the dominant culture hostile towards immigrants and ethnic populations. Drawing a connection between my reading of the novel and the COVID-fueled racism, I debunk the myth of assimilation as a means of integration and attest that race and ethnicity still play crucial role in the politics of power. That is, despite the ethnic protagonists' negotiation of their subjectivity, their ethnic body will continue to be victims of racial politics and used as scapegoats as long as white supremacy prevails and is considered normative. Revisiting Manhattan Music and rendering the connection between race, ethnicity and racism opens up avenues for critical rethinking and compels us to ever more consider the intersection of ethnic identity and race today in an era of global pandemic that has targeted Asian migrants in and beyond America. [ABSTRACT FROM AUTHOR]

• Urban traffic control is achieved by optimal route guidance based on avoidance maps. • Avoidance maps define which areas vehicles should better circumvent to sustain optimal internal traffic conditions. • Avoidance maps are derived from a reactive proportional control scheme. • A thorough sensitivity analysis shows how to tune the main parameters of the new concept (region sizes, safety distance, controller time horizon). • The new concept can improve total travel time by around 15% with little harm to individual vehicles. • Privacy by design is guaranteed as vehicles are not sharing data with the control system. This paper presents a new concept for operating traffic management at a sizeable urban scale. The overall principle is to partition the network into multiple regions, where traffic conditions should remain optimal. Instead of monitoring the inflow, like for perimeter control, deviations in regional mean speed compared to a reference value are transformed into avoidance levels by a centralized controller. That information is broadcasted to vehicles through a public avoidance map. The onboard navigation systems then interpret this map to deliver individual route guidance according to the avoidance levels. In that way, the concept tends to distribute vehicles in the network optimally while preserving privacy. In addition to the controller parameters itself, three other critical parameters define the system: the safety distance, the controller time horizon, and the region sizes. Thorough sensitivity analysis leads to the optimal setting. The concept is proven effective using microsimulation for both a Manhattan and a realistic network. The total travel time is improved by about 15% when traffic is severely congested compared to the uncontrolled case. In the meantime, the mean individual travel distance increase for rerouted vehicles is kept below 10%. Those results have been obtained with a simple decentralized reactive control framework, i.e., an individual proportional feedback system independently governs each region. More advanced formulations introducing cooperation between the regions are also tested. In a nutshell, this paper is a proof of concept for a new control system that appears both practical and valuable to alleviate congestion in urban areas. © 2021 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 24th International Symposium on Transportation and Traffic Theory [ABSTRACT FROM AUTHOR]

The paper presents a new mixed-integer programming formulation for the maximally diverse grouping problem (MDGP) with attribute values. The MDGP is the problem of assigning items to groups such that all groups are as heterogeneous as possible. In the version with attribute values, the heterogeneity of groups is measured by the sum of pairwise absolute differences of the attribute values of the assigned items, i.e. by the Manhattan metric. The advantage of the version with attribute values is that the objective function can be reformulated such that it is linear instead of quadratic like in the standard MDGP formulation. We evaluate the new model formulation for the MDGP with attribute values in comparison with two different MDGP formulations from the literature. Our model formulation leads to substantially improved computation times and solves instances of realistic sizes (for example the assignment of students to seminars) with up to 70 items and three attributes, 50 items and five attributes, and 30 items and ten attributes to (near) optimality within half an hour. [ABSTRACT FROM AUTHOR]

In this paper, a drift rejection SLAM (Simultaneous Localization and Mapping) method is proposed targeting indoor scenarios, where SLAM generates large drifts due to the lack of reliable features. To provide sufficient features, we leverage multiple feature primitives and geometric restraints (parallel or perpendicular) restraint in man-made environments. Under some satisfy Manhattan World (MW) assumption scene, such as corridors, we can get absolute and drift-free rotation estimation using a Gaussian sphere. By fully utilizing drift-free rotation estimation under MW assumption and the local stability of purely track restricted by point, line, and plane features, our drift rejection SLAM method becomes more accurate and robust. Additionally, by exploiting the constraint of planar motion on ground robot, we propose an ingenious strategy to reduce translation drift by eliminating vertical movement in the Manhattan world. Advantages of our method over other state-of-the-art algorithms are validated on public datasets and real-world experiments. The code is released at . [ABSTRACT FROM AUTHOR]

Hypergraph matching has been attractive in the application of computer vision in recent years. The interference of external factors, such as squeezing, pulling, occlusion, and noise, results in the same target displaying different image characteristics under different influencing factors. After extracting the image feature point description, the traditional method directly measures the feature description using distance measurement methods such as Euclidean distance, cosine distance, and Manhattan distance, which lack a sufficient generalization ability and negatively impact the accuracy and effectiveness of matching. This paper proposes a metric-learning-based hypergraph matching (MLGM) approach that employs metric learning to express the similarity relationship between high-order image descriptors and learns a new metric function based on scene requirements and target characteristics. The experimental results show that our proposed method performs better than state-of-the-art algorithms on both synthetic and natural images. [ABSTRACT FROM AUTHOR]

The reconstruction of the floorplan for a building requires the creation of a two-dimensional floorplan from a 3D model. This task is widely employed in interior design and decoration. In reality, the structures of indoor environments are complex with much clutter and occlusions, making it difficult to reconstruct a complete and accurate floorplan. It is well known that a suitable dataset is a key point to drive an effective algorithm, while existing datasets of floorplan reconstruction are synthetic and small. Without reliable accumulations of real datasets, the robustness of methods to real scene reconstruction is weakened. In this paper, we first annotate a large-scale realistic benchmark, which contains RGBD image sequences and 3D models of 80 indoor scenes with more than 10,000 square meters. We also introduce a framework for the floorplan reconstruction with mesh-based point cloud normalization. The loose-Manhattan constraint is performed in our optimization process, and the optimal floorplan is reconstructed via constraint integer programming. The experimental results on public and our own datasets demonstrate that the proposed method outperforms FloorNet and Floor-SP. [ABSTRACT FROM AUTHOR]

Autonomous ride-hailing platforms, such as Waymo and Cruise, are quickly expanding their services, but their interactions with the existing ride-hailing companies, such as Uber and Lyft, are rarely discussed. To fill this gap, this paper focuses on the competition between an emerging autonomous ride-hailing platform and a traditional ride-hailing platform by characterizing the equilibrium of their competition and the impact of technology transfer. In particular, we consider an autonomous ride-hailing platform that owns the AV technology and offers ride-hailing services to passengers through a fleet of AVs. In the meanwhile, it competes with a traditional ride-hailing platform that primarily relies on a fleet of human-driver vehicles (HDVs) but may rent a sub-fleet of AVs from the autonomous ride-hailing platform to complement the human-driver fleet (referred to as AV technology transfer). A game-theoretic model is formulated to characterize the competition between the autonomous ride-hailing platform and the traditional ride-hailing platform over a transportation network, encapsulating the passengers' mode choices, the drivers' job options, the network traffic flows and the strategic decisions of the platforms. An algorithm is proposed to compute the approximate Nash equilibrium of the game and conduct an ex-post evaluation on the performance of the obtained solutions. The proposed framework and solution algorithm are validated through a realistic case study for Manhattan. Based on numerical simulations, we find that technology transfer of AVs between the two platforms can lead to a win-win situation where both two platforms get a higher profit, but this comes at the cost of reduced surpluses for human drivers and passengers. In the simulation, a critical trade-off is revealed for the autonomous ride-hailing platform: it strategically forfeits some of its market share in ride-hailing services to encourage the traditional ride-hailing platform to rent more AVs, thereby increasing its rental revenue and consequently, the overall profit. Furthermore, we also find it intriguing that as AV technology improves and operational costs decrease, the traditional ride-hailing platform cannot enjoy any benefit in its profit although it has the option of leasing AVs from the autonomous ride-hailing platform at lower operational costs. Instead, it is compelled to rent a larger fleet of AVs from the autonomous ride-hailing platform at a higher rental price, consequently suffering a reduced profit. Conversely, the autonomous ride-hailing platform significantly benefits from the reduced AV operational cost by capturing a larger market share in the ride-hailing market and earning higher revenue from the AV technology transfer. • Investigated the competition between autonomous and traditional ride-hailing platforms. • A game-theoretic model to capture the incentives of players over transportation networks. • An algorithm is proposed to obtain an approximate Nash equilibrium. • Examined the impacts of AVs on traditional ride-hailing market under platform competition. [ABSTRACT FROM AUTHOR]

Focused crawler traverses the Web to only collect pages that are relevant to a particular topic, and is increasingly considered as a way to get around the scalability issues with current general-purpose search engines. But the data diversity in the Web forces these crawlers to face three significant problems: (i) inconsistency, (ii) ubiquity, and (iii) ambiguity, which causes misguidance in crawling. To handle these issues, this paper proposes a weakly supervised Gated Recurrent Unit (GRU) mechanism for an adaptive focused web crawler framework that matches semantically relevant t o p i c s and w e b p a g e c o n t e n t. This weakly supervised Gated Recurrent Unit model accepts the vector form of the t o p i c and the fetched w e b p a g e as input to produce meaningful s e m a n t i c vectors and incorporates the Manhattan distance rule to compute the topical relevance of the w e b p a g e. The proposed mechanism guides the focused crawler in downloading more relevant web pages by finding the relevant hyperlinks and omitting the irrelevant hyperlinks concerning the topic. The proposed method helps the focused crawler to semantically find, arrange, and index the web pages in a relatively narrow segment of the web to solve the inconsistency, ubiquity, and ambiguity problems of the focused crawlers. The experimental results indicate that the proposed technique outperforms the s t a t e − o f − t h e − a r t approaches in terms of h a r v e s t r a t e , p r e c i s i o n , r e c a l l , h a r m o n i c m e a n , and i r r e l e v a n c e r a t i o. In summary, the strategy described here works well and is important for focused crawlers. [ABSTRACT FROM AUTHOR]

• This study proposes a multi wavelength optical image feature fusion method for improving the effectiveness of subdural hematoma image reconstruction. • CT images of real patients was used in this study to establish a 3D three-layer tissue brain tissue model. • The average VRE and average AMD of hematoma reconstruction in the model which is using this method are only 0.66 % and 0.0052 mm−1. • Compared with the model which is directly using optical information as input, the method make the average VRE and average AMD of hematoma reconstruction respectively decreased by 80.0% and 27.8%. Diffuse optical tomography (DOT) under medical image guidance can be used to reconstruct images of subdural hematomas for monitoring purposes. However, when the shape of hematoma is irregular or the signal to noise ratio(SNR) of input signal is low, the accuracy and robustness of this method both decrease. In order to alleviate this situation and improve the reconstruction effect of cerebral hematoma images, a multi wavelength optical image feature fusion method was proposed in this paper. Experimental results show that the mean relative volume error (VRE) of the hematoma reconstruction model based on the multi-wavelength optical image feature fusion method is only 0.66 %, and the mean value of the average Manhattan distance (AMD) between the reconstructed absorption coefficient and the true absorption coefficient is 0.0052 mm−1. Compared with the model using single-wavelength optical image mapping feature extraction method, the multi-wavelength optical image feature fusion method reduces the average VRE of the model by 16.5 %, and the average AMD by 16.1 %. And compared with the model directly inputting single-wavelength optical information, its average VRE and average AMD decreased by 80.0 % and 27.8 %. This method has clinical application significance for the monitoring of subdural hematomas. [ABSTRACT FROM AUTHOR]

The paper presents a novel method for automatically creating semantic digital models for buildings in Manhattan environments from dense point cloud data. Unlike previous approaches, which rely solely on data-driven methods, our method integrates artificial intelligence with domain engineering knowledge to overcome challenges in indoor point cloud processing and geometry representation in complex layouts. A feature-based Decision Tree classifier extracts main building elements, utilized in a knowledge-based algorithm for 3D space parsing. On this basis, an optimization process generates parameterized floor plans, used to finally create volumetric digital models. The method was validated on datasets from the Technical University of Munich and Stanford University, achieving a mean accuracy of approximately 0.08 m for model placement and 0.06 m for estimating element parameters, which highlights its effectiveness for generating a building's semantic digital model. This approach underscores the potential of AI integration in digital twinning workflows for more automated solutions. [Display omitted] • Designing a hybrid bottom-up, top-down approach for automatic building digital model creation. • Align the capabilities of AI in scene understanding with domain knowledge to overcome challenges in indoor 3D reconstruction. • Developing a knowledge-based method for 3D space parsing in largescale indoor point cloud. • Reconstruction of the parametric building's floor plan using point cloud with low sensitivity to poor data quality. • Creation of dynamic parametric building digital models (LOD200) for flexible facility management and space analysis. [ABSTRACT FROM AUTHOR]

A newly discovered letter written by Hermann J. Muller in August 1948, reveals that he claimed to have evidence that multiple papers by Frederick Hanson and Florence Heys, including those that supported the linear non-threshold (LNT) dose response model for hereditary and cancer risk assessment, were fraudulent and thus untrustworthy. Muller failed to bring this issue, which he referred to as a major scientific scandal, to the attention of the scientific community, remaining silent for the remainder of his career. Since Muller was a recipient of substantial funding by the Rockefeller Foundation (RF) and Hanson was a senior RF program director, instrumental in the process that awarded funding for Muller and other geneticists, it suggested that Muller may have been conflicted in his recognized obligation to the scientific community to expose possible scientific misconduct, and his desire to ensure both continuing funding from the RF and his advocacy for the adoption of the LNT model of radiation risk assessment. In this conflicted situation, Muller seems to have opted for self-interest, failing to bring his concerns/challenges about the publications of his RF funding colleague Hanson to public forum via acceptable venues that typically permit full exposition of disputes. Muller's decision to act in this manner permitted the papers that he deemed as untrustworthy to be widely, and continuously cited (to the present), and in this way, affect worldwide acceptance of the LNT model by the scientific community and regulatory agencies in ways that may negatively impact radiation science, subsequent LNT interpretation, and the public health. • Hermann Muller claimed key early LNT supporting papers were falsified. • Muller never made these assertions public. • Muller was funded by an author of the accused papers in his position at the RF. • Muller's failure to report the allegations may have affected LNT acceptance. • These findings are revealed in Muller's private correspondence. [ABSTRACT FROM AUTHOR]

The efficient and accurate prediction of urban travel demand, which is a hot topic in intelligent transportation research, is challenging due to its complicated spatial-temporal dependencies, dynamic nature, and uneven distribution. Most existing forecasting methods merely considered the static spatial dependencies while ignoring the influence of the diversity of dynamic demand patterns and/or uneven distribution. In this paper, we propose a traffic demand forecasting framework of a hybrid dynamic graph convolutional network (HDGCN) model to deeply capture the characteristics of urban travel demand and improve prediction accuracy. In HDGCN, traffic flow similarity graphs are designed according to the dynamic nature of travel demand, and a dynamic graph sequence is generated according to time sequence. Then, the dynamic graph convolution module and the standard graph convolution module are introduced to extract the spatial features from dynamic graphs and static graphs, respectively. Finally, the spatial features of the two components are fused and combined with the gated recurrent unit (GRU) to learn the temporal features. The efficiency and accuracy of the HDGCN model in predicting urban taxi travel demand are verified by using the taxi data from Manhattan, New York City. The modeling and comparison results demonstrate that the HDGCN model can achieve stable and effective prediction for taxi travel demand compared with the state-of-the-art baseline models. The proposed model could be used for the real-time, accurate, and efficient travel demand prediction of urban taxi and other urban transportation systems. [ABSTRACT FROM AUTHOR]

To improve mobile robot positioning accuracy in building environments and construct structural three-dimensional (3D) maps, this paper proposes a stereo visual-inertial odometry (VIO) system based on structural lines and points belonging to lines. The 2-degree-of-freedom (DoF) spatial structural lines based on the Manhattan world assumption are used to establish visual measurement constraints. The property of point belonging to a line (PPBL) is used to initialize the structural lines and establish spatial distance-residual constraints between point and line landmarks in the reconstructed 3D map. Compared with the 4-DoF spatial straight line, the 2-DoF structural line reduces the variables to be estimated and introduces the orientation information of scenes to the VIO system. The utilization of PPBL makes the proposed system fully exploit the prior geometric information of environments and then achieves better performance. Tests on public data sets and real-world experiments show that the proposed system can achieve higher positioning accuracy and construct 3D maps that better reflect the structure of scenes than existing VIO approaches. [ABSTRACT FROM AUTHOR]