Distance Constraints Research Articles

Balancing the cardinality of clusters with a distance constraint: a fast algorithm

AbstractLogistics companies partition the customers they serve into delivery zones as a tactical decision and manage the customers assigned to each zone as a cluster for the purpose of routing, workload allocation, etc. Frequently, this partition is made in accordance with customers’ geographical location, which can result in very unbalanced clusters in terms of the number of customers they include. In addition, in the day-to-day operations, not necessarily all customers need to be served every day so, even if the clusters originally created are balanced, daily needs may lead to unbalanced clusters. Given an a priori assignment of customers to clusters, improving the balance between clusters in advance of workload management is therefore a key issue. This paper addresses the problem of balancing clusters, when there is a distance constraint that prevents reassigning customers to clusters far away from their original pre-assignment. This problem is formulated as a lexicographic biobjective optimization model. The highest priority objective function minimizes the variance of the number of customers in the clusters. The second ranked objective function minimizes the total distance resulting from all reassignments. A fast and effective heuristic algorithm is developed, based on exploring customer reassignments, either by comparing clusters two by two or by extending the search to allow for sequential customer swaps among clusters. Both the quality of the solution and the computational time required encourage the use of this algorithm by logistics companies to balance clusters in real scenarios.

A comprehensive framework for electric vehicle charging station siting along highways using weighted sum method

The proliferation of electric vehicles (EVs) has led to an increased demand for strategically located EV charging stations (EVCSs) to ensure a balanced and accessible charging network. The siting of EVCS involves a multifaceted process that includes technological, economic, social, geographical, and environmental factors. An EVCS network should deliver high-quality service, alleviate drivers' range anxiety, and be compatible with renewable energy system integration, while also accounting for technical infrastructure and future expansion expectations. Resting areas along highways, equipped with amenities and grid connections, emerge as potential sites for EVCS installation. Accordingly, this study presents an EVCS siting framework along highways, integrating expert opinions from diverse disciplines. The proposed method employs expert opinions to weigh site selection criteria and then uses a clustering-based approach to identify suitable locations for EVCS siting, utilizing the weighted sum method. According to the experts, the most important criterion is determined as the service level of the candidate point with a weight of 0.375, followed by traffic density (0.218), and proximity to connection roads (0.215). The method is tested both on a test highway and the Edirne-Ankara highway, which is the most heavily used in Türkiye. The results demonstrate that the test highway identifies 13 out of 18 optimal locations using the clustering strategy. Similarly, the siting method identifies 18 out of 31 optimal EVCS locations along the Edirne-Ankara highway ensuring the distance constraints. This approach is scalable and adaptable for application on highways in various countries where EVCS infrastructure is still developing.

Freeprotmap: waiting-free prediction method for protein distance map

BackgroundProtein residue–residue distance maps are used for remote homology detection, protein information estimation, and protein structure research. However, existing prediction approaches are time-consuming, and hundreds of millions of proteins are discovered each year, necessitating the development of a rapid and reliable prediction method for protein residue–residue distances. Moreover, because many proteins lack known homologous sequences, a waiting-free and alignment-free deep learning method is needed.ResultIn this study, we propose a learning framework named FreeProtMap. In terms of protein representation processing, the proposed group pooling in FreeProtMap effectively mitigates issues arising from high-dimensional sparseness in protein representation. In terms of model structure, we have made several careful designs. Firstly, it is designed based on the locality of protein structures and triangular inequality distance constraints to improve prediction accuracy. Secondly, inference speed is improved by using additive attention and lightweight design. Besides, the generalization ability is improved by using bottlenecks and a neural network block named local microformer. As a result, FreeProtMap can predict protein residue–residue distances in tens of milliseconds and has higher precision than the best structure prediction method.ConclusionSeveral groups of comparative experiments and ablation experiments verify the effectiveness of the designs. The results demonstrate that FreeProtMap significantly outperforms other state-of-the-art methods in accurate protein residue–residue distance prediction, which is beneficial for lots of protein research works. It is worth mentioning that we could scan all proteins discovered each year based on FreeProtMap to find structurally similar proteins in a short time because the fact that the structure similarity calculation method based on distance maps is much less time-consuming than algorithms based on 3D structures.

Multimodal Mass Spectrometry Identifies a Conserved Protective Epitope in S. pyogenes Streptolysin O.

An important element of antibody-guided vaccine design is the use of neutralizing or opsonic monoclonal antibodies to define protective epitopes in their native three-dimensional conformation. Here, we demonstrate a multimodal mass spectrometry-based strategy for in-depth characterization of antigen-antibody complexes to enable the identification of protective epitopes using the cytolytic exotoxin Streptolysin O (SLO) from Streptococcus pyogenes as a showcase. We first discovered a monoclonal antibody with an undisclosed sequence capable of neutralizing SLO-mediated cytolysis. The amino acid sequence of both the antibody light and the heavy chain was determined using mass-spectrometry-based de novo sequencing, followed by chemical cross-linking mass spectrometry to generate distance constraints between the antibody fragment antigen-binding region and SLO. Subsequent integrative computational modeling revealed a discontinuous epitope located in domain 3 of SLO that was experimentally validated by hydrogen-deuterium exchange mass spectrometry and reverse engineering of the targeted epitope. The results show that the antibody inhibits SLO-mediated cytolysis by binding to a discontinuous epitope in domain 3, likely preventing oligomerization and subsequent secondary structure transitions critical for pore-formation. The epitope is highly conserved across >98% of the characterized S. pyogenes isolates, making it an attractive target for antibody-based therapy and vaccine design against severe streptococcal infections.

An Adaptive Robust EKF Based on Mahalanobis Distance and Non-Holonomic Constraints for Enhancing Vehicle Positioning Accuracy

An Adaptive Robust EKF Based on Mahalanobis Distance and Non-Holonomic Constraints for Enhancing Vehicle Positioning Accuracy

A Clustering Method for Single-Cell RNA-Seq Data Based on Automatic Weighting Penalty and Low-Rank Representation.

Advances in high-throughput single-cell RNA sequencing (scRNA-seq) technology have provided more comprehensive biological information on cell expression. Clustering analysis is a critical step in scRNA-seq research and provides clear knowledge of the cell identity. Unfortunately, the characteristics of scRNA-seq data and the limitations of existing technologies make clustering encounter a considerable challenge. Meanwhile, some existing methods treat different features equally and ignore differences in feature contributions, which leads to a loss of information. To overcome limitations, we introduce a weighted distance constraint into the construction of the similarity graph and combine the similarity constraint. We propose the Joint Automatic Weighting Similarity Graph and Low-rank Representation (JAGLRR) clustering method. Evaluating the contributions of each feature and assigning various weight values can increase the significance of valuable features while decreasing the interference of redundant features. The similarity constraint allows the model to generate a more symmetric affinity matrix. Benefitting from that affinity matrix, JAGLRR recovers the original linear relationship of the data more accurately and obtains more discriminative information. The results on simulated datasets and 8 real datasets show that JAGLRR outperforms 11 existing comparison methods in clustering experiments, with higher clustering accuracy and stability.

Analysis of Coverage and Capacity for UAV-Aided Networks with Directional mmWave Communications

Millimeter wave (mmWave) unmanned aerial vehicle (UAV)-aided networks have enormous application potential due to their large bandwidth and ultra-high speed, being regarded as an effective technology for improving the reliability of military and civilian fields. However, due to their complex electromagnetic spectrum environment and the sensitivity of mmWaves to blocking effects, its performance analysis faces certain difficulties. This article investigates the coverage and network capacity of mmWave UAV-aided networks under significant blocking effects and complex electromagnetic environments; for this purpose, we equipped each UAV with mmWave antennas featuring adjustable beamwidth and direction. A Matérn hard-core point process (MHCPP) with repulsion constraints was also employed to reflect the minimum distance constraints to isolate the mutual interference between UAVs. Then, using a stochastic geometric analysis, we derived the coverage and capacity characteristics and further obtained a closed-form expression for the network coverage probability. Finally, the simulation results showed that the network throughput could reach 86% when the density of UAVs was half of that of ground base stations (GBSs) in the city center, validating the efficiency and accuracy of our theoretical derivations.

A powered descent trajectory planning method with quantitative consideration of safe distance to obstacle

High scientific value areas on celestial bodies such as the Moon and Mars are often located in hazardous terrains. To achieve safe landing exploration, a novel planning method is proposed, which can ensure that the planned trajectory maintains a user-specified distance from obstacles, thus reducing potential collision risk induced by factors such as the body size and model uncertainties. Firstly, the basic model of the trajectory optimization problem and its convexification version is given. The obstacles are modeled as polynomial functions, based on which the “if-then” obstacle avoidance logic explicitly considering the safe distance, is described as a sum of squares constraint. This constraint formulation applies to any obstacle described by a finite number of polynomials, independent of the specific expression of the polynomials (reflecting the shape of the obstacle). Subsequently, the convexification process for the obstacle avoidance constraint is given. Finally, the sequential sum of squares programming problem for the obstacle avoidance trajectory is established, which boils down to a series of semidefinite programming problems. Simulation results show that the closest distance between the planned trajectory and obstacles strictly satisfies the specified distance constraint, and the trajectory could avoid non-convex obstacles. With the promising convergence properties of underlying convex optimization algorithms, advanced autonomous obstacle avoidance guidance schemes are expected to be formed based on the proposed trajectory planning method.

Forward and inversion approach for direct current resistivity based on an unstructured mesh and its application to tunnel engineering

AbstractThe accurate identification of water‐bearing structures is urgently required for the safe construction of tunnel engineering. Currently, the direct current resistivity method is an effective method for detecting water‐bearing structures in tunnels. In the advanced detection of the direct current resistivity based on the finite element method, the traditional hexahedron mesh performs poorly for the discretization of models of complex tunnel structure sections such as horseshoe‐shaped and round sections. Therefore, this study adopts unstructured grid generation technology combining tetrahedra and hexahedra to achieve more accurate modelling of complex structures, such as round and horseshoe‐shaped sections, and establishes a forward modelling method of the direct current resistivity in tunnels based on an unstructured mesh. The maximum error between the numerical simulation and theoretical results for an infinite tabular body in full space is less than 0.8%. It is more complicated to calculate the sensitivity matrix and model constraint term for the inversion region containing two types of grid than for one. For this purpose, the sensitivity matrix of different types of grid areas is calculated, a model constraint term based on the dual constraints of volume and distance is constructed, and finally, a partitioned domain‐weighted least‐squares inversion method based on an unstructured mesh is proposed. Synthetic examples of typical water‐bearing structures are analysed, and the results show that the proposed forward and inverse methods of the direct current resistivity in tunnels based on an unstructured mesh can effectively capture the position and morphology of the water‐bearing structure. Finally, an on‐site application was conducted in the Yellow River Diversion Project in central Shanxi. The proposed method could effectively identify the water body in front of the tunnel face and guide the on‐site construction of the project. These results can improve the interpretation of the direct current resistivity data in tunnels and play a positive role in promoting the use of the direct current resistivity method to prevent and control water‐inrush disasters in tunnels with complex structures.

Analysis of Youtube Content of Mixed-Marriage Women Living Overseas from The Perspective of Anxiety/Uncertainty Management

The advancement of technology has reduced distance and time constraints, leading to the intercultural marriages experienced by Indonesian women. This research aims to analyze the socio-cultural adjustment of mixed-marriage women from the Anxiety Uncertainty Management (AUM) perspective. Referring to AUM theory, the presence of uncertainty and anxiety serves a significant role in maintaining the focus of mixed-marriage women residing in foreign countries, particularly during cross-cultural interactions. This research utilizes a qualitative content analysis method with the assistance of NVivo 14 Plus to code the distribution of adjustment factors within the YouTube content. The selection of research samples was based on the diversity of husbands' origins, current country of residence, length of marriage, and the depth of women's engagement in sharing personal stories in their content, resulting in the selection of six account owners. The categorization of adjustment factors that emerged in the YouTube video content includes stages of uncertainty management, anxiety management, self-awareness, and effective communication. The results of this research indicate that all women go through the four stages of the AUM model in diverse forms. An additional finding in this study is that support systems play a significant role in assisting women to navigate during the adjustment phases.

Interpretable prediction of mRNA abundance from promoter sequence using contextual regression models.

While machine learning models have been successfully applied to predicting gene expression from promoter sequences, it remains a great challenge to derive intuitive interpretation of the model and reveal DNA motif grammar such as motif cooperation and distance constraint between motif sites. Previous interpretation approaches are often time-consuming or have difficulty to learn the combinatory rules. In this work, we designed interpretable neural network models to predict the mRNA expression levels from DNA sequences. By applying the Contextual Regression framework we developed, we extracted weighted features to cluster samples into different groups, which have different gene expression levels. We performed motif analysis in each cluster and found motifs with active or repressive regulation on gene expression. By comparing the co-occurrence locations of discovered motifs, we also uncovered multiple grammars of motif combination including communities of cooperative motifs and distance constraints between motif pairs. These results revealed new insights of the regulatory architecture of promoter sequences.

Analysis of the Photogrammetric Use of 360-Degree Cameras in Complex Heritage-Related Scenes: Case of the Necropolis of Qubbet el-Hawa (Aswan Egypt).

This study shows the results of the analysis of the photogrammetric use of 360-degree cameras in complex heritage-related scenes. The goal is to take advantage of the large field of view provided by these sensors and reduce the number of images used to cover the entire scene compared to those needed using conventional cameras. We also try to minimize problems derived from camera geometry and lens characteristics. In this regard, we used a multi-sensor camera composed of six fisheye lenses, applying photogrammetric procedures to several funerary structures. The methodology includes the analysis of several types of spherical images obtained using different stitching techniques and the comparison of the results of image orientation processes considering these images and the original fisheye images. Subsequently, we analyze the possible use of the fisheye images to model complex scenes by reducing the use of ground control points, thus minimizing the need to apply surveying techniques to determine their coordinates. In this regard, we applied distance constraints based on a previous extrinsic calibration of the camera, obtaining results similar to those obtained using a traditional schema based on points. The results have allowed us to determine the advantages and disadvantages of each type of image and configuration, providing several recommendations regarding their use in complex scenes.

The Role of Facebook in Shaping Voting Behavior of Youth: Perspective of a Developing Country

This paper explores Facebook’s role in shaping the political perception of youth during the 2018 general elections in Pakistan. It also gauges the ideological standpoint of youth in Pakistan that influences their voting decisions. An online campaign has become an indispensable source for politicians to influence the masses without the constraints of distance and time. It is a quantitative study based on a questionnaire survey of young Pakistani voters. Findings show that Facebook has presumed the role of an instigator in shaping the voting behavior of educated youth. Respondents perceived Facebook as biased while expressing views and assisted political entities in manipulating the data to increase their vote bank. Though the majority of them haven’t experienced fake news so either they are not conscious or they trust blindly the information. Furthermore, respondents’ cognitive abilities are not obstructed by politicians using Facebook, instead, friends and family network seems more persuasive in determining voters’ choice. Results manifest that ideological standpoint impacts voters’ decisions substantially. There is no statistical difference in the voting behavior of respondents based on gender and/or age. The individuality of this study is that it focuses on the medium of communication between politicians and youth instead of emphasizing the social cleavages and their impact on voters’ decisions, which Pakistani scholars largely study. This study will prompt the field of psephology in Pakistan, which is largely ignored, and help scholars to move out of the domain of identity politics.

The students’ perceptions of their online learning

This study intends to determine the perceptions of the in-service Teacher profession education program (PPG) students towards Indonesian network learning system (SPADA) hybrid learning. This study employed a survey method with a qualitative approach. Subjects in this study were 82 of 87 PPG Daljab 1 Batch 1 students. The technique of collecting data was through the instruments of questionnaires and interviews. The questionnaire was used to obtain data about the quality of online content and the assessment of online learning that has been carried out. Interviews were conducted to confirm the results of the questionnaire data to see the compatibility between the two data. Data analysis techniques in this study were conducted qualitatively and quantitatively by considering to the research objectives. The results showed that online learning had sharp quality content, can be accepted and well understood, easy to operate and use, overcomes distance and time constraints, and has benefits that are in line with the objectives of PPG implementation.

Intraseasonal movements between staging sites by migrating great knots: Longer distances to alternatives decrease the probability of such moves

Distance is a key constraint for animals in moving between suitable habitats, but is this also the case in staging long-distance migrating shorebirds that habitually cover thousands of kilometers during migrations? We conducted multi-year field observations, benthic prey sampling and satellite tracking, to compare how endangered great knots Calidris tenuirostris respond to the food shortage at two similarly functioning staging sites (Gaizhou and Beijingzi) in the northern Yellow Sea, China. Food availability declined by >95 % at both sites across the study period, with the intake rates of great knots declining by 87 %. However, whereas the number of great knots declined by 91 % at Gaizhou, only a 29 % decrease was seen at Beiijngzi. Satellite tracking showed that during the time when food was poor in Gaizhou, tagged great knots crossed 20 km to suitable alternative high-quality sites where food was not scarce. From Beijingzi, tagged great knots flew at least 124 km to find a good alternative. We show that longer distances to alternative sites decreased the probability of a bird leaving. Thus, habitat degradation in staging sites induced great knots to move to alternative sites, but only if such alternatives were relatively close. As staging habitats become more isolated, the negative effects of habitat degradation will be more serious due to a distance constraint on exploratory movements. This emphasizes the importance of maintaining networks of nearby high-quality refueling sites for migratory birds to provide buffers in seasons when local food conditions are lean.

Contrasting packing modes for tubular assemblies in chlorosomes

The largest light-harvesting antenna in nature, the chlorosome, is a heterogeneous helical BChl self-assembly that has evolved in green bacteria to harvest light for performing photosynthesis in low-light environments. Guided by NMR chemical shifts and distance constraints for Chlorobaculum tepidum wild-type chlorosomes, the two contrasting packing modes for syn-anti parallel stacks of BChl c to form polar 2D arrays, with dipole moments adding up, are explored. Layered assemblies were optimized using local orbital density functional and plane wave pseudopotential methods. The packing mode with the lowest energy contains syn-anti and anti-syn H-bonding between stacks. It can accommodate R and S epimers, and side chain variability. For this packing, a match with the available EM data on the subunit axial repeat and optical data is obtained with multiple concentric cylinders for a rolling vector with the stacks running at an angle of 21° to the cylinder axis and with the BChl dipole moments running at an angle ß ∼ 55° to the tube axis, in accordance with optical data. A packing mode involving alternating syn and anti parallel stacks that is at variance with EM appears higher in energy. A weak cross-peak at -6 ppm in the MAS NMR with 50 kHz spinning, assigned to C-181, matches the shift of antiparallel dimers, which possibly reflects a minor impurity-type fraction in the self-assembled BChl c.

Norm Tweaking: High-Performance Low-Bit Quantization of Large Language Models

As the size of large language models (LLMs) continues to grow, model compression without sacrificing accuracy has become a crucial challenge for deployment. While some quantization methods, such as GPTQ, have made progress in achieving acceptable 4-bit weight-only quantization, attempts at lower-bit quantization often result in severe performance degradation. In this paper, we introduce a technique called norm tweaking, which can be used as a plugin in current PTQ methods to achieve high precision while being cost-efficient. Our approach is inspired by the observation that rectifying the quantized activation distribution to match its float counterpart can readily restore accuracy for LLMs. To achieve this, we carefully design a tweaking strategy that includes calibration data generation and channel-wise distance constraint to update the weights of normalization layers for better generalization. We conduct extensive experiments on various datasets using several open-sourced LLMs. Our method demonstrates significant improvements in both weight-only quantization and joint quantization of weights and activations, surpassing existing PTQ methods. On GLM-130B and OPT-66B, our method even achieves the same level of accuracy at 2-bit quantization as their float ones. Our simple and effective approach makes it more practical for real-world applications.

Fair Representation Learning with Maximum Mean Discrepancy Distance Constraint (Student Abstract)

Unsupervised learning methods such as principal component analysis (PCA), t-distributed stochastic neighbor embedding (t-SNE), and autoencoding are regularly used in dimensionality reduction within the statistical learning scene. However, despite a pivot toward fairness and explainability in machine learning over the past few years, there have been few rigorous attempts toward a generalized framework of fair and explainable representation learning. Our paper explores the possibility of such a framework that leverages maximum mean discrepancy to remove information derived from a protected class from generated representations. For the optimization, we introduce a binary search component to optimize the Lagrangian coefficients. We present rigorous mathematical analysis and experimental results of our framework applied to t-SNE.

An adaptive fault detection model based on variational auto-encoders and unsupervised transfer learning

Aiming at the problem of insufficient generalization of fault detection in traditional machine learning, an SDN controller fault detection method based on unsupervised transfer learning is proposed. The method mainly includes two parts. (1) A Gaussian mixture variational autoencoder based on the autoregressive flow is proposed. First, the encoder and decoder of variational autocoding are improved with gated recurrent units, and the improved variational autocoding can process time series data. Secondly, the gated recurrent unit is improved by using the gravitational search algorithm, which speeds up the search of the weight of the gated recurrent unit. Further, considering that the latent space of the variational autoencoder is a single Gaussian distribution, and the complex data in reality is often too simple to be represented by a single Gaussian distribution. (2) Aiming at the problem of poor generalization of fault detection models in practical scenarios, a domain adaptive fault detection algorithm based on multi-kernel maximum mean difference and intra-class distance constraints is proposed. Map the features into the manifold space to eliminate the distortion of the features in the original space. After mapping, the distance between fields needs to be measured, and the maximum mean difference of a single kernel cannot determine which kernel function is more suitable for the current task in practical applications. Therefore, the maximum mean difference based on multi-core is introduced to measure between the two fields. The experimental results show that the algorithm proposed improves the accuracy about 5% compared with the previous algorithm.

Point Cloud Registration Method Based on Geometric Constraint and Transformation Evaluation.

Existing point-to-point registration methods often suffer from inaccuracies caused by erroneous matches and noisy correspondences, leading to significant decreases in registration accuracy and efficiency. To address these challenges, this paper presents a new coarse registration method based on a geometric constraint and a matrix evaluation. Compared to traditional registration methods that require a minimum of three correspondences to complete the registration, the proposed method only requires two correspondences to generate a transformation matrix. Additionally, by using geometric constraints to select out high-quality correspondences and evaluating the matrix, we greatly increase the likelihood of finding the optimal result. In the proposed method, we first employ a combination of descriptors and keypoint detection techniques to generate initial correspondences. Next, we utilize the nearest neighbor similarity ratio (NNSR) to select high-quality correspondences. Subsequently, we evaluate the quality of these correspondences using rigidity constraints and salient points' distance constraints, favoring higher-scoring correspondences. For each selected correspondence pair, we compute the rotation and translation matrix based on their centroids and local reference frames. With the transformation matrices of the source and target point clouds known, we deduce the transformation matrix of the source point cloud in reverse. To identify the best-transformed point cloud, we propose an evaluation method based on the overlap ratio and inliers points. Through parameter experiments, we investigate the performance of the proposed method under various parameter settings. By conducting comparative experiments, we verified that the proposed method's geometric constraints, evaluation methods, and transformation matrix computation consistently outperformed other methods in terms of root mean square error (RMSE) values. Additionally, we validated that our chosen combination for generating initial correspondences outperforms other descriptor and keypoint detection combinations in terms of the registration result accuracy. Furthermore, we compared our method with several feature-matching registration methods, and the results demonstrate the superior accuracy of our approach. Ultimately, by testing the proposed method on various types of point cloud datasets, we convincingly established its effectiveness. Based on the evaluation and selection of correspondences and the registration result's quality, our proposed method offers a solution with fewer iterations and higher accuracy.