Candidate Positions Research Articles

Distinguishing exoplanet companions from field stars in direct imaging using Gaia astrometry

Direct imaging searches for exoplanets around stars detect many spurious candidates that are in fact background field stars. To help distinguish these from genuine companions, multi-epoch astrometry can be used to identify a common proper motion with the host star. Although this is frequently done, many approaches lack an appropriate model for the motions of the background population, or do not use a statistical framework to properly quantify the results. For this study we used Gaia astrometry combined with 2MASS photometry to model the parallax and proper motion distributions of field stars around exoplanet host stars as a function of candidate magnitude. We developed a likelihood-based method that compares the positions of a candidate at multiple epochs with the positions expected under both this field star model and a co-moving companion model. Our method propagates the covariances in the Gaia astrometry and the candidate positions. True companions are assumed to have long periods compared to the observational baseline, so we currently neglect orbital motion. We applied our method to a sample of 23 host stars with 263 candidates identified in the B-Star Exoplanet Abundance Study (BEAST) survey on VLT/SPHERE. We identified seven candidates in which the odds ratio favours the co-moving companion model by a factor of 100 or more. Most of these detections are based on only two or three epochs separated by less than three years, so further epochs should be obtained to reassess the companion probabilities. Our method is publicly available as an open-source python package from 1em

Teaching Translanguaging in an English-Only Teacher-Preparation Program: Perspectives and Positions of Teacher Candidates

ABSTRACT Translanguaging as a pedagogical approach has disrupted traditional ways of teaching multilingual students. As such, it is common that translanguaging pedagogies are received with resistance in mainstream education, by both pre- and in-service teachers. Using a linguistic ideologies framework coupled with positioning theory and translanguaging, this study explores the perspectives of Mainstream or English-only Teacher Candidates (MTCs) towards translanguaging pedagogies in a mainstream teacher education program. In addition, this research examines how these candidates position themselves as teachers of bilingual and emergent bilingual students. Findings presented frame translanguaging as a tool for MTCs who can create inclusive translanguaging spaces for their multilingual students. In addition, the study describes how MTCs position themselves as translanguaging practitioners who genuinely want to better serve multilingual and emergent bilingual student populations. Implications for teacher educators and teacher preparation programs, from the perspectives of MTCs, are discussed as highly needed curricular material for their courses.

PerFSeeB: designing long high-weight single spaced seeds for full sensitivity alignment with a given number of mismatches

BackgroundTechnical progress in computational hardware allows researchers to use new approaches for sequence alignment problems. For a given sequence, we usually use smaller subsequences (anchors) to find possible candidate positions within a reference sequence. We may create pairs (“position”, “subsequence”) for the reference sequence and keep all such records without compression, even on a budget computer. As sequences for new and reference genomes differ, the goal is to find anchors, so we tolerate differences and keep the number of candidate positions with the same anchors to a minimum. Spaced seeds (masks ignoring symbols at specific locations) are a way to approach the task. An ideal (full sensitivity) spaced seed should enable us to find all such positions subject to a given maximum number of mismatches permitted.ResultsSeveral algorithms to assist seed generation are presented. The first one finds all permitted spaced seeds iteratively. We observe specific patterns for the seeds of the highest weight. There are often periodic seeds with a simple relation between block size, length of the seed and read. The second algorithm produces blocks for periodic seeds for blocks of up to 50 symbols and up to nine mismatches. The third algorithm uses those lists to find spaced seeds for reads of an arbitrary length. Finally, we apply seeds to a real dataset and compare results for other popular seeds.ConclusionsPerFSeeB approach helps to significantly reduce the number of reads’ possible alignment positions for a known number of mismatches. Lists of long, high-weight spaced seeds are available in Additional file 1. The seeds are best in weight compared to seeds from other papers and can usually be applied to shorter reads. Codes for all algorithms and periodic blocks can be found at https://github.com/vtman/PerFSeeB.

Friends with the Good: Moral Relativism and Moral Progress

Abstract The aim of this paper is to defend moral relativism from the accusation that it would make it irrational to classify past changes in public opinion as instances of moral progress, for they would constitute an improvement only from our current point of view. The argument is this. For our assessment of a change in public opinion as an instance of moral progress to be rational, we need to take the moral claims made before the change to be false simpliciter while being open to the possibility that we ourselves change our minds at some point. These two things can be made compatible if we construe moral relativism as taking the truth of moral claims to be relative to the context of assessment. Thus understood, moral relativism is in fact the only view that makes room for talk of moral progress, as the rest of candidate positions make it irrational.

Robust IMU-Based Mitigation of Human Body Shadowing in UWB Indoor Positioning.

Ultra-wideband (UWB) indoor positioning systems have the potential to achieve sub-decimeter-level accuracy. However, the ranging performance degrades significantly under non-line-of-sight (NLoS) conditions. The detection and mitigation of NLoS conditions is a complex problem and has been the subject of many works over the past decades. When localizing pedestrians, human body shadowing (HBS) is a particular and specific cause of NLoS. In this paper, we present an HBS mitigation strategy based on the orientation of the body and tag relative to the UWB anchors. Our HBS mitigation strategy involves a robust range error model that interacts with a tracking algorithm. The model consists of a bank of Gaussian Mixture Models (GMMs), from which an appropriate GMM is selected based on the relative body-tag-anchor orientation. The relative orientation is estimated by means of an inertial measurement unit (IMU) attached to the tag and a candidate position provided by the tracking algorithm. The selected GMM is used as a likelihood function for the tracking algorithm to improve localization accuracy. Our proposed approach was realized for two tracking algorithms. We validated the implemented algorithms on dynamic UWB ranging measurements, which were performed in an industrial lab environment. The proposed algorithms outperform other state-of-the-art algorithms, achieving a 37% reduction of the p75 error.

Dynamic Repositioning of Aerial Base Stations for Enhanced User Experience in 5G and Beyond.

The ultra-dense deployment (UDD) of small cells in 5G and beyond to enhance capacity and data rate is promising, but since user densities continually change, the static deployment of small cells can lead to wastes of capital, the underutilization of resources, and user dissatisfaction. This work proposes the use of Aerial Base Stations (ABSs) wherein small cells are mounted on Unmanned Aerial Vehicles (UAVs), which can be deployed to a set of candidate locations. Furthermore, based on the current user densities, this work studies the optimal placement of the ABSs, at a subset of potential candidate positions, to maximize the total received power and signal-to-interference ratio. The problems of the optimal placement for increasing received power and signal-to-interference ratio are formulated, and optimal placement solutions are designed. The proposed solutions compute the optimal candidate locations for the ABSs based on the current user densities. When the user densities change significantly, the proposed solutions can be re-executed to re-compute the optimal candidate locations for the ABSs, and hence the ABSs can be moved to their new candidate locations. Simulation results show that a 22% or more increase in the total received power can be achieved through the optimal placement of the Aerial BSs and that more than 60% users have more than 80% chance to have their individual received power increased.

IoT Resource Allocation and Optimization Using Improved Reptile Search Algorithm

The Internet of Things (IoT) is a dispersed network system that connects the world through the Internet. The architecture of IoT consists of more gateways and resources which cannot be allocated in a manual process. The allocation of resources in IoT is a challenging process due to the higher consumption of energy and high latency rate. To overcome the challenges in existing works, this research introduced an Improved Reptile Search Algorithm (IRSA) to solve the optimization problem which occurs during the time of allocation resources among IoT networks. IRSA employs the methodology of levy flight and cross-over to update the candidate position and enhance the search speed in a single iteration. The proposed method consumes less energy and has low latency during data transmission from User equipment (UE) to the base station.IRSA has been compared with the existing Scalable Resource Allocation Framework (SRAF) and Improved Chaotic Firefly Algorithm (ICFA). The obtained experimental results show that the proposed IRSA attained better performance with an allocation rate of 96.40% which is comparatively higher than SRAF and ICFA with 92.40% and 91.67% respectively.

CMF-Net: craniomaxillofacial landmark localization on CBCT images using geometric constraint and transformer

Accurate and robust anatomical landmark localization is a mandatory and crucial step in deformation diagnosis and treatment planning for patients with craniomaxillofacial (CMF) malformations. In this paper, we propose a trainable end-to-end cephalometric landmark localization framework on Cone-beam computed tomography (CBCT) scans, referred to as CMF-Net, which combines the appearance with transformers, geometric constraint, and adaptive wing (AWing) loss. More precisely: (1) we decompose the localization task into two branches: the appearance branch integrates transformers for identifying the exact positions of candidates, while the geometric constraint branch at low resolution allows the implicit spatial relationships to be effectively learned on the reduced training data. (2) We use the AWing loss to leverage the difference between the pixel values of the target heatmaps and the automatic prediction heatmaps. We verify our CMF-Net by identifying the 24 most relevant clinical landmarks on 150 dental CBCT scans with complicated scenarios collected from real-world clinics. Comprehensive experiments show that it performs better than the state-of-the-art deep learning methods, with an average localization error of 1.108 mm (the clinically acceptable precision range being 1.5 mm) and a correct landmark detection rate equal to 79.28%. Our CMF-Net is time-efficient and able to locate skull landmarks with high accuracy and significant robustness. This approach could be applied in 3D cephalometric measurement, analysis, and surgical planning.

The waning of ideology? Presidential elections in the Republic of Cyprus, 5 February 2023

ABSTRACT This article analyses the 2023 Presidential Election in the Republic of Cyprus, an unexpectedly close election and victory of Nikos Christodoulides that took place against a backdrop of political distrust and disillusionment towards the political system. There is a discussion of the background context of the elections before focusing on an analysis of the media campaign, candidate positioning, and voter preferences drawing on a variety of data sources to add further insight. The article concludes with the significance of this election for the future of politics in the Republic of Cyprus, where the political transformation and fluidity is manifested towards the declining role of ideology and the preference of the electorate towards the centre of the political map.

Repositioning of Benzodiazepine Drugs and Synergistic Effect with Ciprofloxacin Against ESKAPE Pathogens.

Infectious diseases are among the leading causes of morbidity and mortality worldwide. Combating them becomes more complex when caused by the pathogens of the ESKAPE group, which are Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa and Enterobacter spp. The purpose of this study was to investigate the repositioning potential of the benzodiazepines clonazepam and diazepam individually and in combination with the antibacterial ciprofloxacin against ESKAPE. The minimum inhibitory concentration and minimum bactericidal concentration against seven American Type Culture Collection (ATCC) reference standard strains and 64 ESKAPE clinical isolates were determined. In addition, the interaction with ciprofloxacin was determined by the checkerboard method and fractional inhibitory concentration index (FICI) of clonazepam against 11 ESKAPE and diazepam against five ESKAPE. We also list the results found and their clinical significance. Benzodiazepines showed similar antibacterial activity against Gram-positive and Gram-negative. The checkerboard and FICI results showed a synergistic effect of these drugs when associated with ciprofloxacin against almost all tested isolates. Viewing the clinical cases studied, benzodiazepines have potential as treatment alternatives. The results allow us to conclude that clonazepam and diazepam, when in combination with ciprofloxacin, have promising activity against ESKAPE, therefore, assuming the position of candidates for repositioning.

The politics of depopulation in Japanese municipalities: Ideas and underlying ideologies

ABSTRACT Recent research has tried to understand Japan’s depopulating countryside and regions through the comparative theory of “peripheralization”. Though these studies argue that a critical determinant of peripheralization in depopulating communities is a lack of political agency, few studies systematically investigate the internal and electoral politics of adapting to depopulation. This paper investigates one aspect of this process: how elected officials and candidates position themselves and contest issues concerning depopulation. We start by analyzing the ideas driving politics in two policy areas related to shrinking populations: 1) various measures aimed at attracting young families and people to relocate to the municipality and 2) the closure and merger of public schools. The article maps the key ideas and assumptions driving policies to adapt and combat depopulation. We find two ideas – what we call “populationism” and “listenism” – prevalent in the discourse. In addition, we find contestation about depopulation largely disconnected from broader ideological debates about the role and size of the state, prioritizing community over individual interests, or being more open or closed as a community, etc.

Artificial-Intelligence-Based Charger Deployment in Wireless Rechargeable Sensor Networks

To extend a network’s lifetime, wireless rechargeable sensor networks are promising solutions. Chargers can be deployed to replenish energy for the sensors. However, deployment cost will increase when the number of chargers increases. Many metrics may affect the final policy for charger deployment, such as distance, the power requirement of the sensors and transmission radius, which makes the charger deployment problem very complex and difficult to solve. In this paper, we propose an efficient method for determining the field of interest (FoI) in which to find suitable candidate positions of chargers with lower computational costs. In addition, we designed four metaheuristic algorithms to address the local optima problem. Since we know that metaheuristic algorithms always require more computational costs for escaping local optima, we designed a new framework to reduce the searching space effectively. The simulation results show that the proposed method can achieve the best price–performance ratio.

An improved differential evolution algorithm based on basis vector type and its application in fringe projection 3D imaging

According to the different generation methods, the types of candidate position (basis vector) in the meta-heuristic algorithm can be divided into stochastic, deterministic, and probabilistic. The stochastic type is beneficial to the preservation of population diversity, deterministic model can accelerate convergence, and probabilistic style emphasizes the balance between population diversity and convergence speed. In order to make full use of various characteristics, an improved differential evolution algorithm based on basis vector type (DEBVT) is proposed. On the one hand, in the process of mutation, individuals in the population choose the appropriate basis vector form for evolution in light of their features and needs. On the other hand, the control parameters are adaptively adjusted for the purpose of balancing exploration and exploitation. Based on twenty-nine benchmark functions, DEBVT is compared with several meta-heuristics and differential evolution variants with different types of basis vectors. All experimental results demonstrate that compared with other competitive algorithms, the optimization performance of the proposed DEBVT is remarkable. In addition, an improved UNET network (IUnet) is proposed for absolute phase extraction in fringe projection 3D imaging technology, and the activation function configuration of the network is optimized by DEBVT to construct the IUnet-DEBVT network, which further reduces the error of absolute phase and verifies of the effectiveness of DEBVT.

A Gap-sharing Planet Pair Shaping the Crescent in HD 163296: A Disk Sculpted by a Resonant Chain

The Atacama Large Millimeter Array observations of the disk around HD 163296 have resolved a crescent-shape substructure at around 55 au, inside and off-center from a gap in the dust that extends from 38 to 62 au. In this work we propose that both the crescent and the dust rings are caused by a compact pair (period ratio ≃4:3) of sub-Saturn-mass planets inside the gap, with the crescent corresponding to dust trapped at the L 5 Lagrange point of the outer planet. This interpretation also reproduces well the gap in the gas recently measured from the CO observations, which is shallower than what is expected in a model where the gap is carved by a single planet. Building on previous works arguing for outer planets at ≈86 and ≈137 au, we provide a global model of the disk that best reproduces the data and shows that all four planets may fall into a long resonant chain, with the outer three planets in a 1:2:4 Laplace resonance. We show that this configuration is not only an expected outcome from disk–planet interaction in this system, but it can also help constrain the radial and angular position of the planet candidates using three-body resonances.

The Analytical Method algorithm for trigger primitives generation at the LHC Drift Tubes detector

The Compact Muon Solenoid (CMS) experiment prepares its Phase-2 upgrade for the high-luminosity era of the LHC operation (HL-LHC). Due to the increase of occupancy, trigger latency and rates, the full electronics of the CMS Drift Tube (DT) chambers will need to be replaced. In the new design, the time bin for the digitization of the chamber signals will be of around 1 ns, and the totality of the signals will be forwarded asynchronously to the service cavern at full resolution. The new backend system will be in charge of building the trigger primitives of each chamber. These trigger primitives contain the information at chamber level about the muon candidates position, direction, and collision time, and are used as input in the L1 CMS trigger. The added functionalities will improve the robustness of the system against ageing. An algorithm based on analytical solutions for reconstructing the DT trigger primitives, called Analytical Method, has been implemented both as a software C++ emulator and in firmware. Its performance has been estimated using the software emulator with simulated and real data samples, and through hardware implementation tests. Measured efficiencies are 96 to 98% for all qualities and time and spatial resolutions are close to the ultimate performance of the DT chambers. A prototype chain of the HL-LHC electronics using the Analytical Method for trigger primitive generation has been installed during Long Shutdown 2 of the LHC and operated in CMS cosmic data taking campaigns in 2020 and 2021. Results from this validation step, the so-called Slice Test, are presented.

RNA Modification Detection Using Nanopore Direct RNA Sequencing and nanoDoc2.

RNA modifications regulate multiple aspects of cellular function including RNA splicing, translation, export, decay, stability, and phase separation. One of the comprehensive ways to detect such modifications is by the recent advancement of direct RNA sequencing from Oxford Nanopore Technologies (ONT). However, this method obtains a large amount of data with high complexity in the form of raw current signal that poses a new informatics challenge to accurately detect those modifications. Here, we provide nanoDoc2, a software to detect multiple types of RNA modification from nanopore direct RNA sequencing data. The nanoDoc2 includes a novel signal segmentation algorithm based on the trace value-a base probability feature that is added by the Guppy basecalling program from ONT during processing of the raw signal. The core of nanoDoc2 includes a machine learning algorithm in which a 6-mer segmented raw current signal is analyzed by deep one-class classification using a WaveNet-based neural network. As an output, an RNA modification is detected by a statistical score in each candidate position. Herein, we describe the detailed instructions on how to use nanoDoc2 for signal segmentation, train/test the neural network, and finally predict RNA modifications present in nanopore direct RNA sequencing data.

Perspectively Equivariant Keypoint Learning for Omnidirectional Images.

Robust keypoint detection on omnidirectional images against large perspective variations, is a key problem in many computer vision tasks. In this paper, we propose a perspectively equivariant keypoint learning framework named OmniKL for addressing this problem. Specifically, the framework is composed of a perspective module and a spherical module, each one including a keypoint detector specific to the type of the input image and a shared descriptor providing uniform description for omnidirectional and perspective images. In these detectors, we propose a differentiable candidate position sorting operation for localizing keypoints, which directly sorts the scores of the candidate positions in a differentiable manner and returns the globally top-K keypoints on the image. This approach does not break the differentiability of the two modules, thus they are end-to-end trainable. Moreover, we design a novel training strategy combining the self-supervised and co-supervised methods to train the framework without any labeled data. Extensive experiments on synthetic and real-world 360° image datasets demonstrate the effectiveness of OmniKL in detecting perspectively equivariant keypoints on omnidirectional images. Our source code are available online at https://github.com/vandeppce/sphkpt.

Automatic Labeling Method of Geological Codes Based on Multi-factor Optimization

Automatic labeling of geological codes is an important part of automatic mapping of geological maps. A multi-factor optimization based automatic labeling method is proposed to address the issue of existing polygon feature label placement methods being unable to achieve multi-position placement in geological codes. Firstly, classify geological bodies based on whether they can accommodate geological codes within the area; Subsequently, sufficient candidate positions are obtained for the geological body and a candidate position evaluation method that integrates multiple factors is proposed; Finally, based on the candidate position evaluation method, sorting and particle swarm optimization algorithms are used to achieve single position labeling and multi-position labeling. The simulation experiment compares it with existing polygon feature placement methods from the perspectives of coverage, shape, and method. The method proposed in this paper can automatically placement non-conflicting geological codes for geological bodies of different shapes and areas, and has better performance than existing methods in complex geological bodies.

ПРЕЗИДЕНТСКИЕ И ПАРЛАМЕНТСКИЕ ВЫБОРЫ В КАЗАХСТАНЕ: ИТОГИ И ПОСЛЕДСТВИЯ

The article attempts to review the results of the extraordinary presidential elections held on November 20, 2022 and the extraordinary parliamentary elections held on March 20, 2023 in Kazakhstan. The author analyzes the results of the presidential elections, taking into account the positions of the main candidates who participated in them. He notes the role and importance of the position of the current President of Kazakhstan K.-Zh. Tokayev, who announced the beginning of significant political reforms in the Republic; main provisions of the reforms are presented. The profiles of the leading political parties of Kazakhstan that took part in the parliamentary elections are considered. A comparison of the results of the previous elections of 2021 with the current elections is made, and an analysis of the reasons for the changes in the composition of the Kazakh parliament from the point of view of party representation is given. There is an analysis of the course of the election campaign and the programs with which the parties entered it. The article also reveals reasons for the decline in the results of the leading party of Kazakhstan “Amanat” (formerly “Nur Otan”) and the appearance of the national Social Democratic Party and the new party “Republic” in the Kazakh parliament and makes a forecast on further development of the Kazakhstan political model in the course on its political modernization, declared by K.-Zh. Tokayev at the last presidential elections

A non-dominated sorting based multi-objective neural network algorithm

Neural Network Algorithm (NNA) is a recently proposed Metaheuristic that is inspired by the idea of artificial neural networks. The performance of NNA on single-objective optimization problems is very promising and effective. In this article, a maiden attempt is made to restructure NNA for its possible use to address multi-objective optimization problems. To make NNA suitable for MOPs several fundamental changes in the original NNA are proposed. A novel concept is proposed to initialize the candidate solution, position update, and selection of target solution. To examine the optimization ability of the proposed scheme, it is tested on several benchmark problems and the results are compared with eight state-of-the-art multi-objective optimization algorithms. Inverse generational distance(IGD) and hypervolume (HV) metrics are also calculated to understand the optimization ability of the proposed scheme. The results are statistically validated using Wilcoxon signed rank test. It is observed that the overall optimization ability of the proposed scheme to solve MOPs is very good.•This paper proposes a method to solve multi-objective optimization problems.•A multi-objective Neural Network Algorithm method is proposed.•The proposed method solves difficult multi-objective optimization problems.