Classical Localization Research Articles

The easternmost occurrence of the Late Miocene schizotheriine chalicothere Ancylotherium pentelicum at the classical locality of Maragheh (Iran)

The renowned Late Miocene locality of Maragheh (northwest Iran) has yielded ample material of chalicotheres, most of which has never been described in detail. The present study concerns the taxonomic evaluation of this material and its attribution to the large schizotheriine Ancylotherium pentelicum. This allows a detailed comparison to schizotheriine material from other localities, and based on this, we provide an overview of the diagnostic features of Ancylotherium that separate it from all other chalicotheres. This improves our understanding of the type species A. pentelicum. Additionally, we discuss the palaeobiogeography of the species, which is best known from the Balkan Peninsula. Maragheh marks the easternmost occurrence of A. pentelicum, but its presence has even been suggested in Africa. Lastly, we discuss the existence of sexual size dimorphism in the species, based on the herein described material along with previously described specimens of A. pentelicum from other fossil sites, such as the famous Late Miocene localities Pikermi and Samos (Greece). Specifically, it is shown that many postcranial elements exhibit great size variability that is most probably associated with significant sexual dimorphism.

An accurate tidal peak localization method in radial arterial pulse signals based on hybrid neural networks

Background: cardiovascular diseases (CVDs) have become the leading causes of death worldwide. Arterial stiffness and elasticity are important indicators of cardiovascular health. Pulse wave analysis (PWA) is essential for analyzing arterial stiffness and elasticity, which are highly dependent on the tidal peak (P 2). P 2 is one of the four key physiological points, which also include percussion peaks (P 1), diastolic notches (P 3), and diastolic peaks (P 4). P 1, P 3, and P 4 are often local maxima or minima, facilitating their identification via the second derivatives method, a classic localization method for key physiological points. Classic methods such as the second derivative method, Empirical Mode Decomposition (EMD), and Wavelet Transform (WT), have been employed for the extraction and analysis of the P 2. Due to individual variation and arterial stiffness, locating the P 2 using classic methods is particularly challenging. Methods: we propose a hybrid neural network based on Residual Networks (ResNet) and bidirectional Long Short-Term Memory Networks (Bi-LSTM), successfully achieving high-precision localization of the P 2 in radial artery pulse signals. Meanwhile, we compared our method with the second derivative method, EMD, WT, Convolutional Neural Networks (CNN) and the hybrid model with ResNet and LSTM. Results: the results indicate that our proposed model exhibits significantly higher accuracy compared to other algorithms. Overall, MAEs and RMSEs for our proposed method are 62.60% and 58.84% on average less than those for other algorithms. The average R Adj 2 is 29.20% higher. The outcomes of the efficiency evaluation suggest that the hybrid model performs more balancedly without any significant shortcomings, which indicates that the Bi-LSTM structure upgrades the performances of LSTM. Significance: our hybrid model can provide the medical field with improved diagnostic tools and promote the development of clinical practice and research.

An empirical study of fault localization in Python programs

Despite its massive popularity as a programming language, especially in novel domains like data science programs, there is comparatively little research about fault localization that targets Python. Even though it is plausible that several findings about programming languages like C/C++ and Java—the most common choices for fault localization research—carry over to other languages, whether the dynamic nature of Python and how the language is used in practice affect the capabilities of classic fault localization approaches remain open questions to investigate. This paper is the first multi-family large-scale empirical study of fault localization on real-world Python programs and faults. Using Zou et al.’s recent large-scale empirical study of fault localization in Java (Zou et al. 2021) as the basis of our study, we investigated the effectiveness (i.e., localization accuracy), efficiency (i.e., runtime performance), and other features (e.g., different entity granularities) of seven well-known fault-localization techniques in four families (spectrum-based, mutation-based, predicate switching, and stack-trace based) on 135 faults from 13 open-source Python projects from the BugsInPy curated collection (Widyasari et al. 2020). The results replicate for Python several results known about Java, and shed light on whether Python’s peculiarities affect the capabilities of fault localization. The replication package that accompanies this paper includes detailed data about our experiments, as well as the tool FauxPy that we implemented to conduct the study.

Classical gravitational anomalies of Liouville theory

We show that for classical Liouville field theory, diffeomorphism invariance, Weyl invariance and locality cannot hold together. This is due to a genuine Virasoro center, present in the theory, that leads to an energy-momentum tensor with non-tensorial conformal transformations, in flat space, and with a non-vanishing trace, in curved space. Our focus is on a field-independent term, proportional to the square of the Weyl gauge field, WμWμ, that makes the action Weyl-invariant and was disregarded in previous investigations of Weyl and conformal symmetry. We show this term to be related to the classical center of the Virasoro algebra. The mechanism uncovered here is a classical version of the quantum anomalous phenomenon: the generalization to curved space only allows to keep one of the two symmetries enjoyed by the flat space theory, either Lorentz (diffeomorphism) or conformal invariance.

DSOMF: A Dynamic Environment Simultaneous Localization and Mapping Technique Based on Machine Learning.

To address the challenges of reduced localization accuracy and incomplete map construction demonstrated using classical semantic simultaneous localization and mapping (SLAM) algorithms in dynamic environments, this study introduces a dynamic scene SLAM technique that builds upon direct sparse odometry (DSO) and incorporates instance segmentation and video completion algorithms. While prioritizing the algorithm's real-time performance, we leverage the rapid matching capabilities of Direct Sparse Odometry (DSO) to link identical dynamic objects in consecutive frames. This association is achieved through merging semantic and geometric data, thereby enhancing the matching accuracy during image tracking through the inclusion of semantic probability. Furthermore, we incorporate a loop closure module based on video inpainting algorithms into our mapping thread. This allows our algorithm to rely on the completed static background for loop closure detection, further enhancing the localization accuracy of our algorithm. The efficacy of this approach is validated using the TUM and KITTI public datasets and the unmanned platform experiment. Experimental results show that, in various dynamic scenes, our method achieves an improvement exceeding 85% in terms of localization accuracy compared with the DSO system.

EOCENE STALKED CRINOIDS IN THE GENUS <em>ISSELICRINUS</em> (ECHINODERMATA, CRINOIDEA, ISOCRINIDA) FROM NORTHEASTERN ITALY

Long fragments of isocrinid stalks belonging to the stalked crinoid genus Isselicrinus (Isocrinida, Isselicrininae) were found at Cambrigar, near Ferrara di Monte Baldo (Verona province, northeastern Italy) in the strata called “Harpactocarcinus punctulatus and Pentacrinus diaboli horizon”, traditionally ascribed to the lower Eocene (Ypresian). Morphometric analysis of this material allowed us to identify two distinct species filtering their food at different levels of the water column. The first species, characterized by a mostly pentagonal stalk section, is ascribable to Isselicrinus diaboli (Bayan), a taxon frequently cited from the lower Eocene of northeastern Italy but until now poorly known. The other species has a marked star-shaped stalk section presenting a combination of pedomorphic characters and is ascribed to the new taxon Isselicrinus baldoensis n. sp. A detailed analysis of numerous columnals and pluricolumnals of Isselicrinus diaboli from the type-locality of San Pancrazio near Mossano (Berici Hills, Vicenza province, northeastern Italy) allowed us to propose a revised and more complete diagnosis of this taxon. The particular burial conditions and the ecology of these two species are discussed as well as a reappraisal of the age of the “Harpactocarcinus punctulatus and Pentacrinus diaboli horizon”. Based on new calcareous nannofossil data, and confirmed by the larger foraminiferal assemblages, the isocrinids from Cambrigar are firmly assigned to the Lutetian (middle Eocene). Calcareous nannofossils identified in the matrix of historical hand specimens from the classical locality of San Pancrazio indicate a similar age.

RSSI-based fingerprint localization in LoRaWAN networks using CNNs with squeeze and excitation blocks

The ability to offer long-range, high scalability, sustainability, and low-power wireless communication, are the key factors driving the rapid adoption of the LoRaWAN technology in large-scale Internet of Things applications. This situation has created high demand to incorporate location estimation capabilities into large-scale IoT applications to meaningfully interpret physical measurements collected from IoT devices. As a result, research aimed at investigating node localization in LoRaWAN networks is on the rise. The poor localization performance of classical range-based localization approaches in LoRaWAN networks is due to the long-range nature of LoRaWAN and the rich scattering nature of outdoor environments, which affects signal transmission. Because of the ability of fingerprint-based localization methods to effectively learn useful positional information even from noisy RSSI data, this work proposes a fingerprinting-based branched convolutional neural network (CNN) localization method enhanced with squeeze and excitation (SE) blocks to localize a node in LoRaWAN using RSSI data. Results from the experiments conducted to evaluate the performance of the proposed method using a publicly available LoRaWAN dataset prove its effectiveness and robustness in localizing a node with satisfactory results even with a 30% reduction in both the principal component analysis (PCA) variances on the training data and the size of the original sample. A localization accuracy of 284.57 m mean error on the test area was achieved using the Powed data representation, which represents an 8.39% increase in localization accuracy compared to the currently best-performing fingerprint method in the literature, evaluated using the same LoRaWAN dataset.

Rejuvenating classical brain electrophysiology source localization methods with spatial graph Fourier filters for source extents estimation

EEG/MEG source imaging (ESI) aims to find the underlying brain sources to explain the observed EEG or MEG measurement. Multiple classical approaches have been proposed to solve the ESI problem based on different neurophysiological assumptions. To support clinical decision-making, it is important to estimate not only the exact location of the source signal but also the extended source activation regions. Existing methods may render over-diffuse or sparse solutions, which limit the source extent estimation accuracy. In this work, we leverage the graph structures defined in the 3D mesh of the brain and the spatial graph Fourier transform (GFT) to decompose the spatial graph structure into sub-spaces of low-, medium-, and high-frequency basis. We propose to use the low-frequency basis of spatial graph filters to approximate the extended areas of brain activation and embed the GFT into the classical ESI methods. We validated the classical source localization methods with the corresponding improved version using GFT in both synthetic data and real data. We found the proposed method can effectively reconstruct focal source patterns and significantly improve the performance compared to the classical algorithms.

Multisource Sparse Inversion Localization with Long-Distance Mobile Sensors

To address the threat posed by unknown signal sources within Mobile Crowd Sensing (MCS) systems to system stability and to realize effective localization of unknown sources in long-distance scenarios, this paper proposes a unilateral branch ratio decision algorithm (UBRD). This approach addresses the inadequacies of traditional sparse localization algorithms in long-distance positioning by introducing a time–frequency domain composite block sparse localization model. Given the complexity of localizing unknown sources, a unilateral branch ratio decision scheme is devised. This scheme derives decision thresholds through the statistical characteristics of branch residual ratios, enabling adaptive control over iterative processes and facilitating multisource localization under conditions of remote blind sparsity. Simulation results indicate that the proposed model and algorithm, compared to classic sparse localization schemes, are more suitable for long-distance localization scenarios, demonstrating robust performance in complex situations like blind sparsity, thereby offering broader scenario adaptability.

A functional and structural comparative analysis of large tumor antigens reveals evolution of different importin α-dependent nuclear localization signals.

Nucleocytoplasmic transport regulates the passage of proteins between the nucleus and cytoplasm. In the best characterized pathway, importin (IMP) α bridges cargoes bearing basic, classical nuclear localization signals (cNLSs) to IMPβ1, which mediates transport through the nuclear pore complex. IMPα recognizes three types of cNLSs via two binding sites: the major binding site accommodates monopartite cNLSs, the minor binding site recognizes atypical cNLSs, while bipartite cNLSs simultaneously interact with both major and minor sites. Despite the growing knowledge regarding IMPα-cNLS interactions, our understanding of the evolution of cNLSs is limited. We combined bioinformatic, biochemical, functional, and structural approaches to study this phenomenon, using polyomaviruses (PyVs) large tumor antigens (LTAs) as a model. We characterized functional cNLSs from all human (H)PyV LTAs, located between the LXCXE motif and origin binding domain. Surprisingly, the prototypical SV40 monopartite NLS is not well conserved; HPyV LTA NLSs are extremely heterogenous in terms of structural organization, IMPα isoform binding, and nuclear targeting abilities, thus influencing the nuclear accumulation properties of full-length proteins. While several LTAs possess bipartite cNLSs, merkel cell PyV contains a hybrid bipartite cNLS whose upstream stretch of basic amino acids can function as an atypical cNLS, specifically binding to the IMPα minor site upon deletion of the downstream amino acids after viral integration in the host genome. Therefore, duplication of a monopartite cNLS and subsequent accumulation of point mutations, optimizing interaction with distinct IMPα binding sites, led to the evolution of bipartite and atypical NLSs binding at the minor site.

Analysis of the effects of importin α1 on the nuclear translocation of IL-1α in HeLa cells

Interleukin-1α (IL-1α), a cytokine released by necrotic cells, causes sterile inflammation. On the other hand, IL-1α is present in the nucleus and also regulates the expression of many proteins. A protein substrate containing a classical nuclear localization signal (cNLS) typically forms a substrate/importin α/β complex, which is subsequently transported to the nucleus. To the best of our knowledge, no study has directly investigated whether IL-1α—which includes cNLS—is imported into the nucleus in an importin α/β-dependent manner. In this study, we noted that all detected importin α subtypes interacted with IL-1α. In HeLa cells, importin α1-mediated nuclear translocation of IL-1α occurred at steady state and was independent of importin β1. Importin α1 not only was engaged in IL-1α nuclear transport but also concurrently functioned as a molecule that regulated IL-1α protein level in the cell. Furthermore, we discussed the underlying mechanism of IL-1α nuclear translocation by importin α1 based on our findings.

Dr John Grant Malcolmson and a reconciliation of the Middle Devonian Lethen Bar and Lethen House fish-bearing nodule localities, with notes on the Middle Devonian nodule beds of the Moray Firth area

The area known as Lethen Bar and Clune, SE of Nairn, Scotland, is a classic Middle Devonian locality, which has yielded nodules or concretions, some of which contain fossil fishes with the highest quality of preservation. The locality was largely centred on farm limestone quarries situated around the perimeter of an isolated outlier of the main fishbed. It was first described in the 19th century, although the upsurge in collecting fossil fishes only occurred some 12 years after the quarries were first mentioned in the scientific literature. Our knowledge of the provenance of the locality is based on very limited accounts, which have never been scientifically tested; these accounts also contain apparent anomalies that have never been adequately addressed. Based on these anomalies, the author of a paper published in 1983 proposed that the locality had been untraceable since the late 19th century and that the outcrop was quarried out. In 2005, the present authors recorded the first scientifically detailed stratigraphical section of the fishbed, followed in 2021–23 by detailed field surveys and by a reappraisal of the literature. This work has resolved the 19th century anomalies and allowed us to confirm the locations of old quarries, to give affirmation to two previously unrecognized sites and to show that the outcrop is still present. Thematic collection: This article is part of the Geology of Scotland collection available at: https://www.lyellcollection.org/topic/collections/geology-of-scotland

Perception and Navigation in Autonomous Systems in the Era of Learning: A Survey.

Autonomous systems possess the features of inferring their own state, understanding their surroundings, and performing autonomous navigation. With the applications of learning systems, like deep learning and reinforcement learning, the visual-based self-state estimation, environment perception, and navigation capabilities of autonomous systems have been efficiently addressed, and many new learning-based algorithms have surfaced with respect to autonomous visual perception and navigation. In this review, we focus on the applications of learning-based monocular approaches in ego-motion perception, environment perception, and navigation in autonomous systems, which is different from previous reviews that discussed traditional methods. First, we delineate the shortcomings of existing classical visual simultaneous localization and mapping (vSLAM) solutions, which demonstrate the necessity to integrate deep learning techniques. Second, we review the visual-based environmental perception and understanding methods based on deep learning, including deep learning-based monocular depth estimation, monocular ego-motion prediction, image enhancement, object detection, semantic segmentation, and their combinations with traditional vSLAM frameworks. Then, we focus on the visual navigation based on learning systems, mainly including reinforcement learning and deep reinforcement learning. Finally, we examine several challenges and promising directions discussed and concluded in related research of learning systems in the era of computer science and robotics.

Quantitative analysis of local plasticity accompanying hydrogen-related fracture in low-carbon martensitic steel

In this study, the local plasticity accompanying hydrogen-related fracture in a low-carbon martensitic steel was quantitatively investigated by correlative microscopy analysis combining digital image correlation technique and electron backscatter diffraction. The results revealed that strain localization occurred mainly in the bulk of martensite blocks and at prior austenite grain boundaries, both with and without the presence of hydrogen. Moreover, hydrogen enhanced strain localization during deformation and facilitated crack nucleation and propagation. The quantitative analysis showed that the hydrogen-related quasi-cleavage cracking was closely related to the local plastic deformation, and increasing hydrogen content decreased the local strain level required for quasi-cleavage cracking. It was found that strain localization also occurred around the intergranular cracks, suggesting that hydrogen-induced intergranular cracking was not a classical simple decohesion process and strain localization was involved in the sequence of hydrogen-induced intergranular cracking. These findings shed light on the understanding of the intrinsic nature of hydrogen embrittlement.

Exploiting the endogenous yeast nuclear proteome to identify short linear motifs in vivo

Peptide-domain interactions mediated by short linear motifs (SLiMs) play crucial roles in cellular biology. The simplicity of SLiMs poses challenges in their computational identification. Existing high-throughput methods for discovering SLiMs lack cellular context as they are typically performed invitro. We developed a functional selection method using yeast to identify peptides that interact with the endogenous yeast nuclear proteome. Remarkably, peptides selected for in yeast also mediated nuclear import in human cells. Notably, the identified peptides did not resemble classical nuclear localization sequences. This platform has the potential to identify and investigate motifs that interact with the nuclear proteome of yeast and human and to aid in the identification and understanding of alternative protein nuclear import mechanisms.

Nuclear Import and Export of YAP and TAZ.

Yes-associated Protein (YAP) and its paralog Transcriptional Coactivator with PDZ-binding Motif (TAZ) are major regulators of gene transcription/expression, primarily controlled by the Hippo pathway and the cytoskeleton. Integrating an array of chemical and mechanical signals, they impact growth, differentiation, and regeneration. Accordingly, they also play key roles in tumorigenesis and metastasis formation. Their activity is primarily regulated by their localization, that is, Hippo pathway- and/or cytoskeleton-controlled cytosolic or nuclear sequestration. While many details of such prevailing retention models have been elucidated, much less is known about their actual nuclear traffic: import and export. Although their size is not far from the cutoff for passive diffusion through the nuclear pore complex (NPC), and they do not contain any classic nuclear localization (NLS) or nuclear export signal (NES), evidence has been accumulating that their shuttling involves mediated and thus regulatable/targetable processes. The aim of this review is to summarize emerging information/concepts about their nucleocytoplasmic shuttling, encompassing the relevant structural requirements (NLS, NES), nuclear transport receptors (NTRs, karyophererins), and NPC components, along with the potential transport mechanisms and their regulation. While dissecting retention vs. transport is often challenging, the emerging picture suggests that YAP/TAZ shuttles across the NPC via multiple, non-exclusive, mediated mechanisms, constituting a novel and intriguing facet of YAP/TAZ biology.

Analysis of improved particle swarm algorithm in wireless sensor network localization

Abstract Among the various WSN technologies, node localization technology is one of the core parts of WSN applications and an important component and key technology, which is also a hot spot and focus of research at this stage. In this paper, firstly, we propose a classical ion swarm localization algorithm for wireless sensor network localization, randomly assigning appropriate velocity and position to each particle in the population in order to find the global optimum in the iterative process. Based on this, a network node localization model is established to convert the functional optimization problem with constraints into an unconstrained optimization problem to solve. At the same time, the space is searched using a chaotic search strategy, which greatly improves the search efficiency. Next, the particle swarm algorithm is further optimized, and simulation experiments are set up using MATLAB software to do comparison experiments on the PSOAPF algorithm and other algorithms. The experimental results show that when the density of beacon nodes is 40%, the average localization error of the PSOAPF algorithm is 14.26%, with the smoothest decreasing trend. When the percentage of anchor nodes is 10%, the localization error is reduced by 6.89%, and the localization accuracy of the PSOAPF algorithm is also higher than other models. This study shows that the improved particle swarm algorithm can effectively improve the localization accuracy, reduce error and accelerate the convergence speed in wireless sensing network localization.

Efficient Localization Method Based on RSSI for AP Clusters.

The localization accuracy is susceptible to the received signal strength indication (RSSI) fluctuations for RSSI-based wireless localization methods. Moreover, the maximum likelihood estimation (MLE) of the target location is nonconvex, and locating target presents a significant computational complexity. In this paper, an RSSI-based access point cluster localization (APCL) method is proposed for locating a moving target. Multiple location-constrained access points (APs) are used in the APCL method to form an AP cluster as an anchor node (AN) in the wireless sensor network (WSN), and the RSSI of the target is estimated with several RSSI samples obtained by the AN. With the estimated RSSI for each AN, the solution for the target location can be obtained quickly and accurately due to the fact that the MLE localization problem is transformed into an eigenvalue problem by constructing an eigenvalue equation. Simulation and experimental results show that the APCL method can meet the requirement of high-precision real-time localization of moving targets in WSN with higher localization accuracy and lower computational effort compared to the existing classical RSSI-based localization methods.

Dual input neural networks for positional sound source localization

In many signal processing applications, metadata may be advantageously used in conjunction with a high dimensional signal to produce a desired output. In the case of classical Sound Source Localization (SSL) algorithms, information from a high dimensional, multichannel audio signals received by many distributed microphones is combined with information describing acoustic properties of the scene, such as the microphones’ coordinates in space, to estimate the position of a sound source. We introduce Dual Input Neural Networks (DI-NNs) as a simple and effective way to model these two data types in a neural network. We train and evaluate our proposed DI-NN on scenarios of varying difficulty and realism and compare it against an alternative architecture, a classical Least-Squares (LS) method as well as a classical Convolutional Recurrent Neural Network (CRNN). Our results show that the DI-NN significantly outperforms the baselines, achieving a five times lower localization error than the LS method and two times lower than the CRNN in a test dataset of real recordings.

Bayesian Filtering for Joint Multi-User Positioning, Synchronization and Anchor State Calibration

Millimeter (mmWave) positioning can go beyond classical localization, allowing to extract more complete situational awareness in terms of, e.g, clock offsets, antenna orientations or landmark locations. In this article, we formulate an extended Kalman filtering (EKF)-based framework called MU-PoSAC ( <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</u> ulti- <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">U</u> ser <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Po</u> sitioning, <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u> ynchronization and <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u> nchor State <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</u> alibration), that allows to jointly estimate and track the locations and clock offsets of multiple users together with the unknown locations and orientation offsets of the anchors, building on angle-of-arrival (AoA) and time-of-arrival (ToA) measurements. We provide an extensive set of numerical results in the context of mmWave 5 G New Radio (NR) deployment in an industrial facility with moving robots and other industrial vehicles, incorporating full-scale ray-tracing for accurate propagation modeling as well as actual uplink reference signal based AoA and ToA estimators. Our numerical results show that estimating and tracking the overall system state is feasible, and that a single reference anchor can further enhance the estimation accuracy. In addition, more users are shown to lead to better performance, due to the beneficial coupling of the anchor state. Therefore, our study demonstrates that in order to maximize the estimation performance, it is desirable to have at least one anchor state precisely known, and to have multiple users in the system. Finally, the important practical aspect of Line-of-Sight (LoS) blockage is addressed. It is shown that in the considered industrial use case, the proposed MU-PoSAC framework can offer robustness against intermittent LoS blockage.