Single Instance Research Articles

The Long-Term Patency of the Internal Jugular Vein and the Common Facial Vein as Entrance Sites for Venous Access in Ventriculoatrial Shunts: A Comparative Analysis from a Single-Center Study

The ventriculoatrial (VA) shunt is a surgical intervention used to manage hydrocephalus, although it is less often utilized compared to the ventriculoperitoneal (VP) shunt or endoscopic third ventriculostomy. Placement of the distal catheter typically involves the utilization of either the common facial vein (CFV) or the internal jugular vein (IJV), 2 frequently employed options for venous access. This study aims to determine whether there is a statistically significant difference between the long-term patency (2years) of the distal end of the VA shunt of these 2 options. A retrospective cohort analysis was conducted of patients who received VA shunt surgeries with the employment of the CFV or IJV as access veins at Rajavithi Hospital in Thailand between January 2015 and December 2020. The analysis focused on long-term patency and potential complications. The study comprised a total of 42 participants. Twenty-six (61.9%) individuals underwent ventriculoatrial (VA) shunt surgery via the CFV, while the other 16 (38.1%) underwent the same procedure using the IJV. Neither of the 2 groups required shunt revision due to distal catheter malfunction. Most cases exhibited no significant complications apart from a single instance of shunt system infection. In VA shunt surgery, both the CFV and IJV can be used as venous access sites for the right atrium because there is no discernible difference between their complications or long-term patency. Anatomical considerations, patient-specific characteristics, and the surgeon's preference should all be considered when choosing the venous access location for the placement of a VA shunt.

QuantumBound – Interactive protein generation with one-shot learning and hybrid quantum neural networks

This paper presents a new approach for protein generation based on one-shot learning and hybrid quantum neural networks. Given a single protein complex, the system learns how to predict the remaining unknown properties, without resorting to autoregression, from the physicochemical properties of the receptor and a prior on the physicochemical properties of the ligand. In contrast with other approaches, QuantumBound learns from a single instance, not from a large dataset, as is common in deep learning. By knowing half of the properties of the ligand, the system can predict the remaining half with an average relative error of 1.43% for a dataset consisting of one hundred and twenty Covid-19 spikes complexes. To the best of our knowledge, this is the first time that one-shot learning and hybrid quantum computing have been applied to protein generation.

Chemical complexity challenge: Is multi‐instance machine learning a solution?

AbstractMolecules are complex dynamic objects that can exist in different molecular forms (conformations, tautomers, stereoisomers, protonation states, etc.) and often it is not known which molecular form is responsible for observed physicochemical and biological properties of a given molecule. This raises the problem of the selection of the correct molecular form for machine learning modeling of target properties. The same problem is common to biological molecules (RNA, DNA, proteins)—long sequences where only key segments, which often cannot be located precisely, are involved in biological functions. Multi‐instance machine learning (MIL) is an efficient approach for solving problems where objects under study cannot be uniquely represented by a single instance, but rather by a set of multiple alternative instances. Multi‐instance learning was formalized in 1997 and motivated by the problem of conformation selection in drug activity prediction tasks. Since then MIL has found a lot of applications in various domains, such as information retrieval, computer vision, signal processing, bankruptcy prediction, and so on. In the given review we describe the MIL framework and its applications to the tasks associated with ambiguity in the representation of small and biological molecules in chemoinformatics and bioinformatics. We have collected examples that demonstrate the advantages of MIL over the traditional single‐instance learning (SIL) approach. Special attention was paid to the ability of MIL models to identify key instances responsible for a modeling property.This article is categorized under: Data Science > Chemoinformatics Data Science > Artificial Intelligence/Machine Learning

On the question of the presence of natural reproduction in the “factory” population of the sterlet of the Oka river

The increase in the number of sterlets in the Oka in recent years has been associated with long-term re-climatization measures, the basis of which was mass releases of resilient juveniles grown in conditions of full-cycle industrial-type fish farms. Despite the high growth rate and relatively early puberty of the Oka sterlet, the completion of the naturalization process of this representative of sturgeon due to the lack of spawning behavior and replenishment of the population through natural reproduction probably does not occur. According to the results of accounting surveys of early juvenile fish with ichthyoplankton traps and net fishing gear for mature sterlet in different parts of the Oka River over a long period of observations, no signs indicating the spawning of sterlet producers of “factory” origin have been established. A complete biological analysis of a significant amount of sterlet samples showed a sharp predominance of males in the sexual structure of the population and the absence of post-spawning gonadal changes in mature fish of both sexes, characteristic of the VI–II stage of gonad maturity in May and June catches. In the structure of catches of ichthyoplankton traps for a long-term period of observations on different hydrological regime and depths of the upper and middle reaches of the Oka, despite the rich (up to 21 species per survey) species diversity and a significant volume (5.66 thousand copies) of identified early juvenile fish caught in the Ruslov stream during the rolling migrations, it was not recorded not a single instance of sterlet in the early stages of development. At the same time, the number and ichthyomass of mature sterlet in the river areas adjacent to the places of setting traps was relatively high.

Retrieval Contrastive Learning for Aspect-Level Sentiment Classification

Aspect-Level Sentiment Classification (ALSC) aims to assign specific sentiments to a sentence toward different aspects, which is one of the crucial challenges in the field of Natural Language Processing (NLP). Despite numerous approaches being proposed and obtaining prominent results, the majority of them focus on leveraging the relationships between the aspect and opinion words in a single instance while ignoring correlations with other instances, which will make models inevitably become trapped in local optima due to the absence of a global viewpoint. Instance representation derived from a single instance, on the one hand, the contained information is insufficient due to the lack of descriptions from other perspectives; on the other hand, its stored knowledge is redundant since the inability to filter extraneous content. To obtain a polished instance representation, we developed a Retrieval Contrastive Learning (RCL) framework to subtly extract intrinsic knowledge across instances. RCL consists of two modules: (a) obtaining retrieval instances by sparse retriever and dense retriever, and (b) extracting and learning the knowledge of the retrieval instances by using Contrastive Learning (CL). To demonstrate the superiority of RCL, five ALSC models are employed to conduct comprehensive experiments on three widely-known benchmarks. Compared with the baselines, ALSC models achieve substantial improvements when trained with RCL. Especially, ABSA-DeBERTa with RCL obtains new state-of-the-art results, which outperform the advanced methods by 0.92%, 0.23%, and 0.47% in terms of Macro F1 gains on Laptops, Restaurants, and Twitter, respectively.

Just between us…: The role of sharing and receiving secrets in friendship across time

AbstractSecrets are prominent in close relationships. Most research has examined people's general tendencies to share secrets or single instances of secret‐keeping or secret‐sharing. These methods limit what we know about how keeping and sharing secrets across time in established close relationships is associated with important relationship characteristics. This research focused on the role that secret exchanges play in how people assess relational closeness and social utility (i.e., being a valuable social connection) in friendships. Across 10 weeks, participants (N = 126) reported bi‐weekly on sharing and receiving personal and secondhand secrets (i.e., third‐party secrets) with three friends. We found that sharing and receiving more personal secrets were robustly associated with higher closeness and social utility. The findings on secondhand secret‐sharing were more complex, suggesting that receiving secondhand secrets from friends and sharing secondhand secrets with friends were differentially associated with closeness and social utility when comparing short‐term versus cumulative effects. Results are discussed in terms of how secret‐sharing behaviors are dynamically associated with close relationship qualities.

Summarized and sequential discrimination - A paradigm for research on the perception of multiple instances of discrimination

Despite a large body of knowledge about factors influencing perceptions of discrimination in single instances, little is known about the perception of discrimination based on multiple instances of discrimination. One reason for this lack of knowledge is that existing methods in discrimination perception research are not optimal for this subject. The present manuscript introduces the Summarized and Sequential Discrimination (SUSED) Paradigm, which was designed to facilitate investigating processes of discrimination perception based on multiple discriminatory instances. The SUSED-paradigm provides participants with statistical information about multiple instances of discrimination and fosters investigating information processing underlying judgments of discrimination. In the SUSED-paradigm, information on discrimination is presented either in a summarized or in a sequential format in which an approximation of the actual statistical strength of discrimination can be derived from the given information. Experiments 1–6 (N = 1130) demonstrate the utility of the SUSED-paradigm for experimentally manipulating statistical discrimination at multiple levels of strength in a manner that can be perceived by participants. The results demonstrate high correlations between subjective and statistical discrimination strengths, even when real marginalized groups were presented. Furthermore, we compared how three different measures of discrimination perception differ in their sensitivity to variations in differential group treatments and the strength of the statistical discrimination. Experiments 7 and 8 (N = 347) replicated the well-documented prototype effect in both formats of the SUSED-paradigm to gauge the paradigms convergent validity with former methods.

Feature extraction and classification of multiple cracks from raw vibrational responses of composite beams using 1D-CNN network

Structures and components need to be constantly monitored to ensure good service life and avoid failures. Structural health monitoring is a wide-ranging concept that tries to ensure the safety of components and structures. Vibration responses of small and large structures contain information about these damages. In the present study, this information is extracted and used to classify the location and severity of cracks. For this purpose, an FE model of Glass Fibre Reinforced Plastic (GFRP) free-free beam is created for beams without any crack and beams with three crack locations and three crack severities. Finite Element (FE) model is validated using modal analysis and is used to generate vibration responses from simulated hammer strikes at different locations on the beam. The vibration data consists of responses of beams that have only one crack present at a time and when two cracks are present. A 1D-CNN network is trained on the generated vibrational responses to classify damage labels assigned for each crack location and crack severity. The network was tested using datasets containing only single crack instances and only dual crack instances. The trained networks were found to be 95% and 93% accurate in determining the damage class respectively. Furthermore, datasets of these two instances combined, containing responses when single cracks and dual cracks are present, were also investigated. These networks had an accuracy of 92%. Hence, in all these instances, the 1D-CNN network classifies the healthy and damaged conditions satisfactorily. There are two advantages of using such a technique. Firstly, this approach simplifies the two-step approach: one for feature extraction and another one for damage prediction into a single step. Secondly, the use of raw vibration data for damage prediction means that real-time damage detection is possible.

Revealing the therapeutic potential of dupilumab: a case report on managing post-COVID-19 chronic pruritus in a geriatric patient

This case report describes a 73-year-old Caucasian male who contracted COVID-19 and struggled with chronic, severe pruritus after that. The patient's pruritus resisted numerous conventional and unconventional therapy modalities, highlighting the difficulty of treating post-COVID-19 dermatological symptoms. Pruritus in the patient was significantly alleviated due to therapy with Dupixent, a monoclonal antibody that inhibits interleukin-4 and interleukin-13 signaling. Treatment of the condition improved significantly with the use of Dupixent. The report describes the story of the patient's journey, from the first-time symptoms showed up after COVID-19 infection, through several treatments that was not effective, to when the patient's symptoms disappeared after starting Dupixent. The patient's recent SARS-CoV-2 infection may be connected to the immune-mediated process shown by the patient's histopathological results. Even though these results are based on a single instance, they highlight COVID-19's ability to cause chronic pruritus and recommend Dupixent as a possible treatment approach. More study is required to confirm these findings and investigate possible immunological connections between COVID-19 and chronic pruritus.

BLogic: A Bayesian Model Combination Approach in Logic Regression

With the increasing complexity and dimensionality of datasets in statistical research, traditional methods of identifying interactions are often more challenging to apply due to the limitations of model assumptions. Logic regression has emerged as an effective tool, leveraging Boolean combinations of binary explanatory variables. However, the prevalent simulated annealing approach in logic regression sometimes faces stability issues. This study introduces the BLogic algorithm, a novel approach that amalgamates multiple runs of simulated annealing on a dataset and synthesizes the results via the Bayesian model combination technique. This algorithm not only facilitates predicting response variables using binary explanatory ones but also offers a score computation for prime implicants, elucidating key variables and their interactions within the data. In simulations with identical parameters, conventional logic regression, when executed with a single instance of simulated annealing, exhibits reduced predictive and interpretative capabilities as soon as the ratio of explanatory variables to sample size surpasses 10. In contrast, the BLogic algorithm maintains its effectiveness until this ratio approaches 50. This underscores its heightened resilience against challenges in high-dimensional settings, especially the large p, small n problem. Moreover, employing real-world data from the UK10K Project, we also showcase the practical performance of the BLogic algorithm.

Implementasi Proyek Penguatan Profil Pelajar Pancasila

The implementation of the Pancasila student profile project is designed with several stages. First, it begins with problem identification, followed by topic selection, topic approval, and topic execution. Finally, an evaluation is conducted for improvement. The implementation of the project to strengthen the Pancasila student profile is guided by the six dimensions within the Pancasila student profile. This approach makes it easier for teachers to assist in the character development of children. The application of the Pancasila student profile project requires a high level of creativity from teachers to make the learning process engaging and enjoyable for students, preventing the class from becoming monotonous. In the implementation carried out by teachers in the project to reinforce the Pancasila student profile, there are continuous activities. This means that the project is not completed in a single instance. The Pancasila student profile is also a response provided by the government to address questions regarding what kind of profile or competencies are produced by the education system in Indonesia. This research employs a qualitative descriptive approach, which is used to describe the implementation of the Pancasila student profile project that references the six dimensions of the Pancasila student profile.

Generating collective counterfactual explanations in score-based classification via mathematical optimization

Due to the increasing use of Machine Learning models in high stakes decision making settings, it has become increasingly important to have tools to understand how models arrive at decisions. Assuming an already trained Supervised Classification model, post-hoc explanations can be obtained via so-called counterfactual analysis: a counterfactual explanation of an instance indicates how this instance should be minimally modified so that the perturbed instance is classified in the desired class by the given Machine Learning classification model.Most of the Counterfactual Analysis literature focuses on the single-instance single-counterfactual setting, in which the analysis is done for one single instance to provide one single counterfactual explanation. Taking a stakeholder’s perspective, in this paper we introduce the so-called collective counterfactual explanations. By means of novel Mathematical Optimization models, we provide a counterfactual explanation for each instance in a group of interest, so that the total cost of the perturbations is minimized under some linking constraints. Making the process of constructing counterfactuals collective instead of individual enables us to detect the features that are critical to the entire dataset to have the individuals classified in the desired class. Our methodology allows for some instances to be treated individually, as in the single-instance single-counterfactual case, performing the collective counterfactual analysis for a fraction of records of the group of interest. This way, outliers are identified and handled appropriately.Under some assumptions on the classifier and the space in which counterfactuals are sought, finding collective counterfactual explanations is reduced to solving a convex quadratic linearly constrained mixed integer optimization problem, which, for datasets of moderate size, can be solved to optimality using existing solvers.The performance of our approach is illustrated on real-world datasets, demonstrating its usefulness.

Multilevel visual motion opponency in Drosophila

Inhibitory interactions between opponent neuronal pathways constitute a common circuit motif across brain areas and species. However, in most cases, synaptic wiring and biophysical, cellular and network mechanisms generating opponency are unknown. Here, we combine optogenetics, voltage and calcium imaging, connectomics, electrophysiology and modeling to reveal multilevel opponent inhibition in the fly visual system. We uncover a circuit architecture in which a single cell type implements direction-selective, motion-opponent inhibition at all three network levels. This inhibition, mediated by GluClα receptors, is balanced with excitation in strength, despite tenfold fewer synapses. The different opponent network levels constitute a nested, hierarchical structure operating at increasing spatiotemporal scales. Electrophysiology and modeling suggest that distributing this computation over consecutive network levels counteracts a reduction in gain, which would result from integrating large opposing conductances at a single instance. We propose that this neural architecture provides resilience to noise while enabling high selectivity for relevant sensory information.

Is the Missing Letter Effect Due Primarily to the Test Word Containing the Target Letter or to the Surrounding Words?

Abstract A simple letter detection task, in which subjects mark instances of a target letter in prose passages, has elucidated numerous cognitive processes involved in reading by examining the “missing letter” effect, in which readers’ detection accuracy is especially low on frequent function words. Two experiments explore the fundamental but novel issue of whether the missing letter effect is due to the test word containing the target letter or to the words surrounding the test word. College students searched for a target letter (e in Experiment 1, o in Experiment 2) in a passage that included unrelated sentences, with each sentence containing a single instance of 1 of 2 test words (the or one in Experiment 1, of or on in Experiment 2). The sentences were intact (prose), or the words in each sentence were randomly rearranged (scrambled). The 2 test words in an experiment were surrounded by the exact same words. If the word containing the target letter is primarily responsible for the missing letter effect, the proportion of correct letter detection responses should depend on the test word, whereas if the surrounding words are primarily responsible, it should depend on the text type (prose, scrambled). In fact, in both experiments a huge effect of test word was found but no effect of text type. These results provide clear evidence for the influence of the test word but, surprisingly, no evidence for the influence of the surrounding words on the missing letter effect in the letter detection task.

Invariant Feature Encoding for Contact Handprints Using Delaunay Triangulated Graph

Contact-based biometric applications primarily use prints from a finger or a palm for a single instance in different applications. For access control, there is an enrollment process using one or more templates which are compared with verification images. In forensics applications, randomly located, partial, and often degraded prints acquired from a crime scene are compared with the images captured from suspects or existing fingerprint databases, like AFIS. In both scenarios, if we need to use handprints which include segments from the finger and palm, what would be the solution? The motivation behind this is the concept of one single algorithm for one hand. Using an algorithm that can incorporate both prints in a common processing framework can be an alternative which will have advantages like scaling to larger existing databases. This work proposes a method that uses minutiae or minutiae-like features, Delaunay triangulation and graph matching with invariant feature representation to overcome the effects of rotation and scaling. Since palm prints have a large surface area with degradation, they tend to have many false minutiae compared to fingerprints, and the existing palm print algorithms fail to tackle this. The proposed algorithm constructs Delaunay triangulated graphs (DTG) using minutiae where Delaunay triangles form from minutiae, and initiate a collection of base triangles for opening the matching process. Several matches may be observed for a single triangle match when two images are compared. Therefore, the set of initially matched triangles may not be a true set of matched triangles. Each matched triangle is then used to extend as a sub-graph, adding more nodes to it until a maximum graph size is reached. When a significant region of the template image is matched with the test image, the highest possible order of this graph will be obtained. To prove the robustness of the algorithm to geometrical variations and working ability with extremely degraded (similar to latent prints) conditions, it is demonstrated with a subset of partial-quality and extremely-low-quality images from the FVC (fingerprint) and the THUPALMLAB (palm print) databases with and without geometrical variations. The algorithm is useful when partial matches between template and test are expected, and alignment or geometrical normalization is not accurately possible in pre-processing. It will also work for cross-comparisons between images that are not known a priori.

Federated Transfer Learning for Rice-Leaf Disease Classification across Multiclient Cross-Silo Datasets

Paddy leaf diseases encompass a range of ailments affecting rice plants’ leaves, arising from factors like bacteria, fungi, viruses, and environmental stress. Precision agriculture leverages technologies for enhanced crop production, with disease detection being a vital element. Prompt identification of diseases in paddy leaves is critical for curtailing their propagation and reducing crop damage. However, manually diagnosing paddy diseases in regions with vast agricultural areas and limited experts proves immensely difficult. The utilization of machine learning (ML) and deep learning (DL) for diagnosing diseases in agricultural crops appears to be effective and well-suited for widespread application. These ML/DL methods cannot ensure data privacy, as they involve sharing training data with a central server, overlooking competitive and regulatory considerations. As a solution, federated learning (FL) aims to facilitate decentralized training to tackle the identified limitations of centralized training. This paper utilizes the FL approach for the classification of rice-leaf diseases. The manuscript presents an effective approach for rice-leaf disease classification with a federated architecture, ensuring data privacy. We have compiled an unbalanced dataset of rice-leaf disease images, categorized into four diseases with their respective image counts: bacterial blight (1584), brown spot (1440), blast (1600), and tungro (1308). The proposed method, called federated transfer learning (F-TL), maintains privacy for all connected devices using a decentralized client-server setup. Both IID (independent and identically distributed) and non-IID datasets were utilized for testing the F-TL framework after preprocessing. Initially, we conducted an effectiveness analysis of CNN and eight transfer learning models for rice-leaf disease classification. Among them, MobileNetV2 and EfficientNetB3 outperformed the other transfer-learned models. Subsequently, we trained these models using both IID and non-IID datasets in a federated learning environment. The framework’s performance was assessed through diverse scenarios, comparing it with traditional and federated learning models. The evaluation considered metrics like validation accuracy, loss as well as resource utilization such as CPU and RAM. EfficientNetB3 excelled in training, achieving 99% accuracy with 0.1 loss for both IID and non-IID datasets. MobilenetV2 showed slightly lower training accuracy at 98% (IID) and 90% (non-IID) with losses of 0.4 and 0.6, respectively. In evaluation, EfficientNetB3 maintained 99% accuracy with 0.1 loss for both datasets, while MobilenetV2 achieved 90% (IID) and 97% (non-IID) accuracy with losses of 0.6 and 0.2, respectively. Results indicated the F-TL framework’s superiority over traditional distributed deep-learning classifiers, demonstrating its effectiveness in both single and multiclient instances. Notably, the framework’s strengths lie in its cost-effectiveness and data-privacy assurance for resource-constrained edge devices, positioning it as a valuable alternative for rice-leaf disease classification compared to existing tools.

Retroperitoneal ganglioneuroma: A five-case series from a single Tunisian center

Introduction and importanceRetroperitoneal ganglioneuroma is an infrequent, benign tumor originating from the sympathetic nervous system. The diagnosis predominantly relies on histological assessment, often as an incidental discovery. Surgical removal stands as the primary treatment modality, and the overall prognosis tends to be favorable. Case presentationObjective: In order to comprehensively examine the diagnostic, therapeutic, and progressive aspects of retroperitoneal ganglioneuroma, this study aims to analyze five cases of retroperitoneal ganglioneuroma that were surgically managed between 1993 and 2013.Patients and methods: We present a series of five cases involving retroperitoneal ganglioneuromas that underwent surgical intervention in the urology department of Charles Nicolle Teaching Hospital of Tunis between the years 1993 and 2013. The study cohort comprised three females and two males, with an average age of 36.4 years (ranging from 10 to 88 years). Among these cases, four instances showcased incidental tumor detection, while one case manifested with cruralgia due to nerve compression. Complete tumor excision was achieved in all cases, with a single instance necessitating concurrent nephrectomy. Accurate diagnosis was ascertained through meticulous histological examination. Clinical discussionResults: The case series encompassed three women and two men, with an average age of 36.4 years (ranging from 10 to 88 years). The majority of cases (four out of five) unveiled tumors incidentally, whereas one case presented with cruralgia. Surgical removal resulted in complete resection of all tumors, although nephrectomy was necessary in one instance. Postoperative complications were minimal, and effective anticoagulant treatment addressed one thrombotic event. While recurrence was noted in a solitary case, subsequent imaging confirmed its stability. The average follow-up duration extended to 23.2 months (ranging from 6 to 72 months). ConclusionRetroperitoneal ganglioneuroma, being a rare and benign neoplasm, mandates meticulous pathological assessment and precise imaging for precise localization. Unquestionably, complete surgical resection remains paramount to forestall recurrence and mitigate the likelihood of malignant transformation. Overall, the prognosis associated with retroperitoneal ganglioneuroma tends to be favorable.

Calorie Compensation Patterns Observed in App-Based Food Diaries.

Self-regulation of food intake is necessary for maintaining a healthy body weight. One of the characteristics of self-regulation is calorie compensation. Calorie compensation refers to adjusting the current meal's energy content based on the energy content of the previous meal(s). Preload test studies measure a single instance of compensation in a controlled setting. The measurement of calorie compensation in free-living conditions has largely remained unexplored. This paper proposes a methodology that leverages extensive app-based observational food diary data to measure an individual's calorie compensation profile in free-living conditions. Instead of a single compensation index followed in preload-test studies, we present the compensation profile as a distribution of days a user exhibits under-compensation, overcompensation, non-compensation, and precise compensation. We applied our methodology to the public food diary data of 1622 MyFitnessPal users. We empirically established that four weeks of food diaries were sufficient to characterize a user's compensation profile accurately. We observed that meal compensation was more likely than day compensation. Dinner compensation had a higher likelihood than lunch compensation. Precise compensation was the least likely. Users were more likely to overcompensate for missing calories than for additional calories. The consequences of poor compensatory behavior were reflected in their adherence to their daily calorie goal. Our methodology could be applied to food diaries to discover behavioral phenotypes of poor compensatory behavior toward forming an early behavioral marker for weight gain.

A 3D Lidar SLAM System Based on Semantic Segmentation for Rubber-Tapping Robot

Simultaneous localization and mapping (SLAM) in rubber plantations is a challenging task for rubber-tapping robots. Due to the long-term stability of tree trunks in rubber plantations, a SLAM system based on semantic segmentation, called Se-LOAM, is proposed in this work. The 3D lidar point cloud datasets of trunks collected in rubber plantations of Hainan University are used to train the semantic model, and the model is used to extract features of trunk point clouds. After clustering the trunk point clouds, each single rubber tree instance is segmented based on the Viterbi algorithm. The point clouds of tree instances are fitted to the cylindrical trunk models for semantic cluster association and positional estimation, which are used for lidar odometry and mapping. The experimental results show that the present SLAM system is accurate in establishing online mapping, and the location of the trunk in the map is clearer. Specifically, the average relative pose error is 0.02 m, which is better than the positioning performance of LOAM and LeGO-LOAM. The average error of estimating the diameter at breast height (DBH) is 0.57 cm, and it only takes 401.4 kB to store a map of the area of approximately 500 m2, which is about 10% less than other classic methods. Therefore, Se-LOAM can meet the requirements of online mapping, providing a robust SLAM method for rubber-tapping robots.

-ata nominalisations in Maltese: a language contact perspective

The -ata derivational suffix is attested frequently in nominalisations in Italo-Romance languages and its functions related to ‘event/single instance’ nouns have been documented extensively. This affix with these functions is also present in the derivational morphology of Maltese, in which diachronic and synchronic developments of -ata nominalisations reflect the evolutionary paths of this language, characterised by contact. By investigating the etymology of verbal and nominal bases, this research provides a classification of forms which take the -ata suffix in Maltese to refer to events and single instances within them, through derivation involving contact with Italian, English and Sicilian base forms. -ata nominalisations resulting from etymologically Arabic bases will also be included in the classification.