Distinct Objects Research Articles

Atypical pattern separation memory and its association with restricted interests and repetitive behaviors in autistic children.

Memory challenges remain understudied in childhood autism. Our study investigates one specific aspect of memory function, known as pattern separation memory, in autistic children. Pattern separation memory refers to the critical ability to store unique memories of similar stimuli; however, its role in childhood autism remains largely uncharted. Our study first uncovered that the pattern separation memory was significantly reduced in autistic children, and then showed that reduced memory performance was linked to their symptoms of repetitive, restricted interest and behavior. We also identified distinct subgroups with profiles of reduced and increased generalization for pattern separation memory. More than 72% of autistic children showed a tendency to reduce memory generalization, focusing heavily on unique details of objects for memorization. This focus made it challenging for them to identify commonalities across similar entities. Interestingly, a smaller proportion of autistic children displayed an opposite pattern of increased generalization, marked by challenges in differentiating between similar yet distinct objects. Our findings advance the understanding of memory function in autism and have practical implications for devising personalized learning strategies that align with the unique memory patterns exhibited by autistic children. This study will be of broad interest to researchers in psychology, psychiatry, and brain development as well as teachers, parents, clinicians, and the wider public.

Surgical Sabermetrics: A Scoping Review of Technology-enhanced Assessment of Nontechnical Skills in the Operating Room.

To evaluate the current evidence for surgical sabermetrics: digital methods of assessing surgical nontechnical skills and investigate the implications for enhancing surgical performance. Surgeons need high-quality, objective, and timely feedback to optimize performance and patient safety. Digital tools to assess nontechnical skills have the potential to reduce human bias and aid scalability. However, we do not fully understand which of the myriad of digital metrics of performance assessment have efficacy for surgeons. A systematic review was conducted by searching PubMed, EMBASE, CINAHL, and PSYCINFO databases following PRISMA-ScR guidelines. MeSH terms and keywords included "Assessment," "Surgeons," and "Technology". Eligible studies included a digital assessment of nontechnical skills for surgeons, residents, and/or medical students within an operative context. From 19,229 articles screened, 81 articles met the inclusion criteria. The studies varied in surgical specialties, settings, and outcome measurements. A total of 122 distinct objective, digital metrics were utilized. Studies digitally measured at least 1 category of surgical nontechnical skill using a single (n=54) or multiple objective measures (n=27). The majority of studies utilized simulation (n=48) over live operative settings (n=32). Surgical Sabermetrics has been demonstrated to be beneficial in measuring cognitive load (n=57), situation awareness (n=24), communication (n=3), teamwork (n=13), and leadership (n=2). No studies measured intraoperative decision-making. The literature detailing the intersection between surgical data science and operative nontechnical skills is diverse and growing rapidly. Surgical Sabermetrics may provide a promising modifiable technique to achieve desirable outcomes for both the surgeon and the patient. This study identifies a diverse array of measurements possible with sensor devices and highlights research gaps, including the need for objective assessment of decision-making. Future studies may advance the integration of physiological sensors to provide a holistic assessment of surgical performance.

The Potential of Quality Target Product Profile in the Optimization of Nanoemulsions

Abstract: The application of Quality Target Product Profile (QTPP) in optimizing nanoemulsion (NEM) shows immense potential in advancing pharmaceutical formulation design for effective drug delivery. By aligning QTPP with nanoemulsion attributes, this approach offers a pathway to tailored formulations that meet specific therapeutic objectives and responses. Incorporating QTPP facilitates informed choices in formulating design, covering pivotal factors like stability, drug re-lease kinetics, bioavailability, and precise targeting. Moreover, this review extensively explores the real-world application of QTPP-guided tactics in refining nanoemulsion optimization. It high-lights their pivotal role in anticipating and regulating in vivo responses, encompassing vital as-pects like pharmacokinetics and pharmacodynamics. By conducting thorough examinations of case studies and research outcomes, this article clarifies the effectiveness of aligning QTPP crite-ria with NEM characteristics. This approach fosters the creation of customized formulations pre-cisely suited to achieve distinct therapeutic objectives. This review amalgamates contemporary insights into harnessing QTPP for nanoemulsion optimization, illuminating its capacity to stream-line formulation design, amplify treatment effectiveness by desiring drug release, and catalyze transformative shifts in pharmaceutical research.

In the eye of the beholder: the role of self-perceived status in the relationship between high-performance work systems and affective commitment

PurposeResearch has suggested that employees interpret high-performance work systems (HPWSs) as targeting two distinct organizational objectives: enhancing performance and promoting employee well-being. These attributions often exert divergent effects on employee attitudes. Thus, this study aims to investigate this dynamic within the context of the Korean nursing occupation, clarifying how the HPWS can simultaneously evoke dual attributions: human resource (HR) well-being and HR performance attributions. Additionally, the authors examine the contrasting effects of these attributions and identify a moderating variable that could reconcile them. Drawing on the psychological experience of status theory, the authors conceptualize and test the moderating effect of employees' self-perceived status on the relationship between HR performance attribution and affective commitment.Design/methodology/approachData were collected from 475 nurses in 82 work units in Korean hospitals. Hypotheses were tested in a multilevel moderated mediation model.FindingsThe findings revealed that an HPWS elicits HR well-being and HR performance attributions. While HR well-being attribution was positively associated with affective commitment, HR performance attribution was positively related to affective commitment when employees' self-perceived status was high. Moreover, the HPWS demonstrated an indirect relationship with affective commitment via increasing HR performance attribution when self-perceived status was high.Originality/valueAlthough the personal meaning of HR attributions differs depending on the perceiver’s situation, this aspect has received little attention in the field of research. This study advances the understanding of HR attributions derived from the HPWS within the specific context of Korean nursing. Furthermore, the authors suggest that the two attributions may not conflict with each other, indicating that the impact of HR performance attribution is conditional on an individual’s self-perceived status.

Translation Strategies of Cultural-specific Items in Translating Subtitles Indonesian Documentary “Perempuan Tana Humba”

Translating Cultural-Specific Items (CSIs) could present numerous challenges for translators because each culture communicates differently. Experts have given some approaches to the issue of translating culturally distinctive objects. The researchers used Aixela's (1996) theory for translation technique and Newmark's (1998) theory for analysis and categorization in relation to CSI. The purpose of this qualitative study is to analyze the translation strategies used for cultural-specific items in the translation of the Indonesian documentary "Perempuan Tana Humba" by Lasja F. Susatyo and Mandy Marahimin. The researcher utilized thematic analysis to analyze the CSIs occurring in the documentary. The research concluded that out of five types of CSI proposed by Newmark, only three types of CSIs were found: material culture, social organization, and social culture. In addition, the most frequently used strategy was repetition. This study also showed that the translation strategy for the CSI was determined by the translator's concerns or considerations, such as the occasions in which the translators applied different strategies when the previously mentioned CSIs appeared again in different point of the documentary. It was noticed that various timestamps led to different translation techniques. The study is significant for the practice of translation. The research's findings, which categorize and identify the translation procedures used to translate the CSIs, may be taken into account by translation researchers as well as by present and future translators. Additionally, this study might help to clarify how to use translation procedures and what kinds of CSIs to translate for documentaries.

Critical factors for effective resident participation in neighborhood rehabilitation in Wuhan, China: From the perspectives of diverse stakeholders

Resident participation is essential for neighborhood rehabilitation. It requires the active involvement of residents and efficient management by organizers. To improve the effectiveness of resident participation, it is necessary to understand the critical success factors (CSFs) underlying it. However, previous research has examined the critical factors from a single-stakeholder perspective, overlooking potential differences in perceptions among stakeholders with diverse roles and rehabilitation experiences. Based on 30 interviews and 255 questionnaires from six stakeholder groups in Wuhan, China, this study explores how the perception of critical factors for effective resident participation varies among local government, community-based organization, designer, contractor, consulting party, and resident. Thirty-seven factors were identified and compared among the stakeholders. Financial Incentive (for participation organizers) was identified as the most critical factor for effective resident participation, followed by Information Disclosure and Transparency, and Trust. Results from the analysis of variance (ANOVA) show that the six groups differed significantly in the importance of most factors (25/37), especially in Participant Education and Prejudice against the Working Group. Interview results indicate that the COVID-19 pandemic and rehabilitation experience changed stakeholders’ perceived importance of some factors. Specifically, in future RP initiatives, extra emphasis could be placed on Trait and Capacity (of the working group) and Participation-assistance Technologies. Stakeholders regarded these two factors as more critical as their rehabilitation experience accumulated. By understanding stakeholders' conflicting and changing perceptions of effective resident participation, suggestions were proposed to each stakeholder group to fulfill their distinct participation objectives and improve the overall effectiveness of participation practices.

(What) Is Feminist Logic? (What) Do We Want It to Be?

‘Feminist logic’ may sound like an impossible, incoherent, or irrelevant project, but it is none of these. We begin by delineating three categories into which projects in feminist logic might fall: philosophical logic, philosophy of logic, and pedagogy. We then defuse two distinct objections to the very idea of feminist logic: the irrelevance argument and the independence argument. Having done so, we turn to a particular kind of project in feminist philosophy of logic: Valerie Plumwood's feminist argument for a relevance logic ( L Plum ) . Plumwood's work serves as our primary case study as we turn to the project of considering three different ways we might understand her argument and revisionist arguments like it: as a priori theorizing, as ameliorative conceptual engineering, or as instances of anti-exceptionalist approaches to logic. After arguing that the anti-exceptionalist approach seems to provide the most promising means of understanding the kind of project undertaken in a feminist challenge to classical logic, we briefly address the consequences that this might have for logic instruction. Here, we argue for the perhaps unexpected conclusion that feminist programs ought to offer more, not less, instruction in logic for those who take interest.

Exercise Across the Phases of Cancer Survivorship: A Narrative Review.

Exercise has long been recognized as an important component of treatment for various diseases. However, the benefits and risks of exercise interventions must be carefully evaluated to ensure the former outweighs the latter. As cancer patients undergo diverse treatment modalities with distinct objectives, a systematic approach partitioning the cancer journey into distinct phases is necessary to inform tailored exercise prescriptions. This narrative review summarizes exercise benefits and mechanisms for cancer patients and survivors across four distinct survivorship periods-before surgery, after surgery and before adjuvant treatment, during nonsurgical treatment (adjuvant and neoadjuvant), and during extended survival. In summary, exercise reduces the risks of complications and declines in physical functioning while improving fatigue, quality of life, and the ability to manage treatment effects. Although additional research is warranted, existing evidence is sufficient to integrate exercise into clinical oncology practice and cancer survivorship programs.

Concurrent Heat and Air Pollution Exposures among People Experiencing Homelessness.

Extreme heat and air pollution are important human health concerns; exposure can affect mental and physical well-being, particularly during periods of co-occurrence. Yet, the impacts on people are largely determined by underlying health conditions, coupled with the length and intensity of exposure. Preexisting adverse health conditions and prolonged exposure times are more common for people experiencing homelessness, particularly those with intersectional identity characteristics (e.g., disease, ability, age, etc.). Partially due to methodological limitations, such as data scarcity, there is a lack of research at the intersection of this at-risk population within the climate-health domain. We have three distinct objectives throughout this article: a) to advance critical discussions around the state of concurrent high heat and air pollution exposure research as it relates to people experiencing homelessness; b) to assert the importance of heat and air pollution exposure research among a highly vulnerable, too-often homogenized population-people experiencing homelessness; and c) to underline challenges in this area of study while presenting potential ways to address such shortcomings. The health insights from concurrent air pollution and heat exposure studies are consequential when studying unhoused communities who are already overexposed to harmful environmental conditions. Without holistic data sets and more advanced methods to study concurrent exposures, appropriate and targeted prevention and intervention strategies cannot be developed to protect this at-risk population. We highlight that a) concurrent high heat and air pollution exposure research among people experiencing homelessness is significantly underdeveloped considering the pressing human health implications; b) the severity of physiological responses elicited by high heat and air pollution are predicated on exposure intensity and time, and thus people without means of seeking climate-controlled shelter are most at risk; and c) collaboration among transdisciplinary teams is needed to resolve data resolution issues and enable targeted prevention and intervention strategies. https://doi.org/10.1289/EHP13402.

Gain Scheduled PI controller design using Multi-Objective Reinforcement Learning

Gain scheduling, a widely adopted control design approach, performs tasks by decomposing into sub-problems. It has found successful applications across diverse fields, including aerospace and industrial process control (Leith and Leithead (2000)). Reinforcement Learning (RL) has become increasingly significant in process control applications, thanks to the advancement of sophisticated algorithms and the rise in computational speeds. An attempt has been made to enhance the performance of a Proportional-Integral (PI) controller by integrating the benefits of both gain scheduling and RL. The manuscript focuses on designing a Gain Scheduled PI controller using Multi-Objective RL (GS-MORL). Proposing the training of multiple RL-based PI controllers with distinct objectives with customized reward functions to derive tuning parameters for each goal. The switching between these parameter sets is facilitated by a gain schedule variable. The controller parameters are adjusted based on the error between actual and desired responses. The aim is to enhance overall performance across various performance indices, including the integral of absolute error and time response specifications. The performance improvement from the proposed work has been demonstrated using two simulation case studies, one involving an underdamped second-order system and an integrating type steam turbine system. Comparative analysis against existing literature revealed superior performance across all indices.

An iterative approach for the solution of fully fuzzy linear fractional programming problems via fuzzy multi-objective optimization

<abstract><p>The primary goal of optimization theory is to formulate solutions for real-life challenges that play a fundamental role in our daily lives. One of the most significant issues within this framework is the Linear Fractional Programming Problem (LFrPP). In practical situations, such as production planning and financial decision-making, it is often feasible to express objectives as a ratio of two distinct objectives. To enhance the efficacy of these problems in representing real-world scenarios, it is reasonable to utilize fuzzy sets for expressing variables and parameters. In this research, we have worked on the Fully Fuzzy Linear Fractional Linear Programming Problem (FFLFrLPP). In our approach to problem-solving, we simplified the intricate structure of the FFLFrLPP into a crisp Linear Programming Problem (LPP) while accommodating the inherent fuzziness. Notably, unlike literature, our proposed technique avoided variable transformation, which is highly competitive in addressing fuzzy-based problems. Our methodology also distinguishes itself from the literature in preserving fuzziness throughout the process, from problem formulation to solution. In this study, we conducted a rigorous evaluation of our proposed methodology by applying it to a selection of numerical examples and production problems sourced from the existing literature. Our findings revealed significant improvements in performance when compared to established solution approaches. Additionally, we presented comprehensive statistical analyses showcasing the robustness and effectiveness of our algorithms when addressing large-scale problem instances. This research underscores the innovative contributions of our methods to the field, further advancing the state-of-the-art in problem-solving techniques.</p></abstract>

Optimization of offset and cant angle winglet on remote control airplane using Taguchi – Particle swarm optimization

Incorporating winglets into aircraft has been empirically proven to notably improve aerodynamic efficiency by reducing vortex-induced effects at the wingtips. However, conducting comprehensive investigations necessitates the exploration of numerous winglet factors and value variations. This study pertains to remote control aircraft winglets, focusing on manipulating cant angle and offset factors across four distinct values. Two primary objectives guide this research: firstly, the maximization Cl/Cdmax, and secondly, the minimization Cd₀. The Taguchi experimental design is employed to randomize the variations in offset and cant angle values systematically. These variations are then used to generate pivotal regression values in the subsequent particle swarm optimization (PSO). The variance analysis evaluates the impact of winglet-related variables on each research goal. Additionally, the winglet design incorporates the open-source XFLR5 software, an accessible resource for aeromodelling clubs in Indonesia. XFLR5 enables the investigation of aerodynamic parameters across various angles of incidence and plays a crucial role in this research. The results of this study reveal that the Taguchi method yields two distinct combinations of factor values, aligning with the two primary research objectives. Conversely, particle swarm optimization generates a combination that effectively addresses both objectives. A comparative analysis of the winglet factor combinations from Taguchi and PSO underscores the greater efficiency of the PSO method in optimizing winglet variations for two distinct objectives.

Process evaluations for the scale-up of complex interventions – A scoping review

Background: Process evaluation (PE) is an essential part of testing and adapting complex health interventions (CHIs) and involves evaluating what is implemented (and how), mechanisms of change, and how context affects implementation and health outcomes. To address complex health needs, the focus is on developing new CHIs, rather than on adapting and scaling up existing interventions to promote stronger integration, increased population coverage, or comprehensiveness. Therefore, more attention is needed to process evaluations conducted during the scale-up process as they may have distinctive features, stakeholders, or objectives. We aim to describe the current practice of process evaluations conducted during the scale-up of CHIs.
 Who is it for? The results of this review are relevant to all stakeholders involved in the scale-up of CHI.
 Who did you involve and engage with? All authors of this review have been involved in the scale-up of integrated care across multiple contexts.
 What did you do? Eight primary data sources (PubMed, Embase, CENTRAL, Web of Science, CINAHL, Global Health, Scielo and African Index Medicus) and grey literature were searched. Studies were screened following gold standard review procedures. Study demographics, scale-up dimensions (i.e., integration, coverage, comprehensiveness), features of the complex intervention and PE were charted. Process evaluation was further assessed in terms of stakeholders involved and proposed objectives of the PE. For this review, the United Kingdom Medical Research Council’s (MRC) definition of PE was adjusted in terms of “scale-up” instead of “delivery and functioning.”
 What results did you get? We identified 10303 unique records of which 34 were included after abstract (n=223) and full-text (n=91) screening. Twenty of the 34 studies were conducted in one or more low-middle income countries (LMICs). Most CHIs studied looked at scale-up of interventions for patient empowerment through digital health, strengthening primary care, or person-centered care models. In the absence of a guiding framework for PEs in the context of scale-up, a variety of other frameworks were often used including the RE-AIM framework (n=5), Consolidated Framework for Implementation Research (n=3), or MRC framework for the PE of CHIs (n=1). Some of these frameworks were subsequently adapted to facilitate unexpected pathways to impact or interactions with contextual factors to emerge as well. Interestingly, in 12 out of 34 studies, beneficiaries (e.g., patients) were not stakeholders in the PE.
 What is the learning for the international audience? Process evaluations specific to the process of scaling up is advertently different from implementing a CHI. In the absence of a gold standard framework to guide PE during scale-up, the development of such guidance could be considered.
 What are the next steps? We plan to seek further engagement, including with those funded under the “Global Alliance of Chronic Disease” scale-up program, to determine the value and content of a process evaluation framework for scale-up.

Deep Learning for Enhanced Marine Vision: Object Detection in Underwater Environments

This study leverages the Semantic Segmentation of Underwater Imagery (SUIM) dataset, encompassing over 1,500 meticulously annotated images that delineate eight distinct object categories. These categories encompass a diverse array, ranging from vertebrate fish and invertebrate reefs to aquatic vegetation, wreckage, human divers, robots, and the seafloor. The use of this dataset involves a methodical synthesis of data through extensive oceanic expeditions and collaborative experiments, featuring both human participants and robots. The research extends its scope to evaluating cutting-edge semantic segmentation techniques, employing established metrics to gauge their performance comprehensively. Additionally, we introduce a fully convolutional encoder-decoder model designed with a dual purpose: delivering competitive performance and computational efficiency. Notably, this model boasts a remarkable accuracy of 88%, underscoring its proficiency in underwater image segmentation. Furthermore, this model's integration within the autonomy pipeline of visually-guided underwater robots presents its tangible applicability. Its rapid end-to-end inference capability addresses the exigencies of real-time decision-making, vital for autonomous systems. This study elucidates the model's practical benefits across diverse applications like visual serving, saliency prediction, and intricate scene comprehension. Crucially, the utilization of the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) elevates image quality, enriching the foundation upon which our model's success rests. This research establishes a solid groundwork for future exploration in underwater robot vision by presenting the model and the benchmark dataset.

Retina image segmentation using the three-path Unet model

Unsupervised image segmentation is a technique that divides an image into distinct regions or objects without prior labeling. This approach offers flexibility and adaptability to various types of image data. Particularly for large datasets, it eliminates the need for manual labeling, thereby it presents advantages in terms of time and labor costs. However, when applied to retinal image segmentation, challenges arise due to variations in data, presence of noise, and manual threshold adjustments, which can lead to over-segmentation or under-segmentation of small blood vessel boundaries and endpoints. In order to enhance the precision and accuracy of retinal image segmentation, we propose a novel image supervised segmentation network based on three-path Unet model.Firstly, the Haar wavelet transform is employed to extract high-frequency image information, which forms the foundation for the proposed HaarNet, a Unet-inspired architecture. Next, the HaarNet is integrated with the Unet and SegNet frameworks to develop a three-path Unet model, referred to as TP-Unet. Finally, the model is further refined into TP-Unet+AE+DSL by incorporating the advantages of auto-encoding (AE) and deep supervised learning (DSL) techniques, thereby enhancing the overall performance of the system. To evaluate the effectiveness of our proposed model, we conduct experiments using the DRIVE and CHASE public datasets. On the DRIVE dataset, our recommended model achieves a Dice coefficient of 0.8291 and a sensitivity index of 0.8184. These results significantly outperform the Unet model by 1.34%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1.34\\%$$\\end{document} and 2.60%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$2.60\\%$$\\end{document}, respectively. Furthermore, our model demonstrates excellent performance on the CHASE dataset, with a Dice coefficient of 0.8162, a sensitivity of 0.8242, and an accuracy of 0.9664. These metrics surpass the Unet model by 3.20%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$3.20\\%$$\\end{document}, 6.66%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$6.66\\%$$\\end{document}, and 0.42%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$0.42\\%$$\\end{document}, respectively. Our proposed model provides more accurate and reliable results for retinal vessel segmentation, which holds significant potential for assisting doctors in their diagnosis.

Enhancing English For Nursing: A Comprehensive Needs Analysis For Effective Communication In Emergency Nursing Study Programs

students enrolled in nursing study programs with a focus on emergency characteristics. The investigation encompasses both target needs and learning needs, emphasizing the specific language requirements in the nursing field. English for Nursing falls under the domain of English for Specific Purposes (ESP), designed to cater to learners' specific language needs, fostering more effective and comprehensive teaching and learning. By aligning English instruction with the demands of the nursing discipline, non-English program students will gain familiarity with English tailored to nursing contexts. The ESP approach aims to address the specific language acquisition challenges faced by learners with distinct objectives. Data collection involves questionnaires administered to 45 students and direct interviews with emergency nursing lecturers. Findings reveal the significance of learning English for improved communication within the context of conversations or practical applications. The identified English materials concentrate on terms relevant to nursing study programs, particularly those associated with emergency characteristics, becoming the focal points of instruction. Additionally, materials focusing on enhancing listening and speaking skills emerge as key priorities in the learning process. The outcomes of this study are anticipated to serve as a valuable reference for the design and development of syllabi, specifically tailored to English for Nursing courses.

Is distinct location evidence of distinct objects? Multilocation and the problem of parsimony

AbstractFor an object to be multilocated is for it to wholly occupy disjoint spatial regions simultaneously. If multilocation is possible, it is possible that a multilocated particle is wholly located at 1080 distinct locations, such that it constitutes a particle‐for‐particle duplicate of the actual universe. Such a universe would presumably be perceptually identical to the actual universe. If we take multilocation as possible, we are thus presented with two accounts between which our perceptual evidence cannot adjudicate: one wherein the universe is constituted by many particles and another wherein it is constituted by one radically multilocated particle. Parsimony concerns dictate that the latter is the more rational to accept. Since this is absurd, we should reject that multilocation is possible. Mooney responds to the problem by arguing that distinct location is evidence of non‐identity, even if acceptance of the possibility of multilocation entails that this evidence is not decisive. If this is right, then the evidence favors a theory featuring many particles. In this paper, I contend that our commitment to taking distinct location as evidence of nonidentity is motivated by a more fundamental intuition that does not apply in the relevant context.

Synchrony in auditory 40-Hz gamma oscillations increases in older age and correlates with hearing abilities and cortical GABA levels

Abstract Synchronized 40-Hz gamma oscillations in specific sensory and higher-order thalamocortical networks provide a neural mechanism for feature binding. Aging-related changes in gamma oscillations may cause deficits in auditory feature binding, contributing to impaired speech-in-noise perception. Gamma synchrony is controlled through inhibitory mechanisms mediated by the neurotransmitter γ-aminobutyric acid (GABA), which has been shown to decline in aging. This study investigated aging-related changes in gamma oscillations and how they relate to auditory function and cortical GABA levels. Magnetoencephalograms of 40-Hz auditory steady-state responses (ASSRs) were recorded in young and older adults by presenting amplitude-modulated tones in quiet and mixed with concurrent multi-talker babble noise. Responses in the quiet condition had longer latencies and more prominent amplitudes, indicating the 40-Hz ASSRs in noise were dominated by a sensory component and in quiet by a component involved in higher-order processing. The ASSR amplitudes increased in older adults under both stimulus conditions. However, larger ASSR amplitudes were associated with more severe hearing and speech-in-noise loss only in the noise condition. This suggests the aging-related increase in synchrony of sensory gamma oscillations has a detrimental effect on auditory processing. It may cause increased interference between competing sounds in the central auditory system, making it difficult for the aging auditory system to separate speech features from noise and bind them into a distinct perceptual object. Also in older adults, larger amplitudes of the 40-Hz ASSRs in the quiet condition were associated with higher left auditory cortex GABA concentrations measured with magnetic resonance spectroscopy, supporting GABA’s role in internally generated gamma synchrony in aging.

Topological spin textures in electronic non-Hermitian systems

Non-Hermitian systems have been discussed mostly in the context of open systems and nonequilibrium. Recent experimental progress is much from optical, cold-atomic, and classical platforms due to the vast tunability and clear identification of observables. However, their counterpart in solid-state electronic systems in equilibrium remains unmasked although highly desired, where a variety of materials are available, calculations are solidly founded, and accurate spectroscopic techniques can be applied. We demonstrate that, in the surface state of a topological insulator with spin-dependent relaxation due to magnetic impurities, highly nontrivial topological soliton spin textures appear in momentum space. Such spin-channel phenomena are delicately related to the type of non-Hermiticity and correctly reveal the most robust non-Hermitian features detectable spectroscopically. Moreover, the distinct topological soliton objects can be deformed to each other, mediated by topological transitions driven by tuning across a critical direction of doped magnetism. These results not only open a solid-state avenue to exotic spin patterns via spin- and angle-resolved photoemission spectroscopy, but also inspire non-Hermitian dissipation engineering of spins in solids.

TOWARDS ACCURATE INSTANCE SEGMENTATION IN LARGE-SCALE LIDAR POINT CLOUDS

Abstract. Panoptic segmentation is the combination of semantic and instance segmentation: assign the points in a 3D point cloud to semantic categories and partition them into distinct object instances. It has many obvious applications for outdoor scene understanding, from city mapping to forest management. Existing methods struggle to segment nearby instances of the same semantic category, like adjacent pieces of street furniture or neighbouring trees, which limits their usability for inventory- or management-type applications that rely on object instances. This study explores the steps of the panoptic segmentation pipeline concerned with clustering points into object instances, with the goal to alleviate that bottleneck. We find that a carefully designed clustering strategy, which leverages multiple types of learned point embeddings, significantly improves instance segmentation. Experiments on the NPM3D urban mobile mapping dataset and the FOR-instance forest dataset demonstrate the effectiveness and versatility of the proposed strategy.