bluuebunny/crossref_metadata_2025 huggingface dataset:
Algorithmic fairness datasets: the story so far
Authors: Fabris Alessandro , Messina Stefano , Silvello Gianmaria , Susto Gian Antonio
Published: September 2022

Data-driven algorithms are studied and deployed in diverse domains to support critical decisions, directly impacting people’s well-being. As a result, a growing community of researchers has been investigating the equity of existing algorithms and proposing novel ones, advancing the understanding of risks and opportunities of automated decision-making for historically disadvantaged populations. Progress in fair machine learning and equitable algorithm design hinges on data, which can be appropriately used only if adequately documented. Unfortunately, the algorithmic fairness community, as a whole, suffers from a collective data documentation debt caused by a lack of information on specific resources (opacity) and scatteredness of available information (sparsity). In this work, we target this data documentation debt by surveying over two hundred datasets employed in algorithmic fairness research, and producing standardized and searchable documentation for each of them. Moreover we rigorously identify the three most popular fairness datasets, namely Adult, COMPAS, and German Credit, for which we compile in-depth documentation. This unifying documentation effort supports multiple contributions. Firstly, we summarize the merits and limitations of Adult, COMPAS, and German Credit, adding to and unifying recent scholarship, calling into question their suitability as general-purpose fairness benchmarks. Secondly, we document hundreds of available alternatives, annotating their domain and supported fairness tasks, along with additional properties of interest for fairness practitioners and researchers, including their format, cardinality, and the sensitive attributes they encode. We summarize this information, zooming in on the tasks, domains, and roles of these resources. Finally, we analyze these datasets from the perspective of five important data curation topics: anonymization, consent, inclusivity, labeling of sensitive attributes, and transparency. We discuss different approaches and levels of attention to these topics, making them tangible, and distill them into a set of best practices for the curation of novel resources.
An external stability audit framework to test the validity of personality prediction in AI hiring
Authors: Rhea Alene K. , Markey Kelsey , D’Arinzo Lauren , Schellmann Hilke , Sloane Mona , Squires Paul , Arif Khan Falaah , Stoyanovich Julia
Published: September 2022

Automated hiring systems are among the fastest-developing of all high-stakes AI systems. Among these are algorithmic personality tests that use insights from psychometric testing, and promise to surface personality traits indicative of future success based on job seekers’ resumes or social media profiles. We interrogate the validity of such systems using stability of the outputs they produce, noting that reliability is a necessary, but not a sufficient, condition for validity. Crucially, rather than challenging or affirming the assumptions made in psychometric testing — that personality is a meaningful and measurable construct, and that personality traits are indicative of future success on the job — we frame our audit methodology around testing the underlying assumptions made by the vendors of the algorithmic personality tests themselves. Our main contribution is the development of a socio-technical framework for auditing the stability of algorithmic systems. This contribution is supplemented with an open-source software library that implements the technical components of the audit, and can be used to conduct similar stability audits of algorithmic systems. We instantiate our framework with the audit of two real-world personality prediction systems, namely, Humantic AI and Crystal. The application of our audit framework demonstrates that both these systems show substantial instability with respect to key facets of measurement, and hence cannot be considered valid testing instruments.
Assessment of the environmental drivers of European glass eel (Anguilla anguilla) recruitment in transitional waters
Authors: O’Leary Ciara , Healy Sarah , Cruikshanks Robert , Kelly Karen , Gargan Patrick
Published: September 2022

The European eel (Anguilla anguilla) has undergone an unprecedented population decline since the 1980s, with current recruitment levels fluctuating from 3 to 15% of historical levels for the last 20 years. Monitoring of glass eels and elvers as 0 + recruitment is an essential step in helping to understand the trend in recruitment and to better quantify the current recruitment time series. Two locations within the Shannon estuary on the west coast of Ireland were monitored for glass eel recruitment from January to April in 2017 and 2018. This study used a generalised linear mixed model to examine a range of environmental variables impacting on glass eel abundance in transitional waters. Results found that water temperature and moon phase were the most important variables. Tidal height and cloud cover also influenced the abundance of glass eels but to a lesser extent. This study found that focussing survey efforts on nights around the full moon when water temperatures exceed 5℃ will allow a catch which is representative of the population in an estuary. Glass eel monitoring needs a long-term sampling plan in order to account for annual fluctuations apparent in glass eel recruitment.
Radon (222Rn) as tracer for submarine groundwater discharge investigation—limitations of the approach at shallow wind-exposed coastal settings
Authors: Schubert Michael , Scholten Jan , Kreuzburg Matthias , Petermann Eric , de Paiva Mariele Lopes , Köhler Dennis , Liebetrau Volker , Rapaglia John , Schlüter Michael
Published: September 2022

Mapping radon (222Rn) distribution patterns in the coastal sea is a widely applied method for localizing and quantifying submarine groundwater discharge (SGD). While the literature reports a wide range of successful case studies, methodical problems that might occur in shallow wind-exposed coastal settings are generally neglected. This paper evaluates causes and effects that resulted in a failure of the radon approach at a distinct shallow wind-exposed location in the Baltic Sea. Based on a simple radon mass balance model, we discuss the effect of both wind speed and wind direction as causal for this failure. We show that at coastal settings, which are dominated by gentle submarine slopes and shallow waters, both parameters have severe impact on coastal radon distribution patterns, thus impeding their use for SGD investigation. In such cases, the radon approach needs necessarily to allow for the impact of wind speed and wind direction not only during but also prior to the field campaign.
Governing Through Ignorance: Swedish Authorities’ Treatment of Detained and Non-deported Migrants during the COVID-19 Pandemic
Authors: Lindberg Annika , Lundberg Anna , Rundqvist Elisabet , Häythiö Sofia
Published: September 2022

Tensions between migration enforcement and migrants’ health and rights have gained renewed urgency during the COVID-19 pandemic. This article critically analyses how the pandemic has affected detained and deportable people in Sweden. Building on an activist methodological approach and collaboration, based on a survey conducted inside Swedish detention centres during the pandemic and the authors’ research and activist engagement with migrants who are detained or legally stranded in Sweden, we argue that migration authorities’ inadequate measures to protect detained and deportable people during the pandemic is a case of governance through ignorance enabled by structural racism. The article traces how this ignorance operates on a structural, institutional and micro-level, enabling public disregard and political irresponsibility for the harmful effects of migration enforcement. A broader aim of the article is to challenge the structural, societal and epistemic ignorance of the conditions for detained and deportable persons and to contribute to political change.
Explaining the Association Between Fetal Growth and Childhood ADHD Symptoms: Cross-cohort Replication
Authors: Dooley Niamh , Healy Colm , Brannigan Ross , Cotter David , Clarke Mary , Cannon Mary
Published: September 2022

The association between restricted fetal growth and symptoms of attention deficit/hyperactivity disorder (ADHD) in childhood is well-replicated and robust. However, fetal growth is determined by many prenatal factors and associations with mental health may be confounded by familial and social context. In this study, we sought to quantify the relative contributions of prenatal factors and familial confounds to the association between fetal growth and ADHD symptoms. Two independent cohorts were analyzed, the Adolescent Brain Cognitive Development study (ABCD; United States) and the Growing Up in Ireland (GUI) study. ADHD symptoms were measured by the Child Behavior Checklist (ABCD) and the Strengths & Difficulties questionnaire (GUI) at age 9–10. Using sequential regression models, we assessed the change-in-association between fetal growth and ADHD symptoms after controlling for sex, familial factors (socioeconomic/demographic factors & family psychiatric history) and prenatal factors (pregnancy complications & maternal substance-use during pregnancy). Converging findings from cohorts suggested that over a quarter of the association between fetal growth and ADHD symptoms is attributable to familial confounds. The degree to which the association was explained by prenatal factors differed by cohort—pregnancy complications explained a larger proportion of the effect in ABCD (7.9%) than GUI (2.7%), and maternal substance-use explained a larger proportion of the effect in GUI (22.7%) compared to ABCD (4.8%). Different explanations of the fetal growth-ADHD association across cohorts suggests cohort-specific, and potentially nationally-specific, risk factors for fetal growth and related neurodevelopmental outcomes. The evidence suggests early prevention of ADHD in Ireland should focus on minimizing maternal smoking during pregnancy. In the US, prevention and treatment of pregnancy complications are highlighted as viable targets for intervention.
Do You See What I See? Longitudinal Associations Between Mothers’ and Adolescents’ Perceptions of Their Relationship and Adolescent Internalizing Symptoms
Authors: Nelemans Stefanie A. , Mastrotheodoros Stefanos , Çiftçi Leyla , Meeus Wim , Branje Susan
Published: September 2022

This 6-year community study examined how discrepancies in mothers’ and adolescents’ perceptions of their relationship were longitudinally associated with adolescent internalizing symptoms, and vice versa. 497 adolescents (57% boys, Mage T1 = 13.03, SDage = 0.46) and their mothers reported in 6 annual waves on conflict and warmth in the mother-adolescent relationship and adolescents reported on their depressive and Generalized Anxiety Disorder (GAD) symptoms. Latent Congruence Models suggested that both adolescent depressive and GAD symptoms significantly predicted higher levels of conflict as well as stronger discrepancies in perceptions of conflict 1-year later. In turn, higher levels of conflict significantly predicted both adolescent depressive and GAD symptoms 1-year later. For warmth, lower levels significantly predicted adolescent depressive symptoms 1-year later. Concluding, these findings suggest (1) more systematic evidence for longitudinal associations between conflict than warmth in the mother-adolescent relationship and adolescent internalizing symptoms; (2) support for a transactional model, including support for both interpersonal scar or symptom-driven effects (concerning both levels of and mother-adolescent discrepancies in conflict) and interpersonal risk or relationship-driven effects (concerning levels of both conflict and warmth); (3) longitudinal effects from adolescent internalizing symptoms to mother-adolescent discrepancies, but not vice versa; and (4) strong consistency in patterns of findings across both adolescent depressive and GAD symptoms, with few differential longitudinal associations with aspects of mother-adolescent relationship quality. Thereby, this study provides a more nuanced understanding of the direction of effects between adolescent internalizing symptoms and both levels of and discrepancies in mothers’ and adolescents’ perceptions of their relationship.
A context-aware unsupervised predictive maintenance solution for fleet management
Authors: Giannoulidis Apostolos , Gounaris Anastasios
Published: September 2022

We deal with the problem of predictive maintenance (PdM) in a vehicle fleet management setting following an unsupervised streaming anomaly detection approach. We investigate a variety of unsupervised methods for anomaly detection, such as proximity-based, hybrid (statistical and proximity-based) and transformers. The proposed methods can properly model the context in which each member of the fleet operates. In our case, the context is both crucial for effective anomaly detection and volatile, which calls for streaming solutions that take into account only the recent values. We propose two novel techniques, a 2-stage proximity-based one and context-aware transformers along with advanced thresholding. In addition, to allow for testing PdM techniques for vehicle fleets in a fair and reproducible manner, we build a new fleet-like benchmarking dataset based on an existing dataset of turbofan simulations. Our evaluation results show that our proposals reduce the maintenance costs compared to existing solutions.
Optimisation-driven design to explore and exploit the process–structure–property–performance linkages in digital manufacturing
Authors: Flores Ituarte Iñigo , Panicker Suraj , Nagarajan Hari P. N. , Coatanea Eric , Rosen David W.
Published: September 2022

An intelligent manufacturing paradigm requires material systems, manufacturing systems, and design engineering to be better connected. Surrogate models are used to couple product-design choices with manufacturing process variables and material systems, hence, to connect and capture knowledge and embed intelligence in the system. Later, optimisation-driven design provides the ability to enhance the human cognitive abilities in decision-making in complex systems. This research proposes a multidisciplinary design optimisation problem to explore and exploit the interactions between different engineering disciplines using a socket prosthetic device as a case study. The originality of this research is in the conceptualisation of a computer-aided expert system capable of exploring process–structure–property–performance linkages in digital manufacturing. Thus, trade-off exploration and optimisation are enabled of competing objectives, including prosthetic socket mass, manufacturing time, and performance-tailored socket stiffness for patient comfort. The material system is modelled by experimental characterisation—the manufacturing time by computer simulations, and the product-design subsystem is simulated using a finite element analysis (FEA) surrogate model. We used polynomial surface response-based surrogate models and a Bayesian Network for design space exploration at the embodiment design stage. Next, at detail design, a gradient descent algorithm-based optimisation exploits the results using desirability functions to isolate Pareto non-dominated solutions. This work demonstrates how advanced engineering design synthesis methods can enhance designers’ cognitive ability to explore and exploit multiple disciplines concurrently and improve overall system performance, thus paving the way for the next generation of computer systems with highly intertwined material, digital design and manufacturing workflows.

Graphical abstract
Comparison of Semi-autonomous Mobile Robot Control Strategies in Presence of Large Delay Fluctuation
Authors: Seppänen Alvari , Vepsäläinen Jari , Ojala Risto , Tammi Kari
Published: September 2022

We propose semi-autonomous control strategies to assist in the teleoperation of mobile robots under unstable communication conditions. A short-term autonomous control system is the assistance in the semi-autonomous control strategies, when the teleoperation is compromised. The short-term autonomous control comprises of lateral and longitudinal functions. The lateral control is based on an artificial potential field method where obstacles are repulsive, and a route is attractive. LiDAR-based artificial potential field methods are well studied. We present a novel artificial potential field method based on color and depth images. Benefit of a camera system compared to a LiDAR is that a camera detects color, is cheaper, and does not have moving parts. Moreover, utilization of active sensors is not desired in the particle accelerator environment. A set of experiments with a robot prototype are carried out to validate this system. The experiments are carried out in an environment which mimics the accelerator tunnel environment. The difficulty of the teleoperation is altered with obstacles. Fully manual and autonomous control are compared with the proposed semi-autonomous control strategies. The results show that the teleoperation is improved with autonomous, delay-dependent, and control-dependent assist compared to the fully manual control. Based on the operation time, control-dependent assist performed the best, reducing the time by 12% on the tunnel section with most obstacles. The presented system can be easily applied to common industrial robots operating e.g. in warehouses or factories due to hardware simplicity and light computational demand.
Polish Criminal Liability of the UAV Operator in Connection with the Unauthorized Flight in Operations Area of Fire Services in the Context of the Possibility of the U-Space Using
Authors: Balcerzak Tomasz , Sieradzka Małgorzata
Published: September 2022

The events of recent weeks have reminded us how difficult it is to fight the element of fire. Knowing where the fire has started allows emergency services to react faster and more efficiently. That is why unmanned aerial vehicles are being used more and more often. A drone with a thermal imaging camera locates fire outbreaks from the air. The drone will not only reach places that are difficult to reach, but also record an image from a wide area and—importantly—allow you to assess the extent of damage and fire extinguishing activities in real time. The unmanned aerial vehicle with a thermal imaging camera installed, despite the clouds of smoke and poor visibility, registers where the fire is located and in which direction the fire is spreading. The obtained data, along with the exact coordinates of the places that are on fire, are forwarded to the fire brigade. On this basis, firefighters can react quickly and move to a specific area to extinguish a fire. The analysis of the temperature of the indicated area also translated into the safety of the services participating in the fire extinguishing operation. This refers to issues in the area of public safety and privacy. UAV, under the international, regional and national regime of aviation law, are considered as aircraft. Since there is no framework existing in connection with the unauthorized flight in operations area of fire services in the context of the possibility of the U-space using, this paper will focus on this analysis. The paper’s conclusion will address the proposed recommendations for the UAV and aviation industries to consider as potential solution- implementation of the U-space concept. This idea, apart from the regulations of aviation law and other branches of law seems to be relevant for sanctioning the use of the airspace in fire services application.
Vector Field based Control of Quadrotor UAVs for Wildfire Boundary Monitoring
Authors: Feng Licheng , Katupitiya Jay
Published: September 2022

Accurate real-time information about an ongoing wildfire event is important for realizing effective and safe wildfire fighting. This paper is intended to solve the problem of guiding Unmanned Air Vehicles (UAVs) equipped with onboard cameras to monitor dynamic wildfire boundaries. According to whether the prior knowledge of the wildfire boundary is available or not, we propose a model-based vector field and a model-free vector field for UAV guidance. By describing the wildfire boundary with a zero level set function, the propagation of the wildfire boundary is modeled with the Hamilton-Jacobi equation. If the prior knowledge of the boundary is available, the typical radial basis function thin-plate spline is adopted to approximate the wildfire boundary and predicts its propagation. Then a 3D analytical vector field is constructed for an implicit function representing the wildfire boundary. If only partial observation of the wildfire boundary within the UAV’s field of view is available, the horizontal error between the UAV and its sensed segment of wildfire boundary and the vertical error between the UAV and the desired altitude are utilized to construct a 3D distance error based vector field, directly. To guide the UAV to converge to and patrol along the advancing wildfire boundary, the complex nonlinear dynamics of the UAV is exploited with differential flatness and incorporated with the above mentioned vector fields to design a nonlinear geometric controller. Computer simulations have been conducted to evaluate the performance of the proposed 3D vector field based controllers with both synthetic and real data, and simulation results demonstrate that the proposed algorithms can be effective methods to monitor the advancing wildfire boundaries.
A Modified Michael’s Selection Theorem with Application to Generalized Nash Equilibrium Problem
Authors: Castellani Marco , Giuli Massimiliano
Published: September 2022

This paper provides a sufficient condition for the existence of solutions for generalized Nash equilibrium problems in the infinite-dimensional setting and with a countable (possibly infinite) number of players. The result has been achieved as a consequence of a modified version of Michael’s selection theorem that works even when the range space is not metrizable and the set-valued map has not closed values.
Osteoporosis and risk of fracture: reference class problems are real
Authors: Binney Nicholas
Published: September 2022

Elselijn Kingma argues that Christopher Boorse’s biostatistical theory does not show how the reference classes it uses—namely, age groups of a sex of a species—are objective and naturalistic. Boorse has replied that this objection is of no concern, because there are no examples of clinicians’ choosing to use reference classes other than the ones he suggests. Boorse argues that clinicians use the reference classes they do because these reflect the natural classes of organisms to which their patients belong. Drawing on a thorough exploration of how the disease osteoporosis is defined in adults, I argue that clinicians do indeed make choices about which reference classes to use in diagnosis. Clinicians use young adult reference classes to diagnose osteoporosis in elderly patients. They also use young female reference classes to diagnose osteoporosis in elderly males. Clinicians adjust their reference classes so that the diagnosis of osteoporosis reflects a person’s risk of sustaining a fragility fracture. The ethical intuition that people with the same risk of fracture should receive the same diagnosis overwhelms the naturalistic intuition that reference classes should reflect natural classes of organisms of uniform functional design. Clinicians construct a variety of reference class types, including pathological reference classes and epidemiological population-specific reference classes, to serve this ethical intuition. I show how clinicians use several reference classes at once so that they can more accurately predict risk of fracture. Ultimately, the reference classes chosen and used in medical practice are quite different from those proposed in naturalistic philosophy of medicine.
Interpersonal relation recognition: a survey
Authors: Guerdelli Hajer , Ferrari Claudio , Berretti Stefano
Published: September 2022

People spend a considerable amount of their time in social activities, where person-to-person relations are of main relevance. Recently, there has been an increasing research interest in automatically analyzing interpersonal relations, for the social and behavioral implications, and the many practical applications it may have. However, to the best of our knowledge, there is not a systematic study providing a harmonized view of the literature in the field. On this ground, we summarize in our work interpersonal relation recognition datasets and methods aiming to help researchers to have a better understanding of the characteristics of the state-of-the-art. In the proposed study, we distinguish between methods that address objective relations that do not depend on behavior or emotional state, and methods that consider subjective ones that depend on emotions. It turns out quite evidently that aiming at the latter recognition task is more challenging, with the existing methods that provide convincing results only on limited and very specific cases. For both the broad categories, we discuss datasets and methods according to the different behavioural and psychological models used to annotate and classify the data. We conclude our review work, by providing a comprehensive discussion pointing out current limitations and future research perspectives.
Visual Processing During the Interictal Period Between Migraines: A Meta-Analysis
Authors: Sezai Timucin , Murphy Melanie J. , Riddell Nina , Nguyen Vinh , Crewther Sheila G.
Published: September 2022

Migraine is a poorly understood neurological disorder and a leading cause of disability in young adults, particularly women. Migraines are characterized by recurring episodes of severe pulsating unilateral headache and usually visual symptoms. Currently there is some disagreement in the electrophysiological literature regarding the universality of all migraineurs exhibiting physiological visual impairments also during interictal periods (i.e., the symptom free period between migraines). Thus, this meta-analysis investigated the evidence for altered visual function as measured electrophysiologically via pattern-reversal visual evoked potential (VEP) amplitudes and habituation in adult migraineurs with or without visual aura and controls in the interictal period. Twenty-three studies were selected for random effects meta-analysis which demonstrated slightly diminished VEP amplitudes in the early fast conducting P100 component but not in N135, and substantially reduced habituation in the P100 and the N135 in migraineurs with and without visual aura symptoms compared to controls. No statistical differences were found between migraineurs with and without aura, possibly due to inadequate studies. Overall, insufficient published data and substantial heterogeneity between studies was observed for all latency components of pattern-reversal VEP, highlighting the need for further electrophysiological experimentation and more targeted temporal analysis of visual function, in episodic migraineurs.
RiskScape: a flexible multi-hazard risk modelling engine
Authors: Paulik Ryan , Horspool Nick , Woods Richard , Griffiths Nick , Beale Tim , Magill Christina , Wild Alec , Popovich Benjamin , Walbran Glenn , Garlick Russel
Published: September 2022

This paper presents the architecture and features of RiskScape software. RiskScape is an open-source software with a flexible modelling engine for multi-hazard risk analysis. The RiskScape engine implements modeller-defined risk quantification workflows as ‘model pipelines’. Model pipeline steps and functions analyse hazard, exposure, and vulnerability data across different spatio-temporal domains using geoprocessing and spatial sampling operations. The RiskScape engine supports deterministic and probabilistic risk quantification, with several probabilistic-based modes described in this paper. RiskScape advances modelling software for multi-hazard risk analysis through several implementation features. The RiskScape engine operates model pipelines independent of system prescribed model input data classifications or standards. Multiple hazard types, metric intensities, and temporal occurrence information is geometry processed and sampled to create coverage data of simultaneous or sequenced multi-hazard events at object-exposure locations. Escalating multi-hazard event impacts are then determined for object-exposures using scripted conditional or nested statements that apply vulnerability functions in a logical sequence of temporal hazard and impact occurrence. These model features, supported by open geospatial consortium standard geospatial data files and operations, expedite RiskScape for modelling multi-hazard risk at any geographical location or scale.
An evaluation of vegetation loss due to the super typhoon Sarika in Hainan Island of China
Authors: Fengjin Xiao , Qiufeng Liu
Published: September 2022

With the continued strengthening of global climate change, various extreme climate events have become increasingly prominent. Typhoons are the most disastrous weather event that cause severe losses to the economy, agriculture, transportation, communication, and ecosystem in coastal regions. The super typhoon Sarika that hit Hainan Island on October 18, 2016, lasted for 15 h, and it was the most severe typhoon that hit this island in October since 1970. The coastal land of Hainan Island experienced gales with an average speed of 35 m/s. In this study, we evaluated the impact of the super typhoon Sarika on vegetation by performing normalized difference vegetative index (NDVI) difference analysis using MODIS multi-temporal images acquired before and after the typhoon. The assessment of typhoons depends on the land-use types and landscape topography of slope, aspect, and altitude. The results indicated that the super typhoon Sarika seriously hit forestry, agriculture, shrubs, plantations, and orchards on Hainan Island. Overall, 79% of vegetation exhibited a negative change, whereas only 21% of vegetation exhibited a positive change in NDVI after the super typhoon Sarika. Agriculture was most severely impacted by the typhoon, where more than 81% of areas exhibited a decrease in NDVI, followed by plantations and orchards, where 77% of areas exhibited a decrease in NDVI. Additionally, the impact of the typhoon on vegetation was affected by the degree of NDVI decrease with the altitude, slope, and aspect. In conclusion, vegetation damage is associated with land cover types, altitude, aspect, and slope. NDVI decreased more in low-altitude plain and coastal areas than in higher altitude montane forest areas.
Decoherence and classicalization of continuous-time quantum walks on graphs
Authors: Bressanini Gabriele , Benedetti Claudia , Paris Matteo G. A.
Published: September 2022

We address decoherence and classicalization of continuous-time quantum walks (CTQWs) on graphs. In particular, we investigate three different models of decoherence and employ the quantum-classical (QC) dynamical distance as a figure of merit to assess whether, and to which extent, decoherence classicalizes the CTQW, i.e. turns it into the analogue classical process. We show that the dynamics arising from intrinsic decoherence, i.e. dephasing in the energy basis, do not fully classicalize the walker and partially preserves quantum features. On the other hand, dephasing in the position basis, as described by the Haken–Strobl master equation or by the quantum stochastic walk (QSW) model, asymptotically destroys the quantumness of the walker, making it equivalent to a classical random walk. We also investigate how fast is the classicalization process and observe a larger rate of convergence of the QC-distance to its asymptotic value for intrinsic decoherence and the QSW models, whereas in the Haken–Strobl scenario, larger values of the decoherence rate induce localization of the walker.
What drives the valuation of entrepreneurial ventures? A map to navigate the literature and research directions
Authors: Colombo Massimo G. , Montanaro Benedetta , Vismara Silvio
Published: September 2022

The drivers of the valuations of entrepreneurial ventures are an important issue in entrepreneurial finance, but related research is fragmented. The theoretical perspectives and the drivers highlighted by previous studies differ based on the financial milestones during a venture’s lifecycle in which the valuation is performed (e.g., venture capital investments, initial public offerings, acquisitions). The introduction of new digital financing channels (e.g., crowdfunding, initial coin offerings) that allow retail investors to directly invest in entrepreneurial ventures challenge our understanding of the drivers of valuation. This change has also increased the diversity in the sequence of financial milestones that ventures go through, with important implications for valuation. We conduct a systematic literature review and develop a map highlighting how and why the drivers of venture valuations and their underlying theoretical lenses vary across the different milestones that ventures go through. The map allows us to outline new promising avenues for future research.
Patterns of white-tailed deer movements in suburban Maryland: implications for zoonotic disease mitigation
Authors: Roden-Reynolds P. , Kent C. M. , Li A. Y. , Mullinax J. M.
Published: September 2022

Understanding the ecology of the often dense white-tailed deer populations in urban and suburban landscapes is important for mitigating a variety of conflicts that arise with dense human populations, such as issues surrounding zoonotic disease mitigation. We collared white-tailed deer in highly suburban areas of Howard County, Maryland with high-resolution GPS collars. Then, we created autocorrelated kernel density home ranges for broader land use analyses and concurrently used general additive models to characterize fine-scale hourly measures of speed, activity, and proximity to residential buildings. Suburban deer home ranges encompassed approximately 35% direct residential land, and an average of 71 and 129 residential properties were found within female and male core ranges, respectively. Sex, time of day, and day of year all influenced fine-scale speeds, activity levels, and proximity to residential property buildings. Deer moved into residential areas nightly, especially in winter, and exhibited bouts of increased speed and activity shortly after sunrise and sunset, with distinctive seasonal changes. We discuss how variation in home ranges and movements may influence population management success and explore year-round periods of increased risk of deer transporting ticks to residential areas. These findings focus our broad understanding of deer movements in suburban and urban landscapes to improve deer population management and to mitigate the spread of ticks into residential areas.
OASIS: Only Adversarial Supervision for Semantic Image Synthesis
Authors: Sushko Vadim , Schönfeld Edgar , Zhang Dan , Gall Juergen , Schiele Bernt , Khoreva Anna
Published: September 2022

Despite their recent successes, generative adversarial networks (GANs) for semantic image synthesis still suffer from poor image quality when trained with only adversarial supervision. Previously, additionally employing the VGG-based perceptual loss has helped to overcome this issue, significantly improving the synthesis quality, but at the same time limited the progress of GAN models for semantic image synthesis. In this work, we propose a novel, simplified GAN model, which needs only adversarial supervision to achieve high quality results. We re-design the discriminator as a semantic segmentation network, directly using the given semantic label maps as the ground truth for training. By providing stronger supervision to the discriminator as well as to the generator through spatially- and semantically-aware discriminator feedback, we are able to synthesize images of higher fidelity and with a better alignment to their input label maps, making the use of the perceptual loss superfluous. Furthermore, we enable high-quality multi-modal image synthesis through global and local sampling of a 3D noise tensor injected into the generator, which allows complete or partial image editing. We show that images synthesized by our model are more diverse and follow the color and texture distributions of real images more closely. We achieve a strong improvement in image synthesis quality over prior state-of-the-art models across the commonly used ADE20K, Cityscapes, and COCO-Stuff datasets using only adversarial supervision. In addition, we investigate semantic image synthesis under severe class imbalance and sparse annotations, which are common aspects in practical applications but were overlooked in prior works. To this end, we evaluate our model on LVIS, a dataset originally introduced for long-tailed object recognition. We thereby demonstrate high performance of our model in the sparse and unbalanced data regimes, achieved by means of the proposed 3D noise and the ability of our discriminator to balance class contributions directly in the loss function. Our code and pretrained models are available at https://github.com/boschresearch/OASIS.
An intelligent access algorithm for large scale multihop wireless networks based on mean field game
Authors: Wang Yu , Ni Qinyin , Yu Junjiang , Jia Enfu , Zhu Xiaorong
Published: September 2022

In a distributed wireless network with a large number of nodes, competitive access of nodes may result in the deterioration of throughput and energy. Therefore, in this paper we propose an intelligent access algorithm based on the mean field game (MFG). First, we formulate the competitive access process between nodes as a game Query ID="Q1" Text="Please check and confirm that the authors and their respective affiliations have been correctly identified and amend if necessary." process by a stochastic differential game model, which maximizes the energy efficiency of nodes and obtain the optimal behavior strategy while meeting the requirements of channel access. However, as the number of nodes increases, the dimension of the matrix used to characterize the interaction between nodes becomes too large, which increases the complexity of the solution procedure. Therefore, we introduce the MFG and the interaction between nodes can be approximately transformed into the interaction between nodes and the mean field, which not only reduces the complexity, but also reduces the computational overhead. In addition, the HJB-FPK equation is solved to obtain the Nash equilibrium of the MFG. Finally, a backoff strategy based on the Markov model is proposed, and the node obtains the corresponding backoff strategy according to the network situation and its own state. Simulation results show that the proposed algorithm has good performances on optimizing network throughput and energy efficiency for a large scale multi-hop wireless network.
Andrographolide ameliorates hepatic steatosis by suppressing FATP2-mediated fatty acid uptake in mice with nonalcoholic fatty liver disease
Authors: Ran Li-Sha , Wu Ya-Zeng , Gan Yi-Wen , Wang Hong-Lian , Wu Li-Juan , Zheng Chun-Mei , Ming Yao , Xiong Ran , Li Yong-Lin , Lei Shi-Hang , Wang Xue , Lao Xiao-Qing , Zhang Hong-Min , Wang Li , Chen Chen , Zhao Chang-Ying
Published: September 2022

Excessive intrahepatocellular lipid accumulation or steatosis is caused by abnormal lipid metabolism and a common character of nonalcoholic fatty liver disease (NAFLD), which may progress into cirrhosis and hepatocellular cancer. Andrographolide (Andro) is the primary active ingredient extracted from Andrographis paniculata, showing a protective role against dietary steatosis with the mechanism not fully understood. In this study, we showed that administration of Andro (50, 100, and 200 mg/kg/day for 8 weeks, respectively) attenuated obesity and metabolic syndrome in high-fat diet (HFD)-fed mice with improved glucose tolerance, insulin sensitivity, and reduced hyperinsulinemia, hyperglycemia, and hyperlipidemia. HFD-fed mice presented hepatic steatosis, which was significantly prevented by Andro. In vitro, Andro decreased the intracellular lipid droplets in oleic acid-treated LO2 cells. The selected RT-PCR array revealed a robust expression suppression of the fatty acid transport proteins (FATPs) by Andro treatment. Most importantly, we found that Andro consistently reduced the expression of FATP2 in both the oleic acid-treated LO2 cells and liver tissues of HFD-fed mice. Overexpression of FATP2 abolished the lipid-lowering effect of Andro in oleic acid-treated LO2 cells. Andro treatment also reduced the fatty acid uptake in oleic acid-treated LO2 cells, which was blunted by FATP2 overexpression. Collectively, our findings reveal a novel mechanism underlying the anti-steatosis effect of Andro by suppressing FATP2-mediated fatty acid uptake, suggesting the potential therapeutic application of Andro in the treatment of NAFLD.

Graphical abstract
An ecosystem service approach to the study of vineyard landscapes in the context of climate change: a review
Authors: Candiago Sebastian , Winkler Klara Johanna , Giombini Valentina , Giupponi Carlo , Egarter Vigl Lukas
Published: September 2022

Vineyard landscapes significantly contribute to the economy, identity, culture, and biodiversity of many regions worldwide. Climate change, however, is increasingly threatening the resilience of vineyard landscapes and of their ecological conditions, undermining the provision of multiple ecosystem services. Previous research has often focused on climate change impacts, ecosystem conditions and ecosystem services without systematically reviewing how they have been studied in the literature on viticulture. Here, we systematically review the literature on vineyard landscapes to identify how ecosystem conditions and services have been investigated, and whether an integrative approach to investigate the effects of climate change was adopted. Our results indicate that there are still very few studies that explicitly address multiple ecosystem conditions and services together. Only 28 and 18% of the reviewed studies considered more than two ecosystem conditions or services, respectively. Moreover, while more than 97% of the relationships between ecosystem conditions and services studied were addressing provisioning and regulating services, only 3% examined cultural services. Finally, this review found that there is a lack of integrative studies that address simultaneously the relationships between ecosystem condition, ecosystem services and climate change (only 15 out of 112 studies). To overcome these gaps and to better understand the functioning of vineyard socio-ecological systems under climate change, multidisciplinary, integrative, and comprehensive approaches should be adopted by future studies. A holistic understanding of vineyard landscapes will indeed be crucial to support researchers and decision makers in developing sustainable adaptation strategies that enhance the ecological condition of vineyards and ensure the provision of multiple ecosystem services under future climate scenarios.
Demographic reporting across a decade of neuroimaging: a systematic review
Authors: Sterling Elijah , Pearl Hannah , Liu Zexuan , Allen Jason W. , Fleischer Candace C.
Published: September 2022

Diversity of participants in biomedical research with respect to race, ethnicity, and biological sex is crucial, particularly given differences in disease prevalence, recovery, and survival rates between demographic groups. The objective of this systematic review was to report on the demographics of neuroimaging studies using magnetic resonance imaging (MRI). The Web of Science database was used and data collection was performed between June 2021 to November 2021; all articles were reviewed independently by at least two researchers. Articles utilizing MR data acquired in the United States, with n ≥ 10 human subjects, and published between 2010–2020 were included. Non-primary research articles and those published in journals that did not meet a quality control check were excluded. Of the 408 studies meeting inclusion criteria, approximately 77% report sex, 10% report race, and 4% report ethnicity. Demographic reporting also varied as function of disease studied, participant age range, funding, and publisher. We anticipate quantitative data on the extent, or lack, of reporting will be necessary to ensure inclusion of diverse populations in biomedical research.
Overcoming Vaccine Hesitancy for Future COVID-19 and HIV Vaccines: Lessons from Measles and HPV Vaccines
Authors: Aguolu Obianuju G. , Malik Amyn A. , Ahmed Noureen , Omer Saad B.
Published: September 2022

Background

The discovery of vaccines significantly reduced morbidity and mortality of infectious diseases and led to the elimination and eradication of some. Development of safe and effective vaccines is a critical step to the control of infectious diseases; however, there is the need to address vaccine hesitancy because of its potential impact on vaccine uptake.

Methods

We conducted a narrative review of studies on interventions to address measles and human papillomavirus vaccine hesitancy. We discussed how lessons learned from these studies could be applied towards COVID-19 and future human immunodeficiency virus vaccines.

Results

We found that there are several successful approaches to improving vaccine acceptance. Interventions should be context specific and build on the challenges highlighted in various settings.

Conclusion

Strategies could be used alone or in combination with others. The most successful interventions directly targeted the population for vaccination. Use of financial incentives could be a potential tool to improve vaccine uptake.
Physiological linkage of thyroid and pituitary sensitivities
Authors: Fitzgerald Stephen Paul , Bean Nigel G. , Falhammar Henrik , Hoermann Rudolf
Published: September 2022

Objectives

The sensitivities of the pituitary to thyroxine feedback, and the thyroid to thyrotropin stimulation determine the free thyroxine /thyrotropin feedback loop and can be described mathematically by two curves. It is not well understood how the two curves combine in a healthy population with normal thyroid function to express the individual balance points that are observed. This study was directed at this issue testing the possibilities of random combination and directed linkage between the two curves.

Methods

We reverse-engineered two sets of population data, on the assumption of independent combinations of thyroid and pituitary sensitivities, to obtain estimates of the curve describing thyroid sensitivity. Sensitivity studies were performed.

Results

No analysis resulted in a physiologically feasible estimate of the curve describing thyroid sensitivity. There was evidence of linkage of the two curves in terms of their combination throughout the normal range. Thyroid response curves reflecting a low free thyroxine response to thyrotropin tended to be combined in individuals with thyrotropin curves reflecting a high thyrotropin response to free thyroxine, and vice versa.

Conclusions

Thyroid and pituitary sensitivities are linked, being combined in individuals in a non-random directed pattern. Direct mutual interaction may contribute to this linkage. This linkage precludes the derivation of the curves describing these sensitivities from population data of the free thyroxine and thyrotropin relationship and complicates their derivation by physiological experimentation. This linkage and probable interaction may also bestow evolutionary advantage by minimising inter-individual variation in free thyroxine levels and by augmenting homeostasis.