bluuebunny/crossref_metadata_2025_split huggingface dataset:
Liars and Trolls and Bots Online: The Problem of Fake Persons
Authors: Harris Keith Raymond 
Published: May 2023

AbstractThis paper describes the ways in which trolls and bots impede the acquisition of knowledge online. I distinguish between three ways in which trolls and bots can impede knowledge acquisition, namely, by deceiving, by encouraging misplaced skepticism, and by interfering with the acquisition of warrant concerning persons and content encountered online. I argue that these threats are difficult to resist simultaneously. I argue, further, that the threat that trolls and bots pose to knowledge acquisition goes beyond the mere threat of online misinformation, or the more familiar threat posed by liars offline. Trolls and bots are, in effect, fake persons. Consequently, trolls and bots can systemically interfere with knowledge acquisition by manipulating the signals whereby individuals acquire knowledge from one another online. I conclude with a brief discussion of some possible remedies for the problem of fake persons.
Towards computational awareness in autonomous robots: an empirical study of computational kernels
Authors: Sifat Ashrarul H. , Bharmal Burhanuddin , Zeng Haibo , Huang Jia-Bin , Jung Changhee , Williams Ryan K. 
Published: May 2023

AbstractThe potential impact of autonomous robots on everyday life is evident in emerging applications such as precision agriculture, search and rescue, and infrastructure inspection. However, such applications necessitate operation in unknown and unstructured environments with a broad and sophisticated set of objectives, all under strict computation and power limitations. We therefore argue that the computational kernels enabling robotic autonomy must be scheduled and optimized to guarantee timely and correct behavior, while allowing for reconfiguration of scheduling parameters at runtime. In this paper, we consider a necessary first step towards this goal of computational awareness in autonomous robots: an empirical study of a base set of computational kernels from the resource management perspective. Specifically, we conduct a data-driven study of the timing, power, and memory performance of kernels for localization and mapping, path planning, task allocation, depth estimation, and optical flow, across three embedded computing platforms. We profile and analyze these kernels to provide insight into scheduling and dynamic resource management for computation-aware autonomous robots. Notably, our results show that there is a correlation of kernel performance with a robot’s operational environment, justifying the notion of computation-aware robots and why our work is a crucial step towards this goal.
Investigation of Microstructure and Mechanical Properties of AlSi7Mg0.3Cu0.5 Alloy with Addition of Zr, Ti and Sr
Authors: Bolibruchová Dana , Matejka Marek , Brůna Marek , Kuriš Michal , Michalcová Alena 
Published: May 2023

AbstractThe main objective of this paper is to investigate the effect of Zr and its synergistic effect together with the addition of an inoculant (Ti) and a modifier (Sr) in terms of structural analysis and mechanical characteristics of AlSi7Mg0.3Cu0.5 alloy. In accordance with the objectives of the paper, in the experimental part, eight material variants were cast by the investment casting method. The presence of Zr in the experimental alloys was manifested by the crystallization of phases with different chemical compositions mainly (AlSi)3(TiZr) and (AlSi)2(TiZr). The graded addition of Ti in the alloys resulted in a higher number of nucleation grains of Zr-rich phases, and alloys with a combination of Zr and Sr were characterized by the formation of (AlSi)2(TiZr) phases. Various types of precipitates based on Zr (Al3(ZrTi)) and Cu (Al2Cu) were observed in the substructure of the alloys. The addition of Zr and its synergistic effect with Ti and Sr led to an increase in the conventional yield strength, hardness and partly tensile strength with reduced elongation of the experimental alloys.
A national survey of antimicrobial stewardship content in Canadian entry-to-practice pharmacy programs
Authors: Sauve Jenna M. , Dresser Linda D. , Rocchi Marie A. , So Miranda 
Published: May 2023

Abstract

Objective:
To describe the current landscape of antimicrobial stewardship (AMS) instruction in Canadian entry-to-practice pharmacy programs and the perceived barriers and facilitators to optimizing teaching and learning.

Design:
Electronic survey.

Participants:
Faculty representatives from the 10 Canadian entry-to-practice pharmacy programs, including content experts and faculty leadership.

Methods:
A review of international literature pertaining to AMS in pharmacy curricula informed a 24-item survey, which was open for completion from March to May of 2021. Curriculum content questions were developed using AMS topics recommended by pharmacy educators in the United States, and professional roles described by the Association of Faculties of Pharmacy of Canada.

Results:
All 10 Canadian faculties returned a completed survey. All programs reported teaching AMS principles in their core curricula. Content coverage varied, with programs teaching, on average, 68% of the recommended AMS topics from the United States. Potential gaps were identified within the professional roles of “communicator” and “collaborator.” Didactic methods of content delivery and student assessment, such as lectures and multiple-choice questions, were most frequently used. Three programs offered additional AMS content in their elective curricula. Experiential rotations in AMS were commonly offered, though teaching AMS in formalized interprofessional settings was rare. Curricular time constraints were identified by all programs as a barrier to enhancing AMS instruction. A course to teach AMS, a curriculum framework, and prioritization by the faculty’s curriculum committee were perceived as facilitators.

Conclusions:
Our findings highlight potential gaps and areas of opportunity within Canadian pharmacy AMS instruction.
Ability of presepsin concentrations to predict mortality in adult patients with sepsis
Authors: Aliu-Bejta Ajete , Kurshumliu Mentor , Namani Sadie , Dreshaj Shemsedin , Baršić Bruno 
Published: May 2023

Abstract

Background:
Early diagnosis of sepsis is essential for a favorable disease outcome. The aim of this study was to evaluate the association of initial and subsequent presepsin concentrations with sepsis outcomes.

Methods:
One hundred sepsis patients were enrolled in the study from two different university centers. Four times during study, concentrations of presepsin, procalcitonin (PCT), and C-reactive protein (CRP) were measured, and Sequential Organ Failure Assessment (SOFA) score and Acute Physiology and Chronic Health Evaluation (APACHE II) score were calculated. Patients were grouped into survivors and nonsurvivors. A sandwich ELISA kit was used to measure presepsin concentrations. To test the changes in biomarkers concentrations and SOFA score and APACHE II score during the disease course and to estimate the differences between outcome groups, generalized linear mixed effects model was used. Receiver operating characteristic curve analysis was performed to determine the prognostic value of presepsin concentrations.

Results:
Initial values of presepsin, SOFA score, and APACHE II score were significantly higher in nonsurvivors compared to survivors. Concentrations of PCT and CRP did not differ significantly between outcome groups. ROC curve analyses show a greater predictive ability of initial presepsin concentrations for predicting mortality compared to subsequent measurements of presepsin concentrations.

Conclusions:
Presepsin has a good ability to predict mortality. Initial presepsin concentrations better reflects poor disease outcome compared to presepsin concentrations 24 and 72 hours after admission.
Age Inequalities in Political Representation: A Review Article
Authors: Stockemer Daniel , Sundström Aksel 
Published: May 2023

AbstractPeople in political decision-making across the globe tend to be much older than the average voter. As such, parliaments and cabinets are unrepresentative of the larger population. This has consequences: it risks favouring policies geared towards the interests of older cohorts, it might alienate youth from voting and could push parties to appeal (even more) to older voters. In this review, we synthesize the growing literature on youth representation. We do so by: (1) delineating the group of young politicians, (2) discussing why youth ought to be present in politics, (3) empirically depicting the state of youth representation, and (4) illustrating the factors that help or harm youth to enter politics. This synthesis shows the degree to which young people are absent from decision-making bodies across the national, subnational and supra-national levels and attempts to make sense of the reasons why there is such a dearth of youth as candidates and representatives. We conclude by discussing gaps in research and suggesting several avenues for future work.
A learning perspective on the emergence of abstractions: the curious case of phone(me)s
Authors: Milin Petar , Tucker Benjamin V. , Divjak Dagmar 
Published: May 2023

AbstractIn this study, we propose an operationalization of the concept ofemergencewhich plays a crucial role in usage-based theories of language. The abstractions linguists operate with are assumed to emerge through a process of generalization over the data language users are exposed to. Here, we use two types of computational learning algorithms that differ in how they formalize and execute generalization and, consequently, abstraction, to probe whether a type of language knowledge that resembles linguistic abstractions could emerge from exposure to raw data only. More specifically, we investigated whether a phone, undisputedly the simplest of all linguistic abstractions, could emerge from exposure to speech sounds using two computational learning processes: memory-based learning and error-correction learning (ECL). Both models were presented with a significant amount of pre-processed speech produced by one speaker. We assessed (1) the consistency or stability of what these simple models learn and (2) their ability to approximate abstract categories. Both types of models fare differently regarding these tests. We show that only ECL models can learn abstractions and that at least part of the phone inventory and its grouping into traditional types can be reliably identified from the input.
Public Disclosure and Consumer Financial Protection
Authors: Dou Yiwei , Roh Yongoh 
Published: May 2023

AbstractThe U.S. Consumer Financial Protection Bureau has released a database of consumer complaints about banks’ financial products to the public since 2013. We find a greater reduction in mortgage applications to banks that receive more mortgage complaints in local markets after the disclosures. The effect is stronger in areas with more sophisticated consumers and higher credit competition, and for banks receiving more severe complaints. The number of monthly mortgage complaints per bank exhibits faster mean reversion after the publication of the database. These findings suggest that the public disclosure of mortgage complaints enhances product market discipline and consumer financial protection.
Mesospheric and Upper Stratospheric Temperatures From OMPS‐LP
Authors: Chen Zhong , Schwartz Michael J. , Bhartia Pawan K. , Schoeberl Mark , Kramarova Natalya , Jaross Glen , DeLand Matthew 
Published: May 2023

AbstractWe report the development of a temperature profile data set from the Ozone Mapping and Profiler Suite Limb Profiler (OMPS‐LP) instrument on the Suomi NPP satellite. The data set covers a roughly 10 year period from 2012 to 2022, and temperatures are provided in the altitude range between 35 and 70 km. The algorithm uses daytime Rayleigh scattered radiation near 350 nm to estimate atmospheric density profiles, which are vertically integrated using the hydrostatic equation to estimate atmospheric pressure. Temperature profiles are then derived using the ideal gas law. Spectral structures in the OMPS‐LP radiances are a source of systematic errors that limit the absolute accuracy to ±2 K. However, since the systematic errors do not change significantly over time, the relative accuracy is better than ±1 K. Our temperature data set has been designed to supplement global temperature maps produced by assimilation of data from traditional meteorological sensors, including GNSS radio occultation sensors, that provide high quality atmospheric temperature profiles up to 40–55 km. We show comparisons to other co‐located temperature datasets to validate our product.
Time‐Variability of Flow Recession Dynamics: Application of Machine Learning and Learning From the Machine
Authors: Kim Minseok , Bauser Hannes H. , Beven Keith , Troch Peter A. 
Published: May 2023

AbstractFlow recession analysis, relating discharge Q and its time rate of change −dQ/dt, has been widely used to understand catchment scale flow dynamics. However, data points in the recession plot, the plot of −dQ/dt versus Q, typically form a wide point cloud due to noise and hysteresis in the storage‐discharge relationship, and it is still unclear what information we can extract from the plot and how to understand the information. There seem to be two contrasting approaches to interpret the plot. One emphasizes the importance of the ensemble characteristics of many recessions (i.e., the lower envelope or a measure of central tendency), and the other highlights the importance of the event scale analysis and questions the meaning of the ensemble characteristics. We examine if those approaches can be reconciled. We utilize a machine learning tool to capture the point cloud using the past trajectory of daily discharge. Our model results for a catchment show that most of the data points can be captured using 5 days of past discharge. We show that we can learn the catchment scale flow recession dynamics from what the machine learned. We analyze patterns learned by the machine and explain and hypothesize why the machine learned those characteristics. The hysteresis in the plot mainly occurs during the early time dynamics, and the flow recession dynamics eventually converge to an attractor in the plot, which represents the master recession curve. We also illustrate that a hysteretic storage‐discharge relationship can be estimated based on the attractor.
The Severity of the 2014–2015 Snow Drought in the Oregon Cascades in a Multicentury Context
Authors: Dye Laura A. , Coulthard Bethany L. , Hatchett Benjamin J. , Homfeld Inga K. , Salazar Taylor N. , Littell Jeremy S. , Anchukaitis Kevin J. 
Published: May 2023

AbstractThe western United States (US) is a hotspot for snow drought. The Oregon Cascade Range is highly sensitive to warming and as a result has experienced the largest mountain snowpack losses in the western US since the mid‐20th century, including a record‐breaking snow drought in 2014–2015 that culminated in a state of emergency. While Oregon Cascade snowpacks serve as the state's primary water supply, short instrumental records limit water managers' ability to fully constrain long‐term natural snowpack variability prior to the influence of ongoing and projected anthropogenic climate change. Here, we use annually‐resolved tree‐ring records to develop the first multi‐century reconstruction of Oregon Cascade April 1st Snow Water Equivalent (SWE). The model explains 58% of observed snowpack variability and extends back to 1688 AD, nearly quintupling the length of the existing snowpack record. Our reconstruction suggests that only one other multiyear event in the last three centuries was as severe as the 2014–2015 snow drought. The 2015 event alone was more severe than nearly any other year in over three centuries. Extreme low‐to‐high snowpack “whiplash” transitions are a consistent feature throughout the reconstructed record. Multi‐decadal intervals of persistent below‐the‐mean peak SWE are prominent features of pre‐instrumental snowpack variability, but are generally absent from the instrumental period and likely not fully accounted for in modern water management. In the face of projected snow drought intensification and warming, our findings motivate adaptive management strategies that address declining snowpack and increasingly variable precipitation regimes.
Stratospheric Gas‐Phase Production Alone Cannot Explain Observations of Atmospheric Perchlorate on Earth
Authors: Chan Yuk‐Chun , Jaeglé Lyatt , Campuzano‐Jost Pedro , Catling David C. , Cole‐Dai Jihong , Furdui Vasile I. , Jackson W. Andrew , Jimenez Jose L. , Kim Dongwook , Wedum Alanna E. , Alexander Becky 
Published: May 2023

AbstractPerchlorate has been observed in many environments on Earth and Mars but its sources remain poorly quantified. In this study, we use a global three‐dimensional chemical transport model to simulate perchlorate's gas‐phase photochemical production, atmospheric transport, and deposition on Earth's surface. Model predictions are compared to newly compiled observations of atmospheric concentrations, deposition flux, and oxygen isotopic composition of perchlorate. We find that the modeled gas‐phase production of perchlorate is consistent with reported stratospheric observations. Nevertheless, we show that this mechanism alone cannot explain the high levels of perchlorate observed at many near‐surface sites (aerosol concentrations >0.1 ng m−3 and deposition fluxes >10 g km−2 yr−1) or the low 17O‐excess observed in perchlorate sampled from pristine environments (<+18.4‰). We discuss four hypotheses to explain the model‐observation discrepancies and recommend laboratory and field observations to address key uncertainties in atmospheric sources of perchlorate.
G protein-coupled receptors in neurodegenerative diseases and psychiatric disorders
Authors: Wong Thian-Sze , Li Guangzhi , Li Shiliang , Gao Wei , Chen Geng , Gan Shiyi , Zhang Manzhan , Li Honglin , Wu Song , Du Yang 
Published: May 2023

AbstractNeuropsychiatric disorders are multifactorial disorders with diverse aetiological factors. Identifying treatment targets is challenging because the diseases are resulting from heterogeneous biological, genetic, and environmental factors. Nevertheless, the increasing understanding of G protein-coupled receptor (GPCR) opens a new possibility in drug discovery. Harnessing our knowledge of molecular mechanisms and structural information of GPCRs will be advantageous for developing effective drugs. This review provides an overview of the role of GPCRs in various neurodegenerative and psychiatric diseases. Besides, we highlight the emerging opportunities of novel GPCR targets and address recent progress in GPCR drug development.
Optical neural network via loose neuron array and functional learning
Authors: Huo Yuchi , Bao Hujun , Peng Yifan , Gao Chen , Hua Wei , Yang Qing , Li Haifeng , Wang Rui , Yoon Sung-Eui 
Published: May 2023

AbstractThis research proposes a deep-learning paradigm, termed functional learning (FL), to physically train a loose neuron array, a group of non-handcrafted, non-differentiable, and loosely connected physical neurons whose connections and gradients are beyond explicit expression. The paradigm targets training non-differentiable hardware, and therefore solves many interdisciplinary challenges at once: the precise modeling and control of high-dimensional systems, the on-site calibration of multimodal hardware imperfectness, and the end-to-end training of non-differentiable and modeless physical neurons through implicit gradient propagation. It offers a methodology to build hardware without handcrafted design, strict fabrication, and precise assembling, thus forging paths for hardware design, chip manufacturing, physical neuron training, and system control. In addition, the functional learning paradigm is numerically and physically verified with an original light field neural network (LFNN). It realizes a programmable incoherent optical neural network, a well-known challenge that delivers light-speed, high-bandwidth, and power-efficient neural network inference via processing parallel visible light signals in the free space. As a promising supplement to existing power- and bandwidth-constrained digital neural networks, light field neural network has various potential applications: brain-inspired optical computation, high-bandwidth power-efficient neural network inference, and light-speed programmable lens/displays/detectors that operate in visible light.
Non-Hertz-Millis scaling of the antiferromagnetic quantum critical metal via scalable Hybrid Monte Carlo
Authors: Lunts Peter , Albergo Michael S. , Lindsey Michael 
Published: May 2023

AbstractA key component of the phase diagram of many iron-based superconductors and electron-doped cuprates is believed to be a quantum critical point (QCP), delineating the onset of antiferromagnetic spin-density wave order in a quasi-two-dimensional metal. The universality class of this QCP is believed to play a fundamental role in the description of the proximate non-Fermi liquid behavior and superconducting phase. A minimal model for this transition is the O(3) spin-fermion model. Despite many efforts, a definitive characterization of its universal properties is still lacking. Here, we numerically study the O(3) spin-fermion model and extract the scaling exponents and functional form of the static and zero-momentum dynamical spin susceptibility. We do this using a Hybrid Monte Carlo (HMC) algorithm with a novel auto-tuning procedure, which allows us to study unprecedentedly large systems of 80 × 80 sites. We find a strong violation of the Hertz-Millis form, contrary to all previous numerical results. Furthermore, the form that we do observe provides good evidence that the universal scaling is actually governed by the analytically tractable fixed point discovered near perfect “hot-spot’" nesting, even for a larger nesting window. Our predictions can be directly tested with neutron scattering. Additionally, the HMC method we introduce is generic and can be used to study other fermionic models of quantum criticality, where there is a strong need to simulate large systems.
A blueprint for a synthetic genetic feedback optimizer
Authors: Gyorgy Andras , Menezes Amor , Arcak Murat 
Published: May 2023

AbstractBiomolecular control enables leveraging cells as biomanufacturing factories. Despite recent advancements, we currently lack genetically encoded modules that can be deployed to dynamically fine-tune and optimize cellular performance. Here, we address this shortcoming by presenting the blueprint of a genetic feedback module to optimize a broadly defined performance metric by adjusting the production and decay rate of a (set of) regulator species. We demonstrate that the optimizer can be implemented by combining available synthetic biology parts and components, and that it can be readily integrated with existing pathways and genetically encoded biosensors to ensure its successful deployment in a variety of settings. We further illustrate that the optimizer successfully locates and tracks the optimum in diverse contexts when relying on mass action kinetics-based dynamics and parameter values typical in Escherichia coli.