bluuebunny/crossref_metadata_2025_split huggingface dataset:
Semantic-aware knowledge distillation with parameter-free feature uniformization
Authors: Guo Guangyu , Han Longfei , Wang Le , Zhang Dingwen , Han Junwei 
Published: May 2023

AbstractKnowledge distillation aims to distill knowledge from teacher networks to train student networks. Distilling intermediate features has attracted much attention in recent years as it can be flexibly applied in various fields such as image classification, object detection and semantic segmentation. A critical obstacle of feature-based knowledge distillation is the dimension gap between the intermediate features of teacher and student, and plenty of methods have been proposed to resolve this problem. However, these works usually implement feature uniformization in an unsupervised way, lacking guidance to help the student network learn meaningful mapping functions in the uniformization process. Moreover, the dimension uniformization process of the student and teacher network is usually not equivalent as the mapping functions are different. To this end, some factors of the feature are discarded during parametric feature alignment, or some factors are blended in some non-parametric operations. In this paper, we propose a novel semantic-aware knowledge distillation scheme to solve these problems. We build a standalone feature-based classification branch to extract semantic-aware knowledge for better guiding the learning process of the student network. In addition, to avoid the inequivalence of feature uniformization between teacher and student, we design a novel parameter-free self-attention operation that can convert features of different dimensions into vectors of the same length. Experimental results show that the proposed knowledge distillation scheme outperforms existing feature-based distillation methods on the widely used CIFAR-100 and CINIC-10 datasets.
Continual learning with Bayesian model based on a fixed pre-trained feature extractor
Authors: Yang Yang , Cui Zhiying , Xu Junjie , Zhong Changhong , Zheng Wei-Shi , Wang Ruixuan 
Published: May 2023

AbstractDeep learning has shown its human-level performance in various applications. However, current deep learning models are characterized by catastrophic forgetting of old knowledge when learning new classes. This poses a challenge such as in intelligent diagnosis systems where initially only training data of a limited number of diseases are available. In this case, updating the intelligent system with data of new diseases would inevitably downgrade its performance on previously learned diseases. Inspired by the process of learning new knowledge in human brains, we propose a Bayesian generative model for continual learning built on a fixed pre-trained feature extractor. In this model, knowledge of each old class can be compactly represented by a collection of statistical distributions, e.g., with Gaussian mixture models, and naturally kept from forgetting in continual learning over time. Unlike existing class-incremental learning methods, the proposed approach is not sensitive to the continual learning process and can be additionally well applied to the data-incremental learning scenario. Experiments on multiple medical and natural image classification tasks reveal that the proposed approach outperforms state-of-the-art approaches that even keep some images of old classes during continual learning of new classes.
A Behavioral Approach to Identify Barriers to Adoption of New Technology: A Case Study of Low-input Turfgrasses
Authors: Yue Chengyan , Lai Yufeng , Watkins Eric , Patton Aaron , Braun Ross 
Published: May 2023

AbstractAdopting eco-friendly technologies, such as converting lawns to alternative low-input grass species, can reduce household expenditures and mitigate negative environmental impacts at the same time. However, the rate of adoption of these technologies has not been as high as expected. This study develops a behavioral framework to identify barriers to new technology adoption by incorporating both prospect theory and present bias. We apply the framework in a choice experiment to investigate the relative importance of several factors that shape decisions associated with adoption of low-input turfgrass. We find that loss aversion plays a significant role. Though consumers exhibit present bias, long-term benefits still matter to them. Insights from the behavior model suggest that marketing and government programs that promote cost–benefit-efficient technologies should focus on eliminating or reducing potential losses caused by product failure.
Lying with Uninformative Speech Acts
Authors: Gaszczyk Grzegorz 
Published: May 2023

AbstractI propose an analysis of lying with uninformative speech acts. The orthodox view states that lying is restricted to assertions. However, the growing case for non-assertoric lies made by presuppositions or conventional implicatures challenges this orthodoxy. So far, the only presuppositions to have been considered as lies were informative presuppositions. In fact, uninformative lies were not discussed in the philosophical literature. However, limiting the possibility of lying to informative speech acts is too restrictive. Firstly, I show that standard, uninformative presuppositions can also be lies. Secondly, I extend this picture into uninformative lies made by declarative statements. To implement my proposal, I do not need a new definition of lying. Recently popular commitment-based definitions of lying are able to properly handle uninformative lies.
Matching enrolled trial participants to disease demographics: Using IRB submissions to identify opportunities for researcher training
Authors: Eckstrom Elizabeth , Zauflik Meredith , De Lima Bryanna 
Published: May 2023

Abstract

Background:
Many diseases are highly prevalent in older adults, yet older adults are often underrepresented in corresponding trials. Our objectives were to (1) determine alignment between Institutional Review Board (IRB) protocol age ranges and enrollment demographics to disease demographics pre- and post-implementation of the 2019 National Institutes of Health (NIH) Lifespan Policy and (2) raise awareness about inclusive recruitment to principal investigators (PIs).

Methods:
This was a pre-post study. We reviewed investigator-initiated studies meeting eligibility criteria at Oregon Health & Science University from 2017 to 2018 to determine baseline alignment. Alignment was defined by the level of matching between protocol/enrollment age and disease demographics: 2 points for full match, 1 point for partial match, and 0 points for mismatch. After the NIH policy implementation, we reviewed new studies for alignment. When a mismatch was determined, we contacted PIs (either at initial IRB protocol submission or during ongoing recruitment) to raise awareness and provide strategies to expand inclusion of older adults in their trials.

Results:
Studies that matched IRB protocol ages to disease demographics significantly improved from 78% pre-implementation to 91.2% post-implementation. Similarly, study enrollment ages matching disease demographics increased by 13.4% following the implementation (74.5%–87.9%). Out of 18 post-implementation mismatched studies, 7 PIs accepted a meeting and 3 subsequently changed their protocol age ranges.

Conclusion:
This study highlights strategies that translational institutes and academic institutions could use to identify research studies whose participants do not align with disease demographics, offering opportunities for researcher awareness and training to enhance inclusion.
A framework for assessing clinical trial site readiness
Authors: Buse John B. , Austin Christopher P. , Johnston S. Claiborne , Lewis-Hall Freda , March Andrew N. , Shore Carolyn K. , Tenaerts Pamela , Rutter Joni L. 
Published: May 2023

AbstractClinical trial processes are unnecessarily inefficient and costly, slowing the translation of medical discoveries into treatments for people living with disease. To reduce redundancies and inefficiencies, a group of clinical trial experts developed a framework for clinical trial site readiness based on existing trial site qualifications from sponsors. The site readiness practices are encompassed within six domains: research team, infrastructure, study management, data collection and management, quality oversight, and ethics and safety. Implementation of this framework for clinical trial sites would reduce inefficiencies in trial conduct and help prepare new sites to enter the clinical trials enterprise, with the potential to improve the reach of clinical trials to underserved communities. Moreover, the framework holds benefits for trial sponsors, contract research organizations, trade associations, trial participants, and the public. For novice sites considering future trials, we provide a framework for site preparation and the engagement of stakeholders. For experienced sites, the framework can be used to assess current practices and inform and engage sponsors, staff, and participants. Details in the supplementary materials provide easy access to key regulatory documents and resources. Invited perspective articles provide greater depth from a systems, DEIA (diversity, equity, inclusion, and accessibility) and decentralized trials perspective.
Homoclinic orbits, multiplier spectrum and rigidity theorems in complex dynamics
Authors: Ji Zhuchao , Xie Junyi 
Published: May 2023

Abstract
The aims of this paper are to answer several conjectures and questions about the multiplier spectrum of rational maps and giving new proofs of several rigidity theorems in complex dynamics by combining tools from complex and non-Archimedean dynamics.
A remarkable theorem due to McMullen asserts that, aside from the flexible Lattès family, the multiplier spectrum of periodic points determines the conjugacy class of rational maps up to finitely many choices. The proof relies on Thurston’s rigidity theorem for post-critically finite maps, in which Teichmüller theory is an essential tool. We will give a new proof of McMullen’s theorem (and therefore a new proof of Thurston’s theorem) without using quasiconformal maps or Teichmüller theory.
We show that, aside from the flexible Lattès family, the length spectrum of periodic points determines the conjugacy class of rational maps up to finitely many choices. This generalizes the aforementioned McMullen’s theorem. We will also prove a rigidity theorem for marked length spectrum. Similar ideas also yield a simple proof of a rigidity theorem due to Zdunik.
We show that a rational map is exceptional if and only if one of the following holds: (i) the multipliers of periodic points are contained in the integer ring of an imaginary quadratic field, and (ii) all but finitely many periodic points have the same Lyapunov exponent. This solves two conjectures of Milnor.
Rivalries, reputation, retaliation, and repetition: Testing plausible mechanisms for the contagion of violence between street gangs using relational event models
Authors: Gravel Jason , Valasik Matthew , Mulder Joris , Leenders Roger , Butts Carter , Brantingham P. Jeffrey , Tita George E. 
Published: May 2023

AbstractThe hypothesis that violence—especially gang violence—behaves like a contagious disease has grown in popularity in recent years. Scholars have long observed the tendency for violence to cluster in time and space, but little research has focused on empirically unpacking the mechanisms that make violence contagious. In the context of gang violence, retaliation is the prototypical mechanism to explain why violence begets violence. In this study, we leverage relational event models (REMs)—an underutilized yet particularly well-suited modeling technique to study the dynamics of inter-gang violence. We use REMs to examine gang violence’s tendency to replicate—for which retaliation is but one plausible mechanism—and its tendency to diffuse to other groups. We rely on data on conflicts between gangs in a region of Los Angeles over 3 years. We consider how the characteristics of gangs, their spatial proximity, networks of established rivalries, and the evolving history, directionality, and structure of conflicts predict future inter-gang conflicts. While retaliation is an important mechanism for the replication of violence, established rivalries, and inertia—a gang’s tendency to continue attacking the same group—are more important drivers of future violence. We also find little evidence for an emerging pecking order or status hierarchy between gangs suggested by other scholars. However, we find that gangs are more likely to attack multiple gangs in quick succession. We propose that gang violence is more likely to diffuse to other groups because of the boost of internal group processes an initial attack provides.
Social learning and the adaptiveness of expressing and perceiving fearfulness
Authors: Olsen Karsten , Selbing Ida 
Published: May 2023

Abstract
The fearful ape hypothesis revolves around our ability to express and perceive fearfulness. Here, we address these abilities from a social learning perspective which casts fearfulness in a slightly different light. Our commentary argues that any theory that characterizes a (human) social signal as being adaptive, needs to address the role of social learning as an alternative candidate explanation.
Fearful apes, happy apes: Is fearfulness associated with uniquely human cooperation?
Authors: Riddell Christopher , Kret Mariska , Zijlstra Tonko , Nikolic Milica 
Published: May 2023

Abstract
In the fearful ape hypothesis, Grossmann argues that heightened fearfulness increases human-unique cooperation. We suggest that this conclusion, however, may be premature. In particular, we question Grossmann's singling out of fear as the affective trait that enhances cooperative care. Additionally, we problematize the extent to which heightened fearfulness in humans, and its association with human-unique cooperation, are supported empirically.
Fearful apes or nervous goats? Another look at functions of dispositions or traits
Authors: Krstić Vladimir 
Published: May 2023

Abstract
In his article, Grossmann argues that, in the context of human cooperative caregiving, heightened fearfulness in children and human sensitivity to fear in others are adaptive traits. I offer and briefly defend a rival hypothesis: Heightened fearfulness among infants and young children is a maladaptive trait that did not get deselected in the process of evolution because human sensitivity to fear in others mitigates its disadvantageous effects to a sufficient extent.
Infants aren't biased toward fearful faces
Authors: Herbert Andrew M. , Condry Kirsten , Sutton Tina M. 
Published: May 2023

Abstract
Grossmann's argument for the “fearful ape hypothesis” rests on an incomplete review of infant responses to emotional faces. An alternate interpretation of the literature argues the opposite, that an early preference for happy faces predicts cooperative learning. Questions remain as to whether infants can interpret affect from faces, limiting the conclusion that any “fear bias” means the infant is fearful.
The power of the weak: When altruism is the equilibrium
Authors: Barkan Rachel , Lahav Yaron 
Published: May 2023

Abstract
A rational economic analysis complements Grossmann's fearful ape hypothesis. Two examples of mixed-motive games with strong inter-dependence (i.e., weak chirping nestling, boxed pigs) demonstrate that signaling weakness is a dominant strategy. Weakness elicits cooperative, caring response, comprising the equilibrium of the game. In extensive form, a reliable reputation of weakness elicits caring as a sequential equilibrium.
Simulation does not just inform choice, it changes choice
Authors: Enz Karalyn F. , Tamir Diana I. 
Published: May 2023

Abstract
Simulation – imagining future events – plays a role in decision-making. In Conviction Narrative Theory, people's emotional responses to their simulations inform their choices. Yet imagining one possible future also increases its plausibility and accessibility relative to other futures. We propose that the act of simulation, in addition to affective evaluation, drives people to choose in accordance with their simulations.
Embodied choices bypass narratives under radical uncertainty
Authors: Cañal-Bruland Rouwen , Raab Markus 
Published: May 2023

Abstract
Johnson et al. suggest that we rely on narratives to make choices under radical uncertainty. We argue that in its current version Conviction Narrative Theory (CNT) does not account for embodied, direct sensorimotor influences on choices under radical uncertainty that may bypass narratives, particularly in highly time-constrained situations. We therefore suggest to extend CNT by an embodied choice perspective.
The small world's problem is everyone's problem, not a reason to favor CNT over probabilistic decision theory
Authors: Greco Daniel 
Published: May 2023

Abstract
The case for the superiority of Conviction Narrative Theory (CNT) over probabilistic approaches rests on selective employment of a double standard. The authors judge probabilistic approaches inadequate for failing to apply to “grand-world” decision problems, while they praise CNT for its treatment of “small-world” decision problems. When both approaches are held to the same standard, the comparative question is murkier.
The global distribution of COVID-19 vaccines by the public-private partnership COVAX from a public-law perspective
Authors: von Achenbach Jelena 
Published: May 2023

AbstractThis article examines COVAX, a public private partnership, from a public law perspective. It asks whether COVAX is a legitimate and appropriate instrument with regard to the goal of distributing COVID-19 vaccines in a globally equitable manner and enabling equal access to vaccination worldwide. By developing public-legal legitimacy standards for this purpose, the article critically distances itself from the outset from considering the use of private actors and forms of action in public functions (‘privatization’) essentially as a release of market economy rationality, which enables efficiency and effectiveness gains and relieves the public sector. With the public law perspective, the article questions precisely whether private-law, market-based action is appropriate with respect to the global distribution of vaccines in the pandemic.
Multiple herbicide-resistant waterhemp control with Group 15 herbicides
Authors: Symington Hannah E. , Soltani Nader , Kaastra Allan C. , Hooker David C. , Robinson Darren E. , Sikkema Peter H. 
Published: May 2023

AbstractWaterhemp has evolved resistance to seven herbicide modes of action in the United States and to five in Canada, which limits weed control options for producers. The objective of this research was to quantify the level and duration of residual control of multiple herbicide-resistant (MHR) waterhemp with five Group 15 herbicides (acetochlor, dimethenamid-p, flufenacet, pyroxasulfone, andS-metolachlor) applied preemergence in a non-crop area. Four field trials were conducted over a 2-yr period (2021, 2022) in southwestern Ontario, Canada. By 4 wk after application (WAA) 91% of waterhemp had emerged in the nontreated control area. The numerical control of waterhemp with all Group 15 herbicides, with the exception of pyroxasulfone, was greatest at 4 WAA, then control declined. Flufenacet provided the lowest waterhemp control; dimethenamid-p andS-metolachlor provided intermediate control, and acetochlor and pyroxasulfone provided the highest control. Waterhemp control with pyroxasulfone peaked at 6 WAA with 99% and declined to 77% at 12 WAA. Flufenacet (low and high rates) was predicted to reduce waterhemp emergence by 50% for 42 to 44 d after application (DAA). Dimethenamid-p,S-metolachlor, and acetochlor (both formulations and three rates) were predicted to reduce waterhemp emergence by 80% for 36, 43, and 33 to 51 DAA, respectively; in contrast, pyroxasulfone was predicted to reduce waterhemp emergence by 80% for 82 DAA. This study concludes that of the Group 15 herbicides evaluated, flufenacet provides the lowest and shortest residual control of waterhemp, and pyroxasulfone provides the highest and longest residual control of waterhemp.
Mika und Asa gehen in die Kita
Authors: Hesse Fabienne , Zemp Martina 
Published: May 2023

Der kleine Affe Asa begleitet Mika bereits ihr ganzes Leben als treuer Begleiter. Die beiden haben schon zahlreiche Abenteuer miteinander erlebt. Asa fürchtet sich vor nichts. Sein Mut wird plötzlich herausgefordert, als er mit Mika zusammen die Kita besuchen soll. Die bevorstehende Veränderung macht Asa zunächst Angst. Aber dann bereiten sich die beiden gemeinsam mit ihrer Familie auf die Zeit in der Kita vor und lernen, dass Veränderungen auch etwas Wunderbares sein können.
Dieses Bilderbuch beantwortet Fragen rund um den Kita-Eintritt und zeigt, was es für einen gelingenden Kita-Besuch zu beachten gibt. Es richtet sich an Eltern und andere Bezugs- und Fachpersonen und unterstützt dabei, die Eltern-Kind-Bindung zu stärken und den Übergang in die Kita zu erleichtern. Das Buch gliedert sich in zwei Teile: Im Bindungs-Teil wird das Konzept der Bindung beleuchtet und erklärt, wie die elterliche Sensitivität zur Unterstützung einer sicheren Bindung des Kindes gestärkt werden kann. Im Kita-Teil wird beschrieben, was eine gute Kita ausmacht, wie man das Kind auf die Kita vorbereitet, was man bei der Eingewöhnungszeit, beim Bringen und Abholen und in der Kooperation mit dem Kita-Personal beachten soll. Neben Erklärungen und Informationen für die Erwachsenen und einer Kindergeschichte, in welcher Bindung als magisches Tier dargestellt wird, gibt es zahlreiche praktische Übungen.
Begeben Sie sich gemeinsam mit Ihrem Kind und mit Mika und Asa auf die spannende Reise in die Kita!
Die Übungen zu diesem Buch können nach erfolgter Registrierung von der Hogrefe Webseite heruntergeladen werden.
A Theoretical Model of Soil Aerosol Generation and Vertical Transport During Rainfall
Authors: Wang Yong , Zhang Jie , Huang Ning , Sun Haitao , Dun Hongchao 
Published: May 2023

AbstractRaindrops impacting on soil have been revealed to be a source of aerosol, which can deliver viruses and microorganisms from the soil into the air and affect the content and composition of aerosol in the atmosphere environment. Here we proposed a mathematical description on the generation of soil aerosol. We measured the production rate of soil aerosols from laboratory simulations of raindrops impinging on soils to support the assumptions made in our theory. By combining this generation model with turbulent diffusion and below‐cloud scavenging of aerosol, we are able to study further the transport of soil aerosol in the air, establish a model of aerosol vertical transport, and deduced its analytical solution. The theory reflects that aerosol content in the atmosphere will first increase to the peak and then slowly decrease, which is consistent with atmospheric observation data; small‐size soil aerosol (<2 µm in diameter) will last for a long time in the atmosphere, so it will cause more harm to human life and industry. This study presents a simplified model for theoretically analyzing the soil aerosol movement, and makes it possible to predict quickly and effectively the vertical transport of soil aerosol in the atmosphere environment during rainfall.