Hi all! I am working on an extension of Kohli&Grover's (2012) IT value co-creation theory in multi-firm environments. There is clearly a gap in the literature on the role of human & knowledge artefacts. I was wondering, can you suggest any recent papers (since 2019) linking IT value co-creation research to organizational learning theories?
Background The World Health Organization has warned that cigarette smoking is an avoidable risk factor for endothelial injury. Myogenin might play a role in muscle metabolism and energy utilization. Electrolytes and minerals are involved in most cellular activities. The objective of this study was to compare myogenin and electrolyte levels between adult male cigarette smokers (CS) and non-smokers (NS). Methods A cross-sectional study was conducted involving 90 subjects, consisting of 55 CS and 35 NS. The sandwich enzyme-linked immunosorbent assay was used to determine myogenin levels while the ion-selective electrode method was used to determine electrolyte levels. The levels of sodium, potassium, and chloride and the body mass index (BMI) were measured. Mann-Whitney and independent t-test were used to analyse the data. Results The BMI of CS was significantly lower than that of NS (p
We study mentorship in scientific collaborations, where a junior scientist is supported by potentially multiple senior collaborators, without them necessarily having formal supervisory roles. We identify 3 million mentor–protégé pairs and survey a random sample, verifying that their relationship involved some form of mentorship. We find that mentorship quality predicts the scientific impact of the papers written by protégés post mentorship without their mentors. We also find that increasing the proportion of female mentors is associated not only with a reduction in post-mentorship impact of female protégés, but also a reduction in the gain of female mentors. While current diversity policies encourage same-gender mentorships to retain women in academia, our findings raise the possibility that opposite-gender mentorship may actually increase the impact of women who pursue a scientific career. These findings add a new perspective to the policy debate on how to best elevate the status of women in science.
Online advertising relies on trackers and data brokers to show targeted ads to users. To improve targeting, different entities in the intricately interwoven online advertising and tracking ecosystems are incentivized to share information with each other through client-side or server-side mechanisms. Inferring data sharing between entities, especially when it happens at the server-side, is an important and challenging research problem. In this paper, we introduce Kashf: a novel method to infer data sharing relationships between advertisers and trackers by studying how an advertiser’s bidding behavior changes as we manipulate the presence of trackers. We operationalize this insight by training an interpretable machine learning model that uses the presence of trackers as features to predict the bidding behavior of an advertiser. By analyzing the machine learning model, we can infer relationships between advertisers and trackers irrespective of whether data sharing occurs at the client-side or the server-side. We are able to identify several server-side data sharing relationships that are validated externally but are not detected by client-side cookie syncing.
Neural Ordinary Differential Equations (ODEs) are elegant reinterpretations of deep networks where continuous time can replace the discrete notion of depth, ODE solvers perform forward propagation, and the adjoint method enables efficient, constant memory backpropagation. Neural ODEs are universal approximators only when they are non-autonomous, that is, the dynamics depends explicitly on time. We propose a novel family of Neural ODEs with time-varying weights, where time-dependence is non-parametric, and the smoothness of weight trajectories can be explicitly controlled to allow a tradeoff between expressiveness and efficiency. Using this enhanced expressiveness, we outperform previous Neural ODE variants in both speed and representational capacity, ultimately outperforming standard ResNet and CNN models on select image classification and video prediction tasks.
There are several distinct failure modes for overoptimization of systems on
the basis of metrics. This occurs when a metric which can be used to improve a
system is used to an extent that further optimization is ineffective or
harmful, and is sometimes termed Goodhart's Law. This class of failure is often
poorly understood, partly because terminology for discussing them is ambiguous,
and partly because discussion using this ambiguous terminology ignores
distinctions between different failure modes of this general type. This paper
expands on an earlier discussion by Garrabrant, which notes there are "(at
least) four different mechanisms" that relate to Goodhart's Law. This paper is
intended to explore these mechanisms further, and specify more clearly how they
occur. This discussion should be helpful in better understanding these types of
failures in economic regulation, in public policy, in machine learning, and in
Artificial Intelligence alignment. The importance of Goodhart effects depends
on the amount of power directed towards optimizing the proxy, and so the
increased optimization power offered by artificial intelligence makes it
especially critical for that field.
Many studies have sought to explain the major crime declines experienced in most advanced countries. Key hypotheses relate to: lead poisoning; abortion legalization; drug markets; demographics; policing numbers and strategies; imprisonment; strong economies; the death penalty; gun control; gun concealment; immigration; consumer confidence; the civilizing process, and; improved security. This paper outlines five tests that a hypothesis should pass to be considered further. It finds that fourteen of the fifteen hypotheses fail two or more tests. The security hypothesis appears to pass the tests, and thereby pave the way for further research.