Echoes of Tomorrow: The Road to Serfdom Revisited

It is now half a century since Hayek published The Road to Serfdom. Much of our population was not even born when he wrote this terse, eloquent work – and a lot has happened since. A lifetime of conflict has raged over the ideas Hayek considered in his slender volume. Unimaginably destructive weapons have been aimed at the world\u27s population centers, menacing the very survival of our species. Even under their shadow, we have seen revolutions reacting against the abuses Hayek identified. Millions have gained their freedom. Walls that seemed permanent came crashing down. We hope they stay down. Our thesis though, is that today, after this frenetic rush of history, Hayek is not less relevant or less persuasive, but more so. In clear prose, he explains why collectivism – even the moderate, supposedly pro-democratic variety that is still popular in the West – can become the road to serfdom. As we read him, Hayek makes three arguments about why this is so. First, he tells us why collectivism cannot bring prosperity. Second, he tells us why a government that takes our economic liberties must surely come after our political rights as well. And finally, he tells us why collectivism\u27s rhetoric about regularity and the common man is misleading-why, contrary to what many believe, it is collectivism that is elitist, while capitalism relies on the values and judgments of ordinary folks. As we go through these arguments, we hope you will notice, as readers in 1944 could, how sensible they are as theories. But we also hope you will recognize something we can only know now – how faithfully history has confirmed Hayek\u27s predictions

Plasma midregional proadrenomedullin (MR-proADM) concentrations and their biological determinants in a reference population

Background: Midregional proadrenomedullin (MR-proADM) is emerging as a prognostic biomarker for detecting the failure of multiple organs. Establishment of scientifically robust reference intervals facilitates interpretation of laboratory test results. The objectives of this study were (i) to establish reliable reference intervals for plasma MR-proADM using a commercially available automated fluoroimmunoassay in apparently healthy individuals, and (ii) to identify biological determinants of MR-proADM concentrations. Methods: A total of 506 questionnaire-identified apparently healthy adults were enrolled in a single-center, cross-sectional study. A final reference group (n = 172) was selected after exclusion of obese individuals, those with increased values of laboratory biomarkers indicating asymptomatic myocardial injury or dysfunction, ongoing inflammation, diabetes, dyslipidemia and renal dysfunction and outliers. Results: The 2.5th and 97.5th percentile intervals for MR-proADM values in the reference group (90% confidence interval) were 0.21 (0.19-0.23) and 0.57 (0.55-0.59) nmol/L, respectively. Although older age, higher values of HbA(1c), C-reactive protein, B-type natriuretic peptide and body mass index, together with a history of smoking and a decreased estimated glomerular filtration rate were significantly associated with increasing concentrations of MR-proADM in both univariate and multivariate analyses, magnitudes of these relationships were modest and did not substantially influence MR-proADM reference intervals. Sex-dependent difference in MR-proADM reference intervals was not detected [0.19 (0.16-0.22)-0.56 (0.54-0.60) nmol/L in females vs. 0.22 (0.20-0.25)-0.58 (0.57-0.63) nmol/L in males]. Conclusions: Our study successfully established robust reference intervals for MR-proADM concentrations in plasma. Considering the negligible influence of potential biological determinants on plasma MR-proADM, we recommend the adoption of single reference intervals for adult population as a whole

LaFTer: Label-Free Tuning of Zero-shot Classifier using Language and Unlabeled Image Collections

Recently, large-scale pre-trained Vision and Language (VL) models have set a new state-of-the-art (SOTA) in zero-shot visual classification enabling open-vocabulary recognition of potentially unlimited set of categories defined as simple language prompts. However, despite these great advances, the performance of these zeroshot classifiers still falls short of the results of dedicated (closed category set) classifiers trained with supervised fine tuning. In this paper we show, for the first time, how to reduce this gap without any labels and without any paired VL data, using an unlabeled image collection and a set of texts auto-generated using a Large Language Model (LLM) describing the categories of interest and effectively substituting labeled visual instances of those categories. Using our label-free approach, we are able to attain significant performance improvements over the zero-shot performance of the base VL model and other contemporary methods and baselines on a wide variety of datasets, demonstrating absolute improvement of up to 11.7% (3.8% on average) in the label-free setting. Moreover, despite our approach being label-free, we observe 1.3% average gains over leading few-shot prompting baselines that do use 5-shot supervision

Montessori's mediation of meaning: a social semiotic perspective

The distinctive objects designed by Dr Maria Montessori as the centrepiece of her approach to pedagogy are the topic of this study. The Montessori approach to pedagogy, celebrating its centenary in 2007, continues to be used in classrooms throughout the world. Despite such widespread and enduring use, there has been little analysis of the Montessori objects to evaluate or understand their pedagogic impact. This study begins by outlining the provenance of the Montessori objects, reaching the conclusion that the tendency to interpret them from the perspective of the progressive education movement of the early twentieth century fails to provide insights into the developmental potential embodied in the objects. In order to appreciate that potential more fully, the study explores the design of the objects, specifically, the way in which the semiotic qualities embodied in their design orient children to the meanings of educational knowledge. A meta-analytic framework comprising three components is used to analyse the semiotic potential of the Montessori objects as educational artefacts. First, Vygotsky’s model of development is used to analyse the objects as external mediational means and to recognise the objects as complexes of signs materialising educational knowledge. In order to understand how the objects capture, in the form of concrete analogues, the linguistic meanings which construe educational knowledge, systemic functional linguistics, the second component of the framework, is used to achieve a rich and detailed social semiotic analysis of these relations, in particular, material and linguistic representations of abstract educational meanings. Finally, the pedagogic device, a central feature of Bernstein’s sociology of pedagogy, is used to analyse how the Montessori objects re-contextualise educational knowledge as developmental pedagogy. Particular attention is paid to the Montessori literacy pedagogy, in which the study of grammar plays a central role. The study reveals a central design principle which distinguishes the Montessori objects. This principle is the redundant representation of educational knowledge across multiple semiotic modes. Each representation holds constant the underlying meaning relations which construe quanta of educational knowledge, giving children the freedom to engage with this knowledge playfully, independently and successfully. The conclusion drawn from this study is that the design of the Montessori objects represents valuable educational potential which deserves continued investigation, as well as wider recognition and application. To initiate this process, the findings in this study may provide insights which can be used to develop tools for evaluating and enhancing the implementation of Montessori pedagogy in Montessori schools. The findings may also be used to adapt Montessori design principles for the benefit of educators working in non-Montessori contexts, in particular, those educators concerned with developing pedagogies which promote equitable access to educational knowledge

Sit Back and Relax: Learning to Drive Incrementally in All Weather Conditions

In autonomous driving scenarios, current object detection models show strong performance when tested in clear weather. However, their performance deteriorates significantly when tested in degrading weather conditions. In addition, even when adapted to perform robustly in a sequence of different weather conditions, they are often unable to perform well in all of them and suffer from catastrophic forgetting. To efficiently mitigate forgetting, we propose Domain-Incremental Learning through Activation Matching (DILAM), which employs unsupervised feature alignment to adapt only the affine parameters of a clear weather pre-trained network to different weather conditions. We propose to store these affine parameters as a memory bank for each weather condition and plug-in their weather-specific parameters during driving (i.e. test time) when the respective weather conditions are encountered. Our memory bank is extremely lightweight, since affine parameters account for less than 2% of a typical object detector. Furthermore, contrary to previous domain-incremental learning approaches, we do not require the weather label when testing and propose to automatically infer the weather condition by a majority voting linear classifier.Comment: Intelligent Vehicle Conference (oral presentation

MATE: Masked Autoencoders are Online 3D Test-Time Learners

We propose MATE, the first Test-Time-Training (TTT) method designed for 3D data. It makes deep networks trained in point cloud classification robust to distribution shifts occurring in test data, which could not be anticipated during training. Like existing TTT methods, which focused on classifying 2D images in the presence of distribution shifts at test-time, MATE also leverages test data for adaptation. Its test-time objective is that of a Masked Autoencoder: Each test point cloud has a large portion of its points removed before it is fed to the network, tasked with reconstructing the full point cloud. Once the network is updated, it is used to classify the point cloud. We test MATE on several 3D object classification datasets and show that it significantly improves robustness of deep networks to several types of corruptions commonly occurring in 3D point clouds. Further, we show that MATE is very efficient in terms of the fraction of points it needs for the adaptation. It can effectively adapt given as few as 5% of tokens of each test sample, which reduces its memory footprint and makes it lightweight. We also highlight that MATE achieves competitive performance by adapting sparingly on the test data, which further reduces its computational overhead, making it ideal for real-time applications.Comment: Minor fix in citation