SQUID-based superconducting microcalorimeter with in-situ tunable gain

Cryogenic microcalorimeters are outstanding tools for X-ray spectroscopy due to their unique combination of excellent energy resolution and close to 100% detection efficiency. While well-established microcalorimeter concepts have already proven impressive performance, their energy resolution has yet to improve to be competitive with cutting-edge wavelength-dispersive grating or crystal spectrometers. We hence present an innovative SQUID-based superconducting microcalorimeter with an in-situ tunable gain as alternative concept that is based on the strong temperature dependence of the magnetic penetration depth of a superconductor operated close to its critical temperature. Measurements using a prototype device show no sign for any hysteresis effects that often spoil the performance of superconducting microcalorimeters. Moreover, our predictions of the achievable energy resolution show that a competitive energy resolution O(300meV) with a suitable combination of absorber and sensor material should be easily possible.Comment: This manuscript has been submitted to the AIP journal "Applied Physics Letters", 6 pages, 5 figure

Entity Tracking in Language Models

Keeping track of how states of entities change as a text or dialog unfolds is a key prerequisite to discourse understanding. Yet, there have been few systematic investigations into the ability of large language models (LLMs) to track discourse entities. In this work, we present a task probing to what extent a language model can infer the final state of an entity given an English description of the initial state and a series of state-changing operations. We use this task to first investigate whether Flan-T5, GPT-3 and GPT-3.5 can track the state of entities, and find that only GPT-3.5 models, which have been pretrained on large amounts of code, exhibit this ability. We then investigate whether smaller models pretrained primarily on text can learn to track entities, through finetuning T5 on several training/evaluation splits. While performance degrades for more complex splits, we find that even when evaluated on a different set of entities from training or longer operation sequences, a finetuned model can perform non-trivial entity tracking. Taken together, these results suggest that language models can learn to track entities but pretraining on text corpora alone does not make this capacity surface.Comment: ACL 2023 Camera-read

Causal strands for social bonds : a case study on the credibility of claims from impact reporting

The study investigates if causal claims based on a theory-of-change approach for impact reporting are credible. The authors use their most recent impact report for a Social Bond to show how theory-based logic models can be used to map the sustainability claims of issuers to quantifiable indicators. A single project family (homeownership loans) is then used as a case study to test the underlying hypotheses of the sustainability claims. By applying Bayes Theorem, evidence for and against the claims is weighted to calculate the degree to which the belief in the claims is warranted. The authors found that only one out of three claims describe a probable cause–effect chain for social benefits from the loans. The other two claims either require more primary data to be corroborated or should be re-defined to link the intervention more closely and robustly with the overarching societal goals. However, all previous reported indicators are below the thresholds of the most conservative estimates for fractions of beneficiaries in the paper at hand. We conclude that the combination of a Theory-of-Change with a Bayesian Analysis is an effective way to test the plausibility of sustainability claims and to mitigate biases. Nevertheless, the method is - in the presented form - also too elaborate and time-consuming for impact reporting in the sustainable finance market