5 YEARS OF ION-LASER INTERACTION MASS SPECTROMETRY—STATUS AND PROSPECTS OF ISOBAR SUPPRESSION IN AMS BY LASERS

ABSTRACT A setup for ion-laser interaction was coupled to the state-of-the-art AMS facility VERA five years ago and its potential and applicability as a new means of isobar suppression in accelerator mass spectrometry (AMS) has since been explored. Laser photodetachment and molecular dissociation processes of anions provide unprecedented isobar suppression factors of >1010 for several established AMS isotopes like 36Cl or 26Al and give access to new AMS isotopes like 90Sr, 135Cs or 182Hf at a 3-MV-tandem facility. Furthermore, Ion-Laser InterAction Mass Spectrometry has been proven to meet AMS requirements regarding reliability and robustness with a typical reproducibility of results of 3%. The benefits of the technique are in principle available to any AMS machine, irrespective of attainable ion beam energy. Since isobar suppression via this technique is so efficient, there often is no need for any additional element separation in the detection setup and selected nuclides may even become accessible without accelerator at all.

Multi-qubit correction for quantum annealers

We present multi-qubit correction (MQC) as a novel postprocessing method for quantum annealers that views the evolution in an open-system as a Gibbs sampler and reduces a set of excited states to a new synthetic state with lower energy value. After sampling from the ground state of a given (Ising) Hamiltonian, MQC compares pairs of excited states to recognize virtual tunnels—i.e., a group of qubits that changing their states simultaneously can result in a new state with lower energy value—and successively converges to the ground state. Experimental results using D-Wave 2000Q quantum annealers demonstrate that MQC finds samples with notably lower energy values and improves the reproducibility of results when compared to recent hardware/software advances in the realm of quantum annealing, such as spin-reversal transforms, classical postprocessing techniques, and increased inter-sample delay between successive measurements.

Open Science for Veterinary Education

“Open Science” encourages researchers to improve the reliability, repeatability and reproducibility of results. While stemming from the reproducibility crisis in psychology, open science is relevant for all fields. In education research these practices include pre-registering experimental designs, sharing analyses where appropriate, and pre-printing research report. How can veterinary education implement these lessons? In this short report accompanying the ‘Open Science for VetEd’ workshop, hosted at the VetEd 2021 Annual Conference, I discuss what opportunities open science presents for veterinary education researchers.

Behavior Testing in Rodents: Highlighting Potential Confounds Affecting Variability and Reproducibility

Rodent models of brain disorders including neurodevelopmental, neuropsychiatric, and neurodegenerative diseases are essential for increasing our understanding of underlying pathology and for preclinical testing of potential treatments. Some of the most important outcome measures in such studies are behavioral. Unfortunately, reports from different labs are often conflicting, and preclinical studies in rodent models are not often corroborated in human trials. There are many well-established tests for assessing various behavioral readouts, but subtle aspects can influence measurements. Features such as housing conditions, conditions of testing, and the sex and strain of the animals can all have effects on tests of behavior. In the conduct of behavior testing, it is important to keep these features in mind to ensure the reliability and reproducibility of results. In this review, we highlight factors that we and others have encountered that can influence behavioral measures. Our goal is to increase awareness of factors that can affect behavior in rodents and to emphasize the need for detailed reporting of methods.

Chronic Cranial Windows for Long Term Multimodal Neurovascular Imaging in Mice

Chronic cranial windows allow for longitudinal brain imaging experiments in awake, behaving mice. Different imaging technologies have their unique advantages and combining multiple imaging modalities offers measurements of a wide spectrum of neuronal, glial, vascular, and metabolic parameters needed for comprehensive investigation of physiological and pathophysiological mechanisms. Here, we detail a suite of surgical techniques for installation of different cranial windows targeted for specific imaging technologies and their combination. Following these techniques and practices will yield higher experimental success and reproducibility of results.

Status of the AMS graphitization system in the dendrochronological laboratory at AGH-UST, Kraków

A new system for the preparation of graphite samples for radiocarbon (14C) measurement using an accelerator mass spectrometer (AMS) has been built in the Dendrochronological Laboratory at AGH-UST, Kraków. This system consists of three independent components. The first is the equipment for mechanical and chemical sample pre-treatment. The second is the vacuum line for sample sealing and the purification of CO2. The third and central part of this system is a graphitization line, where graphite is produced from CO2. In the first stage, chemical sample preparation was carried out to remove impurities. IAEA and NIST OxII standard materials were converted to CO2 without pre-treatment. In the next step, samples were combusted to CO2. The resulting CO2 was released under vacuum and cryogenically purified for subsequent graphitization. The performance of the system was tested with NIST OxII, IAEA standards (IAEA C5, C6 and C7) and background samples. The test confirms good reproducibility of results obtained for the samples prepared using this system. The results of the 49 samples of NIST Ox-II, IAEA standards and blank samples were presented in this article.

A survey of benchmarks for reinforcement learning algorithms

Reinforcement learning has recently experienced increased prominence in the machine learning community. There are many approaches to solving reinforcement learning problems with new techniques developed constantly. When solving problems using reinforcement learning, there are various difficult challenges to overcome. \par To ensure progress in the field, benchmarks are important for testing new algorithms and comparing with other approaches. The reproducibility of results for fair comparison is therefore vital in ensuring that improvements are accurately judged. This paper provides an overview of different contributions to reinforcement learning benchmarking and discusses how they can assist researchers to address the challenges facing reinforcement learning. The contributions discussed are the most used and recent in the literature. The paper discusses the contributions in terms of implementation, tasks and provided algorithm implementations with benchmarks. \par The survey aims to bring attention to the wide range of reinforcement learning benchmarking tasks available and to encourage research to take place in a standardised manner. Additionally, this survey acts as an overview for researchers not familiar with the different tasks that can be used to develop and test new reinforcement learning algorithms.

Broad Phase Collision Detection: New Methodology and Solution for Standardized Analysis of Algorithms

Collision detection is a computational problem focused on the identification of geometric intersections between objects and, in general, proximity relationships among them. Despite its notorious relevance and applications in various computing fields, few authors have proposed solutions that are both general and scalable. Additionally, until the time of publication of the results of this work, there was no standard methodology for the analysis of algorithms, neither in academia nor in the industry: only proprietary scenes and comparative studies had been developed, making it difficult to reproduce and compare results. To tackle the issues previously mentioned, we present a new general and scalable solution for broad phase collision detection and a new methodology for comparative analysis of algorithms, named Broadmark, whose open-source code is publicly available, with the goal of transferring knowledge to academia, industry, and society, so far lacking in the scientific literature. Thus, by doing so, we aim to contribute to the generation of robust and multi-faceted solutions applied to various scenarios and, consequently, to greater transparency, ease of modification/extension and reproducibility of results.1