Bayesian Analysis of Femtosecond Pump-Probe Photoelectron-Photoion Coincidence Spectra with Fluctuating Laser Intensities

This paper employs Bayesian probability theory for analyzing data generated in femtosecond pump-probe photoelectron-photoion coincidence (PEPICO) experiments. These experiments allow investigating ultrafast dynamical processes in photoexcited molecules. Bayesian probability theory is consistently applied to data analysis problems occurring in these types of experiments such as background subtraction and false coincidences. We previously demonstrated that the Bayesian formalism has many advantages, amongst which are compensation of false coincidences, no overestimation of pump-only contributions, significantly increased signal-to-noise ratio, and applicability to any experimental situation and noise statistics. Most importantly, by accounting for false coincidences, our approach allows running experiments at higher ionization rates, resulting in an appreciable reduction of data acquisition times. In addition to our previous paper, we include fluctuating laser intensities, of which the straightforward implementation highlights yet another advantage of the Bayesian formalism. Our method is thoroughly scrutinized by challenging mock data, where we find a minor impact of laser fluctuations on false coincidences, yet a noteworthy influence on background subtraction. We apply our algorithm to data obtained in experiments and discuss the impact of laser fluctuations on the data analysis

Contrastive Tuning: A Little Help to Make Masked Autoencoders Forget

Masked Image Modeling (MIM) methods, like Masked Autoencoders (MAE), efficiently learn a rich representation of the input. However, for adapting to downstream tasks, they require a sufficient amount of labeled data since their rich features code not only objects but also less relevant image background. In contrast, Instance Discrimination (ID) methods focus on objects. In this work, we study how to combine the efficiency and scalability of MIM with the ability of ID to perform downstream classification in the absence of large amounts of labeled data. To this end, we introduce Masked Autoencoder Contrastive Tuning (MAE-CT), a sequential approach that utilizes the implicit clustering of the Nearest Neighbor Contrastive Learning (NNCLR) objective to induce abstraction in the topmost layers of a pre-trained MAE. MAE-CT tunes the rich features such that they form semantic clusters of objects without using any labels. Notably, MAE-CT does not rely on hand-crafted augmentations and frequently achieves its best performances while using only minimal augmentations (crop & flip). Further, MAE-CT is compute efficient as it requires at most 10% overhead compared to MAE re-training. Applied to large and huge Vision Transformer (ViT) models, MAE-CT excels over previous self-supervised methods trained on ImageNet in linear probing, k-NN and low-shot classification accuracy as well as in unsupervised clustering accuracy. With ViT-H/16 MAE-CT achieves a new state-of-the-art in linear probing of 82.2%

High Rates of Treatment Success in Pulmonary Multidrug-Resistant Tuberculosis by Individually Tailored Treatment Regimens.

RATIONALE: We evaluated whether treatment outcomes for patients with multidrug-resistant and extensively drug-resistant tuberculosis can be substantially improved when sufficient resources for personalizing medical care are available. OBJECTIVES: To describe the characteristics and outcomes of patients with pulmonary multidrug-resistant tuberculosis at the Otto Wagner Hospital in Vienna, Austria. METHODS: We conducted a retrospective single-center study of patients initiated on treatment for multi-drug resistant tuberculosis between January 2003 and December 2012 at the Otto Wagner Hospital, Vienna, Austria. The records of patients with multidrug-resistant tuberculosis were reviewed for epidemiological, clinical, laboratory, treatment, and outcome data. MEASUREMENTS AND MAIN RESULTS: Ninety patients with pulmonary multidrug-resistant tuberculosis were identified. The median age was 30 years (interquartile range, 26-37). All patients were of non-Austrian origin, and 70 (78%) came from former states of the Soviet Union. Thirty-nine (43%) patients had multidrug-resistant tuberculosis; 28 (31%) had additional bacillary resistance to at least one second-line injectable drug and 9 (10%) to a fluoroquinolone. Fourteen (16%) patients had extensively drug-resistant tuberculosis. Eighty-eight different drug combinations were used for the treatment of the 90 patients. Surgery was performed on 10 (11.1%) of the patients. Sixty-five (72.2%) patients had a successful treatment outcome, 8 (8.9%) defaulted, 3 (3.3%) died, 8 (8.9%) continued treatment in another country and their outcome was unknown, and 6 (6.7%) were still on therapy. None of the patients experienced treatment failure. Treatment outcomes for patients with extensively drug-resistant tuberculosis were similar to those of patients with multidrug-resistant tuberculosis. CONCLUSIONS: High rates of treatment success can be achieved in patients with multidrug-resistant and extensively drug-resistant tuberculosis when individually tailored treatment regimens can be provided in a high-resource setting

Progress in information technology and tourism management: 20 years on and 10 years after the Internet—The state of eTourism research

This paper reviews the published articles on eTourism in the past 20 years. Using a wide variety of sources, mainly in the tourism literature, this paper comprehensively reviews and analyzes prior studies in the context of Internet applications to Tourism. The paper also projects future developments in eTourism and demonstrates critical changes that will influence the tourism industry structure. A major contribution of this paper is its overview of the research and development efforts that have been endeavoured in the field, and the challenges that tourism researchers are, and will be, facing