Twitter and the US stock market: the influence of micro‑bloggers on share prices

With the increased interest in social media over recent years, the role of information disseminated through avenues such as Twitter has become more widely perceived. This paper examines the mention of stocks on the US markets (NYSE and NASDAQ) by a number of financial micro-bloggers to establish whether their posts are reflected in price movements. The Twitter feeds are selected from syndicated and nonsyndicated authors. A substantial number of tweets were linked to the price movements of the mentioned assets and an event study methodology was used to ascertain whether these mentions carry any significant information or whether they are merely noise

Performance of single photon-counting X-ray charge coupled devices

Results of intial performance tests on X-ray sensing properties of charge-coupled devices (CCDs) are presented. CCDs have demonstrated excellent spatial resolution and good spectral resolution, superior to that of non-imaging proportional counters

Parallelization of chip-based fluorescence immuno-assays with quantum-dot labelled beads

This paper presents an optical concept for the read-out of a parallel, bead-based fluorescence immunoassay conducted on a lab-on-a-disk platform. The reusable part of the modular setup comprises a detection unit featuring a single LED as light source, two emission-filters, and a color CCD-camera as standard components together with a spinning drive as actuation unit. The miniaturized lab-on-a-disk is devised as a disposable. In the read-out process of the parallel assay, beads are first identified by the color of incorporated quantum dots (QDs). Next, the reaction-specific fluorescence signal is quantified with FluoSpheres-labeled detection anti-bodies. To enable a fast and automated read-out, suitable algorithms have been implemented in this work. Based on this concept, we successfully demonstrated a Hepatitis-A assay on our disk-based lab-on-a-chip

Bistability of vortex core dynamics in a single perpendicularly magnetized nano-disk

Microwave spectroscopy of individual vortex-state magnetic nano-disks in a perpendicular bias magnetic field, $H$, is performed using a magnetic resonance force microscope (MRFM). It reveals the splitting induced by $H$ on the gyrotropic frequency of the vortex core rotation related to the existence of the two stable polarities of the core. This splitting enables spectroscopic detection of the core polarity. The bistability extends up to a large negative (antiparallel to the core) value of the bias magnetic field $H_r$, at which the core polarity is reversed. The difference between the frequencies of the two stable rotational modes corresponding to each core polarity is proportional to $H$ and to the ratio of the disk thickness to its radius. Simple analytic theory in combination with micromagnetic simulations give quantitative description of the observed bistable dynamics.Comment: 4 pages, 3 figures, 1 table, 16 references. Submitted to Physical Review Letters on December 19th, 200

On-Line AdaTron Learning of Unlearnable Rules

We study the on-line AdaTron learning of linearly non-separable rules by a simple perceptron. Training examples are provided by a perceptron with a non-monotonic transfer function which reduces to the usual monotonic relation in a certain limit. We find that, although the on-line AdaTron learning is a powerful algorithm for the learnable rule, it does not give the best possible generalization error for unlearnable problems. Optimization of the learning rate is shown to greatly improve the performance of the AdaTron algorithm, leading to the best possible generalization error for a wide range of the parameter which controls the shape of the transfer function.)Comment: RevTeX 17 pages, 8 figures, to appear in Phys.Rev.

Cardiovascular Magnetic Resonance Imaging in Experimental Models

Cardiovascular magnetic resonance (CMR) imaging is the modality of choice for clinical studies of the heart and vasculature, offering detailed images of both structure and function with high temporal resolution

GastroVision: A Multi-class Endoscopy Image Dataset for Computer Aided Gastrointestinal Disease Detection

Integrating real-time artificial intelligence (AI) systems in clinical practices faces challenges such as scalability and acceptance. These challenges include data availability, biased outcomes, data quality, lack of transparency, and underperformance on unseen datasets from different distributions. The scarcity of large-scale, precisely labeled, and diverse datasets are the major challenge for clinical integration. This scarcity is also due to the legal restrictions and extensive manual efforts required for accurate annotations from clinicians. To address these challenges, we present \textit{GastroVision}, a multi-center open-access gastrointestinal (GI) endoscopy dataset that includes different anatomical landmarks, pathological abnormalities, polyp removal cases and normal findings (a total of 27 classes) from the GI tract. The dataset comprises 8,000 images acquired from B{\ae}rum Hospital in Norway and Karolinska University Hospital in Sweden and was annotated and verified by experienced GI endoscopists. Furthermore, we validate the significance of our dataset with extensive benchmarking based on the popular deep learning based baseline models. We believe our dataset can facilitate the development of AI-based algorithms for GI disease detection and classification. Our dataset is available at \url{https://osf.io/84e7f/}