Interpretable Anomaly Detection in Echocardiograms with Dynamic Variational Trajectory Models

We propose a novel anomaly detection method for echocardiogram videos. The introduced method takes advantage of the periodic nature of the heart cycle to learn three variants of a variational latent trajectory model (TVAE). While the first two variants (TVAE-C and TVAE-R) model strict periodic movements of the heart, the third (TVAE-S) is more general and allows shifts in the spatial representation throughout the video. All models are trained on the healthy samples of a novel in-house dataset of infant echocardiogram videos consisting of multiple chamber views to learn a normative prior of the healthy population. During inference, maximum a posteriori (MAP) based anomaly detection is performed to detect out-of-distribution samples in our dataset. The proposed method reliably identifies severe congenital heart defects, such as Ebstein's Anomaly or Shone-complex. Moreover, it achieves superior performance over MAP-based anomaly detection with standard variational autoencoders when detecting pulmonary hypertension and right ventricular dilation. Finally, we demonstrate that the proposed method enables interpretable explanations of its output through heatmaps highlighting the regions corresponding to anomalous heart structures.Comment: accepted at IMLH workshop ICML 202

AnnotCompute: annotation-based exploration and meta-analysis of genomics experiments

The ever-increasing scale of biological data sets, particularly those arising in the context of high-throughput technologies, requires the development of rich data exploration tools. In this article, we present AnnotCompute, an information discovery platform for repositories of functional genomics experiments such as ArrayExpress. Our system leverages semantic annotations of functional genomics experiments with controlled vocabulary and ontology terms, such as those from the MGED Ontology, to compute conceptual dissimilarities between pairs of experiments. These dissimilarities are then used to support two types of exploratory analysis—clustering and query-by-example. We show that our proposed dissimilarity measures correspond to a user's intuition about conceptual dissimilarity, and can be used to support effective query-by-example. We also evaluate the quality of clustering based on these measures. While AnnotCompute can support a richer data exploration experience, its effectiveness is limited in some cases, due to the quality of available annotations. Nonetheless, tools such as AnnotCompute may provide an incentive for richer annotations of experiments. Code is available for download at http://www.cbil.upenn.edu/downloads/AnnotCompute

Automatic Procedure for Thermal NDE of Delaminations in CFRP by Using Neural Networks

This work is a first step in detecting and characterizing defects in an automatic way by using artificial intelligence. Transient thermal NDE by IR thermography is the method used for such a purpose. Data are processed by Neural Networks

Free daily newspapers : too strong incentives to print?

A free daily newspaper distributes news to readers and sells ad-space to advertisers, having private information about its audience. For a given number of distributed copies, depending on the type of audience (favorable or unfavorable), the newspaper may either have a large readership or a small readership. A large readership provides a greater return to advertisers, because ads are visualized by more people. A favorable audience has also the advantage of requiring a lower distribution cost (for a given number of distributed copies), because readers are willing to exert more effort to obtain a copy of the free newspaper and are less likely to reject a copy that is handed to them. We find that when the audience is unfavorable, the number of distributed copies and the price of ad-space coincide with those of the perfect information scenario. In contrast, if the audience is favorable, the newspaper prints extra copies to send a credible signal to the advertisers that the audience is favorable. Overprinting is not necessarily welfare-detrimental since readers benefit from the existence of additional copies.info:eu-repo/semantics/publishedVersio

Functional genomics of the beta-cell: short-chain 3-hydroxyacyl-coenzyme A dehydrogenase regulates insulin secretion independent of K+ currents

Recent advances in functional genomics afford the opportunity to interrogate the expression profiles of thousands of genes simultaneously and examine the function of these genes in a high-throughput manner. In this study, we describe a rational and efficient approach to identifying novel regulators of insulin secretion by the pancreatic beta-cell. Computational analysis of expression profiles of several mouse and cellular models of impaired insulin secretion identified 373 candidate genes involved in regulation of insulin secretion. Using RNA interference, we assessed the requirements of 10 of these candidates and identified four genes (40%) as being essential for normal insulin secretion. Among the genes identified was Hadhsc, which encodes short-chain 3-hydroxyacyl-coenzyme A dehydrogenase (SCHAD), an enzyme of mitochondrial beta-oxidation of fatty acids whose mutation results in congenital hyperinsulinism. RNA interference-mediated gene suppression of Hadhsc in insulinoma cells and primary rodent islets revealed enhanced basal but normal glucose-stimulated insulin secretion. This increase in basal insulin secretion was not attenuated by the opening of the KATP channel with diazoxide, suggesting that SCHAD regulates insulin secretion through a KATP channel-independent mechanism. Our results suggest a molecular explanation for the hyperinsulinemia hypoglycemic seen in patients with SCHAD deficiency

Start of SPIDER operation towards ITER neutral beams

Heating Neutral Beam (HNB) Injectors will constitute the main plasma heating and current drive tool both in ITER and JT60-SA, which are the next major experimental steps for demonstrating nuclear fusion as viable energy source. In ITER, in order to achieve the required thermonuclear fusion power gain Q=10 for short pulse operation and Q=5 for long pulse operation (up to 3600s), two HNB injectors will be needed [1], each delivering a total power of about 16.5 MW into the magnetically-confined plasma, by means of neutral hydrogen or deuterium particles having a specific energy of about 1 MeV. Since only negatively charged particles can be efficiently neutralized at such energy, the ITER HNB injectors [2] will be based on negative ions, generated by caesium-catalysed surface conversion of atoms in a radio-frequency driven plasma source. A negative deuterium ion current of more than 40 A will be extracted, accelerated and focused in a multi-aperture, multi-stage electrostatic accelerator, having 1280 apertures (~ 14 mm diam.) and 5 acceleration stages (~200 kV each) [3]. After passing through a narrow gas-cell neutralizer, the residual ions will be deflected and discarded, whereas the neutralized particles will continue their trajectory through a duct into the tokamak vessels to deliver the required heating power to the ITER plasma for a pulse duration of about 3600 s. Although the operating principles and the implementation of the most critical parts of the injector have been tested in different experiments, the ITER NBI requirements have never been simultaneously attained. In order to reduce the risks and to optimize the design and operating procedures of the HNB for ITER, a dedicated Neutral Beam Test Facility (NBTF) [4] has been promoted by the ITER Organization with the contribution of the European Union\u2019s Joint Undertaking for ITER and of the Italian Government, with the participation of the Japanese and Indian Domestic Agencies (JADA and INDA) and of several European laboratories, such as IPP-Garching, KIT-Karlsruhe, CCFE-Culham, CEA-Cadarache. The NBTF, nicknamed PRIMA, has been set up at Consorzio RFX in Padova, Italy [5]. The planned experiments will verify continuous HNB operation for one hour, under stringent requirements for beam divergence (< 7 mrad) and aiming (within 2 mrad). To study and optimise HNB performances, the NBTF includes two experiments: MITICA, full-scale NBI prototype with 1 MeV particle energy and SPIDER, with 100 keV particle energy and 40 A current, aiming at testing and optimizing the full-scale ion source. SPIDER will focus on source uniformity, negative ion current density and beam optics. In June 2018 the experimental operation of SPIDER has started

Reactive direction control for a mobile robot: A locust-like control of escape direction emerges when a bilateral pair of model locust visual neurons are integrated

Locusts possess a bilateral pair of uniquely identifiable visual neurons that respond vigorously to the image of an approaching object. These neurons are called the lobula giant movement detectors (LGMDs). The locust LGMDs have been extensively studied and this has lead to the development of an LGMD model for use as an artificial collision detector in robotic applications. To date, robots have been equipped with only a single, central artificial LGMD sensor, and this triggers a non-directional stop or rotation when a potentially colliding object is detected. Clearly, for a robot to behave autonomously, it must react differently to stimuli approaching from different directions. In this study, we implement a bilateral pair of LGMD models in Khepera robots equipped with normal and panoramic cameras. We integrate the responses of these LGMD models using methodologies inspired by research on escape direction control in cockroaches. Using ‘randomised winner-take-all’ or ‘steering wheel’ algorithms for LGMD model integration, the khepera robots could escape an approaching threat in real time and with a similar distribution of escape directions as real locusts. We also found that by optimising these algorithms, we could use them to integrate the left and right DCMD responses of real jumping locusts offline and reproduce the actual escape directions that the locusts took in a particular trial. Our results significantly advance the development of an artificial collision detection and evasion system based on the locust LGMD by allowing it reactive control over robot behaviour. The success of this approach may also indicate some important areas to be pursued in future biological research

On the Challenges and Opportunities in Generative AI

The field of deep generative modeling has grown rapidly and consistently over the years. With the availability of massive amounts of training data coupled with advances in scalable unsupervised learning paradigms, recent large-scale generative models show tremendous promise in synthesizing high-resolution images and text, as well as structured data such as videos and molecules. However, we argue that current large-scale generative AI models do not sufficiently address several fundamental issues that hinder their widespread adoption across domains. In this work, we aim to identify key unresolved challenges in modern generative AI paradigms that should be tackled to further enhance their capabilities, versatility, and reliability. By identifying these challenges, we aim to provide researchers with valuable insights for exploring fruitful research directions, thereby fostering the development of more robust and accessible generative AI solutions