How should computer science and social science collaborate to build a common model? How should they proceed to gather data that is really useful to the modelling? How can they design a survey that is tailored to the target model? This paper aims to answer those crucial questions in the framework of a multidisciplinary research project. This research addresses the issue of co-constructing a model when several disciplines are involved, and is applied to modelling human behaviour immediately after an earthquake. The main contribution of the work is to propose a tool dedicated to multidisciplinary dialogue. It also proposes a reflexive analysis of the enriching intellectual process carried out by the different disciplines involved. Finally, from working with an anthropologist, a complementary view of the multidisciplinary process is given.

Handling correctly incomplete datasets in machine learning is a fundamental and classical challenge. In this paper, the problem of training a classifier on a dataset with missing features, and its application to a complete or incomplete test dataset, is addressed. A supervised learning method is developed to train a general classifier, such as a logistic regression or a deep neural network, using only a limited number of features per sample, while assuming sparse representations of data vectors on an unknown dictionary. The pattern of missing features is allowed to be different for each input data instance and can be either random or structured. The proposed method simultaneously learns the classifier, the dictionary and the corresponding sparse representation of each input data sample. A theoretical analysis is provided, comparing this method with the standard imputation approach, which consists of performing data completion followed by training the classifier with those reconstructions. Sufficient conditions are identified such that, if it is possible to train a classifier on incomplete observations so that their reconstructions are well separated by a hyperplane, then the same classifier also correctly separates the original (unobserved) data samples. Extensive simulation results on synthetic and well-known reference datasets are presented that validate our theoretical findings and demonstrate the effectiveness of the proposed method compared to traditional data imputation approaches and one state of the art algorithm.

The announcement in September took the world by storm: In radio emissions from Venus's atmosphere, researchers found signs of phosphine, a toxic compound that on Earth is made in significant amounts only by microbes and chemists. The unexpected detection could point to a microbial biosphere floating in the venusian clouds, the researchers suggested in Nature Astronomy . But almost immediately, other astronomers began to point out questionable methods or said they couldn't reproduce results. Now, after reanalyzing their data, the original proponents are downgrading their claims. Phosphine levels are at least seven times lower than first claimed, the authors reported in a preprint posted on 17 November to arXiv. But the team still believes the gas is there, Jane Greaves, an astronomer at Cardiff University who led the work, said in a talk last week to a NASA Venus science group. “We have again a phosphine line.” The original observations were made in 2017 at the James Clerk Maxwell Telescope (JCMT) in Hawaii, and in 2019 at the Atacama Large Millimeter/submillimeter Array (ALMA) in Chile. In Venus's radio spectrum, Greaves and her colleagues detected an absorption line they attributed to phosphine. The researchers went to great lengths to remove confounding effects such as absorption by Earth's own atmosphere. But critics said such aggressive fixes made the discovery of a false positive more likely. ALMA scientists have since found a new noise source: telescope calibration errors. After reanalyzing the ALMA data, Greaves said her team now finds phosphine at just 1 part per billion (ppb). That's still above levels that can be explained by natural processes such as volcanic eruptions or lightning strikes, Greaves said. A study published last month in Astronomy & Astrophysics , led by Therese Encrenaz, an astronomer at the Paris Observatory, ruled out higher phosphine levels. Her team analyzed observations made in 2015 by NASA's Infrared Telescope Facility in Hawaii. Phosphine should have popped out if it had existed at levels above 5 ppb. “It's easy to see there's no phosphine line,” Encrenaz says. If the line does exist, it might not be due to phosphine, according to a critique submitted to Nature Astronomy . It argues that the dip in the JCMT spectrum can be explained by an overlapping absorption line from sulfur dioxide (SO2), the gas that makes up most venusian clouds. The Greaves team concedes the point in its reanalysis. “We emphasize that there could be a contribution from SO2,” they write. But the width of the absorption line in the ALMA data suggests the feature isn't “solely SO2,” they write. Just where any signal is coming from is also in dispute. ALMA is only sensitive to absorption from substances at altitudes above 70 kilometers (km), Encrenaz says. But the Nature Astronomy paper suggested the signal originated some 55 km up, in warmer, more hospitable cloud layers. “This is very difficult to conceive,” Encrenaz says. Greaves and her co-authors argue in their reanalysis that ALMA is unable to capture the full width—and therefore depth—of the signal. “There is no empirical evidence that [phosphine] lies only above 70 km.” Colin Wilson, a co-author of the Nature Astronomy critique, says it's too early to say where the “phosphine roller coaster will end up.” More observations at ALMA might settle the issue, he says. “Whether or not we find phosphine, we're likely to find something new.”

Bengaluru, Nov 23: Artificial intelligence (AI), Machine learning, 5G and Internet of Things (IoT) would be the most important technologies in 2021, according to a new study by the Institute of Electrical and Electronics Engineers (IEEE). The technical professional organisation on Monday released the results of a survey of Chief Information Officers (CIO) and Chief Technology Officers (CTO) in the US, the UK, China, India and Brazil. The survey was on the most important technologies for 2021, the impact of the COVID-19 pandemic on the speed of their technology adoption and the industries expected to be most impacted by technology. On which would be the most important technologies, nearly one-third of the total respondents (32 per cent) said AI and ML followed by 5G (20 per cent) and IoT (14 per cent), according to an IEEE statement. Manufacturing (19 per cent), healthcare (18 per cent), financial services (15 per cent) and education (13 per cent) are the industries that most believe would be impacted by technology, according to the CIOs and CTOs surveyed.

Email security designed around machine-learning makes it possible to automatically prevent data breaches. Learn more about how it works.

Bright red cranberries are visible from space during the harvest season, which occurs from mid-September through mid-November in North America. These images show a sample of bog harvests in Wisconsin between 2015 and 2019 captured by the Landsat 8 and Sentinel-2 satellites. In 1959, a nationwide food panic erupted over a treasured Thanksgiving dish. Two weeks before the holiday, the federal government announced that cranberries had been contaminated by a cancer-causing chemical. Cranberry sales plummeted, schools tossed out cranberry products, restaurants eliminated the suspect fruit from menus.

Register for our blog to get new articles as we release them. Demand Forecasting is a technique for estimation of probable demand for a product or services. It is based on the analysis of past demand for that product or service in the present market condition. Demand forecasting should be done on a scientific basis and facts and events related to forecasting should be considered. After gathering information about various aspects of the market and demand based on the past, is possible to estimate future demand.

What are the opportunities and threats faced by the vendors in the international market?

While long short-term memory (LSTM) models have demonstrated stellar performance with streamflow predictions, there are major risks in applying these models in contiguous regions with no gauges, or predictions in ungauged regions (PUR) problems. However, softer data such as the flow duration curve (FDC) may be already available from nearby stations, or may become available. Here we demonstrate that sparse FDC data can be migrated and assimilated by an LSTM-based network, via an encoder. A stringent region-based holdout test showed a median Kling-Gupta efficiency (KGE) of 0.62 for a US dataset, substantially higher than previous state-of-the-art global-scale ungauged basin tests. The baseline model without FDC was already competitive (median KGE 0.56), but integrating FDCs had substantial value. Because of the inaccurate representation of inputs, the baseline models might sometimes produce catastrophic results. However, model generalizability was further meaningfully improved by compiling an ensemble based on models with different input selections.

We present an accurate, fast and efficient method for segmentation and muscle mask propagation in 3D freehand ultrasound data, towards accurate volume quantification. To this end, we propose a deep Siamese 3D Encoder-Decoder network that captures the evolution of the muscle appearance and shape for contiguous slices and uses it to propagate a reference mask annotated by a clinical expert. To handle longer changes of the muscle shape over the entire volume and to provide an accurate propagation, we devised a Bidirectional Long Short Term Memory module. To train our model with a minimal amount of training samples, we propose a strategy to combine learning from few annotated 2D ultrasound slices with sequential pseudo-labeling of the unannotated slices. To promote few-shot learning, we propose a decremental update of the objective function to guide the model convergence in the absence of large amounts of annotated data. Finally, to handle the class-imbalance between foreground and background muscle pixels, we propose a parametric Tversky loss function that learns to adaptively penalize false positives and false negatives. We validate our approach for the segmentation, label propagation, and volume computation of the three low-limb muscles on a dataset of 44 subjects. We achieve a dice score coefficient of over $95~\%$ and a small fraction of error with $1.6035~\pm~0.587$.