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Experimental Study

Every Single Way You Can Tell Trump World Is Lying About Its Latest COVID Scandal


Donald Trump and his former White House chief of staff Mark Meadows are peddling a new story about the ex-president's coronavirus infection. Their first story was that Trump didn't test positive until Oct. 1, 2020, two days after he debated Joe Biden. Then Meadows admitted in his new book, The Chief's Chief, that Trump actually tested positive on Sept. 26, three days before the debate. That admission was problematic, since Trump never informed Biden--or hundreds of other unwitting people who interacted closely with the maskless president in the intervening five days--about the test result. So now Trump and Meadows have concocted yet another story: The Sept. 26 result was a "false positive."

Real-World Data Analyst, Pharma R&D


Combine data from EMR's, observational notes, claims and other databases at the patient level for analysis. Deliver advanced analysis of real world and clinical data to discover new research opportunities, run clinical trial simulations, perform endpoint (e.g.

4 Reasons Your Deal Forecasts Probably Aren't Accurate


Sales and deal forecasting are vital parts of any business's planning, but it is also hard to argue that there are major issues with how we prepare for the future. Based on a number of sources, the level of inaccuracy with current tools is astounding. A study found that only 28.1% of sales teams were within a 5% deviation of their forecast, and 47% of 90-day predictions were off by a margin of more than half -- and sales reps overestimated by an average $91,000 and underestimated by only $47,000. CSO Insights cites that 60% of forecast deals do not close, and even organizations that formally track and review their processes still lose 40% of predicted closures. A SiriusDecisions' analysis pegged that "79 percent of sales organizations miss their sales forecast by more than 10 percent", and in another analysis, an asset manager says he just cuts 20% off the top of a forecast since he doesn't think they're reliable.

A Cartel of Influential Datasets Is Dominating Machine Learning Research, New Study Suggests


A new paper from the University of California and Google Research has found that a small number of'benchmark' machine learning datasets, largely from influential western institutions, and frequently from government organizations, are increasingly dominating the AI research sector. The researchers conclude that this tendency to'default' to highly popular open source datasets, such as ImageNet, brings up a number of practical, ethical and even political causes for concern. Among their findings – based on core data from the Facebook-led community project Papers With Code (PWC) – the authors contend that'widely-used datasets are introduced by only a handful of elite institutions', and that this'consolidation' has increased to 80% in recent years. '[We] find that there is increasing inequality in dataset usage globally, and that more than 50% of all dataset usages in our sample of 43,140 corresponded to datasets introduced by twelve elite, primarily Western, institutions.' Criteria for inclusion is where the institution or company accounts for more than 50% of known usages.

Electrical impedance spectroscopy (EIS) in plant roots research: a review - Plant Methods


Nondestructive testing of plant roots is a hot topic in recent years. The traditional measurement process is time-consuming and laborious, and it is impossible to analyze the state of plant roots without destroying the sample. Recent studies have shown that as an excellent nondestructive measurement method, although electrical impedance spectroscopy (EIS) has made great achievements in many botanical research fields such as plant morphology and stress resistance, there are still limitations. This review summarizes the application of EIS in plant root measurement. The experiment scheme, instrument and electrode, excitation frequency range, root electrical characteristics, equivalent circuit, and combination of EIS and artificial intelligence (AI) are discussed. Furthermore, the review suggests that future research should focus on miniaturization of measurement equipment, standardization of planting environment and intelligentization of root diagnosis, so as to better apply EIS technology to in situ root nondestructive measurement.

Deep learning algorithm cuts spine MRI exam time by about 50 percent, study finds


USA: The use of a deep learning algorithm for reconstructing spine MRI exams gives images comparable to those taken with a conventional MRI, reveals a recent study. Also, deep learning reduces spine MRI exam time by up to 50%. The findings of the study were presented at the Radiological Society of North America (RSNA) meeting by Dr. Sanders Chang of the Icahn School of Medicine at Mount Sinai in New York City. Some of the downsides of MRI are being addressed in the study. The drawbacks of MRI include low throughput, lengthy acquisition time, patient discomfort, high cost, and motion artifacts.

Analyzing Patient Trajectories With Artificial Intelligence


For example, electronic health records store the history of a patient's diagnoses, medications, laboratory values, and treatment plans [1-3]. Wearables collect granular sensor measurements of various neurophysiological body functions over time [4-6]. Intensive care units (ICUs) monitor disease progression via continuous physiological measurements (eg, electrocardiograms) [7-10]. As a result, patient data in digital medicine are regularly of longitudinal form (ie, consisting of health events from multiple time points) and thus form patient trajectories. Analyzing patient trajectories provides opportunities for more effective care in digital medicine [2,7,11]. Patient trajectories encode rich information on the history of health states that are also predictive of the future course of a disease (eg, individualized differences in disease progression or responsiveness to medications) [9,10,12]. As such, it is possible to construct patient trajectories that capture the entire disease course and characterize the many possible disease progression patterns, such as recurrent, stable, or rapidly deteriorating disease states (Figure 1). Hence, modeling the patient trajectories allows one to build robust models of diseases that capture disease dynamics seen in patient trajectories. Here, we replace disease models with data from only a single or a small number of time points by disease models that account for the longitudinal nature of patient trajectories, thus offering vast potential for digital medicine. Several studies have previously introduced artificial intelligence (AI) in medicine for practitioners [13,14].

Analysis on the Biodiversity in National Parks Projects


Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. In this blog, we are going to be performing an analysis on the data set "Biodiversity in National Parks Projects", which is available in Kaggle.



To determine whether deep learning algorithms developed in a public competition could identify lung cancer on low-dose CT scans with a performance similar to that of radiologists. In this retrospective study, a dataset consisting of 300 patient scans was used for model assessment; 150 patient scans were from the competition set and 150 were from an independent dataset. Both test datasets contained 50 cancer-positive scans and 100 cancer-negative scans. The reference standard was set by histopathologic examination for cancer-positive scans and imaging follow-up for at least 2 years for cancer-negative scans. The test datasets were applied to the three top-performing algorithms from the Kaggle Data Science Bowl 2017 public competition: grt123, Julian de Wit and Daniel Hammack (JWDH), and Aidence.

La veille de la cybersécurité


A University of California-Berkeley study revealed that lenders charge higher rates to Black and Hispanic borrowers. According to the study, algorithmic strategic pricing uses machine learning to find shoppers who might do less comparison shopping and accept higher-priced offerings. This algorithm is biased against Blacks and Hispanics. Including the word "transgender" in video titles has resulted in YouTubers receiving lower ad revenue on their videos. Commented Meg Green, a user experience researcher for Rocket Homes, "Being gay or being Black or being a trans woman does not mean these things are negative and that you don't want to read this information. Anything about being bisexual and gay is pornographic and not acceptable for children, according to some biased data found with AI."