Purpose: To utilize high-resolution quantitative CT (QCT) imaging features for prediction of diagnosis and prognosis in fibrosing interstitial lung diseases (ILD). Approach: 40 ILD patients (20 usual interstitial pneumonia (UIP), 20 non-UIP pattern ILD) were classified by expert consensus of 2 radiologists and followed for 7 years. Clinical variables were recorded. Following segmentation of the lung field, a total of 26 texture features were extracted using a lattice-based approach (TM model). The TM model was compared with previously histogram-based model (HM) for their abilities to classify UIP vs non-UIP. For prognostic assessment, survival analysis was performed comparing the expert diagnostic labels versus TM metrics. Results: In the classification analysis, the TM model outperformed the HM method with AUC of 0.70. While survival curves of UIP vs non-UIP expert labels in Cox regression analysis were not statistically different, TM QCT features allowed statistically significant partition of the cohort. Conclusions: TM model outperformed HM model in distinguishing UIP from non-UIP patterns. Most importantly, TM allows for partitioning of the cohort into distinct survival groups, whereas expert UIP vs non-UIP labeling does not. QCT TM models may improve diagnosis of ILD and offer more accurate prognostication, better guiding patient management.

Now senior director for AI and legal ethics at NVIDIA, Pope spends her days working with internal teams across the company to ensure its products engender trust across industries. In a recent "Solving for Tech Ethics" podcast, Pope joined Beena Ammanath, Deloitte LLP's Trustworthy and Ethical Technology leader, to discuss the challenges and opportunities associated with creating trustworthy AI. Ammanath: Five or 10 years ago, roles like yours just didn't exist. What does a day in your job look like? Pope: One day does not look like the next. Take yesterday as an example.

The way Timnit Gebru sees it, the foundations of the future are being built now. In Silicon Valley, home to the world's biggest tech companies, the artificial intelligence (AI) revolution is already well under way. Software is being written and algorithms are being trained that will determine the shape of our lives for decades or even centuries to come. If the tech billionaires get their way, the world will run on artificial intelligence. Cars will drive themselves and computers will diagnose and cure diseases. Art, music and movies will be automatically generated.

Grand master Viswanathan Anand and sports minister Anurag Thakur were Times of India's guest editor on Saturday, 18th June. Sports guest editors Viswanathan Anand and Anurag Thakur talk to TOI about the game-changing dynamics of the Chess Olympiad... Viswanathan Anand has decided to don a new hat. The five-time world chess champion, who is still an active p layer at 52 and has defeated the world's top player, Magnus Carlsen, twice recently, will contest for the post of deputy president in the FIDE (world chess body) elections. He is also the face of the Chess Olympiad, which will be held in India for the very first time, in Chennai in July-August this year. Anand, looking relaxed in a light summer coat, joined sports minister Anurag Thakur during an interaction with TOI in the Capital on Saturday. The two Guest Sports Editors talked about the way ahead for chess, plans to popularise the sport at the grassroots level and stage more international tournaments to give aspiring youngsters exposure to top-quality chess. Excerpts from the interaction... India is hosting such a big chess event for the first time since the Anand versus Carlsen battle in 2003.

Air pollution is still a problem almost everywhere, so researchers investigate using AI to fight air pollution. Although other environmental topics such as global warming, loss of biodiversity, soil degradation and unsustainable use of freshwater resources have become more prominent in recent years, air pollution has remained an issue which deserves our attention and action. According to the World Health Organisation, between 3 and 8 million people die prematurely every year, because the air they breathe frequently contains harmful substances which may affect the respiratory system, lead to inflammatory diseases or impact the human immune system. Despite several regulations that aim to reduce emissions of air pollutants and put limits on the levels of ambient air pollutant concentrations, measurements across Europe still regularly exhibit concentration levels beyond the threshold values that are deemed safe for human health and food production. Other world regions face even larger problems; sometimes the pollution in the megacities of Southern and Eastern Asia, Africa and South America is so severe that it is almost impossible for people to go about their work or navigate through the streets.

Temperature and humidity are two of the rudimentary factors that must be controlled during egg incubation. Improper temperature and humidity levels during the incubation period often result in unwanted conditions. This paper proposes the design of an efficient Mamdani fuzzy interference system instead of the widely used Takagi-Sugeno system in this field for controlling the temperature and humidity levels of an egg incubator. Though the optimum incubation temperature and humidity levels used here are that of chicken egg, the proposed methodology is applicable to other avian species as well. Theinput functions have been used here as per estimated values forsafe hatching using Mamdani whereas defuzzification method, COA, has been applied for output. From the model output,a stabilized heat from temperature level and fan speed to control the humidity level of an egg incubator can be obtained. This maximizes the hatching rate of healthy chicks under any conditions in the field.

For many applications, analyzing the uncertainty of a machine learning model is indispensable. While research of uncertainty quantification (UQ) techniques is very advanced for computer vision applications, UQ methods for spatio-temporal data are less studied. In this paper, we focus on models for online handwriting recognition, one particular type of spatio-temporal data. The data is observed from a sensor-enhanced pen with the goal to classify written characters. We conduct a broad evaluation of aleatoric (data) and epistemic (model) UQ based on two prominent techniques for Bayesian inference, Stochastic Weight Averaging-Gaussian (SWAG) and Deep Ensembles. Next to a better understanding of the model, UQ techniques can detect out-of-distribution data and domain shifts when combining right-handed and left-handed writers (an underrepresented group).

Despite recent advances in algorithmic fairness, To address these issues there has recently been a significant methodologies for achieving fairness with generalized body of work in the machine learning community on linear models (GLMs) have yet to be algorithmic fairness in the context of predictive modeling, explored in general, despite GLMs being widely including (i) data preprocessing methods that try to reduce used in practice. In this paper we introduce two disparities, (ii) in-process approaches which enforce fairness fairness criteria for GLMs based on equalizing during model training, and (iii) post-process approaches expected outcomes or log-likelihoods. We prove which adjust a model's predictions to achieve fairness after that for GLMs both criteria can be achieved via training is completed. However, the majority of this work a convex penalty term based solely on the linear has focused on classification problems with binary outcome components of the GLM, thus permitting efficient variables, and to a lesser extent on regression.

We model the problem of optimizing the schedule of courses a student at the American College of Greece will need to take to complete their studies. We model all constraints set forth by the institution and the department, so that we guarantee the validity of all produced schedules. We formulate several different objectives to optimize in the resulting schedule, including fastest completion time, course difficulty balance, and so on, with a very important objective our model is capable of capturing being the maximization of the expected student GPA given their performance on passed courses using Machine Learning and Data Mining techniques. All resulting problems are Mixed Integer Linear Programming problems with a number of binary variables that is in the order of the maximum number of terms times the number of courses available for the student to take. The resulting Mathematical Programming problem is always solvable by the GUROBI solver in less than 10 seconds on a modern commercial off-the-self PC, whereas the manual process that was installed before used to take department heads that are designated as student advisors more than one hour of their time for every student and was resulting in sub-optimal schedules as measured by the objectives set forth.

The classical Cox model emerged in 1972 promoting breakthroughs in how patient prognosis is quantified using time-to-event analysis in biomedicine. One of the most useful characteristics of the model for practitioners is the interpretability of the variables in the analysis. However, this comes at the price of introducing strong assumptions concerning the functional form of the regression model. To break this gap, this paper aims to exploit the explainability advantages of the classical Cox model in the setting of interval-censoring using a new Lasso neural network that simultaneously selects the most relevant variables while quantifying non-linear relations between predictors and survival times. The gain of the new method is illustrated empirically in an extensive simulation study with examples that involve linear and non-linear ground dependencies. We also demonstrate the performance of our strategy in the analysis of physiological, clinical and accelerometer data from the NHANES 2003-2006 waves to predict the effect of physical activity on the survival of patients. Our method outperforms the prior results in the literature that use the traditional Cox model.