Exporting Prolog source code

In this paper we present a simple source code configuration tool. ExLibris operates on libraries and can be used to extract from local libraries all code relevant to a particular project. Our approach is not designed to address problems arising in code production lines, but rather, to support the needs of individual or small teams of researchers who wish to communicate their Prolog programs. In the process, we also wish to accommodate and encourage the writing of reusable code. Moreover, we support and propose ways of dealing with issues arising in the development of code that can be run on a variety of like-minded Prolog systems. With consideration to these aims we have made the following decisions: (i) support file-based source development, (ii) require minimal program transformation, (iii) target simplicity of usage, and (iv) introduce minimum number of new primitives

Exploiting informative priors for Bayesian classification and regression trees

A general method for defining informative priors on statistical models is presented and applied specifically to the space of classification and regression trees. A Bayesian approach to learning such models from data is taken, with the Metropolis- Hastings algorithm being used to approximately sample from the posterior. By only using proposal distributions closely tied to the prior, acceptance probabilities are easily computable via marginal likelihood ratios, whatever the prior used. Our approach is empirically tested by varying (i) the data, (ii) the prior and (iii) the proposal distribution. A comparison with related work is given

A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification

Black-box machine learning learning methods are now routinely used in high-risk settings, like medical diagnostics, which demand uncertainty quantification to avoid consequential model failures. Distribution-free uncertainty quantification (distribution-free UQ) is a user-friendly paradigm for creating statistically rigorous confidence intervals/sets for such predictions. Critically, the intervals/sets are valid without distributional assumptions or model assumptions, possessing explicit guarantees even with finitely many datapoints. Moreover, they adapt to the difficulty of the input; when the input example is difficult, the uncertainty intervals/sets are large, signaling that the model might be wrong. Without much work and without retraining, one can use distribution-free methods on any underlying algorithm, such as a neural network, to produce confidence sets guaranteed to contain the ground truth with a user-specified probability, such as 90%. Indeed, the methods are easy-to-understand and general, applying to many modern prediction problems arising in the fields of computer vision, natural language processing, deep reinforcement learning, and so on. This hands-on introduction is aimed at a reader interested in the practical implementation of distribution-free UQ who is not necessarily a statistician. We lead the reader through the practical theory and applications of distribution-free UQ, beginning with conformal prediction and culminating with distribution-free control of any risk, such as the false-discovery rate, false positive rate of out-of-distribution detection, and so on. We will include many explanatory illustrations, examples, and code samples in Python, with PyTorch syntax. The goal is to provide the reader a working understanding of distribution-free UQ, allowing them to put confidence intervals on their algorithms, with one self-contained document.Comment: Blog and tutorial video http://angelopoulos.ai/blog/posts/gentle-intro

Dynamical response of the magnetotail to changes of the solar wind direction: an MHD modeling perspective

We performed global MHD simulations to investigate the magnetotail response to the solar wind directional changes (<I>V<sub>z</sub></I>-variations). These changes, although small, cause significant variations of the neutral sheet shape and location even in the near and middle tail regions. They display a complicated temporal response, in which ~60 to 80% of the final shift of the neutral sheet in <I>Z</I> direction occurs within first 10–15 min (less for faster solar wind), whereas a much longer time (exceeding half hour) is required to reach a new equilibrium. The asymptotic equilibrium shape of the simulated neutral sheet is consistent with predictions of Tsyganenko-Fairfield (2004) empirical model. To visualize a physical origin of the north-south tail motion we compared the values of the total pressure in the northern and southern tail lobes and found a considerable difference (10–15% for only 6° change of the solar wind direction used in the simulation). That difference builds up during the passage of the solar wind directional discontinuity and is responsible for the vertical shift of the neutral sheet, although some pressure difference remains in the near tail even near the new equilibrium. Surprisingly, at a given tailward distance, the response was found to be first initiated in the tail center (the "leader effect"), rather than near the flanks, which can be explained by the wave propagation in the tail, and which may have interesting implications for the substorm triggering studies. The present results have serious implications for the data-based modeling, as they place constraints on the accuracy of tail magnetic configurations to be derived for specific events using data of multi-spacecraft missions, e.g. such as THEMIS

Probabilistic Finite Domains

This thesis presents a set of programming constructs that are capable of modelling probabilistic concepts and of computing with such concepts. The main objectives are to provide: a theoretically sound, practically achievable and notationally intuitive formalism. The probabilistic programming constructs are presented in the form of a system called probabilistic finite domains, which enhances the Logic Programming paradigm with a novel constraint solver. In doing so, we are able to take advantage of the knowledge representation power of probability. In particular we investigate: first, the duality of the two interpretations of probability to the problems researchers face when wishing to create a probabilistic formalism and second, the use of probability as a unifying model for computational derivations. Some programming examples and a simple implementation are also described

Conformal PID Control for Time Series Prediction

We study the problem of uncertainty quantification for time series prediction, with the goal of providing easy-to-use algorithms with formal guarantees. The algorithms we present build upon ideas from conformal prediction and control theory, are able to prospectively model conformal scores in an online setting, and adapt to the presence of systematic errors due to seasonality, trends, and general distribution shifts. Our theory both simplifies and strengthens existing analyses in online conformal prediction. Experiments on 4-week-ahead forecasting of statewide COVID-19 death counts in the U.S. show an improvement in coverage over the ensemble forecaster used in official CDC communications. We also run experiments on predicting electricity demand, market returns, and temperature using autoregressive, Theta, Prophet, and Transformer models. We provide an extendable codebase for testing our methods and for the integration of new algorithms, data sets, and forecasting rules.Comment: Code available at https://github.com/aangelopoulos/conformal-time-serie

Integrated planning framework for successful river restoration projects: upscaling lessons learnt from European case studies

Despite considerable investment in river restoration projects, there is still limited information on the efficacy and success of river restoration activities. One of the main reasons is poor or improper project design, resulting in common problems such as: not addressing the root cause of habitat degradation; not establishing reference conditions, benchmarks and not defining endpoints against which to measure success; inappropriate uses of common restoration techniques because of lack of pre-planning; and inadequate monitoring or appraisal of restoration projects. In this paper peer-reviewed and grey literature and a large database of existing case studies were reviewed to identify the prevailing challenges river managers face when planning and developing river restoration projects. To overcome these current challe nges an integrated project planning framework has been developed that incorporates adaptive management and project management techniques. It encapsulates key concepts and decision support tools to advance the existing sequence of project identification, project formulation, project implementation and post-project monitoring to incorporate multidisciplinary decision making to meet specific environmental and socio-economic objectives. The proposed river restoration project planning framework is adaptable and can therefore be applied to any project development scenario locally, regionally or internationally

HYDRAULIC AND LEACHING BEHAVIOUR OF BELITE CEMENTS PRODUCED WITH ELECTRIC ARC FURNACE STEEL SLAG AS RAW MATERIAL

Three belite-rich cements consisting of a clinker made with 0 (BC), 5 (BC5) and 10 wt. % (BC10) electric arc furnace steel slag (EAFS) as raw material, were studied for their hydraulic and leaching behaviour. Hydration behaviour was studied by FTIR, TG/DTG and SEM analyses. The cements with EAFS resulted in a higher C2S/C3S and C4AF/C3A ratio compared to the reference body. As a result, the rate of hydration was low at early days whereas the structure was porous with scattered AFm and C–S–H crystals. At 28 days, a comparable dense microstructure consisting largely of C–S–H is observed in all mortars. Leaching was studied for V and Cr by means of tank test according to standard NEN 7345. The results showed V release below 2 μg/l. Chromium release calculated per 24 h was 1.4 μg/l in BC5 and 2.4 μg/l in BC10, which is much lower than the parametric value of 50 μg/l specified by the European Directive for drinking water (98/83/EC)