A collective, probabilistic approach to schema mapping using diverse noisy evidence

We propose a probabilistic approach to the problem of schema mapping. Our approach is declarative, scalable, and extensible. It builds upon recent results in both schema mapping and probabilistic reasoning and contributes novel techniques in both fields. We introduce the problem of schema mapping selection, that is, choosing the best mapping from a space of potential mappings, given both metadata constraints and a data example. As selection has to reason holistically about the inputs and the dependencies between the chosen mappings, we define a new schema mapping optimization problem which captures interactions between mappings as well as inconsistencies and incompleteness in the input. We then introduce Collective Mapping Discovery (CMD), our solution to this problem using state-of-the-art probabilistic reasoning techniques. Our evaluation on a wide range of integration scenarios, including several real-world domains, demonstrates that CMD effectively combines data and metadata information to infer highly accurate mappings even with significant levels of noise

Relationship between saccadic eye movements and cortical activity as measured by fMRI: quantitative and qualitative aspects

We investigated the quantitative relationship between saccadic activity (as reflected in frequency of occurrence and amplitude of saccades) and blood oxygenation level dependent (BOLD) changes in the cerebral cortex using functional magnetic resonance imaging (fMRI). Furthermore, we investigated quantitative changes in cortical activity associated with qualitative changes in the saccade task for comparable levels of saccadic activity. All experiments required the simultaneous acquisition of eye movement and fMRI data. For this purpose we used a new high-resolution limbus-tracking technique for recording eye movements in the magnetic resonance tomograph. In the first two experimental series we varied both frequency and amplitude of saccade stimuli (target jumps). In the third series we varied task difficulty; subjects performed either pro-saccades or anti-saccades. The brain volume investigated comprised the frontal and supplementary eye fields, parietal as well as striate cortex, and the motion sensitive area of the parieto-occipital cortex. All these regions showed saccade-related BOLD responses. The responses in these regions were highly correlated with saccade frequency, indicating that repeated processing of saccades is integrated over time in the BOLD response. In contrast, there was no comparable BOLD change with variation of saccade amplitude. This finding speaks for a topological rather than activity-dependent coding of saccade amplitudes in most cortical regions. In the experiments comparing pro- vs anti-saccades we found higher BOLD activation in the "anti" task than in the "pro" task. A comparison of saccade parameters revealed that saccade frequency and cumulative amplitude were comparable between the two tasks, whereas reaction times were longer in the "anti" task than the pro task. The latter finding is taken to indicate a more demanding cortical processing in the "anti" task than the "pro" task, which could explain the observed difference in BOLD activation. We hold that a quantitative analysis of saccade parameters (especially saccade frequency and latency) is important for the interpretation of the BOLD changes observed with visual stimuli in fMRI

No differences in value-based decision-making due to use of oral contraceptives

Fluctuating ovarian hormones have been shown to affect decision-making processes in women. While emerging evidence suggests effects of endogenous ovarian hormones such as estradiol and progesterone on value-based decision-making in women, the impact of exogenous synthetic hormones, as in most oral contraceptives, is not clear. In a between-subjects design, we assessed measures of value-based decision-making in three groups of women aged 18 to 29 years, during (1) active oral contraceptive intake (N = 22), (2) the early follicular phase of the natural menstrual cycle (N = 20), and (3) the periovulatory phase of the natural menstrual cycle (N = 20). Estradiol, progesterone, testosterone, and sex-hormone binding globulin levels were assessed in all groups via blood samples. We used a test battery which measured different facets of value-based decision-making: delay discounting, risk-aversion, risk-seeking, and loss aversion. While hormonal levels did show the expected patterns for the three groups, there were no differences in value-based decision-making parameters. Consequently, Bayes factors showed conclusive evidence in support of the null hypothesis. We conclude that women on oral contraceptives show no differences in value-based decision-making compared to the early follicular and periovulatory natural menstrual cycle phases. Copyright © 2022 Lewis, Kimmig, Kroemer, Pooseh, Smolka, Sacher and Derntl

Evidence That Hydra I is a Tidally Disrupting Milky Way Dwarf Galaxy

The Eastern Banded Structure (EBS) and Hydra I halo overdensities are very nearby (d ~ 10 kpc) objects discovered in Sloan Digital Sky Survey (SDSS) data. Previous studies of the region have shown that EBS and Hydra I are spatially coincident, cold structures at the same distance, suggesting that Hydra I may be the EBS's progenitor. We combine new wide-field Dark Energy Camera (DECam) imaging and MMT/Hectochelle spectroscopic observations of Hydra I with SDSS archival spectroscopic observations to quantify Hydra I's present-day chemodynamical properties, and to infer whether it originated as a star cluster or dwarf galaxy. While previous work using shallow SDSS imaging assumed a standard old, metal-poor stellar population, our deeper DECam imaging reveals that Hydra I has a thin, well-defined main sequence turnoff of intermediate age (~5–6 Gyr) and metallicity ([Fe/H] = −0.9 dex). We measure statistically significant spreads in both the iron and alpha-element abundances of σ_[(Fe)/H}= 0.13 ± 0.02 dex and σ_[ɑ/Fe] = 0.09 ± 0.03 dex, respectively, and place upper limits on both the rotation and its proper motion. Hydra I's intermediate age and [Fe/H]—as well as its low [α/Fe], apparent [Fe/H] spread, and present-day low luminosity—suggest that its progenitor was a dwarf galaxy, which has subsequently lost more than 99.99% of its stellar mass

Probabilistic (logic) programming concepts

A multitude of different probabilistic programming languages exists today, all extending a traditional programming language with primitives to support modeling of complex, structured probability distributions. Each of these languages employs its own probabilistic primitives, and comes with a particular syntax, semantics and inference procedure. This makes it hard to understand the underlying programming concepts and appreciate the differences between the different languages. To obtain a better understanding of probabilistic programming, we identify a number of core programming concepts underlying the primitives used by various probabilistic languages, discuss the execution mechanisms that they require and use these to position and survey state-of-the-art probabilistic languages and their implementation. While doing so, we focus on probabilistic extensions of logic programming languages such as Prolog, which have been considered for over 20 years

Solving Probability Problems in Natural Language

The ability to solve probability word problems such as those found in introductory discrete mathematics textbooks, is an important cognitive and intellectual skill. In this paper, we develop a two-step end-to-end fully automated approach for solving such questions that is able to automatically provide answers to exercises about probability formulated in natural language. In the first step, a question formulated in natural language is analysed and transformed into a high-level model specified in a declarative language. In the second step, a solution to the high-level model is computed using a probabilistic programming system. On a dataset of 2160 probability problems, our solver is able to correctly answer 97.5% of the questions given a correct model. On the end-to-end evaluation, we are able to answer 12.5% of the questions (or 31.1% if we exclude examples not supported by design).status: publishe

An extended star formation history in an ultra-compact dwarf

There has been significant controversy over the mechanisms responsible for forming compact stellar systems like ultra-compact dwarfs (UCDs), with suggestions that UCDs are simply the high-mass extension of the globular cluster population, or alternatively, the liberated nuclei of galaxies tidally stripped by larger companions. Definitive examples of UCDs formed by either route have been difficult to find, with only a handful of persuasive examples of stripped-nucleus-type UCDs being known. In this paper, we present very deep Gemini/GMOS spectroscopic observations of the suspected stripped-nucleus UCD NGC 4546-UCD1 taken in good seeing conditions (<0.7 arcsec). With these data we examine the spatially resolved kinematics and star formation history of this unusual object. We find no evidence of a rise in the central velocity dispersion of the UCD, suggesting that this UCD lacks a massive central black hole like those found in some other compact stellar systems, a conclusion confirmed by detailed dynamical modelling. Finally, we are able to use our extremely high signal-to-noise spectrum to detect a temporally extended star formation history for this UCD. We find that the UCD was forming stars since the earliest epochs until at least 1–2 Gyr ago. Taken together these observations confirm that NGC 4546-UCD1 is the remnant nucleus of a nucleated dwarf galaxy that was tidally destroyed by NGC 4546 within the last 1–2 Gyr

Generating Random Logic Programs Using Constraint Programming

Testing algorithms across a wide range of problem instances is crucial to ensure the validity of any claim about one algorithm's superiority over another. However, when it comes to inference algorithms for probabilistic logic programs, experimental evaluations are limited to only a few programs. Existing methods to generate random logic programs are limited to propositional programs and often impose stringent syntactic restrictions. We present a novel approach to generating random logic programs and random probabilistic logic programs using constraint programming, introducing a new constraint to control the independence structure of the underlying probability distribution. We also provide a combinatorial argument for the correctness of the model, show how the model scales with parameter values, and use the model to compare probabilistic inference algorithms across a range of synthetic problems. Our model allows inference algorithm developers to evaluate and compare the algorithms across a wide range of instances, providing a detailed picture of their (comparative) strengths and weaknesses.Comment: This is an extended version of the paper published in CP 202