Minimum Description Research Articles

Learning MDL Logic Programs from Noisy Data

Many inductive logic programming approaches struggle to learn programs from noisy data. To overcome this limitation, we introduce an approach that learns minimal description length programs from noisy data, including recursive programs. Our experiments on several domains, including drug design, game playing, and program synthesis, show that our approach can outperform existing approaches in terms of predictive accuracies and scale to moderate amounts of noise.

Use of Carotid Web Angioarchitecture for Stroke Risk Assessment

To examine the usefulness of carotid web (CW), carotid bifurcation and their combined angioarchitectural measurements in assessing stroke risk. Anatomic data on the internal carotid artery (ICA), common carotid artery (CCA), and the CW were gathered as part of a retrospective study from symptomatic (stroke) and asymptomatic (nonstroke) patients with CW. We built a model of stroke risk using principal-component analysis, Firth regression trained with 5-fold cross-validation, and heuristic binary cutoffs based on the Minimal Description Length principle. The study included 22 patients, with a mean age of 55.9 ± 12.8 years; 72.9% were female. Eleven patients experienced an ischemic stroke. The first 2 principal components distinguished between patients with stroke and patients without stroke. The model showed that ICA-pouch tip angle (P= 0.036), CCA-pouch tip angle (P= 0.036), ICA web-pouch angle (P= 0.036), and CCA web-pouch angle (P= 0.036) are the most important features associated with stroke risk. Conversely, CCA and ICA anatomy (diameter and angle) were not found to be risk factors. This pilot study shows that using data from computed tomography angiography, carotid bifurcation, and CW angioarchitecture may be used to assess stroke risk, allowing physicians to tailor care for each patient according to risk stratification.

Minimal and versatile description of diffusion and swelling in polymer-solvent systems: Modeling and experimental validation

Minimal and versatile description of diffusion and swelling in polymer-solvent systems: Modeling and experimental validation

Brain-imaging evidence for compression of binary sound sequences in human memory.

According to the language-of-thought hypothesis, regular sequences are compressed in human memory using recursive loops akin to a mental program that predicts future items. We tested this theory by probing memory for 16-item sequences made of two sounds. We recorded brain activity with functional MRI and magneto-encephalography (MEG) while participants listened to a hierarchy of sequences of variable complexity, whose minimal description required transition probabilities, chunking, or nested structures. Occasional deviant sounds probed the participants' knowledge of the sequence. We predicted that task difficulty and brain activity would be proportional to the complexity derived from the minimal description length in our formal language. Furthermore, activity should increase with complexity for learned sequences, and decrease with complexity for deviants. These predictions were upheld in both fMRI and MEG, indicating that sequence predictions are highly dependent on sequence structure and become weaker and delayed as complexity increases. The proposed language recruited bilateral superior temporal, precentral, anterior intraparietal, and cerebellar cortices. These regions overlapped extensively with a localizer for mathematical calculation, and much less with spoken or written language processing. We propose that these areas collectively encode regular sequences as repetitions with variations and their recursive composition into nested structures.

Specification Mining over Temporal Data

Current specification mining algorithms for temporal data rely on exhaustive search approaches, which become detrimental in real data settings where a plethora of distinct temporal behaviours are recorded over prolonged observations. This paper proposes a novel algorithm, Bolt2, based on a refined heuristic search of our previous algorithm, Bolt. Our experiments show that the proposed approach not only surpasses exhaustive search methods in terms of running time but also guarantees a minimal description that captures the overall temporal behaviour. This is achieved through a hypothesis lattice search that exploits support metrics. Our novel specification mining algorithm also outperforms the results achieved in our previous contribution.

Chiral-induced spin selectivity in photo-induced electron transfer: Investigating charge and spin dynamics in a master equation framework.

Investigating the role of chiral-induced spin selectivity in the generation of spin correlated radical pairs in a photoexcited donor-chiral bridge-acceptor system is fundamental to exploit it in quantum technologies. This requires a minimal master equation description of both charge separation and recombination through a chiral bridge. To achieve this without adding complexity and entering in the microscopic origin of the phenomenon, we investigate the implications of spin-polarizing reaction operators to the master equation. The explicit inclusion of coherent evolution yields non-trivial behaviors in the charge and spin dynamics of the system. Finally, we apply this master equation to a setup comprising a molecular qubit attached to the donor-bridge-acceptor molecule, enabling qubit initialization, control, and read-out. Promising results are found by simulating this sequence of operations assuming realistic parameters and achievable experimental conditions.

Qualitative Study of Paediatric Advance Care Planning Through Simulation: How we did it and the Lessons Learned

Simulation is a well-established experiential educational tool in health care, which allows clinicians to learn and practise skills via a replication of reality. However, its use as a research tool is novel and emerging. As we designed a phenomenological study of communication practices around paediatric advance care planning, we came to the view that simulation would be a very useful tool to enable feasible and ethical qualitative study of this complex and sensitive process, which involves discussion about a child’s death in the future and how to prepare for it. There is minimal description in the literature about how to use simulation as a qualitative research method. This article describes how we designed our study, focusing on: (1) the academic rationale for using simulation, (2) designing the simulation-based research method, (3) ensuring psychological safety of all participants in the simulation, (4) trialling the simulation, (5) the logistics of the study and (6) our reflections and learnings after using this novel method. We hope this discussion encourages other researchers to consider simulation as an innovative qualitative research method.

A novel approach to estimating the debt capacity of European SMEs

Research background: The concept of debt capacity assumes that a maximum value of debt ratio exists that when exceeded triggers unfavourable consequences, such as drop in market value, default or a change in the business' creditworthiness. With the current state of the art there is a priori no theoretical assurance that such a specific value exists, or rather it is represented by an interval of values. Beyond that, our understanding of debt capacity is often limited to a theoretical approximation by firm-specific factors, while the context of macroeconomic factors, especially those critical for SMEs, is neglected. Purpose of the article: The aim of this paper is to present a novel approach to estimating SMEs' debt capacity. Further, the aim is to answer the question of what firm-level and macroeconomy conditions lead to exhausting the SMEs' debt capacity and under what conditions a specific value of maximum debt capacity could be estimated. Methods: To estimate the debt capacity, we suggest a use of an information entropy minimising heuristic and the Minimal Description Length Principle. In this approach, the observed feature space is categorised into several regions. In this case, such a region represents a set of firm- and macroeconomy-specific conditions forming the debt capacity of the SMEs. To the best of our knowledge, such an approach has not yet been used in debt capacity applications. Findings & value added: We found out that the debt ratio itself provides little explanation of exhausted debt capacity, suggesting that high debt levels are compensated for by other factors. By using the suggested approach, a set of more than 100 different regions was analysed. It was found that in case of five regions (sets of conditions) the debt capacity is exhausted, as the high level of debt has significant distress consequences.

Inhomogeneous disordering at a photoinduced charge density wave transition

Symmetry-breaking phase transitions are ubiquitous in nature. The study of their dynamics in solids has often been performed using homogeneous order parameter theories. By microscopically modeling the photoinduced melting and recovery of a charge density wave, the authors instead show here that the transient dynamics can lead to disordered state, where the average order parameter is not representative of the atomic-scale configuration. This provides a possible minimal model description of the experimentally proposed inhomogeneous disordering pathway in photoinduced phase transitions.

Local food procurement by hospitals: a scoping review.

Local food procurement by hospitals is gaining traction as governments and advocacy groups seek to influence food systems and strengthen local communities, but there is little empirical evidence as to its practical application or efficacy. The aims of this review were to describe the extent, range and nature of local food procurement models in healthcare foodservices, and to understand the barriers and enablers to implementation, including from the perspective of stakeholders across the supply chain. A scoping review was conducted following the protocol published in the Open Science Framework Registration (DOI: 10.17605/OSF.IO/T3AX2). Five electronic databases were searched for the following concepts: 'hospital foodservice', 'local food procurement practices', 'the extent, range and nature' and/or 'the barriers and enablers of procurement'. Eligible peer-reviewed original research published in English from the year 2000 was included following a two-step selection process. The final library included nine studies. Most studies (7 of 9) were from the United States. Three studies used survey methods and reported high rates (58%-91%) of US hospital participation in local food procurement. Studies offered minimal description of local procurement models, but two models, conventional ('on-contract') or off-contract, were typically used. Barriers to local food procurement included restricted access to suitable local food supply, limited kitchen resources and inadequate technology to trace local food purchase thereby limiting evaluation capabilities. Enablers included organisational support, passionate champions and opportunistic, incremental change. There is a paucity of peer-reviewed studies describing local food procurement by hospitals. Details of local food procurement models were generally lacking: categorisable as either purchases made 'on-contract' via conventional means or 'off-contract'. If hospital foodservices are to increase their local food procurement, they require access to a suitable, reliable and traceable supply, that acknowledges their complexity and budgetary constraints.

The best-worst-choice polytope on four alternatives

Several papers co-authored by A.A.J. Marley helped in popularizing the best-worst-choice paradigm due to Finn and Louviere (1992). Inspired by Block and Marschak (1960), Marley conceived a random utility model for the choice frequencies of the best and worst alternatives in any proposed set of alternatives (Marley and Louviere, 2005). He then asked for a characterization of the prediction range of the model. The range being a convex polytope, an affine description of this polytope would provide a solution to Marley problem. For four alternatives, we show that a minimal such description consists in 26 affine equalities and 144 affine inequalities. The result derives from the Gale transform of the set of polytope vertices: the transform being a family of 24 vectors in a one-dimensional vector space, it plainly reveals the affine structure of the polytope. As far as we know, Marley problem is still open when the number of alternatives exceeds 4.

A tube model for predicting the stress and dielectric relaxations of polydisperse linear polymers

We present an algorithm to predict the linear relaxation spectra for linear polymers of fully general and arbitrary polydispersity. As is common in the tube theory descriptions of linear polymers, we assume that the stress relaxation is affected by both the constraint release and tube escape modes, but unlike most existing descriptions we consider how these two modes of relaxation affect each other. We argue that the proper description for relaxation in an arbitrary blend of linear polymers requires consideration of multiple embedded tubes affecting the different relaxation pathways; we propose a novel but minimal description involving five embedded tubes. Building on prior work for binary blends, we derive the scaling level descriptions of the relaxation pathways. We use a large number of existing experimental results on the stress and dielectric relaxations to validate our model, ensuring we explore a very broad range of parameter space.

Emergent activity, heterogeneity, and robustness in a calcium feedback model of the sinoatrial node

The sinoatrial node (SAN) is the primary pacemaker of the heart. SAN activity emerges at an early point in life and maintains a steady rhythm for the lifetime of the organism. The ion channel composition and currents of SAN cells can be influenced by a variety of factors. Therefore, the emergent activity and long-term stability imply some form of dynamical feedback control of SAN activity. We adapt a recent feedback model—previously utilized to describe control of ion conductances in neurons—to a model of SAN cells and tissue. The model describes a minimal regulatory mechanism of ion channel conductances via feedback between intracellular calcium and an intrinsic target calcium level. By coupling a SAN cell to the calcium feedback model, we show that spontaneous electrical activity emerges from quiescence and is maintained at steady state. In a 2D SAN tissue model, spatial variability in intracellular calcium targets lead to significant, self-organized heterogeneous ion channel expression and calcium transients throughout the tissue. Furthermore, multiple pacemaking regions appear, which interact and lead to time-varying cycle length, demonstrating that variability in heart rate is an emergent property of the feedback model. Finally, we demonstrate that the SAN tissue is robust to the silencing of leading cells or ion channel knockouts. Thus, the calcium feedback model can reproduce and explain many fundamental emergent properties of activity in the SAN that have been observed experimentally based on a minimal description of intracellular calcium and ion channel regulatory networks.

Emergent pacemaking and tissue heterogeneity in a calcium feedback regulatory model of the sinoatrial node.

The sinoatrial node (SAN) is the primary pacemaker of the heart. SAN activity emerges at an early point in life, and maintains a steady rhythm for the lifetime of the organism. The ion channel composition and currents of the cells can be influenced by a variety of factors. Therefore, the emergent activity and long-term stability imply some form of dynamical feedback control of the SAN cell activity. Here, we adapt a recent neuronal model to the SAN rabbit cell. The model describes a minimal regulatory mechanism of neuronal ion channel conductance based on a feedback loop defined by an intracellular [Ca2+] level as its target. Briefly, the cell upregulates or downregulates its channel mRNA and membrane expression levels based on the difference between the intercellular Ca2+ level in the cell and a set intercellular Ca2+ target. Based on this feedback model, spontaneous electrical activity emerges in the SAN cell from a quiescent state with low initial conductances. As conductances increase, the intracellular [Ca2+] level reaches the target, and ion channel conductance reach a steady state consistent with sustained spontaneous activity. In a 2D tissue, variability in [Ca2+] target leads to heterogeneous ion channel expression and Ca2+ transients throughout the tissue. Further, dominant focal clusters appear, which interact with one another leading to a heterogeneous tissue cycle length, implying that variability in heart rate is an emergent property of the feedback model. Finally, the 2D tissue is robust to the silencing of leading cells or ion channel knock-outs. Thus, the calcium feedback regulatory model explains a number of experimental data using a minimal description of intracellular calcium and ion channel regulatory networks.

Surgical versus non-surgical interventions for treating patellar dislocation.

Surgical versus non-surgical interventions for treating patellar dislocation.

The security mindset: characteristics, development, and consequences

Abstract The world is facing a cybersecurity skills gap as cybercrime and cyberwarfare grow in importance. One often-discussed quality that is potentially relevant to cybersecurity recruitment and education is the so-called “security mindset”: a way of thinking characteristic of some security professionals that they believe to be especially advantageous in their work. Although some employers express a desire to hire people with a security mindset, and initiatives to cultivate the security mindset are being implemented, it has no common definition and little is known about its characteristics, its development, and its consequences. We interviewed 21 cybersecurity professionals who strongly identified as having a security mindset based on a minimal description drawn from existing literature. Thematic analysis of the interview data suggests that the security mindset can be conceptualized as consisting of three interconnected aspects—“monitoring” for potential security anomalies, “investigating” anomalies more deeply to identify security flaws, and “evaluating” the relevance of those flaws in a larger context. These three aspects develop in different ways and have different personal and professional consequences. Participants mostly spoke positively of the security mindset, but they also mentioned several disadvantages not mentioned by existing security-mindset literature, such as mental health pressures, workplace tensions, and negative effects on personal relationships. We discuss the implications of these findings for future study of the security mindset and suggest practical implications for cybersecurity management, education, and recruitment.

The cheese was green with… envy: An EEG study on minimal fictional descriptions

Inconsistent information can be hard to understand, but in cases like fiction readers can integrate it with little to no difficulties. The present study aimed at examining if perspective switching can take place when only a minimal fictional description is provided (fictional world condition), as compared with general world knowledge (real world condition). Participants read sentences where food items had animated or inanimate features while EEG was recorded and performed a sentence completion task to evaluate recall. In the real-world condition, the N400 was significantly larger for sentences incongruent, rather than congruent, with general world knowledge. In the fictional world condition, the N400 elicited by congruent and incongruent sentences did not differ, confirming that the minimal description impacted online information processing. Information consistent with general knowledge was better recalled in both conditions. The current results highlight how contextual information is integrated during sentence comprehension.

Quantifiers satisfying semantic universals have shorter minimal description length

Despite wide variation among natural languages, there are linguistic properties thought to be universal to all or nearly all languages. Here, we consider universals at the semantic level, in the domain of quantifiers, which are given by the properties of monotonicity, quantity, and conservativity, and we investigate whether these universals might be explained by differences in complexity. First, we use a minimal pair methodology and compare the complexities of individual quantifiers using approximate Kolmogorov complexity. Second, we use a simple yet expressive grammar to generate a large collection of quantifiers and we investigate their complexities at an aggregate level in terms of both their minimal description lengths and their approximate Kolmogorov complexities. For minimal description length we find that quantifiers satisfying semantic universals are simpler: they have a shorter minimal description length. For approximate Kolmogorov complexity we find that monotone quantifiers have a lower Kolmogorov complexity than non-monotone quantifiers and for quantity and conservativity we find that approximate Kolmogorov complexity does not scale robustly. These results suggest that the simplicity of quantifier meanings, in terms of their minimal description length, partially explains the presence of semantic universals in the domain of quantifiers.

Data-Driven Discovery of Active Nematic Hydrodynamics

Active nematics can be modeled using phenomenological continuum theories that account for the dynamics of the nematic director and fluid velocity through partial differential equations (PDEs). While these models provide a statistical description of the experiments, the relevant terms in the PDEs and their parameters are usually identified indirectly. We adapt a recently developed method to automatically identify optimal continuum models for active nematics directly from spatiotemporal data, via sparse regression of the coarse-grained fields onto generic low order PDEs. After extensive benchmarking, we apply the method to experiments with microtubule-based active nematics, finding a surprisingly minimal description of the system. Our approach can be generalized to gain insights into active gels, microswimmers, and diverse other experimental active matter systems.

Query-adaptive training data recommendation for cross-building predictive modeling

Predictive modeling in buildings is a key task for the optimal management of building energy. Relevant building operational data are a prerequisite for such task, notably when deep learning is used. However, building operational data are not always available, such is the case in newly built, newly renovated, or even not yet built buildings. To address this problem, we propose a deep similarity learning approach to recommend relevant training data to a target building solely by using a minimal contextual description on it. Contextual descriptions are modeled as user queries. We further propose to ensemble most used machine learning algorithms in the context of predictive modeling. This contributes to the genericity of the proposed methodology. Experimental evaluations show that our methodology offers a generic methodology for cross-building predictive modeling and achieves good generalization performance.