Towards a neural hierarchy of time scales for motor control

Animals show remarkable rich motion skills which are still far from realizable with robots. Inspired by the neural circuits which generate rhythmic motion patterns in the spinal cord of all vertebrates, one main research direction points towards the use of central pattern generators in robots. On of the key advantages of this, is that the dimensionality of the control problem is reduced. In this work we investigate this further by introducing a multi-timescale control hierarchy with at its core a hierarchy of recurrent neural networks. By means of some robot experiments, we demonstrate that this hierarchy can embed any rhythmic motor signal by imitation learning. Furthermore, the proposed hierarchy allows the tracking of several high level motion properties (e.g.: amplitude and offset), which are usually observed at a slower rate than the generated motion. Although these experiments are preliminary, the results are promising and have the potential to open the door for rich motor skills and advanced control

Multi-Target Prediction: A Unifying View on Problems and Methods

Multi-target prediction (MTP) is concerned with the simultaneous prediction of multiple target variables of diverse type. Due to its enormous application potential, it has developed into an active and rapidly expanding research field that combines several subfields of machine learning, including multivariate regression, multi-label classification, multi-task learning, dyadic prediction, zero-shot learning, network inference, and matrix completion. In this paper, we present a unifying view on MTP problems and methods. First, we formally discuss commonalities and differences between existing MTP problems. To this end, we introduce a general framework that covers the above subfields as special cases. As a second contribution, we provide a structured overview of MTP methods. This is accomplished by identifying a number of key properties, which distinguish such methods and determine their suitability for different types of problems. Finally, we also discuss a few challenges for future research

Enzymatic production of defined chitosan oligomers with a specific pattern of acetylation using a combination of chitin oligosaccharide deacetylases

Chitin and chitosan oligomers have diverse biological activities with potentially valuable applications in fields like medicine, cosmetics, or agriculture. These properties may depend not only on the degrees of polymerization and acetylation, but also on a specific pattern of acetylation (PA) that cannot be controlled when the oligomers are produced by chemical hydrolysis. To determine the influence of the PA on the biological activities, defined chitosan oligomers in sufficient amounts are needed. Chitosan oligomers with specific PA can be produced by enzymatic deacetylation of chitin oligomers, but the diversity is limited by the low number of chitin deacetylases available. We have produced specific chitosan oligomers which are deacetylated at the first two units starting from the non-reducing end by the combined use of two different chitin deacetylases, namely NodB from Rhizobium sp. GRH2 that deacetylates the first unit and COD from Vibrio cholerae that deacetylates the second unit starting from the non-reducing end. Both chitin deacetylases accept the product of each other resulting in production of chitosan oligomers with a novel and defined PA. When extended to further chitin deacetylases, this approach has the potential to yield a large range of novel chitosan oligomers with a fully defined architecture

Seasonal prediction of Horn of Africa long rains using machine learning: the pitfalls of preselecting correlated predictors

The Horn of Africa is highly vulnerable to droughts and floods, and reliable long-term forecasting is a key part of building resilience. However, the prediction of the “long rains” season (March–May) is particularly challenging for dynamical climate prediction models. Meanwhile, the potential for machine learning to improve seasonal precipitation forecasts in the region has yet to be uncovered. Here, we implement and evaluate four data-driven models for prediction of long rains rainfall: ridge and lasso linear regressions, random forests and a single-layer neural network. Predictors are based on SSTs, zonal winds, land state, and climate indices, and the target variables are precipitation totals for each separate month (March, April, and May) in the Horn of Africa drylands, with separate predictions made for lead-times of 1–3 months. Results reveal a tendency for overfitting when predictors are preselected based on correlations to the target variable over the entire historical period, a frequent practice in machine learning-based seasonal forecasting. Using this conventional approach, the data-driven methods—and particularly the lasso and ridge regressions—often outperform dynamical seasonal hindcasts. However, when the selection of predictors is done independently of both the train and test data, by performing this predictor selection within the cross-validation loop, the performance of all four data-driven models is poorer than that of the dynamical hindcasts. These findings should not discourage future applications of machine learning for rainfall forecasting in the region. Yet, they should be seen as a note of caution to prevent optimistically biased results that are not indicative of the true power in operational forecast systems

Learning Interpretable Rules for Multi-label Classification

Multi-label classification (MLC) is a supervised learning problem in which, contrary to standard multiclass classification, an instance can be associated with several class labels simultaneously. In this chapter, we advocate a rule-based approach to multi-label classification. Rule learning algorithms are often employed when one is not only interested in accurate predictions, but also requires an interpretable theory that can be understood, analyzed, and qualitatively evaluated by domain experts. Ideally, by revealing patterns and regularities contained in the data, a rule-based theory yields new insights in the application domain. Recently, several authors have started to investigate how rule-based models can be used for modeling multi-label data. Discussing this task in detail, we highlight some of the problems that make rule learning considerably more challenging for MLC than for conventional classification. While mainly focusing on our own previous work, we also provide a short overview of related work in this area.Comment: Preprint version. To appear in: Explainable and Interpretable Models in Computer Vision and Machine Learning. The Springer Series on Challenges in Machine Learning. Springer (2018). See http://www.ke.tu-darmstadt.de/bibtex/publications/show/3077 for further informatio

A framework for tracing timber following the Ukraine invasion

Scientific testing including stable isotope ratio analysis (SIRA) and trace element analysis (TEA) is critical for establishing plant origin, tackling deforestation and enforcing economic sanctions. Yet methods combining SIRA and TEA into robust models for origin verification and determination are lacking. Here we report a (1) large Eastern European timber reference database (Betula, Fagus, Pinus, Quercus) tailored to sanctioned products following the Ukraine invasion; (2) statistical test to verify samples against a claimed origin; (3) probabilistic model of SIRA, TEA and genus distribution data, using Gaussian processes, to determine timber harvest location. Our verification method rejects 40–60% of simulated false claims, depending on the spatial scale of the claim, and maintains a low probability of rejecting correct origin claims. Our determination method predicts harvest location within 180 to 230 km of true location. Our results showcase the power of combining data types with probabilistic modelling to identify and scrutinize timber harvest location claims

Evaluation of midkine and anterior gradient 2 in a multimarker panel for the detection of ovarian cancer

The aims of this study were: to characterise and compare plasma concentrations of midkine (MDK) in normal healthy women with concentrations observed in women with ovarian cancer; and to establish and compare the performance of MDK with that of anterior gradient 2 protein (AGR2) and CA125 in the development of multi-analyte classification algorithms for ovarian cancer. Median plasma concentrations of immunoreactive MDK, AGR2 and CA125 were significantly greater in the case cohort (909 pg/ml, 765 pg/ml and 502 U/ml, respectively n = 46) than in the control cohort (383 pg/ml, 188 pg/ml and 13 U/ml, respectively n = 61) (p < 0.001). The area under the receiver operator characteristic curve (AUC) for MDK and AGR2 was not significantly different (0.734 ± 0.046 and 0.784 ± 0.049, respectively, mean ± SE) but were both significantly less than the AUC for CA125 (0.934 ± 0.030, p < 0.003). When subjected to stochastic gradient boosted logistic regression modelling, the AUC of the multi-analyte panel (MDK, AGR2 and CA125, 0.988 ± 0.010) was significantly greater than that of CA125 alone (0.934 ± 0.030, p = 0.035). The sensitivity and specificity of the multi-analyte algorithm were 95.2 and 97.7%, respectively. Within the study cohort, CA125 displayed a sensitivity and specificity of 87.0 and 94.6%, respectively. The data obtained in this study confirm that both MDK and AGR2 individually display utility as biomarkers for ovarian cancer and that in a multi-analyte panel significantly improve the diagnostic utility of CA125 in symptomatic women

Metabolic Deficiences Revealed in the Biotechnologically Important Model Bacterium Escherichia coli BL21(DE3)

The Escherichia coli B strain BL21(DE3) has had a profound impact on biotechnology through its use in the production of recombinant proteins. Little is understood, however, regarding the physiology of this important E. coli strain. We show here that BL21(DE3) totally lacks activity of the four [NiFe]-hydrogenases, the three molybdenum- and selenium-containing formate dehydrogenases and molybdenum-dependent nitrate reductase. Nevertheless, all of the structural genes necessary for the synthesis of the respective anaerobic metalloenzymes are present in the genome. However, the genes encoding the high-affinity molybdate transport system and the molybdenum-responsive transcriptional regulator ModE are absent from the genome. Moreover, BL21(DE3) has a nonsense mutation in the gene encoding the global oxygen-responsive transcriptional regulator FNR. The activities of the two hydrogen-oxidizing hydrogenases, therefore, could be restored to BL21(DE3) by supplementing the growth medium with high concentrations of Ni2+ (Ni2+-transport is FNR-dependent) or by introducing a wild-type copy of the fnr gene. Only combined addition of plasmid-encoded fnr and high concentrations of MoO42− ions could restore hydrogen production to BL21(DE3); however, to only 25–30% of a K-12 wildtype. We could show that limited hydrogen production from the enzyme complex responsible for formate-dependent hydrogen evolution was due solely to reduced activity of the formate dehydrogenase (FDH-H), not the hydrogenase component. The activity of the FNR-dependent formate dehydrogenase, FDH-N, could not be restored, even when the fnr gene and MoO42− were supplied; however, nitrate reductase activity could be recovered by combined addition of MoO42− and the fnr gene. This suggested that a further component specific for biosynthesis or activity of formate dehydrogenases H and N was missing. Re-introduction of the gene encoding ModE could only partially restore the activities of both enzymes. Taken together these results demonstrate that BL21(DE3) has major defects in anaerobic metabolism, metal ion transport and metalloprotein biosynthesis

Maximizing upgrading and downgrading margins for ordinal regression

In ordinal regression, a score function and threshold values are sought to classify a set of objects into a set of ranked classes. Classifying an individual in a class with higher (respectively lower) rank than its actual rank is called an upgrading (respectively downgrading) error. Since upgrading and downgrading errors may not have the same importance, they should be considered as two different criteria to be taken into account when measuring the quality of a classifier. In Support Vector Machines, margin maximization is used as an effective and computationally tractable surrogate of the minimization of misclassification errors. As an extension, we consider in this paper the maximization of upgrading and downgrading margins as a surrogate of the minimization of upgrading and downgrading errors, and we address the biobjective problem of finding a classifier maximizing simultaneously the two margins. The whole set of Pareto-optimal solutions of such biobjective problem is described as translations of the optimal solutions of a scalar optimization problem. For the most popular case in which the Euclidean norm is considered, the scalar problem has a unique solution, yielding that all the Pareto-optimal solutions of the biobjective problem are translations of each other. Hence, the Pareto-optimal solutions can easily be provided to the analyst, who, after inspection of the misclassification errors caused, should choose in a later stage the most convenient classifier. The consequence of this analysis is that it provides a theoretical foundation for a popular strategy among practitioners, based on the so-called ROC curve, which is shown here to equal the set of Pareto-optimal solutions of maximizing simultaneously the downgrading and upgrading margins