Applying robotic frameworks in a simulated multi-agent contest

In the Multi-Agent Programming Contest 2017 the TUBDAI team of the Technische Universität Berlin is using the complex multi-agent scenario to evaluate the application of two frameworks from the field (multi-)robot systems. In particular the task-level decision-making and planning framework ROS Hybrid Behaviour Planner (RHBP) is used to implement the execution and decision-making for single agents. The RHBP framework builds on top of the framework Robot Operating System (ROS) that is used to implement the communication and scenario specific coordination strategy of the agents. The united team for the official contest is formed by volunteering students from a project course and their supervisors.     

Linear Discriminative Star Coordinates for Exploring Class and Cluster Separation of High Dimensional Data

One main task for domain experts in analysing their nD data is to detect and interpret class/cluster separations and outliers. In fact, an important question is, which features/dimensions separate classes best or allow a cluster‐based data classification. Common approaches rely on projections from nD to 2D, which comes with some challenges, such as: The space of projection contains an infinite number of items. How to find the right one? The projection approaches suffers from distortions and misleading effects. How to rely to the projected class/cluster separation? The projections involve the complete set of dimensions/features. How to identify irrelevant dimensions? Thus, to address these challenges, we introduce a visual analytics concept for the feature selection based on linear discriminative star coordinates (DSC), which generate optimal cluster separating views in a linear sense for both labeled and unlabeled data. This way the user is able to explore how each dimension contributes to clustering. To support to explore relations between clusters and data dimensions, we provide a set of cluster‐aware interactions allowing to smartly iterate through subspaces of both records and features in a guided manner. We demonstrate our features selection approach for optimal cluster/class separation analysis with a couple of experiments on real‐life benchmark high‐dimensional data sets.     

Towards User‐Centered Active Learning Algorithms

The labeling of data sets is a time‐consuming task, which is, however, an important prerequisite for machine learning and visual analytics. Visual‐interactive labeling (VIAL) provides users an active role in the process of labeling, with the goal to combine the potentials of humans and machines to make labeling more efficient. Recent experiments showed that users apply different strategies when selecting instances for labeling with visual‐interactive interfaces. In this paper, we contribute a systematic quantitative analysis of such user strategies. We identify computational building blocks of user strategies, formalize them, and investigate their potentials for different machine learning tasks in systematic experiments. The core insights of our experiments are as follows. First, we identified that particular user strategies can be used to considerably mitigate the bootstrap (cold start) problem in early labeling phases. Second, we observed that they have the potential to outperform existing active learning strategies in later phases. Third, we analyzed the identified core building blocks, which can serve as the basis for novel selection strategies. Overall, we observed that data‐based user strategies (clusters, dense areas) work considerably well in early phases, while model‐based user strategies (e.g., class separation) perform better during later phases. The insights gained from this work can be applied to develop novel active learning approaches as well as to better guide users in visual interactive labeling.     

An analog CMOS chip set for neural networks with arbitrarytopologies

An analog CMOS chip set for implementations of artificial neural networks (ANNs) has been fabricated and tested. The chip set consists of two cascadable chips: a neuron chip and a synapse chip. Neurons on the neuron chips can be interconnected at random via synapses on the synapse chips thus implementing an ANN with arbitrary topology. The neuron test chip contains an array of 4 neurons with well defined hyperbolic tangent activation functions which is implemented by using parasitic lateral bipolar transistors. The synapse test chip is a cascadable 4x4 matrix-vector multiplier with variable, 10-b resolution matrix elements. The propagation delay of the test chips was measured to 2.6 mus per layer.     

Surface Cu depletion of Cu(In,Ga)Se2 films: An investigation by hard X-ray photoelectron spectroscopy

The origin of surface Cu depletion of polycrystalline chalcopyrite thin films and its consequences for the physics of related solar cells have been discussed for the past 15 years. In order to shed light on the composition and thickness of this Cu-depleted surface layer, depth-dependent compositional analysis by hard X-ray photoelectron spectroscopy was performed. The data from Cu-poor grown Cu(In,Ga)Se₂ samples point to a surface layer in the sub-nanometer regime, which is completely depleted of Cu. This result supports the surface reconstruction model proposed by first-principles calculations by other authors. Analysis of the surface morphology of the investigated samples confirms the conjunction of Cu depletion and faceting of the surface. Theoretical considerations show that the apparent surface concentration ratio of [Cu]/([In] + [Ga]) = 1/3 found by conventional photoelectron spectroscopy studies can be explained by the surface reconstruction model.     

Telomerase protein rather than its RNA is the target of phosphorothioate-modified oligonucleotides

Human telomerase is a ribonucleoprotein which uses its internal RNA moiety as a template for telomeric DNA synthesis. This enzyme is up-regulated in most malignant tumors and is therefore considered as a possible cancer target. Here we examined the effects of differently modified oligomers on telomeraseactivity from HL-60 cell extracts (TRAP-ezetrade mark assay). Phosphorothioate-modified oligonucleotides (PS-ODNs) inhibited telomerase activity at subnanomolar concen-trations and proved to be more efficient than peptide nucleic acids. In contrast to all the investigated oligomers, PS-ODNs were found to bind to the protein motif of telomerase called the primer binding site but poorly to its RNA. This is suggested by kinetic investigations demonstrating a competitive interaction of PS-ODNs and TS primer at the primer binding site. The K m value of the TS primer was 10.8 nM, the K i value of a 20mer PS-ODN was 1.6 nM. When the TS primer was PS-modified a striking increase in the telomerase activity was found which correlates with the number of phosphodiesters replaced. The K m value of a completely PS-modified TS primer was 0.56 nM. Based on these results the design of chimeric ODNs is proposed consisting of a 5'-PS-modified part targeting the primer binding site and a 3'-terminus part targeting the telomerase RNA.     

A new genetic algorithm in proteomics: Feature selection for SELDI-TOF data

Mass spectrometry from clinical specimens is used in order to identify biomarkers in a diagnosis. Thus, a reliable method for both feature selection and classification is required. A novel method is proposed to find biomarkers in SELDI-TOF in order to perform robust classification.The feature selection is based on a new genetic algorithm. Concerning the classification, a method which takes into account the great variability on intensity by using decision stumps has been developed. Moreover, as the samples are often small, it is more appropriate to use the decision stumps simultaneously than building a complete tree. The thresholds of the decision stumps are determined in the same genetic algorithm. Finally, the method was generalized to more than two groups based on pairwise coupling. The obtained algorithm was applied on two data sets: a publicly available one containing two groups allowing a comparison with other methods from the literature and a new one containing three groups.