Miracle-dz/UniFAR-FTUs huggingface dataset:
Mobiler Roboterassistent Care-O-Bot 3: Entwicklung, Fähigkeiten und Einsatzmöglichkeiten

Das Fraunhofer IPA beschäftigt sich seit über zehn Jahren mit der Entwicklung eines mobilen Roboterassistenten „Care-O-bot“ zur Unterstützung des Menschen im Haushalt. Insbesondere hilfsbedürftigen Personen kann dadurch mehr Selbstständigkeit ermöglicht und gegebenenfalls sogar der unliebsame Umzug in stationäre Pflegeeinrichtungen verhindert werden. Der erste Care-O-bot Prototyp (Bild 1 links, Mitte) wurde 1998 am Fraunhofer IPA aufgebaut [1]. Der Roboter bestand aus einer mobilen Basisplattform sowie einem drehund schwenkbaren Touchscreen, der eine intuitive Kommunikation mit Menschen ermöglichte. Care-O-bot I war bereits in der Lage, sich sicher und verlässlich zwischen Menschen zu bewegen. Damit konnte er einfache Transportaufgaben im Haushalt durchführen. Zudem wurde er bei zahlreichen Gelegenheiten erfolgreich als Messeund Ausstellungsführer eingesetzt und diente als Basis für die Entwicklung dreier Roboter, die im Berliner Museum für Kommunikation seit inzwischen über zehn Jahren tagtäglich die Besucher unterhalten und informieren (Bild 1 rechts). Care-O-bot II (Bild 2), gebaut im Jahr 2002, war zusätzlich mit einem Manipulator, höhenverstellbaren Gehstützen und einem kippbaren Sensorkopf, der zwei Kameras und einen Laserscanner zur Umgebungserfassung enthielt, ausgestattet [2]. Der Manipulator wurde
Constant Time Quantum search Algorithm Over A Datasets: An Experimental Study Using IBM Q Experience

In this work, a constant time Quantum searching algorithm over a datasets is proposed and subsequently the algorithm is executed in real chip quantum computer developed by IBM Quantum experience (IBMQ). QISKit, the software platform developed by IBM is used for this algorithm implementation. Quantum interference, Quantum superposition and {\pi} phase shift of quantum state applied for this constant time search algorithm. The proposed quantum algorithm is executed in QISKit SDK local backend 'local_qasm_simulator', real chip 'ibmq_16_melbourne' and 'ibmqx4' IBMQ. Result also suggest that real chip ibmq_16_melbourne is more quantum error or noise prone than ibmqx4.
Sex Influences SAMHD1 Activity and Susceptibility to Human Immunodeficiency Virus-1 in Primary Human Macrophages

BACKGROUND Macrophages are major targets for HIV-1, contribute to viral propagation in vivo, and are instrumental in the pathogenesis of HAND. While it is known that host sex affects HIV-1 viremia and influences the severity of HIV-1-associated neurocognitive disease, a cellular or molecular basis for these findings remains elusive. METHODS We explored whether sex affects HIV-1 infectivity of primary human macrophages and CD4+ T cells in vitro. RESULTS Macrophages derived from female donors were less susceptible to HIV-1 infection than those derived from males. This sex-dependent difference in macrophage infectivity was independent of the requirement for CD4/CCR5-mediated virus entry and was not observed in CD4+ T cells. Investigations into the mechanism governing these sex-dependent differences revealed that the host restriction factor SAMHD1 exists in a hyperphosphorylated, less active state in male-derived macrophages. In addition, the major kinase responsible for SAMHD1 phosphorylation, CDK1, exhibited lower levels of expression in female-derived macrophages in all tested donor pairs. The sex-dependent differences in viral restriction imposed by SAMHD1 were abrogated upon its depletion. CONCLUSIONS We conclude that SAMHD1 is an essential modulator of infectivity in a sex-dependent manner in macrophages, constituting a novel component of sex differences in innate immune control of HIV-1.
Cross-Subject Transfer Learning on High-Speed Steady-State Visual Evoked Potential-Based Brain-Computer Interface

Steady-state visual evoked potential (SSVEP)-based brain computer-interfaces (BCIs) have shown its robustness in achieving high information transfer rate. State-of-the-art training-based SSVEP decoding methods such as extended Canonical Correlation Analysis (CCA) and Task-Related Component Analysis (TRCA) are the major players that elevate the efficiency of the SSVEP-based BCIs through an individualized calibration process. However, collecting sufficient calibration (e.g. training templates) data could be laborious and time-consuming, hindering the practicality in a real-world context. This study aims to develop a cross-subject transferring approach to reduce the need for training data from a new user. Study results showed that a new least-squares transformation (LST) method was able to significantly reduce the training templates required for a 40-class TRCA-based SSVEP BCI. The LST method may lead to numerous practical applications of plug-and-play high-speed SSVEP-based BCIs.
Characterization of a Novel N-Acylhomoserine Lactonase RmmL from Ruegeria mobilis YJ3

Gram-negative bacteria utilize N-acylhomoserine lactones (AHLs) as quorum sensing (QS) signaling molecules for intercellular communication. Cell-to-cell communication depends on cell population density, and AHL-dependent QS is related to the production of multiple genes including virulence factors. Quorum quenching (QQ), signal inactivation by enzymatic degradation, is a potential strategy for attenuating QS regulated bacterial infections. Both Gram-positive and -negative bacteria have QQ enzymes that can degrade AHLs. In our previous study, strain Ruegeria mobilis YJ3, isolated from healthy shrimp, showed strong AHLs degradative activity. In the current study, an AHL lactonase (designated RmmL) was cloned and characterized from Ruegeria mobilis YJ3. Amino acid sequence analysis showed that RmmL has a conserved “HXHXDH” motif and clusters together with lactonase AidC that belongs to the metallo-β-lactamase superfamily. Recombinant RmmL could degrade either short- or long-chain AHLs in vitro. High-performance liquid chromatography analysis indicated that RmmL works as an AHL lactonase catalyzing AHL ring-opening by hydrolyzing lactones. Furthermore, RmmL can reduce the production of pyocyanin by Pseudomonas aeruginosa PAO1, while for the violacein and the extracellular protease activities by Chromobacterium violaceum CV026 and Vibrio anguillarum VIB72, no significant reduction was observed. This study suggests that RmmL might be used as a therapeutic agent in aquaculture.
Multi-Task Learning for Domain-General Spoken Disfluency Detection in Dialogue Systems

Spontaneous spoken dialogue is often disfluent, containing pauses, hesitations, self-corrections and false starts. Processing such phenomena is essential in understanding a speaker's intended meaning and controlling the flow of the conversation. Furthermore, this processing needs to be word-by-word incremental to allow further downstream processing to begin as early as possible in order to handle real spontaneous human conversational behaviour. In addition, from a developer's point of view, it is highly desirable to be able to develop systems which can be trained from `clean' examples while also able to generalise to the very diverse disfluent variations on the same data -- thereby enhancing both data-efficiency and robustness. In this paper, we present a multi-task LSTM-based model for incremental detection of disfluency structure, which can be hooked up to any component for incremental interpretation (e.g. an incremental semantic parser), or else simply used to `clean up' the current utterance as it is being produced. We train the system on the Switchboard Dialogue Acts (SWDA) corpus and present its accuracy on this dataset. Our model outperforms prior neural network-based incremental approaches by about 10 percentage points on SWDA while employing a simpler architecture. To test the model's generalisation potential, we evaluate the same model on the bAbI+ dataset, without any additional training. bAbI+ is a dataset of synthesised goal-oriented dialogues where we control the distribution of disfluencies and their types. This shows that our approach has good generalisation potential, and sheds more light on which types of disfluency might be amenable to domain-general processing.
Diversity of oil yield, fatty acids, tocopherols, tocotrienols, and sterols in the seeds of 19 interspecific grapes crosses.

BACKGROUND The seeds of Vitis vinifera grapes have been studied extensively but knowledge about the interspecific crosses of other Vitis species (e.g. V. vinifera, V. amurensis, V. rupestris, V. riparia, and V. labruska) is very limited. RESULTS The oil yields recovered from the grape seeds ranged between 7 and 160 g kg-1 dw. The main fatty acids were linoleic (72.5-83.1%), oleic (6.2-15.5%), and palmitic (5.4-13.2%), which together constituted 92.8-97.1% of the total detected fatty acids. The total concentration of tocopherol (T) and tocotrienol (T3) homologues was between 0.785 and 9.033 g kg-1 oil. The concentration of sterols varied significantly and ranged between 2.912 and 105.962 g kg-1 oil. The β-sitosterol constituted 68.2-86.3% of the total content of sterols. The oil yield in grape seeds significantly correlated with the oleic acid, α-linolenic acid, α-T, α-T3, γ-T3, campesterol, Δ5-stigmasterol, β-sitosterol, and total Ts + T3 s and sterols. CONCLUSION The present study demonstrated that seed oil recovered from different interspecific Vitis crosses is a rich source of minor lipophilic bioactive compounds, especially genotypes with low oil content. They can be used to enrich plant oils that are poor in tocotrienols and/or phytosterols without changing the fatty acid composition of main oil, due to low enrichment quantities (micro-blends). © 2018 Society of Chemical Industry.
Automatic Playlist Continuation using Subprofile-Aware Diversification

The ACM RecSys Challenge 2018 involves the task of automatic playlist continuation (APC), aiming to help users to create and extend their own music playlists. In this paper, we explain teamrozik's approach to the Challenge. Our approach to APC is twofold: Cold-Start-APC for short playlists and SPAD-APC for other playlists. Cold-Start-APC is a rudimentary popularity-based recommender. SPAD-APC treats playlists as if they were user profiles. It builds an implicit matrix factorization model to generate initial recommendations. But it re-ranks those recommendations using SubProfile-Aware Diversification (SPAD), which is a personalized intent-aware diversification method. The SPAD re-ranking method aims to ensure that the final set of recommendations covers different interests or tastes in the playlists of the users, which we refer to as subprofiles. We show that such subprofiles do exist within playlists and we show that the SPAD method achieves higher precision than matrix factorization alone.
Information processing in a simple one-step cascade

Using the formalism of information theory, we analyze the mechanism of information transduction in a simple one-step signaling cascade S$\rightarrow$X representing the gene regulatory network. Approximating the signaling channel to be Gaussian, we describe the dynamics using Langevin equations. Upon discretization, we calculate the associated second moments for linear and nonlinear regulation of the output by the input, which follows the birth-death process. While mutual information between the input and the output characterizes the channel capacity, the Fano factor of the output gives a clear idea of how internal and external fluctuations assemble at the output level. To quantify the contribution of the present state of the input to predict the future output, transfer entropy is computed. We find that higher amount of transfer entropy is accompanied by the greater magnitude of external fluctuations (quantified by the Fano factor of the output) propagation from the input to the output. We notice that low input population characterized by the number of signaling molecules S, which fluctuates in a relatively slower fashion compared to its downstream (target) species X, is maximally able to predict (as quantified by transfer entropy) the future state of the output. Our computations also reveal that with increased linear nature of the input-output interaction, all three metrics of mutual information, Fano factor and, transfer entropy achieve relatively larger magnitudes.
Reassessment of the Groundwater Amphipod Paramoera relicta Synonymizes the Genus Relictomoera with Paramoera (Crustacea: Amphipoda: Pontogeneiidae)

The systematic status of the stygobitic amphipod, Paramoera relicta Uéno, 1971, was revisited based on morphological examination of the paratypes, and molecular phylogenetic analyses using a newly collected specimen from its type locality. The paratypes clearly showed that this species does not possess the “sinusoid” antennal sinus in the head, which was erroneously described in the original description of this species; P. relicta is unquestionably classified within the genus Paramoera Miers, 1875. Molecular phylogenetic trees obtained with nuclear histone H3 and mitochondrial 16S rRNA demonstrated that P. relicta clearly belongs to the clade consisting of Paramoera species. Since the genus Relictomoera Barnard and Karaman, 1982 was erected according to the “sinusoid” characteristic of its type species P. relicta, this genus is no longer valid, and should be treated as a junior subjective synonym of Paramoera. One of the paratypes of P. relicta is herein designated as a neotype for the species to clarify its taxonomic status and emend the morphological characteristics of this pontogeneiid amphipod.
1D eyewear: peripheral, hidden LEDs and near-eye holographic displays for unobtrusive augmentation

1D Eyewear uses 1D arrays of LEDs and pre-recorded holographic symbols to enable minimal head-worn displays. Our approach uses computer-generated holograms (CGHs) to create diffraction gratings which project a pre-recorded static image when illuminated with coherent light. Specifically, we develop a set of transmissive, reflective, and steerable optical configurations that can be embedded in conventional eyewear designs. This approach enables high resolution symbolic display in discreet digital eyewear.
Recovering Quantized Data with Missing Information Using Bilinear Factorization and Augmented Lagrangian Method

In this paper, we propose a novel approach in order to recover a quantized matrix with missing information. We propose a regularized convex cost function composed of a log-likelihood term and a Trace norm term. The Bi-factorization approach and the Augmented Lagrangian Method (ALM) are applied to find the global minimizer of the cost function in order to recover the genuine data. We provide mathematical convergence analysis for our proposed algorithm. In the Numerical Experiments Section, we show the superiority of our method in accuracy and also its robustness in computational complexity compared to the state-of-the-art literature methods.
Assessment of modeling and simulation in secondary computing science education

The introduction of the new computing science curriculum in the Netherlands in 2019 raises the need for new evidence-based teaching materials that include practical assignments and guidelines for their assessment. As a part of our research project on teaching Computational Science (modeling and simulation), we participate in these efforts and developed a curriculum intervention including a practical assignment and an accompanying assessment instrument consisting of grading rubrics based on the SOLO taxonomy. In this research paper we focus on the assessment instrument. We describe its development and report on a pilot study carried out in the secondary computing science course implementing the curriculum intervention. The instrument proved to be reliable and effective in tracing high and low levels of the students' achievements in modeling and simulation projects and exposed the expected differences in performance levels of various groups of students, which renders it useful for both formative and summative assessment. Furthermore, our application of the instrument has provided new insights into the needs of specific groups of students to receive instruction prior to and during the work on the assignments.
Measuring and Predicting the Relevance Ratings between FLOSS Projects using Topic Features

Understanding the relevance between the Free/Libra Open Source Software projects is important for developers to perform code and design reuse, discover and develop new features, keep their projects up-to-date, and etc. However, it is challenging to perform relevance ratings between the FLOSS projects mainly because: 1) beyond simple code similarity, there are complex aspects considered when measuring the relevance; and 2) the prohibitive large amount of FLOSS projects available. To address the problem, in this paper, we propose a method to measure and further predict the relevance ratings between FLOSS projects. Our method uses topic features extracted by the LDA topic model to describe the characteristics of a project. By using the topic features, multiple aspects of FLOSS projects such as the application domain, technology used, and programming language are extracted and further used to measure and predict their relevance ratings. Based on the topic features, our method uses matrix factorization to leverage the partially known relevance ratings between the projects to learn the mapping between different topic features to the relevance ratings. Finally, our method combines the topic modeling and matrix factorization technologies to predict the relevance ratings between software projects without human intervention, which is scalable to a large amount of projects. We evaluate the performance of the proposed method by applying our topic extraction and relevance modeling methods using 300 projects from GitHub. The result of topic extraction experiment shows that, for topic modeling, our LDA-based approach achieves the highest hit rate of 98.3% and the highest average accuracy of 29.8%. And the relevance modeling experiment shows that our relevance modeling approach achieves the minimum average predict error of 0.093, suggesting the effectiveness of applying the proposed method on real-world data sets.
Interactive reservoir computing for chunking information streams

Chunking is the process by which frequently repeated segments of temporal inputs are concatenated into single units that are easy to process. Such a process is fundamental to time-series analysis in biological and artificial information processing systems. The brain efficiently acquires chunks from various information streams in an unsupervised manner; however, the underlying mechanisms of this process remain elusive. A widely-adopted statistical method for chunking consists of predicting frequently repeated contiguous elements in an input sequence based on unequal transition probabilities over sequence elements. However, recent experimental findings suggest that the brain is unlikely to adopt this method, as human subjects can chunk sequences with uniform transition probabilities. In this study, we propose a novel conceptual framework to overcome this limitation. In this process, neural networks learn to predict dynamical response patterns to sequence input rather than to directly learn transition patterns. Using a mutually supervising pair of reservoir computing modules, we demonstrate how this mechanism works in chunking sequences of letters or visual images with variable regularity and complexity. In addition, we demonstrate that background noise plays a crucial role in correctly learning chunks in this model. In particular, the model can successfully chunk sequences that conventional statistical approaches fail to chunk due to uniform transition probabilities. In addition, the neural responses of the model exhibit an interesting similarity to those of the basal ganglia observed after motor habit formation.
Slicing UML-based Models of Real-time Embedded Systems

Models of Real-time Embedded (RTE) systems may encompass many components with often many different kinds of dependencies describing, e.g., structural relationships or the flow of data, control, or messages. Understanding and properly accounting for them during development, testing and debugging can be challenging. This paper presents an approach for slicing models of RTE systems to facilitate model understanding and other model-level activities. A key novelty of our approach is the support for models with composite components (with multiple hierarchical levels) and capturing the dependencies that involve structural and behavioural model elements, including a combination of the two. Moreover, we describe an implementation of our approach in the context of Papyrus-RT, an open source Model Driven Engineering (MDE) tool based on the modeling language UML-RT. We conclude the paper with the results of applying our slicer to a set of UML-RT models to validate our approach and to demonstrate the applications of our approach for facilitating model-level analysis tasks, such as testing and debugging.
Automatic playlist continuation using a hybrid recommender system combining features from text and audio

The ACM RecSys Challenge 2018 focuses on music recommendation in the context of automatic playlist continuation. In this paper, we describe our approach to the problem and the final hybrid system that was submitted to the challenge by our team Cocoplaya. This system consists in combining the recommendations produced by two different models using ranking fusion. The first model is based on Matrix Factorization and it incorporates information from tracks' audio and playlist titles. The second model generates recommendations based on typical track co-occurrences considering their proximity in the playlists. The proposed approach is efficient and achieves a good overall performance, with our model ranked 4th on the creative track of the challenge leaderboard.
Exploring Potency

The original notion of potency -- one of the core features underpinning many forms of multi-level modeling -- has come under pressure in several ways: First, since its inception new modeling challenges have come to the fore that raise serious questions about classic potency. Second, classic potency was developed in the context of constructive modeling and does not accommodate exploratory modeling, thus representing a major hindrance to the unification of constructive and exploratory modeling in a multi-level modeling context. Third, as the discipline of multi-level modeling has evolved, a number of alternative interpretations of potency have emerged. In part, these are based on different underlying principles, yet an explicit recognition of the respective differences at a foundational level and an explicit discussion of the tradeoffs involved has been missing from the literature to date. In this paper, I identify limitations of classic potency, propose to evolve it to a potency notion based on a new foundation which -- along with further novel proposals -- addresses the aforementioned challenges, and finally conduct a comparison to three alternative definitions of potency.