Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid Parameter-less Evolutionary Algorithm in Production Planning

In the real-world production planning problems there are many constraints that need to be considered. Usually, these constraints and interdependent and the optimization algorithms has to efficiently solve that. This paper presents the hybrid parameter-less evolutionary algorithm used for construction of an optimal production plan. The algorithm is based on genetic algorithm, but is modified to work without the parameter setting. All algorithm control parameters are automatically determined during the optimization. The algorithm was able to solve the constraints and to make an optimal production plan. Additionally, we evaluated the influence of different ratios of orders with fixed deadlines on the performance of the algorithm. The used algorithm can successfully solve also these additional constraints.
Sequence and Functional Characterization of a Renal Sodium/Dicarboxylate Cotransporter (*)

The cDNA coding for a rabbit renal Na/dicarboxylate cotransporter, designated NaDC-1, was isolated by functional expression in Xenopus oocytes. NaDC-1 cDNA is approximately 2.3 kilobases in length and codes for a protein of 593 amino acids. NaDC-1 protein contains eight putative transmembrane domains, and the sequence and secondary structure are related to the renal Na/sulfate transporter, NaSi-1. Northern analysis shows that the NaDC-1 message is abundant in kidney and small intestine, and related transporters may be found in liver, lung, and adrenal. The transport of succinate by NaDC-1 was sodium-dependent, sensitive to inhibition by lithium, and inhibited by a range of di- and tricarboxylic acids. This transporter also carries citrate, but it does not transport lactate. In kinetic experiments, the K for succinate was around 0.4 mM and the Vmax was 15 nmol/oocyte/h, while the Hill coefficient of Na activation of succinate transport was 1.9. The transport of succinate by NaDC-1 was insensitive to changes in pH, whereas the transport of citrate increased with decreasing pH, in parallel with the concentration of divalent citrate in the medium. The results of the functional characterization indicate that NaDC-1 likely corresponds to the renal brush-border Na/dicarboxylate cotransporter.
Using the crowd for top-k and group-by queries

Group-by and top-k are fundamental constructs in database queries. However, the criteria used for grouping and ordering certain types of data -- such as unlabeled photos clustered by the same person ordered by age -- are difficult to evaluate by machines. In contrast, these tasks are easy for humans to evaluate and are therefore natural candidates for being crowd-sourced. We study the problem of evaluating top-k and group-by queries using the crowd to answer either type or value questions. Given two data elements, the answer to a type question is "yes" if the elements have the same type and therefore belong to the same group or cluster; the answer to a value question orders the two data elements. The assumption here is that there is an underlying ground truth, but that the answers returned by the crowd may sometimes be erroneous. We formalize the problems of top-k and group-by in the crowd-sourced setting, and give efficient algorithms that are guaranteed to achieve good results with high probability. We analyze the crowd-sourced cost of these algorithms in terms of the total number of type and value questions, and show that they are essentially the best possible. We also show that fewer questions are needed when values and types are correlated, or when the error model is one in which the error decreases as the distance between the two elements in the sorted order increases.
Effects of voice rehabilitation after radiotherapy for laryngeal cancer: a longitudinal study

Abstract Objectives: The study aimed to investigate the effects of voice rehabilitation on health-related quality of life (HRQL) and voice function in patients treated for laryngeal cancer six months post-rehabilitation completion. A secondary aim was to identify factors that predict significant communication improvement. Study design: Longitudinal follow-up of randomised controlled trial. Methods: In total, 33 patients received voice rehabilitation post-radiotherapy and 32 patients constituted the control group. Outcome measures included patient-reported HRQL, communication and voice function (acoustic measures and perceptual analysis). Outcome measures were analysed one (baseline), six and 12 months post-radiotherapy, where voice rehabilitation was conducted between the first two time-points. Results: Patients improved after voice rehabilitation with regard to communication function and HRQL and remained unchanged after 12 months post-radiotherapy. A significant roughness deterioration in the control group occurred between six and 12 months post-radiotherapy, yet remained unchanged in the intervention group. A factors increasing odds of significant communication improvement 12 months post-radiotherapy was voice rehabilitation. Smoking affected communication negatively. Conclusion: Voice rehabilitation appears to have positive effect on voice function and HRQL, which persist up to 12 months of follow-up and appears to prevent deterioration of perceived roughness. Patients who experience voice and communication problems at baseline are most likely to benefit from voice rehabilitation.
Wnt signaling is required for thymocyte development and activates Tcf‐1 mediated transcription

T cell factor / lymphocyte enhancer factor (Tcf / Lef) transcription factors complex with the transcriptional co‐activator β‐catenin to transduce Wnt signals in a variety of developmental systems. The prototypic family member Tcf‐1 is highly expressed in T lineage cells. Tcf1– / – mice are defective in cell cycling of early thymocyte stages. Here, we show that the interaction of β‐catenin with Tcf‐1 is required for full thymocyte development. This interaction may be established by signals mediated by Wnt1 and Wnt4, leading to increased Tcf‐dependent transcriptional activity in thymocytes, as demonstrated in Tcf‐LacZ reporter mice. Transduction of fetal thymocytes with Wnt1 and Wnt4 results in increased survival in an in vitro cell culture system. Retroviral expression of soluble Wnt receptor mutants that block Wnt signaling inhibits thymocyte development. These results imply an important role for the Wnt cascade in thymocyte development.
Evaluating Satellite and Supercomputing Technologies for Improved Coastal Ecosystem Assessments

Coastal water-quality is both a primary driver and also a consequence of coastal ecosystem health. Turbidity, a measure of dissolved and particulate water-quality matter, varies daily and can also have large interannual fluctuations. Water quality is influenced by a variety of factors. Identifying which factors drive trends and extreme events in turbidity in an estuary helps environmental managers and decision makers plan for and mitigate against water-quality issues. Efforts to do so on large spatial scales have been hampered due to limitations of turbidity data, including coarse and irregular temporal resolution and poor spatial coverage. We addressed these issues by deriving a proxy for turbidity using ocean color satellite products for 11 Gulf of Mexico estuaries from 2000-2014 on weekly, monthly, seasonal and annual time-steps. Turbidity variability was best explained (R > 0.2, p < 0.05) by wind speed over short time scales (weekly to monthly), while occurrence of extreme turbidity events was closely related (R > 0.2, p < 0.05) to El Niño-Southern Oscillation cycles in six estuaries over long time frames (months to seasons). As expected, river discharge drove both seasonal as well as event-scale variability in turbidity across estuaries.
CORUM: the comprehensive resource of mammalian protein complexes—2009

CORUM is a database that provides a manually curated repository of experimentally characterized protein complexes from mammalian organisms, mainly human (64%), mouse (16%) and rat (12%). Protein complexes are key molecular entities that integrate multiple gene products to perform cellular functions. The new CORUM 2.0 release encompasses 2837 protein complexes offering the largest and most comprehensive publicly available dataset of mammalian protein complexes. The CORUM dataset is built from 3198 different genes, representing ∼16% of the protein coding genes in humans. Each protein complex is described by a protein complex name, subunit composition, function as well as the literature reference that characterizes the respective protein complex. Recent developments include mapping of functional annotation to Gene Ontology terms as well as cross-references to Entrez Gene identifiers. In addition, a ‘Phylogenetic Conservation’ analysis tool was implemented that analyses the potential occurrence of orthologous protein complex subunits in mammals and other selected groups of organisms. This allows one to predict the occurrence of protein complexes in different phylogenetic groups. CORUM is freely accessible at (http://mips.helmholtz-muenchen.de/genre/proj/corum/index.html).
Sample size calculation in metabolic phenotyping studies

The number of samples needed to identify significant effects is a key question in biomedical studies, with consequences on experimental designs, costs and potential discoveries. In metabolic phenotyping studies, sample size determination remains a complex step. This is due particularly to the multiple hypothesis-testing framework and the top-down hypothesis-free approach, with no a priori known metabolic target. Until now, there was no standard procedure available to address this purpose. In this review, we discuss sample size estimation procedures for metabolic phenotyping studies. We release an automated implementation of the Data-driven Sample size Determination (DSD) algorithm for MATLAB and GNU Octave. Original research concerning DSD was published elsewhere. DSD allows the determination of an optimized sample size in metabolic phenotyping studies. The procedure uses analytical data only from a small pilot cohort to generate an expanded data set. The statistical recoupling of variables procedure is used to identify metabolic variables, and their intensity distributions are estimated by Kernel smoothing or log-normal density fitting. Statistically significant metabolic variations are evaluated using the Benjamini-Yekutieli correction and processed for data sets of various sizes. Optimal sample size determination is achieved in a context of biomarker discovery (at least one statistically significant variation) or metabolic exploration (a maximum of statistically significant variations). DSD toolbox is encoded in MATLAB R2008A (Mathworks, Natick, MA) for Kernel and log-normal estimates, and in GNU Octave for log-normal estimates (Kernel density estimates are not robust enough in GNU octave). It is available at http://www.prabi. fr/redmine/projects/dsd/repository, with a tutorial at http://www.prabi. fr/redmine/projects/dsd/wiki.
Assessing Intangibles in Lean Start-Ups Using Repertory Grids

The assessment of intangibles in business plans by investors is an important factor of recent interest, particularly in the evaluation of early-stage technology-based ventures. On one hand, investors are challenged to properly assess new opportunities. At the same time, entrepreneurs or innovators face the formidable task of communicating what is, sometimes, nothing more than just an 'extraordinary' idea. In such situations, the decision to continue with the due diligence process, and finally to invest, is based frequently on those aspects that are intangible. In an attempt to reveal some of the intangibles assessed by investors and communicated by entrepreneurs, an investigation was conducted using repertory grid, a technique based on personal construct psychology. Five venture capitalists and five entrepreneurs were interviewed. Evidence was found for the importance of intangibles during the investment-decision process of early stage technology-based ventures. For these ventures, the consideration of only tangible criteria is not a guaranteed predictor of success. The repertory grid technique makes a significant contribution to the identification of intangibles assessed by investors and communicated by entrepreneurs.
Spectroscopic characterization of the iron-oxo intermediate in cytochrome P450

Abstract From analogy to chloroperoxidase from Caldariomyces fumago, it is believed that the electronic structure of the intermediate iron-oxo species in the catalytic cycle of cytochrome P450 corresponds to an iron(IV) porphyrin-π-cation radical (compound I). However, our recent studies on P450cam revealed that after 8 ms a tyrosine radical and iron(IV) were formed in the reaction of ferric P450 with external oxidants in the shunt pathway. The present study on the heme domain of P450BM3 (P450BMP) shows a similar result. In addition to a tyrosine radical, a contribution from a tryptophan radical was found in the electron paramagnetic resonance (EPR) spectra of P450BMP. Here we present comparative multi-frequency EPR (9.6, 94 and 285 GHz) and Mössbauer spectroscopic studies on freeze-quenched intermediates produced using peroxy acetic acid as oxidant for both P450 cytochromes. After 8 ms in both systems, amino acid radicals occurred instead of the proposed iron(IV) porphyrin-π-cation radical, which may be transiently formed on a much faster time scale. These findings are discussed with respect to other heme thiolate proteins. Our studies demonstrate that intramolecular electron transfer from aromatic amino acids is a common feature in these enzymes. The electron transfer quenches the presumably transiently formed porphyrin-π-cation radical, which makes it extremely difficult to trap compound I.
Practical anomaly detection based on classifying frequent traffic patterns

Detecting network traffic anomalies is crucial for network operators as it helps to identify security incidents and to monitor the availability of networked services. Although anomaly detection has received significant attention in the literature, the automatic classification of network anomalies still remains an open problem. In this paper, we introduce a novel scheme and build a system to detect and classify anomalies that is based on an elegant combination of frequent item-set mining with decision tree learning. Our approach has two key features: 1) effectiveness, it has a very low false-positive rate; and 2) simplicity, an operator can easily comprehend how our detector and classifier operates. We evaluate our scheme using traffic traces from two real networks, namely from the European-wide backbone network of GEÁNT and from a regional peering link in Spain. In both cases, we achieve an overall classification accuracy greater than 98% and a false-positive rate of approximately only 1%. In addition, we show that it is possible to train our classifier with data from one network and use it to effectively classify anomalies in a different network. Finally, we have built a corresponding anomaly detection and classification system and have deployed it as part of an operational platform, where it is successfully used to monitor two 10Gb/s peering links between the Catalan and the Spanish national research and education networks (NREN).
Nesnelerin İnterneti Tabanlı Akıllı Sulama ve Uzaktan İzleme Sistemi

Bu calismada, Nesnelerin Interneti (IoT) tabanli bir akilli sulama sistemi onerilmistir. Onerilen bu akilli sulama sistemi, havanin sicakligi ve nem degerleri ile topragin nem degerini kullanarak sulama zamani ve suresini belirlemektedir. Akilli sulama sistemi, havanin sicaklik ve nem degerlerini kullanarak yagis tahmininde bulunmakta ve gerektiginde sulama zamanini otelemektedir. Android uygulama gelistiricisi kullanilarak olusturulan kullanici arayuzu, sensor verileri, sulama suresi, hava tahmini, elektrik ve su tuketimi gibi parametrelerin goruntulenmesini ve bu verilerin bulut sisteminde depolanmasini saglamaktadir. Akilli sulama sistemi, android arayuz ve sensorler sayesinde ideal sulama periyodu ve sulama suresini belirleyerek, hem elektrik enerjisi hem de su tasarrufu saglamaktadir.
Manifold Warping: Manifold Alignment over Time

Knowledge transfer is computationally challenging, due in part to the curse of dimensionality, compounded by source and target domains expressed using different features (e.g., documents written in different languages). Recent work on manifold learning has shown that data collected in real-world settings often have high-dimensional representations, but lie on low-dimensional manifolds. Furthermore, data sets collected from similar generating processes often present different high-dimensional views, even though their underlying manifolds are similar. The ability to align these data sets and extract this common structure is critical for many transfer learning tasks. In this paper, we present a novel framework for aligning two sequentially-ordered data sets, taking advantage of a shared low-dimensional manifold representation. Our approach combines traditional manifold alignment and dynamic time warping algorithms using alternating projections. We also show that the previously-proposed canonical time warping algorithm is a special case of our approach. We provide a theoretical formulation as well as experimental results on synthetic and real-world data, comparing manifold warping to other alignment methods.
Recent Advances in Morphological Cell Image Analysis

This paper summarizes the recent advances in image processing methods for morphological cell analysis. The topic of morphological analysis has received much attention with the increasing demands in both bioinformatics and biomedical applications. Among many factors that affect the diagnosis of a disease, morphological cell analysis and statistics have made great contributions to results and effects for a doctor. Morphological cell analysis finds the cellar shape, cellar regularity, classification, statistics, diagnosis, and so forth. In the last 20 years, about 1000 publications have reported the use of morphological cell analysis in biomedical research. Relevant solutions encompass a rather wide application area, such as cell clumps segmentation, morphological characteristics extraction, 3D reconstruction, abnormal cells identification, and statistical analysis. These reports are summarized in this paper to enable easy referral to suitable methods for practical solutions. Representative contributions and future research trends are also addressed.
Aesthetics by Numbers: Links between Perceived Texture Qualities and Computed Visual Texture Properties

Our world is filled with texture. For the human visual system, this is an important source of information for assessing environmental and material properties. Indeed—and presumably for this reason—the human visual system has regions dedicated to processing textures. Despite their abundance and apparent relevance, only recently the relationships between texture features and high-level judgments have captured the interest of mainstream science, despite long-standing indications for such relationships. In this study, we explore such relationships, as these might be used to predict perceived texture qualities. This is relevant, not only from a psychological/neuroscience perspective, but also for more applied fields such as design, architecture, and the visual arts. In two separate experiments, observers judged various qualities of visual textures such as beauty, roughness, naturalness, elegance, and complexity. Based on factor analysis, we find that in both experiments, ~75% of the variability in the judgments could be explained by a two-dimensional space, with axes that are closely aligned to the beauty and roughness judgments. That a two-dimensional judgment space suffices to capture most of the variability in the perceived texture qualities suggests that observers use a relatively limited set of internal scales on which to base various judgments, including aesthetic ones. Finally, for both of these judgments, we determined the relationship with a large number of texture features computed for each of the texture stimuli. We find that the presence of lower spatial frequencies, oblique orientations, higher intensity variation, higher saturation, and redness correlates with higher beauty ratings. Features that captured image intensity and uniformity correlated with roughness ratings. Therefore, a number of computational texture features are predictive of these judgments. This suggests that perceived texture qualities—including the aesthetic appreciation—are sufficiently universal to be predicted—with reasonable accuracy—based on the computed feature content of the textures.
Saliency Detection Using Sparse and Nonlinear Feature Representation

An important aspect of visual saliency detection is how features that form an input image are represented. A popular theory supports sparse feature representation, an image being represented with a basis dictionary having sparse weighting coefficient. Another method uses a nonlinear combination of image features for representation. In our work, we combine the two methods and propose a scheme that takes advantage of both sparse and nonlinear feature representation. To this end, we use independent component analysis (ICA) and covariant matrices, respectively. To compute saliency, we use a biologically plausible center surround difference (CSD) mechanism. Our sparse features are adaptive in nature; the ICA basis function are learnt at every image representation, rather than being fixed. We show that Adaptive Sparse Features when used with a CSD mechanism yield better results compared to fixed sparse representations. We also show that covariant matrices consisting of nonlinear integration of color information alone are sufficient to efficiently estimate saliency from an image. The proposed dual representation scheme is then evaluated against human eye fixation prediction, response to psychological patterns, and salient object detection on well-known datasets. We conclude that having two forms of representation compliments one another and results in better saliency detection.
Automatic selection of task spaces for imitation learning

Previous work [1] shows that the movement representation in task spaces offers many advantages for learning object-related and goal-directed movement tasks through imitation. It allows to reduce the dimensionality of the data that is learned and simplifies the correspondence problem that results from different kinematic structures of teacher and robot. Further, the task space representation provides a first generalization, for example wrt. differing absolute positions, if bi-manual movements are represented in relation to each other. Although task spaces are widely used, even if they are not mentioned explicitly, they are mostly defined a priori. This work is a step towards an automatic selection of task spaces. Observed movements are mapped into a pool of possibly even conflicting task spaces and we present methods that analyze this task space pool in order to acquire task space descriptors that match the observation best. As statistical measures cannot explain importance for all kinds of movements, the presented selection scheme incorporates additional criteria such as an attention-based measure. Further, we introduce methods that make a significant step from purely statistically-driven task space selection towards model-based movement analysis using a simulation of a complex human model. Effort and discomfort of the human teacher is being analyzed and used as a hint for important task elements. All methods are validated with real-world data, gathered using color tracking with a stereo vision system and a VICON motion capturing system.