Miracle-dz/UniFAR-FTUs huggingface dataset:
Crystal structure and Hirshfeld-surface analysis of (benzenecarbothioamide-κS)bromidobis(triphenylphosphane-κP)silver(I)

The mononuclear complex exhibits a distorted tetrahedral coordination geometry about the metal atom, arising from one S atom of the benzenecarbothioamide ligand, two P atoms of two triphenylphosphane molecules and one bromide ion. An intramolecular N—H⋯Br hydrogen bond is observed and in the crystal structure, inversion dimers linked by pairs of N—H⋯Br and C—H⋯Br hydrogen bonds are observed. In addition, C—H⋯π interactions occur, leading to [101] chains.
Real time railway extraction by angle alignment measure

Rail extraction is a fundamental and important step in railway Driver Assistant System, which is now an important application of image processing. The task is challenging as the railway is exposed to different environments. This paper proposes a railway extraction scheme, using a novel connectivity measure method named Angle Alignment Measure. The proposed scheme is robust to luminance and color variation, without edge extraction process. Railways with different lengths and patterns can be extracted under various lighting and weather conditions. More importantly, the computation complexity of the proposed scheme is very low, requiring only on average 25ms to process a frame on smart phone and 5ms on desktop computer, which are significantly better than algorithms in the literature.
Show me the Money! the Quest for an Intermedia Currency in the Nordic Countries

Abstract Legacy media are under threat of gradually losing a major source of their income – advertising. Consequently, media audience measurements are being developed to better suit the multimedia world in three Nordic countries: Norway, Denmark and Finland. The findings suggest that: (1) media convergence is bringing pressure to build strategic alliances among competing media that have previously operated autonomously, (2) strategic competitive alliances are not always voluntary but forced, can be formed around core resources, and may be zero-sum relationships, and (3) path dependency and dominant designs in knowledge and business processes impede change in the media industry.
Memory-Efficient Dynamic Programming for Learning Optimal Bayesian Networks

We describe a memory-efficient implementation of a dynamic programming algorithm for learning the optimal structure of a Bayesian network from training data. The algorithm leverages the layered structure of the dynamic programming graphs representing the recursive decomposition of the problem to reduce the memory requirements of the algorithm from O(n2n) to O(C(n, n/2)), where C(n, n/2) is the binomial coefficient. Experimental results show that the approach runs up to an order of magnitude faster and scales to datasets with more variables than previous approaches.
Optimal operation point in electrical grids using a MOPSO algorithm

The paper presents a study about optimal supply of the energy service, using simulations of network operation scenarios, in order to optimize resources and minimize the variables: operation cost, energy losses, generation cost and consumers shedding. These simulations create optimal operation models of the network, allowing the system operator obtain knowledge to take pre-established procedures that must be performed in situations of contingency in order to forecast and minimize drawbacks. The simulations were performed using a multiobjective particle swarm optimization algorithm. The algorithm was applied to the IEEE 14 Bus network where the optimal power flow was evaluated by the MATPOWER tool to establish an optimal electrical working model to minimize the associated costs.
Interaction Effects of Selected Pesticides on Soil Enzymes

The laboratory studies were conducted to resolute the effects of imidacloprid (insecticide) and triadimefon (fungicide) singly and in combination on enzymatic activities of soil microorganisms in tomato cultivated soils at different concentrations of 0.2, 0.5 and 0.7 kg/ha. The rate of amylase activity was stimulated by the application of pesticides at field rate. High dosage decreased the activity of amylase. Decline in the activity of cellulase was observed at all concentrations than control. Imidacloprid had an improved activity of cellulase at 0.5 μg/g than tridimefon and combination. At higher concentration (0.7 μg/g), the combination of insecticide and fungicide showed an antagonistic interaction toward cellulase. After 24 h, maximum inhibition was observed in invertase enzyme rate at all examined dosages. After 48 h, the activity was revived to some extent and imidacloprid showed enhanced activity at 0.5 μg/g (field rate). However at 0.7 μg/g, imidacloprid has a noticeable effect on the invertase. The pesticide application in single and in combination (0.2-0.7 μg/g soil) triggered the dehydrogenase activity. At field rate triadimefon significantly quickened the activity.
Feature Selection for Nonlinear Regression and its Application to Cancer Research

Feature selection is a fundamental problem in machine learning. With the advent of high-throughput technologies, it becomes increasingly important in a wide range of scientific disciplines. In this paper, we consider the problem of feature selection for high-dimensional nonlinear regression. This problem has not yet been well addressed in the community, and existing methods suffer from issues such as local minima, simplified model assumptions, high computational complexity and selected features not directly related to learning accuracy. We propose a new wrapper method that addresses some of these issues. We start by developing a new approach to estimating sample responses and prediction errors, and then deploy a feature weighting strategy to find a feature subspace where a prediction error function is minimized. We formulate it as an optimization problem within the SVM framework and solve it using an iterative approach. In each iteration, a gradient descent based approach is derived to efficiently find a solution. A large-scale simulation study is performed on four synthetic and nine cancer microarray datasets that demonstrates the effectiveness of the proposed method.
Some Common Fixed Point Theorems in Complex Valued b-Metric Spaces

Azam et al. (2011), introduce the notion of complex valued metric spaces and obtained common fixed point result for mappings in the context of complex valued metric spaces. Rao et al. (2013) introduce the notion of complex valued b-metric spaces. In this paper, we generalize the results of Azam et al. (2011), and Bhatt et al. (2011), by improving the conditions of contraction to establish the existence and uniqueness of common fixed point for two self-mappings on complex valued b-metric spaces. Some examples are given to illustrate the main results.
Colorimetric measurement of iron in plasma samples anticoagulated with EDTA.

AIMS: To determine if the iron in EDTA anticoagulated plasma samples can be measured by colorimetric assays using Ferrozine. METHODS: Paired samples of serum and EDTA plasma were obtained from 24 patients and analysed by three commercial iron methods. The EDTA plasmas were also analysed using methods modified by the addition of zinc sulphate or with different concentrations of Ferrozine. The iron contamination of EDTA sample tubes was measured by atomic absorption spectroscopy. RESULTS: Two commercial colorimetric iron methods gave results of zero for EDTA plasma samples. A third commercial reagent gave plasma results that were about 30% lower than their corresponding serum samples. Addition of 7 mmol/l zinc sulphate to this reagent system and extending the sample preincubation time to 300 seconds yielded comparable results from paired serum and EDTA plasma samples. Linear regression analysis gave a slope of 0.97 with an intercept of 0.60 mumol/l and R2 = 0.9943. Measurements by atomic absorption spectroscopy showed that this positive intercept was due to contamination of the blood collection tubes with about 90 ng of iron. CONCLUSIONS: Modification of commercial colorimetric iron methods permits the biochemical assessment of iron status and a full blood count from a single EDTA anticoagulated blood sample.
Fault Diagnosis of a Hydro Turbine Generating Set Based on Support Vector Machine

With the application of large capacity hydro turbine generating set, it is important for the hydro turbine generating set to monitoring its vibration and diagnoses its faulty. In this paper, fault diagnosis based on support vector machine is proposed for hydro turbine generating set. The most important advantage of SVM is effective for the case of lack of training samples. Some key parameters of SVM and kernel functions are surveyed. Compared with the artificial neural network methods, SVM methods are more effective. The experiment shows that the SVM method has good classification ability and robust performances.
Multimodal control for human-robot cooperation

For intuitive human-robot collaboration, the robot must quickly adapt to the human behavior. To this end, we propose a multimodal sensor-based control framework, enabling a robot to recognize human intention, and consequently adapt its control strategy. Our approach is marker-less, relies on a Kinect and on an on-board camera, and is based on a unified task formalism. Moreover, we validate it in a mock-up industrial scenario, where human and robot must collaborate to insert screws in a flank.
Oxidized Lipoprotein as a Major Vessel Cell Proliferator in Oxidized Human Serum

Oxidative stress is correlated with the incidence of several diseases such as atherosclerosis and cancer, and oxidized biomolecules have been determined as biomarkers of oxidative stress; however, the detailed molecular relationship between generated oxidation products and the promotion of diseases has not been fully elucidated. In the present study, to clarify the role of serum oxidation products in vessel cell proliferation, which is related to the incidence of atherosclerosis and cancer, the major vessel cell proliferator in oxidized human serum was investigated. Oxidized human serum was prepared by free radical exposure, separated using gel chromatography, and then each fraction was added to several kinds of vessel cells including endothelial cells and smooth muscle cells. It was found that a high molecular weight fraction in oxidized human serum specifically induced vessel cell proliferation. Oxidized lipids were contained in this high molecular weight fraction, while cell proliferation activity was not observed in oxidized lipoprotein-deficient serum. Oxidized low-density lipoproteins induced vessel cell proliferation in a concentration-dependent manner. Taken together, these results indicate that oxidized lipoproteins containing lipid oxidation products function as a major vessel cell proliferator in oxidized human serum. These findings strongly indicate the relevance of determination of oxidized lipoproteins and lipid oxidation products in the diagnosis of vessel cell proliferation-related diseases such as atherosclerosis and cancer.
Coordinate Interleaved Coded Cooperation

In this paper, a two-user cooperative diversity scheme is proposed where coded cooperation and modulation diversity techniques are properly combined to take their full advantage. Users perform selective cooperation in transmission according to the correct estimation of symbols from their partners. Pairwise error probability analysis is performed for all cooperation cases and a tight upper bound on the bit error probability is obtained for rate compatible punctured convolutional codes and QPSK modulation. Numerical results show that the proposed system outperforms the coded cooperation technique given in by achieving two times more diversity.
Suppressor and Activator Functions Mediated by a Repeated Heptad Sequence in the Liver Fatty Acid-binding Protein Gene (Fabpl)

A 35-nucleotide sequence in the liver fatty acid-binding protein gene (Fabpl) has been identified that interacts with nuclear proteins present in adult mouse liver, kidney, stomach, small intestine, and colon. The binding site consists of a direct heptad repeat (TTCTGNNTT) separated by five nucleotides. Both heptads are required for formation of stable complexes with nuclear proteins in gel mobility shift assays. Thein vivo functions mediated by the repeats were determined by comparing the expression of four Fabpl/human growth hormone fusion genes in multiple pedigrees of adult transgenic mice. The transgenes contained (i) nucleotides −596 to +21 ofFabpl linked to the human growth hormone reporter, (ii) 4 additional copies of the 35-base pair element placed at nucleotide −596 of Fabpl, (iii) 4 additional copies of the sequence placed just upstream of its endogenous site at nucleotide −132, and (iv) a sequence identical to (iii) but with all heptad repeats mutated within each of the 4 additional copies of the 35-base pair element. Transgene expression was defined by RNA blot hybridizations and by light and electron microscopic immunohistochemistry. The heptad repeat functions to suppress expression in tubular epithelial cells of the proximal nephron, in hepatocytes, in the mucus-producing pit cells of the gastric epithelium, and in absorptive enterocytes located in the proximal small intestine. There is a gradient of escape from enterocytic suppression as one moves from the proximal to distal small intestine. This escape progresses to involve successively less differentiated cells located closer and closer to the stem cell zone in crypts of Lieberkühn. The heptad repeat activates gene expression in the colonic epithelium so that all proliferating and nonproliferating cells in colonic crypts distributed from the cecum to the rectum support transgene expression. The heptad has no obvious sequence similarities to known transcription factor binding sites, suggesting that mediators of its in vivo activities are likely to be novel. One candidate factor is a 90-kDa protein identified in Southwestern blots. The 90-kDa protein also binds to an element in the matrix metalloproteinase-2 gene that functions as an enhancer in renal cells, shares sequence homology with the heptad, and generates similar-sized complexes in gel mobility shift assays as theFabpl repeat. The heptad repeat represents a target for identifying transcription factors that regulate gene expression between gut and renal epithelia and that also regulate the differentiation program of the intestine’s principal epithelial lineage as a function of its location along the duodenal-colonic axis. Finally, theFabpl regulatory elements described in this report should be useful for delivering a variety of gene products throughout the colonic epithelium of transgenic mice.
Independent automatic segmentation by self-learning categorial pronunciation rules

The goal of this paper is to present a new method to automatically generate pronunciation rules for automatic segmentation of speech - the German MAUSER system. MAUSER is an algorithm which generates pronunciation rules independently of any domain dependent training data either by clustering and statistically weighting self-learned rules according to a small set of phonological rules clustered by categories or by re-weighting “seen” phonological rules. By this method we are able to automatically segment cost-effectively large corpora of mainly unprompted speech.
FPGA-based colour image classification for mobile robot navigation

This paper presents a real-time colour image classification algorithm for mobile robot navigation based on the advanced technology of field programmable gate arrays (FPGA). In order to calibrate the object colour in different lighting conditions, a kind of statistic ellipsoidal model is adopted. We build a 3-D colour look-up table (CLUT) in which only 18 bits are used to represent a kind of colour instead of conventional 24 bits. This method resolves the problem of object overlapping in a colour space, and has the merits of higher classification accuracy and lower memory cost than traditional ones. It is implemented on FPGA, which can highly reduce the CPU computation burden and remarkably improve the performance of robot vision systems. This method is validated by the applications on a mobile robot in the research.
Mathematical analysis and algorithms for efficiently and accurately implementing stochastic simulations of short-term synaptic depression and facilitation

The release of neurotransmitter vesicles after arrival of a pre-synaptic action potential (AP) at cortical synapses is known to be a stochastic process, as is the availability of vesicles for release. These processes are known to also depend on the recent history of AP arrivals, and this can be described in terms of time-varying probabilities of vesicle release. Mathematical models of such synaptic dynamics frequently are based only on the mean number of vesicles released by each pre-synaptic AP, since if it is assumed there are sufficiently many vesicle sites, then variance is small. However, it has been shown recently that variance across sites can be significant for neuron and network dynamics, and this suggests the potential importance of studying short-term plasticity using simulations that do generate trial-to-trial variability. Therefore, in this paper we study several well-known conceptual models for stochastic availability and release. We state explicitly the random variables that these models describe and propose efficient algorithms for accurately implementing stochastic simulations of these random variables in software or hardware. Our results are complemented by mathematical analysis and statement of pseudo-code algorithms.
Visual sentiment analysis for brand monitoring enhancement

Brand monitoring and reputation management are vital tasks in all modern business intelligence frameworks. However, recent related technologies rely mostly on the textual aspect of online content, in order to extract the underlying sentiment with respect to particular brands. In this work, we demonstrate the sentiment analysis method in the context of a brand monitoring framework, breaking the text-only barrier in the field. Towards this end, a wide range of visual features is extracted, some of which focus on the underlying semiotics and aesthetics of the images. In addition, we employ textual information embedded in the images under study, by adopting text mining techniques that focus on extracting sentiment. We evaluate the classification task for the particular binary task (negative vs positive sentiment) and propose a fusion approach that combines the two different modalities. Finally, the evaluation procedure has been carried out in the context of two different use cases, namely: (a) a general image sentiment classifier for brand and advertising images and (b) a brand-specific classification procedure, according to which the brand of the input images is known a-priori. Results have proven that the visual-based sentiment classification of brand and advertising information can outperform the respective text-based classification. In addition, fusing the two modalities leads to significant performance boosting.
Search and treat strategy to eliminateHelicobacter pylori associated ulcer disease

Peptic ulcer disease is a serious disorder affecting many people and carrying substantial morbidity and even mortality as a result of bleeding or perforation.1 2 The discovery of Helicobacter pylori as its main aetiological factor is a major breakthrough in gastroenterology. We can cure patients that before were suffering from a chronic recurrent ailment. After curing the infection, H pylori associated ulcers do not recur, nor do ulcer complications.3 Quality of life improves, use of medication diminishes or stops, and the number of doctor visits, hospital admissions, and absenteeism from work decreases. Curing the infection may even increase life expectancy.2 Hence treating H pylori infection in ulcer patients is associated not only with improved health but also with significant economic benefits. It was demonstrated that the use of acid suppressants decreases significantly in ulcer patients whereas it usually does not decrease in those with non-ulcer dyspepsia.4-7 Some have claimed that reflux symptoms and/or reflux oesophagitis may develop in successfully treated patients. Whether this is a true development of a new disease or merely the unmasking of an already present but occult diathesis is controversial.8 9 This possible disadvantage does not outweigh the benefits of curing the infection in this specific ulcer patient group. The major clinical discoveries regarding this infection are well known to general practitioners, internists, gastroenterologists, and microbiologists. Unfortunately, implementation of this new knowledge to the level of actually treating the right patients remains problematic. In general it is hard to change physician behaviour.10Many ulcer patients even today are still being treated with chronic or on demand acid suppressants, a therapy now considered obsolete.11 12 Vreeburg et al showed that only a minority of patients admitted with an upper gastrointestinal bleed in the Amsterdam area was tested for the presence …
Achieving AWGN Channel Capacity With Lattice Gaussian Coding

We propose a new coding scheme using only one lattice that achieves the 1/2 log(1 + SNR) capacity of the additive white Gaussian noise (AWGN) channel with lattice decoding, which is provable for signal-to-noise ratio SNR > e at present. The scheme applies a discrete Gaussian distribution over an AWGN-good lattice, but otherwise does not require a shaping lattice or dither. Thus, it significantly simplifies the default lattice coding scheme of Erez and Zamir which involves a quantization good lattice as well as an AWGN-good lattice. Using the flatness factor, we show that the error probability of the proposed scheme under minimum mean-square error lattice decoding is almost the same as that of Erez and Zamir, for any rate up to the AWGN channel capacity. We introduce the notion of good constellations, which carry almost the same mutual information as that of continuous Gaussian inputs. We also address the implementation of Gaussian shaping for the proposed lattice Gaussian coding scheme.
Efficient approximate computation of scene visibility based on navigation meshes and applications for navigation and scene analysis

Scene visibility—the information of which parts of the scene are visible from a certain location—can be used to derive various properties of a virtual environment. For example, it enables the computation of viewpoint quality to determine the informativeness of a viewpoint, helps in constructing virtual tours, and allows to keep track of the objects a user may already have seen. However, computing visibility at runtime may be too computationally expensive for many applications, while sampling the entire scene beforehand introduces a costly precomputation step and may include many samples not needed later on. Therefore, in this paper, we propose a novel approach to precompute visibility information based on navigation meshes, a polygonal representation of a scene's navigable areas. We show that with only limited precomputation, high accuracy can be achieved in these areas. Furthermore, we demonstrate the usefulness of the approach by means of several applications, including viewpoint quality computation, landmark and room detection, and exploration assistance. In addition, we present a travel interface based on common visibility that we found to result in less cybersickness in a user study.