Miracle-dz/UniFAR-FTUs huggingface dataset:
Polymorphisms in DNA-Repair Genes in a Cohort of Prostate Cancer Patients from Different Areas in Spain: Heterogeneity between Populations as a Confounding Factor in Association Studies

Background Differences in the distribution of genotypes between individuals of the same ethnicity are an important confounder factor commonly undervalued in typical association studies conducted in radiogenomics. Objective To evaluate the genotypic distribution of SNPs in a wide set of Spanish prostate cancer patients for determine the homogeneity of the population and to disclose potential bias. Design, Setting, and Participants A total of 601 prostate cancer patients from Andalusia, Basque Country, Canary and Catalonia were genotyped for 10 SNPs located in 6 different genes associated to DNA repair: XRCC1 (rs25487, rs25489, rs1799782), ERCC2 (rs13181), ERCC1 (rs11615), LIG4 (rs1805388, rs1805386), ATM (rs17503908, rs1800057) and P53 (rs1042522). The SNP genotyping was made in a Biotrove OpenArray® NT Cycler. Outcome Measurements and Statistical Analysis Comparisons of genotypic and allelic frequencies among populations, as well as haplotype analyses were determined using the web-based environment SNPator. Principal component analysis was made using the SnpMatrix and XSnpMatrix classes and methods implemented as an R package. Non-supervised hierarchical cluster of SNP was made using MultiExperiment Viewer. Results and Limitations We observed that genotype distribution of 4 out 10 SNPs was statistically different among the studied populations, showing the greatest differences between Andalusia and Catalonia. These observations were confirmed in cluster analysis, principal component analysis and in the differential distribution of haplotypes among the populations. Because tumor characteristics have not been taken into account, it is possible that some polymorphisms may influence tumor characteristics in the same way that it may pose a risk factor for other disease characteristics. Conclusion Differences in distribution of genotypes within different populations of the same ethnicity could be an important confounding factor responsible for the lack of validation of SNPs associated with radiation-induced toxicity, especially when extensive meta-analysis with subjects from different countries are carried out.
A new look at software plagiarism investigation and copyright infringement

In the context of detecting possible copyright infringement, finding a correlation between the source code files for two different programs does not necessarily mean that illicit behavior occurred. Ideas, concepts, facts, processes and methods are not in themselves protected by copyright law. Therefore, not everything in software is subject to copyright or patent protection. It is possible to write software that is not protected by either. Expert opinion may be needed in court in order to determine whether an intellectual property infringement has occurred or not. This may involve a comparison of the source code. This basic legal retirement is explicitly the core of the case. The main contribution of this paper is the suggestion of a framework for understanding of the legal consequences of similarity in software code. Because comparing two works of software is not as simple as a line-by-line comparison.
Energy efficient transmission strategies for distributed detection in wireless sensor networks

In this paper, we propose two energy efficient ordered transmission schemes with fusion rules for distributed detection in wireless sensor networks (WSNs). The fusion rule at the fusion center (FC) has been derived to take into account not only the reliability of local decision at sensors, but also channel gains from sensors to the FC. In the first scheme, the sensors' transmission order is based on only local log-likelihood ratio (LLR), while it is based on both the reliability of local decision and quality of transmission channels in the second scheme. Due to the ordered transmission, the target detection performance can be achieved with a small number of sensors, which leads to energy efficient distributed detection in WSNs. At the high signal-to-noise ratio (SNR) regime, the performances of both strategies converge to the best achievable one in the case that the sensors perfectly know the received signals at the FC.
Semi-supervised Learning of Domain-Specific Language Models from General Domain Data

We present a semi-supervised learning method for building domain-specific language models (LM) from general-domain data. This method is aimed to use small amount of domain-specific data as seeds to tap domain-specific resources residing in larger amount of general-domain data with the help of topic modeling technologies. The proposed algorithm first performs topic decomposition (TD) on the combined dataset of domain-specific and general-domain data using probabilistic latent semantic analysis (PLSA). Then it derives domain-specific word n-gram counts with mixture modeling scheme of PLSA. Finally, it uses traditional n-gram modeling approach to construct domain-specific LMs from the domain-specific word n-gram counts. Experimental results show that this approach can outperform both stat-of-the-art methods and the simulated supervised learning method with our data sets. In particular, the semi-supervised learning method can achieve better performance even with very small amount of domain-specific data.
Peer Effects in Social Networks: Search, Matching Markets, and Epidemics

Social network analysis emerged as an important area in sociology in the early 1930s, marking a shift from looking at individual attribute data to examining the relationships between people and groups. Surveying many different types of real-world networks, researchers quickly found that different types of social networks tend to share a common set of structural characteristics, including small diameter, high clustering, and heavy-tailed degree distributions. Moving beyond real networks, in the 1990s researchers began to propose random network models to explain these commonly observed social network structures. These models laid the foundation for investigation into problems where the underlying network plays a key role, from the spread of information and disease, to the design of distributed communication and search algorithms, to mechanism design and public policy. Here we focus on the role of peer effects in social networks. Through this lens, we develop a mathematically tractable random network model incorporating searchability, propose a novel way to model and analyze two-sided matching markets with externalities, model and calculate the cost of an epidemic spreading on a complex network, and examine the impact of conforming and non-conforming peer effects in vaccination decisions on public health policy. Throughout this work, the goal is to bring together knowledge and techniques from diverse fields like sociology, engineering, and economics, exploiting our understanding of social network structure and generative models to understand deeper problems that — without this knowledge — could be intractable. Instead of crippling our analysis, social network characteristics allow us to reach deeper insights about the interaction between a particular problem and the network underlying it.
Stimulation of GABA-Induced Ca2+ Influx Enhances Maturation of Human Induced Pluripotent Stem Cell-Derived Neurons

Optimal use of patient-derived, induced pluripotent stem cells for modeling neuronal diseases is crucially dependent upon the proper physiological maturation of derived neurons. As a strategy to develop defined differentiation protocols that optimize electrophysiological function, we investigated the role of Ca2+ channel regulation by astrocyte conditioned medium in neuronal maturation, using whole-cell patch clamp and Ca2+ imaging. Standard control medium supported basic differentiation of induced pluripotent stem cell-derived neurons, as assayed by the ability to fire simple, single, induced action potentials. In contrast, treatment with astrocyte conditioned medium elicited complex and spontaneous neuronal activity, often with rhythmic and biphasic characteristics. Such augmented spontaneous activity correlated with astrocyte conditioned medium-evoked hyperpolarization and was dependent upon regulated function of L-, N- and R-type Ca2+ channels. The requirement for astrocyte conditioned medium could be substituted by simply supplementing control differentiation medium with high Ca2+ or γ-amino butyric acid (GABA). Importantly, even in the absence of GABA signalling, opening Ca2+ channels directly using Bay K8644 was able to hyperpolarise neurons and enhance excitability, producing fully functional neurons. These data provide mechanistic insight into how secreted astrocyte factors control differentiation and, importantly, suggest that pharmacological modulation of Ca2+ channel function leads to the development of a defined protocol for improved maturation of induced pluripotent stem cell-derived neurons.
Extracting discriminative information from cohort models

Cohort models are non-match models available in a biometric system. They could be other enrolled models in the gallery of the system. Cohort models have been widely used in biometric systems. A well-established scheme such as T-norm exploits cohort models to predict the statistical parameters of non-match scores for biometric authentication. They have also been used to predict failure or recognition performance of biometric system. In this paper we show that cohort models that are sorted by their similarity to the claimed target model, can produce a discriminative score pattern. We also show that polynomial regression can be used to extract discriminative parameters from these patterns. These parameters can be combined with the raw score to improve the recognition performance of an authentication system. The experimental results obtained for the face and fingerprint modalities of the Biosecure database validate this claim.
Neural Combination of ANN and HMM for Handwritten Devanagari Numeral Recognition

In this article, a two-stage classification system for recognition of handwritten Devanagari numerals is presented. A shape feature vector computed from certain directional-view-based strokes of an input character image, has been used by both the HMM and ANN classifiers of the present recognition system. The two sets of posterior probabilities obtained from the outputs of the above two classifiers are combined by using another ANN classifier. Finally, the numeral image is classified according to the maximum score provided by the ANN of the second stage. In the proposed scheme, we achieved 92.83% recognition accuracy on the test set of a recently developed large image database[1] of handwritten isolated numerals of Devanagari, the first and third most popular language and script in India and the world respectively. This recognition result improves the previously reported[2] accuracy of 91.28% on the same data set.
Decreased fatty acid esterification compensates for the reduced lipolytic activity in hormone-sensitive lipase-deficient white adipose tissue Published, JLR Papers in Press, August 16, 2003. DOI 10.1194/jlr.M300190-JLR200

It has been observed previously that hormone-sensitive lipase-deficient (HSL-ko) mice have reduced white adipose tissue (WAT) stores compared to control mice. These findings contradict the expectation that the decreased lipolytic activity in WAT of HSL-ko mice would cause accumulation of triglycerides (TGs) in that tissue. Here we demonstrate that the cellular TG synthesis in HSL-deficient WAT is markedly reduced due to downregulation of the enzymatic activities of glycerophosphate acyltransferase, dihydroxyacetonphosphate acyltransferase, lysophosphatidate acyltransferase, and diacylglycerol acyltransferase. Fatty acid de novo synthesis is also decreased due to reduced cellular glucose uptake, reduced glucose incorporation into adipose tissue lipids, and reduced activities of acetyl:CoA carboxylase and fatty acid synthase. Finally, the activities of phosphoenolpyruvate carboxykinase (PEPCK), acyl:CoA synthetase (ACS), and glucose 6-phosphate dehydrogenase, the enzymes that provide glycerol-3-phosphate, acyl-CoA, and NADPH for TG synthesis, respectively, are decreased in HSL-ko mice. The reduced expression of the peroxisome proliferator-activated receptor γ (PPARγ) target genes PEPCK, ACS, and aP2, as well as reduced mRNA levels of PPARγ itself, suggest the involvement of this transcription factor in the downregulation of lipogenesis. Taken together, these results establish that in the absence of HSL, the reduced NEFA production is counteracted by a drastic reduction of NEFA reesterification that provides sufficient quantities of NEFA for release into the circulation. These metabolic adaptations result in decreased fat mass in HSL-ko mice.
Numeral Gesture Recognition Using Leap Motion Sensor

3-Dimensional gesture tracking and recognition systems are changing traditional methods of human computer interaction with gesture interaction. These systems offer advantage of tracking and recognizing unencumbered gestures which are drawn in free air. In this paper, we present numeral gesture recognition using Leap Motion. Leap motion is a 3D motion sensor which provides unencumbered, 3-dimensional gestures drawn in free air. Researchers has done ton of work related to numeral gestures recognition with the help of Microsoft Kinect 3D sensor, Prima Sense 3D motion sensor and other similar devices. In place of traditional gesture capturing devices, we used Leap Motion to capture free air numeral gestures and applied Geometric Template Matching method to classify gesture numerals ranging from 0-9. The experimental results shows that average classification rate of 70.2% is achieved by Geometric Template Matching.
Probabilistic foot contact estimation by fusing information from dynamics and differential/forward kinematics

Legged robots require a robust and fast responding feet contact detection strategy. Common force sensors are often too heavy and can be easily damaged during impacts with the terrain. Therefore, it is desirable to detect a contact without a force sensor. This paper introduces a probabilistic contact detection strategy which considers full dynamics and differential/forward kinematics to maximize the use of available information for contact estimation. This papers shows that such strategy is much more accurate than the state-of-the-art strategy that only take one measure into account, with a quadrupedal robot.
Some contributions to the metatheory of the situation calculus

We focus on a rich axiomatization for actions in the situation calculus that includes, among other features, a solution to the frame problem for deterministic actions. Our work is foundational in nature, directed at simplifying the entailment problem for these axioms. Specifically, we make four contributions to the metatheory of situation calculus axiomatizations of dynamical systems: (1) We prove that the above-mentioned axiomatization for actions has a relative satisfiability property; the full axiomatization is satisfiable iff the axioms for the initial state are. (2)We define the concept of regression relative to these axioms, and prove a soundness and completeness theorem for a regression-based approach to the entailment problem for a wide class of queries. (3) Our formalization of the situation calculus requires certain foundational axioms specifying the domain of situations. These include an induction axiom, whose presence complicates human and automated reasoning in the situation calculus. We characterize various classes of sentences whose proofs do not require induction, and in some cases, some of the other foundational axioms. (4)We prove that the logic programming language GOLOG never requires any of the foundational axioms for the evaluation of programs.
Combining multi-view stereo and super resolution in a unified framework

In multi-view stereo setting, pixel correspondence problem and super resolution problem are inter-related in a sense that the result of each problem could help to solve the other. In this paper, we propose a novel method to solve two problems together by optimizing a unified energy functional. Main difference from the previous works is that the consistency between high resolution images is considered along with consideration to the consistency of high-resolution and low-resolution image pair with the same viewpoint. Experimental results show that our method outperforms the naive combination of single image super resolution and multi-view stereo method.
Evidential Reasoning for Object Recognition

The authors present a framework to guide development of evidential reasoning in object recognition systems. Principles of evidential reasoning processes for open-world object recognition are proposed and applied to build evidential reasoning capabilities. The principles summarize research and findings by the authors up through the mid-1990s, including seminal results in object-centered computer vision, figure-ground discrimination, and the application of hierarchical Bayesian inference, Bayesian networks, and decision graphs to evidential reasoning for object recognition.
Face recognition using different surveillance cameras

Face recognition using images taken by surveillance cameras of low resolution is an important and challenging research topic. In this paper we propose a face recognition system based on a recently published database called SCface comprised of images taken under different surveillance conditions. More specifically, the recognition is done using different cameras of different resolutions and imaging sensors. A combination of image filtering, segmentation, frequency domain feature extraction and linear classification is used in the proposed work. The proposed system is compared with the well-known eigenfaces recognition solution. Experimental results show that the proposed system yields superior recognition rates compared to those obtained by the standard eigenfaces solution.
Functional Response (FR) and Relative Growth Rate (RGR) Do Not Show the Known Invasiveness of Lemna minuta (Kunth)

Growing travel and trade threatens biodiversity as it increases the rate of biological invasions globally, either by accidental or intentional introduction. Therefore, avoiding these impacts by forecasting invasions and impeding further spread is of utmost importance. In this study, three forecasting approaches were tested and combined to predict the invasive behaviour of the alien macrophyte Lemna minuta in comparison with the native Lemna minor: the functional response (FR) and relative growth rate (RGR), supplemented with a combined biomass-based nutrient removal (BBNR). Based on the idea that widespread invasive species are more successful competitors than local, native species, a higher FR and RGR were expected for the invasive compared to the native species. Five different nutrient concentrations were tested, ranging from low (4 mgN.L-1 and 1 mgP.L-1) to high (70 mgN.L-1 and 21 mgP.L-1). After four days, a significant amount of nutrients was removed by both Lemna spp. , though significant differences among L. minor and L. minuta were only observed at lower nutrient concentrations (lower than 17 mgN.L-1 and 6 mgP.L-1) with higher nutrient removal exerted by L. minor. The derived FR did not show a clear dominance of the invasive L. minuta, contradicting field observations. Similarly, the RGR ranged from 0.4 to 0.6 d-1, but did not show a biomass-based dominance of L. minuta (0.5 ± 0.1 d-1 versus 0.63 ± 0.09 d-1 for L. minor). BBNR showed similar results as the FR. Contrary to our expectations, all three approaches resulted in higher values for L. minor. Consequently, based on our results FR is sensitive to differences, though contradicted the expectations, while RGR and BBNR do not provide sufficient power to differentiate between a native and an invasive alien macrophyte and should be supplemented with additional ecosystem-based experiments to determine the invasion impact.
Design considerations for pipelined continuous-time incremental Sigma-Delta ADCs

This paper addresses design considerations for power-efficient pipelined continuous-time (CT) incremental Sigma-Delta (IΣΔ) ADC architectures. By pipelining identical CT IΣΔ ADC stages, the proposed architecture provides the design freedom coming from both the pipeline ADC and the IΣΔ ADC. In searching for a low-power solution given a target resolution, different configurations are examined analytically and simulated using behavioral models. For further power reduction, power-efficient circuits are proposed to implement the active blocks in each configuration. Based on the architecture-level analysis, a configuration that leads to minimum power-area consumption is chosen and implemented as a test-case using the proposed circuit blocks. Post-layout simulations show that the test-case ADC, with 3.2-kHz bandwidth, achieves a peak SNDR of 82.5-dB while dissipating a total power of 18.27-μW.
Compression of Web and Social Graphs supporting Neighbor and Community Queries

Motivated by the needs of mining and advanced analysis of large Web graphs and social networks, we study graph patterns that simultaneously provide compression and query opportunities, so that the compressed representation provides eﬃcient support for search and mining queries. We ﬁrst analyze patterns used for Web graph compression while supporting neighbor queries. Our results show that composing edge-reducing patterns with other methods achieves new space/time tradeoﬀs, in particular breaking the smallest known space barrier for Web graphs when supporting neighbor queries. Second, we propose a novel graph compression method based on representing communities with compact data structures. These oﬀer competitive support for neighbor queries, but excel especially at answering community queries. As far as we know, ours is the ﬁrst graph compression method supporting such a wide range of community queries.
Swarm Intelligence for Multiobjective Optimization of Extraction Process

Multi objective (MO) optimization is an emerging field which is increasingly being implemented in many industries globally. In this work, the MO optimization of the extraction process of bioactive compounds from the Gardenia Jasminoides Ellis fruit was solved. Three swarm-based algorithms have been applied in conjunction with normal-boundary intersection (NBI) method to solve this MO problem. The gravitational search algorithm (GSA) and the particle swarm optimization (PSO) technique were implemented in this work. In addition, a novel Hopfield-enhanced particle swarm optimization was developed and applied to the extraction problem. By measuring the levels of dominance, the optimality of the approximate Pareto frontiers produced by all the algorithms were gauged and compared. Besides, by measuring the levels of convergence of the frontier, some understanding regarding the structure of the objective space in terms of its relation to the level of frontier dominance is uncovered. Detail comparative studies were conducted on all the algorithms employed and developed in this work.