Miracle-dz/UniFAR-FTUs huggingface dataset:
Genomic analysis of regulatory regions in organelle DNA

Introduction The prediction and analysis of transcriptional regulatory regions in DNA are of central importance to understand the mechanisms governing gene expression. Computational approaches to identify promoters and regulatory sites in DNA have been mainly directed to the statistical detection of overrepresented “words” on the DNA sequence. Various algorithms and strategies based on weight matrices and Hidden Markov Models have been successfully applied to identify biologically relevant regulatory sites (1). The availability of many completely sequenced genomes allowed the identification of clusters of orthologous sequences present in different organisms. Comparative analysis of these orthologous groups offers a novel possibility for the study of candidate regulatory elements, by the localization of conserved sequence signals in the upstream regions of these genes. The presence of a common signal in several organisms would imply a similar regulatory mechanism, which is based on the premise that regulatory elements have been conserved during evolution due to functional constrains. It is known that some regulatory proteins strictly recognize a conserved sequence, but others bind to less conserved sites and in certain cases regulatory proteins are known to recognize bent DNA structures regardless of sequence conservation (2). Exhaustive knowledge on the structure of the DNA molecule has lead to the development of programs that predict the spatial structure of a given DNA sequence (i.e. DNA curvature). This technology enables us to identify the sites which may have a role in transcriptional regulation due to their conserved structural profile (similar bent sites). These regulatory elements usually couldn't be detected by cross-species orthologous gene comparison because of poor or nonexistent consensus sites. Both of these sequence-based and structure-based approaches have successfully identified biologically relevant signatures in several organisms (2,3). Complete sequences of 433 mitochondrial, and 27 chloroplast genomes are publicly available, and the transcriptional regulation of their genes is not yet fully understood. This offers the possibility of making a multi-genome analysis of their regulatory regions through the use of clusters of orthologous proteins. Using this data set and implementing sequence-based and structure-based approaches, we performed an analysis to identify three kinds of conserved signals: a) sequence based signals, b) sequence based signals that present a conserved structure and c) structure based signals without sequence conservation.
Direct and Parallel QR based subspace decomposition methods for system identification

In this paper we present two computationally efficient methods for computing the past and future input-output data for subspace system identification. The proposed methods employ Direct and Parallel QR decomposition for tall and skinny matrix where many more rows than columns are involved. Data matrix of deterministic and stochastic system has been used. The proposed methods compared to a standard QR decomposition show a significant reduction in the computational time and the complexity of the system in terms of number of operations and memory storage. The system matrices are identified by making use of Kalman filter states and Canonical Variate Algorithm (CVA). The simulation results illustrate that proposed methods require less processing time and low complexity, and provide high accuracy in identifying the system parameters compared to the standard QR decomposition.
Robust pedestrian detection and tracking from a moving vehicle

In this paper, we address the problem of multi-person detection, tracking and distance estimation in a complex scenario using multi-cameras. Specifically, we are interested in a vision system for supporting the driver in avoiding any unwanted collision with the pedestrian. We propose an approach using Histograms of Oriented Gradients (HOG) to detect pedestrians on static images and a particle filter as a robust tracking technique to follow targets from frame to frame. Because the depth map requires expensive computation, we extract depth information of targets using Direct Linear Transformation (DLT) to reconstruct 3D-coordinates of correspondent points found by running Speeded Up Robust Features (SURF) on two input images. Using the particle filter the proposed tracker can efficiently handle target occlusions in a simple background environment. However, to achieve reliable performance in complex scenarios with frequent target occlusions and complex cluttered background, results from the detection module are integrated to create feedback and recover the tracker from tracking failures due to the complexity of the environment and target appearance model variability. The proposed approach is evaluated on different data sets both in a simple background scenario and a cluttered background environment. The result shows that, by integrating detector and tracker, a reliable and stable performance is possible even if occlusion occurs frequently in highly complex environment. A vision-based collision avoidance system for an intelligent car, as a result, can be achieved.
Semi-automated 3D Leaf Reconstruction and Analysis of Trichome Patterning from Light Microscopic Images

Trichomes are leaf hairs that are formed by single cells on the leaf surface. They are known to be involved in pathogen resistance. Their patterning is considered to emerge from a field of initially equivalent cells through the action of a gene regulatory network involving trichome fate promoting and inhibiting factors. For a quantitative analysis of single and double mutants or the phenotypic variation of patterns in different ecotypes, it is imperative to statistically evaluate the pattern reliably on a large number of leaves. Here we present a method that enables the analysis of trichome patterns at early developmental leaf stages and the automatic analysis of various spatial parameters. We focus on the most challenging young leaf stages that require the analysis in three dimensions, as the leaves are typically not flat. Our software TrichEratops reconstructs 3D surface models from 2D stacks of conventional light-microscope pictures. It allows the GUI-based annotation of different stages of trichome development, which can be analyzed with respect to their spatial distribution to capture trichome patterning events. We show that 3D modeling removes biases of simpler 2D models and that novel trichome patterning features increase the sensitivity for inter-accession comparisons.
Dimensionality reduction by rank preservation

Dimensionality reduction techniques aim at representing high-dimensional data in low-dimensional spaces. To be faithful and reliable, the representation is usually required to preserve proximity relationships. In practice, methods like multidimensional scaling try to fulfill this requirement by preserving pairwise distances in the low-dimensional representation. However, such a simplification does not easily allow for local scalings in the representation. It also makes these methods suboptimal with respect to recent quality criteria that are based on distance rankings. This paper addresses this issue by introducing a dimensionality reduction method that works with ranks. Appropriate hypotheses enable the minimization of a rank-based cost function. In particular, the scale indeterminacy that is inherent to ranks is circumvented by representing data on a space with a spherical topology.
An Empirical Study into the State of Practice and Challenges in IT Project Portfolio Management

Optimizing IT investments in alignment with key business drivers is a challenge for all organizations. Organizations need to prioritize and select a portfolio of IT projects that provides optimal benefit and balances utility, risk, and resources. This paper reports on an exploratory empirical study into the processes and project-selection criteria used by several large companies. The objective of this research is to identify successful practices as well as some common challenges that companies have in performing IT portfolio management. These empirical findings provide insight into the current state of practice and common challenges. These form the basis for guidelines for implementing and improving IT portfolio processes.
Fast, Flexible Filtering with Phlat — Personal Search and Organization Made Easy

Systems for fast search of personal information are rapidly becoming ubiquitous. Such systems promise to dramatically improve personal information management, yet most are modeled on Web search in which users know very little about the content that they are searching. We describe the design and deployment of a system called Phlat that optimizes search for personal information with an intuitive interface that merges search and browsing through a variety of associative and contextual cues. In addition, Phlat supports a unified tagging (labeling) scheme for organizing personal content across storage systems (files, email, etc.). The system has been deployed to hundreds of employees within our organization. We report on both quantitative and qualitative aspects of system use. Phlat is available as a free download at http://research.microsoft. com/adapt/phlat. Author Keywords Personal information management, user interfaces, interactive information retrieval, tagging.
International Journal of Advanced Robotic Systems An Architecture to Develop Multimodal Educative Applications with Chatbots Regular Paper

Animated characters are beginning to be used as pedagogical tools, as they have the power to capture students’ attention and foster their motivation for discovery and learning. However, in order for them to be widely employed and accepted as a learning resource, they must be easy to use and friendly. In this paper we present an architecture that facilitates building interactive pedagogical chatbots that can interact with students in natural language. Our proposal provides a modular and scalable framework to develop such systems efficiently. Additionally, we present Geranium, a system that helps children to appreciate and protect their environment with an interactive chatbot developed following our scheme.
Learning non-visual graphical information using a touch-based vibro-audio interface

This paper evaluates an inexpensive and intuitive approach for providing non-visual access to graphic material, called a vibro-audio interface. The system works by allowing users to freely explore graphical information on the touchscreen of a commercially available tablet and synchronously triggering vibration patterns and auditory information whenever an on-screen visual element is touched. Three studies were conducted that assessed legibility and comprehension of the relative relations and global structure of a bar graph (Exp 1), Pattern recognition via a letter identification task (Exp 2), and orientation discrimination of geometric shapes (Exp 3). Performance with the touch-based device was compared to the same tasks performed using standard hardcopy tactile graphics. Results showed similar error performance between modes for all measures, indicating that the vibro-audio interface is a viable multimodal solution for providing access to dynamic visual information and supporting accurate spatial learning and the development of mental representations of graphical material.
Allophones in automatic whispery speech recognition

The article presents studies on the automatic whispery speech recognition. In the performed research a new corpus with whispery speech has been used. It has been checked whether the extended set of articulatory units (allophones have been used instead of phonemes) improves quality of whispery speech recognition. Experimental results show that the small changes in the allophone set may provide better speech recognition quality than using phonemes approach. The authors also made available the trained g2p (grapheme-to-phoneme) model of Polish language for the Sequitur toolkit.
Rapunzel Syndrome Reviewed and Redefined

Rapunzel syndrome is a rare presentation of a trichobezoar, with 24 cases having been reported in literature so far. This syndrome has been reported in 23 female and 1 male patient with a mean age of 10.8 years. The patients commonly present with abdominal pain, nausea, vomiting and signs of obstruction. The distal end of the bezoar may be in the jejunum, ileum or the colon. We evaluate the various cases reported and discuss the various modes of presentation of this uncommon syndrome, and also present a series of 3 cases, all females, aged 16, 18 and 21 years, and had a trichobezoar in the stomach with a tail extending into the ileum.
Real-time coarse-to-fine topologically preserving segmentation

In this paper, we tackle the problem of unsupervised segmentation in the form of superpixels. Our main emphasis is on speed and accuracy. We build on [31] to define the problem as a boundary and topology preserving Markov random field. We propose a coarse to fine optimization technique that speeds up inference in terms of the number of updates by an order of magnitude. Our approach is shown to outperform [31] while employing a single iteration. We evaluate and compare our approach to state-of-the-art superpixel algorithms on the BSD and KITTI benchmarks. Our approach significantly outperforms the baselines in the segmentation metrics and achieves the lowest error on the stereo task.
Generalized Mathematical Model for Biological Growths

In this paper, we present a generalization of the commonly used growth models. We introduce Koya-Goshu biological growth model, as a more general solution of the rate-state ordinary differential equation. It is shown that the commonly used growth models such as Brody, Von Bertalanffy, Richards, Weibull, Monomolecular, Mitscherlich, Gompertz, Logistic, and generalized Logistic functions are its special cases. We have constructed growth and relative growth functions as solutions of the rate-state equation. The generalized growth function is the most flexible so that it can be useful in model selection problems. It is also capable of generating new useful models that have never been used so far. The function incorporates two parameters with one influencing growth pattern and the other influencing asymptotic behaviors. The relationships among these growth models are studies in details and provided in a flow chart.
Context-Based Plot Detection from Online Review Comments for Preventing Spoilers

The plot (content or storyline of a story) may disappoint users who read review comments associated with items containing a story such as comics, novels, and movies. This paper proposes a new method for identifying sentences that include descriptions of the plot of the story. Conventional methods only use information based on the words contained in a target sentence, however, our new method uses contextual information in addition to word information. We identify contextual information by using the sentence location and the plot probability of surrounding sentences. An experiment showed that this method improved the accuracy with which plot-related information can be identified.
Video stabilization with L1–L2 optimization

Digital videos often suffer from undesirable camera jitters because of unstable camera motions. In this paper we present a novel video stabilization algorithm by mixed L1-L2 optimization, aiming at removing unwanted camera movements as well as keeping the original video information to the greatest extent. In the proposed algorithm, we compute smoothed camera paths that are composed of constant, linear and parabolic segments by L1 constraints, meanwhile using the L2 norm of the difference between smoothed and original camera paths to retain the video information. Different from other existing methods, there is only one parameter to control effects of two terms, which is both flexible and easy to meet different requirements in practice. We further design an efficient moving window scheme to support online processing or unlimited length video. Experimental results demonstrate the good performance of our proposed algorithm.
Wavelet Transformations as Diversity Enhancers 1

Discrete wavelet transformations have became indispensable analytical tools in data compression and data denoising. In this paper we give some empirical accounts of wavelet transformations and propose novel thresholding and wavelet selection methods. This is achieved via connections with measures of inequality, that have been used in economics for a long time. We compare our methods with standard thresholding and wavelet selection procedures.
The binding site distance test score: a robust method for the assessment of predicted protein binding sites

MOTIVATION We propose a novel method for scoring the accuracy of protein binding site predictions-the Binding-site Distance Test (BDT) score. Recently, the Matthews Correlation Coefficient (MCC) has been used to evaluate binding site predictions, both by developers of new methods and by the assessors for the community-wide prediction experiment-CASP8. While being a rigorous scoring method, the MCC does not take into account the actual 3D location of the predicted residues from the observed binding site. Thus, an incorrectly predicted site that is nevertheless close to the observed binding site will obtain an identical score to the same number of non-binding residues predicted at random. The MCC is somewhat affected by the subjectivity of determining observed binding residues and the ambiguity of choosing distance cutoffs. By contrast the BDT method produces continuous scores ranging between 0 and 1, relating to the distance between the predicted and observed residues. Residues predicted close to the binding site will score higher than those more distant, providing a better reflection of the true accuracy of predictions. The CASP8 function predictions were evaluated using both the MCC and BDT methods and the scores were compared. The BDT was found to strongly correlate with the MCC scores while also being less susceptible to the subjectivity of defining binding residues. We therefore suggest that this new simple score is a potentially more robust method for future evaluations of protein-ligand binding site predictions. AVAILABILITY http://www.reading. ac.uk/bioinf/downloads/.
Extinguishing Lady Liberty’s Torch? Online Public Responses to the U.S. Executive Order to Ban Immigrants from 7 Countries

ABSTRACT In January 2017, a seven-country ban blocked immigrants from entering the U.S. When the story broke, many people posted comments on online news sites. Analysis revealed themes of rejection and acceptance. Rejection themes focused on security, demeaning Muslims, and exclusion. Acceptance themes called for inclusivity and providing a safe haven for immigrants. Comments aligned into partisan camps reflecting political branding of the sites. Analyses revealed both pro-ban and anti-ban emotionally charged comments. Hate speech was used to justify support of the ban on ultra-conservative sites. These analyses described the emotional response to admitting Muslim immigrants into the United States.
Association of TLR2 and TLR4 Polymorphisms with Risk of Cancer: A Meta-Analysis

Backgrounds The activation of Toll-like receptors (TLRs) may be an important event in the immune evasion of tumor cell. Recently, numerous studies have investigated the associations between TLR2 −196 to −174 del and two SNPs of TLR4 (rs4986790 and rs4986791) and the susceptibility to different types of cancer; however, the results remain conflicting. The aim of this study was to assess the association between TLR2 and TLR4 polymorphisms and cancer risk in a meta-analysis with eligible published studies. Methodology/Principle Findings A dataset composed of 14627 cases and 17438 controls from 34 publications were included in a meta-analysis to evaluate the association between overall cancer risk or cancer-specific risk and three SNPs of TLRs (TLR2 −196 to −174 del, TLR4 rs4986790 and rs4986791). The results showed that all of these three polymorphisms were significantly associated with the increased cancer risk (dominant model: OR = 1.64, 95% CI: 1.04–2.60 for TLR2 −196 to −174 del; OR = 1.19, 95% CI: 1.01–1.41 for TLR4 rs4986790; and OR = 1.47, 95% CI: 1.120–1.80 for TLR4 rs4986791; respectively). In stratified analysis, we found the effect of TLR2 −196 to −174 del on cancer risk remained significant in the subgroup of Caucasians and South Asians, but not in East Asians. However, the association between rs4986791 and cancer risk was significant in both South Asians and East Asians, but not in Caucasians. Furthermore, the association between rs4986790 and cancer risk was statistically significant in digestive cancers (dominant model: OR = 1.76, 95% CI: 1.13–2.73) and female-specific cancers (dominant model: OR = 1.50, 95% CI: 1.16–1.94). However, no significant association with risk of digestive system cancers was observed for TLR2 −196 to −174 del and TLR4 rs4986791. Conclusions/Significance This meta-analysis presented additional evidence for the association between TLR2 and TLR4 polymorphisms and cancer risk. Further well-designed investigations with large sample sizes are required to confirm this conclusion.
A new Feature Selection Algorithm using Modified PSO for an Anaerobic Wastewater Treatment System

Wastewater treatment is necessary to preserve the environment and living organisms. COD (Chemical Oxygen Demand) measures the amount of oxygen consumed by the water in the decomposition of organic matter and oxidation of inorganic matter and chemicals. Predicting effluents in the water is a time consuming process. The proposed method uses anaerobic treatment process to utilize anaerobic bacteria (biomass) to convert organic pollutants or COD into biogas in an oxygen free environment. Upflow Anaerobic Filter(UAF) is used as an anaerobic reactor and Cheese whey is used as a wastewater in the UAF. Artificial Neural Networks (ANN) are used as an estimation tool for predicting the performance of filtering treatment process in wastewater treatment plant. Hence the proposed method uses an cost effective techniques such as Dynamic Score Normalization technique with Mahalanobis distance(DSN-M) as a preprocessing step , Modified Particle Swarm Optimization for feature selection process and Adaptive Neuro-Fuzzy Inference System (ANFIS) as a learning algorithm for prediction. The experiment was carried out by comparing dataset without normalization, without feature selection and with ANFIS(Method 1) and dataset with the proposed normalization(DSN-M), feature selection(Modified PSO) and ANFIS (Method 2). The results proved that the proposed method achieves better performance in predicting the COD effluents present in wastewater.
From first order logic to Nd: a data driven reformulation

First order logic (FOL) o(cid:11)ers a natural way of modeling domains such as chemistry: a molecule is most adequately described as a graph of atoms linked by simple or double bonds. To overcome the speci(cid:12)c di(cid:14)culties of dealing with FOL, this paper presents an automatic mapping from the initial problem domain onto the set of integer vectors IN d , where d is a user-supplied integer. This mapping onto a metric space induces a (semi)-distance on the problem domain. Within supervised learning, the quality of the reformulation can thus be estimated from the predictive accuracy of a k -nearest neighbor classi(cid:12)er based on this distance. The approach is validated on a real-world problem pertaining to organic chemistry: toxicology prediction.