Miracle-dz/UniFAR-FTUs huggingface dataset:
DEVELOPING A NANOPORE SEQUENCING DATA PROCESSING PIPELINE FOR STRUCTURAL VARIATION IDENTIFICATION

Many genomic sequencing technologies have been developed since the Human Genome Project. These next-generation sequencing (NGS) technologies from various companies reshaped the genomics field and have improved rapidly. However, NGS has limitations for certain applications due to its short read length. The third generation of sequencing technology uses single molecule real-time sequencer that can generate long reads. Recently Oxford Nanopore entered the market with the release of its MinION sequencer. Oxford Nanopore’s unique third generation sequencing technology allows for much longer read length than NGS technologies, potentially addressing some of the limitations of NGS. Due to the novelty of nanopore sequencing technology, the available tools for aligning long read data and detecting structural variants have not been thoroughly evaluated. Here we evaluate the performance of several alignment and structural variation detection tools on long read MinION data.
ASP-Based Signal Plan Adjustments for Traffic Flow Optimization

Worldwide, many cities spend considerable effort to reduce trafﬁc and speciﬁcally to avoid trafﬁc congestions. Adaptive trafﬁc control systems serve this purpose by dynamically adjusting trafﬁc signals for optimizing the trafﬁc ﬂow on intersections. Systems such as SCOOT are based on an “intelligent” combination of different trafﬁc optimization strategies. However, they miss the possibility (i) to add and change on-demand rules to implement new optimization strategies, and (ii) to simulate the outcome of new strategies on-the-ﬂy which is similar to the capabilities of microscopic trafﬁc simulation tools such as SUMO. In order to overcome the above limitations, we present a novel approach for calculating signal phase plans (SPPs) used for optimizations in trafﬁc control systems. Our approach is based on Answer Set Programming (ASP) and combines ASP encodings of an abstract mesoscopic ﬂow model and a strategy for generating possible SPPs. Experimental results shows that trafﬁc simulation can be well approximated and that the generated SPPs improve the trafﬁc ﬂow effectively.
A Persuasive Chatbot Using a Crowd-Sourced Argument Graph and Concerns

Chatbots are versatile tools that have the potential of being used for computational persuasion where the chatbot acts as the persuader and the human agent as the persuadee. To allow the user to type his or her arguments, as opposed to selecting them from a menu, the chatbot needs a sufﬁciently large knowledge base of arguments and counterarguments. And in order to make the user change their current stance on a subject, the chatbot needs a method to select persuasive counterarguments. To address this, we present a chatbot that is equipped with an argument graph and the ability to identify the concerns of the user argument in order to select appropriate counterarguments. We evaluate the bot in a study with participants and show how using our method can make the chatbot more persuasive.
Combination of Active Learning and Self-Paced Learning for Deep Answer Selection with Bayesian Neural Network

Answer Selection is an important subtask of Question Answering tasks. For this learning-to-rank problem, deep learning methods have outperformed traditional methods. To train a high-quality deep answer selection model, it often requires large amounts of labeled data, which is a costly and noise-prone process. Active learning and semi-supervised learning are usually applied in the modelling training procedure to achieve optimal accuracy with fewer labeled training samples. However, traditional active learning meth-ods rely on good uncertainty estimates that are hard to obtain with standard neural networks. And the performance of semi-supervised learning methods are always affected adversely by the quality of the pseudo-labeled data. In this work, we propose a new framework integrating active learning and self-paced learning in training deep answer selection models. This framework proposes an uncertainty quantiﬁcation method based on Bayesian neural network, which can guide active learning and self-paced learning in the same iterative process of model training. Experiments were conducted on two kinds of deep answer selection models with real-world datasets including YahooCQA and SemiEvalCQA. The results reveal that the proposed method can signiﬁcantly reduce the labeled samples for model training.
Gradual Semantics for Logic-Based Bipolar Graphs Using T-(Co)norms

In this paper we consider a bipolar graph structure en-coding conﬂicting knowledge with logic formulas. Gradual semantics provide a way to assign strength values in the unit interval to nodes (i.e. logical inference steps) in the bipolar graph. Here, we introduce a new class of semantics based on the notions of T-norms and T-conorms and show that they handle circular reasoning and satisfy desirable properties such as provability and rewriting.
Fine-Grained Text Sentiment Transfer via Dependency Parsing

Fine-grained sentiment transfer demands to edit an input sentence on a given sentiment intensity while preserving its content, which largely extends traditional binary sentiment transfer. Previous works on sentiment transfer usually attempt to learn latent content representation disentangled from sentiment. However, it is difﬁcult to completely separate these two factors and it is also not necessary. In this paper, we propose a novel model that learns the latent representation without disentanglement and leverages sentiment intensity as input to decoder for ﬁne-grained sentiment control. Moreover, aligned sentences with the same content but different sentiment intensities are usually unavailable. Due to the lack of parallel data, we construct pseudo-parallel sentences (i.e, sentences with similar content but different intensities) to relieve the burden of our model. In speciﬁc, motivated by the fact that the sentiment word (e.g., “deli-cious”) has a close relationship with the non-sentiment context word (e.g., “food”), we use dependency parsing to capture the dependency relationship. The pseudo-parallel sentences are produced by replacing the sentiment word with a new one according to the speciﬁc context word. Besides, the difference between pseudo-parallel sentences and generated sentences and other constraints are utilized to guide the model precisely revising sentiment. Experiments on the Yelp dataset show that our method substantially improves the degree of content preservation and sentiment accuracy and achieves state-of-the-art performance.
Phylogeny and historical biogeography of Hydrophyllaceae and Namaceae, with a special reference to Phacelia and Wigandia

This study aimed to examine the systematic position of South American species of Phacelia (Hydrophyllaceae) and Wigandia (Namaceae) and the historical biogeography of Hydrophyllaceae and Namaceae using molecular dating and ancestral area reconstruction. To this end, we constructed two datasets, one with a plastid (ndhF) and one with a nuclear marker (ITS), using previously published and newly generated sequences. We inferred the phylogeny of Hydrophyllaceae and Namaceae implementing both likelihood and Bayesian methods. We also estimated divergence times and ancestral areas for all major clades using a relaxed Bayesian uncorrelated molecular clock and the dispersal-extinction-cladogenesis (DEC) approach, respectively. The South American representatives of Phacelia are placed in three different clades of the genus and two colonizations of South America by North American species took place in the Miocene and at least one in the late Pliocene. Wigandia forms a well-supported monophylum with interspecific relationships partly unresolved. Within Namaceae a colonization of South America by North American species occurred during the Oligocene–Miocene transition. The MRCA of Wigandia was distributed in North and Central America in late Oligocene. Long-distance dispersal may have been necessary for the colonization of South America by Namaceae during the Oligocene–Miocene transition, when North and South America were not connected.
Increasing the Naturalness of an Argumentative Dialogue System Through Argument Chains

This work introduces chained arguments into a dialogue game for argu- mentation to allow a more natural and intuitive interaction with a respective system. Thus, the turn taking rules of the game are improved while still preserving the general consistency that is ensured by the framework. The improved system is used to generate artiﬁcial dialogues between two virtual agents which are assessed in a user study. The results show a signiﬁcant improvement in the perceived naturalness without violating the logical consistency.
Application of Imbalanced Data Classification Quality Metrics as Weighting Methods of the Ensemble Data Stream Classification Algorithms

In the era of a large number of tools and applications that constantly produce massive amounts of data, their processing and proper classification is becoming both increasingly hard and important. This task is hindered by changing the distribution of data over time, called the concept drift, and the emergence of a problem of disproportion between classes—such as in the detection of network attacks or fraud detection problems. In the following work, we propose methods to modify existing stream processing solutions—Accuracy Weighted Ensemble (AWE) and Accuracy Updated Ensemble (AUE), which have demonstrated their effectiveness in adapting to time-varying class distribution. The introduced changes are aimed at increasing their quality on binary classification of imbalanced data. The proposed modifications contain the inclusion of aggregate metrics, such as F1-score, G-mean and balanced accuracy score in calculation of the member classifiers weights, which affects their composition and final prediction. Moreover, the impact of data sampling on the algorithm’s effectiveness was also checked. Complex experiments were conducted to define the most promising modification type, as well as to compare proposed methods with existing solutions. Experimental evaluation shows an improvement in the quality of classification compared to the underlying algorithms and other solutions for processing imbalanced data streams.
A comparison of four commercial kits used for isolating circulating cell-free DNA: QuickGeneMINI8L (Kurabo), Maxwell RSC cfDNA Plasma Kit (Promega), cfKapture 21 Kit (MagBio), and QIAamp MinElute ccfDNA Kit (Qiagen)

A minimally-invasive alternative to surgical biopsies is a liquid biopsy (LB), a technique that has recently revolutionized the management of a number of tumors. One potential target biomarker of LB is cell-free DNA (cfDNA), which can act as a very sensitive indicator for certain tumors. Currently, clinical efforts are focused on increasing the quality of the cfDNA isolated for analysis. The present study compares the efficiency of isolation by four commercial kits: QuickGeneMINI8L (Kurabo), Maxwell RSC cfDNA Plasma Kit (Promega), cfKapture 21 Kit (MagBio), and QIAamp MinElute ccfDNA Kit (Qiagen). In each case, cfDNA was isolated from three plasma samples and one serum sample. Available method for the isolation give the ability to enrich optimal diagnostic quantity of cfDNA. cfDNA can be successfully separated using all investigated kits. The greatest efficiency was demonstrated by the QIAamp MinElute ccfDNA Kit (Qiagen) and cfKapture 21 (MagBio). Large amounts of cell-free DNA can be successfully isolated from small volumes of plasma.
Realizing Simion’s type B associahedron as a pulling triangulation of the Legendre polytope

We show that Simion’s type B associahedron is combinatorially equivalent to a pulling triangulation of a type B root polytope called the Legendre polytope. Furthermore, we show that every pulling triangulation of the Legendre polytope yields a flag complex. Our triangulation refines a decomposition of the Legendre polytope given by Cho. We extend Cho’s cyclic group action to the triangulation in such a way that it corresponds to rotating centrally symmetric triangulations of a regular (2n + 2)gon. Résumé. Nous montrons que l’associaèdre du type B de Simion est combinatoriellement èquivalent à une triangulation obtenue en tirant les sommets d’un polytope des racines du type B, appelé le polytope de Legendre. De plus, nous montrons que toute triangulation obtenue en tirant les sommets de ce polytope est un complex de drapeau. Notre triangulation raffine une décomposition du polytope de Legendre donné par Cho. Nous éteignons l’action du groupe cyclique de Cho à la triangulation tel qu’elle correspond à la rotation des triangulations centrallement symétriques d’un (2n + 2)-gone régulier.
The Last Voting Rule Is Home: Complexity of Control by Partition of Candidates or Voters in Maximin Elections

One of the key topics of computational social choice is electoral control, which models certain ways of how an election chair can seek to inﬂuence the outcome of elections via structural changes such as adding, deleting, or partitioning either candidates or voters. Faliszewski and Rothe [13] have surveyed the rich literature on control, giving an overview of previous results on the complexity of the associated problems for the most important voting rules. Among those, only a few results were known for two quite prominent voting rules: Borda Count and maximin voting (a.k.a. the Simpson– Kramer rule). Neveling and Rothe [26, 25] recently settled the remaining open cases for Borda. In this paper, we solve all remaining open cases for the complexity of control in maximin elections all of which concern control by partition of either candidates or voters.
Effects of Genetic Background on Expression of QTL for Nitrogen Efficiency in Irrigated Rice and Upland Rice

【Objective】 Elite alleles of nitrogen efficiency derived from different irrigated rice and upland rice were detected, and the effects of genetic background on expression of quantitative trait locus (QTL) were analyzed for providing a theoretical basis for fine mapping and molecular marker-assisted breeding of nitrogen efficiency in rice. 【Method】 QTL of some traits associated with nitrogen utilization efficiency were identified using two japonica × japonica recombinant inbred lines (RILs). The first RILs was from the cross IAPAR-9 (upland rice)/Liaoyan241 (irrigated rice), and the second RILs was from the cross IAPAR-9/Akihikari (irrigated rice). 【Result】 A total of 31 QTLs referring some traits correlated with nitrogen utilization efficiency in the first RILs were detected on all chromosomes except chromosomes 6, 7 and 10. QTLs controlling different traits were mapped on the same or adjacent intervals, forming 9 clusters in rice chromosomes. In the second RILs, 33 QTLs for some traits associated with nitrogen utilization efficiency were found on all chromosomes except chromosomes 4 and 10, forming 7 clusters in rice chromosomes. 【Conclusion】 Genetic background had a significant impact on QTL expression of some traits associated with nitrogen utilization efficiency. The interval, RM3421-RM5404, in chromosome 2 and the adjacent interval with RM8264 in chromosome 8, where QTLs underlying traits associated with nitrogen utilization efficiency stably expressed across different genetic backgrounds, may be beneficial to MAS for nitrogen efficiency in rice.
The ‘Reading the mind in the Eyes' test and emotional intelligence

The ‘Reading the Mind in the Eyes' test (Eyes Test) has been widely used to measure theory of mind (ToM) or the ability to recognize the thoughts and feelings of others. Although previous studies have analysed its relationship with the ability to perceive emotions, the potential links with more complex emotional abilities remain unclear. The aim of the present research was to analyse the relationship between the Eyes Test and each of the emotional intelligence (EI) branches: perceiving, facilitating, understanding and managing emotions. In addition, we were interested in studying these relationships as a function of the Eyes Test difficulty. Eight hundred and seventy-four participants completed the Eyes Test and the Mayer–Salovey–Caruso Emotional Intelligence Test. A stepwise multiple regression analysis for the total score on the Eyes Test revealed that the best fitting model included the understanding, perceiving and managing emotion branches, with the understanding branch being the one most strongly associated with performance on the Eyes Test. Interestingly, stepwise multiple regression analysis for the easiest items of the Eyes Test revealed the same predictors, but, in the case of the most difficult items only the understanding branch was a predictor. These outcomes were not moderated by the influence of gender. Our findings support the notion that the Eyes Test can be used as a ToM task and that it is associated with complex EI abilities. Limitations and future lines of investigation are discussed.
Variable Selection Using Nonlocal Priors in High-Dimensional Generalized Linear Models With Application to fMRI Data Analysis

High-dimensional variable selection is an important research topic in modern statistics. While methods using nonlocal priors have been thoroughly studied for variable selection in linear regression, the crucial high-dimensional model selection properties for nonlocal priors in generalized linear models have not been investigated. In this paper, we consider a hierarchical generalized linear regression model with the product moment nonlocal prior over coefficients and examine its properties. Under standard regularity assumptions, we establish strong model selection consistency in a high-dimensional setting, where the number of covariates is allowed to increase at a sub-exponential rate with the sample size. The Laplace approximation is implemented for computing the posterior probabilities and the shotgun stochastic search procedure is suggested for exploring the posterior space. The proposed method is validated through simulation studies and illustrated by a real data example on functional activity analysis in fMRI study for predicting Parkinson’s disease.
Optic Disc Segmentation Using Attention-Based U-Net and the Improved Cross-Entropy Convolutional Neural Network

Medical image segmentation is an important part of medical image analysis. With the rapid development of convolutional neural networks in image processing, deep learning methods have achieved great success in the field of medical image processing. Deep learning is also used in the field of auxiliary diagnosis of glaucoma, and the effective segmentation of the optic disc area plays an important assistant role in the diagnosis of doctors in the clinical diagnosis of glaucoma. Previously, many U-Net-based optic disc segmentation methods have been proposed. However, the channel dependence of different levels of features is ignored. The performance of fundus image segmentation in small areas is not satisfactory. In this paper, we propose a new aggregation channel attention network to make full use of the influence of context information on semantic segmentation. Different from the existing attention mechanism, we exploit channel dependencies and integrate information of different scales into the attention mechanism. At the same time, we improved the basic classification framework based on cross entropy, combined the dice coefficient and cross entropy, and balanced the contribution of dice coefficients and cross entropy loss to the segmentation task, which enhanced the performance of the network in small area segmentation. The network retains more image features, restores the significant features more accurately, and further improves the segmentation performance of medical images. We apply it to the fundus optic disc segmentation task. We demonstrate the segmentation performance of the model on the Messidor dataset and the RIM-ONE dataset, and evaluate the proposed architecture. Experimental results show that our network architecture improves the prediction performance of the base architectures under different datasets while maintaining the computational efficiency. The results render that the proposed technologies improve the segmentation with 0.0469 overlapping error on Messidor.
Simplifying Graph Attention Networks with Source-Target Separation

We present a novel Graph Neural Networks (GNN) architecture as an simpliﬁcation of Graph Attentional Network (GAT) model with implicit computation of edge attention coefﬁcients and shared sparse-dense matrix multiplication between heads. These improvements reduce training time and memory consumption while keeping the model capacity of GAT. On several established benchmarks, our model has a performance on par with state-of-the-art, yet with improved efﬁciency and scalability similar to simpler models including Graph Convolutional Network (GCN). Notably, we are able to apply the model to the large-scale Reddit social network dataset within a reasonable training time and memory constraint, which is previously infeasible for models with similar complexity including GAT.
A Lightweight Recurrent Attention Network for Real-Time Guidewire Segmentation and Tracking in Interventional X-Ray Fluoroscopy

In endovascular surgery and cardiology, interventional therapy is currently the treatment of choice for most patients. Ro-bust guidewire detection in 2D X-ray ﬂuoroscopy can greatly assist physicians in interventional therapy. Nevertheless, this task often comes with the challenge of the extreme foreground-background class imbalance caused by the slenderer guidewire structure compared to other interventional tools. To address this challenge, a novel efﬁcient network architecture, termed Fast Recurrent Attention Network (FRA-Net), is proposed for fully automatic mono-guidewire and dual-guidewire segmentation and tracking. The main contributions of the proposed network are threefold: 1) We propose a novel attention module that improves model sensitivity to guidewire pixels without requiring complicated heuristics. 2) We design a recurrent convolutional layer that ensures better feature representation. 3) Focal Loss is reinforced to better address the problems of extreme class imbalance and misclassiﬁed examples. Quantitative and qualitative evaluation on various datasets demonstrates that the proposed network signiﬁcantly outperforms simpler baselines as well as the best previously-published result for this task, achieving the state-of-the-art performance. To the best of our knowledge, this is the ﬁrst end-to-end approach capable of real-time segmenting and tracking mono-guidewire and dual-guidewire in 2D X-ray ﬂuoroscopy.
Strategic Monitor Placement Against Malicious Flows

Security Games have been widely adopted to model scenarios in which one player, the Defender , has to decide how to deploy her resources to minimize the loss that can be caused by an attack performed by another player, the Attacker , aiming at maximizing such loss. In the present paper, we focus on scenarios in which the De-fender has lexicographic-like preferences on the targets, being primarily interested in defending the integrity of a subset of the targets and, only secondarily , to reduce the amount of the other damaged targets. Our central motivation for studying this problem comes from the need to reduce the impact of malicious ﬂows in networks, that can be either physical, like cities, or virtual, e.g., social networks. In this work, we introduce a new class of security games to model these scenarios, characterizing it and proving the NP -hardness of computing a leader-follower equilibrium, which is the most appropriate solution concept for this setting. To compute such an equilibrium, we then provide an exact exponential-time algorithm, capable of exploiting the topological properties of the network. Finally, we show that, with opportune optimizations, this algorithm can work ef-ﬁciently even on network of 10000 nodes.