Miracle-dz/UniFAR-FTUs huggingface dataset:
On Computing Top-t Most Influential Spatial Sites

Given a set O of weighted objects, a set S of sites, and a query site s, the bichromatic RNN query computes the influence set of s, or the set of objects in O that consider s as the nearest site among all sites in S. The influence of a site s can be defined as the total weight of its RNNs. This paper addresses the new and interesting problem of finding the top-t most influential sites from S, inside a given spatial region Q. A straightforward approach is to find the sites in Q, and compute the RNNs of every such site. This approach is not efficient for two reasons. First, all sites in Q need to be identified whatsoever, and the number may be large. Second, both the site R-tree and the object R-tree need to be browsed a large number of times. For each site in Q, the R-tree of sites is browsed to identify the influence region -- a polygonal region that may contain RNNs, and then the R-tree of objects is browsed to find the RNN set. This paper proposes an algorithm called TopInfluential-Sites, which finds the top-t most influential sites by browsing both trees once systematically. Novel pruning techniques are provided, based on a new metric called minExistDNN. There is no need to compute the influence for all sites in Q, or even to visit all sites in Q. Experimental results verify that our proposed method outperforms the straightforward approach.
Currency recognition system using image processing

In this paper, we propose a system for automated currency recognition using image processing techniques. The proposed method can be used for recognizing both the country or origin as well as the denomination or value of a given banknote. Only paper currencies have been considered. This method works by first identifying the country of origin using certain predefined areas of interest, and then extracting the denomination value using characteristics such as size, color, or text on the note, depending on how much the notes within the same country differ. We have considered 20 of the most traded currencies, as well as their denominations. Our system is able to accurately and quickly identify test notes.
Combining Shape and Appearance for Automatic Pedestrian Segmentation

In this paper we present an approach to automatically segmenting non-rigid pedestrians in still images. Inspired by global shape matching as well as interactive figure-ground separation methods, this approach fulfills the task combining shape and appearance cues in a unified framework. The main idea is to initially extract pedestrian silhouette and skeleton via hierarchical shape matching, and then generate an appearance trimap to refine segmentation. The major contributions of this paper include: 1) a novel shape matching scheme, which is proposed to replace the commonly used Chamfer matching in the shape matching stage, 2) a head-torso parsing method, which is developed for localizing pedestrian to reduce the search space, 3) an automatic trimap generation method used to refine segmentation. Experiments on public datasets demonstrate that the approach improves pedestrian segmentation efficiently and effectively.
Rule Based Temporal Inference

Time-wise knowledge is relevant in knowledge graphs as the majority facts are true in some time period, for instance, (Barack Obama, president of, USA, 2009, 2017). Consequently, temporal information extraction and temporal scoping of facts in knowledge graphs have been a focus of recent research. Due to this, a number of temporal knowledge graphs have become available such as YAGO and Wikidata. In addition, since the temporal facts are obtained from open text, they can be weighted, i.e., the extraction tools assign each fact with a confidence score indicating how likely that fact is to be true. Temporal facts coupled with confidence scores result in a probabilistic temporal knowledge graph. In such a graph, probabilistic query evaluation (marginal inference) and computing most probable explanations (MPE inference) are fundamental problems. In addition, in these problems temporal coalescing, an important research in temporal databases, is very challenging. In this work, we study these problems by using probabilistic programming. We report experimental results comparing the efficiency of several state of the art systems.
Greater involvement of action simulation mechanisms in emotional vs cognitive empathy

Abstract Empathy is crucial for successful interpersonal interactions, and it is impaired in many psychiatric and neurological disorders. Action-perception matching, or action simulation mechanisms, has been suggested to facilitate empathy by supporting the simulation of perceived experience in others. However, this remains unclear, and the involvement of the action simulation circuit in cognitive empathy (the ability to adopt another’s perspective) vs emotional empathy (the capacity to share and react affectively to another’s emotional experience) has not been quantitatively compared. Presently, healthy adults completed a classic cognitive empathy task (false belief), an emotional empathy task and an action simulation button-pressing task during functional magnetic resonance imaging. Conjunction analyses revealed common recruitment of the inferior frontal gyrus (IFG), thought to be critical for action-perception matching, during both action simulation and emotional, but not cognitive, empathy. Furthermore, activation was significantly greater in action simulation regions in the left IFG during emotional vs cognitive empathy, and activity in this region was positively correlated with mean feeling ratings during the emotional empathy task. These findings provide evidence for greater involvement of action simulation mechanisms in emotional than cognitive empathy. Thus, the action simulation circuit may be an important target for delineating the pathophysiology of disorders featuring emotional empathy impairments.
On detecting Association-Based Clique Outliers in heterogeneous information networks

In the real world, various systems can be modeled using heterogeneous networks which consist of entities of different types. People like to discover groups (or cliques) of entities linked to each other with rare and surprising associations from such networks. We define such anomalous cliques as Association-Based Clique Outliers (ABCOutliers) for heterogeneous information networks, and design effective approaches to detect them. The need to find such outlier cliques from networks can be formulated as a conjunctive select query consisting of a set of (type, predicate) pairs. Answering such conjunctive queries efficiently involves two main challenges: (1) computing all matching cliques which satisfy the query and (2) ranking such results based on the rarity and the interestingness of the associations among entities in the cliques. In this paper, we address these two challenges as follows. First, we introduce a new low-cost graph index to assist clique matching. Second, we define the outlierness of an association between two entities based on their attribute values and provide a methodology to efficiently compute such outliers given a conjunctive select query. Experimental results on several synthetic datasets and the Wikipedia dataset containing thousands of entities show the effectiveness of the proposed approach in computing interesting ABCOutliers.
No Effect of Adding Brief Dynamic Therapy to Pharmacotherapy in the Treatment of Obsessive-Compulsive Disorder with Concurrent Major Depression

Background: Until now no studies have investigated the benefits of adding brief dynamic therapy (BDT) to medication in obsessive-compulsive disorder (OCD), while a number of recent investigations have demonstrated the efficacy of supplemental BDT among patients with major depressive disorders (MDD). The objective of the present study was to explore the efficacy of BDT combined with pharmacotherapy in comparison with pharmacotherapy alone in the treatment of OCD with concurrent MDD. Methods: A 12-month randomized clinical trial compared a standard selective serotonin reuptake inhibitor treatment with (n = 27) or without (n = 30) supplemental BDT in patients with OCD and concurrent MDD. Supplemental BDT was added during the first 16-week trial; all patients continued to be treated with only pharmacotherapy in the following continuation phase. The primary efficacy assessments were the Yale-Brown Obsessive Compulsive Scale and the 17-item Hamilton Rating Scale for Depression; the secondary efficacy measures included the Clinical Global Impression scale and the Global Assessment of Functioning. The data analysis was conducted on the ‘intent-to-treat (ITT) efficacy patient sample’. Results: Fifty patients completed the study. No difference between the 2 treatment groups was found at any point by any assessment method in the ITT study sample. Conclusions: Supplemental BDT in the treatment of patients with OCD with concurrent MDD who are receiving effective medication has no significant clinical effect on both obsessive and depressive symptoms.
Vertically integrated As2S3 waveguides on lithium niobate waveguides

A planar waveguide design is presented that integrates As2S3 chalcogenide glass with Ti-diffused lithium-niobate (LN) waveguides to increase functionality. As2S3 is a higher index material that is introduced to create small bend radii structures that are crucial for making high density optical circuits. As2S3 waveguide patterns are aligned on top of the straight LN waveguide. Power launched into LN is coupled to the higher index As2S3 cladding structure and propagates through a tight bend before coupling back to the LN waveguide. Experimental results are given for s-bends of 2.48cm minimum radius of curvature.
Software Reliability Modeling and Analysis via Kernel-Based Approach

Traditional software reliability analysis utilizes only the fault count data observed in testing phase, and is done independently of the source code itself. Recently, it is known that utilization of software metrics in software reliability modeling and analysis can lead to more accurate reliability estimation and fault prediction through many empirical studies. However, such a metrics-based modeling also requires a careful selection of software metrics and their measurement, which are often troublesome and cost-consuming in practice. In this paper, we propose a kernel-based approach to estimate the quantitative software reliability, where two cases are considered; multiple software metrics are used and not. In the former case, we combine the kernel regression with the well-known non-homogeneous Poisson process-based software reliability growth model (SRGM), and propose a new metrics-based SRGM. In the latter case, we perform a similarity-based analysis through a source code transformation algorithm and try to estimate the quantitative software reliability from the source code directly without measuring multiple software metrics. Numerical examples with real application programs are presented to validate our kernel-based approach in the above two cases.
Parameter estimation of polynomial-phase signal using the hybrid LvHAF

Estimating the coefficients of a noisy polynomial phase signal is important in fields including radar, biology and radio communications. In this paper, we consider a novel algorithm for estimating the high-order polynomial-phase signal (PPS). The proposed method, which is called hybrid LvHAF, combines the frequency domain method which is called Lv's Distribution (LVD) and the time domain method which is called high order ambiguity function (HAF) to improve the estimation performance. The HAF-based approach provides a simple order-recursive algorithm for estimating the polynomial-phase coefficients. Due to low complexity, the HAF algorithm is widely used in the field of radar. The LVD is a novel algorithm for estimating the LFM signal, which is simple and only requires a two-dimensional Fourier transform of a parametric scaled symmetric instantaneous autocorrelation function (PSIAF). It can be easily implemented by using the complex multiplications and fast Fourier transforms (FFT) based on the scaling principle. The LVD is searching free and without introducing any nonphysical attributes such as order or rotation angle. It only introduces a time delay into the time-lag instantaneous autocorrelation function and rescales the time axis to eliminate the effects of linear frequency migration for the LFM components on the time-frequency plane. The hybrid LvHAF is following two stage approaches. First, the phase differentiation is applied on the PPSs to produce a linear frequency signal (LFM). Second, the parameters of LFM are estimated by the LVD. The main significance of the LVD is to convert a 1-D LFM into a 2-D single-frequency signal. Through simulations and analyses, we verify the effectiveness of the Hybrid LvHAF algorithm.
Topic-constrained influence maximization in social networks

Understanding the spread of influence and maximizing it through online social networks lead to gain a lot of benefits for various practical applications such as viral marketing, personalized recommendation, cascade detection, and information monitoring. However, most existing studies have commonly concentrated on models of social influence propagation without considering information of individuals' interests and experiences, whereas influence and adoption are in fact evidently topic-dependent. Therefore, in this paper, we incorporate topical aspect with the analysis of social influence. We propose the topic-constrained influence maximization model that aims to accurately describe real-world cascades. Particularly, we first introduce a graph clustering-based topic modeling technique to extract topics of textual content associated with each user in the network. For a given topic, we consider user-to-user interactions concerning that topic and further estimate inter-personal influence likelihoods over the network before invoking influence propagation using a standard greedy-based algorithm. Experimental results conducted on real Twitter data show that the proposed approach explores promising efforts for topic-dependent cascades with significantly higher performance than state-of-the-art methods.
Endoscopy in Aneurysm Surgery

BACKGROUND: Surgical clipping with complete occlusion of the aneurysm and preservation of parent, branching, and perforating vessels remains the most definitive treatment for intracranial aneurysms. OBJECTIVE: To evaluate the benefit of endoscopic application during microsurgical procedures in a retrospective study. METHODS: One hundred eighty aneurysms were microsurgically treated in 124 operations. Three different applications of endoscopic visualization were used, depending on the respective requirements: inspection before clipping, clipping under endoscopic view, and postclipping evaluation. RESULTS: Of 1380 aneurysms, 292 procedures were done with application of the endoscope. Of these 292, a complete data set, including video recording of the procedures for retrospective evaluation, was available in 180 cases. In these, the endoscope provided a favorable enhancement of the visual field, particularly in complex or deep-seated lesions. No adverse effects were observed. Before clipping, the endoscope was used to gain additional topographic information in 150 of 180 cases (83%). Clipping under endoscopic view was performed in 4 cases. After clipping, endoscopic inspection was performed in 130 of 180 procedures. Depending on the endoscopic findings, rearrangement of the applied clip or additional clipping was found to be necessary in 26 of 130 cases (20.0%). CONCLUSION: Endoscopic enhancement of the visual field provided by the endoscope before, during, and after microsurgical aneurysm occlusion may be a safe and effective application to increase the quality of treatment. Although unexpected findings concerning completeness of aneurysm occlusion and compromise of involved vessels could be diminished by endoscopic assessment, total prevention was not accomplished. Abbreviations: ACA, anterior cerebral artery AChoA, anterior choroidal artery AComA, anterior communicating artery AICA, anterior inferior cerebellar artery BA, basilar artery DSA, digital subtraction angiography ICA, internal carotid artery MCA, middle cerebral artery OphthA, ophthalmic artery PCA, posterior cerebral artery PComA, posterior communicating artery PICA, posterior inferior cerebellar artery SCA, superior cerebellar artery VA, vertebral artery
Proteogenomic Analysis and Discovery of Immune Antigens in Mycobacterium vaccae*

Tuberculosis (TB) is one of the leading causes of death worldwide, especially in developing countries. Neonatal BCG vaccination occurs in various regions, but the level of protection varies in different populations. Recently, Mycobacterium vaccae is found to be an immunomodulating therapeutic agent that could confer a significant level of protection against TB. It is the only vaccine in a phase III trial from WHO to assess its efficacy and safety in preventing TB disease in people with latent TB infection. However, the mechanism of immunotherapy of M. vaccae remains poorly understood. In this study, the full genome of M. vaccae was obtained by next-generation sequencing technology, and a proteogenomic approach was successfully applied to further perform genome annotation using high resolution and high accuracy MS data. A total of 3,387 proteins (22,508 unique peptides) were identified, and 581 proteins annotated as hypothetical proteins in the genome database were confirmed. Furthermore, 38 novel protein products not annotated at the genome level were detected and validated. Additionally, the translational start sites of 445 proteins were confirmed, and 98 proteins were validated through extension of their translational start sites based on N terminus-derived peptides. The physicochemical characteristics of the identified proteins were determined. Thirty-five immunogenic proteins of M. vaccae were identified by immunoproteomic analysis, and 20 of them were selected to be expressed and validated by Western blot for immunoreactivity to serum from patients infected with M. tuberculosis. The results revealed that eight of them showed strong specific reactive signals on the immunoblots. Furthermore, cellular immune response was further examined and one protein displayed a higher cellular immune level in pulmonary TB patients. Twelve identified immunogenic proteins have orthologous in H37Rv and BCG. This is the first study to obtain the full genome and annotation of M. vaccae using a proteogenomic approach, and some immunogenic proteins that were validated by immunoproteomic analysis could contribute to the understanding of the mechanism of M. vaccae immunotherapy.
An Object-Based Visual Selection Model with Bottom-Up and Top-Down Modulations

Research in qualitative models of visual attention has mainly focused on the bottom-up guidance of early visual features. Here we propose a new model which combine both bottom-up and top-down modulation into the visual selection model. The proposed model is composed of five main components: a Visual Feature Extraction module, a LEGION network for image segmentation to deal with objects, a Multi-Layer Perceptron (MLP) network for object recognition, a Kohonen's Self-Organizing Maps (SOM) combined with a network of integrate and fire neurons which creates our attribute-saliency map, and finally, and object selection module which highlights the most salient object in the scene. Experiments with synthetic and real images is conducted and the results demonstrate the effectiveness of the proposed approach.
Genetics of Floral Traits Influencing Reproductive Isolation between Aquilegia formosa and Aquilegia pubescens

Reproductive isolation between Aquilegia formosa and Aquilegia pubescens is influenced by differences in their flowers through their effects on pollinator visitation and pollen transfer. Here, we investigate the genetic basis of floral characters differentiating these species. We found that in addition to the effects of flower orientation and the length of nectar spurs previously described, other characters such as flower color or odor affect hawkmoth visitation. Repeatability of measurements in an F2 population ranged from 0.53 to 0.83 among five floral traits, indicating that using the means of multiple measures per plant will substantially increase the power of a quantitative trait locus (QTL) analysis. Integration of floral traits was indicated by significant correlations among traits in an F2 population. In a separate F2 population we found that QTL for different floral traits were often closely associated, indicating that linkage or pleiotropy cause at least some of this integration. In addition, we found QTL for all floral traits examined. Because Aquilegia species are largely interfertile and vary extensively in both floral morphology and ecology, they offer the opportunity for QTL studies of a wide range of characters affecting reproductive isolation.
Transient receptor potential vanilloid 4 (TRPV4) activation by arachidonic acid requires protein kinase A–mediated phosphorylation

Transient receptor potential vanilloid 4 (TRPV4) is a Ca2+-permeable channel of the transient receptor potential (TRP) superfamily activated by diverse stimuli, including warm temperature, mechanical forces, and lipid mediators such as arachidonic acid (AA) and its metabolites. This activation is tightly regulated by protein phosphorylation carried out by various serine/threonine or tyrosine kinases. It remains poorly understood how phosphorylation differentially regulates TRPV4 activation in response to different stimuli. We investigated how TRPV4 activation by AA, an important signaling process in the dilation of coronary arterioles, is affected by protein kinase A (PKA)–mediated phosphorylation at Ser-824. Wildtype and mutant TRPV4 channels were expressed in human coronary artery endothelial cells (HCAECs). AA-induced TRPV4 activation was blunted in the S824A mutant but was enhanced in the phosphomimetic S824E mutant, whereas the channel activation by the synthetic agonist GSK1016790A was not affected. The low level of basal phosphorylation at Ser-824 was robustly increased by the redox signaling molecule hydrogen peroxide (H2O2). The H2O2-induced phosphorylation was accompanied by an enhanced channel activation by AA, and this enhanced response was largely abolished by PKA inhibition or S824A mutation. We further identified a potential structural context dependence of Ser-824 phosphorylation–mediated TRPV4 regulation involving an interplay between AA binding and the possible phosphorylation-induced rearrangements of the C-terminal helix bearing Ser-824. These results provide insight into how phosphorylation specifically regulates TRPV4 activation. Redox-mediated TRPV4 phosphorylation may contribute to pathologies associated with enhanced TRPV4 activity in endothelial and other systems.