Miracle-dz/UniFAR-FTUs huggingface dataset:
Prosodic topic- and turn-finality cues

This paper describes an acoustic analysis of prosody in a variety of experimental types of dialogue. Subjects cooperatively had to perform a speaking task (i.e. describe simple rows of differently colored figures and signal their structure) and a listening task (i.e. respond to discourse boundaries in the speech produced by the interlocutor and take over as soon as the other had finished). It was found that the demarcation of discourse units by means of various intonation contours and accent shifts is largely dependent on the kind of discourse setting, in that speakers clearly take into account whether a conversational partner is likely to interrupt or not. Moreover, subjects appear not just to exploit local cues to signal the boundaries of larger-scale units. Our study reveals that they also have at their disposal: (1) a specific type of intonation contour (a level tone), occurring well before the actual end, that pre-signals that a unit will soon be rounded off; (2) topline-declination over the course of a topical unit that is different in final position than in non-final position; (3) a gradual shift in prominence in a NP from the adjective to the noun position over the course of a discourse unit.
Involvement of Allosteric Effect and KCa Channels in Crosstalk between β2-Adrenergic and Muscarinic M2 Receptors in Airway Smooth Muscle

To advance the development of bronchodilators for asthma and chronic obstructive pulmonary disease (COPD), this study was designed to investigate the mechanism of functional antagonism between β2-adrenergic and muscarinic M2 receptors, focusing on allosteric effects and G proteins/ion channels coupling. Muscarinic receptor antagonists (tiotropium, glycopyrronium, atropine) synergistically enhanced the relaxant effects of β2-adrenergic receptor agonists (procaterol, salbutamol, formoterol) in guinea pig trachealis. This crosstalk was inhibited by iberitoxin, a large-conductance Ca2+-activated K+ (KCa) channel inhibitor, whereas it was increased by verapamil, a L-type voltage-dependent Ca2+ (VDC) channel inhibitor; additionally, it was enhanced after tissues were incubated with pertussis or cholera toxin. This synergism converges in the G proteins (Gi, Gs)/KCa channel/VDC channel linkages. Muscarinic receptor antagonists competitively suppressed, whereas, β2-adrenergic receptor agonists noncompetitively suppressed muscarinic contraction. In concentration-inhibition curves for β2-adrenergic receptor agonists with muscarinic receptor antagonists, EC50 was markedly decreased, and maximal inhibition was markedly increased. Hence, muscarinic receptor antagonists do not bind to allosteric sites on muscarinic receptors. β2-Adrenergic receptor agonists bind to allosteric sites on these receptors; their intrinsic efficacy is attenuated by allosteric modulation (partial agonism). Muscarinic receptor antagonists enhance affinity and efficacy of β2-adrenergic action via allosteric sites in β2-adrenergic receptors (synergism). In conclusion, KCa channels and allosterism may be novel targets of bronchodilator therapy for diseases such as asthma and COPD.
A new landscape of host–protozoa interactions involving the extracellular vesicles world

Abstract Extracellular vesicles (EVs) are released by a wide number of cells including blood cells, immune system cells, tumour cells, adult and embryonic stem cells. EVs are a heterogeneous group of vesicles (~30–1000 nm) including microvesicles and exosomes. The physiological release of EVs represents a normal state of the cell, raising a metabolic equilibrium between catabolic and anabolic processes. Moreover, when the cells are submitted to stress with different inducers or in pathological situations (malignancies, chronic diseases, infectious diseases.), they respond with an intense and dynamic release of EVs. The EVs released from stimulated cells vs those that are released constitutively may themselves differ, both physically and in their cargo. EVs contain protein, lipids, nucleic acids and biomolecules that can alter cell phenotypes or modulate neighbouring cells. In this review, we have summarized findings involving EVs in certain protozoan diseases. We have commented on strategies to study the communicative roles of EVs during host–pathogen interaction and hypothesized on the use of EVs for diagnostic, preventative and therapeutic purposes in infectious diseases. This kind of communication could modulate the innate immune system and reformulate concepts in parasitism. Moreover, the information provided within EVs could produce alternatives in translational medicine.
3D-Aided Deep Pose-Invariant Face Recognition

Learning from synthetic faces, though perhaps appealing for high data efficiency, may not bring satisfactory performance due to the distribution discrepancy of the synthetic and real face images. To mitigate this gap, we propose a 3D-Aided Deep Pose-Invariant Face Recognition Model (3D-PIM), which automatically recovers realistic frontal faces from arbitrary poses through a 3D face model in a novel way. Specifically, 3D-PIM incorporates a simulator with the aid of a 3D Morphable Model (3D MM) to obtain shape and appearance prior for accelerating face normalization learning, requiring less training data. It further leverages a global-local Generative Adversarial Network (GAN) with multiple critical improvements as a refiner to enhance the realism of both global structures and local details of the face simulator’s output using unlabelled real data only, while preserving the identity information. Qualitative and quantitative experiments on both controlled and in-the-wild benchmarks clearly demonstrate superiority of the proposed model over state-of-the-arts.
Deeply-Supervised CNN Model for Action Recognition with Trainable Feature Aggregation

In this paper, we propose a deeply-supervised CNN model for action recognition that fully exploits powerful hierarchical features of CNNs. In this model, we build multi-level video representations by applying our proposed aggregation module at different convolutional layers. Moreover, we train this model in a deep supervision manner, which brings improvement in both performance and efficiency. Meanwhile, in order to capture the temporal structure as well as preserve more details about actions, we propose a trainable aggregation module. It models the temporal evolution of each spatial location and projects them into a semantic space using the Vector of Locally Aggregated Descriptors (VLAD) technique. This deeply-supervised CNN model integrating the powerful aggregation module provides a promising solution to recognize actions in videos. We conduct experiments on two action recognition datasets: HMDB51 and UCF101. Results show that our model outperforms the state-of-the-art methods.
Unary Integer Linear Programming with Structural Restrictions

Recently a number of algorithmic results have appeared which show the tractability of Integer Linear Programming (ILP) instances under strong restrictions on variable domains and/or coefficients (AAAI 2016, AAAI 2017, IJCAI 2017). In this paper, we target ILPs where neither the variable domains nor the coefficients are restricted by a fixed constant or parameter; instead, we only require that our instances can be encoded in unary. We provide new algorithms and lower bounds for such ILPs by exploiting the structure of their variable interactions, represented as a graph. Our first set of results focuses on solving ILP instances through the use of a graph parameter called clique-width, which can be seen as an extension of treewidth which also captures well-structured dense graphs. In particular, we obtain a polynomial-time algorithm for instances of bounded clique-width whose domain and coefficients are polynomially bounded by the input size, and we complement this positive result by a number of algorithmic lower bounds. Afterwards, we turn our attention to ILPs with acyclic variable interactions. In this setting, we obtain a complexity map for the problem with respect to the graph representation used and restrictions on the encoding.
Identification of Synergistic Interaction Between Cannabis-Derived Compounds for Cytotoxic Activity in Colorectal Cancer Cell Lines and Colon Polyps That Induces Apoptosis-Related Cell Death and Distinct Gene Expression

Abstract Introduction: Colorectal cancer remains the third most common cancer diagnosis and fourth leading cause of cancer-related mortality worldwide. Purified cannabinoids have been reported to prevent proliferation, metastasis, and induce apoptosis in a variety of cancer cell types. However, the active compounds from Cannabis sativa flowers and their interactions remain elusive. Research Aim: This study was aimed to specify the cytotoxic effect of C. sativa-derived extracts on colon cancer cells and adenomatous polyps by identification of active compound(s) and characterization of their interaction. Materials and Methods: Ethanol extracts of C. sativa were analyzed by high-performance liquid chromatography and gas chromatograph/mass spectrometry and their cytotoxic activity was determined using alamarBlue-based assay (Resazurin) and tetrazolium dye-based assay (XTT) on cancer and normal colon cell lines and on dysplastic adenomatous polyp cells. Annexin V Assay and fluorescence-activated cell sorting (FACS) were used to determine apoptosis and cell cycle, and RNA sequencing was used to determine gene expression. Results: The unheated cannabis extracts (C2F), fraction 7 (F7), and fraction 3 (F3) had cytotoxic activity on colon cancer cells, but reduced activity on normal colon cell lines. Moreover, synergistic interaction was found between F7 and F3 and the latter contains mainly cannabigerolic acid. The F7 and F7+F3 cytotoxic activity involved cell apoptosis and cell cycle arrest in S or G0/G1 phases, respectively. RNA profiling identified 2283 differentially expressed genes in F7+F3 treatment, among them genes related to the Wnt signaling pathway and apoptosis-related genes. Moreover, F7, F3, and F7+F3 treatments induced cell death of polyp cells. Conclusions: C. sativa compounds interact synergistically for cytotoxic activity against colon cancer cells and induce cell cycle arrest, apoptotic cell death, and distinct gene expression. F3, F7, and F7+F3 are also active on adenomatous polyps, suggesting possible future therapeutic value.
Approximately Nearest Neighborhood Image Search Using Unsupervised Hashing via Homogeneous Kernels

We propose an approximation search algorithm that uses additive homogeneous kernel mapping to search for an image approximation based on kernelized locality-sensitive hashing. To address problems related to the unstable search accuracy of an unsupervised image hashing function and degradation of the search-time performance with increases in the number of hashing bits, we propose a method that combines additive explicit homogeneous kernel mapping and image feature histograms to construct a search algorithm based on a locality-sensitive hashing function. Moreover, to address the problem of semantic gaps caused by using image data that lack type information in semantic modeling, we describe an approximation searching algorithm based on the homogeneous kernel mapping of similarities between pairs of images and dissimilar constraint relationships. Our image search experiments confirmed that the proposed algorithm can construct a locality-sensitive hash function more accurately, thereby effectively improving the similarity search performance.
Evaluating Brush Movements for Chinese Calligraphy: A Computer Vision Based Approach

Chinese calligraphy is a popular, highly esteemed art form in the Chinese cultural sphere and worldwide. Ink brushes are the traditional writing tool for Chinese calligraphy and the subtle nuances of brush movements have a great impact on the aesthetics of the written characters. However, mastering the brush movement is a challenging task for many calligraphy learners as it requires many years’ practice and expert supervision. This paper presents a novel approach to help Chinese calligraphy learners to quantify the quality of brush movements without expert involvement. Our approach extracts the brush trajectories from a video stream; it then compares them with example templates of reputed calligraphers to produce a score for the writing quality. We achieve this by first developing a novel neural network to extract the spatial and temporal movement features from the video stream. We then employ methods developed in the computer vision and signal processing domains to track the brush movement trajectory and calculate the score. We conducted extensive experiments and user studies to evaluate our approach. Experimental results show that our approach is highly accurate in identifying brush movements, yielding an average accuracy of 90%, and the generated score is within 3% of errors when compared to the one given by human experts.
Posttraumatic stress, uncontrollability, and emotional distress tolerance

Although individuals with posttraumatic stress disorder (PTSD) report poor tolerance for distress, the mechanisms underpinning this are unknown. Cognitive models of PTSD propose that appraisals of lacking or losing control impair adaptation to trauma. This study investigated whether avoidance of emotional distress was affected by loss of control in a web‐based community sample with and without PTSD symptomatology, expecting that those with PTSD may show more detrimental effects following greater loss of control.
Egalitarian Committee Scoring Rules

We introduce and study the class of egalitarian variants of committee scoring rules, where instead of summing up the scores that voters assign to committees---as is done in the utilitarian variants---the score of a committee is taken to be the lowest score assigned to it by any voter. We focus on five rules, which are egalitarian analogues of SNTV, the k-Borda rule, the Chamberlin--Courant rule, the Bloc rule, and the Pessimist rule. We establish their computational complexity, provide their initial axiomatic study, and perform experiments to represent the action of these rules graphically.
An Experimental Examination of Alternative Forms of Web Assurance for Business-to-Consumer e-Commerce

This study reports the results of an experiment that examined alternative forms of web assurance for “B2C” e‐commerce. Specifically, using a repeated measures design, variations in purchase intent were examined across four within‐subjects web site conditions (two MP3 players and two digital cameras from different web sites) and five between‐subjects assurance conditions (undirected or directed retailer disclosures, VISA, TRUSTe, and WebTrust™). The effects of assurance (present vs. absent) on purchase intent were studied relative to the roles of retailer disclosures, information risk (security, disclosure, and product risk), and familiarity (with the retailer and product). Control variables encompassing previous Internet and online purchase experiences, general intent to buy online, and web site design were also incorporated in the study. No differences in intent to purchase were found for the three assurance conditions and they were combined for further data analysis. Results indicate that higher intent .
Manifold Adaptive Kernelized Low-Rank Representation for Semisupervised Image Classification

Constructing a powerful graph that can effectively depict the intrinsic connection of data points is the critical step to make the graph-based semisupervised learning algorithms achieve promising performance. Among popular graph construction algorithms, low-rank representation (LRR) is a very competitive one that can simultaneously explore the global structure of data and recover the data from noisy environments. Therefore, the learned low-rank coefficient matrix in LRR can be used to construct the data affinity matrix. Consider the existing problems such as the following: the essentially linear property of LRR makes it not appropriate to process the possible nonlinear structure of data and learning performance can be greatly enhanced by exploring the structure information of data; we propose a new manifold kernelized low-rank representation (MKLRR) model that can perform LRR in the data manifold adaptive kernel space. Specifically, the manifold structure can be incorporated into the kernel space by using graph Laplacian and thus the underlying geometry of data is reflected by the wrapped kernel space. Experimental results of semisupervised image classification tasks show the effectiveness of MKLRR. For example, MKLRR can, respectively, obtain 96.13%, 98.09%, and 96.08% accuracies on ORL, Extended Yale B, and PIE data sets when given 5, 20, and 20 labeled face images per subject.
A mixed methods review to develop and confirm a framework for assessing midwifery practice in perinatal mental health

AIM To ascertain whether a new framework examining midwifery practice in perinatal mental health (PMH) is supported by the research literature. BACKGROUND The identification and care of women with PMH problems is increasingly considered part of midwifery practice. Research suggests that many midwives lack knowledge, skills and confidence. It would be useful to be able to determine barriers and facilitators to effective clinical practice. The authors propose a framework comprising five potentially measurable domains which have an impact on midwives' ability to identify, assess and care for women with PMH problems. DESIGN This mixed-methods review uses an innovative qualitative convergent design based on framework synthesis. DATA SOURCES Relevant electronic databases were searched for the period from January 2007-December 2016; 33 studies from nine countries met the inclusion criteria. REVIEW METHODS Study quality was assessed using critical appraisal tools. Study findings were mapped onto the five domains of the framework: knowledge, confidence, attitudes, illness perception and infrastructure. Findings were then synthesized for each domain. RESULTS All five domains are substantially represented in the literature, thus supporting the proposed framework. Several sub-domains and relationships between domains were identified. Varying levels of knowledge, confidence, attitudes and illness perceptions were found; evidence suggests that midwives benefit from further training within these domains. Features of organizational infrastructure act as barriers or facilitators to effective care; these need to be addressed at organizational level. CONCLUSION The proposed framework was confirmed and can be used to inform practice, policy and research.
H-Net: Neural Network for Cross-domain Image Patch Matching

Describing the same scene with different imaging style or rendering image from its 3D model gives us different domain images. Different domain images tend to have a gap and different local appearances, which raise the main challenge on the cross-domain image patch matching. In this paper, we propose to incorporate AutoEncoder into the Siamese network, named as H-Net, of which the structural shape resembles the letter H. The H-Net achieves state-of-the-art performance on the cross-domain image patch matching. Furthermore, we improved H-Net to H-Net++. The H-Net++ extracts invariant feature descriptors in cross-domain image patches and achieves state-of-the-art performance by feature retrieval in Euclidean space. As there is no benchmark dataset including cross-domain images, we made a cross-domain image dataset which consists of camera images, rendering images from UAV 3D model, and images generated by CycleGAN algorithm. Experiments show that the proposed H-Net and H-Net++ outperform the existing algorithms. Our code and cross-domain image dataset are available at https://github.com/Xylon-Sean/H-Net.
Periodic-CRN: A Convolutional Recurrent Model for Crowd Density Prediction with Recurring Periodic Patterns

Time-series forecasting in geo-spatial domains has important applications, including urban planning, traffic management and behavioral analysis. We observed recurring periodic patterns in some spatio-temporal data, which were not considered explicitly by previous non-linear works. To address this lack, we propose novel `Periodic-CRN' (PCRN) method, which adapts convolutional recurrent network (CRN) to accurately capture spatial and temporal correlations, learns and incorporates explicit periodic representations, and can be optimized with multi-step ahead prediction. We show that PCRN consistently outperforms the state-of-the-art methods for crowd density prediction across two taxi datasets from Beijing and Singapore.
Information Systems Outsourcing Announcements: Investigating the Impact on the Market Value of Contract-Granting Firms

The objective of this research project is to examine the impact of information systems (IS) outsourcing announcements on the market value of contract‐granting firms. This study is important to researchers, professionals, and policy makers as it provides empirical evidence from the capital market that outsourcing is considered to be a value‐added business exchange for contract‐granting firms. Research findings indicate positive and significant market value gains for smaller vs. larger firms and service vs. nonservice industry firms. The significant effects observed in this study are explained by the existence of information asymmetry in the capital market for smaller firms and service firms, and by the relatively higher investment made by service firms in information technology. This study contributes to accounting research by examining the importance of providing financial report users with nonfinancial forward‐looking information concerning management's strategic decisions, such as the decision to outsou.
The Effect of Food Unit Sizes and Meal Serving Occasions on Eating Behaviour Characteristics: Within Person Randomised Crossover Studies on Healthy Women

Manipulating food properties and serving environment during a meal can significantly change food intake at group level. However, the evaluation of the usefulness of such manipulations requires an understanding of individual behavioural changes. Three studies were conducted to explore the effect of unit size and meal occasion on eating behaviour characteristics (food intake, meal duration, number of bites and chews). All studies used a randomised crossover design, with a one-week wash-out period, starting with a familiarisation meal, with the participation of healthy, normal weight females between the ages of 18–35 years. In Study 1 (n = 19) three cube sizes (0.5, 1.0 and 1.5 cm3) of vegetable hash and chicken were compared. In Study 2 (n = 18) mashed potatoes and mincemeat were compared to whole potatoes and meatballs. In Study 3 (n = 29) meals served at lunch time (11:00–13:00) were compared to identical meals served at dinner time (17:00–19:00). The largest food unit size lead to significantly increased meal duration in Study 2 (mean difference 0.9 min, 95% confidence interval (CI) 0.0–1.8), but not in Study 1 (mean difference 1 min, 95% CI 0.1–2.0). There was a significant increase in number of chews in the large unit size condition of both Study 1 (mean difference 88, 95% CI 12–158) and Study 2 (mean difference 95, 95% CI 12–179). Different serving occasions did not significantly change any of the eating behaviours measured. Except for number of bites in Study 2 (R2 = 0.60), most individuals maintained their eating behaviour relative to the group across unit sizes and serving occasions conditions (R2 > 0.75), which suggests single meal testing can provide information about the behavioural characteristics of individual eating styles under different conditions.