Miracle-dz/UniFAR-FTUs huggingface dataset:
Word Emotion Induction for Multiple Languages as a Deep Multi-Task Learning Problem

Predicting the emotional value of lexical items is a well-known problem in sentiment analysis. While research has focused on polarity for quite a long time, meanwhile this early focus has been shifted to more expressive emotion representation models (such as Basic Emotions or Valence-Arousal-Dominance). This change resulted in a proliferation of heterogeneous formats and, in parallel, often small-sized, non-interoperable resources (lexicons and corpus annotations). In particular, the limitations in size hampered the application of deep learning methods in this area because they typically require large amounts of input data. We here present a solution to get around this language data bottleneck by rephrasing word emotion induction as a multi-task learning problem. In this approach, the prediction of each independent emotion dimension is considered as an individual task and hidden layers are shared between these dimensions. We investigate whether multi-task learning is more advantageous than single-task learning for emotion prediction by comparing our model against a wide range of alternative emotion and polarity induction methods featuring 9 typologically diverse languages and a total of 15 conditions. Our model turns out to outperform each one of them. Against all odds, the proposed deep learning approach yields the largest gain on the smallest data sets, merely composed of one thousand samples.
What Do Viewers Say to Their TVs?: An Analysis of Voice Queries to Entertainment Systems

A recently-introduced product of Comcast, a large cable company in the United States, is a "voice remote" that accepts spoken queries from viewers. We present an analysis of a large query log from this service to answer the question: "What do viewers say to their TVs? " In addition to a descriptive characterization of queries and sessions, we describe two complementary types of analyses to support query understanding. First, we propose a domain-specific intent taxonomy to characterize viewer behavior: as expected, most intents revolve around watching programs---both direct navigation as well as browsing---but there is a non-trivial fraction of non-viewing intents as well. Second, we propose a domain-specific tagging scheme for labeling query tokens, that when combined with intent and program prediction, provides a multi-faceted approach to understand voice queries directed at entertainment systems.
Furofuran lignans and alkaloids from Clinacanthus nutans

Abstract One new furofuran lignan 2-methoxy-9β-hydroxydiasesamin (1), and four analogues (2–5), together with eight alkaloids (6–13), were isolated from the ethanol extract of the aerial part of Clinacanthus nutans. Their structures were elucidated by the detailed analysis of comprehensive spectroscopic data. All the compounds were isolated from the genus of Clinacanthus for the first time. In addition, lignans isolated from C. nutans was reported for the first time. Compound 1 exhibited modest activity against three human tumor cell lines Hela, MCF-7, and A549, with IC50 values of 68.55, 60.00, and 59.17 μM, respectively.
Dynamical Properties of Postural Control in Obese Community-Dwelling Older Adults †

Postural control is a key aspect in preventing falls. The aim of this study was to determine if obesity affected balance in community-dwelling older adults and serve as an indicator of fall risk. The participants were randomly assigned to receive a comprehensive geriatric assessment followed by a longitudinal assessment of their fall history. The standing postural balance was measured for 98 participants with a Body Mass Index (BMI) ranging from 18 to 63 kg/m2, using a force plate and an inertial measurement unit affixed at the sternum. Participants’ fall history was recorded over 2 years and participants with at least one fall in the prior year were classified as fallers. The results suggest that body weight/BMI is an additional risk factor for falling in elderly persons and may be an important marker for fall risk. The linear variables of postural analysis suggest that the obese fallers have significantly higher sway area and sway ranges, along with higher root mean square and standard deviation of time series. Additionally, it was found that obese fallers have lower complexity of anterior-posterior center of pressure time series. Future studies should examine more closely the combined effect of aging and obesity on dynamic balance.
An Active Relearning Framework for Remote Sensing Image Classification

Classification is an important technique for remote sensing data interpretation. In order to enhance the performance of a supervised classifier and ensure the lowest possible cost of the training samples used in the process, active learning (AL) can be used to optimize the training sample set. At the same time, integrating spatial information can help to enhance the separability between similar classes, which can in turn reduce the need for training samples in AL. To effectively integrate spatial information into the AL framework, this paper proposes a new active relearning (ARL) model for remote sensing image classification. In particular, our model is used to relearn the spatial features on the classification map, which contributes significantly to enhancing the performance of the classifier. We integrate the relearning model into the AL framework, with the aim to accelerate the convergence of AL and further reduce the labeling cost. Under the newly developed ARL framework, we propose two spatial–spectral uncertainty criteria to optimize the procedure for selecting new training samples. Furthermore, an adaptive multiwindow ARL model is also introduced in this paper. Our experiments with two hyperspectral images and two very high resolution images indicate that the ARL model exhibits faster convergence speed with fewer samples than traditional AL methods. Our results also suggest that the proposed spatial–spectral uncertainty criteria and the multiwindow version can further improve the performance when implementing ARL.
Time-Aware Diversified Query Suggestion

In this paper, we study query suggestion diversi cation for time- aware queries, where query suggestions are diversified from multiple dimensions (e.g., topic and time). More precisely, we introduce the method of Time-aware Diversified Query Suggestion (TDQS) which ensures that the generated suggested queries indicate possible time points in which a searcher who issued the original query may be interested. Preliminary experiments on AOL query log demonstrate that our proposed method can significantly improve the diversity and relevance e ectiveness for time-aware queries in comparison with two state-of-the-art methods.
A Usability Study of Secure Email Deletion

Messaging applications like SnapChat illustrate that users are concerned about the permanence of information. We find that this concern extends to email. In this paper we present a usability study of an end-to-end secure email tool with the option to securely delete messages. This tool uses ephemeral keys, one per message thread, and default expiration times, with a user prompt to renew or delete keys. Deleting keys causes the messages in the thread to be unreadable for that user. We compare the usability of this tool to a nearly identical tool that uses long term keys and lacks a feature to expire keys. We also interview participants about their email use patterns and attitudes towards information permanence. We find that participants are especially interested in the ability to control the lifetime of an email message. Participants also report trusting the tool that allowed them to make their email messages ephemeral more than the tool that just encrypted their email.
Radio galaxy morphology generation using DNN autoencoder and Gaussian mixture models

The morphology of a radio galaxy is highly affected by its central active galactic nuclei (AGN), which is studied to reveal the evolution of the super massive black hole (SMBH). In this work, we propose a morphology generation framework for two typical radio galaxies namely Fanaroff-Riley type-I (FRI) and type-II (FRII) with deep neural network based autoencoder (DNNAE) and Gaussian mixture models (GMMs). The encoder and decoder subnets in the DNNAE are symmetric aside a fully-connected layer namely code layer hosting the extracted feature vectors. By randomly generating the feature vectors later with a three-component Gaussian Mixture models, new FRI or FRII radio galaxy morphologies are simulated. Experiments were demonstrated on real radio galaxy images, where we discussed the length of feature vectors, selection of lost functions, and made comparisons on batch normalization and dropout techniques for training the network. The results suggest a high efficiency and performance of our morphology generation framework. Code is available at: https://github.com/myinxd/dnnae-gmm.
Maximizing acquisition functions for Bayesian optimization

Bayesian optimization is a sample-efficient approach to global optimization that relies on theoretically motivated value heuristics (acquisition functions) to guide its search process. Fully maximizing acquisition functions produces the Bayes' decision rule, but this ideal is difficult to achieve since these functions are frequently non-trivial to optimize. This statement is especially true when evaluating queries in parallel, where acquisition functions are routinely non-convex, high-dimensional, and intractable. We first show that acquisition functions estimated via Monte Carlo integration are consistently amenable to gradient-based optimization. Subsequently, we identify a common family of acquisition functions, including EI and UCB, whose characteristics not only facilitate but justify use of greedy approaches for their maximization.
Batch Normalization: Is Learning An Adaptive Gain and Bias Necessary?

The state-of-the-art training of deep neural networks requires to normalize the activities of the neurons for accelerating the training process. A standard approach is to employ batch normalization (BN), in which the activations are normalized by the mean and standard deviation of the training mini-batch. To be invertible, BN also introduces an adaptive gain and bias which are applied after the normalization but often before the non-linearity. In this paper, we investigate the effects of learnable parameters, gain and bias, on the training of various typical deep neural nets, including ALL-CNNs, Network In Network (NIN), ResNets. Through extensive experiments, we show that there is no big difference in both training convergence and final test accuracy if we remove the BN layer following the final convolutional layer from a convolutional neural network (CNN) for standard classification tasks. We also observed that without adaptively updating learnable parameters for BN layers, it often requires less time for training of very deep neural nets such as ResNet-101.
Wavelet-enhanced convolutional neural network: a new idea in a deep learning paradigm

Abstract Purpose: Manual brain tumor segmentation is a challenging task that requires the use of machine learning techniques. One of the machine learning techniques that has been given much attention is the convolutional neural network (CNN). The performance of the CNN can be enhanced by combining other data analysis tools such as wavelet transform. Materials and methods: In this study, one of the famous implementations of CNN, a fully convolutional network (FCN), was used in brain tumor segmentation and its architecture was enhanced by wavelet transform. In this combination, a wavelet transform was used as a complementary and enhancing tool for CNN in brain tumor segmentation. Results: Comparing the performance of basic FCN architecture against the wavelet-enhanced form revealed a remarkable superiority of enhanced architecture in brain tumor segmentation tasks. Conclusion: Using mathematical functions and enhancing tools such as wavelet transform and other mathematical functions can improve the performance of CNN in any image processing task such as segmentation and classification.
Influenza Viral Shedding in a Prospective Cohort of HIV-Infected and Uninfected Children and Adults in 2 Provinces of South Africa, 2012–2014

Background Prolonged shedding of influenza viruses may be associated with increased transmissibility and resistance mutation acquisition due to therapy. We compared duration and magnitude of influenza shedding between human immunodeficiency virus (HIV)-infected and -uninfected individuals. Methods A prospective cohort study during 3 influenza seasons enrolled patients with influenza-like illness and a positive influenza rapid test. Influenza viruses were detected by real-time reverse transcription polymerase chain reaction. Weibull accelerated failure time regression models were used to describe influenza virus shedding. Mann-Whitney U tests explored initial influenza viral loads (VL). Results Influenza virus shedding duration was similar in 65 HIV-infected (6 days; interquartile range [IQR] 3-10) and 176 HIV-uninfected individuals (7 days; IQR 4-11; P = .97), as was initial influenza VL (HIV-uninfected 5.28 ± 1.33 log10 copies/mL, HIV-infected 4.73 ± 1.68 log10 copies/mL; P = .08). Adjusted for age, HIV-infected individuals with low CD4 counts shed influenza virus for longer than those with higher counts (adjusted hazard ratio 3.55; 95% confidence interval, 1.05-12.08). Discussion A longer duration of influenza virus shedding in HIV-infected individuals with low CD4 counts may suggest a possible increased risk for transmission or viral evolution in severely immunocompromised individuals. HIV-infected individuals should be prioritized for annual influenza immunization.
Five-Year-olds' Acoustic Realization of Mandarin Tone Sandhi and Lexical Tones in Context Are Not Yet Fully Adult-Like

Large numbers of children around the world are learning tone languages, but few studies have examined the acoustic properties of children's early tone productions. Even more scarce are acquisition studies on tone sandhi, a tone change phenomenon which alters the surface realization of lexical tones. Two studies using perceptual coding report the emergence of lexical tone and tone sandhi at around 2 years (Li and Thompson, 1977; Hua and Dodd, 2000). However, the only acoustic study available shows that 3-year-olds are not yet adult-like in their lexical tone productions (Wong, 2012). This raises questions about when children's productions become acoustically adult-like and how their tone productions differ from those of adults. These questions were addressed in the current study which compared Mandarin-speaking pre-schoolers' (3–5-year-olds) tone productions to that of adults. A picture naming task was used with disyllabic real words familiar to pre-schoolers. Overall children produced appropriate tone contours for all tones, i.e., level for tone 1, rising for tones 2, 3 and full sandhi, falling for tone 4 and half sandhi. However, children's productions were not adult-like for tones 3, 4, and the sandhi forms, in terms of coordinating pitch range, slope and curvature, with little evidence of development across ages. These results suggest a protracted process in achieving adult-like acoustic realization of both lexical and sandhi tones.
Alameda Song Sparrow Abundance Related to Salt Marsh Vegetation Patch Size and Shape Metrics Quantified from Remote Sensing Imagery

Understanding the characteristics of high-quality avian habitat is critical for guiding salt marsh man- agement and restoration. Existing insights into salt marsh avian habitat are often based on the com- position of marsh vegetation, e.g., individual plant species cover. This study investigated whether the spatial configuration of marsh surface cover (e.g., patch number, density, size, shape complexity and compactness, degree of dissection of the landscape, variation and repetition of cover type, and the vari- ance within these metrics) is a useful, additional indi- cator of avian habitat quality for the Alameda Song Sparrow (Melospiza melodia pusillula), a non-migra- tory California Species of Special Concern endemic to southern San Francisco Bay. M. m. pusillula density during the breeding seasons of 2002 through 2005 was estimated at 82 observation points in 10 marsh sites within the bird's geographic range. The mean bird density index (overall mean: 5.61 birds detected per hectare of marsh) was not significantly different among marshes of different ages. We mapped the vegetation zones, open water, and upland areas with- in each marsh site using high resolution aerial pho- tographs and automated classification analysis. We quantified the configuration of surface cover around each bird observation point by 31 metrics. Bird den- sity index was best modeled by a multiple linear regression containing positive relationships with the metrics Mean Core Area Index and Patch Core Area Coefficient of Variation (R 2 = 0.210, p < 0.0001). Qualitatively, this model suggested that M. m. pusil- lula abundance during the breeding season was greatest in marsh areas with compact patches that spanned a variety of patch sizes from moderate-to- large, uninterrupted by other cover. We conclude that configuration-based vegetation pattern analysis could usefully complement more customary composition- based habitat assessments to aid wetland habitat research, management, and restoration.
Higher Lung Cancer Incidence in Young Women Than Young Men in the United States

BACKGROUND Previous studies showed a higher incidence of lung cancer among young women than among young men in the United States. Whether this pattern has continued in contemporary birth cohorts and, if so, whether it can be fully explained by sex differences in smoking behaviors are unknown. METHODS We examined the nationwide population‐based incidence of lung cancer according to sex, race or ethnic group, age group (30 to 34, 35 to 39, 40 to 44, 45 to 49, and 50 to 54 years), year of birth (1945 to 1980), and calendar period of diagnosis (1995–1999, 2000–2004, 2005–2009, and 2010–2014), and we calculated female‐to‐male incidence rate ratios. We also examined the prevalence of cigarette smoking, using data from the National Health Interview Survey from 1970 to 2016. RESULTS Over the past two decades, the age‐specific incidence of lung cancer has generally decreased among both men and women 30 to 54 years of age in all races and ethnic groups, but the declines among men have been steeper. Consequently, among non‐Hispanic whites, the female‐to‐male incidence rate ratios increased, exceeding 1.0 in the age groups of 30 to 34, 35 to 39, 40 to 44, and 45 to 49 years. For example, the female‐to‐male incidence rate ratio among whites 40 to 44 years of age increased from 0.88 (95% confidence interval [CI], 0.84 to 0.92) during the 1995–1999 period to 1.17 (95% CI, 1.11 to 1.23) during the 2010–2014 period. The crossover in sex‐specific rates occurred among non‐Hispanic whites born since 1965. Sex‐specific incidence rates converged among non‐Hispanic blacks, Hispanics, and non‐Hispanic Asians and Pacific Islanders but crossed over from a higher incidence among men to a higher incidence among women only among Hispanics. The prevalence of cigarette smoking among women born since 1965 has approached, but generally not exceeded, the prevalence among men. CONCLUSIONS The patterns of historically higher incidence rates of lung cancer among men than among women have reversed among non‐Hispanic whites and Hispanics born since the mid‐1960s, and they are not fully explained by sex differences in smoking behaviors. Future studies are needed to identify reasons for the higher incidence of lung cancer among young women. (Funded by the American Cancer Society.)
Anti-inflammatory effect of xanthomicrol, a major colonic metabolite of 5-demethyltangeretin.

5-Demethyltengeretin (5DT) is a citrus flavonoid with various potential health benefits. To provide physiologically relevant information on the anti-inflammatory properties of 5DT, we identified the major metabolite of 5DT in the mouse colon and established its anti-inflammatory effects in lipopolysaccharide (LPS)-stimulated RAW 264.7 macrophages. CD-1 mice were fed with a 5DT-containing diet for four weeks, and colonic mucosa samples were collected and subjected to LC-MS analysis. Xanthomicrol (XAN) was identified as the major metabolite of 5DT in the mouse colon. More importantly, the colonic level of XAN was about 3.1-fold higher than that of 5DT. The anti-inflammatory effects of 5DT and XAN were determined in LPS-stimulated macrophages. XAN produced significant inhibitory effects on the production of nitric oxide and PGE2. Western blotting and real-time PCR analyses demonstrated that XAN greatly decreased the protein and mRNA levels of iNOS as well as the protein level of COX-2. Furthermore, XAN also reduced the production of pro-inflammatory cytokine IL-1β and induced the expression of anti-oxidative enzyme HO-1. CONCLUSION Our results demonstrated that XAN is a major metabolite of 5DT in the colon of mice fed with 5DT, and XAN may play important roles in the anti-inflammatory effects elicited by orally administered 5DT.
Magnetic Nanoparticles Conjugated with Peptides Derived from Monocyte Chemoattractant Protein-1 as a Tool for Targeting Atherosclerosis

Atherosclerosis is a multifactorial inflammatory disease that may progress silently for long period, and it is also widely accepted as the main cause of cardiovascular diseases. To prevent atherosclerotic plaques from generating, imaging early molecular markers and quantifying the extent of disease progression are desired. During inflammation, circulating monocytes leave the bloodstream and migrate into incipient lipid accumulation in the artery wall, following conditioning by local growth factors and proinflammatory cytokines; therefore, monocyte accumulation in the arterial wall can be observed in fatty streaks, rupture-prone plaques, and experimental atherosclerosis. In this work, we synthesized monocyte-targeting iron oxide magnetic nanoparticles (MNPs), which were incorporated with the peptides derived from the chemokine receptor C-C chemokine receptor type 2 (CCR2)-binding motif of monocytes chemoattractant protein-1 (MCP-1) as a diagnostic tool for potential atherosclerosis. MCP-1-motif MNPs co-localized with monocytes in in vitro fluorescence imaging. In addition, with MNPs injection in ApoE knockout mice (ApoE KO mice), the well-characterized animal model of atherosclerosis, MNPs were found in specific organs or regions which had monocytes accumulation, especially the aorta of atherosclerosis model mice, through in vivo imaging system (IVIS) imaging and magnetic resonance imaging (MRI). We also performed Oil Red O staining and Prussian Blue staining to confirm the co-localization of MCP-1-motif MNPs and atherosclerosis. The results showed the promising potential of MCP-1-motif MNPs as a diagnostic agent of atherosclerosis.