Miracle-dz/UniFAR-FTUs huggingface dataset:
The metric properties of a novel non‐motor symptoms scale for Parkinson's disease: Results from an international pilot study

Non‐motor symptoms (NMS) in Parkinson's disease (PD) are common, significantly reduce quality of life and at present there is no validated clinical tool to assess the progress or potential response to treatment of NMS. A new 30‐item scale for the assessment of NMS in PD (NMSS) was developed. NMSS contains nine dimensions: cardiovascular, sleep/fatigue, mood/cognition, perceptual problems, attention/memory, gastrointestinal, urinary, sexual function, and miscellany. The metric attributes of this instrument were analyzed. Data from 242 patients mean age 67.2 ± 11 years, duration of disease 6.4 ± 6 years, and 57.3% male across all stages of PD were collected from the centers in Europe, USA, and Japan. The mean NMSS score was 56.5 ± 40.7, (range: 0–243) and only one declared no NMS. The scale provided 99.2% complete data for the analysis with the total score being free of floor and ceiling effect. Satisfactory scaling assumptions (multitrait scaling success rate >95% for all domains except miscellany) and internal consistency were reported for most of the domains (mean α, 0.61). Factor analysis supported the a prori nine domain structure (63% of the variance) while a small test–retest study showed satisfactory reproducibility (ICC > 0.80) for all domains except cardiovascular (ICC = 0.45). In terms of validity, the scale showed modest association with indicators of motor symptom severity and disease progression but a high correlation with other measures of NMS (NMSQuest) and health‐related quality of life measure (PDQ‐8) (both, rS = 0.70). In conclusion, NMSS can be used to assess the frequency and severity of NMS in PD patients across all stages in conjunction with the recently validated non‐motor questionnaire. © 2007 Movement Disorder Society
Conservation systematics of the shield-backed trapdoor spiders of the nigrum-group (Mygalomorphae, Idiopidae, Idiosoma): integrative taxonomy reveals a diverse and threatened fauna from south-western Australia

Abstract The aganippine shield-backed trapdoor spiders of the monophyletic nigrum-group of Idiosoma Ausserer s. l. are revised, and 15 new species are described from Western Australia and the Eyre Peninsula of South Australia: I. arenaceum Rix & Harvey, sp. n. , I. corrugatum Rix & Harvey, sp. n. , I. clypeatum Rix & Harvey, sp. n. , I. dandaragan Rix & Harvey, sp. n. , I. formosum Rix & Harvey, sp. n. , I. gardneri Rix & Harvey, sp. n. , I. gutharuka Rix & Harvey, sp. n. , I. incomptum Rix & Harvey, sp. n. , I. intermedium Rix & Harvey, sp. n. , I. jarrah Rix & Harvey, sp. n. , I. kopejtkaorum Rix & Harvey, sp. n. , I. kwongan Rix & Harvey, sp. n. , I. mcclementsorum Rix & Harvey, sp. n. , I. mcnamarai Rix & Harvey, sp. n. , and I. schoknechtorum Rix & Harvey, sp. n. Two previously described species from south-western Western Australia, I. nigrum Main, 1952 and I. sigillatum (O. P.-Cambridge, 1870), are re-illustrated and re-diagnosed, and complementary molecular data for 14 species and seven genes are analysed with Bayesian methods. Members of the nigrum-group are of long-standing conservation significance, and I. nigrum is the only spider in Australia to be afforded threatened species status under both State and Commonwealth legislation. Two other species, I. formosum Rix & Harvey, sp. n. and I. kopejtkaorum Rix & Harvey, sp. n. , are also formally listed as Endangered under Western Australian State legislation. Here we significantly relimit I. nigrum to include only those populations from the central and central-western Wheatbelt bioregion, and further document the known diversity and conservation status of all known species.
Selective prevention of combat-related post-traumatic stress disorder using attention bias modification training: a randomized controlled trial

Background Efficacy of pre-trauma prevention for post-traumatic stress disorder (PTSD) has not yet been established in a randomized controlled trial. Attention bias modification training (ABMT), a computerized intervention, is thought to mitigate stress-related symptoms by targeting disruptions in threat monitoring. We examined the efficacy of ABMT delivered before combat in mitigating risk for PTSD following combat. Method We conducted a double-blind, four-arm randomized controlled trial of 719 infantry soldiers to compare the efficacy of eight sessions of ABMT (n = 179), four sessions of ABMT (n = 184), four sessions of attention control training (ACT; n = 180), or no-training control (n = 176). Outcome symptoms were measured at baseline, 6-month follow-up, 10 days following combat exposure, and 4 months following combat. Primary outcome was PTSD prevalence 4 months post-combat determined in a clinical interview using the Clinician-Administered PTSD Scale. Secondary outcomes were self-reported PTSD and depression symptoms, collected at all four assessments. Results PTSD prevalence 4 months post-combat was 7.8% in the no-training control group, 6.7% with eight-session ABMT, 2.6% with four-session ABMT, and 5% with ACT. Four sessions of ABMT reduced risk for PTSD relative to the no-training condition (odds ratio 3.13, 95% confidence interval 1.01–9.22, p < 0.05, number needed to treat = 19.2). No other between-group differences were found. The results were consistent across a variety of analytic techniques and data imputation approaches. Conclusions Four sessions of ABMT, delivered prior to combat deployment, mitigated PTSD risk following combat exposure. Given its low cost and high scalability potential, and observed number needed to treat, research into larger-scale applications is warranted. The ClinicalTrials.gov identifier is NCT01723215.
Cognitive Schemas Have the Ability to Predict Emotional Schemas in Different Types of Anxiety Disorders

Objective: This study intends to evaluate the Cognitive Schemas have the ability to predict Emotional schemas in different types of anxiety disorders. Method: The study was ex post facto (causal-comparative) is. 109 people suffering from anxiety disorders in six groups of panic disorder, without Through fear, social phobia, specific phobia, obsessive - compulsive disorder, post-traumatic stress and anxiety and acute stress that psychologists and psychiatrists (private and public centers) Tehran, referring and the random sampling method was applied. To evaluate the results of the tests of Young Schema and Leahy Schema, the variance analysis, Tukey and multivariate regression Questionnaire was used. The Cognitive Schemas have the ability to predict Emotional schemas in different types of anxiety disorders. Results: The results showed that, impaired performance (conversely), other-directedness and rejection/disconnection fields have the ability to predict emotional schema of controllability, rejection/disconnection and impaired limits had the ability to predict comprehensibility. The emotional schema of guilt could be predicted by the use of rejection/disconnection and other-directedness, over-vigilance-inhibition (conversely), other-directedness and rejection/disconnection (conversely) have the ability to explain emotional schema of higher values. Results indicate that emotional schema of duration could be predicted by over-vigilance-inhibition (conversely) and rejection/disconnection. Conclusions: In the model of emotional schema of controllability they have an obsessive desire to be under control by others, but this extreme fear of catastrophic events prevents them to trust to anybody. For the same, impaired performance (conversely) is able to determine emotional schema of consensus.
Silencing of type Iγ phosphatidylinositol phosphate kinase suppresses ovarian cancer cell proliferation, migration and invasion

Metastasis is the major cause of death in ovarian cancer patients. Given that the molecular mechanism underlying metastasis formation is critical for improving therapeutic development and clinical treatment, it must be fully understood. Recent studies have revealed that lipid kinase type Iγ phosphatidylinositol phosphate kinase (PIPKIγ) participates in the metastasis of breast cancer and colon cancer by regulating cell migration and invasion. However, its role in the progression of ovarian cancer is unclear. Here we showed that PIPKIγ expression is upregulated in multiple epithelial ovarian cancer cell lines. Silencing of PIPKIγ impaired PI3K/AKT signaling and inhibited the aggressive behaviors of epithelial ovarian cancer cells, including proliferation, migration and invasion. Moreover, we found that PIPKIγ was required for the activation of signal transducer and activator of transcription 3 (STAT3) in epithelial ovarian cancer cells, indicating that STAT3 may also be engaged in the PIPKIγ-dependent aggressiveness of epithelial ovarian cancer cells. Our results, for the first time, identified PIPKIγ as a novel regulator in epithelial ovarian cancer cells that promotes cell proliferation, migration and invasion by activating multiple signaling pathways. Therefore, we propose that PIPKIγ could potentially be a therapeutic target for the early detection and treatment of epithelial ovarian cancer. Further studies employing in vivo models are necessary to test this possibility.
Effects of deforestation on attitudes and levels of tolerance towards commensal primates (Cercopithecidae) in Sri Lanka

Attitudes of people to wildlife, particularly to animals that live in close proximity to them, are an important element of conservation efforts and management. Attitudes may vary according to age and levels of conflict. We assessed the influence of proximity of forest on the attitudes of people towards two commensal primates, the purple-faced langur and the toque macaque. Data were collected in Sri Lanka by interviews in three villages where there is no continuous forest remaining and in three villages with adjacent forest. We found high levels of tolerance towards commensal primates, but significantly higher levels of negative perceptions in villages where forest was no longer present. Perceptions were not related to age or sex. The total disappearance of forest, with primates being dependent on fruit crops and living permanently on the village grounds, inevitably leads to conflict. These changing views have important management implications. Animals surviving in a human-dominated landscape may become more common, and the experiences in Sri Lanka may provide insight into what the future holds for other sites.
Ear verification under uncontrolled conditions with convolutional neural networks

The capabilities of biometric systems have recently made extraordinary leaps by the emergence of deep learning. However, due to the lack of enough training data, the applications of the deep neural network in the ear recognition filed have run into the bottleneck. Moreover, the effect of fine-tuning from some pre-trained models is far less than expected due to the diversity among different tasks. Therefore, the authors propose a large-scale ear database and explore the robust convolutional neural network (CNN) architecture for the ear feature representation. The images in this USTB-Helloear database were taken under uncontrolled conditions with illumination, pose variation and different level of ear occlusions. Then they fine-tuned and modified some deep models on the proposed database through the ear verification experiments. First, they replaced the last pooling layers by spatial pyramid pooling layers to fit arbitrary data size and obtain multi-level features. In the training phase, the CNNs were trained both under the supervision of the softmax loss and centre loss to obtain more compact and discriminative features to identify unseen ears. Finally, three CNNs with different scales of ear images were assembled as the multi-scale ear representations for ear verification. The experimental results demonstrate the effectiveness of the proposed modified CNN deep model.
Binocular Summation Revisited: Beyond √2

Our ability to detect faint images is better with two eyes than with one, but how great is this improvement? A meta-analysis of 65 studies published across more than 5 decades shows definitively that psychophysical binocular summation (the ratio of binocular to monocular contrast sensitivity) is significantly greater than the canonical value of √2. Several methodological factors were also found to affect summation estimates. Binocular summation was significantly affected by both the spatial and temporal frequency of the stimulus, and stimulus speed (the ratio of temporal to spatial frequency) systematically predicts summation levels, with slow speeds (high spatial and low temporal frequencies) producing the strongest summation. We furthermore show that empirical summation estimates are affected by the ratio of monocular sensitivities, which varies across individuals, and is abnormal in visual disorders such as amblyopia. A simple modeling framework is presented to interpret the results of summation experiments. In combination with the empirical results, this model suggests that there is no single value for binocular summation, but instead that summation ratios depend on methodological factors that influence the strength of a nonlinearity occurring early in the visual pathway, before binocular combination of signals. Best practice methodological guidelines are proposed for obtaining accurate estimates of neural summation in future studies, including those involving patient groups with impaired binocular vision.
Oak Woodland Restoration: Understory Response to Removal of Encroaching Conifers

Oregon white oak (or Garry oak, Quercus garryana) woodlands and savannas of the coastal Pacific Northwest are legacies of an anthropogenic fire regime that ended with European settlement in the mid-1800s. Historically, these oak stands had a sparse overstory and an understory dominated by fire-tolerant grasses and forbs. Post-settlement fire suppression resulted in widespread invasion and subsequent overstory dominance by conifers, causing mortality of shade-intolerant oak trees and shifting understory plant communities to shade-tolerant species. In a study on four southwestern Washington sites, our objective was to determine the effects of overstory conifer removal, primarily Douglas-fir (Pseudotsuga menziesii), on microclimate, native and non-native understory cover, and sapling growth. Overstory conifer removal created a warmer, drier understory microclimate during summer months. Conifer removal had little effect on native understory cover during five years post-treatment; however, cover of non-native plants, primarily grasses and woody understory species, increased significantly during the same period. Height growth of Oregon white oak and Douglas-fir saplings exhibited a delayed, but positive, response to overstory conifer removal, although the treatment response of Douglas-fir was 133% greater than that of oak. Increases in non-native understory cover and the rapid growth of young Douglas-fir indicate the importance of pre- and post-treatment understory management to control undesirable plants and promote native species such as Oregon white oak.
User association and wireless backhaul bandwidth allocation for 5G heterogeneous networks in the millimeter-wave band

The user association and wireless backhaul bandwidth allocation for a two-tier heterogeneous network (HetNet) in the millimeter wave (mmWave) band is proposed in this article. The two-tier HetNet is built up with a macro base station (MBS) and several small cell SBSs, where the MBS is assumed to be equipped with large-scale antenna arrays but the SBSs only have single-antenna capability and they rely on the wireless link to the MBS for backhaul. The sum of logarithmic user rate, which is established according to the result of multi-user Multiple Input Multiple Output (MIMO) downlink employing Zero-Force Beamforming (ZFBF), is chosen as the network utility for the objective function. And a distributed optimization algorithm based on primal and dual decomposition is used to jointly optimize the user association variable xj,i and the wireless backhaul bandwidth factor β. Simulation results reveal that the distributed optimization algorithm jointly optimizing two variables outperforms the conventional SINR-based user association strategies.
Policy learning and transfer in regional lifelong learning policies

The context of this research is the study of the processes whereby the effectiveness of training and lifelong learning policies are improved. This covers, in particular, the area of research into the policy-making procedure adopted by the regional governments. Regional policy-making is the outcome of formalised standards and procedures, and does not depend on legal, contextual or cultural variables, or combinations thereof, which differ considerably from context to context. (DIPF/Orig.)
Distributed Economic Dispatch in Microgrids Based on Cooperative Reinforcement Learning

Microgrids incorporated with distributed generation (DG) units and energy storage (ES) devices are expected to play more and more important roles in the future power systems. Yet, achieving efficient distributed economic dispatch in microgrids is a challenging issue due to the randomness and nonlinear characteristics of DG units and loads. This paper proposes a cooperative reinforcement learning algorithm for distributed economic dispatch in microgrids. Utilizing the learning algorithm can avoid the difficulty of stochastic modeling and high computational complexity. In the cooperative reinforcement learning algorithm, the function approximation is leveraged to deal with the large and continuous state spaces. And a diffusion strategy is incorporated to coordinate the actions of DG units and ES devices. Based on the proposed algorithm, each node in microgrids only needs to communicate with its local neighbors, without relying on any centralized controllers. Algorithm convergence is analyzed, and simulations based on real-world meteorological and load data are conducted to validate the performance of the proposed algorithm.
Scale Invariant Feature Transform

Scale Invariant Feature Transform (SIFT) is an image descriptor for image-based matching and recognition developed by David Lowe (1999, 2004). This descriptor as well as related image descriptors are used for a large number of purposes in computer vision related to point matching between different views of a 3-D scene and view-based object recognition. The SIFT descriptor is invariant to translations, rotations and scaling transformations in the image domain and robust to moderate perspective transformations and illumination variations. Experimentally, the SIFT descriptor has been proven to be very useful in practice for image matching and object recognition under real-world conditions. In its original formulation, the SIFT descriptor comprised a method for detecting interest points from a grey-level image at which statistics of local gradient directions of image intensities were accumulated to give a summarizing description of the local image structures in a local neighbourhood around each interest point, with the intention that this descriptor should be used for matching corresponding interest points between different images. Later, the SIFT descriptor has also been applied at dense grids (dense SIFT) which have been shown to lead to better performance for tasks such as object categorization, texture classification, image alignment and biometrics . The SIFT descriptor has also been extended from grey-level to colour images and from 2-D spatial images to 2+1-D spatio-temporal video.
Human Dimensions of Coral Reef Social-Ecological Systems

Coral reefs are among the most diverse ecosystems on the planet but are declining because of human activities. Despite general recognition of the human role in the plight of coral reefs, the vast majority of research focuses on the ecological rather than the human dimensions of reef ecosystems, limiting our understanding of social relationships with these environments as well as potential solutions for reef recovery. General frameworks for social-ecological systems (SESs) have been advanced, but system-specific approaches are needed to develop a more nuanced view of human-environmental interactions for specific contexts and resource systems, and at specific scales. We synthesize existing concepts related to SESs and present a human dimensions framework that explores the linkages between social system structural traits, human activities, ecosystem services, and human well-being in coral reef SESs. Key features of the framework include social-ecological reciprocity, proximate and underlying dimensions, and the directionality of key relationships and feedback loops. Such frameworks are needed if human dimensions research is to be more fully integrated into studies of ecosystem change and the sustainability of linked SESs.
FontLex: A Typographical Lexicon based on Affective Associations

The task of selecting suitable fonts for a given text is non-trivial, as tens of thousands of fonts are available, and the choice of font has been shown to affect the perception of the text as well as of the author or of the brand being advertized. Aiming to support the development of font recommendation tools, we create a typographical lexicon providing associations between words and fonts. We achieve this by means of affective evocations, making use of font–emotion and word–emotion relationships. For this purpose, we ﬁrst determine font vectors for a set of ten emotion attributes, based on word similarities and antonymy information. We evaluate these associations through a user study via Mechanical Turk, which, for eight of the ten emotions, shows a strong user preference towards the fonts that are found to be congruent by our predicted data. Subsequently, this data is used to calculate font vectors for speciﬁc words, by relying on the emotion associations of a given word. This leads to a set of font associations for 6.4K words. We again evaluate the resulting dataset using Mechanical Turk, on 25 randomly sampled words. For the majority of these words, the responses indicate that fonts with strong associations are preferred, and for all except 2 words, fonts with weak associations are dispreferred. Finally, we further extend the dataset using synonyms of font attributes and emotion names. The resulting FontLex resource provides mappings between 6.7K words and 200 fonts.
Undersampling Improves Hypernymy Prototypicality Learning

This paper focuses on supervised hypernymy detection using distributional representations for unknown word pairs. Levy et al. (2015) demonstrated that supervised hypernymy detection suffers from overﬁtting hypernyms in training data. We show that the problem of overﬁtting on this task is caused by a characteristic of datasets, which stems from the inherent structure of the language resources used, hierarchical thesauri. The simple data preprocessing method proposed in this paper alleviates this problem. To be more precise, we demonstrate through experiments that the problem that hypernymy classiﬁers overﬁt hypernyms in training data comes from a skewed word frequency distribution brought by the quasi-tree structure of a thesaurus, which is a major resource of lexical semantic relation data, and propose a simple undersampling method based on word frequencies that can effectively alleviate overﬁtting and improve distributional prototypicality learning for unknown word pairs.