Miracle-dz/UniFAR-FTUs huggingface dataset:
CropNet: Real-Time Thumbnailing

We present a deep learning-based thumbnail generation method called CropNet in this paper. Unlike previous deep learning-based methods, such as Fast-AT, which can utilize detectors introduced in object detection frameworks and generate thousands of proposals, our detector is straightforward and concise, thereby ensuring that the final cropping window is computed by its center and width, with the input aspect ratio. To achieve this goal, CropNet learns specific filters to estimate the center position and utilizes a cascade structure of filters and single neuron for width inference. In addition, CropNet optimizes the center and width jointly for optimal results. We collect a data set of more than 29,000 thumbnail annotations to train CropNet and perform cross-validation between existing data sets. Experiments show that CropNet outperforms existing techniques. Our result is achieved at a test-time speed of 17 ms per image, which is six times faster than the fastest method at present.
Deep Triplet Quantization

Deep hashing establishes efficient and effective image retrieval by end-to-end learning of deep representations and hash codes from similarity data. We present a compact coding solution, focusing on deep learning to quantization approach that has shown superior performance over hashing solutions for similarity retrieval. We propose Deep Triplet Quantization (DTQ), a novel approach to learning deep quantization models from the similarity triplets. To enable more effective triplet training, we design a new triplet selection approach, Group Hard, that randomly selects hard triplets in each image group. To generate compact binary codes, we further apply a triplet quantization with weak orthogonality during triplet training. The quantization loss reduces the codebook redundancy and enhances the quantizability of deep representations through back-propagation. Extensive experiments demonstrate that DTQ can generate high-quality and compact binary codes, which yields state-of-the-art image retrieval performance on three benchmark datasets, NUS-WIDE, CIFAR-10, and MS-COCO.
Access to Data and Number of Iterations: Dual Primal Algorithms for Maximum Matching under Resource Constraints

In this paper we consider graph algorithms in models of computation where the space usage (random accessible storage, in addition to the read only input) is sublinear in the number of edges m and the access to input data is constrained. These questions arises in many natural settings, and in particular in the analysis of MapReduce or similar algorithms that model constrained parallelism with sublinear central processing. In SPAA 2011, Lattanzi etal. provided a O(1) approximation of maximum matching using O(p) rounds of iterative filtering via mapreduce and O(n1+1/p) space of central processing for a graph with n nodes and m edges. We focus on weighted nonbipartite maximum matching in this paper. For any constant p>1, we provide an iterative sampling based algorithm for computing a (1--ε)-approximation of the weighted nonbipartite maximum matching that uses O(p/ε) rounds of sampling, and O(n1+1/p) space. The results extends to b-Matching with small changes.This paper combines adaptive sketching literature and fast primal-dual algorithms based on relaxed Dantzig-Wolfe decision procedures. Each round of sampling is implemented through linear sketches and executed in a single round of MapReduce. The paper also proves that nonstandard linear relaxations of a problem, in particular penalty based formulations, are helpful in mapreduce and similar settings in reducing the adaptive dependence of the iterations.
SCRATCH: A Scalable Discrete Matrix Factorization Hashing for Cross-Modal Retrieval

In recent years, many hashing methods have been proposed for the cross-modal retrieval task. However, there are still some issues that need to be further explored. For example, some of them relax the binary constraints to generate the hash codes, which may generate large quantization error. Although some discrete schemes have been proposed, most of them are time-consuming. In addition, most of the existing supervised hashing methods use an n x n similarity matrix during the optimization, making them unscalable. To address these issues, in this paper, we present a novel supervised cross-modal hashing method---Scalable disCRete mATrix faCtorization Hashing, SCRATCH for short. It leverages the collective matrix factorization on the kernelized features and the semantic embedding with labels to find a latent semantic space to preserve the intra- and inter-modality similarities. In addition, it incorporates the label matrix instead of the similarity matrix into the loss function. Based on the proposed loss function and the iterative optimization algorithm, it can learn the hash functions and binary codes simultaneously. Moreover, the binary codes can be generated discretely, reducing the quantization error generated by the relaxation scheme. Its time complexity is linear to the size of the dataset, making it scalable to large-scale datasets. Extensive experiments on three benchmark datasets, namely, Wiki, MIRFlickr-25K, and NUS-WIDE, have verified that our proposed SCRATCH model outperforms several state-of-the-art unsupervised and supervised hashing methods for cross-modal retrieval.
Relationship between education, leisure activities, and cognitive functions in older adults

Abstract This study aimed to reveal the relationship between life activities and cognitive function and to evaluate the interaction between education and various leisure activities in predicting cognitive function. Using a cross-sectional research design with retrospective data, a total of 210 healthy Korean older adults participated and reported their years of education, working, and lifelong leisure activities. Cognitive function was measured using the Mini Mental State Examination. A hierarchical multiple regression analysis showed that education was positively associated with cognitive function, whereas working activity was not. Craft activities positively predicted cognitive function. Furthermore, education moderated the relationship between leisure activities and cognitive function. Only low-educated participants showed a decrease in cognitive function as they performed domestic chores and an increase in cognitive function as they participated in social activities and volunteering. High-educated participants showed no relation between leisure activities and cognitive function. The results of the current study suggest that the relationship between various leisure activities and cognitive function can vary based on the nature of the leisure activity and educational level. Professionals examining older adults’ cognitive function should pay closer attention to educational level, as well as life styles (i.e. leisure activities), to provide appropriate interventions.
Effectiveness of microteaching as a method of developing teaching competence among in-service medical teachers

Introduction: In spite of the fact that microteaching has been practiced extensively in most universities, its actual efficacy has not been studied systematically. In this study, there was an attempt to quantify the efficacy of microteaching in inducing behavioral change in teachers. We also aimed to determine the perceived utility and ease of this process in teacher training, using the feedback received from the participants. This feedback along with efficacy can collectively predict the effectiveness of microteaching. Methods: A prospective experimental study was designed using a convenient sample of 30 faculty volunteers. After the institutional ethics committee approval, the videos of pre-microteaching and post-microteaching sessions from the 30 participants undergoing 5 sessions of microteaching were graded with a seven point teaching competency scale and the participant’s perceived usefulness and perceived ease of use was studied using a validated questionnaire. Paired sample t-test was used to determine the efficacy of the study. Results: Microteaching showed a statistically significant improvement among the behavior of the participants after five sessions of microteaching. All the parameters in the scale showed a statistically significant improvement. Though the participants felt that this method was useful, the majority of them felt it is a very time consuming process requiring resources. Conclusion: Hence, the overall effectiveness in in-service teaching process was limited for microteaching in this current scenario; though microteaching induced positive behaviour change, it was time consuming and also it was difficult to arrange a peer group to enrole.
Interactions Modeling in Multi-Task Multi-View Learning with Consistent Task Diversity

Multi-task Multi-view (MTMV) learning has recently undergone noticeable development for dealing with heterogeneous data. To exploit information from both related tasks and related views, a common strategy is to model task relatedness and view consistency separately. The drawback of this strategy is that it did not consider the interactions between tasks and views. To remedy this, we propose a novel method, racBFA, by adding rank constraints to asymmetric bilinear factor analyzers (aBFA). We then adapt racBFA to our MTMV learning problem and design a new MTMV learning algorithm, racMTMV. We evaluated racMTMV on 3 real-world data sets. The experimental results demonstrated the effectiveness of our proposed method.
Hybrid Improper Ferroelectricity in (Sr,Ca)3Sn2O7 and Beyond: Universal Relationship between Ferroelectric Transition Temperature and Tolerance Factor in n = 2 Ruddlesden-Popper Phases.

Hybrid improper ferroelectricity, which utilizes nonpolar but ubiquitous rotational/tilting distortions to create polarization, offers an attractive route to the discovery of new ferroelectric and multiferroic materials because its activity derives from geometric rather than electronic origins. Design approaches blending group theory and first principles can be utilized to explore the crystal symmetries of ferroelectric ground states, but in general, they do not make accurate predictions for some important parameters of ferroelectrics, such as Curie temperature ( TC). Here, we establish a predictive and quantitative relationship between TC and the Goldschmidt tolerance factor, t, by employing n = 2 Ruddlesden-Popper (RP) A3B2O7 as a prototypical example of hybrid improper ferroelectrics. The focus is placed on an RP system, (Sr1- xCa x)3Sn2O7 ( x = 0, 0.1, and 0.2), which allows for the investigation of the purely geometric (ionic size) effect on ferroelectric transitions, due to the absence of the second-order Jahn-Teller active (d0 and 6s2) cations that often lead to ferroelectric distortions through electronic mechanisms. We observe a ferroelectric-to-paraelectric transition with TC = 410 K for Sr3Sn2O7. We also find that the TC increases linearly up to 800 K upon increasing the Ca2+ content, i.e., upon decreasing the value of t. Remarkably, this linear relationship is applicable to the suite of all known A3B2O7 hybrid improper ferroelectrics, indicating that the TC correlates with the simple crystal chemistry descriptor, t, based on the ionic size mismatch. This study provides a predictive guideline for estimating the TC of a given material, which would complement the convergent group-theoretical and first-principles design approach.
The Mediating Role of Conceptions of Learning in the Relationship Between Metacognitive Skills/Strategies and Academic Outcomes Among Middle-School Students

This study investigated the mediating role of conceptions of learning in the relationship between metacognitive skills/strategies and academic outcomes among middle-school students. The self-report “Learning Conceptions Questionnaire” (LCQ) and “Metacognitive questionnaire on the method of study” (QMS—in Italian) were administered to 136 middle-school students and their academic outcomes were collected. Correlation analyses revealed that within metacognitive skills/strategies only self-assessment was positively correlated with academic outcomes. Mediation analysis indicated that a conception of learning as internal attribution of success and failure was significantly involved as mediator in the relationship between metacognitive skills/strategies and academic outcomes. This study permitted to advance our knowledge about the relationship between metacognitive skills/strategies and academic outcomes and it has opened the way to practical implications.
Waist‐to‐hip ratio affects female body attractiveness and modulates early brain responses

This investigation examined the electrophysiological response underlying the visual processing of waist‐to‐hip ratio (WHR) in female bodies, a characteristic known to affect perceived attractiveness. WHRs of female bodies were artificially adjusted to values of 0.6, 0.7, 0.8 or 0.9. Behavioural ratings of attractiveness of the bodies revealed a preference for WHRs of 0.7 in the overall group of participants, which included both male and female heterosexual individuals. Event‐related potentials (ERPs) were then recorded while participants performed a selective attention task involving photographs of female models and scrambled images. Results showed that the P1 (80–120 ms) and N1 (130–170 ms) components situated over posterior brain regions were the earliest components to be modulated by attention and bodies. Interestingly, the vertex‐positive potential, occurring between 120–180 ms, produced a greater positivity for WHRs of 0.7 compared to the other ratios. However, this increase was only observed when the body stimuli were attended, while no effect was observed for unattended bodies. These findings provide evidence of an early brain sensitivity to visual attributes that constitute secondary sexual characteristics. Although they are relatively discrete from the point of view of their physical quality, these signs possess strong behavioural significance, producing greater reported attractiveness, likely by conveying the biological meaning that signals good health and greater reproductive success. Our results therefore reveal that attributes associated with sexual attractiveness in female bodies are processed rapidly in the stream of visual processing.
Sequential Gastroenteritis Outbreaks in a Single Year Caused by Norovirus Genotypes GII.2 and GII.6 in an Institutional Setting

Abstract Background Norovirus is a leading cause of acute gastroenteritis worldwide. Improved diagnostic capability has been instrumental in the characterization of archival norovirus strains associated with gastroenteritis outbreaks that were investigated decades ago. One such investigation was that of 2 sequential gastroenteritis outbreaks that occurred in 1971 at the former Henryton State Hospital in Maryland. Approximately 40% of the resident population experienced clinical symptoms in both outbreaks, which occurred 11 months apart. Methods Stored stools and paired sera were re-analyzed to investigate the etiology of the 2 outbreaks. Results Different norovirus genotypes were identified as the etiological agents responsible for the illnesses, with GII.2 associated with the first outbreak and GII.6 with the second. The viruses were antigenically distinct as determined by analyses of hyperimmune sera raised against the corresponding virus-like particles in animals, as well as paired sera from infected individuals. Conclusions The observed antigenic differences were consistent with the failure of the GII.2 strain to provide cross-protective immunity to the GII.6 strain a few months later. An understanding of antigenic diversity among norovirus genotypes will be important in the design of norovirus vaccines.
Dance with Melody: An LSTM-autoencoder Approach to Music-oriented Dance Synthesis

Dance is greatly influenced by music. Studies on how to synthesize music-oriented dance choreography can promote research in many fields, such as dance teaching and human behavior research. Although considerable effort has been directed toward investigating the relationship between music and dance, the synthesis of appropriate dance choreography based on music remains an open problem. There are two main challenges: 1) how to choose appropriate dance figures, i.e., groups of steps that are named and specified in technical dance manuals, in accordance with music and 2) how to artistically enhance choreography in accordance with music. To solve these problems, in this paper, we propose a music-oriented dance choreography synthesis method using a long short-term memory (LSTM)-autoencoder model to extract a mapping between acoustic and motion features. Moreover, we improve our model with temporal indexes and a masking method to achieve better performance. Because of the lack of data available for model training, we constructed a music-dance dataset containing choreographies for four types of dance, totaling 907,200 frames of 3D dance motions and accompanying music, and extracted multidimensional features for model training. We employed this dataset to train and optimize the proposed models and conducted several qualitative and quantitative experiments to select the best-fitted model. Finally, our model proved to be effective and efficient in synthesizing valid choreographies that are also capable of musical expression.
Contraction speed and type influences rapid utilisation of available muscle force: neural and contractile mechanisms

ABSTRACT This study investigated the influence of contraction speed and type on the human ability to rapidly increase torque and utilise the available maximum voluntary torque (MVT) as well as the neuromuscular mechanisms underpinning any effects. Fifteen young, healthy males completed explosive voluntary knee extensions in five conditions: isometric (ISO), and both concentric and eccentric at two constant accelerations of 500 deg s−2 (CONSLOW and ECCSLOW) and 2000 deg s−2 (CONFAST and ECCFAST). Explosive torque and quadriceps EMG were recorded every 25 ms up to 150 ms from their respective onsets and normalised to the available MVT and EMG at MVT, respectively, specific to that joint angle and velocity. Neural efficacy (explosive voluntary:evoked octet torque) was also measured, and torque data were entered into a Hill-type muscle model to estimate muscle performance. Explosive torques normalised to MVT (and normalised muscle forces) were greatest in the concentric followed by the isometric and eccentric conditions, and in the fast compared with slow speeds within the same contraction type (CONFAST>CONSLOW>ISO, and ECCFAST>ECCSLOW). Normalised explosive-phase EMG and neural efficacy were greatest in concentric conditions, followed by isometric and eccentric conditions, but were similar for fast and slow contractions of the same type. Thus, distinct neuromuscular activation appeared to explain the effect of contraction type but not speed on normalised explosive torque, suggesting the speed effect is an intrinsic contractile property. These results provide novel evidence that the ability to rapidly increase torque/force and utilise the available MVT is influenced by both contraction type and speed, owing to neural and contractile mechanisms, respectively. Summary: Time to maximum force in skeletal muscle is shorter at fast versus slow contraction speeds, owing to contractile mechanisms, and in concentric versus isometric or eccentric contractions, owing to neural mechanisms.
Human Pose Estimation Method Based on Flexible Model and Deep Learning

This paper1 proposes a mixture model that combines joint appearance-based and inter-joint spatial relationship-based models with a deep neural architecture called deep convolutional neural network (DCNN). This method has been applied to tackle the human pose estimation problem. Firstly we construct a graphical model for the human body. Secondly, the images are decomposed into several image patches which are used as positive input samples. And finally, a DCNN network that can solve multiple classifications is obtained to perform human pose estimation. The quantitative results obtained from the experiments are impressive and show that our method outperforms recent works of the state-of-the-art that used same considered datasets.
Role of sexual imprinting in assortative mating and premating isolation in Darwin’s finches

Significance It is important to preserve the potential for future speciation because global biodiversity is being rapidly degraded. A key question is how incipient species become reproductively isolated from each other. Here we provide evidence that two species of Darwin’s finches choose mates on the basis of learning morphological features of their parents and possibly from inherited preferences. The evidence for imprinting is stronger in sons than in daughters and for imprinting by both sons and daughters is stronger on fathers, which sing, than on mothers, which do not. Imprinting establishes a barrier to interbreeding between morphologically different species. The barrier is leaky, species occasionally hybridize, and the hybrids can give rise to a new species based on learned mate preferences. Global biodiversity is being degraded at an unprecedented rate, so it is important to preserve the potential for future speciation. Providing for the future requires understanding speciation as a contemporary ecological process. Phylogenetically young adaptive radiations are a good choice for detailed study because diversification is ongoing. A key question is how incipient species become reproductively isolated from each other. Barriers to gene exchange have been investigated experimentally in the laboratory and in the field, but little information exists from the quantitative study of mating patterns in nature. Although the degree to which genetic variation underlying mate-preference learning is unknown, we provide evidence that two species of Darwin’s finches imprint on morphological cues of their parents and mate assortatively. Statistical evidence of presumed imprinting is stronger for sons than for daughters and is stronger for imprinting on fathers than on mothers. In combination, morphology and species-specific song learned from the father constitute a barrier to interbreeding. The barrier becomes stronger the more the species diverge morphologically and ecologically. It occasionally breaks down, and the species hybridize. Hybridization is most likely to happen when species are similar to each other in adaptive morphological traits, e.g., body size and beak size and shape. Hybridization can lead to the formation of a new species reproductively isolated from the parental species as a result of sexual imprinting. Conservation of sufficiently diverse natural habitat is needed to sustain a large sample of extant biota and preserve the potential for future speciation.
Teleoperated Robotic Arm Movement Using EMG Signal With Wearable MYO Armband

The main purpose of this research is to move the robotic arm (5DoF) in real-time, based on the surface Electromyography (sEMG) signals, as obtained from the wireless Myo gesture armband to distinguish seven hand movements. The sEMG signals are biomedical signals that estimate and record the electrical signals produced in muscles through their contraction and relaxation, representing neuromuscular activities. Therefore, controlling the robotic arm via the muscles of the human arm using sEMG signals is considered to be one of the most significant methods. The wireless Myo gesture armband is used to record sEMG signals from the forearm. In order to analyze these signals, the pattern recognition system is employed, which consists of three main parts: segmentation, feature extraction, and classification. Overlap technique is chosen for segmenting part of the signal. Six time domain features (MAV, WL, RMS, AR, ZC, and SSC) are extracted from each segment. The classifiers (SVM, LDA, and KNN) are employed to enable comparison between them in order to obtain optimum accuracy of the system. The results show that the SVM achieves higher system accuracy at 96.57 %, compared to LDA reaching 96.01 %, and 92.67 % accuracy achieved by KNN.