Miracle-dz/UniFAR-FTUs huggingface dataset:
Correlation Filter Tracking with Multiscale Spatial View

Visual tracking has already become one of the most important research focuses in the field of computer vision. Due to such interference as serious occlusion or severe illumination change and so on, the appearance model of the target tends to vary heavily, posing great challenges on tracking. In this paper, a correlation filter tracking with multiscale spatial view (CFMSV) is proposed in which a group of multiscale spatial filters of different view areas is established. We adopt the filters to perform collaborative location by introducing the method of pre-location and exploiting the multiscale spatial view around the target. A large number of tracking experiments have been made, which confirmed that the CFMSV tracking method proposed in our work is superior to the state-of-the-art methods in tracking performance.
Respiratory Viruses and Atypical Bacteria Co-Infection in Children with Acute Respiratory Infection

BACKGROUND: Acute respiratory infections (ARI) are one of the prevalent pediatric diseases. Coinfections of respiratory viruses and atypical bacterial respiratory pathogens are common. AIM: This study aimed to determine the prevalence of co-infection between respiratory pathogens including viruses, bacteria and atypical bacteria in a sample of Egyptian children presenting with symptoms of acute respiratory tract infection. METHODS: This one-year prospective cohort study conducted in Abo El Rish Pediatric Hospital, Cairo University over one year included children presenting with symptoms of acute respiratory infection. Enrolled children were subjected to nasopharyngeal swabs or throat swabs and then processed to detect viral, bacterial and atypical bacterial causative agents by culture), retrotranscription polymerase, Monoplex polymerase chain reaction (PCR) and Multiplex PCR. RESULTS: Viral etiological agents were detected in 20 cases (20.8%), while 76 patients (79.2%) had no definite viral aetiology. The most abundant virus detected was Rhinovirus in 36 (27.3%), followed by 21 (15.9%) were positive for RSV, 12 (9.1%) were positive for HMPV, 6 (4.5%) were positive for adenovirus and 3 (2.3%) were positive for influenza B. For Atypical bacterial causes Mycoplasma were positive for 9 (6.8%) cases and one case was positive for Bordetella parapertussis. Viral and atypical bacteria Co infection were detected in 14 (10.6%) of cases. CONCLUSION: These results suggest that coinfection with bacteria or atypical bacteria in children with acute respiratory tract infection is common and this co-infection can induce serious illness. The multiplex reverse-transcriptase polymerase chain reaction should become an essential tool for epidemiological studies and can fill the gap between clinical presentation and definitive diagnosis.
Comparative proteomic analysis of cat eye syndrome critical region protein 1- function in tumor-associated macrophages and immune response regulation of glial tumors

Introduction Tumor associated macrophages (TAMs) promote tumor development, angiogenesis and distal metastasis. In previous studies, we showed that Cat Eye Syndrome Critical Region Protein 1 (CECR1) is expressed by M2-like TAMs in human glioma samples. CECR1 promoted M2 TAMs differentiation and affected glioma cell proliferation and migration. Here we investigated the proteomic profile of TAMs expressing CECR1 in absence or presence of glioma cells. Results CECR1 siRNA transfection upregulated 67 proteins in THP-1-derived Macrophages (MQs). Pathway annotation mapped this set to 3 major pathways relevant for MQ function, including 'MHC-I antigen presentation', 'phagosome maturation' and 'endocytosis'. Co-culture of siCECR1 THP-1-derived MQs with U87 glioma cells attenuated the changes observed on protein and mRNA level in response to MQ CECR1 silencing. SiCECR1 in U87 co-cultured MQs was associated with an IL-10low, IL-12high M1-like phenotype. In U87 co-culture conditions, SiCECR1 also downregulated S20 proteasome complex proteins PSMA5, PSMA7, PSMC6 and PSMD8. This protein profile was linked to a low proliferation rate of siCECR1 MQs. Overlap analysis identified S100A9 and PLAU as CECR1-related proteins that were significantly correlated with expression of CECR1 and macrophage lineage markers in three large public GBM datasets. Conclusion This study reports the molecular pathways and key molecules that are mediated by CECR1 function in THP- 1-derived MQs and TAMs in glioma. Methods PMA-treated THP-1 cells (MQs) were siRNA transfected for CECR1 in vitro, with or without stimulation of the primary glioma cell line U87. Lysates were analyzed by (nano)LC-MS. Significant altered protein levels were identified (P < 0.05), followed by pathway annotation.
Effects of Negatively Worded Items and the Provision of a Warning about the Inclusion of Negatively Worded Items in an Attitude Questionnaire

It has been widely demonstrated that the inclusion of negatively worded items in attitude questionnaires can have adverse effects on the responses (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). The present study examined the effect of a varying number of negatively worded items and the provision of a warning about the inclusion of negatively worded items on the responses of 333 students in a 12 item attitude questionnaire. Five questionnaire forms were used. The results revealed that the varying number of negatively worded items did not have an effect on the responses. The results furthermore indicated that no wording effects associated with negatively worded items were found except for one negatively worded item that exhibited high intensity in its negative form. Another finding was that there is no need for the provision of a warning. The implications of the results for practice and directions for future research are discussed.
Spinal control of muscle synergies for adult mammalian locomotion

The control of locomotion is thought to be generated by activating groups of muscles that perform similar actions, which are termed muscle synergies. Here, we investigated if muscle synergies are controlled at the level of the spinal cord. We did this by comparing muscle activity in the legs of cats during stepping on a treadmill before and after a complete spinal transection that abolishes commands from the brain. We show that muscle synergies were maintained following spinal transection, validating the concept that muscle synergies for locomotion are primarily controlled by circuits of neurons within the spinal cord.
A Phase II/III, randomized, double-masked, vehicle-controlled, dose-ranging study of the safety and efficacy of OTX-101 in the treatment of dry eye disease

Purpose The aim of this study was to evaluate the safety and efficacy of OTX-101, a clear nanomicellar aqueous solution of cyclosporine, in the treatment of dry eye disease (DED). Patients and methods This was a 12-week multicenter, randomized, prospective, double-masked, vehicle-controlled, dose-ranging clinical trial. Subjects were adults aged ≥18 years, with a total conjunctival staining score of ≥3 and ≤9, and global DED symptom score ≥40 (0–100 visual analogue scale). Following a 14-day vehicle run-in, subjects were randomized in a 1:1:1 ratio to twice daily treatment with OTX-101 0.09%, OTX-101 0.05%, or vehicle for 84 days. Co-primary efficacy end points were changes, from baseline to Day 84, in the total lissamine green conjunctival staining score in the designated study eye and in the global symptom score (both eyes). Secondary end points included total corneal fluorescein staining score, tear breakup time, and Schirmer’s test score. Results In total, 455 subjects were randomized. Subjects treated with active drug experienced greater improvement in conjunctival staining than vehicle-treated patients (P<0.01 for both concentrations). All groups demonstrated improvements in global symptom score, but there were no differences among groups. Nominally significant differences were found between the active drug arms and vehicle for corneal staining scores and Schirmer’s test scores. Most treatment-emergent adverse events were mild in severity; no serious ocular adverse events were reported. Conclusions Both concentrations of OTX-101 met the co-primary sign end point (conjunctival staining) but not the co-primary symptom end point. OTX-101 0.09% demonstrated a notable impact on multiple signs of DED relative to vehicle and was well-tolerated.
Approximating Electromagnetic Exposure in Dense Indoor Environments

The roll out of the upcoming 5G technology is highly anticipated as it will provide very high-speed network connections and unprecedented user experience. Since 5G requires deployment of a number of transmitters tuned at very high frequency, it is imperative to know whether it will have any detrimental impact on human well being. Using simulation analysis and the existing mathematical models specific to mm-wave communications, this work reports power density approximations in dense indoor environments. The transmission frequencies are assumed to be 28 GHz and 38 GHz. The results reported in this paper indicate that the estimated power density still remains within the permissible thresholds.
Evaluating the Feasibility of Deep Learning for Action Recognition in Small Datasets

Action recognition is the computer vision task of identifying what action is happening in a given sequence of frames. Traditional approaches rely on handcrafted features and domain-specific image processing algorithms, often resulting in limited accuracy. The substantial advances in deep learning and the availability of larger datasets have allowed techniques that yield much better performance without domain-specific knowledge to recognize actions being performed based on the raw information from video sequences, a strategy called representation learning. However, deep neural networks usually require very large labeled datasets for training and properly generalizing, and due to their high capacity, they often overfit small data, hence providing suboptimal results. This work aims to check the real feasibility of employing deep learning in the context of smallsized action recognition datasets. Our goal is to verify whether deep learning approaches can provide improved performance in cases in which labeled data is not abundant. In order to do so, we perform a thorough empirical analysis in which we investigate distinct network architectures with hyperparameter optimization, as well as different data pre-processing techniques and fusion methods.
Sequence to Sequence Mixture Model for Diverse Machine Translation

Sequence to sequence (SEQ2SEQ) models lack diversity in their generated translations. This can be attributed to their limitations in capturing lexical and syntactic variations in parallel corpora, due to different styles, genres, topics, or ambiguity of human translation process. In this paper, we develop a novel sequence to sequence mixture (S2SMIX) model that improves both translation diversity and quality by adopting a committee of specialized translation models rather than a single translation model. Each mixture component selects its own training dataset via optimization of the marginal log-likelihood, which leads to a soft clustering of the parallel corpus. Experiments on four language pairs demonstrate the superiority of our mixture model compared to SEQ2SEQ model with the standard and diversity encouraged beam search. Our mixture model incurs negligible additional parameters and no extra computation in the decoding time.
Applying the Monod-Wyman-Changeux Allosteric Activation Model to Pseudo–Steady-State Responses from GABAA Receptors

The Monod-Wyman-Changeux (MWC) cyclic model was described as a kinetic scheme to explain enzyme function and modulation more than 50 years ago and was proposed as a model for understanding the activation of transmitter-gated channels soon afterward. More recently, the MWC model has been used to describe the activation of the GABAA receptor by the transmitter, GABA, and drugs that bind to separate sites on the receptor. It is most interesting that the MWC formalism can also describe the interactions among drugs that activate the receptor. In this review, we describe properties of the MWC model that have been explored experimentally using the GABAA receptor, summarize analytical expressions for activation and interaction for drugs, and briefly review experimental results.
A review of Internet use among older adults

As the world population ages and older adults comprise a growing proportion of current and potential Internet users, understanding the state of Internet use among older adults as well as the ways their use has evolved may clarify how best to support digital media use within this population. This article synthesizes the quantitative literature on Internet use among older adults, including trends in access, skills, and types of use, while exploring social inequalities in relation to each domain. We also review work on the relationship between health and Internet use, particularly relevant for older adults. We close with specific recommendations for future work, including a call for studies better representing the diversity of older adulthood and greater standardization of question design.
Heavy-Hitter Detection Using a Hardware Sketch with the Countmin-CU Algorithm

We present a custom hardware architecture for fast heavy hitter detection in large data streams. The architecture probabilistically estimates the frequency of each element in the data stream using the Countmin-CU sketch with the H3 family of hash functions. The sketch is stored in on-chip memory, and the architecture exploits the parallelism available in the data by simultaneously processing each row of the sketch. The hash functions map each element to a set of counters on the sketch, and the sketch increments the counters that hold the minimum value, which corresponds to the estimated frequency of the element. The hash functions and sorting network are implemented in hardware as fully pipelined circuits, in order to maximize their operating clock frequency. We show a prototype of the architecture running on a Xilinx Kintex-7 XC7K325T FPGA operating with a 300MHz clock, which can process a stream of 3,982,496 32-bit elements and detect the heavy hitters with an 4x16,384-element sketch in 13.27ms, achieving a speedup of 768 compared to a modern desktop computer.
Scale-Free Evolutionary Level Generation

This work proposes a new approach for the graph-based procedural level generation for games, through evolutionary algorithms. The levels are encoded as a graph structure inspired by the concepts of a scale-free network. A scale-free network lacks an internal scale as a consequence of the coexistence of nodes with different degrees in the same network. This approach aims to avoid the generation of linear, repetitive and grid-like levels, giving the algorithm additional freedom to explore the search space for diverse solutions. The algorithm was designed to provide a smooth mixed-initiative authorial control, allowing the designer to adjust constraints parameters, input aesthetically desired properties, manage critical contents of the level and even edit edges and nodes in a drag-and-drop manner. The results show that the algorithm can evolve scale-free structures with moderated nonlinearity, which is taken as an ideal measure for game levels that feature player progression like lock-and-key puzzles.
Interpretable Convolutional Filter Pruning

The sophisticated structure of Convolutional Neural Network (CNN) allows for outstanding performance, but at the cost of intensive computation. As significant redundancies inevitably present in such a structure, many works have been proposed to prune the convolutional filters for computation cost reduction. Although extremely effective, most works are based only on quantitative characteristics of the convolutional filters, and highly overlook the qualitative interpretation of individual filter's specific functionality. In this work, we interpreted the functionality and redundancy of the convolutional filters from different perspectives, and proposed a functionality-oriented filter pruning method. With extensive experiment results, we proved the convolutional filters' qualitative significance regardless of magnitude, demonstrated significant neural network redundancy due to repetitive filter functions, and analyzed the filter functionality defection under inappropriate retraining process. Such an interpretable pruning approach not only offers outstanding computation cost optimization over previous filter pruning methods, but also interprets filter pruning process.
Formal Education and Migration Aspirations in Ethiopia

Expanding access to formal education is a universal aim of development policy worldwide, and young people today are gaining access to schooling on unprecedented levels. Taking Ethiopia as a case study, this paper explores the mobility impacts of increasing educational attainment. First, we analyse internal migration data for Ethiopia using national Labour Force Survey data, and find that that rural-to-urban migration has now replaced rural-to-rural migration as most common migration trajectory within Ethiopia. The pursuit of work and education were key motivations for rural-to-urban migration, and those with higher levels of education moved more. Second, we show how rising levels of primary and secondary education influence aspirations to migrate, distinguishing between internal and international destinations. Using novel survey data collected among rural and urban Ethiopian youth for the Young Lives project, we find that even completing primary levels of education increases the aspiration to live elsewhere. By studying the linkages between education and migration aspirations, alongside other development indicators like wealth, employment, and levels of self-efficacy, this paper contributes to an on-going debate about the relationship between development and migration and challenges common assumptions that migration is simply driven by poverty and need in poorer countries.
Measuring Semantic Similarity Between Sentences Using A Siamese Neural Network

The task of measure semantic redundancy between sentences demands a thorough interpretation from the reader because phrase meaning may be ambiguous. Detecting semantic similarity is a difficult problem because natural language, besides ambiguity, offers almost infinite possibilities to express the same idea. This paper adapts a siamese neural network architecture trained to measure the semantic similarity between two sentences through metric learning. The resulting solution should help in writing more efficient and informative text.
FAME: Supporting Continuous Requirements Elicitation by Combining User Feedback and Monitoring

Context: Software evolution ensures that software systems in use stay up to date and provide value for end-users. However, it is challenging for requirements engineers to continuously elicit needs for systems used by heterogeneous end-users who are out of organisational reach. Objective: We aim at supporting continuous requirements elicitation by combining user feedback and usage monitoring. Online feedback mechanisms enable end-users to remotely communicate problems, experiences, and opinions, while monitoring provides valuable information about runtime events. It is argued that bringing both information sources together can help requirements engineers to understand end-user needs better. Method/Tool: We present FAME, a framework for the combined and simultaneous collection of feedback and monitoring data in web and mobile contexts to support continuous requirements elicitation. In addition to a detailed discussion of our technical solution, we present the first evidence that FAME can be successfully introduced in real-world contexts. Therefore, we deployed FAME in a web application of a German small and medium-sized enterprise (SME) to collect user feedback and usage data. Results/Conclusion: Our results suggest that FAME not only can be successfully used in industrial environments but that bringing feedback and monitoring data together helps the SME to improve their understanding of end-user needs, ultimately supporting continuous requirements elicitation.
Classification via Deep Fuzzy c-Means Clustering

While deep learning has proven to be a powerful new tool for modeling and predicting a wide variety of complex phenomena, those models remain incomprehensible black boxes. This is a critical impediment to the widespread deployment of deep learning technology, as decades of research have found that users simply will not trust (i.e. make decisions based on) a model whose solutions cannot be explained. Fuzzy systems, on the other hand, are by design much more easily understood. We propose to create more comprehensible deep networks by hybridizing them with fuzzy logic. Our proposed architecture first employs a convolutional neural network as an automated feature extractor, and then clusters datasets in that feature space using the fuzzy c- means algorithm. After hardening the clusters, we employ Roccio’s algorithm to classify the data points. Experiments on the MNIST dataset show that this classifier is comparable to other deep learning algorithms, and substantially more accurate than the same fuzzy classifier applied to the original image.
NDVI-derived forest area change and its driving factors in China

China harbors diversified forest types, from tropical rainforest to boreal coniferous forest, and has implemented large-scale reforestation/afforestation programs over the past several decades. However, little information is available on changes in China’s forest area and the causes. In this study, we used the classified forest distribution thematic map derived from Normalized Difference Vegetation Index (NDVI) datasets and a revised IPAT model to examine China’s forest area change and the possible driving factors from 1982 to 2006. Overall, NDVI-derived forest areas were numerically consistent with those reported in the 3rd, 4th, 5th, and 6th National Forest Inventories, respectively. Over the past 25 years, China’s forest area was estimated to have an average of 169.18 million hectares with an annual increase of 0.15 million hectares (c.a. a total net increment of 3.60 million hectares), which is equivalent to 0.089% of the relative annual change rate. However, a large difference in the changing rate and direction of forest area at the province level was found; for instance, forest area has declined in 10 provinces, mainly in Northeastern and Southern China, while 21 provinces showed an increase. The changes were most likely attributed to the policy regarding the import and export of timber and affluence (per capita gross domestic product), and both contributed more than 80% of the total contribution of the six factors of the revised IPAT model.
Unsupervised Depth Estimation from Light Field Using a Convolutional Neural Network

This paper proposes an unsupervised CNN-based method for explicit depth estimation from light field, which learns an end-to-end mapping from a 4D light field to the corresponding disparity map without the supervision of groundtruth depth. Specifically, we design a combined loss function imposing both compliance and divergence constraints on the warped sub-aperture images to the central view, which guarantees our network to generate an accurate and robust disparity map. Furthermore, we find that increasing the number of referenced views in depth feature extraction and complementing missing information caused by warping greatly boost the performance of our network. Due to the difficulty of obtaining groundtruth depth of real-world scenes in practice, the proposed method is much more feasible than supervised learning. On the other hand, compared with traditional non-learning methods, the proposed method better exploits the correlations in the 4D light field and generates superior depth results both quantitatively and qualitatively. Also, the proposed method helps improve the performance of subsequent applications based on the estimated depth, e.g., spatial super-resolution of light field.