Miracle-dz/UniFAR-FTUs huggingface dataset:
Predictors and Acceptability of Shared Physical Activity Participation in Parent–Child Dyads

Shared physical activity (PA) in children and their parents is an approach to increase PA participation for both children and adults. Understanding variables associated with shared PA is essential to inform effective, tailored PA interventions for families. The purpose of this study was to identify predictors and to assess the acceptability of shared PA in parent – child dyads. Each parent – child dyad ( n = 31; mean ± SD age, parents: 38.0 ± 6.6 yr, children: 5.9 ± 1.7 yr) was guided through five standardized PAs (brisk walking, jumping games, body-weight exercises, dancing, and tag games) at a research fitness center. Parents reported demographic characteristics and PA-related variables (i.e., PA self-efficacy, family chaos, and annual household income). One week later, parents ( n = 28) reported their dyad ’ s weekly minutes spent in shared PA. A forward selection procedure was used to determine the optimal regression model for predicting minutes of shared PA. McNemar ’ s tests were used to determine the dyad ’ s acceptability of each shared PA. Lower family chaos ( β = − 19.41, P = 0.034), higher parent body mass index ( β = 7.65, P = 0.003), and higher annual household income ( β = 11.85, P = 0.023) predicted minutes of shared PA. Jumping games, body-weight exercises, dancing, and tag games were not acceptable for parent – child dyads to participate in together ( P < 0.05). Brisk walking was an acceptable shared PA ( P = 0.125). Lower family chaos, higher parent body mass index, and higher annual household income predicted shared PA. Brisk walking was an acceptable PA for parent – child dyads to participate outside of a research laboratory setting. These results can aid future PA programs that include children and parents together.
PyPlutchik: Visualising and comparing emotion-annotated corpora

The increasing availability of textual corpora and data fetched from social networks is fuelling a huge production of works based on the model proposed by psychologist Robert Plutchik, often referred simply as the “Plutchik Wheel”. Related researches range from annotation tasks description to emotions detection tools. Visualisation of such emotions is traditionally carried out using the most popular layouts, as bar plots or tables, which are however sub-optimal. The classic representation of the Plutchik’s wheel follows the principles of proximity and opposition between pairs of emotions: spatial proximity in this model is also a semantic proximity, as adjacent emotions elicit a complex emotion (a primary dyad) when triggered together; spatial opposition is a semantic opposition as well, as positive emotions are opposite to negative emotions. The most common layouts fail to preserve both features, not to mention the need of visually allowing comparisons between different corpora in a blink of an eye, that is hard with basic design solutions. We introduce PyPlutchik the Pyplutchik package is available as a Github repository (http://github.com/alfonsosemeraro/pyplutchik) or through the installation commands pip or conda. For any enquiry about usage or installation feel free to contact the corresponding author, a Python module specifically designed for the visualisation of Plutchik’s emotions in texts or in corpora. PyPlutchik draws the Plutchik’s flower with each emotion petal sized after how much that emotion is detected or annotated in the corpus, also representing three degrees of intensity for each of them. Notably, PyPlutchik allows users to display also primary, secondary, tertiary and opposite dyads in a compact, intuitive way. We substantiate our claim that PyPlutchik outperforms other classic visualisations when displaying Plutchik emotions and we showcase a few examples that display our module’s most compelling features.
Applicability of overlay non-delay tolerant position-based protocols in highways and urban environments for vanet

Vehicular Ad hoc Networks (VANETs) is new sort in wireless ad-hoc networks. Vehicle-to-Vehicle (V2V) communication is one of the main communication paradigms that provide a level of safety and convenience to drivers and passengers on the road. In such environment, routing data packet is challenging due to frequently changed of network topology because of highly dynamic nature of vehicles. Thus, routing in VANETs in require for efficient protocols that guarantee message transmission among vehicles. Numerous routing protocols and algorithms have been proposed or enhanced to solve the aforementioned problems. Many position based routing protocols have been developed for routing messages that have been identified to be appropriate for VANETs. This work explores the performances of selected unicast non-delay tolerant overlay position-based routing protocols. The evaluation has been conducted in highway and urban environment in two different scenarios. The evaluation metrics that are used are Packet Delivery Ratio (PDR), Void Problem Occurrence (VPO), and Average Hop Count (AHC).
Spatial cluster analysis of COVID-19 in Malaysia (Mar-Sep, 2020).

Coronavirus disease 2019 (COVID-19) is the current worldwide pandemic as declared by the World Health Organization (WHO) in March 2020. Being part of the ongoing global pandemic, Malaysia has recorded a total of 8639 COVID-19 cases and 121 deaths as of 30th June 2020. This study aims to detect spatial clusters of COVID-19 in Malaysia using the Spatial Scan Statistic (SaTScan™) to guide control authorities on prioritizing locations for targeted interventions. The spatial analyses were conducted on a monthly basis at the state-level from March to September 2020. The results show that the most likely cluster of COVID-19 occurred in West Malaysia repeatedly from March to June, covering three counties (two federal territories and one neighbouring state) and moved to East Malaysia in July covering two other counties. The most likely cluster shows a tendency of having moved from the western part to the eastern part of the country. These results provide information that can be used for the evidence- based interventions to control the spread of COVID-19 in Malaysia.
Benchmarking a New Paradigm: An Experimental Analysis of a Real Processing-in-Memory Architecture

Many modern workloads, such as neural networks, databases, and graph processing, are fundamentally memory-bound. For such workloads, the data movement between main memory and CPU cores imposes a significant overhead in terms of both latency and energy. A major reason is that this communication happens through a narrow bus with high latency and limited bandwidth, and the low data reuse in memory-bound workloads is insufficient to amortize the cost of main memory access. Fundamentally addressing this data movement bottleneck requires a paradigm where the memory system assumes an active role in computing by integrating processing capabilities. This paradigm is known as processing-in-memory (PIM). Recent research explores different forms of PIM architectures, motivated by the emergence of new 3D-stacked memory technologies that integrate memory with a logic layer where processing elements can be easily placed. Past works evaluate these architectures in simulation or, at best, with simplified hardware prototypes. In contrast, the UPMEM company has designed and manufactured the first publicly-available real-world PIM architecture. This paper provides the first comprehensive analysis of the first publicly-available real-world PIM architecture. We make two key contributions. First, we conduct an experimental characterization of the UPMEM-based PIM system using microbenchmarks to assess various architecture limits such as compute throughput and memory bandwidth, yielding new insights. Second, we present PrIM, a benchmark suite of 16 workloads from different application domains (e.g., linear algebra, databases, graph processing, neural networks, bioinformatics).
A Novel Triplet Sampling Method for Multi-Label Remote Sensing Image Search and Retrieval

—Learning the similarity between remote sensing (RS) images forms the foundation for content based RS image retrieval (CBIR). Recently, deep metric learning approaches that map the semantic similarity of images into an embedding space have been found very popular in RS. A common approach for learning the metric space relies on the selection of triplets of similar (positive) and dissimilar (negative) images to a reference image called as an anchor. Choosing triplets is a difﬁcult task particularly for multi-label RS CBIR, where each training image is annotated by multiple class labels. To address this problem, in this paper we propose a novel triplet sampling method in the framework of deep neural networks (DNNs) deﬁned for multi-label RS CBIR problems. The proposed method selects a small set of the most representative and informative triplets based on two main steps. In the ﬁrst step, a set of anchors that are diverse to each other in the embedding space is selected from the current mini-batch using an iterative algorithm. In the second step, different sets of positive and negative images are chosen for each anchor by evaluating relevancy, hardness, and diversity of the images among each other based on a novel ranking strategy. Experimental results obtained on two multi-label benchmark achieves show that the selection of the most informative and representative triplets in the context of DNNs results in: i) reducing the computational complexity of the training phase of the DNNs without any signiﬁcant loss on the performance; and ii) an increase in learning speed since informative triplets allow fast convergence. The code of the proposed method is publicly available at https://git.tu-berlin.de/rsim/image-retrieval-from-triplets.
FishDeTec: A Fish Identification Application using Image Recognition Approach

The underwater imagery processing is always in high demand, especially the fish species identification. This activity is as important not only for the biologist, scientist, and fisherman, but it is also important for the education purpose. It has been reported that there are more than 200 species of freshwater fish in Malaysia. Many attempts have been made to develop the fish recognition and classification via image processing approach, however, most of the existing work are developed for the saltwater fish species identification and used for a specific group of users. This research work focuses on the development of a prototype system named FishDeTec to the detect the freshwater fish species found in Malaysia through the image processing approach. In this study, the proposed predictive model of the FishDeTec is developed using the VGG16, is a deep Convolutional Neural Network (CNN) model for a large-scale image classification processing. The experimental study indicates that our proposed model is a promising result.
Delay-Tolerant Constrained OCO with Application to Network Resource Allocation

We consider online convex optimization (OCO) with multi-slot feedback delay, where an agent makes a sequence of online decisions to minimize the accumulation of time-varying convex loss functions, subject to short-term and long-term constraints that are possibly time-varying. The current convex loss function and the long-term constraint function are revealed to the agent only after the decision is made, and they may be delayed for multiple time slots. Existing work on OCO under this general setting has focused on the static regret, which measures the gap of losses between the online decision sequence and an offline benchmark that is fixed over time. In this work, we consider both the static regret and the more practically meaningful dynamic regret, where the benchmark is a time-varying sequence of per-slot optimizers. We propose an efficient algorithm, termed Delay-Tolerant Constrained-OCO (DTC-OCO), which uses a novel constraint penalty with double regularization to tackle the asynchrony between information feedback and decision updates. We derive upper bounds on its dynamic regret, static regret, and constraint violation, proving them to be sublinear under mild conditions. We further apply DTC-OCO to a general network resource allocation problem, which arises in many systems such as data networks and cloud computing. Simulation results demonstrate substantial performance gain of DTC-OCO over the known best alternative.
Oral Vitamin D supplementation induces transcriptomic changes in rectal mucosa that are consistent with anti-tumour effects.

Background Risk for several common cancers is influenced by the transcriptomic landscape of the respective tissue-of-origin. Vitamin D influences in-vitro gene expression and cancer cell growth. We sought to determine whether oral vitamin D induces beneficial gene expression effects in human rectal epithelium and identify biomarkers of response. Methods Blood and rectal mucosa was sampled from 191 human subjects and mucosa gene expression (HT12) correlated with plasma vitamin D (25-OHD) to identify differentially expressed genes. Fifty subjects were then administered 3200IU/day oral vitamin D3 and matched blood/mucosa resampled after 12 weeks. Transcriptomic changes (HT12/RNAseq) after supplementation were tested against the prioritised genes for gene-set and GO-process enrichment. To identify blood biomarkers of mucosal response, we derived receiver-operator curves and C-statistic (AUC) and tested biomarker reproducibility in an independent Supplementation Trial (BEST-D). Results 629 genes were associated with 25-OHD level (P<0.01), highlighting 453 GO-term processes (FDR<0.05). In the whole intervention cohort, vitamin D supplementation enriched the prioritised mucosal gene-set (upregulated gene-set P<1.0E-07; downregulated gene-set P<2.6E-05) and corresponding GO terms (P=2.90E-02), highlighting gene expression patterns consistent with anti-tumour effects. However, only 9 individual participants (18%) showed a significant response (NM gene-set enrichment P<0.001) to supplementation. Expression changes in HIPK2 and PPP1CC expression served as blood biomarkers of mucosal transcriptomic response (AUC=0.84 [95%CI:0.66-1.00]), and replicated in BEST-D trial subjects (HIPK2 AUC=0.83 [95%CI:0.77-0.89]; PPP1CC AUC=0.91 [95%CI:0.86-0.95]). Conclusions Higher plasma 25-OHD correlates with rectal mucosa gene expression patterns consistent with anti-tumour effects and this beneficial signature is induced by short-term vitamin D supplementation. Heterogenous gene expression responses to vitamin D may limit the ability of randomised trials to identify beneficial effects of supplementation on CRC risk. However, in the current study blood expression changes in HIPK2 and PPP1CC identify those participants with significant anti-tumor transcriptomic responses to supplementation in the rectum. These data provide compelling rationale for a trial of vitamin D and CRC prevention using easily assayed blood gene expression signatures as intermediate biomarkers of response.
Comparative study of the effects of Abexol and atorvastatin in patients with non-alcoholic fatty liver disease

Aim of the study To investigate the efficacy and safety of Abexol and atorvastatin in patients with non-alcoholic fatty liver disease (NAFLD). Material and methods: The present study had a monocentric, randomized, double-blinded, comparative design with 4 parallel groups – group 1 (Abexol), group 2 (atorvastatin), group 3 (combined therapy) and group 4 (placebo) – to which dietary recommendations and physical activity practice were provided twice a day, for 24 weeks. Significant changes in the ultrasound analysis of the liver were considered a primary efficacy variable. Insulin resistance improvement (HOMA2-IR) was considered as a co-primary efficacy criterion. Significant changes in the serum levels of alanine aminotransferase (ALT) and aspartate aminotransferase (AST), lipid profile variables and the anthropometric variables were evaluated as secondary variables of effectiveness. Statistical analysis of all data was according to the intention to treat method. Results The groups were statistically homogeneous at baseline conditions. At the end of the 6 months of treatment about 50% of the patients in all groups showed a decrease of at least one degree in echogenicity, while the rest remained the same. There were no significant changes in the values of liver enzymes or anthropometric variables evaluated. Treatment with atorvastatin and combined therapy significantly reduced levels of low-density lipoprotein-cholesterol (LDL-C) and total cholesterol. The treatments were safe and well tolerated, although in the atorvastatin group the number of adverse events reported was greater than in the rest of the groups. Conclusions Abexol and atorvastatin showed comparable efficacy and safety in patients with NAFLD, with advantages for treatment with atorvastatin with respect to its effects on the lipid profile of these patients.
The influence of agricultural occupation and climate on the spatial distribution of Plusiinae (Lepidoptera: Noctuidae) on a latitudinal gradient in Brazil

R A C T Studies have reported the presence of certain Plusiinae species in both natural and agricultural landscapes, but their turnover in association with agricultural activities remains unexplored. Aiming to understand how the assemblages of Plusiinae are structured by agricultural occupation and climate, this study used automated light traps sampled moths in 18 sites in Brazil, across a broad latitudinal gradient. Our data has demonstrated that climate variables prevails as the most important variables influencing both the composition of Plusiinae and the abundance of its dominant species Chrysodeixis includens. On the other hand, the lack of significance found for the effect of variables representing agricultural occupation evidences that pest species are present both in agricultural and natural ecosystems, also sharing similar abundances at those locations. In other words, instead of following a gradient of agricultural occupation (e.g. crop sizes around sample sites) the composition of these extremely polyphagous insects is more clearly shaped by the latitudinal gradient, in which temperature and precipitation are better predictors. Thus, in contrary to our expectations, pest species inhabits both natural and agricultural landscapes at similar latitudinal sites, probably due to their wide polyphagy spectrum. These results can be used in management and monitoring programs of pest species in South America, since the local abundance variation and species composition can be more reliable predicted by
Combining an Edge-Based Method and a Direct Method for Robust 3D Object Tracking

In the field of augmented reality, edge-based methods have been popularly used in tracking textureless 3D objects. However, edge-based methods are inherently vulnerable to cluttered backgrounds. Another way to track textureless or poorly-textured 3D objects is to directly align image intensity of 3D object between consecutive frames. Although the direct methods enable more reliable and stable tracking compared to using local features such as edges, they are more sensitive to occlusion and less accurate than the edge-based methods. Therefore, we propose a method that combines an edge-based method and a direct method to leverage the advantages from each approach. Experimental results show that the proposed method is much robust to both fast camera (or object) movements and occlusion while still working in real time at a frame rate of 18 Hz. The tracking success rate and tracking accuracy were improved by up to 84% and 1.4 pixels, respectively, compared to using the edge-based method or the direct method solely.
Duplex Sequence-to-Sequence Learning for Reversible Machine Translation

Sequence-to-sequence learning naturally has two directions. How to effectively utilize supervision signals from both directions? Existing approaches either require two separate models, or a multitask-learned model but with inferior performance. In this paper, we propose REDER (Reversible Duplex Transformer), a parameter-efficient model and apply it to machine translation. Either end of REDER can simultaneously input and output a distinct language. Thus REDER enables reversible machine translation by simply flipping the input and output ends. Experiments verify that REDER achieves the first success of reversible machine translation, which helps outperform its multitask-trained baselines by up to 1.3 BLEU.
T-EMDE: Sketching-based global similarity for cross-modal retrieval

The key challenge in cross-modal retrieval is to find similarities between objects represented with different modalities, such as image and text. However, each modality embeddings stem from non-related feature spaces, which causes the notorious 'heterogeneity gap'. Currently, many cross-modal systems try to bridge the gap with self-attention. However, self-attention has been widely criticized for its quadratic complexity, which prevents many real-life applications. In response to this, we propose T-EMDE - a neural density estimator inspired by the recently introduced Efficient Manifold Density Estimator (EMDE) from the area of recommender systems. EMDE operates on sketches - representations especially suitable for multimodal operations. However, EMDE is non-differentiable and ingests precomputed, static embeddings. With T-EMDE we introduce a trainable version of EMDE which allows full end-to-end training. In contrast to self-attention, the complexity of our solution is linear to the number of tokens/segments. As such, T-EMDE is a drop-in replacement for the self-attention module, with beneficial influence on both speed and metric performance in cross-modal settings. It facilitates communication between modalities, as each global text/image representation is expressed with a standardized sketch histogram which represents the same manifold structures irrespective of the underlying modality. We evaluate T-EMDE by introducing it into two recent cross-modal SOTA models and achieving new state-of-the-art results on multiple datasets and decreasing model latency by up to 20%.