Miracle-dz/UniFAR-FTUs huggingface dataset:
BGMM: a body gauss-markov based mobility model for body area networks

Existing mobility models have limitations in their ability to simulate the movement of Wireless Body Area Network (WBAN) since body nodes do not exactly follow either classic mobility models or human contact distributions. In this paper, we propose a new mobility model called Body Gauss-Markov Mobility (BGMM) model, which is oriented specially to WBAN. First, we present the random Gauss-Markov mobility model as the most suitable theoretical basis for developing our new model, as its movement pattern can reveal real human body movements. Next, we examine the transfer of human movement states and derive a simplified mathematical Human Mobility Model (HMM). We then construct the BGMM model by combining the RGMM and HMM models. Finally, we simulate the traces of the new mobility model. We use four direct metrics in our proposed mobility model to evaluate its performance. The simulation results show that the proposed BGMM model performs with respect to the direct mobility metrics and can effectively represent a general WBAN-nodes movement pattern.
Enhanced postprandial glucagon-like peptide-1 secretion during obesity development has a protective role against glucose intolerance induction in rats

Abstract Glucagon-like peptide-1 (GLP-1) is an incretin hormone that regulates postprandial glycaemic response by enhancing insulin secretion. We previously demonstrated that the postprandial GLP-1 response was enhanced during the development of diet-induced obesity in rats. However, the physiological relevance of the enhanced GLP-1 response remained unclear. We aimed to determine the role of endogenous GLP-1 during obesity development. Male Sprague–Dawley rats were given either a control diet or a high-fat/high-sucrose (HFS, 30 % fat and 40 % sucrose, weight basis) diet with or without continuous administration of the GLP-1 receptor antagonist, exendin (9–39) (Ex9, 100 µg/d), for 5 weeks. Meal tolerance tests (MTT) were performed to assess postprandial glucose, insulin and GLP-1 responses to a liquid diet administration (15 kcal (63 kJ)/10 ml per kg body weight) every 2 weeks. The AUC of postprandial glucose in the HFS group was similar to the control group in both MTT (P = 0·9665 and P = 0·3475, respectively), whereas AUC of postprandial GLP-1 (after 4 weeks,P = 0·0457) and of insulin (after 2 and 4 weeks, P = 0·0486 and P = 0·0110) was higher in the HFS group compared with the control group. In the Ex9 group, AUC of postprandial glucose (P = 0·0297 and P = 0·0486) was higher along with a lower insulin response compared with the HFS group (P = 0·0564 and P = 0·0281). These results suggest that enhancement of the postprandial GLP-1 response during obesity development has a role in maintaining a normal postprandial glycaemic response. Hence, enhancing endogenous GLP-1 secretion by certain materials could be a potential target for prevention of glucose intolerance.
Automated Empirical Tuning of a Multiresolution Analysis Kernel

As CPU speeds double every couple of years following Moore’s law[1], memory speed lags behind. Because of this increasing gap between the speeds of processors and memory, in order to achieve high performance on modern systems new techniques such as longer pipeline, deeper memory hierarchy, and hyper threading have been introduced into the hardware design. Meanwhile, compiler optimization techniques have been developed to transform programs written in high-level languages to run efficiently on modern architectures[2, 3]. These program transformations include loop blocking[4, 5], loop unrolling[2], loop permutation, fusion and distribution[6, 7]. To select optimal parameters such as block size, unrolling factor, and loop order, most compilers compute these values with analytical models referred to as model-driven optimization. In contrast, empirical optimization techniques generate a large number of code variants with different parameter values for an algorithm, for example matrix multiplication. All these candidates run on the target machine, and the one that gives the best performance is picked. With this empirical optimization approach ATLAS[8, 9], PHiPAC[10], OSKI[11], and FFTW[12] successfully generate highly optimized libraries for dense and sparse linear algebra kernels and FFT respectively. It has been shown that empirical optimization is more effective than model-driven optimization[13].
Influence of between‐limb asymmetry in muscle mass, strength, and power on functional capacity in healthy older adults

Numerous daily tasks such as walking and rising from a chair involve bilateral lower limb movements. During such tasks, lower extremity function (LEF) may be compromised among older adults. LEF may be further impaired due to high degrees of between‐limb asymmetry. The present study investigated the prevalence of between‐limb asymmetry in muscle mass, strength, and power in a cohort of healthy older adults and examined the influence of between‐limb asymmetry on LEF.
Public involvement in the dissemination of the North West Coast Household Health Survey: Experiences and lessons of co‐producing research together

Engaging with the public is a key element of health research; however, little work has examined experiences of public involvement in research dissemination. The aim of this paper was to assess the extent of public involvement, experiences of public advisers and resulting changes in the dissemination of the North West Coast Household Health Survey (HHS).
Masked hypertension and submaximal exercise blood pressure among adolescents from the Avon Longitudinal Study of Parents and Children (ALSPAC)

Masked hypertension is associated with increased cardiovascular risk but is undetectable by clinic blood pressure (BP). Elevated systolic BP responses to submaximal exercise reveal the presence of masked hypertension in adults, but it is unknown whether this is the case during adolescence. We aimed to determine if exercise BP was raised in adolescents with masked hypertension, and its association with cardiovascular risk markers.
Identifying Consistent Variables in a Heterogeneous Data Set: Evaluation of a Web-Based Pre-Course in Mathematics.

E-learning has made course evaluation easier in many ways, as a multitude of learner data can be collected and related to student performance. At the same time, open learning environments can be a difficult field for evaluation, with a large variance in participants’ knowledge level, learner behaviour, and commitment. In this study the effectiveness of a mathematics pre-course administered to four cohorts of prospective students at a technical faculty in Germany was evaluated. Deficits in basic mathematics knowledge are considered one risk factor regarding graduation in STEM-related subjects, thus the overall goal was to investigate if the pre-course enabled “at risk” students to improve their starting position. A data analysis was performed, relating students’ preconditions when entering university, their attitude towards mathematics, and their use of learning strategies with further study success. The strongest determinant of first year performance were results in a diagnostic pretest, confirming both the importance of basic mathematics knowledge for academic achievement in engineering and the reliability of the chosen pre-posttest design. Other outcomes were quite unexpected and demanded deeper analyses. Students who had participated in additional face-toface courses, for example, showed less learning gains than students who had participated in an e-tutoring version. It also could be observed that meta-cognitive variables failed to explain successful course participation. Reasons for these outcomes are discussed, suggesting reliability threats and interactions between students’ preconditions and their learner behaviour. A significant and unmoderated impact on students’ learning gains in the pre-course was found for the number of online test attempts, making this variable a reliable indicator of student engagement. The evaluations show that open learning designs with heterogeneous learner groups can deliver meaningful information, provided that limitations are considered and that external references, like academic grades, are available in order to establish consistency.
Use of Digital Photography With Newcomer Immigrant and Refugee Youth to Examine Behaviors and Promote Health

This project examined health behaviors of newly arrived immigrant and refugee youth through digital photography as part of a health promotion intervention. Students (n = 23) aged 12 to 17 years at a school for newcomer youth in North Carolina used digital cameras to record dietary and physical activity behaviors, barriers, and promoters in their home environment. Eighteen participants completed individual photography projects that revealed consumption of soda, processed foods, limited calcium-rich foods, and limited evidence of physical activity. Photography project results and topics from nutrition education provided during the program were used to tailor health promotion resources. Students used digital cameras and props to create visual health-promoting materials (e.g., large posters for school hallways) that included and represented the wide range of cultures and ethnicities at the school. Facilitated discussion analysis revealed students preferred positive health promotion messages (health benefits of fruits and vegetables) versus negative (sugar or salt content of foods). Students reported they enjoyed using the cameras and developing materials and that as result of their experience they want to increase fruit and vegetable consumption, reduce sugar sweetened beverages, and increase physical activity. Digital photography shows promise as a tool to identify health behaviors and environmental barriers and to tailor health messages, especially with populations experiencing language, literacy, and/or cultural barriers.
Sub-second dynamics of theta-gamma coupling in hippocampal CA1

Oscillatory brain activity reflects different internal brain states including neurons’ excitatory state and synchrony among neurons. However, characterizing these states is complicated by the fact that different oscillations are often coupled, such as gamma oscillations nested in theta in the hippocampus, and changes in coupling are thought to reflect distinct states. Here, we describe a new method to separate single oscillatory cycles into distinct states based on frequency and phase coupling. Using this method, we identified four theta-gamma coupling states in rat hippocampal CA1. These states differed in abundance across behaviors, phase synchrony with other hippocampal subregions, and neural coding properties suggesting that these states are functionally distinct. We captured cycle-to-cycle changes in oscillatory coupling states and found frequent switching between theta-gamma states showing that the hippocampus rapidly shifts between different functional states. This method provides a new approach to investigate oscillatory brain dynamics broadly.
Functional Connectivity of Language Regions of Stroke Patients with Expressive Aphasia During Real-Time Functional Magnetic Resonance Imaging Based Neurofeedback

Abstract Stroke lesions in the language centers of the brain impair the language areas and their connectivity. This article describes the dynamics of functional connectivity (FC) of language areas (FCL) during real-time functional magnetic resonance imaging (RT-fMRI)-based neurofeedback training for poststroke patients with expressive aphasia. The hypothesis is that FCL increases during the upregulation of language areas during neurofeedback training and that the training improves FCL with an increasing number of sessions and restores it toward normalcy. Four test and four control patients with expressive aphasia were recruited for the study along with four healthy volunteers termed as the normal group. The test and normal groups were administered four neurofeedback training sessions in between two test sessions, whereas the control group underwent only the two test sessions. The training session requires the subject to exercise language activity covertly so that it upregulates the feedback signal obtained from the Broca's area (in left inferior frontal gyrus) and amplifies the feedback when it is correlated with the Wernicke's area (in left superior temporal gyrus) using RT-fMRI. FC was measured by Pearson's correlation coefficient. The results indicate that the FC of the test group was weaker in the left hemisphere than that of the normal group, and post-training the connections have strengthened (correlation coefficient increases) in the left hemisphere when compared with the control group. The connections of language areas strengthened in both hemispheres during neurofeedback-based upregulation, and multiple training sessions strengthened new pathways and restored left hemispheric connections toward normalcy.
Depressive Symptoms among Former Spousal Caregivers: Comparing Stressors, Resources, and Circumstances of Caregiving Cessation among Older Husbands and Wives

ABSTRACT Little research focuses on the mental health of caregivers (CGs) who stop providing care to their community-dwelling spouse. We examine depressive symptoms of former primary CG spouses who stopped caregiving over a two-year follow-up period when the care recipient (CR): (1) no longer has functional problems; (2) continues having functional problems; or (3) dies. Using data from the Health and Retirement Study (2000–2014), we located 2,370 couples who were both 50+ at baseline and where one partner provided help with ADL and/or IADL limitations but did not do so two years later. OLS regressions stratified by gender indicated that both male and female former spousal CGs whose CR died had significantly more depressive symptoms than those who ceased caregiving when their spouse did or did not still have functional problems. Former wife CGs who were older and whose husbands had more baseline ADLs had fewer follow-up depressive symptoms; wife CGs whose husbands had a nursing home stay had more depressive symptoms. Former husband CGs who had provided longer monthly hours of care had fewer follow-up symptoms. Findings underscore the importance of targeting mental and physical health services to both former caregiving husbands and wives, especially after spousal death.
Low capacity to oxidize fat and body weight

For a given positive energy balance, a low capacity to oxidize fat could contribute to weight gain (low fat oxidation hypothesis). This hypothesis is based on the arguments that for a given stable diet and food quotient (FQ), the respiratory quotient (RQ) is higher in obesity prone (OP) than in obesity resistant individuals (OR) and that a high RQ predicts higher future weight gain. A review of 42 studies shows that there is no convincing experimental support to these arguments and thus for the low fat oxidation hypothesis. A power analysis also shows that this hypothesis might be impossible to experimentally confirm because very large numbers of subjects would be needed to reject the null hypotheses that the 24‐h RQ is not different in OP and OR or that future weight gain is not different in individuals with a low and high 24‐h RQ at baseline. A re‐examination of the significance of the 24‐hour and fasting RQ also shows that the assumption underlying the low fat oxidation hypothesis that a high RQ reflects a low capacity to oxidize fat is not valid: For a stable diet, the 24‐h RQ entirely depends on FQ and energy balance, and the fasting RQ mainly depends on the FQ and energy balance and on the size of glycogen stores.
4‐1BB Delineates Distinct Activation Status of Exhausted Tumor‐Infiltrating CD8+ T Cells in Hepatocellular Carcinoma

Targeting costimulatory receptors with agonistic antibodies is a promising cancer immunotherapy option. We aimed to investigate costimulatory receptor expression, particularly 4‐1BB (CD137 or tumor necrosis factor receptor superfamily member 9), on tumor‐infiltrating CD8+ T cells (CD8+ tumor‐infiltrating lymphocytes [TILs]) and its association with distinct T‐cell activation features among exhausted CD8+ TILs in hepatocellular carcinoma (HCC).
On the limitations of the univariate marginal distribution algorithm to deception and where bivariate EDAs might help

We introduce a new benchmark problem called Deceptive Leading Blocks (DLB) to rigorously study the runtime of the Univariate Marginal Distribution Algorithm (UMDA) in the presence of epistasis and deception. We show that simple Evolutionary Algorithms (EAs) outperform the UMDA unless the selective pressure µ/λ is extremely high, where µ and λ are the parent and offspring population sizes, respectively. More precisely, we show that the UMDA with a parent population size of µ = Ω (log n) has an expected runtime of eΩ(µ) on the DLB problem assuming any selective pressure [EQUATION], as opposed to the expected runtime of O (nλ log λ + n3) for the non-elitist (µ, λ) EA with µ/λ ≤ 1/e. These results illustrate inherent limitations of univariate EDAs against deception and epistasis, which are common characteristics of real-world problems. In contrast, empirical evidence reveals the efficiency of the bi-variate MIMIC algorithm on the DLB problem. Our results suggest that one should consider EDAs with more complex probabilistic models when optimising problems with some degree of epistasis and deception.
FSS-1000: A 1000-Class Dataset for Few-Shot Segmentation

Over the past few years, we have witnessed the success of deep learning in image recognition thanks to the availability of large-scale human-annotated datasets such as PASCAL VOC, ImageNet, and COCO. Although these datasets have covered a wide range of object categories, there are still a significant number of objects that are not included. Can we perform the same task without a lot of human annotations? In this paper, we are interested in few-shot object segmentation where the number of annotated training examples are limited to 5 only. To evaluate and validate the performance of our approach, we have built a few-shot segmentation dataset, FSS-1000, which consists of 1000 object classes with pixelwise annotation of ground-truth segmentation. Unique in FSS-1000, our dataset contains significant number of objects that have never been seen or annotated in previous datasets, such as tiny daily objects, merchandise, cartoon characters, logos, etc. We build our baseline model using standard backbone networks such as VGG-16, ResNet-101, and Inception. To our surprise, we found that training our model from scratch using FSS-1000 achieves comparable and even better results than training with weights pre-trained by ImageNet which is more than 100 times larger than FSS-1000. Both our approach and dataset are simple, effective, and easily extensible to learn segmentation of new object classes given very few annotated training examples. Dataset is available at https://github.com/HKUSTCV/FSS-1000
Optimizing Energy Efficiency of Wearable Sensors Using Fog-assisted Control

Recent advances in the Internet of Things (IoT) technologies have enabled the use of wearables for remote patient monitoring. Wearable sensors capture the patient's vital signs, and provide alerts or diagnosis based on the collected data. Unfortunately, wearables typically have limited energy and computational capacity, making their use challenging for healthcare applications where monitoring must continue uninterrupted long time, without the need to charge or change the battery. Fog computing can alleviate this problem by offloading computationally intensive tasks from the sensor layer to higher layers, thereby not only meeting the sensors' limited computational capacity but also enabling the use of local closed-loop energy optimization algorithms to increase the battery life.
Parameterized Pre-coloring Extension and List Coloring Problems

Golovach, Paulusma and Song (Inf. Comput. 2014) asked to determine the parameterized complexity of the following problems parameterized by $k$: (1) Given a graph $G$, a clique modulator $D$ (a clique modulator is a set of vertices, whose removal results in a clique) of size $k$ for $G$, and a list $L(v)$ of colors for every $v\in V(G)$, decide whether $G$ has a proper list coloring; (2) Given a graph $G$, a clique modulator $D$ of size $k$ for $G$, and a pre-coloring $\lambda_P: X \rightarrow Q$ for $X \subseteq V(G),$ decide whether $\lambda_P$ can be extended to a proper coloring of $G$ using only colors from $Q. $ For Problem 1 we design an $O^*(2^k)$-time randomized algorithm and for Problem 2 we obtain a kernel with at most $3k$ vertices. Banik et al. (IWOCA 2019) proved the the following problem is fixed-parameter tractable and asked whether it admits a polynomial kernel: Given a graph $G$, an integer $k$, and a list $L(v)$ of exactly $n-k$ colors for every $v \in V(G),$ decide whether there is a proper list coloring for $G. $ We obtain a kernel with $O(k^2)$ vertices and colors and a compression to a variation of the problem with $O(k)$ vertices and $O(k^2)$ colors.
A HVS-Inspired Attention to Improve Loss Metrics for CNN-Based Perception-Oriented Super-Resolution

Deep Convolutional Neural Network (CNN) features have been demonstrated to be effective perceptual quality features. The perceptual loss, based on feature maps of pre-trained CNN's has proven to be remarkably effective for CNN based perceptual image restoration problems. In this work, taking inspiration from the the Human Visual System (HVS) and visual perception, we propose a spatial attention mechanism based on the dependency human contrast sensitivity on spatial frequency. We identify regions in input images, based on the underlying spatial frequency, which are not generally well reconstructed during Super-Resolution but are most important in terms of visual sensitivity. Based on this prior, we design a spatial attention map that is applied to feature maps in the perceptual loss and its variants, helping them to identify regions that are of more perceptual importance. The results demonstrate the our technique improves the ability of the perceptual loss and contextual loss to deliver more natural images in CNN based super-resolution.
On Hard Exploration for Reinforcement Learning: a Case Study in Pommerman

How to best explore in domains with sparse, delayed, and deceptive rewards is an important open problem for reinforcement learning (RL). This paper considers one such domain, the recently-proposed multi-agent benchmark of Pommerman. This domain is very challenging for RL — past work has shown that model-free RL algorithms fail to achieve significant learning without artificially reducing the environment’s complexity. In this paper, we illuminate reasons behind this failure by providing a thorough analysis on the hardness of random exploration in Pommerman. While model-free random exploration is typically futile, we develop a model-based automatic reasoning module that can be used for safer exploration by pruning actions that will surely lead the agent to death. We empirically demonstrate that this module can significantly improve learning.
A Hybrid Neural Network Model for Commonsense Reasoning

This paper proposes a hybrid neural network(HNN) model for commonsense reasoning. An HNN consists of two component models, a masked language model and a semantic similarity model, which share a BERTbased contextual encoder but use different model-specific input and output layers. HNN obtains new state-of-the-art results on three classic commonsense reasoning tasks, pushing the WNLI benchmark to 89%, the Winograd Schema Challenge (WSC) benchmark to 75.1%, and the PDP60 benchmark to 90.0%. An ablation study shows that language models and semantic similarity models are complementary approaches to commonsense reasoning, and HNN effectively combines the strengths of both. The code and pre-trained models will be publicly available at https: //github.com/namisan/mt-dnn.