Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting Mobile Users Traffic and Access-Time Behavior Using Recurrent Neural Networks

Predicting mobile users’ web-access behavior can have substantial impacts on resource allocation and cost reduction for wireless networks. Therefore, we propose a machine learning platform to forecast the web traffic and access time of mobile users. Based on the observation, the traffic patterns exhibit complex dependency on time, location, and popularity of webpages. Thus, a Recurrent Neural Network (RNN) with Long Short Term Memory (LSTM) is developed based on distinct engineered features to learn and predict users’ web browsing activities. Then, to forecast the future access-time of mobile users, we propose a Self-exciting Memory Neural Network (SMNN). The access activities are modeled as self-exciting point processes, and intensities are adopted for prediction. Moreover, we extend the proposed predicting framework to cell towers. To cope with the diversity of traffic at the cell tower, we resort to clustering methods to group the similar users of each tower. Then, we develop an LSTM model for each cluster separately to predict the web domain traffic activities for the cell tower. Finally, we show that our proposed models outperform the baseline prediction models based on cellular networks dataset. We also show that for the cell tower access prediction task, the clustering method can significantly improve the prediction accuracy.
Capacity Analysis of Wireless Powered Cooperative NOMA Networks over Generalized Fading

This paper provides a performance evaluation of two-hop non-orthogonal multiple access (NOMA) architecture with energy harvesting (EH) cooperative agent and channel gains following the k-m fading, through analysis of ergodic capacity with respect to hardware impairments and channel conditions. The performance results of the distant user over two EH protocols, namely power splitting and time-switching relaying, are obtained and compared with simulation outcomes. The developed framework allows to evaluate the network under a range of external conditions and infers the importance of considering the hardware impairments.
Overhead-efficient Channel Estimation and Beamforming for Hybrid Architecture-based mmWave Systems

In millimeter wave (mmWave) communications, massive multiple-input multiple-output (MIMO) systems can achieve high gain and increase spectral efficiency significantly. To reduce the hardware complexity and energy consumption of massive MIMO systems, hybrid architecture has attracted extensive attention. Channel estimation and beamformer design based on the hybrid architecture are two critical techniques. Motivated by the advantages of sparse recovery, we propose an algorithm based on compressive sensing (CS) for channel estimation to estimate the dominant paths in the sparse mmWave channel. Moreover, benefited by the limited components of multipath in the angle-domain, an overhead-efficient method for hybrid beamformer design without singular value decompose (SVD) for obtaining the optimal hybrid precoder is proposed followed by the mmWave channel estimation, and only the indices of orthogonal array response vectors of the estimated dominated paths need to be transmitted as the feedback information for hybrid precoder design. Numerical experiments demonstrate that the proposed algorithm for channel estimation can recovery the channel matrix by estimating the parameters of dominant paths. Through the theoretical analysis and numerical experiments, we prove that the proposed algorithm for hybrid beamformer design can perform near-optimal spectral efficiency. Finally, we analyze the feedback overhead cost by the proposed algorithm and prove that the proposed algorithm can significantly reduce the feedback overhead.
Delay-Aware Cache-Enabled Cooperative D2D Transmission in Mobile Cellular Networks

By caching popular contents at edge nodes, the content can be delivered via device-to-device (D2D) communications to reduce the delay significantly. In this paper, we study cooperative content delivery from multiple edge nodes with duplicate caching, where the content caching and the cooperative transmission algorithms are designed jointly to minimize the average delay. Specifically, we decompose the delay optimization problem as short time-scale transmission subproblems for each time slot and a long time-scale caching master problem by primal decomposition. The transmission subproblem is modelled as a finite horizon Markov decision problem (MDP), and we propose a link selection algorithm based on the value function in the MDP model. For the caching master problem, we propose a caching algorithm by the simultaneous perturbation stochastic approximation (SPSA) method. Simulation results show that our proposed transmission and caching algorithms achieve lower delay than the conventional schemes.
Intramuscular Temperature Changes in the Quadriceps Femoris Muscle After Post-Exercise Cold-Water Immersion (10°C for 10 min): A Systematic Review With Meta-Analysis

Post-exercise cold-water immersion (CWI) is a widely accepted recovery strategy for maintaining physical performance output. However, existing review articles about the effects of CWI commonly pool data from very heterogenous study designs and thus, do rarely differentiate between different muscles, different CWI-protocols (duration, temperature, etc.), different forms of activating the muscles before CWI, and different thickness of the subcutaneous adipose tissue. This systematic review therefore aimed to investigate the effects of one particular post-exercise CWI protocol (10°C for 10 min) on intramuscular temperature changes in the quadriceps femoris muscle while accounting for skinfold thickness. An electronic search was conducted on PubMed, LIVIVO, Cochrane Library, and PEDro databases. Pooled data on intramuscular temperature changes were plotted with respect to intramuscular depth to visualize the influence of skinfold thickness. Spearman's rho (rs) was used to assess a possible linear association between skinfold thickness and intramuscular temperature changes. A meta-analysis was performed to investigate the effect of CWI on pre-post intramuscular temperature for each measurement depth. A total of six articles met the inclusion criteria. Maximum intramuscular temperature reduction was 6.40°C with skinfold thickness of 6.50 mm at a depth of 1 cm, 4.50°C with skinfold thickness of 11.00 mm at a depth of 2 cm, and only 1.61°C with skinfold thickness of 10.79 mm at a depth of 3 cm. However, no significant correlations between skinfold thickness and intramuscular temperature reductions were observed at a depth of 1 cm (rs = 0.0), at 2 cm (rs = −0.8) and at 3 cm (rs = −0.5; all p > 0.05). The CWI protocol resulted in significant temperature reductions in the muscle tissue layers at 1 cm (d = −1.92 [95% CI: −3.01 to −0.83] and 2 cm (d = −1.63 [95% CI: −2.20 to −1.06]) but not at 3 cm (p < 0.05). Skinfold thickness and thus, subcutaneous adipose tissue, seems to influence temperature reductions in the muscle tissue only to a small degree. These findings might be useful for practitioners as they demonstrate different intramuscular temperature reductions after a specific post-exercise CWI protocol (10°C for 10 min) in the quadriceps femoris muscle.
Use of a Persona to Support the Interdisciplinary Design of an Assistive Technology for Meal Preparation in Traumatic Brain Injury

User-centered design (UCD) facilitates the creation of technologies that are specifically designed to answer users’ needs. This paper presents the first step of a UCD using a persona, a fictitious character representing the targeted population, which in this case is people having sustained a traumatic brain injury (TBI). The persona is used to better understand the possible interactions of a TBI population with a prototype of a technology that we wish to develop, namely the Cognitive Orthosis for coOKing (COOK). COOK is meant to be an assistive technology that will be designed to promote independence for cooking within a supported-living residence. More specifically, this paper presents the persona’s creation methodology based on the first four phases of the persona’s lifecycle. It also describes how the persona methodology served as a facilitator to initiate an interdisciplinary collaboration between a clinical team and a computer science team. Creation of personas relied on a clinical model (Disability Creation Process) that contextualized the needs of this population and an evaluation tool [Instrumental Activities of Daily Living (IADL) Profile] that presented a wide range of cognitive assistance needs found in this same population. This paper provides an in-depth description of some of the most frequent everyday difficulties experienced by individuals with TBI as well as the persona’s abilities, limitations and social participation during the realization of IADL, and an evaluation of the manifestations of these difficulties during IADL performance as represented through scenarios. The interdisciplinary team used the persona to complete a first description of the interactions of a persona with TBI with COOK. This work is an attempt at offering a communication tool, the persona, to facilitate interdisciplinary research among diverse disciplines who wish to learn to develop a common language, models and methodologies at the beginning of the design process.
AutoRoot: A Novel Fault Localization Schema of Multi-dimensional Root Causes

The key challenge for large scale software system maintenance is to minimize the troubleshooting time when severe system anomaly (e.g., server failure, link congestion, software bugs) happens. It often takes hours for operators to manually locate the fault and thus degrades the service performance in terms of user experience and economics. Previous root cause localization algorithms are usually time-consuming and error-prone. In this paper, we present AutoRoot, a fast and accurate multi-dimensional root cause localization algorithm. Specifically, AutoRoot uses an adaptive density clustering to improve the accuracy and an effective filtering mechanism to reduce the search time. Extensive experiments using multiple real data traces validate the performance of AutoRoot compared with existing algorithms.
Manager Selection and Resource Allocation for 5G-V2X Platoon Systems with Finite Blocklength

In this paper, we propose a novel dynamic manager selection scheme for vehicles platooning systems and investigate the corresponding resource allocation in the finite blocklength regime, to meet the ultra-reliable and low-latency communication (URLLC) requirements of safety-related data. To the best of our knowledge, this is the first work to investigate the impact of finite blocklength on communications of vehicles platooning systems. By taking into account the factors of communication and the changes in the platoon structure, the proposed dynamic platoon manager selection scheme enables a significant performance enhancement over the conventional fixed manager scheme where the head vehicle in the platoon acts as a manager. Based on the proposed platoon manager selection scheme, a joint resource allocation and coding rate optimization algorithm is proposed to minimize the intra-platoon groupcast latency. Thanks to the closed-form expression of the optimal coding rate and transmission power derived, the proposed optimization algorithm achieves optimal performance in terms of the intra-platoon groupcast latency and converges within only 3 iterations, with a significant complexity reduction over exhaustive search.
Effects of Food Provisioning on the Daily Ration and Dive Site Use of Great Hammerhead Sharks, Sphyrna mokarran

Wildlife provisioning is popular, economically valuable, and a rapidly growing part of marine tourism, with great potential to benefit conservation. However, it remains controversial due to limited understanding of its implications on the behavior and ecology of target species. In this study, we modeled how various abiotic and biotic factors influenced great hammerhead sharks’ (Sphyrna mokarran) use of a recreational dive site in Bimini, the Bahamas, where shark-feeding has been conducted since 2012. Further, we calculated bioenergetic models to estimate their daily ration and examined if individual sharks fulfilled their daily energetic requirements from food uptake during dives. Between December 2016 and May 2017, we collected data during 104 provisioning dives in collaboration with a local dive operator. Twenty-eight individual great hammerhead sharks were observed, 11 were philopatric (i.e., identified at the dive site in previous years), and 17 were new (i.e., identified at the dive site for the first time during this study) individuals. On average, four sharks were observed daily, occasionally up to nine individuals, with some individuals spending more than 2 h attending each dive, consuming up to 4.75 kg of provisioned food per dive and returning repeatedly throughout the study period. When we grouped sharks based on their previous experience of the dive site (i.e., philopatric vs. new sharks), we found significantly higher attendance indices, i.e., the number of attended dives divided by the total number dives, and longer presence times during dives in philopatric sharks and different responses toward the number of boats and conspecifics between the two groups. Overall, great hammerhead sharks increased their bait uptake during longer dives and when more boats were present at the dive site. Finally, nine of 12 provisioned great hammerhead sharks were regularly able to fuel their daily energetic requirements from provisioned food alone, with two sharks doing so on 77.8% of all dives. Our study provides insights into how large-bodied marine predators react toward wildlife tourism associated provisioning and allows further discussion about daily energy uptake during provisioning dives, its potential impacts on the ecological role of the target species and associated management measures.
Initiation of Experimental Temporal Lobe Epilepsy by Early Astrocyte Uncoupling Is Independent of TGFβR1/ALK5 Signaling

Blood–brain barrier (BBB) dysfunction following brain insults has been associated with the development and progression of focal epilepsy, although the underlying molecular mechanisms are not fully elucidated yet. Activation of transforming growth factor beta (TGFβ) signaling in astrocytes by extravasated albumin impairs the ability of astrocytes to properly interact with neurons, eventually leading to epileptiform activity. We used the unilateral intracortical kainate mouse model of temporal lobe epilepsy (TLE) with hippocampal sclerosis (HS) to gain further insights into the role of BBB leakage in status epilepticus (SE)-induced epileptogenesis. Immunohistochemical examination revealed pronounced albumin extravasation already 4 h after SE induction. Astrocytes were virtually devoid of albumin immunoreactivity (IR), indicating the lack of uptake by this time point. Inhibition of the TGFβ pathway by the specific TGFβ receptor 1 (TGFβR1) kinase inhibitor IPW-5371 did not prevent seizure-induced reduction of astrocytic gap junction coupling. Thus, loss of coupling, which is thought to play a causative role in triggering TLE-HS, is most likely not mediated by extravasated albumin. Continuous telemetric EEG recordings and video monitoring performed over a period of 4 weeks after epilepsy induction revealed that inhibition of the TGFβ pathway during the initial phase of epileptogenesis slightly attenuated acute and chronic epileptiform activity, but did not reduce the extent of HS. Together, these data indicate that albumin extravasation due to increased BBB permeability and TGFβ pathway activation during the first hours after SE induction are not significantly involved in initiating TLE.
An Exploratory Meta-Analytic Review on the Empirical Evidence of Differential Learning as an Enhanced Motor Learning Method

Background: Differential learning (DL) is a motor learning method characterized by high amounts of variability during practice and is claimed to provide the learner with a higher learning rate than other methods. However, some controversy surrounds DL theory, and to date, no overview exists that compares the effects of DL to other motor learning methods. Objective: To evaluate the effectiveness of DL in comparison to other motor learning methods in the acquisition and retention phase. Design: Systematic review and exploratory meta-analysis. Methods: PubMed (MEDLINE), Web of Science, and Google Scholar were searched until February 3, 2020. To be included, (1) studies had to be experiments where the DL group was compared to a control group engaged in a different motor learning method (lack of practice was not eligible), (2) studies had to describe the effects on one or more measures of performance in a skill or movement task, and (3) the study report had to be published as a full paper in a journal or as a book chapter. Results: Twenty-seven studies encompassing 31 experiments were included. Overall heterogeneity for the acquisition phase (post-pre; I2 = 77%) as well as for the retention phase (retention-pre; I2 = 79%) was large, and risk of bias was high. The meta-analysis showed an overall small effect size of 0.26 [0.10, 0.42] in the acquisition phase for participants in the DL group compared to other motor learning methods. In the retention phase, an overall medium effect size of 0.61 [0.30, 0.91] was observed for participants in the DL group compared to other motor learning methods. Discussion/Conclusion: Given the large amount of heterogeneity, limited number of studies, low sample sizes, low statistical power, possible publication bias, and high risk of bias in general, inferences about the effectiveness of DL would be premature. Even though DL shows potential to result in greater average improvements between pre- and post/retention test compared to non-variability-based motor learning methods, more high-quality research is needed before issuing such a statement. For robust comparisons on the relative effectiveness of DL to different variability-based motor learning methods, scarce and inconclusive evidence was found.
Brain Activation of Patients With Obsessive-Compulsive Disorder During a Mental Rotation Task: A Functional MRI Study

Functional neuroimaging studies have implicated alterations in frontostriatal and frontoparietal circuits in obsessive-compulsive disorder (OCD) during various tasks. To date, however, brain activation for visuospatial function in conjunction with symptoms in OCD has not been comprehensively evaluated. To elucidate the relationship between neural activity, cognitive function, and obsessive-compulsive symptoms, we investigated regional brain activation during the performance of a visuospatial task in patients with OCD using functional magnetic resonance imaging (fMRI). Seventeen medication-free patients with OCD and 21 age-, sex-, and IQ-matched healthy controls participated in this study. Functional magnetic resonance imaging data were obtained while the subjects performed a mental rotation (MR) task. Brain activation during the task was compared between the two groups using a two-sample t-test. Voxel-wise whole-brain multiple regression analyses were also performed to examine the relationship between obsessive-compulsive symptom severity and neural activity during the task. The two groups did not differ in MR task performance. Both groups also showed similar task-related activation patterns in frontoparietal regions with no significant differences. Activation in the right dorsolateral prefrontal cortex in patients with OCD during the MR task was positively associated with their total Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) scores. This study identified the specific brain areas associated with the interaction between symptom severity and visuospatial cognitive function during an MR task in medication-free patients with OCD. These findings may serve as potential neuromodulation targets for OCD treatment.
A ‘Real Life’ Service Evaluation Model for Multidisciplinary Thyroid Eye Services

Background/Aims There is no universal consensus on the practical implementation and evaluation of the Amsterdam Declaration on Graves Orbitopathy in a Multidisciplinary Thyroid Eye Disease (MDTED) pathway. Recent recommendations from the UK TEAMeD-5 and BOPSS initiative highlight the importance of prevention, screening, and prompt referral of patients with moderate to severe and sight-threatening thyroid eye disease to multidisciplinary (MDTED) clinics and recommends annual auditing. We propose a practical service evaluation model with Key Performance Indicators (KPI) that are achievable and could be implemented across most TED pathways. Material and Methods We conducted a service evaluation from an integrated TED pathway in London with three MDTED clinics. Data was collected retrospectively from consecutive TED patients included: 1) Patient demographics, 2) Referral to first appointment time, 3) Documented smoking cessation and selenium supplementation advice, 4) Presenting disease activity and severity, 5) Investigations and treatments, including radio-iodine, 6) Time from decision to treatment initiation, 7) Initial and subsequent thyroid status. Results The median age was 49.0 yrs, 77.5% (183/236) were female and 49.5% (101/204) Afro-Caribbean or Asian. At their first clinic attendance, 47.6% (110/231) were biochemically euthyroid and 76.7% (79/103) at discharge. All 23.1% (52/225) current smokers received smoking cessation advice and 64.8% (153/236) received selenium supplementation advice. Intravenous methylprednisolone was given to 33.9% (80/236) patients and 12.7% (30/236) received second-line immunosuppression. All 7.2% (17/236) patients with sight-threatening disease received treatment within two weeks of diagnosis. Conclusions This study forms a waymark for other units using TEAMeD-5 and BOPSS audit criteria. Dedicated electronic patient records with ongoing data capture, including quality of life assessments, and diagnostic coding would significantly aid future auditing, improve patient care, and facilitate a national audit of TED management. A future survey when the TED standards have become embedded would be instructive to see whether this has improved TED care.