Miracle-dz/UniFAR-FTUs huggingface dataset:
Use of cognitive correction training improves learning for children with mathematics learning disability

Abstract Children with normal intelligence may experience varying degrees of mathematics learning disabilities (MD). This study aims to conduct training to improve the brain’s cognitive ability for mathematics learning by focusing on two important mathematical cognitive abilities. This was a prospective study of 70 children in grades 2–5 from two primary schools in Changzhou and with MD enrolled from June 2015 to February 2017. The children were randomized 1:1 to the training and control groups. A training cycle included 40 sessions (5/weeks) (30 min each session). The efficacy of learning was assessed by assessing number learning and graph reasoning, and by using the Raven standard reasoning test score. In the training group, backward number memory (from 6.1 ± 1.8 to 6.7 ± 1.3, P = 0.02), number sequential connection (from 54.4 ± 14.5 to 47.1 ± 12.1, P < 0.01), and rapid graph judgment (from 531.9 ± 76.3 to 557.8 ± 85.7, P = 0.04) were improved by training, while there was no effect on forward number memory (P = 0.13). There were significant differences in total score and scores of b, c, and e series before and after training (all P < 0.05). The children in the control group had no improvement after 8 weeks. There was a correlation between the ability of rapid graphic judgment before and after training and the score of the Raven E series (r = 0.384, P = 0.024), and between the score of the Raven C series and the score of the Raven D series (r = 0.468, P = 0.013). Cognitive correction training improved the sensitivity to numbers and mathematics learning in children with MD.
Artificial Intelligence-Based Handoff Management for Dense WLANs: A Deep Reinforcement Learning Approach

So far, the handoff management involved in the wireless local area network (WLAN) has mainly fallen into the handoff mechanism and the decision algorithm. The traditional handoff mechanism generates noticeable delays during the handoff process, resulting in discontinuity of service, which is more evident in dense WLANs. Inspired by software-defined networking (SDN), prior works put forward many seamless handoff mechanisms to ensure service continuity. With respect to the handoff decision algorithm, when to trigger handoff and which access point to reconnect to, however, are still tricky problems. In this paper, we first design a self-learning architecture applicable to the SDN-based WLAN frameworks. Along with it, we propose DCRQN, a novel handoff management scheme based on deep reinforcement learning, specifically deep $Q$ -network. The proposed scheme enables the network to learn from actual users’ behaviors and network status from scratch, adapting its learning in time-varying dense WLANs. Due to the temporal correlation property, the handoff decision is modeled as the Markov decision process (MDP). In the modeled MDP, the proposed scheme depends on the real-time network statistics at the time of decisions. Moreover, the convolutional neural network and the recurrent neural network are leveraged to extract fine-grained discriminative features. The numerical results through simulation demonstrate that DCRQN can effectively improve the data rate during the handoff process, outperforming the traditional handoff scheme.
Evaluating Built-In ECC of FPGA On-Chip Memories for the Mitigation of Undervolting Faults

Voltage underscaling below the nominal level is an effective solution for improving energy efficiency in digital circuits, e.g., Field Programmable Gate Arrays (FPGAs). However, further undervolting below a safe voltage level and without accompanying frequency scaling leads to timing related faults, potentially undermining the energy savings. Through experimental voltage underscaling studies on commercial FPGAs, we observed that the rate of these faults exponentially increases for on-chip memories, or Block RAMs (BRAMs). To mitigate these faults, we evaluated the efficiency of the built-in Error-Correction Code (ECC) and observed that more than 90 % of the faults are correctable and further 7 % are detectable (but not correctable). This efficiency is the result of the single-bit type of these faults, which are then effectively covered by the Single-Error Correction and Double-Error Detection (SECDED) design of the built-in ECC. Finally, motivated by the above experimental observations, we evaluated an FPGA-based Neural Network (NN) accelerator under low-voltage operations, while built-in ECC is leveraged to mitigate undervolting faults and thus, prevent NN significant accuracy loss. In consequence, we achieve 40 % of the BRAM power saving through undervolting below the minimum safe voltage level, with a negligible NN accuracy loss, thanks to the substantial fault coverage by the built-in ECC.
Human-Robot-Collaboration (HRC): Social Robots as Teaching Assistants for Training Activities in Small Groups

Can we find real value for educational social robots in the very near future? We argue that the answer is yes. Specifically, in a classroom we observed, we identified a common gap: the instructor divided the class into small groups to work on a learning activity and could not address all their questions simultaneously. The purpose of this study was to examine whether social robots can assist in this scenario. In particular, we were interested to find whether a physical robot serves this purpose better than other technologies such as tablets. Benefits and drawbacks of the robot facilitator are discussed.
Workplace intrusions and employee strain: the interactive effects of extraversion and emotional stability

ABSTRACT Background/objectives: Intrusions are a type of workplace interruption defined as unexpected interpersonal contact that disrupts workflow. Drawing from the Job Demands-Resources model of work stress, we examined how two personal resources – extraversion and emotional stability – influence relations among intrusions and strain outcomes. Design/methodology: Self-reported, online questionnaire data were collected from two samples; 323 faculty and staff from a university (sample 1) and 574 full-time employees recruited from an online crowdsourcing forum (sample 2). Results: In sample 1 extraversion was positively related to intrusions, whereas in sample 2, extraversion and emotional stability were negatively related to intrusions. There were no interactive effects of personality on relations among intrusions and strain outcomes in sample 1. In sample 2, low emotional stability strengthened relations among intrusions and work tension. Additionally, intrusions were negatively associated with stress for individuals high on emotional stability. Finally, introverts experienced less stress in response to intrusions. Conclusion: Taking a person-situation interactionist approach, we examined personal resources that may mitigate interruption-related strain. Our findings suggest that for employees high in emotional stability, interruptions may be both less detrimental and, in some cases, beneficial. This study has practical implications for the structuring of work environments to mitigate strain and maximize person-environment fit.
Prevalence of carpal tunnel syndrome symptoms among dentists working in Riyadh

BACKGROUND Approximately 3–6% of the general population have carpal tunnel syndrome (CTS). CTS is more prevalent among people with occupations that involve repetitive and forceful maneuvers, such as dentists. It is important to identify risk factors for these symptoms and to understand the impact they may have on clinical practice. OBJECTIVES Measure the prevalence of CTS-symptoms and identify factors associated with CTS. DESIGN Cross-sectional. SETTING Dentists working in Riyadh. SUBJECTS AND METHODS In-person interviews from 15 July to 10 September 2017 of subjects obtained using random cluster sampling. MAIN OUTCOME MEASURES Prevalence of CTS-related symptoms among dentists. SAMPLE SIZE 223 dentists (134 males and 89 females). RESULTS The prevalence of CTS-related symptoms among dentists working in Riyadh was 30.5% (95% CI 0.25 to 0.36). Female dentists had a significantly greater risk of having CTS symptoms than male dentists (OR 2.13; 95% CI 1.09–4.17). Obese dentists were also more likely to complain of CTS symptoms than dentists within normal weight limits (OR 3.66; 95% CI 1.55–8.64). Left-hand dominance was strongly associated with CTS symptoms, with an estimated OR of 6.28 (95% CI 1.24–31.90). However, there was no relationship between CTS symptoms and age, marital status, history of smoking, exercise, dental specialty, occupation period, or having other educational degrees. CONCLUSION Thirty percent of dentists working in Riyadh had experienced severe or mild symptoms related to CTS. Several risk factors for CTS have been identified. Future research could explore the reasons behind these risk factors to identify and implement prevention measures. LIMITATIONS Cannot be generalized to the whole of Saudi Arabia, or the region. Larger controlled studies are needed to further identify the risk factors associated with CTS among dentists. In addition, since the Boston Carpal Tunnel Questionnaire was used, the self-reporting nature of the study might be affected by external bias.
Geomorphic and Hydrological Mapping of Sheskinmore Lough and Wetland, Co. Donegal, Ireland

Wetlands are one of the most protected ecosystems in Europe (Glinmann, 1994). Sheskinmore Lough and wetland in County Donegal, Ireland is a sedimentary lake formed around c. 1000 years ago and it is a protected site (Shaw & Carter, 1994). Wetlands are complex hydrological systems where water flow is restricted, obstructed and retained. In recent years the water levels have been dropping due to natural siltation and ad hoc drainage, this has had a negative impact on the water levels and subsequently on the flora and fauna of the site. A sluice has been installed to maintain the water levels in the Lough. Topography is one of the main influences on water bodies and water flow (Pervalo, 2004). Therefore to accurately model the hydrology of the site a digital elevation model (DEM) is essential. This paper presents a high resolution raster grid DEM generated from a differential GPS (DGPS) survey. Six interpolation models are analysed, they include Inverse distant weighting, kriging, minimum curvature splines (both regularised and with tension), natural neighbour and the Australian National University DEM interpolation model (ANUDEM). Different DEM smoothing techniques are also analysed in an attempt to create a more realistic surface, these include subsampling the dataset, smoothing the dataset and smoothing the output DEM. Superficial sediment is modelled, soils are sampled using a stainless steel hand auger. The DEM and soil data presented in this paper will be used to accurately model the hydrological processes of the site using a MIKE SHE model in future studies. Water volume in the Lough is modelled under different scenarios to assess the performance of the sluice using the DEM generated.
The Persistence of Preschool Effects From Early Childhood Through Adolescence

Using data from the Early Childhood Longitudinal Study Kindergarten Cohort of 1998 (n = 15,070), this study used propensity scores to examine the short- and long-term academic and psychosocial benefits of preschool education for a diverse sample of middle-class children. Compared with children who attended informal care at age 4, preschool attendees consistently performed better on achievement tests from age 5 through early adolescence, but exhibited less optimal psychosocial skills. These negative behavioral effects of preschool were concentrated among children who attended preschool for 20 or more hours per week, but otherwise, there was little evidence of heterogeneity as a function of program type or child- and family characteristics. The long-term academic advantages of preschool were, however, largely explained by their positive effects on academic skills early in formal schooling and there was evidence for convergence in children’s academic test scores, which was partially attributed to the differences in children’s social skills during the early elementary school years.
Antifungal and plant growth promotion activity of volatile organic compounds produced by Bacillus amyloliquefaciens

Fusarium wilt of watermelon, caused by F. oxysporum f.sp. niveum (FON), is a devastating disease that causes extensive losses throughout the world. Five bacterial strains (L3, h, β, b, and L) isolated from the watermelon rhizosphere showed antagonistic activity against FON during in vitro tests. Strain L3 produced diffusible and volatile organic compounds (VOCs) which showed the strongest antifungal activity. Arabidopsis thaliana plantlets exposed to VOCs produced by strain L3 showed a 2.39‐fold increase in biomass, 1.40‐fold increase in primary root length, and 5.05‐fold increase in number of lateral roots. Confocal laser scanning microscope showed that the GFP‐labeled strain L3 could colonize along the elongation and differentiation zones of watermelon roots. In greenhouse pot experiments, the biocontrol efficiency of strain L3 against fusarium wilt of watermelon was up to 68.4% in comparison with the control treatment. In addition, inoculation of the strain L3 resulted in a 23.4% increase in plant fresh weight. Based on 16S rDNA sequence analysis, the strain L3 was identified as Bacillus amyloliquefaciens L3. Fourteen VOCs produced by strain L3 were identified through GC‐MS analysis. Of nine VOCs tested, 2‐nonanone and 2‐heptanone were proved to have strong antifungal properties. Acetoin and 2,3‐butanediol were found to promote plant growth. The results suggested B. amyloliquefaciens L3 was a potential biocontrol agent, and that VOCs produced by B. amyloliquefaciens L3 play important roles in the process of biocontrol and plant growth promotion.
Discovery of potential sclerostin inhibitors from plants with loop2 region of sclerostin inhibition by interacting with residues outside Pro-Asn-Ala-Ile-Gly motif

Abstract Sclerostin, an antagonist of the Wnt/β-catenin signaling pathway, was discovered as a potential therapeutic target for stimulating bone formation in osteoporosis. In this study, molecular docking was employed to predict the binding of 29 herbal compounds, which were reported as bone formation stimulators, to the loop2 region of sclerostin. Then, the 50 ns molecular dynamics (MD) simulation of the complexes between sclerostin and the top 10 hits obtained from molecular docking were carried out. Root mean square deviations (RMSDs) analysis of MD trajectories pointed out that all ligands-complexes remain stable throughout the duration of MD simulations. In addition, the molecular mechanics/generalized born surface area (MM/GBSA) binding free energy and energy decomposition analyses were determined. The results here suggested that baicalin is the most promising inhibitor of sclerostin. Interestingly, baicalin binds to sclerostin via the hydrophobic interaction with the amino acid residues on loop2 region but outside the Pro-Asn-Ala-Ile-Gly (PNAIG) motif, particularly the Arg-Gly-Lys-Trp-Trp-Arg (RGKWWR) motif. This finding could be a novel strategy for developing new sclerostin inhibitors in the future. Communicated by Ramaswamy H. Sarma
Human Activity Recognition Using Federated Learning

State-of-the-art deep learning models for human activity recognition use large amount of sensor data to achieve high accuracy. However, training of such models in a data center using data collected from smart devices leads to high communication costs and possible privacy infringement. In order to mitigate aforementioned issues, federated learning can be employed to train a generic classifier by combining multiple local models trained on data originating from multiple clients. In this work we evaluate federated learning to train a human activity recognition classifier and compare its performance to centralized learning by building two models, namely a deep neural network and a softmax regression trained on both synthetic and real-world datasets. We study communication costs as well as the influence of erroneous clients with corrupted data in federated learning setting. We have found that federated learning for the task of human activity recognition is capable of producing models with slightly worse, but acceptable, accuracy compared to centralized models. In our experiments federated learning achieved an accuracy of up to 89% compared to 93% in centralized training for the deep neural network. The global model trained with federated learning on skewed datasets achieves accuracy comparable to centralized learning. Furthermore, we identified an important issue of clients with corrupted data and proposed a federated learning algorithm that identifies and rejects erroneous clients. Lastly, we have identified a trade-off between communication cost and the complexity of a model. We show that more complex models such as deep neural network require more communication in federated learning settings for human activity recognition compared to less complex models, such as multinomial logistic regression.
Concentration of Measure and Large Random Matrices with an application to Sample Covariance Matrices

The present work provides an original framework for random matrix analysis based on revisiting the concentration of measure theory for random vectors. By providing various notions of vector concentration (q-exponential, linear, Lipschitz, convex), a set of elementary tools is laid out that allows for the immediate extension of classical results from random matrix theory involving random concentrated vectors in place of vectors with independent entries. These findings are exemplified here in the context of sample covariance matrices but find a large range of applications in statistical learning and beyond, starting with the capacity to easily analyze the performance of artificial neural networks and random feature maps.
Gliese 49: activity evolution and detection of a super-Earth

Context. Small planets around low-mass stars often show orbital periods in a range that corresponds to the temperate zones of their host stars which are therefore of prime interest for planet searches. Surface phenomena such as spots and faculae create periodic signals in radial velocities and in observational activity tracers in the same range, so they can mimic or hide true planetary signals. Aims. We aim to detect Doppler signals corresponding to planetary companions, determine their most probable orbital configurations, and understand the stellar activity and its impact on different datasets. Methods. We analyzed 22 yr of data of the M1.5 V-type star Gl 49 (BD+61 195) including HARPS-N and CARMENES spectrographs, complemented by APT2 and SNO photometry. Activity indices are calculated from the observed spectra, and all datasets are analyzed with periodograms and noise models. We investigated how the variation of stellar activity imprints on our datasets. We further tested the origin of the signals and investigate phase shifts between the different sets. To search for the best-fit model we maximize the likelihood function in a Markov chain Monte Carlo approach. Results. As a result of this study, we are able to detect the super-Earth Gl 49b with a minimum mass of 5.6 M⊕. It orbits its host star with a period of 13.85 d at a semi-major axis of 0.090 au and we calculate an equilibrium temperature of 350 K and a transit probability of 2.0%. The contribution from the spot-dominated host star to the different datasets is complex, and includes signals from the stellar rotation at 18.86 d, evolutionary timescales of activity phenomena at 40–80 d, and a long-term variation of at least four years.
Magic Number Theory of Superconducting Proximity Effects and Wigner Delay Times in Graphene-Like Molecules

When a single molecule is connected to external electrodes by linker groups, the connectivity of the linkers to the molecular core can be controlled to atomic precision by appropriate chemical synthesis. Recently, the connectivity dependence of the electrical conductance and Seebeck coefficient of single molecules has been investigated both theoretically and experimentally. Here, we study the connectivity dependence of the Wigner delay time of single-molecule junctions and connectivity dependence of superconducting proximity effects, which occur when the external electrodes are replaced by superconductors. Although absolute values of transport properties depend on complex and often uncontrolled details of the coupling between the molecule and electrodes, we demonstrate that ratios of transport properties can be predicted using tables of “magic numbers,” which capture the connectivity dependence of superconducting proximity effects and Wigner delay times within molecules. These numbers are calculated easil.
Exploring high multiplicity amplitudes: The quantum mechanics analogue of the spontaneously broken case

Calculations of high multiplicity Higgs amplitudes exhibit a rapid growth that may signal an end of perturbative behavior or even the need for new physics phenomena. As a step towards this problem we consider the quantum mechanical equivalent of 1 → n scattering amplitudes in a spontaneously broken φ-theory by extending our previous results on the quartic oscillator with a single minimum [1] to transitions 〈n|x̂|0〉 in the symmetric double-well potential with quartic coupling λ. Using recursive techniques to high order in perturbation theory, we argue that these transitions are of exponential form 〈n|x̂|0〉 ∼ exp (F (λn)/λ) in the limit of large n and λn fixed. We apply the methods of “exact perturbation theory” put forward by Serone et al. in [2,3] to obtain the exponent F and investigate its structure in the regime where tree-level perturbation theory violates unitarity constraints. We find that the resummed exponent is in agreement with unitarity and rigorous bounds derived by Bachas [4]. 1 ar X iv :1 81 1. 12 11 6v 2 [ he pph ] 2 5 M ar 2 01 9
Chemical pressure effect on the optical conductivity of the nodal-line semimetals 
ZrSiY(Y=S,Se,Te)
 and 
ZrGeY(Y=S,Te)

ZrSiS is a nodal-line semimetal, whose electronic band structure contains a diamond-shaped line of Dirac nodes. We carried out a comparative study on the optical conductivity of ZrSiS and related compounds ZrSiSe, ZrSiTe, ZrGeS, and ZrGeTe by reflectivity measurements over a broad frequency range combined with density functional theory calculations. The optical conductivity exhibits a distinct U shape, ending at a sharp peak at around 10000~cm$^{-1}$ for all studied compounds, except for ZrSiTe. The U shape of the optical conductivity is due to transitions between the linearly dispersing bands crossing each other along the nodal line. The sharp high-energy peak is related to transitions between almost parallel bands, and its energy position depends on the interlayer bonding correlated with the $c$/$a$ ratio, which can be tuned by either chemical or external pressure. For ZrSiTe, another pair of crossing bands appears in the vicinity of the Fermi level, corrugating the nodal-line electronic structure and leading to the observed difference in optical conductivity. The findings suggest that the Dirac physics in Zr$XY$ compounds with $X$=Si, Ge and $Y$=S, Se, Te is closely connected to the interlayer bonding.