Miracle-dz/UniFAR-FTUs huggingface dataset:
Theta-Phase Connectivity between Medial Prefrontal and Posterior Areas Underlies Novel Instructions Implementation

Abstract Implementing novel instructions is a complex and uniquely human cognitive ability, which requires the rapid and flexible conversion of symbolic content into a format that enables the execution of the instructed behavior. Preparing to implement novel instructions, as opposed to their mere maintenance, involves the activation of the instructed motor plans, and the binding of the action information to the specific context in which this should be executed. Recent evidence and prominent computational models suggest that this efficient configuration of the system might involve a central role of frontal theta oscillations in establishing top-down long-range synchronization between distant and task-relevant brain areas. In the present EEG study (human subjects, 30 females, 4 males), we demonstrate that proactively preparing for the implementation of novels instructions, as opposed to their maintenance, involves a strengthened degree of connectivity in the theta frequency range between medial prefrontal and motor/visual areas. Moreover, we replicated previous results showing oscillatory features associated specifically with implementation demands, and extended on them demonstrating the role of theta oscillations in mediating the effect of task demands on behavioral performance. Taken together, these findings support our hypothesis that the modulation of connectivity patterns between frontal and task-relevant posterior brain areas is a core factor in the emergence of a behavior-guiding format from novel instructions.
Social play is critical for the development of prefrontal inhibitory synapses and cognitive flexibility in rats

Sensory driven activity during early life is critical for setting up the proper connectivity of the sensory cortices. Here we ask if social play behavior, a particular form of social interaction that is highly abundant during post-weaning development, is equally important for setting up connections in the developing prefrontal cortex (PFC). Young rats were deprived from social play with peers for 3 weeks during the period in life when social play behavior normally peaks (P21-42; SPD rats), followed by resocialization until adulthood. We recorded synaptic currents in L5 cells in slices from medial PFC of adult SPD and control rats and observed that inhibitory synaptic currents were reduced in SPD slices, while excitatory synaptic currents were unaffected. This was associated with a decrease in perisomatic inhibitory synapses from parvalbumin-positive GABAergic cells. In parallel experiments, adult SPD rats achieved more reversals in a probabilistic reversal learning task (PRL), which depends on the integrity of the PFC. They appeared to use a different cognitive strategy than controls. One hour of intense play during SPD did not prevent the decrease in inhibitory synaptic inputs and had only a limited effect on behavioral outcomes in the PRL. Our data demonstrate the importance of unrestricted social play for the development of inhibitory synapses in the PFC and cognitive skills in adulthood.
Regulation of the PKD2 Channel Function by TACAN

Autosomal dominant polycystic kidney disease (ADPKD) is caused by mutations in membrane receptor PKD1 or cation channel PKD2. TACAN (also named TMEM120A), recently reported as an ion channel in neuron cells for mechano and pain sensing, is also distributed in diverse non-neuronal tissues such as kidney, heart and intestine, suggesting its involvement in other functions. In this study, we found that TACAN is in complex with PKD2 in native renal cell lines. Using the two-electrode voltage clamp in Xenopus oocytes we found that TACAN inhibited the channel activity of PKD2 gain-of-function mutant F604P. The first and last transmembrane domains of TACAN were found to interact with the PKD2 C-and N-terminal portions, respectively. We showed that the TACAN N-terminus acted as a blocking peptide and that TACAN inhibits the PKD2 function through the PKD2/TACAN binding. By patch clamping in mammalian cells, we found that TACAN inhibits both the single channel conductance and open probability of PKD2 and mutant F604P. PKD2 co-expressed with TACAN, but not PKD2 alone, exhibited pressure sensitivity. Furthermore, we also found that TACAN aggravates PKD2-dependent tail curvature and pronephric cysts in larval zebrafish, in support of the in vitro inhibitory effects of TACAN. In summary, this study revealed that TACAN acts as a PKD2 inhibitor and mediates mechano sensitivity of the PKD2/TACAN channel complex.
Admixture of evolutionary rates across a hybrid zone

Hybridization is a major evolutionary force that can erode genetic differentiation between species, whereas reproductive isolation maintains such differentiation. In studying a hybrid zone between the swallowtail butterflies Papilio syfanius and Papilio maackii, we made the unexpected discovery that genomic substitution rates are unequal between the parental species. This phenomenon creates a novel process in hybridization, where genomic regions most affected by gene flow evolve at similar rates, while genomic regions with greater reproductive isolation evolve at divergent rates. Thus, hybridization mixes evolutionary rates in a way similar to its effect on ancestry. Using coalescent theory, we show that the rate-mixing process provides distinct information about levels of gene flow across different parts of genomes, and that maintenance of divergent substitution rates can be predicted quantitatively from relative sequence divergence (FST) between the hybridizing species at equilibrium. A corollary is that divergent rates will be maintained in regions linked to barrier loci. Overall, we demonstrate that reproductive isolation maintains not only the final outcome of genomic differentiation, but also the rate at which differentiation accumulates. This new information also suggests that the separation of evolutionary rates co-localizes with the separation of gene pools between genomes of incipient species.
Education Data Analysis using Ensemble Models

In this evolving era of machine learning, educational data mining has immense potential in transforming the pedagogy methods. In higher educational institutes, improvement of students' performance is an important aspect for NIRF parameters such as Teaching and Learning. It is a crucial task to identify significant attributes present in the education dataset that contribute most to academic performance prediction. Such work helps educational stakeholders in developing better methods which will improve the effectiveness of Teaching and Learning. In this paper, various classifier models along with ensemble learning models have been used to compare performance of the students. The novelty of this work is to observe the effects of feature engineering on the classification accuracy of the model when applied to the educational dataset. The results obtained show that the Ensemble algorithms perform better than the base algorithms when making student grade predictions.
Stability of Self-Consistent States of Flow in a Short-Circuited Diode in a Mode with a Through Passage of Particles

The purpose of this article is to study the operation of a short-circuited diode, and more precisely the frequency stability of the system and the state of the current electron flow during the operation of the diode system, taking into account the passing particles. In this study, the equations for one-dimensional electron flux in a flat diode were used as a basis, in which the parameters were reduced to dimensionless due to the input parameters of the diode length and the flight time of an individual electron. As a result of substitutions, transformations and solutions of systems of equations, the equations of the spectrum of oscillations that occur in the diode system in the mode without particle mappings and for the investigated state are obtained. During the study, the boundary conditions used in previous studies of the stability of electronic fluxes in diode-type devices were selected, resulting in an analysis of the stability of the system in the studied mode with dependences P(q) and Q(q), which showed the limits of stability of the mode with through flow of particles with a variable parameter q.
Comparison of linear and nonlinear aerodynamic parameter estimation approaches for an unmanned aerial vehicle using unscented Kalman filter

Aerodynamic parameter estimation provides an effective way for aerospace system modeling using measured data from flight tests,especially for the purpose of developing elaborate simulation environments and designing control systems of unmanned aerial vehicle(UAV)with short design cycles and reduced cost.However,parameter identification of airplane dynamics by nonlinear models is complicated because of the noisy and biased sensor measurements.Using linear models for system identification is an alternative way if the fidelity can be guaranteed,as control design procedures are better established in linear systems.This paper considers the application and comparison of linear as well as nonlinear aerodynamic parameter estimation approaches of an UAV using unscented Kalman filter(UKF). It also highlights the degree of deterioration of the linear model in the UKF identification process.The results show that both the linear and nonlinear methodologies can accurately estimate the control system design.Furthermore,considering loss of accuracy to be negligible,the linear model can be employed for control design of the UAV as presented here.
Teacher Assistance System to Detect Distracted Students in Online Classroom Environment

Perceiving students' engagement accurately is important for teachers and educational institutions, especially in online classrooms. In this paper, we address the problem of automated detection of distracted students from videos recorded in online classroom environment. To tackle this difficult challenge, we propose a Teacher Assistance System that detects inattentive students using their head pose from video data. We gathered data in two sessions by using Google Meet and WebEx portals to conduct online classes for a group of nineteen undergraduate Computer Science students. The authors used the OpenCV Caffe model to detect the faces. We used the Perspective-n-Problem approach to estimate the head pose of all the pupils in the class, and we also used the distance between the nose tip and the chin point to improve the model's accuracy. In an online classroom environment, the proposed system detected distracted pupils with 90.45% accuracy. The proposed Teacher Assistance System is precise and does not demand the use of any advanced tools. As a result, it might be used in both online and offline classroom scenarios, as well as other places where people who are distracted need to be identified.
The anthropomorphic design and experiments of HIT/DLR five-fingered dexterous hand

This paper presents a new developed anthropomorphic robot dexterous hand: HIT/DLR Hand II. The hand is composed of an independent palm and five identical modular fingers, and each finger has three degree of freedom (DOFs) and four joints. All the actuators and electronics are integrated in the finger body and the palm. Owing to using a new actuator, drivers and a novel arrangement, both the length and width of the finger is about two third of its former version. By using the wire coupling mechanism, the distal phalanx transmission ratio is kept exactly 1:1 in the whole movement range. The packing mechanism which is implemented directly in the finger body and palm not only reduces the size of whole hand but also make it more anthropomorphic. Additionally, the new designed force/torque and position sensors are integrated in the hand for increasing multisensory capability. To evaluate the performances of the finger mechanism, the position and impedance control experiments are conducted.
Time Delay Attack Detection Using Recurrent Variational Autoencoder and K-means Clustering

Conventional security methods deployed in power plants have difficulty detecting time delay attacks, since they do not alter network packets. However, these attacks can cause damage and instability in power systems, so detecting them is an urgent anomaly detection problem. Current state-of-the-art anomaly detection methods employ machine learning (ML) or statistical regression models in a supervised fashion, which require large amounts of labeled data for training. This data may be hard to practically obtain, so it is preferable to use unsupervised methods, which do not need labeled data. However, unsupervised anomaly detection solutions suffer from high false positive rates, especially under weak and moderate attacks. To improve on existing unsupervised solutions, we develop and present a dual-stage anomaly detection method using a Recurrent Variational Autoencoder (RVAE) and K-means clustering for detecting time delay attacks in power systems. We focus on samples including weak or moderate attacks which existing solutions cannot accurately detect, but can still harm the system if strategically targeted. We call these cases borderline samples, and use an additional clustering enhancement to more accurately classify them. Evaluation of the proposed approach on our power plant dataset demonstrates that our approach is effective in detecting time delay attacks, with 14.7% higher area under the ROC curve (AUC) than RVAE for borderline samples.
Optimal Power Sharing in DC Microgrid Under Load and Generation Uncertainties Based on GWO Algorithm

Microgrids (MGs) are drawing growing attention due to their advantages in the distribution. Hierarchical control strategy including local and global layers has been employed in the MG. The local control layers of the DC MG are the focus in this study. These local control layers may face some problems under load disruptions and power generation variations. Therefore, this paper proposes to develop local control layers of the DC MG depending on Grey Wolf Optimizer (GWO) algorithm to overcome such issues. The simulation studies has been carried out and the main results indicate that the proposed control approach applied in the local layers has superior performance in terms of the voltage adjustment besides the power-exchange amongst PV, and battery accompanied by the supercapacitor as compared to that of the existing approaches.
Deep Unrolled Recovery in Sparse Biological Imaging: Achieving fast, accurate results

Deep algorithm unrolling has emerged as a powerful, model-based approach to developing deep architectures that combine the interpretability of iterative algorithms with the performance gains of supervised deep learning, especially in cases of sparse optimization. This framework is well suited to applications in biological imaging, where physics-based models exist to describe the measurement process and the information to be recovered is often highly structured. Here we review the method of deep unrolling and show how it improves source localization in several biological imaging settings.
Making sense of the relation between number sense and math

While several studies have shown that the performance on numerosity comparison tasks is related to individual differences in math abilities, others have failed to find such a link. These inconsistencies could be due to variations in which math was assessed, different stimulus generation protocols for the numerosity comparison task, or differences in inhibitory control. This within-subject study is a conceptual replication tapping into the relation between numerosity comparison, math, and inhibition in adults (N = 122). Three aspects of math ability were measured using standardized assessments: Arithmetic fluency, calculation, and applied problem solving skills. Participants’ inhibitory skills were measured using Stroop and Go/No-Go tasks with numerical and non-numerical stimuli. Finally, non-symbolic number sense was measured using two different versions of a numerosity comparison task that differed in the stimulus generation protocols (Panamath; Halberda, Mazzocco & Feigenson, 2008, https://doi.org/10.1038/nature07246; G&R, Gebuis & Reynvoet, 2011, https://doi.org/10.3758/s13428-011-0097-5). We find that performance on the Panamath task, but not the G&R task, related to measures of calculation and applied problem solving but not arithmetic fluency, even when controlling for inhibitory control. One possible explanation is that depending on the characteristics of the stimuli in the numerosity comparison task, the reliance on numerical and non-numerical information may vary and only when performance relies more on numerical representations, a relation with math achievement is found. Our findings help to explain prior mixed findings regarding the link between non-symbolic number sense and math and highlight the need to carefully consider variations in numerosity comparison tasks and math measures.
A New Laboratory Test for Predicting Very Early Chip Seal Performance

This paper describes how the adhesive and cohesive strength of the emulsion residue used as the binder in a chip seal is directly related to when the chip seal can be opened to traffic after construction. This strength is usually judged subjectively during construction by experienced personnel. Unfortunately, this experience is often gained by trial and error, the error leading to vehicle damage when residues that have not gained sufficient strength release chips under traffic loads. This research was conducted to help eliminate the subjectivity involved in determining when a chip seal can be safely opened to traffic without undue chip loss. The study began with the hypothesis that the existing ASTM D7000 sweep test could be used for this purpose. The ASTM procedure was evaluated and it was determined the test would be adequate for relative comparison of different emulsions for the same aggregates, but not sufficient to evaluate different aggregates for the same or different emulsions. The reason for this is because D7000 uses a template to establish residue depth. Chip gradation is roughly controlled so embedment depth is relatively equal for different chips but variability remains high so predicting actual project materials could be used and time to trafficking could be estimated. Results indicate the amount of water remaining in the emulsion is directly related to residue strength, as expected. Therefore, by establishing the relationship between water content and chip loss in the revised sweep test the time required in the field before traffic is allowed on the fresh chip seal can be estimated in advance. Results indicate little correlation between emulsion particle charge and aggregate type and also that chip loss is directly related to aggregate moisture content upon embedment.
COUPLING EFFECT OF FLEXIBLE JOINT AND FLEXIBLE LINK ON DYNAMIC SINGULARITY OF FLEXIBLE MANIPULATOR

The coupling effect of the flexible joint and the flexible link on the dynamic singularity of the flexible manipulator is addressed. Firstly,the dynamic equations of a flexible manipulator with a flexible joint and a flexible link are derived. Secondly,the relationship and property between the flexible joint and the flexible link are analyzed. It shows that the flexible joint’s amplitude will increase abruptly,thereby the dynamic singularity occurs if the frequency of a flexible joint is near or equal to some natural frequency of a flexible link. Finally,some numerical simulations which will verify the correctness of the theoretical analysis,are carried out. The results are fundamental for the design of a flexible manipulator and for the avoidance of the dynamic singularity.
Comparison of Account Survival on Twitter During the First Wave of COVID-19 in Four Countries

The user accounts on online social networks (OSNs) are constantly changing. New ones are created at an increasing pace, while others are deleted by users, protected from public view, or suspended by the online service providers. The study of OSN account survival can shed light on aspects such as OSN adoption and abandonment, incidence of bots, or enforcement of OSN platform rules, but the ongoing pandemic has signiﬁcantly altered the online behaviour of people. Therefore, in this thesis I compare the extent to which the ﬁrst wave of the COVID-19 pandemic between March and April 2020 affected account survival patterns in four countries with different approaches to managing COVID-19: the United States, the United Kingdom, Singapore, and New Zealand. The data set for the analysis was obtained from the Twitter Streaming API between March 10 and April 29, 2020. It consists of 17.3M unique tweets in English by 15.3M unique accounts. The thesis presents a detailed quantitative analysis of this data using statistical methods. The results reveal signiﬁcant and consistent differences among patterns of account survival. Overall trends are similar across the four countries studied, with suspended accounts being the most active. Surprisingly, the frequency of COVID-19 mentions was largely unaffected by case-counts, and mostly followed a steady downward trend over time. For the US and the UK, COVID-19 mentioning patterns were similar, regardless of account type. However, New Zealand showed substantial variation over time, while suspended accounts from Singapore were less likely to mention COVID-19 than all other account types. Based on the results of the thesis, further temporal and qualitative studies of account survival can be undertaken, while additional comparisons across various socio-economic variables could shed light on digital exclusion and bias. The thesis concludes that account survival type should be taken into account when examining how the pandemic is discussed on Twitter.
Physics-Conditioned Generative Adversarial Networks for State Estimation in Active Power Distribution Systems with Low Observability

A novel method is proposed to address the issue of low-observability in Distribution System State Estimation (DSSE). We first use the historical data at the unobservable locations to construct and train proper Generative Adversarial Network (GAN) models to compensate for lack of direct real-time measurements. We then integrate the trained GAN models, together with the direct synchronized measurements at the observable locations, into the formulation of the DSSE problem. In this regard, we simultaneously take advantage of the forecasting capabilities of the GAN models, the available real-time synchronized measurements, and the DSSE formulations based on physical laws in the power system. As a result, on one hand we conduct a physics-conditioned estimation of the unknown power injections at the unobservable locations; and on the other hand, we also achieve a complete DSSE solution for the understudy low-observable active power distribution system.