Miracle-dz/UniFAR-FTUs huggingface dataset:
Attention-deficit/hyperactivity disorder and risk for psychiatric and neurodevelopmental disorders in siblings

Abstract Background Probands with attention-deficit/hyperactivity disorder (ADHD) are at increased risk for several psychiatric and neurodevelopmental disorders. The risk of these disorders among the siblings of probands has not been thoroughly assessed in a population-based cohort. Methods Every child born in Finland in 1991–2005 and diagnosed with ADHD in 1995–2011 were identified from national registers. Each case was matched with four controls on sex, place, and date of birth. The full siblings of the cases and controls were born in 1981–2007 and diagnosed in 1981–2013. In total, 7369 cases with 12 565 siblings and 23 181 controls with 42 753 siblings were included in the analyses conducted using generalized estimating equations. Results 44.2% of the cases and 22.2% of the controls had at least one sibling diagnosed with any psychiatric or neurodevelopmental disorder (risk ratio, RR = 2.1; 95% CI 2.0–2.2). The strongest associations were demonstrated for childhood-onset disorders including ADHD (RR = 5.7; 95% CI 5.1–6.3), conduct and oppositional disorders (RR = 4.0; 95% CI 3.5–4.5), autism spectrum disorders (RR = 3.9; 95% CI 3.3–4.6), other emotional and social interaction disorders (RR = 2.7; 95% CI 2.4–3.1), learning and coordination disorders (RR = 2.6; 95% CI 2.4–2.8), and intellectual disability (RR = 2.4; 95% CI 2.0–2.8). Also, bipolar disorder, unipolar mood disorders, schizophrenia spectrum disorders, other neurotic and personality disorders, substance abuse disorders, and anxiety disorders occurred at increased frequency among the siblings of cases. Conclusions The results offer potential utility for early identification of neurodevelopmental and psychiatric disorders in at-risk siblings of ADHD probands and also argue for more studies on common etiologies.
Regional Priority Based Anomaly Detection using Autoencoders

In the recent times, autoencoders, besides being used for compression, have been proven quite useful even for regenerating similar images or help in image denoising. They have also been explored for anomaly detection in a few cases. However, due to location invariance property of convolutional neural network, autoencoders tend to learn from or search for learned features in the complete image. This creates issues when all the items in the image are not equally important and their location matters. For such cases, a semi supervised solution - regional priority based autoencoder (RPAE) has been proposed. In this model, similar to object detection models, a region proposal network identifies the relevant areas in the images as belonging to one of the predefined categories and then those bounding boxes are fed into appropriate decoder based on the category they belong to. Finally, the error scores from all the decoders are combined based on their importance to provide total reconstruction error.
Nutritional limitations to increased production on pasture-based systems

The constraints to high levels of milk production imposed by a high-quality-pasture diet, and development of feeding strategies to overcome these limitations, were examined by modelling the nutritional status of New Zealand Friesian and North American Holstein–Friesian dairy cows grazing high-quality pasture. The Cornell Net Carbohydrate and Protein System (CNCPS) was used to predict sensitivity of milk production to a 10 % change in the composition of pasture nutrients. The rate at which fibre and protein were degraded in the rumen and the value given to effective fibre and lignin content significantly affected the supply of metabolisable energy and protein, and the profile of amino acid supply. The first limiting factor in milk production when only high-quality pasture was fed was metabolisable energy supply, while specific amino acids, particularly methionine and lysine, limited milk production when > 20 g/kg diet consisted of a grain supplement. Compared with cows fed a total mixed ration in confinement, North American Holstein–Friesians grazing all pasture produced less milk (29.6 v. 44.1 kg/d). Of the difference in milk production 61 % could be attributed to a lower DM intake (19 kg/d v. 23.4 kg/d). Predictions using the CNCPS indicated that supply of metabolisable energy was the first-limiting factor for milk production from high-quality pasture (251 g crude protein (N× 6.25)/kg, 432 g neutraldetergent fibre/kg, 77 % in vitro DM digestibility), rather than metabolisable protein or amino acids. In addition, these nutritional limitations imposed by pasture diets will be greater for dairy cow genotypes that have not been selected for high performance within a pasture system.
Real-Time Event Search Corresponding to Place and Time Using Social Stream

Since the decision was made to hold the Olympic Games in 2020 in Tokyo, the number of foreign tourists visiting the city has been increasing rapidly. Accordingly, tourists have been seeking more sightseeing information. Their interests and visit durations are varied, so they should be provided information that is interesting and timely to each of them. While guidebooks are good for pointing out popular tourist attractions, it is more difficult for tourists to get information on local events and spots that are just becoming popular. We developed a tourist information distribution system that sends information corresponding to a place and time. The system extracts recent major and minor event information from social media streams in a per place and time manner and provides it to tourists. We evaluated the accuracy of its event classification of actual Twitter data with two representative methods: support vector machine (SVM) and random forests. Furthermore, we found that supplementary information from the web can be used to provide more accurate event information.
A novel overlapping community detection using parallel CFM and sequential nash equilibrium

Detecting Overlapping Community in Social Networks is one of the challenging and complex problem. Several approaches based on heuristic, modularity & modularity density, graph partitioning and game theory are available for community detection. However getting an optimum and stable solution with less computation cost for large datasets is not possible using these existing approaches. Hence, in this work, we propose a novel overlapping community detection algorithm based on parallel community forest model and sequential Nash Equilibrium for large datasets. In this paper, community forest model (CFM) is implemented in parallel using Spark framework to get the initial community structure and then a Nash Equilibrium is computed to find a stable overlapping community structure. We conducted experiments on the benchmark LFR dataset with different sizes like 500, 1000, 2000 upto 10,000 nodes to evaluate the proposed method. Our experimental results clearly demonstrate that the proposed approach outperforms the existing works in terms of quality, scalability, stability and less computation time.
Fracture Properties of Gap & Dense Graded Thin Bonded Overlays

When a pavement surface becomes heavily deteriorated, the pavement designer can choose from a number of maintenance and rehabilitation alternatives spanning from thin surface-type treatments to more substantial surface renewal programs involving milling and the placement of a hot-mix asphalt overlay. In the past, thin surface treatments were generally limited to lower volume roads, as ride quality and the existence of loose aggregate prevented its use on higher volume, higher speed facilities. However, this is no longer the case with the advent of bonded overlay systems, where a spray paver application system enables heavier tack coat application rates to be used in conjunction with aggregates already coated with asphalt. Thin bonded overlay systems, or TBOs, appear to have performance benefits over traditional surface treatments and overlay systems. However, the assessment and, moreover, the quantification of the crack resistance of these thin pavement surface renewal systems presents a significant scientific challenge. The development of such a system is the focus of this paper. Nineteen pavement sections representing five pavement projects, various tack coat application rates and types, asphalt concrete gradations (dense and gap), construction techniques (conventional and spray paver) are evaluated in this study. Core samples from each of the pavement projects were procured and fracture characterization tests were performed using the disk-shaped compact tension fracture test (ASTM D7313-07b) and/or a variation of the test. The test variation, termed the compact tension test, or C(T), is a specifically tailored version of the DC(T) test designed for testing TBOs. The laboratory fracture results obtained from both of these tests demonstrate significantly higher fracture resistance characteristics for gap-graded TBOs as compared to conventionally placed asphalt overlays. This appears to be due to the significant upwards wicking of the heavy tack coat material into the gap graded mix. The dense graded TBOs, which are a relatively new concept, showed fracture energies that were slightly higher than conventionally placed overlays but significantly lower than the gap graded TBOs, probably as a result of the lower amount of upwards wicking of the tack coat binder as compared to the gap graded system. In order to further explore the cracking resistance of TBOs, a series of numerical simulations were performed to evaluate the relative thermal cracking potential of dense and gap graded TBOs as compared to conventional HMA overlays. The simulation results further reinforce the finding obtained from laboratory fracture testing.
Left-Right Comparative Recurrent Model for Stereo Matching

Leveraging the disparity information from both left and right views is crucial for stereo disparity estimation. Left-right consistency check is an effective way to enhance the disparity estimation by referring to the information from the opposite view. However, the conventional left-right consistency check is an isolated post-processing step and heavily hand-crafted. This paper proposes a novel left-right comparative recurrent model to perform left-right consistency checking jointly with disparity estimation. At each recurrent step, the model produces disparity results for both views, and then performs online left-right comparison to identify the mismatched regions which may probably contain erroneously labeled pixels. A soft attention mechanism is introduced, which employs the learned error maps for better guiding the model to selectively focus on refining the unreliable regions at the next recurrent step. In this way, the generated disparity maps are progressively improved by the proposed recurrent model. Extensive evaluations on KITTI 2015, Scene Flow and Middlebury benchmarks validate the effectiveness of our model, demonstrating that state-of-the-art stereo disparity estimation results can be achieved by this new model.
Radio Resource Management Scheme in NB-IoT Systems

Narrowband Internet of Things (NB-IoT) is the prominent technology that fits the requirements of future IoT networks. However, due to the limited spectrum (i.e., 180 kHz) availability for NB-IoT systems, one of the key issues is how to efficiently use these resources to support massive IoT devices? Furthermore, in NB-IoT, to reduce the computation complexity and to provide coverage extension, the concept of time offset and repetition has been introduced. Considering these new features, the existing resource management schemes are no longer applicable. Moreover, the allocation of frequency band for NB-IoT within LTE band, or as a standalone, might not be synchronous in all the cells, resulting in intercell interference (ICI) from the neighboring cells’ LTE users or NB-IoT users (synchronous case). In this paper, first a theoretical framework for the upper bound on the achievable data rate is formulated in the presence of control channel and repetition factor. From the conducted analysis, it is shown that the maximum achievable data rates are 89.2 Kbps and 92 Kbps for downlink and uplink, respectively. Second, we propose an interference aware resource allocation for NB-IoT by formulating the rate maximization problem considering the overhead of control channels, time offset, and repetition factor. Due to the complexity of finding the globally optimum solution of the formulated problem, a sub-optimal solution with an iterative algorithm based on cooperative approaches is proposed. The proposed algorithm is then evaluated to investigate the impact of repetition factor, time offset and ICI on the NB-IoT data rate, and energy consumption. Furthermore, a detailed comparison between the non-cooperative, cooperative, and optimal scheme (i.e., no repetition) is also presented. It is shown through the simulation results that the cooperative scheme provides up to 8% rate improvement and 17% energy reduction as compared with the non-cooperative scheme.
Dynamic cerebral autoregulation is preserved during isometric handgrip and head‐down tilt in healthy volunteers

In healthy humans, cerebral blood flow (CBF) is autoregulated against changes in arterial blood pressure. Spontaneous fluctuations in mean arterial pressure (MAP) and CBF can be used to assess cerebral autoregulation. We hypothesized that dynamic cerebral autoregulation is affected by changes in autonomic activity, MAP, and cardiac output (CO) induced by handgrip (HG), head‐down tilt (HDT), and their combination. In thirteen healthy volunteers, we recorded blood velocity by ultrasound in the internal carotid artery (ICA), HR, MAP and CO‐estimates from continuous finger blood pressure, and end‐tidal CO2. Instantaneous ICA beat volume (ICABV, mL) and ICA blood flow (ICABF, mL/min) were calculated. Wavelet synchronization index γ (0–1) was calculated for the pairs: MAP–ICABF, CO–ICABF and HR–ICABV in the low (0.05–0.15 Hz; LF) and high (0.15–0.4 Hz; HF) frequency bands. ICABF did not change between experimental states. MAP and CO were increased during HG (+16% and +15%, respectively, P < 0.001) and during HDT + HG (+12% and +23%, respectively, P < 0.001). In the LF interval, median γ for the MAP–ICABF pair (baseline: 0.23 [0.12–0.28]) and the CO–ICABF pair (baseline: 0.22 [0.15–0.28]) did not change with HG, HDT, or their combination. High γ was observed for the HR–ICABV pair at the respiratory frequency, the oscillations in these variables being in inverse phase. The unaltered ICABF and the low synchronization between MAP and ICABF in the LF interval suggest intact dynamic cerebral autoregulation during HG, HDT, and their combination.
Adaptive Online Learning Based Robust Visual Tracking

Accurate location estimation of a target is a classical and very popular problem in visual object tracking, for which correlation filters have been proven highly effective in real-time scenarios. However, the great variation of the target’s appearance and the surrounding background throughout a video sequence would lead to failure tracking for the sake of the model drift, using trackers based on correlation filters. In our approach, we present a simple and fast method to improve the robustness of the model based on sum of template and pixel-wise learners (Staple). On the one hand, a confidence regression model is established to adjust adaptively the model online learning rate to alleviate the model drift. On the other hand, instead of likelihood, the scale with maximal posterior probability is selected as the target scale to obtain the more accurate estimation. Extensive experimental results demonstrate that the proposed approach performs favorably against several state-of-the-art algorithms on large-scale challenging benchmark data sets at speed in excess of 42 frames/s.
Combining STDP and Reward-Modulated STDP in Deep Convolutional Spiking Neural Networks for Digit Recognition

The primate visual system has inspired the development of deep artificial neural networks, which have revolutionized the computer vision domain. Yet these networks are much less energy-efficient than their biological counterparts, and they are typically trained with backpropagation, which is extremely data-hungry. To address these limitations, we used a deep convolutional spiking neural network (DCSNN) and a latency-coding scheme. We trained it using a combination of spike-timing-dependent plasticity (STDP) for the lowest layers and reward-modulated STDP (R-STDP) for the highest ones. In short, with R-STDP a correct (resp. incorrect) decision leads to STDP (resp. anti-STDP). This approach led to an accuracy of 97.2% on MNIST, without requiring an external classifier. In addition, we demonstrated that R-STDP extracts features that are diagnostic for the task at hand, and discards the other ones, whereas STDP extracts any feature that repeats. Finally, our approach is biologically plausible, hardware friendly, and energy-efficient.
Learning to Diversify Recommendations Based on Matrix Factorization

Besides relevancy, diversity of recommendation results is also an important requirement for both users and content providers. In this paper, we propose a novel ranking model utilizing factorized category features of items for diversified top-N recommendation. To combine relevancy and diversity in a unified function, we define the score formula as product of user and item feature vectors, plus discounted product of user and category feature vectors, which respectively correspond to contributions in relevancy and in diversity. Moreover, to obtain the best recommendation lists, we learn these feature vectors from approximate optimal ranking sequences in the training dataset, by newly defined pair-wise loss function. The parameters are finally solved using stochastic gradient descent. Experimental results on public dataset demonstrate the effectiveness of our approach.
Feasibility of using respiratory correlated mega voltage cone beam computed tomography to measure tumor motion

The purpose of this study was to test the feasibility of using respiratory correlated mega voltage cone‐beam computed tomography (MVCBCT), taken during patient localization, to quantify the size and motion of lung tumors. An imaging phantom was constructed of a basswood frame embedded with six different‐sized spherical pieces of paraffin wax. The Quasar respiratory motion phantom was programmed to move the imaging phantom using typical respiratory motion. The moving imaging phantom was scanned using various MVCBCT imaging parameters, including two beam line types, two protocols with different ranges of rotation and different imaging doses. A static phantom was also imaged as a control. For all the 3D volumetric images, the contours of the six spherical inserts were measured manually. Compared with the nominal sphere diameter, the average relative error in the size of the respiratory correlated MVCBCT spheres ranged from 5.3% to 12.6% for the four largest spheres, ranging in size from 3.6 cc to 29 cc. Larger errors were recorded for the two smallest inserts. The average relative error in motion was 5.1% smaller than the programmed amplitude of 3.0 cm. We are able to conclude that it is feasible to use respiratory correlated MVCBCT to quantify tumor motion for lung cancer patients. PACS numbers: 87.19. Wx, 87.57. Q
Analysis and Design of Filters for the Energy Storage System: Optimal Tradeoff Between Frequency Guarantee and Energy Capacity/Power Rating

This paper proposes a new filter design method that can smooth the wind power generation output while keeping the energy capacity and power rating of the energy storage system (ESS) small. Wind power generation causes frequency fluctuations in power systems. Therefore, in many cases, the ESS is needed for smoothing the fluctuations of wind generator output. In order to improve the performance of the ESS controlled with the conventional low-pass filter (LPF), this paper investigates other types of filters. First, the relations between filter and energy capacity/power rating of the ESS are disclosed through the worst-case study. Then, the frequency fluctuation analysis of the power system with respect to the wind power generation is carried out in order to realize a pin-point smoothing. Finally, a metaheuristic optimization method is proposed for the multiobjective design of the filter based on these relations so as to achieve an optimal tradeoff between the output smoothing and energy capacity/power rating of the ESS. It is shown via simulation analysis with real wind data that the proposed method can suppress the frequency fluctuation of the power system effectively with the ESS having smaller energy capacity and power rating.
Beat-to-Beat Variability of Ventricular Action Potential Duration Oscillates at Low Frequency During Sympathetic Provocation in Humans

Background: The temporal pattern of ventricular repolarization is of critical importance in arrhythmogenesis. Enhanced beat-to-beat variability (BBV) of ventricular action potential duration (APD) is pro-arrhythmic and is increased during sympathetic provocation. Since sympathetic nerve activity characteristically exhibits burst patterning in the low frequency range, we hypothesized that physiologically enhanced sympathetic activity may not only increase BBV of left ventricular APD but also impose a low frequency oscillation which further increases repolarization instability in humans. Methods and Results: Heart failure patients with cardiac resynchronization therapy defibrillator devices (n = 11) had activation recovery intervals (ARI, surrogate for APD) recorded from left ventricular epicardial electrodes alongside simultaneous non-invasive blood pressure and respiratory recordings. Fixed cycle length was achieved by right ventricular pacing. Recordings took place during resting conditions and following an autonomic stimulus (Valsalva). The variability of ARI and the normalized variability of ARI showed significant increases post Valsalva when compared to control (p = 0.019 and p = 0.032, respectively). The oscillatory behavior was quantified by spectral analysis. Significant increases in low frequency (LF) power (p = 0.002) and normalized LF power (p = 0.019) of ARI were seen following Valsalva. The Valsalva did not induce changes in conduction variability nor the LF oscillatory behavior of conduction. However, increases in the LF power of ARI were accompanied by increases in the LF power of systolic blood pressure (SBP) and the rate of systolic pressure increase (dP/dtmax). Positive correlations were found between LF-SBP and LF-dP/dtmax (rs = 0.933, p < 0.001), LF-ARI and LF-SBP (rs = 0.681, p = 0.001) and between LF-ARI and LF-dP/dtmax (rs = 0.623, p = 0.004). There was a strong positive correlation between the variability of ARI and LF power of ARI (rs = 0.679, p < 0.001). Conclusions: In heart failure patients, physiological sympathetic provocation induced low frequency oscillation (~0.1 Hz) of left ventricular APD with a strong positive correlation between the LF power of APD and the BBV of APD. These findings may be of importance in mechanisms underlying stability/instability of repolarization and arrhythmogenesis in humans.