Miracle-dz/UniFAR-FTUs huggingface dataset:
Adaptive solution of differential equations for the regularization of artificial neural networks

The authors propose using artificial neural networks to model the solution of the two-dimensional Euler equation which models inviscid and compressible fluid flow. The code thus created should not only have the same accuracy as a more conventional computer code, but should still retain the ability of an ANN to modify itself when exposed to experimental data, thus yielding software that could be specialized with experimental results. To accomplish these objectives, the method of optimal incremental function approximation has been developed for the adaptive solution of differential equations using ANN architecture. Two major attractive features of this approach are that: (1) the developed method is flexible enough to use any of the popular transfer functions and (2) the developed method requires minimal user interaction. The latter is especially advantageous when dealing with complicated physical or computational domains. Numerical results are presented and compared to conventional methods. The accuracy is considered satisfactory.
Cross-Sectional Areas of Calf Soft Tissue Measured with MRI as a New Method for Staging Gynecologic Oncology-Related Extremity Lymphedema

Objectives: To determine whether the cross-sectional area (CSA) of the soft tissue of the calf measured with magnetic resonance imaging (MRI) could stage unilateral lower extremity lymphedema (LEL) secondary to gynecological cancer treatments. Materials and Methods: One hundred and twenty females with unilateral LEL and Thirty-two without LEL after gynecological cancer treatments underwent calf MRI and water displacement. Total soft tissue CSA (T), muscle CSA (M) and subcutaneous tissue CSA (S) of affected calf, and difference of T (DT), M (DM) and S (DS) between calves were obtained on MRI at mid-calf level. Volume of affected calf and difference of volume (DV) between calves were obtained by water displacement. Statistical analysis was performed to determine feasibility of MRI measurements for accessing LEL. Results: There were close correlations between volume and T or S of affected calf, and between DV and DT or DS of calves. The correlations of stages of LEL with T and S of affected calf as well as DT and DS were stronger than with the volume of affected calf and DV (all p < 0.01). Multivariate analysis showed more significant differences between LEL stages in T and S than in volume of affected calf, and in DS than in DV (all p < 0.05). No difference was found between stage 0 and 1 in volume of affected calf and in DV. For staging LEL, DS showed the most profound discrimination ability among all measurable parameters. Conclusions: DS of calves could be the most reliable parameter recommended for staging unilateral LEL.
Population Growth, Malthusian concern and sustainable development - some key Policies and demographic issues in India

Population size and growth continue to be the paramount issues regarding sustainable development in India, notwithstanding the fact that the Indian economy has grown by a little over 5 percent during the last two decades compared to the population growth of about 2 per cent during the same period. The reason is that the Malthusian concern has been the core of Indian policy planning thinking. This has while perpetuated the belief that population growth is the immediate culprit of high poverty and environmental degradation, demographic issues such as distribution of Population, age composition, migration and urbanization are being neglected in the planning for sustainable development. The paper highlights the lack of integration of comprehensive demographic issues with sustainable development planning in India and explores the political economy reasons for lopsided consideration of demographic issues in sustainable development planning during the last five decades since independence.
Combined Inversion Of Borehole Resistivity And Sonic Measurements To Estimate Water Saturation, Porosity And Dry-Rock Elastic Moduli In The Presence Of Invasion

Water saturation, porosity and dry-rock (skeleton) elastic moduli are often evaluated independently using measurements that obey different physical principles. Such an interpretation methodology does not take into account possible deterministic or statistical relationships between different physical measurements when probing the same rock formation. We describe a new inversion-based method that combines resistivity and sonic borehole measurements to estimate the four properties simultaneously. The objective of the combined inversion is to suppress ambiguity in the estimation of in-situ properties thereby improving the accuracy and reliability of the results over traditional methods. We assume a radial one-dimensional model to develop the numerical simulation and inversion components of the study. Moreover, for simplicity we assume invasion in the form of a single piston-like fluid saturation front with one invaded and one virgin zone. The inversion is driven by array-induction and sonic waveform measurements. We use the numerical-mode-matching method and the frequency-wavenumber domain method to simulate induction and sonic measurements, respectively. Induction measurements are related to water saturation and porosity via Archie’s equations. For sonic measurements, we resort to Biot-Gassmann’s equation to relate the saturated rock’s elastic moduli to porosity and fluid saturation. The distorted Born iterative method and a modified preconditioned conjugate gradient method are used for the inversion of induction and sonic measurements, respectively
Vertical Integration of High- $Q$  Filter With Circularly Polarized Patch Antenna With Enhanced Impedance-Axial Ratio Bandwidth

Directly coupling antennas to filters can remove the transition loss between the two and reduce the size of radio frequency front ends. Circularly polarized (CP) patch antennas with a single feed are very popular in many applications due to their compact size and ease of design. However, a probe-fed CP patch antenna typically exhibits only 1%–2% useful fractional bandwidth by considering both impedance matching and axial-ratio (AR) bandwidths. Thick substrates were used to realize single-feed CP antennas with wider bandwidth. However, thick substrates may cause issues, such as surface wave excitation, mutual coupling, and low efficiency. In this paper, we present a new approach to enhance the impedance-AR bandwidth of probe-fed CP patch antennas without using thick substrates. By integrating CP patch antennas with cavity filters and sequentially rotating the CP filter/patch antenna elements to form a <inline-formula> <tex-math notation="LaTeX">$2 \times 2$ </tex-math></inline-formula> array, an impedance-AR bandwidth of 8.3% is achieved with the thickness of the antenna layer to be <inline-formula> <tex-math notation="LaTeX">$0.0292\lambda _{0}$ </tex-math></inline-formula>. In addition, a third-order Chebyshev filtering function is embedded in this antenna array to enhance the out-of-band interference rejection.
Investigation of three-phase capacitor-tapped multi-module voltage source converter with selective harmonic elimination

In conventional Two-Level Voltage Source Converters (2L-VSCs), connecting between high/medium dc voltage and medium/low ac voltage requires semiconductor devices rated at the high/medium dc-voltage, which negatively affects the converter cost. Alternatively, the Capacitor-Tapped Multi-module VSC (CT-MC) can be used, where it has a good bucking capability. The CT-MC consists of three (or multiple of three) Half-Bridge Inverter (HBI) modules, where the dc terminals of these modules are connected in series across the high/medium dc-link voltage, i.e. semiconductor devices with a lower voltage rating can be employed. The ac outputs of the involved HBIs are integrated to the ac gird through proper transformer(s). In this paper, the performance of the CT-MC is investigated, when Selective Harmonic Elimination (SHE) method is employed for dc-ac conversion to reduce the converter switching losses, while keeping the Total Harmonic Distortion (THD) of the ac currents within the recommended range. Simulation results are presented to show the performance of CT-MC when SHE is applied.
Using contextual information for automatic triage of posts in a peer-support forum

Mental health forums are online spaces where people can share their experiences anonymously and get peer support. These forums, require the supervision of moderators to provide support in delicate cases, such as posts expressing suicide ideation. The large increase in the number of forum users makes the task of the moderators unmanageable without the help of automatic triage systems. In the present paper, we present a Machine Learning approach for the triage of posts. Most approaches in the literature focus on the content of the posts, but only a few authors take advantage of features extracted from the context in which they appear. Our approach consists of the development and implementation of a large variety of new features from both, the content and the context of posts, such as previous messages, interaction with other users and author’s history. Our method has competed in the CLPsych 2017 Shared Task, obtaining the first place for several of the subtasks. Moreover, we also found that models that take advantage of post context improve significantly its performance in the detection of flagged posts (posts that require moderators attention), as well as those that focus on post content outperforms in the detection of most urgent events.
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation

Generating novel graph structures that optimize given objectives while obeying some given underlying rules is fundamental for chemistry, biology and social science research. This is especially important in the task of molecular graph generation, whose goal is to discover novel molecules with desired properties such as drug-likeness and synthetic accessibility, while obeying physical laws such as chemical valency. However, designing models to find molecules that optimize desired properties while incorporating highly complex and non-differentiable rules remains to be a challenging task. Here we propose Graph Convolutional Policy Network (GCPN), a general graph convolutional network based model for goal-directed graph generation through reinforcement learning. The model is trained to optimize domain-specific rewards and adversarial loss through policy gradient, and acts in an environment that incorporates domain-specific rules. Experimental results show that GCPN can achieve 61% improvement on chemical property optimization over state-of-the-art baselines while resembling known molecules, and achieve 184% improvement on the constrained property optimization task.
Data Summarization at Scale: A Two-Stage Submodular Approach

The sheer scale of modern datasets has resulted in a dire need for summarization techniques that identify representative elements in a dataset. Fortunately, the vast majority of data summarization tasks satisfy an intuitive diminishing returns condition known as submodularity, which allows us to find nearly-optimal solutions in linear time. We focus on a two-stage submodular framework where the goal is to use some given training functions to reduce the ground set so that optimizing new functions (drawn from the same distribution) over the reduced set provides almost as much value as optimizing them over the entire ground set. In this paper, we develop the first streaming and distributed solutions to this problem. In addition to providing strong theoretical guarantees, we demonstrate both the utility and efficiency of our algorithms on real-world tasks including image summarization and ride-share optimization.
Head Pose Estimation on Low-Quality Images

Head pose estimation methods can be broadly classified into learning-based methods and model-based methods. The learning based methods use machine learning techniques to directly predict the pose from image appearance, while the model-based methods link the 2D observation (e.g. facial landmarks) and 3D model through the projection model for pose estimation. However, both methods may have difficulty on images with very low quality (e.g. low resolution, occlusion, and noisy images). For example, there would be limited appearance information to generate accurate landmark detection on lowquality images for reliable face pose estimation. To tackle pose estimation on low-quality images, we propose to combine the learning and model based methods. Specifically, we first build the relationship between facial landmark locations and image appearance using the Restricted Boltzmann Machine (RBM) model. Then, we link the landmark locations and 3D model analytically using the projection model. By combining the RBM model with the projection model, without explicit landmark detection, we predict the head pose with a KL-divergence based method and a gradient-based method. Experimental results demonstrate the effectiveness of the proposed method.
Accurate and fast obstacle detection method for automotive applications based on stereo vision

This paper describes a simple yet efficient method of obstacle detection. Different from other methods, this study utilizes the coordinate models of and the depth map generated from stereo cameras to accurately detect possible obstacles. The proposed algorithm searches on the depth map along vertical direction for pixels having the same disparity. Having the same disparity indicates that these pixels are from the same object, and such object is an obstacle on the road. After implementation, the average processing time of the proposed obstacle detection algorithm for HD 720p image requires only 4.0 milliseconds (ms) on Intel Core i7 3.6 GHz processor, and 16.3 ms on an embedded system, i.e., the NVIDIA Jetson TX1. The detection performance based on stereo vision is more precise and faster compared with 2-D image object recognition. By directly comparing the purchasing price, the hardware cost to use stereo camera is also much lower than a RADAR or LiDAR system.
StableEyes—A Portable Vestibular Rehabilitation Device

The vestibulo-ocular reflex (VOR) is the primary mechanism for stabilizing vision during rapid head movements. We have developed a training technique that typically increases the VOR response a minimum of 15% after 15 mins of training. This technique relies on subjects tracking a visual target that moves as a function of head motion, but at a different speed, so that the VOR is challenged to increase in order to stabilize the retinal image of the target. We have developed a portable device, StableEyes, which implements this technique so that unassisted training can be performed at home by patients with VOR hypofunction. The device consists of a head unit and base unit. The head unit contains inertial sensors to measure the instantaneous 3-D orientation of the head in space at 250 Hz, and an integrated circuit mirror to dynamically control the position of a laser target in space. The base unit consists of a touch screen interface that allows users to calibrate and set the device, in addition to recording compliance. The laser target range is ±12.5°. The device latency is 6 ms with a frequency response stable up to 6 Hz for velocities >80°/s, i.e., head velocities, where the VOR contributes most to visual stability. StableEyes was used to increase the VOR response in 10 normal subjects. In these, the VOR towards the adapting side increased by ~11%, which is comparable to our laboratory findings. The adoption of StableEyes could improve the efficacy of vestibular rehabilitation and its outcomes.
Study of the Subjective Visibility of Packet Loss Artifacts in Decoded Video Sequences

Packet loss is a significant cause of visual impairments in video broadcasting over packet-switched networks. There are several subjective and objective video quality assessment methods focused on the overall perception of video quality. However, less attention has been paid on the visibility of packet loss artifacts appearing in spatially and temporally limited regions of a video sequence. In this paper, we present the results of a subjective study, using a methodology where a video sequence is displayed on a touchscreen and the users tap it in the positions where they observe artifacts. We also analyze the objective features derived from those artifacts, and propose different models for combining those features into an objective metric for assessing the noticeability of the artifacts. The practical results show that the proposed metric predicts visibility of packet loss impairments with a reasonable accuracy. The proposed method can be applied for developing packetization and error recovery schemes to minimize the subjectively experienced distortion in error-prone networked video systems.
Type-2 fuzzy logic systems: type-reduction

Type-reduction in a type-2 fuzzy logic system (FLS) is an "extended" version of the defuzzification operation in a type-1 FLS. In this paper, we briefly review the structure of a type-2 FLS and describe type-reduction in detail. We focus on a center-of-sets type-reducer, and provide some examples to illustrate it. We also provide some practical approximations to type-reduction computations for certain type-2 membership functions.
Impulsivity in Alcohol-Dependent Patients with and without ADHD: The Role of Atomoxetine

ABSTRACT Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by symptoms of hyperactivity, inattention, and impulsivity. As shown in the literature, this disorder is frequently associated with alcohol and drug abuse. Patients affected by ADHD show high levels of impulsivity and sensation seeking. These characteristics can significantly increase the risk of alcohol abuse, which is itself a clinical condition associated with high levels of impulsivity. Clinical studies suggest that atomoxetine is effective and safe in patients affected by both ADHD and alcohol dependence; however, information focused specifically on impulsivity is very limited. In an open-label study, we evaluated the difference in impulsivity level between alcohol-dependent patients with and without a diagnosis of ADHD. Furthermore, we hypothesized that, in patients with ADHD, atomoxetine could reduce the impulsivity trait.
Wideband Phase Shifter in Groove Gap Waveguide Technology Implemented With Glide-Symmetric Holey EBG

A wideband phase shifter in <inline-formula> <tex-math notation="LaTeX">$U$ </tex-math></inline-formula>-band realized in groove gap waveguide (GGWG) technology is presented as a good solution to be integrated in antenna feed structures. The GGWG is implemented by glide-symmetric holes to reduce manufacturing complexity. The design is also proposed to be mechanically variable by changing the height of the dielectric slab used to change the phase. Experimental results evidence the potential of this solution.
Robust Inverse Planning Approaches for Policy Estimation of Semi-autonomous Agents

Most of existing coordination techniques for autonomous agents assume the knowledge or the estimation of the other agents’ policy. However, this assumption is not valid in semi-autonomous agents because an external entity can take the control and modify the behavior of the agent. We face this problem in applications where an operator can take the control of the system (Robot/UAV). Many human factors may affect this behavior, such as stress, hesitations and preferences. Estimating the policy in such contexts is a difficult problem. Many existing algorithms using Inverse Reinforcement learning or imitation have been developed. However most of them have weak performance when non-optimal policy is followed. In this paper, we investigate techniques for estimating the followed policies of semi-autonomous agents that could be nonoptimal due to critical situations We extend some prediction methods and algorithms based on Factored MDPs and Inverse Reinforcement Learning to improve their stability and their efficiency during the execution of a mission. Then, we develop various experiments showing the performance on efficiency and stability of our approach in different conditions and comparing with it.
Predicting Premorbid Scores on the Repeatable Battery for the Assessment of Neuropsychological Status and their Validation in an Elderly Sample

OBJECTIVE Assessing cognitive change during a single visit requires the comparison of estimated premorbid abilities and current neuropsychological functioning. Although premorbid intellect has been widely examined, premorbid expectations for other cognitive abilities have received less attention. The current study sought to develop and validate premorbid estimates for the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). METHOD Using demographic variables and an estimate of premorbid intellect, premorbid performance on the RBANS was predicted in a sample of 143 community-dwelling, cognitively intact older adults. RESULTS On all six Indexes of the RBANS, premorbid intellect was the best predictor of current cognitive functioning, with gender adding to one of the prediction models (R2 = 0.04-0.16, ps < .02). These prediction formulae were then applied to a sample of 122 individuals with amnestic Mild Cognitive Impairment to look for discrepancies between premorbid and current RBANS scores. Despite minimal differences between premorbid and current RBANS scores in the intact sample, large, and statistically significant differences were observed in the impaired sample, especially on the Immediate Memory Index (discrepancy = -29.00, p < .001), Delayed Memory Index (discrepancy = -32.28, p < .001), and Total Scale score (discrepancy = -25.58, p < .001). CONCLUSION Although validation in larger samples is needed, the current estimates of premorbid RBANS abilities may aid clinicians in determining change across time.
When Social Media Delivers Customer Service: Differential Customer Treatment in the Airline Industry

Companies increasingly are providing customer service through social media, helping customers on a real-time basis. Although some traditional call centers might prioritize customers based on their expected business values, the grounds for differential customer service on social media are unclear, since there has been little theoretical or empirical investigation of this new phenomenon. Building on the literature of social psychology and complaint management, we hypothesize two main drivers of differential treatment: the social media influence effect, which refers to the impact of the customer’s relative standing on social media, and the bystander effect, which refers to the impact of the presence of other social media users. To test these effects, we analyzed more than three million tweets to seven major U.S. airlines on Twitter from September 2014 to May 2015. The evidence is clear that airlines respond to less than half of the tweets directed at them by complaining customers—in contrast with traditional call centers, which are expected to address all callers. Interestingly, we find that the airlines are more likely to respond to complaints from customers with more followers, and customers with more followers are more likely to receive faster responses, thus confirming the existence of a concealed (or at least unpublicized) social media influence effect. We also find that airlines are less likely to respond to complaints with multiple parties mentioned, confirming the existence of the bystander effect. To the best of our knowledge, this is the first paper in the literature to study the existence and drivers of differential treatment when customer service is delivered on social media, and we expect our findings will have important implications for companies, customers, and regulators.