Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigating Classification Techniques with Feature Selection For Intention Mining From Twitter Feed

In the last decade, social networks became most popular medium for communication and interaction. As an example, micro-blogging service Twitter has more than 200 million registered users who exchange more than 65 million posts per day. Users express their thoughts, ideas, and even their intentions through these tweets. Most of the tweets are written informally and often in slang language, that contains misspelt and abbreviated words. This paper investigates the problem of selecting features that affect extracting user's intention from Twitter feeds based on text mining techniques. It starts by presenting the method we used to construct our own dataset from extracted Twitter feeds. Following that, we present two techniques of feature selection followed by classification. In the first technique, we use Information Gain as a one-phase feature selection, followed by supervised classification algorithms. In the second technique, we use a hybrid approach based on forward feature selection algorithm in which two feature selection techniques employed followed by classification algorithms. We examine these two techniques with four classification algorithms. We evaluate them using our own dataset, and we critically review the results.
Tri-graph Information Propagation for Polypharmacy Side Effect Prediction

The use of drug combinations often leads to polypharmacy side effects (POSE). A recent method formulates POSE prediction as a link prediction problem on a graph of drugs and proteins, and solves it with Graph Convolutional Networks (GCNs). However, due to the complex relationships in POSE, this method has high computational cost and memory demand. This paper proposes a flexible Tri-graph Information Propagation (TIP) model that operates on three subgraphs to learn representations progressively by propagation from protein-protein graph to drug-drug graph via protein-drug graph. Experiments show that TIP improves accuracy by 7%+, time efficiency by 83$\times$, and space efficiency by 3$\times$.
The Impact of Metallicity on the Evolution of the Rotation and Magnetic Activity of Sun-like Stars

The rotation rates and magnetic activity of Sun-like and low-mass (≲1.4M⊙) main-sequence stars are known to decline with time, and there now exist several models for the evolution of rotation and activity. However, the role that chemical composition plays during stellar spin-down has not yet been explored. In this work, we use a structural evolution code to compute the rotational evolution of stars with three different masses (0.7, 1.0, and 1.3 M⊙) and six different metallicities, ranging from [Fe/H] = −1.0 to [Fe/H] = +0.5. We also implement three different wind-braking formulations from the literature (two modern and one classical) and compare their predictions for rotational evolution. The effect that metallicity has on stellar structural properties, and in particular the convective turnover timescale, leads the two modern wind-braking formulations to predict a strong dependence of the torque on metallicity. Consequently, they predict that metal-rich stars spin down more effectively at late ages (≳1 Gyr) than metal-poor stars, and the effect is large enough to be detectable with current observing facilities. For example, the formulations predict that a Sun-like (solar-mass and solar-aged) star with [Fe/H] = −0.3 will have a rotation period of less than 20 days. Even though old, metal-poor stars are predicted to rotate more rapidly at a given age, they have larger Rossby numbers and are thus expected to have lower magnetic activity levels. Finally, the different wind-braking formulations predict quantitative differences in the metallicity dependence of stellar rotation, which may be used to test them.
Technological Challenges in Low-mass Interstellar Probe Communication

Building on a preliminary paper design of a downlink from a swarm of low-mass interstellar probes for returning scientific data from the vicinity of Proxima Centauri, the most critical technology issues are summarized, and their significance is explained in the context of the overall system design. The primary goal is to identify major challenges or showstoppers if such a downlink were to be constructed using currently available off-the-shelf technology, and thereby provide direction and motivation to future research on the constituent design challenges and technologies. While there are not any fundamental physical limits that prevent such communication systems, currently available technologies fall significantly short in several areas and there are other major design challenges with uncertain solutions. The greatest identified challenges are in mass constraints, multiplexing simultaneous communication from multiple probes to the same target exoplanet, attitude control and pointing accuracy, and Doppler shifts due to uncertainty in probe velocity. The greatest technology challenges are electrical power, high power and wavelength-agile optical sources, very selective and wavelength-agile banks of optical bandpass filters, and single-photon detectors with extremely low dark-count rates. For a critical subset of these, we describe the nature of the difficulties we encounter and their origins in the overall system context. A receiver that limits reception to a single probe is also considered and compared to the swarm case.
Business Process Full Compliance with Respect to a Set of Conditional Obligation in Polynomial Time

In this paper, we present a new methodology to evaluate whether a business process model is fully compliant with a regulatory framework composed of a set of conditional obligations. The methodology is based failure delta-constraints that are evaluated on bottom-up aggregations of a tree-like representation of business process models. While the generic problem of proving full compliance is in coNP-complete, we show that verifying full compliance can be done in polynomial time using our methodology, for an acyclic structured process model given a regulatory framework composed by a set of conditional obligations, whose elements are restricted to be represented by propositional literals
Predictive analysis of Bitcoin price considering social sentiments

We report on the use of sentiment analysis on news and social media to analyze and predict the price of Bitcoin. Bitcoin is the leading cryptocurrency and has the highest market capitalization among digital currencies. Predicting Bitcoin values may help understand and predict potential market movement and future growth of the technology. Unlike (mostly) repeating phenomena like weather, cryptocurrency values do not follow a repeating pattern and mere past value of Bitcoin does not reveal any secret of future Bitcoin value. Humans follow general sentiments and technical analysis to invest in the market. Hence considering people's sentiment can give a good degree of prediction. We focus on using social sentiment as a feature to predict future Bitcoin value, and in particular, consider Google News and Reddit posts. We find that social sentiment gives a good estimate of how future Bitcoin values may move. We achieve the lowest test RMSE of 434.87 using an LSTM that takes as inputs the historical price of various cryptocurrencies, the sentiment of news articles and the sentiment of Reddit posts.
An Adaptive and Near Parameter-free Evolutionary Computation Approach Towards True Automation in AutoML

A common claim of evolutionary computation methods is that they can achieve good results without the need for human intervention. However, one criticism of this is that there are still hyperparameters which must be tuned in order to achieve good performance. In this work, we propose a near parameter-free genetic programming approach, which adapts the hyperparameter values throughout evolution without ever needing to be specified manually. We apply this to the area of automated machine learning (by extending TPOT), to produce pipelines which can effectively be claimed to be free from human input, and show that the results are competitive with existing state-of-the-art which use hand-selected hyperparameter values. Pipelines begin with a randomly chosen estimator and evolve to competitive pipelines automatically. This work moves towards a truly automated approach to AutoML.
Selective Weak Supervision for Neural Information Retrieval

This paper democratizes neural information retrieval to scenarios where large scale relevance training signals are not available. We revisit the classic IR intuition that anchor-document relations approximate query-document relevance and propose a reinforcement weak supervision selection method, ReInfoSelect, which learns to select anchor-document pairs that best weakly supervise the neural ranker (action), using the ranking performance on a handful of relevance labels as the reward. Iteratively, for a batch of anchor-document pairs, ReInfoSelect back propagates the gradients through the neural ranker, gathers its NDCG reward, and optimizes the data selection network using policy gradients, until the neural ranker’s performance peaks on target relevance metrics (convergence). In our experiments on three TREC benchmarks, neural rankers trained by ReInfoSelect, with only publicly available anchor data, significantly outperform feature-based learning to rank methods and match the effectiveness of neural rankers trained with private commercial search logs. Our analyses show that ReInfoSelect effectively selects weak supervision signals based on the stage of the neural ranker training, and intuitively picks anchor-document pairs similar to query-document pairs.
Pointwise Gradient Estimates in Multi-dimensional Slow Diffusion Equations with a Singular Quenching Term

Abstract We consider the high-dimensional equation ∂t⁡u-Δ⁢um+u-β⁢χ{u>0}=0{\partial_{t}u-\Delta u^{m}+u^{-\beta}{\chi_{\{u>0\}}}=0}, extending the mathematical treatment made in 1992 by B. Kawohl and R. Kersner for the one-dimensional case. Besides the existence of a very weak solution u∈𝒞⁢([0,T];Lδ1⁢(Ω)){u\in\mathcal{C}([0,T];L_{\delta}^{1}(\Omega))}, with u-β⁢χ{u>0}∈L1⁢((0,T)×Ω){u^{-\beta}\chi_{\{u>0\}}\in L^{1}((0,T)\times\Omega)}, δ⁢(x)=d⁢(x,∂⁡Ω){\delta(x)=d(x,\partial\Omega)}, we prove some pointwise gradient estimates for a certain range of the dimension N, m≥1{m\geq 1} and β∈(0,m){\beta\in(0,m)}, mainly when the absorption dominates over the diffusion (1≤m<2+β{1\leq m<2+\beta}). In particular, a new kind of universal gradient estimate is proved when m+β≤2{m+\beta\leq 2}. Several qualitative properties (such as the finite time quenching phenomena and the finite speed of propagation) and the study of the Cauchy problem are also considered.
Automated Parallel Kernel Extraction from Dynamic Application Traces

Modern program runtime is dominated by segments of repeating code called kernels. Kernels are accelerated by increasing memory locality, increasing data-parallelism, and exploiting producer-consumer parallelism among kernels - which requires hardware specialized for a particular class of kernels. Programming this hardware can be difficult, requiring that the kernels be identified and annotated in the code or translated to a domain-specific language. This paper describes a technique to automatically localize parallel kernels from a dynamic application trace, facilitating further code optimization. Dynamic trace collection is fast and compact. With optimization, it only incurs a time-dilation of a factor on nine and file-size of one megabyte per second, addressing a significant criticism of this approach. Kernel extraction is accurate and performed in linear time within logarithmic memory, detecting a wide range of kernels. This approach was validated across 16 libraries, comprised of 10,507 kernels instances. To validate the accuracy of our detected kernels, five test programs were written that spans traditional kernel definitions and were certified to contain all the kernels that were expected.
Planet formation around M dwarfs via disc instability

Context. Around 30 per cent of the observed exoplanets that orbit M dwarf stars are gas giants that are more massive than Jupiter. These planets are prime candidates for formation by disc instability. Aims. We want to determine the conditions for disc fragmentation around M dwarfs and the properties of the planets that are formed by disc instability. Methods. We performed hydrodynamic simulations of M dwarf protostellar discs in order to determine the minimum disc mass required for gravitational fragmentation to occur. Different stellar masses, disc radii, and metallicities were considered. The mass of each protostellar disc was steadily increased until the disc fragmented and a protoplanet was formed. Results. We find that a disc-to-star mass ratio between ~0.3 and ~0.6 is required for fragmentation to happen. The minimum mass at which a disc fragment increases with the stellar mass and the disc size. Metallicity does not significantly affect the minimum disc fragmentation mass but high metallicity may suppress fragmentation. Protoplanets form quickly (within a few thousand years) at distances around ~50 AU from the host star, and they are initially very hot; their centres have temperatures similar to the ones expected at the accretion shocks around planets formed by core accretion (up to 12 000 K). The final properties of these planets (e.g. mass and orbital radius) are determined through long-term disc-planet or planet–planet interactions. Conclusions. Disc instability is a plausible way to form gas giant planets around M dwarfs provided that discs have at least 30% the mass of their host stars during the initial stages of their formation. Future observations of massive M dwarf discs or planets around very young M dwarfs are required to establish the importance of disc instability for planet formation around low-mass stars.
An Investigation of the Factors Affecting the Transition of Lightning Surges through Underground Cables and Sub-Transmission Substations: A case study.

Lightning is a natural phenomenon that takes place all around the world. The enormous amount of energy released by lightning is extremely ruinous, so some suitable protection schemes are required for power systems to minimize its destructive effects. Given that the lightning surge passes through the transmission line, the destruction caused by lightning would not always happen at the strike point. Hence, the failure could take place at a point far from the striking point. This paper investigates factors affecting the transition of lightning surges through underground cables. These cables are utilized to supply a sub-transmission substation whose repeated cable failures have imposed enormous costs to regional electric distribution companies. To obtain reliable results, the highly capable software of EMTP was employed to calculate transient phenomena. In addition, various scenarios were simulated to identify the effects of some factors, including the effectiveness of surge arresters, bus-coupler switches, the grounding resistance of sheaths, and the like, on the susceptibility of the studied substation. Based on simulation results, the effects of the grounding resistance of cable sheaths at terminations and along the cable are significant.
CLCNET: Deep Learning-Based Noise Reduction for Hearing aids using Complex Linear Coding

Noise reduction is an important part of modern hearing aids and is included in most commercially available devices. Deep learning-based state-of-the-art algorithms, however, either do not consider real-time and frequency resolution constrains or result in poor quality under very noisy conditions.To improve monaural speech enhancement in noisy environments, we propose CLCNet, a framework based on complex valued linear coding. First, we define complex linear coding (CLC) motivated by linear predictive coding (LPC) that is applied in the complex frequency domain. Second, we propose a framework that incorporates complex spectrogram input and coefficient output. Third, we define a parametric normalization for complex valued spectrograms that complies with low-latency and on-line processing.Our CLCNet was evaluated on a mixture of the EUROM database and a real-world noise dataset recorded with hearing aids and compared to traditional real-valued Wiener-Filter gains.
An IoT based Real-time Low Cost Smart Energy Meter Monitoring System using Android Application

Nowadays IoT based applications are becoming more popular because it provides efficient solutions for many real time problems. In this paper, an IoT based electric meter monitoring system using android application has been proposed that aims to reduce manual efforts for measuring the electricity units and make users concern about the excessive usage of electricity. Aurdino Uno and an optical sensor are used to fetch the pulse of the electric meter. In order to reduce human error and cost in energy consumption, a low cost wireless sensor network is implemented for digital energy meter and a mobile application that automatically capable of interpret the units meter.
A Python based automated tracking routine for myosin II filaments

The study of motor protein dynamics within cytoskeletal networks is of high interest to physicists and biologists to understand how the dynamics and properties of individual motors lead to cooperative effects and control of overall network behaviour. Here, we report a method to detect and track muscle myosin II filaments within an actin network tethered to supported lipid bilayers. Based on the characteristic shape of myosin II filaments, this automated tracking routine allowed us to follow the position and orientation of myosin II filaments over time, and to reliably classify their dynamics into segments of diffusive and processive motion based on the analysis of displacements and angular changes between time steps. This automated, high throughput method will allow scientists to efficiently analyse motor dynamics in different conditions, and will grant access to more detailed information than provided by common tracking methods, without any need for time consuming manual tracking or generation of kymographs.
Analysis and visualization of the output mode-matching requirements for squeezing in Advanced LIGO and future gravitational wave detectors

The sensitivity of ground-based gravitational-wave (GW) detectors will be improved in the future via the injection of frequency-dependent squeezed vacuum. The achievable improvement is ultimately limited by losses of the interferometer electromagnetic field that carries the GW signal. The analysis and reduction of optical loss in the GW signal chain will be critical for optimal squeezed light-enhanced interferometry. In this work, we analyze a strategy for reducing output-side losses due to spatial mode mismatch between optical cavities with the use of adaptive optics. Our goal is not to design a detector from the top down, but rather to minimize losses within the current design. Accordingly, we consider actuation on optics already present and one transmissive optic to be added between the signal recycling mirror and the output mode cleaner. The results of our calculation show that adaptive mode-matching with the current Advanced LIGO design is a suitable strategy for loss reduction that provides less than 2% mean output mode-matching loss. The range of actuation required is + 47 μD on SR3, +140 mD on OM1 and OM2, +50 mD on the SRM substrate, and −50 mD on the added new transmissive optic. These requirements are within the demonstrated ranges of real actuators in similar or identical configurations to the proposed implementation. We also present a novel technique that graphically illustrates the matching of interferometer modes and allows for a quantitative comparison of different combinations of actuators.
Matrix models for stationary Gromov–Witten invariants of the Riemann sphere

Inspired by recent formulæ of Dubrovin, Yang, and Zagier, we interpret the tau function enumerating stationary Gromov–Witten invariants of P1 as an isomonodromic tau function associated with a difference equation. As a byproduct we obtain an analogue of the Kontsevich matrix model for this tau function. A connection with the Charlier ensemble is also considered.
Distal Explanations for Explainable Reinforcement Learning Agents

Causal explanations present an intuitive way to understand the course of events through causal chains, and are widely accepted in cognitive science as the prominent model humans use for explanation. Importantly, causal models can generate opportunity chains, which take the form of `A enables B and B causes C'. We ground the notion of opportunity chains in human-agent experimental data, where we present participants with explanations from different models and ask them to provide their own explanations for agent behaviour. Results indicate that humans do in-fact use the concept of opportunity chains frequently for describing artificial agent behaviour. Recently, action influence models have been proposed to provide causal explanations for model-free reinforcement learning (RL). While these models can generate counterfactuals---things that did not happen but could have under different conditions---they lack the ability to generate explanations of opportunity chains. We introduce a distal explanation model that can analyse counterfactuals and opportunity chains using decision trees and causal models. We employ a recurrent neural network to learn opportunity chains and make use of decision trees to improve the accuracy of task prediction and the generated counterfactuals. We computationally evaluate the model in 6 RL benchmarks using different RL algorithms, and show that our model performs better in task prediction. We report on a study with 90 participants who receive explanations of RL agents behaviour in solving three scenarios: 1) Adversarial; 2) Search and rescue; and 3) Human-Agent collaborative scenarios. We investigate the participants' understanding of the agent through task prediction and their subjective satisfaction of the explanations and show that our distal explanation model results in improved outcomes over the three scenarios compared with two baseline explanation models.
Multi-Trek Separation in Linear Structural Equation Models

Building on the theory of causal discovery from observational data, we study interactions between multiple (sets of) random variables in a linear structural equation model with non-Gaussian error terms. We give a correspondence between structure in the higher order cumulants and combinatorial structure in the causal graph. It has previously been shown that low rank of the covariance matrix corresponds to trek separation in the graph. Generalizing this criterion to multiple sets of vertices, we characterize when determinants of subtensors of the higher order cumulant tensors vanish. This criterion applies when hidden variables are present as well. For instance, it allows us to identify the presence of a hidden common cause of k of the observed variables.
1D Kohn anomaly in 3D topological Dirac semimetals

Kohn anomalies are distortions of phonon dispersions in metals and semimetals caused by electron-phonon interactions. Typically, lower-dimensional materials show stronger Kohn anomalies determined by the geometry of their Fermi surfaces. For this reason, the archetypal model systems for studying Kohn anomalies have been 1D and 2D conductors. In this work, combining ab initio simulations and theoretical analysis, we investigate strong Kohn anomalies in 3D topological Dirac semimetals. Owing to their unique electronic structure consisting of 3D Dirac cones, we show that their Kohn anomalies have the same features as those in a 1D simple metal. By analyzing model systems $\mathrm{ZrTe_5}$ and $\mathrm{Na_3Bi}$, in addition to our previous results on $\mathrm{Cd_3As_2}$, we further illustrate the relationship between their strong Kohn anomalies and ultralow lattice thermal conductivities. Our work opens up a material platform to probe and understand Kohn anomalies and indicates the potential application of 3D topological Dirac semimetals as thermoelectric materials.