Miracle-dz/UniFAR-FTUs huggingface dataset:
Revisiting the Prepositional-Phrase Attachment Problem Using Explicit Commonsense Knowledge

We revisit the challenging problem of resolving prepositional-phrase (PP) attachment ambiguity. To date, proposed solutions are either rule-based, where explicit grammar rules direct how to resolve ambiguities; or statistical, where the decision is learned from a corpus of labeled examples. We argue that explicit commonsense knowledge bases can provide an essential ingredient for making good attachment decisions. We implemented a module, named Patch-Comm, that can be used by a variety of conventional parsers, to make attachment decisions. Where the commonsense KB does not provide direct answers, we fall back on a more general system that infers"out-of-knowledge-base"assertions in a manner similar to the way some NLP systems handle out-of-vocabulary words. Our results suggest that the commonsense knowledge-based approach can provide the best of both worlds, integrating rule-based and statistical techniques. As the field is increasingly coming to recognize the importance of explainability in AI, a commonsense approach can enable NLP developers to better understand the behavior of systems, and facilitate natural dialogues with end users.
Improved gravitational radiation time-scales II: Spin–orbit contributions and environmental perturbations

Peters’ formula is an analytical estimate of the time-scale of gravitational wave (GW)-induced coalescence of binary systems. It is used in countless applications, where the convenience of a simple formula outweighs the need for precision. However, many promising sources of the Laser Interferometer Space Antenna (LISA), such as supermassive black hole binaries and extreme mass-ratio inspirals (EMRIs), are expected to enter the LISA band with highly eccentric (e ≳ 0.9) and highly relativistic orbits. These are exactly the two limits in which Peters’ estimate performs the worst. In this work, we expand upon previous results and give simple analytical fits to quantify how the inspiral time-scale is affected by the relative 1.5 post-Newtonian (PN) hereditary fluxes and spin–orbit couplings. We discuss several cases that demand a more accurate GW time-scale. We show how this can have a major influence on quantities that are relevant for LISA event-rate estimates, such as the EMRI critical semimajor axis. We further discuss two types of environmental perturbations that can play a role in the inspiral phase: the gravitational interaction with a third massive body and the energy loss due to dynamical friction and torques from a surrounding gas medium ubiquitous in galactic nuclei. With the aid of PN corrections to the time-scale in vacuum, we find simple analytical expressions for the regions of phase space in which environmental perturbations are of comparable strength to the effects of any particular PN order, being able to qualitatively reproduce the results of much more sophisticated analyses.
A multiple-relaxation-time collision model by Hermite expansion

The Bhatnagar–Gross–Krook (BGK) single-relaxation-time collision model for the Boltzmann equation serves as the foundation of the lattice BGK (LBGK) method developed in recent years. The description of the collision as a uniform relaxation process of the distribution function towards its equilibrium is, in many scenarios, simplistic. Based on a previous series of papers, we present a collision model formulated as independent relaxations of the irreducible components of the Hermite coefficients in the reference frame moving with the fluid. These components, corresponding to the irreducible representation of the rotation group, are the minimum tensor components that can be separately relaxed without violating rotation symmetry. For the 2nd, 3rd and 4th moments, respectively, two, two and three independent relaxation rates can exist, giving rise to the shear and bulk viscosity, thermal diffusivity and some high-order relaxation process not explicitly manifested in the Navier–Stokes-Fourier equations. Using the binomial transform, the Hermite coefficients are evaluated in the absolute frame to avoid the numerical dissipation introduced by interpolation. Extensive numerical verification is also provided. This article is part of the theme issue ‘Progress in mesoscale methods for fluid dynamics simulation’.
Learning to Explain Recommendations

Explaining to users why some items are recommended is critical, as it helps users to make better decisions, increase their satisfaction, and gain their trust in recommender systems (RS). However, existing explainable RS usually consider explanations as side outputs of the recommendation model, which has two problems: (1) it is difficult to evaluate the produced explanations because they are usually model-dependent, and (2) as a result, the possible impacts of those explanations are less investigated. To address the evaluation problem, we propose learning to explain for explainable recommendation. The basic idea is to train a model that selects explanations from a collection as a ranking-oriented task. A great challenge, however, is that the sparsity issue in the user-item-explanation data would be severer than that in traditional user-item relation data, since not every user-item pair can associate with multiple explanations. To mitigate this issue, we propose to perform two sets of matrix factorization by considering the ternary relationship as two groups of binary relationships. To further investigate the impacts of explanations, we extend the traditional item ranking of recommendation to an item-explanation joint-ranking formalization. We study if purposely selecting explanations could achieve certain learning goals, e.g., in this paper, improving the recommendation performance. Experiments on three large datasets verify our solution’s effectiveness on both item recommendation and explanation ranking. In addition, our user-item-explanation datasets open up new ways of modeling and evaluating recommendation explanations. To facilitate the development of explainable RS, we will make our datasets and code publicly available.
Alleviation of salt stress on Moringa peregrina using foliar application of nanofertilizers

Moringa peregrina plants were grown under four levels of saline water (0, 3000, 6000, 9000 ppm), and treated with sprayed Hoagland solution containing different concentrations of ZnO and Fe3O4 Nano-Particles (NP) (30, 60 and 90 mg/L); the normal Hoagland solution was used as a control. Results show that salinity levels significantly reduced growth parameters (plant height, root length, number of leaves, number of branches, shoot and root fresh and dry weights). Also, chlorophyll, carotenoids and crude protein levels decreased meanwhile proline and total carbohydrate levels, antioxidant non-enzymes (vitamins A and C) and enzymes (POD and SOD) increased. Moringa plants sprayed with Hoagland-containing ZnO and Fe3O4 NP showed an enhancement in growth parameters either under normal or saline conditions when compared to control. Also, spraying plants with Hoagland-containing ZnO and Fe3O4 NP resulted in significant reduction in Na+ and Cl- and an increase in N, P, K+, Mg2+, Mn2+, Fe, Zn; total chlorophyll, carotenoids, proline, carbohydrates, crude protein levels, antioxidant non-enzymes and enzymes when compared to control, normal Hoagland sprayed-plants. Generally, this enhancement of salt tolerance was considerable in plants sprayed with 60 mg/L ZnO and Fe3O4 NP and grown either in saline and non-saline conditions. Key words: Moringa peregrine, nanofertilizers, salt stress, growth parameters, chemical composition.
EdgeWorkflowReal: An Edge Computing based Workflow Execution Engine for Smart Systems

Current cloud-based smart systems suffer from weaknesses such as high response latency, limited network bandwidth and the restricted computing power of smart end devices which seriously affect the system's QoS (Quality of Service). Recently, given its advantages of low latency, high bandwidth and location awareness, edge computing has become a promising solution for smart systems. However, the development of edge computing based smart systems is a very challenging job for software developers who do not have the skills for the creation of edge computing environments. The management of edge computing resources and computing tasks is also very challenging. Workflow technology has been widely used in smart systems to automate task and resource management, but there does not yet exist a real-world deployable edge computing based workflow execution engine. To fill this gap, we present EdgeWorkflowReal, an edge computing based workflow execution engine for smart systems. EdgeWorkflowReal supports: 1) automatic creation of a real edge computing environment according to user settings; 2) visualized modelling of edge workflow applications; and 3) automatic deployment, monitoring and performance evaluation of edge workflow applications in a smart system.
Importance of feature engineering and database selection in a machine learning model: A case study on carbon crystal structures

Franz M. Rohrhofer,1, 2 Santanu Saha,1, ∗ Simone Di Cataldo,1, 3 Bernhard C. Geiger,2 Wolfgang von der Linden,1 and Lilia Boeri3 Institute of Theoretical and Computational Physics, Graz University of Technology, NAWI Graz, 8010 Graz, Austria Know-Center GmbH, Research Center for Data-Driven Business & Big Data Analytics, Inffeldgasse 13, 8010 Graz, Austria Dipartimento di Fisica, Università di Roma La Sapienza, Piazzale Aldo Moro 5, I-00185 Roma, Italy (Dated: February 2, 2021)
Laser and Electric Field Induced Birefringence Studies on the Cyanobiphenyl Homologues

Abstract The optical and d.c. pulsed field Kerr Effects have been used to study the pretransitional behaviour in the isotropic phase for the alkyl cyano-biphenyl homologues (CN φ.φ CnH2n+1) for n = 5–8. From the dynamic and static measurements both the relaxation time (τ) and the Kerr Constant B have been found to be proportional to (T-T*)−1. These results are interpreted in terms of the Landau - de Gennes model and the characteristic parameters of this formalism have been given. The results are discussed in terms of the changing chemical structure.
The Harrington Yowlumne Narrative Corpus

Minority languages continue to lack adequate resources for their development, especially in the technological domain. Likewise, the J.P. Harrington Papers collection at the Smithsonian Institution are difficult to access in practical terms for community members and researchers due to its handwritten and disorganized format. Our current work seeks to make a portion of this publicly-available yet problematic material practically accessible for natural language processing use. Here, we present the Harrington Yowlumne Narrative Corpus, a corpus of 20 narrative texts that derive from the Tejoneño Yowlumne community of the Tinliw rancheria in Kern County, California between 1910 and 1925. We digitally transcribe the texts and, through a Levenshtein distance-based algorithm and manual checking, we provide gold-standard aligned normalized and lemmatized text. We likewise provide POS tags for each lemmatized token via a lexicon-based deterministic approach. Altogether, the corpus contains 57,136 transcribed characters aligned with 10,719 gold standard text-normalized words.
DRLDO: A novel DRL based De-ObfuscationSystem for Defense against Metamorphic Malware

In this paper, we propose a novel mechanism to normalise metamorphic and obfuscated malware down at the opcode level and hence create an advanced metamorphic malware de-obfuscation and defence system. We name this system as DRLDO, for deep reinforcement learning based de-obfuscator. With the inclusion of the DRLDO as a sub-component, an existing Intrusion Detection System could be augmented with defensive capabilities against ‘zero-day’ attack from obfuscated and metamorphic variants of existing malware. This gains importance, not only because there exists no system till date that use advance DRL to intelligently and automatically normalise obfuscation down even to the opcode level, but also because the DRLDO system does not mandate any changes to the existing IDS. The DRLDO system does not even mandate the IDS’ classifier to be retrained with any new dataset containing obfuscated samples. Hence DRLDO could be easily retrofitted into any existing IDS deployment. We designed, developed, and conducted experiments on the system to evaluate the same against multiple-simultaneous attacks from obfuscations generated from malware samples from a standardised dataset that contain multiple generations of malware. Experimental results prove that DRLDO was able to successfully make the otherwise undetectable obfuscated variants of the malware detectable by an existing pre-trained malware classifier. The detection probability was raised well above the cut-off mark to 0.6 for the classifier to detect the obfuscated malware unambiguously. Further, the de-obfuscated variants generated by DRLDO achieved a very high correlation (of ≈ 0.99) with the base malware. This observation validates that the DRLDO system is actually learning to de-obfuscate and not exploiting a trivial trick.
Analytic approaches to periodically driven closed quantum systems: methods and applications

We present a brief overview of some of the analytic perturbative techniques for the computation of the Floquet Hamiltonian for a periodically driven, or Floquet, quantum many-body system. The key technical points about each of the methods discussed are presented in a pedagogical manner. They are followed by a brief account of some chosen phenomena where these methods have provided useful insights. We provide an extensive discussion of the Floquet–Magnus (FM) expansion, the adiabatic-impulse approximation, and the Floquet perturbation theory. This is followed by a relatively short discourse on the rotating wave approximation, a FM resummation technique and the Hamiltonian flow method. We also provide a discussion of some open problems which may possibly be addressed using these methods.
Exploiting Multi-Temporal Information for Improved Speckle Reduction of Sentinel-1 SAR Images by Deep Learning

Deep learning approaches show unprecedented results for speckle reduction in SAR amplitude images. The wide availability of multi-temporal stacks of SAR images can improve even further the quality of denoising. In this paper, we propose a flexible yet efficient way to integrate temporal information into a deep neural network for speckle suppression. Archives provide access to long time-series of SAR images, from which multi-temporal averages can be computed with virtually no remaining speckle fluctuations. The proposed method combines this multi-temporal average and the image at a given date in the form of a ratio image and uses a state-of-the-art neural network to remove the speckle in this ratio image. This simple strategy is shown to offer a noticeable improvement compared to filtering the original image without knowledge of the multi-temporal average.
Completion Time Minimization in Wireless-Powered UAV-Assisted Data Collection System

In unmanned aerial vehicle (UAV)-assisted data collection system, UAVs can be deployed to charge ground terminals (GTs) via wireless power transfer (WPT) and collect data from them via wireless information transmission (WIT). In this letter, we aim to minimize the time required by a UAV via jointly optimizing the trajectory of the UAV and the transmission scheduling for all the GTs. This problem is formulated as a mixed integer nonlinear programming (MINLP) which are difficult to address in general. To this end, we develop an iterative algorithm based on binary search and successive convex optimization (SCO) to solve it. The simulation shows that our proposed solution outperforms the benchmark algorithms.
Box Re-Ranking: Unsupervised False Positive Suppression for Domain Adaptive Pedestrian Detection

False positive is one of the most serious problems brought by agnostic domain shift in domain adaptive pedestrian detection. However, it is impossible to label each box in countless target domains. Therefore, it yields our attention to suppress false positive in each target domain in an unsupervised way. In this paper, we model an object detection task into a ranking task among positive and negative boxes innovatively, and thus transform a false positive suppression problem into a box re-ranking problem elegantly, which makes it feasible to solve without manual annotation. An attached problem during box re-ranking appears that no labeled validation data is available for cherrypicking. Considering we aim to keep the detection of true positive unchanged, we propose box number alignment, a self-supervised evaluation metric, to prevent the optimized model from capacity degeneration. Extensive experiments conducted on cross-domain pedestrian detection datasets have demonstrated the effectiveness of our proposed framework. Furthermore, the extension to two general unsupervised domain adaptive object detection benchmarks also supports our superiority to other state-of-the-arts.
Evolutionary Algorithms for Fuzzy Cognitive Maps

Fuzzy Cognitive Maps (FCMs) is a complex systems modeling technique which, due to its unique advantages, has lately risen in popularity. They are based on graphs that represent the causal relationships among the parameters of the system to be modeled, and they stand out for their interpretability and flexibility. With the late popularity of FCMs, a plethora of research efforts have taken place to develop and optimize the model. One of the most important elements of FCMs is the learning algorithm they use, and their effectiveness is largely determined by it. The learning algorithms learn the node weights of an FCM, with the goal of converging towards the desired behavior. The present study reviews the genetic algorithms used for training FCMs, as well as gives a general overview of the FCM learning algorithms, putting evolutionary computing into the wider context.
ALMA/NICER observations of GRS 1915+105 indicate a return to a hard state

Context. GRS 1915+105 is a transient black hole X-ray binary consistently emitting 10–100% of the Eddington luminosity in the X-ray band over the last three decades until mid-2018 when the source luminosity suddenly decreased by an order of magnitude. This phase was followed by a change to a state with even lower average X-ray fluxes never seen before during the outburst but presenting renewed flaring activity at different wavelengths, albeit with mean fluxes still in decline. Aims. GRS 1915+105 has the longest orbital period known among low-mass X-ray binaries, the largest accretion disk size, and therefore the largest mass supply for accretion. The high inclination of the disk allows the study of geometrical effects of the accretion flow such as changes in the height-to-radius ratio or the effect of accretion disk winds on the intrinsic emission that is expected during the outburst decay. In addition, the transient jet is expected to change to a compact, self-absorbed, steady jet. Methods. We conducted two full polarization Atacama Large Millimeter Array observations to study the jet properties during the outburst decay by analyzing the spectral, polarization, and intra-epoch variability for both observation epochs. In addition, we analyzed almost daily Neutron Star Interior Composition Explorer pointing observations, modeling X-ray power spectral densities, spectral energy distributions, and light curves with a physically motivated model to follow the changing accretion disk properties throughout the outburst decay and relating them to the jet emission. Results. We show that the X-ray and millimeter (mm) spectral, timing, and polarization properties are consistent with those of a typical decaying X-ray binary outburst and that GRS 1915+105 has descended into the low-luminosity hard X-ray state. The jet emission in the mm is consistent with a compact, steady jet with ∼1% linear polarization, and the magnetic field is likely aligned with the jet position angle. Relating the mm emission to the X-ray emission reveals that the source has changed from a higher radio/X-ray correlation index to a lower one; Lradio ∝ LX0.6.
A Note on Effects of Generalized and Extended Uncertainty Principles on Jüttner Gas

In recent years, the implications of the generalized (GUP) and extended (EUP) uncertainty principles on Maxwell–Boltzmann distribution have been widely investigated. However, at high energy regimes, the validity of Maxwell–Boltzmann statistics is under debate and instead, the Jüttner distribution is proposed as the distribution function in relativistic limit. Motivated by these considerations, in the present work, our aim is to study the effects of GUP and EUP on a system that obeys the Jüttner distribution. To achieve this goal, we address a method to get the distribution function by starting from the partition function and its relation with thermal energy which finally helps us in finding the corresponding energy density states.
Comparison of Cloud-Based Ion Trap and Superconducting Quantum Computer Architectures

Quantum computing represents a radical departure from conventional approaches to information processing, offering the potential for solving problems that can never be approached classically. While large scale quantum computer hardware is still in development, several quantum computing systems have recently become available as commercial cloud services. We compare the performance of these systems on several simple quantum circuits and algorithms, and examine component performance in the context of each system's architecture.
Gesture Recognition in Robotic Surgery: A Review

Objective: Surgical activity recognition is a fundamental step in computer-assisted interventions. This paper reviews the state-of-the-art in methods for automatic recognition of fine-grained gestures in robotic surgery focusing on recent data-driven approaches and outlines the open questions and future research directions. Methods: An article search was performed on 5 bibliographic databases with the following search terms: robotic, robot-assisted, JIGSAWS, surgery, surgical, gesture, fine-grained, surgeme, action, trajectory, segmentation, recognition, parsing. Selected articles were classified based on the level of supervision required for training and divided into different groups representing major frameworks for time series analysis and data modelling. Results: A total of 52 articles were reviewed. The research field is showing rapid expansion, with the majority of articles published in the last 4 years. Deep-learning-based temporal models with discriminative feature extraction and multi-modal data integration have demonstrated promising results on small surgical datasets. Currently, unsupervised methods perform significantly less well than the supervised approaches. Conclusion: The development of large and diverse open-source datasets of annotated demonstrations is essential for development and validation of robust solutions for surgical gesture recognition. While new strategies for discriminative feature extraction and knowledge transfer, or unsupervised and semi-supervised approaches, can mitigate the need for data and labels, they have not yet been demonstrated to achieve comparable performance. Important future research directions include detection and forecast of gesture-specific errors and anomalies. Significance: This paper is a comprehensive and structured analysis of surgical gesture recognition methods aiming to summarize the status of this rapidly evolving field.