Miracle-dz/UniFAR-FTUs huggingface dataset:
Resource Trading in Edge Computing-Enabled IoV: An Efficient Futures-Based Approach

Mobile edge computing (MEC) has become a promising solution to utilize distributed computing resources for supporting computation-intensive vehicular applications in dynamic driving environments. To facilitate this paradigm, onsite resource trading serves as a critical enabler. However, dynamic communications and resource conditions could lead unpredictable trading latency, trading failure, and unfair pricing to the conventional resource trading process. To overcome these challenges, we introduce a novel futures-based resource trading approach in edge computing-enabled internet of vehicles (EC-IoV), where a forward contract is used to facilitate resource trading-related negotiations between an MEC server (seller) and a vehicle (buyer) in a given future term. Through estimating the historical statistics of future resource supply and network condition, we formulate the futures-based resource trading as the optimization problem aiming to maximize the seller's and the buyer's expected utility, while applying risk evaluations to relieve possible losses incurred by the uncertainties of the system. To tackle this problem, we propose an efficient bilateral negotiation approach which facilitates the participants reaching a consensus. Extensive simulations demonstrate that the proposed futures-based resource trading brings mutually beneficial utilities to both participants, while significantly outperforming the baseline methods on critical factors, e.g., trading failures and fairness, negotiation latency and cost.
Stochastic Action Prediction for Imitation Learning

Imitation learning is a data-driven approach to acquiring skills that relies on expert demonstrations to learn a policy that maps observations to actions. When performing demonstrations, experts are not always consistent and might accomplish the same task in slightly different ways. In this paper, we demonstrate inherent stochasticity in demonstrations collected for tasks including line following with a remotecontrolled car and manipulation tasks including reaching, pushing, and picking and placing an object. We model stochasticity in the data distribution using autoregressive action generation, generative adversarial nets, and variational prediction and compare the performance of these approaches. We find that accounting for stochasticity in the expert data leads to substantial improvement in the success rate of task completion.
CRSLab: An Open-Source Toolkit for Building Conversational Recommender System

In recent years, conversational recommender systems (CRSs) have drawn a wide attention in the research community, which focus on providing high-quality recommendations to users via natural language conversations. However, due to diverse scenarios and data formats, existing studies on CRSs lack unified and standardized implementation or comparison. To tackle this challenge, we release an open-source toolkit CRSLab, which provides a unified and extensible framework with highly-decoupled modules to develop CRSs. Based on this framework, we collect 6 commonly used human-annotated CRS datasets and implement 19 models that include advanced techniques such as graph neural networks and pre-training models. Besides, our toolkit provides a series of automatic evaluation protocols and a human-machine interaction interface to evaluate and compare different CRS methods. The project and documents are released at https://github.com/RUCAIBox/CRSLab.
Optical follow-up observation for GW event S190510g using Subaru/Hyper Suprime-Cam

A gravitational wave event, S190510g, which was classified as a binary-neutron-star coalescence at the time of preliminary alert, was detected by LIGO/Virgo collaboration on 2019 May 10. At 1.7 hours after the issue of its preliminary alert, we started a target-of-opportunity imaging observation in the Y band to search for its optical counterpart using the Hyper Suprime-Cam (HSC) on the Subaru Telescope. The observation covers a 118.8 deg2 sky area corresponding to $11.6\%$ confidence in the localization skymap released in the preliminary alert and $1.2\%$ in the updated skymap. We divided the observed area into two fields based on the availability of HSC reference images. For the fields with the HSC reference images, we applied an image subtraction technique; for the fields without the HSC reference images, we sought individual HSC images by matching a catalog of observed objects with the PS1 catalog. The search depth is 22.28 mag in the former method and the limit of search depth is 21.3 mag in the latter method. Subsequently, we performed visual inspection and obtained 83 candidates using the former method and 50 candidates using the latter method. Since we only have the one-day photometric data, we evaluated the probability of candidates being located inside the 3D skymap by estimating their distances with photometry of associated extended objects. We found three candidates are likely located inside the 3D skymap and concluded they could be a counterpart of S190510g, while most of the 133 candidates were likely to be supernovae because the number density of candidates was consistent with the expected number of supernova detections. By comparing our observational depth with a light-curve model of such a kilonova reproducing AT2017gfo, we show that early deep observations with the Subaru/HSC can capture the rising phase of the blue component of a kilonova at the estimated distance of S190510g (∼230 Mpc).
Representation Learning of Reconstructed Graphs Using Random Walk Graph Convolutional Network

Graphs are often used to organize data because of their simple topological structure, and therefore play a key role in machine learning. And it turns out that the low-dimensional embedded representation obtained by graph representation learning are extremely useful in various typical tasks, such as node classification, content recommendation and link prediction. However, the existing methods mostly start from the microstructure (i.e., the edges) in the graph, ignoring the mesoscopic structure (high-order local structure). Here, we propose wGCN -- a novel framework that utilizes random walk to obtain the node-specific mesoscopic structures of the graph, and utilizes these mesoscopic structures to reconstruct the graph And organize the characteristic information of the nodes. Our method can effectively generate node embeddings for previously unseen data, which has been proven in a series of experiments conducted on citation networks and social networks (our method has advantages over baseline methods). We believe that combining high-order local structural information can more efficiently explore the potential of the network, which will greatly improve the learning efficiency of graph neural network and promote the establishment of new learning models.
Structural motifs and bonding in two families of boron structures predicted at megabar pressures

The complex crystal chemistry of elemental boron has led to numerous proposed structures with distinctive motifs as well as contradictory findings. Herein, evolutionary structure searches performed at 100 GPa have uncovered a series of potential new metastable phases of boron, and bonding analyses were carried out to elucidate their electronic structure. These polymorphs, dynamically stable at 100 GPa, were grouped into two families. The first was derived from the thermodynamic minimum at these conditions, α-Ga, whereas channels comprised the second. Two additional intergrowth structures were uncovered, and it was shown they could be constructed by stacking layers of α-Ga-like and channel-like allotropes on top of each other. A detailed bonding analysis revealed networks of four-center σ-bonding functions linked by two-center B-B bonds in the α-Ga based structures, and networks that were largely composed of three-center σ-bonding functions in the channelbased structures. Seven of these high pressure phases were found to be metastable at atmospheric conditions, and their Vickers hardnesses were estimated to be ∼36 GPa.
Achievable Rates for Binary Two-hop Channel with Energy Harvesting Relay With Finite Battery

We study the problem of joint information and energy transfer in a binary two-hop channel with an energy harvesting relay. We consider a finite battery size at the relay and energy loss in transmitting energy. In other words, to be able to send an energy-contained symbol, the relay must receive multiple energy-contained symbols. Thus, we face a kind of channel with memory. We model the energy saved in the battery as the channel state with the challenge that the receiver does not know the channel state. We propose two different achievable schemes, the first one is based on state-dependent superposition coding and the second one is based on the equivalent timing channel approach. Both of our schemes are based on block Markov coding and backward decoding techniques. Due to these two approaches, we find achievable rates with a single-letter expression for the model.
On Koopman Mode Decomposition and Tensor Component Analysis

Koopman mode decomposition and tensor component analysis [also known as CANDECOMP (canonical decomposition)/PARAFAC (parallel factorization)] are two popular approaches of decomposing high dimensional datasets into modes that capture the most relevant features and/or dynamics. Despite their similar goal, the two methods are largely used by different scientific communities and are formulated in distinct mathematical languages. We examine the two together and show that, under certain conditions on the data, the theoretical decomposition given by the tensor component analysis is the same as that given by Koopman mode decomposition. This provides a "bridge" with which the two communities should be able to more effectively communicate. Our work provides new possibilities for algorithmic approaches to Koopman mode decomposition and tensor component analysis and offers a principled way in which to compare the two methods. Additionally, it builds upon a growing body of work showing that dynamical systems theory and Koopman operator theory, in particular, can be useful for problems that have historically made use of optimization theory.
Qian Jian and His Contribution to Small-Scale Turbulence Studies

Qian [Tsien] Jian (1939–2018), a Chinese theoretical physicist and fluid dynamicist, devoted the second part of his scientific life to the physical understanding of small-scale turbulence to the exclusion of all else. To place Qian’s contribution in an appropriate position in the field of small-scale turbulence, a historical overview and a state-of-the art review are attempted. Qian developed his own statistical theory of small-scale turbulence, based on the Liouville (1853) equation and a perturbation variational approach to non-equilibrium statistical mechanics, which is compatible with the Kolmogorov–Oboukhov energy spectrum. Qian’s statistical theory of small-scale turbulence, which appears mathematically and physically valid, successfully led to his contributions to (i) the closure problem of turbulence; (ii) onedimensional turbulence; (iii) two-dimensional turbulence; (iv) the turbulent passive scalar field; (v) the cascade model of turbulence; (vi) the universal equilibrium range of turbulence; (vii) a simple model of the bump phenomenon; (viii) universal constants of turbulence; (ix) the intermittency of turbulence; and perhaps most importantly, (x) the effect of the Taylor microscale Reynolds number (Rλ) on both the width of the inertial range of finite Rλ turbulence and the scaling exponents of velocity structure functions. In particular, Qian found that the inertial range cannot exist when Rλ ≪ 2000. In contrast to the prevailing intermittency models, he discovered that normal scaling is valid in the real Kolmogorov inertial range when Rλ approaches infinity while the anomalous scaling observed in experiments reflects the finite Rλ effect (Qe). He then made a correction to the famous Kolmogorov (1941c) equation and obtained the finite Rλ effect equation or the Kolmogorov–Novikov–Qian equation. He also independently derived the decay law of the finite Rλ effect. Following up Kraichnan, Qian steered all of us along the right path to an improved understanding of small-scale turbulence and solutions to its problems. Qian is credited with his contribution to enhanced knowledge about the finite Rλ effect of turbulence, which has profoundly shaped and stimulated thinking about the K41 turbulence, the K62 turbulence and the finite Rλ turbulence.
VLBI Observations of Supernova PTF11qcj: Direct Constraints on the Size of the Radio Ejecta

We present High Sensitivity Array and enhanced Multi-Element Remotely Linked Interferometer Network observations of the radio-loud broad-lined Type Ic supernova PTF11qcj obtained ∼7.5 yr after the explosion. Previous observations of this supernova at 5.5 yr since explosion showed a double-peaked radio light curve accompanied by a detection in the X-rays, but no evidence for broad Hα spectral features. The Very Long Baseline Interferometry (VLBI) observations presented here show that the PTF11qcj GHz radio ejecta remains marginally resolved at the submilliarcsecond level ≈7.5 yr after the explosion, pointing toward a nonrelativistic expansion. Our VLBI observations thus favor a scenario in which the second peak of the PTF11qcj radio light curve is related to the strong interaction of the supernova ejecta with a circumstellar medium of variable density, rather than to the emergence of an off-axis jet. Continued VLBI monitoring of PTF11qcj in the radio may further strengthen this conclusion.
Lex-BERT: Enhancing BERT based NER with lexicons

In this work, we represent Lex-BERT, which incorporates the lexicon information into Chinese BERT for named entity recognition (NER) tasks in a natural manner. Instead of using word embeddings and a newly designed transformer layer as in FLAT, we identify the boundary of words in the sentences using special tokens, and the modified sentence will be encoded directly by BERT. Our model does not introduce any new parameters and are more efficient than FLAT. In addition, we do not require any word embeddings accompanying the lexicon collection. Experiments on Ontonotes and ZhCrossNER show that our model outperforms FLAT and other baselines.
Mystery of Bloom-Gilman duality: A light-front holographic QCD perspective

Light front wave functions motivated by holographic constructions are used to study Bloom − Gilman duality, a feature of deep inelastic scattering. Separate expressions for structure functions in terms of quark and hadronic degrees of freedom (involving transition form factors) are presented, with an ultimate goal of obtaining a relationship between the two expressions. A specific two-parton model is defined and resonance transition form factors are computed using previously derived light front wave functions. A new form of global duality (integral over all values of x between 0 and 1) is derived from the valence quark-number sum rule. Using a complete set of hadronic states is necessary for this new global duality to be achieved, and the previous original work does not provide such a set. This feature is remedied by amending the model to include a longitudinal confining potential, and the resulting complete set is sufficient to carry out the study of Bloom − Gilman duality. Specific expressions for transition form factors are obtained and all are shown to fall as 1/Q, at asymptotically large values. This is because the Feynman mechanism dominates the asymptotic behavior of the model. These transition form factors are used to assess the validity of the global and local duality sum rules, with the result that both are not satisfied within the given model. Evaluations of the hadronic expression for q(x,Q) provide more details about this lack. This result is not a failure of the current model because it shows that the observed validity of both global and local forms of duality for deep inelastic scattering must be related to a feature of QCD that is deeper than completeness. Our simple present model suggests a prediction that Bloom − Gilman duality would not be observed if deep inelastic scattering experiments were to be made on the pion. The underlying origin of the duality phenomenon in deep inelastic scattering is deeply buried within the confinement aspects of QCD, and its origin remains a mystery.
Learning Neural Networks on SVD Boosted Latent Spaces for Semantic Classification

The availability of large amounts of data and compelling computation power have made deep learning models much popular for text classification and sentiment analysis. Deep neural networks have achieved competitive performance on the above tasks when trained on naive text representations such as word count, term frequency, and binary matrix embeddings. However, many of the above representations result in the input space having a dimension of the order of the vocabulary size, which is enormous. This leads to a blow-up in the number of parameters to be learned, and the computational cost becomes infeasible when scaling to domains that require retaining a colossal vocabulary. This work proposes using singular value decomposition to transform the high dimensional input space to a lower-dimensional latent space. We show that neural networks trained on this lower-dimensional space are not only able to retain performance while savoring significant reduction in the computational complexity but, in many situations, also outperforms the classical neural networks trained on the native input space.
Free-Fall of Solid Particles through Fluids

A comprehensive, up-to-date review is presented of predictive relationships for the terminal, free-fall velocity of solid particles falling in an infinite Newtonian fluid. The study explores accuracy of the implicit and explicit equations in terms of the drag coefficient and the terminal velocity. Problems of predicting the terminal velocity of non-spherical, isometric as well as non-isometric, particles is discussed.
Porous Film Deposition by Electrohydrodynamic Atomization of Nanoparticle Sols

Film deposition by electrohydrodynamic atomization of nanoparticle sols was studied experimentally using zinc oxide nanoparticles in ethanol and using sequential Monte Carlo simulations, in which proper statistics and particle transport to the substrate were accounted for. Experimentally produced and simulated films were in good agreement, and simulations were used to determine the influences of deposition time, nanoparticle concentration in the sol, mean droplet size, and droplet polydispersity on the film thickness, porosity, surface roughness, and lateral feature size. Film growth rates were non-constant due to decreases in the film porosity early in the deposition process. Over time, the thickness of films increased, while the porosity decreased. After sufficient deposition time, the porosity reached a constant value and consequently the film growth rate was constant. The use of high concentration sols or large droplets resulted in the deposition of agglomerated particles, which gave rise to thicker, more porous films. Control over film morphology using droplets from size distributions with geometric standard deviations greater than 2.0 was not possible and the film thickness and air to particle volume ratio for all films produced from polydisperse droplets were convergent regardless of the number concentration of nanoparticles in the sol. Film surface roughnesses were less than the volume equivalent diameter of the agglomerated particles deposited, indicating that films were relatively uniform in thickness. Radial distribution functions were calculated for both experimentally deposited and simulated films and were used to determine lateral feature sizes. Lateral feature sizes increased with increasing deposition time and deposited agglomerate size until the deposition surface was completely covered. This work provides the necessary link between electrohydrodynamic atomization parameters and the resulting morphology of deposited films.
Automatic classification of endogenous seismic sources within a landslide body using random forest algorithm

Different studies have shown the presence of microseismic activity in soft-rock landslides. The seismic signals exhibit significantly different features in the time and frequency domains which allow their classification and interpretation. Most of the classes could be associated with different mechanisms of deformation occurring within and at the surface (e.g. rockfall, slide-quake, fissure opening, fluid circulation). However, some signals remain not fully understood and some classes contain few examples that prevent any interpretation. To move toward a more complete interpretation of the links between the dynamics of soft-rock landslides and the physical processes controlling their behaviour, a complete catalog of the endogeneous seismicity is needed.
Novel Wood Processing by Maleic Acid - Glycerol Mixture System: Improvement of Water Resistance and Mechanical Property of Cellulose by the Processing

Abstract Maleic acid (M) - glycerol (G) mixture (MG) treatment for woods have been developed for improvement of water resistance and durability of woods and particle boards. Characteristics of MG and the mechanical strength of MG-treated cellulose as a model of wood were investigated to clarify the effect of MG treatment. Formation of ester bond between M and G was found during the preparation of MG solution, resulting in production of polyesters with more than 1,000 of molecular weight. Double bonds in M were maintained after the reaction of MG with cellulose. Reaction temperature of MG with cellulose over 180°C affected a decrease in the moisture content and the reinforcement of tensile strength of filter paper.
Pulse Radiolysis Studies on the Fragmentation of Arylmethyl Quaternary Nitrogen Mustards by One-Electron Reduction in Aqueous Solution

The radical species formed on the reaction of eaq- with a series of aryl compounds, all containing a N,N-bis(2-chloroethyl)-N-methylammoniomethyl substituent, have been studied in neutral aqueous solutions by pulse radiolysis using optical absorption spectrophotometry. The benzene, 1, and 4-methylsulfonylbenzene, 2, derivatives fragmented immediately to yield different amounts of benzyl radicals, but the 2-nitrobenzene, 3, and 4-nitrobenzene, 4, derivatives were reduced to long-lived radical anions which decayed by bimolecular processes. These nitrobenzene derivatives differed from the corresponding benzyl halides in that they did not fragment to benzyl radicals. Similarly, no evidence was found for the formation of a benzyl-type radical from the radical anion of the 8-nitronaphthalene derivative, 5. However, benzyl-type radicals were produced upon intramolecular electron transfer from the initially formed radical anions of the 4-nitro-5-imidazole, 6, and the 5-nitro-2-pyrrole, 7, derivatives at rate cons.
Medium modified Fragmentation Functions with open source xFitter

A new analysis of the modification of the hadronization process in the nuclear medium for pions is presented. The effective description is condensed in a set of medium modified fragmentation functions (nFFs) obtained at next-to-leading order (NLO) accuracy. The study was made using the open-source tool xFitter, conveniently modified to incorporate semi-inclusive processes. Theoretical uncertainties for the nFFs are also provided.