Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning Relation Prototype From Unlabeled Texts for Long-Tail Relation Extraction

Relation Extraction (RE) is a vital step to complete Knowledge Graph (KG) by extracting entity relations from texts. However, it usually suffers from the long-tail issue. The training data mainly concentrates on a few types of relations, leading to the lack of sufficient annotations for the remaining types of relations. In this paper, we propose a general approach to learn relation prototypes from unlabeled texts, to facilitate the long-tail relation extraction by transferring knowledge from the relation types with sufficient training data. We learn relation prototypes as an implicit factor between entities, which reflects the meanings of relations as well as their proximities for transfer learning. Specifically, we construct a co-occurrence graph from texts, and capture both first-order and second-order entity proximities for embedding learning. Based on this, we further optimize the distance from entity pairs to corresponding prototypes, which can be easily adapted to almost arbitrary RE frameworks. Thus, the learning of infrequent or even unseen relation types will benefit from semantically proximate relations through pairs of entities and large-scale textual information. We have conducted extensive experiments on two publicly available datasets: New York Times and Google Distant Supervision. Compared with eight state-of-the-art baselines, our proposed model achieves significant improvements (4.1 percent F1 on average). Further results on long-tail relations demonstrate the effectiveness of the learned relation prototypes. We further conduct an ablation study to investigate the impacts of varying components, and apply it to four basic relation extraction models to verify the generalization ability. Finally, we analyze several example cases to give intuitive impressions as qualitative analysis. Our codes and data can be found in https://github.com/CrisJk/PA-TRP.
Turnpike Properties in Optimal Control: An Overview of Discrete-Time and Continuous-Time Results

The turnpike property refers to the phenomenon that in many optimal control problems, the solutions for different initial conditions and varying horizons approach a neighborhood of a specific steady state, then stay in this neighborhood for the major part of the time horizon, until they may finally depart. While early observations of the phenomenon can be traced back to works of Ramsey and von Neumann on problems in economics in 1928 and 1938, the turnpike property received continuous interest in economics since the 1960s and recent interest in systems and control. The present chapter provides an introductory overview of discrete-time and continuous-time results in finite and infinite-dimensions. We comment on dissipativity-based approaches and infinite-horizon results, which enable the exploitation of turnpike properties for the numerical solution of problems with long and infinite horizons. After drawing upon numerical examples, the chapter concludes with an outlook on time-varying, discounted, and open problems.
Influence of inhomogeneous broadening on the dynamics of quantum dot lasers

This work theoretically studies the impacts of the inhomogeneous broadening on the modulation dynamics of quantum dot lasers using a multi-population rate equation model. The modulation dynamics shows two distinct regimes depending on the energy separation between the GS and the ES. For broadenings smaller than the GS-ES separation, the K-factor increases while the damping factor offset, the differential gain and the gain compression factor decrease with the inhomogeneous broadening. For broadenings larger than the GS-ES separation, the damping factor offset keeps almost constant while the K-factor, the differential gain and the gain compression factor increases with the inhomogeneous broadening.
Photometric Dissection of Intracluster Light and Its Correlations with Host Cluster Properties

We explore several ways to dissect brightest cluster galaxies (BCGs) and their surrounding intracluster light (ICL) using a surface brightness (SB) cut, a luminosity cut, excess light above a de Vaucouleurs profile, or a double Sérsic decomposition. Assuming that all light above is attributable to the ICL, we find that an average fraction of of all diffuse light centered on the BCG belongs to the ICL. Likewise, if we assume that all light fainter than mag arcsec−2 belongs to the ICL, the average ICL fraction is . After fitting a de Vaucouleurs profile to the inner parts of the SB profile, we detect excess light at large radii, corresponding to an average ICL fraction of . Finally, by decomposing the SB profile into two Sérsic functions, we find an average ICL fraction of associated with the outer Sérsic component. Our measured ICL and BCG+ICL luminosities agree well with predictions from high-resolution simulations where the outer Sérsic component traces the unrelaxed, accreted stellar material. BCG and ICL properties defined in this way are correlated with cluster parameters to study the coevolution of BCGs, ICL, and their host clusters. We find positive correlations between BCG+ICL brightness and cluster mass, cluster velocity dispersion, cluster radius, and integrated satellite brightness, confirming that BCG/ICL growth is indeed coupled with cluster growth. On average, the ICL is better aligned than the BCG with the host cluster in terms of position angle, ellipticity, and centering. That makes it a potential dark-matter tracer.
Study of Two-Dimensional Open EWOD System using Printed Circuit Board Technology

Digital micro fluidics (DMF) emerged as a popular technology for lab on chip (LOC) application, which allows full and independent control over droplets on an array of electrodes. In this work, open electro wetting on dielectric technique (EWOD) based on printed circuit board has been investigated, which has a wide range of applications. The most of the traditional EWOD chips array of electrode pads typically in 1-D (one-dimensional) line pattern designed for specific operation at a time, limit the utilization of estate. In the proposed system, 2-D (two-dimensional) array of 22 electrodes, controlled by 8 control signals designed and fabricated to enhance reconfigurable paths. Bio-compatible polydimethylsiloxane (PDMS) is used as a dielectric as well as a hydrophobic layer. The controlled droplet transport and mixing are successfully done on the fabricated device. The effect of ground wire configuration on droplet velocity is investigated and results are verified with estimated droplet velocity.
Palladium-catalyzed heck and suzuki coupling in glycerol

The Heck coupling of halobenzenes with various alkenes and the Suzuki cross coupling of halobenzenes with phenylboronic acid were successfully performed in glycerol as the reaction solvent using homogeneous and heterogeneous palladium catalysts. Glycerol is a renewable and recyclable green solvent that is able to dissolve organic substrates, inorganic bases, and palladium complexes, and that allows easy isolation of the reaction product by simple extraction with glycerol-immiscible solvents such as diethyl ether, hexane, and dichloromethane.
An open-ended learning architecture to face the REAL 2020 simulated robot competition

Open-ended learning is a core research field of machine learning and robotics aiming to build learning machines and robots able to autonomously acquire knowledge and skills and to reuse them to solve novel tasks. The multiple challenges posed by open-ended learning have been operationalized in the robotic competition REAL 2020. This requires a simulated camera-arm-gripper robot to (a) autonomously learn to interact with objects during an intrinsic phase where it can learn how to move objects and then (b) during an extrinsic phase, to re-use the acquired knowledge to accomplish externally given goals requiring the robot to move objects to specific locations unknown during the intrinsic phase. Here we present a 'baseline architecture' for solving the challenge, provided as baseline model for REAL 2020. Few models have all the functionalities needed to solve the REAL 2020 benchmark and none has been tested with it yet. The architecture we propose is formed by three components: (1) Abstractor: abstracting sensory input to learn relevant control variables from images; (2) Explorer: generating experience to learn goals and actions; (3) Planner: formulating and executing action plans to accomplish the externally provided goals. The architecture represents the first model to solve the simpler REAL 2020 'Round 1' allowing the use of a simple parameterised push action. On Round 2, the architecture was used with a more general action (sequence of joints positions) achieving again higher than chance level performance. The baseline software is well documented and available for download and use at this https URL.
An End-to-end Deep Reinforcement Learning Approach for the Long-term Short-term Planning on the Frenet Space

Tactical decision making and strategic motion planning for autonomous highway driving are challenging due to the complication of predicting other road users' behaviors, diversity of environments, and complexity of the traffic interactions. This paper presents a novel end-to-end continuous deep reinforcement learning approach towards autonomous cars' decision-making and motion planning. For the first time, we define both states and action spaces on the Frenet space to make the driving behavior less variant to the road curvatures than the surrounding actors' dynamics and traffic interactions. The agent receives time-series data of past trajectories of the surrounding vehicles and applies convolutional neural networks along the time channels to extract features in the backbone. The algorithm generates continuous spatiotemporal trajectories on the Frenet frame for the feedback controller to track. Extensive high-fidelity highway simulations on CARLA show the superiority of the presented approach compared with commonly used baselines and discrete reinforcement learning on various traffic scenarios. Furthermore, the proposed method's advantage is confirmed with a more comprehensive performance evaluation against 1000 randomly generated test scenarios.
The NEOLIX Open Dataset for AutonomousDriving

With the gradual maturity of 5G technology,autonomous driving technology has attracted moreand more attention among the research commu-nity. Autonomous driving vehicles rely on the co-operation of artificial intelligence, visual comput-ing, radar, monitoring equipment and GPS, whichenables computers to operate motor vehicles auto-matically and safely without human interference.However, the large-scale dataset for training andsystem evaluation is still a hot potato in the devel-opment of robust perception models. In this paper,we present the NEOLIX dataset and its applica-tions in the autonomous driving area. Our datasetincludes about 30,000 frames with point cloud la-bels, and more than 600k 3D bounding boxes withannotations. The data collection covers multipleregions, and various driving conditions, includingday, night, dawn, dusk and sunny day. In orderto label this complete dataset, we developed vari-ous tools and algorithms specified for each task tospeed up the labelling process. It is expected thatour dataset and related algorithms can support andmotivate researchers for the further developmentof autonomous driving in the field of computer vi-sion.
Combined Economic Emission Dispatch for Microgrid Considering Solar and Wind Power Cost Functions Using Harmony Search Algorithm

The Energy Management System (EMS) in microgrids includes the problem of economic dispatch (ED) and environmental economic dispatch which called combined economic emission dispatch (CEED). In this paper, the harmony search (HS) algorithm is employed to solve the environmental economic dispatch problem for the microgrid considering the cost functions of wind power, solar power and battery sources. The test system used in this paper consists of three thermal generators, wind power source, solar power source and battery as energy storage device. To show the effectiveness of the employed method, three different scenarios are used. Also, the results are compared with those obtained by other recently published methods.
Electron Localization and Spin Polarization in a Quantum Circle

Localization and magnetic properties of electrons in a thin, cyclic quasi onedimensional GaAs wire with a central potential barrier were studied using the Hartree-Fock and LSDA (Local Spin Density Approximation, exchange only) and compared to more time consuming Quantum Monte-Carlo calculations. Within LSDA, evidence of true localization was found as well as evidence for the existence of both ferromagnetic as well as anti-ferromagnetic states. Also signs of twodimensional spin localization was found, without associated localized electrons.
First-principles free energies and Ginzburg-Landau theory of domains and ferroelectric phase transitions in BaTiO 3

We present a method based on combination of (a) constrained polarization molecular dynamics and (b) thermodynamic integration to determine the free-energy landscape relevant to structural phase transitions and related phenomena in ferroelectric materials, bridging the gap between first-principles calculations and phenomenological Landau-type theories. We illustrate it using a first-principles effective Hamiltonian of ${\text{BaTiO}}_{3}$ to (a) uncover the quantitative features of the free-energy function that are responsible for its first-order ferroelectric transitions, (b) calculate the minimum free-energy pathways for the polarization switching and (c) evaluate temperature-dependent free energy of domain walls, and a minimum free-energy pathway to formation of ferroelectric domains. We use our method within a variational mean-field theory to connect with Landau theory and show through comparison with numerically exact simulations that (a) the state constrained to have vanishing order (away from the equilibrium) below the transition temperature is highly degenerate due to fluctuations that drive the phase transition first order, and (b) certain terms need to be added to the phenomenological Landau-Devonshire free-energy functions to capture the physics of spatial fluctuation in order parameter. Our method can be readily generalized to any classical microscopic Hamiltonian and ensembles characterized with a given constraint.
Supplement Epidemiological impact and budget effect of pre-exposure prophylaxis for HIV-1 prevention in Germany from 2018 to 2058

This supplementary material is hosted by Eurosurveillance as supporting information alongside the article Epidemiological impact and budget effect of pre-exposure prophylaxis for HIV-1 prevention in Germany from 2018 to 2058 on behalf of the authors who remain responsible for the accuracy and appropriateness of the content. The same standards for ethics, copyright, attributions and permissions as for the article apply. Eurosurveillance is not responsible for the maintenance of any links or email addresses provided therein. "
Deep Reinforcement Learning for Wireless Scheduling with Multiclass Services

In this paper, we investigate the problem of scheduling and resource allocation over a time varying set of clients with heterogeneous demands. This problem appears when service providers need to serve traffic generated by users with different classes of requirements. We thus have to allocate bandwidth resources over time to efficiently satisfy these demands within a limited time horizon. This is a highly intricate problem and solutions may involve tools stemming from diverse fields like combinatorics and optimization. Recent work has successfully proposed Deep Reinforcement Learning (DRL) solutions, although not yet for heterogeneous user traffic. We propose a deep deterministic policy gradient algorithm combining state of the art techniques, namely Distributional RL and Deep Sets, to train a model for heterogeneous traffic scheduling. We test on diverse number scenarios with different time dependence dynamics, users’ requirements, and resources available, demonstrating consistent results. We evaluate the algorithm on a wireless communication setting and show significant gains against state-of-the-art conventional algorithms from combinatorics and optimization (e.g. Knapsack, Integer Linear Programming, Frank-Wolfe).
Insights into the electronic structure of 
OsO2
 using soft and hard x-ray photoelectron spectroscopy in combination with density functional theory

Theory and experiment are combined to gain an understanding of the electronic properties of OsO2, a poorly studied metallic oxide that crystallizes in the rutile structure. Hard and soft valence-band x-ray photoemission spectra of OsO2 single crystals are in broad agreement with the results of density-functional-theory calculations, aside from a feature shifted to high binding energy of the conduction band. The energy shift corresponds to the conduction electron plasmon energy measured by reflection electron energy loss spectroscopy. The plasmon satellite is reproduced by many-body perturbation theory.
Evaluation of Waste Express Bag as a Novel Bitumen Modifier

With the rapid development of China’s e-commerce and logistics industry, a large number of waste express bags (WEBs) have been produced, which are difficult to recycle. The existing methods of waste express bag disposal often cause severe environmental pollution. It was discovered in this paper that the use of WEBs to modify bitumen could be an environmentally friendly way to recycle WEBs. This study aimed to investigate the feasibility of using WEBs to modify bitumen and promote the performance of WEB-modified bitumen. In order to verify this assumption, a series of basic or rheological experiments were conducted on different dosages of WEB-modified bitumen. The test results and phenomenon showed that the WEBs could be used to modify the binder homogeneously by using a high-speed shearing machine. The basic experiments showed that the WEBs could decrease the penetration and ductility while increasing the softening point and the rotational viscosity. Additionally, the rheological experimental data indicated that the high temperature performance was improved while the low temperature performance stayed the same. The Fourier-transform infrared spectroscopy (FTIR) results demonstrated that modification of the WEB was a physical modification without chemical reaction and the main component of the modifier was polyethylene. The fluorescence microscope (FM) data revealed the micro-structures of different dosages of WEB-modified bitumen. In conclusion, WEB can be a feasible binder modifier.
Efficient Unsupervised Word Sense Induction , Disambiguation and Embedding

We present an efficient word sense disambiguation and embedding algorithm that learns multi-prototype sense vectors to accommodate different meanings of words. Our method provides both sparse sense vectors to be used for word sense induction, and dense sense embeddings to be used in training of machine learning models for downstream NLP tasks. We disambiguate the ambiguous words by clustering their context words. However, unlike previous methods where each context is assigned to one sense, we use a soft clustering approach to learn the distribution of context words over different senses and use it to break the global context vectors into multiple sense-specific sparse vectors. After constructing the sense-word relation matrix from the word-word co-occurrence matrix, we use it for learning sense embeddings using a fast matrix factorization approach. Our experiments on SemEval 2013 competition data show that our method achieves the state-of-the-art accuracy in a more efficient way.
Explainable Tensorized Neural Ordinary Differential Equations for Arbitrary-Step Time Series Prediction

In this work, we propose a continuous neural network architecture, referred to as Explainable Tensorized Neural - Ordinary Differential Equations (ETN-ODE) network for multi-step time series prediction at arbitrary time points. Unlike existing approaches which mainly handle univariate time series for multi-step prediction, or multivariate time series for single-step predictions, ETN-ODE is capable of handling multivariate time series with arbitrary-step predictions. An additional benefit is its tandem attention mechanism, with respect to temporal and variable attention, which enable it to greatly facilitate data interpretability. Specifically, the proposed model combines an explainable tensorized gated recurrent unit with ordinary differential equations, with the derivatives of the latent states parameterized through a neural network. We quantitatively and qualitatively demonstrate the effectiveness and interpretability of ETN-ODE on one arbitrary-step prediction task and five standard multi-step prediction tasks. Extensive experiments show that the proposed method achieves very accurate predictions at arbitrary time points while attaining very competitive performance against the baseline methods in standard multi-step time series prediction.
Frequency-Domain Filtered-x LMS Algorithms for Active Noise Control: A Review and New Insights

This paper presents a comprehensive overview of the frequency-domain filtered-x least mean-square (FxLMS) algorithms for active noise control (ANC). The direct use of frequency-domain adaptive filters for ANC results in two kinds of delays, i.e., delay in the signal path and delay in the weight adaptation. The effects of the two kinds of delays on the convergence behavior and stability of the adaptive algorithms are analyzed in this paper. The first delay can violate the so-called causality constraint, which is a major concern for broadband ANC, and the second delay can reduce the upper bound of the step size. The modified filter-x scheme has been employed to remove the delay in the weight adaptation, and several delayless filtering approaches have been presented to remove the delay in the signal path. However, state-of-the-art frequency-domain FxLMS algorithms only remove one kind of delay, and some of these algorithms have a very high peak complexity and hence are impractical for real-time systems. This paper thus proposes a new delayless frequency-domain ANC algorithm that completely removes the two kinds of delays and has a low complexity. The performance advantages and limitations of each algorithm are discussed based on an extensive evaluation, and the complexities are evaluated in terms of both the peak and average complexities.
Generalized Pose-and-Scale Estimation using 4-Point Congruence Constraints

We present gP4Pc, a new method for computing the absolute pose of a generalized camera with unknown internal scale from four corresponding 3D point-and-ray pairs. Unlike most pose-and-scale methods, gP4Pc is based on constraints arising from the congruence of shapes defined by two sets of four points related by an unknown similarity transformation. By choosing a novel parametrization for the problem, we derive a system of four quadratic equations in four scalar variables. The variables represent the distances of 3D points along the rays from the camera centers. After solving this system via Gröbner basis-based automatic polynomial solvers, we compute the similarity transformation using an efficient 3D point-point alignment method. We also propose a specialized variant of our solver for the case of coplanar points, which is computationally very efficient and about $ 3\times$ faster than the fastest existing solver. Our experiments on real and synthetic datasets, demonstrate that gP4Pc is among the fastest methods in terms of total running time when used within a RANSAC framework, while achieving competitive numerical stability, accuracy, and robustness to noise.