Miracle-dz/UniFAR-FTUs huggingface dataset:
2 The Elastic Qualities of Soft Honeycombs

We examine solid cellular structures within the theoretical framework of finite elasticity, whereby we assume that the cell wall material is nonlinear elastic. This enables us to identify new mechanical effects which appear in cellular materials when elastically deformed, and to explore the physical properties that influence them. We find that, when a honeycomb structure of hyperelastic material and standard geometry, such as rectangular, hexagonal, or diamond shaped cells, contains walls which are inclined relative to an applied uniaxial tensile load, these walls tend to expand both in the direction of the load and in the perpendicular direction, producing an apparent negative Poisson’s ratio at local cell level. Moreover, we show that this (negative) Poisson ratio decreases as the magnitude of the tensile load increases. For these structures, Poisson’s ratios greater than 0.5 are obtained in uniaxial compression. Similar effects in structures with linearly elastic cell walls do not occur.
Surveying of the Relationship between Investment Level and Corporate Governance Mechanisms with Firm Value

the present study seeks to examine the relationship between investment level and corporate governance mechanisms with corporate value in companies listed at Tehran Stock Exchange. Independent variables include investment level and corporate governance mechanisms which, in turn,comprises in dependent directors, ownership concentration, institutional ownership, auditor type and free floating shares.firm value is our dependent variable measured by Tobin-Q index. Also,control variables such as firm size, leverage, and growth opportunity are used. The study is an applied research withcorrelative, post-event methodology. Population of the study includes 104 companies listed at Tehran Stock Exchange during 2003 to 2011 using screening method. Normal least squares regression model is used for testing hypotheses. Results indicate that level of investment has no effects on firm value in general companies in sense.Also, from among corporate governance mechanisms in general companies , independent directors has positive and significant, while auditor type has negative and significant, effects on firm value, And other corporate governance mechanisms have no effect on firm value. Key words : investment level, corporate governance mechanisms, firm value.
THE ROLE OF PRODUCTION TECHNOLOGY AND NITROGEN ADDITION ON THE MICROSTRUCTURE AND THE CORROSION BEHAVIUOR OF HIGH-SPEED TOOL STEEL

Conventional and nitrogen-containing high-speed tool steel AISI M41 are produced by open-air induction melting furnace (IF) and electroslag refining (ESR) technologies. Corrosion behaviors of the prepared samples in acidic and in neutral chloride solutions at different temperatures have been investigated by open-circuit potential time measurements, electrochemical polarization Tafel plots and weight loss techniques. The corrosion potential and corrosion current density values indicate that the corrosion potential and the corrosion rate of nitrogencontaining open-air induction melting and electroslag refining high-speed tool steels AISI M41 are in order of nobler and lower magnitude than that of conventional and nitrogen-free steels produced by the same processes. Moreover, the apparent activation energy of corrosion (Ea) of the investigated electroslag refining steel is found to be lower than that of the open-air induction melting high-speed tool steel AISI M41. On the other hand, apparent activation energy of corrosion (Ea) of the nitrogen-containing steel is lower than nitrogen–free steel produced by open-air induction melting and electroslag refining. The aims of this work are to study the role of production technology and nitrogen addition on the microstructure and the corrosion behavior of high–speed tool steel.
Influence of Various Business Regulations on the Amount of Foreign Direct Investments

The aim of this paper is to establish connections between 25 quantitative indicators on business regulations, laws and the protection of property rights, and the amount of foreign direct investment that can be expected accordingly. In this area, a growing number of econometric and survey-based studies have been published already. Their focus is mostly on the macroeconomic factors which determine the level of FDI in a country, but relatively few studies cover the link between FDI and laws, regulations and institutional arrangements that shape the daily economic activity in every country. For the purposes of this research publicly available databases from the Internet were used. The data mining rule-based approach, CN2 rule method was used. The findings of this research could be used to build an expert system for predicting the amount of FDI, based on the input of relevant business indicators.
Joint Switch Upgrade and Controller Deployment in Hybrid Software-Defined Networks

To improve traffic management ability, Internet Service Providers (ISPs) are gradually upgrading legacy network devices to programmable devices that support Software-Defined Networking (SDN). The coexistence of legacy and SDN devices gives rise to a hybrid SDN. Existing hybrid SDNs do not consider the potential performance issues introduced by a centralized SDN controller: flow requests processed by a highly loaded controller may experience long-tail processing delay; inappropriate multi-controller deployment could increase the propagation delay of flow requests. In this paper, we propose to jointly consider the deployment of SDN switches and their controllers for hybrid SDNs. We formulate the joint problem as an optimization problem that maximizes the number of flows that can be controlled and managed by the SDN and minimizes the propagation delay of flow requests between SDN controllers and switches under a given upgrade budget constraint. We show this problem is NP-hard. To efficiently solve the problem, we propose some techniques (e.g., strengthening the constraints and adding additional valid inequalities) to accelerate the global optimization solver for solving the problem for small networks and an efficient heuristic algorithm for solving it for large networks. The simulation results from real network topologies illustrate the effectiveness of the proposed techniques and show that our proposed heuristic algorithm uses a small number of controllers to manage a high amount of flows with good performance.
Deep Hierarchical Reinforcement Learning Based Recommendations via Multi-goals Abstraction

The recommender system is an important form of intelligent application, which assists users to alleviate from information redundancy. Among the metrics used to evaluate a recommender system, the metric of conversion has become more and more important. The majority of existing recommender systems perform poorly on the metric of conversion due to its extremely sparse feedback signal. To tackle this challenge, we propose a deep hierarchical reinforcement learning based recommendation framework, which consists of two components, i.e., high-level agent and low-level agent. The high-level agent catches long-term sparse conversion signals, and automatically sets abstract goals for low-level agent, while the low-level agent follows the abstract goals and interacts with real-time environment. To solve the inherent problem in hierarchical reinforcement learning, we propose a novel deep hierarchical reinforcement learning algorithm via multi-goals abstraction (HRL-MG). Our proposed algorithm contains three characteristics: 1) the high-level agent generates multiple goals to guide the low-level agent in different stages, which reduces the difficulty of approaching high-level goals; 2) different goals share the same state encoder parameters, which increases the update frequency of the high-level agent and thus accelerates the convergence of our proposed algorithm; 3) an appreciate benefit assignment function is designed to allocate rewards in each goal so as to coordinate different goals in a consistent direction. We evaluate our proposed algorithm based on a real-world e-commerce dataset and validate its effectiveness.
A Machine Learning approach to Risk Minimisation in Electricity Markets with Coregionalized Sparse Gaussian Processes

The non-storability of electricity makes it unique among commodity assets, and it is an important driver of its price behaviour in secondary financial markets. The instantaneous and continuous matching of power supply with demand is a key factor explaining its volatility. During periods of high demand, costlier generation capabilities are utilised since electricity cannot be stored and this has the impact of driving prices up very quickly. Furthermore, the non-storability also complicates physical hedging. Owing to these, the problem of joint price-quantity risk in electricity markets is a commonly studied theme. We propose using Gaussian Processes (GPs) to tackle this problem since GPs provide a versatile and elegant non-parametric approach for regression and time-series modelling. However, GPs scale poorly with the amount of training data due to a cubic complexity. These considerations suggest that knowledge transfer between price and load is vital for effective hedging, and that a computationally efficient method is required. To this end, we use the coregionalized (or multi-task) sparse GPs which addresses the aforementioned issues. To gauge the performance of our model, we use an average-load strategy as comparator. The latter is a robust approach commonly used by industry. If the spot and load are uncorrelated and Gaussian, then hedging with the expected load will result in the minimum variance position. Our main contributions are twofold. Firstly, in developing a coregionalized sparse GP-based approach for hedging. Secondly, in demonstrating that our model-based strategy outperforms the comparator, and can thus be employed for effective hedging in electricity markets.
NOMA in the Uplink: Delay Analysis With Imperfect CSI and Finite-Length Coding

We study whether using non-orthogonal multiple access (NOMA) in the uplink of a mobile network can reduce the queueing delay compared to orthogonal multiple access (OMA) when the system requires communications at very low latency and high reliability. We first consider an ideal system model with perfect channel state information (CSI) at the transmitter and long codewords, where we determine the optimal decoding orders when the decoder uses successive interference cancellation (SIC) and derive closed-form expressions for the optimal rate when joint decoding is used. While joint decoding performs well even under tight delay constraints, NOMA with SIC decoding often performs worse than OMA. For low-latency systems, we must also consider the impact of finite-length channel coding, as well as rate adaptation based imperfect CSI. We derive closed-form approximations for the corresponding outage or error probabilities and find that those effects create a larger performance penalty for NOMA than for OMA. Thus, NOMA with SIC decoding may often be unsuitable for low-latency systems.
Bayesian optimization of chemical composition: A comprehensive framework and its application to 
RFe12
-type magnet compounds

We propose a framework for optimization of the chemical composition of multinary compounds with the aid of machine learning. The scheme is based on first-principles calculation using the Korringa-Kohn-Rostoker method and the coherent potential approximation (KKR-CPA). We introduce a method for integrating datasets to reduce systematic errors in a dataset, where the data are corrected using a smaller and more accurate dataset. We apply this method to values of the formation energy calculated by KKR-CPA for nonstoichiometric systems to improve them using a small dataset for stoichiometric systems obtained by the projector-augmented-wave (PAW) method. We apply our framework to optimization of $R$Fe$_{12}$-type magnet compounds (R$_{1-\alpha}$Z$_{\alpha}$)(Fe$_{1-\beta}$Co$_{\beta}$)$_{12-\gamma}$Ti$_{\gamma}$, and benchmark the efficiency in determination of the optimal choice of elements (R and Z) and ratio ($\alpha$, $\beta$ and $\gamma$) with respect to magnetization, Curie temperature and formation energy. We find that the optimization efficiency depends on descriptors significantly. The variable $\beta$, $\gamma$ and the number of electrons from the R and Z elements per cell are important in improving the efficiency. When the descriptor is appropriately chosen, the Bayesian optimization becomes much more efficient than random sampling.
Monte Carlo Neural Fictitious Self-Play: Achieve Approximate Nash equilibrium of Imperfect-Information Games

Researchers on artificial intelligence have achieved human-level intelligence in large-scale perfectinformation games, but it is still a challenge to achieve (nearly) optimal results (in other words, an approximate Nash Equilibrium) in large-scale imperfect-information games (i.e. war games, football coach or business strategies). Neural Fictitious Self Play (NFSP) is an effective algorithm for learning approximate Nash equilibrium of imperfect-information games from self-play without prior domain knowledge. However, it relies on Deep Q-Network, which is off-line and is hard to converge in online games with changing opponent strategy, so it can’t approach approximate Nash equilibrium in games with large search scale and deep search depth. In this paper, we propose Monte Carlo Neural Fictitious Self Play (MC-NFSP), an algorithm combines Monte Carlo tree search with NFSP, which greatly improves the performance on large-scale zero-sum imperfect-information games. Experimentally, we demonstrate that the proposed Monte Carlo Neural Fictitious Self Play can converge to approximate Nash equilibrium in games with large-scale search depth while the Neural Fictitious Self Play can’t. Furthermore, we develop Asynchronous Neural Fictitious Self Play (ANFSP). It use asynchronous and parallel architecture to collect game experience. In experiments, we show that parallel actor-learners have a further accelerated and stabilizing effect on training.
Joint 3D Face Reconstruction and Dense Face Alignment from A Single Image with 2D-Assisted Self-Supervised Learning

3D face reconstruction from a single 2D image is a challenging problem with broad applications. Recent methods typically aim to learn a CNN-based 3D face model that regresses coefficients of 3D Morphable Model (3DMM) from 2D images to render 3D face reconstruction or dense face alignment. However, the shortage of training data with 3D annotations considerably limits performance of those methods. To alleviate this issue, we propose a novel 2D-assisted self-supervised learning (2DASL) method that can effectively use "in-the-wild" 2D face images with noisy landmark information to substantially improve 3D face model learning. Specifically, taking the sparse 2D facial landmarks as additional information, 2DSAL introduces four novel self-supervision schemes that view the 2D landmark and 3D landmark prediction as a self-mapping process, including the 2D and 3D landmark self-prediction consistency, cycle-consistency over the 2D landmark prediction and self-critic over the predicted 3DMM coefficients based on landmark predictions. Using these four self-supervision schemes, the 2DASL method significantly relieves demands on the the conventional paired 2D-to-3D annotations and gives much higher-quality 3D face models without requiring any additional 3D annotations. Experiments on multiple challenging datasets show that our method outperforms state-of-the-arts for both 3D face reconstruction and dense face alignment by a large margin.
A Model Counter's Guide to Probabilistic Systems

In this paper, we systematize the modeling of probabilistic systems for the purpose of analyzing them with model counting techniques. Starting from unbiased coin flips, we show how to model biased coins, correlated coins, and distributions over finite sets. From there, we continue with modeling sequential systems, such as Markov chains, and revisit the relationship between weighted and unweighted model counting. Thereby, this work provides a conceptual framework for deriving #SAT encodings for probabilistic inference.
The ALMA Spectroscopic Survey in the HUDF: CO Luminosity Functions and the Molecular Gas Content of Galaxies through Cosmic History

We use the results from the ALMA large program ASPECS, the spectroscopic survey in the Hubble Ultra Deep Field (HUDF), to constrain CO luminosity functions of galaxies and the resulting redshift evolution of ρ(H2). The broad frequency range covered enables us to identify CO emission lines of different rotational transitions in the HUDF at z > 1. We find strong evidence that the CO luminosity function evolves with redshift, with the knee of the CO luminosity function decreasing in luminosity by an order of magnitude from ∼2 to the local universe. Based on Schechter fits, we estimate that our observations recover the majority (up to ∼90%, depending on the assumptions on the faint end) of the total cosmic CO luminosity at z = 1.0–3.1. After correcting for CO excitation, and adopting a Galactic CO-to-H2 conversion factor, we constrain the evolution of the cosmic molecular gas density ρ(H2): this cosmic gas density peaks at z ∼ 1.5 and drops by a factor of to the value measured locally. The observed evolution in ρ(H2), therefore, closely matches the evolution of the cosmic star formation rate density ρSFR. We verify the robustness of our result with respect to assumptions on source inclusion and/or CO excitation. As the cosmic star formation history can be expressed as the product of the star formation efficiency and the cosmic density of molecular gas, the similar evolution of ρ(H2) and ρSFR leaves only little room for a significant evolution of the average star formation efficiency in galaxies since z ∼ 3 (85% of cosmic history).
Mean squared displacement in a generalized Lévy walk model.

Lévy walks represent a class of stochastic models (space-time-coupled continuous-time random walks) with applications ranging from the laser cooling to the description of animal motion. The initial model was intended for the description of turbulent dispersion as given by the Richardson law. The existence of this Richardson regime in the original model was recently challenged [T. Albers and G. Radons, Phys. Rev. Lett. 120, 104501 (2018)PRLTAO0031-900710.1103/PhysRevLett.120. 104501]: The mean squared displacement (MSD) in this model diverges, i.e., does not exist, in the regime, where it presumably should reproduce the Richardson law. In the Supplemental Material to that work the authors present (but do not investigate in detail) a generalized model interpolating between the original one and the Drude-like models known to show no divergences. In the present work we give a detailed investigation of the ensemble MSD in this generalized model, show that the behavior of the MSD in this model is the same (up to prefactors) as in the original one in the domains where the MSD in the original model does exist, and investigate the conditions under which the MSD in the generalized model does exist or diverges. Both ordinary and aged situations are considered.
Factorised Representation Learning in Cardiac Image Analysis

Typically, a medical image offers spatial information on the anatomy (and pathology) modulated by imaging specific characteristics. Many imaging modalities including Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) can be interpreted in this way. We can venture further and consider that a medical image naturally factors into some spatial factors depicting anatomy and factors that denote the imaging characteristics. Here, we explicitly learn this decomposed (factorised) representation of imaging data, focusing in particular on cardiac images. We propose Spatial Decomposition Network (SDNet), which factorises 2D medical images into spatial anatomical factors and non-spatial imaging factors. We demonstrate that this high-level representation is ideally suited for several medical image analysis tasks, such as semi-supervised segmentation, multi-task segmentation and regression, and image-to-image synthesis. Specifically, we show that our model can match the performance of fully supervised segmentation models, using only a fraction of the labelled images. Critically, we show that our factorised representation also benefits from supervision obtained either when we use auxiliary tasks to train the model in a multi-task setting (e.g. regressing to known cardiac indices), or when aggregating multimodal data from different sources (e.g. pooling together MRI and CT data). To explore the properties of the learned factorisation, we perform latent-space arithmetic and show that we can synthesise CT from MR and vice versa, by swapping the modality factors. We also demonstrate that the factor holding image specific information can be used to predict the input modality with high accuracy.
Framework for the chiral extrapolation of the charmed baryon ground-state masses

We consider the chiral Lagrangian for charmed baryon fields with $J^P =\frac{1}{2}^+$ or $J^P =\frac{3}{2}^+$ quantum numbers. A chiral expansion framework for the baryon ground state masses is worked out to N$^3$LO as to compute their dependence on the up, down and strange quark masses for finite box QCD lattice simulations. It is formulated in terms of on-shell meson and baryon masses. The convergence of such a scheme is illustrated with physical masses as taken from the PDG. The counter terms relevant at N$^3$LO are correlated systematically by large-$N_c$ sum rules to leading and subleading order in a manner that keeps the renormalization scale invariance of the approach.
Heuchera Creme Brulee and Mahogany Medicinal Value under Water Stress and Oligosaccharide (COS) Treatment

Food borne pathogens cause serious human illnesses and diseases and their control using natural bioactive compounds becomes essential for the progress of agricultural and food industries. Developing novel tools to enhance the medicinal values of traditional horticultural medicinal crops is one of the promising methods for achieving food borne pathogens control. In this study, oligosaccharide water solutions were applied to Heuchera Creme Brulee and Mahogany subjected to a normal irrigation interval (2 days) or to prolonged irrigation intervals (6 days) for 6 weeks. Plant morphological, physiological, and metabolic markers associated with the bioactivity of leaf extracts against selected microbes. Oligosaccharide-treated plants showed significant increases in all morphological parameters during normal and prolonged irrigation intervals as compared to those of the controls. Morphological improvement associated with a significant increase in chlorophyll, carbohydrates, proline, K, Ca, phenols, and free and total ascorbate and antioxidants. Superoxide dismutase, catalase, and ascorbate peroxidase activities were higher, while H2O2 accumulated to a lower extent in oligosaccharide-treated plants. These morphological and metabolic changes associated with increased antibacterial and antifungal activities of leaf extracts and their activities were comparable to antibiotics and antifungal agents (minimum inhibitory concentrations values were 0.5 -0.20 mg−1mL for bacteria and 0.08 -0.20 mg−1mL for fungi in Mahogany). The application of oligosaccharide and/or water stress might be of great value for producing natural bioactive compounds for food borne pathogens control.