Miracle-dz/UniFAR-FTUs huggingface dataset:
Titania Nanotubes Arrays Based-Gas Sensor: NO2-Oxidizing Gas and H2-Reducing Gas

Gas sensor based on titanium dioxide (TiO2) nanotube was manufactured and its sensitivity to hydrogen (H2) and to nitrogen dioxide (NO2) gasses was investigated using anodization method. The TiO2 NT structure was studied using X-ray diffraction (XRD). The surface morphology of prepared Titania was analysed using field-emission electron-scanning microscopy (FE-SEM). Starting with (XRD) study it confirms the tetragonal phase structure of the prepared Titania (anatase and rutile). In addition, the TiO2 anatase averaged crystallite size was 25.9 nm. The FE-SEM images revealed that the nanotube’s average diameters are within 70 ± 2 nm. Gas response measurements at room temperature (27 °C) for hydrogen and nitrogen dioxide gases at various concentrations (100, 150, 200, 250 and 300 ppm) were investigated. Our study has shown that the higher resistance of NO2 gas was 30 Ω at 300 ppm while it was equal 18.29 Ω at 150 ppm for H2 gas at room temperature.
Experimental, numerical and theoretical investigation of the converging Richtmyer-Meshkov instability in a conventional shock tube

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Experimental, numerical and theoretical investigation of the converging Richtmyer-Meshkov instability in a conventional shock tube Marc Vandenboomgaerde, Pascal Rouzier, Denis Souffland, Christian Mariani, Laurent Biamino, Georges Jourdan, Lazhar Houas
Derivation of Theil’s Inequality Measure from Lorenz Curves

Theil’s T inequality measure is a commonly used tool for inequality measurement as it satisfies important axiomatic requirements such as the principle of transfers and decomposability. Derived from the concept of information theory, Theil’s measure represents a special case of the Generalised Entropy (GE) type measure. The main objective of this paper is to establish a relationship between Theil’s inequality measure and the Lorenz curve, thus providing a useful link between these two popular tools for studying inequality. The result established in this paper also provides a basis for studying generalised entropy measures for inequality measurement using Lorenz curves. Analytical expressions for Theil’s information theoretic measures are derived for three popular specifications of Lorenz curves. Empirical estimates of Theil’s measures for these Lorenz curves are also presented for a number of cases. JEL Classification: C10, D63, I32
Exploring Information Communication Technology and mobility linkage for smart and sustainable cities in developing countries

Mobility in developing countries like India is a challenge, Poor mobility influences both economy and environment of cities through loss in productive man hours and Gross Domestic Product (GDP) and exacerbates environmental degradation. Therefore, using a case study of an emerging Information Communication Technology (ICT) enabled city in India, this study examines the influence of the use of ICT in socio-economic activities and during travel on the travel pattern, economy and environment of the city. A survey research method and System Dynamics modelling approach were used. Findings revealed that definite linkage between ICT use and travel pattern exists. Significant increase in the use of ICT in socio-economic activities and during the travel in combination would enable reduction in trip generation and travel distance in the city. As a result, reduction in loss of man hours and loss of GDP of the city would be experienced. Furthermore, there would be appreciable decrease in vehicular emissions alleviating environmental pollution. The combined effect of the reduction in the mobility challenges, economic growth (GDP) on account of reduction loss of GDP and reduction in environmental deterioration can assist the cities to become smart and sustainable.
Daily and seasonal runoff forecasting with a water budget model

: The paper describes a computer model for forecasting daily values and seasonal volumes of streamflow which arise from snowmelt and rain. The model carries out a total water balance budget and identifies the contributions to runoff, soil moisture, and evaporation by area-elevation bands in the mountainous catchment. The model coefficients are held constant both throughout the season and from year to year, a modelling criterion which is considered essential for forecasting. Short-term forecasts are made from preceding meteorological data and weather forecasts for the next few days. forecasts of seasonal volume inflows are made by using snowpack measurements and antecedent conditions together with recorded weather patterns from previous years. can be classified as average, extreme-high, and extreme-low, so that the most probable volume together with upper and lower bounds can be forecast.
A Hybrid BERT and LightGBM based Model for Predicting Emotion GIF Categories on Twitter

The animated Graphical Interchange Format (GIF) images have been widely used on social media as an intuitive way of expression emotion. Given their expressiveness, GIFs offer a more nuanced and precise way to convey emotions. In this paper, we present our solution for the EmotionGIF 2020 challenge, the shared task of SocialNLP 2020. To recommend GIF categories for unlabeled tweets, we regarded this problem as a kind of matching tasks and proposed a learning to rank framework based on Bidirectional Encoder Representations from Transformer (BERT) and LightGBM. Our team won the 4th place with a Mean Average Precision @ 6 (MAP@6) score of 0.5394 on the round 1 leaderboard.
Fan-out and Fan-in properties of superconducting neuromorphic circuits

Neuromorphic computing has the potential to further the success of software-based artificial neural networks (ANNs) by designing hardware from a different perspective. Current research in neuromorphic hardware targets dramatic improvements to ANN performance by increasing energy efficiency, speed of operation, and even seeks to extend the utility of ANNs by natively adding functionality such as spiking operation. One promising neuromorphic hardware platform is based on superconductive electronics, which has the potential to incorporate all of these advantages at the device level in addition to offering the potential of near lossless communications both within the neuromorphic circuits as well as between disparate superconductive chips. Here we explore one of the fundamental brain-inspired architecture components, the fan-in and fan-out as realized in superconductive circuits based on Josephson junctions. From our calculations and WRSPICE simulations we find that the fan-out should be limited only by junction count and circuit size limitations, and we demonstrate results in simulation at a level of 1-to-10,000, similar to that of the human brain. We find that fan-in has more limitations, but a fan-in level on the order of a few 100-to-1 should be achievable based on current technology. We discuss our findings and the critical parameters that set the limits on fan-in and fan-out in the context of superconductive neuromorphic circuits.
Efficient lattice dynamics calculations for correlated materials with 
DFT+DMFT

Phonons are fundamentally important for many materials properties, including thermal and electronic transport, superconductivity, and structural stability. Here, we describe a method to compute phonons in correlated materials using state-of-the-art DFT+DMFT calculations. Our approach combines a robust DFT+DMFT implementation to calculate forces with the direct method for lattice dynamics using nondiagonal supercells. The use of nondiagonal instead of diagonal supercells drastically reduces the computational expense associated with the DFT+DMFT calculations. We benchmark the method for typical correlated materials (Fe, NiO, MnO, SrVO$_3$), testing for $\mathbf{q}$-point grid convergence and different computational parameters of the DFT+DMFT calculations. The efficiency of the nondiagonal supercell method allows us to access $\mathbf{q}$-point grids of up to $6\times6\times6$. In addition, we discover that for the small displacements that atoms are subject to in the lattice dynamics calculation, fixing the self-energy to that of the equilibrium configuration is in many cases an excellent approximation that further reduces the cost of the DFT+DMFT calculations. Overall, our work provides an efficient and general method for the calculation of phonons using DFT+DMFT, opening many possibilities for the study of lattice dynamics and associated phenomena in correlated materials.
Influence of demographic factors on the use of farm management tools by New Zealand farmers

Knowledge of the use of farm management tools by New Zealand farmers is important to enable the identification of areas where better uptake of tools can result in improvements in farm profitability. In October 2012, a survey was sent to all New Zealand sheep farmers asking them to indicate which farm management tools they had used in the previous 3 years. A total of 962 usable survey responses were returned which represented 7.8% of sheep farmers. The results showed that a number of the management tools were used by a small percentage of farmers thus indicating that improvements in farm productivity could be achieved by increasing their use. In addition, use of these management tools was associated with a number of demographic factors including farm size, farmer age and highest education level. This new knowledge indicates that the delivery of technology transfer should be tailored to farmers based on at least these socioeconomic factors.
User Association in Coexisting RF and TeraHertz Networks in 6G

While fifth generation (5G) networks are ready for deployment, discussions over sixth generation (6G) networks are down the road. Since high frequencies like terahertz (THz) will be central to 6G, in this paper, we propose two user association (UE) algorithms considering a coexisting RF and THz network that balances the traffic load across the network by minimizing the standard deviation of the network traffic load. Our algorithms capture the heterogeneity observed at RF and THz frequencies such as transmission bandwidth, molecular absorption, transmit powers, etc. Unlike typical unsupervised clustering algorithms (e.g. k-means, k-medoid, etc.) that search for appropriate cluster centers' locations, our algorithms identify the appropriate UEs to be associated to a certain BS such that the overall network load standard deviation (STD) can be minimized subject to users' rate constraints. In particular, our algorithms cluster UEs to every base station (BS) such that the traffic load across the network can be balanced, i.e., by minimizing the STD of network traffic load. Numerical results show that the proposed algorithms outperform the classical user association algorithms in terms of data rate, traffic load balancing, and user's fairness.
Deconfounding and Causal Regularization for Stability and External Validity

We review some recent work on removing hidden confounding and causal regularization from a unified viewpoint. We describe how simple and user-friendly techniques improve stability, replicability and distributional robustness in heterogeneous data. In this sense, we provide additional thoughts to the issue on concept drift, raised by Efron (2020), when the data generating distribution is changing.
Composite Q-learning: Multi-scale Q-function Decomposition and Separable Optimization

In the past few years, off-policy reinforcement learning methods have shown promising results in their application for robot control. Deep Q-learning, however, still suffers from poor data-efficiency and is susceptible to stochasticity in the environment or reward functions which is limiting with regard to real-world applications. We alleviate these problems by proposing two novel off-policy Temporal-Difference formulations: (1) Truncated Q-functions which represent the return for the first n steps of a target-policy rollout w.r. t. the full action-value and (2) Shifted Q-functions, acting as the farsighted return after this truncated rollout. This decomposition allows us to optimize both parts with their individual learning rates, achieving significant learning speedup. We prove that the combination of these short- and long-term predictions is a representation of the full return, leading to the Composite Q-learning algorithm. We show the efficacy of Composite Q-learning in the tabular case and compare Deep Composite Q-learning with TD3 and TD3(Delta), which we introduce as an off-policy variant of TD(Delta). Moreover, we show that Composite TD3 outperforms TD3 as well as state-of-the-art compositional Q-learning approaches significantly in terms of data-efficiency in multiple simulated robot tasks and that Composite Q-learning is robust to stochastic environments and reward functions.
Faint Stars in a Faint Galaxy. I. Ultradeep Photometry of the Boötes I Ultrafaint Dwarf Galaxy

We present an analysis of new, extremely deep images of the resolved stellar population of the Boötes I ultrafaint dwarf spheroidal galaxy. These new data were taken with the Hubble Space Telescope, using the Advanced Camera for Surveys (Wide Field Camera) and Wide Field Camera 3 (UVIS), with filters F606W and F814W (essentially V and I), as part of a program to derive the low-mass stellar initial mass function in this galaxy. We compare and contrast two approaches to obtaining the stellar photometry, namely, ePSF and DAOPHOT. We identify likely members of Boötes I based on the location of each star on the color–magnitude diagram, obtained with the DAOPHOT photometry from the ACS/WFC data. The probable members lie close to stellar isochrones that were chosen to encompass the known metallicity distribution derived from spectroscopic data of brighter radial velocity member stars and are consistent with the main-sequence turnoff. The resulting luminosity function of the Boötes I galaxy has a 50% completeness limit of 27.4 in F814W and 28.2 in F606W (Vega magnitude system), which corresponds to a limiting stellar mass of .
Reconfigurable network for quantum transport simulations

In 1981, Richard Feynman discussed the possibility of performing quantum mechanical simulations of nature. Ever since, there has been an enormous interest in using quantum mechanical systems, known as quantum simulators, to mimic specific physical systems. Hitherto, these controllable systems have been implemented on different platforms that rely on trapped atoms, superconducting circuits and photonic arrays. Unfortunately, these platforms do not seem to satisfy, at once, all desirable features of an universal simulator, namely long-lived coherence, full control of system parameters, low losses, and scalability. Here, we overcome these challenges and demonstrate robust simulation of quantum transport phenomena using a state-of-art reconfigurable electronic network. To test the robustness and precise control of our platform, we explore the ballistic propagation of a single-excitation wavefunction in an ordered lattice, and its localization due to disorder. We implement the Su-Schrieffer-Heeger model to directly observe the emergence of topologically-protected one-dimensional edge states. Furthermore, we present the realization of the so-called perfect transport protocol, a key milestone for the development of scalable quantum computing and communication. Finally, we show the first simulation of the exciton dynamics in the B800 ring of the purple bacteria LH2 complex. The high fidelity of our simulations together with the low decoherence of our device make it a robust, versatile and promising platform for the simulation of quantum transport phenomena.
Constructed emotions and superinformation: a constructor-theoretic approach

In this paper we apply the constructor-theoretic approach to the theory of constructed emotions, showing that core affect valence and knowledge can be considered as two different observables, leading to information or superinformation conditions: this depends on subject's strategy, coherently with the affect infusion model. In the second part of the article we show that additional hypotheses on the structure of information allows to study emotions in terms of the contructor-theoretic version of phase task. Quantum algorithms are presented as an example of the connection between emotions and memory tasks.
Laser cooling of transition-metal atoms

We propose the application of laser cooling to a number of transition-metal atoms, allowing numerous bosonic and fermionic atomic gases to be cooled to ultra-low temperatures. The non-zero electron orbital angular momentum of these atoms implies that strongly atom-state-dependent light-atom interactions occur even for light that is far-detuned from atomic transitions. At the same time, many transition-metal atoms have small magnetic dipole moments in their low-energy states, reducing the rate of dipolar-relaxation collisions. Altogether, these features provide compelling opportunities for future ultracold-atom research. Focusing on the case of atomic titanium, we identify the metastable $a ^5F_5$ state as supporting a $J \rightarrow J+1$ optical transition with properties similar to the D2 transition of alkali atoms, and suited for laser cooling. The high total angular momentum and electron spin of this state suppresses leakage out of the the nearly closed optical transition to a branching ratio estimated below $\sim 10^{-5}$. Following the pattern exemplified by titanium, we identify optical transitions that are suited for laser cooling of elements in the scandium group (Sc, Y, La), the titanium group (Ti, Zr), the vanadium group (V, Nb), the manganese group (Mn, Tc), and the iron group (Fe, Ru).
ALLELOPATHIC EFFECTS OF DIFFERENT PHENOLOGICAL STAGES OF CASSIA OCCIDENTALIS L. ON PARTHENIUM HYSTEROPHORUS L.

The effect of leachates of different phenological stages of Cassia occidentalis L. on Parthenium hysterophorus L. was studied. Seed germination, radicle length, plumule length and fresh and dry weight of Parthenium were recorded. Pigments and protein contents were also measured to evaluate relation between biochemical and biophysical parameters of Parthenium under stress. The leaf leachate of Cassia in different developmental stages and pod and seed leachates exhibited varying effect. The Cassia leachate obtained from the leaves of vegetative and flowering stages was more phytotoxic. It caused maximum inhibition of germination and seedling growth of Parthenium. Biochemical parameters, viz. protein and chlorophyll were also influenced. Root and shoot growth and fresh and dry weight decreased in almost all the treatments. The inhibition of germination was in order of vegetative > flowering > fruiting > fruit ripening > pod > seed while 25 and 50% concentrations of seed leachate stimulated the germination. The 100% concentration of leaf leachate from vegetative stage completely inhibited the germination while highest concentration of leaf leachate from vegetative, flowering, fruiting and fruit ripening stages caused the death of the plants. The average inhibition of parameters of seedlings in pot culture was in order of fruiting > vegetative > fruit ripening > flowering > pod > seed.
Optimal 1D Ly α forest power spectrum estimation – I. DESI-lite spectra

The 1D Ly α forest flux power spectrum P1D is sensitive to scales smaller than a typical galaxy survey, and hence ties to the intergalactic medium’s thermal state, suppression from neutrino masses, and new dark matter models. It has emerged as a competitive framework to study new physics, but also has come with various challenges and systematic errors in analysis. In this work, we revisit the optimal quadratic estimator for P1D, which is robust against the relevant problems such as pixel masking, time evolution within spectrum, and quasar continuum errors. We further improve the estimator by introducing a fiducial power spectrum, which enables us to extract more information by alleviating the discreteness of band powers. We meticulously apply our method to synthetic Dark Energy Spectroscopic Instrument (DESI) spectra and demonstrate how the estimator overcomes each challenge. We further apply an optimization scheme that approximates the Fisher matrix to three elements per row and reduces computation time by 60 per cent. We show that we can achieve per cent precision in P1D with 5-yr DESI data in the absence of systematics and provide forecasts for different spectral qualities.
Seedling Composition and Facilitative Effects of the Herbaceous Layer in a Monsoon-Affected Forest in Nanjenshan, Southern Taiwan

Tree seedlings play an important role in forest regeneration. To understand the factors that control seedling establishment, we (1) compared the composition patterns of tree seedlings and their corresponding overstories, (2) examined the relationships between seedling composition and environmental factors and (3) evaluated the interaction (competition or facilitation) between seedlings and herbaceous layer in a wind-stressed forest in Nanjenshan, southern Taiwan. In the study plot, seedling abundance of canopy, subcanopy and shrub species (with true leaves and ＜1 cm diameter at breast height) and coverage of herbaceous species (including herbaceous species, climbers and tree ferns ≤ca. 1 m in height) were investigated on three transects with a total of 180 contiguous 5×5 m quadrats. Clustering classification and ordination methods were used to reveal the tree seedling composition patterns and the relationships between seedling composition and environmental factors. Correlation coefficients were computed between herbaceous coverage and seedling abundance among herb-seedling species pairs and between tall (≥1 m high)/short (＜0.5 m high) herbs and seedlings pairs to test the herb-seedling interaction. The spatial distribution of tree seedlings presented a perfect match to the overstory vegetation pattern. There was a strong relationship among seedling composition, herbaceous composition and topographic features, especially exposure to monsoon winds. Because of the absence of strong correlations between herbaceous structure/species and seedling abundances, the strong linkage in spatial patterns between seedling and herbaceous compositions suggests that certain plant species in the study plot have similar responses to the monsoon exposure. Our results also indicated that seedlings ＜1 cm in diameter were strongly influenced by wind stress, similar to the response of the overstory composition, and that the facilitative/competitive effects of the herbaceous layer on tree seedlings were relatively weak.
OR-Gym: A Reinforcement Learning Library for Operations Research Problem

Reinforcement learning (RL) has been widely applied to game-playing and surpassed the best human-level performance in many domains, yet there are few use-cases in industrial or commercial settings. We introduce OR-Gym, an open-source library for developing reinforcement learning algorithms to address operations research problems. In this paper, we apply reinforcement learning to the knapsack, multi-dimensional bin packing, multi-echelon supply chain, and multi-period asset allocation model problems, as well as benchmark the RL solutions against MILP and heuristic models. These problems are used in logistics, finance, engineering, and are common in many business operation settings. We develop environments based on prototypical models in the literature and implement various optimization and heuristic models in order to benchmark the RL results. By re-framing a series of classic optimization problems as RL tasks, we seek to provide a new tool for the operations research community, while also opening those in the RL community to many of the problems and challenges in the OR field.