Miracle-dz/UniFAR-FTUs huggingface dataset:
Dispersed Nickel Nanoparticles on Flower-like Layered Nickel-Cobalt Double Hydroxides for Non-enzymic Amperometric Sensing of Glucose

The article describes the preparation of supported nickel nanoparticles (NiNPs) by partial reduction of Ni,Co-layered double hydroxide (NiCo-LDH). The nanocomposites were characterized by X-ray diffraction and their morphology and composition were characterized by scanning electron microscopy and transmission electron microscopy. The electrochemical properties of the nanocomposite were explored by cyclic voltammetry and amperometry, which revealed significant electrocatalytic behavior towards the oxidation of glucose. The resulted non-enzymatic glucose sensor has a linear response to glucose in the 5.0 μM to 14.8 mM concentration range, a low detection limit of 1.6 μM, high sensitivity, and excellent selectivity.
Study on the working electrolyte of aluminum electrolytic capacitors for energy saving lamp

The simplex method was adopted to study the working electrolyte for aluminum capacitor,four solutes were used as the experimental factors.The results show that when the ethylene glycol is 93%(mass fraction,same below),ammonium sebacate is 4.14%,ammonium dodecanedioate is 0.9%,nitro-benzoic acid is 0.16%,inferior phosphite is 0.2% and the polymer additive is 1.6%,the comprehensive performance of aluminum capacitor(400 V,φ10 mm×20 mm) is best,its capacitance is 2 247 μF,tanδ is 3.64×10-2,leakage current is 6.7 μA,and the conductivity of electrolyte is 2 224×10-6 S? cm-1,the sparking voltage is 428 V.
Iodide Chemistry in Dye-Sensitized Solar Cells: Making and Breaking I−I Bonds for Solar Energy Conversion

The photo-oxidation of iodide (I−) results in the formation of I−I bonds relevant to solar energy conversion. The making (and breaking) of I−I bonds is specifically important to the operation of high-efficiency dye-sensitized solar cells. In this Perspective, the redox chemistry of iodide in aqueous solution is briefly reviewed, followed by recent photoinduced studies in nonaqueous solution. Analogous to thermal electron-transfer studies, two mechanisms have been identified for photodriven I−I bond formation in solution. With regard to breaking I−I bonds, the photodriven cleavage of I−I bonds has been quantified by the reduction of diiodide (I2•−) and triiodide (I3−). Studies at the solution-semiconductor interface present in dye-sensitized solar cells have also revealed that I−I bonds are formed, and I2•− is a product of iodide oxidation. Rapid disproportionation of I2•− to yield I3− and I− products that are not easily reduced by electrons injected into TiO2 is proposed to be key to the success of the I−.
On molecular topological properties of dendrimers

Topological indices are numerical parameters of a graph that characterize its topology and are usually graph invariant. In a QSAR/QSPR study, physicochemical properties and topological indices such as the Randic, atom–bond connectivity (ABC), and geometric–arithmetic (GA) indices are used to predict the bioactivity of different chemical compounds. Graph theory has found a considerable use in this area of research. In this paper, we study the degree-based molecular topological indices such as ABC4 and GA5 for certain families of dendrimers. We derive the analytical closed formulae for these classes of dendrimers.
Speech Enhancement Combined with Post processing Technique in Non-Stationary Noise Environments

A two stage novel speech enhancement algorithm is presented in this paper. First stage is designed such that it can substantially improve the signal to residual spectrum ratio by combining statistical estimators of the spectral magnitude of the speech and noise. By expressing the signal to residual spectrum ratio as a function of the estimators gain function. We derive a hybrid strategy that can improve the signal to residual spectrum ratio when the apriori and the posteriori SNR are detected to the lower than 0dB. The enhanced signals still suffer from undesirable speech distortion due to harmonic distortion. Some harmonics are considered as noise only components and are suppressed. To overcome harmonic distortion introduced in enhanced speech, in the proposed method the suppressed harmonics are regenerated. Objective and subjective tests were carried out to demonstrate improvement in the perceptual quality of speeches by the proposed technique.
Hate Crime Increases with Minoritized Group Rank

People are on the move in unprecedented numbers across the globe. How does migration affect local intergroup dynamics? In contrast to accounts that emphasize stereotypical features of groups as determinants of their treatment, we propose the group reference dependence hypothesis: violence and negative attitudes toward each minoritized group will depend on the number and size of other minoritized groups in a community. Specifically, as groups increase or decrease in rank in terms of their size (e.g., to largest minority within a community), discriminatory behavior and attitudes toward them should change accordingly. We test this hypothesis across U.S. counties between 1990 and 2010. Consistent with this prediction we find that, as Black, Hispanic/Latinx, Asian, and Arab populations increase in rank relative to one another, they become more likely to be targeted with hate crimes and more negative attitudes. The rank effect holds above and beyond group size/proportion, growth rate, and a number of other alternative explanations. This framework makes novel predictions about how demographic shifts may affect coalitional structures in the coming years and helps explain previous findings in the literature. More broadly, our results complement the existing literature by indicating that attitudes and behaviors toward social categories are not fixed or driven only by features associated with those groups, such as stereotypes.
The Preferential Treatment of Green Bonds

We study the preferential treatment of green bonds in the Central Bank collateral framework as an environmental policy instrument within a DSGE model with environmental and financial frictions. Green and conventional entrepreneurs issue bonds to banks that use them as collateral. The associated collateral premium induce entrepreneurs to increase bond issuance, investment, leverage, and default risk. Collateral policy solves a trade-off between increasing collateral supply, adverse effects on entrepreneur risk-taking, and subsidizing green investment. Due to these adverse side effects, optimal collateral policy is characterized by modest preferential treatment, thereby increasing the green bond share and, to a smaller extent, the green investment share, which in turn reduces pollution. The limited response of green investment is directly related to higher risk-taking of green entrepreneurs. Furthermore, we show that preferential treatment is an imperfect substitute of Pigouvian taxation on pollution: only if the optimal tax can not be implemented, optimal collateral policy features preferential treatment of green bonds.
How Can Innovation Screening Be Improved? A Machine Learning Analysis With Economic Consequences for Firm Performance

Using USPTO patent application data, I apply a machine-learning algorithm to analyze how the current patent examination process in the U.S. can be improved in terms of granting higher quality patents. Surprisingly and unsurprisingly, combining human examiners' expertise with machine learning predictions would yield a 15.5% gain in patent generality and a 35.6% gain in patent citations for granted patents. My economic analysis in the second part of the paper shows that getting a patent on which humans and machines disagree is a winner's curse. First, these patents are more likely to expire early. Second, patents granted by examiners with higher false acceptance rates cause lower announcement returns around patent grant news for public firms. Third, public firms who get these patents are more likely to get sued in patent litigation cases. Consequently, these firms cut R&D investments and have worse operating performance. Fourth, private firms whose patents are granted by such examiners are less likely to exit successfully by an IPO or an M&A. Overall, this study suggests that the social and economic cost of the current patent screening system is large and could potentially be mitigated with the help of a machine learning algorithm.
N-Doped Hollow Carbon Tubes Derived N-HCTs@NiCo2O4 as Bifunctional Oxygen Electrocatalysts for Rechargeable Zinc-Air Batteries

It is paramount important to design and explore excellent bifunctional oxygen electrocatalysts to conquer or weaken the shortcomings encountered by Zinc-air batteries at present. Herein, a novel N-doped hollow carbon tubes modified by uniform NiCo2O4 nanocrystals (N-HCTs@NiCo2O4), has been developed as an extremely efficient bifunctional catalyst. The hollow structure of N-HCTs possesses a large specific surface area of 610.60 cm g-1 which provides abundant active N-dopant sites for the oxygen reduction reaction, whereas the NiCo2O4 nanocrystals serve as the positive phase for the oxygen evolution reaction. It is found that the efficient bifunctional activity is obtained by establishing a coupling interface between N-HCTs and NiCo2O4. The N-HCTs@NiCo2O4 composite has a half-wave potential of 0.81 V toward ORR and the overpotential of 330 mV at a current density of 10 mA cm-2 toward OER as compared with noble metal catalysts. The N-HCTs@NiCo2O4 as the cathode catalyst for Zinc-air batteries exhibits superior peak power density (150 mW cm-2), specific capacity up to 779 mAh g-1, and good stability even after 100 h of cyclic charge/discharge cycles N-HCTs@NiCo2O4 has good prospects for practical applications of non-precious metal catalysts in the cathode of Zinc-air batteries.
Development-Aid Supply Chains for Economic Development and Post- Disaster Recovery

This paper seeks to conceptualize supply chains that use funding from large donors or governments for long-term recovery following a disaster, or more generally, for economic development in a region. We call these development-aid supply chains (DASC) distinct from commercial or humanitarian supply chains. With little available formally on DASCs in the literature, we carried out a field study across five solar-lantern supply chains in Haiti set up for recovery following the massive 2010 earthquake. Stakeholder Resource-Based View allowed us to use stakeholder theory, utility theory, and the resource- based view in analyzing how these supply chains work. We observed how donor cash in these supply chains brings together global original equipment manufacturers; national-level distributors; impact investors; microfinance institutions; retailers; and microentrepreneurs. Many of these entities are social enterprises that bridge development-minded donors with commercially oriented retailers and microentrepreneurs. The result of these bridging efforts is the flow of goods, cash, and social impact data. Our conceptual model flags the problem that donor funding, while crucial for reducing deprivation in the short term, may increase the dependence on aid rather than reduce it.
Public Accountability and University Characteristics in South Africa

Purpose: We examine public accountability and university characteristics (governance, age, liquidity, audit committee quality and size) in South Africa following a period of rapid changes in regulation and operating environment in the higher education sector. We regard disclosures in annual reports as mechanisms for public accountability. <br><br>Design/methodology: We apply public accountability models similar to the one developed by Coy and Dixons to examine the relationship between public accountability and university characteristics. Our study applies disclosure theories and tests the hypothesis that public accountability is related to university characteristics. <br><br>Findings: There is large variability in the level of public accountability by universities averaging 41.3%. University governance levels average 89.4% and explains 36.9% of the disclosures. We find improved compliance over time. The results show low disclosures on self-assessments, board evaluations, futuristic information, greening issues, employer satisfaction, occupational health and safety, employment equity, destination of students, admission qualifications and budgetary controls. <br><br>Originality/value: This is the first study that examines the relationship between public accountability and university characteristics in South Africa. It adds to the literature on public accountability and extends existing studies by including composite governance index and university characteristics as possible explanatory variables for public accountability in geographical areas which has not been examined before. Our findings provide support to the view that disclosures are influenced by university governance and can be explained by a range of theories including public accountability, legitimacy, stakeholder’s political cost and resource dependence theory. As policy implications, we provide evidence to university management and regulators on the importance of good governance
Input Augmentation Improves Constrained Beam Search for Neural Machine Translation: NTT at WAT 2021

This paper describes our systems that were submitted to the restricted translation task at WAT 2021. In this task, the systems are required to output translated sentences that contain all given word constraints. Our system combined input augmentation and constrained beam search algorithms. Through experiments, we found that this combination significantly improves translation accuracy and can save inference time while containing all the constraints in the output. For both En->Ja and Ja->En, our systems obtained the best evaluation performances in automatic and human evaluation.
Empirical observations on the effects of data transformation in machine learning classification of geological domains

In the literature, a large body of work advocates the use of log-ratio transformation for multivariate statistical analysis of compositional data. In contrast, few studies have looked at how data transformation changes the efficacy of machine learning classifiers within geoscience. This letter presents experiment results and empirical observations to further explore this issue. The objective is to study the effects of data transformation on geozone classification performance when machine learning (ML) classifiers/estimators are trained using geochemical data. The training input consists of exploration hole assay samples obtained from a Pilbara iron-ore deposit in Western Australia, and geozone labels assigned based on stratigraphic units, the absence or presence and type of mineralization. The ML techniques considered are multinomial logistic regression, Gaussian na\"{i}ve Bayes, kNN, linear support vector classifier, RBF-SVM, gradient boosting and extreme GB, random forest (RF) and multi-layer perceptron (MLP). The transformations examined include isometric log-ratio (ILR), center log-ratio (CLR) coupled with principal component analysis (PCA) or independent component analysis (ICA), and a manifold learning approach based on local linear embedding (LLE). The results reveal that different ML classifiers exhibit varying sensitivity to these transformations, with some clearly more advantageous or deleterious than others. Overall, the best performing candidate is ILR which is unsurprising considering the compositional nature of the data. The performance of pairwise log-ratio (PWLR) transformation is better than ILR for ensemble and tree-based learners such as boosting and RF; but worse for MLP, SVM and other classifiers.
Very Compact Clusters with Structural Regularization via Similarity and Connectivity

Clustering algorithms have significantly improved along with Deep Neural Networks which provide effective representation of data. Existing methods are built upon deep autoencoder and self-training process that leverages the distribution of cluster assignments of samples. However, as the fundamental objective of the autoencoder is focused on efficient data reconstruction, the learnt space may be sub-optimal for clustering. Moreover, it requires highly effective codes (i.e., representation) of data, otherwise the initial cluster centers often cause stability issues during self-training. Many state-of-the-art clustering algorithms use convolution operation to extract efficient codes but their applications are limited to image data. In this regard, we propose an end-to-end deep clustering algorithm, i.e., Very Compact Clusters (VCC), for the general datasets, which takes advantage of distributions of local relationships of samples near the boundary of clusters, so that they can be properly separated and pulled to cluster centers to form compact clusters. Experimental results on various datasets illustrate that our proposed approach achieves better clustering performance over most of the state-of-the-art clustering methods, and the data embeddings learned by VCC without convolution for image data are even comparable with specialized convolutional methods.
Model-independent constraints on superfluidity from the cooling neutron star in Cassiopeia A

We present a new model-independent (applicable for a broad range of equations of state) analysis of the neutrino emissivity due to triplet neutron pairing in neutron star cores. We find that the integrated neutrino luminosity of the Cooper Pair Formation (CPF) process can be written as a product of two factors. The first factor depends on the neutron star mass, radius and maximal critical temperature of neutron pairing in the core, TCnmax, but not on the particular superfluidity model; it can be expressed by an analytical formula valid for many nucleon equations of state. The second factor depends on the shape of the critical temperature profile within the star, the ratio of the temperature T to TCnmax, but not on the maximal critical temperature itself. While this second factor depends on the superfluidity model, it obeys several model-independent constraints. This property allows one to analyse the thermal evolution of neutron stars with superfluid cores without relying on a specific model of their interiors. The constructed expressions allow us to perform a self-consistent analysis of spectral data and neutron star cooling theory. We apply these findings to the cooling neutron star in the Cassiopeia A supernova remnant using 14 sets of observations taken over 19 years. We constrain TCnmax to the range of (5− 10) × 108 K. This value depends weakly on the equation of state and superfluidity model, and will not change much if cooling is slower than the current data suggest. We also constrain the overall efficiency of the CPF neutrino luminosity.
Adaptive Streaming Perception using Deep Reinforcement Learning

Executing computer vision models on streaming visual data, or streaming perception is an emerging problem, with applications in self-driving, embodied agents, and augmented/virtual reality. The development of such systems is largely governed by the accuracy and latency of the processing pipeline. While past work has proposed numerous approximate execution frameworks, their decision functions solely focus on optimizing latency, accuracy, or energy, etc. This results in sub-optimum decisions, affecting the overall system performance. We argue that the streaming perception systems should holistically maximize the overall system performance (i.e., considering both accuracy and latency simultaneously). To this end, we describe a new approach based on deep reinforcement learning to learn these tradeoffs at runtime for streaming perception. This tradeoff optimization is formulated as a novel deep contextual bandit problem and we design a new reward function that holistically integrates latency and accuracy into a single metric. We show that our agent can learn a competitive policy across multiple decision dimensions, which outperforms state-of-the-art policies on public datasets.
What Does Rotation Prediction Tell Us about Classifier Accuracy under Varying Testing Environments?

Understanding classifier decision under novel environments is central to the community, and a common practice is evaluating it on labeled test sets. However, in real-world testing, image annotations are difficult and expensive to obtain, especially when the test environment is changing. A natural question then arises: given a trained classifier, can we evaluate its accuracy on varying unlabeled test sets? In this work, we train semantic classification and rotation prediction in a multi-task way. On a series of datasets, we report an interesting finding, i.e., the semantic classification accuracy exhibits a strong linear relationship with the accuracy of the rotation prediction task (Pearson's Correlation r>0.88). This finding allows us to utilize linear regression to estimate classifier performance from the accuracy of rotation prediction which can be obtained on the test set through the freely generated rotation labels.
Mode recovery in neural autoregressive sequence modeling

Despite its wide use, recent studies have revealed unexpected and undesirable properties of neural autoregressive sequence models trained with maximum likelihood, such as an unreasonably high affinity to short sequences after training and to infinitely long sequences at decoding time. We propose to study these phenomena by investigating how the modes, or local maxima, of a distribution are maintained throughout the full learning chain of the ground-truth, empirical, learned and decoding-induced distributions, via the newly proposed mode recovery cost. We design a tractable testbed where we build three types of ground-truth distributions: (1) an LSTM based structured distribution, (2) an unstructured distribution where probability of a sequence does not depend on its content, and (3) a product of these two which we call a semi-structured distribution. Our study reveals both expected and unexpected findings. First, starting with data collection, mode recovery cost strongly relies on the ground-truth distribution and is most costly with the semi-structured distribution. Second, after learning, mode recovery cost from the ground-truth distribution may increase or decrease compared to data collection, with the largest cost degradation occurring with the semi-structured ground-truth distribution. Finally, the ability of the decoding-induced distribution to recover modes from the learned distribution is highly impacted by the choices made earlier in the learning chain. We conclude that future research must consider the entire learning chain in order to fully understand the potentials and perils and to further improve neural autoregressive sequence models.
Interferometric Graph Transform for Community Labeling

We present a new approach for learning unsupervised node representations in community graphs. We significantly extend the Interferometric Graph Transform (IGT) to community labeling: this non-linear operator iteratively extracts features that take advantage of the graph topology through demodulation operations. An unsupervised feature extraction step cascades modulus non-linearity with linear operators that aim at building relevant invariants for community labeling. Via a simplified model, we show that the IGT concentrates around the E-IGT: those two representations are related through some ergodicity properties. Experiments on community labeling tasks show that this unsupervised representation achieves performances at the level of the state of the art on the standard and challenging datasets Cora, Citeseer, Pubmed and WikiCS.