Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancement of gas storage and separation properties of microporous polymers by simple chemical modifications

Owing to their large surface area and good solvent processability, polymers of intrinsic microporosity (PIMs) have been widely investigated for gas storage and separation processes. In this article, we show how chemically modifying the polymers can fine-tune their properties for specific, targeted applications. We find that converting the archetypal microporous polymer PIM-1 into a polycarboxylate salt enhances its separation capabilities for H2/CO2 mixtures (relevant to hydrogen production), whereas appending multiple amine groups significantly improves gas separation properties for N2/CO2 mixtures (relevant to flue gas treatment). Adsorption-based separation processes have received less attention than size-sieving processes in porous polymeric materials, however they could provide a suitable alternative technology to energy-intensive separation processes such as cryogenic distillation. We also report the hydrogen storage properties of the modified polymers, which we find to depend on the chemical modification carried out. By coupling the simplicity of the proposed chemical modifications with the scalability and porous properties of PIMs, we provide a blueprint to create new multifunctional materials with adapted properties for targeted applications.
Integrated solid waste management in urban India: A mini review

Blooming of industrialization and urbanization are directly corelated with increased solid waste generation. Every year India faces a massive challenge as it deals with municipal solid waste equating to approximately 62 million tonnes including E-waste, plastic and medical waste. This has mounted and stands as a budding hazard to public and environmental health. Within India solid waste management predominantly adheres to sustainable development. The waste management rules are generally framed and handled through the Ministry of Environment and Forest (MoEF); however, compliance varies among states. In this article we are describing the present status, challenges and implementation followed in the waste reduction processes including segregation/collection, recycling, energy recovery (composting, bio methanation, and bioremediation), land filling of household and industrial waste; its impact on the environment. This review concludes with some simple and effective international trends in waste management, with a vision of improving in current Indian systems.
LiI ( Eu ) in Neutron and γ – ray Spectrometry – a High Sensitive Thermal Neutron Detector

Europium activated LiI crystal (enriched to 96% Li) has been studied in neutron and γ-ray spectrometry. Two crystals of ∅50 mm × 5 mm and ∅30 mm × 3 mm size coupled to a calibrated Photonis XP5200 photomultiplier were tested. A response of LiI(Eu) to neutrons emitted from a paraffin moderated Pu-Be source has been investigated and the thermal neutron peak has been found to be located at Gamma Equivalent Energy (GEE) of about 3.5 MeV. High sensitivity of the LiI(Eu) crystal is demonstrated as the neutron peak is observed at the rate as low as 10 counts per second. Apart from neutron spectra a light output, energy resolution and nonproportionality of the LiI(Eu) response versus γ-ray energy have been measured for the two crystals. The light yield of 1.5×10 ph/MeV (about 40% of NaI(Tl)) was obtained and the energy resolution at 662 keV (Cs-137) of 7.5±0.1% was found for the large crystal. The non-proportionality has been measured to have more proportional character as compared to NaI(Tl). Due to the high sensitivity to thermal neutrons and good proportionality against γ-ray energy, the LiI(Eu) crystal was brought under testing with few samples of fissile nuclear materials. Shielded fissile samples can be recognized via detection of neutrons followed spontaneous fission.
The 3 D shear experiment over the Natih field in Oman . Reservoir geology , data acquisition and anisotropy analysis 1

This paper describes a large-scale reservoir characterization experiment carried out in Oman in 1991 which comprised the acquisition, processing and interpretation of a 28.4 km2 3D multicomponent seismic experiment over the Natih field. The objective of the survey was to obtain information on the fracture network present in the Natih carbonates from shear-wave anisotropy. Shear-wave anisotropy in excess of 20% time splitting was encountered over a large part of the survey. The seismic results are confirmed by geological and well data but provide additional qualitative information on fracturing where this was not available before. Regions of stronger and weaker shearwave anisotropy appear to be fault-bounded. The average well flow rates (which are fracture-dominated) within such blocks correlate with the average anisotropy of the blocks. The further observation that the anisotropy is largest in the fracture gas cap of the reservoir suggests that shear waves can provide a direct hydrocarbon indicator for fractured rock.
‘Healthy’ Business Cycle

A macro-health model with endogenous survival probability dependent on health history builds in a real business cycle (RBC) framework three channels of endogenous health accumulation documented in various scientific disciplines: (1) health affects utility; (2) health affects productivity but depreciates with production; and (3) health is maintained with medical care or leisure activity. The model generates pro-cyclical health care expenditure and counter-cyclical life expectancy, in line with the data, and other aggregate properties seen in the RBC literature. All three channels work to the right direction in matching the observables, and their interactions also play a role in helping improve the model's quantitative fit.
Scale or Yield? A Present-Value Identity

We propose a loglinear present-value identity in which investment ("scale"), profitability ("yield"), and discount rates determine a firm’s market-to-book ratio. Our identity reconciles existing influential market-to-book decompositions and facilitates novel insights from three empirical applications: (1) Both investment and profitability are important contributors to the value spread and stock return news variance. (2) Any cross-sectional return predictability has a mirror image in cash-flow fundamentals, providing asset pricing theories with additional moments to match. (3) The investment spread significantly improves the predictability of time-series variation in the value premium and justifies the poor performance of value in recent years.
LGBTQ-Friendly Employee Policies and Corporate Innovation

This paper contributes to the literature by examining whether LGBTQ-friendly employee policies foster corporate innovation. Using data on U.S. firms from 2003 to 2017, we document that LGBTQ friendliness has a positive influence on innovation intensity and quality. Specifically, our results demonstrate that LGBTQ-friendly firms produce more patents, have more patent citations, and are associated with higher innovation quality as measured by patent originality, generality, and internationality. Furthermore, our empirical findings indicate that LGBTQ friendliness is positively associated with the firm-level concentration of innovative talent. Overall, our results are consistent with the view that diversity considerations and inclusive corporate policies may lead to competitive advantages for the firm.
Market Size and Research: Evidence from the Pharmaceutical Industry

Prior literature has established a link between changes in market size and pharmaceutical innovation; whether a link exists with scientific research remains an open question. If upstream research is not responsive to these changes, the kinds of scientific discoveries that flow into future drug development could be disconnected from downstream demand. We explore this question by exploiting the effects of quasi-experimental variation in market size introduced by Medicare Part D. We find no causal relationship between market size and biomedical research in the decade following the implementation of Medicare Part D. While many factors have been shown to motivate scientists to conduct research, this result suggests that changes in market size provide no such incentive. We do find, however, limited support for a response by corporate scientists conducting applied research. Implications for pharmaceutical innovation policy are discussed.
Market Timing, Farmer Expectations, and Liquidity Constraints

This paper uses data on farmers' price expectations from a survey of randomly sampled smallholder farmers in Mozambique. Survey data show that across all crops most interviewed farmers expect prices to be higher in the lean season. Yet, farmers report selling most of their output shortly after harvest when prices are lower. We find that higher expected prices and lower current sale prices are associated with increased storage for liquidity constrained farmers versus unconstrained farmers. We develop an intertemporal model of market timing in the presence of liquidity constraints that is consistent with these findings and discuss other model predictions.
Does Corporate Tax Avoidance Affect Firm Productivity?

This study examines the relation between corporate tax avoidance and firm-level productivity. Using a sample of U.S.-listed firms from 1994 to 2017, we show that, ceteris paribus, greater tax avoidance leads to increased productivity. Consistent with the ''funding gap'' of innovative investments due to debt market frictions, we further provide direct evidence that productivity is more sensitive to tax avoidance when firms exhibit high knowledge capital intensity. Supplemental analysis shows that the productivity effects of tax avoidance are stronger for financially constrained firms. To provide evidence consistent with a causal tax avoidance effect on productivity, we employ a battery of robustness tests, including a quasi-natural research design that exploits a plausibly exogenous increase in certain firms' tax planning opportunities. Our findings extend the ongoing debate on internal capital markets for productivity-enhancing investments, and have policy implications for academics and regulators investigating firm-specific responses to productivity windfalls, such as those that contribute to the aggregate productivity slowdown.
Leadership Styles of Principals Based on Setting, Zone, and Location of Public Colleges of Education in Ghana

This study investigated the leadership styles of principals in public Colleges of Education (CoEs) in Ghana based on the setting, zone and location of the colleges. The quantitative method of the cross-sectional survey design was adopted for the study. The census sampling technique was employed to obtain 38 principals for the study. The Multifactor Leadership Questionnaire (MLQ) by Bass and Avolio (2004) was adapted to collect data from the principals. Means, standard deviation, t-test and ANOVA were utilised in analysing the data. The descriptive analysis established that generally, principals of public CoEs in Ghana mostly used transformational leadership style as compared to transactional and laissez-faire leadership styles. The inferential analyses also revealed that there were no statistically significant differences in the leadership styles of the principals based on setting, zone and location of the colleges. Among the recommendations is that the Ghana Tertiary Education Commission (GTEC), formerly known as the National Council for Teacher Education (NCTE), should consider competence when selecting and appointing principals and not the setting, zone and location of public CoEs because generally, these variables did not statistically significantly determine the leadership styles adopted by the principals.
Leveraged ETFs with Market Closure and Frictions

Although leveraged ETFs are popular products for retail investors, how to hedge them poses a great challenge to financial institutions. We develop an optimal rebalancing (hedging) model for leveraged ETFs in a comprehensive setting, including overnight market closure and market frictions. The model allows for an analytical optimal rebalancing strategy. The result extends the principle of “aiming in front of target” introduced by Garleanu and Pedersen (2013) from a constant weight between current and future positions to a time-varying weight, because the rebalancing performance is monitored only at discrete time points but the rebalancing takes place continuously. Empirical findings and implications for the weekend effect, return deviations during multi-day periods, and the intraday trading volume are also presented.
Behavioral Bias, Distorted Stock Prices, and Stock Splits

We propose that to correct price distortion caused by investors’ 52-week high anchoring bias, firms can split their stocks to attract attention and induce more information production. Indeed, firms are more likely to split stocks when their prices are near 52-week highs, especially those with higher profitability and more undervaluation. Furthermore, the likelihood of stock splits increases with CEO wealth-performance sensitivity, and firms’ investment-price sensitivity increases after splits. In addition, stock splits decline during 2008-2017 when price distortion due to the anchoring bias is weaker. Our findings suggest that firms’ split decisions and the anchoring-bias-induced undervaluation are closely related.
The Demise of Branch Banking Technology, Consolidation, Bank Fragility

Banks are closing branches at an unprecedented rate. In some OECD countries, four out of five branches have been or will be closed, while in the US more than 10,000 branches representing eleven percent of its stock in 2009 have been closed by now. To understand the drivers of this fundamental transformation, we study a 15-year panel covering 36 OECD countries and 40 years of county and branch level data from the US. We find that technological progression corresponds to branch closure across countries, but plays less of a discernible local or branch-specific role. In contrast, bank fragility and especially consolidation through mergers and acquisitions robustly explain the de-branching we observe at all levels.
Auditor Use of Benchmarks to Assess Fraud Risk: The Case for Industry Data

Fraud detection is among the highest priorities for the accounting profession, standard setters, regulators, and stakeholders in the financial reporting process. During planning, auditors are required to perform preliminary analytical procedures with the objective of identifying unusual or inconsistent relationships between expectations and recorded balances. Auditors use the results of preliminary analytical procedures when assessing the risk that financial statements are materially misstated due to fraud. Via a survey of practicing auditors, we find that auditors rely heavily on prior year balances and relations within the client’s financial data (e.g., comparing revenue growth to growth in accounts receivable) as benchmarks when developing expectations during planning. Auditing standards describe additional benchmarks that are less susceptible to management manipulation, but our survey results indicate that auditors are less apt to employ these benchmarks on their engagements. Our empirical analyses reveal that benchmarks derived from industry data, nonfinancial measures, and cash flows outperform both prior year balances and relations within the client’s financial data when assessing fraud risk. Of all the benchmarks suggested by auditing standards, we observe that the difference between a company’s revenue growth and the revenue growth of its industry has historically been the best indicator of fraud. When a firm reports revenue growth that substantially exceeds that of its industry, it may be too good to be true and auditors should consider increasing their fraud risk assessments and proceeding with skepticism.
Correcting Momentum in Temporal Difference Learning

A common optimization tool used in deep reinforcement learning is momentum, which consists in accumulating and discounting past gradients, reapplying them at each iteration. We argue that, unlike in supervised learning, momentum in Temporal Difference (TD) learning accumulates gradients that become doubly stale: not only does the gradient of the loss change due to parameter updates, the loss itself changes due to bootstrapping. We first show that this phenomenon exists, and then propose a first-order correction term to momentum. We show that this correction term improves sample efficiency in policy evaluation by correcting target value drift. An important insight of this work is that deep RL methods are not always best served by directly importing techniques from the supervised setting.
THE EISENSTEIN IDEAL OF WEIGHT k AND RANKS OF HECKE ALGEBRAS

<jats:p>Let <jats:italic>p</jats:italic> and <jats:inline-formula> <jats:alternatives> <jats:inline-graphic xmlns:xlink="http://www.w3. org/1999/xlink" mime-subtype="png" xlink:href="S1474748023000129_inline1. png" /> <jats:tex-math> $\ell $ </jats:tex-math> </jats:alternatives> </jats:inline-formula> be primes such that <jats:inline-formula> <jats:alternatives> <jats:inline-graphic xmlns:xlink="http://www.w3. org/1999/xlink" mime-subtype="png" xlink:href="S1474748023000129_inline2. png" /> <jats:tex-math> $p> 3$ </jats:tex-math> </jats:alternatives> </jats:inline-formula> and <jats:inline-formula> <jats:alternatives> <jats:inline-graphic xmlns:xlink="http://www.w3. org/1999/xlink" mime-subtype="png" xlink:href="S1474748023000129_inline3. png" /> <jats:tex-math> $p \mid \ell -1$ </jats:tex-math> </jats:alternatives> </jats:inline-formula> and <jats:italic>k</jats:italic> be an even integer. We use deformation theory of pseudo-representations to study the completion of the Hecke algebra acting on the space of cuspidal modular forms of weight <jats:italic>k</jats:italic> and level <jats:inline-formula> <jats:alternatives> <jats:inline-graphic xmlns:xlink="http://www.w3. org/1999/xlink" mime-subtype="png" xlink:href="S1474748023000129_inline4. png" /> <jats:tex-math> $\Gamma _0(\ell )$ </jats:tex-math> </jats:alternatives> </jats:inline-formula> at the maximal Eisenstein ideal containing <jats:italic>p</jats:italic>. We give a necessary and sufficient condition for the <jats:inline-formula> <jats:alternatives> <jats:inline-graphic xmlns:xlink="http://www.w3. org/1999/xlink" mime-subtype="png" xlink:href="S1474748023000129_inline5. png" /> <jats:tex-math> $\mathbb {Z}_p$ </jats:tex-math> </jats:alternatives> </jats:inline-formula>-rank of this Hecke algebra to be greater than <jats:inline-formula> <jats:alternatives> <jats:inline-graphic xmlns:xlink="http://www.w3. org/1999/xlink" mime-subtype="png" xlink:href="S1474748023000129_inline6. png" /> <jats:tex-math> $1$ </jats:tex-math> </jats:alternatives> </jats:inline-formula> in terms of vanishing of the cup products of certain global Galois cohomology classes. We also recover some of the results proven by Wake and Wang-Erickson for <jats:inline-formula> <jats:alternatives> <jats:inline-graphic xmlns:xlink="http://www.w3. org/1999/xlink" mime-subtype="png" xlink:href="S1474748023000129_inline7. png" /> <jats:tex-math> $k=2$ </jats:tex-math> </jats:alternatives> </jats:inline-formula> using our methods. In addition, we prove some <jats:inline-formula> <jats:alternatives> <jats:inline-graphic xmlns:xlink="http://www.w3. org/1999/xlink" mime-subtype="png" xlink:href="S1474748023000129_inline8. png" /> <jats:tex-math> $R=\mathbb {T}$ </jats:tex-math> </jats:alternatives> </jats:inline-formula> theorems under certain hypotheses. </jats:p>
LaplaceNet: A Hybrid Energy-Neural Model for Deep Semi-Supervised Classification

—Semi-supervised learning has received a lot of recent attention as it alleviates the need for large amounts of labelled data which can often be expensive, requires expert knowledge and be time consuming to collect. Recent developments in deep semi-supervised classiﬁcation have reached unprecedented performance and the gap between supervised and semi-supervised learning is ever-decreasing. This improvement in performance has been based on the inclusion of numerous technical tricks, strong augmentation techniques and costly optimisation schemes with multi-term loss functions. We propose a new framework, LaplaceNet, for deep semi-supervised classiﬁcation that has a greatly reduced model complexity. We utilise a hybrid energy-neural network where graph based pseudo-labels, generated by minimising the graphical Laplacian, are used to iteratively improve a neural-network backbone. Our model outperforms state-of-the-art methods for deep semi-supervised classiﬁcation, over several benchmark datasets. Furthermore, we consider the application of strong-augmentations to neural networks theoretically and justify the use of a multi-sampling approach for semi-supervised learning. We demonstrate, through rigorous experimentation, that a multi-sampling augmentation approach improves generalisation and reduces the sensitivity of the network to augmentation. Code available at https://github.com/psellcam/ LaplaceNet.
Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluation

Proxy causal learning (PCL) is a method for estimating the causal effect of treatments on outcomes in the presence of unobserved confounding, using proxies (structured side information) for the confounder. This is achieved via two-stage regression: in the first stage, we model relations among the treatment and proxies; in the second stage, we use this model to learn the effect of treatment on the outcome, given the context provided by the proxies. PCL guarantees recovery of the true causal effect, subject to identifiability conditions. We propose a novel method for PCL, the deep feature proxy variable method (DFPV), to address the case where the proxies, treatments, and outcomes are high-dimensional and have nonlinear complex relationships, as represented by deep neural network features. We show that DFPV outperforms recent state-of-the-art PCL methods on challenging synthetic benchmarks, including settings involving high dimensional image data. Furthermore, we show that PCL can be applied to off-policy evaluation for the confounded bandit problem, in which DFPV also exhibits competitive performance.
Towards a Theoretical Framework of Out-of-Distribution Generalization

Generalization to out-of-distribution (OOD) data is one of the central problems in modern machine learning. Recently, there is a surge of attempts to propose algorithms that mainly build upon the idea of extracting invariant features. Although intuitively reasonable, theoretical understanding of what kind of invariance can guarantee OOD generalization is still limited, and generalization to arbitrary out-of-distribution is clearly impossible. In this work, we take the first step towards rigorous and quantitative definitions of 1) what is OOD; and 2) what does it mean by saying an OOD problem is learnable. We also introduce a new concept of expansion function, which characterizes to what extent the variance is amplified in the test domains over the training domains, and therefore give a quantitative meaning of invariant features. Based on these, we prove OOD generalization error bounds. It turns out that OOD generalization largely depends on the expansion function. As recently pointed out by Gulrajani and Lopez-Paz (2020), any OOD learning algorithm without a model selection module is incomplete. Our theory naturally induces a model selection criterion. Extensive experiments on benchmark OOD datasets demonstrate that our model selection criterion has a significant advantage over baselines.