Miracle-dz/UniFAR-FTUs huggingface dataset:
Can a Machine Correct Option Pricing Models?

Abstract We introduce a novel two-step approach to predict implied volatility surfaces. Given any fitted parametric option pricing model, we train a feedforward neural network on the model-implied pricing errors to correct for mispricing and boost performance. Using a large dataset of S&P 500 options, we test our nonparametric correction on several parametric models ranging from ad-hoc Black–Scholes to structural stochastic volatility models and demonstrate the boosted performance for each model. Out-of-sample prediction exercises in the cross-section and in the option panel show that machine-corrected models always outperform their respective original ones, often by a large extent. Our method is relatively indiscriminate, bringing pricing errors down to a similar magnitude regardless of the misspecification of the original parametric model. Even so, correcting models that are less misspecified usually leads to additional improvements in performance and also outperforms a neural network fitted directly to the implied volatility surface.
Capitalization of Local Government Grants on Land Values: Evidence from Tokyo Metropolitan Area, Japan

This study investigates the capitalization effect of intergovernmental fiscal transfers from central to local government into house prices. The extent to which unconditional fiscal transfers to municipalities in the Tokyo metropolitan area are attributed to land prices will be examined using the event of a drop in the size amount of fiscal transfers following the fiscal reform in the early 2000s. The result shows that the drop in the transfer has decreased the land prices in the municipalities. Furthermore, a reduction of one unit in the per capita grant decreased the value of housing area per capita by one or more units, even if the real discount rate was estimated to be as low as 2%. We can conclude that the fiscal transfer reform focused on in this paper had a more negative impact on the benefits of residing in a municipality in the area than the amount of the reduction.
Organized Crime, Corruption and the Challenges of Economic Growth in the Economic Community of West African States

Purpose This study aims to examine organized crime, corruption and their challenges to the economic growth of the Economic Community of West African States (ECOWAS). Organized crime and corruption can have a grossly negative impact on the economic growth process of every system, but the extent of damage they have needs to be measured to determine the policy implications to the region. Design/methodology/approach The study uses the fixed effects model and the feasible generalized least square known also as the random-effects model with the pooled ordinary least square as a control on the defined objectives using secondary time series data that covers the period 2000 to 2019 for 11 countries in the ECOWAS region. The panel nature of the data set provides a rich degree of freedom with 220 (20 years for 11 countries – 20*11 = 220) observations. Findings Results show among others that organized crime does not have a significant impact on economic growth, whereas corruption significantly reduces economic growth. Research limitations/implications Unavailability of data affected the scope of the study in the areas of a number of countries selected and years chosen for the study. The implication is that it would have given the study better degrees of freedom. Practical implications The practical implication of this study has exposed corruption hinders economic growth in West Africa. Social implications The social implication of the study is that it has exposed that though the organized crime was a bad phenomenon it does not retard economic growth significantly in West Africa. Originality/value This study is original and of immense importance as its the first study to focus on organized crime and corruption’s influence on economic growth among West African states.
Technical Analysis in the Stock Market: A Review

Technical analysis is the study for forecasting future asset prices with past data. In this survey, we review and extend studies on not only the time-series predictive power of technical indicators on the aggregated stock market and various portfolios, but also the cross-sectional predictability with various firm characteristics. While we focus on reviewing major academic research on using traditional technical indicators, but also discuss briefly recent studies that apply machine learning approaches, such as Lasso, neural network and genetic programming, to forecast returns both in the time-series and on the cross-section.
Stakeholder Engagement and Firms’ Innovation: Evidence from LGBT-Supportive Policies

There is a paucity of research on the effects corporate policies have on fostering innovative activities in firms, while research and development (R&amp;D) investments offer a source of innovations in firms. Using governance data from Kinder, Lyndenberg, and Domini (KLD) Research and Analytics, Inc., R&amp;D and financial data from Compustat, and market data from CRSP during the period from 1996 through 2011 this paper investigates whether Lesbian, Gay, Bisexual and Transgender (LGBT) -supportive corporate policies promote innovative activities in US firms. To the extent that LGBT–friendly policies allow inclusive working environments and therefore attract and retain talents, corporate can engage more in innovative activities. The findings show that firms with LGBT–supportive policies nurture employees’ commitment to the firms and engage more in innovative activities. Heckman selection model, propensity score matching (PSM) and difference-in-difference results all confirm the main findings from instrumental variable (IV) analyses.
Emergence of a new SARS-CoV-2 variant from GR clade with a novel S glycoprotein mutation V1230L in West Bengal, India

India is currently facing the devastating second wave of COVID-19 pandemic resulting in approximately 4000 deaths per day. To control this pandemic continuous mutational surveillance and genomic epidemiology of circulating strains is very important. In this study, we performed mutational analysis of the protein coding genes of SARS-CoV-2 strains (n=2000) collected during January 2021 to March 2021. Our data revealed the emergence of a new variant in West Bengal, India, which is characterized by the presence of 11 co-existing mutations including D614G, P681H and V1230L in S-glycoprotein. This new variant was identified in 70 out of 412 sequences submitted from West Bengal. Interestingly, among these 70 sequences, 16 sequences also harbored E484K in the S glycoprotein. Phylogenetic analysis revealed strains of this new variant emerged from GR clade (B.1.1) and formed a new cluster. We propose to name this variant as GRL or lineage B.1. 1/S:V1230L due to the presence of V1230L in S glycoprotein along with GR clade specific mutations. Co-occurrence of P681H, previously observed in UK variant, and E484K, previously observed in South African variant and California variant, demonstrates the convergent evolution of SARS-CoV-2 mutation. V1230L, present within the transmembrane domain of S2 subunit of S glycoprotein, has not yet been reported from any country. Substitution of valine with more hydrophobic amino acid leucine at position 1230 of the transmembrane domain, having role in S protein binding to the viral envelope, could strengthen the interaction of S protein with the viral envelope and also increase the deposition of S protein to the viral envelope, and thus positively regulate virus infection. P618H and E484K mutation have already been demonstrated in favor of increased infectivity and immune invasion respectively. Therefore, the new variant having G614G, P618H, P1230L and E484K is expected to have better infectivity, transmissibility and immune invasion characteristics, which may pose additional threat along with B.1. 617 in the ongoing COVID-19 pandemic in India.
Segregated Patterns of Racial and Socioeconomic Inclusivity of Access to Hospital Care Among the Medicare Population

Background: A hospital-level metric of inclusion is defined, which measures disparities in hospitalizations by race and socioeconomic status. This measure can help frame research agendas around inequities in health care and guide policy. The primary objective was to quantify, for each US hospital in our sample, the difference in the patient population and the surrounding area measured by income level, education level, and racial groups. The secondary objective was to assess the associations of these inclusivity metrics with hospital characteristics. Methods: Cross sectional observational study using MEDPAR claims data for calendar year 2018 involving inpatient admissions to non-specialty, non-federal hospitals of Medicare fee-for-service beneficiaries over the age of 65 years. A community area radius was calculated for each hospital based on their patient counts from surrounding zip codes. The main outcome was a difference metric between the patient demographics and the demographics of the zip codes in the community area. Hospital characteristics associated with the lowest and highest scores were reported using quantile regressions. Results: There were 4,230,080 patients included in the sample at 3,548 hospitals. The median community area (CA) radius was 24.7 miles, contributing 90.1% of a hospital's total number of patients. Estimated median incomes for hospital patients' zip codes ranged from $26,805 less than their CA zip codes to $29,041 more. Education differences varied by approximately one level of attainment in each direction. The widest percentage differences in racialized groups between hospital patient zip codes and CA codes were for Black and African American populations, which varied from 39.2% greater than the CA to 30.6% less than the CA. Metro areas, larger hospitals and major teaching hospitals were more likely to be found in the highest inclusivity scores and also in the lowest. Some Hospital Referral Regions had wide differences in inclusivity scores, indicating patterns of localized segregation in hospital utilization among Medicare beneficiaries. Safety net hospitals consistently were more inclusive. Conclusions and Relevance: Race and class inclusivity of US hospitals varies widely, with larger metro areas and teaching hospitals showing the widest patterns of segregation. Safety net hospitals overperform in both urban and rural settings, providing an essential social service of socioeconomic inclusivity.
Brightness Fluctuation Spectra of Sun-like Stars. I. The Mid-frequency Continuum

We analyze space-based time-series photometry of Sun-like stars, mostly in the Pleiades, but also field stars and the Sun itself. We focus on timescales between roughly 1 hr and 1 day. In the corresponding frequency band these stars display brightness fluctuations with a decreasing power-law continuous spectrum. K2 and Kepler observations show that the rms flicker due to this mid-frequency continuum (MFC) can reach almost 1%, approaching the modulation amplitude from active regions. The MFC amplitude varies by a factor up to 40 among Pleiades members with similar T eff, depending mainly on the stellar Rossby number Ro. For Ro ≤ 0.04, the mean amplitude is roughly constant at about 0.4%; at larger Ro the amplitude decreases rapidly, shrinking by about two orders of magnitude for Ro ≃ 1. Among stars, the MFC amplitude correlates poorly with that of modulation from rotating active regions. Among field stars observed for 3 yr by Kepler, the quarterly average modulation amplitudes from active regions are much more time variable than the quarterly MFC amplitudes. We argue that the process causing the MFC is largely magnetic in nature and that its power-law spectrum comes from magnetic processes distinct from the star’s global dynamo, with shorter timescales. By analogy with solar phenomena, we hypothesize that the MFC arises from a (sometimes energetic) variant of the solar magnetic network, perhaps combined with rotation-related changes in the morphology of supergranules.
Climate Action During COVID-19 Recovery and Beyond: A Twitter Text Mining Study

The Coronavirus pandemic created a global crisis that prompted immediate large-scale action, including economic shutdowns and mobility restrictions. These actions have had devastating effects on the economy, but some positive effects on the environment. As the world recovers from the pandemic, we ask the following question: What is the public attitude towards climate action during COVID-19 recovery and beyond? We answer this question by analyzing discussions on the Twitter social media platform. We find that most discussions support climate action and point out lessons learned during pandemic response that can shape future climate policy, although skeptics continue to have a presence. Additionally, concerns arise in the context of climate action during the pandemic, such as mitigating the risk of COVID-19 transmission on public transit.
Joint Optimization of Tokenization and Downstream Model

Since traditional tokenizers are isolated from a downstream task and model, they cannot output an appropriate tokenization depending on the task and model, although recent studies imply that the appropriate tokenization improves the performance. In this paper, we propose a novel method to find an appropriate tokenization to a given downstream model by jointly optimizing a tokenizer and the model. The proposed method has no restriction except for using loss values computed by the downstream model to train the tokenizer, and thus, we can apply the proposed method to any NLP task. Moreover, the proposed method can be used to explore the appropriate tokenization for an already trained model as post-processing. Therefore, the proposed method is applicable to various situations. We evaluated whether our method contributes to improving performance on text classification in three languages and machine translation in eight language pairs. Experimental results show that our proposed method improves the performance by determining appropriate tokenizations.
A Search for the 3.5 keV Line from the Milky Way’s Dark Matter Halo with HaloSat

Previous detections of an X-ray emission line near 3.5 keV in galaxy clusters and other dark-matter-dominated objects have been interpreted as observational evidence for the decay of sterile neutrino dark matter. Motivated by this, we report on a search for a 3.5 keV emission line from the Milky Way’s galactic dark matter halo with HaloSat. As a single pixel, collimated instrument, HaloSat observations are impervious to potential systematic effects due to grazing incidence reflection and CCD pixelization, and thus may offer a check on possible instrumental systematic errors in previous analyses. We report nondetections of a ∼3.5 keV emission line in four HaloSat observations near the Galactic center. In the context of the sterile neutrino decay interpretation of the putative line feature, we provide 90% confidence level upper limits on the 3.5 keV line flux for a field centered 18.6 degrees from the Galactic center and the corresponding 7.1 keV sterile neutrino mixing angle: F ≤ 0.077 ph cm−2 s−1 sr−1 and sin2(2θ)≤4.25×10−11 . The HaloSat mixing angle upper limit was calculated using a modern parameterization of the Milky Way’s dark matter distribution, and in order to compare with previous limits, we also report the limit calculated using a common historical model. The HaloSat mixing angle upper limit places constraints on a number of previous mixing angle estimates derived from observations of the Milky Way’s dark matter halo and galaxy clusters, and excludes several previous detections of the line. The upper limits cannot, however, entirely rule out the sterile neutrino decay interpretation of the 3.5 keV line feature.
Robust Value Iteration for Continuous Control Tasks

When transferring a control policy from simulation to a physical system, the policy needs to be robust to variations in the dynamics to perform well. Commonly, the optimal policy overfits to the approximate model and the corresponding state-distribution, often resulting in failure to trasnfer underlying distributional shifts. In this paper, we present Robust Fitted Value Iteration, which uses dynamic programming to compute the optimal value function on the compact state domain and incorporates adversarial perturbations of the system dynamics. The adversarial perturbations encourage a optimal policy that is robust to changes in the dynamics. Utilizing the continuous-time perspective of reinforcement learning, we derive the optimal perturbations for the states, actions, observations and model parameters in closed-form. Notably, the resulting algorithm does not require discretization of states or actions. Therefore, the optimal adversarial perturbations can be efficiently incorporated in the min-max value function update. We apply the resulting algorithm to the physical Furuta pendulum and cartpole. By changing the masses of the systems we evaluate the quantitative and qualitative performance across different model parameters. We show that robust value iteration is more robust compared to deep reinforcement learning algorithm and the non-robust version of the algorithm. Videos of the experiments are shown at https://sites.google. com/view/rfvi
Training Speech Enhancement Systems with Noisy Speech Datasets

Recently, deep neural network (DNN)-based speech enhancement (SE) systems have been used with great success. During training, such systems require clean speech data - ideally, in large quantity with a variety of acoustic conditions, many different speaker characteristics and for a given sampling rate (e.g., 48kHz for fullband SE). However, obtaining such clean speech data is not straightforward - especially, if only considering publicly available datasets. At the same time, a lot of material for automatic speech recognition (ASR) with the desired acoustic/speaker/sampling rate characteristics is publicly available except being clean, i.e., it also contains background noise as this is even often desired in order to have ASR systems that are noise-robust. Hence, using such data to train SE systems is not straightforward. In this paper, we propose two improvements to train SE systems on noisy speech data. First, we propose several modifications of the loss functions, which make them robust against noisy speech targets. In particular, computing the median over the sample axis before averaging over time-frequency bins allows to use such data. Furthermore, we propose a noise augmentation scheme for mixture-invariant training (MixIT), which allows using it also in such scenarios. For our experiments, we use the Mozilla Common Voice dataset and we show that using our robust loss function improves PESQ by up to 0.19 compared to a system trained in the traditional way. Similarly, for MixIT we can see an improvement of up to 0.27 in PESQ when using our proposed noise augmentation.
TALBOT EFFECT FOR THE SCHRÖDINGER EQUATION

We study fractal properties of the solution to the periodic Schrödinger equation with singular potentials and bounded variation initial data. The solution turns out to have fractal behavior at irrational times, whereas it resembles the initial data in regularity at rational times. This extends the results of Oskolkov [18] and Rodnianski [20] on the free Schrödinger equation to general cases having potentials. For the purpose, we employ the Bourgain space and obtain smoothing estimates for the Duhamel part involving singular potentials.
Finite Sample Guarantees for Quantile Estimation: An Application to Detector Threshold Tuning

In threshold-based anomaly detection, we want to tune the threshold of a detector to achieve an acceptable false alarm rate. However, tuning the threshold is often a non-trivial task due to unknown detector output distributions. A detector threshold that provides an acceptable false alarm rate is equivalent to a specific quantile of the detector output distribution. Therefore, we use quantile estimators based on order statistics to estimate the detector threshold. The estimation of quantiles from sample data has a more than a century-long tradition and we provide three different distribution-free finite sample guarantees for a class of quantile estimators. The first is based on the Dvoretzky–Kiefer–Wolfowitz (DKW) inequality, the second utilizes the Vysochanskij–Petunin inequality, and the third is based on exact confidence intervals for a beta distribution. These guarantees are then compared and used in the detector threshold tuning problem. We use both simulated data as well as data obtained from an experimental setup with the Temperature Control Lab to validate the guarantees provided.
A Flexible FPGA-Based ISA Configurable SoC platform

We proposes a platform which can generate hardware/software description based on flexible in-struction set architectures (ISAs). The platform takes advantage of the flexibility of field pro-grammable gate array (FPGA) to design many micro control units (MCUs) based on different ISAs. The platform can generate many ISAs, MCUs, and Assemblers according to a pre-defined ISA and user applications. Although the MCU performance is not optimized, the FPGA shows a great potential on resource reduction and enough performance at very low system clock rate. The flexible ISA has shown great importance for the design targeted to specific purpose. We also show a case study of the proposed flexible ISA-based FPGA-MCU. It can control many specifi-cally designed hardware IPs and a customized multi-task OS with tasks. Not only the case works correctly, but also the proposed FPGA-MCU of the case is flexible with reduced FPGA resources, low cost, and within time constraints.
On the Advantages of Multiple Stereo Vision Camera Designs for Autonomous Drone Navigation

In this work we showcase the design and assessment of the performance of a multi-camera UAV, when coupled with state-of-the-art planning and mapping algorithms for autonomous navigation. The system leverages state-of-the-art receding horizon exploration techniques for Next-Best-View (NBV) planning with 3D and semantic information, provided by a reconfigurable multi stereo camera system. We employ our approaches in an autonomous drone-based inspection task and evaluate them in an autonomous exploration and mapping scenario. We discuss the advantages and limitations of using multi stereo camera flying systems, and the trade-off between number of cameras and mapping performance.
Out-of-Vocabulary Entities in Link Prediction

Knowledge graph embedding techniques are key to making knowledge graphs amenable to the plethora of machine learning approaches based on vector representations. Link prediction is often used as a proxy to evaluate the quality of these embeddings. Given that the creation of benchmarks for link prediction is a time-consuming endeavor, most work on the subject matter uses only a few benchmarks. As benchmarks are crucial for the fair comparison of algorithms, ensuring their quality is tantamount to providing a solid ground for developing better solutions to link prediction and ipso facto embedding knowledge graphs. First studies of benchmarks pointed to limitations pertaining to information leaking from the development to the test fragments of some benchmark datasets. We spotted a further common limitation of three of the benchmarks commonly used for evaluating link prediction approaches: out-of-vocabulary entities in the test and validation sets. We provide an implementation of an approach for spotting and removing such entities and provide corrected versions of the datasets WN18RR, FB15K-237, and YAGO3-10. Our experiments on the corrected versions of WN18RR, FB15K-237, and YAGO3-10 suggest that the measured performance of state-of-the-art approaches is altered significantly with p-values<1%,<1.4%, and<1%, respectively. Overall, state-of-the-art approaches gain on average absolute $3.29 \pm 0.24\%$ in all metrics on WN18RR. This means that some of the conclusions achieved in previous works might need to be revisited. We provide an open-source implementation of our experiments and corrected datasets at at https://github.com/dice-group/OOV-In-Link-Prediction.