Miracle-dz/UniFAR-FTUs huggingface dataset:
Like a Moth to a Flame: Does the Stock Market Exacerbate Credit Risks of Peer-to-Peer (P2P) Lending?

In this paper, we establish a causal link between fluctuations in the stock market and credit risks from the P2P lending market, by exploring information of more than 450 thousand loans on Renrendai.com, a leading Chinese P2P crowd lending platform. Based on the fact that retail investors exhibit a disproportional level of attention when the Shanghai Stock Exchange (SSE) composite index is above the 3,500 threshold, we find that both the default rate and the degree of delinquency have a disproportional jump for P2P loans borrowed above the 3,500 threshold of the SSE composite index. This effect is more pronounced when the loan quality is lower and when investors are more overconfident. Furthermore, when the SSE composite index exceeds 3,500, the overall creditworthiness of borrowers disproportionally deteriorates, indicated by higher interest rates, longer fulfill time needed to achieve the target loan amount, lower credit scores, and lower loan amounts. Overall, our results suggest that FinTech developments could amplify financial risks and hamper financial stability.
Undefine Chart Patters: An Image-Based Approach

Academic literature of asset technical analysis and price series pattern recognition typically involves characterising heuristic chart patterns with piecewise linear functions and filtering characteristic price sequences with local regression. We propose an image-based approach to encode price series as a two-dimensional density matrix and analyse price geometries with convolution filters. The method scales local prices based on volatility within a sliding window and creates a density array via one-hot encoding and aggregation. By comparing convolutional network classifiers and autoregressive networks, we show that the image-based price representation improves pattern-related feature interpretability. However, forecasting future price movements remains challenging due to global insignificance of consistent patterns. Period-adaptive pattern filters are necessary to denoise series and separate patterns prior to image-based encoding and forecasting.
The Impact of Global Value Chain on the Performance on SMEs Special Reference on East Asian Countries

The purpose of this study is to examine the impact of global value chain on the performance on SME’s mainly based on five research articles which relate to Indonesia, Thailand, Viet Nam and Sri Lanka published by Asian Development Bank Institute (2021). The said study is based on the empirical findings mainly carried through five research findings that relate to enhancing SME participation in global value chains. As this study was qualitative nature, it has been employed through secondary data and collects data through these five research findings and mainly consists with four countries data and firm level data which relate to SME performance. In terms of measuring the relationship use the descriptive analysis technique. This paper found that the SME’s participation and performance in GVCs limit by several factors and some factors limit in country policies and procedures. Moreover, this paper discussed the importance factors which enhance SMEs performance to participate GVCs. Hence this study has been significantly contributes to the existing knowledge explaining the need of SME’s for the economic growth and global value chain.
Price Exuberance in the Carbon Emissions Trading Market

The pricing of carbon is an essential element in addressing climate change through market activity. Price exuberance can potentially result in a loss of confidence in these markets. We test to see whether the time-series changes from a random walk to an explosive process during the first five months of the European Union’s Emission Trading System Phase IV. Our finding is that the market exhibits market exuberant behavior over this period.
Do Liquidity Limits Amplify Money Market Fund Redemptions During the COVID Crisis?

Regulation of Money Market Funds (MMFs) in the EU requires some categories of MMFs to consider applying liquidity management tools if they breach a minimum ‘weekly’ liquidity requirement. Anticipation of the application of such tools is a plausible amplifier of run risks. Using a larger European dataset than previously studied, we assess whether proximity to liquidity thresholds explains differences in redemptions both at the start of the COVID-19 crisis and in the following months. We assess this effect for MMFs subject to and exempt from the liquidity regulation. The evidence shows that outflows can be robustly associated with proximity to minimum liquidity requirements in the peak of the crisis for funds required to consider suspending redemptions if breaches occur. In the post-crisis phase the redemption-liquidity relationship does not appear to be specifically related to mandated consideration of the suspension of redemptions. The evidence supports consideration of countercyclical liquidity requirements or buffers that are more usable in times of stress.
News Implied Volatility and Aggregate Economic Activity: Evidence from the Japanese Government Bond Market

Because options on 10-year Japanese government bonds (JGB) futures are relatively new in the market, their implied volatility, JGB-VIX, is not available before 2007. For the period when JGB-VIX is available, we conduct supervised learning by using the daily newspaper articles as input and JGB-VIX as output. We then construct a new JGB market uncertainty measure, which we called JGB-NU, based on the predicted values of JGB-VIX from the estimated model and contents of the newspaper articles from 1981 to 2017. In the VAR analysis with JGB-NU, we confirm that JGB market uncertainty shocks have a negative impact on real economic activities in Japan.
A Closed-Loop Linear Covariance Framework for Vehicle Path Planning in a Static Uncertain Obstacle Fiel

Path planning in an uncertain environment is a key enabler of true vehicle autonomy. Over the past two decades, numerous approaches have been developed to account for errors in the vehicle path while navigating complex and often uncertain environments. An important capability of such planning is the prediction of vehicle dispersion covariances about a candidate path. This work develops a new closed-loop linear covariance (CL-LinCov) framework applicable to a wide range of autonomous system architectures. Important features of the developed framework include the (1) separation of high-level guidance from low-level control, (2) support for output-feedback controllers with internal states, dynamics, and output, and (3) multi-use continuous sensors for navigation state propagation, guidance, and feedback control. The closed-loop nature of the framework preserves the important coupling between the system dynamics, exogenous disturbances, and the guidance, navigation, and control algorithms. The developed framework is applied to a simplified model of an unmanned aerial vehicle and validated by comparison via Monte Carlo analysis. The utility of the CL-LinCov information is illustrated by its application to path planning in a static, uncertain obstacle field via a modified version of the Rapidly Exploring Random Tree algorithm.
Long-tailed Distribution Adaptation

Recognizing images with long-tailed distributions remains a challenging problem while there lacks an interpretable mechanism to solve this problem. In this study, we formulate Long-tailed recognition as Domain Adaption (LDA), by modeling the long-tailed distribution as an unbalanced domain and the general distribution as a balanced domain. Within the balanced domain, we propose to slack the generalization error bound, which is defined upon the empirical risks of unbalanced and balanced domains and the divergence between them. We propose to jointly optimize empirical risks of the unbalanced and balanced domains and approximate their domain divergence by intra-class and inter-class distances, with the aim to adapt models trained on the long-tailed distribution to general distributions in an interpretable way. Experiments on benchmark datasets for image recognition, object detection, and instance segmentation validate that our LDA approach, beyond its interpretability, achieves state-of-the-art performance.
Presentation of the fundamental groups of complements of shadows

A shadowed polyhedron is a simple polyhedron equipped with half integers on regions, called gleams, which represents a compact, oriented, smooth 4-manifold. The polyhedron is embedded in the 4-manifold and it is called a shadow of that manifold. A subpolyhedron of a shadow represents a possibly singular subsurface in the 4-manifold. In this paper, we focus on contractible shadows obtained from the unit disk by attaching annuli along generically immersed closed curves on the disk. In this case, the 4-manifold is always a 4-ball. Milnor fibers of plane curve singularities and complexified real line arrangements can be represented in this way. We give a presentation of the fundamental group of the complement of a subpolyhedron of such a shadow in the 4-ball. The method is very similar to the Wirtinger presentation of links in knot theory.
Deploying the Conditional Randomization Test in High Multiplicity Problems

This paper introduces the sequential CRT, which is a variable selection procedure that combines the conditional randomization test (CRT) and Selective SeqStep+. Valid p -values are constructed via the ﬂexible CRT, which are then ordered and passed through the selective SeqStep+ ﬁlter to produce a list of discoveries. We develop theory guaranteeing control on the false discovery rate (FDR) even though the p -values are not independent. We show in simulations that our novel procedure indeed controls the FDR and are competitive with—and sometimes outperform—state-of-the-art alternatives in terms of power. Finally, we apply our methodology to a breast cancer dataset with the goal of identifying biomarkers associated with cancer stage.
Transparent forwarders: an unnoticed component of the open DNS infrastructure

In this paper, we revisit the open DNS (ODNS) infrastructure and, for the first time, systematically measure and analyze transparent forwarders, DNS components that transparently relay between stub resolvers and recursive resolvers. Our key findings include four takeaways. First, transparent forwarders contribute 26% (563k) to the current ODNS infrastructure. Unfortunately, common periodic scanning campaigns such as Shadowserver do not capture transparent forwarders and thus underestimate the current threat potential of the ODNS. Second, we find an increased deployment of transparent forwarders in Asia and South America. In India alone, the ODNS consists of 80% transparent forwarders. Third, many transparent forwarders relay to a few selected public resolvers such as Google and Cloudflare, which confirms a consolidation trend of DNS stakeholders. Finally, we introduce DNSRoute++, a new traceroute approach to understand the network infrastructure connecting transparent forwarders and resolvers.
Reconstruction of Sets of Strings From Prefix/Suffix Compositions

The problem of reconstructing strings from substring information has found many applications due to its importance in genomic data sequencing and DNA- and polymer-based data storage. One important paradigm requires reconstructing mixtures of strings based on the union of compositions of their prefixes and suffixes, generated by mass spectrometry devices. We describe new coding methods that allow for unique joint reconstruction of subsets of strings selected from a code and provide upper and lower bounds on the asymptotic rate of the underlying codebooks. Our code constructions combine properties of binary <inline-formula> <tex-math notation="LaTeX">$B_{h}$ </tex-math></inline-formula> and Dyck strings that can be extended to accommodate missing substrings in the pool. As auxiliary results, we present simple entropy upper bounds for binary <inline-formula> <tex-math notation="LaTeX">$B_{h}$ </tex-math></inline-formula> codes and an improved bound for <inline-formula> <tex-math notation="LaTeX">$h=4$ </tex-math></inline-formula>, and also describe errors that arise during mass spectrometry.
Quasi-Newton policy gradient algorithms

Policy gradient algorithms have been widely applied to Markov decision process and reinforcement learning problems in recent years. Regularization with various entropy functions is often used to encourage exploration and improve stability. In this paper, we propose a quasi-Newton method for the policy gradient algorithm with entropy regularization. In the case of Shannon entropy, the resulting algorithm reproduces the natural policy gradient algorithm. For other entropy functions, this method results in brand new policy gradient algorithms. We provide a simple proof that all these algorithms enjoy the Newton-type quadratic convergence and that the corresponding gradient ﬂow converges globally to the optimal solution. Using both synthetic and industrial-scale examples, we demonstrate that the proposed quasi-Newton method typically converges in single-digit iterations, often orders of magnitude faster than other state-of-the-art algorithms.
Data-Centric AI Requires Rethinking Data Notion

The transition towards data-centric AI requires revisiting data notions from mathematical and implementational standpoints to obtain unified data-centric machine learning packages. Towards this end, this work proposes unifying principles offered by categorical and cochain notions of data, and discusses the importance of these principles in data-centric AI transition. In the categorical notion, data is viewed as a mathematical structure that we act upon via morphisms to preserve this structure. As for cochain notion, data can be viewed as a function defined in a discrete domain of interest and acted upon via operators. While these notions are almost orthogonal, they provide a unifying definition to view data, ultimately impacting the way machine learning packages are developed, implemented, and utilized by practitioners.
Deployment of Polar Codes for Mission-Critical Machine-Type Communication Over Wireless Networks

Mission critical Machine-type Communication, also referred to as Ultra-reliable Low Latency Communication is primarily characterized by communication that provides ultra-high reliability and very low latency to concurrently transmit short commands to a massive number of connected devices. While the reduction in PHY layer overhead and improvement in channel coding techniques are pivotal in reducing latency and improving reliability, the current wireless standards dedicated to support mcMTC rely heavily on adopting the bottom layers of general-purpose wireless standards and customizing only the upper layers. The mcMTC has a significant technical impact on the design of all layers of the communication protocol stack. In this paper, an innovative bottom-up approach has been proposed for mcMTC applications through PHY layer targeted at improving the transmission reliability by implementing ultra-reliable channel coding scheme in the PHY layer of IEEE 802.11a bearing in mind short packet transmission system. To achieve this aim, we analyzed and compared the channel coding performance of convolutional codes, LDPC codes, and polar codes in wireless network on the condition of short data packet transmission. The Viterbi decoding algorithm, logarithmic belief propagation algorithm, and cyclic redundancy check - successive cancellation list decoding algorithm were adopted to CC, LDPC codes, and polar codes, respectively. Consequently, a new PHY layer for mcMTC has been proposed. The reliability of the proposed approach has been validated by simulation in terms of Bit error rate vs. SNR. The simulation results demonstrate that the reliability of IEEE 802.11a standard has been significantly improved to be at PER less 10e-5 with the implementation of polar codes. The results also show that the general-purpose wireless networks are prominent in providing short packet mcMTC with the modification needed.
Relative Entropy Gradient Sampler for Unnormalized Distributions

We propose a relative entropy gradient sampler (REGS) for sampling from unnormalized distributions. REGS is a particle method that seeks a sequence of simple nonlinear transforms iteratively pushing the initial samples from a reference distribution into the samples from an unnormalized target distribution. To determine the nonlinear transforms at each iteration, we consider the Wasserstein gradient flow of relative entropy. This gradient flow determines a path of probability distributions that interpolates the reference distribution and the target distribution. It is characterized by an ODE system with velocity fields depending on the density ratios of the density of evolving particles and the unnormalized target density. To sample with REGS, we need to estimate the density ratios and simulate the ODE system with particle evolution. We propose a novel nonparametric approach to estimating the logarithmic density ratio using neural networks. Extensive simulation studies on challenging multimodal 1D and 2D mixture distributions and Bayesian logistic regression on real datasets demonstrate that the REGS outperforms the state-of-the-art sampling methods included in the comparison.
Hybrid Classical-Quantum method for Diabetic Foot Ulcer Classification

Diabetes is a raising problem that affects many people globally. Diabetic patients are at risk of developing foot ulcer that usually leads to limb amputation, causing significant morbidity, and psychological distress. In order to develop a self monitoring mobile application, it is necessary to be able to classify such ulcers into either of the following classes: Infection, Ischaemia, None, or Both. In this work, we compare the performance of a classical transfer-learning-based method, with the performance of a hybrid classical-quantum Classifier on diabetic foot ulcer classification task. As such, we merge the pre-trained Xception network with a multi-class variational classifier. Thus, after modifying and re-training the Xception network, we extract the output of a mid-layer and employ it as deep-features presenters of the given images. Finally, we use those deep-features to train multi-class variational classifier, where each classifier is implemented on an individual variational circuit. The method is then evaluated on the blind test set DFUC2021. The results proves that our proposed hybrid classical-quantum Classifier leads to considerable improvement compared to solely relying on transfer learning concept through training the modified version of Xception network.
Reversible adversarial examples against local visual perturbation

Recently, studies have indicated that adversarial attacks pose a threat to deep learning systems. However, when there are only adversarial examples, people cannot get the original images, so there is research on reversible adversarial attacks. However, the existing strategies are aimed at invisible adversarial perturbation, and do not consider the case of locally visible adversarial perturbation. In this article, we generate reversible adversarial examples for local visual adversarial perturbation, and use reversible data embedding technology to embed the information needed to restore the original image into the adversarial examples to generate examples that are both adversarial and reversible. Experiments on ImageNet dataset show that our method can restore the original image losslessly while ensuring the attack capability
A note on the definability of genus for Zariski geometries

In this work we propose a notion of genus in the context of Zariski geometries and we obtain natural generalizations of the Riemann–Hurwitz Theorem and the Hurwitz Theorem in the context of very ample Zariski geometries. As a corollary, we show that such notion of genus cannot be first-order definable in the full language of a Zariski geometry.
Lead acetate genitoxicicty in suckling rats

The mutagenic and carcinogenic potentials of lead are still being investigated. The aim of this study was to evaluate the genotoxic effect of lead acetate in the early period of life, when the organism is extremely sensitive to toxic effects of lead. Six-day-old suckling Wistar rats were exposed to lead (as acetate) either orally for 9 days (daily dose 2 mg Pb/kg b. wt., 18 mg/kg b.wt. total dose) or by a single intraperitoneal injection (5 mg Pb/kg b. wt.). DNA damage was investigated using the comet assay and in vivo micronucleus test. The results of the comet assay showed statistically significant differences between the control (unexposed) animals and the two groups of exposed animals by ANOVA weighted for unequal variance (heterogeneity of variances was found by Levene's test), followed by Tukey's post hoc test at the level of significance of p<0.05. The two groups of lead-exposed animals were also significantly different from one another. Orally lead-exposed animals showed a significant increase of MN frequencies in reticulocytes and erythrocytes compared to unexposed animals (ANOVA, p<0.05).
A Weighted Generalized Coherence Approach for Sensing Matrix Design

As compared to using randomly generated sensing matrices, optimizing the sensing matrix w.r. t. a carefully designed criterion is known to lead to better quality signal recovery given a set of compressive measurements. In this paper, we propose generalizations of the well-known mutual coherence criterion for optimizing sensing matrices starting from random initial conditions. We term these generalizations as bi-coherence or tri-coherence and they are based on a criterion that discourages any one column of the sensing matrix from being close to a sparse linear combination of other columns. We also incorporate training data to further improve the sensing matrices through weighted coherence, weighted bi-coherence, or weighted tri-coherence criteria, which assign weights to sensing matrix columns as per their importance. An algorithm is also presented to solve the optimization problems. Finally, the effectiveness of the proposed algorithm is demonstrated through empirical results.
Residual Overfit Method of Exploration

Exploration is a crucial aspect of bandit and reinforcement learning algorithms. The uncertainty quantification necessary for exploration often comes from either closed-form expressions based on simple models or resampling and posterior approximations that are computationally intensive. We propose instead an approximate exploration methodology based on fitting only two point estimates, one tuned and one overfit. The approach, which we term the residual overfit method of exploration (ROME), drives exploration towards actions where the overfit model exhibits the most overfitting compared to the tuned model. The intuition is that overfitting occurs the most at actions and contexts with insufficient data to form accurate predictions of the reward. We justify this intuition formally from both a frequentist and a Bayesian information theoretic perspective. The result is a method that generalizes to a wide variety of models and avoids the computational overhead of resampling or posterior approximations. We compare ROME against a set of established contextual bandit methods on three datasets and find it to be one of the best performing.