Miracle-dz/UniFAR-FTUs huggingface dataset:
Financial Shocks and Credit Cycles 1

This paper compares the contribution of internal and external financial shocks to the formation of credit cycle phases using cross-country quarterly data for 27 countries, including advanced and emerging economies, for the period from 1990 through 2019. To conduct comparative analysis, we apply IV Probit models of the credit cycle which take into account the relationship between the credit and business cycles the inertia of the cycles and the non-linearity of the transmission of internal and external financial shocks to the economy through the credit market. In our sample of countries, the transmission of shocks to credit cycle phases proves to be non-linear (a switching effect is observed depending on the time elapsed since the shocks occurred); with the economic effect of the external capital inflow shock being in absolute value twice as strong as that of the bank credit supply shock (on average for the current and subsequent quarters); in turn, the bank credit supply shock is twice as strong as the monetary policy shock. A counterfactual analysis of the role of financial shocks in the formation of the credit cycle in Russia indicates an increase in the effectiveness of the monetary authorities in terms of their ability to control the phases of the credit cycle and, accordingly, a relative decrease in the role of credit supply shocks, while the global financial cycle retains its dominance.
GENDER DIFFERENCES IN CYCLING 1 Gender Differences in Cycling Patterns and Attitudes towards Cycling in a Sample of European Regular Cyclists GENDER DIFFERENCES IN CYCLING

Men cycle more than women and gender differences in perceptions and attitudes towards cycling may be influenced by such difference in bicycle use. However, to our knowledge, no previous research has focused on gender differences among regular cyclists. In our study, we investigated gender differences in attitudes towards cycling and towards cycling infrastructure, purpose of cycling, risk perception, and exposure to severe crashes in a large sample of regular cyclists. Following a cross-sectional design, we collected data from 2417 participants from Hungary, Italy, Spain, Sweden, Netherlands, and United Kingdom. A survey was administered to an online panel of respondents. Gender differences in attitudes towards cycling were small in terms of effect size or non-significant, with women having more positive attitudes in personal benefits rather than mobility benefits. Women reported gender-stereotyped reasons for cycling more than men, except for social activities. Also, women showed higher discomfort than men cycling in mixed traffic and higher risk perception than men. Furthermore, men reported higher exposure to severe crashes than women. We contend that bicycle use and gender role (i.e. society’s shared beliefs concerning a range of attitudes, norms, and behaviours that are generally considered appropriate or desirable for individuals based on their actual or perceived sex) can affect differences between male and female cyclists in perceptions, attitudes towards cycling, and cycling behaviours.
Efficient and Near-Optimal Smoothed Online Learning for Generalized Linear Functions

Due to the drastic gap in complexity between sequential and batch statistical learning, recent work has studied a smoothed sequential learning setting, where Nature is constrained to select contexts with density bounded by 1/{\sigma} with respect to a known measure {\mu}. Unfortunately, for some function classes, there is an exponential gap between the statistically optimal regret and that which can be achieved efficiently. In this paper, we give a computationally efficient algorithm that is the first to enjoy the statistically optimal log(T/{\sigma}) regret for realizable K-wise linear classification. We extend our results to settings where the true classifier is linear in an over-parameterized polynomial featurization of the contexts, as well as to a realizable piecewise-regression setting assuming access to an appropriate ERM oracle. Somewhat surprisingly, standard disagreement-based analyses are insufficient to achieve regret logarithmic in 1/{\sigma}. Instead, we develop a novel characterization of the geometry of the disagreement region induced by generalized linear classifiers. Along the way, we develop numerous technical tools of independent interest, including a general anti-concentration bound for the determinant of certain matrix averages.
BiT: Robustly Binarized Multi-distilled Transformer

Modern pre-trained transformers have rapidly advanced the state-of-the-art in machine learning, but have also grown in parameters and computational complexity, making them increasingly difficult to deploy in resource-constrained environments. Binarization of the weights and activations of the network can significantly alleviate these issues, however, is technically challenging from an optimization perspective. In this work, we identify a series of improvements that enables binary transformers at a much higher accuracy than what was possible previously. These include a two-set binarization scheme, a novel elastic binary activation function with learned parameters, and a method to quantize a network to its limit by successively distilling higher precision models into lower precision students. These approaches allow for the first time, fully binarized transformer models that are at a practical level of accuracy, approaching a full-precision BERT baseline on the GLUE language understanding benchmark within as little as 5.9%. Code and models are available at: https://github.com/facebookresearch/bit.
Trainable Weight Averaging for Fast Convergence and Better Generalization

Stochastic gradient descent (SGD) and its variants are commonly considered as the de-facto methods to train deep neural networks (DNNs). While recent improvements to SGD mainly focus on the descent algorithm itself, few works pay attention to utilizing the historical solutions—as an iterative method, SGD has actually gone through substantial explorations before its ﬁnal convergence. Recently, an interesting attempt is stochastic weight averaging (SWA), which signiﬁcantly improves the generalization by simply averaging the solutions at the tail stage of training. In this paper, we propose to optimize the averaging coefﬁcients, leading to our Trainable Weight Averaging (TWA), essentially a novel training method in a reduced subspace spanned by historical solutions. TWA is quite efﬁcient and has good generalization capability as the degree of freedom for training is small. It largely reduces the estimation error from SWA, making it not only further improve the SWA solutions but also take full advantage of the solutions generated in the head of training where SWA fails. In the extensive numerical experiments, (i) TWA achieves consistent improvements over SWA with less sensitivity to learning rate; (ii) applying TWA in the head stage of training largely speeds up the convergence, resulting in over 40% time saving on CIFAR and 30% on ImageNet with improved generalization compared with regular training. The code is released at https://github.com/nblt/TWA .
A DLT enabled smart mask system to enable social compliance

As Covid-19 remains a cause of concern, especially due to its mutations, wearing masks correctly and efficiently remains a priority in order to limit the spread of the disease. In this paper we present a wearable smart-mask prototype using concepts from Internet of Things, Control Theory and Distributed Ledger Technologies. Its purpose is to encourage people to comply with social distancing norms, through the use of incentives. The smart mask is designed to monitor Carbon Dioxide and Total Volatile Organic Compounds concentrations. The detected data is appended to a DAG-based DLT, named the IOTA Tangle. The IOTA Tangle ensures that the data is secure and immutable and acts as a communication backbone for the incentive mechanism. A hardware-in-the-loop simulation, based on indoor positioning, is developed to validate the effectiveness of the designed prototype.
Green Hierarchical Vision Transformer for Masked Image Modeling

We present an efficient approach for Masked Image Modeling (MIM) with hierarchical Vision Transformers (ViTs), allowing the hierarchical ViTs to discard masked patches and operate only on the visible ones. Our approach consists of three key designs. First, for window attention, we propose a Group Window Attention scheme following the Divide-and-Conquer strategy. To mitigate the quadratic complexity of the self-attention w.r. t. the number of patches, group attention encourages a uniform partition that visible patches within each local window of arbitrary size can be grouped with equal size, where masked self-attention is then performed within each group. Second, we further improve the grouping strategy via the Dynamic Programming algorithm to minimize the overall computation cost of the attention on the grouped patches. Third, as for the convolution layers, we convert them to the Sparse Convolution that works seamlessly with the sparse data, i.e., the visible patches in MIM. As a result, MIM can now work on most, if not all, hierarchical ViTs in a green and efficient way. For example, we can train the hierarchical ViTs, e.g., Swin Transformer and Twins Transformer, about 2.7$\times$ faster and reduce the GPU memory usage by 70%, while still enjoying competitive performance on ImageNet classification and the superiority on downstream COCO object detection benchmarks. Code and pre-trained models have been made publicly available at https://github.com/LayneH/GreenMIM.
Distributed Contextual Linear Bandits with Minimax Optimal Communication Cost

We study distributed contextual linear bandits with stochastic contexts, where $N$ agents act cooperatively to solve a linear bandit-optimization problem with $d$-dimensional features over the course of $T$ rounds. For this problem, we derive the first ever information-theoretic lower bound $\Omega(dN)$ on the communication cost of any algorithm that performs optimally in a regret minimization setup. We then propose a distributed batch elimination version of the LinUCB algorithm, DisBE-LUCB, where the agents share information among each other through a central server. We prove that the communication cost of DisBE-LUCB matches our lower bound up to logarithmic factors. In particular, for scenarios with known context distribution, the communication cost of DisBE-LUCB is only $\tilde{\mathcal{O}}(dN)$ and its regret is ${\tilde{\mathcal{O}}}(\sqrt{dNT})$, which is of the same order as that incurred by an optimal single-agent algorithm for $NT$ rounds. We also provide similar bounds for practical settings where the context distribution can only be estimated. Therefore, our proposed algorithm is nearly minimax optimal in terms of \emph{both regret and communication cost}. Finally, we propose DecBE-LUCB, a fully decentralized version of DisBE-LUCB, which operates without a central server, where agents share information with their \emph{immediate neighbors} through a carefully designed consensus procedure.
PerDoor: Persistent Non-Uniform Backdoors in Federated Learning using Adversarial Perturbations

Federated Learning (FL) enables numerous participants to train deep learning models collaboratively without exposing their personal, potentially sensitive data, making it a promising solution for data privacy in collaborative training. The distributed nature of FL and unvetted data, however, makes it inherently vulnerable to backdoor attacks: In this scenario, an adversary injects backdoor functionality into the centralized model during training, which can be triggered to cause the desired misclassification for a specific adversary-chosen input. A range of prior work establishes successful backdoor injection in an FL system; however, these backdoors are not demonstrated to be long-lasting. The backdoor functionality does not remain in the system if the adversary is removed from the training process since the centralized model parameters continuously mutate during successive FL training rounds. Therefore, in this work, we propose PerDoor, a persistent-by-construction backdoor injection technique for FL, driven by adversarial perturbation and targeting parameters of the centralized model that deviate less in successive FL rounds and contribute the least to the main task accuracy. An exhaustive evaluation considering an image classification scenario portrays on average $10.5\times$ persistence over multiple FL rounds compared to traditional backdoor attacks. Through experiments, we further exhibit the potency of PerDoor in the presence of state-of-the-art backdoor prevention techniques in an FL system. Additionally, the operation of adversarial perturbation also assists PerDoor in developing non-uniform trigger patterns for backdoor inputs compared to uniform triggers (with fixed patterns and locations) of existing backdoor techniques, which are prone to be easily mitigated.
Follow-the-Perturbed-Leader for Adversarial Markov Decision Processes with Bandit Feedback

We consider regret minimization for Adversarial Markov Decision Processes (AMDPs), where the loss functions are changing over time and adversarially chosen, and the learner only observes the losses for the visited state-action pairs (i.e., bandit feedback). While there has been a surge of studies on this problem using Online-Mirror-Descent (OMD) methods, very little is known about the Follow-the-Perturbed-Leader (FTPL) methods, which are usually computationally more efficient and also easier to implement since it only requires solving an offline planning problem. Motivated by this, we take a closer look at FTPL for learning AMDPs, starting from the standard episodic finite-horizon setting. We find some unique and intriguing difficulties in the analysis and propose a workaround to eventually show that FTPL is also able to achieve near-optimal regret bounds in this case. More importantly, we then find two significant applications: First, the analysis of FTPL turns out to be readily generalizable to delayed bandit feedback with order-optimal regret, while OMD methods exhibit extra difficulties (Jin et al., 2022). Second, using FTPL, we also develop the first no-regret algorithm for learning communicating AMDPs in the infinite-horizon setting with bandit feedback and stochastic transitions. Our algorithm is efficient assuming access to an offline planning oracle, while even for the easier full-information setting, the only existing algorithm (Chandrasekaran and Tewari, 2021) is computationally inefficient.
A Learned-SVD approach for Regularization in Diffuse Optical Tomography

—Diffuse Optical Tomography (DOT) is an emerging technology in medical imaging which employs light in the NIR spectrum to estimate the distribution of optical coefﬁcients in biological tissues for diagnostic and monitoring purposes. DOT reconstruction implies the solution of a severely ill–posed inverse problem, for which regularization techniques are mandatory in order to achieve reasonable results. Traditionally, regularization techniques put a variance prior on the desired solution/gradient via regularization parameters, whose choice requires a ﬁne tuning, speciﬁc for each case. In this work we explore deep learning techniques in a fully data–driven approach, able of reconstructing the generating signal (optical absorption coefﬁcient) in an automated way. We base our approach on the so- called Learned Singular Value Decomposition, which has been proposed for general inverse problems, and we tailor it to the DOT application. We perform tests with increasing levels of noise on the measure, and compare it with standard variational approaches.
The Journal of Toxicological Sciences

Total mercury (THg) and methylmercury (MeHg) bioaccumulation was explored in the Bimastus parvus species of earthworm (B. parvus) native to the leachate-contaminated forest soils around a Hg-polluted traditional landfill in Japan. General soil properties and concentrations of THg and MeHg in forest soils and in B. parvus were determined. The results indicated that the average THg concentrations in B. parvus and in forest soils in the leachate-contaminated sites were 10.21 and 14.90 times higher than those in the reference sites, respectively, whereas similar average MeHg concentrations were observed in forest soils (< 0.01 mg kg-1) and in B. parvus (0.100-0.114 mg kg-1) across all sampled sites. The average bioaccumulation factors of THg in B. parvus (BAFTHg) in forest soil were similar between the leachatecontaminated sites and the reference sites. Cluster and regression analyses demonstrated that the B. parvus Hg (THg and MeHg) and soil THg were positively correlated with each other and with soil organic matter (SOM) and clays, but were negatively correlated with sand and hardly correlated with silts and pH in leachate-contaminated forest soils. From these results, it was proposed that Hg exposure to food chains is possible through B. parvus, because B. parvus showed a high ability to accumulate THg and MeHg in both leachate-contaminated and reference forest soils. Together, these findings indicated that the role of B. parvus in MeHg production is not clear, and it is possible that the MeHg in B. parvus was firstly formed within forest soils and then accumulated in their tissues.
Contribution of 52-week low to the momentum strategy on 52-week high

I examine whether the momentum strategy based on 52-week high price can use 52-week low price as another reference point. I find that the remoteness of current price from 52-week low price plays an incremental role in the momentum profits of the strategy based on 52-week high price. Furthermore, I find that the role depends on the level of the nearness to 52-week high price. I find that the profits of the 52-week high price momentum strategy that also uses remoteness of current price from 52-week low price increase as the level of the nearness to 52-week high price increases. I also find that the profits of this strategy based on both 52-week high price and 52-week low price come largely from small stocks as the profits of the traditional momentum strategy is known to come largely from small stocks. APPROVAL FOR SCHOLARLY DISSEMINATION The author grants to the Prescott Memorial Library of Louisiana Tech University the right to reproduce, by appropriate methods, upon request, any or all portions of this Dissertation. It is understood that “proper request” consists of the agreement, on the part of the requesting party, that said reproduction is for his personal use and that subsequent reproduction will not occur without written approval of the author of this Dissertation. Further, any portions of the Dissertation used in books, papers, and other works must be appropriately referenced to this Dissertation. Finally, the author of this Dissertation reserves the right to publish freely, in the literature, at any time, any or all portions of this Dissertation.
Disabilities Sports & Health Science Comparison of Shoulder Muscle Strength of Deaf and Healthy Basketball Players

Purpose: It has still not been made clear whether the audio inputs from the environment may have on the muscle strength characteristics of people who spend long periods of their lives with hearing loss. The purpose is to evaluate shoulder muscle strength and endurance of deaf basketball (DB) players and to compare them with healthy basketball (HB) players. Methods: The study was carried out with a total of 26-man players including 13 players (years: 24±3) in DB group and 13 players (years: 22±2) in HB group. Internal rotator (IR) and external rotator (ER) muscles strength of shoulder dominant and non-dominant side was measured using an isokinetic dynamometer. Results: According to isokinetic test results, there was not a difference between two groups on either dominant or nondominant side in terms of ER and IR Nm/kg (p>0.05). The ER / IR ratio was different between the two groups in favor of HB players (p=0.017). The strength parameters of dominant extremities of players in both groups were found to be higher than nondominant extremity. Conclusions: As a result, IR and ER muscle strengths were similar in two groups, both groups were at risk for musculoskeletal injuries in terms of ER/IR ratio. It could be speculated that it is essential to design training programs with additional ER strengthening exercises to improve the balance between IR and ER muscles and minimize risk of injury. The dominant extremity was found to be stronger in both groups. In our opinion, symmetrical movement patterns will positively affect performance on dominant extremity.
Alpinia galanga extract induces senescence in human epidermal growth factor receptor 2-overexpressing breast cancer cells

Background: Alpinia galanga L. (galangal) is a widely known spice in Southeast Asia. A previous study indicated that its active compounds might effectively inhibit (human epidermal growth factor receptor 2 [HER2])-overexpressing breast cancer cell proliferation. Objective: Our study aimed to investigate the antiproliferation activity of galangal extract (GE) on HER2-overexpressing breast cancer cells by analyzing GE cytotoxicity and its mechanism of action. Materials and Methods: We used HCC1954 cell line as a model. We observed the effect of GE (10–200 µ g/mL) on cell growth using 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay. The modulation of GE on cell cycle, apoptosis, and the intracellular reactive oxygen species (ROS) level was analyzed using flow cytometry. Moreover, SA- β -galactosidase staining used to observe the cell senescence. Results: Cytotoxic test indicated that GE has modest cytotoxicity toward HCC1954 (IC 50 >200 µ g/mL) and GE increased cell accumulation at the G2/M phase. Although GE showed no induction on apoptosis, it increased the number of senescent cells. This effect correlated with the increasing intracellular ROS level on treatment with GE. Furthermore, when combined with doxorubicin (Dox), GE increased the Dox’s cytotoxicity. Conclusion: A. galanga antiproliferation activity against HER2-overexpressing breast cancer was associated with its capability to induce cell senescence and intracellular ROS level, causing halted cell cycle progression.
Valuation Effects of Capital Structure and Earnings Quality

Introduction Interdisciplinary Research in Hospitality Finance and Accounting Considerable research has been conducted in management accounting topics since the mid-1990s (Foster & Young, 1997; Otley, 2016; Vailatti, Da Silva Rosa, & Vicente, 2017). In particular, the past two decades have witnessed a growing interest from researchers in interdisciplinary studies on hospitality management accounting (Hesford, Lee, Van der Stede, & Young, 2006). Several authors (Kwangmin Park & Jang, 2014; Potter & Schmidgall, 1999; Tsai, Pan, & Lee, 2011) extensively review hospitality finance and accounting (HFA) research and outline key topics, issues, methodologies, and findings.