Miracle-dz/UniFAR-FTUs huggingface dataset:
Coding with Knots

In this paper we explore new ways to approach understanding of the mysterious Precolumbian quipus, using both visual and sonic interpretations. We base our investigation on the Harvard Quipu Archive, starting with graphical visualisation techniques that give us an overall view so we can compare textile structures and perform basic cryptanalysis. We use listening and sonification in order to filter and compare the different modes of data representation (knot type, colour, twist and material). This provides new ways to explore both currently understood and unknown patterns of meaning in quipus.
Explaining Information Flow Inside Vision Transformers Using Markov Chain

Transformer-based models are receiving increasingly popularity in the field of computer vision, however, the corresponding interpretability study is less. As the simplest explainability method, visualization of attention weights exerts poor performance because of lacking association between the input and model decisions. In this study, we propose a method, named Transition Attention Maps , to generate the saliency map concerning a specific target category. The proposed approach connects the idea of the Markov chain, to investigate the information flow across layers of the Transformer and combine the integrated gradients to compute the relevance of input tokens for the model decisions. We compare with other explainability meth-ods using Vision Transformer as a benchmark and demonstrate that our method achieves better performance in various aspects. We open source the implementation of our approach at https://github.com/PaddlePaddle/InterpretDL .
Random testing of a higher-order blockchain language (experience report)

We describe our experience of using property-based testing---an approach for automatically generating random inputs to check executable program specifications---in a development of a higher-order smart contract language that powers a state-of-the-art blockchain with thousands of active daily users. We outline the process of integrating QuickChick---a framework for property-based testing built on top of the Coq proof assistant---into a real-world language implementation in OCaml. We discuss the challenges we have encountered when generating well-typed programs for a realistic higher-order smart contract language, which mixes purely functional and imperative computations and features runtime resource accounting. We describe the set of the language implementation properties that we tested, as well as the semantic harness required to enable their validation. The properties range from the standard type safety to the soundness of a control- and type-flow analysis used by the optimizing compiler. Finally, we present the list of bugs discovered and rediscovered with the help of QuickChick and discuss their severity and possible ramifications.
Medicinal plants in oral health and the intergenerational transfer of knowledge: Resilience to cocoa culture in southern Bahia

1 Programa de Pós-Graduação em Ciências da Saúde, Universidade Estadual de Santa Cruz, Ilhéus, Bahia, Brazil. 2 Departamento de Ciências Biológicas, Universidade Estadual de Santa Cruz, Ilhéus, Bahia, Brazil. 3 Departamento de Ciências Exatas e Tecnológicas, Universidade Estadual de Santa Cruz, Ilhéus, Bahia, Brazil. 4 Programa de Pós-Graduação em Ciências da Saúde, Universidade Estadual de Santa Cruz, Ilhéus, Bahia, Brazil. 5 Departamento de Ciências Biológicas, Universidade Estadual de Santa Cruz, Ilhéus, Bahia, Brazil.
Using System Calls to Predict Changes in Application Energy Consumption Proﬁles

Battery is a limited resource that affects the smartphone availability. Hence, it is the responsibility of developers to develop and maintain energy efficient applications to enhance end user satisfaction. As the impact of software code change on energy consumption is not known, developers need special instrumentation to assess the impact of change on their application’s energy consumption profile. Unfortunately, this instrumentation is costly and generally not available. In order to address this issue, we use system calls to predict the impact of code changes on the energy consumption profile. We find that significant changes to system call profiles often induce significant changes in energy consumption. Using this simple observation, we introduce GreenAdvisor, a first of its kind tool that systematically records and analyzes an application’s system-calls in order to predict whether the energy-consumption profile of an application has changed. The GreenAdvisor tool was evaluated using a user study whereby numerous software teams, whose members used GreenAdvisor while developing Android applications to examine the energy-consumption impact of selected commits from the teams’ projects. The evaluation confirms the usefulness of our tool in assisting developers analyze and understand the energy-consumption profile changes of a new version. This work is useful for the developers who are grappling with paucity of tools or knowledge to find out impact of changes on their application’s energy consumption.
0 Anticipation , Tax Avoidance , and the Price Elasticity of Gasoline Demand February 15 , 2015

Traditional least squares estimates of the responsiveness of gasoline consumption to changes in gasoline prices are biased toward zero, given the endogeneity of gasoline prices. A seemingly natural solution to this problem is to instrument for gasoline prices using gasoline taxes, but this approach tends to yield implausibly large price elasticities. We demonstrate that anticipatory behavior provides an important explanation for this result. We provide evidence that gasoline buyers increase gasoline purchases before tax increases and delay gasoline purchases before tax decreases. This intertemporal substitution renders the tax instrument endogenous, invalidating conventional IV analysis. We show that including suitable leads and lags in the regression restores the validity of the IV estimator, resulting in much lower and more plausible elasticity estimates. Our analysis has implications more broadly for the IV analysis of markets in which buyers may store purchases for future consumption. 1 John F. Kennedy School of Government, Harvard University, Littauer Center, 1805 Cambridge Street, Cambridge, MA 02138. Email: coglianese@fas.harvard.edu. 2 Haas School of Business, University of California, Berkeley, CA 94720, Energy Institute at Haas, and National Bureau of Economic Research. Email: ldavis@haas.berkeley. edu. 3 Department of Economics, University of Michigan, Lorch Hall, Ann Arbor, MI 48109, and Centre for Economic Policy Research. Email: lkilian@umich.edu. 4 Department of Economics and John F. Kennedy School of Government, Harvard University, Littauer Center, 1805 Cambridge Street, Cambridge, MA 02138, and National Bureau of Economic Research. Email: james_stock@harvard.edu.
Coping strategies and post-trauma outcome. 1 Adaptive and Maladaptive Coping Strategies Predict Post-trauma Outcomes in Ambulance Personnel

Attending potentially dangerous and traumatic incidents is inherent in the role of emergency workers, yet there is a paucity of literature aimed at examining variables that impact on the outcomes of such exposure. As coping strategies have been implicated in adjusting to life challenges in other contexts, this study explored adaptive and/or maladaptive coping strategies in relation to work-related post-trauma outcomes. One hundred and twenty-five Paramedics completed a survey battery including the Posttraumatic Growth Inventory (PTGI; Tedeschi & Calhoun, 1996), the Impact of Events Scale-Revised (IES-R; Weiss & Marmar, 1997), and the Revised-COPE (Zuckerman & Gagne, 2003). Results from the regression analysis demonstrated that specific adaptive and maladaptive coping strategies were differentially associated with post-trauma outcomes. The research contributes to a more comprehensive understanding regarding the effectiveness of coping strategies employed by paramedics in managing trauma, with implications for their psychological well-being, as well as the training and support services available.
INVESTIGATING THE RELATIONSHIPS BETWEEN ASEAN STOCK MARKETS: AN APPROACH USING THE GRANGER CAUSALITY TEST OF TIME-VARYING INFORMATION EFFICIENCY

The information efficiency and the relationships between ASEAN stock markets are two of the issues that are of great research interest. However, these two issues were often investigated separately in previous studies. Therefore, this paper combines these two issues in the same analysis. Data on the daily closing index of six ASEAN stock markets, including Indonesia, Malaysia, the Philippines, Singapore, Thailand, and Vietnam are used to calculate Shannon entropy to measure the stock market information efficiency. In addition, this paper conducts the Granger causality test to reveal the relationships between the ASEAN stock markets. The results show that all six stock markets are not in the state of information efficiency, which means the stock indices, stock returns, and volatility are not purely random, but patterned. In addition, the Granger test results show that the ASEAN stock markets are logically correlated. The two markets that are more integrated than the others are Indonesia and Malaysia. Vietnam participates in regional economics in a passive way, while the Philippines is more proactive. The Singapore stock market is also less integrated with the other ASEAN markets, although it is a mature stock market that outperforms the rest.
Applying Machine Learning for Duplicate Detection, Throttling and Prioritization of Equipment Commissioning Audits at Fulfillment Network

VQ (Vendor Qualification) and IOQ (Installation and Operation Qualification) audits are implemented in warehouses to ensure all equipment being turned over in the fulfillment network meets the quality standards. Audit checks are likely to be skipped if there are many checks to be performed in a short time. In addition, exploratory data analysis reveals several instances of similar checks being performed on the same assets and thus, duplicating the effort. In this work, Natural Language Processing and Machine Learning are applied to trim a large checklist dataset for a network of warehouses by identifying similarities and duplicates, and predict the non-critical ones with a high passing rate. The study proposes ML classifiers to identify checks which have a high passing probability of IOQ and VQ and assign priorities to checks to be prioritized when the time is not available to perform all checks. This research proposes using NLP-based BlazingText classifier to throttle the checklists with a high passing rate, which can reduce 10%-37% of the checks and achieve significant cost reduction. The applied algorithm over performs Random Forest and Neural Network classifiers and achieves an area under the curve of 90%. Because of imbalanced data, down-sampling and upweighting have shown a positive impact on the models' accuracy using F1 score, which improve from 8% to 75%. In addition, the proposed duplicate detection process identifies 17% possible redundant checks to be trimmed.
Breaking accessibility barriers: development & evaluation of an mHealth platform for remotely delivered exercise-based cardiac rehabilitation

I ACKNOWLEDGEMENTS . III FUNDING & SUPPORT . V CONTRIBUTION FROM STUDY INVESTIGATORS . VII PUBLICATIONS, PRESENTATIONS, REPORTS, & AWARDS . IX PUBLICATIONS . IX CONFERENCE PRESENTATIONS . X OTHER PRESENTATIONS . X REPORTS . X AWARDS DURING CANDIDATURE . XI TABLE OF CONTENTS . XIII LIST OF FIGURES . XVII LIST OF TABLES . XIX GLOSSARY . XXI THESIS INTRODUCTION . 1 1.1. THESIS AIMS . 1 1.2. THESIS OBJECTIVES . 2 1.3. THESIS STRUCTURE . 2 CORONARY HEART DISEASE: BURDEN & PREVENTION . 5 2.1. CORONARY HEART DISEASE BURDEN . 5 Coronary Heart Disease Burden In New Zealand . 6 2.2. CARDIOVASCULAR RISK FACTORS: KEY SECONDARY PREVENTION TARGETS . 6 2.3. CARDIAC REHABILITATION . 7 Cardiac Rehabilitation In New Zealand . 9 2.4. EXERCISE AS A MULTIFACTORIAL SECONDARY PREVENTION INTERVENTION . 10 2.5. CENTRE-BASED EXERCISE PROGRAMMES . 11 Effect On Mortality . 11 Effect On Recurrent Cardiac Events . 12 Effect On Exercise Capacity . 13 Effect On Blood Pressure . 14 Effect On Blood Lipid Profile . 15 Effect On Glucose Metabolism . 16 Effect On Body Composition. 17 Effect On Psychosocial Risk Factors . 18 Summary Of Evidence For exCR . 19 2.6. UTILISATION . 20 Utilisation In New Zealand . 21 2.7. HOME-BASED EXERCISE PROGRAMMES . 21 2.8. TELEHEALTH CARDIAC REHABILITATION . 22 2.9. MOBILE HEALTH CARDIAC REHABILITATION . 23 2.10. SUMMARY . 25 TELEHEALTH EXERCISE-BASED CARDIAC REHABILITATION: A SYSTEMATIC REVIEW & META-ANALYSIS . 27 3.
Baseless-Rumor Alert Bot to Promote Reliability of Information

: The dissemination of information by individuals, which has recently gained widespread attention, signiﬁ-cantly a ﬀ ects multitudes of people regardless of the accuracy of the information. There is a vast amount of information available, making it di ﬃ cult to identify baseless rumors, when the information is unveriﬁed. This study presents a bot called “Chillmo,” which has been developed to alert users of such rumors. The proposed system aims to raise users’ awareness of the reliability of information by actively alerting the users regarding the rumors receiving attention and providing the accurate information about such rumors through quizzes. Based on the experimental results, it was conﬁrmed that Chillmo can make the users skeptical about rumors, promote the veriﬁcation of authenticity, and raise their interest in the reliability of the information. In the future, we intend to conduct experiments with a larger number of people using the system to investigate trends by age and changes in the system usage over time.
Selective Data Acquisition in the Wild for Model Charging

The lack of sufficient labeled data is a key bottleneck for practitioners in many real-world supervised machine learning (ML) tasks. In this paper, we study a new problem, namely selective data acquisition in the wild for model charging : given a supervised ML task and data in the wild (e.g., enterprise data warehouses, online data repositories, data markets, and so on), the problem is to select labeled data points from the data in the wild as additional train data that can help the ML task. It consists of two steps (Fig. 1). The first step is to discover relevant datasets ( e.g. , tables with similar relational schema), which will result in a set of candidate datasets. Because these candidate datasets come from different sources and may follow different distributions, not all data points they contain can help. The second step is to select which data points from these candidate datasets should be used. We build an end-to-end solution. For step 1, we piggyback off-the-shelf data discovery tools. Technically, our focus is on step 2, for which we propose a solution framework called AutoData. It first clusters all data points from candidate datasets such that each cluster contains similar data points from different sources. It then iteratively picks which cluster to use, samples data points ( i.e. , a mini-batch) from the picked cluster, evaluates the mini-batch, and then revises the search criteria by learning from the feedback ( i.e. , reward) based on the evaluation. We propose a multi-armed bandit based solution and a Deep Q Networks-based reinforcement learning solution. Experiments using both relational and image datasets show the effectiveness of our solutions.
Package ‘ terminaldigits ’ May 13 , 2022

May 13, 2022 Title Tests of Uniformity and Independence for Terminal Digits Version 0.1. 0 Description Implements simulated tests for the hypothesis that terminal digits are uniformly distributed (chi-squared goodness-of-fit) and the hypothesis that terminal digits are independent from preceding digits (several tests of independence for r x c contingency tables). Also, for a number of distributions, implements Monte Carlo simulations for type I errors and power for the test of independence. License MIT + file LICENSE Depends R (>= 2.10) Imports discretefit, Rcpp Suggests dplyr, gt, ggplot2, knitr, rmarkdown, testthat (>= 3.0.0) LinkingTo Rcpp Config/testthat/edition 3 Encoding UTF-8 LazyData true RoxygenNote 7.1. 2 VignetteBuilder knitr NeedsCompilation yes Author Josh McCormick [aut, cre] (<https://orcid.org/0000-0002-2920-1119>) Maintainer Josh McCormick <josh.mccormick@aya.yale. edu> Repository CRAN Date/Publication 2022-05-13 09:10:12 UTC
Sucrose metabolism and candidate genes during catkin fibers 1 development in poplar 2 Running title : Sucrose metabolism in poplar catkin fiber

29 Poplar is an important tree species for ecological protection, wood production, 30 bioenergy and urban greening; it has been widely planted worldwide. However, the 31 catkin fibers produced by female poplars can cause environmental pollution and 32 safety hazards during spring. This study focused on Populus tomentosa, and revealed 33 the sucrose metabolism regulatory mechanism of catkin fibers development from 34 morphological, physiological and molecular aspects. Paraffin section suggested that 35 poplar catkin fibers were not seed hairs and produced from the epidermal cells of 36 funicle and placenta. Sucrose degradation via invertase and sucrose synthase played 37 the dominant role during poplar catkin fibers development. The expression patterns 38 revealed that sucrose metabolism-related genes played important roles during catkin 39 fibers development. Y1H analysis indicated that there was a potential interaction 40 between sucrose synthase 2 (PtoSUS2)/vacuolar invertase 3 (PtoVIN3) and MYB/ 41 bHLH transcription factors in poplar. Finally, the two key genes, PtoSUS2 and 42 PtoVIN3, had roles in Arabidopsis trichome density, indicating that sucrose 43 metabolism is important in poplar catkin fibers development. This study is not only 44 helpful for clarifying the mechanism of sucrose regulation during trichome 45 development in perennial woody plants, but also establishes a foundation to solve 46 poplar catkin fibers pollution through genetic engineering methods. 47
Porous Single-Crystalline Monolith to Enhance Catalytic Activity and Stability

Engineering the catalytic activity and stability of materials would require the identification of the structural features that can tailor active sites at surfaces. Porous single crystals combine the ordered lattice structures and disordered interconnected pores, and they would therefore provide the advantages of precise structure features to identify and engineer the active sites at surfaces. Herein, we fabricate porous single-crystalline vanadium nitride (VN) at centimeter scale and further dope Fe (Fe0.1V0.9N) and Co (Co0.1V0.9N) in lattice to engineer the active sites at surface. We demonstrate that the active surface is composed of unsaturated coordination of V-N, Fe-N, and Co-N structures which lead to the generation of high-density active sites at the porous single-crystalline monolith surface. The interconnected pores aid the pore-enhanced fluxion to facilitate species diffusion in the porous architectures. In the nonoxidative dehydrogenation of ethane to ethylene, we demonstrate the outstanding performance with ethane conversion of 36% and ethylene selectivity of 99% at 660°C. Remarkably stability as a result of their single-crystalline structure, the monoliths achieve the outstanding performance without degradation being observed even after 200 hours of a continuous operation in a monolithic reactor. This work not only demonstrates the effective structural engineering to simultaneously enhance the stability and overall performance for practically useful catalytic materials but also provide a new route for the element doping of porous single crystals at large scale for the potential application in other fields.
Efficacy and Safety of Botulinum Toxin A and Pulsed Radiofrequency on Postherpetic Neuralgia: A Randomized Clinical Trial

This study evaluated the effectiveness and safety of botulinum toxin type A (BoNT-A) and pulsed radiofrequency (RF) in the clinical treatment of postherpetic neuralgia (PHN). A total of 100 patients with PHN were randomly divided into two groups (n = 50 per group): RF group and BoNT-A group. Based on conventional drug treatment, patients were treated with either a single nerve root pulsed radiofrequency therapy or a single local subcutaneous injection of BoNT-A in the lesion area. All the patients were followed up for 24 weeks on pain scores, sleep quality, anxiety, and depression scores, etc. In the last follow-up at the end of 24 weeks postoperation, the pain scores of patients in both groups were significantly lower than those before the operation (P < 0.05), indicating that both treatments were effective against PHN; however, there was no significant difference between these two groups (P > 0.05). It is noteworthy that the subcutaneous injection of BoNT-A is relatively easy to administer and less expensive compared to RF. Therefore, we believe that the subcutaneous injection of BoNT-A is an effective and safe method for the treatment of PHN.
Multi-objective Neural Network Optimization for Boiler Combustion Systems

There are double requirements of both reducing operating cost and decreasing contaminant emission for power station boilers.A boiler combustion model has been obtained with the training of neural network based on combustion data collected,which can be used to forecast the NOx emission and combustion efficiency of the boiler with different combustion parameters.A reasonable balance point is found between high efficiency and low NOx emission of the boiler based on multi-objective optimization and operation data adjustment.
Strong Hydrogen Bonds in Acetylenedicarboxylic Acid Dihydrate

Acetylenedicarboxylic acid dihydrate (ADAD) represents a complex with strong hydrogen bonding between the carboxylic OH and the water molecule. An X-ray re-examination of the ADAD crystal structure confirms the O…O distance of the short hydrogen bonds, and clearly shows different bond lengths between the two oxygen atoms with respect to the carbon atom in the carboxyl group, indicating a neutral structure for the complex. The neutral structure was also confirmed by vibrational spectroscopy, as no proton transfer was observed. The diffraction studies also revealed two polymorph modifications: room temperature (α) and low temperature (β), with a phase transition at approximately 4.9 °C. The calculated vibrational spectra are in satisfactory agreement with the experimental spectra. A comparison of the structure and the vibrational spectra between the ADAD and the oxalic acid dihydrate reveals some interesting details. The crystal structures of both crystal hydrates are almost identical; only the O…O distances of the strongest hydrogen bonds differ by 0.08 Å. Although it was expected that a larger O…O spacing in the ADAD crystal may significantly change the infrared and Raman spectra, especially for the frequency and the shape of the acidic OH stretching vibration, both the shape and frequency are almost identical, with all subpeaks topped on the broad OH stretching vibration. The O…O distance dependent are only in- and out-of-plane OH deformations modes. The presence of polarons due to the ionized defects was not observed in the vibrational spectra of ADAD. Therefore, the origin of the broad OH band shape was explained in a similar way to the acid dimers. The anharmonicity of a potential enhances the coupling of the OH stretching with the low-frequency hydrogen bond stretching, which, in addition to the Fermi resonance, structures the band shape of the OH stretching. The fine structure found as a superposition of a broad OH stretching is attributed to Davydov coupling.