Miracle-dz/UniFAR-FTUs huggingface dataset:
A proposal for a minimal model of free reeds

In this paper we propose a minimal model for free reeds taking into account the significant phenomena. This free reed model may be used to build models of free reed instruments which permit numerical simulations. Several definitions for the section by which the airflow passes through the reed are reviewed and a new one is proposed which takes into account the entire escape area under the reed and the reed thickness. To derive this section, it is necessary to distinguish the neutral section (the only section of the reed which always keeps its length constant while moving) from the upstream or downstream sections. A minimal configuration is chosen to permit the instabilities of both (-,+) and (+,-) reeds on the basis of a linear analysis of instabilities conditions. This configuration is used to illustrate, with temporal simulations, the minimal model for both kinds of reeds and to discuss the model assumptions. Some clues are given about the influence, on the playing frequency and on the dynamic of the sound, of two main parameters of the geometrical model: the size of the volume and the level of the excitation. It is shown that the playing frequency of a (+,-) reed can vary in a large range according to the size of the volume upstream of the reed; that the playing frequency is nearly independent of the excitation but that the dynamic of the sound increases with the excitation level. Some clues are also proposed to determine the nature of the bifurcation for free reeds: it seems that free reeds may present inverse bifurcations. The influence of the reed thickness is also studied for configurations where the reed length or the reed width vary to keep the mass constant. This study shows that the reed thickness can have a great influence on the sound magnitude, the playing frequency and the magnitude of the reed displacement which justifies its introduction in the reed model.
Comparison of six approaches to predicting droplet activation of surface active aerosol – Part 2: Strong surfactants

Abstract. Surfactants have been a focus of investigation in atmospheric sciences for decades due to their ability to modify the water uptake and cloud formation potential of aerosols. Surfactants adsorb at the surface and can decrease the surface tension of aqueous solutions. In microscopic aqueous droplets with finite amounts of solute, surface adsorption may simultaneously deplete the droplet bulk of the surfactant. While this mechanism is now broadly accepted, the representation in atmospheric and cloud droplet models is still not well constrained. We compare the predictions of five bulk–surface partitioning models documented in the literature to represent aerosol surface activity in Köhler calculations of cloud droplet activation. The models are applied to common aerosol systems, consisting of strong atmospheric surfactants (sodium myristate or myristic acid) and sodium chloride in a wide range of relative mixing ratios. For the same particles, the partitioning models predict similar critical droplet properties at small surfactant mass fractions, but differences between the model predictions increase significantly with the surfactant mass fraction in the particles. Furthermore, significantly different surface tensions are predicted for growing droplets at given ambient conditions along the Köhler curves. The inter-model variation for these strong surfactant particles is different than previously observed for moderately surface active atmospheric aerosol components. Our results highlight the importance of establishing bulk–surface partitioning effects in Köhler calculations for a wide range of conditions and aerosol types relevant to the atmosphere. In particular, conclusions made for a single type of surface active aerosol and surface activity model may not be immediately generalized.
Supplementary of PointConvFormer: Revenge of the Point-based Convolution

As in Fig. 1, we use a U-Net structure for semantic segmentation tasks where we specify a number of base channels, and then have each downsampling stage to use successively more channels. The U-Net contains 5 resolution levels. For each resolution level, we use grid subsampling to downsample the input point clouds, then followed by several pointconvformer residual blocks. The number of layers in the blocks at each level is [3, 2, 4, 6, 6] respectively for the main model. For the Lite model at 10cm voxel size, the number of layers is [3, 2, 2, 2, 2] at each level, respectively. For the Lite model at 5cm voxel size, the number of layers at each level is [3, 3, 3, 3, 3], respectively (Table 1). For the 9.4M model at the 2cm grid resolution, because it is too fine to be captured by 5 downsampling levels, we utilize a sixth block which contains 2 layers. For the 5.6M parameter model at the 2cm resolution, we followed PointTransformerv2 to use a set of resolution levels of [0.02, 0.06, 0.15, 0.375, 0.9375], and also replaced the 3 costly initial layers with a single linear layer, which significantly reduced the size and runtime of the model. This model obtained only 73.0% without mix3D, however, mix3D boosted its performance to 74.4% which is as good as the original model that is significantly larger. For the full model, we have Cmid = 16, and for the Lite model we have Cmid = 4 which greatly reduced the parameter count with no performance drop at the Lite model scale. Latter blocks have more layers since they are cheaper to compute, similar to image convolutional models. For deconvolution, we just use PointConv as described in the main paper. And each block has a single PointConvTranspose layer, which is a PointConv layer that upsamples to locations without any features. For the encoder, we utilize the bottleneck residual architecture described in the paper. For the decoder, because the output dimensionality is always smaller than the input dimensionality, we find that having a bottleneck to 1/4 of the output dimensionality reduced too much capac-
Deep Learning-based Handheld Device-Enabled Symptom-driven Recording: A Pragmatic Approach for the Detection of Post-ablation Atrial Fibrillation Recurrence

Objective: Symptom-driven electrocardiogram (ECG) recording plays a significant role in the detection of post-ablation atrial fibrillation recurrence (AFR). However, making timely medical contact whenever symptoms occur may not be practical. Herein, a deep learning (DL)-based handheld device was deployed to facilitate symptom-driven monitoring. Methods: A cohort of patients with paroxysmal atrial fibrillation (AF) was trained to use a DL-based handheld device to record ECG signals whenever symptoms presented after the ablation. Additionally, 24-hour Holter monitoring and 12-lead ECG were scheduled at 3, 6, 9, and 12 months post-ablation. The detection of AFR by the different modalities was explored. Results: A total of 22 of 67 patients experienced AFR. The handheld device and 24-hour Holter monitor detected 19 and 8 AFR events, respectively, five of which were identified by both modalities. A larger portion of ECG tracings was recorded for patients with than without AFR [362(330) vs. 132(133), P=0.01)], and substantial numbers of AFR events were recorded from 18:00 to 24:00. Compared to Holter, more AFR events were detected by the handheld device in earlier stages (HR=1.6, 95% CI 1.2–2.2, P<0.01). Conclusions: The DL-based handheld device-enabled symptom-driven recording, compared with the conventional monitoring strategy, improved AFR detection and enabled more timely identification of symptomatic episodes.
Biological Neurons vs Deep Reinforcement Learning: Sample efficiency in a simulated game-world

How do synthetic biological systems and artificial neural networks compete in their performance in a game environment? Reinforcement learning has undergone significant advances, however remains behind biological neural intelligence in terms of sample efficiency. Yet most biological systems are significantly more complicated than most algorithms. Here we compare the inherent intelligence of in vitro biological neuronal networks to state-of-the-art deep reinforcement learning algorithms in the arcade game ’pong’. We employed DishBrain, a system that embodies in vitro neural networks with in silico computation using a high-density multielectrode array. We compared the learning curve and the performance of these biological systems against time-matched learning from DQN, A2C, and PPO algorithms. Agents were implemented in a reward-based environment of the ‘Pong’ game. Key learning characteristics of the deep reinforcement learning agents were tested with those of the biological neuronal cultures in the same game environment. We find that even these very simple biological cultures typically outperform deep reinforcement learning systems in terms of various game performance characteristics, such as the average rally length implying a higher sample efficiency. Furthermore, the human cell cultures proved to have the overall highest relative improvement in the average number of hits in a rally when comparing the initial 5 minutes and the last 15 minutes of each designed gameplay session.
Transition from superhydrophilic to superhydrophobic of silicon wafer by a combination of laser treatment and fluoropolymer deposition

We propose a novel approach for synthesizing silicon surfaces ensuring arbitrary apparent contact angle within the range from 0° to 170° for water. Superhydrophilicity of silicon substrates is achieved by nanosecond laser treatment and subsequent change in the contact angle is obtained by deposition of fluoropolymer films with different thicknesses by a hot wire chemical vapor deposition (HWCVD) method. The deposited film thickness needed to obtain superhydrophobicity was determined. The wettability properties of the produced fluoropolymer–silicon system are analyzed using the Wenzel and Cassie theories.
Impact of Job Crafting Intervention on Psychological Empowerment, Work Engagement, and Affective Well-being in Teachers

Organizations are under a lot of pressure to survive in the ever-changing world of competition. Human resources are a critical aspect for which there is no substitute. Job crafting is a component that can assist employees to improve the positive aspects of their work while reducing the negative aspects' impact. The study's main goal is to see how a job crafting intervention affects employees' psychological empowerment, work engagement, and affective well-being. It is a quasi-experimental design including both experimental and control groups. The population consists of teachers. Job crafting intervention was provided to the employees. It consists of six core elements including three sessions and pre-post testing taking a total of the six-week time period. Pre-testing and post-testing were done by using the Job Crafting questionnaire developed by Tims et al. (2012), Psychological Empowerment Scale (Spreitzer, 1995), Utrecht Work Engagement Scale (UWES; Schaufeli et al., 2006) and Job-related Affective Well-being Scale, JAWS (Van Katwyk et al., 2000). A paired sample t-test was run to see the effect of job crafting intervention on job crafting, psychological empowerment, work engagement, and affective well-being. The results were positive and significant. The relationship between job crafting, psychological empowerment, work engagement, and affective well-being was also assessed and there was a positive and significant correlation. The job crafting training can make it achievable to initiate employees’ active job crafting behavior and work engagement. Employers should recognize the need to assist and motivate staff to maximize their resources and meet their challenges.
An Analysis of the Management Practice Using Organizational Metaphor Theory

Analysis of the organization becomes necessary when the organization shows certain phenomena such as performance decreasing or increasing sharply, organizations structural changes, organizational climate is not conducive, the number of employee changes, and so on. This study explores the implementation of Morgan’s Organizational Metaphor Theory in the management practice within a private higher education institution in Jakarta. This study helps to interpret the advancements and barriers in the current management practice. This study uses a qualitative research method with a case study approach. Some analyses were made based on the eight metaphors about organizations according to Morgan, namely the metaphor of the organization 1) as a machine, 2) as an organism, 3) as the brain, 4) as culture, 5) as a political system, 6) as a physical prison, 7) as a dynamic and transforming reality, and 8) as an instrument of domination. The results show that the institutions should strive to make some important changes in their management practice, especially by applying the recent management practices, including treating lecturers and staff as partners. This study implies that the implementation of modern management must be accompanied by the leadership. Keywords: organizational metaphor theory, modern management, leadership commitment
Counterfactual Replacement Analysis for Interpretation of Counterfactual Replacement Analysis for Interpretation of Blackbox Sexism Classification Models Blackbox Sexism Classification Models

This paper describes the AKD team’s system designed for SemEval-2023 Task 10: Explain-able Detection of Online Sexism (EDOS). We implement a simple fine-tuned GPT-3 model, ranking 26 on the leaderboard for task A. We also discuss different approaches to interpretability in the context of critiquing the EDOS task’s sub-category oriented approach. Finally, we propose counterfactual replacement analysis, a novel prototype technique for approaching explainability.
Value Relevance of GRI Economic and Ethics/Integrity Disclosure among Listed Manufacturing Firms in Nigeria: The Role of Ownership Concentration

This study evaluates the value relevance of GRI Economic and Ethics/Integrity Disclosures among listed Manufacturing Firms in Nigeria. It also investigates the role of ownership concentration in influencing the value relevance of GRI disclosures. The study adopted the purposive sampling technique to select a sample of 43 listed Manufacturing Firms on the Nigerian Exchange Group (NGX) for the 8-year period from 2014–2021. The study used the Ohlson (1995) model to analyze the value relevance of GRI Economic and Ethics/Integrity Disclosures. The results of the study showed that GRI Economic Disclosure, Ethics/Integrity Disclosure and Ownership Concentration are value relevant in the Nigerian listed Manufacturing Firms. Further, the study found that ownership concentration has a significant moderating influence on the value relevance of GRI Economic and Ethics/Integrity Disclosures among listed Manufacturing Firms in Nigeria. The results of the study provide evidence that listed Manufacturing Firms should pay special attention to GRI Economic and Ethics/Integrity Disclosures in order to enhance firm value and investor decision-making. The study contributes to the extant literature by being the first to investigate the value relevance of GRI Economic and Ethics/Integrity Disclosures and the role of ownership concentration among listed Manufacturing Firms in Nigeria. The study recommends that listed Manufacturing Firms should respond to the GRI Economic and Ethics/Integrity Disclosure requirements in order to enhance firm value.
Using twitter data to measure inflation perception

Anchoring inflation expectations and measuring the current and future impact of prices evolution is a crucial issue for central banks. With the current rise of social networks, a new source of information has appeared to measure inflation perception. In this study, we propose an indicator of inflation perception based on Twitter data, focusing on the specific community of users who retweeted posts of the Banque de France account. Despite the bias induced, this strategy allows to efficiently extract information from an expert community on economic and financial subjects, capturing a potentially more relevant signal. We combine supervised machine learning and natural language processing methods with dictionarybased filters to classify all tweets posted by these retweeters, by first detecting whether they relate to prices issues, and then assessing which of the selected tweets mention inflation, deflation or is off-topic. Finally, we create a Twitter indicator of inflation perception, which is the difference between the number of tweets about inflation and the number of tweets about deflation. The resulting Twitter indicator is consistent with monthly household surveys on inflation expectations and perception, and is highly correlated with the inflation rate. We also show that it has a strong anticipatory power of the future inflation. These results suggest that it is both possible and relevant to use Twitter data to construct a daily measure of inflation perceptions. JEL Classification: E31, C53, C55, D84, E58.
Subjective and contextual determinants of engagement in actions beyond basic professional duties

: The objectives of the study were to recognize the main motives for engagement in actions beyond basic professional duties among various professional groups and to identify selected properties of the subject and context of activities that contribute to such beyond-duties engagement. A set of questionnaires in electronic or paper form was completed by 209 employees, including 104 representatives of assistance professions and 95 representatives of creative professions, aged between 21 and 67 years (M = 37.54, SD = 9.55). It was established that the main motives for beyond-duties engagement in both groups of professionals under study were self-actualization and acting for the benefit of others. With the use of structural equation modelling, subjective and contextual determinants of engagement in actions beyond basic professional duties were identified as self-efficacy and action meaningfulness. The research discusses the phenomenon of engagement in extra activities at work from the perspective of the acting person/employee. The study results can be useful for managers in setting goals of appropriate type and manner to their employees as well as for task assignment.
(2016). Expression of Prolyl hydroxylases 2 and 3 in chick embryos. Gene Expression Patterns, 21(2), 97-102.

Hypoxic cellular response is crucial for normal development as well as in pathological conditions in order to tolerate low oxygen. The response is mediated by Hypoxia Inducible Factors (HIFs), where the α -subunit of HIF is stabilised and able to function only in low oxygen. Prolyl hydroxyrases (PHDs) are oxygen dependent dioxygenase enzymes that hydroxyrate HIF-α leading to HIF degradation. Thus PHDs function as an oxygen sensor for the function of HIFs. Here we describe the mRNA expression pattern of PHDs in chick embryos. Up to embryonic day 2, PHDs are weak without specific localisation, whereas from day 3 localised expression was observed in the eye, branchial arches and dermomyotome. Later in the limb development PHDs were expressed in the perichondral mesenchyme, excluded from the developing limb cartilages.
Shaping the spectrum of a down-converted mid-infrared frequency comb

New methods to frequency down-convert a broadband near infrared frequency comb into the mid-infrared by three-wave mixing are studied. Modulation of the second-order nonlinear coefficient based on the concepts of either nonlinear spectral holography or quasi-periodic modulation enables us to obtain different spectral shapes of the mid-infrared comb. It includes flat and broadband single-band or dual-band spectra, or a shape that exhibits two or even three sharp peaks at chosen frequencies. The methods we present can be used to tailor the frequency comb spectra to selected molecular absorption lines in the mid-infrared.
To Buy or Not to Buy: A Study of Halal Labelling and Celebrity Endorser on Purchase Decision

Aims: This study aims to determine the effect of Halal Labelling and Celebrity Endorser on Wardah Cosmetics Purchasing Decisions at students of the Faculty of Economics, Makassar State University. Study Design: Quantitative descriptive. Place and Duration of Study: Makassar City, from January to April 2023. Methodology: The population in this study were active female students of the Faculty of Economics UNM class of 2022 with a population of 861 female students. Sampling using purposive sampling technique and obtained a sample size of 100 respondents. The coming analysis technique used is multiple linear regression analysis using SPSS. Results: The results of this study obtained a value of the Correlation Coefficient Test of 0.716 which indicates that there is a strong relationship between the two variables. Based on T test, it is found that the Halal Labelling has the strongest influence on purchasing decision. And R square of 0.503 means that the halal labelling variable and celebrity endorser have an influence of 50.3% on purchasing decisions, and the rest is influenced by other variables. This study shows that: 1) Halal labelling has a positive and significant effect on purchasing decisions; 2) Celebrity Endorser has a positive and significant effect on purchasing decisions. Conclusion: There is an influence between Halal Labelling and Celebrity Endorser partially and simultaneously on Wardah Cosmetics Purchasing Decisions on students of the Faculty of Economics, Makassar State University.
Bivariate Normal-Power Series Class of Distributions: Model, Properties and Applications

Recently Mahmoudi and Mahmoodian (2017a) introduced a new class of distributions which obtain by compounding normal and power series distributions. This class of distributions are very flexible and can be used quite effectively to analysis skewd data. In this paper we proposed a new bivariate class of distributions with the normal-power series distributions marginals.
HMMN: Hard Sample Mining Memory Network for Re-identiﬁcation

In this paper, we exploit a novel memory-based strategy for mining hard triplets in the ﬁeld of re-identiﬁcation. This strategy is realized with an end-to-end deep learning based framework using an external memory pool. It could be used in the security system. We show that the pipeline is able to explicitly provide hard negative and positive samples to generate effective triplets, which are important for online metric learning. That’s because the representative triplets could provide distinctive information to help understand the concept of metric learning between categories. In addition, a ‘focal-triplet loss’ function is proposed to help deal with the lack of positive or negative samples for one anchor, and imbalance between easy and hard triplets for mini-batch. Experimental re-sults on Market-1501, CUHK03 and DukeMTMC-reID demonstrate the effectiveness of our method, and its performance outperforms that of some existing methods.
SUB-RIEMANNIAN GEODESIC FLOW ON META-ABELIAN CARNOT GROUPS

The paper establishes a correspondence, making a symplectic reduction, between the regular geodesics in a meta-abelian Carnot group G and the space of solutions to a family of classical electro-mechanical systems on Euclidean space. The correspondence characterizes when these normal subRiemannian geodesic ﬂows are integrable or admit no closed geodesics. More-over, we can classify the integrable subRiemannian geodesic ﬂow on the Carnot group with a rank higher than 2, which is a more general perspective than the previously done.