Miracle-dz/UniFAR-FTUs huggingface dataset:
Are We Ready For Learned Cardinality Estimation?

Cardinality estimation is a fundamental but long unresolved problem in query optimization. Recently, multiple papers from different research groups consistently report that learned models have the potential to replace existing cardinality estimators. In this paper, we ask a forward-thinking question: Are we ready to deploy these learned cardinality models in production? Our study consists of three main parts. Firstly, we focus on the static environment (i.e., no data updates) and compare five new learned methods with eight traditional methods on four real-world datasets under a unified workload setting. The results show that learned models are indeed more accurate than traditional methods, but they often suffer from high training and inference costs. Secondly, we explore whether these learned models are ready for dynamic environments (i.e., frequent data updates). We find that they cannot catch up with fast data up-dates and return large errors for different reasons. For less frequent updates, they can perform better but there is no clear winner among themselves. Thirdly, we take a deeper look into learned models and explore when they may go wrong. Our results show that the performance of learned methods can be greatly affected by the changes in correlation, skewness, or domain size. More importantly, their behaviors are much harder to interpret and often unpredictable. Based on these findings, we identify two promising research directions (control the cost of learned models and make learned models trustworthy) and suggest a number of research opportunities. We hope that our study can guide researchers and practitioners to work together to eventually push learned cardinality estimators into real database systems.
AUTOMATION OF INTERNAL FRICTION MEASUREMENT APPARATUS OF INVERTED TORSION PENDULUM TYPE

Waveform analysis instead of traditional wave height analysis was employed. A complete internal friction against temperature profile is obtainable as well as temperature, period of oscillation and internal friction data in table format. In addition to the elimination of manual operation and supervision, the accuracy of measurement was greatly increased because by using Fourier transformation of the waveform of damped oscillation the disturbing components of parasitic motions such as flexural or precessional are separated out from the genuine torsional motion and eliminated on calculating the decay constant.
Estádio de maturação dos frutos e fatores relacionados aos aspectos nutritivos e de textura da polpa de pequi (Caryocar brasiliense Camb.)

The experiment was carried out with the purpose to obtain information about changes in the constituents responsible for nutritive and texture traits of pequi pulp related with harvest time and types of freezing. Fruits were collected in the tree before natural fall, in the ground after natural fall and in the ground three days after natural fall. In each harvest time, while one batch of the putamens was frozen in liquid nitrogen and afterwards stored in a freezer, another batch was frozen directly in the freezer. After six months the following analyses were performed in the pulp: contents of proteins, total carotenoid, b-carotene, licopene, vitamin A and lipids (nutritive characters); content of cellulose, hemicelluloses, total pectin, total and bound calcium, activity of polygalacturonase (PG) and pectinmethilesterase (PME) of the cell wall. The contents of pigments, lipids, proteins and activity of PG were the highest in the most advanced stages of maturation of fruits. On the other hand the content of cellulose decreased with the advanced stages of maturation whiles the content of hemicelluloses, total and bound calcium were not affected at the stages of maturation of the fruits. The types of freezing affected the contents of pigments; higher in the pulp frozen in liquid nitrogen; and the activity of PG was higher in the pulp frozen directly in the freezer. The fruits collected in the tree showed the lowest eating quality as judged by their lowest contents of pigments, protein and lipids. The results indicate that the most appropriate harvest time to higher quality of pequi fruits is after natural fall.
Digital Twins Are Not Monozygotic – Cross-Replicating ADAS Testing in Two Industry-Grade Automotive Simulators

The increasing levels of software- and data-intensive driving automation call for an evolution of automotive soft-ware testing. As a recommended practice of the Verification and Validation (V&V) process of ISO/PAS 21448, a candidate standard for safety of the intended functionality for road vehicles, simulation-based testing has the potential to reduce both risks and costs. There is a growing body of research on devising test automation techniques using simulators for Advanced Driver-Assistance Systems (ADAS). However, how similar are the results if the same test scenarios are executed in different simulators? We conduct a replication study of applying a Search-Based Software Testing (SBST) solution to a real-world ADAS (PeVi, a pedestrian vision detection system) using two different commercial simulators, namely, TASS/Siemens PreScan and ESI Pro-SiVIC. Based on a minimalistic scene, we compare critical test scenarios generated using our SBST solution in these two simulators. We show that SBST can be used to effectively generate critical test scenarios in both simulators, and the test results obtained from the two simulators can reveal several weaknesses of the ADAS under test. However, executing the same test scenarios in the two simulators leads to notable differences in the details of the test outputs, in particular, related to (1) safety violations revealed by tests, and (2) dynamics of cars and pedestrians. Based on our findings, we recommend future V&V plans to include multiple simulators to support robust simulation-based testing and to base test objectives on measures that are less dependant on the internals of the simulators.
High‐Temperature Vaporization Behavior of Oxides II. Oxides of Be, Mg, Ca, Sr, Ba, B, Al, Ga, In, Tl, Si, Ge, Sn, Pb, Zn, Cd, and Hg

In order to assess the high‐temperature vaporization behavior and equilibrium gas phase compositions over the condensed oxides of Be, Mg, Ca, Sr, Ba, B, Al, Ga, In, Tl, Si, Ge, Sn, Pb, Zn, Cd, and Hg, the relevant thermodynamic and molecular constant data have been compiled and critically evaluated. Selected values of the Gibbs energy functions of condensed and vapor phases are given in the form of equations valid over wide temperature ranges, along with the standard entropies and enthalpies of formation. These data were used to generate plots of equilibrium partial pressures of vapor species as functions of temperature for representative environmental conditions ranging from reducing to oxidizing. The calculated partial pressures and compositions agree, for the most part, with experimental results obtained under comparable conditions. Maximum vaporization rates have been calculated using the Hertz–Knudsen equation. Literature references are given.
The high-bias stability of monatomic chains

For the metals Au, Pt and Ir it is possible to form freely suspended monatomic chains between bulk electrodes. The atomic chains sustain very large current densities, but finally fail at high bias. We investigate the breaking mechanism, that involves current-induced heating of the atomic wires and electromigration forces. We find good agreement of the observations for Au based on models due to Todorov and co-workers. The high-bias breaking of atomic chains for Pt can also be described by the models, although here the parameters have not been obtained independently. In the limit of long chains the breaking voltage decreases inversely proportional to the length.
Reconstructing unseen modalities and pathology with an efficient Recurrent Inference Machine

Objective: To allow efficient learning using the Recurrent Inference Machine (RIM) for image reconstruction whereas not being strictly dependent on the training data distribution so that unseen modalities and pathologies are still accurately recovered. Methods: Theoretically, the RIM learns to solve the inverse problem of accelerated-MRI reconstruction whereas being robust to variable imaging conditions. The efficiency and generalization capabilities with different training datasets were studied, as well as recurrent network units with decreasing complexity: the Gated Recurrent Unit (GRU), the Minimal Gated Unit (MGU), and the Independently Recurrent Neural Network (IndRNN), to reduce inference times. Validation was performed against Compressed Sensing (CS) and further assessed based on data unseen during training. A pathology study was conducted by reconstructing simulated white matter lesions and prospectively undersampled data of a Multiple Sclerosis patient. Results: Training on a single modality of 3T $T_1$-weighted brain data appeared sufficient to also reconstruct 7T $T_{2}^*$-weighted brain and 3T $T_2$-weighted knee data. The IndRNN is an efficient recurrent unit, reducing inference time by 68\% compared to CS, whereas maintaining performance. The RIM was able to reconstruct lesions unseen during training more accurately than CS when trained on $T_2$-weighted knee data. Training on $T_1$-weighted brain data and on combined data slightly enhanced the signal compared to CS. Conclusion: The RIM is efficient when decreasing its complexity, which reduces the inference time, whereas still being able to reconstruct data and pathology that was unseen during training.
Ba8Ge43 revisited: a 2a' x2a' x2a' Superstructure of the Clathrate-I Type with Full Vacancy Ordering

The reinvestigation of the crystal structure of Ba8Ge43□3 (space group , no. 230; a = 21.3123(5) A; Z = 8; Pearson symbol cI408) shows a full ordering of the vacancies (□) in the germanium framework. This ordered crystal structure can be considered as a derivative of an ideal “Ba8Ge46” clathrate-I type structure (, a′ = a/2) in which three Ge vacancies (per formula unit) are allowed to order in a cubic superstructure with a doubled unit cell parameter (□ at the 24c site, space group ). In the resulting Ge framework, each vacancy □ is surrounded by four three-bonded (3b)Ge- species. The ordering in crystals of as-cast samples (cooled in argon atmosphere, non-annealed) is partially disrupted. For the “as-cast” crystals, a short-range model is proposed based on the partial distribution of Ge on the 24c and 24d sites. From the electron balance, Ba8Ge43 can be considered as a metallic Zintl phase with four excess electrons per formula unit. The Ba8Ge43 phase is stable in the temperature range 770 - 810 °C and exists in equilibrium with Ba6Ge25 and Ge. By decomposition of undercooled (metastable) Ba8Ge43, a new metastable binary BaGe˜5 phase is formed. Ba8Ge43 erneut untersucht: eine 2a′×2a′×2a′ Clathrat-I Uberstruktur mit vollstandig geordneten Leerstellen Die erneute Untersuchung der Kristallstruktur von Ba8Ge43□3 (Raumgruppe , Nr. 230; a = 21.3123(5) A; Z = 8; Pearson-Symbol cI408) zeigt eine komplette Ordnung der Defekte (□) innerhalb des Germaniumgerustes. Diese geordnete Struktur kann als ein Abkommling des idealen “Ba8Ge46” Clathrat-I-Typs (, a′ = a/2) beschrieben werden, in der drei Defekte pro Formeleinheit in einer kubischen Uberstrukturzelle mit verdoppeltem Gitterparameter (□ auf der Lage 24c in der Raumgruppe ) geordnet vorliegen mit vier dreibindigen (3b)Ge--Spezies um jeden Defekt herum. Die Ordnung in Kristallen aus nicht-getemperter Proben wird gestort. Fur die nicht-getemperten Kristalle wurde ein Ordnungsmodell mit partieller Verteilung von Ge auf den Lagen 24c und 24d vorgeschlagen. Die Elektronenbilanz zeigt, dass Ba8Ge43 als metallische Zintl-Phase mit vier Uberschusselektronen pro Formeleinheit beschrieben werden kann. Die Phase Ba8Ge43 ist im Temperaturbereich von 770 °C bis 810 °C stabil und steht im Gleichgewicht mit Ba6Ge25 und Ge. Durch Zersetzung von unterkuhltem, metastabilen Ba8Ge43 bildet sich eine neue metastabile binare Phase BaGe˜5.
Modelling of composting of food waste in a column reactor

The composting of organic solid waste, the mixture of fruit and vegetable leftovers enriched with night soil, was investigated in a closed thermally insulated reactor. It was found that 80.9 % of the original substrate biodegraded after 14 days. A mathematical model of the column reactor was proposed where the biodegradation rate of the organic solid waste was described using simple n-thorder kinetics. A good prediction of process performance was obtained using the proposed kinetics and experimentally obtained reaction heat.
Graph representation learning and software homology matching based A study of JAVA code vulnerability detection techniques

In nowadays using different tools and apps is a basic need of people's behavior in life, but the security issues arising from the existence of source code plagiarism among tools and apps are likely to bring huge losses to companies and even countries, so detecting the existence of vulnerabilities or malicious code in software becomes an important part of protecting information and detecting software security. This project is based on the aspect of JAVA code vulnerability detection based on graph representation learning and software homology comparison to carry out research. This project will be based on the content of deep learning, using a large number of vulnerable source code, extracting its features, and transforming it into a graph so that it can be tested source code for comparison and report the vulnerability content. The main work and results of this project are as follows: 1.By extracting the example dataset and generating json files to save the feature information of relevant java code; by generating vector files, bytecode files and dot files, and batch extracting nodes and edges based on the contents of the dot files for subsequent machine learning use, the before and after steps and operations form a logical self-consistency to ensure the integrity of the project. 2.Through the study of graph neural networks and graph convolutional neural networks, relevant models are selected for machine learning using predecessor files and manual model tuning is performed to ensure good learning results and feedback for the machine learning part of the project. 3.This project training dataset negative samples for sard above the shared dataset, which contains 46636 java vulnerability source code, and dataset support environment, test dataset negative samples dataset also from sard, positive samples dataset are generated from the relevant person in charge. 4.Based on Graph Neural Network (GNN) and Graph Convolutional Neural Network (GCN), this project will design and implement a whole set of automated vulnerability detection system for java code. 5. All the related contents of this project, after the human extensive search of domestic and foreign related papers and materials, there are not all projects or contents similar to this project, the same papers and materials appear, all the problems involved in this project and related ideas are for the project this group of people thinking, looking for solutions.
A Comparative Study on Consumption Functions: The Case of the European Union*

The consumption function describes the relationship between consumption expenditures and income. As is well known, the distribution of macroeconomic data such as income and consumption is unequal. Accordingly, estimators derived from linear models may be inefficient. This study attempted to reach efficient estimators, using gamma distribution, within the limits of this study. The main purpose of this study is to estimate parameters of mainstream consumption functions using the panel-data of EU members and negotiating countries. Using data from the World Bank, the European Union Macroeconomic Database (AMECO), and the Bank of International Settlement (BIS), consumption functions were estimated by the Generalized Linear Model (GLM) approach. The reliability of the estimators was tested with the Generalized Moments Method (GMM). Furthermore, the study uses GLM, based on machine learning, to obtain robust estimators for overfitting. Findings of all three methods are compatible with each other and the “Permanent Income” hypothesis verified.
Multi-stakeholder Perspectives on the Maternal, Provider, Institutional, Community, and Policy Drivers of Disrespectful Maternity Care in South-East Nigeria

Purpose Understanding the contextualized perspectives of stakeholders involved in maternal health care is critical to promoting respectful maternity care. This study explored maternal, provider, institutional, community, and policy level drivers of disrespectful maternity care in Southeast Nigeria. This study also identified multi-stakeholder perspectives on solutions to implementing respectful maternity care in health facilities. Materials and Methods This was a mixed-methods cross-sectional study conducted in two urban cities of Ebonyi State, South-eastern Nigeria. Data were collected using semi-structured questionnaires, focus group discussions, and key informant interviews with mothers, providers, senior facility obstetric decision-makers, ministry of health policymaker, and community members. Quantitative data and qualitative data were analysed using SPSS version 20 and manual thematic analysis, respectively. Results Maternal level drivers were poor antenatal clinic attendance, uncooperative clients, non-provision of birthing materials, and low awareness of rights. Provider factors included work overload/stress, training gaps, desire for good obstetric outcome, under-remuneration and under-appreciation. Institutional drivers were poor work environments including poorly designed wards for privacy, stressful hospital protocols, and non-provision of work equipment. Community-level drivers were poor female autonomy, empowerment, and normalization of disrespect and abuse during childbirth. The absence of targeted policies and the high cost of maternal health services were identified as policy-related drivers. Conclusion A variety of multi-level drivers of disrespectful maternity care were identified. A diverse and inclusive multi-stakeholder approach should underline efforts to mitigate disrespectful maternity care and promote respectful, equitable, and quality maternal health care.
Synthesis of TiO2/Nanozeolite Composites for Highly Efficient Photocatalytic Oxidation of Propene in the Gas Phase

In this work, we reported the preparation of composites based on titania (TiO2) and Zeolite Socony Mobil-5 (ZSM-5) nanozeolite, following two approaches (i.e., incorporating the presynthesized zeolite in the synthesis medium of TiO2 and incorporating presynthesized TiO2 in the synthesis medium of ZSM-5). The materials synthesized were characterized by X-ray fluorescence (XRF), X-ray diffraction (XRD), nitrogen adsorption, X-ray photoelectron spectroscopy (XPS), ultraviolet–visible spectroscopy (UV–vis), transmission electron microscopy (TEM), scanning electron microscopy (SEM), and energy-dispersive X-ray (EDX) spectrometry analysis, and their photocatalytic activities were assessed in the oxidation of propene in the gas phase. It was observed that the synthesis methodology affects the final properties of the composite, which ultimately affected their photocatalytic performance in the studied application. It was found that the Nano-ZSM5/TiO2 composite was the most active among the investigated samples, which was attributed to the intimate contact between the two components of the composite, the preserved properties of the photocatalytic active phase in the final material, and the positive contribution of the nanozeolite by increasing the local concentration of propene.
An NMR-Smell Module for the First-Semester General Chemistry Laboratory

This article describes a module of NMR-smell experiments for the general chemistry laboratory that can be taught in two-week or three-week formats; the module has also been run in a modified form with high school students during the summer. The series of experiments involves an exploration of organic stereochemistry via hands-on model building, several chemosensory smell tests, and hands-on use of 13C NMR, thus serving as a powerful interdisciplinary lab involving chemistry, physics, and neuroscience. Similarities and differences between NMR and MRI methods are emphasized. By the end of the NMR-smell module, students have a very basic knowledge of the physics of the NMR process, the ability to identify different types of organic isomerism, the ability to identify stereogenic centers in molecules, and a basic knowledge of the importance of 3-D structure in chemistry. The less quantifiable, but in some ways more important, benefits of the module that students really appreciate are getting to "touch and play.