Tabular data: Deep learning is not all you need

A key element in solving real-life data science problems is selecting the types of models to use. Tree ensemble models (such as XGBoost) are usually recommended for classification and regression problems with tabular data. However, several deep learning models for tabular data have recently been proposed, claiming to outperform XGBoost for some use cases. This paper explores whether these deep models should be a recommended option for tabular data by rigorously comparing the new deep models to XGBoost on various datasets. In addition to systematically comparing their performance, we consider the tuning and computation they require. Our study shows that XGBoost outperforms these deep models across the datasets, including the datasets used in the papers that proposed the deep models. We also demonstrate that XGBoost requires much less tuning. On the positive side, we show that an ensemble of deep models and XGBoost performs better on these datasets than XGBoost alone.     

LinkingPark: An automatic semantic table interpretation system

In this paper, we present LinkingPark, an automatic semantic annotation system for tabular data to knowledge graph matching. LinkingPark is designed as a modular framework which can handle Cell-Entity Annotation (CEA), Column-Type Annotation (CTA), and Columns-Property Annotation (CPA) altogether. It is built upon our previous SemTab 2020 system, which won the 2nd prize among 28 different teams after four rounds of evaluations. Moreover, the system is unsupervised, stand-alone, and flexible for multilingual support. Its backend offers an efficient RESTful API for programmatic access, as well as an Excel Add-in for ease of use. Users can interact with LinkingPark in near real-time, further demonstrating its efficiency.     

Benchmark experiment of large-angle scattering reaction cross section of iron at 14 MeV using two shadow bars – Comparison of experimental results with ENDF/B-VIII –

ABSTRACT

It is important to perform neutron transport simulations with accurate nuclear data in the neutronics design of a fusion reactor. However, absolute values of large-angle scattering cross sections vary among nuclear data libraries even for well-examined nuclide of iron. Benchmark experiments focusing on large-angle scattering cross sections were thus performed to confirm the correctness of nuclear data libraries. The series benchmark experiments were performed at a DT neutron source facility, OKTAVIAN of Osaka University, Japan, by the unique experimental system established by the authors’ group, which can extract only the contribution of large-angle scattering reactions. This system consists of two shadow bars, target plate (iron), and neutron detector (niobium). Two types of shadow bars were used and four irradiations were conducted for one experiment, so that contribution of room-return neutrons was effectively removed and only large-angle scattering neutrons were extracted from the measured four Nb reaction rates. The obtained experimental results were compared with calculations for five nuclear data libraries including JENDL-4.0, JEFF.-3.3, FENDL-3.1, ENDF/B- VII, and recently released ENDF/B-VIII. It was found from the comparison that ENDF/B-VIII showed the best result, though ENDF/B-VII showed overestimation and others are in large underestimation at 14 MeV.     

A new signal processing-based islanding detection method using pyramidal algorithm with undecimated wavelet transform for distributed generators of hydrogen energy

Machine learning-based fault detection methods are frequently combined with wavelet transform (WT) to detect an unintentional islanding condition. In contrast to this condition, these methods have long detection and computation time. Thus, selecting a useful signal processing-based approach is required for reliable islanding detection, especially in real-time applications. This paper presents a new modified signal processing-based islanding detection method (IDM) for real-time applications of hydrogen energy-based distributed generators. In the study, a new IDM using a modified pyramidal algorithm approach with an undecimated wavelet transform (UWT) is presented. The proposed method is performed with different grid conditions with the presence of electric noise in real-time. Experimental results show that oscillations in the acquired signal can be reduced by the UWT, and noise sensitivity is lower than other WT-based methods. The non-detection zone is zero and the maximum detection and computational time is also 75 ms at a close power match.     

Thiol-Functionalized Covalent Organic Frameworks as Thermal History Indictor for Temperature and Time History Monitoring

Various products, including foods and pharmaceuticals, are sensitive to temperature fluctuations. Thus, temperature monitoring during production, transportation, and storage is critical. Facile indicators are required to monitor temperature conditions via color changes in real time. This study aimed to prepare and apply thiol-functionalized covalent organic frameworks (COFs) as a novel indicator for monitoring thermal history and temperature abuse. The COFs underwent obvious color changes from bright yellow to purple after exposure to different temperatures for varying durations. The reaction kinetics are analyzed under isothermal conditions, which reveal that the order of reaction rates is k_−20°C < k_4°C < k_20°C < k_35°C < k_55°C. The activation energy (E_a) of the COFs is calculated using the Arrhenius equation as 50.71 kJ moL⁻¹. The COFs are capable of sensitive color changes and offer a broad temperature tracking range, thereby demonstrating their application potential for the monitoring of temperature and time exposure history during production, transportation, and storage. This excellent performance thermal history indicator also shows promise for expanding the application field of COFs.     

基于面元素重排的三维网格模型数据隐藏算法

目的 为了解决目前三维数据隐藏算法不能兼顾无失真和盲提取的问题，提出一种新的完全无失真的三维网格模型数据隐藏盲算法。方法 首先使用混沌逻辑映射选择嵌入与提取模式，保证数据的安全性。然后利用面元素重排，完全不会造成三维模型失真的性质，通过不同嵌入模式规则对三角面元素进行重排，以嵌入秘密数据。接收端则可根据相应的提取模式规则提取秘密数据。结果 仿真结果与分析表明，该算法不会对三维模型造成任何失真，嵌入容量为每顶点2比特，且能抵抗仿射变换攻击、噪声攻击和平滑攻击等。结论 这种三维数据隐藏盲算法无失真，容量大、安全性高、鲁棒性强，适用于三维载体不容修改的情形，如军事、医学、秘密通信和版权保护等。     

Patterns in environmental priorities revealed through government open data portals

The ways in which environmental priorities are framed are varied and influenced by political forces. One technological advance--the proliferation of government open data portals (ODPs)--has the potential to improve governance through facilitating access to data. Yet it is also known that the data hosted on ODPs may simply reflect the goals and interests of multiple levels of political power. In this article, I use traditional statistical correlation and regression techniques along with newer natural language processing and machine learning algorithms to analyze the corpus of datasets hosted on government ODPs (total: 49,066) to extract patterns that relate scales of governance and political liberalism/conservatism to the priorities and meaning attached to environmental issues. I find that state-level and municipal-level ODPs host different categories of environmental datasets, with municipal-level ODPs generally hosting more datasets pertaining to services and amenities and state-level ODPs hosting more datasets pertaining to resource protection and extraction. Stronger trends were observed for the influences of political conservatism/liberalism among state-level ODPs than for municipal-level ODPs.     

Novel approaches to purifying bacteriocin: A review

ABSTRACT

Bacteriocin is a proteinaceous biomolecule produced by bacteria (both Gram-positive and Gram-negative) that exhibits antimicrobial activity against closely related species, and food-borne pathogens. It has recently gained importance and attracted the attention of several researchers looking to produce it from various substrates and bacterial strains. This ushers in a new era of food preservation where the use of bacteriocin in food products will be an alternative to chemical preservatives, and heat treatment which are understood to cause unwanted side effects, and reduce sensory and nutritional quality. However, this new market depends on the success of novel downstream separation schemes from various types of crude feedstocks which are both effective and economic. This review focuses on the downstream separation of bacteriocin from various sources using both conventional and novel techniques. Finally, recommendations for future interesting areas of research that need to be pursued are highlighted.     

A red code triggers an unintended approach motivation toward sweet ultra-processed foods: Possible implications for front-of-pack labels

Front-of-package labels (FOPL) are recommended to reduce consumer intake of ultra-processed food products (UPP). The multiple traffic-light label is one example of FOPL that indicates the content of target nutrients in products by displaying red (high), amber (medium), and/or green (low) color-coding. The red code may implicitly enhance sweetness perception and approach dispositions toward sweet UPP via cross-modal visual-taste interactions. We conducted two experiments to examine the possibility of contradictory influence of explicitly learned and implicit cross-modal associations on the emotional responses evoked by UPP pictures. In both experiments, we first explicitly associated the color codes with health-related meanings. In Experiment I (n = 78), a psychometric tool estimated the emotional responses (pleasantness and arousal ratings) evoked by UPP pictures when preceded by red, amber, or green color-codes. In Experiment II (n = 24), we recorded participants’ electrocortical brain activity to assess the early posterior negativity (EPN) component as an index of the emotional responses to UPP. The reported pleasantness (Experiment I) and the EPN amplitude (Experiment II) were greater for sweet UPP relative to salty UPP when primed with red codes but not when primed with green or amber. A red code increased positive emotions toward sweet UPP despite its explicit association with increased health-risks. Thus, the use of multiple traffic-lights might lead to an unintended implicit approach behavior toward sweet UPP. Designers, researchers, and policy makers may consider color-taste cross-modal associations when designing, testing, and applying FOPL.