Miracle-dz/UniFAR-FTUs huggingface dataset:
Latent Space Simulation for Carbon Capture Design Optimization

The CO2 capture efficiency in solvent-based carbon capture systems (CCSs) critically depends on the gas-solvent interfacial area (IA), making maximization of IA a foundational challenge in CCS design. While the IA associated with a particular CCS design can be estimated via a computational fluid dynamics (CFD) simulation, using CFD to derive the IAs associated with numerous CCS designs is prohibitively costly. Fortunately, previous works such as Deep Fluids (DF) (Kim et al., 2019) show that large simulation speedups are achievable by replacing CFD simulators with neural network (NN) surrogates that faithfully mimic the CFD simulation process. This raises the possibility of a fast, accurate replacement for a CFD simulator and therefore efficient approximation of the IAs required by CCS design optimization. Thus, here, we build on the DF approach to develop surrogates that can successfully be applied to our complex carbon-capture CFD simulations. Our optimized DF-style surrogates produce large speedups (4000x) while obtaining IA relative errors as low as 4% on unseen CCS configurations that lie within the range of training configurations. This hints at the promise of NN surrogates for our CCS design optimization problem. Nonetheless, DF has inherent limitations with respect to CCS design (e.g., limited transferability of trained models to new CCS packings). We conclude with ideas to address these challenges.
Hubble distancing: focusing on distance measurements in cosmology

The Hubble-Lemaître tension is currently one of the most important questions in cosmology. Most of the focus so far has been on reconciling the Hubble constant value inferred from detailed cosmic microwave background measurement with that from the local distance ladder. This emphasis on one number — namely H 0 — misses the fact that the tension fundamentally arises from disagreements of distance measurements. To be successful, a proposed cosmological model must accurately fit these distances rather than simply infer a given value of H 0. Using the newly developed likelihood package `distanceladder', which integrates the local distance ladder into MontePython, we show that focusing on H 0 at the expense of distances can lead to the spurious detection of new physics in models which change late-time cosmology. As such, we encourage the observational cosmology community to make their actual distance measurements broadly available to model builders instead of simply quoting their derived Hubble constant values.
Simple and Effective Balance of Contrastive Losses

Contrastive losses have long been a key ingredient of deep metric learning and are now becoming more popular due to the success of self-supervised learning. Recent research has shown the benefit of decomposing such losses into two sub-losses which act in a complementary way when learning the representation network: a positive term and an entropy term. Although the overall loss is thus defined as a combination of two terms, the balance of these two terms is often hidden behind implementation details and is largely ignored and sub-optimal in practice. In this work, we approach the balance of contrastive losses as a hyper-parameter optimization problem, and propose a coordinate descent-based search method that efficiently find the hyper-parameters that optimize evaluation performance. In the process, we extend existing balance analyses to the contrastive margin loss, include batch size in the balance, and explain how to aggregate loss elements from the batch to maintain near-optimal performance over a larger range of batch sizes. Extensive experiments with benchmarks from deep metric learning and self-supervised learning show that optimal hyper-parameters are found faster with our method than with other common search methods.
Towards Interactive Language Modeling

Interaction between caregivers and children plays a critical role in human language acquisition and development. Given this observation, it is remarkable that explicit interaction plays little to no role in artificial language modeling -- which also targets the acquisition of human language, yet by artificial models. Moreover, an interactive approach to language modeling has the potential to make language models substantially more versatile and to considerably impact downstream applications. Motivated by these considerations, we pioneer the space of interactive language modeling. As a first contribution we present a road map in which we detail the steps that need to be taken towards interactive language modeling. We then lead by example and take the first steps on this road map, showing the initial feasibility of our approach. As such, this work aims to be the start of a larger research agenda on interactive language modeling.
Multi-Coil MRI Reconstruction Challenge—Assessing Brain MRI Reconstruction Models and Their Generalizability to Varying Coil Configurations

Deep-learning-based brain magnetic resonance imaging (MRI) reconstruction methods have the potential to accelerate the MRI acquisition process. Nevertheless, the scientific community lacks appropriate benchmarks to assess the MRI reconstruction quality of high-resolution brain images, and evaluate how these proposed algorithms will behave in the presence of small, but expected data distribution shifts. The multi-coil MRI (MC-MRI) reconstruction challenge provides a benchmark that aims at addressing these issues, using a large dataset of high-resolution, three-dimensional, T1-weighted MRI scans. The challenge has two primary goals: (1) to compare different MRI reconstruction models on this dataset and (2) to assess the generalizability of these models to data acquired with a different number of receiver coils. In this paper, we describe the challenge experimental design and summarize the results of a set of baseline and state-of-the-art brain MRI reconstruction models. We provide relevant comparative information on the current MRI reconstruction state-of-the-art and highlight the challenges of obtaining generalizable models that are required prior to broader clinical adoption. The MC-MRI benchmark data, evaluation code, and current challenge leaderboard are publicly available. They provide an objective performance assessment for future developments in the field of brain MRI reconstruction.
Lux: Always-on Visualization Recommendations for Exploratory Dataframe Workflows

Exploratory data science largely happens in computational notebooks with dataframe APIs, such as pandas, that support flexible means to transform, clean, and analyze data. Yet, visually exploring data in dataframes remains tedious, requiring substantial programming effort for visualization and mental effort to determine what analysis to perform next. We propose Lux, an always-on framework for accelerating visual insight discovery in dataframe workflows. When users print a dataframe in their notebooks, Lux recommends visualizations to provide a quick overview of the patterns and trends and suggests promising analysis directions. Lux features a high-level language for generating visualizations on demand to encourage rapid visual experimentation with data. We demonstrate that through the use of a careful design and three system optimizations, Lux adds no more than two seconds of overhead on top of pandas for over 98% of datasets in the UCI repository. We evaluate Lux in terms of usability via interviews with early adopters, finding that Lux helps fulfill the needs of data scientists for visualization support within their dataframe workflows. Lux has already been embraced by data science practitioners, with over 3.1k stars on Github.
Popularity Prediction for Social Media over Arbitrary Time Horizons

Predicting the popularity of social media content in real time requires approaches that efficiently operate at global scale. Popularity prediction is important for many applications, including detection of harmful viral content to enable timely content moderation. The prediction task is difficult because views result from interactions between user interests, content features, resharing, feed ranking, and network structure. We consider the problem of accurately predicting popularity both at any given prediction time since a content item's creation and for arbitrary time horizons into the future. In order to achieve high accuracy for different prediction time horizons, it is essential for models to use static features (of content and user) as well as observed popularity growth up to prediction time. We propose a feature-based approach based on a self-excited Hawkes point process model, which involves prediction of the content's popularity at one or more reference horizons in tandem with a point predictor of an effective growth parameter that reflects the timescale of popularity growth. This results in a highly scalable method for popularity prediction over arbitrary prediction time horizons that also achieves a high degree of accuracy, compared to several leading baselines, on a dataset of public page content on Facebook over a two-month period, covering billions of content views and hundreds of thousands of distinct content items. The model has shown competitive prediction accuracy against a strong baseline that consists of separately trained models for specific prediction time horizons.
Generative Adversarial Networks with Conditional Neural Movement Primitives for An Interactive Generative Drawing Tool

Sketches are abstract representations of visual perception and visuospatial construction. In this work, we proposed a new framework, Generative Adversarial Networks with Conditional Neural Movement Primitives (GAN-CNMP), that incorporates a novel adversarial loss on CNMP to increase sketch smoothness and consistency. Through the experiments, we show that our model can be trained with few unlabeled samples, can construct distributions automatically in the latent space, and produces better results than the base model in terms of shape consistency and smoothness.
A Compact Survey on Event Extraction: Approaches and Applications

—Event extraction is a critical technique to apprehend the essential content of events promptly. With the rapid development of deep learning technology, event extraction technology based on deep learning has become a research hotspot. Numerous methods, datasets, and evaluation metrics have been proposed in the literature, raising the need for a comprehensive and updated survey. This paper ﬁlls the gap by reviewing the state-of-the-art approaches, focusing on deep learning-based models. We summarize the task deﬁnition, paradigm, and models of event extraction and then discuss each of these in detail. We introduce benchmark datasets that support tests of predictions and evaluation metrics. A comprehensive comparison between different techniques is also provided in this survey. Finally, we conclude by summarizing future research directions facing the research area.
Distance Learners Acceptance of a Course Website

The influence of perceived ease of use and perceived usefulness on a course website acceptance and usage by distance learning students of a higher learning institution in Malaysia was investigated. The results showed that the usefulness and ease of use of the course website exerted significant impact on the usage of the website by the students. Perceived ease of use influenced perceived usefulness of the course website and perceived usefulness also partially mediated the impact of perceived ease of use on usage. In the case of mandated use such as this, perceived ease of use was more influential in determining usage as compared with perceived usefulness. Abstrak Pengaruh perasaan kesenangan mengguna dan perasaan kebergunaan terhadap penerimaan suatu laman web kursus dan kegunaannya oleh para pelajar pendidikan jarak jauh di institusi pengajian tinggi di Malaysia telah diselidiki. Dapatan kajian menunjukkan bahawa perasaan kebergunaan dan kesenangan mengguna laman web kursus mengenakan impak yang signifikan terhadap kegunaan laman web oleh para pelajar. Perasaan kesenangan mengguna mempengaruhi perasaan kebergunaan bagi laman web kursus dan perasaan kebergunaan juga menjadi perantara separa impak perasaan kesenangan mengguna terhadap kegunaan. Di dalam kes gunaan bermandat seperti ini, perasaan kesenangan mengguna memberi pengaruh yang lebih tinggi dalam menentukan kegunaan jika dibandingkan dengan perasaan kebergunaan.
To Make It 'Total': Quality Management over Subcultures

Although the concept of Total Quality Management has been fuzzy for decades, academics currently appear to agree that it should concern the entire organization. Total Quality Management inevitably involves a change in the organizational culture, and this, therefore, seems to be a key area in the success of any TQM-efforts. Moreover, any culture change programme in a large company will have to take into account the existence of subcultures with their values and beliefs. Any universally planned uniform change will probably fail to satisfy the expectations in large sectors of the organization. This article first discusses the concept and existence of subcultures and the difficulties in analysing them through two studies conducted by the authors. Then, some culture change problems encountered following the introduction of TQM are addressed through yet another study. Finally, conclusions are drawn to act as guidelines for practitioners.
The Design of Bending Parts Compound Die

Through analyzing U type bending parts that has bottom-holes, a suitable Compound die that can complete 4 procedures - lancing, bending , punching and be cut off at in the same forming-position was synchronously designed. The die working process and the main points of the design are introduced here. Production testing confirmed that there were different levels of improvements in the product quality, production efficiency and economic efficiency. In order to meet the design and development requirement of the die, the proportion of the compound and progressive dies in the dies production has been increased in consideration of the structure and production costs of the die.
A New Species of Decuanellus Osella (Coleoptera: Curculionidae: Molytinae: Lymantini) from Maricao State Forest, Puerto Rico

Abstract A new species of Decuanellus Osella is described from the insular serpentine soil forests of Maricao State Forest, Puerto Rico. Decuanellus coronarostris Rodriguez Ruiz and Van Dam, new species, is a new Caribbean endemic species of eyeless weevil, which was collected from a leaf litter survey of Maricao State Forest (El Bosque Estatal de Maricao) during 2017. This species differs from other Decuanellus species by its lack of tubercles on the distal part of the elytra and by a peculiar crown-shaped projection on its rostrum from which the species name is derived. A revised key to Decuanellus is presented.
Transfer Learning based Recognition Algorithm for Common Tea Disease

The technology of recognition for tea disease is help to increase the efficiency of disease control. On the basis of extensive analyses, a transfer learning based recognition algorithm for common tea disease (TLB_RA) was proposed to improve the identification accuracy of disease, such as anthracnose, tea blister blight and tea white scab. In order to improve the training accuracy, the inception v3 is exploited to built up the deep learning model under the condition of small sample set. Experiment results reveal that compared with the typical method, the recall level average and unit estimation of time is improved by the proposed algorithm.
Material Selection in Green Buildings: Optimization of Cost and CO 2 Emissions

Material selection in green buildings is one of the important parts of construction of such buildings. It can be considered as a multi-criteria decision making problem; since not only are the environmental impacts of green buildings important, but also the invested budgets. This paper addresses this issue and aims at solving the material selection problem in the form of a multi-criteria decision making problem under two criteria: CO2 and cost. The proposed method, integrates a very useful simulation method called fuzzy Monte Carlo simulation (FMCS) and fuzzy ordered weighted averaging (F-OWA) operator. The FMCS is utilized to evaluate the feasible alternatives under uncertainties. The F-OWA is a new method which is developed and proposed in this paper, selects the best and optimal alternative for a green building. The information of a case study is used to evaluate the workability and capabilities of the proposed method. Keywords—Greean buildings, Material selection, CO2 emissions, Cost, Decision making, Fuzzy Monte Carlo Simulation
Automatic diagnosis of multiple lesions in fundus images based on dual attention mechanism

Glaucomatous optic neuropathy (GON), retinal exudates and retinal hemorrhage are the main basis for the diagnosis of fundus diseases. Traditional methods can diagnose fundus diseases and their severity, but there are few studies on the characteristics of fundus diseases, which cannot give a reasonable explanation for the diagnosis of fundus diseases. Therefore, a convolutional neural network based on dual attention mechanism was proposed to realize automatic diagnosis of multiple fundus lesions with high accuracy. Convolutional neural network uses a residual structure with jumping connections, and channels and spatial attention mechanisms are embedded after each group of convolution to improve the accuracy of fundus lesions diagnosis. The model was tested on the clinical data of Ningbo Eye Hospital Affiliated to Wenzhou Medical University. The diagnostic accuracy of GON, retinal exudates and retinal hemorrhage were 98.17%, 97.49% and 97.15%, respectively. The experimental results showed that: the model showed good feature extraction ability and diagnostic performance in multi-lesion diagnosis of fundus, which provided reference value for subsequent medical artificial intelligence diagnosis research.
Assortativity of an Elastic Network with Implicit Use of Information about Nodes Degree

The problem of modeling dynamics of complex networks is considered. The network model with non-unit elasticity controlled by mediation-driven attachment rule is studied. According to that rule, new nodes do not need a direct access to node statistics of the network: this data affects the links of new nodes, but is used implicitly. The assortative property of the considered model of complex networks is studied. It was shown that the proposed model generates networks with non-zero assortativity coefficient that is an important feature of this model. The dependence of the assortativity coefficient on the network size and copy-factor is obtained by numerical simulation.
Conceptual Optimization Model of Inventory/Distribution Network with Multi Role Nodes

Production and distribution planning might come as one integrated system or separately managed. The distribution planning may include the storage and handling of products, transportation and delivery of products, and so on. Some industries may have inventory/distribution network which are very complex, due to its distribution network and may also be subjected to specific routing restriction. The restriction may affect the optimization of the performance of the distribution network. The aim of this paper is to present a conceptual model which minimizes the total distribution cost for a multi production plant, multi distribution center and multi retailers under seasonal demand and multi time period. The supply chain under consideration comprises of multi role nodes as the production plant and/or distribution center.
HUNGARIAN GPS NETWORK TRANSFORMATION INTO DIFFERENT DATUMS AND PROJECTION SYSTEMS

The results of the GPS network adjustment are the adjusted co-ordinate components of vectors, the co-ordinates of network stations and accuracy data. We have more options to convert the co-ordinates into national datum depending on whether the co-ordinates of the national initial station are obtained from absolute point positioning (local datum) or the initial station is included in the EUREF network having known co-ordinates. In the latter case a quasi transformation can be performed with the known transformation parameters. To reduce discrepancies we apply 2D Helmert, affine and 5 th The application of GPS technology becomes more important nowadays. Every user can choose from several manufacturers products including geodetic accuracy GPS receivers and navigational receivers as well according to their demand and financial situation. Most of the users apply such software systems for data processing which belong to receivers. Consequently the obtained results are very rarely suitable for immediate practical aims. In most cases further computations needed for the ob- tained data can be applied to a user defined co-ordinate system. In the following we present kinds of computation methods and procedures which allow to convert GPS observable results processed by manufacturer's software into the Hungarian unified national projection system (EOV), Gauss-Kruger and UTM projection sys- tems. The applied methods satisfy the accuracy requirements both for the geodetic and the geomatic fields. Using the national GPS network point with known EUREF co-ordinates, the adjusted co-ordinates will be in EUREF system as well. It is very important to distinguish these two cases (EUREF and local datum) because one can obtain even 100 meter differences in co-ordinates for the same point applying the above mentioned two methods (HOFMANN-WELLENHOF et al., 1994).
Mass Transport in a Microchannel Bioreactor with a Porous Wall

A two-dimensional flow model, incorporating mass transport, has been developed to simulate flow in a microchannel bioreactor with a porous wall. A two-domain method was implemented which was based on finite volume method. For the porous-fluid interface, a stress jump condition was used with continuity of normal stress; and the mass interfacial conditions were continuities of mass and mass flux. Two parameters are defined to characterize the mass transports in the fluid and porous regions. The porous Damkohler number is the ratio of consumption to diffusion of the substrates in the porous medium. The fluid Damkohler number is the ratio of substrate consumption in the porous medium to substrate convection in the fluid region. The concentration results are found to be well correlated by the use of a reaction-convection distance parameter which incorporates the effects of axial distance, substrate consumption and convection.