Miracle-dz/UniFAR-FTUs huggingface dataset:
GAUSS -- A Sample Return Mission to Ceres

The goal of Project GAUSS is to return samples from the dwarf planet Ceres. Ceres is the most accessible ocean world candidate and the largest reservoir of water in the inner solar system. It shows active cryovolcanism and hydrothermal activities in recent history that resulted in minerals not found in any other planets to date except for Earth's upper crust. The possible occurrence of recent subsurface ocean on Ceres and the complex geochemistry suggest possible past habitability and even the potential for ongoing habitability. Aiming to answer a broad spectrum of questions about the origin and evolution of Ceres and its potential habitability, GAUSS will return samples from this possible ocean world for the first time. The project will address the following top-level scientific questions: 1) What is the origin of Ceres and the origin and transfer of water and other volatiles in the inner solar system? 2) What are the physical properties and internal structure of Ceres? What do they tell us about the evolutionary and aqueous alteration history of icy dwarf planets? 3) What are the astrobiological implications of Ceres? Was it habitable in the past and is it still today? 4) What are the mineralogical connections between Ceres and our current collections of primitive meteorites? GAUSS will first perform a high-resolution global remote sensing investigation, characterizing the geophysical and geochemical properties of Ceres. Candidate sampling sites will then be identified, and observation campaigns will be run for an in-depth assessment of the candidate sites. Once the sampling site is selected, a lander will be deployed on the surface to collect samples and return them to Earth in cryogenic conditions that preserves the volatile and organic composition as well as the original physical status as much as possible.
GeoBlocks: A Query-Driven Storage Layout for Geospatial Data

City authorities need to analyze urban geospatial data to improve transportation and infrastructure. Current tools do not address the exploratory and interactive nature of these analyses and in many cases consult the raw data to compute query results. While pre-aggregation and materializing intermediate query results is common practice in many OLAP settings, it is rarely used to speed up geospatial queries. We introduce GeoBlocks, a pre-aggregating, query-driven storage layout for geospatial point data that can provide approximate, yet precision-bounded aggregation results over arbitrary query polygons. GeoBlocks adapt to the skew naturally present in query workloads to improve query performance over time. In summary, GeoBlocks outperform on-the-fly aggregation by up to several orders of magnitude, providing the sub-second query latencies required for interactive analytics.
Boosting the Rating Prediction with Click Data and Textual Contents

Matrix factorization (MF) is one of the most efficient methods for rating predictions. MF learns user and item representations by factorizing the user-item rating matrix. Further, textual contents are integrated to conventional MF to address the cold-start problem. However, the textual contents do not reflect all aspects of the items. In this paper, we propose a model that leverages the information hidden in the item co-click (i.e., items that are often clicked together by a user) into learning item representations. We develop TCMF (Textual Co Matrix Factorization) that learns the user and item representations jointly from the user-item matrix, textual contents and item co-click matrix built from click data. Item co-click information captures the relationships between items which are not captured via textual contents. The experiments on two real-world datasets MovieTweetings, and Bookcrossing) demonstrate that our method outperforms competing methods in terms of rating prediction. Further, we show that the proposed model can learn effective item representations by comparing with state-of-the-art methods in classification task which uses the item representations as input vectors.
Scene Graph Reasoning with Prior Visual Relationship for Visual Question Answering

One of the key issues of Visual Question Answering (VQA) is to reason with semantic clues in the visual content under the guidance of the question, how to model relational semantics still remains as a great challenge. To fully capture visual semantics, we propose to reason over a structured visual representation - scene graph, with embedded objects and inter-object relationships. This shows great benefit over vanilla vector representations and implicit visual relationship learning. Based on existing visual relationship models, we propose a visual relationship encoder that projects visual relationships into a learned deep semantic space constrained by visual context and language priors. Upon the constructed graph, we propose a Scene Graph Convolutional Network (SceneGCN) to jointly reason the object properties and relational semantics for the correct answer. We demonstrate the model's effectiveness and interpretability on the challenging GQA dataset and the classical VQA 2.0 dataset, remarkably achieving state-of-the-art 54.56% accuracy on GQA compared to the existing best model.
Stellar population astrophysics (SPA) with the TNG

Context. Open clusters exquisitely track the Galactic disc chemical properties and its time evolution; a substantial number of studies and large spectroscopic surveys focus mostly on the chemical content of relatively old clusters (age ≳ 1 Gyr). Interestingly, the less studied young counterpart populating the solar surrounding has been found to be solar (at most), with a notable surprising lack of young metal-rich objects. While there is wide consensus about the moderately above-solar composition of the Hyades cluster, the metallicity of Praesepe is still controversial. Recent studies suggest that these two clusters share identical chemical composition and age, but this conclusion is disputed. Aims. With the aim of reassessing the metallicity of Praesepe, and its difference (if any) with the Hyades cluster, we present in this paper a spectroscopic investigation of ten solar-type dwarf members. Methods. We exploited GIARPS at the TNG to acquire high-resolution, high-quality optical and near-IR spectra and derived stellar parameters, metallicity ([Fe/H]), light elements, α- and iron-peak elements, by using a strictly differential (line-by-line) approach. We also analysed in the very same way the solar spectrum and the Hyades solar analogue HD 28099. Results. Our findings suggest that Praesepe is more metal-rich than the Hyades, at the level of Δ[Fe/H] = +0.05 ± 0.01 dex, with a mean value of [Fe/H] = +0.21 ± 0.01 dex. All the other elements scale with iron, as expected. This result seems to reject the hypothesis of a common origin for these two open clusters. Most importantly, Praesepe is currently the most metal-rich, young open cluster living in the solar neighbourhood.
A physiological model of the inflammatory-thermal-pain-cardiovascular interactions during a pathogen challenge

Uncontrolled, excessive production of pro-inflammatory mediators from immune cells and traumatized tissues can cause systemic inflammatory issues like sepsis, one of the ten leading causes of death in the United States and one of the three leading causes of death in the intensive care unit. Understanding the effects of inflammation on the autonomic control system can improve a patient's chance of recovery after an inflammatory event such as surgery. Though the effects of the autonomic response on the inflammatory system are well defined, there remains a gap in understanding the reverse response. Specifically, the impact of the inflammatory response on the autonomic control system remains unknown. In this study, we investigate hypothesized interactions of the inflammatory system with the thermal and cardiovascular regulatory systems in response to an endotoxin challenge using mathematical modeling. We calibrate the model to data from two independent studies: a) of the inflammatory response in healthy young men and b) a comparative study of the inflammatory response between mice and humans. Simulation analysis is used to explore how the model responds to pathological input and treatment, specifically antibiotics, antipyretics, vasopressors, and combination therapy. Our findings show that multimodal treatment that simultaneously targets both the pathogen and the infection symptoms gives the most favorable recovery outcome.
Visualization in the preprocessing phase: an interview study with enterprise professionals

The current information age has increasingly required organizations to become data-driven. However, analyzing and managing raw data is still a challenging part of the data mining process. Even though we can find interview studies proposing design implications or recommendations for future visualization solutions in the data mining scope, they cover the entire workflow and do not fully focus on the challenges during the preprocessing phase and on how visualization can support it. Moreover, they do not organize a final list of insights consolidating the findings of other related studies. Hence, to better understand the current practice of enterprise professionals in data mining workflows, in particular during the preprocessing phase, and how visualization supports this process, we conducted semi-structured interviews with thirteen data analysts. The discussion about the challenges and opportunities based on the responses of the interviewees resulted in a list of ten insights. This list was compared with the closest related works, improving the reliability of our findings and providing background, as a consolidated set of requirements, for future visualization research papers applied to visual data exploration in data mining. Furthermore, we provide greater details on the profile of the data analysts, the main challenges they face, and the opportunities that arise while they are engaged in data mining projects in diverse organizational areas.
Fatigue-resistant high-performance elastocaloric materials made by additive manufacturing

A million times cooler Elastocaloric materials can be used for solid-state cooling applications because they can pump heat out of a system using a reversible phase transformation. However, many such materials fail after a small number of cycles. Hou et al. found that laser melting of elastocaloric metals can create fatigue-resistant microstructures. A nickel-titanium–based alloy could be cycled a million times and still produce a cooling of about 4 kelvin. This processing method could improve elastocaloric performance and move us closer to using these materials more widely for solid-state cooling applications. Science, this issue p. 1116 The microstructure of elastocaloric metals created by laser melting promotes fatigue resistance. Elastocaloric cooling, a solid-state cooling technology, exploits the latent heat released and absorbed by stress-induced phase transformations. Hysteresis associated with transformation, however, is detrimental to efficient energy conversion and functional durability. We have created thermodynamically efficient, low-hysteresis elastocaloric cooling materials by means of additive manufacturing of nickel-titanium. The use of a localized molten environment and near-eutectic mixing of elemental powders has led to the formation of nanocomposite microstructures composed of a nickel-rich intermetallic compound interspersed among a binary alloy matrix. The microstructure allowed extremely small hysteresis in quasi-linear stress-strain behaviors—enhancing the materials efficiency by a factor of four to seven—and repeatable elastocaloric performance over 1 million cycles. Implementing additive manufacturing to elastocaloric cooling materials enables distinct microstructure control of high-performance metallic refrigerants with long fatigue life.
Machine learning at CERN: ATLAS, LHCb, and more

The use of machine learning is increasing at the LHC experiments including both the ATLAS and LHCb collaborations, in terms of the number of users, the breadth of applications, and the set of different techniques under study. While traditionally applied in the context of improving the final analysis selection for a given physics result, machine learning is now also being applied in many other places, including object reconstruction, object calibration, object identification, simulation, and automation. The variety of machine learning tools being used is also expanding, and many areas are benefiting from the use of deep learning methods. It is expected that this growth in machine learning within particle physics will continue, as the large and rapidly increasing datasets provide the perfect environment to develop and refine new machine learning algorithms which can maximally exploit the complex data.
Comparison between Clustering Algorithms Based On Ontology Based Text Mining Techniques

Large number of documents is grouped according to their similarities. The Text Mining methods have been proposed to solve the problem by automatically classifying text documents, mainly in English. But this method has some limitations when dealing with non-English language texts, e.g., Chinese research proposals. An ontology based text MINING approach is to cluster the documents based on their similarities. For ontology making in text documents, the clustering algorithm is need to be applied. This paper focuses on two clustering techniques SOM and DVA for clustering documents based on their similarities. SOM and DVA algorithms are applied after the preprocessing process with doc-uments such as Stop Word Removal, Stemming. DVA includes Vector Dimension Reduction also. The proposed Ontology based text mining technique is efficient and effective in terms of time, reliability and quantity. DVA out performs than SOM in terms of all the factors. Keywords: Text Mining, Ontology, OTMM, Clustering, SOM and DVA Algorithm
Driver Hazard Detection and Avoidance Performance as a Function of Eyes-Off-Road Interval Under Partially Automated Driving

There has been concern that automation may impair a person’s ability to detect and appropriately respond to environmental hazards. This study sought to examine a driver’s ability to not only detect hazards, but also to implement appropriate responses following extended Eyes-Off-Road (EOR) intervals. Thirty-six drivers were exposed to a series of filmed roadway vignettes with discernible hazards embedded within a range of traffic situations. Driver’s visual gaze was directed “off-road” for 2, 7 or 20 seconds, and then re-oriented to the roadway. Drivers were tasked to respond appropriately as quickly as possible once their gaze returned to the forward road scene by steering and/or braking in response to the hazards. Results found no significant differences among the EOR intervals in terms of the driver’s ability to detect and appropriately respond to the hazard when it emerged after the driver’s eyes were directed forward.
How to Find Variable Active Galactic Nuclei with Machine Learning

Machine-learning (ML) algorithms will play a crucial role in studying the large data sets delivered by new facilities over the next decade and beyond. Here, we investigate the capabilities and limits of such methods in finding galaxies with brightness-variable active galactic nuclei (AGNs). Specifically, we focus on an unsupervised method based on self-organizing maps (SOM) that we apply to a set of nonparametric variability estimators. This technique allows us to maintain domain knowledge and systematics control while using all the advantages of ML. Using simulated light curves that match the noise properties of observations, we verify the potential of this algorithm in identifying variable light curves. We then apply our method to a sample of ∼8300 WISE color-selected AGN candidates in Stripe 82, in which we have identified variable light curves by visual inspection. We find that with ML we can identify these variable classified AGN with a purity of 86% and a completeness of 66%, a performance that is comparable to that of more commonly used supervised deep-learning neural networks. The advantage of the SOM framework is that it enables not only a robust identification of variable light curves in a given data set, but it is also a tool to investigate correlations between physical parameters in multidimensional space—such as the link between AGN variability and the properties of their host galaxies. Finally, we note that our method can be applied to any time-sampled light curve (e.g., supernovae, exoplanets, pulsars, and other transient events).
Risks Analysis of Discarding Water in Power Generation Process of Hydropower Station

Hydropower, as a kind of clean energy, has the characteristics of rapid response to load changes, and is often used to undertake peak regulation and frequency regulation tasks. However, due to the uncertainties of reservoir inflow and runoff, the relative lag of power grid outgoing channels, and the interconnection of intermittent new energy, such as wind power and solar energy, the hydropower system is facing enormous peak regulation pressure and the continuous prominent problem of discarding water. In the study, aiming at the problem of risk control of discarding water in power generation process of hydropower station, a practical calculation method of risk of discarding water in power generation is proposed based on the analysis of frequency of runoff. Taking the Ertan hydropower station as an example, the corresponding water level processes of different discarding water risks under the control of pre-flood falling water level, flood limit water level and storage water level after flood season are deduced, and in the routine dispatching process, the water level operation control guidance corresponding to different discarding risk is introduced. The simulation results show that the proposed discarding water risk control method is practical and effective, and can provide guidance for discarding water risk control in the actual dispatching process of hydropower station.
A Novel Privacy-Preserving Deep Learning Scheme without Using Cryptography Component

Recently, deep learning, which uses Deep Neural Networks (DNN), plays an important role in many fields. A secure neural network model with a secure training/inference scheme is indispensable to many applications. To accomplish such a task usually needs one of the entities (the customer or the service provider) to provide private information (customer's data or the model) to the other. Without a secure scheme and the mutual trust between the service providers and their customers, it will be an impossible mission. In this paper, we propose a novel privacy-preserving deep learning model and a secure training/inference scheme to protect the input, the output, and the model in the application of the neural network. We utilize the innate properties of a deep neural network to design a secure mechanism without using any complicated cryptography component. The security analysis shows our proposed scheme is secure and the experimental results also demonstrate that our method is very efficient and suitable for real applications.
Graze-and-merge Collisions under External Perturbers

Graze-and-merge collisions are common multi-step mergers occurring in low-velocity, off-axis impacts between similar-sized planetary bodies. The first impact happens at somewhat faster than the mutual escape velocity; for typical impact angles this does not result in immediate accretion, but the smaller body is slowed down so that it loops back around and collides again, ultimately accreting. The scenario changes in the presence of a third major body, i.e., planets accreting around a star, or satellites around a planet. We find that when the loop-back orbit remains inside roughly one third of the Hill radius from the target, then the overall process is not strongly affected. As the loop-back orbit increases in radius, the return velocity and angle of the second collision become increasingly random, with no record of the first collision’s orientation. When the loop-back orbit gets to about three quarters of the Hill radius, the path of smaller body is disturbed up to the point that it will usually escape the target.
Modeling Of Self-Organization Fish School System By Neural Network System

In this paper, a Neural Network (NN) model system for self-organization fish school system is identified. Monitoring and data extraction from fish school video has been achieved by using image processing technique in order to generate the data suitable for parameter identification of NN model system. Data obtained have been used to identify the parameters of a model based on a black-box represented by nonlinear autoregressive exogenous model (NARX). The obtained results show that this system can be used for multi robot formation system.
3D Sensitive Modeling of PEM FC for Automotive Applications

In recent years, the fault diagnosis in Proton Exchange Membrane Fuel cell (PEMFC) becomes challenging issues in order to increase performance of the Fuel cell electric vehicle (FCEV). In fact, the occurrence of faults on different operation conditions into the FC unavoidable thus, lead-up to malfunctions in the FCEV and consequently, reduces its detect faults based on operating condition in different points is one of the most tackle in this area. Therefore, the main purpose of this paper is to evaluate the fault's effect on stack PEM fuel cell on a based on diving cycle tests by using a proposed faults sensitive model.
Quantifying the impact of variable BLR diffuse continuum contributions on measured continuum interband delays

We investigate the contribution of reprocessed continuum emission (1000–10 000 Å) originating in broad-line region (BLR) gas, the diffuse continuum (DC), to the wavelength-dependent continuum delays measured in AGN disc reverberation mapping experiments. Assuming a spherical BLR geometry, we adopt a Local Optimally emitting Cloud (LOC) model for the BLR that approximately reproduces the broad emission-line strengths of the strongest UV lines (Ly α and C iv) in NGC 5548. Within this LOC framework, we explore how assumptions about the gas hydrogen density and column density distributions influence flux and delay spectra of the DC. We find that: (i) models which match well measured emission-line luminosities and time delays also produce a significant DC component, (ii) increased $\rm {\mathit{ n}_H}$ and/or $\rm {\mathit{ N}_H}$, particularly at smaller BLR radii, result in larger DC luminosities and reduced DC delays, (iii) in a given continuum band the relative importance of the DC component to the measured interband delays is proportional (though not 1:1) to its fractional contribution to the total light in that band, (iv) the measured DC delays and DC variability amplitude depend also on the variability amplitude and characteristic variability time-scale of the driving continuum, (v) the DC radial surface emissivity distributions F(r) approximate power laws in radius with indices close to −2 (≈1:1 response to variations in the driving continuum flux), thus their physics is relatively simple and less sensitive to the unknown geometry and uncertainties in radiative transfer. Finally, we provide a simple recipe for estimating the DC contribution in disc reverberation mapping experiments.