Miracle-dz/UniFAR-FTUs huggingface dataset:
Overcapacity Risk of China’s Coal Power Industry: A Comprehensive Assessment and Driving Factors

The comprehensive and accurate monitoring of coal power overcapacity is the key link and an important foundation for the prevention and control of overcapacity. The previous research fails to fully consider the impact of the industry correlation effect; making it difficult to reflect the state of overcapacity accurately. In this paper; we comprehensively consider the fundamentals; supply; demand; economic and environmental performance of the coal power industry and its upstream; downstream; competitive; and complementary industries to construct an index system for assessing coal power overcapacity risk. Besides; a new evaluation model based on a correlation-based feature selection-association rules-data envelopment analysis (CFS-ARs-DEA) integrated algorithm is proposed by using a data-driven model. The results show that from 2008 to 2017; the risk of coal power overcapacity in China presented a cyclical feature of “decline-rise-decline”, and the risk level has remained high in recent years. In addition to the impact of supply and demand; the environmental benefits and fundamentals of related industries also have a significant impact on coal power overcapacity. Therefore; it is necessary to monitor and govern coal power overcapacity from the overall perspective of the industrial network, and coordinate the advancement of environmental protection and overcapacity control.
SkyCam: A Dataset of Sky Images and their Irradiance values

Recent advances in Computer Vision and Deep Learning have enabled astonishing results in a variety of fields and applications. Motivated by this success, the SkyCam Dataset aims to enable image-based Deep Learning solutions for short-term, precise prediction of solar radiation on a local level. For the span of a year, three different cameras in three topographically different locations in Switzerland are acquiring images of the sky every 10 seconds. Thirteen high resolution images with different exposure times are captured and used to create an additional HDR image. The images are paired with highly precise irradiance values gathered from a high-accuracy pyranometer.
A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers

Readers of academic research papers often read with the goal of answering specific questions. Question Answering systems that can answer those questions can make consumption of the content much more efficient. However, building such tools requires data that reflect the difficulty of the task arising from complex reasoning about claims made in multiple parts of a paper. In contrast, existing information-seeking question answering datasets usually contain questions about generic factoid-type information. We therefore present Qasper, a dataset of 5049 questions over 1585 Natural Language Processing papers. Each question is written by an NLP practitioner who read only the title and abstract of the corresponding paper, and the question seeks information present in the full text. The questions are then answered by a separate set of NLP practitioners who also provide supporting evidence to answers. We find that existing models that do well on other QA tasks do not perform well on answering these questions, underperforming humans by at least 27 F1 points when answering them from entire papers, motivating further research in document-grounded, information-seeking QA, which our dataset is designed to facilitate.
The Influence of Ocean Acidification and Warming on DMSP & DMS in New Zealand Coastal Water

The cycling of the trace gas dimethyl sulfide (DMS) and its precursor dimethylsulfoniopropionate (DMSP) may be affected by future ocean acidification and warming. DMSP and DMS concentrations were monitored over 20-days in four mesocosm experiments in which the temperature and pH of coastal water were manipulated to projected values for the year 2100 and 2150. This had no effect on DMSP in the two-initial nutrient-depleted experiments; however, in the two nutrient-amended experiments, warmer temperature combined with lower pH had a more significant effect on DMSP & DMS concentrations than lower pH alone. Overall, this indicates that future warming may have greater influence on DMS production than ocean acidification. The observed reduction in DMSP at warmer temperatures was associated with changes in phytoplankton community and in particular with small flagellate biomass. A small decrease in DMS concentration was measured in the treatments relative to other studies, from −2% in the nutrient-amended low pH treatment to −16% in the year 2150 pH and temperature conditions. Temporal variation was also observed with DMS concentration increasing earlier in the higher temperature treatment. Nutrient availability and community composition should be considered in models of future DMS.
Learning Shared Semantic Space for Speech-to-Text Translation

Having numerous potential applications and great impact, end-to-end speech translation (ST) has long been treated as an independent task, failing to fully draw strength from the rapid advances of its sibling - text machine translation (MT). With text and audio inputs represented differently, the modality gap has rendered MT data and its end-to-end models incompatible with their ST counterparts. In observation of this obstacle, we propose to bridge this representation gap with Chimera. By projecting audio and text features to a common semantic representation, Chimera unifies MT and ST tasks and boosts the performance on ST benchmarks, MuST-C and Augmented Librispeech, to a new state-of-the-art. Specifically, Chimera obtains 27.1 BLEU on MuST-C EN-DE, improving the SOTA by a +1.9 BLEU margin. Further experimental analyses demonstrate that the shared semantic space indeed conveys common knowledge between these two tasks and thus paves a new way for augmenting training resources across modalities. Code, data, and resources are available at https://github.com/Glaciohound/Chimera-ST.
LASR: Learning Articulated Shape Reconstruction from a Monocular Video

Remarkable progress has been made in 3D reconstruction of rigid structures from a video or a collection of images. However, it is still challenging to reconstruct nonrigid structures from RGB inputs, due to its under-constrained nature. While template-based approaches, such as parametric shape models, have achieved great success in modeling the "closed world" of known object categories, they cannot well handle the "open-world" of novel object categories or outlier shapes. In this work, we introduce a template-free approach to learn 3D shapes from a single video. It adopts an analysis-by-synthesis strategy that forward-renders object silhouette, optical flow, and pixel values to compare with video observations, which generates gradients to adjust the camera, shape and motion parameters. Without using a category-specific shape template, our method faithfully reconstructs nonrigid 3D structures from videos of human, animals, and objects of unknown classes. Our code is available at lasr-google.github. io.
MGCN: Graph Convolutional Network for Drug Molecular Screening

The intersection of drug research and development and neural network research is getting closer, new methods and new technologies are emerging one after another, using neural network to improve the efficiency of drug research and development, and rapidly innovate the treatment of diseases. In drug development, one target protein corresponds to multiple drug molecules. In order to save the time and cost of new drug development, virtual screening of key steps in new drug development is studied. A virtual screening model for drug molecules is proposed, and the graph convolutional neural network is used to classify drug molecules to achieve the purpose of preliminary screening. The graph convolutional network model is constructed based on DGL and combined with cross-entropy and fully connected networks. The model is carried out by experimental means analysis. The experimental results show that the graph neural network can complete the classification task of drug molecules. Through the verification of molecular docking software, the screened molecules are real and effective, reducing the time spent on virtual screening, and it has become an inspiration for further exploration of graph neural networks in drug development.
4-Iodo-N-(phenylsulfonyl)benzamide hemihydrate

In the title hemihydrate, 2C13H10INO3S·H2O, there are two organic mol­ecules (A and B) and one water mol­ecule in the asymmetric unit. The benzene rings are inclined to one another by 77.98 (1)° in A and 79.81 (9)° in B. The A and B mol­ecules are connected through a water mol­ecule via N—H⋯O and O—H⋯O hydrogen bonds. In the extended structure, the A mol­ecules are inter­linked via two water mol­ecules through O—H⋯O hydrogen bonds to generate R44(12) loops. Further, the A and B mol­ecules are linked by N—H⋯O hydrogen bonds, and thus an [010] chain is formed. Several C—H⋯O inter­actions extend the chains into sheet lying parallel to the ab-plane. The sheets are further extended into a three dimensional architecture via a C—H⋯π inter­action.
∂-Explainer: Abductive Natural Language Inference via Differentiable Convex Optimization

Constrained optimization solvers with Integer Linear programming (ILP) have been the cornerstone for explainable natural language inference during its inception. ILP based approaches provide a way to encode explicit and controllable assumptions casting natural language inference as an abductive reasoning problem, where the solver constructs a plausible explanation for a given hypothesis. While constrained based solvers provide explanations, they are often limited by the use of explicit constraints and cannot be integrated as part of broader deep neural architectures. In contrast, state-of-the-art transformer-based models can learn from data and implicitly encode complex constraints. However, these models are intrinsically black boxes. This paper presents a novel framework named ∂ -Explainer (Diff-Explainer) that combines the best of both worlds by casting the constrained optimization as part of a deep neural network via differentiable convex optimization and ﬁne-tuning pre-trained transformers for downstream ex-plainable NLP tasks. To demonstrate the ef-ﬁcacy of the framework, we transform the constraints presented by TupleILP and integrate them with sentence embedding trans-formers for the task of explainable science QA. Our experiments show up to ≈ 10% improvement over non-differentiable solver while still providing explanations for supporting its inference.
Commissioning of the TrueBeam STx 6 MV FFF Beam in the RayStation Treatment Planning System for SRS and SBRT Treatments

Purpose: The purpose of this study is to provide technical information on commissioning the TrueBeam STx 6 MV flattening-filter free (FFF) beam in the RayStation treatment planning system (TPS) for stereotactic radiosurgery (SRS) and stereotactic body radiation therapy (SBRT) treatments. Methods: For beam modeling, percent depth dose curves, profiles and output factors for jaw-collimated fields and stereotactic cones as well as X-jaws transmission were measured. For multi-leaf collimator (MLC) modeling, MLC model parameters such as offset, gain, curvature, leaf tip width, tongue and groove and transmission were determined and output factors for MLC-collimated fields were measured. Absolute dose calibration was also performed. For beam model and MLC model validation, the American Association of Physicists in Medicine Task Group-119 plans, clinical SRS and SBRT plans and end-to-end testing were performed. Results: Beam characteristics of the 6 MV FFF beam agreed well with those in the literature. Validation results showed that our beam model and MLC model were acceptable for SRS and SBRT treatments. Conclusions: The technical information and dosimetric data provided in this study will be a useful reference for other clinics/institutions which will commission the same machine energy in the RayStation TPS.
Accuracy and stability analysis of the Semi-Lagrangian method for stiff hyperbolic relaxation systems and kinetic BGK model

In this paper, we develop a family of third order asymptotic-preserving (AP) and asymptotically accurate (AA) diagonally implicit Runge-Kutta (DIRK) time discretization methods for the stiff hyperbolic relaxation systems and kinetic Bhatnagar-Gross-Krook (BGK) model in the semi-Lagrangian (SL) setting. The methods are constructed based on an accuracy analysis of the SL scheme for stiff hyperbolic relaxation systems and kinetic BGK model in the limiting fluid regime when the Knudsen number approaches $0$. An extra order condition for the asymptotic third order accuracy in the limiting regime is derived. Linear Von Neumann stability analysis of the proposed third order DIRK methods are performed to a simplified two-velocity linear kinetic model. Extensive numerical tests are presented to demonstrate the AA, AP and stability properties of our proposed schemes.
Influence of Temperature Humidity Index and Dietary Energy on Certain Stress Marker Profile of Murrah Buffalo Heifers

The present study was undertaken to assess the influence of temperature humidity index (THI) and dietary energy (DE) on certain metabolic and endocrine profile of Murrah buffalo heifers. Thirty six heifers were distributed into three groups (T1, T2, and T3) comprising six animals in each group. The ration was formulated as control group (T1) having ME content according to ICAR and T2 and T3 having 115% and 85% than control respectively, in total mixed based ration. The effect of rations was observed in high THI, 78-85 (summer) and low THI, 50-61 (winter) separately for a period of 120days. The Blood samples were collected aseptically at monthly interval. The respiratory rate, pulse rate and rectal temperature were higher during high THI (summer season) in compression to low THI (winter season). Though there was no significant variation on plasma thyroid (T3, T4) and IGF-1 level among the groups but in high THI period, the level were significantly (P<0.01) decrease in comparison to low THI period. Plasma cortisol level showed significantly (P<0.01) higher in T3 group in both the season in comparisons to T1 and T2 which indicated stress to the animals. In conclusion, increasing ME content could be helpful in alleviating stress during both the seasons and moreover, during summer period, where the animals were at higher stress, increasing ME content will support to climatic resilient livestock production.
Gate-defined wires in twisted bilayer graphene: From electrical detection of intervalley coherence to internally engineered Majorana modes

Twisted bilayer graphene (TBG) realizes a highly tunable, strongly interacting system featuring superconductivity and various correlated insulating states. We establish gate-defined wires in TBG with proximity-induced spin-orbit coupling as $(i)$ a tool for revealing the nature of correlated insulators and $(ii)$ a platform for Majorana-based topological qubits. In particular, we show that the band structure of a gate-defined wire immersed in an `inter-valley coherent' correlated insulator inherits electrically detectable fingerprints of symmetry breaking native to the latter. Surrounding the wire by a superconducting TBG region on one side and an inter-valley coherent correlated insulator on the other further enables the formation of Majorana zero modes--possibly even at zero magnetic field depending on the precise symmetry-breaking order present. Our proposal not only introduces a highly gate-tunable topological qubit medium relying on internally generated proximity effects, but can also shed light on the Cooper-pairing mechanism in TBG.
Evidence of topological edge states in a superconducting nonsymmorphic nodal-line semimetal

nonsymmorphic nodal-line semimetal L. X. Xu1,2,3*, Y. Y. Y. Xia1,4*, S. Liu1,3,5*, Y. W. Li1, L.Y. Wei1, H. Y. Wang1, C. W. Wang1,2,3, H. F. Yang1, A. J. Liang1, K. Huang1, T. Deng1,2,3, W. Xia1,4, X. Zhang1, H. J. Zheng1, Y. J. Chen6, L. X. Yang6, M. X. Wang1, Y. F. Guo1,4‡, G. Li1,4§, Z. K. Liu1,4†, Y. L. Chen1,4,6,7 1School of Physical Science and Technology, ShanghaiTech University, CASShanghai Science Research Center, Shanghai 200031, China 2 State Key Laboratory of Functional Material for Informatics, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China 3University of Chinese Academy of Sciences, Beijing 100049, China 4ShanghaiTech Laboratory for Topological Physics, Shanghai 201210, China 5Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, China 6State Key Laboratory of Low Dimensional Quantum Physics, Department of Physics, Tsinghua University, Beijing 100084, China 7Department of Physics, University of Oxford, Oxford, United Kingdom *These authors contributed equally to this work
Asymmetric Organocatalytic Michael/Michael/Henry Sequence to Construct Cyclohexanes with Six Vicinal Stereogenic Centers

Abstract An efficient, asymmetric, catalytic, triple-cascade reaction between α-keto esters and nitroalkenes to construct cyclohexanes with six vicinal stereogenic centers in good yields and with high enantioselectivities has been established. A bifunctional guanidine–amide organocatalyst proved to be useful for the Michael/Michael/Henry sequence through Brønsted base and hydrogen-bonding cooperative catalysis.
Leveraging Multiple Relations for Fashion Trend Forecasting Based on Social Media

Fashion trend forecasting is of great research significance in providing useful suggestions for both fashion companies and fashion lovers. Although various studies have been devoted to tackling this challenging task, they only studied limited fashion elements with highly seasonal or simple patterns, which could hardly reveal the real complex fashion trends. Moreover, the mainstream solutions for this task are still statistical-based and solely focus on time-series data modeling, which limit the forecast accuracy. Towards insightful fashion trend forecasting, previous work [1] proposed to analyze more fine-grained fashion elements which can informatively reveal fashion trends. Specifically, it focused on detailed fashion element trend forecasting for specific user groups based on social media data. In addition, it proposed a neural network-based method, namely KERN, to address the problem of fashion trend modeling and forecasting. In this work, to extend the previous work [1], we propose an improved model named Relation Enhanced Attention Recurrent (REAR) network. Compared to KERN, the REAR model leverages not only the relations among fashion elements, but also those among user groups, thus capturing more types of correlations among various fashion trends. To further improve the performance of long-range trend forecasting, the REAR method devises a sliding temporal attention mechanism, which is able to capture temporal patterns on future horizons better. Extensive experiments and more analysis have been conducted on the FIT [1] and GeoStyle [2] datasets to evaluate the performance of REAR. Experimental and analytical results demonstrate the effectiveness of the proposed REAR model in fashion trend forecasting, which also show the improvement of REAR compared to the KERN.
A Symbolic Regression Method for Dynamic Modeling and Control of Quadrotor UAVs

This paper presents a mathematic dynamic model of a quadrotor unmanned aerial vehicle (QUAV) by using the symbolic regression approach and then proposes a hierarchical control scheme for trajectory tracking. The symbolic regression approach is capable of constructing analytical quadrotor dynamic equations only through the collected data, which relieves the burden of first principle modeling. To facilitate position tracking control of a QUAV, the design of controller can be decomposed into two parts: a proportional-integral controller for the position subsystem is first designed to obtain the desired horizontal position and the backstepping method for the attitude subsystem is developed to ensure that the Euler angles and the altitude can fast converge to the reference values. The effectiveness is verified through experiments on a benchmark multicopter simulator.
Attention-augmented Spatio-Temporal Segmentation for Land Cover Mapping

The availability of massive earth observing satellite data provides huge opportunities for land use and land cover mapping. However, such mapping effort is challenging due to the existence of various land cover classes, noisy data, and the lack of proper labels. Also, each land cover class typically has its own unique temporal pattern and can be identified only during certain periods. In this article, we introduce a novel architecture that incorporates the UNet structure with Bidirectional LSTM and Attention mechanism to jointly exploit the spatial and temporal nature of satellite data and to better identify the unique temporal patterns of each land cover class. We compare our method with other state-of-the-art methods both quantitatively and qualitatively on two real-world datasets which involve multiple land cover classes. We also visualise the attention weights to study its effectiveness in mitigating noise and in identifying discriminative time periods of different classes. The code and dataset used in this work are made publicly available for reproducibility.
Short Communication: Molecular study on mt-DNA COX2 gene of Sumatran elephant (Elephas maximus sumatranus)

Abstract. Wibowo DS, Widiyanti R, Asvan M, Restanti PD, Wijayanto H. 2021. Short Communication: Molecular study on mt-DNA COX2 gene of Sumatran elephant (Elephas maximus sumatranus). Biodiversitas 22: 1063-1068. Sumatran elephant is the only subspecies of Asian elephants that receives a critically endangered status from the International Union for Conservation and Nature Resources (IUCN). Identifying the genetic marker of Sumatran elephants is, therefore, important for their conservation. This study aimed to identify the Sumatran elephant based on specific mitochondrial DNA markers of the Cytochrome c oxidase subunit II (COX2) gene. It is an exploratory research considering the limited data and research about the genetic, especially the COX2 of Sumatran elephant (Elephas maximus sumatranus). Forward and reverse sequencing of PCR products was conducted using the primary COX2 from Sumatran elephant samples. The results of the subsequent gene sequencing were aligned with the sequences of other Asian elephants from Genbank using Clustal W software and analyzed using the MEGA program version 6.06. The analysis of genetic distance based on COX2 constituent nucleotides calculated with Kimura’s two-parameter method showed that the genetic distance between Elephas maximus sumatranus and Elephas maximus outside of Sumatra was 0.25%. The phylogenetic trees analyzed using the maximum-likelihood based on nucleotide sequences showed a high homogeneity. The ratio of Elephas maximus sumatranus with Elephas maximus shows levels of nucleotide mutations which are nine nucleotides and four nucleotides. These results indicated that the COX2 gene could not identify the individual species of Sumatran elephant because of the high intraspecies homogeneity, but it detected the interspecies divergence clustered in Asian elephant clade.