Miracle-dz/UniFAR-FTUs huggingface dataset:
A feminist project of belonging for the Anthropocene

At the core of J.K. Gibson-Graham's feminist political imaginary is the vision of a decentralized movement that connects globally dispersed subjects and places through webs of signification. We view these subjects and places both as sites of becoming and as opportunities for belonging. But no longer can we see subjects as simply human and places as human-centered. The ‘arrival’ of the Anthropocene has thrown us onto new terrain. Feminist critiques of hyper-separation are pushing us to move beyond the divisive binaries of human/nonhuman, subject/object, economy/ecology and thinking/acting. The reframing of our living worlds as vast uncontrolled experiments is inspiring us to reposition ourselves as learners, increasingly open to our interconnections with earth others and more willing to intervene in adventurous ways. In this article we begin to think about more-than-human regional development and regional research collectives that have the potential to perform resilient worlds. For us the project of belong.
MRC is failing to turn basic research into commercial success, report says

The performance of the Medical Research Council (MRC) has been transformed in the past few years, but serious failings remain, a parliamentary investigation into the United Kingdom's research councils has found. Mounting concern over the management of the council culminated in a scathing attack on its finances and strategic planning by the House of Commons Science and Technology Committee in March 2003 ( BMJ 2003;326:732). Phil Willis, the committee's current chairman, said much progress had since been made. “It now has a clarity of vision and focus. That wasn't the case three or …
Challenges to sustainable Arctic tourist lodging: a proposed solution for Greenland

ABSTRACT The beauty of nature in Greenland and the selling point of Greenlandic tourism ‘Our ice is melting, yet it is still here’ have been attracting more tourists in recent years. Therefore, demand for tourist accommodation is expected to grow in the future. Staying overnight in small huts is a traditional way for nomadic Greenlanders and for tourists. However, the austere condition of the existing huts cannot meet requirements nowadays. Meanwhile, climate change is causing retreat of ice, so tourist attractions might change in the future. Therefore the contradiction of ‘improved lodging’, ‘environmental friendliness’ and ‘flexibility of location’ raise the problem for the future development of tourist accommodation. The aim of this work is to explore a desirable solution by developing stand-alone mobile tourist huts with light and heating based on renewable energy supply, and sustainable black waste water management. The cabin can be disassembled into pieces and transported to new areas by trucks, snow scooters, boats or dog-sledges with almost no disturbance to the environment of the original location. In addition, cultural sustainability is also taken into account.
Event-triggered impulsive containment control for fixed-time consensus of a class of heterogeneous multi-agent system with disturbances

This paper studies the fixed-time containment consensus (FTCC) of a class of nonlinear heterogeneous multi-agent systems (NHMASs) with disturbances. A reference model-based event-triggered impulsive control (ETIC) mechanism is devised to realize the FTCC of NHMASs. The impulsive controller is performed only at the triggering instant, which can substantially decrease energy consumption. Then, some sufficient conditions are presented to guarantee that the NHMASs with multiple leaders can reach consensus within fixed time in the case of that agents’ dynamics are different and high order. It is proved that Zeno behavior can be excluded. Finally, a simulation example is provided to verify the feasibility of the derived results.
Sampling problems on a Quantum Computer

Due to the advances in the manufacturing of quantum hardware in the recent years, significant research efforts have been directed towards employing quantum methods to solving problems in various areas of interest. Thus a plethora of novel quantum methods have been developed in recent years. In this paper, we provide a survey of quantum sampling methods along-side needed theory and applications of those sampling methods as a starting point for research in this area. This work focuses in particular on Gaussian Boson sampling, quantum Monte Carlo methods, quantum variational Monte Carlo, quantum Boltzmann Machines and quantum Bayesian networks. We strive to provide a self-contained overview over the mathematical background, technical feasibility, applicability for other problems and point out potential areas of future research.
AlphaSyn: Logic Synthesis Optimization with Efficient Monte Carlo Tree Search

Recent years have seen rising research in logic synthesis recipe generation to improve the Quality-of-Result (QoR). However, existing approaches typically have low efficiency and are stuck at local optima. In this work, we propose a logic synthesis optimization framework, AlphaSyn, that incorporates a domain-specific Monte Carlo tree search (MCTS) algorithm. AlphaSyn enables exploration across the entire search space while optimizing sampling points utilization. We further develop a synthesis-specific upper confidence bound for trees (SynUCT) algorithm for the selection phase and a well-designed learning strategy to enhance the stability of the MCTS algorithm. The AlphaSyn algorithm is fully parallelized for efficiency with asynchronous MCTS exploration and significance-base resource allocation. For standard-cell technology mapping on the ASAP 7nm library among other tasks, experimental results show that AlphaSyn outperforms SOTA FlowTune with an average 8.74% area reduction and $\boldsymbol{1.24}\times$ runtime speedup.
Research on multimodal information fusion dynamic target recognition method for autonomous driving

In order to improve the ability of autonomous driving to sense the surrounding environment and promote the development of autonomous driving technology, this paper proposes a multimodal information fusion dynamic target recognition method for autonomous driving. First, in order to solve the distributed modal gap caused by the heterogeneity of different signals, this paper proposes to construct two different subspaces of modes for multimodal information. The first subspace is mode-invariant, in which cross-modal representations learn their commonalities and narrow the modal gap. The second subspace is modality-specific, private to each modality, and guarantees their characteristics. Second, in video signals, existing methods use completely independent approaches to individual modal signals or treat all modal signals equally using the same approach, which do not fully consider modal differences and exploit modal correlations. This section proposes a new RGB-D target recognition method capable of learning complementary functional modes from multiple and common features between different modes. A joint loss activation function is used for cascading multimodal information features. Experimental results show that the RD-DSST model proposed in this paper, which performs better on the publicly available dataset of KITTI, compared with Unet, YOLOv5 and Faster R-CNN, proving the effectiveness of the model proposed in this paper.
Predicting Ejection Fraction from Electrocardiogram Signals using a Multi-task Learning Model

The aim of this study was to investigate the feasibility and effectiveness of using electrocardiogram (ECG) signals to predict ejection fraction (EF) in an extremely imbalanced dataset. We collected ECG signals from 9365 patients and conducted a correlation analysis with EF. After collecting and preprocessing the ECG signal, we developed a deep learning-based multi-task learning model designed to extract features from ECG signals and perform predictions. Our study employed a model based on Transformer, multi-scale convolutional neural networks (CNN), channel attention, and pre-trained ResNet to predict EF (EF > 50 or EF ≤ 50) and EF value. The experimental results demonstrate that the proposed model exhibits excellent predictive performance, with an AUC of 0.825 and MAE of 4.855 for predicting EF. It outperforms other models and shows better results in comparison. Our study validates the feasibility and effectiveness of using ECG signals to predict EF and provides strong support for early diagnosis and treatment of cardiovascular diseases.
Relationship between team learning profiles and outcomes in team project-based learning: a cluster analysis

ABSTRACT Team project-based learning has become increasingly common in higher education. This study aimed to characterise and understand students’ team learning experiences in team project-based learning by considering various aspects, such as individual qualities, teamwork, task, and instructor support. K-means clustering analysis was performed using eleven variables: team learning preference, co-regulation, team efficacy, team climate, team trust, psychological safety, team interaction, perceived team loafing, task authenticity, the frequency of instructor feedback, and usefulness of instructor feedback. Data were collected from students who were involved in team project-based learning at a South Korean university in the 2nd semester of 2021, and 782 responses were used in the analysis. Consequently, six clusters were identified, each showing different patterns of individual team learning attitudes and skills, teamwork, task authenticity, and instructor feedback. Following the cluster analysis, one-way ANOVA was performed, finding significant differences in team performance and team learning satisfaction between clusters. Based on these findings, important factors in team learning are discussed, and implications of enhancing team project-based learning are presented.
A Deep Supervised Pavement Crack Detection Network with Multiscale Feature Fusion and Feature Learning

Cracks are one of the most serious road diseases. Accurate detection of road cracks is an important part of road maintenance. With the development of modern society, infrastructure is increasing day by day. Traditional manual detection is not suitable for modern large-scale crack detection because it is time consuming and depends on people’s subjective consciousness. Due to the complex background and the complexity of the cracks, high-precision autonomous crack detection is still a challenging task. To solve the above problems, this paper takes the attention U-Net as the baseline network, and proposed a deep supervised pavement crack detection network for end-to-end crack detection. To realize effective feature fusion, an attention fusion block (AFB) is proposed for multi-scale feature aggregation. Meanwhile, based on the advantages of feature learning scheme, a progressive fusion block (PCF) is also proposed to capture more global context information. Faced with the problem of limited receptive fields, a multiple receptive field fusion (MRFF) block is proposed for multi-scale feature representation. Experiments on two public datasets show that the proposed network has obtained a better segmentation performance than the mainstream segmentation models.
An oxygen, hydrogen, sulfur, and carbon isotope study of carbonate-replacement (skarn) tin deposits of the Dachang tin field, China

The Dachang tin field, located in Guangxi province of southern China, is one of the largest in the world. It comprises the Changpo, Bali, and Longtaoshan carbonate replacement tin deposits and the Lamo Cu-Zn skarn deposit. These deposits are hosted by Devonian carbonate-rich sedimentary rocks, which have been metamorphosed to calc-silicate skarn and marble near the contact of the Longxianggei granite.Calculated delta 18 O (sub H 2 O) values of the hydrothermal fluids in equilibrium with quartz, cassiterite, and calcite are 10.6 and 11.6, 9.1 to 10.2, and 7.5 to 9.8 per mil, respectively. Measured delta D values of fluid inclusion waters in cassiterite and calcite are -115 and -120, and -73 to -93 per mil, respectively. The decrease in the calculated delta 18 O (sub H 2 O) values from 11.6 to 7.5 per mil is attributed to increase in the CO 2 content of the hydrothermal fluids. The low delta D values of fluid inclusion waters in cassiterite may be the result of decreasing CH 4 /CO 2 as the originally magmatic fluid migrated from reducing conditions in carbonaceous black shale to oxidizing conditions in pure limestone. Oxygen and hydrogen isotope data indicate that the hydrothermal fluid was derived from the Longxianggei granite.Variations in delta 13 C and delta 18 O values of calcite in orebodies, altered limestone, and marble are due to continuous isotopic exchange reaction between magmatic fluid and carbonate rocks at variable water to rock ratios between 300 degrees to 600 degrees C.Sulfur isotope data indicate that host rocks controlled the variations in sulfur isotope compositions of sulfide minerals in different deposits of the tin field, with significant contribution of sulfur from magmatic fluids.
The formation of Ca‐, Fe‐rich silicates in reduced and oxidized CV chondrites: The roles of impact‐modified porosity and permeability, and heterogeneous distribution of water ices

CV (Vigarano type) carbonaceous chondrites, comprising Allende‐like (CVoxA) and Bali‐like (CVoxB) oxidized and reduced (CVred) subgroups, experienced differing degrees of fluid‐assisted thermal and shock metamorphism. The abundance and speciation of secondary minerals produced during asteroidal alteration differ among the subgroups: (1) ferroan olivine and diopside–hedenbergite solid solution pyroxenes are common in all CVs; (2) nepheline and sodalite are abundant in CVoxA, rare in CVred, and absent in CVoxB; (3) phyllosilicates and nearly pure fayalite are common in CVoxB, rare in CVred, and virtually absent in CVoxA; (4) andradite, magnetite, and Fe‐Ni‐sulfides are common in oxidized CVs, but rare in reduced CVs; the latter contain kirschsteinite instead. Thus, a previously unrecognized correlation exists between meteorite bulk permeabilities and porosities with the speciation of the Ca‐, Fe‐rich silicates (pyroxenes, andradite, kirschsteinite) among the CVox and CVred meteorites. The extent of secondary mineralization was controlled by the distribution of water ices, permeability, and porosity, which in turn were controlled by impacts on the asteroidal parent body. More intense shock metamorphism in the region where the reduced CVs originated decreased their porosity and permeability while simultaneously expelling intergranular ices and fluids. The mineralogy, petrography, and bulk chemical compositions of both the reduced and oxidized CV chondrites indicate that mobile elements were redistributed between Ca,Al‐rich inclusions, dark inclusions, chondrules, and matrices only locally; there is no evidence for large‐scale (>several cm) fluid transport. Published 53Mn‐53Cr ages of secondary fayalite in CV, CO, and unequilibrated ordinary chondrites, and carbonates in CI, CM, and CR chondrites are consistent with aqueous alteration initiated by heating of water ice‐bearing asteroids by decay of 26Al, not shock metamorphism.
Welcome Interferences: Dealing with Obstacles Promotes Creative Thought in Goal Pursuit

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Simultaneous input and state estimation for uncertain discrete-time stochastic systems

This paper investigates the simultaneous unknown input and state estimation (SUISE) problem for a class of uncertain linear discrete-time stochastic systems with unknown input. First, the augmented system is constructed with the aid of the input model, which is described as a random walk process with finite covariance. Next, a robust filter for the augmented system is proposed. Then, a new SUISE algorithm is obtained from the robust filter as the covariance of the input approaches infinity. Finally, a simulation example illustrates the proposed SUISE algorithm’s effectiveness.
Type-C olivine fabric in the Zhimafang garnet peridotite of the southern Sulu ultrahigh-pressure metamorphic terrane:Formation conditions and tectonic implications.

Upper mantle seismic anisotropy is mainly attributed to the lattice-preferred orientation ( LPO) of olivine, which is regarded as an indicator of different dynamic processes. Five types of olivine fabric have been identified for olivine aggregates in simple shear deformation experiments under various water contents and stresses. Using the electron backscatter diffraction (EBSD) technique, we measured the LPOs of olivine porphyroclasts in the Zhimafang garnet peridotite of the southern Sulu ultrahigh-pressure metamorphic terrane. Although these olivine grains have experienced different degrees of deformation, all of them have distinctive type-C fabric in which [100] axes is concentrated subnormal to the foliation and [001] axes subparallel to the lineation. Our data do not show any correlation between the types of olivine fabric and degrees of deformation. This observation together with the differences in the shear direction inferred from olivine fabric from that from quartz fabricformed during exhumation demonstrate that the type-C olivine fabric did not result from deformation associated with the exhumation phases of the Sulu UHP rocks. In stead, the type-C olivine fabric was formed in a subduction zone under high water activity, as suggested by water contents of olivine crystals and the temperature and pressure conditions of the Zhimafang garnet peridotite. These results suggest that during subduction of the Yangtze plate, the Zhimafang garnet peridotite was captured from the overlying mantle wedge and then experienced ultrahigh-pressure metamorphism as the Yangtze continental block was subducted to mantle depths.
Enhancing Scholarly Understanding: A Comparison of Knowledge Injection Strategies in Large Language Models

The use of transformer-based models like BERT for natural language processing has achieved remarkable performance across multiple domains. However, these models face challenges when dealing with very specialized domains, such as scientific literature. In this paper, we conduct a comprehensive analysis of knowledge injection strategies for transformers in the scientific domain, evaluating four distinct methods for injecting external knowledge into transformers. We assess these strategies in a single-label multi-class classification task involving scientific papers. For this, we develop a public benchmark based on 12k scientific papers from the AIDA knowledge graph, categorized into three fields. We utilize the Computer Science Ontology as our external knowledge source. Our findings indicate that most proposed knowledge injection techniques outperform the BERT baseline.
Hygroscopicity and CCN activity of atmospheric aerosol particles and their relation to organics: Characteristics of urban aerosols in Nagoya, Japan

The size‐resolved distributions of hygroscopic growth factor g and the ratios of cloud condensation nuclei (CCN) to condensation nuclei of atmospheric aerosols were investigated in Nagoya, Japan. The average of the distributions of g at 85% relative humidity was bimodal. The size‐resolved mean κ derived from g showed an increasing trend with diameter: 0.17–0.33 at 24–359 nm. The κ values calculated from CCN activation curves were 37% higher than those derived from g. Only 9% of the 37% difference is explained by the difference in the κ of inorganics under subsaturated and supersaturated conditions, suggesting a contribution of organics to the remaining 28% difference. The size‐averaged κ of organics (κorg) was calculated as 0.14 and 0.19 by two different methods. The number fractions of CCN predicted from the hygroscopicity data over the range of 24–359 nm are loosely consistent with those observed if the size‐ and time‐averaged g is applied to all particles (differences: −30% to +10%). This consistency improves if size‐ and time‐resolved g and g distribution are used (differences: −19% to −3%). Whereas the number fractions of CCN predicted from the composition data are greatly underestimated if organics are assumed to be insoluble (differences: −64% to −45%), they are more consistent if κorg of 0.14 or 0.19 is applied (differences: −10% to +14%). The results demonstrate the importance of the dependence of the g of particles on time and particle size and the hygroscopicity of organics for CCN number concentrations in the urban atmosphere.
Fluids in granulites

Since the discovery of CO2 fluid inclusions in granulites, the role of fluids in the formation of these rocks has been widely studied. Owing to the complexity of the tectono-metamorphic history of granulite terrains, fluid inclusion data alone are not sufficient. They need to be integrated with geochemical and mineralogical studies done on the same rock samples. A clear understanding of the tectono-metamorphic history of granulite terranes is also indispensable. The widespread occurrence of CO2 and the later discovered high-salinity aqueous fluid inclusions support the idea that the lower crust underwent fluid flow and that both carbonic and brine fluids played a role in its formation. Both low-H2O-activity fluids play a similar role in destabilizing hydrous mineral phases. Furthermore, experimental studies have shown that brine fluids have a much larger geochemical effect on granulites than initially expected. These fluids are far more mobile in the lower crust compared with CO2 and also have the capability for dissolving numerous minerals. As in the example of the Limpopo Complex, fluid inclusions and many metasomatic features observed in granulite terranes can thus be explained only by large-scale movement of high-salinity aqueous fluids and, to a lesser extent, CO2, implying that lower-crustal granulites are not as dry as previously assumed. Similar brines and CO2-rich fluids are also found in mantle material, most likely derived from deeply subducted supracrustal protoliths.