Test-Time Adaptation for Nighttime Color-Thermal Semantic Segmentation

The ability to scene understanding in adverse visual conditions, e.g., nighttime, has sparked active research for RGB-Thermal (RGB-T) semantic segmentation. However, it is essentially hampered by two critical problems: 1) the day-night gap of RGB images is larger than that of thermal images, and 2) the class-wise performance of RGB images at night is not consistently higher or lower than that of thermal images. we propose the first test-time adaptation (TTA) framework, dubbed Night-TTA, to address the problems for nighttime RGBT semantic segmentation without access to the source (daytime) data during adaptation. Our method enjoys three key technical parts. Firstly, as one modality (e.g., RGB) suffers from a larger domain gap than that of the other (e.g., thermal), Imaging Heterogeneity Refinement (IHR) employs an interaction branch on the basis of RGB and thermal branches to prevent cross-modal discrepancy and performance degradation. Then, Class Aware Refinement (CAR) is introduced to obtain reliable ensemble logits based on pixel-level distribution aggregation of the three branches. In addition, we also design a specific learning scheme for our TTA framework, which enables the ensemble logits and three student logits to collaboratively learn to improve the quality of predictions during the testing phase of our Night TTA. Extensive experiments show that our method achieves state-of-the-art (SoTA) performance with a 13.07% boost in mIoU

Rational Design of Single Atomic Co in CoNx Moieties on Graphene Matrix as an Ultra‐Highly Efficient Active Site for Oxygen Reduction Reaction

The sharp increase in current energy consumption needs the development of fuel cells (FCs) as one of sustainable, renewable, efficient and eco‐friendly electrochemical conversion systems of energy. The performance of electrocatalysts is crucially important for commercialization of FCs. Commercial Pt based catalysts are used due to their high catalytic activity. However, widespread commercialization is impossible because of the scarcity and poor durability of Pt based catalysts. We are on our quest to find a more stable and affordable alternative catalyst of Pt based catalysts. In particular, single‐atom catalysts supported on graphene are greatly attractive because of their unique characteristic and high catalytic activity. In this work, graphene is hydrothermally treated by sulfuric acid to introduce the ion‐exchanging sites. Then, Co2+ ion‐exchanging, 2‐methylimidazole coordination and pyrolysis process are subsequently conducted to prepare highly‐dispersed single‐atom Co species catalyst with outstanding ORR activity and durability. This work presents a new direction for a rational design of single‐atom catalyst on carbon matrix.We would like to thank MICIINUN and FEDER for financial support (Project RTI2018-095291-B-I00)

CivRealm: A Learning and Reasoning Odyssey in Civilization for Decision-Making Agents

The generalization of decision-making agents encompasses two fundamental elements: learning from past experiences and reasoning in novel contexts. However, the predominant emphasis in most interactive environments is on learning, often at the expense of complexity in reasoning. In this paper, we introduce CivRealm, an environment inspired by the Civilization game. Civilization's profound alignment with human history and society necessitates sophisticated learning, while its ever-changing situations demand strong reasoning to generalize. Particularly, CivRealm sets up an imperfect-information general-sum game with a changing number of players; it presents a plethora of complex features, challenging the agent to deal with open-ended stochastic environments that require diplomacy and negotiation skills. Within CivRealm, we provide interfaces for two typical agent types: tensor-based agents that focus on learning, and language-based agents that emphasize reasoning. To catalyze further research, we present initial results for both paradigms. The canonical RL-based agents exhibit reasonable performance in mini-games, whereas both RL- and LLM-based agents struggle to make substantial progress in the full game. Overall, CivRealm stands as a unique learning and reasoning challenge for decision-making agents. The code is available at https://github.com/bigai-ai/civrealm

Analysis of Arsenic Speciation Distribution in Agaric, Shiitake Mushroom, Matsutake and Agrocybe by IC-ICP-MS Method

To analyze the speciation distribution of arsenic in agaric, shiitake mushroom, matsutake and agrocybe, the ion chromatography-inductive coupled plasma mass spectrometer (IC-ICP-MS) was used to determine arsenobetaine, dimethyl arsenic, arsenous acid, arsenic choline, monomethyl arsenic and arsenic acid, and the methodological investigation and content determination were carried out. The results showed that the method could completely separate all six arsenic forms within 5 minutes, and the peak patterns were good. The linear relationship of the method was good (mass concentration of 0.5~20 μg/L, r>0.999). The detection limit and quantification limit of six arsenic species were not more than 0.005 and 0.017 mg/kg respectively. The recovery rate of six arsenic forms in agaric, agrocybe and shiitake mushroom could reach 80%~120% with standard addition. For matsutake, the standard addition recovery rate could also reach 80%~120% when adding right standard amounts (0.05 mg/kg dimethyl arsenic, arsenic choline and arsenic acid; 0.2 mg/kg arsenite and monomethyl arsenic; 5 mg/kg arsenic betaine). Combined with the dehydration rate of dry products, the inorganic arsenic content of the tested samples met the requirements of GB 2762-2022. The content of total arsenic in matsutake was the highest, but the proportion of inorganic arsenic（arsenic choline+arsenic acid） in total arsenic was the lowest 3.6%~6.8%, and the highest proportion was arsenobetaine (75.8%~87.3%). The main form of arsenic in agaric, agrocybe, and shiitake mushroom were inorganic arsenic. The proportion of inorganic arsenic to total arsenic could reach 58.4%~66.1%、60.0%~66.7%、81.2%~91.7%, respectively. There was a risk of food safety when the total arsenic content was high

Prediction of ESRD in IgA Nephropathy Patients from an Asian Cohort: A Random Forest Model

Background/Aims: There is an increasing risk of end-stage renal disease (ESRD) among Asian people with immunoglobulin A nephropathy (IgAN). A computer-aided system for ESRD prediction in Asian IgAN patients has not been well studied. Methods: We retrospectively reviewed biopsy-proven IgAN patients treated at the Department of Nephrology of the Second Xiangya Hospital from January 2009 to November 2013. Demographic and clinicopathological data were obtained within 1 month of renal biopsy. A random forest (RF) model was employed to predict the ESRD status in IgAN patients. All cases were initially trained and validated, taking advantage of the out-of-bagging(OOB) error. Predictors used in the model were selected according to the Gini impurity index in the RF model and verified by logistic regression analysis. The area under the receiver operating characteristic(ROC) curve (AUC) and F-measure were used to evaluate the RF model. Results: A total of 262 IgAN patients were enrolled in this study with a median follow-up time of 4.66 years. The importance rankings of predictors of ESRD in the RF model were first obtained, indicating some of the most important predictors. Logistic regression also showed that these factors were statistically associated with ESRD status. We first trained an initial RF model using gender, age, hypertension, serum creatinine, 24-hour proteinuria and histological grading suggested by the Clinical Decision Support System for IgAN (CDSS, www.IgAN.net). This 6-predictor model achieved a F-measure of 0.8 and an AUC of 92.57%. By adding Oxford-MEST scores, this model outperformed the initial model with an improved AUC (96.1%) and F-measure (0.823). When C3 staining was incorporated, the AUC was 97.29% and F-measure increased to 0.83. Adding the estimated glomerular filtration rate (eGFR) improved the AUC to 95.45%. We also observed improved performance of the model with additional inputs of blood urea nitrogen (BUN), uric acid, hemoglobin and albumin. Conclusion: In addition to the predictors in the CDSS, Oxford-MEST scores, C3 staining and eGFR conveyed additional information for ESRD prediction in Chinese IgAN patients using a RF model

The Assessment of Ice-snow Tourism Resources Value and Its Realization Degree

The development of the ice-snow tourism needs a scientific and effective method to assess the value of ice-snow tour-ism resource and its realization degree effectively and reasonability. Based on the analysis of expectation value and realization value of ice-snow tourism resource, this paper designs a three-dimension assessment index system and con-structs the method of assessing the value of ice-snow tourism resource and its realization degree. Finally using ISW scenic area in Heilongjiang Province as an example for empirical analysis, the results show that the method can be ef-fective in assessing the value of ice-snow tourism resource and its realization degree

Will Green CSR Enhance Innovation? A Perspective of Public Visibility and Firm Transparency

In response to the asking and requiring of stakeholders to be more environmentally responsible, firms must commit to green corporate social responsibility (CSR). Firms being green and responsible always can acquire intangible resources that are important for firm innovation. Given the scarcity of existing research addressing relevant issues in depth, this paper expands our understanding of green CSR by revealing its antecedent effects on firm innovation performance. We also include public visibility and firm transparency as contingency factors to explore the relationship between green CSR and firm innovation performance. Using data collected from publicly listed firms in China, we find that greater innovation performance is associated with an increase in firm green CSR, and the positive relationship between green CSR and innovation performance is moderated by public visibility and firm transparency. Based on the results, theoretical contributions and practical implications are outlined

Industry-energy system management by a Copula-based stochastic programming approach

In this study, a Copula-based stochastic industry-energy system management (CSIE) model was developed based on Copula-based stochastic programming and interval linear programming. CSIE model can not only deal with extreme random events in industry-energy system (IES) of resource-dependent cities, but also quantify the risks of industrial energy demand-supply. To prove the practicability, a case study of IES planning in Yulin city was represented. Reasonable solutions of energy production and industrial energy consumption strategy were obtained, which can guarantee that pollutant emission meets the environmental requirements, and the system cost gets the lowest during 2021-2035. Furthermore, CSIE model could be spread to IES management in similar resource-dependent cities

Mapping out the reaction network of humin formation at the initial stage of fructose dehydration in water

The formation of humins hampers the large-scale production of 5-hydroxymethylfurfural (HMF) in biorefinery. Here, a detailed reaction network of humin formation at the initial stage of fructose-to-HMF dehydration in water is delineated by combined experimental, spectroscopic, and theoretical studies. Three bimolecular reaction pathways to build up soluble humins are demonstrated. That is, the intermolecular etherification of β-furanose at room temperature initiates the C12 path, whereas the C–C cleavage of α-furanose at 130–150 °C leads to C11 path, and that of open-chain fructose at 180 °C to C11′ path. The successive intramolecular dehydrations and condensations of the as-formed bimolecular intermediates lead to three types of soluble humins. We show that the C12 path could be restrained by using HCl or AlCl3 catalyst, and both the C12 and C11′ paths could be effectively inhibited by adding THF as a co-solvent or accelerating heating rate via microwave heating