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The majority of ixodid ticks display host-specificity to varying extents. Feeding on different hosts affects their development and reproduction. Consequences can be analyzed at the level of the egg, as it is the initial stage of tick development. Tick egg proteins are abundant and diverse, providing nutrients for embryonic development. However, studies on tick egg profiles are scarce. In this study, we aimed to analyze whether feeding Haemaphysalis qinghaiensis ticks on the yaks (Bos grunniens) and domestic sheep (Ovis aries) has an impact on the variety and variability of the egg proteome. Detached engorged females were used to lay eggs, which were then collected, dewaxed, and subjected to protein extraction. The extracted egg proteins were enzymatically digested using Filter-Aided Sample Preparation (FASP), and the unique peptides were separated and detected by Liquid Chromatography-tandem Mass Spectrometry (LC–MS/MS). The MS data were searched against the previously constructed whole tick transcriptome library of H. qinghaiensis, and the UniProt database for the identification of tick-derived egg proteins. The analysis revealed 49 and 53 high-confidence proteins identified in eggs collected from B. grunniens (EggBg) and O. aries (EggOa), respectively. Of these, 46 high-confidence proteins were common to both egg types, while three were unique to EggBg and seven to EggOa. All the identified proteins mainly belonged to enzymes, enzyme inhibitors, transporters, and proteins with unknown functions. The differential abundance analysis showed that nine proteins were significantly more present in EggBg, while six were significantly more present in EggOa. Overall, enzymes were the most diverse group, while vitellogenin (Vg) was the most abundant. Blood meal uptake on different hosts has a certain effect on the egg proteome composition and the abundance of some proteins, but it may also lead to compensation of protein roles.

Robert Zhao Renhui is an artist from Singapore trained in photography, and the founder of the Institute of Critical Zoologists (ICZ). He is known for his artistic investigations of the representation of Asia’s and Southeast Asia’s nature, history, and culture in the scientific and colonial archive, and his research often focuses on the evolutionary pressure exerted on other-than-human species by urbanization, global transportation, and the built environment. For the Singapore Pavilion, Zhao turns his gaze towards secondary forests and neglected pockets of greenery amid Singapore’s mostly urban environment. His project Seeing Forest is the fruit of several years of careful visitations to a terrain of secondary woodland that has proliferated on the grounds of a former, mid-twentieth century military camp and which is encompassed by high-rise apartment blocks, multi-lane roads, and numerous construction sites in the Singapore district along Alexander Road. Littered with the detritus of previous human use and inhabitation, this patch of ruderal forest also conceals the collective and improbable habitat of myriad wild species of vastly different origins. As the artist learns to be quiet and to attune himself to these creatures’ chrono-diverse rhythms and improvised rituals of survival, urbanization is recalibrated as a relational refuge for heterogeneous trans-territorial, temporally cadenced, and unexpected encounters., https://www.librarystack.org/robert-zhao-renhui-seeing-forest-volume-1-of-2/?ref=unknown

The final publication is available at Springer via http://dx.doi.org/10.1007/s10346-024-02316-7., Debris flows are considered one of the most hazardous types of mass movement. China has a long history of monitoring debris flows, which has enhanced the understanding of debris flows and the development of strategies for their prevention. This study reviewed case studies and outputs related to debris flow monitoring in China. The monitoring systems are set in seven catchments, with area between 2 and 40 km2, covering various types of debris flows in different climate conditions. This review also introduced the definitions and classifications adopted for debris flows in China for comparison with those used in Western literatures. A comprehensive analysis was conducted of debris flow parameters, including the grain size distribution, density, Froude number, velocity–depth relationship, volume–peak discharge relationship, volume–drainage area relationship, and velocity and peak discharge calculation methods. Additionally, the rainfall intensity–duration thresholds were compared. Accurate identification of such information is fundamental for enhancing comprehension of debris flow characteristics, facilitating monitoring, and the implementation of early warning and alarm systems., Peer Reviewed, Postprint (author's final draft)

A comparative analysis of the factors controlling organic matter (OM) enrichment between marine-continental transitional (transitional hereafter) and marine or lacustrine shales is lacking. The early Permian Taiyuan Formation in the Ordos Basin, deposited during a shift from marine to continental settings in northern China, provides a unique opportunity to unravel the differences in OM enrichment mechanisms among these shales. The Taiyuan Formation is characterized by high TOC content (average 4.50%) and kerogen type II2-III. Most samples are thermally mature with a few high to post-mature samples relating to the Late Jurassic–Early Cretaceous Yanshanian magmatism. Rare earth elements and yttrium (REY) are dominated by light- and medium-types enrichments, with distinctly positive Gd anomaly, likely due to seawater incursion. A warm and humid climate prevailed during deposition of the Taiyuan Formation, consistent with the tropical-subtropical location of the North China Plate in the early Permian. The climatic conditions promoted intense continental weathering as reflected by high Th/Sc ratios, chemical index of alteration values, and feldspar alteration to scaly kaolinite. The V/(V + Ni) ratio is inconsistent with the other redox proxies, presumably due to variations in the redox buffer supply in the transitional facies (e.g., OM and pyrite), varying burial rates and dissimilar redox potential of different elements. Hence, this proxy should be interpreted with caution in such settings. Most redox proxies indicate oxic bottom water during deposition of the Taiyuan Formation transitional shale, in contrast to typical OM enriched marine and lacustrine shales where redox stratification or euxinic conditions are common. Instead, the dominant factor for OM enrichment in transitional shales appears to have been a high influx of terrestrial weathering products, including abundant higher-plants OM, associated with preservation of OM due to rapid burial. This process minimiz, A comparative analysis of the factors controlling organic matter (OM) enrichment between marine-continental transitional (transitional hereafter) and marine or lacustrine shales is lacking. The early Permian Taiyuan Formation in the Ordos Basin, deposited during a shift from marine to continental settings in northern China, provides a unique opportunity to unravel the differences in OM enrichment mechanisms among these shales. The Taiyuan Formation is characterized by high TOC content (average 4.50%) and kerogen type II2-III. Most samples are thermally mature with a few high to post-mature samples relating to the Late Jurassic–Early Cretaceous Yanshanian magmatism. Rare earth elements and yttrium (REY) are dominated by light- and medium-types enrichments, with distinctly positive Gd anomaly, likely due to seawater incursion. A warm and humid climate prevailed during deposition of the Taiyuan Formation, consistent with the tropical-subtropical location of the North China Plate in the early Permian. The climatic conditions promoted intense continental weathering as reflected by high Th/Sc ratios, chemical index of alteration values, and feldspar alteration to scaly kaolinite. The V/(V + Ni) ratio is inconsistent with the other redox proxies, presumably due to variations in the redox buffer supply in the transitional facies (e.g., OM and pyrite), varying burial rates and dissimilar redox potential of different elements. Hence, this proxy should be interpreted with caution in such settings. Most redox proxies indicate oxic bottom water during deposition of the Taiyuan Formation transitional shale, in contrast to typical OM enriched marine and lacustrine shales where redox stratification or euxinic conditions are common. Instead, the dominant factor for OM enrichment in transitional shales appears to have been a high influx of terrestrial weathering products, including abundant higher-plants OM, associated with preservation of OM due to rapid burial. This p

Hyperlipidemia, obesity and gut dysbiosis are pivotal risk factors for atherosclerotic cardiovascular disease (ACVD). Supplementation of Akkermansia muciniphila (AKK) has also been proven to be effective in the prevention and treatment of obesity and other metabolic disorders. Here we found that AKK was more abundant in healthy control than ACVD patients via metagenomic sequencing on fecal samples. Subsequently, we investigated the role and underlying mechanism of AKK on obesity-associated atherosclerosis. AKK intervention partially reversed the exacerbation of atherosclerotic lesion formation in ApoE-/- mice by improving dyslipidemia. Interestingly, replenishment with AKK significantly enhanced cardiac function and reduced the body weight. It also reduced pro-inflammatory cytokine IL-6 and increased anti-inflammatory IL-10 in the circulation. Additionally, AKK colonization dramatically regulated gut microbiota and increased the abundance of Lactobacillaceae. Our findings have provided novel insights into the therapeutic potential of AKK as a beneficial microbe for treating atherosclerotic-associated cardiovascular diseases., Hyperlipidemia, obesity and gut dysbiosis are pivotal risk factors for atherosclerotic cardiovascular disease (ACVD). Supplementation of Akkermansia muciniphila (AKK) has also been proven to be effective in the prevention and treatment of obesity and other metabolic disorders. Here we found that AKK was more abundant in healthy control than ACVD patients via metagenomic sequencing on fecal samples. Subsequently, we investigated the role and underlying mechanism of AKK on obesity-associated atherosclerosis. AKK intervention partially reversed the exacerbation of atherosclerotic lesion formation in ApoE-/- mice by improving dyslipidemia. Interestingly, replenishment with AKK significantly enhanced cardiac function and reduced the body weight. It also reduced pro-inflammatory cytokine IL-6 and increased anti-inflammatory IL-10 in the circulation. Additionally, AKK colonization dramatically regulated gut microbiota and increased the abundance of Lactobacillaceae. Our findings have provided novel insights into the therapeutic potential of AKK as a beneficial microbe for treating atherosclerotic-associated cardiovascular diseases.

Oxygenated organic molecules (OOMs) produced by the oxidation of aromatic compounds are key components of secondary organic aerosol (SOA) in urban environments. The steric effects of substitutions and rings and the role of key reaction pathways in altering the OOM distributions remain unclear because of the lack of systematic multi-precursor study over a wide range of OH exposure. In this study, we conducted flow-tube experiments and used the nitrate adduct time-of-flight chemical ionization mass spectrometer (NO3 - TOF-CIMS) to measure the OOMs produced by the photooxidation of six key aromatic precursors under low-NOx conditions. For single aromatic precursors, the detected OOM peak clusters show an oxygen atom difference of one or two, indicating the involvement of multi-step auto-oxidation and alkoxy radical pathways. Multi-generation OH oxidation is needed to explain the diverse hydrogen numbers in the observed formulae. In particular, for double-ring precursors at higher OH exposure, multi-generation OH oxidation may have significantly enriched the dimer formulae. The results suggest that methyl substitutions in precursor lead to less fragmented OOM products, while the double-ring structure corresponds to less efficient formation of closed-shell monomeric and dimeric products, both highlighting significant steric effects of precursor molecular structure on the OOM formation. Naphthalene-derived OOMs however have lower volatilities and greater SOA contributions than the other-type of OOMs, which may be more important in initial particle growth. Overall, the OOMs identified by the NO3 - TOF-CIMS may have contributed up to 30.0% of the measured SOA mass, suggesting significant mass contributions of less oxygenated, undetected semi-volatile products. Our results highlight the key roles of progressive OH oxidation, methyl substitution and ring structure in the OOM formation from aromatic precursors, which need to be considered in future model developments to improv

Large-scale thermal storage systems are crucial for solar district heating systems. Currently, there is less engineering guidance on the heat loss patterns of underground water pits, especially in the special climatic conditions of the Xizang Plateau. Therefore, this study analyzes the heat transfer characteristics of underground water pits in the plateau climate. A cylindrical underground water pit model is constructed, and the heat loss law of underground water pits of the heating season and seasonal heat storage is investigated under multiyear operation. The results show that the heat loss rate of the underground water pit with seasonal heat storage is 0.8 % higher than that of the heating season heat storage for the same volume. The difference in heat loss between the two heat storage modes is small. The heat loss from the top, sides and bottom of an underground water pit accounted for 34.2 %, 36 % and 29.8 % of the heat loss in the first year, respectively. The percentages of heat loss from each surface after ten years of operation were 46.7 %, 36.6 %, and 16.7 %. In addition, the functions of storage volume and heat loss per unit volume of the first year in Xizang were fitted. The relationship equation between heat loss per unit volume of the first year and the annual heat loss per unit volume in the nth year was obtained. This study provides support for the heat loss of underground water pits that operate continuously for many years.

Maternal use of e-cigarette (e-cig) aerosols poses significant risks to fetal brain development, potentially increasing susceptibility to neurodevelopmental disorders in later life. However, the underlying mechanisms remain incompletely understood. This study aimed to understand the effects of fetal e-cig exposure on DNA methylome and transcriptomic changes in the neonatal brain. Pregnant rats were exposed to e-cig aerosols, and neonatal brains (5 males and 5 females/group) from both control and e-cig-exposed groups were used for experimental analysis. Results indicated that prenatal e-cig exposure altered site-specific DNA methylation patterns at both CpG and CH (non-CpG) sites, predominantly in intergenic and intronic regions, with sex dimorphism in methylation and gene expression changes. Gene ontology analysis revealed that e-cig exposure not only affected neuron projection development and axonogenesis but also altered pathways related to neurodegeneration and long-term depression. These findings provide novel insights into the dynamic changes of CpG and CH methylation induced by e-cig exposure, underscoring the susceptibility of the developing brain to maternal e-cig exposure and its potential implications for developmental disorders and neurodegenerative diseases later in life.

The cAMP cascade is increasingly recognized to transduce physiological effects locally through spatially limited cAMP gradients. However, little is known about how adenylyl cyclase enzymes that initiate cAMP gradients are localized. Here we address this question in physiologically relevant striatal neurons and investigate how AC localization impacts downstream signaling function. We show that the major striatal AC isoforms are differentially sorted between ciliary and extraciliary domains of the plasma membrane, and that one isoform, AC9, is uniquely concentrated in endosomes. We identify key sorting determinants in the N-terminal cytoplasmic domain responsible for isoform-specific localization. We further show that AC9-containing endosomes accumulate activated dopamine receptors and form an elaborately intertwined network with juxtanuclear PKA stores bound to Golgi membranes. Finally, we provide evidence that endosomal localization enables AC9 to selectively elevate PKA activity in the nucleus relative to the cytoplasm. Together, these results reveal a precise spatial landscape of the cAMP cascade in neurons and a key role of AC localization in directing downstream PKA signaling to the nucleus.

Background: With the implementation of the 11th edition of the International Classification of Diseases (ICD-11) and the publication of the metabolic dysfunction-associated fatty liver disease (MAFLD) nomenclature in 2020, it is important to establish consensus for the coding of MAFLD in ICD-11. This will inform subsequent revisions of ICD-11. Methods: Using the Qualtrics XM and WJX platforms, questionnaires were sent online to MAFLD-ICD-11 coding collaborators, authors of papers, and relevant association members. Results: A total of 890 international experts in various fields from 61 countries responded to the survey. We also achieved full coverage of provincial-level administrative regions in China. 77.1% of respondents agreed that MAFLD should be represented in ICD-11 by updating NAFLD, with no significant regional differences (77.3% in Asia and 76.6% in non-Asia, p = 0.819). Over 80% of respondents agreed or somewhat agreed with the need to assign specific codes for progressive stages of MAFLD (i.e. steatohepatitis) (92.2%), MAFLD combined with comorbidities (84.1%), or MAFLD subtypes (i.e., lean, overweight/obese, and diabetic) (86.1%). Conclusions: This global survey by a collaborative panel of clinical, coding, health management and policy experts, indicates agreement that MAFLD should be coded in ICD-11. The data serves as a foundation for corresponding adjustments in the ICD-11 revision.

This study examines the impact of Generative AI (GAI) financing on financial development (FD) across 21 countries using cross-sectional data from 2020 to 2022. Employing both simple linear regression and two-stage least squares (2SLS) to address endogeneity, we find that GAI financing significantly contributes to financial development, with stronger effects observed in Asian and non-European regions. Regional heterogeneity is evident, highlighting varying impacts across different subcontinents. Policy implications suggest promoting GAI ecosystems, attracting foreign investment, and enhancing publicity for GAI startups. The study highlights the need for future research on the ethical implications and dynamic effects of GAI financing.

Yong Li,1 Ju Gao,1 Lin Jiang,2 Canlin Sun,2 Hua Hong,2 Dapeng Yu2 1Department of Anesthesiology, Northern Jiangsu People’s Hospital Affiliated to Yangzhou University, Yangzhou, Jiangsu Province, People’s Republic of China; 2Department of Anesthesiology, The Affiliated Taizhou People’s Hospital of Nanjing Medical University, Taizhou, Jiangsu Province, People’s Republic of ChinaCorrespondence: Dapeng Yu, Department of Anesthesiology, The Affiliated Taizhou People’s Hospital of Nanjing Medical University, Taizhou, Jiangsu Province, 225300, People’s Republic of China, Email xiaosiyu1@163.comAbstract: Dexmedetomidine is a selective and potent α2-adrenoceptor agonist used for sedation, analgesia, and anxiolysis, with minimal respiratory depression; therefore, it is widely used in clinical practice. Transient hypertension has been reported to be an indication for the use of dexmedetomidine. The authors report three female patients who experienced hypertensive crisis when used atropine to treat bradycardia caused by dexmedetomidine. The transient hypertension is a relatively common side effect of dexmedetomidine, hypertensive crisis seen with coadministration of atropine is much less frequently reported. This is the first report to describe the use of atropine to treat bradycardia induced by dexmedetomidine, which may cause severe hypertension in female patients. They discuss the reason for and treatment of hypertension caused by administration of atropine and dexmedetomidine together and review the relevant literature.Keywords: dexmedetomidine, atropine, bradycardia, hypertension

Yan-jiao Zhang,1 Chao Huang,2 Xiu-guang Zu,1 Jin-ming Liu,1 Yong-jun Li1 1Department of Cardiology, The Second Hospital of Hebei Medical University, Shijiazhuang, 050000, People’s Republic of China; 2Department of Thoracic Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050011, People’s Republic of ChinaCorrespondence: Yong-jun Li, Department of Cardiology, The Second Hospital of Hebei Medical University, Heping West Road No. 215, Shijiazhuang, 050000, People’s Republic of China, Tel +86 15803210938, Email liyongjunbgr@126.comObjective: Immunogenic cell death (ICD) is part of the immune system’s response to coronary artery disease (CAD). In this study, we bioinformatically evaluated the diagnostic and therapeutic utility of immunogenic cell death-related genes (IRGs) and their relationship with immune infiltration features in CAD.Methods: We acquired the CAD-related datasets GSE12288, GSE71226, and GSE120521 from the Gene Expression Omnibus (GEO) database and the IRGs from the GeneCards database. After identifying the immune cell death-related differentially expressed genes (IRDEGs), we developed a risk model and detected immune subtypes in CAD. IRDEGs were identified using least absolute shrinkage and selection operator (LASSO) analysis. Using a nomogram, we confirmed that both the LASSO model and ICD signature genes had good diagnostic performance.Results: There was a high degree of coincidence and immune representativeness between two CAD groups based on characteristic genes and hub genes. Hub genes were associated with the interaction of neuroactive ligands with receptors and cell adhesion receptors. The two groups differed in terms of adipogenesis, allograft rejection, and apoptosis, as well as the ICD signature and hub gene expression levels. The two CAD-ICD subtypes differed in terms of immune infiltration.Conclusion: Quantitative real-time PCR (qRT-PCR) correlated CAD with the expression of OAS3, ITGAV, and PIBF1. The ICD sign

Spin logics have emerged as a promising avenue for the development of logic-in-memory architectures. In particular, the realization of XOR spin logic gates using a single spin–orbit torque device shows great potential for low-power stateful logic circuits in the next generation. In this study, we successfully obtained the XOR logic gate by utilizing a spin–orbit torque device with a lateral interface, which was created by local ion implantation in the Ta/Pt/Co/Ta Hall device exhibiting perpendicular magnetic anisotropy. The angle of the lateral interface is set at 45° relative to the current direction, leading to the competition between symmetry breaking and current-driven Néel-type domain wall motion. Consequently, the field-free magnetic switching reversed is realized by the same sign of current amplitude at this interface. Based on this field-free magnetic switching behavior, we successfully proposed an XOR logic gate that could be implemented using only a single spin–orbit torque Hall device. This study provides a potentially viable approach toward efficient spin logics and in-memory computing architectures. Graphical abstract: (Figure presented.) © Youke Publishing Co.,Ltd 2024.

Here, we present a finite element method-based scheme for solving coupled partial differential equations (PDEs) for the analysis of lithiation-induced stress in largely deformed spherical nanoparticles via the PDE module in COMSOL. We describe steps for software installation and setting PDEs, initial/boundary conditions, and mesh parameters. We then detail procedures for dividing the mesh and analyzing lithium trapping during electrochemical cycling. This protocol can also be extended to analyze a wide range of problems involving diffusion-induced stress. For complete details on the use and execution of this protocol, please refer to Li et al.1 © 2024 The Author(s)

To date only a fraction of the genetic footprint of thyroid function has been clarified. We report a genome-wide association study meta-analysis of thyroid function in up to 271,040 individuals of European ancestry, including reference range thyrotropin (TSH), free thyroxine (FT4), free and total triiodothyronine (T3), proxies for metabolism (T3/FT4 ratio) as well as dichotomized high and low TSH levels. We revealed 259 independent significant associations for TSH (61% novel), 85 for FT4 (67% novel), and 62 novel signals for the T3 related traits. The loci explained 14.1%, 6.0%, 9.5% and 1.1% of the total variation in TSH, FT4, total T3 and free T3 concentrations, respectively. Genetic correlations indicate that TSH associated loci reflect the thyroid function determined by free T3, whereas the FT4 associations represent the thyroid hormone metabolism. Polygenic risk score and Mendelian randomization analyses showed the effects of genetically determined variation in thyroid function on various clinical outcomes, including cardiovascular risk factors and diseases, autoimmune diseases, and cancer. In conclusion, our results improve the understanding of thyroid hormone physiology and highlight the pleiotropic effects of thyroid function on various diseases., Pattern Recognition and Bioinformatics

Over the past decade, China has experienced a decline in atmospheric reactive nitrogen (Nr) emissions. Given that China's subtropical region is a significant nitrogen (N) deposition hotspot, it is essential to accurately quantify the ten-year variations in dry and wet N depositions in the context of reductions in atmospheric Nr emissions. Here, we evaluated the spatiotemporal variation in N deposition on forest, paddy field and tea field ecosystems in a typical subtropical agricultural catchment from 2011 to 2020. Our findings indicated a significant decrease in total N deposition in both the tea field ecosystem (41.5–30.5 kg N ha−1) and the forest ecosystem (40.8–25.7 kg N ha−1) (P < 0.05), but no significant change in the paddy field ecosystem (29.3–32.9 kg N ha−1). Specifically, dry N deposition exhibited significant declines except in the paddy field ecosystem, whereas wet N deposition had no significant change. The reduction in total oxidized and reduced N depositions in forest and tea field ecosystems is attributed to the decrease in NOx and NH3 emissions. Additionally, The ratio of NHx deposition to total N deposition all exceeded 0.5 in three ecosystems and the NHx/NOy ratio had an increasing trend (P < 0.05) in the paddy field, indicating that reactive N emissions from agricultural sources were the primary contributor to overall N deposition. Our study emphasizes that despite the decreasing trend in N deposition, it still exceeds the critical loads of natural ecosystems and requires stringent N emissions control, particularly from agricultural sources, in the future.

To date only a fraction of the genetic footprint of thyroid function has been clarified. We report a genome-wide association study meta-analysis of thyroid function in up to 271,040 individuals of European ancestry, including reference range thyrotropin (TSH), free thyroxine (FT4), free and total triiodothyronine (T3), proxies for metabolism (T3/FT4 ratio) as well as dichotomized high and low TSH levels. We revealed 259 independent significant associations for TSH (61% novel), 85 for FT4 (67% novel), and 62 novel signals for the T3 related traits. The loci explained 14.1%, 6.0%, 9.5% and 1.1% of the total variation in TSH, FT4, total T3 and free T3 concentrations, respectively. Genetic correlations indicate that TSH associated loci reflect the thyroid function determined by free T3, whereas the FT4 associations represent the thyroid hormone metabolism. Polygenic risk score and Mendelian randomization analyses showed the effects of genetically determined variation in thyroid function on various clinical outcomes, including cardiovascular risk factors and diseases, autoimmune diseases, and cancer. In conclusion, our results improve the understanding of thyroid hormone physiology and highlight the pleiotropic effects of thyroid function on various diseases.

Dicyandiamide decreased N2O emissions even under 40°C. Ammonia oxidizers and nirS were well adapted to 40°C in manured soils. Fungal nirK tolerated high temperature better in urea than manure treatment. Compared to nosZII, nosZI adapted to all temperature regardless of fertilization. nirS-denitrifier dominated N2O emissions at high temperature in fertilized soil. Heat waves associated with global warming and extreme climates would arouse serious consequences on nitrogen (N) cycle. However, the responses of the functional guilds to different temperatures, especially high temperature and the cascading effect on N2O emissions remain unclear. An incubation study was conducted to examine the effect of different temperatures (20°C, 30°C, and 40°C) and fertilizer types (urea and manure) on N2O-producers and N2O-reducers, as well as the efficacy of dicyandiamide (DCD) on N2O emissions in a vegetable soil. Results showed that ammonia oxidizers and nirS-type denitrifiers were well adapted to high temperature (40°C) with manure application, while the fungal nirK-denitrifiers had better tolerance with urea application. The nosZ clade I microbes had a strong adaptability to various temperatures regardless of fertilization type, while the growth of nosZ clade II group microbes in non-fertilized soil (control) were significantly inhibited at higher temperature. The N2O emissions were significantly decreased with increasing temperature and DCD application (up to 60%, even at 40°C). Under high temperature conditions, fungal denitrifiers play a significant role in N-limited soils (non-fertilized) while nirS-type denitrifiers was more important in fertilized soils in N2O emissions, which should be specially targeted when mitigating N2O emissions under global warming climate.

Contains fulltext : 304858.pdf (Publisher’s version ) (Open Access), To date only a fraction of the genetic footprint of thyroid function has been clarified. We report a genome-wide association study meta-analysis of thyroid function in up to 271,040 individuals of European ancestry, including reference range thyrotropin (TSH), free thyroxine (FT4), free and total triiodothyronine (T3), proxies for metabolism (T3/FT4 ratio) as well as dichotomized high and low TSH levels. We revealed 259 independent significant associations for TSH (61% novel), 85 for FT4 (67% novel), and 62 novel signals for the T3 related traits. The loci explained 14.1%, 6.0%, 9.5% and 1.1% of the total variation in TSH, FT4, total T3 and free T3 concentrations, respectively. Genetic correlations indicate that TSH associated loci reflect the thyroid function determined by free T3, whereas the FT4 associations represent the thyroid hormone metabolism. Polygenic risk score and Mendelian randomization analyses showed the effects of genetically determined variation in thyroid function on various clinical outcomes, including cardiovascular risk factors and diseases, autoimmune diseases, and cancer. In conclusion, our results improve the understanding of thyroid hormone physiology and highlight the pleiotropic effects of thyroid function on various diseases.

FIB-SEM tomography is a powerful technique that integrates a focused ion beam (FIB) and a scanning electron microscope (SEM) to capture high-resolution imaging data of nanostructures. This approach involves collecting in-plane SEM images and using FIB to remove material layers for imaging subsequent planes, thereby producing image stacks. However, these image stacks in FIB-SEM tomography are subject to the shine-through effect, which makes structures visible from the posterior regions of the current plane. This artifact introduces an ambiguity between image intensity and structures in the current plane, making conventional segmentation methods such as thresholding or the k-means algorithm insufficient. In this study, we propose a multimodal machine learning approach that combines intensity information obtained at different electron beam accelerating voltages to improve the three-dimensional (3D) reconstruction of nanostructures. By treating the increased shine-through effect at higher accelerating voltages as a form of additional information, the proposed method significantly improves segmentation accuracy and leads to more precise 3D reconstructions for real FIB tomography data.

Living in cities affords expanded access to various resources, infrastructures, and services at reduced travel costs, which improves social life and promotes systemic gains. However, recent research shows that urban dwellers also experience inequality in accessing urban facilities, which manifests in distinct travel and visitation patterns for residents with different demographic backgrounds. Here, we go beyond simple flawed correlation analysis and reveal prevalent accessibility gaps by quantifying the causal effects of resident demographics on mobility patterns extracted from U.S. residents’ detailed interactions with millions of urban venues. Moreover, to efficiently reveal micro neighborhood-level accessibility gaps, we design a novel Counterfactual RANdom-walks-based Embedding (CRANE) method to learn continuous embedding vectors on urban mobility networks with confounding effects disentangled. Our analysis reveals significant income and racial gaps in mobility frequency and visitation rates to sports and education venues. Besides, bachelor’s degree holders experience greater mobility reduction during the COVID-19 crisis. With extensive experiments on neighborhood-level accessibility prediction and visualizing accessibility gaps with embeddings vectors, we demonstrate that the counterfactual mobility network embeddings can improve the explanatory capacity and robustness of revealed accessibility gaps by extending them from aggregate statistics to individual neighborhoods and allowing for cross-city knowledge transfer. As such, urban mobility networks can reveal consistent accessibility gaps in the U.S., calling for urgent urban design policies to fill in the gaps. © 2024, The Author(s).

Frame-level micro- and macro-expression spotting methods require time-consuming frame-by-frame observation during annotation. Meanwhile, video-level spotting lacks sufficient information about the location and number of expressions during training, resulting in significantly inferior performance compared with fully-supervised spotting. To bridge this gap, we propose a point-level weakly-supervised expression spotting (PWES) framework, where each expression requires to be annotated with only one random frame (i.e., a point). To mitigate the issue of sparse label distribution, the prevailing solution is pseudo-label mining, which, however, introduces new problems: localizing contextual background snippets results in inaccurate boundaries and discarding foreground snippets leads to fragmentary predictions. Therefore, we design the strategies of multi-refined pseudo label generation (MPLG) and distribution-guided feature contrastive learning (DFCL) to address these problems. Specifically, MPLG generates more reliable pseudo labels by merging class-specific probabilities, attention scores, fused features, and point-level labels. DFCL is utilized to enhance feature similarity for the same categories and feature variability for different categories while capturing global representations across the entire datasets. Extensive experiments on the CAS(ME)^2, CAS(ME)^3, and SAMM-LV datasets demonstrate PWES achieves promising performance comparable to that of recent fully-supervised methods.

Depression harms. However, due to a lack of mental health awareness and fear of stigma, many patients do not actively seek diagnosis and treatment, leading to detrimental outcomes. Depression detection aims to determine whether an individual suffers from depression by analyzing their history of posts on social media, which can significantly aid in early detection and intervention. It mainly faces two key challenges: 1) it requires professional medical knowledge, and 2) it necessitates both high accuracy and explainability. To address it, we propose a novel depression detection system called DORIS, combining medical knowledge and the recent advances in large language models (LLMs). Specifically, to tackle the first challenge, we proposed an LLM-based solution to first annotate whether high-risk texts meet medical diagnostic criteria. Further, we retrieve texts with high emotional intensity and summarize critical information from the historical mood records of users, so-called mood courses. To tackle the second challenge, we combine LLM and traditional classifiers to integrate medical knowledge-guided features, for which the model can also explain its prediction results, achieving both high accuracy and explainability. Extensive experimental results on benchmarking datasets show that, compared to the current best baseline, our approach improves by 0.036 in AUPRC, which can be considered significant, demonstrating the effectiveness of our approach and its high value as an NLP application.

Social media platforms have become one of the main channels where people disseminate and acquire information, of which the reliability is severely threatened by rumors widespread in the network. Existing approaches such as suspending users or broadcasting real information to combat rumors are either with high cost or disturbing users. In this paper, we introduce a novel rumor mitigation paradigm, where only a minimal set of links in the social network are intervened to decelerate the propagation of rumors, countering misinformation with low business cost and user awareness. A knowledge-informed agent embodying rumor propagation mechanisms is developed, which intervenes the social network with a graph neural network for capturing information flow in the social media platforms and a policy network for selecting links. Experiments on real social media platforms demonstrate that the proposed approach can effectively alleviate the influence of rumors, substantially reducing the affected populations by over 25%. Codes for this paper are released at https://github.com/tsinghua-fib-lab/DRL-Rumor-Mitigation., Comment: WWW24 short

Selecting urban regions for metro network expansion to meet maximal transportation demands is crucial for urban development, while computationally challenging to solve. The expansion process relies not only on complicated features like urban demographics and origin-destination (OD) flow but is also constrained by the existing metro network and urban geography. In this paper, we introduce a reinforcement learning framework to address a Markov decision process within an urban heterogeneous multi-graph. Our approach employs an attentive policy network that intelligently selects nodes based on information captured by a graph neural network. Experiments on real-world urban data demonstrate that our proposed methodology substantially improve the satisfied transportation demands by over 30\% when compared with state-of-the-art methods. Codes are published at https://github.com/tsinghua-fib-lab/MetroGNN., Comment: WWW24 short

Cities are crucibles of numerous opportunities, but also hotbeds of inequality. The plight of disadvantaged populations who are “left behind” within urban environments has been an increasingly pressing concern, which poses substantial threats to the realization of the UN SDG agenda. However, a comprehensive framework for studying this urban dilemma is currently absent, preventing researchers from developing AI models for social good prediction and intervention. To fill this gap, we construct VulnerabilityMap, a framework to meticulously dissect the challenges faced by urban disadvantaged populations, unraveling their vulnerability to a spectrum of shocks and stresses that are categorized through the prism of Maslow’s hierarchy of needs. Specifically, we systematically collect large-scale multi-sourced census and web-based data covering more than 328 million people in the United States regarding demographic features, neighborhood environments, offline mobility behaviors, and online social connections. These features are further related to vulnerability outcomes from short-term shocks such as COVID-19 and long-term physiological, social, and self-actualization stresses. Leveraging our framework, we construct machine learning models that exhibit strong performance in predicting vulnerability outcomes from various disadvantage features, which shows the promising utility of our framework to support targeted AI models. Moreover, we provide model-based explainability analysis to interpret the reasons underlying model predictions, shedding light on intricate social factors that trap certain populations inside vulnerable situations. Our constructed dataset is publicly available at https://github.com/LinChen-65/VulnerabilityMap/.

Identifying critical nodes in networks is a classical decision-making task, and many methods struggle to strike a balance between adaptability and utility. Therefore, we propose an approach that empowers Evolutionary Algorithm (EA) with Large Language Models (LLMs), to generate a function called "score\_nodes" which can further be used to identify crucial nodes based on their assigned scores. Our model consists of three main components: Manual Initialization, Population Management, and LLMs-based Evolution. It evolves from initial populations with a set of designed node scoring functions created manually. LLMs leverage their strong contextual understanding and rich programming skills to perform crossover and mutation operations on the individuals, generating excellent new functions. These functions are then categorized, ranked, and eliminated to ensure the stable development of the populations while preserving diversity. Extensive experiments demonstrate the excellent performance of our method, showcasing its strong generalization ability compared to other state-of-the-art algorithms. It can consistently and orderly generate diverse and efficient node scoring functions. All source codes and models that can reproduce all results in this work are publicly available at this link: \url{https://anonymous.4open.science/r/LLM4CN-6520}

Understanding and effectively managing urban spaces hinges upon the vital practice of sensing urban physical environments. It not only aids urban researchers in uncovering hidden complexities within the cityscape but also allows for a broader exploration of urban attributes that extend beyond mere physicality. In this age of information technology, a novel urban sensing paradigm has emerged, driven by geospatial artificial intelligence (GeoAI) and street-level imagery (SI), revolutionizing our ability to comprehensively perceive urban environments. Distinguished from conventional sources, SI offers a unique human-centric perspective. This viewpoint enables the extraction of extensive information, encompassing tangible elements within images, as well as intangible factors such as socio-economic characteristics, urban functionality, and even human perception—all analyzed through the prism of GeoAI. This chapter provides a comprehensive overview of research in urban physical environment sensing through the integration of GeoAI and SI. It delves into the existing imagery datasets, the GeoAI methodologies employed for their interpretation, and explores present applications while considering future trends. With the continuous advancement of robust AI models and the availability of high-quality SI, the amalgamation of GeoAI and SI holds great promise for amplifying urban sensing capabilities, enabling more profound understandings and effective administration of our cities.

As cities continue to burgeon, Urban Computing emerges as a pivotal discipline for sustainable development by harnessing the power of cross-domain data fusion from diverse sources (e.g., geographical, traffic, social media, and environmental data) and modalities (e.g., spatio-temporal, visual, and textual modalities). Recently, we are witnessing a rising trend that utilizes various deep-learning methods to facilitate cross-domain data fusion in smart cities. To this end, we propose the first survey that systematically reviews the latest advancements in deep learning-based data fusion methods tailored for urban computing. Specifically, we first delve into data perspective to comprehend the role of each modality and data source. Secondly, we classify the methodology into four primary categories: feature-based, alignment-based, contrast-based, and generation-based fusion methods. Thirdly, we further categorize multi-modal urban applications into seven types: urban planning, transportation, economy, public safety, society, environment, and energy. Compared with previous surveys, we focus more on the synergy of deep learning methods with urban computing applications. Furthermore, we shed light on the interplay between Large Language Models (LLMs) and urban computing, postulating future research directions that could revolutionize the field. We firmly believe that the taxonomy, progress, and prospects delineated in our survey stand poised to significantly enrich the research community. The summary of the comprehensive and up-to-date paper list can be found at https://github.com/yoshall/Awesome-Multimodal-Urban-Computing.

Facial action unit (AU) detection, aiming to classify AU present in the facial image, has long suffered from insufficient AU annotations. In this paper, we aim to mitigate this data scarcity issue by learning AU representations from a large number of unlabelled facial videos in a contrastive learning paradigm. We formulate the self-supervised AU representation learning signals in two-fold: (1) AU representation should be frame-wisely discriminative within a short video clip; (2) Facial frames sampled from different identities but show analogous facial AUs should have consistent AU representations. As to achieve these goals, we propose to contrastively learn the AU representation within a video clip and devise a cross-identity reconstruction mechanism to learn the person-independent representations. Specially, we adopt a margin-based temporal contrastive learning paradigm to perceive the temporal AU coherence and evolution characteristics within a clip that consists of consecutive input facial frames. Moreover, the cross-identity reconstruction mechanism facilitates pushing the faces from different identities but show analogous AUs close in the latent embedding space. Experimental results on three public AU datasets demonstrate that the learned AU representation is discriminative for AU detection. Our method outperforms other contrastive learning methods and significantly closes the performance gap between the self-supervised and supervised AU detection approaches.

Participatory urban planning is the mainstream of modern urban planning that involves the active engagement of residents. However, the traditional participatory paradigm requires experienced planning experts and is often time-consuming and costly. Fortunately, the emerging Large Language Models (LLMs) have shown considerable ability to simulate human-like agents, which can be used to emulate the participatory process easily. In this work, we introduce an LLM-based multi-agent collaboration framework for participatory urban planning, which can generate land-use plans for urban regions considering the diverse needs of residents. Specifically, we construct LLM agents to simulate a planner and thousands of residents with diverse profiles and backgrounds. We first ask the planner to carry out an initial land-use plan. To deal with the different facilities needs of residents, we initiate a discussion among the residents in each community about the plan, where residents provide feedback based on their profiles. Furthermore, to improve the efficiency of discussion, we adopt a fishbowl discussion mechanism, where part of the residents discuss and the rest of them act as listeners in each round. Finally, we let the planner modify the plan based on residents' feedback. We deploy our method on two real-world regions in Beijing. Experiments show that our method achieves state-of-the-art performance in residents satisfaction and inclusion metrics, and also outperforms human experts in terms of service accessibility and ecology metrics., Comment: arXiv admin note: text overlap with arXiv:2402.01698

Complex networks pervade various real-world systems, from the natural environment to human societies. The essence of these networks is in their ability to transition and evolve from microscopic disorder-where network topology and node dynamics intertwine-to a macroscopic order characterized by certain collective behaviors. Over the past two decades, complex network science has significantly enhanced our understanding of the statistical mechanics, structures, and dynamics underlying real-world networks. Despite these advancements, there remain considerable challenges in exploring more realistic systems and enhancing practical applications. The emergence of artificial intelligence (AI) technologies, coupled with the abundance of diverse real-world network data, has heralded a new era in complex network science research. This survey aims to systematically address the potential advantages of AI in overcoming the lingering challenges of complex network research. It endeavors to summarize the pivotal research problems and provide an exhaustive review of the corresponding methodologies and applications. Through this comprehensive survey-the first of its kind on AI for complex networks-we expect to provide valuable insights that will drive further research and advancement in this interdisciplinary field., Comment: 51 pages, 4 figures, 10 tables

Traditional session-based recommendation (SBR) utilizes session behavior sequences from anonymous users for recommendation. Although this strategy is highly efficient, it sacrifices the inherent semantic information of the items, making it difficult for the model to understand the true intent of the session and resulting in a lack of interpretability in the recommended results. Recently, large language models (LLMs) have flourished across various domains, offering a glimpse of hope in addressing the aforementioned challenges. Inspired by the impact of LLMs, research exploring the integration of LLMs with the Recommender system (RS) has surged like mushrooms after rain. However, constrained by high time and space costs, as well as the brief and anonymous nature of session data, the first LLM recommendation framework suitable for industrial deployment has yet to emerge in the field of SBR. To address the aforementioned challenges, we have proposed the LLM Integration Framework for SBR (LLM4SBR). Serving as a lightweight and plug-and-play framework, LLM4SBR adopts a two-step strategy. Firstly, we transform session data into a bimodal form of text and behavior. In the first step, leveraging the inferential capabilities of LLMs, we conduct inference on session text data from different perspectives and design the component for auxiliary enhancement. In the second step, the SBR model is trained on behavior data, aligning and averaging two modal session representations from different perspectives. Finally, we fuse session representations from different perspectives and modalities as the ultimate session representation for recommendation. We conducted experiments on two real-world datasets, and the results demonstrate that LLM4SBR significantly improves the performance of traditional SBR models and is highly lightweight and efficient, making it suitable for industrial deployment.

Spatio-temporal modeling is foundational for smart city applications, yet it is often hindered by data scarcity in many cities and regions. To bridge this gap, we propose a novel generative pre-training framework, GPD, for spatio-temporal few-shot learning with urban knowledge transfer. Unlike conventional approaches that heavily rely on common feature extraction or intricate few-shot learning designs, our solution takes a novel approach by performing generative pre-training on a collection of neural network parameters optimized with data from source cities. We recast spatio-temporal few-shot learning as pre-training a generative diffusion model, which generates tailored neural networks guided by prompts, allowing for adaptability to diverse data distributions and city-specific characteristics. GPD employs a Transformer-based denoising diffusion model, which is model-agnostic to integrate with powerful spatio-temporal neural networks. By addressing challenges arising from data gaps and the complexity of generalizing knowledge across cities, our framework consistently outperforms state-of-the-art baselines on multiple real-world datasets for tasks such as traffic speed prediction and crowd flow prediction. The implementation of our approach is available: https://github.com/tsinghua-fib-lab/GPD.

Urban spatio-temporal prediction is crucial for informed decision-making, such as transportation management, resource optimization, and urban planning. Although pretrained foundation models for natural languages have experienced remarkable breakthroughs, wherein one general-purpose model can tackle multiple tasks across various domains, urban spatio-temporal modeling lags behind. Existing approaches for urban prediction are usually tailored for specific spatio-temporal scenarios, requiring task-specific model designs and extensive in-domain training data. In this work, we propose a universal model, UniST, for urban spatio-temporal prediction. Drawing inspiration from large language models, UniST achieves success through: (i) flexibility towards diverse spatio-temporal data characteristics, (ii) effective generative pre-training with elaborated masking strategies to capture complex spatio-temporal relationships, (iii) spatio-temporal knowledge-guided prompts that align and leverage intrinsic and shared knowledge across scenarios. These designs together unlock the potential of a one-for-all model for spatio-temporal prediction with powerful generalization capability. Extensive experiments on 15 cities and 6 domains demonstrate the universality of UniST in advancing state-of-the-art prediction performance, especially in few-shot and zero-shot scenarios. The implementation is available at this repository: https://github.com/tsinghua-fib-lab/UniST.

The powerful reasoning capabilities of large language models (LLMs) have brought revolutionary changes to many fields, but their performance in human behaviour generation has not yet been extensively explored. This gap likely emerges because the internal processes governing behavioral intentions cannot be solely explained by abstract reasoning. Instead, they are also influenced by a multitude of factors, including social norms and personal preference. Inspired by the Theory of Planned Behaviour (TPB), we develop a LLM workflow named Chain-of-Planned Behaviour (CoPB) for mobility behaviour generation, which reflects the important spatio-temporal dynamics of human activities. Through exploiting the cognitive structures of attitude, subjective norms, and perceived behaviour control in TPB, CoPB significantly enhance the ability of LLMs to reason the intention of next movement. Specifically, CoPB substantially reduces the error rate of mobility intention generation from 57.8% to 19.4%. To improve the scalability of the proposed CoPB workflow, we further explore the synergy between LLMs and mechanistic models. We find mechanistic mobility models, such as gravity model, can effectively map mobility intentions to physical mobility behaviours. The strategy of integrating CoPB with gravity model can reduce the token cost by 97.7% and achieve better performance simultaneously. Besides, the proposed CoPB workflow can facilitate GPT-4-turbo to automatically generate high quality labels for mobility behavior reasoning. We show such labels can be leveraged to fine-tune the smaller-scale, open source LLaMA 3-8B, which significantly reduces usage costs without sacrificing the quality of the generated behaviours.

Crowd simulation holds crucial applications in various domains, such as urban planning, architectural design, and traffic arrangement. In recent years, physics-informed machine learning methods have achieved state-of-the-art performance in crowd simulation but fail to model the heterogeneity and multi-modality of human movement comprehensively. In this paper, we propose a social physics-informed diffusion model named SPDiff to mitigate the above gap. SPDiff takes both the interactive and historical information of crowds in the current timeframe to reverse the diffusion process, thereby generating the distribution of pedestrian movement in the subsequent timeframe. Inspired by the well-known social physics model, i.e., Social Force, regarding crowd dynamics, we design a crowd interaction module to guide the denoising process and further enhance this module with the equivariant properties of crowd interactions. To mitigate error accumulation in long-term simulations, we propose a multi-frame rollout training algorithm for diffusion modeling. Experiments conducted on two real-world datasets demonstrate the superior performance of SPDiff in terms of macroscopic and microscopic evaluation metrics. Code and appendix are available at https://github.com/tsinghua-fib-lab/SPDiff.

Accounting for over 20% of the total carbon emissions, the precise estimation of on-road transportation carbon emissions is crucial for carbon emission monitoring and efficient mitigation policy formulation. However, existing estimation methods typically depend on hard-to-collect individual statistics of vehicle miles traveled to calculate emissions, thereby suffering from high data collection difficulty. To relieve this issue by utilizing the strong pattern recognition of artificial intelligence, we incorporate two sources of open data representative of the transportation demand and capacity factors, the origin-destination (OD) flow data and the road network data, to build a hierarchical heterogeneous graph learning method for on-road carbon emission estimation (HENCE). Specifically, a hierarchical graph consisting of the road network level, community level, and region level is constructed to model the multi-scale road network-based connectivity and travel connection between spatial areas. Heterogeneous graphs consisting of OD links and spatial links are further built at both the community level and region level to capture the intrinsic interactions between travel demand and road network accessibility. Extensive experiments on two large-scale real-world datasets demonstrate HENCE's effectiveness and superiority with R-squared exceeding 0.75 and outperforming baselines by 9.60% on average, validating its success in pioneering the use of artificial intelligence to empower carbon emission management and sustainability development. The implementation codes are available at this link: https://github.com/tsinghua-fib-lab/HENCE.

Hyperparameter optimization is critical in modern machine learning, requiring expert knowledge, numerous trials, and high computational and human resources. Despite the advancements in Automated Machine Learning (AutoML), challenges in terms of trial efficiency, setup complexity, and interoperability still persist. To address these issues, we introduce a novel paradigm leveraging Large Language Models (LLMs) to automate hyperparameter optimization across diverse machine learning tasks, which is named AgentHPO (short for LLM Agent-based Hyperparameter Optimization). Specifically, AgentHPO processes the task information autonomously, conducts experiments with specific hyperparameters (HPs), and iteratively optimizes them based on historical trials. This human-like optimization process largely reduces the number of required trials, simplifies the setup process, and enhances interpretability and user trust, compared to traditional AutoML methods. Extensive empirical experiments conducted on 12 representative machine-learning tasks indicate that AgentHPO not only matches but also often surpasses the best human trials in terms of performance while simultaneously providing explainable results. Further analysis sheds light on the strategies employed by the LLM in optimizing these tasks, highlighting its effectiveness and adaptability in various scenarios.

Large language models (LLMs) have shown impressive emergent abilities in a wide range of tasks, but still face challenges in handling complex reasoning problems. Previous works like chain-of-thought (CoT) and tree-of-thoughts (ToT) have predominately focused on enhancing accuracy, but overlook the rapidly increasing token cost, which could be particularly problematic for open-ended real-world tasks with huge solution spaces. Motivated by the dual process theory of human cognition, we propose "Synergy of Thoughts" (SoT) to unleash the synergistic potential of hybrid LLMs for efficient reasoning. By default, SoT uses smaller-scale language models to generate multiple low-cost reasoning thoughts, which resembles the parallel intuitions produced by System 1. If these intuitions exhibit conflicts, SoT will invoke the reflective reasoning of scaled-up language models to emulate the intervention of System 2, which will override the intuitive thoughts and rectify the reasoning process. This framework is model-agnostic and training-free, which can be flexibly implemented with various off-the-shelf LLMs. Experiments on six representative reasoning tasks show that SoT substantially reduces the token cost by 38.3%-75.1%, and simultaneously achieves state-of-the-art reasoning accuracy and solution diversity. Notably, the average token cost reduction on open-ended tasks reaches up to 69.1%. Code repo with all prompts will be released upon publication., Comment: 20 pages, 14 figures, 14 tables

Participatory urban planning is the mainstream of modern urban planning and involves the active engagement of different stakeholders. However, the traditional participatory paradigm encounters challenges in time and manpower, while the generative planning tools fail to provide adjustable and inclusive solutions. This research introduces an innovative urban planning approach integrating Large Language Models (LLMs) within the participatory process. The framework, based on the crafted LLM agent, consists of role-play, collaborative generation, and feedback iteration, solving a community-level land-use task catering to 1000 distinct interests. Empirical experiments in diverse urban communities exhibit LLM's adaptability and effectiveness across varied planning scenarios. The results were evaluated on four metrics, surpassing human experts in satisfaction and inclusion, and rivaling state-of-the-art reinforcement learning methods in service and ecology. Further analysis shows the advantage of LLM agents in providing adjustable and inclusive solutions with natural language reasoning and strong scalability. While implementing the recent advancements in emulating human behavior for planning, this work envisions both planners and citizens benefiting from low-cost, efficient LLM agents, which is crucial for enhancing participation and realizing participatory urban planning., Comment: 26 pages, 7 figures, 2 tables

Face recognition technology has been deployed in various real-life applications. The most sophisticated deep learning-based face recognition systems rely on training millions of face images through complex deep neural networks to achieve high accuracy. It is quite common for clients to upload face images to the service provider in order to access the model inference. However, the face image is a type of sensitive biometric attribute tied to the identity information of each user. Directly exposing the raw face image to the service provider poses a threat to the user's privacy. Current privacy-preserving approaches to face recognition focus on either concealing visual information on model input or protecting model output face embedding. The noticeable drop in recognition accuracy is a pitfall for most methods. This paper proposes a hybrid frequency-color fusion approach to reduce the input dimensionality of face recognition in the frequency domain. Moreover, sparse color information is also introduced to alleviate significant accuracy degradation after adding differential privacy noise. Besides, an identity-specific embedding mapping scheme is applied to protect original face embedding by enlarging the distance among identities. Lastly, secure multiparty computation is implemented for safely computing the embedding distance during model inference. The proposed method performs well on multiple widely used verification datasets. Moreover, it has around 2.6% to 4.2% higher accuracy than the state-of-the-art in the 1:N verification scenario., Comment: This work is already accepted at the conference International Conference on Computer Vision Theory and Applications (VISAPP) 2024 as a regular paper

As a critical technology for next-generation communication networks, integrated sensing and communication (ISAC) aims to achieve the harmonious coexistence of communication and sensing. The degrees-of-freedom (DoF) of ISAC is limited due to multiple performance metrics used for communication and sensing. Reconfigurable Intelligent Surfaces (RIS) composed of metamaterials can enhance the DoF in the spatial domain of ISAC systems. However, the availability of perfect Channel State Information (CSI) is a prerequisite for the gain brought by RIS, which is not realistic in practical environments. Therefore, under the imperfect CSI condition, we propose a decomposition-based large deviation inequality approach to eliminate the impact of CSI error on communication rate and sensing Cram\'er-Rao bound (CRB). Then, an alternating optimization (AO) algorithm based on semi-definite relaxation (SDR) and gradient extrapolated majorization-maximization (GEMM) is proposed to solve the transmit beamforming and discrete RIS beamforming problems. We also analyze the complexity and convergence of the proposed algorithm. Simulation results show that the proposed algorithms can effectively eliminate the influence of CSI error and have good convergence performance. Notably, when CSI error exists, the gain brought by RIS will decrease with the increase of the number of RIS elements. Finally, we summarize and outline future research directions., Comment: This paper has been submitted to a IEEE journal. arXiv admin note: text overlap with arXiv:2303.01771

Urban villages, defined as informal residential areas in or around urban centers, are characterized by inadequate infrastructures and poor living conditions, closely related to the Sustainable Development Goals (SDGs) on poverty, adequate housing, and sustainable cities. Traditionally, governments heavily depend on field survey methods to monitor the urban villages, which however are time-consuming, labor-intensive, and possibly delayed. Thanks to widely available and timely updated satellite images, recent studies develop computer vision techniques to detect urban villages efficiently. However, existing studies either focus on simple urban village image classification or fail to provide accurate boundary information. To accurately identify urban village boundaries from satellite images, we harness the power of the vision foundation model and adapt the Segment Anything Model (SAM) to urban village segmentation, named UV-SAM. Specifically, UV-SAM first leverages a small-sized semantic segmentation model to produce mixed prompts for urban villages, including mask, bounding box, and image representations, which are then fed into SAM for fine-grained boundary identification. Extensive experimental results on two datasets in China demonstrate that UV-SAM outperforms existing baselines, and identification results over multiple years show that both the number and area of urban villages are decreasing over time, providing deeper insights into the development trends of urban villages and sheds light on the vision foundation models for sustainable cities. The dataset and codes of this study are available at https://github.com/tsinghua-fib-lab/UV-SAM., Comment: Accepted by AAAI 2024

The rapid proliferation of Large Language Models (LLMs) has been a driving force in the growth of cloud-based LLM services, which are now integral to advancing AI applications. However, the dynamic auto-regressive nature of LLM service, along with the need to support exceptionally long context lengths, demands the flexible allocation and release of substantial resources. This presents considerable challenges in designing cloud-based LLM service systems, where inefficient management can lead to performance degradation or resource wastage. In response to these challenges, this work introduces DistAttention, a novel distributed attention algorithm that segments the KV Cache into smaller, manageable units, enabling distributed processing and storage of the attention module. Based on that, we propose DistKV-LLM, a distributed LLM serving system that dynamically manages KV Cache and effectively orchestrates all accessible GPU and CPU memories spanning across the data center. This ensures a high-performance LLM service on the cloud, adaptable to a broad range of context lengths. Validated in a cloud environment with 32 NVIDIA A100 GPUs in configurations from 2 to 32 instances, our system exhibited 1.03-2.4x end-to-end throughput improvements and supported context lengths 2-19x longer than current state-of-the-art LLM service systems, as evidenced by extensive testing across 18 datasets with context lengths up to 1,900K.