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Boye Zhang,1,2 Jianliang Man,3,4 Lingyun Guo,1 Xiaoxia Ru,1 Chengwu Zhang,1,2 Wen Liu,1,2,5 Lihong Li,1,2,5 Sufang Ma,1,2 Lixia Guo,2,4 Haojiang Wang,1,2 Bin Wang,1,2 Haipeng Diao,1,5 Renchao Che,6,7 Lili Yan1,2,5 1School of Basic Medical Sciences, Shanxi Medical University, Taiyuan, 030001, People’s Republic of China; 2Shanxi Province Brain Degenerative Diseases Precision Diagnosis and Treatment Engineering Research Center, Shanxi Medical University, Jinzhong, 030606, People’s Republic of China; 3Academy of Medical Sciences, Shanxi Medical University, Taiyuan, 030001, People’s Republic of China; 4College of Pharmacy, Shanxi Medical University, Taiyuan, 030001, People’s Republic of China; 5Key Laboratory of Cellular Physiology, Ministry of Education, Shanxi Medical University, Taiyuan, 030001, People’s Republic of China; 6Laboratory of Advanced Materials, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Academy for Engineering &Technology, Fudan University, Shanghai, 200438, People’s Republic of China; 7Zhejiang Laboratory, Hangzhou, 311100, People’s Republic of ChinaCorrespondence: Lili Yan; Renchao Che, Email yanlili@sxmu.edu.cn; rcche@fudan.edu.cnBackground: Challenges such as poor drug selectivity, non-target reactivity, and the development of drug resistance continue to pose significant obstacles in the clinical application of cancer therapeutic drugs. To overcome the limitations of drug resistance in chemotherapy, a viable treatment strategy involves designing multifunctional nano-platforms that exploit the unique physicochemical properties of tumor microenvironment (TME).Methods: Herein, layer-by-layer nanoparticles with polyporous CuS as delivery vehicles, loaded with a sonosensitizer (tetra-(4-aminophenyl) porphyrin, TAPP) and sequentially functionalized with pH‐responsive CaCO3, targeting group hyaluronic acid (HA) were designed and synthesized for synergistic treatment involving chemodynamic therapy (CDT), sonod

Objective: Prediabetes and lifestyle factors have been associated with the risks of multiple adverse outcomes, but the effect of a healthy lifestyle on prediabetes-related complications remains unknown. We aimed to investigate whether the risks of multiple adverse outcomes including incident type 2 diabetes mellitus (T2DM), cardiovascular disease (CVD), and chronic kidney disease (CKD) among individuals with prediabetes can be offset by a broad combination of healthy lifestyle factors. Methods: This prospective study used data from the UK Biobank cohort. An overall lifestyle score ranging from 0 to 6 was created with 1 point for each of the 6 healthy lifestyle factors: no current smoking, moderate alcohol consumption, regular physical activity, healthy diet, no overweight or obese, and adequate sleep duration. T2DM, CVD, and CKD were ascertained during a median follow-up of 14 years. Cox proportional hazard regression models were used to estimate the associations. Sensitivity analyses were performed to test the robustness of the results. Results: We included 202,993 participants without T2DM, CVD, and CKD at baseline (mean age 55.5 years [SD 8.1]; 54.7% were women). Among these participants, 6,745, 16,961, and 6,260 participants eventually developed T2DM, CVD, and CKD, respectively. Compared with the participants with normoglycaemia, those with prediabetes showed a higher risk of these adverse outcomes. In addition, those prediabetic participants with a lifestyle score of 0-1 had a significantly higher risk of T2DM (hazard ratio [HR] 16.73, 95% CI 14.24, 19.65), CVD (HR 1.96, 95% CI 1.74, 2.21), and CKD (HR 1.92, 95% CI 1.58, 2.34) compared with those with no prediabetes and a score of 5-6. Moreover, among the participants with prediabetes, the HRs for T2DM, CVD, and CKD comparing a lifestyle score of 5-6 versus 0-1 decreased to 0.43 (95% CI 0.36, 0.51), 0.52 (95% CI 0.44, 0.62), and 0.60 (95% CI 0.46, 0.79), respectively. Conclusions: Combined healthy lifestyle fac

Background: Type 2 diabetes (T2D) is associated with an increased risk of premature death. Whether multifactorial risk factor modification could attenuate T2D-related excess risk of death is unclear. We aimed to examine the association of risk factor target achievement with mortality and life expectancy among patients with T2D, compared with individuals without diabetes. Methods: In this longitudinal cohort study, we included 316 995 participants (14 162 with T2D and 302 833 without T2D) free from cardiovascular disease (CVD) or cancer at baseline between 2006 and 2010 from the UK Biobank. Participants with T2D were categorised according to the number of risk factors within target range (non-smoking, being physically active, healthy diet, guideline-recommended levels of glycated haemoglobin, body mass index, blood pressure, and total cholesterol). Survival models were applied to calculate hazard ratios (HRs) for mortality and predict life expectancy differences. Results: Over a median follow-up of 13.8 (IQR 13.1-14.4) years, deaths occurred among 2105 (14.9%) participants with T2D and 18 505 (6.1%) participants without T2D. Compared with participants without T2D (death rate per 1000 person-years 4.51 [95% CI 4.44 to 4.57]), the risk of all-cause mortality among those with T2D decreased stepwise with an increasing number of risk factors within target range (0-1 risk factor target achieved: absolute rate difference per 1000 person-years 7.34 [4.91 to 9.78], HR 2.70 [2.25 to 3.25]; 6-7 risk factors target achieved: absolute rate difference per 1000 person-years 0.68 [-0.62 to 1.99], HR 1.16 [0.93 to 1.43]). A similar pattern was observed for CVD and cancer mortality. The association between risk factors target achievement and all-cause mortality was more prominent among participants younger than 60 years than those 60 years or older (P for interaction = 0.012). At age 50 years, participants with T2D who had 0-1 and 6-7 risk factors within target range had an average 7.

Background: Prenatal and postnatal factors may have joint effects on cardiovascular health, and we aimed to assess the joint association of birth weight and ideal cardiovascular health metrics (ICVHMs) prospectively in adulthood with incident cardiovascular disease (CVD). Methods: In the UK Biobank, 227,833 participants with data on ICVHM components and birth weight and without CVD at baseline were included. The ICVHMs included smoking, body mass index, physical activity, diet information, total cholesterol, blood pressure, and hemoglobin A1c. The Cox proportional hazards model was used to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) in men and women. Results: Over a median follow-up period of 13.0 years (2,831,236 person-years), we documented 17,477 patients with incident CVD. Compared with participants with birth weights of 2.5–4.0 kg, the HRs (95% CIs) of CVD among those with low birth weights was 1.08 (1.00–1.16) in men and 1.23 (1.16–1.31) in women. The association between having a birth weight <2.5 kg and CVD risk in men was more prominent for those aged <50 years than for those of older age (P for interaction = 0.026). Lower birth weight and non-ideal cardiovascular health metrics were jointly related to an increased risk of CVD. Participants with birth weights <2.5 kg and ICVHMs score 0–1 had the highest risk of incident CVD (HR [95% CI]: 3.93 [3.01–5.13] in men; 4.24 [3.33–5.40] in women). The joint effect (HR [95% CI]: 1.36 [1.17–1.58]) could be decomposed into 24.7% (95% CI: 15.0%–34.4%) for a lower birth weight, 64.7% (95% CI: 56.7%–72.6%) for a lower ICVHM score, and 10.6% (95% CI: 2.7%–18.6%) for their additive interaction in women. Conclusions: Birth weight and ICVHMs were jointly related to CVD risk. Attaining a normal birth weight and ideal ICVHMs may reduce the risk of CVD, and a simultaneous improvement of both prenatal and postnatal factors could further prevent additional cases in women.

Background and aims: To investigate the associations of ideal cardiovascular health metrics (ICVHMs) with all-cause mortality among former and current smokers compared with never smokers. Methods and results: A total of 378,147 participants [mean age (SD) years: 56.3 (8.1); 47.2 % men] were included from the UK Biobank cohort. The ICVHMs were combined Life's simple 7 from the American Heart Association and sleep duration time. The association was explored using COX regression models. During a median follow-up of 13.3 years, we documented 24,594 deaths. Compared with never smokers, among former smokers, the multivariable-adjusted hazard ratio (HR) for all-cause mortality was 1.82 (95%CI 1.71-1.92) for participants who had <= 2 ICVHMs and 1.03 (0.97-1.10) for participants who had >= 6 ICVHMs; among current smokers, the HRs for mortality were 2.74 (2.60-2.89) and 2.18 (1.78-2.67). The phenomenon was more pronounced among participants younger than 60 years [HR (95%CI), 1.82 (1.71-1.95) for <= 2 ICVHMs vs 1.04 (0.96-1.12) for >= 6 ICVHMs with age >= 60 years and 1.83 (1.62-2.06) vs 0.98 (0.88-1.11) with age <60 years among former smokers; 2.66 (2.49-2.85) vs 2.44 (1.84-3.24) with age >= 60 years and 2.85 (2.62-3.10) vs 1.96 (1.47-2.61) with age <60 years among current smokers]. In addition, the HR for mortality of each 1-number increment in ICVHMs was 0.87 (0.86-0.89) among former smokers and 0.91 (0.89-0.94) among current smokers. Conclusion: Our findings indicated the importance of adherence to have more ICVHMs in the mortality risk among former smokers, and priority of smoking cessation in current smokers.

Objectives Disorders of immune system may impact cardiovascular health; however, comprehensive study is lacking. We aimed to analyse the association of total and 20 individual immune-mediated diseases (IMDs) with risk of incident cardiovascular disease (CVD). Methods In this prospective cohort study, 414 495 participants (55.6% women; mean age 55.9 years) from UK Biobank with baseline assessment at 2006-10 were included. Among them, 21 784 participants had prevalent IMDs. Information on IMDs at baseline and incidence of CVDs during follow-up were recorded. Cox proportional hazard models were used to estimate the association between IMDs and CVDs risk. Results During the median follow-up of 12.1 years, there were 6506 cases of CVDs in participants with IMDs (29.9%) and 77 699 cases in those without IMDs (19.8%). After multivariable adjustment, participants with IMDs were significantly associated with an increased risk of total CVD [hazard ratio (HR) 1.57; 95% CI 1.52-1.61]. Among the 20 IMDs, 16 showed significant associations with CVD (all P < 0.0025 after Bonferroni correction), with HR ranging from 1.34 (1.16-1.54) for celiac disease to 2.75 (2.10-3.61) for SLE. Participants with any IMD exposure had a higher risk of all individual CVD events, with HR ranging from 1.34 (1.14-1.58) for cerebral hemorrhage to 1.80 (1.54-2.11) for pericardium diseases. IMD duration <5, 5-10 and >10 years was associated with 55%, 59% and 56% increased risk of total CVD, respectively. Conclusion Total and individual IMDs were associated with an increased risk of overall CVDs. It is important to consider primary prevention of CVD in patients with IMD and dysregulation of immune system in the cardiovascular health.

BACKGROUND: An association between sweetened beverages and several cardiometabolic diseases has been reported, but their association with atrial fibrillation (AF) is unclear. We aimed to investigate the associations between consumption of sugar-sweetened beverages (SSB), artificially sweetened beverages (ASB), and pure fruit juice (PJ) and risk of consumption with AF risk and further evaluate whether genetic susceptibility modifies these associations. METHODS:A total of 201 856 participants who were free of baseline AF, had genetic data available, and completed a 24-hour diet questionnaire were included. Cox proportional hazard models were used to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs). RESULTS: During a median follow-up of 9.9 years, 9362 incident AF cases were documented. Compared with nonconsumers, individuals who consumed >2 L/wk of SSB or ASB had an increased risk of AF (HR, 1.10 [95% CI, 1.01-1.20] and HR, 1.20 [95% CI, 1.10-1.31]) in the multivariable-adjusted model. A negative association was observed between the consumption of ≤ 1 L/wk of PJ and the risk of AF (HR, 0.92 [95% CI, 0.87-0.97]). The highest HRs (95% CIs) of AF were observed for participants at high genetic risk who consumed >2 L/wk of ASB (HR, 3.51 [95% CI, 2.94-4.19]), and the lowest HR were observed for those at low genetic risk who consumed ≤ 1 L/wk of PJ (HR, 0.77 [95% CI, 0.65-0.92]). No significant interactions were observed between the consumption of SSB, ASB, or PJ and genetic predisposition to AF. CONCLUSIONS: Consumption of SSB and ASB at >2 L/wk was associated with an increased risk for AF. PJ consumption ≤ 1 L/wk was associated with a modestly lower risk for AF. The association between sweetened beverages and AF risk persisted after adjustment for genetic susceptibility to AF. This study does not demonstrate that consumption of SSB and ASB alters AF risk but rather that the consumption of SSB and ASB may predict AF risk beyond traditional risk f

The monsoon holds great significance in Asian-Australian civilization. Recent studies realized the link between the monsoon onset and the seasonal migration of the Intertropical Convergence Zone (ITCZ). However, no clear ITCZ band is observed in the Asian-Australian sector due to the strong influence of topography. Instead, there exists a large-scale (∼1,500 km) tropical convective cell––a perennial system that we hereafter coin as the “intertropical convective cell (ITCC).” Using ERA5 reanalysis and satellite-based outgoing longwave radiation products, here we show by objective detection and tracking that the ITCC exhibits eight phases during its seasonal migration along the Asian-Maritime land bridge. Particularly, its sudden northward jump in boreal spring coincides well with the earliest (abrupt) onset of the Asian rainy season, while its equatorward retreat heralds the overall (gradual) monsoon withdrawal. These findings demonstrate the close link of the ITCC behavior to the spring-fall asymmetry of the monsoon. Dynamically, the off-equatorial ITCC features a monsoon regime with a cross-equatorial overturning circulation, differing markedly from its equatorial regime with two weak overturning cells on each side. Further budget analyses prove our hypothesis that the north-south charging gradient of the moist static energy determines the ITCC's spring-fall asymmetric propagation, illuminating the physical origin of the spring-fall asymmetry in the monsoon. Our results demonstrate the usefulness of the ITCC framework in understanding the Asian-Australian monsoon complexity in a fresh and holistic manner. The framework will facilitate monsoon diagnosis, modeling and subseasonal-to-seasonal forecasting in the Asian-Australian sector. © 2024 The Authors.

For segmented telescopes, achieving fine co-focus adjustment is essential for realizing co-phase adjustment and maintenance, which involves adjusting the millimeter-scale piston between segments to fall within the capture range of the co-phase detection system. CGST proposes using a SHWFS for piston detection during the co-focus adjustment stage. However, the residual piston after adjustment exceeds the capture range of the broadband PSF phasing algorithm$(\pm 30 \mu m) $, and the multi-wavelength PSF algorithm requires even higher precision in co-focus adjustment. To improve the co-focus adjustment accuracy of CGST, a fine co-focus adjustment based on cross-calibration is proposed. This method utilizes a high-precision detector to calibrate and fit the measurements from the SHWFS, thereby reducing the impact of atmospheric turbulence and systematic errors on piston measurement accuracy during co-focus adjustment. Simulation results using CGST demonstrate that the proposed method significantly enhances adjustment accuracy compared to the SHWFS detection method. Additionally, the residual piston after fine co-focus adjustment using this method falls within the capture range of the multi-wavelength PSF algorithm. To verify the feasibility of this method, experiments were conducted on an 800mm ring segmented mirror system, successfully achieving fine co-focus adjustment where the remaining piston of all segments fell within $\pm 15 \mu m$.

Huaichang Wen,1,2,&ast; Yi Wang,2,&ast; Hao Cheng,2 Bin Wang,2 Xianwen Hu1 1Department of Anesthesiology, The Second Hospital of Anhui Medical University, Hefei, People’s Republic of China; 2Department of Anesthesiology, First Affiliated Hospital of Wannan Medical College, Wuhu, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Xianwen Hu, Department of Anesthesiology, The Second Hospital of Anhui Medical University, Hefei, 230032, People’s Republic of China, Email huxianwen0002@126.comBackground: The treatment of herpes zoster-related pain is challenging, and requires a variety of methods including pulse radio frequency modulation. Among them, single-time high-voltage long-term pulsed radiofrequency (HL-PRF) has been proved to be an effective treatment for subacute postherpetic neuralgia. However, it has the possibility of poor long-term curative effect and recurrence of neuralgia. In this study, we aim to identify the clinical efficacy and safety of twice repeated HL-PRF treatment in patients with subacute postherpetic neuralgia.Design: We conducted a retrospective analysis of subacute postherpetic neuralgia patients who underwent HL-PRF treatment.Setting: Pain Management Department of First Affiliated Hospital of Wannan Medical College.Patients: We enrolled all patients with subacute postherpetic neuralgia, who underwent HL-PRF treatment from January 2023 to October 2023.Measurements: The primary outcome variable was the visual Analog Scale (VAS) scores at 1, 4, 8, and 12 weeks after treatment. Secondary outcomes included Pittsburgh sleep quality index (PSQI), 36-item short-form health survey (SF-36) score, and total effective rate after treatment.Results: A total of 63 patients were included in the analysis. Among them, 33 patients received single-time HL-PRF treatment (Group S) and 30 patients received twice repeated HL-PRF treatment (Group T). Pain scores, PSQI scores, and SF-36 score were reduced in

Yuanli Ai,1,&ast; Kun Peng,2,&ast; Chunli Li,1,&ast; Jun Zhang,1 Gang Wang,3,4 Bin Wang,1 Enyi Huang3,4 1Institute of Life Sciences, College of Basic Medicine, Chongqing Medical University, Chongqing, People’s Republic of China; 2Chongqing Medical and Pharmaceutical College, Chongqing, People’s Republic of China; 3Chongqing Key Laboratory of Oral Diseases and Biomedical Sciences, Stomatological Hospital of Chongqing Medical University, Chongqing, People’s Republic of China; 4Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education College of Stomatology, College of Stomatology, Chongqing Medical University, Chongqing, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Bin Wang; Enyi Huang, Email bwang@cqmu.edu.cn; enyihuang@hospital.cqmu.edu.cnIntroduction: Bone, a pivotal structural organ, is susceptible to disorders with profound health implications. The investigation of gene expression in bone tissue is imperative, particularly within the context of metabolic diseases such as obesity and diabetes that augment the susceptibility to bone fractures. The objective of this study is to identify a set of internal control genes for the analysis of gene expression.Methods: This study employs reverse transcription-quantitative real-time polymerase chain reaction (RT-qPCR) to assess gene expression in bone tissue. We selected fourteen housekeeping genes and assessed their stability in the cortical bone of mouse models for obesity and diabetes using four well-established algorithms (GeNorm, BestKeeper, NormFinder, and the comparative Delta Ct method).Results and Conclusion: We identified Rpl13a as the mostly stably expressed reference gene in cortical bone tissue from mouse models of obesity and diabetes (db/db), while Gapdh was found to be the most stable reference gene in another diabetes model, KKAy mice. Additionally, Ef1a, Ppia, Rplp0, and Rpl22 were identified as alternative

Renzhe Tang,1,&ast; Chong Li,2,&ast; Dongmei Di,1 Lin Zhou,2 Yongxiang Qian,1 Chaohua Qiang,3 Chao Ma,1 Rui Zhou,1 Bin Wang,1 Min Wang1 1Department of Cardiothoracic Surgery, The Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu Province, 213003, People’s Republic of China; 2Department of Respiratory Medicine, The Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu Province, 213003, People’s Republic of China; 3Department of Echocardiography, The Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu Province, 213003, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Min Wang; Bin Wang, Department of Cardiothoracic Surgery, The Third Affiliated Hospital of Soochow University, No. 185 of Juqian Street, Changzhou, Jiangsu Province, 213003, People’s Republic of China, Email wangmin80808@163.com; wangbin1987@suda.edu.cnObjective: Obstructive sleep apnea (OSA) is a disorder characterized by disruption in breathing and hypoventilation. In parallel, metabolic syndrome (MetS) mainly co-occur with OSA, however, their association has not been fully elucidated. Therefore, this study aimed to reveal the relationship between OSA and MetS using data from the National Health And Nutrition Examination Survey (NHANES) database and pooled data from Genome-Wide Association Studies (GWAS).Material and Methods: Data from the National Health and Nutrition Examination Survey and pooled data from genome-wide association analysis (GWAS) were used univariate and multivariate logistic regression analyses were carried out to evaluate the correlation between OSA and MetS, and multivariate logistic regression models were utilized for adjusting for potential confounders. Two-sample Mendelian randomization (MR) was used to assess the causal relationship between OSA and MetS. The variance-weighted inverse method was employed as the main method of analysis.Results: A positive relationship of

Tianshan Mountains are the headwater regions for the central Asia rivers, providing water resources for ecological protection and economic development in semiarid regions. Due to scarce observations, the hydroclimatic characteristics of the Tianshan Mountains Precipitation (TMP) measured over highland (>1500 m) regions remain to be revealed. Here, we show the TMP belongs to a monsoon-like climate regime, with a distinct annual range and a high ratio of summer-to-yearly rainfall, and exhibits six abrupt changes, dividing the annual cycle into six precipitation sub-seasons. Over the past 60 years, the yearly TMP has significantly increased by 17.3%, with a dramatic increase in winter (135.7%). The TMP displays a significant 40-day climatological intra-seasonal oscillation (CISO) in summer. The TMP CISO’s wet phase results from the confrontation of the eastward propagating mid-tropospheric Balkhash Lake Low and the southward migrating Mongolian High. The sudden changes in the two climatological circulation systems trigger TMP’s changes, shaping the 40-day CISO. Emerging scientific issues are also discussed. © 2024, The Author(s).

Hard carbon is a promising negative electrode material for rechargeable sodium-ion batteries due to the ready availability of their precursors and high reversible charge storage. The reaction mechanisms that drive the sodiation properties in hard carbons and subsequent electrochemical performance are strictly linked to the characteristic slope and plateau regions observed in the voltage profile of these materials. This work shows that electron paramagnetic resonance (EPR) spectroscopy is a powerful and fast diagnostic tool to predict the extent of the charge stored in the slope and plateau regions during galvanostatic tests in hard carbon materials. EPR lineshape simulation and temperature-dependent measurements help to separate the nature of the spins in mechanochemically modified hard carbon materials synthesised at different temperatures. This proves relationships between structure modification and electrochemical signatures in the galvanostatic curves to obtain information on their sodium storage mechanism. Furthermore, through ex situ EPR studies we study the evolution of these EPR signals at different states of charge to further elucidate the storage mechanisms in these carbons. Finally, we discuss the interrelationship between EPR spectroscopy data of the hard carbon samples studied and their corresponding charging storage mechanism.

The evolution of Large Language Models (LLMs) like ChatGPT and GPT-4 has sparked discussions on the advent of Artificial General Intelligence (AGI). However, replicating such advancements in open-source models has been challenging. This paper introduces InternLM2, an open-source LLM that outperforms its predecessors in comprehensive evaluations across 6 dimensions and 30 benchmarks, long-context modeling, and open-ended subjective evaluations through innovative pre-training and optimization techniques. The pre-training process of InternLM2 is meticulously detailed, highlighting the preparation of diverse data types including text, code, and long-context data. InternLM2 efficiently captures long-term dependencies, initially trained on 4k tokens before advancing to 32k tokens in pre-training and fine-tuning stages, exhibiting remarkable performance on the 200k ``Needle-in-a-Haystack" test. InternLM2 is further aligned using Supervised Fine-Tuning (SFT) and a novel Conditional Online Reinforcement Learning from Human Feedback (COOL RLHF) strategy that addresses conflicting human preferences and reward hacking. By releasing InternLM2 models in different training stages and model sizes, we provide the community with insights into the model's evolution.

In this paper, we propose and analyze a novel third-order low-regularity trigonometric integrator for the semilinear Klein-Gordon equation in the $d$-dimensional space with $d=1,2,3$. The integrator is constructed based on the full use of Duhamel's formula and the technique of twisted function to the trigonometric integrals. Rigorous error estimates are presented and the proposed method is shown to have third-order accuracy in the energy space under a weak regularity requirement in $H^{2}\times H^{1}$. A numerical experiment shows that the proposed third-order low-regularity integrator is much more accurate than the well-known exponential integrators of order three for approximating the Klein-Gordon equation with nonsmooth solutions.

Estimating robot pose and joint angles is significant in advanced robotics, enabling applications like robot collaboration and online hand-eye calibration.However, the introduction of unknown joint angles makes prediction more complex than simple robot pose estimation, due to its higher dimensionality.Previous methods either regress 3D keypoints directly or utilise a render&compare strategy. These approaches often falter in terms of performance or efficiency and grapple with the cross-camera gap problem.This paper presents a novel framework that bifurcates the high-dimensional prediction task into two manageable subtasks: 2D keypoints detection and lifting 2D keypoints to 3D. This separation promises enhanced performance without sacrificing the efficiency innate to keypoint-based techniques.A vital component of our method is the lifting of 2D keypoints to 3D keypoints. Common deterministic regression methods may falter when faced with uncertainties from 2D detection errors or self-occlusions.Leveraging the robust modeling potential of diffusion models, we reframe this issue as a conditional 3D keypoints generation task. To bolster cross-camera adaptability, we introduce theNormalised Camera Coordinate Space (NCCS), ensuring alignment of estimated 2D keypoints across varying camera intrinsics.Experimental results demonstrate that the proposed method outperforms the state-of-the-art render\&compare method and achieves higher inference speed.Furthermore, the tests accentuate our method's robust cross-camera generalisation capabilities.We intend to release both the dataset and code in https://nimolty.github.io/Robokeygen, Comment: Accepted by ICRA 2024

We present a novel topology-preserving 3D medial axis computation framework based on volumetric restricted power diagram (RPD), while preserving the medial features and geometric convergence simultaneously, for both 3D CAD and organic shapes. The volumetric RPD discretizes the input 3D volume into sub-regions given a set of medial spheres. With this intermediate structure, we convert the homotopy equivalency between the generated medial mesh and the input 3D shape into a localized contractibility checking for each restricted element (power cell, power face, power edge), by checking their connected components and Euler characteristics. We further propose a fractional Euler characteristic algorithm for efficient GPU-based computation of Euler characteristic for each restricted element on the fly while computing the volumetric RPD. Compared with existing voxel-based or point-cloud-based methods, our approach is the first to adaptively and directly revise the medial mesh without globally modifying the dependent structure, such as voxel size or sampling density, while preserving its topology and medial features. In comparison with the feature preservation method MATFP, our method provides geometrically comparable results with fewer spheres and more robustly captures the topology of the input 3D shape.

The scale and quality of a dataset significantly impact the performance of deep models. However, acquiring large-scale annotated datasets is both a costly and time-consuming endeavor. To address this challenge, dataset expansion technologies aim to automatically augment datasets, unlocking the full potential of deep models. Current data expansion techniques include image transformation and image synthesis methods. Transformation-based methods introduce only local variations, leading to limited diversity. In contrast, synthesis-based methods generate entirely new content, greatly enhancing informativeness. However, existing synthesis methods carry the risk of distribution deviations, potentially degrading model performance with out-of-distribution samples. In this paper, we propose DistDiff, a training-free data expansion framework based on the distribution-aware diffusion model. DistDiff constructs hierarchical prototypes to approximate the real data distribution, optimizing latent data points within diffusion models with hierarchical energy guidance. We demonstrate its capability to generate distribution-consistent samples, significantly improving data expansion tasks. DistDiff consistently enhances accuracy across a diverse range of datasets compared to models trained solely on original data. Furthermore, our approach consistently outperforms existing synthesis-based techniques and demonstrates compatibility with widely adopted transformation-based augmentation methods. Additionally, the expanded dataset exhibits robustness across various architectural frameworks. Our code is available at https://github.com/haoweiz23/DistDiff, Comment: Project: https://github.com/haoweiz23/DistDiff

Tool learning aims to extend the capabilities of large language models (LLMs) with external tools. A major challenge in tool learning is how to support a large number of tools, including unseen tools. To address this challenge, previous studies have proposed retrieving suitable tools for the LLM based on the user query. However, previously proposed methods do not consider the differences between seen and unseen tools, nor do they take the hierarchy of the tool library into account, which may lead to suboptimal performance for tool retrieval. Therefore, to address the aforementioned issues, we propose ToolRerank, an adaptive and hierarchy-aware reranking method for tool retrieval to further refine the retrieval results. Specifically, our proposed ToolRerank includes Adaptive Truncation, which truncates the retrieval results related to seen and unseen tools at different positions, and Hierarchy-Aware Reranking, which makes retrieval results more concentrated for single-tool queries and more diverse for multi-tool queries. Experimental results show that ToolRerank can improve the quality of the retrieval results, leading to better execution results generated by the LLM., Comment: This paper is accepted for LREC-COLING 2024

Clustering extreme weather events are concurrent or consecutive occurrences of disastrous weather in multiple regions, resulting in cumulative impacts. Here we discovered a significant increasing trend in clustering extreme precipitation events over the Indo-Pacific rim over the past four decades. This trend can be largely attributable to the increasing frequency of the Rossby wave response, including the circum-Pacific and cross-Pacific patterns due to Rossby wave activity propagation, and the Pacific anticyclone pattern due to Rossby wave breaking. The three patterns show remarkable disparity in seasonality, persistence, and hydrological impacts. They can increase the occurrences of most severe precipitation by up to 5, 8, and 25 times, respectively. The Indian Summer Monsoon heat sources and La Niña are identified as key drivers, and the mid-latitude jet streams are modulators contributing to the events. Our findings suggest that specific Rossby wave patterns may influence the potential evolution of future clustering extremes.

This paper introduces CN-RMA, a novel approach for 3D indoor object detection from multi-view images. We observe the key challenge as the ambiguity of image and 3D correspondence without explicit geometry to provide occlusion information. To address this issue, CN-RMA leverages the synergy of 3D reconstruction networks and 3D object detection networks, where the reconstruction network provides a rough Truncated Signed Distance Function (TSDF) and guides image features to vote to 3D space correctly in an end-to-end manner. Specifically, we associate weights to sampled points of each ray through ray marching, representing the contribution of a pixel in an image to corresponding 3D locations. Such weights are determined by the predicted signed distances so that image features vote only to regions near the reconstructed surface. Our method achieves state-of-the-art performance in 3D object detection from multi-view images, as measured by mAP@0.25 and mAP@0.5 on the ScanNet and ARKitScenes datasets. The code and models are released at https://github.com/SerCharles/CN-RMA., Comment: CVPR2024 poster paper, 8 pages of main part, and 4 pages of supplementary material

Abrupt monsoon onsets/retreats are indispensable targets for climate prediction and future projection, but the origins of their abruptness remain elusive. This study establishes the existence of three climatological Madden-Julian Oscillation (CMJO) episodes contributing to the rapid Australian summer monsoon retreat in mid-March, the South China Sea (or East Asian) summer monsoon onset in mid-May, and the Indian summer monsoon onset in early June. The CMJO displays a dynamically coherent convection-circulation structure resembling its transitionary counterpart, demonstrating its robustness as a convectively coupled circulation system and the tendency of the transient MJOs’ phase-lock to the annual cycle. The CMJO is inactive during the boreal winter due to destructive year-to-year modulations of El Niño-Southern Oscillation. We hypothesize that the interaction between atmospheric internal variability (MJO) and the insolation-forced slow annual cycle generates the sudden monsoon withdrawal/onset during the boreal spring. Understanding the factors determining the timing and location of the MJO’s phase-locking and its variability is vital for monsoon forecasting and climate projection.

Federated learning (FL) is a machine learning paradigm that targets model training without gathering the local data dispersed over various data sources. Standard FL, which employs a single server, can only support a limited number of users, leading to degraded learning capability. In this work, we consider a multi-server FL framework, referred to as \emph{Confederated Learning} (CFL), in order to accommodate a larger number of users. A CFL system is composed of multiple networked edge servers, with each server connected to an individual set of users. Decentralized collaboration among servers is leveraged to harness all users' data for model training. Due to the potentially massive number of users involved, it is crucial to reduce the communication overhead of the CFL system. We propose a stochastic gradient method for distributed learning in the CFL framework. The proposed method incorporates a conditionally-triggered user selection (CTUS) mechanism as the central component to effectively reduce communication overhead. Relying on a delicately designed triggering condition, the CTUS mechanism allows each server to select only a small number of users to upload their gradients, without significantly jeopardizing the convergence performance of the algorithm. Our theoretical analysis reveals that the proposed algorithm enjoys a linear convergence rate. Simulation results show that it achieves substantial improvement over state-of-the-art algorithms in terms of communication efficiency.

Increasing the number of parameters in large language models (LLMs) usually improves performance in downstream tasks but raises compute and memory costs, making deployment difficult in resource-limited settings. Quantization techniques, which reduce the bits needed for model weights or activations with minimal performance loss, have become popular due to the rise of LLMs. However, most quantization studies use pre-trained LLMs, and the impact of quantization on instruction-tuned LLMs and the relationship between perplexity and benchmark performance of quantized LLMs are not well understood. Evaluation of quantized LLMs is often limited to language modeling and a few classification tasks, leaving their performance on other benchmarks unclear. To address these gaps, we propose a structured evaluation framework consisting of three critical dimensions: (1) knowledge \& capacity, (2) alignment, and (3) efficiency, and conduct extensive experiments across ten diverse benchmarks. Our experimental results indicate that LLMs with 4-bit quantization can retain performance comparable to their non-quantized counterparts, and perplexity can serve as a proxy metric for quantized LLMs on most benchmarks. Furthermore, quantized LLMs with larger parameter scales can outperform smaller LLMs. Despite the memory savings achieved through quantization, it can also slow down the inference speed of LLMs. Consequently, substantial engineering efforts and hardware support are imperative to achieve a balanced optimization of decoding speed and memory consumption in the context of quantized LLMs., Comment: ACL 2024 Findings

Multi-person pose estimation (MPPE), which aims to locate the key points for all persons in the frames, is an active research branch of computer vision. Variable human poses and complex scenes make MPPE dependent on local details and global structures; their absence may cause key point feature misalignment. In this case, high-order spatial interactions that can effectively link the local and global information of features are particularly important. However, most methods do not include spatial interactions. A few methods have low-order spatial interactions, but achieving a good balance between accuracy and complexity is challenging. To address the above problems, a dual-residual spatial interaction network (DRSI-Net) for MPPE with high accuracy and low complexity is proposed herein. Compared to other methods, DRSI-Net recursively performs residual spatial information interactions on the neighbouring features so that more useful spatial information can be retained and more similarities can be obtained between shallow and deep extracted features. The channel and spatial dual attention mechanism introduced in the multi-scale feature fusion also helps the network to adaptively focus on features relevant to the target key points and further refine the generated poses. Simultaneously, by optimising the interactive channel dimensions and dividing the gradient flow, the spatial interaction module is designed to be lightweight, thus reducing the complexity of the network. According to the experimental results on the COCO dataset, the proposed DRSI-Net outperforms other state-of-the-art methods in accuracy and complexity.

Anthropomorphic social bots are engineered to emulate human verbal communication and generate toxic or inflammatory content across social networking services (SNSs). Bot-disseminated misinformation could subtly yet profoundly reshape societal processes by complexly interweaving factors like repeated disinformation exposure, amplified political polarization, compromised indicators of democratic health, shifted perceptions of national identity, propagation of false social norms, and manipulation of collective memory over time. However, extrapolating bots' pluripotency across hybridized, multilingual, and heterogeneous media ecologies from isolated SNS analyses remains largely unknown, underscoring the need for a comprehensive framework to characterise bots' emergent risks to civic discourse. Here we propose an interdisciplinary framework to characterise bots' pluripotency, incorporating quantification of influence, network dynamics monitoring, and interlingual feature analysis. When applied to the geopolitical discourse around the Russo-Ukrainian conflict, results from interlanguage toxicity profiling and network analysis elucidated spatiotemporal trajectories of pro-Russian and pro-Ukrainian human and bots across hybrid SNSs. Weaponized bots predominantly inhabited X, while human primarily populated Reddit in the social media warfare. This rigorous framework promises to elucidate interlingual homogeneity and heterogeneity in bots' pluripotent behaviours, revealing synergistic human-bot mechanisms underlying regimes of information manipulation, echo chamber formation, and collective memory manifestation in algorithmically structured societies.

In nested Named entity recognition (NER), entities are nested with each other, and thus requiring more data annotations to address. This leads to the development of few-shot nested NER, where the prevalence of pretrained language models with in-context learning (ICL) offers promising solutions. In this work, we introduce an effective and innovative ICL framework for the setting of few-shot nested NER. We improve the ICL prompt by devising a novel example demonstration selection mechanism, EnDe retriever. In EnDe retriever, we employ contrastive learning to perform three types of representation learning, in terms of semantic similarity, boundary similarity, and label similarity, to generate high-quality demonstration examples. Extensive experiments over three nested NER and four flat NER datasets demonstrate the efficacy of our system., Comment: 5 figures

Due to the energy crisis and environmental pollution, the installed capacity of inverter-based resources (IBRs) in power grids is rapidly increasing, and grid-following control (GFL) is the most prevalent at present. Meanwhile, grid-forming control-based (GFM) devices have been installed in the grid to provide active support for frequency and voltage. In the future GFL devices combined with GFM will be promising, especially in power systems with high penetration or 100% IBRs. When a short-circuit fault occurs in the grid, the controlled current source characteristic of the GFL devices leads to insufficient dynamic voltage support (DVS), while the GFM devices usually reduce the internal voltage to limit the current. Thus, deep voltage sags and undesired disconnections of IBRs may occur. Moreover, due to the dispersed locations and the control strategies' diversity of IBRs, the voltage support of different devices may not be fully coordinated, which is not conducive to short-term voltage security (STVS). To address this issue, a control scheme based on the simulation of transient characteristics of synchronous machines (SMs) is proposed. Then, a new fault ride-through strategy (FRT) is proposed based on the characteristic differences between GFL and GFM devices, and an optimization model of multi-device control parameters is formulated to meet the short-term voltage security constraints (SVSCs) and device capacity constraints. Finally, a fast solution method based on analytical modeling is proposed for the model. Test results based on the doublegenerator-one-load system, the IEEE 14-bus system, and other systems of different sizes show that the proposed method can effectively enhance the active support capability of GFL and GFM to the grid voltage, and avoid the large-scale disconnection of IBRs, Comment: 10 pages, 13 figures

We introduce InternLM-XComposer2, a cutting-edge vision-language model excelling in free-form text-image composition and comprehension. This model goes beyond conventional vision-language understanding, adeptly crafting interleaved text-image content from diverse inputs like outlines, detailed textual specifications, and reference images, enabling highly customizable content creation. InternLM-XComposer2 proposes a Partial LoRA (PLoRA) approach that applies additional LoRA parameters exclusively to image tokens to preserve the integrity of pre-trained language knowledge, striking a balance between precise vision understanding and text composition with literary talent. Experimental results demonstrate the superiority of InternLM-XComposer2 based on InternLM2-7B in producing high-quality long-text multi-modal content and its exceptional vision-language understanding performance across various benchmarks, where it not only significantly outperforms existing multimodal models but also matches or even surpasses GPT-4V and Gemini Pro in certain assessments. This highlights its remarkable proficiency in the realm of multimodal understanding. The InternLM-XComposer2 model series with 7B parameters are publicly available at https://github.com/InternLM/InternLM-XComposer., Comment: Code and models are available at https://github.com/InternLM/InternLM-XComposer

We propose to explore the properties of raw point clouds through the \emph{winding clearness}, a concept we first introduce for assessing the clarity of the interior/exterior relationships represented by the winding number field of the point cloud. In geometric modeling, the winding number is a powerful tool for distinguishing the interior and exterior of a given surface $\partial \Omega$, and it has been previously used for point normal orientation and surface reconstruction. In this work, we introduce a novel approach to assess and optimize the quality of point clouds based on the winding clearness. We observe that point clouds with reduced noise tend to exhibit improved winding clearness. Accordingly, we propose an objective function that quantifies the error in winding clearness, solely utilizing the positions of the point clouds. Moreover, we demonstrate that the winding clearness error is differentiable and can serve as a loss function in optimization-based and learning-based point cloud processing. In the optimization-based method, the loss function is directly back-propagated to update the point positions, resulting in an overall improvement of the point cloud. In the learning-based method, we incorporate the winding clearness as a geometric constraint in the diffusion-based 3D generative model. Experimental results demonstrate the effectiveness of optimizing the winding clearness in enhancing the quality of the point clouds. Our method exhibits superior performance in handling noisy point clouds with thin structures, highlighting the benefits of the global perspective enabled by the winding number.

California has committed to ambitious decarbonization targets across multiple sectors, including decarbonizing the electrical grid by 2045. In addition, the medium- and heavy-duty truck fleets are expected to see rapid electrification over the next two decades. Considering these two pathways in tandem is critical for ensuring cost optimality and reliable power system operation. In particular, we examine the potential cost savings of electrical generation infrastructure by enabling flexible charging and bidirectional charging for these trucks. We also examine costs adjacent to enabling these services, such as charger upgrades and battery degradation. We deploy a large mixed-integer decarbonization planning model to quantify the costs associated with the electric generation decarbonization pathway. Example scenarios governing truck driving and charging behaviors are implemented to reveal the sensitivity of temporal driving patterns. Our experiments show that cost savings on the order of multiple billions of dollars are possible by enabling flexible and bidirectional charging in medium- and heavy-duty trucks in California.

New categories may be introduced over time, or existing categories may need to be reclassified. Class incremental learning (CIL) is employed for the gradual acquisition of knowledge about new categories while preserving information about previously learned ones in such dynamic environments. It might also be necessary to also eliminate the influence of related categories on the model to adapt to reclassification. We thus introduce class-level machine unlearning (MU) within CIL. Typically, MU methods tend to be time-consuming and can potentially harm the model's performance. A continuous stream of unlearning requests could lead to catastrophic forgetting. To address these issues, we propose a non-destructive eCIL-MU framework based on embedding techniques to map data into vectors and then be stored in vector databases. Our approach exploits the overlap between CIL and MU tasks for acceleration. Experiments demonstrate the capability of achieving unlearning effectiveness and orders of magnitude (upto $\sim 278\times$) of acceleration.

In the field of information extraction (IE), tasks across a wide range of modalities and their combinations have been traditionally studied in isolation, leaving a gap in deeply recognizing and analyzing cross-modal information. To address this, this work for the first time introduces the concept of grounded Multimodal Universal Information Extraction (MUIE), providing a unified task framework to analyze any IE tasks over various modalities, along with their fine-grained groundings. To tackle MUIE, we tailor a multimodal large language model (MLLM), Reamo, capable of extracting and grounding information from all modalities, i.e., recognizing everything from all modalities at once. Reamo is updated via varied tuning strategies, equipping it with powerful capabilities for information recognition and fine-grained multimodal grounding. To address the absence of a suitable benchmark for grounded MUIE, we curate a high-quality, diverse, and challenging test set, which encompasses IE tasks across 9 common modality combinations with the corresponding multimodal groundings. The extensive comparison of Reamo with existing MLLMs integrated into pipeline approaches demonstrates its advantages across all evaluation dimensions, establishing a strong benchmark for the follow-up research. Our resources are publicly released at https://haofei.vip/MUIE.

We present a grid-free fluid solver featuring a novel Gaussian representation. Drawing inspiration from the expressive capabilities of 3D Gaussian Splatting in multi-view image reconstruction, we model the continuous flow velocity as a weighted sum of multiple Gaussian functions. Leveraging this representation, we derive differential operators for the field and implement a time-dependent PDE solver using the traditional operator splitting method. Compared to implicit neural representations as another continuous spatial representation with increasing attention, our method with flexible 3D Gaussians presents enhanced accuracy on vorticity preservation. Moreover, we apply physics-driven strategies to accelerate the optimization-based time integration of Gaussian functions. This temporal evolution surpasses previous work based on implicit neural representation with reduced computational time and memory. Although not surpassing the quality of state-of-the-art Eulerian methods in fluid simulation, experiments and ablation studies indicate the potential of our memory-efficient representation. With enriched spatial information, our method exhibits a distinctive perspective combining the advantages of Eulerian and Lagrangian approaches.

In the era of artificial intelligence, the diversity of data modalities and annotation formats often renders data unusable directly, requiring understanding and format conversion before it can be used by researchers or developers with different needs. To tackle this problem, this article introduces a framework called Dataset Description Language (DSDL) that aims to simplify dataset processing by providing a unified standard for AI datasets. DSDL adheres to the three basic practical principles of generic, portable, and extensible, using a unified standard to express data of different modalities and structures, facilitating the dissemination of AI data, and easily extending to new modalities and tasks. The standardized specifications of DSDL reduce the workload for users in data dissemination, processing, and usage. To further improve user convenience, we provide predefined DSDL templates for various tasks, convert mainstream datasets to comply with DSDL specifications, and provide comprehensive documentation and DSDL tools. These efforts aim to simplify the use of AI data, thereby improving the efficiency of AI development.

Unoriented surface reconstructions based on the Gauss formula have attracted much attention due to their elegant mathematical formulation and excellent performance. However, the isotropic characteristics of the formulation limit their capacity to leverage the anisotropic information within the point cloud. In this work, we propose a novel anisotropic formulation by introducing a convection term in the original Laplace operator. By choosing different velocity vectors, the anisotropic feature can be exploited to construct more effective linear equations. Moreover, an adaptive selection strategy is introduced for the velocity vector to further enhance the orientation and reconstruction performance of thin structures. Extensive experiments demonstrate that our method achieves state-of-the-art performance and manages various challenging situations, especially for models with thin structures or small holes. The source code will be released on GitHub., Comment: 17pages;14figures

Recent advances in vision-language foundational models, such as CLIP, have demonstrated significant strides in zero-shot classification. However, the extensive parameterization of models like CLIP necessitates a resource-intensive fine-tuning process. In response, TIP-Adapter and SuS-X have introduced training-free methods aimed at bolstering the efficacy of downstream tasks. While these approaches incorporate support sets to maintain data distribution consistency between knowledge cache and test sets, they often fall short in terms of generalization on the test set, particularly when faced with test data exhibiting substantial distributional variations. In this work, we present CapS-Adapter, an innovative method that employs a caption-based support set, effectively harnessing both image and caption features to exceed existing state-of-the-art techniques in training-free scenarios. CapS-Adapter adeptly constructs support sets that closely mirror target distributions, utilizing instance-level distribution features extracted from multimodal large models. By leveraging CLIP's single and cross-modal strengths, CapS-Adapter enhances predictive accuracy through the use of multimodal support sets. Our method achieves outstanding zero-shot classification results across 19 benchmark datasets, improving accuracy by 2.19\% over the previous leading method. Our contributions are substantiated through extensive validation on multiple benchmark datasets, demonstrating superior performance and robust generalization capabilities. Our code is made publicly available at https://github.com/WLuLi/CapS-Adapter.

Recommender Systems (RSs) provide personalized recommendation service based on user interest, which are widely used in various platforms. However, there are lots of users with sparse interest due to lacking consumption behaviors, which leads to poor recommendation results for them. This problem is widespread in large-scale RSs and is particularly difficult to address. To solve this problem, we propose a novel solution named User Interest Enhancement (UIE) which enhances user interest including user profile and user history behavior sequences using the enhancement vectors and personalized enhancement vector generated based on stream clustering and memory networks from different perspectives. UIE not only remarkably improves model performance on the users with sparse interest but also significantly enhance model performance on other users. UIE is an end-to-end solution which is easy to be implemented based on ranking model. Moreover, we expand our solution and apply similar methods to long-tail items, which also achieves excellent improvement. Furthermore, we conduct extensive offline and online experiments in a large-scale industrial RS. The results demonstrate that our model outperforms other models remarkably, especially for the users with sparse interest. Until now, UIE has been fully deployed in multiple large-scale RSs and achieved remarkable improvements.

Automated volumetric segmentation of the pancreas on cross-sectional imaging is needed for diagnosis and follow-up of pancreatic diseases. While CT-based pancreatic segmentation is more established, MRI-based segmentation methods are understudied, largely due to a lack of publicly available datasets, benchmarking research efforts, and domain-specific deep learning methods. In this retrospective study, we collected a large dataset (767 scans from 499 participants) of T1-weighted (T1W) and T2-weighted (T2W) abdominal MRI series from five centers between March 2004 and November 2022. We also collected CT scans of 1,350 patients from publicly available sources for benchmarking purposes. We developed a new pancreas segmentation method, called PanSegNet, combining the strengths of nnUNet and a Transformer network with a new linear attention module enabling volumetric computation. We tested PanSegNet's accuracy in cross-modality (a total of 2,117 scans) and cross-center settings with Dice and Hausdorff distance (HD95) evaluation metrics. We used Cohen's kappa statistics for intra and inter-rater agreement evaluation and paired t-tests for volume and Dice comparisons, respectively. For segmentation accuracy, we achieved Dice coefficients of 88.3% (std: 7.2%, at case level) with CT, 85.0% (std: 7.9%) with T1W MRI, and 86.3% (std: 6.4%) with T2W MRI. There was a high correlation for pancreas volume prediction with R^2 of 0.91, 0.84, and 0.85 for CT, T1W, and T2W, respectively. We found moderate inter-observer (0.624 and 0.638 for T1W and T2W MRI, respectively) and high intra-observer agreement scores. All MRI data is made available at https://osf.io/kysnj/. Our source code is available at https://github.com/NUBagciLab/PaNSegNet., Comment: under review version

The rise of Web3 social ecosystems signifies the dawn of a new chapter in digital interaction, offering significant prospects for user engagement and financial advancement. Nonetheless, this progress is shadowed by potential privacy concessions, especially as these platforms frequently merge with existing Web2.0 social media accounts, amplifying data privacy risks for users. In this study, we investigate the nuanced dynamics between user engagement on Web3 social platforms and the consequent privacy concerns. We scrutinize the widespread phenomenon of fabricated activities, which encompasses the establishment of bogus accounts aimed at mimicking popularity and the deliberate distortion of social interactions by some individuals to gain financial rewards. Such deceptive maneuvers not only distort the true measure of the active user base but also amplify privacy threats for all members of the user community. We also find that, notwithstanding their attempts to limit social exposure, users remain entangled in privacy vulnerabilities. The actions of those highly engaged users, albeit often a minority group, can inadvertently breach the privacy of the larger collective. By casting light on the delicate interplay between user engagement, financial motives, and privacy issues, we offer a comprehensive examination of the intrinsic challenges and hazards present in the Web3 social milieu. We highlight the urgent need for more stringent privacy measures and ethical protocols to navigate the complex web of social exchanges and financial ambitions in the rapidly evolving Web3.

Multilingual proficiency presents a significant challenge for large language models (LLMs). English-centric models are usually suboptimal in other languages, particularly those that are linguistically distant from English. This performance discrepancy mainly stems from the imbalanced distribution of training data across languages during pre-training and instruction tuning stages. To address this problem, we propose a novel approach called CrossIn, which utilizes a mixed composition of cross-lingual instruction tuning data. Our method leverages the compressed representation shared by various languages to efficiently enhance the model's task-solving capabilities and multilingual proficiency within a single process. In addition, we introduce a multi-task and multi-faceted benchmark to evaluate the effectiveness of CrossIn. Experimental results demonstrate that our method substantially improves performance across tasks and languages, and we provide extensive insights into the impact of cross-lingual data volume and the integration of translation data on enhancing multilingual consistency and accuracy., Comment: 11 pages

As the rapidly advancing domain of natural language processing (NLP), large language models (LLMs) have emerged as powerful tools for interpreting human commands and generating text across various tasks. Nonetheless, the resilience of LLMs to handle text containing inherent errors, stemming from human interactions and collaborative systems, has not been thoroughly explored. Our study investigates the resilience of LLMs against five common types of disruptions including 1) ASR (Automatic Speech Recognition) errors, 2) OCR (Optical Character Recognition) errors, 3) grammatical mistakes, 4) typographical errors, and 5) distractive content. We aim to investigate how these models react by deliberately embedding these errors into instructions. Our findings reveal that while some LLMs show a degree of resistance to certain types of noise, their overall performance significantly suffers. This emphasizes the importance of further investigation into enhancing model resilience. In response to the observed decline in performance, our study also evaluates a "re-pass" strategy, designed to purify the instructions of noise before the LLMs process them. Our analysis indicates that correcting noisy instructions, particularly for open-source LLMs, presents significant challenges., Comment: 12 pages

Low-dose computed tomography (LDCT) has become the technology of choice for diagnostic medical imaging, given its lower radiation dose compared to standard CT, despite increasing image noise and potentially affecting diagnostic accuracy. To address this, advanced deep learning-based LDCT denoising algorithms have been developed, primarily using Convolutional Neural Networks (CNNs) or Transformer Networks with the Unet architecture. This architecture enhances image detail by integrating feature maps from the encoder and decoder via skip connections. However, current methods often overlook enhancements to the Unet architecture itself, focusing instead on optimizing encoder and decoder structures. This approach can be problematic due to the significant differences in feature map characteristics between the encoder and decoder, where simple fusion strategies may not effectively reconstruct images.In this paper, we introduce WiTUnet, a novel LDCT image denoising method that utilizes nested, dense skip pathways instead of traditional skip connections to improve feature integration. WiTUnet also incorporates a windowed Transformer structure to process images in smaller, non-overlapping segments, reducing computational load. Additionally, the integration of a Local Image Perception Enhancement (LiPe) module in both the encoder and decoder replaces the standard multi-layer perceptron (MLP) in Transformers, enhancing local feature capture and representation. Through extensive experimental comparisons, WiTUnet has demonstrated superior performance over existing methods in key metrics such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), and Root Mean Square Error (RMSE), significantly improving noise removal and image quality.