Balanced Audiovisual Dataset for Imbalance Analysis

The imbalance problem is widespread in the field of machine learning, which also exists in multimodal learning areas caused by the intrinsic discrepancy between modalities of samples. Recent works have attempted to solve the modality imbalance problem from algorithm perspective, however, they do not fully analyze the influence of modality bias in datasets. Concretely, existing multimodal datasets are usually collected under specific tasks, where one modality tends to perform better than other ones in most conditions. In this work, to comprehensively explore the influence of modality bias, we first split existing datasets into different subsets by estimating sample-wise modality discrepancy. We surprisingly find that: the multimodal models with existing imbalance algorithms consistently perform worse than the unimodal one on specific subsets, in accordance with the modality bias. To further explore the influence of modality bias and analyze the effectiveness of existing imbalance algorithms, we build a balanced audiovisual dataset, with uniformly distributed modality discrepancy over the whole dataset. We then conduct extensive experiments to re-evaluate existing imbalance algorithms and draw some interesting findings: existing algorithms only provide a compromise between modalities and suffer from the large modality discrepancy of samples. We hope that these findings could facilitate future research on the modality imbalance problem.Comment: website:https://gewu-lab.github.io/Balanced-Audiovisual-Dataset

Kinematic-aware Prompting for Generalizable Articulated Object Manipulation with LLMs

Generalizable articulated object manipulation is essential for home-assistant robots. Recent efforts focus on imitation learning from demonstrations or reinforcement learning in simulation, however, due to the prohibitive costs of real-world data collection and precise object simulation, it still remains challenging for these works to achieve broad adaptability across diverse articulated objects. Recently, many works have tried to utilize the strong in-context learning ability of Large Language Models (LLMs) to achieve generalizable robotic manipulation, but most of these researches focus on high-level task planning, sidelining low-level robotic control. In this work, building on the idea that the kinematic structure of the object determines how we can manipulate it, we propose a kinematic-aware prompting framework that prompts LLMs with kinematic knowledge of objects to generate low-level motion trajectory waypoints, supporting various object manipulation. To effectively prompt LLMs with the kinematic structure of different objects, we design a unified kinematic knowledge parser, which represents various articulated objects as a unified textual description containing kinematic joints and contact location. Building upon this unified description, a kinematic-aware planner model is proposed to generate precise 3D manipulation waypoints via a designed kinematic-aware chain-of-thoughts prompting method. Our evaluation spanned 48 instances across 16 distinct categories, revealing that our framework not only outperforms traditional methods on 8 seen categories but also shows a powerful zero-shot capability for 8 unseen articulated object categories. Moreover, the real-world experiments on 7 different object categories prove our framework's adaptability in practical scenarios. Code is released at https://github.com/GeWu-Lab/LLM_articulated_object_manipulation/tree/main.Comment: Accepted by ICRA 202

Groundwater Diffuse Recharge and its Response to Climate Changes in Semi-Arid Northwestern China

Understanding the processes and rates of groundwater recharge in arid and semi-arid areas is crucial for utilizing and managing groundwater resources sustainably. We obtained three chloride profiles of the unsaturated-zone in the desert/loess transition zone of northwestern China and reconstructed the groundwater recharge variations over the last 11, 21, and 37 years, respectively, using the generalized chloride mass balance (GCMB) method. The average recharge rates were 43.7, 43.5, and 45.1 mm yr-1, respectively, which are similar to those evaluated by the chloride mass balance (CMB) or GCMB methods in other semi-arid regions. The results indicate that the annual recharge rates were not in complete linear proportion to the corresponding annual precipitations, although both exhibited descending tendencies on the whole. Comparisons between the daily precipitation aggregate at different intensity and recharge rates reveal that the occurrence of relatively heavy daily precipitation per year may contribute to such nonlinearity between annual precipitation and recharge. The possible influences of vegetation cover alterations following precipitation change cannot be excluded as well. The approximately negative correlation between the average annual recharge and temperature suggests that changes in temperature have had significant influences on recharge

TikTalk: A Video-Based Dialogue Dataset for Multi-Modal Chitchat in Real World

To facilitate the research on intelligent and human-like chatbots with multi-modal context, we introduce a new video-based multi-modal dialogue dataset, called TikTalk. We collect 38K videos from a popular video-sharing platform, along with 367K conversations posted by users beneath them. Users engage in spontaneous conversations based on their multi-modal experiences from watching videos, which helps recreate real-world chitchat context. Compared to previous multi-modal dialogue datasets, the richer context types in TikTalk lead to more diverse conversations, but also increase the difficulty in capturing human interests from intricate multi-modal information to generate personalized responses. Moreover, external knowledge is more frequently evoked in our dataset. These facts reveal new challenges for multi-modal dialogue models. We quantitatively demonstrate the characteristics of TikTalk, propose a video-based multi-modal chitchat task, and evaluate several dialogue baselines. Experimental results indicate that the models incorporating large language models (LLM) can generate more diverse responses, while the model utilizing knowledge graphs to introduce external knowledge performs the best overall. Furthermore, no existing model can solve all the above challenges well. There is still a large room for future improvements, even for LLM with visual extensions. Our dataset is available at \url{https://ruc-aimind.github.io/projects/TikTalk/}.Comment: Accepted to ACM Multimedia 202

Efficacy and safety of ultrasound-assisted wound debridement in the treatment of diabetic foot ulcers: a systematic review and meta-analysis of 11 randomized controlled trials

ObjectiveResearch data suggests that ultrasound-assisted wound debridement (UAWD) can effectively promote the healing of diabetic foot ulcers (DFU). However, existing research is not consistent with this viewpoint. Therefore, we conducted this study to investigate the effect of UAWD on the healing of diabetic foot ulcers.MethodsFrom the establishment of the database to January 2024, we searched 8 databases to study the effectiveness and safety of UAWD in the treatment of DFU. Two authors independently screened the qualifications of the articles, while two authors extracted relevant data. Statistical analysis was conducted using Review Manager 5.4 and STATA 18.0 software.ResultsA total of 11 randomized controlled studies were included, with 6 countries and 696 participants participating. Our findings showed that UAWD was associated with a significant benefit in healing rate (OR = 2.60, 95% CI: [1.67, 4.03], P < 0.0001, I2 = 25%), wound healing time (MD = -11.94, 95% CI: [-23.65, -0.23], P = 0.05, I2 = 99%), percentage reduction in wound size (MD = 14.2, 95% CI: [10.8, 17.6], P = 0.47, I2 = 32%), effectiveness of treatment (OR = 10.3, 95% CI: [4.68, 22.66], P < 0.00001, I2 = 0%). Moreover, UAWD did not cause any significant adverse reactions. However, there was no obvious difference in wound blood perfusion (MD = 0.25, 95% CI: [-0.01, 0.52], P = 0.06, I2 = 90%), transcutaneous oxygen partial pressure (MD = 14.34, 95% CI: [-10.03, 38.71], P = 0.25, I2 = 98%).ConclusionUAWD can significantly improve wound healing rate, shorten wound healing time, accelerate wound area reduction, and improve clinical treatment effectiveness without significant adverse reactions. Although there is no significant difference in transcutaneous oxygen pressure and wound blood flow perfusion between UAWD and SWC. So we look forward to more scientifically blinded, placebo-controlled, high-quality studies in the future, to enable researchers to obtain more complete and accurate analytical data, in order to improve the scientific and credibility of the evidence.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/, identifier CRD42024501198

Novel paradigms for the gut–brain axis during alcohol withdrawal, withdrawal-associated depression, and craving in patients with alcohol use disorder

IntroductionPatients with alcohol use disorder (AUD) exhibit symptoms such as alcohol withdrawal, depression, and cravings. The gut-immune response may play a significant role in manifesting these specific symptoms associated with AUD. This study examined the role of gut dysfunction, proinflammatory cytokines, and hormones in characterizing AUD symptoms.MethodsForty-eight AUD patients [men (n = 34) and women (n = 14)] aged 23–63 years were grouped using the Clinical Institute Withdrawal Assessment of Alcohol Scale (CIWA) as clinically significant (CS-CIWA [score > 10] [n = 22]) and a clinically not-significant group (NCS-CIWA [score ≤ 10] [n = 26]). Clinical data (CIWA, 90-day timeline followback [TLFB90], and lifetime drinking history [LTDH]) and blood samples (for testing proinflammatory cytokines, hormones, and markers of intestinal permeability) were analyzed. A subset of 16 AUD patients was assessed upon admission for their craving tendencies related to drug-seeking behavior using the Penn-Alcohol Craving Score (PACS).ResultsCS-CIWA group patients exhibited unique and significantly higher levels of adiponectin and interleukin (IL)-6 compared to NCS-CIWA. In the CS group, there were significant and high effects of association for the withdrawal score with gut-immune markers (lipopolysaccharide [LPS], adiponectin, IL-6, and IL-8) and for withdrawal-associated depression with gut-immune markers (scored using MADRS with LPS, soluble cells of differentiation type 14 [sCD14], IL-6, and IL-8). Craving (assessed by PACS, the Penn-Alcohol Craving Scale) was significantly characterized by what could be described as gut dysregulation (LBP [lipopolysaccharide binding protein] and leptin) and candidate proinflammatory (IL-1β and TNF-α) markers. Such a pathway model describes the heavy drinking phenotype, HDD90 (heavy drinking days past 90 days), with even higher effects (R2 = 0.955, p = 0.006) in the AUD patients, who had higher ratings for cravings (PACS > 5).DiscussionThe interaction of gut dysfunction cytokines involved in both inflammation and mediating activity constitutes a novel pathophysiological gut–brain axis for withdrawal symptoms and withdrawal-associated depression and craving symptoms in AUD. AUD patients with reported cravings show a significant characterization of the gut–brain axis response to heavy drinking.Trial registrationClinicalTrials.gov, identifier: NCT# 00106106

The effects of psychiatric disorders on the risk of chronic heart failure: a univariable and multivariable Mendelian randomization study

BackgroundSubstantial evidence suggests an association between psychiatric disorders and chronic heart failure. However, further investigation is needed to confirm the causal relationship between these psychiatric disorders and chronic heart failure. To address this, we evaluated the potential effects of five psychiatric disorders on chronic heart failure using two-sample Mendelian Randomization (MR).MethodsWe selected single nucleotide polymorphisms (SNPs) associated with chronic heart failure and five psychiatric disorders (Attention-Deficit Hyperactivity Disorder (ADHD), Autism Spectrum Disorder (ASD), Major Depression, Bipolar Disorder and Schizophrenia (SCZ)). Univariable (UVMR) and multivariable two-sample Mendelian Randomization (MVMR) were employed to assess causality between these conditions. Ever smoked and alcohol consumption were controlled for mediating effects in the multivariable MR. The inverse variance weighting (IVW) and Wald ratio estimator methods served as the primary analytical methods for estimating potential causal effects. MR-Egger and weighted median analyses were also conducted to validate the results. Sensitivity analyses included the funnel plot, leave-one-out, and MR-Egger intercept tests. Additionally, potential mediators were investigated through risk factor analyses.ResultsGenetically predicted heart failure was significantly associated with ADHD (odds ratio (OR), 1.12; 95% CI, 1.04–1.20; p = 0.001), ASD (OR, 1.29; 95% CI, 1.07–1.56; p = 0.008), bipolar disorder (OR, 0.89; 95% CI, 0.83–0.96; p = 0.001), major depression (OR, 1.15; 95% CI, 1.03–1.29; p = 0.015), SCZ (OR, 1.04; 95% CI, 1.00–1.07; p = 0.024). Several risk factors for heart failure are implicated in the above cause-and-effect relationship, including ever smoked and alcohol consumption.ConclusionOur study demonstrated ADHD, ASD, SCZ and major depression may have a causal relationship with an increased risk of heart failure. In contrast, bipolar disorder was associated with a reduced risk of heart failure, which could potentially be mediated by ever smoked and alcohol consumption. Therefore, prevention strategies for heart failure should also incorporate mental health considerations, and vice versa

Predicting crack patterns in SiC-based cladding for LWR applications using peridynamics

SiC continuous fibre reinforced SiC matrix (SiC-SiC) composites are a proposed material for accident tolerant fuel cladding. Thermomechanical models of SiC-based cladding under light water conditions indicate that microcracking in the radial direction of the tubing may lead to a loss of hermicity. SiC-based tubing is known to have anisotropic elastic properties but the effect of this anisotropy have not been incorporated into existing thermomechanical models of clad cracking. This work augments an existing isotropic 2D peridynamic model of cracking and damage in the r-θ plane of a SiC-based cladding to account for the orthotropic elastic properties of SiC-SiC composite tubing. Three SiC-based architectures are modelled under normal operating conditions of a UO2-fuelled pressurised water reactor (PWR). The results of the anisotropic SiC-cladding model are compared with the results of the isotropic model, and the sensitivity of results to material anisotropy, thermal conductivity, and applied linear power rating are analysed. The results of this analysis show that anisotropy has a significant effect on the damage and crack patterns observed in the r-θ plane of SiC-based cladding, if either an inner or outer monolith is present. The anisotropic model predicts more cracks in two layer clad with an inner monolith and higher levels of damage in a two layer clad with an outer monolith than the isotropic model. Under normal reactor conditions the outer monolith clad architecture was found to remain hermetic