Robot Structure Prior Guided Temporal Attention for Camera-to-Robot Pose Estimation from Image Sequence

In this work, we tackle the problem of online camera-to-robot pose estimation from single-view successive frames of an image sequence, a crucial task for robots to interact with the world

Efficient Compression of Overparameterized Deep Models through Low-Dimensional Learning Dynamics

Overparameterized models have proven to be powerful tools for solving various machine learning tasks. However, overparameterization often leads to a substantial increase in computational and memory costs, which in turn requires extensive resources to train. In this work, we present a novel approach for compressing overparameterized models, developed through studying their learning dynamics. We observe that for many deep models, updates to the weight matrices occur within a low-dimensional invariant subspace. For deep linear models, we demonstrate that their principal components are fitted incrementally within a small subspace, and use these insights to propose a compression algorithm for deep linear networks that involve decreasing the width of their intermediate layers. We empirically evaluate the effectiveness of our compression technique on matrix recovery problems. Remarkably, by using an initialization that exploits the structure of the problem, we observe that our compressed network converges faster than the original network, consistently yielding smaller recovery errors. We substantiate this observation by developing a theory focused on deep matrix factorization. Finally, we empirically demonstrate how our compressed model has the potential to improve the utility of deep nonlinear models. Overall, our algorithm improves the training efficiency by more than 2x, without compromising generalization.Comment: International Conference on Artificial Intelligence and Statistics (AISTATS 2024

UC-14 TeleClinic

TeleClinic is a telemedicine web application that provides ease of access and a medium for interaction between patients and their respective doctors and administrators. In particular, this web portal includes a chat feature, an area for medical reports, an area for appointment requests, and an area for video recordings. Additionally, TeleClinic meets the requirements prescribed by the Health Insurance Portability and Accountability Act (H.I.P.A.A.) via upholding data privacy and safeguarding medical information. To maximize its overall utility, TeleClinic utilizes the React and React Redux libraries for its front-end and a NoSQL database in Google Firebase for its back-end.Advisors(s): Dr. Ken HogansonTopic(s): IoT/Cloud/NetworkingCS 485

Study on the quality change of crown pear during storage

Using the high-quality Crown Pear as the subject of experimental research, an analysis of the changes in the quality of Crown Pears during a storage period is conducted to provide a theoretical basis for the development of the pear cold storage industry. The study utilizes a handheld digital refractometer, texture analyzer, colorimeter, T-type thermocouple, and electronic balance to explore six aspects of Crown Pears: soluble solids content, hardness, color difference, freezing point, drying loss, and taste. The results reveal the following changes in pear quality during different storage periods within one cycle: the content of soluble solids in Crown Pears initially increases and then decreases during the storage period; hardness decreases with increasing storage time; the external appearance of pears gradually darkens; and drying loss increases with storage time. During the cold storage process of Crown Pears, the optimal temperature setting for the cold storage should be maintained at -1°C to 0.5°C. The flavor of Crown Pears is not optimal during the early stage of storage. The storage time for Crown Pears should be within four months

Mediators of the association between educational attainment and type 2 diabetes mellitus:a two-step multivariable Mendelian randomisation study

Aims/hypothesis: Type 2 diabetes mellitus is a major health burden disproportionately affecting those with lower educational attainment (EA). We aimed to obtain causal estimates of the association between EA and type 2 diabetes and to quantify mediating effects of known modifiable risk factors. Methods: We applied two-step, two-sample multivariable Mendelian randomisation (MR) techniques using SNPs as genetic instruments for exposure and mediators, thereby minimising bias due to confounding and reverse causation. We leveraged summary data on genome-wide association studies for EA, proposed mediators (i.e. BMI, blood pressure, smoking, television watching) and type 2 diabetes. The total effect of EA on type 2 diabetes was decomposed into a direct effect and indirect effects through multiple mediators. Additionally, traditional mediation analysis was performed in a subset of the National Health and Nutrition Examination Survey 2013–2014. Results: EA was inversely associated with type 2 diabetes (OR 0.53 for each 4.2 years of schooling; 95% CI 0.49, 0.56). Individually, the largest contributors were BMI (51.18% mediation; 95% CI 46.39%, 55.98%) and television watching (50.79% mediation; 95% CI 19.42%, 82.15%). Combined, the mediators explained 83.93% (95% CI 70.51%, 96.78%) of the EA–type 2 diabetes association. Traditional analysis yielded smaller effects but showed consistent direction and priority ranking of mediators. Conclusions/interpretation: These results support a potentially causal protective effect of EA against type 2 diabetes, with considerable mediation by a number of modifiable risk factors. Interventions on these factors thus have the potential of substantially reducing the burden of type 2 diabetes attributable to low EA

Observational and Genetic Evidence for Bidirectional Effects Between Red Blood Cell Traits and Diastolic Blood Pressure

Background: Previous studies have found associations of red blood cell traits (hemoglobin and red blood cell count, RBC) with blood pressure; whether these associations are causal is unknown.Methods: We performed cross-sectional analyses in the Lifelines Cohort Study (n=167,785). Additionally, we performed bidirectional two sample Mendelian randomization (MR) analyses to explore the causal effect of the two traits on systolic (SBP) and diastolic blood pressure (DBP), using genetic instrumental variables regarding hemoglobin and RBC identified in UK Biobank (n=350,475) and International Consortium of Blood Pressure studies for SBP and DBP (n= 757,601).Results: In cross-sectional analyses we observed positive associations with hypertension and blood pressure for both hemoglobin (OR=1.18, 95% CI: 1.16 to 1.20 for hypertension; B=0.11, 95% CI: 0.11 to 0.12 for SBP; B=0.11, 95% CI: 0.10 to 0.11 for DBP, all per SD) and RBC (OR=1.14, 95% CI: 1.12 to 1.16 for hypertension; B=0.11, 95% CI: 0.10 to 0.12 for SBP; B=0.08, 95% CI: 0.08 to 0.09 for DBP, all per SD). MR analyses suggested that higher hemoglobin and RBC cause higher DBP (inverse variance weighted [IVW] B=0.11, 95% CI: 0.07 to 0.16 for hemoglobin; B=0.07, 95% CI: 0.04 to 0.10 for RBC, all per SD). Reverse MR analyses (all per SD) suggested causal effects of DBP on both hemoglobin (B=0.06, 95% CI: 0.03 to 0.09) and RBC (B=0.08, 95% CI: 0.04 to 0.11). No significant effects on SBP were found.Conclusions: Our results suggest bidirectional causal relationships of hemoglobin and RBC with DBP, but not with SBP

LEVA : Using large language models to enhance visual analytics

Visual analytics supports data analysis tasks within complex domain problems. However, due to the richness of data types, visual designs, and interaction designs, users need to recall and process a significant amount of information when they visually analyze data. These challenges emphasize the need for more intelligent visual analytics methods. Large language models have demonstrated the ability to interpret various forms of textual data, offering the potential to facilitate intelligent support for visual analytics. We propose LEVA, a framework that uses large language models to enhance users' VA workflows at multiple stages: onboarding, exploration, and summarization. To support onboarding, we use large language models to interpret visualization designs and view relationships based on system specifications. For exploration, we use large language models to recommend insights based on the analysis of system status and data to facilitate mixed-initiative exploration. For summarization, we present a selective reporting strategy to retrace analysis history through a stream visualization and generate insight reports with the help of large language models. We demonstrate how LEVA can be integrated into existing visual analytics systems. Two usage scenarios and a user study suggest that LEVA effectively aids users in conducting visual analytics

Overvoltage risk regulation strategy with distributed energy application in a distribution network based on the Stackelberg game

Along with the increasing low-carbon demand of the power system, the access of a high percentage of renewable energy resources to the distribution network has a large impact on the voltage fluctuation of the system and reduces the operational reliability. In this paper, we consider utilizing the reactive capacity of distributed resources to participate in system voltage regulation to reduce node loss of load probability (LOLP) caused by node overvoltage faults and propose an overvoltage risk regulation strategy for the interaction between distribution network operators (DSOs) and distributed users in the framework of the Stackelberg game. First, the nodes are clustered and analyzed based on the two-dimensional indexes of node voltage regulation ability, and different voltage regulation compensation tariffs are assigned. Second, the cost-benefit model of voltage regulation for the leader and follower sides and the node LOLP model are constructed to measure the reliability of the system. The Stackelberg game is used to co-optimize the two parties’ compensation tariffs and voltage regulation strategies. The optimal solution of voltage regulation under the equilibrium of the game is obtained by solving using the particle swarm optimization (PSO) algorithm. Based on the IEEE-33 node system, a case study is carried out to verify that the proposed overvoltage risk regulation strategy can maximize the benefits of the regulator participants while enhancing the operational reliability of the system

SpemNet: A Cotton Disease and Pest Identification Method Based on Efficient Multi-Scale Attention and Stacking Patch Embedding

Simple Summary: Cotton is a crucial economic crop, but it is often threatened by various pests and diseases during its growth, significantly impacting its yield and quality. Earlier image classification methods often suffer from low accuracy and struggle to perform effectively in complex real-world environments. This paper proposes a novel image classification network named SpemNet, specifically designed for cotton pest and disease recognition. By introducing the Efficient Multi-Scale Attention (EMA) module and the Stacking Patch Embedding (SPE) module, the network enhances the ability to learn local features and integrate multi-scale information, thereby significantly improving the accuracy and efficiency of cotton pest and disease recognition. Extensive experiments conducted on the publicly available CottonInsect and IP102 datasets, as well as a self-collected cotton leaf disease dataset, demonstrate that SpemNet exhibits significant advantages in key metrics such as precision, recall, and F1 score, confirming its effectiveness and superiority in the task of cotton pest and disease recognition. Abstract: We propose a cotton pest and disease recognition method, SpemNet, based on efficient multi-scale attention and stacking patch embedding. By introducing the SPE module and the EMA module, we successfully solve the problems of local feature learning difficulty and insufficient multi-scale feature integration in the traditional Vision Transformer model, which significantly improve the performance and efficiency of the model. In our experiments, we comprehensively validate the SpemNet model on the CottonInsect dataset, and the results show that SpemNet performs well in the cotton pest recognition task, with significant effectiveness and superiority. The SpemNet model excels in key metrics such as precision and F1 score, demonstrating significant potential and superiority in the cotton pest and disease recognition task. This study provides an efficient and reliable solution in the field of cotton pest and disease identification, which is of great theoretical and applied significance