Graph Meets LLM: A Novel Approach to Collaborative Filtering for Robust Conversational Understanding

Conversational AI systems such as Alexa need to understand defective queries to ensure robust conversational understanding and reduce user friction. These defective queries often arise from user ambiguities, mistakes, or errors in automatic speech recognition (ASR) and natural language understanding (NLU). Personalized query rewriting is an approach that focuses on reducing defects in queries by taking into account the user's individual behavior and preferences. It typically relies on an index of past successful user interactions with the conversational AI. However, unseen interactions within the user's history present additional challenges for personalized query rewriting. This paper presents our "Collaborative Query Rewriting" approach, which specifically addresses the task of rewriting new user interactions that have not been previously observed in the user's history. This approach builds a "User Feedback Interaction Graph" (FIG) of historical user-entity interactions and leverages multi-hop graph traversal to enrich each user's index to cover future unseen defective queries. The enriched user index is called a Collaborative User Index and contains hundreds of additional entries. To counteract precision degradation from the enlarged index, we add additional transformer layers to the L1 retrieval model and incorporate graph-based and guardrail features into the L2 ranking model. Since the user index can be pre-computed, we further investigate the utilization of a Large Language Model (LLM) to enhance the FIG for user-entity link prediction in the Video/Music domains. Specifically, this paper investigates the Dolly-V2 7B model. We found that the user index augmented by the fine-tuned Dolly-V2 generation significantly enhanced the coverage of future unseen user interactions, thereby boosting QR performance on unseen queries compared with the graph traversal only approach

Fuzzy neural network PID control design of camellia fruit vibration picking manipulator

Due to the growth characteristics of the flowers and fruits of camellia in the same period, the vibrating camellia fruit picking machine needs to ensure the constant rotational speed of the vibrating hydraulic motor when the picking mechanism is operating, to achieve a constant vibration frequency, to ensure that the camellia fruit can smoothly fall off the branches through vibration. In contrast, the camellia fruit does not fall off. In this regard, this paper deduced the state space equation of the camellia fruit picking machine’s valve-controlled vibrating hydraulic motor system and designed a fuzzy wavelet neural network PID controller (FWNN PID controller) based on the traditional incremental PID control principle. Then the designed vibration picking manipulator control system was simulated under no-load, 5 s load conditions, and load start conditions with MATLAB/Simulink, a general PID controller and a fuzzy RBF neural network PID controller (FRBFNN PID controller) were used to contrast with it. The results show that the general PID controller has a slow response speed and poor robustness, while fuzzy neural network PID controllers (including FWNN PID controller and FRBFNN PID controller) have a fast response speed and strong robustness, which can well meet the requirements of a specific vibration frequency. Finally, a field test was carried out. The results show that the FWNN PID control is better than the FRBFNN PID control. Furthermore, the FWNN PID controller obviously reduced the drop rate of camellia flowers within 6% while ensuring the picking efficiency above 90%, which can well meet the needs of the camellia fruit picking operation

Electrolyte Optimization to Improve the High-Voltage Operation of Single-Crystal LiNi0.83_{0.83}Co0.11_{0.11}Mn0.06_{0.06}O2_2 in Lithium-Ion Batteries

Single-crystal Ni-rich layered oxide materials LiNi$_{1−x−y}$Co$_x$Mn$_y$O$_2$ (NCM, 1 – x − y ≥ 0.6) are emerging as promising cathode materials that do not show intergranular cracks as a result of the lack of grain boundaries and anisotropy of the bulk structure, enabling extended cyclability in lithium-ion batteries (LIBs) operating at high voltage. However, SC-NCM materials still suffer from capacity fading upon extended cycling. This degradation of capacity can be attributed to a reconstruction of the surface. A phase transformation from layered structures to disordered spinel/rock-salt structures was found to be responsible for impedance growth and capacity loss. Film-forming additives are a straightforward approach for the mitigation of surface reconstruction via the formation of a robust protection layer at the cathode’s surface. In this work, we investigate various additives on the electrochemical performance of single-crystal LiNi$_{0.83}$Co$_{0.11}$Mn$_{0.06}$O$_2$ (SC-NCM83). The results demonstrate that the use of 1% lithium difluoroxalate borate (LiDFOB) and 1% lithium difluorophosphate (LiPO$_2$F$_2$) additives substantially enhanced the cycling performance (with a capacity retention of 93.6% after 150 cycles) and rate capability in comparison to the baseline electrolyte (72.7%) as well as electrolytes using 1% LiDFOB (90.5%) or 1% LiPO$_2$F$_2$ (88.3%) individually. The superior cycling stability of the cell using the combination of both additives was attributed to the formation of a conformal cathode/electrolyte interface (CEI) layer, resulting in a stabilized bulk structure and decreased impedance upon long-term cycling, as evidenced via a combination of state-of-the-art analytical techniques

Lipid exchange promotes fusion of model protocells

Vesicle fusion is an important process underlying cell division, transport, and membrane trafficking. In phospholipid systems, a range of fusogens including divalent cations and depletants have been shown to induce adhesion, hemifusion, and then full content fusion between vesicles. This works shows that these fusogens do not perform the same function for fatty acid vesicles, which are used as model protocells (primitive cells). Even when fatty acid vesicles appear adhered or hemifused to each other, the intervening barriers between vesicles do not rupture. This difference is likely because fatty acids have a single aliphatic tail, and are more dynamic than their phospholipid counterparts. To address this, we postulate that fusion could instead occur under conditions, such as lipid exchange, that disrupt lipid packing. Using both experiments and molecular dynamics simulations, we verify that fusion in fatty acid systems can indeed be induced by lipid exchange. These results begin to probe how membrane biophysics could constrain the evolutionary dynamics of protocells.Comment: 15 pages, 7 figure

Improved fuzzy neural network control for the clamping force of Camellia fruit picking manipulator

During the operation of the vibrating mechanism, the push-shaking camellia fruit picking manipulator needs to ensure a constant force output of the clamping hydraulic motor in order to make sure that the camellia fruit tree trunk wouldn't loosen or damage, which may affect its later growth, during the picking process. In this regard, this paper derived the state space model of the valve-controlled clamping hydraulic motor system of the push-shaking camellia fruit picking manipulator, and the fuzzy wavelet neural network (FWNN) was designed on the basis of the traditional incremental PID control principle and the parameters of the neural network were optimized by the improved grey wolf optimizer (GWO). And then, the control system was simulated with the MATLAB/Simulink software without and with external interference, and compared and analyzed it with traditional PID controller and fuzzy PID (FPID) controller. The results show that the traditional PID controller and the FPID control have slow response and poor robustness, while the improved fuzzy wavelet neural network PID (IFWNN PID) controller possesses the characteristics of fast response and strong robustness, which can well meet the requirement of the constant clamping force of hydraulic motors. Finally, the field clamping test was carried out on the picking manipulator. The results show that the manipulator controlled by the IFWNN PID controller shortens the clamping time by 20.0% and reduces the clamping damage by 13.6% compared with the PID controller, which is verified that the designed controller can meet the clamping operation requirements of the camellia fruit picking machine

Critical review on the thermal conductivity modelling of silica aerogel composites

As a new generation of thermal insulation materials, the effective thermal conductivity of aerogel and its composites is extremely low. The nanoporous structure of aerogels demobilises the movement of gas molecules, and the nano-skeleton system restricts solid heat transfer because of the size effect. Numerous research and modelling works have been carried out to understand and predict heat transfers. This review thoroughly discusses the existing theories and models of silica aerogel composites in gas, solid and radiative heat transfers. It investigates the correlation of the pore size distribution and solid skeleton network of the composites with the thermal conductivity. The review then assesses the advances of the development and questions remaining for further development, including 1) some unexplainable performance of existing models and 2) improvements required for gas and solid thermal conductivity models. Bridging the identified research gaps shall lead researchers to understand existing models better, develop a more accurate model based on more realistic microstructure simulation and further innovate the models for other emerging composites

Physical activity and weight loss among adults with type 2 diabetes and overweight or obesity: a post hoc analysis of the Look AHEAD trial

Importance: Prior findings from the Look AHEAD trial showed no significant reduction in the risk of cardiovascular events by lifestyle-induced weight loss among individuals with type 2 diabetes (T2D) and overweight or obesity. However, physical activity (PA) may modify the changes in cardiovascular risk associated with weight loss. Objective: To examine the joint association of weight loss and PA with the risk of adverse cardiovascular events in patients with T2D and overweight or obesity. Design, Setting, and Participants: This cohort study was a post hoc analysis of the Look AHEAD randomized clinical trial, which compared the cardiovascular effects of weight loss by intensive lifestyle intervention vs diabetes support and education among individuals with T2D and overweight or obesity. The study was conducted from June 2001 to September 2012, and participants were patients in the substudy of accelerometry-measured PA from 8 locations in the United States. Data were analyzed from June to August 2023. Exposures: Body weight change and accelerometer-derived PA volume across the first 4 years. Main Outcomes and Measures: The primary outcome was a composite cardiovascular outcome including cardiovascular death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for angina. Results: Among a total of 1229 participants (mean [SD] age, 60 [7] years; 533 male [43%]), 333 (27%) achieved and maintained weight loss for the first 4 years. Among the individuals who maintained weight loss, 105 (32%) maintained high PA volume. During a median of 9.5 years of follow-up, 198 participants (16.1%) experienced the primary outcome. Compared with those with low PA volume and no weight loss (105 [15.8%]), maintaining high PA volume and weight loss was associated with a 61% lower risk of the primary end point (hazard ratio, 0.39; 95% CI, 0.19-0.81; P = .01). However, there was no significant difference in the risk of the primary end point among those with either weight loss only or high PA only. The multiplicative interaction between weight loss and PA for the risk of cardiovascular events was also significant (P for interaction = .01). Conclusions and Relevance: In this cohort study, maintaining weight loss and higher PA volume was associated with a lower risk of the composite cardiovascular outcome. The findings suggest that the cardiovascular benefits of PA may vary and be enhanced by weight loss among individuals with T2D and overweight or obesity

Magnetic resonance imaging based deep-learning model: a rapid, high-performance, automated tool for testicular volume measurements

BackgroundTesticular volume (TV) is an essential parameter for monitoring testicular functions and pathologies. Nevertheless, current measurement tools, including orchidometers and ultrasonography, encounter challenges in obtaining accurate and personalized TV measurements.PurposeBased on magnetic resonance imaging (MRI), this study aimed to establish a deep learning model and evaluate its efficacy in segmenting the testes and measuring TV.Materials and methodsThe study cohort consisted of retrospectively collected patient data (N = 200) and a prospectively collected dataset comprising 10 healthy volunteers. The retrospective dataset was divided into training and independent validation sets, with an 8:2 random distribution. Each of the 10 healthy volunteers underwent 5 scans (forming the testing dataset) to evaluate the measurement reproducibility. A ResUNet algorithm was applied to segment the testes. Volume of each testis was calculated by multiplying the voxel volume by the number of voxels. Manually determined masks by experts were used as ground truth to assess the performance of the deep learning model.ResultsThe deep learning model achieved a mean Dice score of 0.926 ± 0.034 (0.921 ± 0.026 for the left testis and 0.926 ± 0.034 for the right testis) in the validation cohort and a mean Dice score of 0.922 ± 0.02 (0.931 ± 0.019 for the left testis and 0.932 ± 0.022 for the right testis) in the testing cohort. There was strong correlation between the manual and automated TV (R2 ranging from 0.974 to 0.987 in the validation cohort; R2 ranging from 0.936 to 0.973 in the testing cohort). The volume differences between the manual and automated measurements were 0.838 ± 0.991 (0.209 ± 0.665 for LTV and 0.630 ± 0.728 for RTV) in the validation cohort and 0.815 ± 0.824 (0.303 ± 0.664 for LTV and 0.511 ± 0.444 for RTV) in the testing cohort. Additionally, the deep-learning model exhibited excellent reproducibility (intraclass correlation >0.9) in determining TV.ConclusionThe MRI-based deep learning model is an accurate and reliable tool for measuring TV

Drivers of cropland abandonment in mountainous areas: A household decision model on farming scale and a case study of Southwest China

Cropland abandonment has emerged as a prevalent phenomenon in the mountainous areas of China.While there is a general understanding that this new trend is driven by the rising opportunity cost of rural labor, rigorous theoretical and empirical analyses are largely absent. This paper first develops a theoretical model to investigate household decisions on farming scale when off-farm labor market is accessible and there is heterogeneity of farmland productivity and distribution. The model is capable of explaining the hidden reasons of cropland abandonment in sloping and agriculturally less-favored locations. The model also unveils the impacts of heterogeneity of household labor on fallow decisions and the efficiency loss due to an imperfect labor market. The model is empirically tested by applying the Probit and Logit estimators to a unique household and land-plot survey dataset which contains 5258 plots of599 rural households in Chongqing, a provincial level municipality, in Southwest China. The survey shows that more than 30% of the sample plots have been abandoned, mainly since 1992. The econometric results are consistent with our theoretical expectations. This work would help policy-makers and stakeholders to identify areas with a high probability of land abandonment and farming practice which is less sustainable in the mountainous areas