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

Automating Catheterization Labs with Real-Time Perception

For decades, three-dimensional C-arm Cone-Beam Computed Tomography (CBCT) imaging system has been a critical component for complex vascular and nonvascular interventional procedures. While it can significantly improve multiplanar soft tissue imaging and provide pre-treatment target lesion roadmapping and guidance, the traditional workflow can be cumbersome and time-consuming, especially for less experienced users. To streamline this process and enhance procedural efficiency overall, we proposed a visual perception system, namely AutoCBCT, seamlessly integrated with an angiography suite. This system dynamically models both the patient's body and the surgical environment in real-time. AutoCBCT enables a novel workflow with automated positioning, navigation and simulated test-runs, eliminating the need for manual operations and interactions. The proposed system has been successfully deployed and studied in both lab and clinical settings, demonstrating significantly improved workflow efficiency

Self-learning Canonical Space for Multi-view 3D Human Pose Estimation

Multi-view 3D human pose estimation is naturally superior to single view one, benefiting from more comprehensive information provided by images of multiple views. The information includes camera poses, 2D/3D human poses, and 3D geometry. However, the accurate annotation of these information is hard to obtain, making it challenging to predict accurate 3D human pose from multi-view images. To deal with this issue, we propose a fully self-supervised framework, named cascaded multi-view aggregating network (CMANet), to construct a canonical parameter space to holistically integrate and exploit multi-view information. In our framework, the multi-view information is grouped into two categories: 1) intra-view information , 2) inter-view information. Accordingly, CMANet consists of two components: intra-view module (IRV) and inter-view module (IEV). IRV is used for extracting initial camera pose and 3D human pose of each view; IEV is to fuse complementary pose information and cross-view 3D geometry for a final 3D human pose. To facilitate the aggregation of the intra- and inter-view, we define a canonical parameter space, depicted by per-view camera pose and human pose and shape parameters ($\theta$ and $\beta$) of SMPL model, and propose a two-stage learning procedure. At first stage, IRV learns to estimate camera pose and view-dependent 3D human pose supervised by confident output of an off-the-shelf 2D keypoint detector. At second stage, IRV is frozen and IEV further refines the camera pose and optimizes the 3D human pose by implicitly encoding the cross-view complement and 3D geometry constraint, achieved by jointly fitting predicted multi-view 2D keypoints. The proposed framework, modules, and learning strategy are demonstrated to be effective by comprehensive experiments and CMANet is superior to state-of-the-art methods in extensive quantitative and qualitative analysis

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

Position-Aware Parameter Efficient Fine-Tuning Approach for Reducing Positional Bias in LLMs

Recent advances in large language models (LLMs) have enhanced their ability to process long input contexts. This development is particularly crucial for tasks that involve retrieving knowledge from an external datastore, which can result in long inputs. However, recent studies show a positional bias in LLMs, demonstrating varying performance depending on the location of useful information within the input sequence. In this study, we conduct extensive experiments to investigate the root causes of positional bias. Our findings indicate that the primary contributor to LLM positional bias stems from the inherent positional preferences of different models. We demonstrate that merely employing prompt-based solutions is inadequate for overcoming the positional preferences. To address this positional bias issue of a pre-trained LLM, we developed a Position-Aware Parameter Efficient Fine-Tuning (PAPEFT) approach which is composed of a data augmentation technique and a parameter efficient adapter, enhancing a uniform attention distribution across the input context. Our experiments demonstrate that the proposed approach effectively reduces positional bias, improving LLMs' effectiveness in handling long context sequences for various tasks that require externally retrieved knowledge

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

Differences between Chronically Hepatitis B Virus-Infected Pregnant Women with and without Intrafamilial Infection: From Viral Gene Sequences to Clinical Manifestations

Introduction: This study aimed to investigate the differences between pregnant women with chronic hepatitis B virus (HBV) infection and intrafamilial infection and those without intrafamilial infection. Methods: HBV-DNA was extracted from the sera of 16 pregnant women with chronic hepatitis B (CHB) and their family members for gene sequencing and phylogenetic analyses. A total of 74 pregnant women with CHB were followed up from the second trimester to 3 months postpartum. Viral markers and other laboratory indicators were compared between pregnant women with CHB with and without intrafamilial infection. Results: The phylogenetic tree showed that HBV lines in the mother-spread pedigree shared a node, whereas there was an unrelated genetic background for HBV lines in individuals without intrafamilial infection. From delivery to 3 months postpartum, compared with those without intrafamilial infection, pregnant women with intrafamilial infection were related negatively to HBV-DNA (β = −0.43, 95% confidence interval [CI]: −0.76 to −0.12, p = 0.009), HBeAg (β = −195.15, 95% CI: −366.35 to −23.96, p = 0.027), and hemoglobin changes (β = −8.09, 95% CI: −15.54 to −0.64, p = 0.035) and positively to changes in the levels of alanine aminotransferase (β = 73.9, 95% CI: 38.92–108.95, p < 0.001) and albumin (β = 2.73, 95% CI: 0.23–5.23, p = 0.033). Conclusion: The mother-spread pedigree spread model differs from that of non-intrafamilial infections. Pregnant women with intrafamilial HBV infection have less hepatitis flares and liver damage, but their HBV-DNA and HBeAg levels rebound faster after delivery, than those without intrafamilial infection by the virus