Grounding Language Model with Chunking-Free In-Context Retrieval

This paper presents a novel Chunking-Free In-Context (CFIC) retrieval approach, specifically tailored for Retrieval-Augmented Generation (RAG) systems. Traditional RAG systems often struggle with grounding responses using precise evidence text due to the challenges of processing lengthy documents and filtering out irrelevant content. Commonly employed solutions, such as document chunking and adapting language models to handle longer contexts, have their limitations. These methods either disrupt the semantic coherence of the text or fail to effectively address the issues of noise and inaccuracy in evidence retrieval. CFIC addresses these challenges by circumventing the conventional chunking process. It utilizes the encoded hidden states of documents for in-context retrieval, employing auto-aggressive decoding to accurately identify the specific evidence text required for user queries, eliminating the need for chunking. CFIC is further enhanced by incorporating two decoding strategies, namely Constrained Sentence Prefix Decoding and Skip Decoding. These strategies not only improve the efficiency of the retrieval process but also ensure that the fidelity of the generated grounding text evidence is maintained. Our evaluations of CFIC on a range of open QA datasets demonstrate its superiority in retrieving relevant and accurate evidence, offering a significant improvement over traditional methods. By doing away with the need for document chunking, CFIC presents a more streamlined, effective, and efficient retrieval solution, making it a valuable advancement in the field of RAG systems

Effect of a poloxamer 407-based thermosensitive gel on minimization of thermal injury to diaphragm during microwave ablation of the liver.

AIM: To assess the insulating effect of a poloxamer 407 (P407)-based gel during microwave ablation of liver adjacent to the diaphragm. METHODS: We prepared serial dilutions of P407, and 22.5% (w/w) concentration was identified as suitable for ablation procedures. Subsequently, microwave ablations were performed on the livers of 24 rabbits (gel, saline, control groups, n = 8 in each). The P407 solution and 0.9% normal saline were injected into the potential space between the diaphragm and liver in experimental groups. No barriers were applied to the controls. After microwave ablations, the frequency, size and degree of thermal injury were compared histologically among the three groups. Subsequently, another 8 rabbits were injected with the P407 solution and microwave ablation was performed. The levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), blood urea nitrogen (BUN) and creatinine (Cr) in serum were tested at 1 d before microwave ablation and 3 and 7 d after operation. RESULTS: In vivo ablation thermal injury to the adjacent diaphragm was evaluated in the control, saline and 22.5% P407 gel groups (P = 0.001-0.040). However, there was no significant difference in the volume of ablation zone among the three groups (P \u3e 0.05). Moreover, there were no statistical differences among the preoperative and postoperative gel groups according to the levels of ALT, AST, BUN and Cr in serum (all P \u3e 0.05). CONCLUSION: Twenty-two point five percent P407 gel could be a more effective choice during microwave ablation of hepatic tumors adjacent to the diaphragm. Further studies for clinical translation are warranted

MicroRNAs involved in neoplastic transformation of liver cancer stem cells

<p>Abstract</p> <p>Background</p> <p>The existence of cancer stem cells in hepatocellular carcinoma (HCC) has been verified by characterizing side population (SP) cells based on efflux of Hoechst 33342 dye from stem cells. Recent advances in microRNA (miRNA) biology have revealed that miRNAs play an important role in embryonic development and tumorigenesis. However, it is still unclear which miRNAs participate in the neoplastic transformation of liver cancer stem cells (LCSCs) during hepatocarcinogenesis.</p> <p>Methods</p> <p>To identify the unique set of miRNAs differentially regulated in LCSCs, we applied SP sorting to primary cultures of F344 rat HCC cancer cells treated with diethylnitrosamine (DEN) and normal syngenic fetal liver cells, and the stem-like characteristics of SP cells were verified through detecting expression of CD90.1, AFP and CK-7. Global miRNA expression profiles of two groups of SP cells were screened through microarray platform.</p> <p>Results</p> <p>A total of 68 miRNAs, including miR-10b, miR-21, miR-470*, miR-34c-3p, and let-7i*, were identified as overexpressed in SP of HCC cells compared to fetal liver cells. Ten miRNAs were underexpressed, including miR-200a* and miR-148b*. These miRNAs were validated using stem-loop real-time reverse transcriptase polymerase chain reaction (RT-PCR).</p> <p>Conclusions</p> <p>Our results suggest that LCSCs may have a distinct miRNA expression fingerprint during hepatocarcinogenesis. Dissecting these relationships will provide a new understanding of the function of miRNA in the process of neoplastic transformation of LCSCs.</p

Performance Analysis and Optimization of Compressed Air Energy Storage Integrated with Latent Thermal Energy Storage

Recovering compression waste heat using latent thermal energy storage (LTES) is a promising method to enhance the round-trip efficiency of compressed air energy storage (CAES) systems. In this study, a systematic thermodynamic model coupled with a concentric diffusion heat transfer model of the cylindrical packed-bed LTES is established for a CAES system, and the numerical simulation model is validated by experimental data in the reference. Based on the numerical model, the charging–discharging performance of LTES and CAES systems is evaluated under different layouts of phase change materials (PCMs) in LTES, and the optimal layout of PCM is specified as a three-stage layout, since the exergy efficiency of LTES and round-trip efficiency are improved by 8.2% and 6.9% compared with a one-stage layout. Then, the proportion of three PCMs is optimized using response surface methods. The optimization results indicate that the exergy efficiency of LTES and round-trip efficiency of the CAES system are expected to be 80.9% and 73.3% under the PCM proportion of 0.48:0.3:0.22 for three stages, which are 7.0% and 13.1% higher than the original three-stage PCMs with equal proportions

A comparative study of grain quality and physicochemical properties of premium japonica rice from three typical production regions

Grain quality indicates rice commodity value. This research compared grain quality and physicochemical properties of premium japonica rice from three production regions, Yangtze River downstream of China (YRDCN), Northeast region of China (NECN) and Japan. Results showed that there were distinct quality and physicochemical characteristics variance among the three groups of japonica rice, while CVs of most quality parameters from low to high was Japan, YRDCN and NECN. YRDCN rice presented obvious lower apparent amylose content (AAC) and ratio of each chain-length sections of amylopectin, and showed higher protein contents especially glutelin and ratio in short and intermediate amylopectin molecules. Among three rice groups, YRDCN rice presented weaker appearance, whereas did not show inferior cooking and eating properties, which was primarily linked to lower AAC. Rice AAC and starch fine structure significantly correlated with pasting parameters, swelling power and solubility, while protein content had close relation with taste analyzer parameters. Results of this study indicated improvement direction for japonica rice of YRDCN, and also provided reference for consumers’ rice purchasing selection in accordance with individual taste preference

Electrochemically Inert g-C3N4 Promotes Water Oxidation Catalysis

Electrode surface wettability is critically important for heterogeneous electrochemical reactions taking place in aqueous and nonaqueous media. Herein, electrochemically inert g-C 3 N 4 (GCN) is successfully demonstrated to significantly enhance water oxidation by constructing a superhydrophilic catalyst surface and promoting substantial exposure of active sites. As a proof-of-concept application, superhydrophilic GCN/Ni(OH) 2 (GCNN) hybrids with monodispersed Ni(OH) 2 nanoplates strongly anchored on GCN are synthesized for enhanced water oxidation catalysis. Owing to the superhydrophilicity of functionalized GCN, the surface wettability of GCNN (contact angle 0°) is substantially improved as compared with bare Ni(OH) 2 (contact angle 21°). Besides, GCN nanosheets can effectively suppress Ni(OH) 2 aggregation to help expose more active sites. Benefiting from the well-defined catalyst surface, the optimal GCNN hybrid shows significantly enhanced electrochemical performance over bare Ni(OH) 2 nanosheets, although GCN is electrochemically inert. In addition, similar catalytic performance promotion resulting from wettability improvement induced by incorporation of hydrophilic GCN is also successfully demonstrated on Co(OH) 2 . The present results demonstrate that, in addition to developing new catalysts, building efficient surface chemistry is also vital to achieve extraordinary water oxidation performance

Learning Deep Nets for Gravitational Dynamics with Unknown Disturbance through Physical Knowledge Distillation: Initial Feasibility Study

Learning high-performance deep neural networks for dynamic modeling of high Degree-Of-Freedom (DOF) robots remains challenging due to the sampling complexity. Typical unknown system disturbance caused by unmodeled dynamics (such as internal compliance, cables) further exacerbates the problem. In this paper, a novel framework characterized by both high data efficiency and disturbance-adapting capability is proposed to address the problem of modeling gravitational dynamics using deep nets in feedforward gravity compensation control for high-DOF master manipulators with unknown disturbance. In particular, Feedforward Deep Neural Networks (FDNNs) are learned from both prior knowledge of an existing analytical model and observation of the robot system by Knowledge Distillation (KD). Through extensive experiments in high-DOF master manipulators with significant disturbance, we show that our method surpasses a standard Learning-from-Scratch (LfS) approach in terms of data efficiency and disturbance adaptation. Our initial feasibility study has demonstrated the potential of outperforming the analytical teacher model as the training data increases