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Mounting studies suggest that working memory (WM) plays a crucial role in language prediction, but how varying types of WM loads influence language prediction remains unclear. This study investigated whether verbal and visual WM loads differentially impact language predictions during speech comprehension. Using a dual-task paradigm combined with eye-tracking in a visual world setting, we asked 48 participants to complete a sentence comprehension task under concurrent WM load conditions. Participants were divided into two groups, one of which performed a visual dots memory task and the other completed a visual words memory task, with memory load being applied in half of the trials. Results revealed anticipatory gaze towards target objects, suggesting the prediction of upcoming linguistic information. Notably, early fixations during the tonal cue window indicated tonal prediction in spoken sentence processing. Furthermore, WM load significantly disrupted participants’ language prediction effects, highlighting the involvement of working memory resources in this process. Importantly, the verbal memory task imposed a more severe disruption to language prediction than the visual memory task, suggesting differential roles of WM subtypes in linguistic prediction. This offers novel insights into how verbal WM and visual-spatial WM differentially influence predictive language processing.

In order to explore the influencing factors of college students' love view, this study used love questionnaire, adult attachment scale and interpersonal trust scale to investigate 790 college students. It was found that the adult attachment is dependent love view and interpersonal trust. There is a linear correlation between them; for girls, interpersonal trust and love are also linearly related, but not for boys; for girls, interpersonal trust depends on the closeness dimension, anxiety dimension and love concept of adult attachment .There is an intermediary role between them; for boys, interpersonal trust does not have an intermediary role. In summary, there is a gender difference in the mediating effect of interpersonal trust, that is, gender has a moderating effect. The results of this study provide a certain theoretical support for better exploring the influencing factors and mechanisms of the concept of love from the perspective of growth factors.

Multimodal sentiment analysis aims to uncover human affective states by integrating data from multiple sensory sources. However, previous studies have focused on optimizing model architecture, neglecting the impact of objective function settings on model performance. Given this, this study introduces a new framework, DMMSA, which utilizes the intrinsic correlation of sentiment signals and enhances the model’s understanding of complex sentiments. DMMSA incorporates coarse-grained sentiment analysis to reduce task complexity. Meanwhile, it embeds a contrastive learning mechanism within the modality, which decomposes unimodal features into similar and dissimilar ones, thus allowing for the simultaneous consideration of both unimodal and multimodal emotions. We tested DMMSA on the CH-SIMS, MOSI, and MOEI datasets. When only changing the optimization objectives, DMMSA achieved accuracy gains of 3.2%, 1.57%, and 1.95% over the baseline in five-class and seven-class classification tasks. In regression tasks, DMMSA reduced the Mean Absolute Error (MAE) by 1.46%, 1.5%, and 2.8% compared to the baseline.

In this work, an integrated numerical model combining combustion and fluid heating was established for a 660 MW coal-fired boiler. To validate the accuracy of the developed model, related experiment was also conducted, and shows that the relative errors (REs) of several predicted parameters on both flue-gas side and water-steam side of the boiler are less than 7 %, which meets the requirement of analysis. The results indicate that the effect of SOFA tilt angle on gas temperature deviation (GTD) is greatest, and that of SOFA’s proportion follows; while GTD presents a smallest sensitivity to the concentration of O2. This is because O2 concentration mainly affects the average value of the gas temperature, but barely changes its distribution. By contrast, altering SOFA’s tilt angle and its proportion directly varies the shape, location, and area of the high-temperature and low-temperature regions at the entrance profile of the horizontal crossover pass, so these two factors play a more important role in the variation of GTD.

The unmanned aerial vehicle (UAV) enabled mobile edge computing (MEC) system is attracting a lot of attentions for the potential of low latency and low transmission energy consumption, due to the advantages of high mobility and easy deployment. It has been widely applied to provide communication and computing services, especially in Internet of Things (IoT). However, there are still some challenges in the UAV-enabled MEC system. Firstly, the endurance of the UAV is limited and further impacts the performance of the system. Secondly, mobile devices are battery-powered and the batteries of some devices are hard to change. Therefore, in this paper, a UAV-enabled MEC system in which the UAV is empowered to have computing capability and provides tasks offloading service is studied. The total energy consumption of the UAV-enabled system, which includes the energy consumption of the UAV and the energy consumption of the ground users, is minimized under the constraints of the UAV’s energy budget, the number of each task’s bits, the causality of the data and the velocity of the UAV. The bits allocation of uploading data, computing data, downloading data and the trajectory of the UAV are jointly optimized with the goal of minimizing the total energy consumption. Moreover, a two-stage alternating algorithm is proposed to solve the non-convex formulated problem. Finally, the simulation results show the superiority of the proposed scheme compared with other benchmark schemes. Finally, the performance of the proposed scheme is demonstrated under different settings.

Triple negative breast cancer (TNBC) is the most aggressive cancer in women, and despite improved treatments, it remains a major cause of morbidity and mortality. We and others have demonstrated that different hybrid compounds targeting PARP/MAPK or other pathways to inhibit cancer progression may lead to promising therapeutic results. We introduced fluorine to alter the physical properties of the compounds. TSC-3C was one of the generated compounds. Upon treatment with TSC-3C, MDA-MB-231 cell proliferation, invasion, and migration were inhibited. TSC-3C induced MDA-MB-231 cell mitochondrial dysfunction and apoptosis, which may be caused by reducing the level of phosphorylated p44/42 MAPK (ERK1/2) and increasing the level of p-JNK. The present study may help to elucidate the role of the MAPK pathway in the development of breast cancer and may promote further research on halogenated heterocyclic compounds for the treatment of breast cancer.

Src homology 2 domain-containing protein tyrosine phosphatase 2 (SHP2) is a non-receptor protein tyrosine phosphatase (PTP) encoded by the PTPN11 gene, which is involved in the RAS/MAPK cell signaling transduction process. SHP2 has been shown to contribute to the progression of various cancers and is emerging as an important target for anti-tumor drug research. However, past efforts to develop SHP2 inhibitors into drugs have been unsuccessful owing to the positively charged nature of the active site pocket tending to bind negatively charged groups that are usually non-drug-like. Here, a series of uncharged pyrazoline derivatives were designed and developed as new SHP2 inhibitors using a structure-based strategy. Compound 4o, which exhibited the strongest SHP2 inhibitory activity, bound directly to the catalytic domain of SHP2 in a competitive manner through multiple hydrogen bonds. Compound 4o affected the RAS/MAPK signaling pathway by inhibiting SHP2, and subsequently induced apoptosis and growth inhibition of HCT116 cells in vitro and in vivo. Notably, the oral administration of compound 4o in large doses showed no obvious toxicity. In summary, our findings provide a basis for the further development of compound 4o as a safe, effective and anti-tumor SHP2 inhibitor.

Automatically generating scientific literature surveys is a valuable task that can significantly enhance research efficiency. However, the diverse and complex nature of information within a literature survey poses substantial challenges for generative models. In this paper, we design a series of prompts to systematically leverage large language models (LLMs), enabling the creation of comprehensive literature surveys through a step-by-step approach. Specifically, we design prompts to guide LLMs to sequentially generate the title, abstract, hierarchical headings, and the main content of the literature survey. We argue that this design enables the generation of the headings from a high-level perspective. During the content generation process, this design effectively harnesses relevant information while minimizing costs by restricting the length of both input and output content in LLM queries. Our implementation with Qwen-long achieved third place in the NLPCC 2024 Scientific Literature Survey Generation evaluation task, with an overall score only 0.03% lower than the second-place team. Additionally, our soft heading recall is 95.84%, the second best among the submissions. Thanks to the efficient prompt design and the low cost of the Qwen-long API, our method reduces the expense for generating each literature survey to 0.1 RMB, enhancing the practical value of our method.

Large language models (LLMs) have revolutionized natural language processing by achieving state-of-the-art performance across various tasks. Recently, their effectiveness as embedding models has gained attention, marking a paradigm shift from traditional encoder-only models like ELMo and BERT to decoder-only, large-scale LLMs such as GPT, LLaMA, and Mistral. This survey provides an in-depth overview of this transition, beginning with foundational techniques before the LLM era, followed by LLM-based embedding models through two main strategies to derive embeddings from LLMs. 1) Direct prompting: We mainly discuss the prompt designs and the underlying rationale for deriving competitive embeddings. 2) Data-centric tuning: We cover extensive aspects that affect tuning an embedding model, including model architecture, training objectives, data constructions, etc. Upon the above, we also cover advanced methods for producing embeddings from longer texts, multilingual, code, cross-modal data, as well as reasoning-aware and other domain-specific scenarios. Furthermore, we discuss factors affecting choices of embedding models, such as performance/efficiency comparisons, dense vs sparse embeddings, pooling strategies, and scaling law. Lastly, the survey highlights the limitations and challenges in adapting LLMs for embeddings, including cross-task embedding quality, trade-offs between efficiency and accuracy, low-resource, long-context, data bias, robustness, etc. This survey serves as a valuable resource for researchers and practitioners by synthesizing current advancements, highlighting key challenges, and offering a comprehensive framework for future work aimed at enhancing the effectiveness and efficiency of LLMs as embedding models.

This paper studies the problem of detecting novel or unexpected instances in text classification. In traditional text classification, the classes appeared in testing must have been seen in training. However, in many applications, this is not the case because in testing, we may see unexpected instances that are not from any of the training classes. In this paper, we propose a significantly more effective approach that converts the original problem to a pair-wise matching problem and then outputs how probable two instances belong to the same class. Under this approach, we present two models. The more effective model uses two embedding matrices of a pair of instances as two channels of a CNN. The output probabilities from such pairs are used to judge whether a test instance is from a seen class or is novel/unexpected. Experimental results show that the proposed method substantially outperforms the state-of-the-art baselines.