The Relationship Between Parental Attachment and Mobile Phone Dependence Among Chinese Rural Adolescents: The Role of Alexithymia and Mindfulness

Mobile phone has experienced a significant increase in popularity among adolescents in recent years. Findings indicate dependence on mobile phone is related to poor parent-child relationship. However, previous research on mobile phone dependence (MPD) is scant and mainly focus on adult samples. In this view, the present study investigated the association between parental attachment and MPD as well as its influence mechanism, in sample of adolescents in rural China. Data were collected from three middle schools in rural areas of Jiangxi and Hubei Province (N = 693, 46.46% female, Mage = 14.88, SD = 1.77). Participants completed the Inventory of Parent and Peer Attachment (IPPA), the twenty-item Toronto alexithymia scale (TAS-20), the Mindful Attention Awareness Scale (MAAS) and the Mobile Phone Addiction Index Scale (MPAI). Among the results, parental attachment negatively predicted MPD and alexithymia were exerting partial mediation effect between parental attachment and MPD. Further, mindfulness acted as moderator of the relationship between alexithymia and MPD: The negative impact of alexithymia on MPD was weakened under the condition of high level of mindfulness. Knowledge of this mechanism could be useful for understanding adolescents’ MPD in terms of the interaction of multiple factors

Molecular Imaging in Tracking Tumor Stem-Like Cells

Cancer remains a major public health problem in many countries. It was found to contain a subset of cancer stem cells (CSCs) that are capable of proliferation and self-renewal, and differentiation into various types of cancer cells. CSCs often display characteristics of chemotherapy resistance and radiotherapy resistance. Numerous putative biomarkers of CSCs are currently identified including CD133, CD44, CD24, ALDH (aldehyde dehydrogenase), and ABCG2. Interestingly, no single marker is exclusively expressed by CSCs. Thus, the various combinations of different biomarkers will be possible to identify CSCs, and considerable work is being done to recognize new ones. In order to demonstrate the mechanisms of resistance and response to therapy and predict the outcome as well as prognosis, the ways to track and identify CSCs will be extremely important. The technologies of molecular imaging will reveal mechanisms of cancer progression and provide visual targets for novel therapeutics. Limited studies were investigated on the detection of various types of CSCs by molecular imaging. Although the tracking of circulating CSCs is still hampered by technological challenges, personalized diagnosis and therapies of cancers are expected to be established based on increased understanding of molecular imaging of cancer stem-like cells biomarkers

HTP: Exploiting Holistic Temporal Patterns for Sequential Recommendation

Sequential recommender systems have demonstrated a huge success for next-item recommendation by explicitly exploiting the temporal order of users' historical interactions. In practice, user interactions contain more useful temporal information beyond order, as shown by some pioneering studies. In this paper, we systematically investigate various temporal information for sequential recommendation and identify three types of advantageous temporal patterns beyond order, including absolute time information, relative item time intervals and relative recommendation time intervals. We are the first to explore item-oriented absolute time patterns. While existing models consider only one or two of these three patterns, we propose a novel holistic temporal pattern based neural network, named HTP, to fully leverage all these three patterns. In particular, we introduce novel components to address the subtle correlations between relative item time intervals and relative recommendation time intervals, which render a major technical challenge. Extensive experiments on three real-world benchmark datasets show that our HTP model consistently and substantially outperforms many state-of-the-art models. Our code is publically available at https://github.com/623851394/HTP/tree/main/HTP-mai

SmartCiteCon: Implicit Citation Context Extraction from Academic Literature Using Unsupervised Learning

We introduce SmartCiteCon (SCC), a Java API for extracting both explicit and implicit citation context from academic literature in English. The tool is built on a Support Vector Machine (SVM) model trained on a set of 7,058 manually annotated citation context sentences, curated from 34,000 papers in the ACL Anthology. The model with 19 features achieves F1=85.6%. SCC supports PDF, XML, and JSON files out-of-box, provided that they are conformed to certain schemas. The API supports single document processing and batch processing in parallel. It takes about 12–45 seconds on average depending on the format to process a document on a dedicated server with 6 multithreaded cores. Using SCC, we extracted 11.8 million citation context sentences from ∼33.3k PMC papers in the CORD19 dataset, released on June 13, 2020. The source code is released at https://gitee.com/irlab/SmartCiteCon

Remote creation of strong and coherent emissions in air with two-color ultrafast laser pulses

We experimentally demonstrate generation of strong narrow-bandwidth emissions with excellent coherent properties at ~391 nm and ~428 nm from molecular ions of nitrogen inside a femtosecond filament in air by an orthogonally polarized two-color driver field (i. e., 800 nm laser pulse and its second harmonic). The durations of the coherent emissions at 391 nm and 428 nm are measured to be ~2.4 ps and ~7.8 ps respectively, both of which are much longer than the duration of the pump and its second harmonic pulses. Furthermore, the measured temporal decay characteristics of the excited molecular systems suggest an "instantaneous" population inversion mechanism that may be achieved in molecular nitrogen ions at an ultrafast time scale comparable to the 800 nm pump pulse.Comment: 19 pages, 4 figure

Impulsive rotational Raman scattering of N2 by a remote "air laser" in femtosecond laser filament

We report on experimental realization of impulsive rotational Raman scattering from neutral nitrogen molecules in a femtosecond laser filament using an intense self-induced white-light seeding "air laser" generated during the filamentation of an 800 nm Ti: Sapphire laser in nitrogen gas. The impulsive rotational Raman fingerprint signals are observed with a maximum conversion efficiency of ~0.8%. Our observation provides a promising way of remote identification and location of chemical species in atmosphere by rotational Raman scattering of molecules.Comment: 4 pages, 4 figure

Real-time observation of dynamics in rotational molecular wave packets by use of "air laser" spectroscopy

Molecular rotational spectroscopy based on strong-field-ionization-induced nitrogen laser is employed to investigate the time evolution of the rotational wave packet composed by a coherent superposition of quantum rotational states created in a field-free molecular alignment. We show that this technique uniquely allows real-time observation of the ultrafast dynamics of the individual rotational states in the rotational wavepacket. Our analysis also shows that there exist two channels of generation of the nitrogen laser, shedding new light on the population inversion mechanism behind the air laser generated by intense femtosecond laser pulses.Comment: 23 pages, 6 figure

Self-induced white-light seeding laser in a femtosecond laser filament

We report, for what we believe to be the first time, on the generation of remote self-seeding laser amplification by using only one 800 nm Ti:Sapphire femtosecond laser pulse. The laser pulse (~ 40 fs) is first used to generate a filament either in pure nitrogen or in ambient air in which population inversion between ground and excited states of nitrogen molecular ions is realized. Self-induced white light inside the filament is then serving as the seed to be amplified. The self-induced narrow-band laser at 428 nm has a pulse duration of ~2.6 ps with perfect linear polarization property. This finding opens new possibilities for remote detection in the atmosphere.Comment: 18 pages, 5 figure

MetaTool Benchmark for Large Language Models: Deciding Whether to Use Tools and Which to Use

Large language models (LLMs) have garnered significant attention due to their impressive natural language processing (NLP) capabilities. Recently, many studies have focused on the tool utilization ability of LLMs. They primarily investigated how LLMs effectively collaborate with given specific tools. However, in scenarios where LLMs serve as intelligent agents, as seen in applications like AutoGPT and MetaGPT, LLMs are expected to engage in intricate decision-making processes that involve deciding whether to employ a tool and selecting the most suitable tool(s) from a collection of available tools to fulfill user requests. Therefore, in this paper, we introduce MetaTool, a benchmark designed to evaluate whether LLMs have tool usage awareness and can correctly choose tools. Specifically, we create a dataset called ToolE within the benchmark. This dataset contains various types of user queries in the form of prompts that trigger LLMs to use tools, including both single-tool and multi-tool scenarios. Subsequently, we set the tasks for both tool usage awareness and tool selection. We define four subtasks from different perspectives in tool selection, including tool selection with similar choices, tool selection in specific scenarios, tool selection with possible reliability issues, and multi-tool selection. We conduct experiments involving nine popular LLMs and find that the majority of them still struggle to effectively select tools, highlighting the existing gaps between LLMs and genuine intelligent agents. However, through the error analysis, we found there is still significant room for improvement. Finally, we conclude with insights for tool developers that follow ChatGPT to provide detailed descriptions that can enhance the tool selection performance of LLMs