166 research outputs found
The effect of “gender fit” on fitness app engagement
The effectiveness of fitness app in health promotion success has been observed and may greatly contribute to large health improvements of the public. However, the actual engagement with these interventions is unexpectedly low. Fitness app developers design Behavior change techniques (BCTs) to enhance user engagement with fitness apps. Although several studies have examined the effectiveness of some BCTs in encouraging user engagement with Internet-based applications in general, investigations remain underspecified. Based on the theory of psychological fit, we focus on the gender boundary condition of the effectiveness of BCTs on user engagement with fitness apps. The purpose of this research is twofold. First, we aim to explore gender differences in preferences for BCTs. The second purpose is to investigate whether there exists “gender fit” effect on user engagement of fitness apps with different BCTs
A Knowledge-Driven Cross-view Contrastive Learning for EEG Representation
Due to the abundant neurophysiological information in the
electroencephalogram (EEG) signal, EEG signals integrated with deep learning
methods have gained substantial traction across numerous real-world tasks.
However, the development of supervised learning methods based on EEG signals
has been hindered by the high cost and significant label discrepancies to
manually label large-scale EEG datasets. Self-supervised frameworks are adopted
in vision and language fields to solve this issue, but the lack of EEG-specific
theoretical foundations hampers their applicability across various tasks. To
solve these challenges, this paper proposes a knowledge-driven cross-view
contrastive learning framework (KDC2), which integrates neurological theory to
extract effective representations from EEG with limited labels. The KDC2 method
creates scalp and neural views of EEG signals, simulating the internal and
external representation of brain activity. Sequentially, inter-view and
cross-view contrastive learning pipelines in combination with various
augmentation methods are applied to capture neural features from different
views. By modeling prior neural knowledge based on homologous neural
information consistency theory, the proposed method extracts invariant and
complementary neural knowledge to generate combined representations.
Experimental results on different downstream tasks demonstrate that our method
outperforms state-of-the-art methods, highlighting the superior generalization
of neural knowledge-supported EEG representations across various brain tasks.Comment: 14pages,7 figure
Strain prioritization and genome mining for enediyne natural products
The enediyne family of natural products has had a profound impact on modern chemistry, biology, and medicine, and yet only 11 enediynes have been structurally characterized to date. Here we report a genome survey of 3,400 actinomycetes, identifying 81 strains that harbor genes encoding the enediyne polyketide synthase cassettes that could be grouped into 28 distinct clades based on phylogenetic analysis. Genome sequencing of 31 representative strains confirmed that each clade harbors a distinct enediyne biosynthetic gene cluster. A genome neighborhood network allows prediction of new structural features and biosynthetic insights that could be exploited for enediyne discovery. We confirmed one clade as new C-1027 producers, with a significantly higher C-1027 titer than the original producer, and discovered a new family of enediyne natural products, the tiancimycins (TNMs), that exhibit potent cytotoxicity against a broad spectrum of cancer cell lines. Our results demonstrate the feasibility of rapid discovery of new enediynes from a large strain collection.
IMPORTANCE Recent advances in microbial genomics clearly revealed that the biosynthetic potential of soil actinomycetes to produce enediynes is underappreciated. A great challenge is to develop innovative methods to discover new enediynes and produce them in sufficient quantities for chemical, biological, and clinical investigations. This work demonstrated the feasibility of rapid discovery of new enediynes from a large strain collection. The new C-1027 producers, with a significantly higher C-1027 titer than the original producer, will impact the practical supply of this important drug lead. The TNMs, with their extremely potent cytotoxicity against various cancer cells and their rapid and complete cancer cell killing characteristics, in comparison with the payloads used in FDA-approved antibody-drug conjugates (ADCs), are poised to be exploited as payload candidates for the next generation of anticancer ADCs. Follow-up studies on the other identified hits promise the discovery of new enediynes, radically expanding the chemical space for the enediyne family
Geodesics in the Engel group with a sub-Lorentzian metric *
Abstract: Let E be the Engel group and D be a rank 2 bracket generating left invariant distribution with a Lorentzian metric, which is a nondegenerate metric of index 1. In this paper, we first study some properties of horizontal curves on E. Second, we prove that time-like normal geodesics are locally maximizers in the Engel group, and calculate the explicit expression of non-space-like geodesics
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
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