Ξ(1620)\Xi(1620) production in K−pK^- p scattering process

In the present work, the production of $\Xi(1620)$ in the $K^- p$ scattering process is investigated by using an effective Lagrangian approach, where $\Xi(1620)$ is considered as a $\bar{K} \Lambda$ molecular state. Our estimations indicate that the cross sections for $K^-p\to K^+ \Xi(1620)^-$ are $(1.48 ^{+ 1.12}_{-0.69}) \ \mathrm{\mu b}$ at $P_K=2.8 \ \mathrm{GeV}$, where the uncertainties are resulted from the variation of the model parameter. As for the $K^-p\to K^+ \pi^0 \Xi^-$ process, the cross sections are estimated to be $(0.61 ^{+0.47}_{-0.29})\ \mathrm{\mu b}$ at $P_K =2.8 \ \mathrm{GeV}$, which is consistent with the experimental measurements.Comment: 5 pages, 5 figure

Pionic and radiative transitions from Tcsˉ0+(2900)T_{c\bar{s}0}^+(2900) to Ds1+(2460)D_{s1}^+(2460) as a probe of the structure of Ds1+(2460)D_{s1}^+(2460)

In this work, we evaluated the widths of the pionic and radiative transitions from the $T_{c\bar{s}0}^{+}(2900)$ to the $D_{s1}^{+}(2460)$ in the $D_{s1}^{+}(2460)$ molecular frame and the $D_{s1}^{+}(2460)$ charmed-strange meson frame. Our estimations demonstrate that the transition widths in the $D_{s1}^{+}(2460)$ molecular frame are much larger than those in the the $D_{s1}^{+}(2460)$ charmed-strange meson frame. Specifically, the ratio of the widths of $\Gamma(T_{c\bar{s}0}^{+}(2900)\to D_{s1}^{+} \pi^{0})$ and $\Gamma(T_{c\bar{s}0}^{+}(2900)\to D^{+(0)}K^{0(+)})$ is estimated to be around 0.1 in the $D_{s1}^{+}(2460)$ charmed-strange meson frame, whereas the lower limit of this ratio is 0.67 in the $D_{s1}^{+}(2460)$ molecular frame. Thus, the aforementioned ratio could be employed as a tool for testing the nature of the $D_{s1}^{+}(2460)$.Comment: 8 pages, 7 figure

Emotional Intelligence of Large Language Models

Large Language Models (LLMs) have demonstrated remarkable abilities across numerous disciplines, primarily assessed through tasks in language generation, knowledge utilization, and complex reasoning. However, their alignment with human emotions and values, which is critical for real-world applications, has not been systematically evaluated. Here, we assessed LLMs' Emotional Intelligence (EI), encompassing emotion recognition, interpretation, and understanding, which is necessary for effective communication and social interactions. Specifically, we first developed a novel psychometric assessment focusing on Emotion Understanding (EU), a core component of EI, suitable for both humans and LLMs. This test requires evaluating complex emotions (e.g., surprised, joyful, puzzled, proud) in realistic scenarios (e.g., despite feeling underperformed, John surprisingly achieved a top score). With a reference frame constructed from over 500 adults, we tested a variety of mainstream LLMs. Most achieved above-average EQ scores, with GPT-4 exceeding 89% of human participants with an EQ of 117. Interestingly, a multivariate pattern analysis revealed that some LLMs apparently did not reply on the human-like mechanism to achieve human-level performance, as their representational patterns were qualitatively distinct from humans. In addition, we discussed the impact of factors such as model size, training method, and architecture on LLMs' EQ. In summary, our study presents one of the first psychometric evaluations of the human-like characteristics of LLMs, which may shed light on the future development of LLMs aiming for both high intellectual and emotional intelligence. Project website: https://emotional-intelligence.github.io/Comment: 34 pages, 4 figure