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Large Language Models (LLMs) have significantly advanced user-bot interactions, enabling more complex and coherent dialogues. However, the prevalent text-only modality might not fully exploit the potential for effective user engagement. This paper explores the impact of multi-modal interactions, which incorporate images and audio alongside text, on user engagement in chatbot conversations. We conduct a comprehensive analysis using a diverse set of chatbots and real-user interaction data, employing metrics such as retention rate and conversation length to evaluate user engagement. Our findings reveal a significant enhancement in user engagement with multi-modal interactions compared to text-only dialogues. Notably, the incorporation of a third modality significantly amplifies engagement beyond the benefits observed with just two modalities. These results suggest that multi-modal interactions optimize cognitive processing and facilitate richer information comprehension. This study underscores the importance of multi-modality in chatbot design, offering valuable insights for creating more engaging and immersive AI communication experiences and informing the broader AI community about the benefits of multi-modal interactions in enhancing user engagement.

Bulk magnetic and thermodynamic measurements, along with mean-field calculations, were conducted on the ferromagnetic K3Gd5(PO4)6 powders. No magnetic ordering was observed until 2 K, while the application of an external field B > 1 T resulted in the splitting of the Gd3+ ground state multiplet and induced a non-cooperative Schottky effect. The average nearest-neighbor exchange strength |J1/kB| is determined to be 0.017 K, which leads to a remarkably large low field magnetic entropy change {\Delta}Sm = 36.2 J kg-1 K-1 under applied field change B = 2 T at temperature T = 2 K, as well as a maximum adiabatic temperature change Tad = 10.9 K. We contend that ferromagnetic gadolinium orthophosphates serve as a promising reservoir for exploring advanced magnetic refrigerants applicable under low magnetic fields., Comment: 7 pages, 5 figures

Magnetars are young, highly magnetized neutron stars that are associated with magnetar short bursts (MSBs), magnetar giant flares (MGFs), and at least a part of fast radio bursts (FRBs). In this work, we consider that a magnetar and a main sequence star are in a binary system and analyze the properties of the electromagnetic signals generated by the interaction between the magnetar bursts and the companion star. During the pre-burst period, the persistent radiation could be generated by the interaction between the $e^+e^-$-pair wind from the magnetar and the companion or its stellar wind. We find that for a newborn magnetar, the pre-burst persistent radiation from the strong magnetar wind can be dominant, and it is mainly at the optical and ultraviolet (UV) bands. For relatively older magnetars, the reemission from a burst interacting with the companion is larger than the pre-burst persistent radiation and the luminosity of the companion itself. The transient reemission produced by the heating process has a duration of $0.1 - 10^5 {\rm~s}$ at the optical, UV, and X-ray bands. Additionally, we find that if these phenomena occur in nearby galaxies within a few hundred kiloparsecs, they could be detected by current or future optical telescopes., Comment: 15 pages, 8 figures; Accepted for publication in A&A

Gamma-ray bursts (GRBs) have long been considered potential sources of ultra-high-energy cosmic rays (UHECRs; with energy $\gtrsim 10^{18} {\rm~eV}$). In this work, we propose a novel model generating MeV emission lines in GRB, which can constrain the properties of heavy nuclei that potentially exist in GRB jets. Specifically, we find that relativistic hydrogen-like high-atomic-number ions originating from the $\beta$ decay of unstable nuclei and/or the recombination entrained in the GRB jet can generate narrow MeV emission lines through the de-excitation of excited-electrons. This model can successfully explain the MeV emission line observed in the most luminous GRB ever recorded, GRB~221009A, with suitable parameters including a Lorentz factor $\gamma \sim 820-1700$ and a total mass of heavy nuclei $M_{\rm tot} \sim 10^{23} - 10^{26}$~g. Especially, the emission line broadening can be reasonably attributed to both the expansion of the jet shell and the thermal motion of nuclei, naturally resulting in a narrow width ($\sigma_{\rm line} / E_{\rm line} \lesssim 0.2$) consistent with the observation. Furthermore, we predict that different GRBs can exhibit lines in different bands with various evolving behaviors, which might be confirmed with further observations. Finally, our model provides indirect evidence that GRBs may be one of the sources of UHECRs., Comment: 13 pages, 4 figures; Published in ApJL, https://doi.org/10.3847/2041-8213/ad4ce1

Shapley value attribution (SVA) is an increasingly popular explainable AI (XAI) method, which quantifies the contribution of each feature to the model's output. However, recent work has shown that most existing methods to implement SVAs have some drawbacks, resulting in biased or unreliable explanations that fail to correctly capture the true intrinsic relationships between features and model outputs. Moreover, the mechanism and consequences of these drawbacks have not been discussed systematically. In this paper, we propose a novel error theoretical analysis framework, in which the explanation errors of SVAs are decomposed into two components: observation bias and structural bias. We further clarify the underlying causes of these two biases and demonstrate that there is a trade-off between them. Based on this error analysis framework, we develop two novel concepts: over-informative and underinformative explanations. We demonstrate how these concepts can be effectively used to understand potential errors of existing SVA methods. In particular, for the widely deployed assumption-based SVAs, we find that they can easily be under-informative due to the distribution drift caused by distributional assumptions. We propose a measurement tool to quantify such a distribution drift. Finally, our experiments illustrate how different existing SVA methods can be over- or under-informative. Our work sheds light on how errors incur in the estimation of SVAs and encourages new less error-prone methods.

Impurities exist inevitably in two-dimensional materials as they spontaneously adsorb onto the surface during fabrication, usually exerting detrimental effects on electronic transport. Here, we focus on a special type of impurities that preferentially adsorb onto the hollow regions of graphene nanoribbons (GNRs), and study how they affect the quantum transport in GNRs. Contrary to previous knowledge that random adatoms should localize electrons, the so-called Anderson localization, noteworthy quantized conductance peaks (QCPs) are observed at specific electron energies. These QCPs are remarkably robust against variations in system size, GNR edge, and adatom properties, and they can reappear at identical energies following an arithmetic sequence of device width. Further investigation of wavefunction reveals a unique transport mode at each QCP energy which transmits through disordered GNRs reflectionlessly, while all the others become fully Anderson localized, indicating the survival of quantum ballistic transport in the localized regime. Our findings highlight the potential utility of hollow adatoms as a powerful tool to manipulate the conductivity of GNRs, and deepen the understanding of the interplay between impurities and graphene., Comment: 8 pages, 4 figures, and 1 table; comments are welcome

The evolution of Large Language Models (LLMs) like ChatGPT and GPT-4 has sparked discussions on the advent of Artificial General Intelligence (AGI). However, replicating such advancements in open-source models has been challenging. This paper introduces InternLM2, an open-source LLM that outperforms its predecessors in comprehensive evaluations across 6 dimensions and 30 benchmarks, long-context modeling, and open-ended subjective evaluations through innovative pre-training and optimization techniques. The pre-training process of InternLM2 is meticulously detailed, highlighting the preparation of diverse data types including text, code, and long-context data. InternLM2 efficiently captures long-term dependencies, initially trained on 4k tokens before advancing to 32k tokens in pre-training and fine-tuning stages, exhibiting remarkable performance on the 200k ``Needle-in-a-Haystack" test. InternLM2 is further aligned using Supervised Fine-Tuning (SFT) and a novel Conditional Online Reinforcement Learning from Human Feedback (COOL RLHF) strategy that addresses conflicting human preferences and reward hacking. By releasing InternLM2 models in different training stages and model sizes, we provide the community with insights into the model's evolution.

This paper presents WanJuan-CC, a safe and high-quality open-sourced English webtext dataset derived from Common Crawl data. The study addresses the challenges of constructing large-scale pre-training datasets for language models, which require vast amounts of high-quality data. A comprehensive process was designed to handle Common Crawl data, including extraction, heuristic rule filtering, fuzzy deduplication, content safety filtering, and data quality filtering. From approximately 68 billion original English documents, we obtained 2.22T Tokens of safe data and selected 1.0T Tokens of high-quality data as part of WanJuan-CC. We have open-sourced 100B Tokens from this dataset. The paper also provides statistical information related to data quality, enabling users to select appropriate data according to their needs. To evaluate the quality and utility of the dataset, we trained 1B-parameter and 3B-parameter models using WanJuan-CC and another dataset, RefinedWeb. Results show that WanJuan-CC performs better on validation datasets and downstream tasks.

With the growing humanlike nature of dialog agents, people are now engaging in extended conversations that can stretch from brief moments to substantial periods of time. Understanding the factors that contribute to sustaining these interactions is crucial, yet existing studies primarily focusing on short-term simulations that rarely explore such prolonged and real conversations. In this paper, we investigate the factors influencing retention rates in real interactions with roleplaying models. By analyzing a large dataset of interactions between real users and thousands of characters, we systematically examine multiple factors and assess their impact on user retention rate. Surprisingly, we find that the degree to which the bot embodies the roles it plays has limited influence on retention rates, while the length of each turn it speaks significantly affects retention rates. This study sheds light on the critical aspects of user engagement with role-playing models and provides valuable insights for future improvements in the development of large language models for role-playing purposes.

Due to its significant application in reducing algorithm depth, fSim gates have attracted a lot of attention. However, during the implementation of quantum gates, fluctuations in control parameters and decoherence caused by the environment may lead to a decrease in the fidelity of the gate. Implementing the fSim gate that is robust to these factors in one step remains an unresolved issue. In this manuscript, we propose a one-step implementation of the nonadiabatic geometric fSim gate composed of a nonadiabatic holonomic controlled phase (CP) gate and a nonadiabatic noncyclic geometric iSWAP gate with parallel paths in a tunable superconducting circuit. Compared to the composite nonadiabatic geometric fSim gate composed of a nonadiabatic holonomic CP gate and a nonadiabatic geometric iSWAP gate, our scheme only takes half the time and demonstrates robustness to parameter fluctuations, as well as to environmental impacts. Moreover, the scheme does not require complex controls, making it very easy to implement in experiments, and can be achieved in various circuit structures. Our scheme may provide a promising path toward quantum computation and simulation.

Charge transport in amorphous semiconductors is considerably more complicated than process in crystalline materials due to abundant localized states. In addition to device-scale characterization, spatially resolved measurements are important to unveil electronic properties. Here, we report gigahertz conductivity mapping in amorphous indium gallium zinc oxide (a-IGZO) thin-film transistors by microwave impedance microscopy (MIM), which probes conductivity without Schottky barrier's influence. The difference between dc and microwave conductivities reflects the efficacy of the injection barrier in an accumulation-mode transistor. The conductivity exhibits significant nanoscale inhomogeneity in the subthreshold regime, presumably due to trapping and releasing from localized states. The characteristic length scale of local fluctuations, as determined by autocorrelation analysis, is about 200 nm. Using random-barrier model, we can simulate the spatial variation of potential landscape, which underlies the mesoscopic conductivity distribution. Our work provides an intuitive way to understand the charge transport mechanism in amorphous semiconductors at microscopic level., Comment: 14 pages, 4 figures

There are two classes of gamma-ray transients with a duration shorter than 2 seconds. One consists of cosmic short Gamma-Ray Bursts (GRBs) taking place in the deep universe via the neutron star mergers, and the other is the magnetar giant flares (GFs) with energies of $\sim 10^{44}-10^{46}$ erg from ``nearby" galaxies. Though the magnetar GFs and the short GRBs have rather similar temporal and spectral properties, their energies ($E_{\rm \gamma,iso}$) are different by quite a few orders of magnitude and hence can be distinguished supposing the host galaxies have been robustly identified. The newly observed GRB 231115A has been widely discussed as a new GF event for its high probability of being associated with M82. Here we conduct a detailed analysis of its prompt emission observed by Fermi-GBM, and compare the parameters with existing observations. The prompt gamma-ray radiation properties of GRB 231115A, if associated with M82, nicely follow the $E_{\rm p,z}-E_{\gamma,\rm iso}$ relation of the GFs, where $E_{\rm p,z}$ is the peak energy of the gamma-ray spectrum after the redshift ($z$) correction. To be a short GRB, the reshift needs to be $\sim 1$. Though such a chance is low, the available X-ray/GeV observation upper limits are not stringent enough to further rule out this possibility. We have also discussed the prospect of convincingly establishing the magnetar origin of GRB 231115A-like events in the future., Comment: 15 pages, 8 figures, 2 tables, accepted for publication in ApJ

The fusion of AI and fashion design has emerged as a promising research area. However, the lack of extensive, interrelated data on clothing and try-on stages has hindered the full potential of AI in this domain. Addressing this, we present the Fashion-Diffusion dataset, a product of multiple years' rigorous effort. This dataset, the first of its kind, comprises over a million high-quality fashion images, paired with detailed text descriptions. Sourced from a diverse range of geographical locations and cultural backgrounds, the dataset encapsulates global fashion trends. The images have been meticulously annotated with fine-grained attributes related to clothing and humans, simplifying the fashion design process into a Text-to-Image (T2I) task. The Fashion-Diffusion dataset not only provides high-quality text-image pairs and diverse human-garment pairs but also serves as a large-scale resource about humans, thereby facilitating research in T2I generation. Moreover, to foster standardization in the T2I-based fashion design field, we propose a new benchmark comprising multiple datasets for evaluating the performance of fashion design models. This work represents a significant leap forward in the realm of AI-driven fashion design, setting a new standard for future research in this field.

Fast radio bursts (FRBs) are transient radio signals with millisecond-duration, large dispersion measure (DM) and extremely high brightness temperature. Among them, FRB 20180916B has been found to have a 16-day periodically modulated activity. However, the physical origin of the periodicity is still a mystery. Here, we utilize the comprehensive observational data to diagnose the periodic models. We find that the ultra-long rotation model is the most probable one for the periodic activity. However, this model cannot reproduce the observed rotation measure (RM) variations. We propose a self-consistent model, i.e., a massive star binary containing a slowly rotational neutron star and a massive star with large mass loss, which can naturally accommodate the wealth of observational features for FRB 20180916B. In this model, the RM variation is periodic, which can be tested by future observations., Comment: 17 pages, 9 figures, 3 tables, accepted for publication in ApJL

Monocular 3D object detection is a crucial and challenging task for autonomous driving vehicle, while it uses only a single camera image to infer 3D objects in the scene. To address the difficulty of predicting depth using only pictorial clue, we propose a novel perspective-aware convolutional layer that captures long-range dependencies in images. By enforcing convolutional kernels to extract features along the depth axis of every image pixel, we incorporates perspective information into network architecture. We integrate our perspective-aware convolutional layer into a 3D object detector and demonstrate improved performance on the KITTI3D dataset, achieving a 23.9\% average precision in the easy benchmark. These results underscore the importance of modeling scene clues for accurate depth inference and highlight the benefits of incorporating scene structure in network design. Our perspective-aware convolutional layer has the potential to enhance object detection accuracy by providing more precise and context-aware feature extraction.

Image segmentation and depth estimation are crucial tasks in computer vision, especially in autonomous driving scenarios. Although these tasks are typically addressed separately, we propose an innovative approach to combine them in our novel deep learning network, Panoptic-DepthLab. By incorporating an additional depth estimation branch into the segmentation network, it can predict the depth of each instance segment. Evaluating on Cityscape dataset, we demonstrate the effectiveness of our method in achieving high-quality segmentation results with depth and visualize it with a color map. Our proposed method demonstrates a new possibility of combining different tasks and networks to generate a more comprehensive image recognition result to facilitate the safety of autonomous driving vehicles.

Backbone members are recognized as essential parts of an organization, yet their role and mechanisms of functioning in networks are not fully understood. In this paper, we propose a new framework called Twotier to analyze the evolution of community sports organizations (CSOs) and the role of backbone members. Tier-one establishes a dynamic user interaction network based on grouping relationships, and weighted k-shell decomposition is used to select backbone members. We perform community detection and capture the evolution of two separate sub-networks: one formed by backbone members and the other formed by other members. In Tier-two, the sub-networks are abstracted, revealing a core-periphery structure in the organization where backbone members serve as bridges connecting all parts of the network. Our findings suggest that relying on backbone members can keep newcomers actively involved in rewarding activities, while non-rewarding activities solidify relations between backbone members.

The improvement of living standards and awareness of chronic diseases have increased the importance of community sports organizations in promoting the physical activity levels of the public. However, limited understanding of human behavior in this context often leads to suboptimal resource utilization. In this study, we analyzed the participation behavior of 2,956 members with a time span of 6 years in a community sports organization. Our study reveals that, at the population level, the participation frequency in activities adheres to a power-law distribution. To understand the underlying mechanisms driving crowd participation, we introduce a novel behavioral model called HFBI (Habit-Formation and Behavioral Inertia), demonstrating a robust fit to the observed power-law distribution. The habit formation mechanism indicates that individuals who are more engaged are more likely to maintain participation, while the behavioral inertia mechanism suggests that individuals' willingness to participate in activities diminishes with their absences from activities. At the individual level, our analysis reveals a burst-quiet participation pattern, with bursts often commencing with incentive activities. We also find a power-law distribution in the intervals between individual participations. Our research offers valuable insights into the complex dynamics of human participation in community sports activity and provides a theoretical foundation to inform intervention design. Furthermore, the flexibility of our model enables its application to other data exhibiting power-law properties, broadening its potential impact beyond the realm of community sports.

In this paper, we presented the 23.3 years of pulsar timing results of PSR J1456-6413 based on the observation of Parkes 64m radio telescope. We detected two new glitches at MJD 57093(3) and 59060(12) and confirmed its first glitch at MJD 54554(10). Using the "Cholesky" timing analysis method, we have determined its position, proper motion, and two-dimensional transverse velocities from the data segments before and after the second glitch, respectively. Furthermore, we detected exponential recovery behavior after the first glitch, with a recovery time scale of approximately 200 days and a corresponding exponential recovery factor Q of approximately 0.15(2), while no exponential recovery was detected for the other two glitches. More interestingly, we found that the leading component of the integral pulse profile after the second glitch became stronger, while the main component became weaker. Our results will expand the sample of pulsars with magnetosphere fluctuation triggered by the glitch event.