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This study investigates how 18-year-old students, parents, and experts in China utilize artificial intelligence (AI) tools to support decision-making in college applications during college entrance exam -- a highly competitive, score-driven, annual national exam. Through 32 interviews, we examine the use of Quark GaoKao, an AI tool that generates college application lists and acceptance probabilities based on exam scores, historical data, preferred locations, etc. Our findings show that AI tools are predominantly used by parents with limited involvement from students, and often focus on immediate exam results, failing to address long-term career goals. We also identify challenges such as misleading AI recommendations, and irresponsible use of AI by third-party consultant agencies. Finally, we offer design insights to better support multi-stakeholders' decision-making in families, especially in the Chinese context, and discuss how emerging AI tools create barriers for families with fewer resources.

Audience feedback is crucial for refining video content, yet it typically comes after publication, limiting creators' ability to make timely adjustments. To bridge this gap, we introduce SimTube, a generative AI system designed to simulate audience feedback in the form of video comments before a video's release. SimTube features a computational pipeline that integrates multimodal data from the video-such as visuals, audio, and metadata-with user personas derived from a broad and diverse corpus of audience demographics, generating varied and contextually relevant feedback. Furthermore, the system's UI allows creators to explore and customize the simulated comments. Through a comprehensive evaluation-comprising quantitative analysis, crowd-sourced assessments, and qualitative user studies-we show that SimTube's generated comments are not only relevant, believable, and diverse but often more detailed and informative than actual audience comments, highlighting its potential to help creators refine their content before release.

Intelligent tutoring systems (ITS) using artificial intelligence (AI) technology have shown promise in supporting learners with diverse abilities; however, they often fail to meet the specific communication needs and cultural nuances needed by d/Deaf and Hard-of-Hearing (DHH) learners. As large language models (LLMs) provide new opportunities to incorporate personas to AI-based tutors and support dynamic interactive dialogue, this paper explores how DHH learners perceive LLM-powered ITS with different personas and identified design suggestions for improving the interaction. We developed an interface that allows DHH learners to interact with ChatGPT and three LLM-powered AI tutors with different experiences in DHH education while the learners watch an educational video. A user study with 16 DHH participants showed that they perceived conversations with the AI tutors who had DHH education experiences to be more human-like and trustworthy due to the tutors' cultural knowledge of DHH communities. Participants also suggested providing more transparency regarding the tutors' background information to clarify each AI tutor's position within the DHH community. We discuss design implications for more inclusive LLM-based systems, such as supports for the multimodality of sign language.

As gravitational-wave observations expand in scope and detection rate, the data analysis infrastructure must be modernized to accommodate rising computational demands and ensure sustainability. We present a scalable gravitational-wave search pipeline which modernizes the GstLAL pipeline by adapting the core filtering engine to the PyTorch framework, enabling flexible execution on both Central Processing Units (CPUs) and Graphics Processing Units (GPUs). Offline search results on the same 8.8 day stretch of public gravitational-wave data indicate that the GstLAL and the PyTorch adaptation demonstrate comparable search performance, even with float16 precision. Lastly, computational benchmarking results show that the GPU float16 configuration of the PyTorch adaptation executed on an A100 GPU can achieve a speedup factor of up to 169 times compared to GstLAL's performance on a single CPU core., Comment: 8 pages, 4 figures

Results: We find that the tensor force affects the height of fission barriers and the double-humped structure of fission path of 240Pu. On the PES, the fission valley becomes larger in the (Q20, Q30) plane, and the triaxial deformation is suppressed around the outer barrier after considering the tensor force. Incorporating the tensor force into the dynamical process enhances the difference in shape evolution between two asymmetric channels. More interestingly, the charge distribution from TDHF with double PNP calculations shows a strong odd-even effect after including the tensor force. In addition, the total mass and charge distributions of fission fragments show a slight shift towards larger asymmetry and are more consistent with the experiments when tensor components are included. We also find that the tensor force enhances energy gaps of the deformed shells for heavy fragments. Moreover, the TKEs of fragments are in accord with the experiments, and after incorporating the tensor force, we observe a higher concentration of the calculated TKEs for heavy fragments at Z = 52 and Z = 56. Conclusions: The tensor force plays a role in both static and dynamical processes in nuclear fission, improving the accuracy of theoretical descriptions within the current framework. Our calculations have shown that the fission-barrier height with SLy5t is closer to the empirical value than that of SLy5. Additionally, the inclusion of the tensor force not only manifests the odd-even effect in charge distribution but also improves the description of total mass and charge distributions., Comment: 16 pages, 13 figures, Submitted to Phys. Rev. C

A notable issue, the proper description of mass and charge distributions of fission fragments within nonadiabatic descriptions of fission dynamics, is investigated by performing double particle number projection on the outcomes of time-dependent Hartree-Fock (TDHF) simulation. The induced fission process of the benchmark nucleus 240Pu is studied. In the three-dimensional Cartesian coordinate without any symmetry restrictions, we get the static fission pathway from the two-dimensional potential energy surface, and then the fission dynamics from saddle to scission point are obtained using TDHF. We show that the charge numbers of primary heavy fragments from TDHF simulation strongly depend on the deformations of initial configurations via the two asymmetric fission channels, which can be distinguished according to the dynamical fission trajectories. The charge distribution of fission fragments is well reproduced using the double particle number projection technique. After applying the Gaussian kernel estimation based on the distribution from the double particle number projection technique, the mass distribution is also consistent with the experimental results. Besides, the results of the total kinetic energy of fission fragments are reasonably consistent with the experiments., Comment: 8 pages, 3 figures

This study investigates innovative interaction designs for communication and collaborative learning between learners of mixed hearing and signing abilities, leveraging advancements in mixed reality technologies like Apple Vision Pro and generative AI for animated avatars. Adopting a participatory design approach, we engaged 15 d/Deaf and hard of hearing (DHH) students to brainstorm ideas for an AI avatar with interpreting ability (sign language to English, voice to English) that would facilitate their face-to-face communication with hearing peers. Participants envisioned the AI avatars to address some issues with human interpreters, such as lack of availability, and provide affordable options to expensive personalized interpreting service. Our findings indicate a range of preferences for integrating the AI avatars with actual human figures of both DHH and hearing communication partners. The participants highlighted the importance of having control over customizing the AI avatar, such as AI-generated signs, voices, facial expressions, and their synchronization for enhanced emotional display in communication. Based on our findings, we propose a suite of design recommendations that balance respecting sign language norms with adherence to hearing social norms. Our study offers insights on improving the authenticity of generative AI in scenarios involving specific, and sometimes unfamiliar, social norms.

Accessibility efforts for d/Deaf and hard of hearing (DHH) learners in video-based learning have mainly focused on captions and interpreters, with limited attention to learners' emotional awareness--an important yet challenging skill for effective learning. Current emotion technologies are designed to support learners' emotional awareness and social needs; however, little is known about whether and how DHH learners could benefit from these technologies. Our study explores how DHH learners perceive and use emotion data from two collection approaches, self-reported and automatic emotion recognition (AER), in video-based learning. By comparing the use of these technologies between DHH (N=20) and hearing learners (N=20), we identified key differences in their usage and perceptions: 1) DHH learners enhanced their emotional awareness by rewatching the video to self-report their emotions and called for alternative methods for self-reporting emotion, such as using sign language or expressive emoji designs; and 2) while the AER technology could be useful for detecting emotional patterns in learning experiences, DHH learners expressed more concerns about the accuracy and intrusiveness of the AER data. Our findings provide novel design implications for improving the inclusiveness of emotion technologies to support DHH learners, such as leveraging DHH peer learners' emotions to elicit reflections.

Deaf and Hard-of-Hearing (DHH) learners face unique challenges in video-based learning due to the complex interplay between visual and auditory information in videos. Traditional approaches to making video content accessible primarily focus on captioning, but these solutions often neglect the cognitive demands of processing both visual and textual information simultaneously. This paper introduces a set of \textit{Motion} design guidelines, aimed at mitigating these cognitive challenges and improving video learning experiences for DHH learners. Through a two-phase research, we identified five key challenges, including misaligned content and visual overload. We proposed five design principles accordingly. User study with 16 DHH participants showed that improving visual-audio relevance and guiding visual attention significantly enhances the learning experience by reducing physical demand, alleviating temporal pressure, and improving learning satisfaction. Our findings highlight the potential of Motion design to transform educational content for DHH learners, and we discuss implications for inclusive video learning tools.

Art appreciation serves as a crucial medium for emotional communication and sociocultural dialogue. In the digital era, fostering deep user engagement on online art appreciation platforms remains a challenge. Leveraging generative AI technologies, we present EyeSee, a system designed to engage users through anthropomorphic characters. We implemented and evaluated three modes (Narrator, Artist, and In-Situ) acting as a third-person narrator, a first-person creator, and first-person created objects, respectively, across two sessions: Narrative and Recommendation. We conducted a within-subject study with 24 participants. In the Narrative session, we found that the In-Situ and Artist modes had higher aesthetic appeal than the Narrator mode, although the Artist mode showed lower perceived usability. Additionally, from the Narrative to Recommendation session, we found that user-perceived relatability and believability within each interaction mode were sustained, but the user-perceived consistency and stereotypicality changed. Our findings suggest novel implications for applying anthropomorphic in-situ narratives to other educational settings.

Evaluating UX in the context of AI's complexity, unpredictability, and generative nature presents unique challenges. HCI scholars lack sufficient tool support to build knowledge around diverse evaluation metrics and develop comprehensive UX evaluation plans. In this paper, we introduce EvAlignUX, an innovative system grounded in scientific literature and powered by large language models (LLMs), designed to help HCI scholars explore evaluation metrics and their relationship to potential research outcomes. A user study involving 19 HCI scholars revealed that EvAlignUX significantly improved the perceived clarity, specificity, feasibility, and overall quality of their evaluation proposals. The use of EvAlignUX enhanced participants' thought processes, resulting in the creation of a Question Bank that can be used to guide UX Evaluation Development. Additionally, the influence of researchers' backgrounds on their perceived inspiration and concerns about over-reliance on AI highlights future research directions for AI's role in fostering critical thinking.

Emotional support is a crucial aspect of communication between community members and police dispatchers during incident reporting. However, there is a lack of understanding about how emotional support is delivered through text-based systems, especially in various non-emergency contexts. In this study, we analyzed two years of chat logs comprising 57,114 messages across 8,239 incidents from 130 higher education institutions. Our empirical findings revealed significant variations in emotional support provided by dispatchers, influenced by the type of incident, service time, and a noticeable decline in support over time across multiple organizations. To improve the consistency and quality of emotional support, we developed and implemented a fine-tuned Large Language Model (LLM), named dispatcherLLM. We evaluated dispatcherLLM by comparing its generated responses to those of human dispatchers and other off-the-shelf models using real chat messages. Additionally, we conducted a human evaluation to assess the perceived effectiveness of the support provided by dispatcherLLM. This study not only contributes new empirical understandings of emotional support in text-based dispatch systems but also demonstrates the significant potential of generative AI in improving service delivery.

Developing novel interdisciplinary research ideas often requires discussions and feedback from experts across different domains. However, obtaining timely inputs is challenging due to the scarce availability of domain experts. Recent advances in Large Language Model (LLM) research have suggested the feasibility of utilizing LLM-simulated expert personas to support research ideation. In this study, we introduce PersonaFlow, an LLM-based system using persona simulation to support the ideation stage of interdisciplinary scientific discovery. Our findings indicate that using multiple personas during ideation significantly enhances user-perceived quality of outcomes (e.g., relevance of critiques, creativity of research questions) without increasing cognitive load. We also found that users' persona customization interactions significantly improved their sense of control and recall of generated ideas. Based on the findings, we discuss highlighting ethical concerns, including potential over-reliance and cognitive biases, and suggest design implications for leveraging LLM-simulated expert personas to support research ideation when human expertise is inaccessible.

This paper examines the governance of multimodal large language models (MM-LLMs) through individual and collective deliberation, focusing on analyses of politically sensitive videos. We conducted a two-step study: first, interviews with 10 journalists established a baseline understanding of expert video interpretation; second, 114 individuals from the general public engaged in deliberation using Inclusive.AI, a platform that facilitates democratic decision-making through decentralized autonomous organization (DAO) mechanisms. Our findings show that while experts emphasized emotion and narrative, the general public prioritized factual clarity, objectivity of the situation, and emotional neutrality. Additionally, we explored the impact of different governance mechanisms: quadratic vs. weighted ranking voting and equal vs. 20-80 power distributions on users decision-making on how AI should behave. Specifically, quadratic voting enhanced perceptions of liberal democracy and political equality, and participants who were more optimistic about AI perceived the voting process to have a higher level of participatory democracy. Our results suggest the potential of applying DAO mechanisms to help democratize AI governance.

As AI systems become more advanced, ensuring their alignment with a diverse range of individuals and societal values becomes increasingly critical. But how can we capture fundamental human values and assess the degree to which AI systems align with them? We introduce ValueCompass, a framework of fundamental values, grounded in psychological theory and a systematic review, to identify and evaluate human-AI alignment. We apply ValueCompass to measure the value alignment of humans and language models (LMs) across four real-world vignettes: collaborative writing, education, public sectors, and healthcare. Our findings uncover risky misalignment between humans and LMs, such as LMs agreeing with values like "Choose Own Goals", which are largely disagreed by humans. We also observe values vary across vignettes, underscoring the necessity for context-aware AI alignment strategies. This work provides insights into the design space of human-AI alignment, offering foundations for developing AI that responsibly reflects societal values and ethics.

The manipulation and transformation of quantum resources are key parts of quantum mechanics. Among them, asymmetry is one of the most useful operational resources, which is widely used in quantum clocks, quantum metrology, and other tasks. Recent studies have shown that the asymmetry of quantum states can be significantly amplified with the assistance of correlating catalysts which are finite-dimensional auxiliaries. In the experiment, we perform translationally invariant operations, ensuring that the asymmetric resources of the entire system remain non-increasing, on a composite system composed of a catalytic system and a quantum system. The experimental results demonstrate an asymmetry amplification of 0.0172\pm0.0022 in the system following the catalytic process. Our work showcases the potential of quantum catalytic processes and is expected to inspire further research in the field of quantum resource theories., Comment: 17pages,7figures

We propose an alternative experimental protocol for the detection of doped Chern insulators and chiral superconductors. Our approach relies on coupling the target chiral system to adatom spins. Due to the substrate chirality, the adatom spins are expected to order in a noncoplanar configuration with a nonzero spin chirality. Here, we obtain concrete results for chiral substrates which are invariant under arbitrary spin rotations, and are coupled to three adatoms carrying classical moments. By exploring all the accessible magnetic ground states, we identify the regimes in which nonzero spin chirality is induced on the adatom complex. We apply our method to valley-polarized bilayer graphene and $d+id$ superconductors, and find qualitatively different ground state diagrams. Our analysis shows that the adatom spin chirality fully encodes the properties of the substrate chirality., Comment: 12 pages and 3 figures in total; 6.5 pages main text with 3 figures and 5.5 pages supplementary material

Leveraging the features of the GstLAL pipeline, we present the results of a matched filtering search for asymmetric binary black hole systems with heavily misaligned spins in LIGO and Virgo data taken during the third observing run. Our target systems show strong imprints of precession whereas current searches have non-optimal sensitivity in detecting them. After measuring the sensitivity improvement brought by our search over standard spin-aligned searches, we report the detection of 30 gravitational wave events already discovered in the latest version of the Gravitational Wave Transient Catalog. However, we do not find any additional significant gravitational wave candidates. Our results allow us to place an upper limit of $R_{90\%} = 0.28^{+0.33}_{-0.04}\;\; \mathrm{Gpc^{-3}yr^{-1}}$ on the merger rate of a hypothetical subpopulation of asymmetric, heavily precessing signals, not identified by other searches. Since our upper limit is consistent with the latest rate estimates from the LIGO-Virgo-KAGRA collaboration, our findings rule out the existence of a yet-to-be-discovered population of precessing binaries.

In cross-modal music processing, translation between visual, auditory, and semantic content opens up new possibilities as well as challenges. The construction of such a transformative scheme depends upon a benchmark corpus with a comprehensive data infrastructure. In particular, the assembly of a large-scale cross-modal dataset presents major challenges. In this paper, we present the MOSA (Music mOtion with Semantic Annotation) dataset, which contains high quality 3-D motion capture data, aligned audio recordings, and note-by-note semantic annotations of pitch, beat, phrase, dynamic, articulation, and harmony for 742 professional music performances by 23 professional musicians, comprising more than 30 hours and 570 K notes of data. To our knowledge, this is the largest cross-modal music dataset with note-level annotations to date. To demonstrate the usage of the MOSA dataset, we present several innovative cross-modal music information retrieval (MIR) and musical content generation tasks, including the detection of beats, downbeats, phrase, and expressive contents from audio, video and motion data, and the generation of musicians' body motion from given music audio. The dataset and codes are available alongside this publication (https://github.com/yufenhuang/MOSA-Music-mOtion-and-Semantic-Annotation-dataset)., Comment: IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2024. 14 pages, 7 figures. Dataset is available on: https://github.com/yufenhuang/MOSA-Music-mOtion-and-Semantic-Annotation-dataset/tree/main and https://zenodo.org/records/11393449

The release of causal structure of physical events from a well-defined order to an indefinite one stimulates remarkable enhancements in various quantum information tasks. Some of these advantages, however, are questioned for the ambiguous role of the control system in the quantum switch that is an experimentally realized process with indefinite causal structure. In communications, for example, not only the superposition of alternative causal orders, but also the superposition of alternative trajectories can accelerate information transmissions. Here, we follow the proposal of Liu et al. [Phys. Rev. Lett. 129, 230604 (2022)], and examine the information enhancement effect of indefinite causal orders with the toolkit of thermodynamics in a photonic platform. Specifically, we simulate the thermal interaction between a system qubit and two heat baths embedded in a quantum switch by implementing the corresponding switched thermal channels. Although its action on the system qubit only is thermally free, our results suggest that the quantum switch should be seen as a resource when the control qubit is also considered. Moreover, we characterize the non-Markovian property in this scenario by measuring the information backflows from the heat baths to the system qubit.

Non-cognitive skills are crucial for personal and social life well-being, and such skill development can be supported by narrative-based (e.g., storytelling) technologies. While generative AI enables interactive and role-playing storytelling, little is known about how users engage with and perceive the use of AI in social life simulation for non-cognitive skills learning. Additionally, the benefits of AI mentorship on self-reflection awareness and ability in this context remain largely underexplored. To this end, we introduced Simulife++, an interactive platform enabled by a large language model (LLM). The system allows users to act as protagonists, creating stories with one or multiple AI-based characters in diverse social scenarios. In particular, we expanded the Human-AI interaction to a Human-AI-AI collaboration by including a Sage Agent, who acts as a bystander, providing users with some perspectives and guidance on their choices and conversations in terms of non-cognitive skills to promote reflection. In a within-subject user study, our quantitative results reveal that, when accompanied by Sage Agent, users exhibit significantly higher levels of reflection on motivation, self-perceptions, and resilience & coping, along with an enhanced experience of narrative transportation. Additionally, our qualitative findings suggest that Sage Agent plays a crucial role in promoting reflection on non-cognitive skills, enhancing social communication and decision-making performance, and improving overall user experience within Simulife++. Multiple supportive relationships between Sage Agent and users were also reported. We offer design implications for the application of generative AI in narrative solutions and the future potential of Sage Agent for non-cognitive skill development in broader social contexts.

Previous research underscored the potential of danmaku--a text-based commenting feature on videos--in engaging hearing audiences. Yet, for many Deaf and hard-of-hearing (DHH) individuals, American Sign Language (ASL) takes precedence over English. To improve inclusivity, we introduce "Signmaku," a new commenting mechanism that uses ASL, serving as a sign language counterpart to danmaku. Through a need-finding study (N=12) and a within-subject experiment (N=20), we evaluated three design styles: real human faces, cartoon-like figures, and robotic representations. The results showed that cartoon-like signmaku not only entertained but also encouraged participants to create and share ASL comments, with fewer privacy concerns compared to the other designs. Conversely, the robotic representations faced challenges in accurately depicting hand movements and facial expressions, resulting in higher cognitive demands on users. Signmaku featuring real human faces elicited the lowest cognitive load and was the most comprehensible among all three types. Our findings offered novel design implications for leveraging generative AI to create signmaku comments, enriching co-learning experiences for DHH individuals., Comment: 14 pages, CHI 2024

Precession in Binary Black Holes (BBH) is caused by the failure of the Black Hole spins to be aligned and its study can open up new perspectives in gravitational waves (GW) astronomy, providing, among other advancements, a precise measure of distance and an accurate characterization of the BBH spins. However, detecting precessing signals is a highly non-trivial task, as standard matched filtering pipelines for GW searches are built on many assumptions that do not hold in the precessing case. This work details the upgrades made to the GstLAL pipeline to facilitate the search for precessing BBH signals. The implemented changes in the search statistics and in the signal consistency test are then described in detail. The performance of the upgraded pipeline is evaluated through two extensive searches of precessing signals, targeting two different regions in the mass space, and the consistency of the results is examined. Additionally, the benefits of the upgrades are assessed by comparing the sensitive volume of the precessing searches with two corresponding traditional aligned-spin searches. While no significant sensitivity improvement is observed for precessing binaries with mass ratio $q\lesssim 6$, a volume increase of up to 100\% is attainable for heavily asymmetric systems with largely misaligned spins. Furthermore, our findings suggest that the primary cause of degraded performance in an aligned-spin search targeting precessing signals is not a poor signal-to-noise-ratio recovery but rather the failure of the $\xi^2$ signal-consistency test. Our work paves the way for a large-scale search for precessing signals, which could potentially result in exciting future detections.

Reference-based metrics such as BLEU and BERTScore are widely used to evaluate question generation (QG). In this study, on QG benchmarks such as SQuAD and HotpotQA, we find that using human-written references cannot guarantee the effectiveness of the reference-based metrics. Most QG benchmarks have only one reference; we replicate the annotation process and collect another reference. A good metric is expected to grade a human-validated question no worse than generated questions. However, the results of reference-based metrics on our newly collected reference disproved the metrics themselves. We propose a reference-free metric consisted of multi-dimensional criteria such as naturalness, answerability, and complexity, utilizing large language models. These criteria are not constrained to the syntactic or semantic of a single reference question, and the metric does not require a diverse set of references. Experiments reveal that our metric accurately distinguishes between high-quality questions and flawed ones, and achieves state-of-the-art alignment with human judgment., Comment: EMNLP 2024 Findings - Camera Ready

The newly discovered Kagome superconductors AV$_3$Sb$_5$ (A=K, Rb and Cs) continue to bring surprises in generating unusual phenomena and physical properties, including anomalous Hall effect, unconventional charge density wave, electronic nematicity and time-reversal symmetry breaking. Here we report an unexpected emergence of multiple flat bands in the AV$_3$Sb$_5$ superconductors. By performing high-resolution angle-resolved photoemission (ARPES) measurements, we observed four branches of flat bands that span over the entire momentum space. The appearance of the flat bands is not anticipated from the band structure calculations and cannot be accounted for by the known mechanisms of flat band generation. It is intimately related to the evolution of van Hove singularities. It is for the first time to observe such emergence of multiple flat bands in solid materials. Our findings provide new insights in revealing the underlying mechanism that governs the unusual behaviors in the Kagome superconductors. They also provide a new pathway in producing flat bands and set a platform to study the flat bands related physics., Comment: 20 pages, 4 figures

In the last decade, it was understood that quantum networks involving several independent sources of entanglement which are distributed and measured by several parties allowed for completely novel forms of nonclassical quantum correlations, when entangled measurements are performed. Here, we experimentally obtain quantum correlations in a triangle network structure, and provide solid evidence of its nonlocality. Specifically, we first obtain the elegant distribution proposed in (Entropy 21, 325) by performing a six-photon experiment. Then, we justify its nonlocality based on machine learning tools to estimate the distance of the experimentally obtained correlation to the local set, and through the violation of a family of conjectured inequalities tailored for the triangle network.

Quantum steering ellipsoid visualizes the set of all qubit states that can be steered by measuring on another correlated qubit in the Bloch picture. Together with local reduced states, it provides a faithful geometric characterization of the underlying two-qubit state so that almost all nonclassical state features can be reflected in its geometric properties. Consequently, the various types of quantum ellipsoids with different geometric properties form an ellipsoid zoo, which, in this work, is experimentally verified via measurements on many polarization-path photonic states. By generating two-qubit states with high fidelity, the corresponding ellipsoids are constructed to certify the presence of entanglement, one-way Einstein-Podolsky-Rosen steering, discord, and steering incompleteness. It is also experimentally verified that the steering ellipsoid can be reconstructed from using the twelve vertices of the icosahedron as measurement directions. Our results aid progress in applying the quantum steering ellipsoid to reveal nonclassical features of the multi-qubit system., Comment: Several references are added and the presentation is close to the publication version

This study explores the impact of AI-generated digital self-clones on improving online presentation skills. We carried out a mixed-design experiment involving 44 international students, comparing self-recorded videos (control) with self-clone videos (AI group) for English presentation practice. The AI videos utilized voice cloning, face swapping, lip-sync, and body-language simulation to refine participants' original presentations in terms of repetition, filler words, and pronunciation. Machine-rated scores indicated enhancements in speech performance for both groups. Though the groups didn't significantly differ, the AI group exhibited a heightened depth of reflection, self-compassion, and a meaningful transition from a corrective to an enhancive approach to self-critique. Within the AI group, congruence between self-perception and AI self-clones resulted in diminished speech anxiety and increased enjoyment. Our findings recommend the ethical employment of digital self-clones to enhance the emotional and cognitive facets of skill development., Comment: 25 pages

This empirical study serves as a primer for interested service providers to determine if and how Large Language Models (LLMs) technology will be integrated for their practitioners and the broader community. We investigate the mutual learning journey of non-AI experts and AI through CoAGent, a service co-creation tool with LLM-based agents. Engaging in a three-stage participatory design processes, we work with with 23 domain experts from public libraries across the U.S., uncovering their fundamental challenges of integrating AI into human workflows. Our findings provide 23 actionable "heuristics for service co-creation with AI", highlighting the nuanced shared responsibilities between humans and AI. We further exemplar 9 foundational agency aspects for AI, emphasizing essentials like ownership, fair treatment, and freedom of expression. Our innovative approach enriches the participatory design model by incorporating AI as crucial stakeholders and utilizing AI-AI interaction to identify blind spots. Collectively, these insights pave the way for synergistic and ethical human-AI co-creation in service contexts, preparing for workforce ecosystems where AI coexists., Comment: 23 pages

We propose an alternative route to stabilize magnetic skyrmions which does not require Dzyaloshinkii-Moriya interactions, magnetic anisotropy, or an external Zeeman field. Our so-called magnetic skyrmion catalysis (MSC) solely relies on the emergence of flux in the system's ground state. We review scenarios that allow for a nonzero flux and summarize the magnetic skyrmion phases that it induces. Among these, we focus on the so-called skyrmionic spin-whirl crystal (Sk-SWC$_4$) phase. We discuss aspects of MSC using a concrete model for topological superconductivity, which describes the surface states of a topological crystalline insulator in the presence of proximity induced pairing. By assuming that the surface states can exhibit the Sk-SWC$_4$ phase, we detail how the addition of a pairing gap generates a chiral superconductor. For this purpose, we construct a low-energy model which renders the mechanism for topological superconductivity transparent. Moreover, by employing this model, we perform a self-consistent investigation of the appearance of the Sk-SWC$_4$ phase for different values of the pairing gap and the ground state's flux. Our analysis verifies the catalytic nature of our mechanism in stabilizing the Sk-SWC$_4$ phase, since the magnetization modulus becomes enhanced upon ramping up the flux. The involvement of MSC further shields magnetism against the suppression induced by the pairing gap. Remarkably, even if the pairing gap fully suppresses the Sk-SWC$_4$ phase for a given value of flux, this skyrmion phase can be restored by further increasing the flux. Our findings demonstrate that MSC enables topological superconductivity in a minimal and robust fashion., Comment: 20 pages, 2 figures, Proceedings SPIE 12656, Spintronics XVI (San Diego USA)

Developing novel research questions (RQs) often requires extensive literature reviews, especially in interdisciplinary fields. To support RQ development through human-AI co-creation, we leveraged Large Language Models (LLMs) to build an LLM-based agent system named CoQuest. We conducted an experiment with 20 HCI researchers to examine the impact of two interaction designs: breadth-first and depth-first RQ generation. The findings revealed that participants perceived the breadth-first approach as more creative and trustworthy upon task completion. Conversely, during the task, participants considered the depth-first generated RQs as more creative. Additionally, we discovered that AI processing delays allowed users to reflect on multiple RQs simultaneously, leading to a higher quantity of generated RQs and an enhanced sense of control. Our work makes both theoretical and practical contributions by proposing and evaluating a mental model for human-AI co-creation of RQs. We also address potential ethical issues, such as biases and over-reliance on AI, advocating for using the system to improve human research creativity rather than automating scientific inquiry., Comment: Accepted to SIGCHI 2024

The $d$-wave pseudospin current order ($d$PSCO) with staggered circulating pseudospin current has been proposed as the hidden electronic order to describe the unexpected breaking of spatial symmetries in stoichiometric Sr$_{2}$IrO$_{4}$ and the unconventional pseudogap phenomena in electron doped Sr$_{2}$IrO$_{4}$. However, a microscopic model for the emergence of $d$PSCO is still lacking. The nearest neighbor Coulomb repulsion $V$, which is expected to be significant in Sr$_{2}$IrO$_{4}$ due to the large spatial extension of the Ir $5d$ orbitals, is capable of driving $d$PSCO on the mean-field level, albeit the latter is energetically degenerate to the staggered flux phase with circulating charge current. We find the in-plane anisotropy $\Gamma_2$ in the effective superexchange interaction between $J_\text{eff}={1\over 2}$ pseudospins, originating from the cooperative interplay between Hund's rule coupling and spin-orbit coupling of Ir $5d$ electrons, is able to lift the degeneracy and stabilize the pseudospin currents. The effective single-orbital model of $J_\text{eff}={1\over 2}$ electrons, including onsite Coulomb repulsion $U$, nearest neighbor Coulomb repulsion $V$, and the in-plane anisotropy $\Gamma_2$, is then studied. We obtain the mean-field ground states, analyze their properties, and determine the phase diagram of stoichiometric Sr$_{2}$IrO$_{4}$ in the plane spanned by $U$ and $V$ at a fixed $\Gamma_2$. We demonstrate the realization of $d$PSCO, and its competition and coexistence with antiferromagnetism. Remarkably, we find the coexistence of $d$PSCO and antiferromagnetism naturally leads to spin bond nematicity, with the spin directions of these three orders forming nontrivial chirality. Furthermore, we show that the emergence of the coexistent state and its chirality can be tuned by carrier doping., Comment: 12 pages, 10 figures

We describe the survey design and science goals for ODIN (One-hundred-deg^2 DECam Imaging in Narrowbands), a NOIRLab survey using the Dark Energy Camera (DECam) to obtain deep (AB~25.7) narrow-band images over an unprecedented area of sky. The three custom-built narrow-band filters, N419, N501, and N673, have central wavelengths of 419, 501, and 673 nm and respective full-widthat-half-maxima of 7.2, 7.4, and 9.8 nm, corresponding to Lya at z=2.4, 3.1, and 4.5 and cosmic times of 2.8, 2.1, and 1.4 Gyr, respectively. When combined with even deeper, public broad-band data from Hyper Suprime-Cam, DECam, and in the future, LSST, the ODIN narrow-band images will enable the selection of over 100,000 Lya-emitting (LAE) galaxies at these epochs. ODIN-selected LAEs will identify protoclusters as galaxy overdensities, and the deep narrow-band images enable detection of highly extended Lya blobs (LABs). Primary science goals include measuring the clustering strength and dark matter halo connection of LAEs, LABs, and protoclusters, and their respective relationship to filaments in the cosmic web. The three epochs allow the redshift evolution of these properties to be determined during the period known as Cosmic Noon, where star formation was at its peak. The two narrow-band filter wavelengths are designed to enable interloper rejection and further scientific studies by revealing [O II] and [O III] at z=0.34, Lya and He II 1640 at z=3.1, and Lyman continuum plus Lya at z=4.5. Ancillary science includes similar studies of the lower-redshift emission-line galaxy samples and investigations of nearby star-forming galaxies resolved into numerous [O III] and [S II] emitting regions., Comment: 21 pages, 8 figures, submitted to Astrophysical Journal