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This presentation opens with a brief review of lattice QCD calculations showing the $2s$ radial excitation of the nucleon sits at approximately 2 GeV, well above the Roper resonance position. We then proceed to reconcile this observation with experimental scattering data. While the idea of dressing quark-model states in a coupled-channel analysis to describe scattering data has been around for decades, it's now possible to bring these descriptions to the finite-volume of lattice QCD for confrontation with lattice-QCD calculations. This combination of lattice QCD and experiment demands that we reconsider our preconceived notions about the quark-model and its excitation spectrum. We close with a discussion of an unanticipated resolution to the missing baryon resonances problem., Comment: 8 pages, 2 figures. To appear as a plenary contribution to the proceedings of HADRON 2023, the 20th International Conference on Hadron Spectroscopy and Structure, 5th-9th June 2023, Genova, Italy

With the continuous accumulation of data from hadron collision experiments, efficient Partial Wave Analysis tools are indispensable for constructing a clear hadron spectrum. Currently, automated computations of scattering amplitudes primarily rely on the helicity scheme and covariant effective Lagrangian method. The automated calculations under the covariant $L$-$S$ scheme, which is one of the commonly used partial wave analysis schemes, have not been fully realized. In this work, we provide a general algorithm for computing partial wave amplitudes under the covariant $L$-$S$ scheme. This will lay the foundation for automated computation of partial wave amplitudes under the covariant $L$-$S$ scheme., Comment: 6 pages

In the digital era, QR codes serve as a linchpin connecting virtual and physical realms. Their pervasive integration across various applications highlights the demand for aesthetically pleasing codes without compromised scannability. However, prevailing methods grapple with the intrinsic challenge of balancing customization and scannability. Notably, stable-diffusion models have ushered in an epoch of high-quality, customizable content generation. This paper introduces Text2QR, a pioneering approach leveraging these advancements to address a fundamental challenge: concurrently achieving user-defined aesthetics and scanning robustness. To ensure stable generation of aesthetic QR codes, we introduce the QR Aesthetic Blueprint (QAB) module, generating a blueprint image exerting control over the entire generation process. Subsequently, the Scannability Enhancing Latent Refinement (SELR) process refines the output iteratively in the latent space, enhancing scanning robustness. This approach harnesses the potent generation capabilities of stable-diffusion models, navigating the trade-off between image aesthetics and QR code scannability. Our experiments demonstrate the seamless fusion of visual appeal with the practical utility of aesthetic QR codes, markedly outperforming prior methods. Codes are available at \url{https://github.com/mulns/Text2QR}, Comment: Accepted by CVPR 2024

Neural code summarization leverages deep learning models to automatically generate brief natural language summaries of code snippets. The development of Transformer models has led to extensive use of attention during model design. While existing work has primarily and almost exclusively focused on static properties of source code and related structural representations like the Abstract Syntax Tree (AST), few studies have considered human attention, that is, where programmers focus while examining and comprehending code. In this paper, we develop a method for incorporating human attention into machine attention to enhance neural code summarization. To facilitate this incorporation and vindicate this hypothesis, we introduce EyeTrans, which consists of three steps: (1) we conduct an extensive eye-tracking human study to collect and pre-analyze data for model training, (2) we devise a data-centric approach to integrate human attention with machine attention in the Transformer architecture, and (3) we conduct comprehensive experiments on two code summarization tasks to demonstrate the effectiveness of incorporating human attention into Transformers. Integrating human attention leads to an improvement of up to 29.91% in Functional Summarization and up to 6.39% in General Code Summarization performance, demonstrating the substantial benefits of this combination. We further explore performance in terms of robustness and efficiency by creating challenging summarization scenarios in which EyeTrans exhibits interesting properties. We also visualize the attention map to depict the simplifying effect of machine attention in the Transformer by incorporating human attention. This work has the potential to propel AI research in software engineering by introducing more human-centered approaches and data.

Recent advancements in HCI and AI research attempt to support user experience (UX) practitioners with AI-enabled tools. Despite the potential of emerging models and new interaction mechanisms, mainstream adoption of such tools remains limited. We took the lens of Human-Centered AI and presented a systematic literature review of 359 papers, aiming to synthesize the current landscape, identify trends, and uncover UX practitioners' unmet needs in AI support. Guided by the Double Diamond design framework, our analysis uncovered that UX practitioners' unique focuses on empathy building and experiences across UI screens are often overlooked. Simplistic AI automation can obstruct the valuable empathy-building process. Furthermore, focusing solely on individual UI screens without considering interactions and user flows reduces the system's practical value for UX designers. Based on these findings, we call for a deeper understanding of UX mindsets and more designer-centric datasets and evaluation metrics, for HCI and AI communities to collaboratively work toward effective AI support for UX.

Blind or Low-Vision (BLV) users often rely on audio descriptions (AD) to access video content. However, conventional static ADs can leave out detailed information in videos, impose a high mental load, neglect the diverse needs and preferences of BLV users, and lack immersion. To tackle these challenges, we introduce SPICA, an AI-powered system that enables BLV users to interactively explore video content. Informed by prior empirical studies on BLV video consumption, SPICA offers novel interactive mechanisms for supporting temporal navigation of frame captions and spatial exploration of objects within key frames. Leveraging an audio-visual machine learning pipeline, SPICA augments existing ADs by adding interactivity, spatial sound effects, and individual object descriptions without requiring additional human annotation. Through a user study with 14 BLV participants, we evaluated the usability and usefulness of SPICA and explored user behaviors, preferences, and mental models when interacting with augmented ADs.

It is well known that for singular inconsistent range-symmetric linear systems, the generalized minimal residual (GMRES) method determines a least squares solution without breakdown. The reached least squares solution may be or not be the pseudoinverse solution. We show that a lift strategy can be used to obtain the pseudoinverse solution. In addition, we propose a new iterative method named RSMAR (minimum $\mathbf A$-residual) for range-symmetric linear systems $\mathbf A\mathbf x=\mathbf b$. At step $k$ RSMAR minimizes $\|\mathbf A\mathbf r_k\|$ in the $k$th Krylov subspace generated with $\{\mathbf A, \mathbf r_0\}$ rather than $\|\mathbf r_k\|$, where $\mathbf r_k$ is the $k$th residual vector and $\|\cdot\|$ denotes the Euclidean vector norm. We show that RSMAR and GMRES terminate with the same least squares solution when applied to range-symmetric linear systems. We provide two implementations for RSMAR. Our numerical experiments show that RSMAR is the most suitable method among GMRES-type methods for singular inconsistent range-symmetric linear systems., Comment: 22 pages, 4 figures

We derive the modified L\"uscher equation in the presence of the long-range force caused by the exchange of a light particle. It is shown that the use of this equation enables one to circumvent the problems related to the strong partial-wave mixing and the t-channel sub-threshold singularities. It is also demonstrated that the present method is intrinsically linked to the so-called modified effective-range expansion (MERE) in the infinite volume. A detailed comparison with the two recently proposed alternative approaches is provided., Comment: 28 pages, 4 figures

We introduce two iterative methods, GPBiLQ and GPQMR, for solving unsymmetric partitioned linear systems. The basic mechanism underlying GPBiLQ and GPQMR is a novel simultaneous tridiagonalization via biorthogonality that allows for short-recurrence iterative schemes. Similar to the biconjugate gradient method, it is possible to develop another method, GPBiCG, whose iterate (if it exists) can be obtained inexpensively from the GPBiLQ iterate. Whereas the iterate of GPBiCG may not exist, the iterates of GPBiLQ and GPQMR are always well defined as long as the biorthogonal tridiagonal reduction process does not break down. We discuss connections between the proposed methods and some existing methods, and give numerical experiments to illustrate the performance of the proposed methods., Comment: 22 pages, 4 figures

Abridged: Programmer attention represents the visual focus of programmers on parts of the source code in pursuit of programming tasks. We conducted an in-depth human study with XY Java programmers, where each programmer generated summaries for 40 methods from five large Java projects over five one-hour sessions. We used eye-tracking equipment to map the visual attention of programmers while they wrote the summaries. We also rate the quality of each summary. We found eye-gaze patterns and metrics that define common behaviors between programmer attention during context-aware code summarization. Specifically, we found that programmers need to read significantly (p<0.01) fewer words and make significantly fewer revisits to words (p\textless0.03) as they summarize more methods during a session, while maintaining the quality of summaries. We also found that the amount of source code a participant looks at correlates with a higher quality summary, but this trend follows a bell-shaped curve, such that after a threshold reading more source code leads to a significant decrease (p<0.01) in the quality of summaries. We also gathered insight into the type of methods in the project that provide the most contextual information for code summarization based on programmer attention. Specifically, we observed that programmers spent a majority of their time looking at methods inside the same class as the target method to be summarized. Surprisingly, we found that programmers spent significantly less time looking at methods in the call graph of the target method. We discuss how our empirical observations may aid future studies towards modeling programmer attention and improving context-aware automatic source code summarization., Comment: 10 pages, 4 figures, 4 tables. this is a pre-print submitted to IEEE Transactions on Software Engineering for review

The increasing use of large language model (LLM)-powered code generation tools, such as GitHub Copilot, is transforming software engineering practices. This paper investigates how developers validate and repair code generated by Copilot and examines the impact of code provenance awareness during these processes. We conducted a lab study with 28 participants, who were tasked with validating and repairing Copilot-generated code in three software projects. Participants were randomly divided into two groups: one informed about the provenance of LLM-generated code and the other not. We collected data on IDE interactions, eye-tracking, cognitive workload assessments, and conducted semi-structured interviews. Our results indicate that, without explicit information, developers often fail to identify the LLM origin of the code. Developers generally employ similar validation and repair strategies for LLM-generated code, but exhibit behaviors such as frequent switching between code and comments, different attentional focus, and a tendency to delete and rewrite code. Being aware of the code's provenance led to improved performance, increased search efforts, more frequent Copilot usage, and higher cognitive workload. These findings enhance our understanding of how developers interact with LLM-generated code and carry implications for designing tools that facilitate effective human-LLM collaboration in software development.

In recent years, there has been a growing interest in employing intelligent agents in writing. Previous work emphasizes the evaluation of the quality of end product-whether it was coherent and polished, overlooking the journey that led to the product, which is an invaluable dimension of the creative process. To understand how to recognize human efforts in co-writing with intelligent writing systems, we adapt Flower and Hayes' cognitive process theory of writing and propose CoCo Matrix, a two-dimensional taxonomy of entropy and information gain, to depict the new human-agent co-writing model. We define four quadrants and situate thirty-four published systems within the taxonomy. Our research found that low entropy and high information gain systems are under-explored, yet offer promising future directions in writing tasks that benefit from the agent's divergent planning and the human's focused translation. CoCo Matrix, not only categorizes different writing systems but also deepens our understanding of the cognitive processes in human-agent co-writing. By analyzing minimal changes in the writing process, CoCo Matrix serves as a proxy for the writer's mental model, allowing writers to reflect on their contributions. This reflection is facilitated through the measured metrics of information gain and entropy, which provide insights irrespective of the writing system used.

In this paper, we present a simple but effective method to enhance blind video quality assessment (BVQA) models for social media videos. Motivated by previous researches that leverage pre-trained features extracted from various computer vision models as the feature representation for BVQA, we further explore rich quality-aware features from pre-trained blind image quality assessment (BIQA) and BVQA models as auxiliary features to help the BVQA model to handle complex distortions and diverse content of social media videos. Specifically, we use SimpleVQA, a BVQA model that consists of a trainable Swin Transformer-B and a fixed SlowFast, as our base model. The Swin Transformer-B and SlowFast components are responsible for extracting spatial and motion features, respectively. Then, we extract three kinds of features from Q-Align, LIQE, and FAST-VQA to capture frame-level quality-aware features, frame-level quality-aware along with scene-specific features, and spatiotemporal quality-aware features, respectively. Through concatenating these features, we employ a multi-layer perceptron (MLP) network to regress them into quality scores. Experimental results demonstrate that the proposed model achieves the best performance on three public social media VQA datasets. Moreover, the proposed model won first place in the CVPR NTIRE 2024 Short-form UGC Video Quality Assessment Challenge. The code is available at \url{https://github.com/sunwei925/RQ-VQA.git}.

This paper reviews the NTIRE 2024 Challenge on Shortform UGC Video Quality Assessment (S-UGC VQA), where various excellent solutions are submitted and evaluated on the collected dataset KVQ from popular short-form video platform, i.e., Kuaishou/Kwai Platform. The KVQ database is divided into three parts, including 2926 videos for training, 420 videos for validation, and 854 videos for testing. The purpose is to build new benchmarks and advance the development of S-UGC VQA. The competition had 200 participants and 13 teams submitted valid solutions for the final testing phase. The proposed solutions achieved state-of-the-art performances for S-UGC VQA. The project can be found at https://github.com/lixinustc/KVQChallenge-CVPR-NTIRE2024., Comment: Accepted by CVPR2024 Workshop. The challenge report for CVPR NTIRE2024 Short-form UGC Video Quality Assessment Challenge

Large language models are increasingly applied in real-world scenarios, including research and education. These models, however, come with well-known ethical issues, which may manifest in unexpected ways in human-computer interaction research due to the extensive engagement with human subjects. This paper reports on research practices related to LLM use, drawing on 16 semi-structured interviews and a survey conducted with 50 HCI researchers. We discuss the ways in which LLMs are already being utilized throughout the entire HCI research pipeline, from ideation to system development and paper writing. While researchers described nuanced understandings of ethical issues, they were rarely or only partially able to identify and address those ethical concerns in their own projects. This lack of action and reliance on workarounds was explained through the perceived lack of control and distributed responsibility in the LLM supply chain, the conditional nature of engaging with ethics, and competing priorities. Finally, we reflect on the implications of our findings and present opportunities to shape emerging norms of engaging with large language models in HCI research.

With ChatGPT's release, conversational prompting has become the most popular form of human-LLM interaction. However, its effectiveness is limited for more complex tasks involving reasoning, creativity, and iteration. Through a systematic analysis of HCI papers published since 2021, we identified four key phases in the human-LLM interaction flow - planning, facilitating, iterating, and testing - to precisely understand the dynamics of this process. Additionally, we have developed a taxonomy of four primary interaction modes: Mode 1: Standard Prompting, Mode 2: User Interface, Mode 3: Context-based, and Mode 4: Agent Facilitator. This taxonomy was further enriched using the "5W1H" guideline method, which involved a detailed examination of definitions, participant roles (Who), the phases that happened (When), human objectives and LLM abilities (What), and the mechanics of each interaction mode (How). We anticipate this taxonomy will contribute to the future design and evaluation of human-LLM interaction., Comment: 11 pages, 4 figures, 3 tables. Accepted at CHI Late-Breaking Work 2024

Portrait images typically consist of a salient person against diverse backgrounds. With the development of mobile devices and image processing techniques, users can conveniently capture portrait images anytime and anywhere. However, the quality of these portraits may suffer from the degradation caused by unfavorable environmental conditions, subpar photography techniques, and inferior capturing devices. In this paper, we introduce a dual-branch network for portrait image quality assessment (PIQA), which can effectively address how the salient person and the background of a portrait image influence its visual quality. Specifically, we utilize two backbone networks (\textit{i.e.,} Swin Transformer-B) to extract the quality-aware features from the entire portrait image and the facial image cropped from it. To enhance the quality-aware feature representation of the backbones, we pre-train them on the large-scale video quality assessment dataset LSVQ and the large-scale facial image quality assessment dataset GFIQA. Additionally, we leverage LIQE, an image scene classification and quality assessment model, to capture the quality-aware and scene-specific features as the auxiliary features. Finally, we concatenate these features and regress them into quality scores via a multi-perception layer (MLP). We employ the fidelity loss to train the model via a learning-to-rank manner to mitigate inconsistencies in quality scores in the portrait image quality assessment dataset PIQ. Experimental results demonstrate that the proposed model achieves superior performance in the PIQ dataset, validating its effectiveness. The code is available at \url{https://github.com/sunwei925/DN-PIQA.git}.

Traditional deep neural network (DNN)-based image quality assessment (IQA) models leverage convolutional neural networks (CNN) or Transformer to learn the quality-aware feature representation, achieving commendable performance on natural scene images. However, when applied to AI-Generated images (AGIs), these DNN-based IQA models exhibit subpar performance. This situation is largely due to the semantic inaccuracies inherent in certain AGIs caused by uncontrollable nature of the generation process. Thus, the capability to discern semantic content becomes crucial for assessing the quality of AGIs. Traditional DNN-based IQA models, constrained by limited parameter complexity and training data, struggle to capture complex fine-grained semantic features, making it challenging to grasp the existence and coherence of semantic content of the entire image. To address the shortfall in semantic content perception of current IQA models, we introduce a large Multi-modality model Assisted AI-Generated Image Quality Assessment (MA-AGIQA) model, which utilizes semantically informed guidance to sense semantic information and extract semantic vectors through carefully designed text prompts. Moreover, it employs a mixture of experts (MoE) structure to dynamically integrate the semantic information with the quality-aware features extracted by traditional DNN-based IQA models. Comprehensive experiments conducted on two AI-generated content datasets, AIGCQA-20k and AGIQA-3k show that MA-AGIQA achieves state-of-the-art performance, and demonstrate its superior generalization capabilities on assessing the quality of AGIs. Code is available at https://github.com/wangpuyi/MA-AGIQA.

This paper reviews the AIS 2024 Video Quality Assessment (VQA) Challenge, focused on User-Generated Content (UGC). The aim of this challenge is to gather deep learning-based methods capable of estimating the perceptual quality of UGC videos. The user-generated videos from the YouTube UGC Dataset include diverse content (sports, games, lyrics, anime, etc.), quality and resolutions. The proposed methods must process 30 FHD frames under 1 second. In the challenge, a total of 102 participants registered, and 15 submitted code and models. The performance of the top-5 submissions is reviewed and provided here as a survey of diverse deep models for efficient video quality assessment of user-generated content., Comment: CVPR 2024 Workshop -- AI for Streaming (AIS) Video Quality Assessment Challenge

Spatial audio offers more immersive video consumption experiences to viewers; however, creating and editing spatial audio often expensive and requires specialized equipment and skills, posing a high barrier for amateur video creators. We present MIMOSA, a human-AI co-creation tool that enables amateur users to computationally generate and manipulate spatial audio effects. For a video with only monaural or stereo audio, MIMOSA automatically grounds each sound source to the corresponding sounding object in the visual scene and enables users to further validate and fix the errors in the locations of sounding objects. Users can also augment the spatial audio effect by flexibly manipulating the sounding source positions and creatively customizing the audio effect. The design of MIMOSA exemplifies a human-AI collaboration approach that, instead of utilizing state-of art end-to-end "black-box" ML models, uses a multistep pipeline that aligns its interpretable intermediate results with the user's workflow. A lab user study with 15 participants demonstrates MIMOSA's usability, usefulness, expressiveness, and capability in creating immersive spatial audio effects in collaboration with users.

In hadron spectrum physics, the partial wave analysis is one of the main methods to extract the properties of hadronic resonances. Due to the obvious Lorentz covariant form and the determined orbital-spin quantum numbers, the covariant orbital-spin coupling scheme has unique advantages among usual partial wave methods. In this paper, the covariant orbital-spin coupling scheme is generalized based on the irreducible tensor of the homogeneous proper Lorentz group and its little groups. The general systematic procedure for constructing the partial wave amplitude in a Lorentz covariant way is given. This scheme is applicable to both massive and massless particles. Some specific examples are given., Comment: 69 pages and 2 figures

We study the Borromean nuclei formed by a core nucleus and two neutrons in a nonrelativistic effective field theory formalism considering both neutron-neutron and neutron-core interactions. We provide formulae of the charge and matter radii, and successfully reproduce the universal relation proposed by Hongo and Son based on the approximation of an infinite neutron-neutron scattering length and neglecting the neutron-core scattering. Once the realistic finite neutron-neutron and neutron-core scattering lengths are used, the charge and matter radii are influenced by the neutron-core channel in a growingly relevant manner. We obtain a relation among the binding energy of the three-body Borromean system, the ratio between charge and matter radii, and the ratio between the neutron-neutron and core-neutron scattering lengths. We find that the two-neutron separation energy for $^{22}$C needs to be $\lesssim 2$ keV in order to be consistent with the experimental constraints of the matter radius of $^{22}$C and the $^{20}{\rm C}\,n$ $S$-wave scattering length., Comment: 26 pages, 14 figures

We have investigated the internal structure of the open- and hidden-charmed ($DD^*$/$\bar DD^*$) molecules in the unified framework. We first fit the experimental lineshape of the $T^+_{cc}$ state and extract the $DD^*$ interaction, from which the $T^+_{cc}$ is assumed to arise solely. Then we obtain the $D\bar{D}^*$ interaction by charge conjugation. Our results show that the $D\bar{D}^*$ interaction is attractive but insufficient to form $X(3872)$. Instead, its formation requires the crucial involvement of the coupled channel effect between the $D\bar D^*$ and $c\bar c$ components, although the $c\bar c$ accounts for approximately $1\%$ only. Besides $X(3872)$, we have obtained a higher-energy state around $3957.9$ MeV with a width of $16.7$ MeV, which may be a potential candidate for the $X(3940)$.In $J^{PC}=1^{+-}$ sector, we have found two resonances related to the iso-vector $Z_c$ and the iso-scalar $h_c(2P)$, respectively. Our combined study provides valuable insights into the nature of these $DD^*$/$D\bar D^*$ exotic states., Comment: 8 pages, 5 figures

Recent lattice QCD results for the low-lying odd-parity excitations of the nucleon near the $N^{*}(1535)$ and $N^{*}(1650)$ resonance positions have revealed that the lattice QCD states have magnetic moments consistent with predictions from a constituent-quark-model. Using Hamiltonian Effective Field Theory (HEFT) to describe pion-nucleon scattering in the $I(J^{P}) = \frac{1}{2}(\frac{1}{2}^{-})$ channel, we represent these two quark-model like states as two single-particle bare basis states, dressed and mixed by meson-baryon scattering channels. By constraining the free parameters of the Hamiltonian with $S_{11}$ pion-nucleon scattering data, we perform the first calculation of the finite-volume spectrum using two bare-baryon basis states. By comparing this spectrum to contemporary lattice QCD results at three lattice volumes, we analyse the eigenvectors of the Hamiltonian to gain insight into the structure and composition of these two low-lying resonances. We find that an interpretation of the two low-lying nucleon resonances as quark-model like states dressed by meson-baryon interactions is consistent with both the $S_{11}$ scattering data and lattice QCD. We introduce a novel HEFT formalism for estimating scattering-state contaminations in lattice QCD correlation functions constructed with standard three-quark operators. Not only are historical lattice QCD results described with excellent accuracy, but correlation functions with large scattering-state contaminations are identified., Comment: 17 pages, 12 figures

Recent LHCb data for $B^-\to J/\psi\Lambda\bar{p}$ show a clear peak structure at the $\Xi_c\bar{D}$ threshold in the $J/\psi\Lambda$ invariant mass ($M_{J/\psi\Lambda}$) distribution. The LHCb's amplitude analysis identified the peak with the first hidden-charm pentaquark with strangeness $P_{\psi s}^\Lambda(4338)$. We conduct a coupled-channel amplitude analysis of the LHCb data by simultaneously fitting the $M_{J/\psi\Lambda}$, $M_{J/\psi\bar{p}}$, $M_{\Lambda\bar{p}}$, and $\cos\theta_{K^*}$ distributions. Rather than the Breit-Wigner fit employed in the LHCb analysis, we consider relevant threshold effects and a unitary $\Xi_c\bar{D}$-$\Lambda_c\bar{D}_s$ coupled-channel scattering amplitude from which $P_{\psi s}^\Lambda$ poles are extracted for the first time. In our default fit, the $P_{\psi s}^\Lambda(4338)$ pole is almost a $\Xi_c \bar{D}$ bound state at $( 4338.2\pm 1.4)-( 1.9\pm 0.5 )\,i$ MeV. Our default model also fits a large fluctuation at the $\Lambda_c\bar{D}_s$ threshold, giving a $\Lambda_c\bar{D}_s$ virtual state, $P_{\psi s}^\Lambda(4255)$, at $4254.7\pm 0.4$ MeV. We also found that the $P_{\psi s}^\Lambda(4338)$ peak cannot solely be a kinematical effect, and a nearby pole is needed., Comment: 4 pages, 4 figures, Contribution to the Proceedings for 16th International Conference on Meson-Nucleon Physics and the Structure of the Nucleon (MENU 2023), October 15-20, 2023, Mainz, Germany

In this work, an unquenched quark model is proposed for describing the heavy-light mesons by taking into account the coupled-channel effects induced by chiral dynamics. After including a relativistic correction term for the strong transition amplitudes, both the mass spectra and decay widths of the observed heavy-light mesons can be successfully described simultaneously in a unified framework, several long-standing puzzles related to the small masses and broad widths are overcome naturally. We also provide valuable guidance in searching new heavy-light mesons by the detailed predictions of their masses, widths, and branching ratios. The success of the unquenched quark model presented in this work indicates it may be an important step for understanding the hadron spectrum., Comment: 12 pages, 3 figures

Non-conjugated luminescent polymers (NCLPs) have the advantages of simple synthesis, optical tunability, and excellent processability. However, the underlying luminous mechanism in NCLPs remains obscure and it is a considerable challenge to obtain NCLPs with ultra-long phosphorescence lifetime and multicolour emission simultaneously. In this article, linear polyurethane derivatives (PUs) with cluster-induced aggregation, multicolour luminescence and ultra-long phosphorescence have been prepared by simply adjusting the reaction temperature and the reaction time. DFT calculations and molecular dynamics simulations provide elaborate microstructural information on the PUs. With the synergistic effect of abundant hydrogen bonding interactions, through-space dative bonds, short interatomic contacts and oxygen clusters various luminous clusters are formed. The energy level splitting caused by clusters with different extents of spatial conjugation endows the NCLPs with multicolour clusteroluminescence, promotes intersystem crossing (ISC), and stabilises the triplet excited state, and finally an ultra-long room temperature phosphorescence (RTP) lifetime of 0.45 s is attained. Experimental encryption/decryption models validate the potential of the PUs in information security. The results have important implications for understanding the intrinsic mechanism of unconventional luminescence in the absence of any traditional conjugative units or heavy atom effects, and they provide a new horizon for the strategic design of multicolour luminescence and ultra-long phosphorescence in NCLPs for a range of practical applications. Cluster-induced aggregation, multicolour luminescence and ultra-long phosphorescence (0.45 s) in NCLPs based on polyurethane derivatives have been achieved by simply adjusting reaction temperature and reaction time; this is the longest reported phosphorescence lifetime for pure NCLPs.

We derive an explicit expression for the Lellouch-L\"uscher (LL) factor in the $K\to 3\pi$ decays at leading order (without derivative couplings). Several important technical details are addressed, like a proper decomposition into the isospin amplitudes, the choice of a minimal set of effective couplings and the renormalization, as well as the algorithm for the solution of the pertinent Faddeev equations in the infinite volume which is based on the contour deformation method. Most importantly, our numerical results demonstrate that the three-body force contributes very little to the LL factor. This result paves the way for the study of the $K\to 3\pi$ decays on the lattice., Comment: 41 pages, 14 figures

We collect spectra extracted in the $I=\ell=1$ $\pi\pi$ sector provided by various lattice QCD collaborations and study the $m_\pi$ dependence of $\rho$-meson properties using Hamiltonian Effective Field Theory (HEFT). In this unified analysis, the coupling constant and cutoff mass, characterizing the $\rho - \pi \pi$ vertex, are both found to be weakly dependent on $m_\pi$, while the mass of the bare $\rho$, associated with a simple quark-model state, shows a linear dependence on $m_\pi^2$. Both the lattice results and experimental data can be described well. Drawing on HEFT's ability to describe the pion mass dependence of resonances in a single formalism, we map the dependence of the phase shift as a function of $m_\pi$, and expose interesting discrepancies in contemporary lattice QCD results., Comment: 24 pages, 16 figures

Low-lying baryon resonances have been explored using Hamiltonian Effective Field Theory (HEFT), in a formalism where resonances with a three-quark component are described by both two-particle meson-baryon states and a bare basis state. Here, we investigate the use of multiple bare states in the Hamiltonian, to extend the formalism to higher energy ranges, and represent a larger portion of the low-lying baryon spectrum. Introducing a second bare state into a toy model extension of the low-energy $\Delta(1232)$ system, we explore the influence of the second bare state on the position of poles in the infinite-volume $T$-matrix. Considering the same system in a finite-volume, we analyse the finite-volume energy spectrum in the presence of a second bare state, providing insight into the interplay between two bare basis states, representing quark-model states, and the relationship between infinite-volume poles and finite-volume eigenstates., Comment: 14 pages, 7 figures

Computer graphics images (CGIs) are artificially generated by means of computer programs and are widely perceived under various scenarios, such as games, streaming media, etc. In practice, the quality of CGIs consistently suffers from poor rendering during production, inevitable compression artifacts during the transmission of multimedia applications, and low aesthetic quality resulting from poor composition and design. However, few works have been dedicated to dealing with the challenge of computer graphics image quality assessment (CGIQA). Most image quality assessment (IQA) metrics are developed for natural scene images (NSIs) and validated on databases consisting of NSIs with synthetic distortions, which are not suitable for in-the-wild CGIs. To bridge the gap between evaluating the quality of NSIs and CGIs, we construct a large-scale in-the-wild CGIQA database consisting of 6,000 CGIs (CGIQA-6k) and carry out the subjective experiment in a well-controlled laboratory environment to obtain the accurate perceptual ratings of the CGIs. Then, we propose an effective deep learning-based no-reference (NR) IQA model by utilizing both distortion and aesthetic quality representation. Experimental results show that the proposed method outperforms all other state-of-the-art NR IQA methods on the constructed CGIQA-6k database and other CGIQA-related databases. The database is released at https://github.com/zzc-1998/CGIQA6K.

Zi-Jiao Wang,1,&ast; Xiao-Ning Liu,1,&ast; Jia-Jun He,1 Yan-Ping Wang,1 Chen-Xi Zhao,2 Xiao-Jing Yang,1 Hong-Yan Yin,3 De-Pin Cao,1,&ast; Shu-E Zhang1,&ast; 1Department of Health Management, School of Health Management, Harbin Medical University, Harbin, People’s Republic of China; 2Academic Affairs Office, First Affiliated Hospital of Harbin Medical University, Harbin, People’s Republic of China; 3Department of Humanities and Social Sciences, Harbin Medical University (Daqing), Daqing, Heilongjiang, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: De-Pin Cao; Shu-E Zhang, Department of Health Management, School of Health Management, Harbin Medical University, Harbin, 150081, People’s Republic of China, Tel +86 13351113008 ; +86 15104694354, Email caodp211@126.com; hydzhangshue@163.comPurpose: The COVID-19 pandemic sets specific circumstances that may accelerate academic procrastination behavior of medical students. Career calling is a protective factor that fights against academic procrastination and may further improve medical students’ mental health and academic achievement. This study aims to determine the status of Chinese medical students’ academic procrastination during controlled COVID-19 pandemic. Moreover, the study investigates the relationships and mechanisms among career calling, peer pressure, a positive learning environment, and academic procrastination.Patients and Methods: Data were collected from several Chinese medical universities through an anonymous cross-sectional survey of 3614 respondents (effective response rate = 60.0%). Using online questionnaires to collect the data and IBM SPSS Statistics 22.0 for statistical analysis.Results: The average score of academic procrastination of Chinese medical students was 2.62± 0.86. This study proved the usage of peer pressure and positive learning environment as moderating roles of relationship between career calling and academ

Relational databases play an important role in business, science, and more. However, many users cannot fully unleash the analytical power of relational databases, because they are not familiar with database languages such as SQL. Many techniques have been proposed to automatically generate SQL from natural language, but they suffer from two issues: (1) they still make many mistakes, particularly for complex queries, and (2) they do not provide a flexible way for non-expert users to validate and refine incorrect queries. To address these issues, we introduce a new interaction mechanism that allows users to directly edit a step-by-step explanation of a query to fix errors. Our experiments on multiple datasets, as well as a user study with 24 participants, demonstrate that our approach can achieve better performance than multiple SOTA approaches. Our code and datasets are available at https://github.com/magic-YuanTian/STEPS., Comment: Accepted to EMNLP 2023

Collaborative Qualitative Analysis (CQA) can enhance qualitative analysis rigor and depth by incorporating varied viewpoints. Nevertheless, ensuring a rigorous CQA procedure itself can be both demanding and costly. To lower this bar, we take a theoretical perspective to design the CollabCoder workflow, that integrates Large Language Models (LLMs) into key inductive CQA stages: independent open coding, iterative discussions, and final codebook creation. In the open coding phase, CollabCoder offers AI-generated code suggestions and records decision-making data. During discussions, it promotes mutual understanding by sharing this data within the coding team and using quantitative metrics to identify coding (dis)agreements, aiding in consensus-building. In the code grouping stage, CollabCoder provides primary code group suggestions, lightening the cognitive load of finalizing the codebook. A 16-user evaluation confirmed the effectiveness of CollabCoder, demonstrating its advantages over existing software and providing empirical insights into the role of LLMs in the CQA practice., Comment: Will be published at the ACM CHI Conference on Human Factors in Computing Systems (CHI'24)

In argumentative writing, writers must brainstorm hierarchical writing goals, ensure the persuasiveness of their arguments, and revise and organize their plans through drafting. Recent advances in large language models (LLMs) have made interactive text generation through a chat interface (e.g., ChatGPT) possible. However, this approach often neglects implicit writing context and user intent, lacks support for user control and autonomy, and provides limited assistance for sensemaking and revising writing plans. To address these challenges, we introduce VISAR, an AI-enabled writing assistant system designed to help writers brainstorm and revise hierarchical goals within their writing context, organize argument structures through synchronized text editing and visual programming, and enhance persuasiveness with argumentation spark recommendations. VISAR allows users to explore, experiment with, and validate their writing plans using automatic draft prototyping. A controlled lab study confirmed the usability and effectiveness of VISAR in facilitating the argumentative writing planning process., Comment: 30 pages, published in UIST'23

The chitin synthase Chs3 is a multipass membrane protein whose trafficking is tightly controlled. Accordingly, its exit from the endoplasmic reticulum (ER) depends on several complementary mechanisms that ensure its correct folding. Despite its potential failure on its exit, Chs3 is very stable in this compartment, which suggests its poor recognition by ER quality control mechanisms such as endoplasmic reticulum-associated degradation (ERAD). Here we show that proper N-glycosylation of its luminal domain is essential to prevent the aggregation of the protein and its subsequent recognition by the Hrd1-dependent ERAD-L machinery. In addition, the interaction of Chs3 with its chaperone Chs7 seems to mask additional cytosolic degrons, thereby avoiding their recognition by the ERAD-C pathway. On top of that, Chs3 molecules that are not degraded by conventional ERAD can move along the ER membrane to reach the inner nuclear membrane, where they are degraded by the inner nuclear membrane-associated degradation (INMAD) system, which contributes to the intracellular homeostasis of Chs3. These results indicate that Chs3 is an excellent model to study quality control mechanisms in the cell and reinforce its role as a paradigm in intracellular trafficking research.

Quantum noise reduction and entanglement-enhanced sensing in the acoustic frequency range is an outstanding challenge relevant for a number of applications including magnetometry and broadband noise reduction in gravitational wave detectors. Here we experimentally demonstrate quantum behavior of a macroscopic atomic spin oscillator in the acoustic frequency range. Quantum back-action of the spin measurement, ponderomotive squeezing of light, and virtual spring softening are observed at oscillation frequencies down to the sub-kHz range. Quantum noise sources characteristic of spin oscillators operating in the near-DC frequency range are identified and means for their mitigation are presented.

The proliferation of Artificial Intelligence-Generated Images (AGIs) has greatly expanded the Image Naturalness Assessment (INA) problem. Different from early definitions that mainly focus on tone-mapped images with limited distortions (e.g., exposure, contrast, and color reproduction), INA on AI-generated images is especially challenging as it has more diverse contents and could be affected by factors from multiple perspectives, including low-level technical distortions and high-level rationality distortions. In this paper, we take the first step to benchmark and assess the visual naturalness of AI-generated images. First, we construct the AI-Generated Image Naturalness (AGIN) database by conducting a large-scale subjective study to collect human opinions on the overall naturalness as well as perceptions from technical and rationality perspectives. AGIN verifies that naturalness is universally and disparately affected by technical and rationality distortions. Second, we propose the Joint Objective Image Naturalness evaluaTor (JOINT), to automatically predict the naturalness of AGIs that aligns human ratings. Specifically, JOINT imitates human reasoning in naturalness evaluation by jointly learning both technical and rationality features. We demonstrate that JOINT significantly outperforms baselines for providing more subjectively consistent results on naturalness assessment., Comment: 33 pages

Singing, as a common facial movement second only to talking, can be regarded as a universal language across ethnicities and cultures, plays an important role in emotional communication, art, and entertainment. However, it is often overlooked in the field of audio-driven facial animation due to the lack of singing head datasets and the domain gap between singing and talking in rhythm and amplitude. To this end, we collect a high-quality large-scale singing head dataset, SingingHead, which consists of more than 27 hours of synchronized singing video, 3D facial motion, singing audio, and background music from 76 individuals and 8 types of music. Along with the SingingHead dataset, we argue that 3D and 2D facial animation tasks can be solved together, and propose a unified singing facial animation framework named UniSinger to achieve both singing audio-driven 3D singing head animation and 2D singing portrait video synthesis. Extensive comparative experiments with both SOTA 3D facial animation and 2D portrait animation methods demonstrate the necessity of singing-specific datasets in singing head animation tasks and the promising performance of our unified facial animation framework., Comment: Project page: https://wsj-sjtu.github.io/SingingHead

Banding, also known as staircase-like contours, frequently occurs in flat areas of images/videos processed by the compression or quantization algorithms. As undesirable artifacts, banding destroys the original image structure, thus degrading users' quality of experience (QoE). In this paper, we systematically investigate the banding image quality assessment (IQA) problem, aiming to detect the image banding artifacts and evaluate their perceptual visual quality. Considering that the existing image banding databases only contain limited content sources and banding generation methods, and lack perceptual quality labels (i.e. mean opinion scores), we first build the largest banding IQA database so far, named Banding Artifact Noticeable Database (BAND-2k), which consists of 2,000 banding images generated by 15 compression and quantization schemes. A total of 23 workers participated in the subjective IQA experiment, yielding over 214,000 patch-level banding class labels and 44,371 reliable image-level quality ratings. Subsequently, we develop an effective no-reference (NR) banding evaluator for banding detection and quality assessment by leveraging frequency characteristics of banding artifacts. A dual convolutional neural network is employed to concurrently learn the feature representation from the high-frequency and low-frequency maps, thereby enhancing the ability to discern banding artifacts. The quality score of a banding image is generated by pooling the banding detection maps masked by the spatial frequency filters. Experiments demonstrate that our banding evaluator achieves a remarkably high accuracy in banding detection and also exhibits high SRCC and PLCC results with the perceptual quality labels. These findings unveil the strong correlations between the intensity of banding artifacts and the perceptual visual quality, thus validating the necessity of banding quality assessment.

Large Language Models (LLMs) have demonstrated considerable advances, and several claims have been made about their exceeding human performance. However, in real-world tasks, domain knowledge is often required. Low-resource learning methods like Active Learning (AL) have been proposed to tackle the cost of domain expert annotation, raising this question: Can LLMs surpass compact models trained with expert annotations in domain-specific tasks? In this work, we conduct an empirical experiment on four datasets from three different domains comparing SOTA LLMs with small models trained on expert annotations with AL. We found that small models can outperform GPT-3.5 with a few hundreds of labeled data, and they achieve higher or similar performance with GPT-4 despite that they are hundreds time smaller. Based on these findings, we posit that LLM predictions can be used as a warmup method in real-world applications and human experts remain indispensable in tasks involving data annotation driven by domain-specific knowledge.

The study of UX dark patterns, i.e., UI designs that seek to manipulate user behaviors, often for the benefit of online services, has drawn significant attention in the CHI and CSCW communities in recent years. To complement previous studies in addressing dark patterns from (1) the designer's perspective on education and advocacy for ethical designs; and (2) the policymaker's perspective on new regulations, we propose an end-user-empowerment intervention approach that helps users (1) raise the awareness of dark patterns and understand their underlying design intents; (2) take actions to counter the effects of dark patterns using a web augmentation approach. Through a two-phase co-design study, including 5 co-design workshops (N=12) and a 2-week technology probe study (N=15), we reported findings on the understanding of users' needs, preferences, and challenges in handling dark patterns and investigated the feedback and reactions to users' awareness of and action on dark patterns being empowered in a realistic in-situ setting., Comment: Accepted to CSCW 2024

The recent advances in Large Language Models (LLMs) have stimulated interest among researchers and industry professionals, particularly in their application to tasks concerning mobile user interfaces (UIs). This position paper investigates the use of LLMs for UI layout generation. Central to our exploration is the introduction of UI grammar -- a novel approach we proposed to represent the hierarchical structure inherent in UI screens. The aim of this approach is to guide the generative capacities of LLMs more effectively and improve the explainability and controllability of the process. Initial experiments conducted with GPT-4 showed the promising capability of LLMs to produce high-quality user interfaces via in-context learning. Furthermore, our preliminary comparative study suggested the potential of the grammar-based approach in improving the quality of generative results in specific aspects., Comment: ICML 2023 Workshop on AI and HCI

Thanks to their generative capabilities, large language models (LLMs) have become an invaluable tool for creative processes. These models have the capacity to produce hundreds and thousands of visual and textual outputs, offering abundant inspiration for creative endeavors. But are we harnessing their full potential? We argue that current interaction paradigms fall short, guiding users towards rapid convergence on a limited set of ideas, rather than empowering them to explore the vast latent design space in generative models. To address this limitation, we propose a framework that facilitates the structured generation of design space in which users can seamlessly explore, evaluate, and synthesize a multitude of responses. We demonstrate the feasibility and usefulness of this framework through the design and development of an interactive system, Luminate, and a user study with 14 professional writers. Our work advances how we interact with LLMs for creative tasks, introducing a way to harness the creative potential of LLMs.

Managing privacy to reach privacy goals is challenging, as evidenced by the privacy attitude-behavior gap. Mitigating this discrepancy requires solutions that account for both system opaqueness and users' hesitations in testing different privacy settings due to fears of unintended data exposure. We introduce an empathy-based approach that allows users to experience how privacy attributes may alter system outcomes in a risk-free sandbox environment from the perspective of artificially generated personas. To generate realistic personas, we introduce a novel pipeline that augments the outputs of large language models (e.g., GPT-4) using few-shot learning, contextualization, and chain of thoughts. Our empirical studies demonstrated the adequate quality of generated personas and highlighted the changes in privacy-related applications (e.g., online advertising) caused by different personas. Furthermore, users demonstrated cognitive and emotional empathy towards the personas when interacting with our sandbox. We offered design implications for downstream applications in improving user privacy literacy.

While AI shows promise for enhancing the efficiency of qualitative analysis, the unique human-AI interaction resulting from varied coding strategies makes it challenging to develop a trustworthy AI-assisted qualitative coding system (AIQCs) that supports coding tasks effectively. We bridge this gap by exploring the impact of varying coding strategies on user trust and reliance on AI. We conducted a mixed-methods split-plot 3x3 study, involving 30 participants, and a follow-up study with 6 participants, exploring varying text selection and code length in the use of our AIQCs system for qualitative analysis. Our results indicate that qualitative open coding should be conceptualized as a series of distinct subtasks, each with differing levels of complexity, and therefore, should be given tailored design considerations. We further observed a discrepancy between perceived and behavioral measures, and emphasized the potential challenges of under- and over-reliance on AIQCs systems. Additional design implications were also proposed for consideration., Comment: 27 pages with references, 9 figures, 5 tables

Mobile task automation is an attractive technique that aims to enable voice-based hands-free user interaction with smartphones. However, existing approaches suffer from poor scalability due to the limited language understanding ability and the non-trivial manual efforts required from developers or end-users. The recent advance of large language models (LLMs) in language understanding and reasoning inspires us to rethink the problem from a model-centric perspective, where task preparation, comprehension, and execution are handled by a unified language model. In this work, we introduce AutoDroid, a mobile task automation system capable of handling arbitrary tasks on any Android application without manual efforts. The key insight is to combine the commonsense knowledge of LLMs and domain-specific knowledge of apps through automated dynamic analysis. The main components include a functionality-aware UI representation method that bridges the UI with the LLM, exploration-based memory injection techniques that augment the app-specific domain knowledge of LLM, and a multi-granularity query optimization module that reduces the cost of model inference. We integrate AutoDroid with off-the-shelf LLMs including online GPT-4/GPT-3.5 and on-device Vicuna, and evaluate its performance on a new benchmark for memory-augmented Android task automation with 158 common tasks. The results demonstrated that AutoDroid is able to precisely generate actions with an accuracy of 90.9%, and complete tasks with a success rate of 71.3%, outperforming the GPT-4-powered baselines by 36.4% and 39.7%. The demo, benchmark suites, and source code of AutoDroid will be released at url{https://autodroid-sys.github.io/}., Comment: Published in MobiCom 2024; Original title: "Empowering LLM to use Smartphone for Intelligent Task Automation"

This paper launches a new effort at modeling programmer attention by predicting eye movement scanpaths. Programmer attention refers to what information people intake when performing programming tasks. Models of programmer attention refer to machine prediction of what information is important to people. Models of programmer attention are important because they help researchers build better interfaces, assistive technologies, and more human-like AI. For many years, researchers in SE have built these models based on features such as mouse clicks, key logging, and IDE interactions. Yet the holy grail in this area is scanpath prediction -- the prediction of the sequence of eye fixations a person would take over a visual stimulus. A person's eye movements are considered the most concrete evidence that a person is taking in a piece of information. Scanpath prediction is a notoriously difficult problem, but we believe that the emergence of lower-cost, higher-accuracy eye tracking equipment and better large language models of source code brings a solution within grasp. We present an eye tracking experiment with 27 programmers and a prototype scanpath predictor to present preliminary results and obtain early community feedback., Comment: Accepter at ASE2023 NIER Track. 4 pages + 1 page for references, 4 figures, 1 table

Video shakiness is an unpleasant distortion of User Generated Content (UGC) videos, which is usually caused by the unstable hold of cameras. In recent years, many video stabilization algorithms have been proposed, yet no specific and accurate metric enables comprehensively evaluating the stability of videos. Indeed, most existing quality assessment models evaluate video quality as a whole without specifically taking the subjective experience of video stability into consideration. Therefore, these models cannot measure the video stability explicitly and precisely when severe shakes are present. In addition, there is no large-scale video database in public that includes various degrees of shaky videos with the corresponding subjective scores available, which hinders the development of Video Quality Assessment for Stability (VQA-S). To this end, we build a new database named StableDB that contains 1,952 diversely-shaky UGC videos, where each video has a Mean Opinion Score (MOS) on the degree of video stability rated by 34 subjects. Moreover, we elaborately design a novel VQA-S model named StableVQA, which consists of three feature extractors to acquire the optical flow, semantic, and blur features respectively, and a regression layer to predict the final stability score. Extensive experiments demonstrate that the StableVQA achieves a higher correlation with subjective opinions than the existing VQA-S models and generic VQA models. The database and codes are available at https://github.com/QMME/StableVQA., Comment: Accepted by ACM MM'23