arXiv subjects

Wenqian Xu

Publications and source records attributed to Wenqian Xu.

17 recordsLinked to original sources

arXiv2026

Data-driven Design of Metal-Organic Frameworks with Tunable Negative Thermal Expansion

Materials with negative thermal expansion (NTE) are essential for applications requiring precise control of thermal expansion. Owing to their exceptional chemical tunability, flexible architectures, and low-energy lattice vibrations, metal-organic frameworks (MOFs) represent a rich platform for exploring NTE. However, uncovering the structural motifs that govern NTE across the enormous MOF design space remains experimentally challenging, and large-scale first-principles phonon calculations are computationally prohibitive. Here, we comprehensively evaluate the factors influencing NTE in MOFs by utilizing a high-throughput workflow based on MACE-MP-MOF0, a machine learning interatomic potential fine-tuned for MOFs with near-ab initio accuracy, to construct PhononMOFdb, a database of phonons, inelastic neutron scattering spectra, bulk moduli, and heat capacities for over 12,000 MOFs. High-throughput screening of this database reveals that highly porous cubic topology frameworks with heavier, lower-valent metal nodes favor strong NTE, while linker functionalization provides a practical handle for tuning NTE magnitude and sign without compromising mechanical stability. Experimental validation via high-resolution temperature-dependent synchrotron powder X-ray diffraction on the Ce-UiO-66 MOF and its brominated variants confirms the design recipe and yields volumetric NTE coefficients surpassing current records. This work establishes a data-driven strategy for engineering NTE in MOFs, showing how machine learning-accelerated discovery and targeted experimental validation together unlock predictive materials design.

cond-mat.mtrl-sci↗

arXiv2026

Sidekick: Designing Communication for Effective Multitasking with Computer Use Agents

Computer Use Agents (CUAs) can autonomously execute complex, multi-step tasks within GUIs, enhancing efficiency through parallel multitasking. However, our formative studies with CUA experts and GenAI users indicated that current feedback is primarily text-based, requiring sustained attention to monitor progress and offering limited visibility to trace past GUI interactions. Based on the findings, we developed a prototype system, Sidekick, for communicating CUAs' status with multimodal feedback across different stages of interaction: (i) When CUAs run in the background, Sidekick signals its execution state through ambient cues. (ii) Upon resuming interaction with CUAs, Sidekick provides multimodal summaries of completed actions to support rapid context resumption. (iii) When CUAs operate in the foreground, Sidekick enhances transparency by verbalizing and visualizing the agent's reasoning. A study with 30 participants demonstrated that Sidekick significantly improved multitasking performance with CUAs compared to baseline systems that presented textual feedback either in a typical chat or in an ambient display. Sidekick supported progress awareness, and error and action traceability more effectively. Finally, we demonstrate the promise of Sidekick through several example applications, and discuss implications for long-horizon human-agent collaboration.

cs.HC↗

arXiv2026

Reinforcement Learning Measurement Model

Interactive assessments generate sequential process data that are not well handled by conventional item response models. Existing MDP-based measurement approaches, such as the Markov decision process measurement model (MDP-MM, LaMar, 2018), link action choices to state-action values, but their reliance on person-specific tabular value functions makes them difficult to scale beyond small, fully enumerated tasks. We propose the Reinforcement Learning Measurement Model (RLMM), a measurement framework that decouples person-level choice sensitivity from task-level value representation through a shared parametric action-value function, making estimation more computationally efficient for larger process-data settings. The model combines a Boltzmann choice rule with normalized advantages, a soft Bellman consistency penalty, and a block-coordinate MAP procedure for joint estimation, while also yielding step-level influence diagnostics for identifying behaviorally critical decisions. In peg-solitaire simulations, the RLMM achieved higher estimation accuracy and substantially lower runtime than the original MDP-MM, with advantages increasing as task complexity grew. In AQUALAB gameplay logs, the estimated person parameter was positively associated with cumulative reward, task completion, and behavioral efficiency. These results show that the RLMM extends decision-process-based psychometric models to larger and more behaviorally realistic environments while preserving an interpretable latent trait tied to decision making steps.

stat.ME↗

arXiv2026

Inverse Energy Cascade in Turbulent Taylor-Couette Flows

The inverse energy cascade in turbulent Taylor-Couette flow is studied in line with the results of the large eddy simulation. The simulation results show that the inverse energy cascade first occurs within the core region of the flow channel of the Taylor-Couette flow at higher Reynolds number. It is uncovered that this phenomenon is induced by the pulsed zero shear stress resulting from the singularities of the Navier-Stokes equation. In the core area between the two cylinders, the shear stress is nearly zero at higher Reynolds number. The turbulence generated there has high turbulent energy due to discontinuity of the tangential velocity. Since the energy transfer between the fluid layers is inhibited due to the low shear stress, the turbulent energy cannot be transferred along the radial direction, and small-scale vortices with high turbulent energy are produced. These small-scale vortices are located with the large-scale vortices and cannot be dissipated owing to low shear stress. A peak in the energy spectrum at middle frequency (or wave number) is formed due to the concentration of the small-scale vortices. As the number of the singular points of the Navier-Stokes equation increases with the increasing Reynolds number, the region with zero shear stress expands along the radial direction, intensifying nonlinear instability and energy accumulation. This, in turn, leads to more prominent peaks in the energy spectrum, resulting in a more pronounced inverse energy cascade.

physics.flu-dyn↗

arXiv2026

Solitary wave structure of transitional flow in the wake of a sphere

The soliton-like coherent structure (SCS), which has been verified to exist in both transitional and turbulent boundary layers1-4, still poses a challenge in the understanding of its formation and behavior. In our previous study (Niu et al.5), the SCS was also found to exist in the transitional wake flow behind a sphere. In present study, the formation and evolution of the SCS is further investigated at four Reynolds numbers by numerical simulation. The results show that at the early stage of the turbulence transition, the SCS appears as a form of wave packet during the Tollmien-Schlichting (T-S) wave stage. With the increase of the Reynolds number, the SCS reaches its maximum amplitude downstream where the velocity discontinuity occurs. This position is located after the breakdown of the T-S wave and the three-dimensional structure is formed. Then, the SCS conserves its shape and amplitude over a long distance downstream. The relationships among the SCS, the spikes, the vortex structures, and the high-shear layers are further analyzed. It is found that the SCS in the wake flow has similarities to the phenomena observed in boundary layer flows during the turbulent transition. The vortex structures and high-shear layers mostly wrap around the border of the SCS. The vortex structure is considered to be as a consequence of the development of the SCS rather than its cause.

physics.flu-dyn↗

arXiv2026

TouchScribe: Augmenting Non-Visual Hand-Object Interactions with Automated Live Visual Descriptions

People who are blind or have low vision regularly use their hands to interact with the physical world to gain access to objects' shape, size, weight, and texture. However, many rich visual features remain inaccessible through touch alone, making it difficult to distinguish similar objects, interpret visual affordances, and form a complete understanding of objects. In this work, we present TouchScribe, a system that augments hand-object interactions with automated live visual descriptions. We trained a custom egocentric hand interaction model to recognize both common gestures (e.g., grab to inspect, hold side-by-side to compare) and unique ones by blind people (e.g., point to explore color, or swipe to read available texts). Furthermore, TouchScribe provides real-time and adaptive feedback based on hand movement, from hand interaction states, to object labels, and to visual details. Our user study and technical evaluations demonstrate that TouchScribe can provide rich and useful descriptions to support object understanding. Finally, we discuss the implications of making live visual descriptions responsive to users' physical reach.

cs.HC↗

arXiv2026

Modulating anomalous thermal quenching behavior of stimulation luminescence via high-orbit electronic satellite-stabilized Trap state in germanate-based phosphors for 5D optical data storage

Persistent luminescence (PersL) materials, widely used in emergency lighting and information storage, are primarily employed at room temperature. However, their luminescent performance deteriorates sharply at high temperatures. Herein, a serials of Mg2GeO4:Ti4+,Ln3+ (Ln = Tb, Eu) phosphors demonstrated anomalous thermal quenching PersL due to the temperature-dependent Fermi-Dirac distribution of bound charge carriers of Ti4+Mg2+ as remote electron traps and VMg2+ as hole traps. The high carrier retention rate of phosphors is attributed to the ability of Ti4+Mg2+ positive charge center to strongly trap non-bonding electrons over a long range (about 20 angstroms) as the electronic satellite for its stable operation. Under external optical/thermal stimulation, the released electrons and holes recombine at the different luminescent levels of Tb3+, resulting in the emission of different PersL branching ratios. Using these phosphors, we have developed 5D optical data storage (2D plane + trap depth + temperature + time) and the encrypted engine program for high-temperature aerospace engines. This study reveals the energy storage process of long-range trapping and releasing electrons by Ti4+ electron traps, and provides a new design concept for the design of PersL materials.

physics.optics↗

arXiv2025

Teaching Data Science Students to Sketch Privacy Designs through Heuristics (Extended Technical Report)

Recent studies reveal that experienced data practitioners often draw sketches to facilitate communication around privacy design concepts. However, there is limited understanding of how we can help novice students develop such communication skills. This paper studies methods for lowering novice data science students' barriers to creating high-quality privacy sketches. We first conducted a need-finding study (N=12) to identify barriers students face when sketching privacy designs. We then used a human-centered design approach to guide the method development, culminating in three simple, text-based heuristics. Our user studies with 24 data science students revealed that simply presenting three heuristics to the participants at the beginning of the study can enhance the coverage of privacy-related design decisions in sketches, reduce the mental effort required for creating sketches, and improve the readability of the final sketches.

cs.HC↗

arXiv2025

Not There Yet: Evaluating Vision Language Models in Simulating the Visual Perception of People with Low Vision

Advances in vision language models (VLMs) have enabled the simulation of general human behavior through their reasoning and problem solving capabilities. However, prior research has not investigated such simulation capabilities in the accessibility domain. In this paper, we evaluate the extent to which VLMs can simulate the vision perception of low vision individuals when interpreting images. We first compile a benchmark dataset through a survey study with 40 low vision participants, collecting their brief and detailed vision information and both open-ended and multiple-choice image perception and recognition responses to up to 25 images. Using these responses, we construct prompts for VLMs (GPT-4o) to create simulated agents of each participant, varying the included information on vision information and example image responses. We evaluate the agreement between VLM-generated responses and participants' original answers. Our results indicate that VLMs tend to infer beyond the specified vision ability when given minimal prompts, resulting in low agreement (0.59). The agreement between the agent' and participants' responses remains low when only either the vision information (0.59) or example image responses (0.59) are provided, whereas a combination of both significantly increase the agreement (0.70, p < 0.0001). Notably, a single example combining both open-ended and multiple-choice responses, offers significant performance improvements over either alone (p < 0.0001), while additional examples provided minimal benefits (p > 0.05).

cs.CV↗

arXiv2025

Probing the Gaps in ChatGPT Live Video Chat for Real-World Assistance for People who are Blind or Visually Impaired

Recent advancements in large multimodal models have provided blind or visually impaired (BVI) individuals with new capabilities to interpret and engage with the real world through interactive systems that utilize live video feeds. However, the potential benefits and challenges of such capabilities to support diverse real-world assistive tasks remain unclear. In this paper, we present findings from an exploratory study with eight BVI participants. Participants used ChatGPT's Advanced Voice with Video, a state-of-the-art live video AI released in late 2024, in various real-world scenarios, from locating objects to recognizing visual landmarks, across unfamiliar indoor and outdoor environments. Our findings indicate that current live video AI effectively provides guidance and answers for static visual scenes but falls short in delivering essential live descriptions required in dynamic situations. Despite inaccuracies in spatial and distance information, participants leveraged the provided visual information to supplement their mobility strategies. Although the system was perceived as human-like due to high-quality voice interactions, assumptions about users' visual abilities, hallucinations, generic responses, and a tendency towards sycophancy led to confusion, distrust, and potential risks for BVI users. Based on the results, we discuss implications for assistive video AI agents, including incorporating additional sensing capabilities for real-world use, determining appropriate intervention timing beyond turn-taking interactions, and addressing ecological and safety concerns.

cs.HC↗

arXiv2025

Rietveld Refinement and NMR Crystallographic Investigations of Multicomponent Crystals Containing Alkali Metal Chlorides and Urea

New mechanochemical preparations of three multicomponent crystals (MCCs) of the form MCl:urea.nH2O (M = Li+, Na+, and Cs+) are reported. Their structures were determined by an NMR crystallography approach, combining Rietveld refinement of synchrotron powder X-ray diffraction data (PXRD), multinuclear (35Cl, 7Li, 23Na, and 133Cs) solid-state NMR (SSNMR) spectroscopy, and thermal analysis. Mechanochemical syntheses of the three MCCs, two of which are novel, are optimized for maximum yield and efficiency. 35Cl SSNMR is well-suited for the structural characterization of these MCCs since it is sensitive to subtle differences and/or changes in chloride ion environments, providing a powerful means of examining H...Cl-bonding environments. Alkali metal NMR is beneficial for identifying the number of unique magnetically and crystallographically distinct sites and enables facile detection of educts and/or impurities. In the case of NaCl:urea.H2O, 23Na MAS NMR spectra are key, both for identifying residual NaCl educt and for monitoring NaCl:urea.H2O degradation, which appears to proceed via an autocatalytic decomposition process driven by water (with a rate constant of k = 1.22x10-3 s-1). SSNMR and PXRD were used to inform the initial structural models. Following Rietveld refinement, the models were subjected to dispersion-corrected plane-wave DFT geometry optimizations and subsequent calculations of the 35Cl EFG tensors, which enable the refinement of hydrogen atom positions, as well as the exploration of their relationships to the local hydrogen bonding environments of the chloride ions and crystallographic symmetry elements.

cond-mat.mtrl-sci↗

arXiv2023

Image Segmentation using U-Net Architecture for Powder X-ray Diffraction Images

Scientific researchers frequently use the in situ synchrotron high-energy powder X-ray diffraction (XRD) technique to examine the crystallographic structures of materials in functional devices such as rechargeable battery materials. We propose a method for identifying artifacts in experimental XRD images. The proposed method uses deep learning convolutional neural network architectures, such as tunable U-Nets to identify the artifacts. In particular, the predicted artifacts are evaluated against the corresponding ground truth (manually implemented) using the overall true positive rate or recall. The result demonstrates that the U-Nets can consistently produce great recall performance at 92.4% on the test dataset, which is not included in the training, with a 34% reduction in average false positives in comparison to the conventional method. The U-Nets also reduce the time required to identify and separate artifacts by more than 50%. Furthermore, the exclusion of the artifacts shows major changes in the integrated 1D XRD pattern, enhancing further analysis of the post-processing XRD data.

cs.LG↗

arXiv2023

Observation of Traveling Breathers and Their Scattering in a Two-Fluid System

The observation of traveling breathers (TBs) with large-amplitude oscillatory tails realizes an almost 50-year-old theoretical prediction (Kuznetsov 1975) and generalizes the notion of a breather. Two strongly nonlinear TB families are created in a core-annular flow by interacting a soliton and a nonlinear periodic (cnoidal) carrier. Bright and dark TBs are observed to move faster or slower, respectively, than the carrier while imparting a phase shift. Agreement with model equations is achieved. Scattering of the TBs are observed to be physically elastic. The observed TBs generalize to many continuum and discrete systems.

nlin.PS↗

arXiv2022

In situ X-ray area detector gain correction at an operating photon energy

Gain calibration of x-ray area detectors is a challenge due to the inability to generate an x-ray flat field at the selected photon energy that the beamline operates at, which has a strong influence on the detector's gain behavior. A method is presented in which a gain map is calculated without flat field measurements. Rather, a series of quick scattering measurements from an amorphous scatterer is used to calculate a gain map. The ability to rapidly obtain a gain map allows for a recalibration of an x-ray detector as needed without significant expenditure of either time or effort. Area detectors on the beamlines used, such as the Pilatus 2M CdTe or Varex XRD 4343CT, were found to have gains which drift slightly over timescales of several weeks or after exposure to high photon flux, suggesting the need to more frequently recalibrate for detector gain.

physics.ins-det↗

arXiv2022

Artifact Identification in X-ray Diffraction Data using Machine Learning Methods

The in situ synchrotron high-energy X-ray powder diffraction (XRD) technique is highly utilized by researchers to analyze the crystallographic structures of materials in functional devices (e.g., battery materials) or in complex sample environments (e.g., diamond anvil cells or syntheses reactors). An atomic structure of a material can be identified by its diffraction pattern, along with detailed analysis such as Rietveld refinement which indicates how the measured structure deviates from the ideal structure (e.g., internal stresses or defects). For in situ experiments, a series of XRD images is usually collected on the same sample at different conditions (e.g., adiabatic conditions), yielding different states of matter, or simply collected continuously as a function of time to track the change of a sample over a chemical or physical process. In situ experiments are usually performed with area detectors, collecting 2D images composed of diffraction rings for ideal powders. Depending on the material's form, one may observe different characteristics other than the typical Debye Scherrer rings for a realistic sample and its environments, such as textures or preferred orientations and single crystal diffraction spots in the 2D XRD image. In this work, we present an investigation of machine learning methods for fast and reliable identification and separation of the single crystal diffraction spots in XRD images. The exclusion of artifacts during an XRD image integration process allows a precise analysis of the powder diffraction rings of interest. We observe that the gradient boosting method can consistently produce high accuracy results when it is trained with small subsets of highly diverse datasets. The method dramatically decreases the amount of time spent on identifying and separating single crystal spots in comparison to the conventional method.

eess.IV↗

arXiv2022

Dual Perceptual Loss for Single Image Super-Resolution Using ESRGAN

The proposal of perceptual loss solves the problem that per-pixel difference loss function causes the reconstructed image to be overly-smooth, which acquires a significant progress in the field of single image super-resolution reconstruction. Furthermore, the generative adversarial networks (GAN) is applied to the super-resolution field, which effectively improves the visual quality of the reconstructed image. However, under the condtion of high upscaling factors, the excessive abnormal reasoning of the network produces some distorted structures, so that there is a certain deviation between the reconstructed image and the ground-truth image. In order to fundamentally improve the quality of reconstructed images, this paper proposes a effective method called Dual Perceptual Loss (DP Loss), which is used to replace the original perceptual loss to solve the problem of single image super-resolution reconstruction. Due to the complementary property between the VGG features and the ResNet features, the proposed DP Loss considers the advantages of learning two features simultaneously, which significantly improves the reconstruction effect of images. The qualitative and quantitative analysis on benchmark datasets demonstrates the superiority of our proposed method over state-of-the-art super-resolution methods.

eess.IV↗

arXiv2018

Stability of boundary layer flow based on energy gradient theory

The flow of the laminar boundary layer on a flat plate is studied with simulation of Navier-Stokes equations. The mechanisms of flow instability at external edge of the boundary layer and near the wall are analyzed using the energy gradient theory. The simulation results show that there is an overshoot on the velocity profile at the external edge of the boundary layer. At this overshoot, the energy gradient function is very large which results in instability according to the energy gradient theory. It is found that the transverse gradient of the total mechanical energy is responsible for the instability at the external edge of the boundary layer, which induces the entrainment of external flow into the boundary layer. Within the boundary layer, there is a maximum of the energy gradient function near the wall, which leads to intensive flow instability near the wall and contributes to the generation of turbulence.

physics.flu-dyn↗