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In this letter, we systematically investigate the microscopic dynamics of collective vibrational excitations in simple liquids. The thermodynamic states of simple liquids are unified to the mean atomic free volume. Our results show that longitudinal acoustic collective vibrational excitations are always observed in simple liquids even when the liquids are viscous, in which the atomic free volume is larger than the cross point of the corresponding mean propagation length and the atomic diffusion limit. This is because some long-wavelength longitudinal acoustic collective vibrational excitations can still propagate in viscous liquids. However, transverse acoustic collective vibrational excitations in viscous liquids become localized since both short- and long-wavelength transverse acoustic collective vibrational excitations have propagation lengths smaller than the atomic diffusion limit. Therefore, transverse acoustic collective vibrational excitations may not be detected in simple liquids. The propagation length of macroscopic elastic and shear waves which are the mechanical response of long-wavelength longitudinal and transverse collective vibrational excitations, respectively, is further calculated to quickly determine the propagation-to-localization crossover of collective vibrational excitations in simple liquids. Our findings here advance the understanding of the microscopic dynamics of collective vibrational excitations in simple liquids.

Automated Machine Learning (AutoML) has simplified complex ML processes such as data pre-processing, model selection, and hyper-parameter searching. However, traditional AutoML frameworks focus solely on discriminative tasks, often falling short in tackling AutoML for generative models. Additionally, these frameworks lack interpretability and user engagement during the training process, primarily due to the absence of human-centered design. It leads to a lack of transparency in final decision-making and limited user control, potentially reducing trust and adoption of AutoML methods. To address these limitations, we introduce UniAutoML, a human-centered AutoML framework that leverages Large Language Models (LLMs) to unify AutoML for both discriminative (e.g., Transformers and CNNs for classification or regression tasks) and generative tasks (e.g., fine-tuning diffusion models or LLMs). The human-centered design of UniAutoML innovatively features a conversational user interface (CUI) that facilitates natural language interactions, providing users with real-time guidance, feedback, and progress updates for better interpretability. This design enhances transparency and user control throughout the AutoML training process, allowing users to seamlessly break down or modify the model being trained. To mitigate potential risks associated with LLM generated content, UniAutoML incorporates a safety guardline that filters inputs and censors outputs. We evaluated UniAutoML's performance and usability through experiments on eight diverse datasets and user studies involving 25 participants, demonstrating that UniAutoML not only enhances performance but also improves user control and trust. Our human-centered design bridges the gap between AutoML capabilities and user understanding, making ML more accessible to a broader audience.

Monolithic metal-organic frameworks (MOFs), which have a continuous structure composed of small primary MOF particles and amorphous networks, are demonstrated to possess larger pore volume and thus better larger gas uptake capacity compared to their powder forms. Here, we systematically investigated the water vapor adsorption kinetics in a prototypical MOF, i.e., MOF-801. Our results show that the total pore volume (average pore diameter) of the monolithic MOF-801 is 0.831 cm3/g (5.20 nm) which is much larger than that of powder MOF-801, i.e., 0.488 cm3/g (1.95 nm). Unexpectedly, we find that the water uptake capacity of monolithic MOF-801 is much lower than that of powder MOF-801 when the RH ranges from 10% to 90%. Our molecular dynamics simulations further demonstrate that the unexpected water uptake capacity of monolithic MOF-801 at RH of 10%~90% is caused by the water film formed by the capillary condensation in these mesopores of monolithic MOF-801. The water molecules can overcome the capillary force when the RH is higher than 90%, and then leads to the increase of the corresponding water uptake capacity of monolithic MOF-801. Our findings reveal the underlying mechanisms for water adsorption kinetics in both powder and monolithic MOFs, which could motivate and benefit the new passive cooling or water harvesting system design based on MOFs.

In the dynamic realms of machine learning and deep learning, the robustness and reliability of models are paramount, especially in critical real-world applications. A fundamental challenge in this sphere is managing Out-of-Distribution (OOD) samples, significantly increasing the risks of model misclassification and uncertainty. Our work addresses this challenge by enhancing the detection and management of OOD samples in neural networks. We introduce OOD-R (Out-of-Distribution-Rectified), a meticulously curated collection of open-source datasets with enhanced noise reduction properties. In-Distribution (ID) noise in existing OOD datasets can lead to inaccurate evaluation of detection algorithms. Recognizing this, OOD-R incorporates noise filtering technologies to refine the datasets, ensuring a more accurate and reliable evaluation of OOD detection algorithms. This approach not only improves the overall quality of data but also aids in better distinguishing between OOD and ID samples, resulting in up to a 2.5\% improvement in model accuracy and a minimum 3.2\% reduction in false positives. Furthermore, we present ActFun, an innovative method that fine-tunes the model's response to diverse inputs, thereby improving the stability of feature extraction and minimizing specificity issues. ActFun addresses the common problem of model overconfidence in OOD detection by strategically reducing the influence of hidden units, which enhances the model's capability to estimate OOD uncertainty more accurately. Implementing ActFun in the OOD-R dataset has led to significant performance enhancements, including an 18.42\% increase in AUROC of the GradNorm method and a 16.93\% decrease in FPR95 of the Energy method. Overall, our research not only advances the methodologies in OOD detection but also emphasizes the importance of dataset integrity for accurate algorithm evaluation.

Tuning the thermal transport properties of hybrid halide perovskites is critical for their applications in optoelectronics, thermoelectrics, and photovoltaics. Here, we demonstrate an effective strategy to modulate the thermal transport property of hybrid perovskites by halide alloying. A highly tunable thermal conductivity of mixed-halide hybrid perovskites is achieved due to halide-alloying and structural distortion. Our experimental measurements show that the room temperature thermal conductivity of MAPb(BrxI1-x)3 (x = 0-1) can be largely modulated from 0.27 W/mK (x = 0.5) to 0.47 W/mK (x = 1). Molecular dynamics simulations further demonstrate that the thermal conductivity reduction of hybrid halide perovskites results from the suppression of the mean free paths of the low-frequency acoustic and optical phonons. It is found that halide alloying and the induced structural distortion can largely increase the scatterings of optical and acoustic phonons, respectively. The confined diffusion of MA+ cations in the octahedra cage is found to act as an additional thermal transport channel in hybrid perovskites and can contribute around 10-20% of the total thermal conductivity. Our findings provide a strategy for tailoring the thermal transport in hybrid halide perovskites which may largely benefit their related applications.

As automation technologies advance, the need for compact and multi-modal sensors in robotic applications is growing. To address this demand, we introduce CompdVision, a novel sensor that employs a compound-eye imaging system to combine near-field 3D visual and tactile sensing within a compact form factor. CompdVision utilizes two types of vision units to address diverse sensing needs, eliminating the need for complex modality conversion. Stereo units with far-focus lenses can see through the transparent elastomer for depth estimation beyond the contact surface. Simultaneously, tactile units with near-focus lenses track the movement of markers embedded in the elastomer to obtain contact deformation. Experimental results validate the sensor's superior performance in 3D visual and tactile sensing, proving its capability for reliable external object depth estimation and precise measurement of tangential and normal contact forces. The dual modalities and compact design make the sensor a versatile tool for robotic manipulation., Comment: Accepted to IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2024

Learning generalized representations from limited training samples is crucial for applying deep neural networks in low-resource scenarios. Recently, methods based on Contrastive Language-Image Pre-training (CLIP) have exhibited promising performance in few-shot adaptation tasks. To avoid catastrophic forgetting and overfitting caused by few-shot fine-tuning, existing works usually freeze the parameters of CLIP pre-trained on large-scale datasets, overlooking the possibility that some parameters might not be suitable for downstream tasks. To this end, we revisit CLIP's visual encoder with a specific focus on its distinctive attention pooling layer, which performs a spatial weighted-sum of the dense feature maps. Given that dense feature maps contain meaningful semantic information, and different semantics hold varying importance for diverse downstream tasks (such as prioritizing semantics like ears and eyes in pet classification tasks rather than side mirrors), using the same weighted-sum operation for dense features across different few-shot tasks might not be appropriate. Hence, we propose fine-tuning the parameters of the attention pooling layer during the training process to encourage the model to focus on task-specific semantics. In the inference process, we perform residual blending between the features pooled by the fine-tuned and the original attention pooling layers to incorporate both the few-shot knowledge and the pre-trained CLIP's prior knowledge. We term this method as Semantic-Aware FinE-tuning (SAFE). SAFE is effective in enhancing the conventional few-shot CLIP and is compatible with the existing adapter approach (termed SAFE-A).

A new resolvent-based method is developed to predict the space-time properties of the flow field. To overcome the deterioration of the prediction accuracy with the increasing distance between the measurements and predictions in the Resolvent-Based Estimation (RBE), the newly proposed method utilizes the RBE to estimate the relative energy distribution near the wall rather than the absolute energy directly estimated from the measurements. Using this extra information from RBE, the new method modifies the energy distribution of the spatially uniform and uncorrelated forcing that drives the flow system by minimizing the norm of the cross-spectral density (CSD) tensor of the error matrix in the near-wall region in comparison with the RBE-estimated one, and therefore it is named as the Resolvent-informed White-noise-based Estimation (RWE) method. For validation, three time-resolved direct numerical simulation (DNS) datasets with the friction Reynolds numbers $Re_\tau = 180$, 550, and 950 are generated, with various locations of measurements ranging from the near-wall region ($y^+ = 40$) to the upper bound of the logarithmic region ($y/h \approx 0.2$) for the predictions. Besides the RWE, three existing methods, i.e., the RBE, the $\lambda$-model, and the White-noise-Based Estimation (WBE), are also included for the validation. The performance of the RBE and $\lambda$-model in predicting the energy spectra shows a strong dependence on the measurement locations. The newly proposed RWE shows a low sensitivity on $Re_{\tau}$ and the measurement locations, which may range from the near-wall region to the upper bound of the logarithmic region, and has a high accuracy in predicting the energy spectra., Comment: 42 pages, 24 figures, accepted by Journal of Fluid Mechanics

We propose a new method for measuring the spatial density distribution of the stellar halo of the Milky Way. Our method is based on a pairwise statistic of the distribution of stars on the sky, the angular two-point correlation function (ATPCF). The ATPCF utilizes two dimensional data of stars only and is therefore immune to the large uncertainties in the determination of distances to stars. We test our method using mock stellar data coming from various models including the single power-law (SPL) and the broken power-law (BPL) density profiles. We also test the influence of axisymmetric flattening factors using both constant and varying values. We find that the ATPCF is a powerful tool for recovering the spatial distributions of the stellar halos in our models. We apply our method to observational data from the type ab RR Lyrae catalog in the Catalina Survey Data Release 1. In the 3-parameter BPL model, we find that $s_{1}=2.46_{-0.20}^{+0.18}, s_{2}=3.99_{-1.33}^{+0.75}$ and $r_{0}=31.11_{-5.88}^{+7.61}$, which are in good agreement with previous results. We also find that introducing an extra parameter, the radially varying flattening factor, greatly improves our ability to model accurately the observed data distribution. This implies perhaps that the stellar halo of the Milky Way should be regarded as oblate., Comment: Accepted for publication in MNRAS