Miracle-dz/UniFAR-FTUs huggingface dataset:
Using AI libraries for Incompressible Computational Fluid Dynamics

Recently, there has been a huge effort focused on developing highly efficient open source libraries to perform Artificial Intelligence (AI) related computations on different computer architectures (for example, CPUs, GPUs and new AI processors). This has not only made the algorithms based on these libraries highly efficient and portable between different architectures, but also has substantially simplified the entry barrier to develop methods using AI. Here, we present a novel methodology to bring the power of both AI software and hardware into the field of numerical modelling by repurposing AI methods, such as Convolutional Neural Networks (CNNs), for the standard operations required in the field of the numerical solution of Partial Differential Equations (PDEs). The aim of this work is to bring the high performance, architecture agnosticism and ease of use into the field of the numerical solution of PDEs. We use the proposed methodology to solve the advection-diffusion equation, the non-linear Burgers equation and incompressible flow past a bluff body. For the latter, a convolutional neural network is used as a multigrid solver in order to enforce the incompressibility constraint. We show that the presented methodology can solve all these problems using repurposed AI libraries in an efficient way, and presents a new avenue to explore in the development of methods to solve PDEs and Computational Fluid Dynamics problems with implicit methods.
Dynamic laser ablation loading of a linear Paul trap

We present a detailed method for accumulating Ca$^{+}$ ions controllably in a linear Paul trap. The ions are generated by pulsed laser ablation and dynamically loaded into the ion trap by switching the trapping potential on and off. The loaded ions are precooled by buffer gas and then laser-cooled to form Coulomb crystals for verifying quantity. The number of ions is controlled by manipulating the trapping potential of the ion trap, partial pressure of buffer gas and turn-on time of the entrance end cap voltage. With single-pulse laser ablation, the number of trapped ions ranges from tens to ten thousand. The kinetic energy of loaded ions can be selected via the optimal turn-on time of the entrance end cap. Using multiple-pulse laser ablation, the number is further increased and reaches about $4 \times 10^{4}$. The dynamic loading method has wide application for accumulating low-yielding ions via laser ablation in the ion trap.
Collaborative learning of common latent representations in routinely collected multivariate ICU physiological signals

In Intensive Care Units (ICU), the abundance of multivariate time series presents an opportunity for machine learning (ML) to enhance patient phenotyping. In contrast to previous research focused on electronic health records (EHR), here we propose an ML approach for phenotyping using routinely collected physiological time series data. Our new algorithm integrates Long Short-Term Memory (LSTM) networks with collaborative filtering concepts to identify common physiological states across patients. Tested on real-world ICU clinical data for intracranial hypertension (IH) detection in patients with brain injury, our method achieved an area under the curve (AUC) of 0.889 and average precision (AP) of 0.725. Moreover, our algorithm outperforms autoencoders in learning more structured latent representations of the physiological signals. These findings highlight the promise of our methodology for patient phenotyping, leveraging routinely collected multivariate time series to improve clinical care practices.
Physics-Informed Machine Learning for the Inverse Design of Wave Scattering Clusters

Clusters of wave-scattering oscillators offer the ability to passively control wave energy in elastic continua. However, designing such clusters to achieve a desired wave energy pattern is a highly nontrivial task. While the forward scattering problem may be readily analyzed, the inverse problem is very challenging as it is ill-posed, high-dimensional, and known to admit non-unique solutions. Therefore, the inverse design of multiple scattering fields and remote sensing of scattering elements remains a topic of great interest. Motivated by recent advances in physics-informed machine learning, we develop a deep neural network that is capable of predicting the locations of scatterers by evaluating the patterns of a target wavefield. We present a modeling and training formulation to optimize the multi-functional nature of our network in the context of inverse design, remote sensing, and wavefield engineering. Namely, we develop a multi-stage training routine with customized physics-based loss functions to optimize models to detect the locations of scatterers and predict cluster configurations that are physically consistent with the target wavefield. We demonstrate the efficacy of our model as a remote sensing and inverse design tool for three scattering problem types, and we subsequently applicability for designing clusters that direct waves along preferred paths or localize wave energy. Hence, we present an effective model for multiple scattering inverse design which may have diverse applications such as wavefield imaging or passive wave energy control.
Location-guided Head Pose Estimation for Fisheye Image

Camera with a fisheye or ultra-wide lens covers a wide field of view that cannot be modeled by the perspective projection. Serious fisheye \textcolor{blue}{lens} distortion in the peripheral region of the image leads to degraded performance of the \textcolor{blue}{existing} head pose estimation models trained on undistorted images. This paper presents a new approach for head pose estimation that uses the knowledge of head location in the image to reduce the negative effect of fisheye distortion. We develop an end-to-end convolutional neural network to estimate the head pose with the multi-task learning of head pose and head location. Our proposed network estimates the head pose directly from the fisheye image without the operation of rectification or calibration. We also created \textcolor{blue}{a} fisheye-\textcolor{blue}{distorted} version of the three popular head pose estimation datasets, BIWI, 300W-LP, and AFLW2000 for our experiments. Experiments results show that our network remarkably improves the accuracy of head pose estimation compared with other state-of-the-art one-stage and two-stage methods.
Supersymmetric AdS Solitons and the interconnection of different vacua of ${\cal N}=4$ Super Yang-Mills

We find AdS soliton solutions in 5-dimensional gauged supergravity, obtained from the $S^5$ compactification of type IIB, with a dilaton saturating the Breitenlohner-Freedman bound. The solutions depend on the value of the periodicity of an $S^1$ cycle and the boundary values for two $U(1)$ gauge fields, and give a scalar VEV in the dual field theory. At certain values of the gauge sources we have supersymmetric solutions, corresponding to supersymmetric flows, which are a deformation of the Coulomb Branch flow in ${\cal N}=4$ SYM. The solutions parameterize quantum phase transitions between a discrete spectrum phase, a continuous above a mass gap phase, and a continuous without a mass gap phase, in 2+1 dimensions. We analyze the phase diagram in terms of the QFT sources and we find that for every value for them, there are always two branches of supergravity solutions. We find that these two branches of solitons correspond to two possible vacua existing in the dual QFT when fermions are anti-periodic on an $S^1$. We describe the interconnection of these states in the QFT at strong 't Hooft coupling in the large $N$ limit. In 10 dimensions, our solutions are related to deformations of D3-brane distributions.
Diffusion Models as Constrained Samplers for Optimization with Unknown Constraints

Addressing real-world optimization problems becomes particularly challenging when analytic objective functions or constraints are unavailable. While numerous studies have addressed the issue of unknown objectives, limited research has focused on scenarios where feasibility constraints are not given explicitly. Overlooking these constraints can lead to spurious solutions that are unrealistic in practice. To deal with such unknown constraints, we propose to perform optimization within the data manifold using diffusion models. To constrain the optimization process to the data manifold, we reformulate the original optimization problem as a sampling problem from the product of the Boltzmann distribution defined by the objective function and the data distribution learned by the diffusion model. To enhance sampling efficiency, we propose a two-stage framework that begins with a guided diffusion process for warm-up, followed by a Langevin dynamics stage for further correction. Theoretical analysis shows that the initial stage results in a distribution focused on feasible solutions, thereby providing a better initialization for the later stage. Comprehensive experiments on a synthetic dataset, six real-world black-box optimization datasets, and a multi-objective optimization dataset show that our method achieves better or comparable performance with previous state-of-the-art baselines.
Opening Cabinets and Drawers in the Real World using a Commodity Mobile Manipulator

Pulling open cabinets and drawers presents many difficult technical challenges in perception (inferring articulation parameters for objects from onboard sensors), planning (producing motion plans that conform to tight task constraints), and control (making and maintaining contact while applying forces on the environment). In this work, we build an end-to-end system that enables a commodity mobile manipulator (Stretch RE2) to pull open cabinets and drawers in diverse previously unseen real world environments. We conduct 4 days of real world testing of this system spanning 31 different objects from across 13 different real world environments. Our system achieves a success rate of 61% on opening novel cabinets and drawers in unseen environments zero-shot. An analysis of the failure modes suggests that errors in perception are the most significant challenge for our system. We will open source code and models for others to replicate and build upon our system.
Whole-body Humanoid Robot Locomotion with Human Reference

Recently, humanoid robots have made significant advances in their ability to perform challenging tasks due to the deployment of Reinforcement Learning (RL), however, the inherent complexity of humanoid robots, including the difficulty of designing complicated reward functions and training entire sophisticated systems, still poses a notable challenge. To conquer these challenges, after many iterations and in-depth investigations, we have meticulously developed a full-size humanoid robot,"Adam", whose innovative structural design greatly improves the efficiency and effectiveness of the imitation learning process. In addition, we have developed a novel imitation learning framework based on an adversarial motion prior, which applies not only to Adam but also to humanoid robots in general. Using the framework, Adam can exhibit unprecedented human-like characteristics in locomotion tasks. Our experimental results demonstrate that the proposed framework enables Adam to achieve human-comparable performance in complex locomotion tasks, marking the first time that human locomotion data has been used for imitation learning in a full-size humanoid robot.
HearHere: Mitigating Echo Chambers in News Consumption through an AI-based Web System

Considerable efforts are currently underway to mitigate the negative impacts of echo chambers, such as increased susceptibility to fake news and resistance towards accepting scientific evidence. Prior research has presented the development of computer systems that support the consumption of news information from diverse political perspectives to mitigate the echo chamber effect. However, existing studies still lack the ability to effectively support the key processes of news information consumption and quantitatively identify a political stance towards the information. In this paper, we present HearHere, an AI-based web system designed to help users accommodate information and opinions from diverse perspectives. HearHere facilitates the key processes of news information consumption through two visualizations. Visualization 1 provides political news with quantitative political stance information, derived from our graph-based political classification model, and users can experience diverse perspectives (Hear). Visualization 2 allows users to express their opinions on specific political issues in a comment form and observe the position of their own opinions relative to pro-liberal and pro-conservative comments presented on a map interface (Here). Through a user study with 94 participants, we demonstrate the feasibility of HearHere in supporting the consumption of information from various perspectives. Our findings highlight the importance of providing political stance information and quantifying users' political status as a means to mitigate political polarization. In addition, we propose design implications for system development, including the consideration of demographics such as political interest and providing users with initiatives.
Plasma-induced magnetic phase in 3D $\mathrm{Mn^{II}-Nb^{IV}}$ octacyanidometalate with magnetic sponge behavior

A new magnetic phase with $T_C = 72 \ \mathrm K$ was obtained by exposing the three-dimensional $\mathrm{\{ [Mn^{II}(H_2O)_2]_2[Nb^{IV}(CN)_8] \cdot 4H_2O \} _n}$ coordination ferrimagnet ($T_C = 49 \ \mathrm K$) to air, oxygen, nitrogen, and argon-based plasma. The X-ray powder diffraction pattern revealed that the unit cell shrank after plasma treatment, leading to a 20% enhancement of the superexchange couplings, as estimated from the mean-field approximation (MFA) model. Although no stable dehydrated form was found in the thermogravimetric analysis, the observed changes are attributed to the removal of crystallization water molecules. The plasma-induced magnetic phase could not be obtained by exposing the studied material to 0% relative humidity during dynamic vapor sorption. Instead, the material underwent a major structural reorganization after dehydration, necessitating an extended MFA model to reproduce the magnetic susceptibility. These findings demonstrate that plasma-induced changes can create unique magnetic phases in molecule-based systems that are otherwise unobtainable.
Windowed-FourierMixer: Enhancing Clutter-Free Room Modeling with Fourier Transform

With the growing demand for immersive digital applications, the need to understand and reconstruct 3D scenes has significantly increased. In this context, inpainting indoor environments from a single image plays a crucial role in modeling the internal structure of interior spaces as it enables the creation of textured and clutter-free reconstructions. While recent methods have shown significant progress in room modeling, they rely on constraining layout estimators to guide the reconstruction process. These methods are highly dependent on the performance of the structure estimator and its generative ability in heavily occluded environments. In response to these issues, we propose an innovative approach based on a U-Former architecture and a new Windowed-FourierMixer block, resulting in a unified, single-phase network capable of effectively handle human-made periodic structures such as indoor spaces. This new architecture proves advantageous for tasks involving indoor scenes where symmetry is prevalent, allowing the model to effectively capture features such as horizon/ceiling height lines and cuboid-shaped rooms. Experiments show the proposed approach outperforms current state-of-the-art methods on the Structured3D dataset demonstrating superior performance in both quantitative metrics and qualitative results. Code and models will be made publicly available.
Exploring Multi-Document Information Consolidation for Scientific Sentiment Summarization

Modern natural language generation systems with LLMs exhibit the capability to generate a plausible summary of multiple documents; however, it is uncertain if models truly possess the ability of information consolidation to generate summaries, especially on those source documents with opinionated information. To make scientific sentiment summarization more grounded, we hypothesize that in peer review human meta-reviewers follow a three-layer framework of sentiment consolidation to write meta-reviews and it represents the logic of summarizing scientific sentiments in meta-review generation. The framework is validated via human annotation. Based on the framework, we propose evaluation metrics to assess the quality of generated meta-reviews, and we find that the hypothesis of the sentiment consolidation framework works out empirically when we incorporate it as prompts for LLMs to generate meta-reviews in extensive experiments.
Attentive Illumination Decomposition Model for Multi-Illuminant White Balancing

White balance (WB) algorithms in many commercial cameras assume single and uniform illumination, leading to undesirable results when multiple lighting sources with different chromaticities exist in the scene. Prior research on multi-illuminant WB typically predicts illumination at the pixel level without fully grasping the scene's actual lighting conditions, including the number and color of light sources. This often results in unnatural outcomes lacking in overall consistency. To handle this problem, we present a deep white balancing model that leverages the slot attention, where each slot is in charge of representing individual illuminants. This design enables the model to generate chromaticities and weight maps for individual illuminants, which are then fused to compose the final illumination map. Furthermore, we propose the centroid-matching loss, which regulates the activation of each slot based on the color range, thereby enhancing the model to separate illumination more effectively. Our method achieves the state-of-the-art performance on both single- and multi-illuminant WB benchmarks, and also offers additional information such as the number of illuminants in the scene and their chromaticity. This capability allows for illumination editing, an application not feasible with prior methods.
G4G: A Generic Framework for High Fidelity Talking Face Generation with Fine-grained Intra-modal Alignment

Despite numerous completed studies, achieving high fidelity talking face generation with highly synchronized lip movements corresponding to arbitrary audio remains a significant challenge in the field. The shortcomings of published studies continue to confuse many researchers. This paper introduces G4G, a generic framework for high fidelity talking face generation with fine-grained intra-modal alignment. G4G can reenact the high fidelity of original video while producing highly synchronized lip movements regardless of given audio tones or volumes. The key to G4G's success is the use of a diagonal matrix to enhance the ordinary alignment of audio-image intra-modal features, which significantly increases the comparative learning between positive and negative samples. Additionally, a multi-scaled supervision module is introduced to comprehensively reenact the perceptional fidelity of original video across the facial region while emphasizing the synchronization of lip movements and the input audio. A fusion network is then used to further fuse the facial region and the rest. Our experimental results demonstrate significant achievements in reenactment of original video quality as well as highly synchronized talking lips. G4G is an outperforming generic framework that can produce talking videos competitively closer to ground truth level than current state-of-the-art methods.
Why does music source separation benefit from cacophony?

In music source separation, a standard training data augmentation procedure is to create new training samples by randomly combining instrument stems from different songs. These random mixes have mismatched characteristics compared to real music, e.g., the different stems do not have consistent beat or tonality, resulting in a cacophony. In this work, we investigate why random mixing is effective when training a state-of-the-art music source separation model in spite of the apparent distribution shift it creates. Additionally, we examine why performance levels off despite potentially limitless combinations, and examine the sensitivity of music source separation performance to differences in beat and tonality of the instrumental sources in a mixture.
Reflection Removal Using Recurrent Polarization-to-Polarization Network

This paper addresses reflection removal, which is the task of separating reflection components from a captured image and deriving the image with only transmission components. Considering that the existence of the reflection changes the polarization state of a scene, some existing methods have exploited polarized images for reflection removal. While these methods apply polarized images as the inputs, they predict the reflection and the transmission directly as non-polarized intensity images. In contrast, we propose a polarization-to-polarization approach that applies polarized images as the inputs and predicts"polarized"reflection and transmission images using two sequential networks to facilitate the separation task by utilizing the interrelated polarization information between the reflection and the transmission. We further adopt a recurrent framework, where the predicted reflection and transmission images are used to iteratively refine each other. Experimental results on a public dataset demonstrate that our method outperforms other state-of-the-art methods.
Continuous Memory Representation for Anomaly Detection

There have been significant advancements in anomaly detection in an unsupervised manner, where only normal images are available for training. Several recent methods aim to detect anomalies based on a memory, comparing or reconstructing the input with directly stored normal features (or trained features with normal images). However, such memory-based approaches operate on a discrete feature space implemented by the nearest neighbor or attention mechanism, suffering from poor generalization or an identity shortcut issue outputting the same as input, respectively. Furthermore, the majority of existing methods are designed to detect single-class anomalies, resulting in unsatisfactory performance when presented with multiple classes of objects. To tackle all of the above challenges, we propose CRAD, a novel anomaly detection method for representing normal features within a"continuous"memory, enabled by transforming spatial features into coordinates and mapping them to continuous grids. Furthermore, we carefully design the grids tailored for anomaly detection, representing both local and global normal features and fusing them effectively. Our extensive experiments demonstrate that CRAD successfully generalizes the normal features and mitigates the identity shortcut, furthermore, CRAD effectively handles diverse classes in a single model thanks to the high-granularity continuous representation. In an evaluation using the MVTec AD dataset, CRAD significantly outperforms the previous state-of-the-art method by reducing 65.0% of the error for multi-class unified anomaly detection. The project page is available at https://tae-mo.github. io/crad/.