Miracle-dz/UniFAR-FTUs huggingface dataset:
RoSI: Recovering 3D Shape Interiors from Few Articulation Images

The dominant majority of 3D models that appear in gaming, VR/AR, and those we use to train geometric deep learning algorithms are incomplete, since they are modeled as surface meshes and missing their interior structures. We present a learning framework to recover the shape interiors (RoSI) of existing 3D models with only their exteriors from multi-view and multi-articulation images. Given a set of RGB images that capture a target 3D object in different articulated poses, possibly from only few views, our method infers the interior planes that are observable in the input images. Our neural architecture is trained in a category-agnostic manner and it consists of a motion-aware multi-view analysis phase including pose, depth, and motion estimations, followed by interior plane detection in images and 3D space, and finally multi-view plane fusion. In addition, our method also predicts part articulations and is able to realize and even extrapolate the captured motions on the target 3D object. We evaluate our method by quantitative and qualitative comparisons to baselines and alternative solutions, as well as testing on untrained object categories and real image inputs to assess its generalization capabilities.
Sub-Optimal Moving Horizon Estimation in Feedback Control of Linear Constrained Systems

Moving horizon estimation (MHE) offers benefits relative to other estimation approaches by its ability to explicitly handle constraints, but suffers increased computation cost. To help enable MHE on platforms with limited computation power, we propose to solve the optimization problem underlying MHE sub-optimally for a fixed number of optimization iterations per time step. The stability of the closed-loop system is analyzed using the small-gain theorem by considering the closed-loop controlled system, the optimization algorithm dynamics, and the estimation error dynamics as three interconnected subsystems. By assuming incremental input/output-to-state stability $(\delta $ -IOSS) of the system and imposing standard ISS conditions on the controller, we derive conditions on the iteration number such that the interconnected system is input-to-state stable (ISS) w.r. t. the external disturbances. A simulation with an MHE-MPC estimator-controller pair is used for validation.
Neutrino Mass in Non-Supersymmetric $SO(10)$ GUT

We study a prediction on neutrino observables in a non-supersymmetric renormalizable $SO(10)$ GUT model that contains a ${\bf 10}$ complex scalar field and a ${\bf 126}$ scalar field whose Yukawa couplings with ${\bf 16}$ matter fields provide the quark and charged lepton Yukawa couplings, neutrino Dirac Yukawa coupling and Majorana mass for the singlet neutrinos. The $SO(10)$ breaking is achieved in two steps by a ${\cal O}(10^{15})$ GeV VEV of a ${\bf 54}$ real scalar field and a ${\cal O}(10^{14})$ GeV VEV of the ${\bf 126}$ field. First, we analyze the gauge coupling unification conditions and determine the VEV of the ${\bf 126}$ field. Next, we constrain the Yukawa couplings of the ${\bf 10}$ and ${\bf 126}$ fields at the scale of the ${\bf 126}$ field's VEV from experimental data on quark and charged lepton masses and quark flavor mixings. Then we express the active neutrino mass with the above Yukawa couplings and the ${\bf 126}$ field's VEV based on the Type-1 seesaw mechanism, and fit neutrino oscillation data, thereby deriving a prediction on poorly or not measured neutrino observables. What distinguishes our work from previous studies is that we do not assign Peccei-Quinn charges on visible sector fields so that the ${\bf 10}$ scalar field and its complex conjugate both have Yukawa couplings with ${\bf 16}$ matter fields. From the fitting of neutrino oscillation data, we find that not only the normal neutrino mass hierarchy, but also the inverted hierarchy can be realized. We also reveal that in the normal hierarchy case, the Dirac CP phase of the neutrino mixing matrix $\delta_{CP}$ is likely in the ranges of $-2.4<\delta_{\rm CP}<-1.2$ and $1.2<\delta_{\rm CP}<2.4$, and not in the region with $\delta_{\rm CP}\sim\pi$, and that in the normal hierarchy case, $\theta_{23}$ is likely in the upper octant and in the range of $0.50\lesssim\sin^2\theta_{23}\lesssim0.55$.
Energy-guided Entropic Neural Optimal Transport

Energy-based models (EBMs) are known in the Machine Learning community for decades. Since the seminal works devoted to EBMs dating back to the noughties, there have been a lot of efficient methods which solve the generative modelling problem by means of energy potentials (unnormalized likelihood functions). In contrast, the realm of Optimal Transport (OT) and, in particular, neural OT solvers is much less explored and limited by few recent works (excluding WGAN-based approaches which utilize OT as a loss function and do not model OT maps themselves). In our work, we bridge the gap between EBMs and Entropy-regularized OT. We present a novel methodology which allows utilizing the recent developments and technical improvements of the former in order to enrich the latter. From the theoretical perspective, we prove generalization bounds for our technique. In practice, we validate its applicability in toy 2D and image domains. To showcase the scalability, we empower our method with a pre-trained StyleGAN and apply it to high-res AFHQ $512\times 512$ unpaired I2I translation. For simplicity, we choose simple short- and long-run EBMs as a backbone of our Energy-guided Entropic OT approach, leaving the application of more sophisticated EBMs for future research. Our code is available at: https://github.com/PetrMokrov/Energy-guided-Entropic-OT
Robust and Context-Aware Real-Time Collaborative Robot Handling via Dynamic Gesture Commands

This letter studies real-time collaborative robot (cobot) handling, where the cobot maneuvers an object under human dynamic gesture commands. Enabling dynamic gesture commands is useful when the human needs to avoid direct contact with the robot or the object handled by the robot. However, the key challenge lies in the heterogeneity in human behaviors and the stochasticity in the perception of dynamic gestures, which requires the robot handling policy to be adaptable and robust. To address these challenges, we introduce Conditional Collaborative Handling Process (CCHP) to encode a context-aware cobot handling policy and a procedure to learn such policy from human-human collaboration. We thoroughly evaluate the adaptability and robustness of CCHP and apply our approach to a real-time cobot assembly task with Kinova Gen3 robot arm. Results show that our method leads to significantly less human effort and smoother human-robot collaboration than state-of-the-art rule-based approach even with first-time users.
On the geometry of the Lehn–Lehn–Sorger–van Straten eightfold

In this note we make a few remarks about the geometry of the holomorphic symplectic manifold Z constructed by C.Lehn, M.Lehn, C.Sorger and D. van Straten as a two-step contraction of the variety of twisted cubic curves on a cubic fourfold Y in P^5. We show that Z is birational to a component of a moduli space of stable sheaves in the Calabi-Yau subcategory of the derived category of Y. Using this description we deduce that the twisted cubics contained in a hyperplane section Y_H of Y give rise to a Lagrangian subvariety Z_H in Z. For a generic choice of the hyperplane, Z_H is birational to the theta-divisor in the intermediate Jacobian of Y_H.
Generation of artificial facial drug abuse images using Deep De-identified anonymous Dataset augmentation through Genetics Algorithm (3DG-GA)

In biomedical research and artificial intelligence, access to large, well-balanced, and representative datasets is crucial for developing trustworthy applications that can be used in real-world scenarios. However, obtaining such datasets can be challenging, as they are often restricted to hospitals and specialized facilities. To address this issue, the study proposes to generate highly realistic synthetic faces exhibiting drug abuse traits through augmentation. The proposed method, called"3DG-GA", Deep De-identified anonymous Dataset Generation, uses Genetics Algorithm as a strategy for synthetic faces generation. The algorithm includes GAN artificial face generation, forgery detection, and face recognition. Initially, a dataset of 120 images of actual facial drug abuse is used. By preserving, the drug traits, the 3DG-GA provides a dataset containing 3000 synthetic facial drug abuse images. The dataset will be open to the scientific community, which can reproduce our results and benefit from the generated datasets while avoiding legal or ethical restrictions.
Modeling Cell Size Distribution With Heterogeneous Flux Balance Analysis

For over two decades, Flux Balance Analysis (FBA) has been successfully used for predicting growth rates and intracellular reaction rates in microbiological metabolism. An aspect that is often omitted from this analysis, is segregation or heterogeneity between different cells. In this letter, we propose an extended FBA method to model cell size distributions in balanced growth conditions. Hereto, a mathematical description of the concept of balanced growth in terms of cell mass distribution is presented. The cell mass distribution, quantified by the Number Density Function (NDF), is affected by cell growth and cell division. An optimization program is formulated in a general manner in which the NDF, average cell culture growth rate and reaction rates per cell mass are treated as optimization variables. As qualitative proof of concept, the methodology is illustrated on a core carbon model of Escherichia coli under aerobic growth conditions. This illustrates feasibility and applications of this method, while indicating some shortcomings intrinsic to the simplified biomass structuring and the time invariant approach.
False Claims against Model Ownership Resolution

Deep neural network (DNN) models are valuable intellectual property of model owners, constituting a competitive advantage. Therefore, it is crucial to develop techniques to protect against model theft. Model ownership resolution (MOR) is a class of techniques that can deter model theft. A MOR scheme enables an accuser to assert an ownership claim for a suspect model by presenting evidence, such as a watermark or fingerprint, to show that the suspect model was stolen or derived from a source model owned by the accuser. Most of the existing MOR schemes prioritize robustness against malicious suspects, ensuring that the accuser will win if the suspect model is indeed a stolen model. In this paper, we show that common MOR schemes in the literature are vulnerable to a different, equally important but insufficiently explored, robustness concern: a malicious accuser. We show how malicious accusers can successfully make false claims against independent suspect models that were not stolen. Our core idea is that a malicious accuser can deviate (without detection) from the specified MOR process by finding (transferable) adversarial examples that successfully serve as evidence against independent suspect models. To this end, we first generalize the procedures of common MOR schemes and show that, under this generalization, defending against false claims is as challenging as preventing (transferable) adversarial examples. Via systematic empirical evaluation, we show that our false claim attacks always succeed in the MOR schemes that follow our generalization, including in a real-world model: Amazon's Rekognition API.
Statistical Analysis of Fixed Mini-Batch Gradient Descent Estimator

Abstract We study here a fixed mini-batch gradient descent (FMGD) algorithm to solve optimization problems with massive datasets. In FMGD, the whole sample is split into multiple nonoverlapping partitions. Once the partitions are formed, they are then fixed throughout the rest of the algorithm. For convenience, we refer to the fixed partitions as fixed mini-batches. Then for each computation iteration, the gradients are sequentially calculated on each fixed mini-batch. Because the size of fixed mini-batches is typically much smaller than the whole sample size, it can be easily computed. This leads to much reduced computation cost for each computational iteration. It makes FMGD computationally efficient and practically more feasible. To demonstrate the theoretical properties of FMGD, we start with a linear regression model with a constant learning rate. We study its numerical convergence and statistical efficiency properties. We find that sufficiently small learning rates are necessarily required for both numerical convergence and statistical efficiency. Nevertheless, an extremely small learning rate might lead to painfully slow numerical convergence. To solve the problem, a diminishing learning rate scheduling strategy can be used. This leads to the FMGD estimator with faster numerical convergence and better statistical efficiency. Finally, the FMGD algorithms with random shuffling and a general loss function are also studied. Supplementary materials for this article are available online.
In-Distribution and Out-of-Distribution Self-Supervised ECG Representation Learning for Arrhythmia Detection

This paper presents a systematic investigation into the effectiveness of Self-Supervised Learning (SSL) methods for Electrocardiogram (ECG) arrhythmia detection. We begin by conducting a novel analysis of the data distributions on three popular ECG-based arrhythmia datasets: PTB-XL, Chapman, and Ribeiro. To the best of our knowledge, our study is the first to quantitatively explore and characterize these distributions in the area. We then perform a comprehensive set of experiments using different augmentations and parameters to evaluate the effectiveness of various SSL methods, namely SimCRL, BYOL, and SwAV, for ECG representation learning, where we observe the best performance achieved by SwAV. Furthermore, our analysis shows that SSL methods achieve highly competitive results to those achieved by supervised state-of-the-art methods. To further assess the performance of these methods on both In-Distribution (ID) and Out-of-Distribution (OOD) ECG data, we conduct cross-dataset training and testing experiments. Our comprehensive experiments show almost identical results when comparing ID and OOD schemes, indicating that SSL techniques can learn highly effective representations that generalize well across different OOD datasets. This finding can have major implications for ECG-based arrhythmia detection. Lastly, to further analyze our results, we perform detailed per-disease studies on the performance of the SSL methods on the three datasets.
Constraints on cosmological birefringence from PLANCK and Bicep2/Keck data

The polarization of cosmic microwave background (CMB) can be used to constrain cosmological birefringence, the rotation of the linear polarization of CMB photons potentially induced by parity violating physics beyond the standard model. This effect produces non-null CMB cross correlations between temperature and B mode-polarization, and between E- and B-mode polarization. Both cross-correlations are otherwise null in the standard cosmological model. We use the recently released 2015 PLANCK likelihood in combination with the Bicep2/Keck/Planck (BKP) likelihood to constrain the birefringence angle α. Our findings, that are compatible with no detection, read α = 0.0° ± 1.3° (stat) ± 1° (sys) for PLANCK data and α = 0.30° ± 0.27° (stat) ± 1° (sys) for BKP data. We finally forecast the expected improvements over present constraints when the PLANCK BB, TB and EB spectra at high ℓ will be included in the analysis.
Learning Over Contracting and Lipschitz Closed-Loops for Partially-Observed Nonlinear Systems

This paper presents a policy parameterization for learning-based control on nonlinear, partially-observed dynamical systems. The parameterization is based on a nonlinear version of the Youla parameterization and the recently proposed Recurrent Equilibrium Network (REN) class of models. We prove that the resulting Youla - Ren parameterization automatically satisfies stability (contraction) and user-tunable robustness (Lipschitz) conditions on the closed-loop system. This means it can be used for safe learning-based control with no additional constraints or projections required to enforce stability or robustness. We test the new policy class in simulation on two reinforcement learning tasks: 1) magnetic suspension, and 2) inverting a rotary-arm pendulum. We find that the Youla-REN performs similarly to existing learning-based and optimal control methods while also ensuring stability and exhibiting improved robustness to adversarial disturbances.
EEG Cortical Source Feature based Hand Kinematics Decoding using Residual CNN-LSTM Neural Network

Motor kinematics decoding (MKD) using brain signal is essential to develop Brain-computer interface (BCI) system for rehabilitation or prosthesis devices. Surface electroencephalogram (EEG) signal has been widely utilized for MKD. However, kinematic decoding from cortical sources is sparsely explored. In this work, the feasibility of hand kinematics decoding using EEG cortical source signals has been explored for grasp and lift task. In particular, pre-movement EEG segment is utilized. A residual convolutional neural network (CNN) - long short-term memory (LSTM) based kinematics decoding model is proposed that utilizes motor neural information present in pre-movement brain activity. Various EEG windows at 50 ms prior to movement onset, are utilized for hand kinematics decoding. Correlation value (CV) between actual and predicted hand kinematics is utilized as performance metric for source and sensor domain. The performance of the proposed deep learning model is compared in sensor and source domain. The results demonstrate the viability of hand kinematics decoding using pre-movement EEG cortical source data.
Pressure-enhanced fractional Chern insulators along a magic line in moiré transition metal dichalcogenides

We show that pressure applied to twisted WSe$_2$ can enhance the many-body gap and region of stability of a fractional Chern insulator at filling $\nu = 1/3$. Our results are based on exact diagonalization of a continuum model, whose pressure-dependence is obtained through {\it ab initio} methods. We interpret our results in terms of a {\it magic line} in the pressure-{\it vs}-twist angle phase diagram: along the magic line, the bandwidth of the topmost moir\'e valence band is minimized while simultaneously its quantum geometry nearly resembles that of an ideal Chern band. We expect our results to generalize to other twisted transition metal dichalcogenide homobilayers.
RSIR Transformer: Hierarchical Vision Transformer using Random Sampling Windows and Important Region Windows

Recently, Transformers have shown promising performance in various vision tasks. However, the high costs of global self-attention remain challenging for Transformers, especially for high-resolution vision tasks. Local self-attention runs attention computation within a limited region for the sake of efficiency, resulting in insufficient context modeling as their receptive fields are small. In this work, we introduce two new attention modules to enhance the global modeling capability of the hierarchical vision transformer, namely, random sampling windows (RS-Win) and important region windows (IR-Win). Specifically, RS-Win sample random image patches to compose the window, following a uniform distribution, i.e., the patches in RS-Win can come from any position in the image. IR-Win composes the window according to the weights of the image patches in the attention map. Notably, RS-Win is able to capture global information throughout the entire model, even in earlier, high-resolution stages. IR-Win enables the self-attention module to focus on important regions of the image and capture more informative features. Incorporated with these designs, RSIR-Win Transformer demonstrates competitive performance on common vision tasks.
Converting ECG Signals to Images for Efficient Image-text Retrieval via Encoding

Automated interpretation of electrocardiograms (ECG) has garnered significant attention with the advancements in machine learning methodologies. Despite the growing interest in automated ECG interpretation using machine learning, most current studies focus solely on classification or regression tasks and overlook a crucial aspect of clinical cardio-disease diagnosis: the diagnostic report generated by experienced human clinicians. In this paper, we introduce a novel approach to ECG interpretation, leveraging recent breakthroughs in Large Language Models (LLMs) and Vision-Transformer (ViT) models. Rather than treating ECG diagnosis as a classification or regression task, we propose an alternative method of automatically identifying the most similar clinical cases based on the input ECG data. Also, since interpreting ECG as images are more affordable and accessible, we process ECG as encoded images and adopt a vision-language learning paradigm to jointly learn vision-language alignment between encoded ECG images and ECG diagnosis reports. Encoding ECG into images can result in an efficient ECG retrieval system, which will be highly practical and useful in clinical applications. More importantly, our findings could serve as a crucial resource for providing diagnostic services in regions where only paper-printed ECG images are accessible due to past underdevelopment.
Far-infrared polarization of the supernova remnant Cassiopeia A with SOFIA HAWC +

We present polarization observations of the young supernova remnant (SNR) Cas A using the High-resolution Airborne Wideband Camera-Plus (HAWC +) instrument onboard the Stratospheric Observatory for Infrared Astronomy (SOFIA). The polarization map at 154 $\mu$m reveals dust grains with strong polarization fractions (5–30 per cent), supporting previous measurements made over a smaller region of the remnant at 850 $\mu$m. The 154-$\mu$m emission and the polarization signal is coincident with a region of cold dust observed in the southeastern shell and in the unshocked central ejecta. The highly polarized far-IR emission implies the grains are large (>0.14 $\mu$m) and silicate-dominated. The polarization level varies across the SNR, with an inverse correlation between the polarization degree and the intensity and smaller polarization angle dispersion for brighter SNR emission. Stronger polarization is detected between the bright structures. This may result from a higher collision rate between the gas and dust producing a lower grain alignment efficiency where the gas density is higher. We use the dust emission to provide an estimate of the magnetic field strength in Cas A using the Davis–Chandrasekhar–Fermi method. The high polarization level is direct evidence that grains are highly elongated and strongly aligned with the magnetic field of the SNR. The dust mass from the polarized region is 0.14 ± 0.04 M⊙, a lower limit of the amount of dust present within the ejecta of Cas A. This result strengthens the hypothesis that core-collapse SNe are an important contributor to the dust mass in high redshift galaxies.
Choose Your Weapon: Survival Strategies for Depressed AI Academics

Are you an AI researcher at an academic institution? Are you anxious you are not coping with the current pace of AI advancements? Do you feel you have no (or very limited) access to the computational and human resources required for an AI research breakthrough? You are not alone; we feel the same way. A growing number of AI academics can no longer find the means and resources to compete at a global scale. This is a somewhat recent phenomenon, but an accelerating one, with private actors investing enormous compute resources into cutting edge AI research. Here, we discuss what you can do to stay competitive while remaining an academic. We also briefly discuss what universities and the private sector could do improve the situation, if they are so inclined. This is not an exhaustive list of strategies, and you may not agree with all of them, but it serves to start a discussion.