Miracle-dz/UniFAR-FTUs huggingface dataset:
Occlusion Aware Student Emotion Recognition based on Facial Action Unit Detection

Given that approximately half of science, technology, engineering, and mathematics (STEM) undergraduate students in U.S. colleges and universities leave by the end of the first year [15], it is crucial to improve the quality of classroom environments. This study focuses on monitoring students' emotions in the classroom as an indicator of their engagement and proposes an approach to address this issue. The impact of different facial parts on the performance of an emotional recognition model is evaluated through experimentation. To test the proposed model under partial occlusion, an artificially occluded dataset is introduced. The novelty of this work lies in the proposal of an occlusion-aware architecture for facial action units (AUs) extraction, which employs attention mechanism and adaptive feature learning. The AUs can be used later to classify facial expressions in classroom settings. This research paper's findings provide valuable insights into handling occlusion in analyzing facial images for emotional engagement analysis. The proposed experiments demonstrate the significance of considering occlusion and enhancing the reliability of facial analysis models in classroom environments. These findings can also be extended to other settings where occlusions are prevalent.
Analysis of a cusped helicon plasma thruster discharge

Experiments and simulations are used to analyze a compact helicon plasma thruster with a cusp in its internal magnetic field. The former rely on a compensated Langmuir probe and a Faraday cup, while the latter employ a hybrid PIC/fluid transport model combined with a frequency-domain electromagnetic field model. Measurements serve to tune the anomalous transport parameters of the model and overall show the same trends as the numerical results, including a secondary peak of electron temperature downstream in the magnetic nozzle, where electron cyclotron resonance conditions for the 13.56 MHz excitation frequency are met. The cusp plays a central role in determining the plasma losses to the walls and the profile of electron temperature, which in turn defines the excitation and ionization losses. While losses to the rear wall are reduced, losses to the lateral wall are increased, which, together with the low production efficiency, limit the performance of the device.
Sparse Gaussian Graphical Models with Discrete Optimization: Computational and Statistical Perspectives

We consider the problem of learning a sparse graph underlying an undirected Gaussian graphical model, a key problem in statistical machine learning. Given $n$ samples from a multivariate Gaussian distribution with $p$ variables, the goal is to estimate the $p \times p$ inverse covariance matrix (aka precision matrix), assuming it is sparse (i.e., has a few nonzero entries). We propose GraphL0BnB, a new estimator based on an $\ell_0$-penalized version of the pseudolikelihood function, while most earlier approaches are based on the $\ell_1$-relaxation. Our estimator can be formulated as a convex mixed integer program (MIP) which can be difficult to compute at scale using off-the-shelf commercial solvers. To solve the MIP, we propose a custom nonlinear branch-and-bound (BnB) framework that solves node relaxations with tailored first-order methods. As a by-product of our BnB framework, we propose large-scale solvers for obtaining good primal solutions that are of independent interest. We derive novel statistical guarantees (estimation and variable selection) for our estimator and discuss how our approach improves upon existing estimators. Our numerical experiments on real/synthetic datasets suggest that our method can solve, to near-optimality, problem instances with $p = 10^4$ -- corresponding to a symmetric matrix of size $p \times p$ with $p^2/2$ binary variables. We demonstrate the usefulness of GraphL0BnB versus various state-of-the-art approaches on a range of datasets.
Regression-free Blind Image Quality Assessment

The optimization objective of regression-based blind image quality assessment (IQA) models is to minimize the mean prediction error across the training dataset, which can lead to biased parameter estimation due to potential training data biases. To mitigate this issue, we propose a regression-free framework for image quality evaluation, which is based upon retrieving locally similar instances by incorporating semantic and distortion feature spaces. The approach is motivated by the observation that the human visual system (HVS) exhibits analogous perceptual responses to semantically similar image contents impaired by identical distortions, which we term as content-distortion consistency. The proposed method constructs a hierarchical k-nearest neighbor (k-NN) algorithm for instance retrieval through two classification modules: semantic classification (SC) module and distortion classification (DC) module. Given a test image and an IQA database, the SC module retrieves multiple pristine images semantically similar to the test image. The DC module then retrieves instances based on distortion similarity from the distorted images that correspond to each retrieved pristine image. Finally, quality prediction is obtained by aggregating the subjective scores of the retrieved instances. Without training on subjective quality scores, the proposed regression-free method achieves competitive, even superior performance compared to state-of-the-art regression-based methods on authentic and synthetic distortion IQA benchmarks.
LEST: Large-scale LiDAR Semantic Segmentation with Transformer

Large-scale LiDAR-based point cloud semantic segmentation is a critical task in autonomous driving perception. Almost all of the previous state-of-the-art LiDAR semantic segmentation methods are variants of sparse 3D convolution. Although the Transformer architecture is becoming popular in the field of natural language processing and 2D computer vision, its application to large-scale point cloud semantic segmentation is still limited. In this paper, we propose a LiDAR sEmantic Segmentation architecture with pure Transformer, LEST. LEST comprises two novel components: a Space Filling Curve (SFC) Grouping strategy and a Distance-based Cosine Linear Transformer, DISCO. On the public nuScenes semantic segmentation validation set and SemanticKITTI test set, our model outperforms all the other state-of-the-art methods.
Differential rotation of stars with multiple transiting planets

Abstract If a star hosts a planet in an orbit such that it eclipses the star periodically, can be estimated the rotation profile of this star. If planets in multiplanetary system occult different stellar areas, spots in more than one latitude of the stellar disc can be detected. The monitored study of theses starspots in different latitudes allow us to infer the rotation profile of the star. We use the model described in Silva (2003) to characterize the starspots of Kepler-210, an active star with two planets. Kepler-210 is a late K star with an estimated age of 350 ± 50 Myrs, average rotation period of 12.33 days, mass of 0.63 M⊙ and radius of 0.69 R⊙. The planets that eclipses this star have radii of 0.0498 R s and 0.0635 R s with orbital periods of 2.4532 ± 0.0007 days and 7.9725 ± 0.0014 days, respectively, where R s is the star radius.
Minimum Target Sets in Non-Progressive Threshold Models: When Timing Matters

Let $G$ be a graph, which represents a social network, and suppose each node $v$ has a threshold value $\tau(v)$. Consider an initial configuration, where each node is either positive or negative. In each discrete time step, a node $v$ becomes/remains positive if at least $\tau(v)$ of its neighbors are positive and negative otherwise. A node set $\mathcal{S}$ is a Target Set (TS) whenever the following holds: if $\mathcal{S}$ is fully positive initially, all nodes in the graph become positive eventually. We focus on a generalization of TS, called Timed TS (TTS), where it is permitted to assign a positive state to a node at any step of the process, rather than just at the beginning. We provide graph structures for which the minimum TTS is significantly smaller than the minimum TS, indicating that timing is an essential aspect of successful target selection strategies. Furthermore, we prove tight bounds on the minimum size of a TTS in terms of the number of nodes and maximum degree when the thresholds are assigned based on the majority rule. We show that the problem of determining the minimum size of a TTS is NP-hard and provide an Integer Linear Programming formulation and a greedy algorithm. We evaluate the performance of our algorithm by conducting experiments on various synthetic and real-world networks. We also present a linear-time exact algorithm for trees.
Pressure-induced formation of cubic lutetium hydrides derived from trigonal LuH$_3$

In recent years, there has been a fervent search for room-temperature superconductivity within the binary hydrides. However, as the number of untested compounds dwindled, it became natural to begin searching within the ternary hydrides. This led to the controversial discovery of room-temperature superconductivity at only 1GPa in nitrogen-doped lutetium hydride [Dasenbrock-Gammon et al., Nature 615, 244 (2023)] and consequently provided much impetus for the synthesis of nitrogen-based ternary hydrides. Here, we report the synthesis of stable trigonal LuH$_3$ by hydrogenating pure lutetium which was subsequently pressurised to $\sim$2GPa in a dilute-N$_2$/He-rich pressure medium. Raman spectroscopy and x-ray diffraction were used to characterise the structures throughout. After depressurising, energy-dispersive and wavelength-dispersive X-ray spectroscopies characterised the final compound. Though our compound under pressure exhibits similar structural behaviour to the Dasenbrock-Gammon et al. sample, we do not observe any nitrogen within the structure of the recovered sample at ambient pressure. We observe two cubic structures under pressure that simultaneously explain the X-ray diffraction and Raman spectra observed: the first corresponds well to $Fm\overline{3}m$ LuH$_{2+x}$, whilst the latter is an $Ia\overline{3}$-type structure.
Landscape Surrogate: Learning Decision Losses for Mathematical Optimization Under Partial Information

Recent works in learning-integrated optimization have shown promise in settings where the optimization problem is only partially observed or where general-purpose optimizers perform poorly without expert tuning. By learning an optimizer $\mathbf{g}$ to tackle these challenging problems with $f$ as the objective, the optimization process can be substantially accelerated by leveraging past experience. The optimizer can be trained with supervision from known optimal solutions or implicitly by optimizing the compound function $f\circ \mathbf{g}$. The implicit approach may not require optimal solutions as labels and is capable of handling problem uncertainty; however, it is slow to train and deploy due to frequent calls to optimizer $\mathbf{g}$ during both training and testing. The training is further challenged by sparse gradients of $\mathbf{g}$, especially for combinatorial solvers. To address these challenges, we propose using a smooth and learnable Landscape Surrogate $M$ as a replacement for $f\circ \mathbf{g}$. This surrogate, learnable by neural networks, can be computed faster than the solver $\mathbf{g}$, provides dense and smooth gradients during training, can generalize to unseen optimization problems, and is efficiently learned via alternating optimization. We test our approach on both synthetic problems, including shortest path and multidimensional knapsack, and real-world problems such as portfolio optimization, achieving comparable or superior objective values compared to state-of-the-art baselines while reducing the number of calls to $\mathbf{g}$. Notably, our approach outperforms existing methods for computationally expensive high-dimensional problems.
Mining of Single-Class by Active Learning for Semantic Segmentation

Several Active Learning (AL) policies require retraining a target model several times in order to identify the most informative samples and rarely offer the option to focus on the acquisition of samples from underrepresented classes. Here the Mining of Single-Class by Active Learning (MiSiCAL) paradigm is introduced where an AL policy is constructed through deep reinforcement learning and exploits quantity-accuracy correlations to build datasets on which high-performance models can be trained with regards to specific classes. MiSiCAL is especially helpful in the case of very large batch sizes since it does not require repeated model training sessions as is common in other AL methods. This is thanks to its ability to exploit fixed representations of the candidate data points. We find that MiSiCAL is able to outperform a random policy on 150 out of 171 COCO10k classes, while the strongest baseline only outperforms random on 101 classes.
No distributed quantum advantage for approximate graph coloring

We give an almost complete characterization of the hardness of $c$-coloring $\chi$-chromatic graphs with distributed algorithms, for a wide range of models of distributed computing. In particular, we show that these problems do not admit any distributed quantum advantage. To do that: 1) We give a new distributed algorithm that finds a $c$-coloring in $\chi$-chromatic graphs in $\tilde{\mathcal{O}}(n^{\frac{1}{\alpha}})$ rounds, with $\alpha = \bigl\lfloor\frac{c-1}{\chi - 1}\bigr\rfloor$. 2) We prove that any distributed algorithm for this problem requires $\Omega(n^{\frac{1}{\alpha}})$ rounds. Our upper bound holds in the classical, deterministic LOCAL model, while the near-matching lower bound holds in the non-signaling model. This model, introduced by Arfaoui and Fraigniaud in 2014, captures all models of distributed graph algorithms that obey physical causality; this includes not only classical deterministic LOCAL and randomized LOCAL but also quantum-LOCAL, even with a pre-shared quantum state. We also show that similar arguments can be used to prove that, e.g., 3-coloring 2-dimensional grids or $c$-coloring trees remain hard problems even for the non-signaling model, and in particular do not admit any quantum advantage. Our lower-bound arguments are purely graph-theoretic at heart; no background on quantum information theory is needed to establish the proofs.
Model for a Fluid Particle Breakup in a Turbulent Flow

A model is developed for predicting the outcome of breakup of a fluid particle (bubble or drop), which is initially deformed (e.g. due to turbulence) and breaks into two daughter particles. An initially dumbbell-shaped deformation of the particle is assumed. The evolution of sizes of particle sub-parts is calculated using Rayleigh-Plesset equations, which consider the inertia of surrounding fluid, capillary action and viscous effects. The redistribution of internal fluid in the particle is calculated using Bernoulli equation. The model computes the sizes of daughter particles after the breakup. By assuming various initial conditions (various initial shapes and initial velocities of deformation), the size distribution of daughter particles is obtained. These size distributions are qualitatively compared with available experimental data and reasonable agreement is observed. Because of strong assumptions, this model cannot be used directly for accurate prediction of size distribution after a breakup. However, it provides an insight in the physics of the breakup, especially on the effect of inner phase properties.
NU-MCC: Multiview Compressive Coding with Neighborhood Decoder and Repulsive UDF

Remarkable progress has been made in 3D reconstruction from single-view RGB-D inputs. MCC is the current state-of-the-art method in this field, which achieves unprecedented success by combining vision Transformers with large-scale training. However, we identified two key limitations of MCC: 1) The Transformer decoder is inefficient in handling large number of query points; 2) The 3D representation struggles to recover high-fidelity details. In this paper, we propose a new approach called NU-MCC that addresses these limitations. NU-MCC includes two key innovations: a Neighborhood decoder and a Repulsive Unsigned Distance Function (Repulsive UDF). First, our Neighborhood decoder introduces center points as an efficient proxy of input visual features, allowing each query point to only attend to a small neighborhood. This design not only results in much faster inference speed but also enables the exploitation of finer-scale visual features for improved recovery of 3D textures. Second, our Repulsive UDF is a novel alternative to the occupancy field used in MCC, significantly improving the quality of 3D object reconstruction. Compared to standard UDFs that suffer from holes in results, our proposed Repulsive UDF can achieve more complete surface reconstruction. Experimental results demonstrate that NU-MCC is able to learn a strong 3D representation, significantly advancing the state of the art in single-view 3D reconstruction. Particularly, it outperforms MCC by 9.7% in terms of the F1-score on the CO3D-v2 dataset with more than 5x faster running speed.
A Large and Variable Leading Tail of Helium in a Hot Saturn Undergoing Runaway Inflation

Atmospheric escape shapes the fate of exoplanets, with statistical evidence for transformative mass loss imprinted across the mass–radius–insolation distribution. Here, we present transit spectroscopy of the highly irradiated, low-gravity, inflated hot Saturn HAT-P-67 b. The Habitable Zone Planet Finder spectra show a detection of up to 10% absorption depth of the 10833 Å helium triplet. The 13.8 hr of on-sky integration time over 39 nights sample the entire planet orbit, uncovering excess helium absorption preceding the transit by up to 130 planetary radii in a large leading tail. This configuration can be understood as the escaping material overflowing its small Roche lobe and advecting most of the gas into the stellar—and not planetary—rest frame, consistent with the Doppler velocity structure seen in the helium line profiles. The prominent leading tail serves as direct evidence for dayside mass loss with a strong day-/nightside asymmetry. We see some transit-to-transit variability in the line profile, consistent with the interplay of stellar and planetary winds. We employ one-dimensional Parker wind models to estimate the mass-loss rate, finding values on the order of 2 × 1013 g s−1, with large uncertainties owing to the unknown X-ray and ultraviolet (XUV) flux of the F host star. The large mass loss in HAT-P-67 b represents a valuable example of an inflated hot Saturn, a class of planets recently identified to be rare, as their atmospheres are predicted to evaporate quickly. We contrast two physical mechanisms for runaway evaporation: ohmic dissipation and XUV irradiation, slightly favoring the latter.
Asymptotic safety guaranteed at four-loop order

We investigate a family of four-dimensional quantum field theories with weakly interacting ultraviolet fixed points up to four loop order in perturbation theory. Key new ingredients are the three loop gauge contributions to quartic scalar beta functions, which we compute in the $\overline{\text{MS}}$ scheme for a template $SU(N_c)$ gauge theory coupled to $N_f$ fundamental fermions and elementary scalars. We then determine fixed point couplings, field and mass anomalous dimensions, and universal scaling exponents up to the first three non-trivial orders in a small Veneziano parameter. The phase diagram and UV-IR connecting trajectories are found and contrasted with asymptotic freedom. Further, the size of the conformal window, unitarity, and mechanisms leading to the loss of conformality are investigated. Our results provide blueprints for concrete 4d non-supersymmetric conformal field theories with standard model-like field content, and invite further model building.
SphereNet: Learning a Noise-Robust and General Descriptor for Point Cloud Registration

Point cloud registration aims to estimate a transformation that aligns point clouds collected from different perspectives. In learning-based point cloud registration, a robust descriptor is crucial for achieving high-accuracy registration. However, most existing methods are susceptible to noise and demonstrate poor generalization ability when applied to unseen datasets. Motivated by this, we introduce SphereNet to learn a noise-robust and unseen-general descriptor for point cloud registration. In our method, first, the spheroid generator builds a geometric domain based on spherical voxelization (SV) to encode geometric information. Then, the spherical interpolation of the sphere is introduced to realize robustness against noise. Finally, a new spherical convolutional neural network (CNN) with spherical integrity padding completes the extraction of descriptors, which reduces the loss of features and fully captures the geometric features. To evaluate our methods, a new benchmark 3DMatch-noise with strong noise is introduced. Extensive experiments are carried out on both indoor and outdoor datasets. Our results demonstrate that SphereNet achieves an increase in feature-matching recall of more than 25 percentage points (pp) on 3DMatch-noise under high-intensity noise. Moreover, SphereNet establishes a new state-of-the-art performance on the 3DMatch and 3DLoMatch benchmarks, achieving 93.5% and 75.6% registration recall (RR), respectively. Furthermore, SphereNet exhibits superior generalization ability on unseen datasets.
Exotic quantum liquids in Bose-Hubbard models with spatially modulated symmetries

We investigate the effect that spatially modulated continuous conserved quantities can have on quantum ground states. We do so by introducing a family of one-dimensional local quantum rotor and bosonic models which conserve finite Fourier momenta of the particle number, but not the particle number itself. These correspond to generalizations of the standard Bose-Hubbard model (BHM), and relate to the physics of Bose surfaces. First, we show that while having an infinite-dimensional local Hilbert space, such systems feature a non-trivial Hilbert space fragmentation for momenta incommensurate with the lattice. This is linked to the nature of the conserved quantities having a dense spectrum and provides the first such example. We then characterize the zero-temperature phase diagram for both commensurate and incommensurate momenta. In both cases, analytical and numerical calculations predict a phase transition between a gapped (Mott insulating) and quasi-long range order phase; the latter is characterized by a two-species Luttinger liquid in the infrared, but dressed by oscillatory contributions when computing microscopic expectation values. Following a rigorous Villain formulation of the corresponding rotor model, we derive a dual description, from where we estimate the robustness of this phase using renormalization group arguments, where the driving perturbation has ultra-local correlations in space but power law correlations in time. We support this conclusion using an equivalent representation of the system as a two-dimensional vortex gas with modulated Coulomb interactions within a fixed symmetry sector. We conjecture that a Berezinskii-Kosterlitz-Thouless-type transition is driven by the unbinding of vortices along the temporal direction.