Miracle-dz/UniFAR-FTUs huggingface dataset:
Interactive Multi-scale Fusion of 2D and 3D Features for Multi-object Tracking

Multiple object tracking (MOT) is a significant task in achieving autonomous driving. Traditional works attempt to complete this task, either based on point clouds (PC) collected by LiDAR, or based on images captured from cameras. However, relying on one single sensor is not robust enough, because it might fail during the tracking process. On the other hand, feature fusion from multiple modalities contributes to the improvement of accuracy. As a result, new techniques based on different sensors integrating features from multiple modalities are being developed. Texture information from RGB cameras and 3D structure information from Lidar have respective advantages under different circumstances. However, it's not easy to achieve effective feature fusion because of completely distinct information modalities. Previous fusion methods usually fuse the top-level features after the backbones extract the features from different modalities. In this paper, we first introduce PointNet++ to obtain multi-scale deep representations of point cloud to make it adaptive to our proposed Interactive Feature Fusion between multi-scale features of images and point clouds. Specifically, through multi-scale interactive query and fusion between pixel-level and point-level features, our method, can obtain more distinguishing features to improve the performance of multiple object tracking. Besides, we explore the effectiveness of pre-training on each single modality and fine-tuning on the fusion-based model. The experimental results demonstrate that our method can achieve good performance on the KITTI benchmark and outperform other approaches without using multi-scale feature fusion. Moreover, the ablation studies indicates the effectiveness of multi-scale feature fusion and pre-training on single modality.
Unraveling the temperature dynamics and hot electron generation in tunable gap-plasmon metasurface absorbers

Abstract Localized plasmons formed in ultrathin metallic nanogaps can lead to robust absorption of incident light. Plasmonic metasurfaces based on this effect can efficiently generate energetic charge carriers, also known as hot electrons, owing to their ability to squeeze and enhance electromagnetic fields in confined subwavelength spaces. However, it is very challenging to accurately identify and quantify the dynamics of hot carriers, mainly due to their ultrafast time decay. Their nonequilibrium temperature response is one of the key factors missing to understand the short time decay and overall transient tunable absorption performance of gap-plasmon metasurfaces. Here, we systematically study the temperature dynamics of hot electrons and their transition into thermal carriers at various timescales from femto to nanoseconds by using the two-temperature model. Additionally, the hot electron temperature and generation rate threshold values are investigated by using a hydrodynamic nonlocal model approach that is more accurate when ultrathin gaps are considered. The derived temperature dependent material properties are used to study the ultrafast transient nonlinear modification in the absorption spectrum before plasmon-induced lattice heating is established leading to efficient tunable nanophotonic absorber designs. We also examine the damage threshold of these plasmonic absorbers under various pulsed laser illuminations, an important quantity to derive the ultimate input intensity limits that can be used in various emerging nonlinear optics and other tunable nanophotonic applications. The presented results elucidate the role of hot electrons in the response of gap-plasmon metasurface absorbers which can be used to design more efficient photocatalysis, photovoltaics, and photodetection devices.
Infrared-Shielding of Plasmonic Random Metasurface Constructed by Cesium-Doped Tungsten Bronze

The heat-shielding properties of random metasurface, composed of spherical or spheroidal nanoparticles with random displacements and/or random deformation, were theoretically investigated using the finite difference time domain method. The effective coverage was defined using the total area of nanoparticles in the metasurface, and the robustness of the near-infrared light reflection against randomness was investigated. When the effective coverage was high, the near-infrared light reflection was reduced by at least 20% in both nanoparticle arrangement and shape randomness compared to the hexagonal close-packed perfect metasurface. In contrast, when effective coverage was low, the randomness of the nanoparticle arrangement had almost no effect on the near-infrared light reflection. Furthermore, the near-infrared light reflection performance was improved by the randomness of the nanoparticle shape.
Long-term Dynamics of Fairness Intervention in Connection Recommender Systems

Recommender system fairness has been studied from the perspectives of a variety of stakeholders including content producers, the content itself and recipients of recommendations. Regardless of which type of stakeholders are considered, most works in this area assess the efficacy of fairness intervention by evaluating a single fixed fairness criterion through the lens of a one-shot, static setting. Yet recommender systems constitute dynamical systems with feedback loops from the recommendations to the underlying population distributions which could lead to unforeseen and adverse consequences if not taken into account. In this paper, we study a connection recommender system patterned after the systems employed by web-scale social networks and analyze the long-term effects of intervening on fairness in the recommendations. We find that, although seemingly fair in aggregate, common exposure and utility parity interventions fail to mitigate amplification of biases in the long term. We theoretically characterize how certain fairness interventions impact the bias amplification dynamics in a stylized Polya urn model.
Kernel-Based Identification of Local Limit Cycle Dynamics with Linear Periodically Parameter-Varying Models

Limit cycle oscillations are phenomena arising in nonlinear dynamical systems and characterized by periodic, locally-stable, and self-sustained state trajectories. Systems controlled in a closed loop along a periodic trajectory can also be modelled as systems experiencing limit cycle behavior. The goal of this work is to identify from data, the local dynamics around the limit cycle using linear periodically parameter-varying models. Using a coordinate transformation onto transversal surfaces, the dynamics are decomposed into two parts: one along the limit cycle, and one on the transversal surfaces. Then, the model is identified from trajectory data using kernel-based methods with a periodic kernel design. The kernel-based model is extended to also account for variations in system parameters associated with different operating conditions. The performance of the proposed identification method is demonstrated on a benchmark nonlinear system and on a simplified airborne wind energy model. The method provides accurate model parameter estimation, compared to the analytical linearization, and good prediction capability.
Matrices and Spans and internalization and enrichment in tricategories

We introduce categories M and S internal in the tricategory Bicat 3 of bicategories, pseudofunctors, pseudonatural transformations and modiﬁcations, for matrices and spans in a 1-strict tricategory V . Their horizontal tricategories are the tricategories of matrices and spans in V . Both the internal and the enriched constructions are tricategoriﬁcations of the corresponding constructions in 1-categories. Following [11] we introduce monads and their vertical morphisms in categories internal in tricategories. We prove an equivalent condition for when the internal categories for matrices M and spans S in a 1-strict tricategory V are equivalent, and deduce that in that case their corresponding categories of (strict) monads and vertical monad morphisms are equivalent, too. We prove that the latter categories are isomorphic to those of categories enriched and discretely internal in V , respectively. As a byproduct of our tricategorical constructions we recover some results from [10]. Truncating to 1-categories we recover results from [6] and [9] on the equivalence of enriched and discretely internal 1-categories.
Adaptive Multi-User Clustering and Power Allocation for NOMA Systems with Imperfect SIC

Non-orthogonal multiple access (NOMA) is recognized as a promising radio access technique for the next generation wireless systems. We consider a practical downlink NOMA system with imperfect successive interference cancellation and derive bounds on the power allocation factors for a given number of users in each cluster. We propose a minimum signal-to-interference-plus-noise ratio difference criterion between two successive NOMA users in a cluster of users to achieve higher rates than an equivalent orthogonal multiple access (OMA) system. We then propose adaptive multi-user clustering and power allocation algorithms for downlink NOMA systems. Through extensive simulations, we show that the proposed algorithms achieve higher rates than the state-of-the-art algorithms.
Crack-path selection in phase-field models for brittle fracture

This work presents a critical overview of the eﬀects of diﬀerent aspects of model formulation on crack path selection in quasi-static phase ﬁeld fracture. We consider diﬀerent evolution methods, mechanics formulations, fracture dissipation energy formulations, and forms of the irreversibility condition. The diﬀerent model variants are implemented with common numerical methods based on staggered solution of the phase-ﬁeld and mechanics sub-problems via FFT-based solvers. These methods mix standard approaches with novel elements, such as the use of bound-constrained conjugate gradients for the phase ﬁeld sub-problem and a heuristic method for near-equilibrium evolution. We examine diﬀerences in crack paths between model variants in simple model systems and microstructures with randomly heterogeneous Young’s modulus. Our results indicate that near-equilibrium evolution methods are preferable for quasi-static fracture of heterogeneous microstructures compared to minimization and time-dependent methods. In examining mechanics formulations, we ﬁnd distinct eﬀects of crack driving force and the model for contact implicit in phase ﬁeld fracture. Our results favor the use of a strain-spectral decomposition for the crack driving force but not the contact model. Irreversibility condition and fracture dissipation energy formulation were also found to aﬀect crack path selection, but systematic eﬀects were diﬃcult to deduce due to the overall sensitivity of crack selection within the heterogeneous microstructures. Our ﬁndings support the use of the AT1 model over the AT2 model and irreversibility of the phase ﬁeld within a crack set rather than the entire domain. Sensitivity to these diﬀerences in formulation was reduced but not eliminated by reducing the crack width parameter (cid:96) relative to the size scale of the random microstructures. phase ﬁeld fracture formulations aﬀect crack paths in a set of randomly generated, elastically heterogeneous two-dimensional (2-D) microstructures.
Coarse-to-Fine Recursive Speech Separation for Unknown Number of Speakers

The vast majority of speech separation methods assume that the number of speakers is known in advance, hence they are specific to the number of speakers. By contrast, a more realistic and challenging task is to separate a mixture in which the number of speakers is unknown. This paper formulates the speech separation with the unknown number of speakers as a multi-pass source extraction problem and proposes a coarse-to-fine recursive speech separation method. This method comprises two stages, namely, recursive cue extraction and target speaker extraction. The recursive cue extraction stage determines how many computational iterations need to be performed and outputs a coarse cue speech by monitoring statistics in the mixture. As the number of recursive iterations increases, the accumulation of distortion eventually comes into the extracted speech and reminder. Therefore, in the second stage, we use a target speaker extraction network to extract a fine speech based on the coarse target cue and the original distortionless mixture. Experiments show that the proposed method archived state-of-the-art performance on the WSJ0 dataset with a different number of speakers. Furthermore, it generalizes well to an unseen large number of speakers.
Optimal control of ensembles of dynamical systems

In this paper we consider the problem of the optimal control of an ensemble of affine-control systems. After proving the well-posedness of the minimization problem under examination, we establish a $\Gamma$-convergence result that allows us to substitute the original (and usually infinite) ensemble with a sequence of finite increasing-in-size sub-ensembles. The solutions of the optimal control problems involving these sub-ensembles provide approximations in the $L^2$-strong topology of the minimizers of the original problem. Using again a $\Gamma$-convergence argument, we manage to derive a Maximum Principle for ensemble optimal control problems with end-point cost. Moreover, in the case of finite sub-ensembles, we can address the minimization of the related cost through numerical schemes. In particular, we propose an algorithm that consists of a subspace projection of the gradient field induced on the space of admissible controls by the approximating cost functional. In addition, we consider an iterative method based on the Pontryagin Maximum Principle. Finally, we test the algorithms on an ensemble of linear systems in $\mathbb{R}^2$.
Zentropy Theory for Ferroelectrics: A Case Study of PbTiO 3

Thermodynamics of ferroelectric to paraelectric (FE-PE) transitions is commonly described by the phenomenological Landau theory and more recently by effective Hamiltonian. Here we show that the zentropy theory, which considers the total entropy of a macroscopic system (macrostate) as a weighted sum of entropies of representative microscopic configurations (microstates) and the configurational entropy among the microstates, can predict the FE-PE transition without fitting parameters. In PbTiO 3 , the microstates are FE states with or without domain walls. With the energetics of microstates determined from first-principles calculations, the FE-PE transition for PbTiO 3 is predicted showing remarkable agreement with experiments, unveiling the microscopic fundamentals of the transition.
Optimization for Classical Machine Learning Problems on the GPU

Constrained optimization problems arise frequently in classical machine learning. There exist frameworks addressing constrained optimization, for instance, CVXPY and GENO. However, in contrast to deep learning frameworks, GPU support is limited. Here, we extend the GENO framework to also solve constrained optimization problems on the GPU. The framework allows the user to specify constrained optimization problems in an easy-to-read modeling language. A solver is then automatically generated from this specification. When run on the GPU, the solver outperforms state-of-the-art approaches like CVXPY combined with a GPU-accelerated solver such as cuOSQP or SCS by a few orders of magnitude.
The interplay between mass-loss and binarity

Abstract Most stars with birth masses larger than that of our Sun belong to binary or higher order multiple systems. Similarly, most stars have stellar winds. Radiation pressure and multiplicity create outflows of material that remove mass from the primary star and inject it into the interstellar medium or transfer it to a companion. Both have strong impact on the subsequent evolution of the stars, yet they are often studied separately. In this short review, I will sketch part of the landscape of the interplay between stellar winds and binarity. I will present several examples where binarity shapes the stellar outflows, providing new opportunities to understand and measure mass loss properties. Stellar winds spectral signatures often help clearly identifying key stages of stellar evolution. The multiplicity properties of these stages then shed a new light onto evolutionary connections between the different categories of evolved stars.
Proactive Image Manipulation Detection

Image manipulation detection algorithms are often trained to discriminate between images manipulated with particular Generative Models (GMs) and genuine/real images, yet generalize poorly to images manipulated with GMs unseen in the training. Conventional detection algorithms receive an input image passively. By contrast, we propose a proactive scheme to image manipulation detection. Our key enabling technique is to estimate a set of templates which when added onto the real image would lead to more accurate manipulation detection. That is, a template protected real image, and its manipulated version, is better discriminated compared to the original real image vs. its manipulated one. These templates are estimated using certain constraints based on the desired properties of templates. For image manipulation detection, our proposed approach outperforms the prior work by an average precision of 16%for CycleGAN and 32% for GauGAN. Our approach is generalizable to a variety of GMs showing an improvement over prior work by an average precision of 10% averaged across 12 GMs. Our code is available at https://www.github. com/vishal3477/proactive_IMD.
Anticipatory Counterplanning

In competitive environments, commonly agents try to prevent opponents from achieving their goals. Most previous preventing approaches assume the opponent's goal is known a priori. Others only start executing actions once the opponent's goal has been inferred. In this work we introduce a novel domain-independent algorithm called Anticipatory Counterplanning. It combines inference of opponent's goals with computation of planning centroids to yield proactive counter strategies in problems where the opponent's goal is unknown. Experimental results show how this novel technique outperforms reactive counterplanning, increasing the chances of stopping the opponent from achieving its goals.
LncRNAs elevate plant adaptation under low temperature by maintaining local chromatin landscape

ABSTRACT Epigenetic regulation is one of the most precise and subtle ways of gene regulation, including DNA modification, histone modification, RNA modification, histone variants, chromatin remodeling, and long non-coding RNAs (lncRNAs). Chromatin modification is the most basic type of epigenetic regulation, which plays a key role in a myriad of developmental and physiological processes that have been thoroughly studied. These modifications are usually completed by a series of conserved chromatin modification complexes in eukaryotes. In recent years, a series of lncRNAs in organisms also have been described as having irreplaceable functions in biological environment adaptation, especially in biotic and abiotic stresses. Moreover, these molecules form a sophisticated regulatory network through mutual cross-regulation to achieve quantitative expression of key environmental response genes to external signals. For instance, the function of lncRNAs will directly or indirectly depend on the function of the chromatin modification complex. In this review, we mainly focus on chromatin modification, lncRNA, and their coordination mechanism to achieve the high adaptability of plants in low-temperature environments. We highlight recent findings and insights into lncRNA-mediated local chromatin environment changes during plant growth under low temperature via chromatin modification complexes, including target gene specificity for different lncRNA.
An algebraic framework for structured epidemic modelling

Pandemic management requires that scientists rapidly formulate and analyse epidemiological models in order to forecast the spread of disease and the effects of mitigation strategies. Scientists must modify existing models and create novel ones in light of new biological data and policy changes such as social distancing and vaccination. Traditional scientific modelling workflows detach the structure of a model—its submodels and their interactions—from its implementation in software. Consequently, incorporating local changes to model components may require global edits to the code base through a manual, time-intensive and error-prone process. We propose a compositional modelling framework that uses high-level algebraic structures to capture domain-specific scientific knowledge and bridge the gap between how scientists think about models and the code that implements them. These algebraic structures, grounded in applied category theory, simplify and expedite modelling tasks such as model specification, stratification, analysis and calibration. With their structure made explicit, models also become easier to communicate, criticize and refine in light of stakeholder feedback. This article is part of the theme issue ‘Technical challenges of modelling real-life epidemics and examples of overcoming these’.
Isotretinoin Impairs the Secretory Function of Meibomian Gland Via the PPARγ Signaling Pathway

Purpose To investigate the effects of isotretinoin on the ocular surface and to explore the possible mechanisms. Methods Rats were treated with isotretinoin 20 mg/kg/d for five months and tested monthly for tear secretion, fluorescein staining, and infrared photography. After five months of treatment, tissues were harvested for routine staining to evaluate the morphological changes; and real-time polymerase chain reaction, Western blot, and immunohistochemistry to study the expression of associated genes and their products such as forkhead box protein O1 (FoxO1), forkhead box protein O3, peroxisome proliferator–activated receptor γ (PPARγ), adipose differentiation–related protein, elongation of very long chain fatty acids protein 4, fatty acid binding protein 4, matrix metalloproteinase-9, and interleukin-6. Results Systemically, isotretinoin-treated rats have a significantly lower body weight that controls and apparent skin damage. Locally, although there was no alteration in tear secretion, a significant corneal involvement indicated by increased fluorescein staining scores, and also the contrast of meibomian gland was significantly reduced but no significant atrophy of the acinus was found. In addition, isotretinoin causes a decrease in conjunctival goblet cells. Furthermore, isotretinoin treatment did not cause the upregulation of FoxO1 and inflammation related genes but significantly suppressed the expression of PPARγ pathway. Conclusions Isotretinoin does not cause a significant atrophy of the acinus and a significant change of FoxO1 expression in the meibomian gland. Isotretinoin causes meibomian gland dysfunction, affecting meibocyte differentiation and qualitative and quantitative changes in the meibum, through PPARγ pathway.