Miracle-dz/UniFAR-FTUs huggingface dataset:
Continuously Generalized Ordinal Regression for Linear and Deep Models

Ordinal regression is a classification task where classes have an order and prediction error increases the further the predicted class is from the true class. The standard approach for modeling ordinal data involves fitting parallel separating hyperplanes that optimize a certain loss function. This assumption offers sample efficient learning via inductive bias, but is often too restrictive in real-world datasets where features may have varying effects across different categories. Allowing class-specific hyperplane slopes creates generalized logistic ordinal regression, increasing the flexibility of the model at a cost to sample efficiency. We explore an extension of the generalized model to the all-thresholds logistic loss and propose a regularization approach that interpolates between these two extremes. Our method, which we term continuously generalized ordinal logistic, significantly outperforms the standard ordinal logistic model over a thorough set of ordinal regression benchmark datasets. We further extend this method to deep learning and show that it achieves competitive or lower prediction error compared to previous models over a range of datasets and modalities. Furthermore, two primary alternative models for deep learning ordinal regression are shown to be special cases of our framework.
Simplicial $q$-connectivity of directed graphs with applications to network analysis

Directed graphs are ubiquitous models for networks, and topological spaces they generate, such as the directed flag complex, have become useful objects in applied topology. The simplices are formed from directed cliques. We extend Atkin's theory of $q$-connectivity to the case of directed simplices. This results in a preorder where simplices are related by sequences of simplices that share a $q$-face with respect to directions specified by chosen face maps. We leverage the Alexandroff equivalence between preorders and topological spaces to introduce a new class of topological spaces for directed graphs, enabling to assign new homotopy types different from those of directed flag complexes as seen by simplicial homology. We further introduce simplicial path analysis enabled by the connectivity preorders. As an application we characterise structural differences between various brain networks by computing their longest simplicial paths.
Astrometric identification of nearby binary stars II: Astrometric binaries in the Gaia Catalogue of Nearby Stars

We examine the capacity to identify binary systems from astrometric deviations alone. We apply our analysis to the Gaia eDR3 and DR2 data, specifically the Gaia Catalogue of Nearby Stars. We show we must renormalize (R)UWE over the local volume to avoid biasing local observations, giving a Local Unit Weight Error (LUWE). We use the simple criterion of LUWE>2, along with a handful of quality cuts to remove likely contaminants, to identify unresolved binary candidates. We identify 22,699 binary candidates within 100 pc of the Sun (just under 10% of sources in this volume). We find an astrometric binary candidate fraction of around 20% for giant stars, 10% on the Main Sequence and lower than 1% for White Dwarfs. We also look for Variability Induced Movers, by computing the correlation between photometric variability and astrometric noise -- and show that VIMs may dominate the binary population of sub-Solar mass MS stars. We discuss the possibility and limitations of identifying non-luminous massive companions from astrometry alone, but find that our method is insensitive to these. Finally, we compare the astrometric deviations of MS binaries to the simulated sample from paper I, which show excellent agreement, and compute the astrometric candidate binary fraction as a function of absolute magnitude.
Modeling Very Complex Geometric Assemblies: The Use of Discrete and Hierarchical Models

AbstractVirtual Reality (VR) Systems allow immersive inspection of very complex environments, with direct interaction with the objects in the virtual scene. Low cost and affordable VR systems are becoming essential in application areas like Industrial Design, Medical Applications and Cultural Heritage and require sophisticated techniques for model inspection and interaction. Discrete geometric models can be very useful for some involved geometry processing algorithms like model repair, occlusion culling and multi-resolution and level of detail. The interest of these techniques and their potential in present and future VR applications will be discussed. This paper addresses the interest of discrete geometric models in volume-based geometric simplification, the use of discrete tiles for impostorbased extreme simplification, topology preserving model repair techniques based on discrete membranes and specific algorithms for recovering polygonal shapes from discrete volume models. The use of out-of-core octree.
A Non-parametric View of FedAvg and FedProx:Beyond Stationary Points

Federated Learning (FL) is a promising decentralized learning framework and has great potentials in privacy preservation and in lowering the computation load at the cloud. Recent work showed that FedAvg and FedProx - the two widely-adopted FL algorithms - fail to reach the stationary points of the global optimization objective even for homogeneous linear regression problems. Further, it is concerned that the common model learned might not generalize well locally at all in the presence of heterogeneity. In this paper, we analyze the convergence and statistical efficiency of FedAvg and FedProx, addressing the above two concerns. Our analysis is based on the standard non-parametric regression in a reproducing kernel Hilbert space (RKHS), and allows for heterogeneous local data distributions and unbalanced local datasets. We prove that the estimation errors, measured in either the empirical norm or the RKHS norm, decay with a rate of 1/t in general and exponentially for finite-rank kernels. In certain heterogeneous settings, these upper bounds also imply that both FedAvg and FedProx achieve the optimal error rate. To further analytically quantify the impact of the heterogeneity at each client, we propose and characterize a novel notion-federation gain, defined as the reduction of the estimation error for a client to join the FL. We discover that when the data heterogeneity is moderate, a client with limited local data can benefit from a common model with a large federation gain. Numerical experiments further corroborate our theoretical findings.
Assessing the Commonly Used Assumptions in Estimating the Principal Causal Effect in Clinical Trials

Abstract–In clinical trials, it is often of interest to understand the Principal Causal Effect (PCE), the average treatment effect for a principal stratum (a subset of patients defined by the potential outcomes of one or more post-baseline variables). Commonly used assumptions include monotonicity, principal ignorability, and cross-world assumptions of principal ignorability and principal strata independence. In this article, we evaluate these assumptions through a 2 × 2 cross-over study in which the potential outcomes under both treatments can be observed, provided there are no carry-over and study period effects. From this example, it seemed the monotonicity assumption and the within-treatment principal ignorability assumptions did not hold well. On the other hand, the assumptions of cross-world principal ignorability and cross-world principal stratum independence conditional on baseline covariates seemed reasonable. With the latter assumptions, we estimated the PCEs, defined by whether the blood glucose standard deviation increased in each treatment period, without relying on the cross-over feature, producing estimates close to the results when exploiting the cross-over feature. To the best of our knowledge, this article is the first attempt to evaluate the plausibility of commonly used assumptions for estimating PCEs using a cross-over trial.
Virtualization of Electromagnetic Operations (VEMO)

Today's operations in the spectrum occur across many disparate and unique devices. For example, a military or commercial maritime platform can have dozens of apertures used for various functions. Utilizing software-defined radios and dynamic analog front ends, we propose that future systems involving multiple applications for RF, virtualize operations to gain performance efficiencies. The concept of virtualization of electromagnetic operations (VEMO) is considered a means to enable flexible use of hardware and spectrum resources to achieve diverse mission requirements in military and commercial settings. This will result in more efficient use of resources, including size/weight/power and integration of objectives across systems that employ the electromagnetic spectrum.
Sim-to-Real Domain Adaptation for Lane Detection and Classification in Autonomous Driving

While supervised detection and classification frameworks in autonomous driving require large labelled datasets to converge, Unsupervised Domain Adaptation (UDA) approaches, facilitated by synthetic data generated from photoreal simulated environments, are considered low-cost and less time-consuming solutions. In this paper, we propose UDA schemes using adversarial discriminative and generative methods for lane detection and classification applications in autonomous driving. We also present Simulanes dataset generator to create a synthetic dataset that is naturalistic utilizing CARLA’s vast traffic scenarios and weather conditions. The proposed UDA frameworks take the synthesized dataset with labels as the source domain, whereas the target domain is the unlabelled real-world data. Using adversarial generative and feature discriminators, the learnt models are tuned to predict the lane location and class in the target domain. The proposed techniques are evaluated using both real-world and our synthetic datasets. The results manifest that the proposed methods have shown superiority over other baseline schemes in terms of detection and classification accuracy and consistency. The ablation study reveals that the size of the simulation dataset plays important roles in the classification performance of the proposed methods. Our UDA frameworks are available at github.com/anita-hu/sim2real-lane-detection and our dataset generator is released at github.com/anita-hu/simulanes.
CUP: A Conservative Update Policy Algorithm for Safe Reinforcement Learning

Safe reinforcement learning (RL) is still very challenging since it requires the agent to consider both return maximization and safe exploration. In this paper, we propose CUP, a Conservative Update Policy algorithm with a theoretical safety guarantee. We derive the CUP based on the new proposed performance bounds and surrogate functions. Although using bounds as surrogate functions to design safe RL algorithms have appeared in some existing works, we develop them at least three aspects: (i) We provide a rigorous theoretical analysis to extend the surrogate functions to generalized advantage estimator (GAE). GAE significantly reduces variance empirically while maintaining a tolerable level of bias, which is an efficient step for us to design CUP; (ii) The proposed bounds are tighter than existing works, i.e., using the proposed bounds as surrogate functions are better local approximations to the objective and safety constraints. (iii) The proposed CUP provides a non-convex implementation via first-order optimizers, which does not depend on any convex approximation. Finally, extensive experiments show the effectiveness of CUP where the agent satisfies safe constraints. We have opened the source code of CUP at https://github.com/RL-boxes/Safe-RL.
Anisotropic Dark Energy from a Coupled 2-form Field

In this paper, we study the cosmological dynamics of a 2-form field coupled to a cold dark matter component in a Bianchi I background. Using a dynamical system analysis, we show that our model has two attractor solutions corresponding to late-time accelerated universes. These solutions have an important distinction between each other: one is isotropic and the other one is not, i.e. there is a non-zero shear contribution induced by the 2-form field. Due to the coupling in the dark sector, we also obtain a scaling solution where the 2-form field behaves as a pressure-less fluid. For a particular set of parameters and initial conditions, we numerically study the expansion history of the model. We find that the value of the shear at redshift zero is within the current observational bounds, which could be a potential alleviation to the quadrupole anomaly, and that the equation of state of dark energy is exactly -1.
Rank-one Boolean tensor factorization and the multilinear polytope

We consider the NP-hard problem of approximating a tensor with binary entries by a rank-one tensor, referred to as rank-one Boolean tensor factorization problem. We formulate this problem, in an extended space of variables, as the problem of minimizing a linear function over a highly structured multilinear set. Leveraging on our prior results regarding the facial structure of multilinear polytopes, we propose novel linear programming relaxations for rank-one Boolean tensor factorization. To analyze the performance of the proposed linear programs, we consider a semi-random corruption model for the input tensor. We first consider the original NP-hard problem and establish necessary and sufficient conditions for the recovery of the ground truth with high probability. Next, we obtain sufficient conditions under which the proposed linear programming relaxations recover the ground truth with high probability. Our theoretical results as well as numerical simulations indicate that certain facets of the multilinear polytope significantly improve the recovery properties of linear programming relaxations for rank-one Boolean tensor factorization.
Mirror-symmetry protected topological phase in a zigzag ladder with staggered potential

We study spinless fermions on a zigzag ladder subjected to staggered on-site potential along both of its legs. Three insulating phases, including a charge-density-wave at half-filling, and two dimer insulators at quarter and three-quarter fillings, are identified. The two dimer insulators admit topological phases at opposite signs of the on-site potential, protected by the underlying mirrorsymmetry. The phases are characterized by a quantized value of the Zak phase as well as of the entanglement entropy. By performing a projection to the lower and upper two bands separately we show that the system reveals an emergent chiral symmetry. Using density-matrix renormalization group simulations, the topological phases are shown to be remarkably robust against any amount of nearest-neighbor inter-leg as well as intra-leg interactions. Our results are supported by quantum Monte-Carlo simulations in applicable limits of the ladder.
Bending waves in velocity space: A first look at the THINGS sample

Detection of bending waves is a highly challenging task even in nearby disc galaxies due to their sub-kpc bending amplitudes. However, simulations show that the harmonic bending of a Milky Way like disc galaxy is associated with a harmonic fluctuation in the measured line of sight (los) velocities as well, and can be regarded as a kinematic signature of a manifested bending wave. Here, we look for similar kinematic signatures of bending waves in H i discs, as they extend to much beyond the optical radii. We present a multipole analysis of the H i los residual velocity fields of six nearby spiral galaxies from the THINGS sample, which uncovers the bending wave-induced velocity peaks. This allows us to identify the radial positions and amplitudes of the different bending modes present in the galaxies. We find that all of our sample discs show a combined kinematic signature of superposition of a few lower-order bending modes, suggesting that bending waves are a common phenomenon. The identified velocity peaks are found to be of modes m = 2, 3 and 4, not more than 15 km s−1 in amplitude and spread across the entire H i disc. Interestingly, they appear to be concentrated near the optical edge of their host galaxies. Also, m = 2 appears to be more common than the other two modes.
Hyper-relationship Learning Network for Scene Graph Generation

Generating informative scene graphs from images requires integrating and reasoning from various graph components, i.e., objects and relationships. However, current scene graph generation (SGG) methods, including the unbiased SGG methods, still struggle to predict informative relationships due to the lack of 1) high-level inference such as transitive inference between relationships and 2) efficient mechanisms that can incorporate all interactions of graph components. To address the issues mentioned above, we devise a hyper-relationship learning network, termed HLN, for SGG. Specifically, the proposed HLN stems from hypergraphs and two graph attention networks (GATs) are designed to infer relationships: 1) the object-relationship GAT or OR-GAT to explore interactions between objects and relationships, and 2) the hyper-relationship GAT or HR-GAT to integrate transitive inference of hyper-relationships, i.e., the sequential relationships between three objects for transitive reasoning. As a result, HLN significantly improves the performance of scene graph generation by integrating and reasoning from object interactions, relationship interactions, and transitive inference of hyper-relationships. We evaluate HLN on the most popular SGG dataset, i.e., the Visual Genome dataset, and the experimental results demonstrate its great superiority over recent state-of-the-art methods. For example, the proposed HLN improves the recall per relationship from 11.3\% to 13.1\%, and maintains the recall per image from 19.8\% to 34.9\%. We will release the source code and pretrained models on GitHub.
A Guide to Computational Reproducibility in Signal Processing and Machine Learning [Tips & Tricks]

A computational experiment is deemed <italic>reproducible</italic> if the same data and methods are available to replicate quantitative results by any independent researcher, anywhere and at any time, granted that they have the required computing power. Such computational reproducibility is a growing challenge that has been extensively studied among computational researchers as well as within the signal processing and machine learning research community <xref ref-type="bibr" rid="ref1">[1]</xref>, <xref ref-type="bibr" rid="ref2">[2]</xref>.
AERODYNAMIC AND AEROACOUSTIC CHARACTERISTICS OF FLATBACK AIRFOILS FOR USE ON LARGE WIND TURBINES

The present paper presents a possible path for developing a large eddy simulation (LES) applicable to high Reynolds-number complex turbulent flows, performance of the coupling of LES with statistical turbulence models, i.e. Reynolds-Averaged Navier-Stokes (RANS) modeling around the flow over a blunt trailing edge configuration was investigated. To discuss the model performance in detail, LES, RANS and embedded LES models were applied to a three-dimensional turbulent flow around a flatback airfoil with massive separation. The hybrid LES/RANS model gave considerable improvement of the prediction accuracy even with moderate grid resolution. The turbulent fluctuations in the boundary layers at the inflow region of the LES domain are generated by synthetic vortex method. The comparison with RANS and LES solution for the full domain and with experimental data shows likewise results for the pressure distributions surrounding flatback airfoils. To predict accurately the noise radiation from the blunt trailing edge and to save computational costs, the near field region is computed by embedded LES and the other surrounding region is computed by RANS simultaneously. The present hybrid LES/RANS method is found to be adequate for predicting noise radiation over a range of frequencies.
A robotic neuro-musculoskeletal simulator for spine research

An influential conceptual framework advanced by Panjabi represents the living spine as a complex neuromusculoskeletal system whose biomechanical functioning is rather finely dependent upon the interactions among and between three principal subsystems: the passive musculoskeletal subsystem (osteoligamentous spine plus passive mechanical contributions of the muscles), the active musculoskeletal subsystem (muscles and tendons), and the neural and feedback subsystem (neural control centers and feedback elements such as mechanoreceptors located in the soft tissues) [1]. The interplay between subsystems readily encourages “thought experiments” of how pathologic changes in one subsystem might influence another—for example, prompting one to speculate how painful arthritic changes in the facet joints might affect the neuromuscular control of spinal movement. To answer clinical questions regarding the interplay between these subsystems the proper experimental tools and techniques are required. Traditional spine biomechanical experiments are able to provide comprehensive characterization of the structural properties of the osteoligamentous spine. However, these technologies do not incorporate a simulated neural feedback from neural elements, such as mechanoreceptors and nociceptors, into the control loop. Doing so enables the study of how this feedback—including pain-related— alters spinal loading and motion patterns. The first
In Silico and Ex Vivo Analyses of the Inhibitory Action of the Alzheimer Drug Posiphen and Primary Metabolites with Human Acetyl- and Butyrylcholinesterase Enzymes

Alzheimer’s disease (AD) is the most common neurodegenerative disorder worldwide. Ongoing research to develop AD treatments has characterized multiple drug targets including the cholinergic system, amyloid-β peptide, phosphorylated tau, and neuroinflammation. These systems have the potential to interact to either drive or slow AD progression. Promising agents that simultaneously impact many of these drug targets are the AD experimental drug Posiphen and its enantiomer phenserine that, currently, are separately being evaluated in clinical trials. To define the cholinergic component of these agents, the anticholinesterase activities of a ligand dataset comprising Posiphen and primary metabolites ((+)-N1-norPosiphen, (+)-N8-norPosiphen, and (+)-N1,N8-bisnorPosiphen) were characterized and compared to those of the enantiomer phenserine. The “target” dataset involved the human cholinesterase enzymes acetylcholinesterase (AChE) and butyrylcholinesterase (BChE). Binding interactions between the ligands and targets were analyzed using Autodock 4.2. The computationally determined inhibitory action of these ligands was then compared to ex vivo laboratory-measured values versus human AChE and BChE. While Posiphen lacked AChE inhibitory action, its major and minor metabolites (+)-N1-norPosiphen and (+)-N1,N8-bisnorPosiphen, respectively, possessed modest AChE inhibitory activity, and Posiphen and all metabolites lacked BChE action. Phenserine, as a positive control, demonstrated AChE-selective inhibitory action. In light of AChE inhibitory action deriving from a major and minor Posiphen metabolite, current Posiphen clinical trials in AD and related disorders should additionally evaluate AChE inhibition; particularly if Posiphen should be combined with a known anticholinesterase, since this drug class is clinically approved and the standard of care for AD subjects, and excessive AChE inhibition may impact drug tolerability.