Miracle-dz/UniFAR-FTUs huggingface dataset:
Bicriteria Multidimensional Mechanism Design with Side Information

We develop a versatile new methodology for multidimensional mechanism design that incorporates side information about agent types with the bicriteria goal of generating high social welfare and high revenue simultaneously. Side information can come from a variety of sources -- examples include advice from a domain expert, predictions from a machine-learning model trained on historical agent data, or even the mechanism designer's own gut instinct -- and in practice such sources are abundant. In this paper we adopt a prior-free perspective that makes no assumptions on the correctness, accuracy, or source of the side information. First, we design a meta-mechanism that integrates input side information with an improvement of the classical VCG mechanism. The welfare, revenue, and incentive properties of our meta-mechanism are characterized by a number of novel constructions we introduce based on the notion of a weakest competitor, which is an agent that has the smallest impact on welfare. We then show that our meta-mechanism -- when carefully instantiated -- simultaneously achieves strong welfare and revenue guarantees that are parameterized by errors in the side information. When the side information is highly informative and accurate, our mechanism achieves welfare and revenue competitive with the total social surplus, and its performance decays continuously and gradually as the quality of the side information decreases. Finally, we apply our meta-mechanism to a setting where each agent's type is determined by a constant number of parameters. Specifically, agent types lie on constant-dimensional subspaces (of the potentially high-dimensional ambient type space) that are known to the mechanism designer. We use our meta-mechanism to obtain the first known welfare and revenue guarantees in this setting.
DAG-Inducing Problems and Algorithms

In this paper, we show that in a parallel processing system, if a partial order is induced among the local states visited by a node, then synchronization cost can be eliminated. As a result of this partial order, a DAG is induced among the global states. Specifically, we show that in such systems, correctness is preserved even if the nodes execute asynchronously and read old information of other nodes. We present two variations for inducing DAGs -- \textit{DAG-inducing problems}, where the problem definition itself induces a DAG, and \textit{DAG-inducing algorithms}, where a DAG is induced by the algorithm. We demonstrate that the dominant clique (DC) problem and shortest path (SP) problem are DAG-inducing problems. Among these, DC allows self-stabilization, whereas the algorithm that we present for SP does not. We demonstrate that maximal matching (MM) is not a DAG-inducing problem. However, a DAG-inducing algorithm can be developed for it. The algorithm for MM allows self-stabilization. This algorithm converges in $2n$ moves and does not require a synchronous environment, which is an improvement over the existing algorithms in the literature. The algorithm for DC converges in $2m$ moves, and the algorithm for SP converges in $\mathcal{D}$ rounds. ($n$ is the number of nodes and $m$ is the number of edges in the input graph, and $\mathcal{D}$ is its diameter.) We also note that DAG-inducing problems are more general than, and encapsulate, lattice linear problems (Garg, SPAA 2020). Similarly, DAG-inducing algorithms encapsulate lattice linear algorithms (Gupta and Kulkarni, SSS 2022). We also show that a partial order induced among the local states visited by a node, as discussed above, is a necessary and sufficient condition to allow asynchrony.
GLOW: Global Layout Aware Attacks on Object Detection

Adversarial attacks aim to perturb images such that a predictor outputs incorrect results. Due to the limited research in structured attacks, imposing consistency checks on natural multi-object scenes is a promising yet practical defense against conventional adversarial attacks. More desired attacks, to this end, should be able to fool defenses with such consistency checks. Therefore, we present the first approach GLOW that copes with various attack requests by generating global layout-aware adversarial attacks, in which both categorical and geometric layout constraints are explicitly established. Specifically, we focus on object detection task and given a victim image, GLOW first localizes victim objects according to target labels. And then it generates multiple attack plans, together with their context-consistency scores. Our proposed GLOW, on the one hand, is capable of handling various types of requests, including single or multiple victim objects, with or without specified victim objects. On the other hand, it produces a consistency score for each attack plan, reflecting the overall contextual consistency that both semantic category and global scene layout are considered. In experiment, we design multiple types of attack requests and validate our ideas on MS COCO and Pascal. Extensive experimental results demonstrate that we can achieve about 30$\%$ average relative improvement compared to state-of-the-art methods in conventional single object attack request; Moreover, our method outperforms SOTAs significantly on more generic attack requests by about 20$\%$ in average; Finally, our method produces superior performance under challenging zero-query black-box setting, or 20$\%$ better than SOTAs. Our code, model and attack requests would be made available.
TabGenie: A Toolkit for Table-to-Text Generation

Heterogenity of data-to-text generation datasets limits the research on data-to-text generation systems. We present TabGenie – a toolkit which enables researchers to explore, preprocess, and analyze a variety of data-to-text generation datasets through the unified framework of table-to-text generation. In TabGenie, all inputs are represented as tables with associated metadata. The tables can be explored through a web interface, which also provides an interactive mode for debugging table-to-text generation, facilitates side-by-side comparison of generated system outputs, and allows easy exports for manual analysis. Furthermore, TabGenie is equipped with command line processing tools and Python bindings for unified dataset loading and processing. We release TabGenie as a PyPI package and provide its open-source code and a live demo at https://github.com/kasnerz/tabgenie.
Multimodal Deep Learning to Differentiate Tumor Recurrence from Treatment Effect in Human Glioblastoma

Differentiating tumor progression (TP) or recurrence from treatment-related necrosis (TN) is critical for clinical management decisions in glioblastoma (GBM). Dynamic FDG PET (dPET), an advance from traditional static FDG PET, may prove advantageous in clinical staging. dPET includes novel methods of a model-corrected blood input function that accounts for partial volume averaging to compute parametric maps that reveal kinetic information. In a preliminary study, a convolution neural network (CNN) was trained to predict classification accuracy between TP and TN for 35 brain tumors from 26 subjects in the PET-MR image space. 3D parametric PET Ki (from dPET), traditional static PET standardized uptake values (SUV), and also the brain tumor MR voxels formed the input for the CNN. The average test accuracy across all leave-one-out cross-validation iterations adjusting for class weights was 0.56 using only the MR, 0.65 using only the SUV, and 0.71 using only the Ki voxels. Combining SUV and MR voxels increased the test accuracy to 0.62. On the other hand, MR and Ki voxels increased the test accuracy to 0.74. Thus, dPET features alone or with MR features in deep learning models would enhance prediction accuracy in differentiating TP vs TN in GBM.
Robust model reference control for multivariable linear systems subject to parameter uncertainties

Abstract Robust model reference control for multivariable linear systems with structural parameter uncertainties is considered. It is shown that the problem can be decomposed into two subproblems: a robust state feedback stabilization problem for multivariable linear systems subject to parameter uncertainties and a robust compensation problem. The latter concerns solution of three coefficient matrices such that four matrix equations are met and, simultaneously, the effect of the uncertainties to the tracking error is minimized. Based on a complete parametric solution to a class of generalized Sylvester matrix equations, the robust compensation problem is turned into a minimization problem with quadratic cost and linear constraints. A set of linear equations is derived that determines the optimal solution to the minimization. An example illustrates the application of the proposed approach.
Robust Field-level Likelihood-free Inference with Galaxies

We train graph neural networks to perform field-level likelihood-free inference using galaxy catalogs from state-of-the-art hydrodynamic simulations of the CAMELS project. Our models are rotational, translational, and permutation invariant and do not impose any cut on scale. From galaxy catalogs that only contain 3D positions and radial velocities of ∼1000 galaxies in tiny (25h−1Mpc)3 volumes our models can infer the value of Ωm with approximately 12% precision. More importantly, by testing the models on galaxy catalogs from thousands of hydrodynamic simulations, each having a different efficiency of supernova and active galactic nucleus feedback, run with five different codes and subgrid models—IllustrisTNG, SIMBA, Astrid, Magneticum, SWIFT-EAGLE—we find that our models are robust to changes in astrophysics, subgrid physics, and subhalo/galaxy finder. Furthermore, we test our models on 1024 simulations that cover a vast region in parameter space—variations in five cosmological and 23 astrophysical parameters—finding that the model extrapolates really well. Our results indicate that the key to building a robust model is the use of both galaxy positions and velocities, suggesting that the network has likely learned an underlying physical relation that does not depend on galaxy formation and is valid on scales larger than ∼10 h −1 kpc.
Mixtures of All Trees

Tree-shaped graphical models are widely used for their tractability. However, they unfortunately lack expressive power as they require committing to a particular sparse dependency structure. We propose a novel class of generative models called mixtures of all trees: that is, a mixture over all possible ($n^{n-2}$) tree-shaped graphical models over $n$ variables. We show that it is possible to parameterize this Mixture of All Trees (MoAT) model compactly (using a polynomial-size representation) in a way that allows for tractable likelihood computation and optimization via stochastic gradient descent. Furthermore, by leveraging the tractability of tree-shaped models, we devise fast-converging conditional sampling algorithms for approximate inference, even though our theoretical analysis suggests that exact computation of marginals in the MoAT model is NP-hard. Empirically, MoAT achieves state-of-the-art performance on density estimation benchmarks when compared against powerful probabilistic models including hidden Chow-Liu Trees.
High frequency behavior of soft magnetic wires using the giant magnetoimpedance effect

We have investigated the high frequency properties of several amorphous and polycrystalline wires mounted as inner conductors in coaxial lines. A static magnetic field was applied along the wire axis. The impedance spectra of the wires, measured using a network analyzer, show peaks in the real part of the impedance, which shift to higher frequency with the strength of the static field, a behavior typical of ferromagnetic resonance. The theoretical resonance condition predicts a straight line on an f02−H0 plot, where f0 is the resonance frequency and H0 is the resonant field, whose slope depends only on the saturation magnetization, Ms, of the material. All our wires obey this relation, and the values of Ms calculated from the slopes are in good agreement with those measured directly using a vibrating sample magnetometer.
An Embedded and Real-Time Pupil Detection Pipeline

Wearable pupil detection systems often separate the analysis of the captured wearer's eye images for wirelessly-tethered back-end systems. We argue in this paper that investigating hardware-software co-designs would bring along opportunities to make such systems smaller and more efficient. We introduce an open-source embedded system for wearable, non-invasive pupil detection in real-time, on the wearable, embedded platform itself. Our system consists of a head-mounted eye tracker prototype, which combines two miniature camera systems with Raspberry Pi-based embedded system. Apart from the hardware design, we also contribute a pupil detection pipeline that operates using edge analysis, natively on the embedded system at 30fps and run-time of 54ms at 480x640 and 23ms at 240x320. Average cumulative error of 5.3368px is found on the LPW dataset for a detection rate of 51.9\% with our detection pipeline. For evaluation on our hardware-specific camera frames, we also contribute a dataset of 35000 images, from 20 participants.
On the consistency of ΛCDM with CMB measurements in light of the latest Planck, ACT and SPT data

Using Gaussian Processes we perform a thorough, non-parametric consistency test of the ΛCDM model when confronted with state-of-the-art TT, TE, and EE measurements of the anisotropies in the Cosmic Microwave Background by the Planck, ACT, and SPT collaborations. Using ΛCDM's best-fit predictions to the TTTEEE data from Planck, we find no statistically significant deviations when looking for signatures in the residuals across the different datasets. The results of SPT are in good agreement with the ΛCDM best-fit predictions to the Planck data, while the results of ACT are only marginally consistent. However, when using the best-fit predictions to CamSpec — a recent reanalysis of the Planck data — as the mean function, we find larger discrepancies between the datasets. Our analysis also reveals an interesting feature in the polarisation (EE) measurements from the CamSpec analysis, which could be explained by a slight underestimation of the covariance matrix. Interestingly, the disagreement between CamSpec and Planck/ACT is mainly visible in the residuals of the TT spectrum, the latter favoring a scale-invariant tilt ns ≃ 1, which is consistent with previous findings from parametric analyses. We also report some features in the EE measurements captured both by ACT and SPT which are independent of the chosen mean function and could be hinting towards a common physical origin. For completeness, we repeat our analysis using the best-fit spectra to ACT+WMAP as the mean function. Finally, we test the internal consistency of the Planck data alone by studying the high and low-ℓ ranges separately, finding no discrepancy between small and large angular scales.
Gradient-Boosted Based Structured and Unstructured Learning

We propose two frameworks to deal with problem settings in which both structured and unstructured data are available. Structured data problems are best solved by traditional machine learning models such as boosting and tree-based algorithms, whereas deep learning has been widely applied to problems dealing with images, text, audio, and other unstructured data sources. However, for the setting in which both structured and unstructured data are accessible, it is not obvious what the best modeling approach is to enhance performance on both data sources simultaneously. Our proposed frameworks allow joint learning on both kinds of data by integrating the paradigms of boosting models and deep neural networks. The first framework, the boosted-feature-vector deep learning network, learns features from the structured data using gradient boosting and combines them with embeddings from unstructured data via a two-branch deep neural network. Secondly, the two-weak-learner boosting framework extends the boosting paradigm to the setting with two input data sources. We present and compare first- and second-order methods of this framework. Our experimental results on both public and real-world datasets show performance gains achieved by the frameworks over selected baselines by magnitudes of 0.1% - 4.7%.
Aberration measurement and correction with a high resolution 1.75D array

Accurate measurement of tissue induced aberrations is necessary for effective adaptive ultrasound imaging. We acquired single channel RF data on a 6.7 MHz, 8/spl times/128 array (Tetrad Co.) operating at F/1.0 in azimuth and F/2.89 in elevation. This array was interfaced to a Siemens Elegra scanner, allowing for data acquisition during routine clinical scanning. Breast images in three patients and four volunteers (for a total of 16 scans), and thyroid and liver images in six volunteers (10 scans each) were taken. A least squares algorithm was employed to estimate the arrival time error induced by the tissue and to generate corrected images. In general, mild (20-40 nsec r.m.s.) and spatially stable aberration profiles were measured.
Stability of multi-solitons for the Benjamin-Ono equation

This paper is concerned with the dynamical stability of the $N$-solitons of the Benjamin-Ono (BO) equation. This extends the work of Neves and Lopes \cite{LN} which was restricted to $N=2$ the double solitons case. By constructing a suitable Lyapunov functional, it is found that the multi-solitons are non-isolated constrained minimizers satisfying a suitable variational nonlocal elliptic equation, the stability issue is reduced to the spectral analysis of higher order nonlocal operators consist of the Hilbert transform. Such operators are isoinertial and the negative eigenvalues of which are located. Our approach in the spectral analysis consists in an invariant for the multi-solitons and new operator identities motivated by the bi-Hamiltonian structure of the BO equation. Since the BO equation is more likely a two dimensional integrable system, its recursion operator is not explicit and which contributes the main difficulties in our analysis. The key ingredient in the spectral analysis is by employing the completeness in $L^2$ of the squared eigenfunctions of the eigenvalue problem for the BO equation. It is demonstrated here that orbital stability of soliton in $H^{\frac{1}{2}}(\R)$ implies that all $N$-solitons are dynamically stable in $H^{\frac{N}{2}}(\R)$.
Fractional Korn’s inequalities without boundary
conditions

This work establishes fractional analogues of Korn's first and second inequalities for vector fields in fractional Sobolev spaces defined over a bounded domain. The validity of the inequalities require no additional boundary condition, extending existing fractional Korn's inequalities that are only applicable for Sobolev vector fields satisfying zero Dirichlet boundary conditions. The domain of definition is required to have a $C^1$-boundary or, more generally, a Lipschitz boundary with small Lipschitz constant. We conjecture that the inequalities remain valid for vector fields defined over any Lipschitz domain. We support this claim by presenting a proof of the inequalities for vector fields defined over planar convex domains.
Towards Surgical Context Inference and Translation to Gestures

Manual labeling of gestures in robot-assisted surgery is labor intensive, prone to errors, and requires expertise or training. We propose a method for automated and explainable generation of gesture transcripts that leverages the abundance of data for image segmentation. Surgical context is detected using segmentation masks by examining the distances and intersections between the tools and objects. Next, context labels are translated into gesture transcripts using knowledge-based Finite State Machine (FSM) and data-driven Long Short Term Memory (LSTM) models. We evaluate the performance of each stage of our method by comparing the results with the ground truth segmentation masks, the consensus context labels, and the gesture labels in the JIGSAWS dataset. Our results show that our segmentation models achieve state-of-the-art performance in recognizing needle and thread in Suturing and we can automatically detect important surgical states with high agreement with crowd-sourced labels (e.g., contact between graspers and objects in Suturing). We also find that the FSM models are more robust to poor segmentation and labeling performance than LSTMs. Our proposed method can significantly shorten the gesture labeling process (~2.8 times).
Framelet Message Passing

Graph neural networks (GNNs) have achieved champion in wide applications. Neural message passing is a typical key module for feature propagation by aggregating neighboring features. In this work, we propose a new message passing based on multiscale framelet transforms, called Framelet Message Passing. Different from traditional spatial methods, it integrates framelet representation of neighbor nodes from multiple hops away in node message update. We also propose a continuous message passing using neural ODE solvers. It turns both discrete and continuous cases can provably achieve network stability and limit oversmoothing due to the multiscale property of framelets. Numerical experiments on real graph datasets show that the continuous version of the framelet message passing significantly outperforms existing methods when learning heterogeneous graphs and achieves state-of-the-art performance on classic node classification tasks with low computational costs.
Modular and Parallelizable Multibody Physics Simulation via Subsystem-Based ADMM

In this paper, we present a new multibody physics simulation framework that utilizes the subsystem-based struc-ture and the Alternating Direction Method of Multiplier (ADMM). The major challenge in simulating complex high degree of freedom systems is a large number of coupled con-straints and large-sized matrices. To address this challenge, we first split the multibody into several subsystems and reformulate the dynamics equation into a subsystem perspective based on the structure of their interconnection. Then we utilize ADMM with our novel subsystem-based variable splitting scheme to solve the equation, which allows parallelizable and modular architecture. The resulting algorithm is fast, scalable, versatile, and converges well while maintaining solution consistency. Sev-eral illustrative examples are implemented with performance evaluation results showing advantages over other state-of-the-art algorithms.
Natural selection for collective action

Collective action -- behavior that arises from the combined actions of multiple individuals -- is observed across living beings. The question of how and why collective action evolves has profound implications for behavioral ecology, multicellularity, and human society. Collective action is challenging to model mathematically, due to nonlinear fitness effects and the consequences of spatial, group, and/or family relationships. We derive a simple condition for collective action to be favored by natural selection. A collective's effect on the fitness of each individual is weighted by the relatedness between them, using a new measure of collective relatedness. If selection is weak, this condition can be evaluated using coalescent theory. More generally, our result applies to any synergistic social behavior, in spatial, group, and/or family-structured populations. We use this result to obtain conditions for the evolution of collective help among diploid siblings, subcommunities of a network, and hyperedges of a hypergraph. We also obtain a condition for which of two strategies is favored in a game between siblings, cousins, or other relatives. Our work provides a rigorous basis for extending the notion of ``actor", in the study of social behavior, from individuals to collectives.