Miracle-dz/UniFAR-FTUs huggingface dataset:
PolyFit: Polynomial-based Indexing Approach for Fast Approximate Range Aggregate Queries

Range aggregate queries find frequent application in data analytics. In some use cases, approximate results are preferred over accurate results if they can be computed rapidly and satisfy approximation guarantees. Inspired by a recent indexing approach, we provide means of representing a discrete point data set by continuous functions that can then serve as compact index structures. More specifically, we develop a polynomial-based indexing approach, called PolyFit, for processing approximate range aggregate queries. PolyFit is capable of supporting multiple types of range aggregate queries, including COUNT, SUM, MIN and MAX aggregates, with guaranteed absolute and relative error bounds. Experiment results show that PolyFit is faster and more accurate and compact than existing learned index structures.
Enumeration of Unordered Forests

Reverse search is a convenient method for enumerating structured objects, that can be used both to address theoretical issues and to solve data mining problems. This method has already been successfully developed to handle unordered trees. If the literature proposes solutions to enumerate singletons of trees, we study in this article a more general, higher combinatorial problem, the enumeration of sets of trees-forests. By compressing each forest into a Directed Acyclic Graph (DAG), we develop a reverse search like method to enumerate DAG compressing forests. Remarkably, we prove that these DAG are in bijection with the row-Fishburn matrices, a well-studied class of combinatorial objects. In a second step, we derive our forest enumeration to provide algorithms for tackling two related problems : (i) the enumeration of "subforests" of a forest, and (ii) the frequent "subforest" mining problem. All the methods presented in this article enumerate each item uniquely, up to isomorphism.
Law of large numbers for Betti numbers of homogeneous and spatially independent random simplicial complexes

The Linial–Meshulam complex model is a natural higher dimensional analog of the Erdős–Rényi graph model. In recent years, Linial and Peled established a limit theorem for Betti numbers of Linial–Meshulam complexes with an appropriate scaling of the underlying parameter. The present article aims to extend that result to more general random simplicial complex models. We introduce a class of homogeneous and spatially independent random simplicial complexes, including the Linial–Meshulam complex model and the random clique complex model as special cases, and we study the asymptotic behavior of their Betti numbers. Moreover, we obtain the convergence of the empirical spectral distributions of their Laplacians. A key element in the argument is the local weak convergence of simplicial complexes. Inspired by the work of Linial and Peled, we establish the local weak limit theorem for homogeneous and spatially independent random simplicial complexes.
Sequential Forecasting of 100,000 Points

Predicting the future is a crucial first step to effective control, since systems that can predict the future can select plans that lead to desired outcomes. In this work, we study the problem of future prediction at the level of 3D scenes, represented by point clouds captured by a LiDAR sensor, i.e., directly learning to forecast the evolution of >100,000 points that comprise a complete scene. We term this Scene Point Cloud Sequence Forecasting (SPCSF). By directly predicting the densest-possible 3D representation of the future, the output contains richer information than other representations such as future object trajectories. We design a method, SPCSFNet, evaluate it on the KITTI and nuScenes datasets, and find that it demonstrates excellent performance on the SPCSF task. To show that SPCSF can benefit downstream tasks such as object trajectory forecasting, we present a new object trajectory forecasting pipeline leveraging SPCSFNet. Specifically, instead of forecasting at the object level as in conventional trajectory forecasting, we propose to forecast at the sensor level and then apply detection and tracking on the predicted sensor data. As a result, our new pipeline can remove the need of object trajectory labels and enable large-scale training with unlabeled sensor data. Surprisingly, we found our new pipeline based on SPCSFNet was able to outperform the conventional pipeline using state-of-the-art trajectory forecasting methods, all of which require future object trajectory labels. Finally, we propose a new evaluation procedure and two new metrics to measure the end-to-end performance of the trajectory forecasting pipeline. Our code will be made publicly available at this https URL.
A Preferred Orientation Angle for Bipolar Planetary Nebulae

We present preliminary results from measuring Galactic orientation angles of 766 elliptical and bipolar Planetary Nebulae (PNe) in the Hong Kong/Australian Astronomical Observatory/Strasbourg Observatory H-alpha Planetary Nebula research platform and database (HASH DB). For elliptical PNe the distribution of orientation angles is found to be more or less uniform. However, for bipolar PNe there is statistically significant evidence for preferred orientation angles (as tentatively reported before with smaller samples) across the whole Galaxy.
A criterion for asymptotic sharpness in the enumeration of simply generated trees

We study the identity $y(x)=xA(y(x))$, from the theory of rooted trees, for appropriate generating functions $y(x)$ and $A(x)$ with non-negative integer coefficients. A problem that has been studied extensively is to determine the asymptotics of the coefficients of $y(x)$ from analytic properties of the complex function $z\mapsto A(z)$, assumed to have a positive radius of convergence $R$. It is well-known that the vanishing of $A(x)-xA'(x)$ on $(0,R)$ is sufficient to ensure that $y(r)<R$, where $r$ is the radius of convergence of $y(x)$. This result has been generalized in the literature to account for more general functional equations than the one above, and used to determine asymptotics for the Taylor coefficients of $y(x)$. What has not been shown is whether that sufficient condition is also necessary. We show here that it is, thus establishing a criterion for sharpness of the inequality $y(r)\leq R$. As an application, we prove a 1996 conjecture of Kuperberg regarding the asymptotic growth rate of an integer sequence arising in the study of Lie algebra representations.
PIC: Permutation Invariant Convolution for Recognizing Long-range Activities

Neural operations as convolutions, self-attention, and vector aggregation are the go-to choices for recognizing short-range actions. However, they have three limitations in modeling long-range activities. This paper presents PIC, Permutation Invariant Convolution, a novel neural layer to model the temporal structure of long-range activities. It has three desirable properties. i. Unlike standard convolution, PIC is invariant to the temporal permutations of features within its receptive field, qualifying it to model the weak temporal structures. ii. Different from vector aggregation, PIC respects local connectivity, enabling it to learn long-range temporal abstractions using cascaded layers. iii. In contrast to self-attention, PIC uses shared weights, making it more capable of detecting the most discriminant visual evidence across long and noisy videos. We study the three properties of PIC and demonstrate its effectiveness in recognizing the long-range activities of Charades, Breakfast, and MultiThumos.
Adversarial Transferability in Wearable Sensor Systems

Machine learning has increasingly become the most used approach for inference and decision making in wearable sensor systems. However, recent studies have found that machine learning systems are easily fooled by the addition of adversarial perturbation to their inputs. What is more interesting is that the adversarial examples generated for one machine learning system can also degrade the performance of another. This property of adversarial examples is called transferability. In this work, we take the first strides in studying adversarial transferability in wearable sensor systems, from the following perspectives: 1) Transferability between machine learning models, 2) Transferability across subjects, 3) Transferability across sensor locations, and 4) Transferability across datasets. With Human Activity Recognition (HAR) as an example sensor system, we found strong untargeted transferability in all cases of transferability. Specifically, gradient-based attacks were able to achieve higher misclassification rates compared to non-gradient attacks. The misclassification rate of untargeted adversarial examples ranged from 20% to 98%. For targeted transferability between machine learning models, the success rate of adversarial examples was 100% for iterative attack methods. However, the success rate for other types of targeted transferability ranged from 20% to 0%. Our findings strongly suggest that adversarial transferability has serious consequences not only in sensor systems but also across the broad spectrum of ubiquitous computing.
Intraductal Carcinoma of the Prostate

The histologic differential diagnosis of IDC-P on prostate needle biopsy includes high-grade prostatic intraepithelial neoplasia (HGPIN), invasive cribriform prostatic adenocarcinoma, prostatic ductal adenocarcinoma, and urothelial carcinoma extending into prostatic ducts. Diagnostic reproducibility is low in the category of intraductal lesions of the prostate (4) whereas, accurate diagnosis is crucial for patient management. When IDC-P is accompanied by invasive carcinoma, it is associated with higher grade and higher stage cancer. Isolated IDC-P (IDC-P unaccompanied by invasive cancer) on needle biopsy is generally associated with highgrade invasive prostatic adenocarcinoma on subsequent radical prostatectomy (2,4). IDC-P requires either definitive therapy or prompt rebiopsy. In contrast, HGPIN is considered a preneoplastic lesion; its presence does not require definitive therapy and in limited quantities, does not require rebiopsy. If urothelial carcinoma extending into prostatic ducts is identified on needle biopsy, radical cystoprostatectomy is recommended. Usual (acinar) prostatic adenocarcinoma and prostatic ductal adenocarcinoma are treated in the same manner and require definitive treatment.
"Pacing Bigeminal" An Alternative Method in Developing a Rapid Pacing-Induced Heart Failure Model

A rapid pacing-induced heart failure model is commonly used in developing dilated cardiomyopathy (DCM). Traditionally, the right ventricular lead was connected with a single chamber pacemaker specific for animals that had a high frequency. However, the pacemaker used in this model is commercially unavailable. We developed a “pacing bigeminal” method using a commercially available dual-chamber (DDD) pacemaker to achieve high-frequency pacing. Twenty beagles were assigned to group A (n = 10) (pacing bigeminal method) and group B (n = 10) (traditional method). Echocardiographic measurements and electrocardiograms were obtained at baseline, at two weeks of pacing, and at 4 weeks of end pacing. LV anterior wall cardiac samples were obtained at 2 weeks of pacing and 4 weeks of end pacing for myocardial microscopic evaluation. Clinical manifestation and exposure time were also observed. After pacing for 10.5 ± 2.3 (714) days, the beagles in group B experienced heart failure, whereas in group A, only 7.9 ± 2.5 (5-12) days (P < 0.05) were needed to reach heart failure. Both methods could induce wide QRS duration, heart rate elevation, and myocardial microscopic changes (P > 0.05). In conclusion, this pacing bigeminal-induced heart failure method is feasible and can induce heart failure faster than the traditional method, which makes it a promising alternative method. (Int Heart J 2016; 57: 747-752)
Speeding up Linear Programming using Randomized Linear Algebra

Linear programming (LP) is an extremely useful tool and has been successfully applied to solve various problems in a wide range of areas, including operations research, engineering, economics, or even more abstract mathematical areas such as combinatorics. It is also used in many machine learning applications, such as $\ell_1$-regularized SVMs, basis pursuit, nonnegative matrix factorization, etc. Interior Point Methods (IPMs) are one of the most popular methods to solve LPs both in theory and in practice. Their underlying complexity is dominated by the cost of solving a system of linear equations at each iteration. In this paper, we consider \emph{infeasible} IPMs for the special case where the number of variables is much larger than the number of constraints. Using tools from Randomized Linear Algebra, we present a preconditioning technique that, when combined with the Conjugate Gradient iterative solver, provably guarantees that infeasible IPM algorithms (suitably modified to account for the error incurred by the approximate solver), converge to a feasible, approximately optimal solution, without increasing their iteration complexity. Our empirical evaluations verify our theoretical results on both real-world and synthetic data.
Deep connections between learning from limited labels & physical parameter estimation - inspiration for regularization

Recently established equivalences between differential equations and the structure of neural networks enabled some interpretation of training of a neural network as partial-differential-equation (PDE) constrained optimization. We add to the previously established connections, explicit regularization that is particularly beneficial in the case of single large-scale examples with partial annotation. We show that explicit regularization of model parameters in PDE constrained optimization translates to regularization of the network output. Examination of the structure of the corresponding Lagrangian and backpropagation algorithm do not reveal additional computational challenges. A hyperspectral imaging example shows that minimum prior information together with cross-validation for optimal regularization parameters boosts the segmentation accuracy.
Finite-time Identification of Stable Linear Systems Optimality of the Least-Squares Estimator

We present a new finite-time analysis of the estimation error of the Ordinary Least Squares (OLS) estimator for stable linear time-invariant systems. We characterize the number of observed samples (the length of the observed trajectory) sufficient for the OLS estimator to be (ε, δ)-PAC, i.e., to yield an estimation error less than ε with probability at least 1 - δ. We show that this number matches existing sample complexity lower bounds [1], [2] up to universal multiplicative factors (independent of (ε, δ) and of the system). This paper hence establishes the optimality of the OLS estimator for stable systems, a result conjectured in [1]. Our analysis of the performance of the OLS estimator is simpler, sharper, and easier to interpret than existing analyses. It relies on new concentration results for the covariates matrix.
State of Charge Estimation of Parallel Connected Battery Cells via Descriptor System Theory*

This manuscript presents an algorithm for individual Lithium-ion (Li-ion) battery cell state of charge (SOC) estimation when multiple cells are connected in parallel, using only terminal voltage and total current measurements. For battery packs consisting of thousands of cells, it is desirable to estimate individual SOCs by only monitoring the total current in order to reduce sensing cost. Mathematically, series connected cells yield dynamics given by ordinary differential equations under classical full voltage sensing. In contrast, parallel connected cells are evidently more challenging because the dynamics are governed by a nonlinear descriptor system, including differential equations and algebraic equations arising from voltage and current balance across cells. This paper designs and analyzes an observer with linear output error injection, where the individual cell SOCs and local currents are locally observable from the total current and voltage measurements. The asymptotic convergence of differential and algebraic states is established by considering local Lipschitz continuity property of system nonlinearities. Simulation results on LiNiMnCoO2/Graphite (NMC) cells illustrate convergence for SOCs, local currents, and terminal voltage.
Nearest Neighbor Dirichlet Process

There is a rich literature on Bayesian nonparametric methods for unknown densities. The most popular approach relies on Dirichlet process mixture models. These models characterize the unknown density as a kernel convolution with an unknown almost surely discrete mixing measure, which is given a Dirichlet process prior. Such models are very flexible and have good performance in many settings, but posterior computation relies on Markov chain Monte Carlo algorithms that can be complex and inefficient. As a simple and general alternative, we propose a class of nearest neighbor-Dirichlet processes. The approach starts by grouping the data into neighborhoods based on standard algorithms. Within each neighborhood, the density is characterized via a Bayesian parametric model, such as a Gaussian with unknown parameters. Assigning a Dirichlet prior to the weights on these local kernels, we obtain a simple pseudo-posterior for the weights and kernel parameters. A simple and embarrassingly parallel Monte Carlo algorithm is proposed to sample from the resulting pseudo-posterior for the unknown density. Desirable asymptotic properties are shown, and the methods are evaluated in simulation studies and applied to a motivating dataset in the context of classification.
X -stance: A Multilingual Multi-Target Dataset for Stance Detection

We extract a large-scale stance detection dataset from comments written by candidates of elections in Switzerland. The dataset consists of German, French and Italian text, allowing for a cross-lingual evaluation of stance detection. It contains 67 000 comments on more than 150 political issues (targets). Unlike stance detection models that have specific target issues, we use the dataset to train a single model on all the issues. To make learning across targets possible, we prepend to each instance a natural question that represents the target (e.g. "Do you support X? "). Baseline results from multilingual BERT show that zero-shot cross-lingual and cross-target transfer of stance detection is moderately successful with this approach.
Segmentation and Optimal Region Selection of Physiological Signals using Deep Neural Networks and Combinatorial Optimization

Physiological signals, such as the electrocardiogram and the phonocardiogram are very often corrupted by noisy sources. Usually, artificial intelligent algorithms analyze the signal regardless of its quality. On the other hand, physicians use a completely orthogonal strategy. They do not assess the entire recording, instead they search for a segment where the fundamental and abnormal waves are easily detected, and only then a prognostic is attempted. Inspired by this fact, a new algorithm that automatically selects an optimal segment for a post-processing stage, according to a criteria defined by the user is proposed. In the process, a Neural Network is used to compute the output state probability distribution for each sample. Using the aforementioned quantities, a graph is designed, whereas state transition constraints are physically imposed into the graph and a set of constraints are used to retrieve a subset of the recording that maximizes the likelihood function, proposed by the user. The developed framework is tested and validated in two applications. In both cases, the system performance is boosted significantly, e.g in heart sound segmentation, sensitivity increases 2.4% when compared to the standard approaches in the literature.
Watching the World Go By: Representation Learning from Unlabeled Videos

Recent single image unsupervised representation learning techniques show remarkable success on a variety of tasks. The basic principle in these works is instance discrimination: learning to differentiate between two augmented versions of the same image and a large batch of unrelated images. Networks learn to ignore the augmentation noise and extract semantically meaningful representations. Prior work uses artificial data augmentation techniques such as cropping, and color jitter which can only affect the image in superficial ways and are not aligned with how objects actually change e.g. occlusion, deformation, viewpoint change. In this paper, we argue that videos offer this natural augmentation for free. Videos can provide entirely new views of objects, show deformation, and even connect semantically similar but visually distinct concepts. We propose Video Noise Contrastive Estimation, a method for using unlabeled video to learn strong, transferable single image representations. We demonstrate improvements over recent unsupervised single image techniques, as well as over fully supervised ImageNet pretraining, across a variety of temporal and non-temporal tasks. Code and the Random Related Video Views dataset are available at this https URL
In Defense of Graph Inference Algorithms for Weakly Supervised Object Localization

Weakly Supervised Object Localization (WSOL) methods have become increasingly popular since they only require image level labels as opposed to expensive bounding box annotations required by fully supervised algorithms. Typically, a WSOL model is first trained to predict class generic objectness scores on an off-the-shelf fully supervised source dataset and then it is progressively adapted to learn the objects in the weakly supervised target dataset. In this work, we argue that learning only an objectness function is a weak form of knowledge transfer and propose to learn a classwise pairwise similarity function that directly compares two input proposals as well. The combined localization model and the estimated object annotations are jointly learned in an alternating optimization paradigm as is typically done in standard WSOL methods. In contrast to the existing work that learns pairwise similarities, our proposed approach optimizes a unified objective with convergence guarantee and it is computationally efficient for large-scale applications. Experiments on the COCO and ILSVRC 2013 detection datasets show that the performance of the localization model improves significantly with the inclusion of pairwise similarity function. For instance, in the ILSVRC dataset, the Correct Localization (CorLoc) performance improves from 72.7% to 78.2% which is a new state-of-the-art for weakly supervised object localization task.