Miracle-dz/UniFAR-FTUs huggingface dataset:
OffRoadTranSeg: Semi-Supervised Segmentation using Transformers on OffRoad environments

We present OffRoadTranSeg, the first end-to-end framework for semi-supervised segmentation in unstructured outdoor environment using transformers and automatic data selection for labelling. The offroad segmentation is a scene understanding approach that is widely used in autonomous driving. The popular offroad segmentation method is to use fully connected convolution layers and large labelled data, however, due to class imbalance, there will be several mismatches and also some classes may not be detected. Our approach is to do the task of offroad segmentation in a semi-supervised manner. The aim is to provide a model where self supervised vision transformer is used to fine-tune offroad datasets with self-supervised data collection for labelling using depth estimation. The proposed method is validated on RELLIS-3D and RUGD offroad datasets. The experiments show that OffRoadTranSeg outperformed other state of the art models, and also solves the RELLIS-3D class imbalance problem.
Dark solitons in a trapped gas of long-range interacting bosons

M. Beau, 2 A. del Campo, 4, 1 D. J. Frantzeskakis, T. P. Horikis, and P. G. Kevrekidis Department of Physics, University of Massachusetts, Boston, Massachusetts 02125, USA Dublin Institute for Advanced Studies, School of Theoretical Physics, 10 Burlington Road, Dublin 4, Ireland Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg, Luxembourg Donostia International Physics Center, E-20018 San Sebastián, Spain Department of Physics, National and Kapodistrian University of Athens, Panepistimiopolis, Zografos, Athens 15784, Greece Department of Mathematics, University of Ioannina, Ioannina 45110, Greece Department of Mathematics and Statistics, University of Massachusetts, Amherst, MA 01003-4515, USA
Regret Analysis in Deterministic Reinforcement Learning

We consider Markov Decision Processes (MDPs) with deterministic transitions and study the problem of regret minimization, which is central to the analysis and design of optimal learning algorithms. We present logarithmic problem-specific regret lower bounds that explicitly depend on the system parameter (in contrast to previous minimax approaches) and thus, truly quantify the fundamental limit of the performance achievable by any learning algorithm. Deterministic MDPs can be interpreted as graphs and analyzed in terms of their cycles, a fact which we leverage in order to identify a class of deterministic MDPs whose regret lower bound can be determined numerically. We further exemplify this result on a deterministic line search problem, and a deterministic MDP with state-dependent rewards, whose regret lower bounds we can state explicitly. These bounds share similarities with the known problem-specific bound of the multi-armed bandit problem and suggest that navigation on a deterministic MDP need not have an effect on the performance of a learning algorithm.
Nonlocal amplification of intense vorticity in turbulent flows

The nonlinear and nonlocal coupling of vorticity and strain-rate constitutes a major hindrance in understanding the self-amplification of velocity gradients in turbulent fluid flows. Utilizing highlyresolved direct numerical simulations of isotropic turbulence in periodic domains of up to 12288 grid points, and Taylor-scale Reynolds number Rλ in the range 140 − 1300, we investigate this nonlocality by decomposing the strain-rate tensor into local and non-local contributions obtained through Biot-Savart integration of vorticity in a sphere of radius R. We find that vorticity is predominantly amplified by the non-local strain coming beyond a characteristic scale size, which varies as a simple power-law of vorticity magnitude. The underlying dynamics preferentially align vorticity with the most extensive eigenvector of non-local strain. The remaining local strain aligns vorticity with the intermediate eigenvector and does not contribute significantly to amplification; instead it surprisingly attenuates intense vorticity, leading to breakdown of the observed power-law and ultimately also the scale-invariance of vorticity amplification, with important implications for prevailing intermittency theories.
Instantaneous Capture Input for Balancing the Variable Height Inverted Pendulum

Balancing is a fundamental need for legged robots due to their unstable floating-base nature. Balance control has been thoroughly studied for simple models such as the linear inverted pendulum thanks to the concept of the instantaneous capture point (ICP), yet the constant center of mass height assumption limits the application. This letter explores balancing of the variable-height inverted pendulum (VHIP) model by introducing the instantaneous capture input (ICI), an extension of the ICP based on its key properties. Namely, the ICI can be computed as a function of the state, and when this function is used as the control policy, the ICI is rendered stationary and the system will eventually come to a stop. This characterization induces an analytical region of capturable states for the VHIP, which can be used to conceptually guide where to step. To further address state and control constraints during recovery, we present and theoretically analyze an explicit ICI-based controller with online optimal feedback gains. Simulations demonstrate the validity of our controller for capturability maintenance compared to an approach based on the divergent component of motion.
Understanding Dynamics of Nonlinear Representation Learning and Its Application

Abstract Representations of the world environment play a crucial role in artificial intelligence. It is often inefficient to conduct reasoning and inference directly in the space of raw sensory representations, such as pixel values of images. Representation learning allows us to automatically discover suitable representations from raw sensory data. For example, given raw sensory data, a deep neural network learns nonlinear representations at its hidden layers, which are subsequently used for classification (or regression) at its output layer. This happens implicitly during training through minimizing a supervised or unsupervised loss. In this letter, we study the dynamics of such implicit nonlinear representation learning. We identify a pair of a new assumption and a novel condition, called the on-model structure assumption and the data architecture alignment condition. Under the on-model structure assumption, the data architecture alignment condition is shown to be sufficient for the global convergence and necessary for global optimality. Moreover, our theory explains how and when increasing network size does and does not improve the training behaviors in the practical regime. Our results provide practical guidance for designing a model structure; for example, the on-model structure assumption can be used as a justification for using a particular model structure instead of others. As an application, we then derive a new training framework, which satisfies the data architecture alignment condition without assuming it by automatically modifying any given training algorithm dependent on data and architecture. Given a standard training algorithm, the framework running its modified version is empirically shown to maintain competitive (practical) test performances while providing global convergence guarantees for deep residual neural networks with convolutions, skip connections, and batch normalization with standard benchmark data sets, including MNIST, CIFAR-10, CIFAR-100, Semeion, KMNIST, and SVHN.
Extensions to Multifidelity Monte Carlo Methods for Simulations of Chaotic Systems

Multifidelity Monte Carlo methods often rely on a preprocessing phase consisting of standard Monte Carlo sampling to estimate correlation coefficients between models of different fidelity to determine the weights and number of samples for each level. For computationally intensive models, as are often encountered in simulations of chaotic systems, this up-front cost can be prohibitive. In this work, a correlation estimation procedure is developed for the case in which the highest and next highest fidelity models are generated via discretizing the same mathematical model using different resolution. The procedure uses discretization error estimates to estimate the required correlation coefficient without the need to sample the highest fidelity model, which can dramatically decrease the cost of the preprocessing phase. The method is extended to chaotic problems by using discretization error estimates that account for the statistical nature of common quantities of interest and the accompanying finite sampling errors that pollute estimates of such quantities of interest. The methodology is then demonstrated on a model problem based on the Kuramoto-Sivashinsky equation.
Online Cognitive Data Sensing and Processing Optimization in Energy-harvesting Edge Computing Systems

Mobile edge computing (MEC) has recently become a prevailing technique to alleviate the intensive computation burden in Internet of Things (IoT) networks. However, the limited device battery capacity and stringent spectrum resource significantly restrict the data processing performance of MEC-enabled IoT networks. To address the two performance limitations, we consider in this paper an MEC-enabled IoT system with a wireless device (WD) replenishing its battery by means of energy harvesting (EH) and opportunistically accessing the licensed spectrum of an overlaid primary communication link to offload its sensing data to an MEC server (MS) for edge processing. Under time-varying fading channel, random energy arrivals, and stochastic ON-OFF state of the primary link, we aim to design an online algorithm to jointly control the cognitive data sensing rate and processing method (i.e., local and edge processing) without knowing future system information. In particular, we aim to maximize the long-term average sensing rate of the WD subject to quality of service (QoS) requirement of primary link, average power constraint of MS and data queue stability of both MS and WD. We formulate the problem as a multi-stage stochastic optimization and propose an online algorithm named PLySE that applies the perturbed Lyapunov optimization technique to decompose the original problem into per-slot deterministic optimization problems. For each per-slot problem, we derive the closed-form optimal solution of data sensing and processing control to facilitate low-complexity real-time implementation. Interestingly, our analysis finds that the optimal solution exhibits an threshold-based structure related to the current energy state, secondary queueing backlogs and primary link activity. Simulation results collaborate with our analysis and demonstrate more than 46.7% data sensing rate improvement of the proposed PLySE over representative benchmark methods.
Dynamic Schnyder Woods

A realizer, commonly known as Schnyder woods, of a triangulation is a partition of its interior edges into three oriented rooted trees. A flip in a realizer is a local operation that transforms one realizer into another. Two types of flips in a realizer have been introduced: colored flips and cycle flips. A corresponding flip graph is defined for each of these two types of flips. The vertex sets are the realizers, and two realizers are adjacent if they can be transformed into each other by one flip. In this paper we study the relation between these two types of flips and their corresponding flip graphs. We show that a cycle flip can be obtained from linearly many colored flips. We also prove an upper bound of $O(n^2)$ on the diameter of the flip graph of realizers defined by colored flips. In addition, a data structure is given to dynamically maintain a realizer over a sequence of colored flips which supports queries, including getting a node's barycentric coordinates, in $O(\log n)$ time per flip or query.
Image content dependent semi-fragile watermarking with localized tamper detection

Content-independent watermarks and block-wise independency can be considered as vulnerabilities in semi-fragile watermarking methods. In this paper to achieve the objectives of semi-fragile watermarking techniques, a method is proposed to not have the mentioned shortcomings. In the proposed method, the watermark is generated by relying on image content and a key. Furthermore, the embedding scheme causes the watermarked blocks to become dependent on each other, using a key. In the embedding phase, the image is partitioned into non-overlapping blocks. In order to detect and separate the different types of attacks more precisely, the proposed method embeds three copies of each watermark bit into LWT coefficients of each 4×4 block. In the authentication phase, by voting between the extracted bits the error maps are created; these maps indicate image authenticity and reveal the modified regions. Also, in order to automate the authentication, the images are classified into four categories using seven features. Classification accuracy in the experiments is 97.97 percent. It is noted that our experiments demonstrate that the proposed method is robust against JPEG compression and is competitive with a state-of-the-art semi-fragile watermarking method, in terms of robustness and semi-fragility.
VAT-Mart: Learning Visual Action Trajectory Proposals for Manipulating 3D ARTiculated Objects

Perceiving and manipulating 3D articulated objects (e.g., cabinets, doors) in human environments is an important yet challenging task for future home-assistant robots. The space of 3D articulated objects is exceptionally rich in their myriad semantic categories, diverse shape geometry, and complicated part functionality. Previous works mostly abstract kinematic structure with estimated joint parameters and part poses as the visual representations for manipulating 3D articulated objects. In this paper, we propose object-centric actionable visual priors as a novel perception-interaction handshaking point that the perception system outputs more actionable guidance than kinematic structure estimation, by predicting dense geometry-aware, interaction-aware, and task-aware visual action affordance and trajectory proposals. We design an interaction-for-perception framework VAT-Mart to learn such actionable visual representations by simultaneously training a curiosity-driven reinforcement learning policy exploring diverse interaction trajectories and a perception module summarizing and generalizing the explored knowledge for pointwise predictions among diverse shapes. Experiments prove the effectiveness of the proposed approach using the large-scale PartNet-Mobility dataset in SAPIEN environment and show promising generalization capabilities to novel test shapes, unseen object categories, and real-world data. Project page: https://hyperplane-lab.github. io/vat-mart
Fully Steerable 3D Spherical Neurons

Emerging from low-level vision theory, steerable filters found their counterpart in deep learning. Earlier works used the steering theorems and presented convolutional networks equivariant to rigid transformations. In our work, we propose a steerable feed-forward learning-based approach that consists of spherical decision surfaces and operates on point clouds. Due to the inherent geometric 3D structure of our theory, we derive a 3D steerability constraint for its atomic parts, the hypersphere neurons. Exploiting the rotational equivariance, we show how the model parameters are fully steerable at inference time. The proposed spherical filter banks enable to make equivariant and, after online optimization, invariant class predictions for known synthetic point sets in unknown orientations.
Effective grey‐box testing with partial FSM models

For partial, nondeterministic, finite state machines, a new conformance relation called strong reduction is presented. It complements other existing conformance relations in the sense that the new relation is well suited for model‐based testing of systems whose inputs are enabled or disabled, depending on the actual system state. Examples of such systems are graphical user interfaces and systems with interfaces that can be enabled or disabled in a mechanical way. We present a new test generation algorithm producing complete test suites for strong reduction. The suites are executed according to the grey‐box testing paradigm: it is assumed that the state‐dependent sets of enabled inputs can be identified during test execution, while the implementation states remain hidden, as in black‐box testing. We show that this grey‐box information is exploited by the generation algorithm in such a way that the resulting best‐case test suite size is only linear in the state space size of the reference model. Moreover, examples show that this may lead to significant reductions of test suite size in comparison to true black‐box testing for strong reduction.
Learning from Synthetic Data for Opinion-free Blind Image Quality Assessment in the Wild

Nowadays, most existing blind image quality assessment (BIQA) models 1) are developed for synthetically-distorted images and often generalize poorly to authentic ones; 2) heavily rely on human ratings, which are prohibitively labor-expensive to collect. Here, we propose an $opinion$-$free$ BIQA method that learns from synthetically-distorted images and multiple agents to assess the perceptual quality of authentically-distorted ones captured in the wild without relying on human labels. Specifically, we first assemble a large number of image pairs from synthetically-distorted images and use a set of full-reference image quality assessment (FR-IQA) models to assign pseudo-binary labels of each pair indicating which image has higher quality as the supervisory signal. We then train a convolutional neural network (CNN)-based BIQA model to rank the perceptual quality, optimized for consistency with the binary labels. Since there exists domain shift between the synthetically- and authentically-distorted images, an unsupervised domain adaptation (UDA) module is introduced to alleviate this issue. Extensive experiments demonstrate the effectiveness of our proposed $opinion$-$free$ BIQA model, yielding state-of-the-art performance in terms of correlation with human opinion scores, as well as gMAD competition. Codes will be made publicly available upon acceptance.
Driving Behavior Modeling Using Naturalistic Human Driving Data With Inverse Reinforcement Learning

Driving behavior modeling is of great importance for designing safe, smart, and personalized autonomous driving systems. In this paper, an internal reward function-based driving model that emulates the human’s decision-making mechanism is utilized. To infer the reward function parameters from naturalistic human driving data, we propose a structural assumption about human driving behavior that focuses on discrete latent driving intentions. It converts the continuous behavior modeling problem to a discrete setting and thus makes maximum entropy inverse reinforcement learning (IRL) tractable to learn reward functions. Specifically, a polynomial trajectory sampler is adopted to generate candidate trajectories considering high-level intentions and approximate the partition function in the maximum entropy IRL framework. An environment model considering interactive behaviors among the ego and surrounding vehicles is built to better estimate the generated trajectories. The proposed method is applied to learn personalized reward functions for individual human drivers from the NGSIM highway driving dataset. The qualitative results demonstrate that the learned reward functions are able to explicitly express the preferences of different drivers and interpret their decisions. The quantitative results reveal that the learned reward functions are robust, which is manifested by only a marginal decline in proximity to the human driving trajectories when applying the reward function in the testing conditions. For the testing performance, the personalized modeling method outperforms the general modeling approach, significantly reducing the modeling errors in human likeness (a custom metric to gauge accuracy), and these two methods deliver better results compared to other baseline methods. Moreover, it is found that predicting the response actions of surrounding vehicles and incorporating their potential decelerations caused by the ego vehicle are critical in estimating the generated trajectories, and the accuracy of personalized planning using the learned reward functions relies on the accuracy of the forecasting model.
Traditional Machine Learning and Deep Learning Models for Argumentation Mining in Russian Texts

Argumentation mining is a field of computational linguistics that is devoted to extracting from texts and classifying arguments and relations between them, as well as constructing an argumentative structure. A significant obstacle to research in this area for the Russian language is the lack of annotated Russian-language text corpora. This article explores the possibility of improving the quality of argumentation mining using the extension of the Russian-language version of the Argumentative Microtext Corpus (ArgMicro) based on the machine translation of the Persuasive Essays Corpus (PersEssays). To make it possible to use these two corpora combined, we propose a Joint Argument Annotation Scheme based on the schemes used in ArgMicro and PersEssays. We solve the problem of classifying argumentative discourse units (ADUs) into two classes -"pro"("for") and"opp"("against") using traditional machine learning techniques (SVM, Bagging and XGBoost) and a deep neural network (BERT model). An ensemble of XGBoost and BERT models was proposed, which showed the highest performance of ADUs classification for both corpora.
On the Parameterized Complexity of Learning First-Order Logic

We analyse the complexity of learning first-order queries in a model-theoretic framework for supervised learning introduced by (Grohe and Turán, TOCS 2004). Previous research on the complexity of learning in this framework focussed on the question of when learning is possible in time sublinear in the background structure. Here we study the parameterized complexity of the learning problem. We have two main results. The first is a hardness result, showing that learning first-order queries is at least as hard as the corresponding model-checking problem, which implies that on general structures it is hard for the parameterized complexity class AW[*]. Our second main contribution is a fixed-parameter tractable agnostic PAC learning algorithm for first-order queries over sparse relational data (more precisely, over nowhere dense background structures).
A Survey on Deep Learning for Human Mobility

The study of human mobility is crucial due to its impact on several aspects of our society, such as disease spreading, urban planning, well-being, pollution, and more. The proliferation of digital mobility data, such as phone records, GPS traces, and social media posts, combined with the predictive power of artificial intelligence, triggered the application of deep learning to human mobility. Existing surveys focus on single tasks, data sources, mechanistic or traditional machine learning approaches, while a comprehensive description of deep learning solutions is missing. This survey provides a taxonomy of mobility tasks, a discussion on the challenges related to each task and how deep learning may overcome the limitations of traditional models, a description of the most relevant solutions to the mobility tasks described above, and the relevant challenges for the future. Our survey is a guide to the leading deep learning solutions to next-location prediction, crowd flow prediction, trajectory generation, and flow generation. At the same time, it helps deep learning scientists and practitioners understand the fundamental concepts and the open challenges of the study of human mobility.