Miracle-dz/UniFAR-FTUs huggingface dataset:
Picasso: Model-free Feature Visualization

Today, Machine Learning (ML) applications can have access to tens of thousands of features. With such feature sets, efficiently browsing and curating subsets of most relevant features is a challenge. In this paper, we present a novel approach to visualize up to several thousands of features in a single image. The image not only shows information on individual features, but also expresses feature interactions via the relative positioning of features.
A comparative analysis of pulse time-of-arrival creation methods

Extracting precise pulse times of arrival (TOAs) and their uncertainties is the first and most fundamental step in high-precision pulsar timing. In the classical method, TOAs are derived from total intensity pulse profiles of pulsars via cross-correlation with an idealised `1D' template of that profile. While a number of results have been presented in the literature relying on the ever increasing sensitivity of such pulsar timing experiments, there is no consensus on the most reliable methods for TOA creation and, more importantly, the associated TOA uncertainties for each scheme. In this article, we present a comprehensive comparison of TOA determination practices, focusing on the creation of timing templates, TOA determination methods and the most useful TOA bandwidth. The aim is both to present a possible approach towards TOA optimisation as well as the (partial) identification of an optimal TOA-creation scheme and the demonstration of optimisation differences between pulsars and data sets. We compare the values of data-derived template profiles as compared to analytic profiles and evaluate the three most commonly used template-matching methods. Finally, we study the relation between timing precision and TOA bandwidth to identify any potential breaks in that relationship. As a practical demonstration, we apply our selected methods to European Pulsar Timing Array data on the three test pulsars PSRs\ J0218+4232, J1713+0747 and J2145$-$0750.
Rapid and Reliable Trajectory Planning Involving Omnidirectional Jumping of Quadruped Robots

—Dynamic jumping with multi-legged robots poses a challenging problem in planning and control. Formulating the jump optimization to allow fast online execution is difﬁ-cult; efﬁciently using this capability to generate long-horizon trajectories further complicates the problem. In this work, we present a novel hierarchical planning framework to address this problem. We ﬁrst formulate a real-time tractable trajectory optimization for performing omnidirectional jumping. We then embed the results of this optimization into a low dimensional jump feasibility classiﬁer. This classiﬁer is leveraged by a high-level planner to produce paths that are both dynamically feasible and also robust to variability in hardware trajectory realizations. We deploy our framework on the Mini Cheetah Vision quadruped, demonstrating robot’s ability to generate and execute reliable, goal-oriented paths that involve forward, lateral, and rotational jumps onto surfaces 1.35 times taller than robot’s nominal hip height. The ability to plan through omnidirectional jumping greatly expands robot’s mobility relative to planners that restrict jumping to the sagittal or frontal planes.
Contrastive Object-level Pre-training with Spatial Noise Curriculum Learning

The goal of contrastive learning based pre-training is to leverage large quantities of unlabeled data to produce a model that can be readily adapted downstream. Current approaches revolve around solving an image discrimination task: given an anchor image, an augmented counterpart of that image, and some other images, the model must produce representations such that the distance between the anchor and its counterpart is small, and the distances between the anchor and the other images are large. There are two significant problems with this approach: (i) by contrasting representations at the image-level, it is hard to generate detailed object-sensitive features that are beneficial to downstream object-level tasks such as instance segmentation; (ii) the augmentation strategy of producing an augmented counterpart is fixed, making learning less effective at the later stages of pre-training. In this work, we introduce Curricular Contrastive Object-level Pre-training (CCOP) to tackle these problems: (i) we use selective search to find rough object regions and use them to build an inter-image object-level contrastive loss and an intra-image object-level discrimination loss into our pre-training objective; (ii) we present a curriculum learning mechanism that adaptively augments the generated regions, which allows the model to consistently acquire a useful learning signal, even in the later stages of pre-training. Our experiments show that our approach improves on the MoCo v2 baseline by a large margin on multiple object-level tasks when pre-training on multi-object scene image datasets. Code is available at https://github.com/ChenhongyiYang/CCOP.
Multiple target tracking with interaction using an MCMC MRF Particle Filter

This paper presents and discusses an implementation of a multiple target tracking method, which is able to deal with target interactions and prevent tracker failures due to hijacking. The referenced approach uses a Markov Chain Monte Carlo (MCMC) sampling step to evaluate the filter and constructs an efficient proposal density to generate new samples. This density integrates target interaction terms based on Markov Random Fields (MRFs) generated per time step. The MRFs model the interactions between targets in an attempt to reduce tracking ambiguity that typical particle filters suffer from when tracking multiple targets. A test sequence of 662 grayscale frames containing 20 interacting ants in a confined space was used to test both the proposed approach and a set of importance sampling based independent particle filters, to establish a performance comparison. It is shown that the implemented approach of modeling target interactions using MRF successfully corrects many of the tracking errors made by the independent, interaction unaware, particle filters.
Computer Vision User Entity Behavior Analytics

Insider threats are costly, hard to detect, and unfortunately rising in occurrence. Seeking to improve detection of such threats, we develop novel techniques to enable us to extract powerful features, generate high quality image encodings, and augment attack vectors for greater classiﬁcation power. Combined, they form Computer Vision User and Entity Behavior Analytics, a detection system designed from the ground up to improve upon advancements in academia and mitigate the issues that prevent the usage of advanced models in industry. The proposed system beats state-of-art methods used in academia and as well as in industry.
QNNVerifier: A Tool for Verifying Neural Networks using SMT-Based Model Checking

QNNVerifier is the first open-source tool for verifying implementations of neural networks that takes into account the finite word-length (i.e. quantization) of their operands. The novel support for quantization is achieved by employing state-of-the-art software model checking (SMC) techniques. It translates the implementation of neural networks to a decidable fragment of first-order logic based on satisfiability modulo theories (SMT). The effects of fixed- and floating-point operations are represented through direct implementations given a hardware-determined precision. Furthermore, QNNVerifier allows to specify bespoke safety properties and verify the resulting model with different verification strategies (incremental and k-induction) and SMT solvers. Finally, QNNVerifier is the first tool that combines invariant inference via interval analysis and discretization of non-linear activation functions to speed up the verification of neural networks by orders of magnitude. A video presentation of QNNVerifier is available at https://youtu.be/7jMgOL41zTY
Generalized Lee Bounds

Lee (2009) is a common approach to bound the average causal effect in the presence of selection bias, assuming the treatment effect on selection has the same sign for all subjects. This paper generalizes Lee bounds to allow the sign of this effect to be identified by pretreatment covariates, relaxing the standard (unconditional) monotonicity to its conditional analog. Asymptotic theory for generalized Lee bounds is proposed in low-dimensional smooth and high-dimensional sparse designs. The paper also generalizes Lee bounds to accommodate multiple outcomes. It characterizes the sharp identified set for the causal parameter and proposes uniform Gaussian inference on the support function. The estimated bounds achieve nearly point-identification in JobCorps job training program (Lee (2009)), where unconditional monotonicity is unlikely to hold.
Correct-by-construction reach-avoid control of partially observable linear stochastic systems

We study feedback controller synthesis for reach-avoid control of discrete-time, linear time-invariant (LTI) systems with Gaussian process and measurement noise. The problem is to compute a controller such that, with at least some required probability, the system reaches a desired goal state in finite time while avoiding unsafe states. Due to stochasticity and nonconvexity, this problem does not admit exact algorithmic or closed-form solutions in general. Our key contribution is a correct-by-construction controller synthesis scheme based on a finite-state abstraction of a Gaussian belief over the unmeasured state, obtained using a Kalman filter. We formalize this abstraction as a Markov decision process (MDP). To be robust against numerical imprecision in approximating transition probabilities, we use MDPs with intervals of transition probabilities. By construction, any policy on the abstraction can be refined into a piecewise linear feedback controller for the LTI system. We prove that the closed-loop LTI system under this controller satisfies the reach-avoid problem with at least the required probability. The numerical experiments show that our method is able to solve reach-avoid problems for systems with up to 6D state spaces, and with control input constraints that cannot be handled by methods such as the rapidly-exploring random belief trees (RRBT).
Diversity policy gradient for sample efficient quality-diversity optimization

A fascinating aspect of nature lies in its ability to produce a large and diverse collection of organisms that are all high-performing in their niche. By contrast, most AI algorithms focus on finding a single eficient solution to a given problem. Aiming for diversity in addition to performance is a convenient way to deal with the exploration-exploitation trade-off that plays a central role in learning. It also allows for increased robustness when the returned collection contains several working solutions to the considered problem, making it well-suited for real applications such as robotics. Quality-Diversity (QD) methods are evolutionary algorithms designed for this purpose. This paper proposes a novel algorithm, qd-pg, which combines the strength of Policy Gradient algorithms and Quality Diversity approaches to produce a collection of diverse and high-performing neural policies in continuous control environments. The main contribution of this work is the introduction of a Diversity Policy Gradient (DPG) that exploits information at the time-step level to drive policies towards more diversity in a sample-efficient manner. Specifically, qd-pg selects neural controllers from a map-elites grid and uses two gradient-based mutation operators to improve both quality and diversity. Our results demonstrate that qd-pg is significantly more sample-eficient than its evolutionary competitors.
Small‐size printed loop‐type antenna integrated with two stacked coupled‐fed shorted strip monopoles for eight‐band LTE/GSM/UMTS operation in the mobile phone

A small‐size internal printed antenna formed by a loop‐type antenna integrated with two stacked coupled‐fed shorted strip monopoles is presented.The proposed antenna has a small uniplanar structure of 40 × 15 mm2 (600 mm2) for eight‐band LTE/GSM/UMTS operation in the mobile phone. The loop‐type antenna comprises a driven monopole and a coupled portion short‐circuited to the system ground plane of the mobile phone; the driven monopole alone contributes a resonant mode for the antenna's upper band, whereas the driven monopole and shorted coupled portion together provide a loop‐type resonant path to generate a resonant mode for the antenna's lower band. Two stacked strips are then integrated with the loop‐type antenna, with the two strips coupled‐fed by the driven monopole and short‐circuited to the ground plane through the shorted coupled portion. That is, two stacked coupled‐fed shorted strip monopoles are formed and incorporated with the loop‐type antenna in an integrated configuration. The two strip monopoles contribute two additional resonant modes to incorporate those generated by the loop‐type antenna to achieve the desired wide lower band (698–960 MHz) and upper band (1710–2690 MHz) for eight‐band LTE/GSM/UMTS operation. Details of the proposed antenna are presented. © 2010 Wiley Periodicals, Inc. Microwave Opt Technol Lett 52: 1471–1476, 2010; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.25257
Conormal homology of manifolds with corners

Given a manifold with corners $X$, we associates to it the corner structure simplicial complex $\Sigma_X$. Its reduced K-homology is isomorphic to the K-theory of the $C^*$-algebra $\mathcal{K}_b(X)$ of b-compact operators on $X$. Moreover, the homology of $\Sigma_X$ is isomorphic to the conormal homology of $X$. In this note, we constract for an arbitrary abstract finite simplicial complex $\Sigma$ a manifold with corners $X$ such that $\Sigma_X\cong \Sigma$. As a consequence, the homology and K-homology which occur for finite simplicial complexes also occur as conormal homology of manifolds with corners and as K-theory of their b-compact operators. In particular, these groups can contain torsion.
Variational Gibbs inference for statistical model estimation from incomplete data

Statistical models are central to machine learning with broad applicability across a range of downstream tasks. The models are controlled by free parameters that are typically estimated from data by maximum-likelihood estimation or approximations thereof. However, when faced with real-world data sets many of the models run into a critical issue: they are formulated in terms of fully-observed data, whereas in practice the data sets are plagued with missing data. The theory of statistical model estimation from incomplete data is conceptually similar to the estimation of latent-variable models, where powerful tools such as variational inference (VI) exist. However, in contrast to standard latent-variable models, parameter estimation with incomplete data often requires estimating exponentially-many conditional distributions of the missing variables, hence making standard VI methods intractable. We address this gap by introducing variational Gibbs inference (VGI), a new general-purpose method to estimate the parameters of statistical models from incomplete data. We validate VGI on a set of synthetic and real-world estimation tasks, estimating important machine learning models such as variational autoencoders and normalising flows from incomplete data. The proposed method, whilst general-purpose, achieves competitive or better performance than existing model-specific estimation methods.
Knotted handlebodies in the 4-sphere and 5-ball

For every integer $g\ge 2$ we construct 3-dimensional genus-$g$ 1-handlebodies smoothly embedded in $S^4$ with the same boundary, and which are defined by the same cut systems of their boundary, yet which are not isotopic rel. boundary via any locally flat isotopy even when their interiors are pushed into $B^5$. This proves a conjecture of Budney-Gabai for genus at least 2.
Robot Skill Adaptation via Soft Actor-Critic Gaussian Mixture Models

$A$ core challenge for an autonomous agent acting in the real world is to adapt its repertoire of skills to cope with its noisy perception and dynamics. To scale learning of skills to long-horizon tasks, robots should be able to learn and later refine their skills in a structured manner through trajectories rather than making instantaneous decisions individually at each time step. To this end, we propose the Soft Actor- Critic Gaussian Mixture Model (SAC-GMM), a novel hybrid approach that learns robot skills through a dynamical system and adapts the learned skills in their own trajectory distribution space through interactions with the environment. Our approach combines classical robotics techniques of learning from demonstration with the deep reinforcement learning framework and exploits their complementary nature. We show that our method utilizes sensors solely available during the execution of preliminarily learned skills to extract relevant features that lead to faster skill refinement. Extensive evaluations in both simulation and real-world environments demonstrate the effectiveness of our method in refining robot skills by leveraging physical interactions, high-dimensional sensory data, and sparse task completion rewards. Videos, code, and pre-trained models are available at http://sac-gmm.cs. uni-freiburg.de.
On the Fragility of Alfv\'en waves in a Stratified Atmosphere

Complete asymptotic expansions are developed for slow,Alfvén and fastmagnetohydrodynamicwaves at the base of an isothermal three-dimensional (3D) plane stratified atmosphere. Together with existing convergent Frobenius series solutions about z = ∞, matchings are numerically calculated that illuminate the fates of slow and Alfvén waves injected from below. An Alfvén wave in a two-dimensional model is 2.5D in the sense that the wave propagates in the plane of the magnetic field but its polarization is normal to it in an ignorable horizontal direction, and the wave remains an Alfvén wave throughout. The rotation of the plane of wave propagation away from the vertical plane of the magnetic field pushes the plasma displacement vector away from horizontal, thereby coupling it to stratification. It is shown that potent slow-Alfvén coupling occurs in such 3D models. It is found that about 50% of direction-averaged Alfvén wave flux generated in the low atmosphere at frequencies comparable to or greater than the acoustic cutoff can reach the top as Alfvén flux for small magnetic field inclinations θ, and this increases to 80% or more with increasing θ. On the other hand, direction-averaged slow waves can be 40% effective in converting to Alfvén waves at small inclination, but this reduces sharply with increasing θ and wave frequency. Together with previously explored fast-slow and fast-Alfvén couplings, this provides valuable insights into which injected transverse waves can reach the upper atmosphere as Alfvén waves, with implications for solar and stellar coronal heating and solar/stellar wind acceleration.
Joint Wireless and Computing Resources Allocation in Multi-Cell MEC

This paper addresses join wireless and computing resource allocation in mobile edge computing (MEC) systems with several access points and with the possibility that users connect to many access points, and utilize the computation capability of many servers at the same time. The problem of sum transmission energy minimization under response time constraints is considered. It is proved, that the optimization problem is non-convex. The complexity of optimization of a part of the system parameters is investigated, and based on these results an Iterative Resource Allocation procedure is proposed, that converges to a local optimum. The performance of the joint resource allocation is evaluated by comparing it to lower and upper bounds defined by less or more flexible multi-cell MEC architectures. The results show that the free selection of the access point is crucial for good system performance.
A vision system for landing an unmanned helicopter in a complex environment

We present a vision-based landing algorithm for an autonomous helicopter under complex environment (there are several suspected targets). The algorithm is integrated with algorithms for visual acquisition, recognition of the target and computing the navigation information. In our algorithm, we use international standard landing mark as our landing target. The experiment results demonstrate that our algorithm has the feature of robustness, accuracy and real time. It can meet the actual flight requirements well: the average processing time of a 640×480 image is less than 40ms; the position error is below 5cm in each axis of translation; the angle error is below 3.5°. Based on the algorithm, we win the champion of the aerial robot competition in the 2008 China robot competition and the RoboCup China open.
Ultra-high frame rate digital light projector using chip-scale LED-on-CMOS technology

Digital light projector systems are crucial components in applications including computational imaging, fluorescence microscopy and highly parallel data communications. Here we present a chip-scale projector system based on emissive micro-LED pixels directly bonded to a smart pixel CMOS drive chip. Enabled by the high modulation bandwidth of the LED devices, the 128×128 pixel array can project binary patterns at up to 0.5 Mfps and toggle between two stored frames at MHz rates. The projector has a 5-bit grayscale resolution that can be updated at rates up to 83 kfps, and can be held in memory as a constant bias for the binary pattern projection. Finally, the projector can be operated in a pulsed mode, with individual pixels emitting pulses down to a few nanoseconds in duration. Again, this mode can be used in conjunction with the high-speed spatial pattern projection. The design of the smart pixels and LED devices are presented along with measurements of each mode of operation. As a demonstration of the data throughput achievable with this system we present an optical camera communications application, exhibiting data rates of >5 Gbps, over three orders of magnitude improvement on current demonstrations.
Generating Synthetic Systems of Interdependent Critical Infrastructure Networks

The lack of data on critical infrastructure systems has hindered research progress in modeling and optimizing the system performance. This study develops a method for generating synthetic interdependent critical infrastructure networks (SICIN) using simulation and nonlinear optimization techniques. SICIN consists of three components: 1) determining the location of facilities in individual networks via a modified simulated annealing algorithm; 2) generating interdependent links based on a novel pseudo-tripartite graph algorithm, and 3) simulating network flow using nonlinear optimization considering the operations of individual networks and their interdependencies. Two existing systems of interdependent infrastructure networks are used to validate the proposed method. The results demonstrate that SICIN outperforms state-of-the-art simulation methods according to multiple topological and flow measures of similarity between the simulated and real networks. The outcomes of this research include 1) a sample of a simulated system of interdependent networks that can serve as a test case for infrastructure models and 2) a generalized algorithm that can be used to generate synthetic interdependent infrastructures given partial data of any real network.