Miracle-dz/UniFAR-FTUs huggingface dataset:
Manipulation via Membranes: High-Resolution and Highly Deformable Tactile Sensing and Control

Collocated tactile sensing is a fundamental enabling technology for dexterous manipulation. However, deformable sensors introduce complex dynamics between the robot, grasped object, and environment that must be considered for fine manipulation. Here, we propose a method to learn soft tactile sensor membrane dynamics that accounts for sensor deformations caused by the physical interaction between the grasped object and environment. Our method combines the perceived 3D geometry of the membrane with proprioceptive reaction wrenches to predict future deformations conditioned on robot action. Grasped object poses are recovered from membrane geometry and reaction wrenches, decoupling interaction dynamics from the tactile observation model. We benchmark our approach on two real-world contact-rich tasks: drawing with a grasped marker and in-hand pivoting. Our results suggest that explicitly modeling membrane dynamics achieves better task performance and generalization to unseen objects than baselines.
Best-Response dynamics in two-person random games with correlated payoffs

We consider finite two-player normal form games with random payoffs. Player A's payoffs are i.i. d. from a uniform distribution. Given p in [0, 1], for any action profile, player B's payoff coincides with player A's payoff with probability p and is i.i. d. from the same uniform distribution with probability 1-p. This model interpolates the model of i.i. d. random payoff used in most of the literature and the model of random potential games. First we study the number of pure Nash equilibria in the above class of games. Then we show that, for any positive p, asymptotically in the number of available actions, best response dynamics reaches a pure Nash equilibrium with high probability.
Habitat classification from satellite observations with sparse annotations

Remote sensing benefits habitat conservation by making monitoring of large areas easier compared to field surveying especially if the remote sensed data can be automatically analyzed. An important aspect of monitoring is classifying and mapping habitat types present in the monitored area. Automatic classification is a difficult task, as classes have fine-grained differences and their distributions are long-tailed and unbalanced. Usually training data used for automatic land cover classification relies on fully annotated segmentation maps, annotated from remote sensed imagery to a fairly high-level taxonomy, i.e., classes such as forest, farmland, or urban area. A challenge with automatic habitat classification is that reliable data annotation requires field-surveys. Therefore, full segmentation maps are expensive to produce, and training data is often sparse, point-like, and limited to areas accessible by foot. Methods for utilizing these limited data more efficiently are needed. We address these problems by proposing a method for habitat classifi∗Corresponding author Email address: mikko.impio@syke.fi (Mikko Impiö) Programme for Environmental Information, Finnish Environment Institute, Finland Data and Information Centre, Finnish Environment Institute, Finland Metsähallitus, National Parks, Finland Preprint submitted to Elsevier September 28, 2022 cation and mapping, and apply this method to classify the entire northern Finnish Lapland area into Natura2000 classes. The method is characterized by using finely-grained, sparse, single-pixel annotations collected from the field, combined with large amounts of unannotated data to produce segmentation maps. Supervised, unsupervised and semi-supervised methods are compared, and the benefits of transfer learning from a larger out-of-domain dataset are demonstrated. We propose a convolutional neural network (CNN) biased towards center pixel classification ensembled with a random forest classifier, that produces higher quality classifications than the models themselves alone. We show that cropping augmentations, test-time augmentation and semisupervised learning can help classification even further.
Going Further With Winograd Convolutions: Tap-Wise Quantization for Efficient Inference on 4x4 Tiles

Most of today’s computer vision pipelines are built around deep neural networks, where convolution operations require most of the generally high compute effort. The Winograd convolution algorithm computes convolutions with fewer multiply–accumulate operations (MACs) compared to the standard algorithm, reducing the operation count by a factor of 2.25x for 3x3 convolutions when using the version with 2x2-sized tiles F2. Even though the gain is significant, the Winograd algorithm with larger tile sizes, i.e., F4, offers even more potential in improving throughput and energy efficiency, as it reduces the required MACs by 4x. Unfortunately, the Winograd algorithm with larger tile sizes introduces numerical issues that prevent its use on integer domain-specific accelerators (DSAs) and higher computational overhead to transform input and output data between spatial and Winograd domains. To unlock the full potential of Winograd F4, we propose a novel tap-wise quantization method that overcomes the numerical issues of using larger tiles, enabling integer-only inference. Moreover, we present custom hardware units that process the Winograd transformations in a power- and area-efficient way, and we show how to integrate such custom modules in an industrial-grade, programmable DSA. An extensive experimental evaluation on a large set of state-of-the-art computer vision benchmarks reveals that the tap-wise quantization algorithm makes the quantized Winograd F4 network almost as accurate as the FP32 baseline. The Winograd-enhanced DSA achieves up to 1.85x gain in energy efficiency and up to 1.83x end-to-end speed-up for state-of-the-art segmentation and detection networks.
IdeaReader: A Machine Reading System for Understanding the Idea Flow of Scientific Publications

Understanding the origin and influence of the publication's idea is critical to conducting scientific research. However, the proliferation of scientific publications makes it difficult for researchers to sort out the evolution of all relevant literature. To this end, we present IdeaReader, a machine reading system that finds out which papers are most likely to inspire or be influenced by the target publication and summarizes the ideas of these papers in natural language. Specifically, IdeaReader first clusters the references and citations (first-order or higher-order) of the target publication, and the obtained clusters are regarded as the topics that inspire or are influenced by the target publication. It then picks out the important papers from each cluster to extract the skeleton of the idea flow. Finally, IdeaReader automatically generates a literature review of the important papers in each topic. Our system can help researchers gain insight into how scientific ideas flow from the target publication's references to citations by the automatically generated survey and the visualization of idea flow.
Learning Self-Supervised Representations from Vision and Touch for Active Sliding Perception of Deformable Surfaces

—Humans make extensive use of vision and touch as complementary senses, with vision providing global information about the scene and touch measuring local information during manipulation without suffering from occlusions. In this work, we propose a novel framework for learning multi-task visuo-tactile representations in a self-supervised manner. We design a mechanism which enables a robot to autonomously collect spatially aligned visual and tactile data, a key property for downstream tasks. We then train visual and tactile encoders to embed these paired sensory inputs into a shared latent space using cross-modal contrastive loss. The learned representations are evaluated without ﬁne-tuning on 5 perception and control tasks involving deformable surfaces: tactile classiﬁcation, contact localization, anomaly detection (e.g., surgical phantom tumor palpation), tactile search from a visual query (e.g., garment feature localization under occlusion), and tactile servoing along cloth edges and cables. The learned representations achieve an 80% success rate on towel feature classiﬁcation, a 73% average success rate on anomaly detection in surgical materials, a 100% average success rate on vision-guided tactile search, and 87.8% average servo distance along cables and garment seams. These results suggest the ﬂexibility of the learned representations and pose a step toward task-agnostic visuo-tactile representation learning for robot control.
Molecular filtration by reduced graphene oxide sandwiched between wood sheets

This study investigates the prospect of using reduced graphene oxide encapsulated between thin wood membranes as a composite filter for water filtration. Graphene oxide (GO) with a volume range of 0.2 to 0.5 mL was deposited on a 95 mm 2 area of thin sheets of Balsa wood with a thickness range of 2.20 - 3.50 mm. The dried GO deposits were laser treated in reduced oxygen environment to prepare a reduced graphene oxide-wood composite (rGO-wood). The rGO substrate of the composite was sandwiched between 2 wood sheets which were glued using polydimethylsiloxane (PDMS) to give a stable membrane. Fabricated membranes improved the Allura red rejection from 8.29 ± 0.69 % in the controls (sandwiched wood sheets without rGO) to 93.14 ± 1.31 % in the 1 mL total rGO composites. The ratio of percentage permeance drop of the treatments over the controls ranged from 62.8 ± 4.3 to 85.4 ± 5.2 % from the 0.4 to 1 mL rGO composites respectively. The correlation coefficient (r) between the GO volume and rejection was 0.94 suggesting that the GO treatments provided some control over the transport of molecules through the membrane. The correlation coefficient between wood thickness, and Allura red rejection shows that the wood sheets did not influence the rejection. The permeance was weakly affected by the membrane thickness (r = -0.46). An 93% improvement in rejection performance of the composite membranes over the controls was observed. The overall data suggests that the wood-rGO composite has potential for water filtration applications.
Statistics of interior current distributions in two-dimensional open chaotic billiards

The probability current statistics of two-dimensional open chaotic ballistic billiards is studied both analytically and numerically. Assuming that the real and imaginary parts of the scattering wave function are both random Gaussian fields, we find a universal distribution function for the interior probability current. As a by-product we recover previous analytic forms for wave function statistics. The expressions bridge the entire region from GOE to GUE type statistics. Our analytic expressions are verified numerically by explicit quantum mechanical calculations of transport through a Bunimovich billiard.
Improving Primal Heuristics for Mixed Integer Programming Problems based on Problem Reduction: A Learning-based Approach

In this paper, we propose a Bi-layer Prediction-based Reduction Branch (BP-RB) framework to speed up the process of finding a high-quality feasible solution for Mixed Integer Programming (MIP) problems. A graph convolutional network (GCN) is employed to predict binary variables' values. After that, a subset of binary variables is fixed to the predicted value by a greedy method conditioned on the predicted probabilities. By exploring the logical consequences, a learning-based problem reduction method is proposed, significantly reducing the variable and constraint sizes. With the reductive sub- MIP problem, the second layer GCN framework is employed to update the prediction for the remaining binary variables' values and to determine the selection of variables which are then used for branching to generate the Branch and Bound (B&B) tree. Numerical examples show that our BP-RB framework speeds up the primal heuristic and finds the feasible solution with high quality.
The effectiveness of factorization and similarity blending

Collaborative Filtering (CF) is a widely used technique which allows to leverage past users' preferences data to identify behavioural patterns and exploit them to predict custom recommendations. In this work, we illustrate our review of different CF techniques in the context of the Computational Intelligence Lab (CIL) CF project at ETH Z\"urich. After evaluating the performances of the individual models, we show that blending factorization-based and similarity-based approaches can lead to a significant error decrease (-9.4%) on the best-performing stand-alone model. Moreover, we propose a novel stochastic extension of a similarity model, SCSR, which consistently reduce the asymptotic complexity of the original algorithm.
Shape optimization for the Laplacian eigenvalue over triangles and its application to interpolation error constant estimation

A computer-assisted proof is proposed for the Laplacian eigenvalue minimization problems over triangular domains under diameter constraints. The proof utilizes recently developed guaranteed computation methods for both eigenvalues and eigenfunctions of differential operators. The paper also provides an elementary and concise proof of the Hadamard shape derivative, which helps to validate the monotonicity of eigenvalue with respect to shape parameters. Besides the model homogeneous Dirichlet eigenvalue problem, the eigenvalue problem associated with a non-homogeneous Neumann boundary condition, which is related to the Crouzeix--Raviart interpolation error constant, is considered. The computer-assisted proof tells that among the triangles with the unit diameter, the equilateral triangle minimizes the first eigenvalue for each concerned eigenvalue problem.
Quantum superposition of causal structures as a universal resource for local implementation of nonlocal quantum operations

Spatial separation restricts the set of locally implementable quantum operations on distributed multipartite quantum systems. We propose that indefinite causal structure arising due to quantum superposition of different space-time geometries can be used as an independent universal resource for local implementation of any quantum operation on spatially distributed quantum systems. Consequently, all such quantum tasks that are not accomplishable by local operations and classical communication (LOCC) only also become locally accomplishable. We show that exploiting indefinite causal structure as the sole resource, it is possible to perfectly teleport the state of one agent's subsystem to the other distant laboratory in such a way that the agent at the distant laboratory can have access to the whole initially shared state in his or her laboratory and can perform any global quantum operation on the joint state locally. We further find that, after the teleportation process, the resource -- indefinite causal structure of the space-time does not get consumed. Hence, after implementing the desired quantum operation the state of the first agent's subsystem can be teleported back to its previous laboratory using the same resource. We show that this two-way teleportation is not always necessary for locally executing all nonlocal quantum tasks that are not realisable by LOCC only. Without invoking any kind of teleportation, we present a protocol for perfect local discrimination of the set of four Bell states that exploits indefinite causal structure as the sole resource. As immediate upshots, we present some more examples of such nonlocal tasks as local discrimination of the set of states exhibiting ``quantum nonlocality without entanglement"and activation of bound entangled states that are also achievable by our proposed protocol incorporating indefinite causal structure as a resource.
Biologically-Plausible Determinant Maximization Neural Networks for Blind Separation of Correlated Sources

Extraction of latent sources of complex stimuli is critical for making sense of the world. While the brain solves this blind source separation (BSS) problem continuously, its algorithms remain unknown. Previous work on biologically-plausible BSS algorithms assumed that observed signals are linear mixtures of statistically independent or uncorrelated sources, limiting the domain of applicability of these algorithms. To overcome this limitation, we propose novel biologically-plausible neural networks for the blind separation of potentially dependent/correlated sources. Differing from previous work, we assume some general geometric, not statistical, conditions on the source vectors allowing separation of potentially dependent/correlated sources. Concretely, we assume that the source vectors are sufficiently scattered in their domains which can be described by certain polytopes. Then, we consider recovery of these sources by the Det-Max criterion, which maximizes the determinant of the output correlation matrix to enforce a similar spread for the source estimates. Starting from this normative principle, and using a weighted similarity matching approach that enables arbitrary linear transformations adaptable by local learning rules, we derive two-layer biologically-plausible neural network algorithms that can separate mixtures into sources coming from a variety of source domains. We demonstrate that our algorithms outperform other biologically-plausible BSS algorithms on correlated source separation problems.
Graph Neural Network Expressivity and Meta-Learning for Molecular Property Regression

We demonstrate the applicability of model-agnostic algorithms for meta-learning, specifically Reptile, to GNN models in molecular regression tasks. Using meta-learning we are able to learn new chemical prediction tasks with only a few model updates, as compared to using randomly initialized GNNs which require learning each regression task from scratch. We experimentally show that GNN layer expressivity is correlated to improved meta-learning. Additionally, we also experiment with GNN emsembles which yield best performance and rapid convergence for k-shot learning.
R-fiducial: Reliable and Scalable Radar Fiducials for Smart mmwave Sensing

Millimeter wave sensing has recently attracted a lot of attention given its environmental robust nature. In situations where visual sensors like cameras fail to perform, mmwave radars can be used to achieve reliable performance. However, because of the poor scattering performance and lack of texture in millimeter waves, radars can not be used in several situations that require precise identification of objects. In this paper, we take insight from camera fiducials which are very easily identifiable by the camera, and present R-fiducial tags, which smartly augment the current infrastructure to enable a myriad of applications with mmwave radars. R-fiducial acts as a fiducial for mmwave sensing, similar to camera fiducials, and can be reliably identified by a mmwave radar. We identify a precise list of requirements that a millimeter wave fiducial has to follow and show how R-fiducial achieves all of them. R-fiducial uses a novel spread-spectrum modulation technique that provides low latency with high reliability. Our evaluations show that R-fiducial can be reliably detected upto 25m and upto 120 degrees field of view with a latency of the order of milliseconds. We also conduct experiments and case studies in adverse and low visibility conditions to showcase the applicability of R-fiducial in a wide range of applications.
Deep Cross-Modality and Resolution Graph Integration for Universal Brain Connectivity Mapping and Augmentation

The connectional brain template (CBT) captures the shared traits across all individuals of a given population of brain connectomes, thereby acting as a ﬁngerprint. Estimating a CBT from a population where brain graphs are derived from diverse neuroimaging modalities (e.g., functional and structural) and at diﬀerent resolutions (i.e., number of nodes) remains a formidable challenge to solve. Such network integration task allows for learning a rich and universal representation of the brain connectivity across varying modalities and resolutions. The resulting CBT can be substantially used to generate entirely new multimodal brain connectomes, which can boost the learning of the downs-stream tasks such as brain state classiﬁcation. Here, we propose the Multimodal Multiresolution Brain Graph Integrator Network (i.e., M2GraphIntegrator), the ﬁrst multimodal multiresolution graph integration framework that maps a given connectomic population into a well-centered CBT. M2GraphIntegrator ﬁrst uniﬁes brain graph resolutions by utilizing resolution-speciﬁc graph autoencoders. Next, it integrates the resulting ﬁxed-size brain graphs into a universal CBT lying at the center of its population. To preserve the population diversity, we further design a novel clustering-based training sample selection strategy which leverages the most heterogeneous training samples. To ensure the biological soundness of the learned CBT, we propose a topological loss that minimizes the topological gap between the ground-truth brain graphs and the learned CBT. Our experiments show that from a single CBT, one can generate realistic connectomic datasets including brain graphs of varying resolutions and modalities. We further demonstrate that our framework signiﬁcantly outperforms benchmarks in reconstruction quality, augmentation task, centeredness and topological soundness. signiﬁcantly outper-formed ablated
Stellar population of the Rosette Nebula and NGC2244. Application of the probabilistic random forest

(Abridged) In this work, we study the 2.8x2. 6 deg2 region in the emblematic Rosette Nebula, centred at the young cluster NGC 2244, with the aim of constructing the most reliable candidate member list to date, determining various structural and kinematic parameters, and learning about the past and the future of the region. Starting from a catalogue containing optical to mid-infrared photometry, as well as positions and proper motions from Gaia EDR3, we apply the Probabilistic Random Forest algorithm and derive membership probability for each source. Based on the list of almost 3000 probable members, of which about a third are concentrated within the radius of 20' from the centre of NGC 2244, we identify various clustered sources and stellar concentrations, and estimate the average distance of 1489+-37 pc (entire region), 1440+-32 pc (NGC 2244) and 1525+-36 pc (NGC 2237). The masses, extinction, and ages are derived by SED fitting, and the internal dynamic is assessed via proper motions relative to the mean proper motion of NGC 2244. NGC 2244 is showing a clear expansion pattern, with an expansion velocity that increases with radius. Its IMF is well represented by two power laws (dN/dM\propto M^{-\alpha}), with slopes \alpha = 1.05+-0.02 for the mass range 0.2 - 1.5 MSun, and \alpha = 2.3+-0.3 for the mass range 1.5 - 20 MSun, in agreement with other star forming regions. The mean age of the region is ~2 Myr. We find evidence for the difference in ages between NGC 2244 and the region associated with the molecular cloud, which appears slightly younger. The velocity dispersion of NGC 2244 is well above the virial velocity dispersion derived from the total mass (1000+-70 MSun) and half-mass radius (3.4+-0.2 pc). From the comparison to other clusters and to numerical simulations, we conclude that NGC 2244 may be unbound, and possibly even formed in a super-virial state.