Miracle-dz/UniFAR-FTUs huggingface dataset:
Interpreting and Predicting Tactile Signals via a Physics-Based and Data-Driven Framework

High-density afferents in the human hand have long been regarded as essential for human grasping and manipulation abilities. In contrast, robotic tactile sensors are typically used to provide low-density contact data, such as center-of-pressure and resultant force. Although useful, this data does not exploit the rich information content that some tactile sensors (e.g., the SynTouch BioTac) naturally provide. This research extends robotic tactile sensing beyond reduced-order models through 1) the automated creation of a precise tactile dataset for the BioTac over diverse physical interactions, 2) a 3D finite element (FE) model of the BioTac, which complements the experimental dataset with high-resolution, distributed contact data, and 3) neural-network-based mappings from raw BioTac signals to low-dimensional experimental data, and more importantly, high-density FE deformation fields. These data streams can provide a far greater quantity of interpretable information for grasping and manipulation algorithms than previously accessible.
An Overview of Neural Network Compression

Overparameterized networks trained to convergence have shown impressive performance in domains such as computer vision and natural language processing. Pushing state of the art on salient tasks within these domains corresponds to these models becoming larger and more difficult for machine learning practitioners to use given the increasing memory and storage requirements, not to mention the larger carbon footprint. Thus, in recent years there has been a resurgence in model compression techniques, particularly for deep convolutional neural networks and self-attention based networks such as the Transformer. Hence, this paper provides a timely overview of both old and current compression techniques for deep neural networks, including pruning, quantization, tensor decomposition, knowledge distillation and combinations thereof. We assume a basic familiarity with deep learning architectures\footnote{For an introduction to deep learning, see ~\citet{goodfellow2016deep}}, namely, Recurrent Neural Networks~\citep[(RNNs)][]{rumelhart1985learning,hochreiter1997long}, Convolutional Neural Networks~\citep{fukushima1980neocognitron}~\footnote{For an up to date overview see~\citet{khan2019survey}} and Self-Attention based networks~\citep{vaswani2017attention}\footnote{For a general overview of self-attention networks, see ~\citet{chaudhari2019attentive}. },\footnote{For more detail and their use in natural language processing, see~\citet{hu2019introductory}}. Most of the papers discussed are proposed in the context of at least one of these DNN architectures.
Environmentally Induced Entanglement - Anomalous Behavior in the Adiabatic Regime

Considering two non-interacting qubits in the context of open quantum systems, it is well known that their common environment may act as an entangling agent. In a perturbative regime the influence of the environment on the system dynamics can effectively be described by a unitary and a dissipative contribution. For the two-spin Boson model with (sub-) Ohmic spectral density considered here, the particular unitary contribution (Lamb shift) easily explains the buildup of entanglement between the two qubits. Furthermore it has been argued that in the adiabatic limit, adding the so-called counterterm to the microscopic model compensates the unitary influence of the environment and, thus, inhibits the generation of entanglement. Investigating this assertion is one of the main objectives of the work presented here. Using the hierarchy of pure states (HOPS) method to numerically calculate the exact reduced dynamics, we find and explain that the degree of inhibition crucially depends on the parameter s determining the low frequency power law behavior of the spectral density J(ω)∼ωse−ω/ωc. Remarkably, we find that for resonant qubits, even in the adiabatic regime (arbitrarily large ωc), the entanglement dynamics is still influenced by an environmentally induced Hamiltonian interaction. Further, we study the model in detail and present the exact entanglement dynamics for a wide range of coupling strengths, distinguish between resonant and detuned qubits, as well as Ohmic and deep sub-Ohmic environments. Notably, we find that in all cases the asymptotic entanglement does not vanish and conjecture a linear relation between the coupling strength and the asymptotic entanglement measured by means of concurrence. Further we discuss the suitability of various perturbative master equations for obtaining approximate entanglement dynamics.
Transience of a symmetric random walk in infinite measure

We consider a random walk on a second countable locally compact topological space endowed with an invariant Radon measure. We show that if the walk is symmetric and if every subset which is invariant by the walk has zero or infinite measure, then one has transience in law for almost every starting point. We then deduce a converse to Eskin-Margulis recurrence theorem.
The microvasculature of the ovary: A review by SEM of vascular corrosion casts

The vascular changes of the ovary were studied by scanning electron microscopy of vascular corrosion casts in estrous, pseudopregnant (stimulated with human chorionic gonadotropin - hCG) and pregnant rabbits. The results demonstrated that ovarian cycle and pregnancy may induce both structural and functional changes in the ovarian vessels. In fact the ovarian blood vessels adapted their structure to the temporary functional needs of the recruited follicles or corpora lutea. These changes involved both larger ovarian blood vessels (supplying the hilus and the medulla) and cortical blood vessels (supplying the luteo-follicular complexes). Hilar and medullary blood vessels also displayed morphological devices for the continuous control of the blood flow (spiral arteries) and for the local recirculation of endocrine products (artero-venous contacts). Such contacts, may likely sustain a "countercurrent mechanism" which was also shown in ovaries of other species. Scanning electron microscopy of vascular corrosion casts even demonstrated that cortical blood vessels are subjected to continuous remodeling. SEM showed different morphological types of vascular plexuses which supplied antral follicles (Types 1-2), atretic follicles (Types 3-4), peri-ovulatory follicles (Type 5), growing pseudopregnant corpora lutea (Type 6) regressing pseudopregnant corpora lutea (Type 7) and pregnant corpora lutea (Type 8). In estrous rabbit, growing to mature follicles (Types 1-2) showed a gradual enlargement and proliferation of the theca capillaries. These changes, associated with capillary hyper-permeabilization were observed in ovulatory and post-ovulatory follicles (Type 5), after hCG stimulation. The corpus luteum formation (Type 6 and 8) was accompanied by additional capillary dilation, diffuse angiogenetic sprouting and organization of conspicuous venous drainage which appeared more enhanced in pregnant (type 8) than in pseudopregnant (type 6) corpora lutea. The regression of the corpus luteum (Type 7) was characterized by the appearance of avascular areas within the gland and by regression of vascular dilatation. Finally, in estrous rabbit the atretic follicles (Types 3-4) wall showed large interruptions (avascular areas) and focal invasion of the central cavity by newly formed capillaries arranged randomly. The hCG stimulation did not affect consistently the interstitial microvasculature. It is concluded that the gradual increase of ovarian blood flow occuring during follicle growth and corpora lutea formation is provided by a functional adaptation of major ovarian vessels and by capillary functional (vasodilation) and structural (angiogenesis) support as well.
Active Learning A Neural Network Model For Gold Clusters & Bulk From Sparse First Principles Training Data

Small metal clusters are of fundamental scientific interest and of tremendous significance in catalysis. These nanoscale clusters display diverse geometries and structural motifs depending on the cluster size; a knowledge of this size‐dependent structural motifs and their dynamical evolution has been of longstanding interest. Given the high computational cost of first‐principles calculations, molecular modeling and atomistic simulations such as molecular dynamics (MD) has proven to be an important complementary tool to aid this understanding. Classical MD typically employ predefined functional forms which limits their ability to capture such complex size‐dependent structural and dynamical transformation. Neural Network (NN) based potentials represent flexible alternatives and in principle, well‐trained NN potentials can provide high level of flexibility, transferability and accuracy on‐par with the reference model used for training. A major challenge, however, is that NN models are interpolative and requires large quantities ( ∼104 or greater) of training data to ensure that the model adequately samples the energy landscape both near and far‐from‐equilibrium. A highly desirable goal is minimize the number of training data, especially if the underlying reference model is first‐principles based and hence expensive. Here, we introduce an active learning (AL) scheme that trains a NN model on‐the‐fly with minimal amount of first‐principles based training data. Our AL workflow is initiated with a sparse training dataset ( ∼ 1 to 5 data points) and is updated on‐the‐fly via a Nested Ensemble Monte Carlo scheme that iteratively queries the energy landscape in regions of failure and updates the training pool to improve the network performance. Using a representative system of gold clusters, we demonstrate that our AL workflow can train a NN with ∼ 500 total reference calculations. Using an extensive DFT test set of ∼1100 configurations, we show that our AL‐NN is able to accurately predict both the DFT energies and the forces for clusters of a myriad of different sizes. Our NN predictions are within 30 meV/atom and 40 meV/Å of the reference DFT calculations. Moreover, our AL‐NN model also adequately captures the various size‐dependent structural and dynamical properties of gold clusters in excellent agreement with DFT calculations and available experiments. We finally show that our AL‐NN model also captures bulk properties reasonably well, even though they were not included in the training data.
Minibatch vs Local SGD for Heterogeneous Distributed Learning

We analyze Local SGD (aka parallel or federated SGD) and Minibatch SGD in the heterogeneous distributed setting, where each machine has access to stochastic gradient estimates for a different, machine-specific, convex objective; the goal is to optimize w.r. t. the average objective; and machines can only communicate intermittently. We argue that, (i) Minibatch SGD (even without acceleration) dominates all existing analysis of Local SGD in this setting, (ii) accelerated Minibatch SGD is optimal when the heterogeneity is high, and (iii) present the first upper bound for Local SGD that improves over Minibatch SGD in a non-homogeneous regime.
No-Reference Image Quality Assessment via Feature Fusion and Multi-Task Learning

Blind or no-reference image quality assessment (NR-IQA) is a fundamental, unsolved, and yet challenging problem due to the unavailability of a reference image. It is vital to the streaming and social media industries that impact billions of viewers daily. Although previous NR-IQA methods leveraged different feature extraction approaches, the performance bottleneck still exists. In this paper, we propose a simple and yet effective general-purpose no-reference (NR) image quality assessment (IQA) framework based on multi-task learning. Our model employs distortion types as well as subjective human scores to predict image quality. We propose a feature fusion method to utilize distortion information to improve the quality score estimation task. In our experiments, we demonstrate that by utilizing multi-task learning and our proposed feature fusion method, our model yields better performance for the NR-IQA task. To demonstrate the effectiveness of our approach, we test our approach on seven standard datasets and show that we achieve state-of-the-art results on various datasets.
A Variational View on Bootstrap Ensembles as Bayesian Inference

In this paper, we employ variational arguments to establish a connection between ensemble methods for Neural Networks and Bayesian inference. We consider an ensemble-based scheme where each model/particle corresponds to a perturbation of the data by means of parametric bootstrap and a perturbation of the prior. We derive conditions under which any optimization steps of the particles makes the associated distribution reduce its divergence to the posterior over model parameters. Such conditions do not require any particular form for the approximation and they are purely geometrical, giving insights on the behavior of the ensemble on a number of interesting models such as Neural Networks with ReLU activations. Experiments confirm that ensemble methods can be a valid alternative to approximate Bayesian inference; the theoretical developments in the paper seek to explain this behavior.
Visual Transformers: Token-based Image Representation and Processing for Computer Vision

Computer vision has achieved great success using standardized image representations -- pixel arrays, and the corresponding deep learning operators -- convolutions. In this work, we challenge this paradigm: we instead (a) represent images as a set of visual tokens and (b) apply visual transformers to find relationships between visual semantic concepts. Given an input image, we dynamically extract a set of visual tokens from the image to obtain a compact representation for high-level semantics. We then use visual transformers to operate over the visual tokens to densely model relationships between them. We find that this paradigm of token-based image representation and processing drastically outperforms its convolutional counterparts on image classification and semantic segmentation. To demonstrate the power of this approach on ImageNet classification, we use ResNet as a convenient baseline and use visual transformers to replace the last stage of convolutions. This reduces the stage's MACs by up to 6.9x, while attaining up to 4.53 points higher top-1 accuracy. For semantic segmentation, we use a visual-transformer-based FPN (VT-FPN) module to replace a convolution-based FPN, saving 6.5x fewer MACs while achieving up to 0.35 points higher mIoU on LIP and COCO-stuff.
A Survey for C II Emission-line Stars in the Large Magellanic Cloud

We present a narrowband imaging survey of the Large Magellanic Cloud (LMC), designed to isolate the C ii λλ7231, 7236 emission lines in objects as faint as mλ7400 ∼ 18. The work is motivated by the recent serendipitous discovery in the LMC of the first confirmed extragalactic [WC11] star, whose spectrum is dominated by C ii emission, and the realization that the number of such objects is currently largely unconstrained. The survey, which imaged ∼50 deg2 using on-band and off-band filters, will significantly increase the total census of these rare stars. In addition, each new LMC [WC] star has a known luminosity, a quantity quite uncertain in the Galactic sample. Multiple known C ii emitters were easily recovered, validating the survey design. We find 38 new C ii emission candidates; spectroscopy of the complete sample will be needed to ascertain their nature. In a preliminary spectroscopic reconnaissance, we observed three candidates, finding C ii emission in each. One is a new [WC11]. Another shows both the narrow C ii emission lines characteristic of a [WC11], but also broad emission of C iv, O v, and He ii characteristic of a much hotter [WC4] star; we speculate that this is a binary [WC]. The third object shows weak C ii emission, but the spectrum is dominated by a dense thicket of strong absorption lines, including numerous O ii transitions. We conclude it is likely an unusual hot, hydrogen-poor post-AGB star, possibly in transition from [WC] to white dwarf. Even lacking a complete spectroscopic program, we can infer that late [WC] stars do not dominate the central stars of LMC planetary nebulae, and that the detected C ii emitters are largely of an old population.
Confidence sequences for sampling without replacement

Many practical tasks involve sampling sequentially without replacement (WoR) from a finite population of size $N$, in an attempt to estimate some parameter $\theta^\star$. Accurately quantifying uncertainty throughout this process is a nontrivial task, but is necessary because it often determines when we stop collecting samples and confidently report a result. We present a suite of tools for designing confidence sequences (CS) for $\theta^\star$. A CS is a sequence of confidence sets $(C_n)_{n=1}^N$, that shrink in size, and all contain $\theta^\star$ simultaneously with high probability. We first exploit a relationship between Bayesian posteriors and martingales to construct a (frequentist) CS for the parameters of a hypergeometric distribution. We then present Hoeffding- and empirical-Bernstein-type time-uniform CSs and fixed-time confidence intervals for sampling WoR which improve on previous bounds in the literature.
Effects of Wheat Cultivar and Nitrogen Application on Storage Protein Composition and Breadmaking Quality

ABSTRACT Influences of cultivar and nitrogen application on protein concentration and composition, and amount and size-distribution of different protein components, were investigated in 10 spring wheat cultivars (Triticum aestivum L.) with widely varying gluten strength, grown under four nitrogen fertilizer conditions. The results showed that cultivar differences in gluten strength were determined by storage protein composition, differences in total amount of HMW glutenin subunits, the glutenin-to-gliadin ratio, and the relationship between SDS-soluble and SDS-insoluble protein polymers. Negative correlations were found between protein parameters related to gluten strength and bread volume. No cultivar stability for gluten strength in relation to differences in nitrogen application was found. Thus, the gluten strength was influenced by the nitrogen application in all the investigated cultivars. Increased nitrogen supply correlated significantly to an increase in all protein components containing gliadins .
Accurately Solving Physical Systems with Graph Learning

Iterative solvers are widely used to accurately simulate physical systems. These solvers require initial guesses to generate a sequence of improving approximate solutions. In this contribution, we introduce a novel method to accelerate iterative solvers for physical systems with graph networks (GNs) by predicting the initial guesses to reduce the number of iterations. Unlike existing methods that aim to learn physical systems in an end-to-end manner, our approach guarantees long-term stability and therefore leads to more accurate solutions. Furthermore, our method improves the run time performance of traditional iterative solvers. To explore our method we make use of position-based dynamics (PBD) as a common solver for physical systems and evaluate it by simulating the dynamics of elastic rods. Our approach is able to generalize across different initial conditions, discretizations, and realistic material properties. Finally, we demonstrate that our method also performs well when taking discontinuous effects into account such as collisions between individual rods. A video showing dynamic results of our graph learning assisted simulations of elastic rods can be found on the project website available at this http URL .
Sharp Thresholds of the Information Cascade Fragility Under a Mismatched Model

We analyze a sequential decision making model in which decision makers (or, players) take their decisions based on their own private information as well as the actions of previous decision makers. Such decision making processes often lead to what is known as the \emph{information cascade} or \emph{herding} phenomenon. Specifically, a cascade develops when it seems rational for some players to abandon their own private information and imitate the actions of earlier players. The risk, however, is that if the initial decisions were wrong, then the whole cascade will be wrong. Nonetheless, information cascade are known to be fragile: there exists a sequence of \emph{revealing} probabilities $\{p_{\ell}\}_{\ell\geq1}$, such that if with probability $p_{\ell}$ player $\ell$ ignores the decisions of previous players, and rely on his private information only, then wrong cascades can be avoided. Previous related papers which study the fragility of information cascades always assume that the revealing probabilities are known to all players perfectly, which might be unrealistic in practice. Accordingly, in this paper we study a mismatch model where players believe that the revealing probabilities are $\{q_\ell\}_{\ell\in\mathbb{N}}$ when they truly are $\{p_\ell\}_{\ell\in\mathbb{N}}$, and study the effect of this mismatch on information cascades. We consider both adversarial and probabilistic sequential decision making models, and derive closed-form expressions for the optimal learning rates at which the error probability associated with a certain decision maker goes to zero. We prove several novel phase transitions in the behaviour of the asymptotic learning rate.
Response of plant functional types to changes in the fire regime in Mediterranean ecosystems: A simulation approach

In the Mediterranean basin, the climate is predicted to be warmer and effectively drier, leading to changes in fuel conditions and fire regime. Land abandonment in the Mediterranean basin is also changing the fire regime through the increase in fuel loads. In the present study, two simulation models of vegetation dynamics were tested in order to predict changes in plant functional types due to changes in fire recurrence in eastern Spain. The two modelling approaches are the FATE-model (based on vital attributes) and the gap model BROLLA (based on the gap-phase theory). The models were arranged to simulate four functional types, based mainly on their regenerative strategies after disturbance: Quercus (resprouter), Pinus (non-resprouter with serotinous cones), Erica (resprouter), and Cistus (non-resprouter with germination stimulated by fire). The simulation results suggested a decrease in Quercus abundance, an increase in Cistus and Erica, and a maximum of Pinus at intermediate recurrence scenarios. Despite their different approaches, both models predicted a similar response to increased fire recurrence, and the results were consistent with field observations.
Recent progress in the $L_p$ theory for elliptic and parabolic equations with discontinuous coefficients

In this paper, we review some results over the last 10-15 years on elliptic and parabolic equations with discontinuous coefficients. We begin with an approach given by N. V. Krylov to parabolic equations in the whole space with VMO$_x$ coefficients. We then discuss some subsequent development including elliptic and parabolic equations with coefficients which are allowed to be merely measurable in one or two space directions, weighted $L_p$ estimates with Muckenhoupt ($A_p$) weights, non-local elliptic and parabolic equations, as well as fully nonlinear elliptic and parabolic equations.