Miracle-dz/UniFAR-FTUs huggingface dataset:
Etching of polymers, proteins and bacterial spores by atmospheric pressure DBD plasma in air

Many studies proved that non-equilibrium discharges generated at atmospheric pressure are highly effective for the bio-decontamination of surfaces of various materials. One of the key processes that leads to a desired result is plasma etching and thus the evaluation of etching rates of organic materials is of high importance. However, the comparison of reported results is rather difficult if impossible as different authors use diverse sources of atmospheric plasma that are operated at significantly different operational parameters. Therefore, we report here on the systematic study of the etching of nine different common polymers that mimic the different structures of more complicated biological systems, bovine serum albumin (BSA) selected as the model protein and spores of Bacillus subtilis taken as a representative of highly resistant micro-organisms. The treatment of these materials was performed by means of atmospheric pressure dielectric barrier discharge (DBD) sustained in open air at constant conditions. All tested polymers, BSA and spores, were readily etched by DBD plasma. However, the measured etching rates were found to be dependent on the chemical structure of treated materials, namely on the presence of oxygen in the structure of polymers.
JAS-GAN: Generative Adversarial Network Based Joint Atrium and Scar Segmentations on Unbalanced Atrial Targets

Automated and accurate segmentations of left atrium (LA) and atrial scars from late gadolinium-enhanced cardiac magnetic resonance (LGE CMR) images are in high demand for quantifying atrial scars. The previous quantification of atrial scars relies on a two-phase segmentation for LA and atrial scars due to their large volume difference (unbalanced atrial targets). In this paper, we propose an inter-cascade generative adversarial network, namely JAS-GAN, to segment the unbalanced atrial targets from LGE CMR images automatically and accurately in an end-to-end way. Firstly, JAS-GAN investigates an adaptive attention cascade to automatically correlate the segmentation tasks of the unbalanced atrial targets. The adaptive attention cascade mainly models the inclusion relationship of the two unbalanced atrial targets, where the estimated LA acts as the attention map to adaptively focus on the small atrial scars roughly. Then, an adversarial regularization is applied to the segmentation tasks of the unbalanced atrial targets for making a consistent optimization. It mainly forces the estimated joint distribution of LA and atrial scars to match the real ones. We evaluated the performance of our JAS-GAN on a 3D LGE CMR dataset with 192 scans. Compared with the state-of-the-art methods, our proposed approach yielded better segmentation performance (Average Dice Similarity Coefficient (DSC) values of 0.946 and 0.821 for LA and atrial scars, respectively), which indicated the effectiveness of our proposed approach for segmenting unbalanced atrial targets.
Local Asymptotic Mixed Normality via Transition Density Approximation and an Application to Ergodic Jump-Diffusion Processes

Abstract. We study sufficient conditions for local asymptotic mixed normality. We weaken the sufficient conditions in Theorem 1 of Jeganathan (Sankhyā Ser. A 1982) so that they can be applied to a wider class of statistical models including a jump-diffusion model. Moreover, we show that local asymptotic mixed normality of a statistical model generated by approximated transition density functions is implied for the original model. Together with density approximation by means of thresholding techniques, we show local asymptotic normality for a statistical model of discretely observed jump-diffusion processes where the drift coefficient, diffusion coefficient, and jump structure are parametrized. As a consequence, the quasi-maximum-likelihood and Bayes-type estimators proposed in Shimizu and Yoshida (Stat. Inference Stoch. Process. 2006) and Ogihara and Yoshida (Stat. Inference Stoch. Process. 2011) are shown to be asymptotically efficient in this model. Moreover, we can construct asymptotically uniformly most powerful tests for the parameters.
Robotic Surgery With Lean Reinforcement Learning

As surgical robots become more common, automating away some of the burden of complex direct human operation becomes ever more feasible. Model-free reinforcement learning (RL) is a promising direction toward generalizable automated surgical performance, but progress has been slowed by the lack of efficient and realistic learning environments. In this paper, we describe adding reinforcement learning support to the da Vinci Skill Simulator, a training simulation used around the world to allow surgeons to learn and rehearse technical skills. We successfully teach an RL-based agent to perform sub-tasks in the simulator environment, using either image or state data. As far as we know, this is the first time an RL-based agent is taught from visual data in a surgical robotics environment. Additionally, we tackle the sample inefficiency of RL using a simple-to-implement system which we term hybrid-batch learning (HBL), effectively adding a second, long-term replay buffer to the Q-learning process. Additionally, this allows us to bootstrap learning from images from the data collected using the easier task of learning from state. We show that HBL decreases our learning times significantly.
ISTR: End-to-End Instance Segmentation with Transformers

End-to-end paradigms significantly improve the accuracy of various deep-learning-based computer vision models. To this end, tasks like object detection have been upgraded by replacing non-end-to-end components, such as removing non-maximum suppression by training with a set loss based on bipartite matching. However, such an upgrade is not applicable to instance segmentation, due to its significantly higher output dimensions compared to object detection. In this paper, we propose an instance segmentation Transformer, termed ISTR, which is the first end-to-end framework of its kind. ISTR predicts low-dimensional mask embeddings, and matches them with ground truth mask embeddings for the set loss. Besides, ISTR concurrently conducts detection and segmentation with a recurrent refinement strategy, which provides a new way to achieve instance segmentation compared to the existing top-down and bottom-up frameworks. Benefiting from the proposed end-to-end mechanism, ISTR demonstrates state-of-the-art performance even with approximation-based suboptimal embeddings. Specifically, ISTR obtains a 46.8/38.6 box/mask AP using ResNet50-FPN, and a 48.1/39.9 box/mask AP using ResNet101-FPN, on the MS COCO dataset. Quantitative and qualitative results reveal the promising potential of ISTR as a solid baseline for instance-level recognition. Code has been made available at: https://github.com/hujiecpp/ISTR.
Hyperspherically Regularized Networks for BYOL Improves Feature Uniformity and Separability

Bootstrap Your Own Latent (BYOL) introduced an approach to self-supervised learning avoiding the contrastive paradigm and subsequently removing the computational burden of negative sampling. However, feature representations under this paradigm are poorly distributed on the surface of the unit-hypersphere representation space compared to contrastive methods. This work empirically demonstrates that feature diversity enforced by contrastive losses is beneﬁcial when employed in BYOL, and as such, provides greater inter-class feature separability. Therefore to achieve a more uniform distribution of features, we advocate the minimization of hyperspherical energy (i.e. maximization of entropy) in BYOL network weights. We show that directly optimizing a measure of uni-formity alongside the standard loss, or regularizing the networks of the BYOL architecture to minimize the hyperspherical energy of neurons can produce more uniformly distributed and better performing representations for downstream tasks.
Audio Transformers: Transformer Architectures For Large Scale Audio Understanding. Adieu Convolutions

Over the past two decades, CNN architectures have produced compelling models of sound perception and cognition, learning hierarchical organizations of features. Analogous to successes in computer vision, audio feature classification can be optimized for a particular task of interest, over a wide variety of datasets and labels. In fact similar architectures designed for image understanding have proven effective for acoustic scene analysis. Here we propose applying Transformer based architectures without convolutional layers to raw audio signals. On a standard dataset of Free Sound 50K,comprising of 200 categories, our model outperforms convolutional models to produce state of the art results. This is significant as unlike in natural language processing and computer vision, we do not perform unsupervised pre-training for outperforming convolutional architectures. On the same training set, with respect mean aver-age precision benchmarks, we show a significant improvement. We further improve the performance of Transformer architectures by using techniques such as pooling inspired from convolutional net-work designed in the past few years. In addition, we also show how multi-rate signal processing ideas inspired from wavelets, can be applied to the Transformer embeddings to improve the results. We also show how our models learns a non-linear non constant band-width filter-bank, which shows an adaptable time frequency front end representation for the task of audio understanding, different from other tasks e.g. pitch estimation.
Jamming of bidisperse frictional spheres

Ishan Srivastava, 2, ∗ Scott A. Roberts, Joel T. Clemmer, Leonardo E. Silbert, Jeremy B. Lechman, and Gary S. Grest Center for Computational Sciences and Engineering, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA Sandia National Laboratories, Albuquerque, New Mexico 87185, USA School of Math, Science, and Engineering, Central New Mexico Community College, Albuquerque, New Mexico 87106, USA
Machine Learning-based Reconfigurable Intelligent Surface-aided MIMO Systems

Reconfigurable intelligent surface (RIS) technology has recently emerged as a spectral- and cost-efficient approach for wireless communications systems. However, existing hand-engineered schemes for passive beamforming design and optimization of RIS, such as the alternating optimization (AO) approaches, require a high computational complexity, especially for multiple-input-multiple-output (MIMO) systems. To over-come this challenge, we propose a low-complexity unsupervised learning scheme, referred to as learning-phase-shift neural net-work (LPSNet), to efficiently find the solution to the spectral efficiency maximization problem in RIS-aided MIMO systems. In particular, the proposed LPSNet has an optimized input structure and requires a small number of layers and nodes to produce efficient phase shifts for the RIS. Simulation results for a 16 × 2 MIMO system assisted by an RIS with 40 elements show that the LPSNet achieves 97.25% of the SE provided by the AO counterpart with more than a 95% reduction in complexity.
BERT memorisation and pitfalls in low-resource scenarios

State-of-the-art pre-trained models have been shown to memorise facts and perform well with limited amounts of training data. To gain a better understanding of how these models learn, we study their generalisation and memo-risation capabilities in noisy and low-resource scenarios. We ﬁnd that the training of these models is almost unaffected by label noise and that it is possible to reach near-optimal performances even on extremely noisy datasets. Conversely, we also ﬁnd that they completely fail when tested on low-resource tasks such as few-shot learning and rare entity recognition. To mitigate such limitations, we propose a novel architecture based on BERT and prototypical networks that improves performance in low-resource named entity recognition tasks.
Representation Learning using Graph Autoencoders with Residual Connections

—Graph autoencoders are very efﬁcient at embedding graph-based complex data sets. However, most of the autoen-coders have shallow depths and their efﬁciency tends to decrease with the increase of layer depth. In this paper, we study the effect of adding residual connections to shallow and deep graph variational and vanilla autoencoders. We show that residual connections improve the accuracy of the deep graph-based autoencoders. Furthermore, we propose Res-VGAE, a graph variational autoencoder with different residual connections. Our experiments show that our model achieves superior results when compared with other autoencoder-based models for the link prediction task.
Fast Particle Acceleration in Three-dimensional Relativistic Reconnection

Magnetic reconnection is invoked as one of the primary mechanisms to produce energetic particles. We employ large-scale 3D particle-in-cell simulations of reconnection in magnetically dominated (σ = 10) pair plasmas to study the energization physics of high-energy particles. We identify an acceleration mechanism that only operates in 3D. For weak guide fields, 3D plasmoids/flux ropes extend along the z-direction of the electric current for a length comparable to their cross-sectional radius. Unlike in 2D simulations, where particles are buried in plasmoids, in 3D we find that a fraction of particles with γ ≳ 3σ can escape from plasmoids by moving along z, and so they can experience the large-scale fields in the upstream region. These “free” particles preferentially move in z along Speiser-like orbits sampling both sides of the layer and are accelerated linearly in time—their Lorentz factor scales as γ ∝ t, in contrast to γ∝t in 2D. The energy gain rate approaches ∼eE rec c, where E rec ≃ 0.1B 0 is the reconnection electric field and B 0 the upstream magnetic field. The spectrum of free particles is hard, dNfree/dγ∝γ−1.5 , contains ∼20% of the dissipated magnetic energy independently of domain size, and extends up to a cutoff energy scaling linearly with box size. Our results demonstrate that relativistic reconnection in GRB and AGN jets may be a promising mechanism for generating ultra-high-energy cosmic rays.
The Power of Tightness for Call-By-Push-Value

We propose tight type systems for Call-by-Name (CBN) and Call-by-Value (CBV) that can be both encoded in a tight type system for Call-by-Push-Value (CBPV). All such systems are quantitative, in the sense that they provide exact information about the length of normalization sequences to normal form (discriminated between multiplicative and exponential steps) as well as the size of these normal forms
Exomoons in Systems with a Strong Perturber: Applications to α Cen AB

The presence of a stellar companion can place constraints on occurrence and orbital evolution of satellites orbiting exoplanets, i.e., exomoons. In this work we revise earlier orbital stability limits for retrograde orbits in the case of a three-body system consisting of a star, planet, and satellite. The revised limit reads asatcrit≈0.668(1−1.236ep) for e p ≤ 0.8 in units of the Hill Radius and represents the lower critical orbit as a function of the planetary eccentricity e p. A similar formula is determined for exomoons hosted by planets in binary star systems, where e p is replaced with the components of free and forced eccentricity from secular orbit evolution theory. By exploring the dynamics of putative exomoons in α Centauri AB we find that the outer stability limit can be much less than half the Hill Radius due to oscillations in the planetary orbital eccentricity caused by the gravitational interaction with the binary star. We show, furthermore, how the resulting truncation of the outer stability limit can affect the outward tidal migration and potential observability of exomoons through transit-timing variations (TTVs). Typical TTV (rms) amplitudes induced by exomoons in binary systems are ≲10 minutes and appear more likely for planets orbiting the less massive stellar component.
Non-asymptotic Excess Risk Bounds for Classification with Deep Convolutional Neural Networks

In this paper, we consider the problem of binary classification with a class of general deep convolutional neural networks, which includes fully-connected neural networks and fully convolutional neural networks as special cases. We establish non-asymptotic excess risk bounds for a class of convex surrogate losses and target functions with different modulus of continuity. An important feature of our results is that we clearly define the prefactors of the risk bounds in terms of the input data dimension and other model parameters and show that they depend polynomially on the dimensionality in some important models. We also show that the classification methods with CNNs can circumvent the curse of dimensionality if the input data is supported on an approximate low-dimensional manifold. To establish these results, we derive an upper bound for the covering number for the class of general convolutional neural networks with a bias term in each convolutional layer, and derive new results on the approximation power of CNNs for any uniformly-continuous target functions. These results provide further insights into the complexity and the approximation power of general convolutional neural networks, which are of independent interest and may have other applications. Finally, we apply our general results to analyze the non-asymptotic excess risk bounds for four widely used methods with different loss functions using CNNs, including the least squares, the logistic, the exponential and the SVM hinge losses.
Mean Field Equilibrium in Multi-Armed Bandit Game with Continuous Reward

Mean field game facilitates analyzing multi-armed bandit (MAB) for a large number of agents by approximating their interactions with an average effect. Existing mean field models for multi-agent MAB mostly assume a binary reward function, which leads to tractable analysis but is usually not applicable in practical scenarios. In this paper, we study the mean field bandit game with a continuous reward function. Specifically, we focus on deriving the existence and uniqueness of mean field equilibrium (MFE), thereby guaranteeing the asymptotic stability of the multi-agent system. To accommodate the continuous reward function, we encode the learned reward into an agent state, which is in turn mapped to its stochastic arm playing policy and updated using realized observations. We show that the state evolution is upper semi-continuous, based on which the existence of MFE is obtained. As the Markov analysis is mainly for the case of discrete state, we transform the stochastic continuous state evolution into a deterministic ordinary differential equation (ODE). On this basis, we can characterize a contraction mapping for the ODE to ensure a unique MFE for the bandit game. Extensive evaluations validate our MFE characterization, and exhibit tight empirical regret of the MAB problem.
Ordinal Pattern Dependence in the Context of Long-Range Dependence

Ordinal pattern dependence is a multivariate dependence measure based on the co-movement of two time series. In strong connection to ordinal time series analysis, the ordinal information is taken into account to derive robust results on the dependence between the two processes. This article deals with ordinal pattern dependence for a long-range dependent time series including mixed cases of short- and long-range dependence. We investigate the limit distributions for estimators of ordinal pattern dependence. In doing so, we point out the differences that arise for the underlying time series having different dependence structures. Depending on these assumptions, central and non-central limit theorems are proven. The limit distributions for the latter ones can be included in the class of multivariate Rosenblatt processes. Finally, a simulation study is provided to illustrate our theoretical findings.
RNA Identification and Detection of Nucleic Acids as Aerosols in Air Samples by Means of Photon and Electron Interactions

This study presents a methodology to reveal traces of viral particles, as aerosol with known chemical and molecular structure, in a sample by means of photon and electron interactions. The method is based on Monte Carlo simulations and on the analysis of photon-electron fluxes-spectra through energy channels counts as a function of different aerosol viral concentrations in the air sample and looking at the peculiar photon/electron interactions with the potential abnormal atomic hydrogen (H), oxygen (O), carbon (C), and phosphorus (P) compositions present in the air sample as a function of living and nonliving matter with PO4 group RNA/DNA strands in a cluster configuration.
Bloch oscillations and the lack of the decay of the false vacuum in a one-dimensional quantum spin chain

We consider the decay of the false vacuum, realised within a quantum quench into an anti-confining regime of the Ising spin chain with a magnetic field opposite to the initial magnetisation. Although the effective linear potential between the domain walls is repulsive, the time evolution of correlations still shows a suppression of the light cone and a reduction of vacuum decay. The suppressed decay is a lattice effect, and can be assigned to emergent Bloch oscillations.
Rate-Splitting Multiple Access for Enhanced URLLC and eMBB in 6G: Invited Paper

Rate-Splitting Multiple Access (RSMA) is a flexible and robust multiple access scheme for downlink multi-antenna wireless networks. RSMA relies on Rate-Splitting (RS) at the transmitter and Successive Interference Cancellation (SIC) at the receivers. In this work, we study the performance of RSMA in the scenarios related with the important core services of New Radio (NR) and 6G, namely, enhanced Ultra-Reliable and Low-Latency (URLLC) and enhanced Mobile Broadband Communications (eMBB). We present the optimal system designs employing RSMA that target short-packet and low-latency communications as well as robust communications with high-throughput under the practical and important setup of imperfect Channel State Information at Transmitter (CSIT) originating from user mobility and latency/delay (between CSI acquisition and data transmission) in the network. We demonstrate via numerical results that RSMA achieves significantly higher performance than Space Division Multiple Access (SDMA) and Non-Orthogonal Multiple Access (NOMA), and is capable of addressing the requirements for enhanced URLLC and eMBB in 6G efficiently.
Security Properties for Stack Safety

What exactly does “stack safety” mean? The phrase is associated with a variety of compiler, run-time, and hardware mechanisms for protecting stack memory. But these mechanisms typically lack precise specifications, relying instead on informal descriptions and examples of bad behaviors that they prevent. We propose a formal characterization of stack safety, formulated with concepts from language-based security: a combination of an integrity property (“the private state in each caller’s stack frame is held invariant by the callee”), a confidentiality property (“the callee’s behavior is insensitive to the caller’s private state”), and a well-bracketedness property (“each callee returns control to its immediate caller”). We use these properties to validate the stack-safety “micro-policies” proposed by Roessler and DeHon [2018]. Specifically, we check (with property-based random testing) that Roessler and Dehon’s “eager” micro-policy, which catches violations as early as possible, enforces a simple “stepwise” variant of our properties and correctly detects several broken variants, and that (a repaired version of) their more performant “lazy” micro-policy corresponds to a slightly weaker and more extensional “observational” variant of our properties.