julilili42/papers huggingface dataset:
Nerve Imaging, Electrodiagnostics, and Clinical Examination — Three Musketeers to Differentiate Polyneuropathies
2022-03-01

Acquired demyelinating polyneuropathies consist of a variety of different disease entities including immune-mediated, hematological, inherited, and paraneoplastic-mediated neuropathies.Early diagnosis is important in order to be able to initiate the appropriate therapy.In recent years, with the help of high-resolution nerve ultrasound, progress has been made in distinguishing different mono-and polyneuropathies, in monitoring the disease course, and further understanding the underlying pathomechanisms.In this issue of Neurotherapeutics, Niu et al.[1] from China present diagnostic criteria using electrophysiological and sonographic data to distinguish polyneuropathy, organomegaly, endocrinopathy, monoclonal protein, and skin changes (POEMS) syndrome from chronic inflammatory demyelinating polyneuropathy (CIDP).With this work, the authors reemphasize the value of nerve ultrasound as a diagnostic tool.Immune-mediated neuropathies represent a heterogeneous group of peripheral nerve disorders that can be classified according to time course, predominant involvement of motor/sensory fibers, distribution of deficits, and paraclinical parameters such as nerve conduction studies, high resolution nerve ultrasound, and serum antibodies [2].Among these, Guillain-Barré syndrome (GBS) and CIDP represent the most common subtypes with a prevalence ranging from 1-3/100,000, respectively [2].Although electrodiagnostics and clinical examination remain the hallmark of a proper diagnosis, the rate of false
Minimally invasive arthroscopy of the knee using a new 2 mm device
2022-03-01

Abstract Arthroscopy has been evolving over the last decades, whereas arthroscopic devices have not changed much. Smaller diameter arthroscopes would potentially reduce the intraoperative trauma for cartilage and soft tissues. Two‐millimeter‐diameter arthroscopy demonstrated very good visualization and reach of intraarticular structures—similar to knee arthroscopy using a standard arthroscopic system.
ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness
2022-03-01

Convolutional Neural Networks (CNNs) are commonly thought to recognise objects by learning increasingly complex representations of object shapes. Some recent studies suggest a more important role of image textures. We here put these conflicting hypotheses to a quantitative test by evaluating CNNs and human observers on images with a texture-shape cue conflict. We show that ImageNet-trained CNNs are strongly biased towards recognising textures rather than shapes, which is in stark contrast to human behavioural evidence and reveals fundamentally different classification strategies. We then demonstrate that the same standard architecture (ResNet-50) that learns a texture-based representation on ImageNet is able to learn a shape-based representation instead when trained on "Stylized-ImageNet", a stylized version of ImageNet. This provides a much better fit for human behavioural performance in our well-controlled psychophysical lab setting (nine experiments totalling 48,560 psychophysical trials across 97 observers) and comes with a number of unexpected emergent benefits such as improved object detection performance and previously unseen robustness towards a wide range of image distortions, highlighting advantages of a shape-based representation.
MMA Training: Direct Input Space Margin Maximization through Adversarial Training
2022-03-01

We study adversarial robustness of neural networks from a margin maximization perspective, where margins are defined as the distances from inputs to a classifier's decision boundary. Our study shows that maximizing margins can be achieved by minimizing the adversarial loss on the decision boundary at the "shortest successful perturbation", demonstrating a close connection between adversarial losses and the margins. We propose Max-Margin Adversarial (MMA) training to directly maximize the margins to achieve adversarial robustness. Instead of adversarial training with a fixed , MMA offers an improvement by enabling adaptive selection of the "correct" as the margin individually for each datapoint. In addition, we rigorously analyze adversarial training with the perspective of margin maximization, and provide an alternative interpretation for adversarial training, maximizing either a lower or an upper bound of the margins. Our experiments empirically confirm our theory and demonstrate MMA training's efficacy on the MNIST and CIFAR10 datasets w.r.t. and robustness. Code and models are available at https://github.com/BorealisAI/mma_training.
Provable Robustness of ReLU networks via Maximization of Linear Regions
2022-03-01

It has been shown that neural network classifiers are not robust. This raises concerns about their usage in safety-critical systems. We propose in this paper a regularization scheme for ReLU networks which provably improves the robustness of the classifier by maximizing the linear regions of the classifier as well as the distance to the decision boundary. Our techniques allow even to find the minimal adversarial perturbation for a fraction of test points for large networks. In the experiments we show that our approach improves upon adversarial training both in terms of lower and upper bounds on the robustness and is comparable or better than the state-of-the-art in terms of test error and robustness.
One-Shot Instance Segmentation
2022-03-01

We tackle the problem of one-shot instance segmentation: Given an example image of a novel, previously unknown object category, find and segment all objects of this category within a complex scene. To address this challenging new task, we propose Siamese Mask R-CNN. It extends Mask R-CNN by a Siamese backbone encoding both reference image and scene, allowing it to target detection and segmentation towards the reference category. We demonstrate empirical results on MS Coco highlighting challenges of the one-shot setting: while transferring knowledge about instance segmentation to novel object categories works very well, targeting the detection network towards the reference category appears to be more difficult. Our work provides a first strong baseline for one-shot instance segmentation and will hopefully inspire further research into more powerful and flexible scene analysis algorithms. Code is available at: https://github.com/bethgelab/siamese-mask-rcnn
A Novel Standard for Systematic Reporting of Neuroblastoma Surgery: The International Neuroblastoma Surgical Report Form (INSRF): A Joint Initiative by the Pediatric Oncological Cooperative Groups SIOPEN∗, COG∗∗, and GPOH∗∗∗.
2022-03-01

The International Neuroblastoma Surgical Report Form is the first universal form for the structured and uniform reporting of NBL-related surgical procedures and their outcomes, aiming to facilitate the postoperative communication, treatment planning and analysis of surgical treatment of NBL.
Predictive Uncertainty Quantification with Compound Density Networks
2022-03-01

Despite the huge success of deep neural networks (NNs), finding good mechanisms for quantifying their prediction uncertainty is still an open problem. Bayesian neural networks are one of the most popular approaches to uncertainty quantification. On the other hand, it was recently shown that ensembles of NNs, which belong to the class of mixture models, can be used to quantify prediction uncertainty. In this paper, we build upon these two approaches. First, we increase the mixture model's flexibility by replacing the fixed mixing weights by an adaptive, input-dependent distribution (specifying the probability of each component) represented by NNs, and by considering uncountably many mixture components. The resulting class of models can be seen as the continuous counterpart to mixture density networks and is therefore referred to as compound density networks (CDNs). We employ both maximum likelihood and variational Bayesian inference to train CDNs, and empirically show that they yield better uncertainty estimates on out-of-distribution data and are more robust to adversarial examples than the previous approaches.
Foundations of Comparison-Based Hierarchical Clustering
2022-03-01

We address the classical problem of hierarchical clustering, but in a framework where one does not have access to a representation of the objects or their pairwise similarities. Instead, we assume that only a set of comparisons between objects is available, that is, statements of the form "objects and are more similar than objects and ." Such a scenario is commonly encountered in crowdsourcing applications. The focus of this work is to develop comparison-based hierarchical clustering algorithms that do not rely on the principles of ordinal embedding. We show that single and complete linkage are inherently comparison-based and we develop variants of average linkage. We provide statistical guarantees for the different methods under a planted hierarchical partition model. We also empirically demonstrate the performance of the proposed approaches on several datasets.
A randomized gradient-free attack on ReLU networks
2022-03-01

It has recently been shown that neural networks but also other classifiers are vulnerable to so called adversarial attacks e.g. in object recognition an almost non-perceivable change of the image changes the decision of the classifier. Relatively fast heuristics have been proposed to produce these adversarial inputs but the problem of finding the optimal adversarial input, that is with the minimal change of the input, is NP-hard. While methods based on mixed-integer optimization which find the optimal adversarial input have been developed, they do not scale to large networks. Currently, the attack scheme proposed by Carlini and Wagner is considered to produce the best adversarial inputs. In this paper we propose a new attack scheme for the class of ReLU networks based on a direct optimization on the resulting linear regions. In our experimental validation we improve in all except one experiment out of 18 over the Carlini-Wagner attack with a relative improvement of up to 9\\%. As our approach is based on the geometrical structure of ReLU networks, it is less susceptible to defences targeting their functional properties.
Efficient Humanoid Contact Planning using Learned Centroidal Dynamics Prediction
2022-03-01

Humanoid robots dynamically navigate an environment by interacting with it via contact wrenches exerted at intermittent contact poses. Therefore, it is important to consider dynamics when planning a contact sequence. Traditional contact planning approaches assume a quasi-static balance criterion to reduce the computational challenges of selecting a contact sequence over a rough terrain. This however limits the applicability of the approach when dynamic motions are required, such as when walking down a steep slope or crossing a wide gap. Recent methods overcome this limitation with the help of efficient mixed integer convex programming solvers capable of synthesizing dynamic contact sequences. Nevertheless, its exponential-time complexity limits its applicability to short time horizon contact sequences within small environments. In this paper, we go beyond current approaches by learning a prediction of the dynamic evolution of the robot centroidal momenta, which can then be used for quickly generating dynamically robust contact sequences for robots with arms and legs using a search-based contact planner. We demonstrate the efficiency and quality of the results of the proposed approach in a set of dynamically challenging scenarios.
Planar Graphs of Bounded Degree have Constant Queue Number
2022-03-01

A \\emph{queue layout} of a graph consists of a \\emph{linear order} of its vertices and a partition of its edges into \\emph{queues}, so that no two independent edges of the same queue are nested. The \\emph{queue number} of a graph is the minimum number of queues required by any of its queue layouts. A long-standing conjecture by Heath, Leighton and Rosenberg states that the queue number of planar graphs is bounded. This conjecture has been partially settled in the positive for several subfamilies of planar graphs (most of which have bounded treewidth). In this paper, we make a further important step towards settling this conjecture. We prove that planar graphs of bounded degree (which may have unbounded treewidth) have bounded queue number. A notable implication of this result is that every planar graph of bounded degree admits a three-dimensional straight-line grid drawing in linear volume. Further implications are that every planar graph of bounded degree has bounded track number, and that every -planar graph (i.e., every graph that can be drawn in the plane with at most crossings per edge) of bounded degree has bounded queue number.
Active Probabilistic Inference on Matrices for Pre-Conditioning in Stochastic Optimization
2022-03-01

Pre-conditioning is a well-known concept that can significantly improve the convergence of optimization algorithms. For noise-free problems, where good pre-conditioners are not known a priori, iterative linear algebra methods offer one way to efficiently construct them. For the stochastic optimization problems that dominate contemporary machine learning, however, this approach is not readily available. We propose an iterative algorithm inspired by classic iterative linear solvers that uses a probabilistic model to actively infer a pre-conditioner in situations where Hessian-projections can only be constructed with strong Gaussian noise. The algorithm is empirically demonstrated to efficiently construct effective pre-conditioners for stochastic gradient descent and its variants. Experiments on problems of comparably low dimensionality show improved convergence. In very high-dimensional problems, such as those encountered in deep learning, the pre-conditioner effectively becomes an automatic learning-rate adaptation scheme, which we also empirically show to work well.
Efficient energy-preserving methods for charged-particle dynamics
2022-03-01

In this paper, energy-preserving methods are formulated and studied for solving charged-particle dynamics. We first formulate the scheme of energy-preserving methods and analyze its basic properties including algebraic order and symmetry. Then it is shown that these novel methods can exactly preserve the energy of charged-particle dynamics. Moreover, the long time momentum conservation is studied along such energy-preserving methods. A numerical experiment is carried out to illustrate the notable superiority of the new methods in comparison with the popular Boris method in the literature.
Symmetric and symplectic exponential integrators for nonlinear Hamiltonian systems
2022-03-01

This letter studies symmetric and symplectic exponential integrators when applied to numerically computing nonlinear Hamiltonian systems. We first establish the symmetry and symplecticity conditions of exponential integrators and then show that these conditions are extensions of the symmetry and symplecticity conditions of Runge-Kutta methods. Based on these conditions, some symmetric and symplectic exponential integrators up to order four are derived. Two numerical experiments are carried out and the results demonstrate the remarkable numerical behavior of the new exponential integrators in comparison with some symmetric and symplectic Runge-Kutta methods in the literature.
Jets and outflows of massive protostars - From cloud collapse to jet launching and cloud dispersal
2022-03-01

In a comprehensive convergence study, we investigate the computational conditions necessary to resolve disk formation and jet-launching processes, and analyze possible caveats. We explore the magneto-hydrodynamic (MHD) processes of the collapse of massive prestellar cores in detail, including an analysis of the forces involved and their temporal evolution for up to two free-fall times. We conduct MHD simulations, combining nonideal MHD, self-gravity, and very high resolutions as they have never been achieved before. Our setup includes a 100 Msol cloud core that collapses under its own self-gravity to self-consistently form a dense disk structure and launch tightly collimated magneto-centrifugal jets and wide-angle winds. Our high-resolution simulations can resolve a magneto-centrifugal jet and a magnetic pressure-driven outflow, separately. The nature of the outflows depends critically on spatial resolution. Only high-resolution simulations are able to differentiate a magneto-centrifugally launched, highly collimated jet from a slow wide-angle magnetic-pressure-driven tower flow. Of these two outflow components, the tower flow dominates angular-momentum transport. The mass outflow rate is dominated by the entrained material from the interaction of the jet with the stellar environment and only part of the ejected medium is directly launched from the accretion disk. A tower flow can only develop to its full extent when much of the original envelope has already dispersed. Taking into account both the mass launched from the surface of the disk and the entrained material from the envelope, we find an ejection-to-accretion efficiency of 10%. Nonideal MHD is required to form centrifugally supported accretion disks and the disk size is strongly dependent on spatial resolution.
Sequence Alignment with Dirichlet Process Mixtures
2022-03-01

We present a probabilistic model for unsupervised alignment of high-dimensional time-warped sequences based on the Dirichlet Process Mixture Model (DPMM). We follow the approach introduced in (Kazlauskaite, 2018) of simultaneously representing each data sequence as a composition of a true underlying function and a time-warping, both of which are modelled using Gaussian processes (GPs) (Rasmussen, 2005), and aligning the underlying functions using an unsupervised alignment method. In (Kazlauskaite, 2018) the alignment is performed using the GP latent variable model (GP-LVM) (Lawrence, 2005) as a model of sequences, while our main contribution is extending this approach to using DPMM, which allows us to align the sequences temporally and cluster them at the same time. We show that the DPMM achieves competitive results in comparison to the GP-LVM on synthetic and real-world data sets, and discuss the different properties of the estimated underlying functions and the time-warps favoured by these models.
Exponential energy-preserving methods for charged-particle dynamics in a strong and constant magnetic field
2022-03-01

In this paper, exponential energy-preserving methods are formulated and analysed for solving charged-particle dynamics in a strong and constant magnetic field. The resulting method can exactly preserve the energy of the dynamics. Moreover, it is shown that the magnetic moment of the considered system is nearly conserved over a long time along this exponential energy-preserving method, which is proved by using modulated Fourier expansions. Other properties of the method including symmetry and convergence are also studied. An illustrated numerical experiment is carried out to demonstrate the long-time behaviour of the method.
Gelfand-type theorems for dynamical Banach modules
2022-03-01

The representation theorems of Gelfand and Kakutani for commutative C*-algebras and AM- and AL-spaces are the basis for the Koopman linearization of topological and measure-preserving dynamical systems. In this article we prove versions of these results for dynamics on topological and measurable Banach bundles and the corresponding weighted Koopman representations on Banach modules.
Parabolic Approximation Line Search for DNNs
2022-03-01

A major challenge in current optimization research for deep learning is to automatically find optimal step sizes for each update step. The optimal step size is closely related to the shape of the loss in the update step direction. However, this shape has not yet been examined in detail. This work shows empirically that the batch loss over lines in negative gradient direction is mostly convex locally and well suited for one-dimensional parabolic approximations. By exploiting this parabolic property we introduce a simple and robust line search approach, which performs loss-shape dependent update steps. Our approach combines well-known methods such as parabolic approximation, line search and conjugate gradient, to perform efficiently. It surpasses other step size estimating methods and competes with common optimization methods on a large variety of experiments without the need of hand-designed step size schedules. Thus, it is of interest for objectives where step-size schedules are unknown or do not perform well. Our extensive evaluation includes multiple comprehensive hyperparameter grid searches on several datasets and architectures. Finally, we provide a general investigation of exact line searches in the context of batch losses and exact losses, including their relation to our line search approach.
Fusing spheroids to aligned μ-tissues in a Heart-on-Chip featuring oxygen sensing and electrical pacing capabilities
2022-03-01

Abstract Over the last decade Organ-on-Chip (OOC) emerged as a promising technology for advanced in vitro models, recapitulating key physiological cues. OOC approaches tailored for cardiac tissue engineering resulted in a variety of platforms, some of which integrate stimulation or probing capabilities. Due to manual handling processes, however, a large-scale standardized and robust tissue generation, applicable in an industrial setting, is still out of reach. Here, we present a novel cell injection and tissue generation concept relying on spheroids, which can be produced in large quantities and uniform size from induced pluripotent stem cell-derived human cardiomyocytes. Hydrostatic flow transports and accumulates spheroids in dogbone-shaped cultivation chambers, which subsequently fuse and form aligned, contracting cardiac muscle fibers. Furthermore, we demonstrate electrical stimulation capabilities by utilizing fluidic media connectors as electrodes and provide the blueprint of a low-cost, open-source, scriptable pulse generator. We report on a novel integration strategy of optical O 2 sensor spots into resin-based microfluidic systems, enabling in situ determination of O 2 partial pressures. Finally, proof-of-concept demonstrating electrical stimulation combined with in situ monitoring of metabolic activity in cardiac tissues is provided. The developed system thus opens the door for advanced OOCs integrating biophysical stimulation as well as probing capabilities and serves as blueprint for the facile and robust generation of high density microtissues in microfluidic modules amenable for scale-up and automation.
Zen as everyday praxis
2022-03-01

In Italo Calvino’s works, dialectical oppositions engender a creeping sense of nervous tension, which this article retraces in its multifarious narrative forms. Contextually, this study addresses the relevance of Calvino’s encounter with Zen praxis as an alternative to nerve-wracking dichotomous choices, towards a harmonious fusion of opposites. Palomar (1983), Calvino’s last major work of fiction, serves as a prism to restore to the author’s writing the cross-cultural dynamics of East–West dialogues and to illuminate the fruitful potential of the interaction between Zen and psychoanalysis. A Zen reading of Palomar shows that Calvino not only reflects on the outward appearance of the real in order to understand it, but allows his own work to be transformed by the thoughts that sustain this process of understanding, thus making the book, and in particular its end, a mystical fusion of theory and practice and of Western analytic rationality and Eastern meditative experience.