julilili42/papers huggingface dataset:
Stereo Radiance Fields (SRF): Learning View Synthesis for Sparse Views of Novel Scenes
2021-06-01

Recent neural view synthesis methods have achieved impressive quality and realism, surpassing classical pipelines which rely on multi-view reconstruction. State-of-the-Art methods, such as NeRF, are designed to learn a single scene with a neural network and require dense multi-view inputs. Testing on a new scene requires re-training from scratch, which takes 2-3 days. In this work, we introduce Stereo Radiance Fields (SRF), a neural view synthesis approach that is trained end-to-end, generalizes to new scenes, and requires only sparse views at test time. The core idea is a neural architecture inspired by classical multi-view stereo methods, which estimates surface points by finding similar image regions in stereo images. In SRF, we predict color and density for each 3D point given an encoding of its stereo correspondence in the input images. The encoding is implicitly learned by an ensemble of pair-wise similarities -- emulating classical stereo. Experiments show that SRF learns structure instead of overfitting on a scene. We train on multiple scenes of the DTU dataset and generalize to new ones without re-training, requiring only 10 sparse and spread-out views as input. We show that 10-15 minutes of fine-tuning further improve the results, achieving significantly sharper, more detailed results than scene-specific models. The code, model, and videos are available at https://virtualhumans.mpi-inf.mpg.de/srf/.
Cluster-driven Graph Federated Learning over Multiple Domains
2021-06-01

Federated Learning (FL) deals with learning a central model (i.e. the server) in privacy-constrained scenarios, where data are stored on multiple devices (i.e. the clients). The central model has no direct access to the data, but only to the updates of the parameters computed locally by each client. This raises a problem, known as statistical heterogeneity, because the clients may have different data distributions (i.e. domains). This is only partly alleviated by clustering the clients. Clustering may reduce heterogeneity by identifying the domains, but it deprives each cluster model of the data and supervision of others. Here we propose a novel Cluster-driven Graph Federated Learning (FedCG). In FedCG, clustering serves to address statistical heterogeneity, while Graph Convolutional Networks (GCNs) enable sharing knowledge across them. FedCG: i) identifies the domains via an FL-compliant clustering and instantiates domain-specific modules (residual branches) for each domain; ii) connects the domain-specific modules through a GCN at training to learn the interactions among domains and share knowledge; and iii) learns to cluster unsupervised via teacher-student classifier-training iterations and to address novel unseen test domains via their domain soft-assignment scores. Thanks to the unique interplay of GCN over clusters, FedCG achieves the state-of-the-art on multiple FL benchmarks.
NormalFusion: Real-Time Acquisition of Surface Normals for High-Resolution RGB-D Scanning
2021-06-01

Multiview shape-from-shading (SfS) has achieved high-detail geometry, but its computation is expensive for solving a multiview registration and an ill-posed inverse rendering problem. Therefore, it has been mainly used for offline methods. Volumetric fusion enables real-time scanning using a conventional RGB-D camera, but its geometry resolution has been limited by the grid resolution of the volumetric distance field and depth registration errors. In this paper, we propose a real-time scanning method that can acquire high-detail geometry by bridging volumetric fusion and multiview SfS in two steps. First, we pro-pose the first real-time acquisition of photometric normals stored in texture space to achieve high-detail geometry. We also introduce geometry-aware texture mapping, which progressively refines geometric registration between the texture space and the volumetric distance field by means of normal texture, achieving real-time multiview SfS. We demonstrate our scanning of high-detail geometry using an RGB-D cam-era at ∼20 fps. Results verify that the geometry quality of our method is strongly competitive with that of offline multi-view SfS methods.
Mobility-Aware Routing and Caching: A Federated Learning Assisted Approach
2021-06-01

We develop mobility-aware routing and caching strategies to solve the network cost minimization problem for dense small-cell networks. The challenge mainly stems from the insufficient backhaul capacity of small-cell networks and the limited storing capacity of small-cell base stations (SBSs). The optimization problem is NP-hard since both the mobility patterns of the mobilized users (MUs), as well as the MUs' preference for contents, are unknown. To tackle this problem, we start by dividing the entire geographical area into small sections, each of which containing one SBS and several MUs. Based on the concept of one-stop-shop (OSS), we propose a federated routing and popularity learning (FRPL) approach in which the SBSs cooperatively learn the routing and preference of their respective MUs, and make caching decision. Notably, FRPL enables the completion of the multi-tasks in one shot, thereby reducing the average processing time per global aggregation. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Theoretical and numerical analyses show the effectiveness of our proposed approach.
SMPLicit: Topology-aware Generative Model for Clothed People
2021-06-01

In this paper we introduce SMPLicit, a novel generative model to jointly represent body pose, shape and clothing geometry. In contrast to existing learning-based approaches that require training specific models for each type of garment, SMPLicit can represent in a unified manner different garment topologies (e.g. from sleeveless tops to hoodies and to open jackets), while controlling other properties like the garment size or tightness/looseness. We show our model to be applicable to a large variety of garments including T-shirts, hoodies, jackets, shorts, pants, skirts, shoes and even hair. The representation flexibility of SMPLicit builds upon an implicit model conditioned with the SMPL human body parameters and a learnable latent space which is semantically interpretable and aligned with the clothing attributes. The proposed model is fully differentiable, allowing for its use into larger end-to-end trainable systems. In the experimental section, we demonstrate SMPLicit can be readily used for fitting 3D scans and for 3D reconstruction in images of dressed people. In both cases we are able to go beyond state of the art, by retrieving complex garment geometries, handling situations with multiple clothing layers and providing a tool for easy outfit editing. To stimulate further research in this direction, we will make our code and model publicly available at http://www.iri.upc.edu/people/ecorona/smplicit/.
A Closer Look at Self-training for Zero-Label Semantic Segmentation
2021-06-01

Being able to segment unseen classes not observed during training is an important technical challenge in deep learning, because of its potential to reduce the expensive annotation required for semantic segmentation. Prior zero-label semantic segmentation works approach this task by learning visual-semantic embeddings or generative models. However, they are prone to overfitting on the seen classes because there is no training signal for them. In this paper, we study the challenging generalized zero-label semantic segmentation task where the model has to segment both seen and unseen classes at test time. We assume that pixels of unseen classes could be present in the training images but without being annotated. Our idea is to capture the latent information on unseen classes by supervising the model with self-produced pseudo-labels for unlabeled pixels. We propose a consistency regularizer to filter out noisy pseudo-labels by taking the intersections of the pseudo-labels generated from different augmentations of the same image. Our framework generates pseudo-labels and then retrain the model with human-annotated and pseudo-labelled data. This procedure is repeated for several iterations. As a result, our approach achieves the new state-of-the-art on PascalVOC12 and COCO-stuff datasets in the challenging generalized zero-label semantic segmentation setting, surpassing other existing methods addressing this task with more complex strategies.
Charge injection capacity of ferroelectric microelectrodes for bioelectronic applications
2021-06-01

We analyze the extracellular stimulation current and the charge injection capacity (CIC) of microelectrodes coated with an insulating layer to prevent toxic electrochemical effects in bioelectronic applications. We show for a microelectrode coated with an insulating ferroelectric layer that the ferroelectric polarization current contributes to the extracellular stimulation current. Depending on the remanent polarization Pr of the ferroelectric, the polarization current in the switching regime can increase the CIC by up to two orders of magnitude as compared to the commonly used extracellular capacitive stimulation with microelectrodes that are coated with a dielectric layer.
Evaluation of Novel Substituted Furopyridines as Inhibitors of Protein Kinases Related to Tau Pathology in Alzheimer´s Disease
2021-06-01

Background: Alzheimer ́s disease (AD) is characterized by a progressive neuronal degeneration caused by two pathological hallmarks, hyperphosphorylated tau protein aggregated into tau filaments and amyloid precursor protein derived beta amyloid peptides aggregated into extracellular amyloid plaques. All attempts so far to find effective drugs failed in clinical trials. AD is a multifactorial disease, so that selective drugs to target one AD-relevant structure alone may not be sufficient. Objective: We built novel furopyridines with various substitution patterns to evaluate them as protein kinases inhibitors of enzymes related to tau pathology. Methods: Furopyridine derivatives were synthesized and purified using column chromatography. The protein kinase inhibitory properties were determined in ATP-competition assays with determined affinity constants for the most active compounds. Results: The compounds were prepared in simple two-component reactions of substituted 1,4- dihydropyridines and respective quinones to obtain various substitutions of the molecular furopyridine scaffold. The substituent effects on the determined kinase inhibitory properties of cdk1, cdk2, Fyn, JNK3 and gsk-3β are discussed. Conclusion: Various 3-substitutions were found most sensitive for the protein kinase inhibition depending on the length, nature and a substituent positioning within. We identified compounds as inhibitors of several kinases as a tool to potentially combat the disease progress in a multitargeting approach.
Evidence-Based Examination Techniques for the Shoulder Joint
2021-06-01

Abstract Objective Digitalisation now almost covers all areas of medical student teaching. Teaching videos are a good way to help students learn practical skills. The existing evidence is a recognised aid for the classification of the respective technology. Method This video presents the usual examination techniques of the shoulder joint on a patient with an unstable shoulder. The respective techniques, if available, were backed up with appropriate evidence. Conclusion The examination techniques presented allow students to view them in a standardized manner on a patient. The evidence for the examination techniques can help with the classification of the respective technique.
Update Breast Cancer 2021 Part 3 – Current Developments in the Treatment of Early Breast Cancer: Review and Assessment of Specialised Treatment Scenarios by an International Expert Panel
2021-06-01

Abstract The continuous availability of findings from new studies repeatedly results in updated treatment recommendations and guidelines. In the case of breast carcinoma in particular, several studies have been published in the last few years that have transformed how early and advanced breast carcinoma is being treated. However, this by no means means implies that there is agreement among all experts on specific issues. It is precisely the diversity of interpretation of guidelines and study findings that reflects the constantly changing available data and its complexity, as well as the availability of new drugs. In recent years, new substances such as pertuzumab, T-DM1, neratinib and capecitabine have become available to treat patients with early stages of breast carcinoma. Furthermore, the first results on the use of CDK4/6 inhibitors for adjuvant treatment have now been published. Last but not least, the use of multigene tests to avoid the necessity of chemotherapy in certain patients is still under discussion. This review summarises the state of the data and publishes the results of the survey completed by experts at the 2021 St. Gallen Breast Cancer Conference on early-stage breast cancer.
Human POSEitioning System (HPS): 3D Human Pose Estimation and Self-localization in Large Scenes from Body-Mounted Sensors
2021-06-01

We introduce (HPS) Human POSEitioning System, a method to recover the full 3D pose of a human registered with a 3D scan of the surrounding environment using wearable sensors. Using IMUs attached at the body limbs and a head mounted camera looking outwards, HPS fuses camera based self-localization with IMU-based human body tracking. The former provides drift-free but noisy position and orientation estimates while the latter is accurate in the short-term but subject to drift over longer periods of time. We show that our optimization-based integration exploits the benefits of the two, resulting in pose accuracy free of drift. Furthermore, we integrate 3D scene constraints into our optimization, such as foot contact with the ground, resulting in physically plausible motion. HPS complements more common third-person-based 3D pose estimation methods. It allows capturing larger recording volumes and longer periods of motion, and could be used for VR/AR applications where humans interact with the scene without requiring direct line of sight with an external camera, or to train agents that navigate and interact with the environment based on first-person visual input, like real humans. With HPS, we recorded a dataset of humans interacting with large 3D scenes (300-1000 sq.m) consisting of 7 subjects and more than 3 hours of diverse motion. The dataset, code and video will be available on the project page: http://virtualhumans.mpi-inf.mpg.de/hps/ .
Learning Decision Trees Recurrently Through Communication
2021-06-01

Integrated interpretability without sacrificing the prediction accuracy of decision making algorithms has the potential of greatly improving their value to the user. Instead of assigning a label to an image directly, we propose to learn iterative binary sub-decisions, inducing sparsity and transparency in the decision making process. The key aspect of our model is its ability to build a decision tree whose structure is encoded into the memory representation of a Recurrent Neural Network jointly learned by two models communicating through message passing. In addition, our model assigns a semantic meaning to each decision in the form of binary attributes, providing concise, semantic and relevant rationalizations to the user. On three benchmark image classification datasets, including the large-scale ImageNet, our model generates human interpretable binary decision sequences explaining the predictions of the network while maintaining state-of-the-art accuracy.
Diversity-sensitive social platforms and responsibility
2021-06-01

There is an ongoing debate on how algorithms and machine learning can and should deal with human diversity while avoiding the pitfalls of statistical stereotyping, the re-enforcement of clichés and the perpetuation of unjust discrimination. Computer scientists try to tackle these issues by developing algorithms and social-interaction protocols for mediating diversity-aware interactions between people, for instance on diversity-sensitive social platforms. At the same time, diversity-related data often comprise sensitive personal data, and their collection, storage and management increases the vulnerability of users to various misuse scenarios. Already this observation leads to the question, how do we need to conceptualize responsibility to do justice to the increased vulnerability? In this paper, I thus focus on the questions a diversity-sensitive social platform raises with regard to responsibility, and propose a tentative ethical framework of responsibility for these platforms.
Phylogenetic context using phylogenetic outlines
2021-06-01

A bstract Microbial studies typically involve the sequencing and assembly of draft genomes for individual microbes or whole microbiomes. Given a draft genome, one first task is to determine its phylogenetic context, that is, to place it relative to the set of related reference genomes. We provide a new interactive graphical tool that addresses this task using Mash sketches to compare against all bacterial and archaeal representative genomes in the GTDB taxonomy, all within the framework of SplitsTree5. The phylogenetic context of the query sequences is then displayed as a phylogenetic outline, a new type of phylogenetic network that is more general that a phylogenetic tree, but significantly less complex than other types of phylogenetic networks. We propose to use such networks, rather than trees, to represent phylogenetic context, because they can express uncertainty in the placement of taxa, whereas a tree must always commit to a specific branching pattern. We illustrate the new method using a number of draft genomes of different assembly quality.
Phonemic Training Modulates Early Speech Processing in Pre-reading Children
2021-06-01

Phonemic awareness and rudimentary grapheme knowledge concurrently develop in pre-school age. In a training study, we tried to disentangle the role of both precursor functions of reading for spoken word recognition. Two groups of children exercised with phonemic materials, but only one of both groups learnt corresponding letters to trained phonemes. A control group exercised finger-number associations (non-linguistic training). After the training, we tested how sensitive children were to prime-target variation in word onset priming. A group of young adults took part in the same experiment to provide data from experienced readers. While decision latencies to the targets suggested fine-grained spoken word processing in all groups, event-related potentials (ERPs) indicated that both phonemic training groups processed phonemic variation in more detail than the non-linguistic training group and young adults at early stages of speech processing. Our results indicate temporal plasticity of implicit speech processing in pre-school age as a function of explicit phonemic training.
SMD-Nets: Stereo Mixture Density Networks
2021-06-01

Despite stereo matching accuracy has greatly improved by deep learning in the last few years, recovering sharp boundaries and high-resolution outputs efficiently remains challenging. In this paper, we propose Stereo Mixture Density Networks (SMD-Nets), a simple yet effective learning framework compatible with a wide class of 2D and 3D architectures which ameliorates both issues. Specifically, we exploit bimodal mixture densities as output representation and show that this allows for sharp and precise disparity estimates near discontinuities while explicitly modeling the aleatoric uncertainty inherent in the observations. Moreover, we formulate disparity estimation as a continuous problem in the image domain, allowing our model to query disparities at arbitrary spatial precision. We carry out comprehensive experiments on a new high-resolution and highly realistic synthetic stereo dataset, consisting of stereo pairs at 8Mpx resolution, as well as on real-world stereo datasets. Our experiments demonstrate increased depth accuracy near object boundaries and prediction of ultra high-resolution disparity maps on standard GPUs. We demonstrate the flexibility of our technique by improving the performance of a variety of stereo backbones.
CRISPR-Cas9 Gene Editing for Sickle Cell Disease and β-Thalassemia
2021-06-01

(Abstracted from N Engl J Med 2021;384:252–260) Transfusion-dependent β-thalassemia (TDT) and sickle cell disease (SCD) are the most common inherited hematologic disorders, affecting approximately 60,000 and 300,000 patients worldwide, respectively. Current therapies, including red blood cell (RBC) transfusion and iron chelation in TDT and transfusion, pain management, and hydroxyurea in SCD, help to manage the disorders but do not address the underlying cause.
Existence, uniqueness and regularity for the stochastic Ericksen–Leslie equation
2021-06-01

Abstract We investigate existence and uniqueness for the liquid crystal flow driven by colored noise on the two-dimensional torus. After giving a natural uniqueness criterion, we prove local solvability in L p -based spaces, for every p &gt; 2. Thanks to a bootstrap principle together with a Gyöngy–Krylov-type compactness argument, this will ultimately lead us to prove the existence of a particular class of global solutions which are partially regular, strong in the probabilistic sense, and taking values in the ‘critical space’ L 2 × H 1 .
David George with Thomas Clayton, Niels Herold, Megan-Marie Johnson and Ashley Spriggs (eds.). 2019. Coriolanus: A New Variorum Edition of Shakespeare. 2 vols. Morrisville, NC: Lulu Press, 654 pp. and 532 pp., $ 60.00 each.
2021-06-01

Article David George with Thomas Clayton, Niels Herold, Megan-Marie Johnson and Ashley Spriggs (eds.). 2019. Coriolanus: A New Variorum Edition of Shakespeare. 2 vols. Morrisville, NC: Lulu Press, 654 pp. and 532 pp., $ 60.00 each. was published on June 1, 2021 in the journal Anglia (volume 139, issue 2).
Doing connoisseurship. Yesterday, today, \\ntomorrow. Introductory remarks
2021-06-01

The introduction seeks to concretize and expand on crucial connoisseurial practices such as comparing or verbalising. Starting from the three domains of connoisseurship, namely the judgment of an artwork’s quality, the attribution to an artist, and the question whether the art work is an original or a copy, it becomes apparent that connoisseurial practices still play a remarkable role in art historical endeavors. This can be demonstrated with a look at the contributions, which are bringing together and reflecting both historical and current connoisseurial practices.