nils-herrmann/clean_scirepeval huggingface dataset:
Technical Considerations for Semantic Segmentation in MRI using Convolutional Neural Networks

High-fidelity semantic segmentation of magnetic resonance volumes is critical for estimating tissue morphometry and relaxation parameters in both clinical and research applications. While manual segmentation is accepted as the gold-standard, recent advances in deep learning and convolutional neural networks (CNNs) have shown promise for efficient automatic segmentation of soft tissues. However, due to the stochastic nature of deep learning and the multitude of hyperparameters in training networks, predicting network behavior is challenging. In this paper, we quantify the impact of three factors associated with CNN segmentation performance: network architecture, training loss functions, and training data characteristics. We evaluate the impact of these variations on the segmentation of femoral cartilage and propose potential modifications to CNN architectures and training protocols to train these models with confidence.
Semi-Supervised Learning Via Feedforward-Designed Convolutional Neural Networks

A semi-supervised learning framework using the feedforward-designed convolutional neural networks (FF-CNNs) is proposed for image classification in this work. One unique property of FF-CNNs is that no backpropagation is used in model parameters determination. Since unlabeled data may not always enhance semi-supervised learning , we define an effective quality score and use it to select a subset of unlabeled data in the training process. We conduct experiments on the MNIST, SVHN, and CIFAR-10 datasets, and show that the proposed semi-supervised FF-CNN solution outperforms the CNN trained by backpropagation (BP-CNN) when the amount of labeled data is reduced. Furthermore, we develop an ensemble system that combines the output decision vectors of different semi-supervised FF-CNNs to boost classification accuracy. The ensemble systems can achieve further performance gains on all three benchmarking datasets.
Geometric pluripotential theory on Kähler
 manifolds

Finite energy pluripotential theory accommodates the variational theory of equations of complex Monge-Amp\`ere type arising in K\"ahler geometry. Recently it has been discovered that many of the potential spaces involved have a rich metric geometry, effectively turning the variational problems in question into problems of infinite dimensional convex optimization, yielding existence results for solutions of the underlying complex Monge-Amp\`ere equations. The purpose of this survey is to describe these developments from basic principles.
Thrust distribution in electron-positron annihilation using the principle of maximum conformality

We present a comprehensive and self-consistent analysis for the thrust distribution by using the Principle of Maximum Conformality (PMC). By absorbing all nonconformal terms into the running coupling using PMC via renormalization group equation, the scale in the running coupling shows the correct physical behavior and the correct number of active flavors is determined. The resulting PMC predictions agree with the precise measurements for both the thrust differential distributions and the thrust mean values. Moreover, we provide a new remarkable way to determine the running of the coupling constant $\alpha_s(Q^2)$ from the measurement of the jet distributions in electron-positron annihilation at a single given value of the center-of-mass energy $\sqrt{s}$.
A lifestyle-based model of household neighbourhood location and individual travel mode choice behaviours

Issues such as urban sprawl, congestion, oil dependence, climate change and public health, are prompting urban and transportation planners to turn to land use and urban design to rein in automobile use. One of the implicit beliefs in this effort is that the right land-use policies will, in fact, help to reduce automobile use and increase the use of alternative modes of transportation. Thus, planners and transport engineers are increasingly viewing land use policies and lifestyle patterns as a way to manage transportation demand. While a substantial body of work has looked at the relationship between the built environment and travel behaviour, as well as the influence of lifestyles and lifestyle-related decisions on using different travel modes and activity behaviours, limited work has been done in capturing these effects simultaneously and also in exploring the effect of intra-household interaction on individual attitudes and beliefs towards travel and activity behavior, and their subsequent influence on lifestyles and modality styles. Therefore, for this study we proposed a framework that captures the concurrent influence of lifestyles and modality styles on both household-level decisions, such as neighbourhood location, and individual-level decisions, such as travel mode choices using a hierarchical Latent Class Choice Model.
Performance of a GridPix TPC readout based on the Timepix3 chip

With the ultimate goal of developing a pixel-based readout for a TPC at the ILC, a GridPix readout system consisting of one Timepix3 chip with an integrated amplification grid was embedded in a prototype detector. The performance was studied in a testbeam with 2.5 GeV electrons at the ELSA accelerator in Bonn. The error on the track position measurement both in the drift direction and in the readout plane is dominated by diffusion. Systematic uncertainties are limited to below 10 $\mu$m. The GridPix can detect single ionization electrons with high efficiency, which allows for energy loss measurements and particle identification. From a truncated sum, an energy loss (dE/dx) resolution of 4.1% is found for an effective track length of 1 m. Using the same type of chips, a Quad module was developed that can be tiled to cover a TPC readout plane at the ILC. Simulation studies show that a pixel readout can improve the momentum resolution of a TPC at the ILC by about 20%.
Colloquium: A Century of Noether's Theorem

In the summer of 1918, Emmy Noether published the theorem that now bears her name, establishing a profound two-way connection between symmetries and conservation laws. The influence of this insight is pervasive in physics; it underlies all of our theories of the fundamental interactions and gives meaning to conservation laws that elevates them beyond useful empirical rules. Noether's papers, lectures, and personal interactions with students and colleagues drove the development of abstract algebra, establishing her in the pantheon of twentieth-century mathematicians. This essay traces her path from Erlangen through Gottingen to a brief but happy exile at Bryn Mawr College in Pennsylvania, illustrating the importance of "Noether's Theorem" for the way we think today. The text draws on a colloquium presented at Fermilab on 15 August 2018.
Are All Layers Created Equal?

Understanding deep neural networks has been a major research objective in recent years with notable theoretical progress. A focal point of those studies stems from the success of excessively large networks which defy the classical wisdom of uniform convergence and learnability. We study empirically the layer-wise functional structure of overparameterized deep models. We provide evidence for the heterogeneous characteristic of layers. To do so, we introduce the notion of robustness to post-training re-initialization and re-randomization. We show that the layers can be categorized as either ``ambient'' or ``critical''. Resetting the ambient layers to their initial values has no negative consequence, and in many cases they barely change throughout training. On the contrary, resetting the critical layers completely destroys the predictor and the performance drops to chanceh. Our study provides further evidence that mere parameter counting or norm accounting is too coarse in studying generalization of deep models, and flatness or robustness analysis of the models needs to respect the network architectures.
Testing Markov Chains without Hitting

We study the problem of identity testing of markov chains. In this setting, we are given access to a single trajectory from a markov chain with unknown transition matrix $Q$ and the goal is to determine whether $Q = P$ for some known matrix $P$ or $\text{Dist}(P, Q) \geq \epsilon$ where $\text{Dist}$ is suitably defined. In recent work by Daskalakis, Dikkala and Gravin, 2018, it was shown that it is possible to distinguish between the two cases provided the length of the observed trajectory is at least super-linear in the hitting time of $P$ which may be arbitrarily large. 
In this paper, we propose an algorithm that avoids this dependence on hitting time thus enabling efficient testing of markov chains even in cases where it is infeasible to observe every state in the chain. Our algorithm is based on combining classical ideas from approximation algorithms with techniques for the spectral analysis of markov chains.
Neural Fictitious Self-Play on ELF Mini-RTS

Despite the notable successes in video games such as Atari 2600, current AI is yet to defeat human champions in the domain of real-time strategy (RTS) games. One of the reasons is that an RTS game is a multi-agent game, in which single-agent reinforcement learning methods cannot simply be applied because the environment is not a stationary Markov Decision Process. In this paper, we present a first step toward finding a game-theoretic solution to RTS games by applying Neural Fictitious Self-Play (NFSP), a game-theoretic approach for finding Nash equilibria, to Mini-RTS, a small but nontrivial RTS game provided on the ELF platform. More specifically, we show that NFSP can be effectively combined with policy gradient reinforcement learning and be applied to Mini-RTS. Experimental results also show that the scalability of NFSP can be substantially improved by pretraining the models with simple self-play using policy gradients, which by itself gives a strong strategy despite its lack of theoretical guarantee of convergence.
Robust Matrix Completion State Estimation in Distribution Systems

Due to the insufficient measurements in the distribution system state estimation (DSSE), full observability and redundant measurements are difficult to achieve without using the pseudo measurements. The matrix completion state estimation (MCSE) combines the matrix completion and power system model to estimate voltage by exploring the low-rank characteristics of the matrix. This paper proposes a robust matrix completion state estimation (RMCSE) to estimate the voltage in a distribution system under a low-observability condition. Tradition state estimation weighted least squares (WLS) method requires full observability to calculate the states and needs redundant measurements to proceed a bad data detection. The proposed method improves the robustness of the MCSE to bad data by minimizing the rank of the matrix and measurements residual with different weights. It can estimate the system state in a low-observability system and has robust estimates without the bad data detection process in the face of multiple bad data. The method is numerically evaluated on the IEEE 33-node radial distribution system. The estimation performance and robustness of RMCSE are compared with the WLS with the largest normalized residual bad data identification (WLS-LNR), and the MCSE.
Heavy User Effect in A/B Testing: Identification and Estimation

On-line experimentation (also known as A/B testing) has become an integral part of software development. To timely incorporate user feedback and continuously improve products, many software companies have adopted the culture of agile deployment, requiring online experiments to be conducted and concluded on limited sets of users for a short period. While conceptually efficient, the result observed during the experiment duration can deviate from what is seen after the feature deployment, which makes the A/B test result highly biased. While such bias can have multiple sources, we provide theoretical analysis as well as empirical evidence to show that the heavy user effect can contribute significantly to it. To address this issue, we propose to use a jackknife-resampling estimator. Simulated and real-life examples show that the jackknife estimator can reduce the bias and make A/B testing results closer to our long term estimate.
On the Limiting Absorption Principle for Schr{ö}dinger operators on waveguides

We prove a Limiting Absorption Principle for Schrodinger operators in tubes about infinite curves embedded in the Euclidian space with different types of boundary conditions. The argument is based on the Mourre theory with conjugate operators different from the generator of dilations which is usually used in this case, and permits to prove a Limiting Absorption Principle for Schrodinger operators in singular waveguides.
Topological band structure of surface acoustic waves on a periodically corrugated surface

Surface acoustic waves (SAWs) are elastic waves localized on a surface of an elastic body. We theoretically study topological edge modes of SAWs for a corrugated surface. We introduce a corrugation forming a triangular lattice on the surface of an elastic body. We treat the corrugation as a perturbation, and construct eigenmodes on a corrugated surface by superposing those for the flat surface at wavevectors which are mutually different by reciprocal lattice vectors. We thereby show emergence of Dirac cones at the $K$ and $K'$ points analytically. Moreover, by breaking the time-reversal symmetry, we show that the Dirac cones open a gap, and that the Chern number for the lowest band has a nonzero value. It means existence of topological chiral edge modes of SAWs in the gap.
Regularizing Generative Models Using Knowledge of Feature Dependence

Generative modeling is a fundamental problem in machine learning with many potential applications. Efficient learning of generative models requires available prior knowledge to be exploited as much as possible. In this paper, we propose a method to exploit prior knowledge of relative dependence between features for learning generative models. Such knowledge is available, for example, when side-information on features is present. We incorporate the prior knowledge by forcing marginals of the learned generative model to follow a prescribed relative feature dependence. To this end, we formulate a regularization term using a kernel-based dependence criterion. The proposed method can be incorporated straightforwardly into many optimization-based learning schemes of generative models, including variational autoencoders and generative adversarial networks. We show the effectiveness of the proposed method in experiments with multiple types of datasets and models.
GEN-SLAM: Generative Modeling for Monocular Simultaneous Localization and Mapping

We present a Deep Learning based system for the twin tasks of localization and obstacle avoidance essential to any mobile robot. Our system learns from conventional geometric SLAM, and outputs, using a single camera, the topological pose of the camera in an environment, and the depth map of obstacles around it. We use a CNN to localize in a topological map, and a conditional VAE to output depth for a camera image, conditional on this topological location estimation. We demonstrate the effectiveness of our monocular localization and depth estimation system on simulated and real datasets.
Quantum Gravity from General Relativity

Although general relativity is a predictively successful theory, it treats matter as classical rather than as quantum. For this reason, it will have to be replaced by a more fundamental quantum theory of gravity. Attempts to formulate a quantum theory of gravity suggest that such a theory may have radical consequences for the nature, and indeed the fate, of spacetime. The present article articulates what this problem of spacetime is and traces it three approaches to quantum gravity taking general relativity as their vantage point: semi-classical gravity, causal set theory, and loop quantum gravity.
Latent Space Cartography: Generalised Metric-Inspired Measures and Measure-Based Transformations for Generative Models

Deep generative models are universal tools for learning data distributions on high dimensional data spaces via a mapping to lower dimensional latent spaces. We provide a study of latent space geometries and extend and build upon previous results on Riemannian metrics. We show how a class of heuristic measures gives more flexibility in finding meaningful, problem-specific distances, and how it can be applied to diverse generator types such as autoregressive generators commonly used in e.g. language and other sequence modeling. We further demonstrate how a diffusion-inspired transformation previously studied in cartography can be used to smooth out latent spaces, stretching them according to a chosen measure. In addition to providing more meaningful distances directly in latent space, this also provides a unique tool for novel kinds of data visualizations. We believe that the proposed methods can be a valuable tool for studying the structure of latent spaces and learned data distributions of generative models.
Progressive Generative Adversarial Networks for Medical Image Super resolution

Anatomical landmark segmentation and pathology localization are important steps in automated analysis of medical images. They are particularly challenging when the anatomy or pathology is small, as in retinal images and cardiac MRI, or when the image is of low quality due to device acquisition parameters as in magnetic resonance (MR) scanners. We propose an image super-resolution method using progressive generative adversarial networks (P-GAN) that can take as input a low-resolution image and generate a high resolution image of desired scaling factor. The super resolved images can be used for more accurate detection of landmarks and pathology. Our primary contribution is in proposing a multistage model where the output image quality of one stage is progressively improved in the next stage by using a triplet loss function. The triplet loss enables stepwise image quality improvement by using the output of the previous stage as the baseline. This facilitates generation of super resolved images of high scaling factor while maintaining good image quality. Experimental results for image super-resolution show that our proposed multistage P-GAN outperforms competing methods and baseline GAN.
Adaptive Artificial Intelligent Q&A Platform

The paper presents an approach to build a question and answer system that is capable of processing the information in a large dataset and allows the user to gain knowledge from this dataset by asking questions in natural language form. Key content of this research covers four dimensions which are; Corpus Preprocessing, Question Preprocessing, Deep Neural Network for Answer Extraction and Answer Generation. The system is capable of understanding the question, responds to the user's query in natural language form as well. The goal is to make the user feel as if they were interacting with a person than a machine.
Blockchain Storage Load Balancing Among DHT Clustered Nodes

In Bitcoin, to independently verify whether new transactions are correct or not, a type of a node called "Full Node" has to hold the whole of historical transactions. The transactions are stored in ledger called "Blockchain. " Blockchain is an append-only data structure. Thus, to operate Full Nodes, the required storage capacity would grow too large for resource-constrained devices. Due to the limitation, the existing lightweight node scheme is that a node relies on other Full Nodes. In this thesis, to reduce storage capacity with keeping the independence of each node, we propose a storage load balancing scheme "KARAKASA" using Distributed Hash Table (DHT). In KARAKASA, nodes distributedly keep the whole blockchain among DHT networked nodes. We evaluated KARAKASA from the view of storage capacity and independence. As a result, a node in a cluster does not need to trust other nodes. We concluded that nodes in a DHT cluster can behave like Full Nodes without holding the whole blockchain.
Assessing Partisan Traits of News Text Attributions

On the topic of journalistic integrity, the current state of accurate, impartial news reporting has garnered much debate in context to the 2016 US Presidential Election. In pursuit of computational evaluation of news text, the statements (attributions) ascribed by media outlets to sources provide a common category of evidence on which to operate. In this paper, we develop an approach to compare partisan traits of news text attributions and apply it to characterize differences in statements ascribed to candidate, Hilary Clinton, and incumbent President, Donald Trump. In doing so, we present a model trained on over 600 in-house annotated attributions to identify each candidate with accuracy > 88%. Finally, we discuss insights from its performance for future research.
Non-Monotonic Sequential Text Generation

Standard sequential generation methods assume a pre-specified generation order, such as text generation methods which generate words from left to right. In this work, we propose a framework for training models of text generation that operate in non-monotonic orders; the model directly learns good orders, without any additional annotation. Our framework operates by generating a word at an arbitrary position, and then recursively generating words to its left and then words to its right, yielding a binary tree. Learning is framed as imitation learning, including a coaching method which moves from imitating an oracle to reinforcing the policy’s own preferences. Experimental results demonstrate that using the proposed method, it is possible to learn policies which generate text without pre-specifying a generation order while achieving competitive performance with conventional left-to-right generation.
Dual Space Preconditioning for Gradient Descent

The conditions of relative smoothness and relative strong convexity were recently introduced for the analysis of Bregman gradient methods for convex optimization. We introduce a fully explicit descent scheme with relative smoothness in the dual space between the convex conjugate of the objective function and a designed dual reference function. For Legendre type convex functions under this dual relative smoothness, our scheme naturally remains in the interior of the domain, despite being fully explicit. We obtain linear convergence under dual relative strong convexity with a condition number that is invariant under horizontal translations. Our method is a non-linear preconditioning of gradient descent that can improve the conditioning of explicit first-order methods on problems with non-smooth or non-strongly convex structure. We show how this method can be applied to p-norm regression and exponential penalty function minimization.
Unsupervised Polyglot Text-to-speech

We present a TTS neural network that is able to produce speech in multiple languages. The proposed network is able to transfer a voice, which was presented as a sample in a source language, into one of several target languages. Training is done without using matching or parallel data, i.e., without samples of the same speaker in multiple languages, making the method much more applicable. The conversion is based on learning a polyglot network that has multiple per-language sub-networks and adding loss terms that preserve the speaker’s identity in multiple languages. We evaluate the proposed polyglot neural network for three languages with a total of more than 400 speakers and demonstrate convincing conversion capabilities.