nils-herrmann/clean_scirepeval huggingface dataset:
Regularly varying functions, generalized
 contents, and the spectrum of fractal
 strings

We revisit the problem of characterizing the eigenvalue distribution of the Dirichlet-Laplacian on bounded open sets $\Omega\subset\mathbb{R}$ with fractal boundaries. It is well-known from the results of Lapidus and Pomerance \cite{LapPo1} that the asymptotic second term of the eigenvalue counting function can be described in terms of the Minkowski content of the boundary of $\Omega$ provided it exists. He and Lapidus \cite{HeLap2} discussed a remarkable extension of this characterization to sets $\Omega$ with boundaries that are not necessarily Minkowski measurable. They employed so-called generalized Minkowski contents given in terms of gauge functions more general than the usual power functions. The class of valid gauge functions in their theory is characterized by some technical conditions, the geometric meaning and necessity of which is not obvious. Therefore, it is not completely clear how general the approach is and which sets $\Omega$ are covered. Here we revisit these results and put them in the context of regularly varying functions. Using Karamata theory, it is possible to get rid of most of the technical conditions and simplify the proofs given by He and Lapidus, revealing thus even more of the beauty of their results. Further simplifications arise from characterization results for Minkowski contents obtained in \cite{RW13}. We hope our new point of view on these spectral problems will initiate some further investigations of this beautiful theory.
A Study on the Extraction and Analysis of a Large Set of Eye Movement Features during Reading

This work presents a study on the extraction and analysis of a set of 101 categories of eye movement features from three types of eye movement events: fixations, saccades, and post-saccadic oscillations. The eye movements were recorded during a reading task. For the categories of features with multiple instances in a recording we extract corresponding feature subtypes by calculating descriptive statistics on the distributions of these instances. A unified framework of detailed descriptions and mathematical formulas are provided for the extraction of the feature set. The analysis of feature values is performed using a large database of eye movement recordings from a normative population of 298 subjects. We demonstrate the central tendency and overall variability of feature values over the experimental population, and more importantly, we quantify the test-retest reliability (repeatability) of each separate feature. The described methods and analysis can provide valuable tools in fields exploring the eye movements, such as in behavioral studies, attention and cognition research, medical research, biometric recognition, and human-computer interaction.
Transfer Learning for Music Classification and Regression Tasks

In this paper, we present a transfer learning approach for music classification and regression tasks. We propose to use a pre-trained convnet feature, a concatenated feature vector using the activations of feature maps of multiple layers in a trained convolutional network. We show how this convnet feature can serve as general-purpose music representation. In the experiments, a convnet is trained for music tagging and then transferred to other music-related classification and regression tasks. The convnet feature outperforms the baseline MFCC feature in all the considered tasks and several previous approaches that are aggregating MFCCs as well as low- and high-level music features.
Introduction To The Monogenic Signal

The monogenic signal is an image analysis methodology that was introduced by Felsberg and Sommer in 2001 and has been employed for a variety of purposes in image processing and computer vision research. In particular, it has been found to be useful in the analysis of ultrasound imagery in several research scenarios mostly in work done within the BioMedIA lab at Oxford. However, the literature on the monogenic signal can be difficult to penetrate due to the lack of a single resource to explain the various principles from basics. The purpose of this document is therefore to introduce the principles, purpose, applications, and limitations of the methodology. It assumes some background knowledge from the fields of image and signal processing, in particular a good knowledge of Fourier transforms as applied to signals and images. We will not attempt to provide a thorough math- ematical description or derivation of the monogenic signal, but rather focus on developing an intuition for understanding and using the methodology and refer the reader elsewhere for a more mathematical treatment.
Deep Poincare Map For Robust Medical Image Segmentation

Precise segmentation is a prerequisite for an accurate quantification of the imaged objects. It is a very challenging task in many medical imaging applications due to relatively poor image quality and data scarcity. In this work, we present an innovative segmentation paradigm, named Deep Poincare Map (DPM), by coupling the dynamical system theory with a novel deep learning based approach. Firstly, we model the image segmentation process as a dynamical system, in which limit cycle models the boundary of the region of interest (ROI). Secondly, instead of segmenting the ROI directly, convolutional neural network is employed to predict the vector field of the dynamical system. Finally, the boundary of the ROI is identified using the Poincare map and the flow integration. We demonstrate that our segmentation model can be built using a very limited number of train- ing data. By cross-validation, we can achieve a mean Dice score of 94% compared to the manual delineation (ground truth) of the left ventricle ROI defined by clinical experts on a cardiac MRI dataset. Compared with other state-of-the-art methods, we can conclude that the proposed DPM method is adaptive, accurate and robust. It is straightforward to apply this method for other medical imaging applications.
Locality from Quantum Gravity: All or Nothing

In a full theory of quantum gravity, local physics is expected to be approximate rather than innate. It is therefore important to understand how approximate locality emerges in the semiclassical limit. Here we show that any notion of locality emergent from a holographic theory of quantum gravity is "all or nothing": local data is not obtained gradually from subregions of the boundary, but is rather obtained all at once when enough of the boundary is accessed. Our assumptions are mild and thus this feature is quite general; in the special case of AdS/CFT, a slightly different manifestation follows from well-known and familiar properties.
Cosmic Equilibration: A Holographic No-Hair Theorem from the Generalized Second Law

In a wide class of cosmological models, a positive cosmological constant drives cosmological evolution toward an asymptotically de Sitter phase. Here we connect this behavior to the increase of entropy over time, based on the idea that de Sitter spacetime is a maximum-entropy state. We prove a cosmic no-hair theorem for Robertson-Walker and Bianchi I spacetimes that admit a Q-screen (“quantum” holographic screen) with certain entropic properties: If generalized entropy, in the sense of the cosmological version of the generalized second law conjectured by Bousso and Engelhardt, increases up to a finite maximum value along the screen, then the spacetime is asymptotically de Sitter in the future. Moreover, the limiting value of generalized entropy coincides with the de Sitter horizon entropy. We do not use the Einstein field equations in our proof, nor do we assume the existence of a positive cosmological constant. As such, asymptotic relaxation to a de Sitter phase can, in a precise sense, be thought of as cosmological equilibration.
Adversarial Source Identification Game With Corrupted Training

We study a variant of the source identification game with training data in which part of the training data is corrupted by an attacker. In the addressed scenario, the defender aims at deciding whether a test sequence has been drawn according to a discrete memoryless source <inline-formula> <tex-math notation="LaTeX">$X \sim P_{X}$ </tex-math></inline-formula>, whose statistics are known to him through the observation of a training sequence generated by <inline-formula> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula>. In order to undermine the correct decision under the alternative hypothesis that the test sequence has not been drawn from <inline-formula> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula>, the attacker can modify a sequence produced by a source <inline-formula> <tex-math notation="LaTeX">$Y \sim P_{Y}$ </tex-math></inline-formula> up to a certain distortion and corrupt the training sequence either by adding some fake samples or by replacing some samples with fake ones. We derive the unique rationalizable equilibrium of the two versions of the game in the asymptotic regime and by assuming that the defender makes his decision by relying only on the first order statistics of the test and the training sequences. By mimicking Stein’s lemma, we derive the best achievable performance for the defender when the first type error probability is required to tend to zero exponentially fast with an arbitrarily small, yet positive, error exponent. We then use such a result to analyze the ultimate distinguishability of any two sources as a function of the allowed distortion and the fraction of corrupted samples injected into the training sequence.
Discriminative Transfer Learning for General Image Restoration

Recently, several discriminative learning approaches have been proposed for effective image restoration, achieving convincing tradeoff between image quality and computational efficiency. However, these methods require separate training for each restoration task (e.g., denoising, deblurring, and demosaicing) and problem condition (e.g., noise level of input images). This makes it time-consuming and difficult to encompass all tasks and conditions during training. In this paper, we propose a discriminative transfer learning method that incorporates formal proximal optimization and discriminative learning for general image restoration. The method requires a single-pass discriminative training and allows for reuse across various problems and conditions while achieving an efficiency comparable to previous discriminative approaches. Furthermore, after being trained, our model can be easily transferred to new likelihood terms to solve untrained tasks, or be combined with existing priors to further improve image restoration quality.
Coordinated Multipoint Transmission in Downlink Multi-Cell NOMA Systems: Models and Spectral Efficiency Performance

We outline a general framework to use CoMP transmission technology in downlink multi-cell NOMA systems considering distributed power allocation at each cell. In this framework, CoMP transmission is used for users experiencing strong received signals from multiple cells, while each cell adopts NOMA for resource allocation to its active users. After a brief review of the working principles of different CoMP schemes, we investigate their applicability and necessary conditions for their use in a downlink multi-cell NOMA system. After that, we discuss various network scenarios with different spatial distributions of users and present the formula for achievable rate of users under each of the CoMP-NOMA scenarios. To this end, a numerical performance evaluation is carried out for the proposed CoMP-NOMA systems, and the results are compared with those for conventional orthogonal multiple access based CoMP systems. The numerical results quantify the spectral efficiency gain of the proposed CoMP-NOMA models over CoMP-OMA. Finally, we conclude this article by identifying the major challenges in implementing CoMP-NOMA in future cellular systems.
Redesigning OP2 Compiler to Use HPX Runtime Asynchronous Techniques

Maximizing parallelism level in applications can be achieved by minimizing overheads due to load imbalances and waiting time due to memory latencies. Compiler optimization is one of the most effective solutions to tackle this problem. The compiler is able to detect the data dependencies in an application and is able to analyze the specific sections of code for parallelization potential. However, all of these techniques provided with a compiler are usually applied at compile time, so they rely on static analysis, which is insufficient for achieving maximum parallelism and producing desired application scalability. One solution to address this challenge is the use of runtime methods. This strategy can be implemented by delaying certain amount of code analysis to be done at runtime. In this research, we improve the parallel application performance generated by the OP2 compiler by leveraging HPX, a C++ runtime system, to provide runtime optimizations. These optimizations include asynchronous tasking, loop interleaving, dynamic chunk sizing, and data prefetching. The results of the research were evaluated using an Airfoil application which showed a 40-50% improvement in parallel performance.
A precise optical transmission spectrum of the inflated exoplanet WASP-52b

We have measured a precise optical transmission spectrum for WASP-52b, a highly inflated hot Jupiter with an equilibrium temperature of 1300 K. Two transits of the planet were observed spectroscopically at low resolution with the auxiliary-port camera (ACAM) on the William Herschel Telescope (WHT), covering a wide range of 4000-8750 \AA. We use a Gaussian process approach to model the correlated noise in the multi-wavelength light curves, resulting in a high precision relative transmission spectrum with errors on the order of a pressure scale height. We attempted to fit a variety of different representative model atmospheres to the transmission spectrum, but did not find a satisfactory match to the entire spectral range. For the majority of the covered wavelength range (4000-7750 \AA) the spectrum is flat, and can be explained by an optically thick and grey cloud layer at 0.1 mbar, but this is inconsistent with a slightly deeper transit at wavelengths $> 7750$ \AA. We were not able to find an obvious systematic source for this feature, so this opacity may be the result of an additional unknown absorber.
Is the cosmological constant problem properly posed

In applications of Einstein gravity, one replaces the quantum-mechanical energy–momentum tensor of sources such as the degenerate electrons in a white dwarf or the black-body photons in the microwave background by c-number matrix elements. And not only that, one ignores the zero-point fluctuations in these sources by only retaining the normal-ordered parts of those matrix elements. There is no apparent justification for this procedure, and we show that it is precisely this procedure that leads to the cosmological constant problem. We suggest that solving the problem requires that gravity be treated just as quantum-mechanically as the sources to which it couples, and show that one can then solve the cosmological constant problem if one replaces Einstein gravity by the fully quantum-mechanically consistent conformal gravity theory.
Behavior of light polarization in photon-scalar interaction

Quantum theories of gravity help us to improve our insight into the gravitational interactions. Motivated by the interesting effect of gravity on the photon trajectory, we treat a quantum recipe concluding a classical interaction of light and a massive object such as the sun. We use the linear quantum gravity to compute the classical potential of a photon interacting with a massive scalar. The leading terms have a traditional 1/r subordinate and demonstrate a polarization-dependent behavior. This result challenges the equivalence principle; attractive and/or repulsive interactions are admissible.
Adaptive Simulation-based Training of AI Decision-makers using Bayesian Optimization

This work studies how an AI-controlled dog-fighting agent with tunable decision-making parameters can learn to optimize performance against an intelligent adversary, as measured by a stochastic objective function evaluated on simulated combat engagements. Gaussian process Bayesian optimization (GPBO) techniques are developed to automatically learn global Gaussian Process (GP) surrogate models, which provide statistical performance predictions in both explored and unexplored areas of the parameter space. This allows a learning engine to sample full-combat simulations at parameter values that are most likely to optimize performance and also provide highly informative data points for improving future predictions. However, standard GPBO methods do not provide a reliable surrogate model for the highly volatile objective functions found in aerial combat, and thus do not reliably identify global maxima. These issues are addressed by novel Repeat Sampling (RS) and Hybrid Repeat/Multi-point Sampling (HRMS) techniques. Simulation studies show that HRMS improves the accuracy of GP surrogate models, allowing AI decision-makers to more accurately predict performance and efficiently tune parameters.
Tracing the Use of Practices Through Networks of Collaboration

An active line of research has used on-line data to study the ways in which discrete units of information---including messages, photos, product recommendations, group invitations---spread through social networks. There is relatively little understanding, however, of how on-line data might help in studying the diffusion of more complex {\em practices}---roughly, routines or styles of work that are generally handed down from one person to another through collaboration or mentorship. In this work, we propose a framework together with a novel type of data analysis that seeks to study the spread of such practices by tracking their syntactic signatures in large document collections. Central to this framework is the notion of an "inheritance graph" that represents how people pass the practice on to others through collaboration. Our analysis of these inheritance graphs demonstrates that we can trace a significant number of practices over long time-spans, and we show that the structure of these graphs can help in predicting the longevity of collaborations within a field, as well as the fitness of the practices themselves.
DART: Noise Injection for Robust Imitation Learning

One approach to Imitation Learning is Behavior Cloning, in which a robot observes a supervisor and infers a control policy. A known problem with this "off-policy" approach is that the robot's errors compound when drifting away from the supervisor's demonstrations. On-policy, techniques alleviate this by iteratively collecting corrective actions for the current robot policy. However, these techniques can be tedious for human supervisors, add significant computation burden, and may visit dangerous states during training. We propose an off-policy approach that injects noise into the supervisor's policy while demonstrating. This forces the supervisor to demonstrate how to recover from errors. We propose a new algorithm, DART (Disturbances for Augmenting Robot Trajectories), that collects demonstrations with injected noise, and optimizes the noise level to approximate the error of the robot's trained policy during data collection. We compare DART with DAgger and Behavior Cloning in two domains: in simulation with an algorithmic supervisor on the MuJoCo tasks (Walker, Humanoid, Hopper, Half-Cheetah) and in physical experiments with human supervisors training a Toyota HSR robot to perform grasping in clutter. For high dimensional tasks like Humanoid, DART can be up to $3x$ faster in computation time and only decreases the supervisor's cumulative reward by $5\%$ during training, whereas DAgger executes policies that have $80\%$ less cumulative reward than the supervisor. On the grasping in clutter task, DART obtains on average a $62\%$ performance increase over Behavior Cloning.
Graph regularized tensor sparse coding for image representation

Sparse coding (SC) is a unsupervised learning scheme that has received an increasing amount of interests in recent years. However, conventional SC vectorizes the input images, which destructs the intrinsic spatial structures of the images. In this paper, we propose a novel graph regularized tensor sparse coding (GTSC) for image representation. GTSC preserves the local proximity of elementary structures in the image by adopting the newly proposed tubal-tensor representation. Simultaneously, it considers the intrinsic geometric properties by imposing graph regularization that has been successfully applied to uncover the geometric distribution for the image data. Moreover, the learned sparse representations by GTSC have better physical explanations as the key operation (i.e., circular convolution) in the tubal-tensor model preserves the shifting invariance property. Experimental results on image clustering demonstrate the effectiveness of the proposed scheme.
Secure and Privacy-Preserving Average Consensus

it underpins key functionalities of such systems ranging from distributed information fusion, decision-making, to decentralized control. In order to reach an agreement, existing average consensus algorithms require each agent to exchange explicit state information with its neighbors. This leads to the disclosure of private state information, which is undesirable in cases where privacy is of concern. In this paper, we propose a novel approach that enables secure and privacy-preserving average consensus in a decentralized architecture in the absence of any trusted third-parties. By leveraging homomorphic cryptography, our approach can guarantee consensus to the exact value in a deterministic manner. The proposed approach is light-weight in computation and communication, and applicable to time-varying interaction topology cases. A hardware implementation is presented to demonstrate the capability of our approach.
Ensembles of Deep LSTM Learners for Activity Recognition using Wearables

Recently, deep learning (DL) methods have been introduced very successfully into human activity recognition (HAR) scenarios in ubiquitous and wearable computing. Especially the prospect of overcoming the need for manual feature design combined with superior classification capabilities render deep neural networks very attractive for real-life HAR applications. Even though DL-based approaches now outperform the state-of-the-art in a number of recognition tasks, still substantial challenges remain. Most prominently, issues with real-life datasets, typically including imbalanced datasets and problematic data quality, still limit the effectiveness of activity recognition using wearables. In this paper we tackle such challenges through Ensembles of deep Long Short Term Memory (LSTM) networks. LSTM networks currently represent the state-of-the-art with superior classification performance on relevant HAR benchmark datasets. We have developed modified training procedures for LSTM networks and combine sets of diverse LSTM learners into classifier collectives. We demonstrate that Ensembles of deep LSTM learners outperform individual LSTM networks and thus push the state-of-the-art in human activity recognition using wearables. Through an extensive experimental evaluation on three standard benchmarks (Opportunity, PAMAP2, Skoda) we demonstrate the excellent recognition capabilities of our approach and its potential for real-life applications of human activity recognition.