nils-herrmann/clean_scirepeval huggingface dataset:
Using Motion and Internal Supervision in Object Recognition

In this thesis we address two related aspects of visual object recognition: the use of motion information, and the use of internal supervision, to help unsupervised learning. These two aspects are inter-related in the current study, since image motion is used for internal supervision, via the detection of spatiotemporal events of active-motion and the use of tracking. Most current work in object recognition deals with static images during both learning and recognition. In contrast, we are interested in a dynamic scene where visual processes, such as detecting motion events and tracking, contribute spatiotemporal information, which is useful for object attention, motion segmentation, 3-D understanding and object interactions. We explore the use of these sources of information in both learning and recognition processes. In the first part of the work, we demonstrate how motion can be used for adaptive detection of object-parts in dynamic environments, while automatically learning new object appearances and poses. In the second and main part of the study we develop methods for using specific types of visual motion to solve two difficult problems in unsupervised visual learning: learning to recognize hands by their appearance and by context, and learning to extract direction of gaze. We use our conclusions in this part to propose a model for several aspects of learning by human infants from their visual environment.
Simulations of the merging cluster of galaxies Cygnus A

The archetype FR-II galaxy Cygnus A lies in a moderately rich cluster about to undergo a major merger. We study the pre-merger Cygnus cluster environment using smoothed particle hydrodynamics simulations constrained by 2Ms of Chandra observations of the hot intracluster medium. The observations constrain the total gravitating mass and concentration parameter, and the simulations provide the quiescent and merger-enhanced temperature profiles of the pre- and post-merger of the cluster excluding the central active galactic nucleus (AGN). We present the first detailed model of the sub cluster north west of Cygnus A, named CygNW. We find a lower baryon fraction and higher concentration parameter for CygA than expected from known scaling relations in the literature. The model suggests the Cygnus cluster hosts a pre-merger with a progenitor mass ratio of about 1.5:1 at the virial radius. We notice that the intra cluster medium is heated as a result of the merger, but we find no evidence for a (pre-)merger shock in the interstitial region between both cluster haloes. We attribute the merger-induced heating to compression of the cluster outskirts. The smooth model obtained from our simulations is subtracted from the observed cluster state and shows residual temperature structure that is neither hydrostatic nor merger-heated cluster gas. We speculate that this residual heating may result from previous AGN activity over the last ∼100 Myr.
Learning Features of Network Structures Using Graphlets.

Networks are fundamental to the study of complex systems, ranging from social contacts, message transactions, to biological regulations and economical networks. In many realistic applications, these networks may vary over time. Modeling and analyzing such temporal properties is of additional interest as it can provide a richer characterization of relations between nodes in networks. In this paper, we explore the role of \emph{graphlets} in network classification for both static and temporal networks. Graphlets are small non-isomorphic induced subgraphs representing connected patterns in a network and their frequency can be used to assess network structures. We show that graphlet features, which are not captured by state-of-the-art methods, play a significant role in enhancing the performance of network classification. To that end, we propose two novel graphlet-based techniques, \emph{gl2vec} for network embedding, and \emph{gl-DCNN} for diffusion-convolutional neural networks. We demonstrate the efficacy and usability of \emph{gl2vec} and \emph{gl-DCNN} through extensive experiments using several real-world static and temporal networks. We find that features learned from graphlets can bring notable performance increases to state-of-the-art methods in network analysis.
Vector Image Generation by Learning Parametric Layer Decomposition

Deep image generation is becoming a tool to enhance artists and designers creativity potential. In this paper, we aim at making the generation process easier to understand and interact with. Inspired by vector graphics systems, we propose a new deep generation paradigm where the images are composed of simple layers, defined by their color and a parametric transparency mask. This presents a number of advantages compared to the commonly used convolutional network architectures. In particular, our layered decomposition allows simple user interaction, for example to update a given mask, or change the color of a selected layer. From a compact code, our architecture also generates images with a virtually infinite resolution, the color at each point in an image being a parametric function of its coordinates. We validate the viability of our approach in the auto-encoding framework by comparing reconstructions with state-of-the-art baselines given similar memory resources on CIFAR10, CelebA and ImageNet datasets and demonstrate several applications. We also show Generative Adversarial Network (GAN) results qualitatively different from the ones obtained with common approaches.
Viability of bigravity cosmology

We revisit the question of viability of bigravity cosmology as a candidate for dark energy. In the context of the low energy limit model, where matter couples to a single metric, we study linear perturbations around homogeneous and isotropic backgrounds to derive the Poisson's equation for the Newtonian potential. Extending to second order perturbations, we identify the Vainshtein radius below which nonlinear scalar self-interactions conspire to reproduce general relativity on local scales. We combine all of these results to determine the parameter space that allows a late-time de Sitter attractor compatible with observations and a successful Vainshtein mechanism. We find that the requirement of having a successful Vainshtein mechanism is not compatible with the existence of cosmological solutions at early times.
Bayesian Spectral Deconvolution Based on Poisson Distribution: Bayesian Measurement and Virtual Measurement Analytics (VMA)

Kenji Nagata1,2,3, Yoh-ichi Mototake4, Rei Muraoka4, Takehiko Sasaki4, and Masato Okada3,4 National Institute of Advanced Industrial Science and Technology, Tsukuba, Ibaraki 305-0045, Japan1 Japan Science and Technology Agency, PRESTO, Kawaguchi, Saitama 332-0012, Japan2 Research and Services  Tsukuba, Ibaraki, 305-0047,  The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8561,
Impact of Data Normalization on Deep Neural Network for Time Series Forecasting

For the last few years it has been observed that the Deep Neural Networks (DNNs) has achieved an excellent success in image classification, speech recognition. But DNNs are suffer great deal of challenges for time series forecasting because most of the time series data are nonlinear in nature and highly dynamic in behaviour. The time series forecasting has a great impact on our socio-economic environment. Hence, to deal with these challenges its need to be redefined the DNN model and keeping this in mind, data pre-processing, network architecture and network parameters are need to be consider before feeding the data into DNN models. Data normalization is the basic data pre-processing technique form which learning is to be done. The effectiveness of time series forecasting is heavily depend on the data normalization technique. In this paper, different normalization methods are used on time series data before feeding the data into the DNN model and we try to find out the impact of each normalization technique on DNN to forecast the time series. Here the Deep Recurrent Neural Network (DRNN) is used to predict the closing index of Bombay Stock Exchange (BSE) and New York Stock Exchange (NYSE) by using BSE and NYSE time series data.
High dimensional inference for the structural health monitoring of lock gates

Locks and dams are critical pieces of inland waterways. However, many components of existing locks have been in operation past their designed lifetime. To ensure safe and cost effective operations, it is therefore important to monitor the structural health of locks. To support lock gate monitoring, this work considers a high dimensional Bayesian inference problem that combines noisy real time strain observations with a detailed finite element model. To solve this problem, we develop a new technique that combines Karhunen-Lo\`eve decompositions, stochastic differential equation representations of Gaussian processes, and Kalman smoothing that scales linearly with the number of observations and could be used for near real-time monitoring. We use quasi-periodic Gaussian processes to model thermal influences on the strain and infer spatially distributed boundary conditions in the model, which are also characterized with Gaussian process prior distributions. The power of this approach is demonstrated on a small synthetic example and then with real observations of Mississippi River Lock 27, which is located near St. Louis, MO USA. The results show that our approach is able to probabilistically characterize the posterior distribution over nearly 1.4 million parameters in under an hour on a standard desktop computer.
Stochastic Image Deformation in Frequency Domain and Parameter Estimation using Moment Evolutions

Modelling deformation of anatomical objects observed in medical images can help describe disease progression patterns and variations in anatomy across populations. We apply a stochastic generalisation of the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework to model differences in the evolution of anatomical objects detected in populations of image data. The computational challenges that are prevalent even in the deterministic LDDMM setting are handled by extending the FLASH LDDMM representation to the stochastic setting keeping a finite discretisation of the infinite dimensional space of image deformations. In this computationally efficient setting, we perform estimation to infer parameters for noise correlations and local variability in datasets of images. Fundamental for the optimisation procedure is using the finite dimensional Fourier representation to derive approximations of the evolution of moments for the stochastic warps. Particularly, the first moment allows us to infer deformation mean trajectories. The second moment encodes variation around the mean, and thus provides information on the noise correlation. We show on simulated datasets of 2D MR brain images that the estimation algorithm can successfully recover parameters of the stochastic model.
Expansions of the Real Field by Canonical Products

We consider expansions of o-minimal structures on the real field by collections of restrictions to the positive real line of the canonical Weierstrass products associated with sequences such as $(-n^{s})_{n>0}$ (for $s>0$) and $(-s^{n})_{n>0}$ (for $s>1$), and also expansions by associated functions such as logarithmic derivatives. There are only three possible outcomes known so far: (i) the expansion is o-minimal (that is, definable sets have only finitely many connected components); (ii) every Borel subset of each $\mathbb{R}^{n}$ is definable; (iii) the expansion is interdefinable with a structure of the form $(\mathfrak{R}^{\prime },\unicode[STIX]{x1D6FC}^{\mathbb{Z}})$ where $\unicode[STIX]{x1D6FC}>1$, $\unicode[STIX]{x1D6FC}^{\mathbb{Z}}$ is the set of all integer powers of $\unicode[STIX]{x1D6FC}$, and $\mathfrak{R}^{\prime }$ is o-minimal and defines no irrational power functions.
Geoneutrinos in large direct detection experiments

Geoneutrinos can provide a unique insight into Earth's interior, its central engine and its formation history. We study the detection of geoneutrinos in large direct detection experiments, which has been considered non-feasible. We compute the geoneutrino-induced electron and nuclear recoil spectra in different materials, under several optimistic assumptions. We identify germanium as the most promising target element due to the low nuclear recoil energy threshold that could be achieved. The minimum exposure required for detection would be $\mathcal{O}(10)$ tonne-years. The realistic low thresholds achievable in germanium and silicon permit the detection of $^{40}$K geoneutrinos. These are particularly important to determine Earth's formation history but they are below the kinematic threshold of inverse beta decay, the detection process used in scintillator-based experiments.
Revisiting Exploration-Conscious Reinforcement Learning

The objective of Reinforcement Learning is to learn an optimal policy by performing actions and observing their long term consequences. Unfortunately, acquiring such a policy can be a hard task. More severely, since one cannot tell if a policy is optimal, there is a constant need for exploration. This is known as the Exploration-Exploitation trade-off. In practice, this trade-off is resolved by using some inherent exploration mechanism, such as the $\epsilon$-greedy exploration, while still trying to learn the optimal policy. In this work, we take a different approach. We define a surrogate optimality objective: an optimal policy with respect to the exploration scheme. As we show throughout the paper, although solving this criterion does not necessarily lead to an optimal policy, the problem becomes easier to solve. We continue by analyzing this notion of optimality, devise algorithms derived from this approach, which reveal connections to existing work, and test them empirically on tabular and deep Reinforcement Learning domains.
OPTIMAL DESIGNS FOR SERIES ESTIMATION IN NONPARAMETRIC REGRESSION WITH CORRELATED DATA

In this paper we investigate the problem of designing experiments for series estimators in nonparametric regression models with correlated observations. We use projection based estimators to derive an explicit solution of the best linear oracle estimator in the continuous time model for all Markovian-type error processes. These solutions are then used to construct estimators, which can be calculated from the available data along with their corresponding optimal design points. Our results are illustrated by means of a simulation study, which demonstrates that the new series estimator has a better performance than the commonly used techniques based on the optimal linear unbiased estimators. Moreover, we show that the performance of the estimators proposed in this paper can be further improved by choosing the design points appropriately.
IDENTITIES OF THE ROGERS-RAMANUJAN-SLATER TYPE

It is shown that (two-variable generalizations of) more than half of Slater's list of 130 Rogers–Ramanujan identities (L. J. Slater, Further identities of the Rogers–Ramanujan type,  (2)54 (1952) 147–167) can be easily derived using just three multiparameter Bailey pairs and their associated q-difference equations. As a bonus, new Rogers–Ramanujan type identities are found along with natural combinatorial interpretations for many of these identities.
Splashback radius in symmetron gravity

The splashback radius rsp has been identified in cosmological N-body simulations as an important scale associated with gravitational collapse and the phase-space distribution of recently accreted material. We employ a semianalytical approach to study the spherical collapse of dark matter halos in symmetron gravity and provide, for the first time, insights into how the phenomenology of splashback is affected by modified gravity. The symmetron is a scalar-tensor theory which exhibits a screening mechanism whereby higher-density regions are screened from the effects of a fifth force. In this model, we find that, as overdensities grow over cosmic time, the inner region becomes heavily screened. In particular, we identify a sector of the parameter space for which material currently sitting at rsp has followed, during the collapse, the formation of this screened region. As a result, we find that for this part of the parameter space the splashback radius is maximally affected by the symmetron force, and we predict changes in rsp up to around 10% compared to its General Relativity value. Because this margin is within the precision of present splashback experiments, we expect this feature to soon provide constraints for symmetron gravity on previously unexplored scales.
Theory of Connections Applied to Support Vector Machines to Solve Differential Equations

Differential equations are used as numerical models to describe physical phenomena throughout the field of engineering and science, including heat and fluid flow, structural bending, and systems dynamics. Although there are many other techniques for finding approximate solutions to these equations, this paper looks to compare the application of the Theory of Connections (ToC) with one based on Support Vector Machines (SVM). The ToC method uses a constrained expression (an expression that always satisfies the differential equation constraints), which transforms the process of solving a differential equation into an unconstrained problem, and is ultimately solved via least-squares. In addition to individual analysis, the two methods are merged into a new methodology, called constrained SMVs (CSVM), by incorporating the SVM method into the ToC framework to solve unconstrained problems. Numerical tests are conducted on three sample problems: one first order linear ODEs, one first order non-linear ODE, and one second order linear ODE. Using the SVM method as a benchmark, a speed comparison is made for all the problems by timing the training period, and an accuracy comparison is made using the maximum error and mean-squared error on the training and test sets. In general, ToC is shown to be slightly faster (by an order of magnitude or less) and more accurate (by multiple orders of magnitude) over the SVM and CSVM approaches.
Lower Assouad Dimension of Measures and Regularity

In analogy with the lower Assouad dimensions of a set, we study the lower Assouad dimensions of a measure. As with the upper Assouad dimensions, the lower Assouad dimensions of a measure provide information about the extreme local behaviour of the measure. We study the connection with other dimensions and with regularity properties. In particular, the quasi-lower Assouad dimension is dominated by the infimum of the measure’s lower local dimensions. Although strict inequality is possible in general, equality holds for the class of self-similar measures of finite type. This class includes all self-similar, equicontractive measures satisfying the open set condition, as well as certain “overlapping” self-similar measures, such as Bernoulli convolutions with contraction factors that are inverses of Pisot numbers. We give lower bounds for the lower Assouad dimension for measures arising from a Moran construction, prove that self-affine measures are uniformly perfect and have positive lower Assouad dimension, prove that the Assouad spectrum of a measure converges to its quasi-Assouad dimension and show that coincidence of the upper and lower Assouad dimension of a measure does not imply that the measure is s-regular.
Benchmark Dataset for Automatic Damaged Building Detection from Post-Hurricane Remotely Sensed Imagery

Rapid damage assessment is of crucial importance to emergency responders during hurricane events, however, the evaluation process is often slow, labor-intensive, costly, and error-prone. New advances in computer vision and remote sensing open possibilities to observe the Earth at a different scale. However, substantial pre-processing work is still required in order to apply state-of-the-art methodology for emergency response. To enable the comparison of methods for automatic detection of damaged buildings from post-hurricane remote sensing imagery taken from both airborne and satellite sensors, this paper presents the development of benchmark datasets from publicly available data. The major contributions of this work include (1) a scalable framework for creating benchmark datasets of hurricane-damaged buildings and (2) public sharing of the resulting benchmark datasets for Greater Houston area after Hurricane Harvey in 2017. The proposed approach can be used to build other hurricane-damaged building datasets on which researchers can train and test object detection models to automatically identify damaged buildings.
Pied Piper: Rethinking Internet Data Delivery

We contend that, analogously to the transition from resource-limited on-prem computing to resource-abundant cloud computing, Internet data delivery should also be adapted to a reality in which the cloud offers a virtually unlimited resource, i.e., network capacity, and virtualization enables delegating local tasks, such as routing and congestion control, to the cloud. This necessitates rethinking the traditional roles of inter- and intra-domain routing and conventional end-to-end congestion control. 
We introduce Optimized Cloudified Delivery (OCD), a holistic approach for optimizing joint Internet/cloud data delivery, and evaluate OCD through hundreds of thousands of file downloads from multiple locations. We start by examining an OCD baseline approach: traffic from a source A to a destination B successively passes through two cloud virtual machines operating as relays - nearest to A and B; and the two cloud relays employ TCP split. 
We show that even this naive strategy can outperform recently proposed improved end-to-end congestion control paradigms (BBR and PCC) by an order of magnitude. 
Next, we present a protocol-free, ideal pipe model of data transmission, and identify where today's Internet data delivery mechanisms diverge from this model. We then design and implement OCD Pied Piper. Pied Piper leverages various techniques, including novel kernel-based transport-layer accelerations, to improve the Internet-Cloud interface so as to approximately match the ideal network pipe model.
Scene Recomposition by Learning-Based ICP

By moving a depth sensor around a room, we compute a 3D CAD model of the environment, capturing the room shape and contents such as chairs, desks, sofas, and tables. Rather than reconstructing geometry, we match, place, and align each object in the scene to thousands of CAD models of objects. In addition to the fully automatic system, the key technical contribution is a novel approach for aligning CAD models to 3D scans, based on deep reinforcement learning. This approach, which we call Learning-based ICP, outperforms prior ICP methods in the literature, by learning the best points to match and conditioning on object viewpoint. LICP learns to align using only synthetic data and does not require ground truth annotation of object pose or keypoint pair matching in real scene scans. While LICP is trained on synthetic data and without 3D real scene annotations, it outperforms both learned local deep feature matching and geometric based alignment methods in real scenes. The proposed method is evaluated on real scenes datasets of SceneNN and ScanNet as well as synthetic scenes of SUNCG. High quality results are demonstrated on a range of real world scenes, with robustness to clutter, viewpoint, and occlusion.