nils-herrmann/clean_scirepeval huggingface dataset:
Stacking models for nearly optimal link prediction in complex networks

Networks are a powerful tool for modeling complex biological and social systems. However, most networks are incomplete, and missing connections can negatively affect scientific analyses. Today, many algorithms can predict missing connections, but it is unknown how accuracy varies across algorithms and networks and whether link predictability varies across scientific domains. Analyzing 203 link prediction algorithms applied to 550 diverse real-world networks, we show that no predictor is best or worst overall. We then combine these many predictors into a single state-of-the-art algorithm that achieves nearly optimal performance on both synthetic networks with known optimality and real-world networks. Not all networks are equally predictable, however, and we find that social networks are easiest, while biological and technological networks are hardest. Most real-world networks are incompletely observed. Algorithms that can accurately predict which links are missing can dramatically speed up network data collection and improve network model validation. Many algorithms now exist for predicting missing links, given a partially observed network, but it has remained unknown whether a single best predictor exists, how link predictability varies across methods and networks from different domains, and how close to optimality current methods are. We answer these questions by systematically evaluating 203 individual link predictor algorithms, representing three popular families of methods, applied to a large corpus of 550 structurally diverse networks from six scientific domains. We first show that individual algorithms exhibit a broad diversity of prediction errors, such that no one predictor or family is best, or worst, across all realistic inputs. We then exploit this diversity using network-based metalearning to construct a series of “stacked” models that combine predictors into a single algorithm. Applied to a broad range of synthetic networks, for which we may analytically calculate optimal performance, these stacked models achieve optimal or nearly optimal levels of accuracy. Applied to real-world networks, stacked models are superior, but their accuracy varies strongly by domain, suggesting that link prediction may be fundamentally easier in social networks than in biological or technological networks. These results indicate that the state of the art for link prediction comes from combining individual algorithms, which can achieve nearly optimal predictions. We close with a brief discussion of limitations and opportunities for further improvements.
Radiopathomics: Integration of radiographic and histologic characteristics for prognostication in glioblastoma

Large number of diverse imaging [e.g., multi-parametric MRI (mpMRI), and digital pathology images] and non-imaging (e.g., clinical) biomedical data streams are being routinely acquired as part of the standard clinical workflow for glioblastoma patients. However, under the current clinical practice, these data streams are not collectively used for diagnosis. We sought to assess the synergies between pathologic, and radiomic features by evaluating the predictive value of each group of features and their combinations through a prognostic classifier.
 
 
 
 The mpMRI (T1,T1-Gd,T2,T2-FLAIR) and corresponding digital pathology images for 135 de novo glioblastoma was acquired from TCIA. An extensive panel of handcrafted features, including shape, volume, intensity distributions, gray-level co-occurrence matrix based texture, was extracted from delineated tumor regions of mpMRI scans. A set of 100 region-of-interest each comprising 1024x1024 that contained viable tumor with descriptive histologic characteristics and that were free of artifacts were extracted from digital pathology images, and were quantified in terms of nuclear texture features, and nuclear intensity and gradient statistics. A support vector regression multivariately integrated these features towards a marker of overall-survival. The accuracy of the predictive model for each group of features, and their combinations, was determined via a 10-fold cross-validation scheme.
 
 
 
 The Pearson correlation coefficient between the survival scores predicted by SVR and the actual survival scores was estimated to be 0.75 and 0.77 for radiographic and pathologic data, however, the integration of these data yielded a clear improvement in correlation (0.81), supporting the synergistic value of these features in the prognostic model.
 
 
 
 Radiomic features extracted from preoperative mpMRI, when used together with digital pathology features, offer synergistic value in assessment of prognosis in individual patients. The proposed radiopathomics marker may contribute to (i) stratification of patients into clinical trials, (ii) patient selection for targeted therapy, and (iii) personalized treatment planning.
Communication-Efficient Distributed Learning via Lazily Aggregated Quantized Gradients

The present paper develops a novel aggregated gradient approach for distributed machine learning that adaptively compresses the gradient communication. The key idea is to first quantize the computed gradients, and then skip less informative quantized gradient communications by reusing outdated gradients. Quantizing and skipping result in 'lazy' worker-server communications, which justifies the term Lazily Aggregated Quantized gradient that is henceforth abbreviated as LAQ. Our LAQ can provably attain the same linear convergence rate as the gradient descent in the strongly convex case, while effecting major savings in the communication overhead both in transmitted bits as well as in communication rounds. Empirically, experiments with real data corroborate a significant communication reduction compared to existing gradient- and stochastic gradient-based algorithms.
Real-time Multi-target Path Prediction and Planning for Autonomous Driving aided by FCN

Real-time multi-target path planning is a key issue in the field of autonomous driving. Although multiple paths can be generated in real-time with polynomial curves, the generated paths are not flexible enough to deal with complex road scenes such as S-shaped road and unstructured scenes such as parking lots. Search and sampling-based methods, such as A* and RRT and their derived methods, are flexible in generating paths for these complex road environments. However, the existing algorithms require significant time to plan to multiple targets, which greatly limits their application in autonomous driving. In this paper, a real-time path planning method for multi-targets is proposed. We train a fully convolutional neural network (FCN) to predict a path region for the target at first. By taking the predicted path region as soft constraints, the A* algorithm is then applied to search the exact path to the target. Experiments show that FCN can make multiple predictions in a very short time (50 times in 40ms), and the predicted path region effectively restrict the searching space for the following A* search. Therefore, the A* can search much faster so that the multi-target path planning can be achieved in real-time (3 targets in less than 100ms).
Conformal Prediction based Spectral Clustering

Spectral Clustering(SC) is a prominent data clustering technique of recent times which has attracted much attention from researchers. It is a highly data-driven method and makes no strict assumptions on the structure of the data to be clustered. One of the central pieces of spectral clustering is the construction of an affinity matrix based on a similarity measure between data points. The way the similarity measure is defined between data points has a direct impact on the performance of the SC technique. Several attempts have been made in the direction of strengthening the pairwise similarity measure to enhance the spectral clustering. In this work, we have defined a novel affinity measure by employing the concept of non-conformity used in Conformal Prediction(CP) framework. The non-conformity based affinity captures the relationship between neighborhoods of data points and has the power to generalize the notion of contextual similarity. We have shown that this formulation of affinity measure gives good results and compares well with the state of the art methods.
A Convergence Proof of Projected Fast Iterative Soft-thresholding Algorithm for Parallel Magnetic Resonance Imaging

The boom of non-uniform sampling and compressed sensing techniques dramatically alleviates the prolonged data acquisition problem of magnetic resonance imaging. Sparse reconstruction, thanks to its fast computation and promising performance, has attracted researchers to put numerous efforts on it and has been adopted in commercial scanners. Algorithms for solving the sparse reconstruction models play an essential role in sparse reconstruction. Being a simple and efficient algorithm for sparse reconstruction, pFISTA has been successfully extended to parallel imaging, however, its convergence criterion is still an open question, confusing users on the setting of the parameter which assures the convergence of the algorithm. In this work, we prove the convergence of the parallel imaging version pFISTA. Specifically, the convergences of two well-known parallel imaging reconstruction models, SENSE and SPIRiT, solved by pFISTA are proved. Experiments on brain images demonstrate the validity of the convergence criterion. The convergence criterion proofed in this work can help users quickly obtain the satisfy parameter that admits faithful results and fast convergence speeds.
Deep End-to-End Alignment and Refinement for Time-of-Flight RGB-D Module

Recently, it is increasingly popular to equip mobile RGB cameras with Time-of-Flight (ToF) sensors for active depth sensing. However, for off-the-shelf ToF sensors, one must tackle two problems in order to obtain high-quality depth with respect to the RGB camera, namely 1) online calibration and alignment; and 2) complicated error correction for ToF depth sensing. In this work, we propose a framework for jointly alignment and refinement via deep learning. First, a cross-modal optical flow between the RGB image and the ToF amplitude image is estimated for alignment. The aligned depth is then refined via an improved kernel predicting network that performs kernel normalization and applies the bias prior to the dynamic convolution. To enrich our data for end-to-end training, we have also synthesized a dataset using tools from computer graphics. Experimental results demonstrate the effectiveness of our approach, achieving state-of-the-art for ToF refinement.
Refined α-Divergence Variational Inference via Rejection Sampling

We present an approximate inference method, based on a synergistic combination of Rényi αdivergence variational inference (RDVI) and rejection sampling (RS). RDVI is based on minimization of Rényi α-divergence Dα(p||q) between the true distribution p(x) and a variational approximation q(x); RS draws samples from a distribution p(x) = p̃(x)/Zp using a proposal q(x), s.t. Mq(x) ≥ p̃(x),∀x. Our inference method is based on a crucial observation that D∞(p||q) equals logM(θ) where M(θ) is the optimal value of the RS constant for a given proposal qθ(x). This enables us to develop a two-stage hybrid inference algorithm.
Communication-Efficient Weighted Sampling and Quantile Summary for GBDT

Gradient boosting decision tree (GBDT) is a powerful and widely-used machine learning model, which has achieved state-of-the-art performance in many academic areas and production environment. However, communication overhead is the main bottleneck in distributed training which can handle the massive data nowadays. In this paper, we propose two novel communication-efficient methods over distributed dataset to mitigate this problem, a weighted sampling approach by which we can estimate the information gain over a small subset efficiently, and distributed protocols for weighted quantile problem used in approximate tree learning.
Realizing a scalable building block of a U(1) gauge theory with cold atomic mixtures

In the fundamental laws of physics, gauge fields mediate the interaction between charged particles. An example is quantum electrodynamics -- the theory of electrons interacting with the electromagnetic field -- based on U(1) gauge symmetry. Solving such gauge theories is in general a hard problem for classical computational techniques. While quantum computers suggest a way forward, it is difficult to build large-scale digital quantum devices required for complex simulations. Here, we propose a fully scalable analog quantum simulator of a U(1) gauge theory in one spatial dimension. To engineer the local gauge symmetry, we employ inter-species spin-changing collisions in an atomic mixture. We demonstrate the experimental realization of the elementary building block as a key step towards a platform for large-scale quantum simulations of continuous gauge theories.
Proceedings 35th International Conference on Logic Programming (Technical Communications), ICLP 2019 Technical Communications, Las Cruces, NM, USA, September 20-25, 2019

Since the first conference held in Marseille in 1982, ICLP has been the premier international event for presenting research in logic programming. Contributions are sought in all areas of logic programming, including but not restricted to: 
 Semantics, Formalisms, Nonmonotonic reasoning, Knowledge representation. 
Languages: Concurrency, Objects, Coordination, Mobility, Higher Order, Types, Modes, Assertions, Modules, Meta-programming, Logic-based domain-specific languages, Programming Techniques. 
Declarative programming: Declarative program development, Analysis, Type and mode inference, Partial evaluation, Abstract interpretation, Transformation, Validation, Verification, Debugging, Profiling, Testing, Execution visualization 
Implementation: Virtual machines, Compilation, Memory management, Parallel/distributed execution, Constraint handling rules, Tabling, Foreign interfaces, User interfaces. 
Related Paradigms and Synergies: Inductive and Co-inductive Logic Programming, Constraint Logic Programming, Answer Set Programming, Interaction with SAT, SMT and CSP solvers, Logic programming techniques for type inference and theorem proving, Argumentation, Probabilistic Logic Programming, Relations to object-oriented and Functional programming. 
Applications: Databases, Big Data, Data integration and federation, Software engineering, Natural language processing, Web and Semantic Web, Agents, Artificial intelligence, Computational life sciences, Education, Cybersecurity, and Robotics.
Social Network Analysis of Corruption Structures: Adjacency Matrices Supporting the Visualization and Quantification of Layeredness

Often, corruption is described as taking place within or supported by a network: A collection of individuals structured in such a way as to enable the transaction of bribes for favors. Surprisingly, despite the network nomenclature, corruption is rarely analyzed from the network perspective using the tools of network science. Here, we will argue that analyzing corruption from the perspective of network science is beneficial to its understanding. In passing this chapter, a contribution to the Liber Amicorum in honor of Leo Huberts, then gives a very short introduction into social network analysis.
Efficient Transfer Bayesian Optimization with Auxiliary Information

We propose an efficient transfer Bayesian optimization method, which finds the maximum of an expensive-to-evaluate black-box function by using data on related optimization tasks. Our method uses auxiliary information that represents the task characteristics to effectively transfer knowledge for estimating a distribution over target functions. In particular, we use a Gaussian process, in which the mean and covariance functions are modeled with neural networks that simultaneously take both the auxiliary information and feature vectors as input. With a neural network mean function, we can estimate the target function even without evaluations. By using the neural network covariance function, we can extract nonlinear correlation among feature vectors that are shared across related tasks. Our Gaussian process-based formulation not only enables an analytic calculation of the posterior distribution but also swiftly adapts the target function to observations. Our method is also advantageous because the computational costs scale linearly with the number of source tasks. Through experiments using a synthetic dataset and datasets for finding the optimal pedestrian traffic regulations and optimal machine learning algorithms, we demonstrate that our method identifies the optimal points with fewer target function evaluations than existing methods.
Spatio-Semantic ConvNet-Based Visual Place Recognition

We present a Visual Place Recognition system that follows the two-stage format common to image retrieval pipelines. The system encodes images of places by employing the activations of different layers of a pre-trained, off-the-shelf, VGG16 Convolutional Neural Network (CNN) architecture. In the first stage of our method and given a query image of a place, a number of top candidate images is retrieved from a previously stored database of places. In the second stage, we propose an exhaustive comparison of the query image against these candidates by encoding semantic and spatial information in the form of CNN features. Results from our approach outperform by a large margin state-of-the-art visual place recognition methods on five of the most commonly used benchmark datasets. The performance gain is especially remarkable on the most challenging datasets, with more than a twofold recognition improvement with respect to the latest published work.
Characterizing and Predicting Repeat Food Consumption Behavior for Just-in-Time Interventions

Human beings are creatures of habit. In their daily life, people tend to repeatedly consume similar types of food items over several days and occasionally switch to consuming different types of items when the consumptions become overly monotonous. However, the novel and repeat consumption behaviors have not been studied in food recommendation research. More importantly, the ability to predict daily eating habits of individuals is crucial to improve the effectiveness of food recommender systems in facilitating healthy lifestyle change. In this study, we analyze the patterns of repeat food consumptions using large-scale consumption data from a popular online fitness community called MyFitnessPal (MFP), conduct an offline evaluation of various state-of-the-art algorithms in predicting the next-day food consumption, and analyze their performance across different demographic groups and contexts. The experiment results show that algorithms incorporating the exploration-and-exploitation and temporal dynamics are more effective in the next-day recommendation task than most state-of-the-art algorithms.
Learning to Find Hydrological Corrections

High resolution Digital Elevation models, such as the grid terrain model of Denmark with more than 200 billion measurements, is a basic requirement for water flow modelling and flood risk analysis. However, a large number of modifications often need to be made to even very accurate terrain models, before they can be used in realistic flow modeling. This include removal of bridges, which otherwise act as dams in flow modeling, and inclusion of culverts that transport water underneath roads. For this reason, there is list of known hydrological corrections for the danish model. However, producing this list is a slow an expensive process, since it is to a large extent done manually, often with only local input. In this paper we propose a new algorithmic approach based on machine learning and convolutional neural networks for automatically detecting hydrological corrections on large terrain data. Our model is able to detect most known hydrological corrections and quite a few more that should have been included in the original list.
Multi-Task Learning for Automotive Foggy Scene Understanding via Domain Adaptation to an Illumination-Invariant Representation

Joint scene understanding and segmentation for automotive applications is a challenging problem in two key aspects:- (1) classifying every pixel in the entire scene and (2) performing this task under unstable weather and illumination changes (e.g. foggy weather), which results in poor outdoor scene visibility. This poor outdoor scene visibility leads to a non-optimal performance of deep convolutional neural network-based scene understanding and segmentation. In this paper, we propose an efficient end-to-end contemporary automotive semantic scene understanding approach under foggy weather conditions, employing domain adaptation and illumination-invariant image per-transformation. As a multi-task pipeline, our proposed model provides:- (1) transferring images from extreme to clear-weather condition using domain transfer approach and (2) semantically segmenting a scene using a competitive encoder-decoder convolutional neural network (CNN) with dense connectivity, skip connections and fusion-based techniques. We evaluate our approach on challenging foggy datasets, including synthetic dataset (Foggy Cityscapes) as well as real-world datasets (Foggy Zurich and Foggy Driving). By incorporating RGB, depth, and illumination-invariant information, our approach outperforms the state-of-the-art within automotive scene understanding, under foggy weather condition.
A Review of Tracking, Prediction and Decision Making Methods for Autonomous Driving

This literature review focuses on three important aspects of an autonomous car system: tracking (assessing the identity of the actors such as cars, pedestrians or obstacles in a sequence of observations), prediction (predicting the future motion of surrounding vehicles in order to navigate through various traffic scenarios) and decision making (analyzing the available actions of the ego car and their consequences to the entire driving context). For tracking and prediction, approaches based on (deep) neural networks and other, especially stochastic techniques, are reported. For decision making, deep reinforcement learning algorithms are presented, together with methods used to explore different alternative actions, such as Monte Carlo Tree Search.
An Image Based Visual Servo Approach with Deep Learning for Robotic Manipulation

Aiming at the difficulty of extracting image features and estimating the Jacobian matrix in image based visual servo, this paper proposes an image based visual servo approach with deep learning. With the powerful learning capabilities of convolutional neural networks(CNN), autonomous learning to extract features from images and fitting the nonlinear relationships from image space to task space is achieved, which can greatly facilitate the image based visual servo procedure. Based on the above ideas a two-stream network based on convolutional neural network is designed and the corresponding control scheme is proposed to realize the four degrees of freedom visual servo of the robot manipulator. Collecting images of observed target under different pose parameters of the manipulator as training samples for CNN, the trained network can be used to estimate the nonlinear relationship from 2D image space to 3D Cartesian space. The two-stream network takes the current image and the desirable image as inputs and makes them equal to guide the manipulator to the desirable pose. The effectiveness of the approach is verified with experimental results.