nils-herrmann/clean_scirepeval huggingface dataset:
Deep convolutional activation features for large scale Brain Tumor histopathology image classification and segmentation

We propose a simple, efficient and effective method using deep convolutional activation features (CNNs) to achieve stat- of-the-art classification and segmentation for the MICCAI 2014 Brain Tumor Digital Pathology Challenge. Common traits of such medical image challenges are characterized by large image dimensions (up to the gigabyte size of an image), a limited amount of training data, and significant clinical feature representations. To tackle these challenges, we transfer the features extracted from CNNs trained with a very large general image database to the medical image challenge. In this paper, we used CNN activations trained by ImageNet to extract features (4096 neurons, 13.3% active). In addition, feature selection, feature pooling, and data augmentation are used in our work. Our system obtained 97.5% accuracy on classification and 84% accuracy on segmentation, demonstrating a significant performance gain over other participating teams.
Approximate querying of RDF graphs via path alignment

A query over RDF data is usually expressed in terms of matching between a graph representing the target and a huge graph representing the source. Unfortunately, graph matching is typically performed in terms of subgraph isomorphism, which makes semantic data querying a hard problem. In this paper we illustrate a novel technique for querying RDF data in which the answers are built by combining paths of the underlying data graph that align with paths specified by the query. The approach is approximate and generates the combinations of the paths that best align with the query. We show that, in this way, the complexity of the overall process is significantly reduced and verify experimentally that our framework exhibits an excellent behavior with respect to other approaches in terms of both efficiency and effectiveness.
STARDATA: A StarCraft AI Research Dataset

We release a dataset of 65646 StarCraft replays that contains 1535 million frames and 496 million player actions. We provide full game state data along with the original replays that can be viewed in StarCraft. The game state data was recorded every 3 frames which ensures suitability for a wide variety of machine learning tasks such as strategy classification, inverse reinforcement learning, imitation learning, forward modeling, partial information extraction, and others. We use TorchCraft to extract and store the data, which standardizes the data format for both reading from replays and reading directly from the game. Furthermore, the data can be used on different operating systems and platforms. The dataset contains valid, non-corrupted replays only and its quality and diversity was ensured by a number of heuristics. We illustrate the diversity of the data with various statistics and provide examples of tasks that benefit from the dataset. We make the dataset available at https://github.com/TorchCraft/StarData.
Topic Modelling with Word Embeddings

English. This work aims at evaluating and comparing two different frameworks for the unsupervised topic modelling of the CompWHoB Corpus, namely our political-linguistic dataset. The first approach is represented by the application of the latent DirichLet Allocation (henceforth LDA), defining the evaluation of this model as baseline of comparison. The second framework employs Word2Vec technique to learn the word vector representations to be later used to topic-model our data. Compared to the previously defined LDA baseline, results show that the use of Word2Vec word embeddings significantly improves topic modelling performance but only when an accurate and taskoriented linguistic pre-processing step is carried out. Italiano. L’obiettivo di questo contributo è di valutare e confrontare due differenti framework per l’apprendimento automatico del topic sul CompWHoB Corpus, la nostra risorsa testuale. Dopo aver implementato il modello della latent DirichLet Allocation, abbiamo definito come standard di riferimento la valutazione di questo stesso approccio. Come secondo framework, abbiamo utilizzato il modello Word2Vec per apprendere le rappresentazioni vettoriali dei termini successivamente impiegati come input per la fase di apprendimento automatico del topic. I risulati mostrano che utilizzando i ‘word embeddings’ generati da Word2Vec, le prestazioni del modello aumentano significativamente ma solo se supportati da una accurata fase di ‘pre-processing’ linguisti-
Distributed real-time cooperative localization and mapping using an uncertainty-aware expectation maximization approach

We demonstrate distributed, online, and real-time cooperative localization and mapping between multiple robots operating throughout an unknown environment using indirect measurements. We present a novel Expectation Maximization (EM) based approach to efficiently identify inlier multi-robot loop closures by incorporating robot pose uncertainty, which significantly improves the trajectory accuracy over long-term navigation. An EM and hypothesis based method is used to determine a common reference frame. We detail a 2D laser scan correspondence method to form robust correspondences between laser scans shared amongst robots. The implementation is experimentally validated using teams of aerial vehicles, and analyzed to determine its accuracy, computational efficiency, scalability to many robots, and robustness to varying environments. We demonstrate through multiple experiments that our method can efficiently build maps of large indoor and outdoor environments in a distributed, online, and real-time setting.
Differential Evolution With an Individual-Dependent Mechanism

Differential evolution (DE) is a well-known optimization algorithm that utilizes the difference of positions between individuals to perturb base vectors and thus generate new mutant individuals. However, the difference between the fitness values of individuals, which may be helpful to improve the performance of the algorithm, has not been used to tune parameters and choose mutation strategies. In this paper, we propose a novel variant of DE with an individual-dependent mechanism that includes an individual-dependent parameter (IDP) setting and an individual-dependent mutation (IDM) strategy. In the IDP setting, control parameters are set for individuals according to the differences in their fitness values. In the IDM strategy, four mutation operators with different searching characteristics are assigned to the superior and inferior individuals, respectively, at different stages of the evolution process. The performance of the proposed algorithm is then extensively evaluated on a suite of the 28 latest benchmark functions developed for the 2013 Congress on Evolutionary Computation special session. Experimental results demonstrate the algorithm's outstanding performance.
An Arabic-Hebrew parallel corpus of TED talks

We describe an Arabic-Hebrew parallel corpus of TED talks built upon WIT, the Web inventory that repurposes the original content of the TED website in a way which is more convenient for MT researchers. The benchmark consists of about 2,000 talks, whose subtitles in Arabic and Hebrew have been accurately aligned and rearranged in sentences, for a total of about 3.5M tokens per language. Talks have been partitioned in train, development and test sets similarly in all respects to the MT tasks of the IWSLT 2016 evaluation campaign. In addition to describing the benchmark, we list the problems encountered in preparing it and the novel methods designed to solve them. Baseline MT results and some measures on sentence length are provided as an extrinsic evaluation of the quality of the benchmark.
Nonparametric Bayesian Inverse Reinforcement Learning for Multiple Reward Functions

We present a nonparametric Bayesian approach to inverse rei nforcement learning (IRL) for multiple reward functions. Most previous IRL algo rithms assume that the behaviour data is obtained from an agent who is optimizin g a single reward function, but this assumption is hard to guarantee in practi ce. Our approach is based on integrating the Dirichlet process mixture model in to Bayesian IRL. We provide an efficient Metropolis-Hastings sampling algorit hm utilizing the gradient of the posterior to estimate the underlying reward function s, and demonstrate that our approach outperforms previous ones via experiments on a number of problem domains.
Interactive Video Object Segmentation in the Wild

In this paper we present our system for human-in-theloop video object segmentation. The backbone of our system is a method for one-shot video object segmentation . While fast, this method requires an accurate pixel-level segmentation of one (or several) frames as input. As manually annotating such a segmentation is impractical, we propose a deep interactive image segmentation method, that can accurately segment objects with only a handful of clicks. On the GrabCut dataset, our method obtains 90% IOU with just 3.8 clicks on average, setting the new state of the art. Furthermore, as our method iteratively refines an initial segmentation, it can effectively correct frames where the video object segmentation fails, thus allowing users to quickly obtain high quality results even on challenging sequences. Finally, we investigate usage patterns and give insights in how many steps users take to annotate frames, what kind of corrections they provide, etc., thus giving important insights for further improving interactive video segmentation.
Surface versus edge-based determinants of visual recognition

Two roles hypothesized for surface characteristics, such as color, brightness, and texture, in object recognition are that such information can (a) define the gradients needed for a 2%-D sketch so that a 3-D representation can be derived (e.g., Marr & Nishihara, 1978) and (b) provide additional distinctive features for accessing memory. In a series of five experiments, subjects either named or verified (against a target name) brief (50100 ms) presentations of slides of common objects. Each object was shown in two versions: professionally photographed in full color or as a simplified line drawing showing only the object’s major components (which typically corresponded to its parts). Although one or the other type of picture would be slightly favored in a particular condition of exposure (duration or masking), overall mean reaction times and error rates were virtually identical for the two types of stimuli. These results support a view that edge-based representations mediate real-time object recognition in contrast to surface gradient or multiple cue representations. A previously unexplored distinction of color diagnosticity allowed us to determine whether color (and brightness) was employed as an additional feature in accessing memory for those objects or conditions where there might have been an advantage for the color slides. For some objects, e.g., banana, fork, fish, and camera, color is diagnostic as to the object’s classification. For other objects, e.g., chair, pen, mitten, and bicycle pump, color is not diagnostic, as such objects can be of any color. If color was employed in accessing memory, color-diagnostic objects should have shown a relative advantage when presented as color slides compared to the line drawing versions of the same objects. Also, this advantage would be magnified when subjects could anticipate the color of an object in the verification task, particularly on NO trials when the foil was of a different color. Neither an overall advantage for color-diagnostic objects when presented in color nor a magnification of a relative advantage on the NO trials in the verification task was obtained. Although differences in surface characteristics such as color, brightness, and texture can be instrumental in defining edges and can provide cues for visual search, they play only a secondary role in the real-time recognition of an intact object when its edges can be readily extracted.
An SVM based churn detector in prepaid mobile telephony

The context of prepaid mobile telephony is specific in the way that customers are not contractually linked to their operator and thus can cease their activity without notice. In order to estimate the retention efforts which can be engaged towards each individual customer, the operator must distinguish the customers presenting a strong churn risk from the other. This work presents a data mining application leading to a churn detector. We compare artificial neural networks (ANN) which have been historically applied to this problem, to support vectors machines (SVM) which are particularly effective in classification and adapted to noisy data. Thus, the objective of this article is to compare the application of SVM and ANN to churn detection in prepaid cellular telephony. We show that SVM gives better results than ANN on this specific problem.
Catch you if you lie to me: Efficient verifiable conjunctive keyword search over large dynamic encrypted cloud data

Encrypted data search allows cloud to offer fundamental information retrieval service to its users in a privacy-preserving way. In most existing schemes, search result is returned by a semi-trusted server and usually considered authentic. However, in practice, the server may malfunction or even be malicious itself. Therefore, users need a result verification mechanism to detect the potential misbehavior in this computation outsourcing model and rebuild their confidence in the whole search process. On the other hand, cloud typically hosts large outsourced data of users in its storage. The verification cost should be efficient enough for practical use, i.e., it only depends on the corresponding search operation, regardless of the file collection size. In this paper, we are among the first to investigate the efficient search result verification problem and propose an encrypted data search scheme that enables users to conduct secure conjunctive keyword search, update the outsourced file collection and verify the authenticity of the search result efficiently. The proposed verification mechanism is efficient and flexible, which can be either delegated to a public trusted authority (TA) or be executed privately by data users. We formally prove the universally composable (UC) security of our scheme. Experimental result shows its practical efficiency even with a large dataset.
Anatomical Landmark Detection in Medical Applications Driven by Synthetic Data

An important initial step in many medical image analysis applications is the accurate detection of anatomical landmarks. Most successful methods for this task rely on data-driven machine learning algorithms. However, modern machine learning techniques, e.g. convolutional neural networks, need a large corpus of training data, which is often an unrealistic setting for medical datasets. In this work, we investigate how to adapt synthetic image datasets from other computer vision tasks to overcome the under-representation of the anatomical pose and shape variations in medical image datasets. We transform both data domains to a common one in such a way that a convolutional neural network can be trained on the larger synthetic image dataset and fine-tuned on the smaller medical image dataset. Our evaluations on data of MR hand and whole body CT images demonstrate that this approach improves the detection results compared to training a convolutional neural network only on the medical data. The proposed approach may also be usable in other medical applications, where training data is scarce.
A 0 . 3 – 25-GHz Ultra-Wideband Mixer Using Commercial 0 . 18-m CMOS Technology

An ultra-wideband mixer using standard complementery metal oxide semiconductor (CMOS) technology was first proposed in this paper. This broadband mixer achieves measured conversion gain of 11 1 5 dB with a bandwidth of 0.3 to 25 GHz. The mixer was fabricated in a commercial 0.18m CMOS technology and demonstrated the highest frequency and bandwidth of operation. It also presented better gain-bandwidth-product performance compared with that of GaAs-based HBT technologies. The chip area is 0.8 1 mm.
Autogynephilia : A Paraphilic Model of Gender Identity Disorder

Autogynephilia is defined as a male’s propensity to be sexually aroused by the thought or image of himself as female. Autogynephilia explains the desire for sex reassignment of some maleto-female (MtF) transsexuals. It can be conceptualized as both a paraphilia and a sexual orientation. The concept of autogynephilia provides an alternative to the traditional model of transsexualism that emphasizes gender identity. Autogynephilia helps explain mid-life MtF gender transition, progression from transvestism to transsexualism, the prevalence of other paraphilias among MtF transsexuals, and late development of sexual interest in male partners. Hormone therapy and sex reassignment surgery can be effective treatments in autogynephilic transsexualism. The concept of autogynephilia can help clinicians better understand MtF transsexual clients who recognize a strong sexual component to their gender dysphoria.
A multiple criteria active learning method for support vector regression

This paper presents a novel active learning method developed in the framework of ε-insensitive support vector regression (SVR) for the solution of regression problems with small size initial training data. The proposed active learning method selects iteratively the most informative as well as representative unlabeled samples to be included in the training set by jointly evaluating three criteria: (i) relevancy, (ii) diversity, and (iii) density of samples. All three criteria are implemented according to the SVR properties and are applied in two clustering-based consecutive steps. In the first step, a novel measure to select the most relevant samples that have high probability to be located either outside or on the boundary of the ε-tube of SVR is defined. To this end, initially a clustering method is applied to all unlabeled samples together with the training samples that are inside the ε-tube (those that are not support vectors, i.e., non-SVs); then the clusters with non-SVs are eliminated. The unlabeled samples in the remaining clusters are considered as the most relevant patterns. In the second step, a novel measure to select diverse samples among the relevant patterns from the high density regions in the feature space is defined to better model the SVR learning function. To this end, initially clusters with the highest density of samples are chosen to identify the highest density regions in the feature space. Then, the sample from each selected cluster that is associated with the portion of feature space having the highest density (i.e., the most representative of the underlying distribution of samples contained in the related cluster) is selected to be included in the training set. In this way diverse samples taken from high density regions are efficiently identified. Experimental results obtained on four different data sets show the robustness of the proposed technique particularly when a small-size initial training set are available. & 2014 Elsevier Ltd. All rights reserved.
Fault tolerance analysis of digital feed-forward deep neural networks

As the homeostatis characteristics of nerve systems show, artificial neural networks are considered to be robust to variation of circuit components and interconnection faults. However, the tolerance of neural networks depends on many factors, such as the fault model, the network size, and the training method. In this study, we analyze the fault tolerance of fixed-point feed-forward deep neural networks for the implementation in CMOS digital VLSI. The circuit errors caused by the interconnection as well as the processing units are considered. In addition to the conventional and dropout training methods, we develop a new technique that randomly disconnects weights during the training to increase the error resiliency. Feed-forward deep neural networks for phoneme recognition are employed for the experiments.
Parsing and Hypergraphs

While symbolic parsers can be viewed as deduction systems, t his view is less natural for probabilistic parsers. We present a view of parsing as directed hypergraph analysis which naturally covers both symbolic and probabilistic parsing. We illustrate the approach by showing how a dynamic extension of Dijkstra’s algorithm can be used to construct a probabilistic chart parser with an O(n3) time bound for arbitrary PCFGs, while preserving as much of t he flexibility of symbolic chart parsers as allowed by the inher ent ordering of probabilistic dependencies.
Multi-view learning overview: Recent progress and new challenges

Multi-view learning is an emerging direction in machine learning which considers learning with multiple views to improve the generalization performance. Multi-view learning is also known as data fusion or data integration from multiple feature sets. Since the last survey of multi-view machine learning in early 2013, multi-view learning has made great progress and developments in recent years, and is facing new challenges. This overview first reviews theoretical underpinnings to understand the properties and behaviors of multi-view learning. Then multi-view learning methods are described in terms of three classes to offer a neat categorization and organization. For each category, representative algorithms and newly proposed algorithms are presented. The main feature of this survey is that we provide comprehensive introduction for the recent developments of multi-view learning methods on the basis of coherence with early methods. We also attempt to identify promising venues and point out some specific challenges which can hopefully promote further research in this rapidly developing field.
Resonant and multi-mode operation of flying capacitor multi-level DC-DC converters

Multi-level converter architectures have been explored for a variety of applications including high-power DC-AC inverters and DC-DC converters. In this work, we explore flying-capacitor multi-level (FCML) DC-DC topologies as a class of hybrid switched-capacitor/inductive converter. Compared to other candidate architectures in this area (e.g. Series-Parallel, Dickson), FCML converters have notable advantages such as the use of single-rated low-voltage switches, potentially lower switching loss, lower passive component volume, and enable regulation across the full VDD-VOUT range. It is shown that multimode operation, including previously published resonant and dynamic off-time modulation, form a single set of techniques that can be used to extend high efficiency over a wide power density range. Some of the general operating considerations of FCML converters, such as the challenge of maintaining voltage balance on flying capacitors, are shown to be of equal concern in other soft-switched SC converter topologies. Experimental verification from a 24V:12V, 3-level converter is presented to show multimode operation with a nominally 2:1 topology. A second 50V:7V 4-level FCML converter demonstrates operation with variable regulation. A method is presented to balance flying capacitor voltages through low frequency closed-loop feedback.