nils-herrmann/clean_scirepeval huggingface dataset:
Exploiting Wikipedia as External Knowledge for Named Entity Recognition

We explore the use of Wikipedia as external knowledge to improve named entity recognition (NER). Our method retrieves the corresponding Wikipedia entry for each candidate word sequence and extracts a category label from the first sentence of the entry, which can be thought of as a definition part. These category labels are used as features in a CRF-based NE tagger. We demonstrate using the CoNLL 2003 dataset that the Wikipedia category labels extracted by such a simple method actually improve the accuracy of NER.
Tag recommendations in social bookmarking systems

Collaborative tagging systems allow users to assign keywords—so called “tags”—to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied. In this paper we evaluate and compare several recommendation algorithms on large-scale real life datasets: an adaptation of user-based collaborative filtering, a graph-based recommender built on top of the FolkRank algorithm, and simple methods based on counting tag occurences. We show that both FolkRank and Collaborative Filtering provide better results than non-personalized baseline methods. Moreover, since methods based on counting tag occurrences are computationally cheap, and thus usually preferable for real time scenarios, we discuss simple approaches for improving the performance of such methods. We show, how a simple recommender based on counting tags from users and resources can perform almost as good as the best recommender.
Symmetry Descriptors and 3D Shape Matching

In this paper, we present the Symmetry Descriptors of a 3D model. This is a collection of spherical functions that describes the measure of a model's rotational and reflective symmetry with respect to every axis passing through the center of mass. We show that Symmetry Descriptors can be computed efficiently using fast signal processing techniques, and demonstrate the empirical value of Symmetry Descriptors by showing that they improve matching performance in a variety of shape retrieval experiments.
Visual Graphs from Motion (VGfM): Scene understanding with object geometry reasoning

Recent approaches on visual scene understanding attempt to build a scene graph – a computational representation of objects and their pairwise relationships. Such rich semantic representation is very appealing, yet difficult to obtain from a single image, especially when considering complex spatial arrangements in the scene. Differently, an image sequence conveys useful information using the multi-view geometric relations arising from camera motions. Indeed, object relationships are naturally related to the 3D scene structure. To this end, this paper proposes a system that first computes the geometrical location of objects in a generic scene and then efficiently constructs scene graphs from video by embedding such geometrical reasoning. Such compelling representation is obtained using a new model where geometric and visual features are merged using an RNN framework. We report results on a dataset we created for the task of 3D scene graph generation in multiple views.
Rationale-Augmented Convolutional Neural Networks for Text Classification

We present a new Convolutional Neural Network (CNN) model for text classification that jointly exploits labels on documents and their constituent sentences. Specifically, we consider scenarios in which annotators explicitly mark sentences (or snippets) that support their overall document categorization, i.e., they provide rationales. Our model exploits such supervision via a hierarchical approach in which each document is represented by a linear combination of the vector representations of its component sentences. We propose a sentence-level convolutional model that estimates the probability that a given sentence is a rationale, and we then scale the contribution of each sentence to the aggregate document representation in proportion to these estimates. Experiments on five classification datasets that have document labels and associated rationales demonstrate that our approach consistently outperforms strong baselines. Moreover, our model naturally provides explanations for its predictions.
Nearness diagram navigation (ND): a new real time collision avoidance approach

This paper presents a new real-time collision avoidance approach for mobile robots. The Nearness Diagram method (ND) performs a high level information extraction and interpr etation of the envir onment. Subsequently, this information is used to gener ate the motion commands. The proposed approach is well-suited to deal with unknown, unstructur edand dynamic envir onments,where problems of other appr oachesare avoided. Some experimental results are shown using an holonomic mobile base to demonstrate the usefulness of the method.
Short-Read Mapping by a Systolic Custom FPGA Computation

The mapping of reads, i.e. short DNA base pair strings, to large genome databases has become a critical operation for genetic analysis and diagnosis. Although this mapping operation is a simple string search tolerant of some character mismatches, it is yet extremely challenging due to the tremendous size of the searched genome databases. It is the heavy use of search heuristics such as BLAST, Maq and Bowtie, which makes the economic deployment of read mappers possible. While these heuristics achieve feasible computation times, they also sacrifice the accuracy of the mapping results, which is itself a high value for reliable diagnostics. The traditional software implementations are unable to exploit the tremendous parallelism, which is available in the mapping of thousands and millions of reads. Merely a handful of concurrent control flows, and thus searches, can be performed efficiently on contemporary multicores. Even GPU assistance only enables a few dozens of parallel searches. This paper proposes a systolic custom computation on FPGA, which implements the read mapping on a massively parallel architecture. It implements a true search and guarantees to find all read mappings under a configurable threshold of base pair mismatches. The highly regular design from compact string matchers enables the implementation of thousands of parallel search engines on a single FPGA device. The presented map per platform combines highest computational performance with an excellent result accuracy. Its performance is more than twice as high as that of a recently published comparable FPGA map per. Already when implemented on a contemporary mid-size FPGA, it meets the search speed of software heuristics, which only detect little more than half of the valid read mappings. The map per easily scales to large FPGA devices, which can, thus, implement accurate high-performance volume mappers. Accurate mapping is made available in application domains that could only afford fuzzy heuristics by now.
U-Air: when urban air quality inference meets big data

Information about urban air quality, e.g., the concentration of PM2.5, is of great importance to protect human health and control air pollution. While there are limited air-quality-monitor-stations in a city, air quality varies in urban spaces non-linearly and depends on multiple factors, such as meteorology, traffic volume, and land uses. In this paper, we infer the real-time and fine-grained air quality information throughout a city, based on the (historical and real-time) air quality data reported by existing monitor stations and a variety of data sources we observed in the city, such as meteorology, traffic flow, human mobility, structure of road networks, and point of interests (POIs). We propose a semi-supervised learning approach based on a co-training framework that consists of two separated classifiers. One is a spatial classifier based on an artificial neural network (ANN), which takes spatially-related features (e.g., the density of POIs and length of highways) as input to model the spatial correlation between air qualities of different locations. The other is a temporal classifier based on a linear-chain conditional random field (CRF), involving temporally-related features (e.g., traffic and meteorology) to model the temporal dependency of air quality in a location. We evaluated our approach with extensive experiments based on five real data sources obtained in Beijing and Shanghai. The results show the advantages of our method over four categories of baselines, including linear/Gaussian interpolations, classical dispersion models, well-known classification models like decision tree and CRF, and ANN.
The Author-Topic Model for Authors and Documents

We introduce the author-topic model, a generative model for documents that extends Latent Dirichlet Allocation (LDA; Blei, Ng, & Jordan, 2003) to include authorship information. Each author is associated with a multinomial distribution over topics and each topic is associated with a multinomial distribution over words. A document with multiple authors is modeled as a distribution over topics that is a mixture of the distributions associated with the authors. We apply the model to a collection of 1,700 NIPS conference papers and 160,000 CiteSeer abstracts. Exact inference is intractable for these datasets and we use Gibbs sampling to estimate the topic and author distributions. We compare the performance with two other generative models for documents, which are special cases of the author-topic model: LDA (a topic model) and a simple author model in which each author is associated with a distribution over words rather than a distribution over topics. We show topics recovered by the authortopic model, and demonstrate applications to computing similarity between authors and entropy of author output.
Tweet2Vec: Character-Based Distributed Representations for Social Media

Text from social media provides a set of challenges that can cause traditional NLP approaches to fail. Informal language, spelling errors, abbreviations, and special characters are all commonplace in these posts, leading to a prohibitively large vocabulary size for word-level approaches. We propose a character composition model, tweet2vec, which finds vectorspace representations of whole tweets by learning complex, non-local dependencies in character sequences. The proposed model outperforms a word-level baseline at predicting user-annotated hashtags associated with the posts, doing significantly better when the input contains many outof-vocabulary words or unusual character sequences. Our tweet2vec encoder is publicly available1.
On the generalized bin packing problem

We introduce the Generalized Bin Packing Problem, a new packing problem where, given a set of items characterized by volume and profit and a set of bins with given volumes and costs, one aims to select the subsets of profitable items and appropriate bins to optimize an objective function combining the cost of using the bins and the profit yielded by loading the selected items. The Generalized Bin Packing Problem thus generalizes many other packing problems, including Bin Packing and Variable Sized Bin Packing, as well as Knapsack, Multiple Homogeneous and Heterogeneous Knapsack. We present two formulations for the problem, as well as an efficient column-generation-based lower bound method, plus first fit, best fit, and column-generation-based upper bound procedures. The paper introduces new instance sets and analyzes the results of extensive computational experiments, which show that the proposed procedures are very efficient and the bounds are very tight.
Staying Cool when Things Get Hot: Emotion Regulation Modulates Neural Mechanisms of Memory Encoding

During times of emotional stress, individuals often engage in emotion regulation to reduce the experiential and physiological impact of negative emotions. Interestingly, emotion regulation strategies also influence memory encoding of the event. Cognitive reappraisal is associated with enhanced memory while expressive suppression is associated with impaired explicit memory of the emotional event. However, the mechanism by which these emotion regulation strategies affect memory is unclear. We used event-related fMRI to investigate the neural mechanisms that give rise to memory formation during emotion regulation. Twenty-five participants viewed negative pictures while alternately engaging in cognitive reappraisal, expressive suppression, or passive viewing. As part of the subsequent memory design, participants returned to the laboratory two weeks later for a surprise memory test. Behavioral results showed a reduction in negative affect and a retention advantage for reappraised stimuli relative to the other conditions. Imaging results showed that successful encoding during reappraisal was uniquely associated with greater co-activation of the left inferior frontal gyrus, amygdala, and hippocampus, suggesting a possible role for elaborative encoding of negative memories. This study provides neurobehavioral evidence that engaging in cognitive reappraisal is advantageous to both affective and mnemonic processes.
SUBWORD LANGUAGE MODELING WITH NEURAL NETWORKS

We explore the performance of several types of language mode ls on the word-level and the character-level language modelin g tasks. This includes two recently proposed recurrent neural netwo rk architectures, a feedforward neural network model, a maximum ent ropy model and the usual smoothed n-gram models. We then propose a simple technique for learning sub-word level units from th e data, and show that it combines advantages of both character and wo rdlevel models. Finally, we show that neural network based lan gu ge models can be order of magnitude smaller than compressed n-g ram models, at the same level of performance when applied to a Bro dcast news RT04 speech recognition task. By using sub-word un its, the size can be reduced even more.
A SICK cure for the evaluation of compositional distributional semantic models

Shared and internationally recognized benchmarks are fundamental for the development of any computational system. We aim to help the research community working on compositional distributional semantic models (CDSMs) by providing SICK (Sentences Involving Compositional Knowldedge), a large size English benchmark tailored for them. SICK consists of about 10,000 English sentence pairs that include many examples of the lexical, syntactic and semantic phenomena that CDSMs are expected to account for, but do not require dealing with other aspects of existing sentential data sets (idiomatic multiword expressions, named entities, telegraphic language) that are not within the scope of CDSMs. By means of crowdsourcing techniques, each pair was annotated for two crucial semantic tasks: relatedness in meaning (with a 5-point rating scale as gold score) and entailment relation between the two elements (with three possible gold labels: entailment, contradiction, and neutral). The SICK data set was used in SemEval-2014 Task 1, and it freely available for research purposes.
Efficacy of Topical Combination of 0.25% Finasteride and 3% Minoxidil Versus 3% Minoxidil Solution in Female Pattern Hair Loss: A Randomized, Double-Blind, Controlled Study.

The relationship between female pattern hair loss (FPHL) and androgenic hormones is not well established, but some evidence indicates oral finasteride may be efficacious in FPHL. Use of a topical formulation has been proposed to minimize unwanted effects.



Our objective was to compare the efficacy and safety of topical 0.25% finasteride combined with 3% minoxidil solution and 3% minoxidil solution as monotherapy in the treatment of FPHL.



This was a prospective, randomized, double-blind study in 30 postmenopausal women with FPHL. Each participant was randomized to receive either topical 0.25% finasteride combined with topical 3% minoxidil or topical 3% minoxidil solution as monotherapy for 24 weeks. To determine efficacy, the hair density and diameter was measured and global photographic assessment was conducted at baseline and 8, 16, and 24 weeks. Side effects and serum dihydrotestosterone levels were also evaluated.



By 24 weeks, hair density and diameter had increased in both groups, and finasteride/minoxidil was significantly superior to minoxidil solution in terms of hair diameter (p = 0.039). No systemic side effects were reported. However, serum dihydrotestosterone levels in the finasteride/minoxidil group significantly decreased from baseline (p = 0.016).



A topical combination of 0.25% finasteride and 3% minoxidil may be a promising option in the treatment of FPHL with an additional benefit of increasing hair diameter. Nevertheless, as it may be absorbed percutaneously, it should be reserved for postmenopausal women.
Sparse deep belief net model for visual area V2

Motivated in part by the hierarchical organization of the cortex, a number of algorithms have recently been proposed that try to learn hierarchical, or “deep,” structure from unlabeled data. While several authors have formally or informally compared their algorithms to computations performed in visual area V1 (and the cochlea), little attempt has been made thus far to evaluate these algorithms in terms of their fidelity for mimicking computations at deeper levels in the cortical hierarchy. This paper presents an unsupervised learning model that faithfully mimics certain properties of visual area V2. Specifically, we develop a sparse variant of the deep belief networks of Hinton et al. (2006). We learn two layers of nodes in the network, and demonstrate that the first layer, similar to prior work on sparse coding and ICA, results in localized, oriented, edge filters, similar to the Gabor functions known to model V1 cell receptive fields. Further, the second layer in our model encodes correlations of the first layer responses in the data. Specifically, it picks up both colinear (“contour”) features as well as corners and junctions. More interestingly, in a quantitative comparison, the encoding of these more complex “corner” features matches well with the results from the Ito & Komatsu’s study of biological V2 responses. This suggests that our sparse variant of deep belief networks holds promise for modeling more higher-order features.
Perceptual evaluation of speech quality (PESQ)-a new method for speech quality assessment of telephone networks and codecs

Previous objective speech quality assessment models, such as bark spectral distortion (BSD), the perceptual speech quality measure (PSQM), and measuring normalizing blocks (MNB), have been found to be suitable for assessing only a limited range of distortions. A new model has therefore been developed for use across a wider range of network conditions, including analogue connections, codecs, packet loss and variable delay. Known as perceptual evaluation of speech quality (PESQ), it is the result of integration of the perceptual analysis measurement system (PAMS) and PSQM99, an enhanced version of PSQM. PESQ is expected to become a new ITU-T recommendation P.862, replacing P.861 which specified PSQM and MNB.
Flower classification based on local and spatial visual cues

This paper addresses flower image classification. The extent of blossom, deformation and inter-class appearance blur of flowers add great difficulties to flower classification task in addition to view, color, illumination changes that commonly occurred in other objects classification tasks. In this paper, SIFT-like feature descriptors and feature context method are used in coding local and spatial information, then LibLinear SVM classifier is employed for classification. Experimental results show that CSIFT is more robust and stable than SIFT and Dense SIFT in representing flower image. The accuracy of classification with CSIFT and feature context is comparable to state-of-the-art method. Since we do not need segment flower out of image in advance, practically, our method is better in performance and efficiency.
An Active Pixel Sensor Fabricated Using CMOS / CCD Process Technology

Paul P. K. Lee, Russell C. Gee*, R. Michael Guidash, T-H. Lee, and Eric R. Fossum* Microelectronics Technology Division, Eastman Kodak Company 1669 Lake Avenue, Rochester, New York 14650-2008 Tel: (716) 477-2869, Fax: (716) 477-4947, Internet: ppklee@Kodak.COM *Jet Propulsion Laboratory, California Institute of Technology 4800 Oak Grove Drive, Pasadena, CA 91109  This paper describes the integration of the active pixel sensor (APS) architecture normally fabricated in conventional complementary metal oxide semiconductor (CMOS) technology with a pinned photodiode (PPD) device using a mixed process technology. This new technology allows mix and match of CMOS and high performance charged coupled device (CCD) modules. The PPD [1] becomes the photoactive element in an XYaddressable area array with each pixel containing active devices for the transfer, readout and reset functions. It is a standard photo-sensitive element, available in a high performance true two-phase CCD technology developed previously for CCD-based image sensors . An n-well 2-μm CMOS technology was combined with the CCD process to provide the best features from both. A Design of Experiment approach was used with Technology Computer Aided Design (TCAD) tools to develop and optimize the new mixed process technology without sacrificing any CCD performance while minimizing impact to the CMOS device characteristics . By replacing polysilicon photocapacitor or photogate in conventional APS with the pinned photodiode, deficiencies in poor blue response and high dark current are minimized .
Theodor Lipps and the shift from "sympathy" to "empathy".

In the course of extensive philosophical debates on aesthetics in nineteenth-century Germany, Robert Vischer introduced the concept of Einfühlung in relation to art. Theodor Lipps subsequently extended it from art to visual illusions and interpersonal understanding. While Lipps had regarded Einfühlung as basically similar to the old notion of sympathy, Edward Titchener in America believed it had a different meaning. Hence, he coined the term empathy as its translation. This term came to be increasingly widely adopted, first in psychology and then more generally. But the lack of agreement about the supposed difference between these concepts suggests that Lipps had probably been right.