nils-herrmann/clean_scirepeval huggingface dataset:
Learning to Draw Samples: With Application to Amortized MLE for Generative Adversarial Learning

We propose a simple algorithm to train stochastic neural networks to draw samples from given target distributions for probabilistic inference. Our method is based on iteratively adjusting the neural network parameters so that the output changes along a Stein variational gradient (Liu & Wang, 2016) that maximumly decreases the KL divergence with the target distribution. Our method works for any target distribution specified by their unnormalized density function, and can train any black-box architectures that are differentiable in terms of the parameters we want to adapt. As an application of our method, we propose an amortized MLE algorithm for training deep energy model, where a neural sampler is adaptively trained to approximate the likelihood function. Our method mimics an adversarial game between the deep energy model and the neural sampler, and obtains realisticlooking images competitive with the state-of-the-art results.
Latent Predictor Networks for Code Generation

Many language generation tasks require the production of text conditioned on both structured and unstructured inputs. We present a novel neural network architecture which generates an output sequence conditioned on an arbitrary number of input functions. Crucially, our approach allows both the choice of conditioning context and the granularity of generation, for example characters or tokens, to be marginalised, thus permitting scalable and effective training. Using this framework, we address the problem of generating programming code from a mixed natural language and structured specification. We create two new data sets for this paradigm derived from the collectible trading card games Magic the Gathering and Hearthstone. On these, and a third preexisting corpus, we demonstrate that marginalising multiple predictors allows our model to outperform strong benchmarks.
An Analysis of Random-Walk Cuckoo Hashing

In this paper, we provide a polylogarithmic bound that holds with high probability on the insertion time for cuckoo hashing under the random-walk insertion method. Cuckoo hashing provides a useful methodology for building practical, high-performance hash tables. The essential idea of cuckoo hashing is to combine the power of schemes that allow multiple hash locations for an item with the power to dynamically change the location of an item among its possible locations. Previous work on the case where the number of choices is larger than two has required a breadth-first search analysis, which is both inefficient in practice and currently has only a polynomial high probability upper bound on the insertion time. Here we significantly advance the state of the art by proving a polylogarithmic bound on the more efficient randomwalk method, where items repeatedly kick out random blocking items until a free location for an item is found.
A Dynamic Theory of Organizational Knowledge Creation

This paper proposes a paradigm for managing the dynamic aspects of organizational knowledge creating processes. Its central theme is that organizational knowledge is created through a continuous dialogue between tacit and explicit knowledge. The nature of this dialogue is examined and four patterns of interaction involving tacit and explicit knowledge are identified. It is argued that while new knowledge is developed by individuals, organizations play a critical role in articulating and amplifying that knowledge. A theoretical framework is developed which provides an analytical perspective on the constituent dimensions of knowledge creation. This framework is then applied in two operational models for facilitating the dynamic creation of appropriate organizational knowledge. (Self-Designing Organization; Teams; Knowledge Conversion; Organizational Innovation;
Broadband focusing flat mirrors based on plasmonic gradient metasurfaces.

We demonstrate that metal-insulator-metal configurations, with the top metal layer consisting of a periodic arrangement of differently sized nanobricks, can be designed to function as broadband focusing flat mirrors. Using 50-nm-high gold nanobricks arranged in a 240-nm-period lattice on the top of a 50-nm-thick layer of silicon dioxide deposited on a continuous 100-nm-thick gold film, we realize a 17.3 × 17.3 μm(2) flat mirror that efficiently reflects (experiment: 14-27%; theory: 50-78%) and focuses a linearly polarized (along the direction of nanobrick size variation) incident beam in the plane of its polarization with the focal length, which changes from ~15 to 11 μm when tuning the light wavelength from 750 to 950 nm, respectively. Our approach can easily be extended to realize the radiation focusing in two dimensions as well as other optical functionalities by suitably controlling the phase distribution of reflected light.
A new retexturing method for virtual fitting room using Kinect 2 camera

This research work proposes a new method for garment retexturing using a single static image along with depth information obtained using the Microsoft Kinect 2 camera. First the garment is segmented out from the image and texture domain coordinates are computed for each pixel of the shirt using 3D information. After that shading is applied on the new colours from the texture image by applying linear stretching of the luminance of the segmented garment. The proposed method is colour and pattern invariant and results in to visually realistic retexturing. The proposed method has been tested on various images and it is shown that it generally performs better and produces more realistic results compared to the state-of-the-art methods. The proposed method can be an application for the virtual fitting room.
Looking Beyond the Visible Scene

A common thread that ties together many prior works in scene understanding is their focus on the aspects directly present in a scene such as its categorical classification or the set of objects. In this work, we propose to look beyond the visible elements of a scene; we demonstrate that a scene is not just a collection of objects and their configuration or the labels assigned to its pixels - it is so much more. From a simple observation of a scene, we can tell a lot about the environment surrounding the scene such as the potential establishments near it, the potential crime rate in the area, or even the economic climate. Here, we explore several of these aspects from both the human perception and computer vision perspective. Specifically, we show that it is possible to predict the distance of surrounding establishments such as McDonald's or hospitals even by using scenes located far from them. We go a step further to show that both humans and computers perform well at navigating the environment based only on visual cues from scenes. Lastly, we show that it is possible to predict the crime rates in an area simply by looking at a scene without any real-time criminal activity. Simply put, here, we illustrate that it is possible to look beyond the visible scene.
An Empirical Study of Algorithms for Point-Feature Label Placement

A major factor affecting the clarity of graphical displays that include text labels is the degree to which labels obscure display features (including other labels) as a result of spatial overlap. Point-feature label placement (PFLP) is the problem of placing text labels adjacent to point features on a map or diagram so as to maximize legibility. This problem occurs frequently in the production of many types of informational graphics, though it arises most often in automated cartography. In this paper we present a comprehensive treatment of the PFLP problem, viewed as a type of combinatorial optimization problem. Complexity analysis reveals that the basic PFLP problem and most interesting variants of it are NP-hard. These negative results help inform a survey of previously reported algorithms for PFLP; not surprisingly, all such algorithms either have exponential time complexity or are incomplete. To solve the PFLP problem in practice, then, we must rely on good heuristic methods. We propose two new methods, one based on a discrete form of gradient descent, the other on simulated annealing, and report on a series of empirical tests comparing these and the other known algorithms for the problem. Based on this study, the first to be conducted, we identify the best approaches as a function of available computation time.
BinMatch: A Semantics-Based Hybrid Approach on Binary Code Clone Analysis

Binary code clone analysis is an important technique which has a wide range of applications in software engineering (e.g., plagiarism detection, bug detection). The main challenge of the topic lies in the semantics-equivalent code transformation (e.g., optimization, obfuscation) which would alter representations of binary code tremendously. Another challenge is the trade-off between detection accuracy and coverage. Unfortunately, existing techniques still rely on semantics-less code features which are susceptible to the code transformation. Besides, they adopt merely either a static or a dynamic approach to detect binary code clones, which cannot achieve high accuracy and coverage simultaneously.  In this paper, we propose a semantics-based hybrid approach to detect binary clone functions. We execute a template binary function with its test cases, and emulate the execution of every target function for clone comparison with the runtime information migrated from that template function. The semantic signatures are extracted during the execution of the template function and emulation of the target function. Lastly, a similarity score is calculated from their signatures to measure their likeness. We implement the approach in a prototype system designated as BinMatch which analyzes IA-32 binary code on the Linux platform. We evaluate BinMatch with eight real-world projects compiled with different compilation configurations and commonly-used obfuscation methods, totally performing over 100 million pairs of function comparison. The experimental results show that BinMatch is robust to the semantics-equivalent code transformation. Besides, it not only covers all target functions for clone analysis, but also improves the detection accuracy comparing to the state-of-the-art solutions.
Data-driven synthesis of smoke flows with CNN-based feature descriptors

We present a novel data-driven algorithm to synthesize high resolution flow simulations with reusable repositories of space-time flow data. In our work, we employ a descriptor learning approach to encode the similarity between fluid regions with differences in resolution and numerical viscosity. We use convolutional neural networks to generate the descriptors from fluid data such as smoke density and flow velocity. At the same time, we present a deformation limiting patch advection method which allows us to robustly track deformable fluid regions. With the help of this patch advection, we generate stable space-time data sets from detailed fluids for our repositories. We can then use our learned descriptors to quickly localize a suitable data set when running a new simulation. This makes our approach very efficient, and resolution independent. We will demonstrate with several examples that our method yields volumes with very high effective resolutions, and non-dissipative small scale details that naturally integrate into the motions of the underlying flow.
Robust Real-time Object Detection

This paper describes a visual object detection framework that is capable of processing images extremely rapidly while achieving high detection rates. There are three key contributions. The first is the introduction of a new image representation called the “Integral Image” which allows the features used by our detector to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features and yields extremely efficient classifiers . The third contribution is a method for combining classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising object-like regions. A set of experiments in the domain of face detection are presented. The system yields face detection performace comparable to the best previous systems [18, 13, 16, 12, 1]. Implemented on a conventional desktop, face detection proceeds at 15 frames per second.
Morph-based speech recognition and modeling of out-of-vocabulary words across languages

We explore the use of morph-based language models in large-vocabulary continuous-speech recognition systems across four so-called morphologically rich languages: Finnish, Estonian, Turkish, and Egyptian Colloquial Arabic. The morphs are subword units discovered in an unsupervised, data-driven way using the Morfessor algorithm. By estimating n-gram language models over sequences of morphs instead of words, the quality of the language model is improved through better vocabulary coverage and reduced data sparsity. Standard word models suffer from high out-of-vocabulary (OOV) rates, whereas the morph models can recognize previously unseen word forms by concatenating morphs. It is shown that the morph models do perform fairly well on OOVs without compromising the recognition accuracy on in-vocabulary words. The Arabic experiment constitutes the only exception since here the standard word model outperforms the morph model. Differences in the datasets and the amount of data are discussed as a plausible explanation.
Anticipating the future by watching unlabeled video

In many computer vision applications, machines will need to reason beyond the present, and predict the future. This task is challenging because it requires leveraging extensive commonsense knowledge of the world that is difficult to write down. We believe that a promising resource for efficiently obtaining this knowledge is through the massive amounts of readily available unlabeled video. In this paper, we present a large scale framework that capitalizes on temporal structure in unlabeled video to learn to anticipate both actions and objects in the future. The key idea behind our approach is that we can train deep networks to predict the visual representation of images in the future. We experimentally validate this idea on two challenging “in the wild” video datasets, and our results suggest that learning with unlabeled videos significantly helps forecast actions and anticipate objects.
Head Pose and Expression Transfer Using Facial Status Score

We propose a method to transfer both head poseand facial expression of a source person in a video to the faceof a target person in an output video. Our method models theentire 2D frame instead of the 3D face, and it generates outputresults using a status score, which includes the relative facialstatus about the head pose and expression in a frame. From thetarget video, the learning process obtains frame features neededfor moving to each frame from the neutral frame for all frames,and generates the basis of these features via principal componentanalysis (PCA). Then, it learns to generate these features from agiven status score sequentially. In the transfer process, it obtainsa status score from a source frame of the video and generatesthe features from the given status score. Then, it generates theoutput frame using the reconstructed features. An output videois generated by repeating these steps for each source frame.Our method generates output results on the trajectory of thetarget video by using the advantage of PCA. Therefore, in theoutput results generated by our methods, both head pose andexpression are transferred correctly while the non-face regionsof the frames are supported. Finally, we experimentally comparethe effectiveness of our method and conventional methods.
Revealing protein–lncRNA interaction

Long non-coding RNAs (lncRNAs) are associated to a plethora of cellular functions, most of which require the interaction with one or more RNA-binding proteins (RBPs); similarly, RBPs are often able to bind a large number of different RNAs. The currently available knowledge is already drawing an intricate network of interactions, whose deregulation is frequently associated to pathological states. Several different techniques were developed in the past years to obtain protein-RNA binding data in a high-throughput fashion. In parallel, in silico inference methods were developed for the accurate computational prediction of the interaction of RBP-lncRNA pairs. The field is growing rapidly, and it is foreseeable that in the near future, the protein-lncRNA interaction network will rise, offering essential clues for a better understanding of lncRNA cellular mechanisms and their disease-associated perturbations.
The role of emotion in computer-mediated communication: A review

It has been argued that the communication of emotions is more difficult in computer-mediated communication (CMC) than in face-to-face (F2F) communication. The aim of this paper is to review the empirical evidence in order to gain insight in whether emotions are communicated differently in these different modes of communication. We review two types of studies: (1) studies that explicitly examine discrete emotions and emotion expressions, and (2) studies that examine emotions more implicitly, namely as self-disclosure or emotional styles. Our conclusion is that there is no indication that CMC is a less emotional or less personally involving medium than F2F. On the contrary, emotional communication online and offline is surprisingly similar, and if differences are found they show more frequent and explicit emotion communication in CMC than in F2F. 2007
Adult Mouse Cortical Cell Taxonomy Revealed by Single Cell Transcriptomics

Nervous systems are composed of various cell types, but the extent of cell type diversity is poorly understood. We construct a cellular taxonomy of one cortical region, primary visual cortex, in adult mice on the basis of single-cell RNA sequencing. We identified 49 transcriptomic cell types, including 23 GABAergic, 19 glutamatergic and 7 non-neuronal types. We also analyzed cell type–specific mRNA processing and characterized genetic access to these transcriptomic types by many transgenic Cre lines. Finally, we found that some of our transcriptomic cell types displayed specific and differential electrophysiological and axon projection properties, thereby confirming that the single-cell transcriptomic signatures can be associated with specific cellular properties.  The mammalian brain is likely the most complex animal organ, given the variety and scope of functions it controls, the diversity of cells it comprises, and the number of genes it expresses. In the mammalian brain, the neocortex is essential for sensory, motor and cognitive behaviors. Although different cortical areas have dedicated roles in information processing, they exhibit a similar layered structure, with each layer harboring distinct neuronal populations. In the adult cortex, many types of neurons have been identified through characterization of their molecular, morphological, connectional, physiological and functional properties. Despite much effort, objective classification on the basis of quantitative features has been challenging, and our understanding of the extent of cell-type diversity remains incomplete. Cell types can be preferentially associated with molecular markers that underlie their unique structural, physiological and functional properties, and these markers have been used for cell classification. Transcriptomic profiling of small cell populations from fine dissections on the basis of cell surface or transgenic markers has been informative; however, any populationlevel profiling obscures potential heterogeneity in collected cells. Recently, robust and scalable transcriptomic single cell profiling has emerged as a powerful approach to characterization and classification of single cells, including neurons. We used single-cell RNA-seq to characterize and classify more than 1,600 cells from the primary visual cortex in adult male mice.
Network biology: understanding the cell's functional organization

A key aim of postgenomic biomedical research is to systematically catalogue all molecules and their interactions within a living cell. There is a clear need to understand how these molecules and the interactions between them determine the function of this enormously complex machinery, both in isolation and when surrounded by other cells. Rapid advances in network biology indicate that cellular networks are governed by universal laws and offer a new conceptual framework that could potentially revolutionize our view of biology and disease pathologies in the twenty-first century.
Clinical and microbiologic characteristics of vulvovaginitis in Korean prepubertal girls, 2009–2014: a single center experience

To update information on the clinical and microbiologic characteristics of pediatric vulvovaginitis in Korean prepubertal girls.



A total of 120 girls (aged 0 to 9 years) with culture-confirmed pediatric vulvovaginitis, diagnosed between 2009 and 2014, were enrolled in the study. The epidemiologic and microbiologic characteristics, and clinical outcomes were assessed. Patients with sexual precocity, as well as those who were referred for suspected sexual abuse, were excluded.



Girls aged 4 to 6 years were at the highest risk of pediatric vulvovaginitis. Seasonal distribution indicated obvious peaks in summer and winter. Of the 120 subjects, specific pathogens were identified in the genital specimens in only 20 cases (16.7%). Streptococcus pyogenes (n=12, 60%) was the leading cause of specific vulvovaginitis. Haemophilus influenzae was isolated in one patient. No cases presented with enteric pathogens, such as Shigella or Yersinia. A history of recent upper respiratory tract infection, swimming, and bubble bath use was reported in 37.5%, 15.8%, and 10.0% of patients, respectively. Recent upper respiratory tract infection was not significantly correlated with the detection of respiratory pathogens in genital specimens (P>0.05). Of 104 patients who underwent perineal hygienic care, 80 (76.9%) showed improvement of symptoms without antibiotic treatment. Furthermore, the efficacy of hygienic care was not significantly different between patients with or without specific pathogens (P>0.05).



Specific pathogens were only found in 16.7% of pediatric vulvovaginitis cases. Our results indicate an excellent outcome with hygienic care, irrespective of the presence of specific pathogens.
Communication-optimal parallel algorithm for strassen's matrix multiplication

Parallel matrix multiplication is one of the most studied fundamental problems in distributed and high performance computing. We obtain a new parallel algorithm that is based on Strassen's fast matrix multiplication and minimizes communication. The algorithm outperforms all known parallel matrix multiplication algorithms, classical and Strassen-based, both asymptotically and in practice. A critical bottleneck in parallelizing Strassen's algorithm is the communication between the processors. Ballard, Demmel, Holtz, and Schwartz (SPAA '11) prove lower bounds on these communication costs, using expansion properties of the underlying computation graph. Our algorithm matches these lower bounds, and so is communication-optimal. It exhibits perfect strong scaling within the maximum possible range.
 Benchmarking our implementation on a Cray XT4, we obtain speedups over classical and Strassen-based algorithms ranging from 24% to 184% for a fixed matrix dimension n=94080, where the number of processors ranges from 49 to 7203.
 Our parallelization approach generalizes to other fast matrix multiplication algorithms.