nils-herrmann/clean_scirepeval huggingface dataset:
The plastic surgery hypothesis

Recent work on genetic-programming-based approaches to automatic program patching have relied on the insight that the content of new code can often be assembled out of fragments of code that already exist in the code base. This insight has been dubbed the plastic surgery hypothesis; successful, well-known automatic repair tools such as GenProg rest on this hypothesis, but it has never been validated. We formalize and validate the plastic surgery hypothesis and empirically measure the extent to which raw material for changes actually already exists in projects. In this paper, we mount a large-scale study of several large Java projects, and examine a history of 15,723 commits to determine the extent to which these commits are graftable, i.e., can be reconstituted from existing code, and find an encouraging degree of graftability, surprisingly independent of commit size and type of commit. For example, we find that changes are 43% graftable from the exact version of the software being changed. With a view to investigating the difficulty of finding these grafts, we study the abundance of such grafts in three possible sources: the immediately previous version, prior history, and other projects. We also examine the contiguity or chunking of these grafts, and the degree to which grafts can be found in the same file. Our results are quite promising and suggest an optimistic future for automatic program patching methods that search for raw material in already extant code in the project being patched.
Tight bounds on quantum searching

We provide a tight analysis of Grover’s recent algorithm for quantum database searching. We give a simple closed-form formula for the probability of success after any given number of iterations of the algorithm. This allows us to determine the number of iterations necessary to achieve almost certainty of finding the answer. Furthermore, we analyse the behaviour of the algorithm when the element to be found appears more than once in the table and we provide a new algorithm to find such an element even when the number of solutions is not known ahead of time. Using techniques from Shor’s quantum factoring algorithm in addition to Grover’s approach, we introduce a new technique for approximate quantum counting, which allows to estimate the number of solutions. Finally we provide a lower bound on the efficiency of any possible quantum database searching algorithm and we show that Grover’s algorithm nearly comes within a factor 2 of being optimal in terms of the number of probes required in the table.
Education, Signaling and Mismatch

We assess the importance education as a signal of workersskills and the e¤ects of poor signaling quality on labor market outcomes. We do so by merging a frictional labor market model with a signaling setup where there is a privately observed idiosyncratic component in the cost of education. Given that highly skilled workers cannot correctly signal their abilities, their wages will be lower and they will not be matched to the "right" vacancies, or may be unemployed. Skilled workers will then have lower incentives to move to high productivity markets. Furthermore, fewer vacancies will be created in labor markets where skills matter, and incentives for workers to invest in education will be lower. Overall, an economy where education is a noisier signal generates lower educational attainment, higher unemployment and lower productivity. In addition, we provide evidence suggesting that education plays a poor signaling role in Latin American countries. We then calibrate our model using Peruvian data, and through a quantitative exercise we show that this mechanism could be relevant to explain the relatively bad performance of labor markets in Latin American countries. 1E-mail: larozamena@utdt.edu,  We are grateful to Fernando Álvarez Parra for his comments and suggestions.
A Middleware for Fast and Flexible Sensor Network Deployment

A key problem in current sensor network technology is the heterogeneity of the available software and hardware platforms which makes deployment and application development a tedious and time consuming task. To minimize the unnecessary and repetitive implementation of identical functionalities for different platforms, we present our Global Sensor Networks (GSN) middleware which supports the flexible integration and discovery of sensor networks and sensor data, enables fast deployment and addition of new platforms, provides distributed querying, filtering, and combination of sensor data, and supports the dynamic adaption of the system configuration during operation. GSN's central concept is the virtual sensor abstraction which enables the user to declaratively specify XML-based deployment descriptors in combination with the possibility to integrate sensor network data through plain SQL queries over local and remote sensor data sources. In this demonstration, we specifically focus on the deployment aspects and allow users to dynamically reconfigure the running system, to add new sensor networks on the fly, and to monitor the effects of the changes via a graphical interface. The GSN implementation is available from http://globalsn.sourceforge.net/.
Privacy-preserving generative deep neural networks support clinical data sharing

Though it is widely recognized that data sharing enables faster scientific progress, the sensible need to protect participant privacy hampers this practice in medicine. We train deep neural networks that generate synthetic subjects closely resembling study participants. Using the SPRINT trial as an example, we show that machine-learning models built from simulated participants generalize to the original dataset. We incorporate differential privacy, which offers strong guarantees on the likelihood that a subject could be identified as a member of the trial. Investigators who have compiled a dataset can use our method to provide a freely accessible public version that enables other scientists to perform discovery-oriented analyses. Generated data can be released alongside analytical code to enable fully reproducible workflows, even when privacy is a concern. By addressing data sharing challenges, deep neural networks can facilitate the rigorous and reproducible investigation of clinical datasets.  Sharing individual-level data from clinical studies remains challenging. The status quo often requires scientists to establish a formal collaboration and execute extensive data usage agreements before sharing data. These requirements slow or even prevent data sharing between researchers in all but the closest collaborations. Recent initiatives have begun to address cultural challenges around data sharing. The New England Journal of Medicine recently held the Systolic Blood Pressure Trial (SPRINT) Data Analysis Challenge to examine possible benefits of clinical trial data sharing (1, 2). The SPRINT clinical trial examined the efficacy of intensive lowering of systolic blood pressure (<120 mmHg) compared with treatment to a standard systolic blood pressure goal (<140 mmHg). Intensive blood pressure lowering resulted in fewer cardiovascular events and the trial was stopped early. Reanalysis of the challenge data led to the development of personalized treatment scores (3) and decision support systems (4), in addition to a more specific analysis of blood pressure management in participants with chronic kidney disease (5). Initiatives such as the SPRINT Data Analysis Challenge have begun to address cultural norms. Even for this effort which focused on data sharing, investigators were required to execute data use agreements that included clauses to maintain security and prohibit re-identification or sharing. . CC-BY 4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this  (which was not . http://dx.doi.org/10.1101/159756 doi: bioRxiv  first posted online Jul. 5, 2017;
Biomedical Event Extraction using Abstract Meaning Representation

We propose a novel,  Meaning Representation (AMR) based approach to identifying molecular events/interactions in biomedical text. Our key contributions are: (1) an empirical validation of our hypothesis that an event is a subgraph of the AMR graph, (2) a neural network-based model that identifies such an event subgraph given an AMR, and (3) a distant supervision based approach to gather additional training data. We evaluate our approach on the 2013 Genia Event Extraction dataset1 (Kim et al., 2013) and show promising results.
Perseus: Randomized Point-based Value Iteration for POMDPs

Partially observable Markov decision processes (POMDPs) form an attractive and principled framework for agent planning under uncertainty. Point-based approximate techniques for POMDPs compute a policy based on a finite set of points collected in advance from the agent’s belief space. We present a randomized point-based value iteration algorithm called Perseus. The algorithm performs approximate value backup stages, ensuring that in each backup stage the value of each point in the belief set is improved; the key observation is that a single backup may improve the value of many belief points. Contrary to other point-based methods, Perseus backs up only a (randomly selected) subset of points in the belief set, sufficient for improving the value of each belief point in the set. We show how the same idea can be extended to dealing with continuous action spaces. Experimental results show the potential of Perseus in large scale POMDP problems.
Leaf Classification Using Shape, Color, and Texture Features

Several methods to identify plants have been proposed by several researchers. Commonly, the methods did not capture color information, because color was not recognized as an important aspect to the identification. In this research, shape and vein, color, and texture features were incorporated to classify a leaf. In this case, a neural network called Probabilistic Neural network (PNN) was used as a classifier. The experimental result shows that the method for classification gives average accuracy of 93.75% when it was tested on Flavia dataset, that contains 32 kinds of plant leaves. It means that the method gives better performance compared to the original work. Color features, Foliage plants, Lacunarity, Leaf classification, PFT, PNN, Texture features.
A Machine Learning Architecture for Optimizing Web Search Engines

Indexing systems for the World Wide Web, such as Lycos and Alta Vista, play an essential role in making the Web useful and usable. These systems are based on Information Retrieval methods for indexing plain text documents, but also include heuristics for adjusting their document rankings based on the special HTML structure of Web documents. In this paper, we describe a wide range of such heuristics|including a novel one inspired by reinforcement learning techniques for propagating rewards through a graph|which can be used to a ect a search engine's rankings. We then demonstrate a system which learns to combine these heuristics automatically, based on feedback collected unintrusively from users, resulting in much improved rankings.
Benign Osteoblastoma Involving Maxilla: A Case Report and Review of the Literature

Osteoblastoma is a rare benign tumor. This tumor is characterized by osteoid and bone formation with the presence of numerous osteoblasts. The lesion is more frequently seen in long bones and rarely involves maxilla and mandible. Due to its clinical and histological similarity with other bone tumors such as osteoid osteoma and fibro-osseous lesions, osteoblastoma presents a diagnostic dilemma.  Very few cases of osteoblastomas involving maxillofacial region have been reported in the literature. This case report involves osteoblastoma involving right maxilla in an 18-year-old male patient. Following detailed clinical examination, radiological interpretation, and histopathological diagnosis, surgical excision was performed. The patient was followed up for a period of 3 years and was disease free.  Benign osteoblastoma involving jaw bones is a rare tumor. There is a close resemblance of this tumor with other lesions such as fibro-osseous lesions and odontogenic tumors and thus faces a diagnostic challenge. Surgical excision with a long-term follow-up gives good prognosis to this lesion-Benign Osteoblastoma.
Color spaces for computer graphics

Normal human color perception is a product of three independent sensory systems. By mirroring this mechanism, full-color display devices create colors as mixtures of three primaries. Any displayable color can be described by the corresponding values of these primaries. Frequently it is more convenient to define various other color spaces, or coordinate systems, for color representation or manipulation. Several such color spaces are presented which are suitable for applications involving user specification of color, along with the defining equations and illustrations. The use of special color spaces for particular kinds of color computations is discussed.
A direct LDA algorithm for high-dimensional data - with application to face recognition

However, for a task with very high-dimensional data such as images, the traditional LDA algorithm encounters several di$culties. Consider face recognition for example. A low-de"nition face image of size 64 64 implies a feature space of 64 64"4096 dimensions, and therefore scatter matrices of size 4096 4096"16M. First, it is computationally challenging to handle big matrices (such as computing eigenvalues). Second, those matrices are almost always singular, as the number of training images needs to be at least 16M for them to be non-degenerate. Due to these di$culties, it is commonly believed that a direct LDA solution for such high-dimensional data is infeasible. Thus, ironically, before LDA can be used to reduce dimensionality, another procedure has to be "rst applied for dimensionality reduction. In face recognition, many techniques have been proposed (for a good review, see Ref. ). Among them, the most notable is a two-stage PCA#LDA approach
How to Train Deep Variational Autoencoders and Probabilistic Ladder Networks

Variational autoencoders are a powerful framework for unsupervised learning. However, previous work has been restricted to shallow models with one or two layers of fully factorized stochastic latent variables, limiting the flexibility of the latent representation. We propose three advances in training algorithms of variational autoencoders, for the first time allowing to train deep models of up to five stochastic layers, (1) using a structure similar to the Ladder network as the inference model, (2) warm-up period to support stochastic units staying active in early training, and (3) use of batch normalization. Using these improvements we show state-of-the-art log-likelihood results for generative modeling on several benchmark datasets.
Survey on Frequent Pattern Mining

Frequent itemsets play an essential role in many data mining tasks that try to find interesting patterns from databases, such as association rules, correlations, sequences, episodes, classifiers, clusters and many more of which the mining of association rules is one of the most popular problems. The original motivation for searching association rules came from the need to analyze so called supermarket transaction data, that is, to examine customer behavior in terms of the purchased products. Association rules describe how often items are purchased together. For example, an association rule “beer ⇒ chips (80%)” states that four out of five customers that bought beer also bought chips. Such rules can be useful for decisions concerning product pricing, promotions, store layout and many others. Since their introduction in 1993 by Argawal et al. , the frequent itemset and association rule mining problems have received a great deal of attention. Within the past decade, hundreds of research papers have been published presenting new algorithms or improvements on existing algorithms to solve these mining problems more efficiently. In this chapter, we explain the basic frequent itemset and association rule mining problems. We describe the main techniques used to solve these problems and give a comprehensive survey of the most influential algorithms that were proposed during the last decade.
The Virtual Reality Modeling Language and Java

Why VRML and Java together? Over 20 million VRML browsers have been shipped with Web browsers, making interactive 3D graphics suddenly available for any desktop. Java adds complete programming capabilities plus network access, making VRML fully functional and portable. This is a powerful new combination, especially as ongoing research shows that VRML plus Java provide extensive support for building large-scale virtual environments (LSVEs). This article provides historical background, a detailed overview of VRML 3D graphics, sample VRML-Java test programs, and a look ahead at future work.
Private memoirs of a smart meter

Household smart meters that measure power consumption in real-time at fine granularities are the foundation of a future smart electricity grid. However, the widespread deployment of smart meters has serious privacy implications since they inadvertently leak detailed information about household activities. In this paper, we show that even without a priori knowledge of household activities or prior training, it is possible to extract complex usage patterns from smart meter data using off-the-shelf statistical methods. Our analysis uses two months of data from three homes, which we instrumented to log aggregate household power consumption every second. With the data from our small-scale deployment, we demonstrate the potential for power consumption patterns to reveal a range of information, such as how many people are in the home, sleeping routines, eating routines, etc. We then sketch out the design of a privacy-enhancing smart meter architecture that allows an electric utility to achieve its net metering goals without compromising the privacy of its customers.
On Bayesian Upper Confidence Bounds for Bandit Problems

Stochastic bandit problems have been analyzed from two different perspectives: a frequentist view, where the parameter is a deterministic unknown quantity, and a Bayesian approach, where the parameter is drawn from a prior distribution. We show in this paper that methods derived from this second perspective prove optimal when evaluated using the frequentist cumulated regret as a measure of performance. We give a general formulation for a class of Bayesian index policies that rely on quantiles of the posterior distribution. For binary bandits, we prove that the corresponding algorithm, termed BayesUCB, satisfies finite-time regret bounds that imply its asymptotic optimality. More generally, Bayes-UCB appears as an unifying framework for several variants of the UCB algorithm addressing different bandit problems (parametric multi-armed bandits, Gaussian bandits with unknown mean and variance, linear bandits). But the generality of the Bayesian approach makes it possible to address more challenging models. In particular, we show how to handle linear bandits with sparsity constraints by resorting to Gibbs sampling.
Managing Multiple Tasks in Complex, Dynamic Environments

Multitask Resource Conflicts Michael Freed NASA Ames Research Center mfieed@mail.arc.nasa.gov Sketchy planners are designed to achieve goals in realistically complex, time-pressured, and uncertain task environments. However, the ability to manage multiple, potentially interacting tasks in such environments requires extensions to the functionality these systems typically provide. This paper identifies a number of factors affecting how interacting tasks should be prioritized, interrupted, and resumed, and then describes a sketchy planner called APEX that takes account of these factors when managing multiple tasks To perform effectively in many enviromnents, an agent must be able to manage multiple tasks in a complex, timepressured, and partially uncertain world. For example, the APEX agent architecture described below has been used to simulate human air traffic controllers in a simulated aerospace environment (Freed and Remington, 1997). Air traffic control consists almost entirely of routine activity; complexity arises largely from the need to manage multiple tasks. For example, the task of guiding a plane to landing at a destination airport typically involves issuing a series of standard turn and descent authorizations to each plane. Since such routines must be carried out over minutes or tens of minutes, the task of handling any individual plane must be periodically interrupted to handle new arrivals or resume a previously interrupted planehandling task. Plan execution systems (e.g. Georgoff and Lansky, 1988; Firby, 1989; Cohen et al., 1989; Gat, 1992;  1994; Hayes-Roth, 1995; Pell, et al., 1997), also called sketchy planners, have been designed specifically to cope with the time-pressure and uncertainty inherent in these kinds of environments. This paper discusses a sketchy ---_ pianner caiied APEX which incorporates and buiids on multitask management capabilities found in previous systems.
Optimistic Optimization of a Deterministic Function without the Knowledge of its Smoothness

We consider a global optimization problem of a deterministic function f in a semimetric space, given a finite budget of n evaluations. The function f is assumed to be locally smooth (around one of its global maxima) with respect to a semi-metric l. We describe two algorithms based on optimistic exploration that use a hierarchical partitioning of the space at all scales. A first contribution is an algorithm, DOO, that requires the knowledge of l. We report a finite-sample performance bound in terms of a measure of the quantity of near-optimal states. We then define a second algorithm, SOO, which does not require the knowledge of the semimetric l under which f is smooth, and whose performance is almost as good as DOO optimally-fitted.
Semantic Parsing on Freebase from Question-Answer Pairs

In this paper, we train a semantic parser that scales up to Freebase. Instead of relying on annotated logical forms, which is especially expensive to obtain at large scale, we learn from question-answer pairs. The main challenge in this setting is narrowing down the huge number of possible logical predicates for a given question. We tackle this problem in two ways: First, we build a coarse mapping from phrases to predicates using a knowledge base and a large text corpus. Second, we use a bridging operation to generate additional predicates based on neighboring predicates. On the dataset of Cai and Yates (2013), despite not having annotated logical forms, our system outperforms their state-of-the-art parser. Additionally, we collected a more realistic and challenging dataset of question-answer pairs and improves over a natural baseline.
Deterministic quantum teleportation of atomic qubits

Quantum teleportation provides a means to transport quantum information efficiently from one location to another, without the physical transfer of the associated quantum-information carrier. This is achieved by using the non-local correlations of previously distributed, entangled quantum bits (qubits). Teleportation is expected to play an integral role in quantum communication and quantum computation. Previous experimental demonstrations have been implemented with optical systems that used both discrete and continuous variables, and with liquid-state nuclear magnetic resonance. Here we report unconditional teleportation of massive particle qubits using atomic (9Be+) ions confined in a segmented ion trap, which aids individual qubit addressing. We achieve an average fidelity of 78 per cent, which exceeds the fidelity of any protocol that does not use entanglement. This demonstration is also important because it incorporates most of the techniques necessary for scalable quantum information processing in an ion-trap system.