nils-herrmann/clean_scirepeval huggingface dataset:
Causes and consequences of microRNA dysregulation in cancer

Over the past several years it has become clear that alterations in the expression of microRNA (miRNA) genes contribute to the pathogenesis of most — if not all — human malignancies. These alterations can be caused by various mechanisms, including deletions, amplifications or mutations involving miRNA loci, epigenetic silencing or the dysregulation of transcription factors that target specific miRNAs. Because malignant cells show dependence on the dysregulated expression of miRNA genes, which in turn control or are controlled by the dysregulation of multiple protein-coding oncogenes or tumour suppressor genes, these small RNAs provide important opportunities for the development of future miRNA-based therapies.
Pattern Matching

An important subtask of the pattern discovery process is pattern matching, where the pattern sought is already known and we want to determine how often and where it occurs in a sequence. In this paper we review the most practical techniques to find patterns of different kinds. We show how regular expressions can be searched for with general techniques, and how simpler patterns can be dealt with more simply and efficiently. We consider exact as well as approximate pattern matching. Also, we cover both sequential searching, where the sequence cannot be preprocessed, and indexed searching, where we have a data structure built over the sequence to speed up the search.
Assessment of factors influencing the tissue culture-independent Agrobacterium-mediated in planta genetic transformation of okra [Abelmoschus esculentus (L.) Moench]

Regeneration of transformed plants from the Agrobacterium-infected tissue is a time-consuming process and requires hard work. Okra [Abelmoschus esculentus (L.) Moench] is highly recalcitrant to Agrobacterium-mediated genetic transformation and regeneration. In this study, we established a tissue culture-independent genetic transformation system for okra using seed as an explant. Agrobacterium tumefaciens EHA 105 harbouring the binary vector pCAMBIA 1301–bar was used to infect the okra seeds. Various parameters influencing the okra genetic transformation including, co-cultivation duration, acetosyringone, sonication, and vacuum infiltration have been evaluated. Maximum transformation efficiency of 18.3 % was recorded when the pre-cultured okra seeds were sonicated for 30 min and vacuum infiltrated for 3 min in Agrobacterium suspension containing 100 µM acetosyringone and co-cultivated for 3 days on a medium containing 100 µM acetosyringone. The GUS histochemical analysis confirmed the gus A gene integration and expression, whereas polymerase chain reaction (PCR) and Southern blot hybridization confirmed the bar gene integration and copy number in the transformed okra genome. The transgene was successfully segregated into the progeny plants with a Mendelian inheritance ratio of 3:1. The in planta transformation protocol developed in the present investigation is applicable to transform the okra plants with disease-resistant traits, and the transformed plants can be generated within 60 days.
Robust Monte Carlo localization for mobile robots

Mobile robot localization is the problem of determining a robot's pose from sensor data. Monte Carlo Localization is a family of algorithms for localization based on particle filters, which are approximate Bayes filters that use random samples for posterior estimation. Recently, they have been applied with great success for robot localization. Unfortunately, regular particle filters perform poorly in certain situations. Mixture-MCL, the algorithm described here, overcomes these problems by using a "dual" sampler, integrating two complimentary ways of generating samples in the estimation. To apply this algorithm for mobile robot localization, a kd-tree is learned from data that permits fast dual sampling. Systematic empirical results obtained using data collected in crowded public places illustrate superior performance, robustness, and efficiency, when compared to other state-of-the-art localization algorithms.
A Content-Adaptively Sparse Reconstruction Method for Abnormal Events Detection With Low-Rank Property

This paper presents a content-adaptively sparse reconstruction method for abnormal events detection by exploiting the low-rank property of video sequences. In dictionary learning phase, the bases which describe more important characteristics of the normal behavior patterns are assigned with lower reconstruction costs. Based on the low-rank property of the bases captured by the low-rank approximation, a weighted sparse reconstruction method is proposed to measure the abnormality of testing samples. Multiscale 3-D gradient features, which encode the spatiotemporal information, are adopted as the low level descriptors. The benefits of the proposed method are threefold: first, the low-rank property is utilized to learn the underlying normal dictionaries, which can represent groups of similar normal features effectively; second, the sparsity-based algorithm can adaptively determine the number of dictionary bases, which makes it a preferable choice for representing the dynamic scene semantics; and third, based on the weighted sparse reconstruction method, the proposed method is more efficient for detecting the abnormal events. Experimental results on the public datasets have shown that the proposed method yields competitive performance comparing with the state-of-the-art methods.
RainForest—A Framework for Fast Decision Tree Construction of Large Datasets

Classification of large datasets is an important data mining problem. Many classification algorithms have been proposed in the literature, but studies have shown that so far no algorithm uniformly outperforms all other algorithms in terms of quality. In this paper, we present a unifying framework called Rain Forest for classification tree construction that separates the scalability aspects of algorithms for constructing a tree from the central features that determine the quality of the tree. The generic algorithm is easy to instantiate with specific split selection methods from the literature (including C4.5, CART, CHAID, FACT, ID3 and extensions, SLIQ, SPRINT and QUEST). In addition to its generality, in that it yields scalable versions of a wide range of classification algorithms, our approach also offers performance improvements of over a factor of three over the SPRINT algorithm, the fastest scalable classification algorithm proposed previously. In contrast to SPRINT, however, our generic algorithm requires a certain minimum amount of main memory, proportional to the set of distinct values in a column of the input relation. Given current main memory costs, this requirement is readily met in most if not all workloads.
Impact Analysis of Flow Shaping in Ethernet-AVB/TSN and AFDX from Network Calculus and Simulation Perspective

Ethernet-AVB/TSN (Audio Video Bridging/Time-Sensitive Networking) and AFDX (Avionics Full DupleX switched Ethernet) are switched Ethernet technologies, which are both candidates for real-time communication in the context of transportation systems. AFDX implements a fixed priority scheduling strategy with two priority levels. Ethernet-AVB/TSN supports a similar fixed priority scheduling with an additional Credit-Based Shaper (CBS) mechanism. Besides, TSN can support time-triggered scheduling strategy. One direct effect of CBS mechanism is to increase the delay of its flows while decreasing the delay of other priority ones. The former effect can be seen as the shaping restriction and the latter effect can be seen as the shaping benefit from CBS. The goal of this paper is to investigate the impact of CBS on different priority flows, especially on the intermediate priority ones, as well as the effect of CBS bandwidth allocation. It is based on a performance comparison of AVB/TSN and AFDX by simulation in an automotive case study. Furthermore, the shaping benefit is modeled based on integral operation from network calculus perspective. Combing with the analysis of shaping restriction and shaping benefit, some configuration suggestions on the setting of CBS bandwidth are given. Results show that the effect of CBS depends on flow loads and CBS configurations. A larger load of high priority flows in AVB tends to a better performance for the intermediate priority flows when compared with AFDX. Shaping benefit can be explained and calculated according to the changing from the permitted maximum burst.
Scalable Object Detection Using Deep Neural Networks

Deep convolutional neural networks have recently achieved state-of-the-art performance on a number of image recognition benchmarks, including the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC-2012). The winning model on the localization sub-task was a network that predicts a single bounding box and a confidence score for each object category in the image. Such a model captures the whole-image context around the objects but cannot handle multiple instances of the same object in the image without naively replicating the number of outputs for each instance. In this work, we propose a saliency-inspired neural network model for detection, which predicts a set of class-agnostic bounding boxes along with a single score for each box, corresponding to its likelihood of containing any object of interest. The model naturally handles a variable number of instances for each class and allows for cross-class generalization at the highest levels of the network. We are able to obtain competitive recognition performance on VOC2007 and ILSVRC2012, while using only the top few predicted locations in each image and a small number of neural network evaluations.
Approaches to Online Learning and Concept Drift for User Identification in Computer Security

The task in the computer security domain of anomaly detection is to characterize the behaviors of a computer user (the "valid’, or "normal’ user) so that unusual occurrences can be detected by comparison f the current input stream to the valid user’s profile. This task requires an online leaming system that can respond to concept drift and handle discrete non-metric time sequence data. We present an architecture for online learning in the anomaly detection domain and address the issues of incremental updating of system parameters and instance selection. We demonstrate a method for measuring direction and magnitude of concept drift in the classification space and present approaches to the above stated issues which make use of the drift measurement. An empirical evaluation demonstrates the relative strengths and weaknesses of these techniques in comparison to a number of baseline techniques. We show that, for some users, our drift adaptive techniques are advantageous.
Object Recognition from Local Scale-Invariant Features

Proc. of the International Conference on Computer Vision, Corfu (Sept. 1999) An object recognition system has been developed that uses a new class of local image features. The features are invariant to image scaling, translation, and rotation, and partially invariant to illumination changes and affine or 3D projection. These features share similar properties with neurons in inferior temporal cortex that are used for object recognition in primate vision. Features are efficiently detected through a staged filtering approach that identifies stable points in scale space. Image keys are created that allow for local geometric deformations by representing blurred image gradients in multiple orientation planes and at multiple scales. The keys are used as input to a nearest-neighbor indexing method that identifies candidate object matches. Final verification of each match is achieved by finding a low-residual least-squares solution for the unknown model parameters. Experimental results show that robust object recognition can be achieved in cluttered partially-occluded images with a computation time of under 2 seconds.
Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach

This paper presents a real-time, object-independent grasp synthesis method which can be used for closed-loop grasping. Our proposed Generative Grasping Convolutional Neural Network (GG-CNN) predicts the quality and pose of grasps at every pixel. This one-to-one mapping from a depth image overcomes limitations of current deep-learning grasping techniques by avoiding discrete sampling of grasp candidates and long computation times. Additionally, our GG-CNN is orders of magnitude smaller while detecting stable grasps with equivalent performance to current state-of-the-art techniques. The lightweight and single-pass generative nature of our GG-CNN allows for closed-loop control at up to 50Hz, enabling accurate grasping in non-static environments where objects move and in the presence of robot control inaccuracies. In our real-world tests, we achieve an 83% grasp success rate on a set of previously unseen objects with adversarial geometry and 88% on a set of household objects that are moved during the grasp attempt. We also achieve 81% accuracy when grasping in dynamic clutter.
Accidental strangulation resulted from hypoxyphilia associated with multiple paraphilias and substance abuse : a psychological autopsy case report

We report a case of a 34-year-old male who was found hanged by his neck in his own apartment. The deceased was dressed in women’s nylon panties, bra, nylon pantyhose, and a wig. The immediate cause of his death was found to be asphyxia due to hanging but the manner of his death was initially considered to be equivocal. The psychological autopsy based on the structured interview with the deceased’s parents, sexual partner, and closed friends using Beck’s Suicide Intent Scale (SIS), Paykel’s Interview for Recent Life Events (IRLE), Duke Social Support Index (DSSI), the Structured Clinical Interview for DSM-IV-TR (SCID-I), Hamilton Depression Rating Scale (HAM-D), and our structured suicide information questionnaire based on the pamphlet “Suicide Prevention and Psychological Autopsy” (Department of U.S. Army) and some published articles was performed and the data were analyzed. The interviews with the informants showed that the deceased was single; was engaged in homosexual behavior; was well educated with a university degree; was employed; has not been diagnosed with any chronic illnesses; never had suicidal attempts in the past; didn’t have any history of alcohol and drug abuse; wasn’t recently emotionally upset; didn’t have any chronic and recent interpersonal problems with his family, sexual partner, and friends; had plans for the future. The deceased has not being diagnosed with any psychiatric illnesses during his life. But the scrupulous crime scene investigation was indicative that diseased was meeting the DSM-IV-TR diagnostic criteria for substance abuse, multiple paraphilias involving sexual masochism (hypoxyphilia), transvestic fetishism, and agalmatophilia (a paraphilia that involves being sexually attracted to a doll, statue or mannequin). Integration of the psychological autopsy data with the crime scene findings allowed identification of this event as an accidental death (autoerotic asphyxia).
Xen and the art of virtualization

Numerous systems have been designed which use virtualization to subdivide the ample resources of a modern computer. Some require specialized hardware, or cannot support commodity operating systems. Some target 100% binary compatibility at the expense of performance. Others sacrifice security or functionality for speed. Few offer resource isolation or performance guarantees; most provide only best-effort provisioning, risking denial of service.This paper presents Xen, an x86 virtual machine monitor which allows multiple commodity operating systems to share conventional hardware in a safe and resource managed fashion, but without sacrificing either performance or functionality. This is achieved by providing an idealized virtual machine abstraction to which operating systems such as Linux, BSD and Windows XP, can be ported with minimal effort.Our design is targeted at hosting up to 100 virtual machine instances simultaneously on a modern server. The virtualization approach taken by Xen is extremely efficient: we allow operating systems such as Linux and Windows XP to be hosted simultaneously for a negligible performance overhead --- at most a few percent compared with the unvirtualized case. We considerably outperform competing commercial and freely available solutions in a range of microbenchmarks and system-wide tests.
Automatic music transcription: challenges and future directions

Automatic music transcription is considered by many to be a key enabling technology in music signal processing. However, the performance of transcription systems is still significantly below that of a human expert, and accuracies reported in recent years seem to have reached a limit, although the field is still very active. In this paper we analyse limitations of current methods and identify promising directions for future research. Current transcription methods use general purpose models which are unable to capture the rich diversity found in music signals. One way to overcome the limited performance of transcription systems is to tailor algorithms to specific use-cases. Semi-automatic approaches are another way of achieving a more reliable transcription. Also, the wealth of musical scores and corresponding audio data now available are a rich potential source of training data, via forced alignment of audio to scores, but large scale utilisation of such data has yet to be attempted. Other promising approaches include the integration of information from multiple algorithms and different musical aspects.
Planning optimal paths for multiple robots on graphs

In this paper, we study the problem of optimal multi-robot path planning (MPP) on graphs. We propose two multiflow based integer linear programming (ILP) models that compute minimum last arrival time and minimum total distance solutions for our MPP formulation, respectively. The resulting algorithms from these ILP models are complete and guaranteed to yield true optimal solutions. In addition, our flexible framework can easily accommodate other variants of the MPP problem. Focusing on the time optimal algorithm, we evaluate its performance, both as a stand alone algorithm and as a generic heuristic for quickly solving large problem instances. Computational results confirm the effectiveness of our method.
Robust Manifold Nonnegative Matrix Factorization

Nonnegative Matrix Factorization (NMF) has been one of the most widely used clustering techniques for exploratory data analysis. However, since each data point enters the objective function with squared residue error, a few outliers with large errors easily dominate the objective function. In this article, we propose a Robust Manifold Nonnegative Matrix Factorization (RMNMF) method using ℓ2,1-norm and integrating NMF and spectral clustering under the same clustering framework. We also point out the solution uniqueness issue for the existing NMF methods and propose an additional orthonormal constraint to address this problem. With the new constraint, the conventional auxiliary function approach no longer works. We tackle this difficult optimization problem via a novel Augmented Lagrangian Method (ALM)--based algorithm and convert the original constrained optimization problem on one variable into a multivariate constrained problem. The new objective function then can be decomposed into several subproblems that each has a closed-form solution. More importantly, we reveal the connection of our method with robust K-means and spectral clustering, and we demonstrate its theoretical significance. Extensive experiments have been conducted on nine benchmark datasets, and all empirical results show the effectiveness of our method.
Nopol: Automatic Repair of Conditional Statement Bugs in Java Programs

We propose Nopol, an approach to automatic repair of buggy conditional statements (i.e.,  if-then-else statements). This approach takes a buggy program as well as a test suite as input and generates a patch with a conditional expression as output. The test suite is required to contain passing test cases to model the expected behavior of the program and at least one failing test case that reveals the bug to be repaired. The process of Nopol consists of three major phases. First, Nopol employs angelic fix localization to identify expected values of a condition during the test execution. Second, runtime trace collection is used to collect variables and their actual values, including primitive data types and objected-oriented features (e.g., nullness checks), to serve as building blocks for patch generation. Third, Nopol encodes these collected data into an instance of a Satisfiability Modulo Theory (SMT) problem; then a feasible solution to the SMT instance is translated back into a code patch. We evaluate Nopol on 22 real-world bugs (16 bugs with buggy if conditions and six bugs with missing preconditions) on two large open-source projects, namely Apache Commons Math and Apache Commons Lang. Empirical analysis on these bugs shows that our approach can effectively fix bugs with buggy if conditions and missing preconditions. We illustrate the capabilities and limitations of Nopol using case studies of real bug fixes.
A 2W, 92% efficiency and 0.01% THD+N class-D audio power amplifier for mobile applications, based on the novel SCOM architecture

This paper presents a high power efficient class-D audio power amplifier. The proposed class-D amplifier adopts the synchronized controlled oscillation modulator (SCOM), which is suitable for mobile applications. The THD+N at half the maximum output power is below 0.01% and the efficiency is better than 92% in an 8 /spl Omega/ speaker load. The one-chip integrated circuit is implemented in a 0.35 /spl mu/m CMOS technology, with a supply voltage range of 1.6 V-3.6 V. it occupies a chip area of 1.2/spl times/1.2 mm/sup 2/ and dissipates only 1.3 mA at idle.
Evolutionary Algorithms for Constrained Parameter Optimization Problems

Evolutionary computation techniques have received a great deal of attention regarding their potential as optimization techniques for complex numerical functions. However, they have not produced a significant breakthrough in the area of nonlinear programming due to the fact that they have not addressed the issue of constraints in a systematic way. Only recently have several methods been proposed for handling nonlinear constraints by evolutionary algorithms for numerical optimization problems; however, these methods have several drawbacks, and the experimental results on many test cases have been disappointing. In this paper we (1) discuss difficulties connected with solving the general nonlinear programming problem; (2) survey several approaches that have emerged in the evolutionary computation community; and (3) provide a set of 11 interesting test cases that may serve as a handy reference for future methods.
Hypothesis spaces for minimum Bayes risk training in large vocabulary speech recognition

The Minimum Bayes Risk (MBR) framework has been a successful strategy for the training of hidden Markov models for large vocabulary speech recognition. Practical implementations of MBR must select an appropriate hypothesis space and loss function. The set of word sequences and a word-based Levenshtein distance may be assumed to be the optimal choice but use of phoneme-based criteria appears to be more successful. This paper compares the use of different hypothesis spaces and loss functions defined using the system constituents of word, phone, physical triphone, physical state and physical mixture component. For practical reasons the competing hypotheses are constrained by sampling. The impact of the sampling technique on the performance of MBR training is also examined.