nils-herrmann/clean_scirepeval huggingface dataset:
Fast and effective orchestration of compiler optimizations for automatic performance tuning

Although compile-time optimizations generally improve program performance, degradations caused by individual techniques are to be expected. One promising research direction to overcome this problem is the development of dynamic, feedback-directed optimization orchestration algorithms, which automatically search for the combination of optimization techniques that achieves the best program performance. The challenge is to develop an orchestration algorithm that finds, in an exponential search space, a solution that is close to the best, in acceptable time. In this paper, we build such a fast and effective algorithm, called Combined Elimination (CE). The key advance of CE over existing techniques is that it takes the least tuning time (57% of the closest alternative), while achieving the same program performance. We conduct the experiments on both a Pentium IV machine and a SPARC II machine, by measuring performance of SPEC CPU2000 benchmarks under a large set of 38 GCC compiler options. Furthermore, through orchestrating a small set of optimizations causing the most degradation, we show that the performance achieved by CE is close to the upper bound obtained by an exhaustive search algorithm. The gap is less than 0.2% on average.
Mitigating Slow Dynamics of Low-Cost Chemical Sensors for Mobile Air Quality Monitoring Sensor Networks

The last decade has seen a growing interest in air quality monitoring using networks of wireless low-cost sensor platforms. One of the unifying characteristics of chemical sensors typically used in real-world deployments is their slow response time. While the impact of sensor dynamics can largely be neglected when considering static scenarios, in mobile applications chemical sensor measurements should not be considered as point measurements (i.e. instantaneous in space and time). In this paper, we study the impact of sensor dynamics on measurement accuracy and locality through systematic experiments in the controlled environment of a wind tunnel. We then propose two methods for dealing with this problem: (i) reducing the effect of the sensor’s slow dynamics by using an open active sampler, and (ii) estimating the underlying true signal using a sensor model and a deconvolution technique. We consider two performance metrics for evaluation: localization accuracy of specific field features and root mean squared error in field estimation. Finally, we show that the deconvolution technique results in consistent performance improvement for all the considered scenarios, and for both metrics, while the active sniffer design considered provides an advantage only for feature localization, particularly for the highest sensor movement speed.
The syntax and semantics of the proposed international algebraic language of the Zurich ACM-GAMM Conference

This paper gives a tutorial summary of the syntax and interpretation rules of the proposed international algebraic language put forward by the Zurich ACM-GAMM Conference, followed by a formal, complete presentation of the same information. Notations are presented for numbers, numerical variables, Boolean variables, relations, n-dimensional arrays, functi ons, operator s and algebraic expre s sions. Means are provided in the language for specifying assignment of values to. variables, conditional execution of statements, iterative proce<i;ures, formation of compound statements from sequences of statements, definition of new statements for arbitrary procedures, reuse and alteration of program segments. The proposed language is intended to provide convenient and concise means for expressing virtually all procedures of numericaL computation while employing relatively few syntactical rules and statement types.
The Infinite PCFG Using Hierarchical Dirichlet Processes

We present a nonparametric Bayesian model of tree structures based on the hierarchical Dirichlet process (HDP). Our HDP-PCFG model allows the complexity of the grammar to grow as more training data is available. In addition to presenting a fully Bayesian model for the PCFG, we also develop an efficient variational inference procedure. On synthetic data, we recover the correct grammar without having to specify its complexity in advance. We also show that our techniques can be applied to full-scale parsing applications by demonstrating its effectiveness in learning state-split grammars.
A guide to convolution arithmetic for deep learning

All models are wrong, but some are useful. 2 Acknowledgements The authors of this guide would like to thank David Warde-Farley, Guillaume Alain and Caglar Gulcehre for their valuable feedback. Special thanks to Ethan Schoonover, creator of the Solarized color scheme, 1 whose colors were used for the figures. Feedback Your feedback is welcomed! We did our best to be as precise, informative and up to the point as possible, but should there be anything you feel might be an error or could be rephrased to be more precise or com-prehensible, please don't refrain from contacting us. Likewise, drop us a line if you think there is something that might fit this technical report and you would like us to discuss – we will make our best effort to update this document. Source code and animations The code used to generate this guide along with its figures is available on GitHub. 2 There the reader can also find an animated version of the figures.
Predicting the Risk of Heart Attacks usingNeural Network and Decision Tree

The healthcare environment is more and more data enriched, but the amount of knowledge getting from those data is very less, because lack of data analysis tools. We need to get the hidden relationships from the data. In the healthcare system to predict the heart attack perfectly, there are some techniques which are already in use. There is some lack of accuracy in the available techniques like Naïve Bayes. Here, this paper proposes the system which uses neural network and Decision tree (ID3) to predict the heart attacks. Here the dataset with 6 attributes is used to diagnose the heart attacks. The dataset used is acath heart attack dataset provided by UCI machine learning repository. The results of the prediction give more accurate output than the other techniques.
Fault-Tolerant Interior-Permanent-Magnet Machines for Hybrid Electric Vehicle Applications

Multiphase interior permanent magnet (IPM) motors are very good candidates for hybrid electric vehicle applications. High torque pulsation is the major disadvantage of most IPM motor configurations. A five-phase IPM motor with low torque pulsation is discussed. The mathematical model of the five-phase motor is given. A control strategy that provides fault tolerance to five-phase permanent-magnet motors is introduced. In this scheme, the five-phase system continues operating safely under loss of up to two phases without any additional hardware connections. This feature is very important in traction and propulsion applications where high reliability is of major importance. The system that is introduced in this paper will guarantee high efficiency, high performance, and high reliability, which are required for automotive applications A prototype four-pole IPM motor with 15 stator slots has been built and is used for experimental verification.
Image Sequences Based Traffic Incident Detection for Signaled Intersections Using HMM

For an intelligent transportation system (ITS), traffic incident detection is one of the most important issues, especially for urban area which is full of signaled intersections. In this paper, we propose a novel traffic incident detection method based on the image signal processing and hidden Markov model (HMM) classifier. First, a traffic surveillance system was set up at a typical intersection of china, traffic videos were recorded and image sequences were extracted for image database forming. Second, compressed features were generated through several image processing steps, image difference with FFT was used to improve the recognition rate. Finally, HMM was used for classification of traffic signal logics (East-West, West-East, South-North, North-South) and accident of crash, the total correct rate is 74% and incident recognition rate is 84%. We believe, with more types of incident adding to the database, our detection algorithm could serve well for the traffic surveillance system.
Task Scheduling Optimization in Cloud Computing Based on Heuristic Algorithm

Cloud computing is an emerging technology and it allows users to pay as you need and has the high performance. Cloud computing is a heterogeneous system as well and it holds large amount of application data. In the process of scheduling some intensive data or computing an intensive application, it is acknowledged that optimizing the transferring and processing time is crucial to an application program. In this paper in order to minimize the cost of the processing we formulate a model for task scheduling and propose a particle swarm optimization (PSO) algorithm which is based on small position value rule. By virtue of comparing PSO algorithm with the PSO algorithm embedded in crossover and mutation and in the local research, the experiment results show the PSO algorithm not only converges faster but also runs faster than the other two algorithms in a large scale. The experiment results prove that the PSO algorithm is more suitable to cloud computing.
Modeling Relationships in Referential Expressions with Compositional Modular Networks

People often refer to entities in an image in terms of their relationships with other entities. For example, the black cat sitting under the table refers to both a black cat entity and its relationship with another table entity. Understanding these relationships is essential for interpreting and grounding such natural language expressions. Most prior work focuses on either grounding entire referential expressions holistically to one region, or localizing relationships based on a fixed set of categories. In this paper we instead present a modular deep architecture capable of analyzing referential expressions into their component parts, identifying entities and relationships mentioned in the input expression and grounding them all in the scene. We call this approach Compositional Modular Networks (CMNs): a novel architecture that learns linguistic analysis and visual inference end-to-end. Our approach is built around two types of neural modules that inspect local regions and pairwise interactions between regions. We evaluate CMNs on multiple referential expression datasets, outperforming state-of-the-art approaches on all tasks.
Motor primitives in vertebrates and invertebrates

In recent years different lines of evidence have led to the idea that motor actions and movements in both vertebrates and invertebrates are composed of elementary building blocks. The entire motor repertoire can be spanned by applying a well-defined set of operations and transformations to these primitives and by combining them in many different ways according to well-defined syntactic rules. Motor and movement primitives and modules might exist at the neural, dynamic and kinematic levels with complicated mapping among the elementary building blocks subserving these different levels of representation. Hence, while considerable progress has been made in recent years in unravelling the nature of these primitives, new experimental, computational and conceptual approaches are needed to further advance our understanding of motor compositionality.
Active query sensing for mobile location search

While much exciting progress is being made in mobile visual search, one important question has been left unexplored in all current systems. When the first query fails to find the right target (up to 50% likelihood), how should the user form his/her search strategy in the subsequent interaction? In this paper, we propose a novel Active Query Sensing system to suggest the best way for sensing the surrounding scenes while forming the second query for location search. We accomplish the goal by developing several unique components -- an offline process for analyzing the saliency of the views associated with each geographical location based on score distribution modeling, predicting the visual search precision of individual views and locations, estimating the view of an unseen query, and suggesting the best subsequent view change. Using a scalable visual search system implemented over a NYC street view data set (0.3 million images), we show a performance gain as high as two folds, reducing the failure rate of mobile location search to only 12% after the second query. This work may open up an exciting new direction for developing interactive mobile media applications through innovative exploitation of active sensing and query formulation.
Automatically finding performance problems with feedback-directed learning software testing

A goal of performance testing is to find situations when applications unexpectedly exhibit worsened characteristics for certain combinations of input values. A fundamental question of performance testing is how to select a manageable subset of the input data faster to find performance problems in applications automatically. 
 We offer a novel solution for finding performance problems in applications automatically using black-box software testing. Our solution is an adaptive, feedback-directed learning testing system that learns rules from execution traces of applications and then uses these rules to select test input data automatically for these applications to find more performance problems when compared with exploratory random testing. We have implemented our solution and applied it to a medium-size application at a major insurance company and to an open-source application. Performance problems were found automatically and confirmed by experienced testers and developers.
Bleichenbacher's Attack Strikes again: Breaking PKCS#1 v1.5 in XML Encryption

We describe several attacks against the PKCS#1 v1.5 key transport mechanism of XML Encryption. Our attacks allow to recover the secret key used to encrypt transmitted payload data within a few minutes or several hours, depending on the considered scenario. The attacks exploit differences in error messages and in the timing behavior of XML frameworks. We show how to attack seemingly invulnerable implementations, by exploiting additional properties of the XML Encryption standard that lead to new side-channels. An interesting novelty of one of our attacks is that it combines a weakness of a public-key scheme (transporting an ephemeral session key) with a different weakness of a symmetric encryption scheme (which transports the payload data, encrypted with the session key). Recently the XML Encryption standard was updated, in response to an attack presented at CCS 2011. The attacks described in this paper work even against the updated version of XML Encryption. Our work shows once more that legacy cryptosystems have to be used with extreme care, and should be avoided wherever possible, since they may lead to practical attacks.
GENERALIZED AUTOREGRESSIVE CONDITIONAL HETEROSKEDASTICITY

A natural generalization of the ARCH (Autoregressive Conditional Heteroskedastic) process introduced in Engle (1982) to allow for past conditional variances in the current conditional variance equation is proposed. Stationarity conditions and autocorrelation structure for this new class of parametric models are derived. Maximum likelihood estimation and testing are also considered. Finally an empirical example relating to the uncertainty of the inflation rate is presented.
A Redundancy-Aware Sentence Regression Framework for Extractive Summarization

Existing sentence regression methods for extractive summarization usually model sentence importance and redundancy in two separate processes. They first evaluate the importance f(s) of each sentence s and then select sentences to generate a summary based on both the importance scores and redundancy among sentences. In this paper, we propose to model importance and redundancy simultaneously by directly evaluating the relative importance f(s|S) of a sentence s given a set of selected sentences S. Specifically, we present a new framework to conduct regression with respect to the relative gain of s given S calculated by the ROUGE metric. Besides the single sentence features, additional features derived from the sentence relations are incorporated. Experiments on the DUC 2001, 2002 and 2004 multi-document summarization datasets show that the proposed method outperforms state-of-the-art extractive summarization approaches.
Learning Spectral Descriptors for Deformable Shape Correspondence

Informative and discriminative feature descriptors play a fundamental role in deformable shape analysis. For example, they have been successfully employed in correspondence, registration, and retrieval tasks. In recent years, significant attention has been devoted to descriptors obtained from the spectral decomposition of the Laplace-Beltrami operator associated with the shape. Notable examples in this family are the heat kernel signature (HKS) and the recently introduced wave kernel signature (WKS). The Laplacian-based descriptors achieve state-of-the-art performance in numerous shape analysis tasks; they are computationally efficient, isometry-invariant by construction, and can gracefully cope with a variety of transformations. In this paper, we formulate a generic family of parametric spectral descriptors. We argue that to be optimized for a specific task, the descriptor should take into account the statistics of the corpus of shapes to which it is applied (the "signal") and those of the class of transformations to which it is made insensitive (the "noise"). While such statistics are hard to model axiomatically, they can be learned from examples. Following the spirit of the Wiener filter in signal processing, we show a learning scheme for the construction of optimized spectral descriptors and relate it to Mahalanobis metric learning. The superiority of the proposed approach in generating correspondences is demonstrated on synthetic and scanned human figures. We also show that the learned descriptors are robust enough to be learned on synthetic data and transferred successfully to scanned shapes.
Neuroanatomical correlates of brainstem coma.

The brainstem tegmentum, including the reticular formation, contains distinct nuclei, each of which has a set of chemical, physiological and anatomical features. Damage to the brainstem tegmentum is known to cause coma, the most radical disturbance of consciousness. However, it has remained unclear which nuclei within the tegmentum are crucial for the maintenance of consciousness in humans. Accordingly, we initiated a retrospective study of MRIs obtained from 47 patients with brainstem stroke. The lesion boundaries were charted on patient MRIs and transferred onto a corresponding series of 4.7 T MRIs obtained from a control brainstem specimen that later was cut on a freezing microtome and analysed histologically. In addition, medical charts and available post-mortem materials were used to obtain relevant clinical and anatomical data to verify the MRI readings in each case. We found that in the 38 patients who did not have coma, brainstem damage either was located outside the tegmentum (n = 29) or produced a very small and unilateral compromise of the tegmentum (n = 9). In contrast, in patients who had coma (n = 9), the lesions in the tegmentum were mostly bilateral (n = 7) and were located either in the pons alone (n = 4) or in the upper pons and the midbrain (n = 5). The maximum overlap territory of the lesions coincided with the location of the rostral raphe complex, locus coeruleus, laterodorsal tegmental nucleus, nucleus pontis oralis, parabrachial nucleus and the white matter in between these nuclei. We also found that four coma subjects developed hyperthermia and died in the absence of any infections. In these cases, the maximum lesion overlap was centred in the core of pontine tegmentum. Our findings suggest that lesions confined to the upper pons can cause coma in humans even in the absence of damage to the midbrain. The findings also point to the brainstem nuclei whose lesions are likely to be associated with loss of consciousness and fatal hyperthermia in humans.
Facial Attractiveness : Evolutionary , Cognitive , and Social Perspectives

Edited volumes are an imperfect format for the presentation of ideas, not least because their goals vary. Sometimes they aim simply to survey the field, at other times to synthesize and advance the field. I prefer the former for disciplines that by their nature are not disposed to achieve definitive statements (philosophy, for example). A volume on an empirical topic, however, by my judgment falls short if it closes without firm conclusions, if not on the topic itself, at least on the state of the art of its study. Facial Attractiveness does fall short of this standard, but not for lack of serious effort (especially appreciated are such features as the summary table in Chapter 5). Although by any measure an excellent and thorough review of the major strands of its topic, the volume’s authors are often in such direct conflict that the reader is disappointed that the editors do not, in the end, provide sufficient guidance about where the most productive research avenues lie. Every contribution is persuasive, but as they cannot all be correct, who is to win the day? An obvious place to begin is with the question, What is “attractiveness”? Most writers seem unaware of the problem, and how it might impact their research methodology. What, the reader wants to know, is the most defensible conceptualization of the focal phenomenon? Often an author focuses explicitly on the aesthetic dimension of “attractive,” treating it as a synonym for “beauty.” A recurring phrase in the book is that “beauty is in the eye of the beholder,” with the authors undertaking to argue whether this standard accurately describes social reality. They reach contradictory conclusions. Chapter 1 (by Adam Rubenstein et al.) finds the maxim to be a “myth” which, by chapter’s end, is presumably dispelled; Anthony Little and his co-authors in Chapter 3, however, view their contribution as “help[ing] to place beauty back into the eye of the beholder.” Other chapters take intermediate positions. Besides the aesthetic, “attractive” can refer to raw sexual appeal, or to more long-term relationship evaluations. Which kind of attractiveness one intends will determine the proper methodology to use, and thereby impact the likely experimental results. As only one example, if one intends to investigate aesthetic attraction, the sexual orientation of the judges does not matter, whereas it matters a great deal if one intends to investigate sexual or relationship attraction. Yet no study discussed in these
Wireless power for mobile devices

Wireless power transfer allows a convenient, easy to use battery charging of mobile phones and other mobile devices. No hassle with cables and plugs, just place the device on a pad and that's it. Such a system even has the potential to become a standard charging solution. Where are the limits for such a solution and which are the side conditions to consider? What are the possibilities to realize such a system? To make the whole idea a success, it is definitely necessary to come to widely accepted standard. Therefore, in 2009 the Wireless Power Consortium was founded with meanwhile more than 80 international companies as members. The consortium recently released the first worldwide standard on wireless power for mobile devices of to 5W called “Qi”. The contribution presents details of this standard and the rationale behind.