nils-herrmann/clean_scirepeval huggingface dataset:
Knowledge-Based Artificial Neural Networks

Hybrid learning methods use theoretical knowledge of a domain and a set of classified examples to develop a method for accurately classifying examples not seen during training. The challenge of hybrid learning systems is to use the information provided by one source of information to offset information missing from the other source. By so doing, a hybrid learning system should learn more effectively than systems that use only one of the information sources. KBANN(Knowledge-Based Artificial Neural Networks) is a hybrid learning system built on top of connectionist learning techniques. It maps problem-specific “domain theories”, represented in propositional logic, into neural networks and then refines this reformulated knowledge using backpropagation. KBANN is evaluated by extensive empirical tests on two problems from molecular biology. Among other results, these tests show that the networks created by KBANN generalize better than a wide variety of learning systems, as well as several techniques proposed by biologists.
Design and Calibration of a Large Open-Ended Coaxial Probe for the Measurement of the Dielectric Properties of Concrete

The subject of this paper is the design and calibration of an open-ended coaxial probe for the nondestructive measurement of the dielectric properties of concrete. Measurements are made between 100-900 MHz, frequencies which are often used in geophysics and civil engineering for ground penetrating radar inspection. The probe is calibrated using measurements on saline solutions in conjunction with three different mathematical techniques for comparative study. Measurements of mortar and concrete specimens having different water/cement ratios were made in order to observe the standard deviations due to their heterogeneous nature. Similar to the case of relatively homogeneous rock specimens (limestone and granite), the standard deviation for heterogeneous concrete samples do not exceed 5%. In addition, the effect of the concrete's porosity on its dielectric properties was clearly observed: measured permittivity between 4-4.5 at 900 MHz for porous concrete, and between 6.5-7.5 at 900 MHz for dense concrete.
Domain Adaptation for Automatic OLED Panel Defect Detection Using Adaptive Support Vector Data Description

Detection of surface defects on organic light emitting diode (OLED) panels pose challenges such as irregular shapes and sizes along with varying textures and patterns on the panels. These challenges can be addressed by designing invariant features and training an anomaly detection algorithm such as support vector data description (SVDD). However, these hand designed features may not be capable of handling test datasets that have undergone distributional shift due to changes in lighting configuration or panel specification. This leads to a degradation of the classifier performance. In this paper, we propose a domain adaptation technique for outlier detection called as adaptive support vector data description (A-SVDD) to tackle distributional change in OLED panel datasets. The proposed method aims to learn an incremental classifier based on the existing classifier using an objective function similar to SVDD. We also investigate the application of features called as local inlier–outlier ratios augmented with modified local binary pattern (LBP) for detection of OLED panel defects in the context of SVDD and A-SVDD. In the experiments, the proposed domain adaptation technique is compared with baseline methods and existing approaches to demonstrate its effectiveness. A detailed evaluation of the features was performed in the context of A-SVDD and SVDD on several defects like scratch, spot, stain and pit to demonstrate that the combination of local inlier–outlier ratios and modified LBP significantly increases the detection accuracy.
Motion Prediction of Traffic Actors for Autonomous Driving using Deep Convolutional Networks

Despite its ubiquity in our daily lives, AI is only just starting to make advances in what may arguably have the largest societal impact thus far, the nascent field of autonomous driving. In this work we discuss this important topic and address one of crucial aspects of the emerging area, the problem of predicting future state of autonomous vehicle’s surrounding necessary for safe and efficient operations. We introduce a deep learning-based approach that takes into account current world state and produces rasterized representations of each actor’s vicinity. The raster images are then used by deep convolutional models to infer future movement of actors while accounting for inherent uncertainty of the prediction task. Extensive experiments on real-world data strongly suggest benefits of the proposed approach. Moreover, following successful tests the system was deployed to a fleet of autonomous vehicles.
Towards an Iterative Reinforcement Approach for Simultaneous Document Summarization and Keyword Extraction

Though both document summarization and keyword extraction aim to extract concise representations from documents, these two tasks have usually been investigated independently. This paper proposes a novel iterative reinforcement approach to simultaneously extracting summary and keywords from single document under the assumption that the summary and keywords of a document can be mutually boosted. The approach can naturally make full use of the reinforcement between sentences and keywords by fusing three kinds of relationships between sentences and words, either homogeneous or heterogeneous. Experimental results show the effectiveness of the proposed approach for both tasks. The corpus-based approach is validated to work almost as well as the knowledge-based approach for computing word semantics.
HoPE: Hot-Cacheline Prediction for Dynamic Early Decompression in Compressed LLCs

Data compression plays a pivotal role in improving system performance and reducing energy consumption, because it increases the logical effective capacity of a compressed memory system without physically increasing the memory size. However, data compression techniques incur some cost, such as non-negligible compression and decompression overhead. This overhead becomes more severe if compression is used in the cache. In this article, we aim to minimize the read-hit decompression penalty in compressed Last-Level Caches (LLCs) by speculatively decompressing frequently used cachelines. To this end, we propose a Hot-cacheline Prediction and Early decompression (HoPE) mechanism that consists of three synergistic techniques: Hot-cacheline Prediction (HP), Early Decompression (ED), and Hit-history-based Insertion (HBI). HP and HBI efficiently identify the hot compressed cachelines, while ED selectively decompresses hot cachelines, based on their size information. Unlike previous approaches, the HoPE framework considers the performance balance/tradeoff between the increased effective cache capacity and the decompression penalty. To evaluate the effectiveness of the proposed HoPE mechanism, we run extensive simulations on memory traces obtained from multi-threaded benchmarks running on a full-system simulation framework. We observe significant performance improvements over compressed cache schemes employing the conventional Least-Recently Used (LRU) replacement policy, the Dynamic Re-Reference Interval Prediction (DRRIP) scheme, and the Effective Capacity Maximizer (ECM) compressed cache management mechanism. Specifically, HoPE exhibits system performance improvements of approximately 11%, on average, over LRU, 8% over DRRIP, and 7% over ECM by reducing the read-hit decompression penalty by around 65%, over a wide range of applications.
The Secure Remote Password Protocol

This paper presents a new password authentication and key-exchange protocol suitable for authenticating users and exchanging keys over an untrusted network. The new protocol resists dictionary attacks mounted by either passive or active network intruders, allowing, in principle, even weak passphrases to be used safely. It also o ers perfect forward secrecy, which protects past sessions and passwords against future compromises. Finally, user passwords are stored in a form that is not plaintext-equivalent to the password itself, so an attacker who captures the password database cannot use it directly to compromise security and gain immediate access to the host. This new protocol combines techniques of zero-knowledge proofs with asymmetric key exchange protocols and o ers signi cantly improved performance over comparably strong extended methods that resist stolen-veri er attacks such as Augmented EKE or B-SPEKE.
300mm low k wafer dicing saw study

With the farther shrink of the IC dimension, the low k material has been widely used to replace the traditional SiO/sub 2/ ILD in order to reduce the interconnect delay. The introduction of low k material into silicon imposed challenges on dicing saw process, ILD and metal layers peeling and its penetration into the sealing ring of the die during dicing saw are the most common defects. In this paper, the low k material structure and its impact on wafer dicing were elaborated. A practical dicing quality inspection matrix was developed to assess the cutting process variation. A 300mm CMOS 90nm dual damascene low k wafer was chosen as a test vehicle to develop robust low k dicing saw process. The critical factors (dicing blade, index speed, spindle speed, cut in depth, test pattern in the saw street...) affecting cutting quality were studied and optimized. The selected C90 dual damascene low k device passed package reliability test with the optimized low k dicing saw recipe and process. The further improvement and solutions in eliminating the low k dicing saw peeling from both wafer fab and packaging assembly were also explored.
Modeling Compositionality with Multiplicative Recurrent Neural Networks

We present the multiplicative recurrent neural network as a general model for compositional meaning in language, and evaluate it on the task of fine-grained sentiment analysis. We establish a connection to the previously investigated matrixspace models for compositionality, and show they are special cases of the multiplicative recurrent net. Our experiments show that these models perform comparably or better than Elman-type additive recurrent neural networks and outperform matrix-space models on a standard fine-grained sentiment analysis corpus. Furthermore, they yield comparable results to structural deep models on the recently published Stanford Sentiment Treebank without the need for generating parse trees.
Transparent air filter for high-efficiency PM2.5 capture.

Particulate matter (PM) pollution has raised serious concerns for public health. Although outdoor individual protection could be achieved by facial masks, indoor air usually relies on expensive and energy-intensive air-filtering devices. Here, we introduce a transparent air filter for indoor air protection through windows that uses natural passive ventilation to effectively protect the indoor air quality. By controlling the surface chemistry to enable strong PM adhesion and also the microstructure of the air filters to increase the capture possibilities, we achieve transparent, high air flow and highly effective air filters of ~90% transparency with >95.00% removal of PM2.5 under extreme hazardous air-quality conditions (PM2.5 mass concentration >250 μg m(-3)). A field test in Beijing shows that the polyacrylonitrile transparent air filter has the best PM2.5 removal efficiency of 98.69% at high transmittance of ~77% during haze occurrence.
How Big Are the Tax Benefits of Debt ?

I integrate under firm-specific benefit functions to estimate that the capitalized tax benefit of debt equals 9.7 percent of firm value ~or as low as 4.3 percent, net of personal taxes!. The typical firm could double tax benefits by issuing debt until the marginal tax benefit begins to decline. I infer how aggressively a firm uses debt by observing the shape of its tax benefit function. Paradoxically, large, liquid, profitable firms with low expected distress costs use debt conservatively. Product market factors, growth options, low asset collateral, and planning for future expenditures lead to conservative debt usage. Conservative debt policy is persistent. DO THE TAX BENEFITS of debt affect corporate financing decisions? How much do they add to firm value? These questions have puzzled researchers since the work of Modigliani and Miller ~1958, 1963!. Recent evidence indicates that tax benefits are one of the factors that affect financing choice ~e.g., MacKie-Mason ~1990!, Graham ~1996a!!, although opinion is not unanimous on which factors are most important or how they contribute to firm value ~Shyam-Sunder and Myers ~1998!, Fama and French ~1998!!. Researchers face several problems when they investigate how tax incentives affect corporate financial policy and firm value. Chief among these problems is the difficulty of calculating corporate tax rates due to data problems and the complexity of the tax code. Other challenges include quantifying the effects of interest taxation at the personal level and understanding the bankruptcy process and the attendant costs of financial distress. In this * Graham is at the Fuqua School of Business, Duke University. Early conversations with Rick Green, Eric Hughson, Mike Lemmon, and S. P. Kothari were helpful in formulating some of the ideas in this paper. I thank Peter Fortune for providing the bond return data and Eli Ofek for supplying the managerial entrenchment data. I also thank three referees for detailed comments; also, Jennifer Babcock, Ron Bagley, Alon Brav, John Campbell, John Chalmers, Bob Dammon, Eugene Fama, Roger Gordon, Mark Grinblatt, Burton Hollifield, Steve Huddart, Arvind Krishnamurthy, Rich Lyons, Robert MacDonald, Ernst Maug, Ed Maydew, Roni Michaely, Phillip O’Conner, John Persons, Dick Rendleman, Oded Sarig, René Stulz ~the editor!, Bob Taggart, S. Viswanathan, Ralph Walkling, and Jaime Zender; seminar participants at Carnegie Mellon, Chicago, Duke, Ohio State, Rice, the University of British Columbia, the University of North Carolina, Washington University, William and Mary, and Yale; seminar participants at the NBER Public Economics and Corporate workshops, the National Tax Association annual conference, the American Economic Association meetings, and the Eighth Annual Conference on Financial Economics and Accounting for helpful suggestions and feedback. Jane Laird gathered the state tax rate information. All errors are my own. This paper previously circulated under the title “tD or Not tD? Using Benefit Functions to Value and Infer the Costs of Interest Deductions.” THE JOURNAL OF FINANCE • VOL. LV, NO. 5 • OCT.
PyElph - a software tool for gel images analysis and phylogenetics

This paper presents PyElph, a software tool which automatically extracts data from gel images, computes the molecular weights of the analyzed molecules or fragments, compares DNA patterns which result from experiments with molecular genetic markers and, also, generates phylogenetic trees computed by five clustering methods, using the information extracted from the analyzed gel image. The software can be successfully used for population genetics, phylogenetics, taxonomic studies and other applications which require gel image analysis. Researchers and students working in molecular biology and genetics would benefit greatly from the proposed software because it is free, open source, easy to use, has a friendly Graphical User Interface and does not depend on specific image acquisition devices like other commercial programs with similar functionalities do. PyElph software tool is entirely implemented in Python which is a very popular programming language among the bioinformatics community. It provides a very friendly Graphical User Interface which was designed in six steps that gradually lead to the results. The user is guided through the following steps: image loading and preparation, lane detection, band detection, molecular weights computation based on a molecular weight marker, band matching and finally, the computation and visualization of phylogenetic trees. A strong point of the software is the visualization component for the processed data. The Graphical User Interface provides operations for image manipulation and highlights lanes, bands and band matching in the analyzed gel image. All the data and images generated in each step can be saved. The software has been tested on several DNA patterns obtained from experiments with different genetic markers. Examples of genetic markers which can be analyzed using PyElph are RFLP (Restriction Fragment Length Polymorphism), AFLP (Amplified Fragment Length Polymorphism), RAPD (Random Amplification of Polymorphic DNA) and STR (Short Tandem Repeat). The similarity between the DNA sequences is computed and used to generate phylogenetic trees which are very useful for population genetics studies and taxonomic classification. PyElph decreases the effort and time spent processing data from gel images by providing an automatic step-by-step gel image analysis system with a friendly Graphical User Interface. The proposed free software tool is suitable for researchers and students which do not have access to expensive commercial software and image acquisition devices.
Advances in Collaborative Filtering

The collaborative filtering (CF) approach to recommenders has recently enjoyed much interest and progress. The fact that it played a central role within the recently completed Netflix competition has contributed to its popularity. This chapter surveys the recent progress in the field. Matrix factorization techniques, which became a first choice for implementing CF, are described together with recent innovations. We also describe several extensions that bring competitive accuracy into neighborhood methods, which used to dominate the field. The chapter demonstrates how to utilize temporal models and implicit feedback to extend models accuracy. In passing, we include detailed descriptions of some the central methods developed for tackling the challenge of the Netflix Prize competition.
Solving DDEs in Matlab

dde23 aims to make it as easy as possible to solve effectively delaydifferential equations (DDEs) with constant delays in Matlab. In this paper we discuss some of its features, including discontinuity tracking, iteration for short delays, and event location. We also develop some theoretical results that underlie the solver, including convergence, error estimation, and the effects of short delays on the evaluation of formulas and stability. Some examples illustrate the use of dde23 and show it to be a capable DDE solver that is exceptionally easy to use for a wide range of complex problems.
Large-Scale Training of SVMs with Automata Kernels

This paper presents a novel application of automata algorithms to machine learning. It introduces the first optimization solution for support vector machines used with sequence kernels that is purely based on weighted automata and transducer algorithms, without requiring any specific solver. The algorithms presented apply to a family of kernels covering all those commonly used in text and speech processing or computational biology. We show that these algorithms have significantly better computational complexity than previous ones and report the results of large-scale experiments demonstrating a dramatic reduction of the training time, typically by several orders of magnitude.
Hash2Vec, Feature Hashing for Word Embeddings

In this paper we propose the application of feature hashing to create word embeddings for natural language processing. Feature hashing has been used successfully to create document vectors in related tasks like document classification. In this work we show that feature hashing can be applied to obtain word embeddings in linear time with the size of the data. The results show that this algorithm, that does not need training, is able to capture the semantic meaning of words. We compare the results against GloVe showing that they are similar. As far as we know this is the first application of feature hashing to the word embeddings problem and the results indicate this is a scalable technique with practical results for NLP applications.
Emotional modulation of cognitive control: approach-withdrawal states double-dissociate spatial from verbal two-back task performance.

Emotional states might selectively modulate components of cognitive control. To test this hypothesis, the author randomly assigned 152 undergraduates (equal numbers of men and women) to watch short videos intended to induce emotional states (approach, neutral, or withdrawal). Each video was followed by a computerized 2-back working memory task (spatial or verbal, equated for difficulty and appearance). Spatial 2-back performance was enhanced by a withdrawal state and impaired by an approach state; the opposite pattern held for verbal performance. The double dissociation held more strongly for participants who made more errors than average across conditions. The results suggest that approach-withdrawal states can have selective influences on components of cognitive control, possibly on a hemispheric basis. They support and extend several frameworks for conceptualizing emotion-cognition interactions.
Computational intelligence in economics and finance: Carrying on the legacy of Herbert Simon

This is an editorial guide for the special issue on computational intelligence (CI) in economics and finance. A historical introduction to the background is given. This research paradigm is traced back to Herbert Simon, who, as a founder of artificial intelligence, pioneered the applications of AI to economics. The move from the classical AI to CI indicates a continuation of the legacy of Herbert Simon. Computational intelligence has proved to be a constructive foundation for economics. In responding to what Herbert Simon referred as procedural rationality, our study of bounded rationality has been enriched by bringing autonomous agents into the economic analysis.
AN AUTOMATIC SYSTEM FOR DETECTING AND COUNTING RBC AND WBC USING FUZZY LOGIC

Blood cell detection and counting is the initial process for detecting and diagnosing diseases. Several image processing algorithms are there for the blood cell classification and counting. The processed image helps to detect different blood related diseases. In all those algorithms several pre-processing steps are there for the process of detection and counting. Though all the algorithms give accurate results, the pre-processing steps are complex and time-consuming. This paper discusses about the RBC and WBC detection using fuzzy logic. Fuzzy logic toolbox software in MATLAB is used to develop the model on virtual platform.
Pulsewidth modulation for electronic power conversion

The efficient and fast control of electric power forms part of the key technologies of modern automated production. It is performed using electronic power converters. The converters transfer energy from a source to a controlled process in a quantized fashion, using semiconductor switches which are turned on and off at fast repetition rates. The algorithms which generate the switching functions – pulsewidth modulation techniques – are manifold. They range from simple averaging schemes to involved methods of real-time optimization. This paper gives an overview.