nils-herrmann/clean_scirepeval huggingface dataset:
Photonic Side-Channel Analysis of Arbiter PUFs

As intended by its name, physically unclonable functions (PUFs) are considered as an ultimate solution to deal with insecure storage, hardware counterfeiting, and many other security problems. However, many different successful attacks have already revealed vulnerabilities of certain digital intrinsic PUFs. This paper demonstrates that legacy arbiter PUF and its popular extended versions (i.e., feed-forward and XOR-enhanced) can be completely and linearly characterized by means of photonic emission analysis. Our experimental setup is capable of measuring every PUF internal delay with a resolution of 6 ps. Due to this resolution, we indeed require only the theoretical minimum number of linear independent equations (i.e., physical measurements) to directly solve the underlying inhomogeneous linear system. Moreover, it is not required to know the actual PUF responses for our physical delay extraction. We present our practical results for an arbiter PUF implementation on a complex programmable logic device manufactured with a 180 nm process. Finally, we give an insight into photonic emission analysis of arbiter PUF on smaller chip architectures by performing experiments on a field programmable gate array manufactured with a 60 nm process.
Multiview Vector-Valued Manifold Regularization for Multilabel Image Classification

In computer vision, image datasets used for classification are naturally associated with multiple labels and comprised of multiple views, because each image may contain several objects (e.g., pedestrian, bicycle, and tree) and is properly characterized by multiple visual features (e.g., color, texture, and shape). Currently, available tools ignore either the label relationship or the view complementarily. Motivated by the success of the vector-valued function that constructs matrix-valued kernels to explore the multilabel structure in the output space, we introduce multiview vector-valued manifold regularization (MV3MR) to integrate multiple features. MV3MR exploits the complementary property of different features and discovers the intrinsic local geometry of the compact support shared by different features under the theme of manifold regularization. We conduct extensive experiments on two challenging, but popular, datasets, PASCAL VOC' 07 and MIR Flickr, and validate the effectiveness of the proposed MV3MR for image classification.
Vocal amusia in a professional tango singer due to a right superior temporal cortex infarction

We describe the psychophysical features of vocal amusia in a professional tango singer caused by an infarction mainly involving the superior temporal cortex of the right hemisphere. The lesion also extended to the supramarginal gyrus, the posterior aspect of the postcentral gyrus and the posterior insula. She presented with impairment of musical perception that was especially pronounced in discriminating timbre and loudness but also in discriminating pitch, and a severely impaired ability to reproduce the pitch just presented. In contrast, language and motor disturbances were almost entirely absent. By comparing her pre- and post-stroke singing, we were able to show that her singing after the stroke lacked the fine control of the subtle stress and pitch changes that characterized her pre-stroke singing. Such impairment could not be explained by the impairment of pitch perception. The findings suggest that damage to the right temporoparietal cortex is enough to produce both perceptive and expressive deficits in music.
Comparison of PCA -, MDF -, and RD-LDA - based feature extraction approaches for hand-based personal recognition

In this paper, Principal Component Analysis (PCA), Most Discriminant Features (MDF), and Regularized-Direct Linear Discriminant Analysis (RD-LDA) - based feature extraction approaches are tested and compared in an experimental personal recognition system. The system is multimodal and bases on features extracted from nine regions of an image of the palmar surface of the hand. For testing purposes 10 gray-scale images of right hand of 184 people were acquired. The experiments have shown that the best results are obtained with the RD-LDA - based features extraction approach (100% correctness for 920 identification tests and EER = 0.01% for 64170 verification tests).
CONTINGENCIES ON MANAGEMENT ACCOUNTING PRACTICES IN ESTONIAN MANUFACTURING COMPANIES

Current paper examines the management accounting practices of Estonian manufacturing companies, exploring the main impacts on them within a contingency theory framework. The methodology comprises an analysis of 62 responses to a postal questionnaire survey carried out among the largest Estonian manufacturing companies. On the one hand, the present research aims to confirm earlier findings related to the ‘contingent factors’ that influence management accounting, on the other, to identify possible new factors, such as, the legal accounting environment and shortage of properly qualified accountants. 1 University of Tartu, Faculty of Economics and Business Administration, Ass. Prof. of Accounting Department, PhD, E-mail: toom@mtk.ut.ee 2 University of Tartu, Faculty of Economics and Business Administration, Lecturer of Accounting Department, PhD student, E-mail: kertu@mtk.ut.ee  The authors are grateful to prof. Robert Chenhall from Monash University for his assistance and to visiting prof. Gary Cunningham from Stuttgart University of Technology for his constructive comments. The financial support from the Estonian Science Foundation is herein acknowledged with gratitude.
Video Normals from Colored Lights

We present an algorithm and the associated single-view capture methodology to acquire the detailed 3D shape, bends, and wrinkles of deforming surfaces. Moving 3D data has been difficult to obtain by methods that rely on known surface features, structured light, or silhouettes. Multispectral photometric stereo is an attractive alternative because it can recover a dense normal field from an untextured surface. We show how to capture such data, which in turn allows us to demonstrate the strengths and limitations of our simple frame-to-frame registration over time. Experiments were performed on monocular video sequences of untextured cloth and faces with and without white makeup. Subjects were filmed under spatially separated red, green, and blue lights. Our first finding is that the color photometric stereo setup is able to produce smoothly varying per-frame reconstructions with high detail. Second, when these 3D reconstructions are augmented with 2D tracking results, one can register both the surfaces and relax the homogenous-color restriction of the single-hue subject. Quantitative and qualitative experiments explore both the practicality and limitations of this simple multispectral capture system.
Overcoming browser cookie churn with clustering

Many large Internet websites are accessed by users anonymously, without requiring registration or logging-in. However, to provide personalized service these sites build anonymous, yet persistent, user models based on repeated user visits. Cookies, issued when a web browser first visits a site, are typically employed to anonymously associate a website visit with a distinct user (web browser). However, users may reset cookies, making such association short-lived and noisy. In this paper we propose a solution to the cookie churn problem: a novel algorithm for grouping similar cookies into clusters that are more persistent than individual cookies. Such clustering could potentially allow more robust estimation of the number of unique visitors of the site over a certain long time period, and also better user modeling which is key to plenty of web applications such as advertising and recommender systems.
 We present a novel method to cluster browser cookies into groups that are likely to belong to the same browser based on a statistical model of browser visitation patterns. We address each step of the clustering as a binary classification problem estimating the probability that two different subsets of cookies belong to the same browser. We observe that our clustering problem is a generalized interval graph coloring problem, and propose a greedy heuristic algorithm for solving it. The scalability of this method allows us to cluster hundreds of millions of browser cookies and provides significant improvements over baselines such as constrained K-means.
A Foundational Architecture for Artificial General Intelligence

Implementing and fleshing out a number of psychological and neuroscience theories of cognition, the LIDA conceptual model aims at being a cognitive “theory of everything.” With modules or processes for perception, working memory, episodic memories, “consciousness,” procedural memory, action selection, perceptual learning, episodic learning, deliberation, volition, and non-routine problem solving, the LIDA model is ideally suited to provide a working ontology that would allow for the discussion, design, and comparison of AGI systems. The LIDA architecture is based on the LIDA cognitive cycle, a sort of “cognitive atom.” The more elementary cognitive modules and processes play a role in each cognitive cycle. Higher-level processes are performed over multiple cycles. In addition to giving a quick overview of the LIDA conceptual model, and its underlying computational technology, we argue for the LIDA architecture’s role as a foundational architecture for an AGI. Finally, lessons For AGI researchers drawn from the model and its architecture are discussed.
Gender-Specific Gene Expression in Post-Mortem Human Brain: Localization to Sex Chromosomes

Gender differences in brain development and in the prevalence of neuropsychiatric disorders such as depression have been reported. Gender differences in human brain might be related to patterns of gene expression. Microarray technology is one useful method for investigation of gene expression in brain. We investigated gene expression, cell types, and regional expression patterns of differentially expressed sex chromosome genes in brain. We profiled gene expression in male and female dorsolateral prefrontal cortex, anterior cingulate cortex, and cerebellum using the Affymetrix oligonucleotide microarray platform. Differentially expressed genes between males and females on the Y chromosome (DBY, SMCY, UTY, RPS4Y, and USP9Y) and X chromosome (XIST) were confirmed using real-time PCR measurements. In situ hybridization confirmed the differential expression of gender-specific genes and neuronal expression of XIST, RPS4Y, SMCY, and UTY in three brain regions examined. The XIST gene, which silences gene expression on regions of the X chromosome, is expressed in a subset of neurons. Since a subset of neurons express gender-specific genes, neural subpopulations may exhibit a subtle sexual dimorphism at the level of differences in gene regulation and function. The distinctive pattern of neuronal expression of XIST, RPS4Y, SMCY, and UTY and other sex chromosome genes in neuronal subpopulations may possibly contribute to gender differences in prevalence noted for some neuropsychiatric disorders. Studies of the protein expression of these sex-chromosome-linked genes in brain tissue are required to address the functional consequences of the observed gene expression differences.
Ultrawideband (UWB) Planar Antenna with Single-, Dual-, and Triple-Band Notched Characteristic Based on Electric Ring Resonator

The ultrawideband (UWB) planar antenna is designed as a circular metallic patch fed by a coplanar waveguide (CPW). This antenna provides the impedance bandwidth of the wideband response from 2.5 to 12 GHz. To achieve the notched characteristics at desirable frequencies, the electric ring resonator (ERR) incorporated into the CPW feedline is proposed for use in the planar configuration of the UWB antenna. The notched frequency band is controlled by dimensions of the ERR structure. The single-notched band can be obtained by placing a single ERR beneath the CPW structure. For implementation of the multinotch band, a modified multimode structure of the ERR is examined. Reconfigurability of the first notched band is provided by using a digital variable capacitor (DVC) instead of ERR's quasi-lumped capacitance. The results of simulations and measurements are in a good agreement.
Hybrid Orthogonal Projection and Estimation (HOPE): A New Framework to Probe and Learn Neural Networks

In this paper, we propose a novel model for high-dimensional data, called the Hybrid Orthogonal Projection and Estimation (HOPE) model, which combines a linear orthogonal projection and a finite mixture model under a unified generative modeling framework. The HOPE model itself can be learned unsupervised from unlabelled data based on the maximum likelihood estimation as well as discriminatively from labelled data. More interestingly, we have shown the proposed HOPE models are closely related to neural networks (NNs) in a sense that each hidden layer can be reformulated as a HOPE model. As a result, the HOPE framework can be used as a novel tool to probe why and how NNs work, more importantly, to learn NNs in either supervised or unsupervised ways. In this work, we have investigated the HOPE framework to learn NNs for several standard tasks, including image recognition on MNIST and speech recognition on TIMIT. Experimental results have shown that the HOPE framework yields significant performance gains over the current state-of-the-art methods in various types of NN learning problems, including unsupervised feature learning, supervised or semi-supervised learning.
A Budget Optimization Framework for Search Advertisements Across Markets

Budget optimization is one of the primary decision-making issues faced by advertisers in search auctions. A quality budget optimization strategy can significantly improve the effectiveness of search advertising campaigns, thus helping advertisers to succeed in the fierce competition of online marketing. This paper investigates budget optimization problems in search advertisements and proposes a novel hierarchical budget optimization framework (BOF), with consideration of the entire life cycle of advertising campaigns. Then, we formulated our BOF framework, made some mathematical analysis on some desirable properties, and presented an effective solution algorithm. Moreover, we established a simple but illustrative instantiation of our BOF framework which can help advertisers to allocate and adjust the budget of search advertising campaigns. Our BOF framework provides an open testbed environment for various strategies of budget allocation and adjustment across search advertising markets. With field reports and logs from real-world search advertising campaigns, we designed some experiments to evaluate the effectiveness of our BOF framework and instantiated strategies. Experimental results are quite promising, where our BOF framework and instantiated strategies perform better than two baseline budget strategies commonly used in practical advertising campaigns.
Correlation induced electron-hole asymmetry in quasi- two-dimensional iridates

The resemblance of crystallographic and magnetic structures of the quasi-two-dimensional iridates Ba2IrO4 and Sr2IrO4 to La2CuO4 points at an analogy to cuprate high-Tc superconductors, even if spin-orbit coupling is very strong in iridates. Here we examine this analogy for the motion of a charge (hole or electron) added to the antiferromagnetic ground state. We show that correlation effects render the hole and electron case in iridates very different. An added electron forms a spin polaron, similar to the cuprates, but the situation of a removed electron is far more complex. Many-body 5d 4 configurations form which can be singlet and triplet states of total angular momentum that strongly affect the hole motion. This not only has ramifications for the interpretation of (inverse–)photoemission experiments but also demonstrates that correlation physics renders electron- and hole-doped iridates fundamentally different. Some iridate compounds such as Sr2IrO4 have electronic and atomic structures similar to quasi-2D copper oxides, raising the prospect of high temperature superconductivity. Here, the authors show that there is significant electron-hole asymmetry in iridates, contrary to expectations from the cuprates.
Discovering charge density functionals and structure-property relationships with PROPhet: A general framework for coupling machine learning and first-principles methods

Modern ab initio methods have rapidly increased our understanding of solid state materials properties, chemical reactions, and the quantum interactions between atoms. However, poor scaling often renders direct ab initio calculations intractable for large or complex systems. There are two obvious avenues through which to remedy this problem: (i) develop new, less expensive methods to calculate system properties, or (ii) make existing methods faster. This paper describes an open source framework designed to pursue both of these avenues. PROPhet (short for PROPerty Prophet) utilizes machine learning techniques to find complex, non-linear mappings between sets of material or system properties. The result is a single code capable of learning analytical potentials, non-linear density functionals, and other structure-property or property-property relationships. These capabilities enable highly accurate mesoscopic simulations, facilitate computation of expensive properties, and enable the development of predictive models for systematic materials design and optimization. This work explores the coupling of machine learning to ab initio methods through means both familiar (e.g., the creation of various potentials and energy functionals) and less familiar (e.g., the creation of density functionals for arbitrary properties), serving both to demonstrate PROPhet’s ability to create exciting post-processing analysis tools and to open the door to improving ab initio methods themselves with these powerful machine learning techniques.
AnO(ND) difference algorithm and its variations

The problems of finding a longest common subsequence of two sequencesA andB and a shortest edit script for transformingA intoB have long been known to be dual problems. In this paper, they are shown to be equivalent to finding a shortest/longest path in an edit graph. Using this perspective, a simpleO(ND) time and space algorithm is developed whereN is the sum of the lengths ofA andB andD is the size of the minimum edit script forA andB. The algorithm performs well when differences are small (sequences are similar) and is consequently fast in typical applications. The algorithm is shown to haveO(N+D 2) expected-time performance under a basic stochastic model. A refinement of the algorithm requires onlyO(N) space, and the use of suffix trees leads to anO(N logN+D 2) time variation.
Further empirical data on the psychoeducational profile-revised (PEP-R): reliability and validation with the Vineland adaptive behavior scales.

The PEP-R (psychoeducational profile revised) is an instrument that has been used in many countries to assess abilities and formulate treatment programs for children with autism and related developmental disorders. To the end to provide further information on the PEP-R's psychometric properties, a large sample (N = 137) of children presenting Autistic Disorder symptoms under the age of 12 years, including low-functioning individuals, was examined.  yielded data of interest especially in terms of: Cronbach's alpha, interrater reliability, and validation with the Vineland Adaptive Behavior Scales. These findings help complete the instrument's statistical description and augment its usefulness, not only in designing treatment programs for these individuals, but also as an instrument for verifying the efficacy of intervention.
Rotor design of a high-speed Permanent Magnet Synchronous Machine rating 100,000 rpm at 10kW

Rotors of electrical high speed machines are subject to high stress, limiting the rated power of the machines. This paper describes the design process of a high-speed rotor of a Permanent Magnet Synchronous Machine (PMSM) for a rated power of 10kW at 100,000 rpm. Therefore, at the initial design the impact of the rotor radius to critical parameters is analyzed analytically. In particular, critical parameters are mechanical stress due to high centrifugal forces and natural bending frequencies. Furthermore, air friction losses, heating the rotor and the stator additionally, are no longer negligible compared to conventional machines and must be considered in the design process. These mechanical attributes are controversial to the electromagnetic design, increasing the effective magnetic air gap, for example. Thus, investigations are performed to achieve sufficient mechanical strength without a significant reduction of air gap flux density or causing thermal problems. After initial design by means of analytical estimations, an optimization of rotor geometry and materials is performed by means of the finite element method (FEM).
Coffee Ingestion Enhances 1-Mile Running Race Performance.

Caffeine, often in the form of coffee, is frequently used as a supplement by athletes in an attempt to facilitate improved performance during exercise.



To investigate the effectiveness of coffee ingestion as an ergogenic aid prior to a 1-mile (1609 m) race.



In a double-blind, randomized, cross-over, and placebo-controlled design, 13 trained male runners completed a 1-mile race 60 minutes following the ingestion of 0.09 g·kg-1 coffee (COF), 0.09 g·kg-1 decaffeinated coffee (DEC), or a placebo (PLA). All trials were dissolved in 300 mL of hot water.



The race completion time was 1.3% faster following the ingestion of COF (04:35.37 [00:10.51] min:s.ms) compared with DEC (04:39.14 [00:11.21] min:s.ms; P = .018; 95% confidence interval [CI], -0.11 to -0.01; d = 0.32) and 1.9% faster compared with PLA (04:41.00 [00:09.57] min:s.ms; P = .006; 95% CI, -0.15 to -0.03; d = 0.51). A large trial and time interaction for salivary caffeine concentration was observed (P < .001; , with a very large increase (6.40 [1.57] μg·mL-1; 95% CI, 5.5-7.3; d = 3.86) following the ingestion of COF. However, only a trivial difference between DEC and PLA was observed (P = .602; 95% CI, -0.09 to 0.03; d = 0.17). Furthermore, only trivial differences were observed for blood glucose (P = .839;  and lactate (P = .096;  and maximal heart rate (P = .286;  between trials.



The results of this study show that 60 minutes after ingesting 0.09 g·kg-1 of caffeinated coffee, 1-mile race performance was enhanced by 1.9% and 1.3% compared with placebo and decaffeinated coffee, respectively, in trained male runners.
Osteomyelitis of the Jaws: Definition and Classification

Classification of Osteomyelitis of the Jaws .................. 11 2.5.1 Acute/Subacute Osteomyelitis .............. 11 2.5.2 Chronic Osteomyelitis ........................... 11 2.5.3 Chronic Suppurative Osteomyelitis: Secondary Chronic Osteomyelitis .......... 11 2.5.4 Chronic Non-suppurative Osteomyelitis 11 2.5.5 Diffuse Sclerosing Osteomyelitis, Primary Chronic Osteomyelitis, Florid Osseous Dysplasia, Juvenile Chronic Osteomyelitis ............. 11 2.5.6 SAPHO Syndrome, Chronic Recurrent Multifocal Osteomyelitis (CRMO) ........... 13 2.5.7 Periostitis Ossificans, Garrès Osteomyelitis ............................. 13 2.5.8 Other Commonly Used Terms ................ 13 2.6 Osteomyelitis of the Jaws: The Zurich Classification System ........... 16 2.6.1 General Aspects of the Zurich Classification System ............................. 16 2.6.2 Acute Osteomyelitis and Secondary Chronic Osteomyelitis ........................... 17 2.6.3 Clinical Presentation ............................. 26 2.6.4 Primary Chronic Osteomyelitis .............. 34 2.7 Differential Diagnosis ............................ 48 2.7.1 General Considerations ......................... 48 2.7.2 Differential Diagnosis of Acute and Secondary Chronic Osteomyelitis ... 50 2.7.3 Differential Diagnosis of Primary Chronic Osteomyelitis ........................... 50 2.1