nils-herrmann/clean_scirepeval huggingface dataset:
Comparison of diagnostic value using a small, single channel, P-wave centric sternal ECG monitoring patch with a standard 3-lead Holter system over 24 hours.

To compare simultaneous recordings from an external patch system specifically designed to ensure better P-wave recordings and standard Holter monitor to determine diagnostic efficacy. Holter monitors are a mainstay of clinical practice, but are cumbersome to access and wear and P-wave signal quality is frequently inadequate.



This study compared the diagnostic efficacy of the P-wave centric electrocardiogram (ECG) patch (Carnation Ambulatory Monitor) to standard 3-channel (leads V1, II, and V5) Holter monitor (Northeast Monitoring, Maynard, MA). Patients were referred to a hospital Holter clinic for standard clinical indications. Each patient wore both devices simultaneously and served as their own control. Holter and Patch reports were read in a blinded fashion by experienced electrophysiologists unaware of the findings in the other corresponding ECG recording. All patients, technicians, and physicians completed a questionnaire on comfort and ease of use, and potential complications.



In all 50 patients, the P-wave centric patch recording system identified rhythms in 23 patients (46%) that altered management, compared to 6 Holter patients (12%), P<.001. The patch ECG intervals PR, QRS and QT correlated well with the Holter ECG intervals having correlation coefficients of 0.93, 0.86, and 0.94, respectively. Finally, 48 patients (96%) preferred wearing the patch monitor.



A single-channel ambulatory patch ECG monitor, designed specifically to ensure that the P-wave component of the ECG be visible, resulted in a significantly improved rhythm diagnosis and avoided inaccurate diagnoses made by the standard 3-channel Holter monitor.
Advanced bibliometric methods as quantitative core of peer review based evaluation and foresight exercises

This paper gives an overview of the potentials and limitations of bibliometric methods for the assessment of strengths and weaknesses in research performance, and for monitoring scientific developments. We distinguish two different methods. In the first application, research performance assessment, the bibliometric method is based on advanced analysis of publication and citation data. We show that the resulting indicators are very useful, and in fact an indispensable element next to peer review in research evaluation procedures. Indicators based on advanced bibliometric methods offer much more than ‘only numbers’. They provide insight into the position of actors at the research front in terms of influence and specializations, as well as into patterns of scientific communication and processes of knowledge dissemination. After a discussion of technical and methodological problems, we present practical examples of the use of research performance indicators. In the second application, monitoring scientific developments, bibliometric methods based on advanced mapping techniques are essential. We discuss these techniques briefly and indicate their most important potentials, particularly their role in foresight exercises. Finally, we give a first outline of how both bibliometric approaches can be combined to a broader and powerful methodology to observe scientific advancement and the role of actors.
GravityFusion: Real-time dense mapping without pose graph using deformation and orientation

In this paper, we propose a novel approach to integrating inertial sensor data into a pose-graph free dense mapping algorithm that we call GravityFusion. A range of dense mapping algorithms have recently been proposed, though few integrate inertial sensing. We build on ElasticFusion, a particularly elegant approach that fuses color and depth information directly into small surface patches called surfels. Traditional inertial integration happens at the level of camera motion, however, a pose graph is not available here. Instead, we present a novel approach that incorporates the gravity measurements directly into the map: Each surfel is annotated by a gravity measurement, and that measurement is updated with each new observation of the surfel. We use mesh deformation, the same mechanism used for loop closure in ElasticFusion, to enforce a consistent gravity direction among all the surfels. This eliminates drift in two degrees of freedom, avoiding the typical curving of maps that are particularly pronounced in long hallways, as we qualitatively show in the experimental evaluation.
A Tutorial on Deep Neural Networks for Intelligent Systems

Developing Intelligent Systems involves artificial intelligence approaches including artificial neural networks. Here, we present a tutorial of Deep Neural Networks (DNNs), and some insights about the origin of the term “deep”; references to deep learning are also given. Restricted Boltzmann Machines, which are the core of DNNs, are discussed in detail. An example of a simple two-layer network, performing unsupervised learning for unlabeled data, is shown. Deep Belief Networks (DBNs), which are used to build networks with more than two layers, are also described. Moreover, examples for supervised learning with DNNs performing simple prediction and classification tasks, are presented and explained. This tutorial includes two intelligent pattern recognition applications: handwritten digits (benchmark known as MNIST) and speech recognition.
A multiple-criteria algorithm for smart parking: making fair and preferred parking reservations in smart cities

Smart cities are struggling with using public space efficiently and decreasing pollution at the same time. For this governments have embraced smart parking initiatives, which should result in a high utilization of public space and minimization of the driving, in this way reducing the emissions of cars. Yet, simply opening data about the availability of public spaces results in more congestions as multiple cars might be heading for the same parking space. In this work, we propose a Multiple Criteria based Parking space Reservation (MCPR) algorithm, for reserving a space for a user to deal with parking space in a fair way. Users' requirements are the main driving factor for the algorithm and used as criteria in MCPR. To evaluate the algorithm, simulations for three set of user preferences were made. The simulation results show that the algorithm satisfied the users' request fairly for all the three preferences. The algorithm helps users automatically to find a parking space according to the users' requirements. The algorithm can be used in a smart parking system to search for a parking space on behalf of user and send parking space information to the user.
Convex optimization for fast image dehazing

This paper presents a new convex optimization method for the image dehazing problem. It is based on reformulation of the hazed image model to solve the bilinearly coupled hazed image and light transmission distribution term as a single optimization variable. This resolves the nonconvex difficulty of the original problem and is sufficient for straightforward reconstruction of the haze free image. The Haar wavelet transform is further applied to derive a subband hazed image model for the convex optimization, which leads to great reduction of the computational workload.
Emulated current mode control system for DC-DC boost converter using FPGA

The proposed system of the digitally emulated current mode control for a DC-DC boost converter using the FPGA is implemented by the emulation technique to generate PWM pulse. A reasonable A/D converter with a few MSPS conversion rate is good enough to control the DC-DC converter with 100 kHz switching frequency. It is found the experimental data show the good static and dynamic-response characteristics, which means that the proposed system can be integrated into one chip digital IC for power-source-control with reasonable price.
On-Chip Millimeter Wave Antennas and Transceivers

The main mechanisms responsible for performance degradation of millimeter wave (mmWave) and terahertz (THz) on-chip antennas are reviewed. Several techniques to improve the performance of the antennas and several high efficiency antenna types are presented. In order to illustrate the effects of the chip topology on the antenna, simulations and measurements of mmWave and THz on-chip antennas are shown. Finally, different transceiver architectures are explored with emphasis on the challenges faced in a wireless multi-core environment.
Preprint prepared for ArXiv submission

Generative Adversarial Networks (GANs) have recently achieved impressive results for many real-world applications, and many GAN variants have emerged with improvements in sample quality and training stability. However, visualization and understanding of GANs is largely missing. How does a GAN represent our visual world internally? What causes the artifacts in GAN results? How do architectural choices affect GAN learning? Answering such questions could enable us to develop new insights and better models. In this work, we present an analytic framework to visualize and understand GANs at the unit-, object-, and scene-level. We first identify a group of interpretable units that are closely related to object concepts with a segmentation-based network dissection method. Then, we quantify the causal effect of interpretable units by measuring the ability of interventions to control objects in the output. Finally, we examine the contextual relationship between these units and their surrounding by inserting the discovered object concepts into new images. We show several practical applications enabled by our framework, from comparing internal representations across different layers, models, and datasets, to improving GANs by locating and removing artifact-causing units, to interactively manipulating objects in the scene. We provide open source interpretation tools to help peer researchers and practitioners better understand their GAN models∗.
Thin Silicon Wafer Device Concept with Advanced Laser Annealing and Sintering Process

A new anode/cathode design and process concept for thin wafer based silicon devices is proposed to achieve the goal of providing improved control for activating the injecting layer and forming a good ohmic contact. The concept is based on laser annealing in a melting regime of a p-type anode layer covered with a thin titanium layer with high melting temperature and high laser light absorption. The improved activation control of a boron anode layer is demonstrated on the Soft Punch Through IGBT with a nominal breakdown voltage of 1700 V. Furthermore, the silicidation of the titanium absorbing layer, which is necessary for achieving a low VCE ON, is discussed in terms of optimization of the device electrical parameters.
Self-monitoring and age.

Two adult samples were surveyed to investigate the relation between individuals' levels of self-monitoring and age. A negative relation was predicted as older individuals were seen as most likely to exhibit the low self-monitoring tendency of behaving in accordance with one's own attitudes and feelings, whereas younger individuals appeared most likely to exhibit the high self-monitoring tendency of behaving according to social cues. A significant negative correlation between age and self-monitoring was found in both samples. The self-monitoring construct is discussed in relation to other social-cognitive life-span differences and to the idea of critical periods throughout the life span.
Visual odometry based on stereo image sequences with RANSAC-based outlier rejection scheme

A common prerequisite for many vision-based driver assistance systems is the knowledge of the vehicle's own movement. In this paper we propose a novel approach for estimating the egomotion of the vehicle from a sequence of stereo images. Our method is directly based on the trifocal geometry between image triples, thus no time expensive recovery of the 3-dimensional scene structure is needed. The only assumption we make is a known camera geometry, where the calibration may also vary over time. We employ an Iterated Sigma Point Kalman Filter in combination with a RANSAC-based outlier rejection scheme which yields robust frame-to-frame motion estimation even in dynamic environments. A high-accuracy inertial navigation system is used to evaluate our results on challenging real-world video sequences. Experiments show that our approach is clearly superior compared to other filtering techniques in terms of both, accuracy and run-time.
[Erythema annulare centrifugum responding to natural ultraviolet light].

Erythema annulare centrifugum (EAC) is a disease of unknown etiology, although it has been variously associated with hypersensitivity reactions, infections, hormonal disorders, rheumatological and liver diseases, dysproteinemias, drugs, and occult tumors. García-Muret et al described a subtype of EAC with annual relapses that occurred in the summer.1 Nonetheless, in our hospital we have observed how 2 patients with longstanding EAC presented a clear clinical improvement in response to natural exposure to sunlight during the summer months. The first patient was a woman aged 22 years, with no known diseases, who presented an 8-year history of lesions on the trunk and upper and lower limbs. The asymptomatic and sometimes slightly pruritic lesions underwent episodes of centrifugal spread in winter. Examination revealed several erythematous plaques varying in size between 2 cm and 8 cm. The larger lesions were annular, with erythematous borders that were slightly more raised and trailing scale . A culture of scales from the lesions was negative on 2 occasions. The Spanish Contact Dermatitis and Skin Allergy Research Group (GEIDAC) standard battery of patch tests were all negative. Complete blood count, basic blood chemistry, antibody test, chest x-ray, and abdominal ultrasound results were normal. Superficial perivascular dermatitis was reported on the 2 occasions biopsies were performed. The patient had failed to respond to treatment with topical corticosteroids and antifungal agents. To prevent possible postinflammatory hyperpigmentation the patient had avoided sunbathing during the summer months. However, her last revision revealed that the lesions had completely disappeared following continuous sun exposure during her holidays . The second patient was a man aged 27 years. Since the age of 16 years he had presented with occasional flare-ups, on the trunk and limbs, of erythematous lesions with centrifugal spread and a scaly border. Routine blood tests and antinuclear antibodies were normal or negative, cultures were negative, and a histopathology study merely showed nonspecific chronic dermatitis. Flare-ups were not associated with any triggering factor, and the lesions had not responded to treatment with antifungal agents or topical corticosteroids. Nonetheless, the patient’s condition had improved during the summer, coinciding with exposure to sunlight. EAC, which was originally described by Darier in 1916, presents as annular plaques with clear central areas and slightly raised erythematous borders with trailing scale. Centrifugal growth gives rise to polycyclic patterns in the plaques. The disease follows a chronic course marked by exacerbations and remissions. The most frequent lesion
Forward RTO-Recovery (F-RTO): An Algorithm for Detecting Spurious Retransmission Timeouts with TCP

Status of this Memo By submitting this Internet-Draft, each author represents that any applicable patent or other IPR claims of which he or she is aware have been or will be disclosed, and any of which he or she becomes aware will be disclosed, in accordance with Section 6 of BCP 79. Internet-Drafts are working documents of the Internet Engineering Task Force (IETF), its areas, and its working groups. Note that other groups may also distribute working documents as Internet-Drafts. Internet-Drafts are draft documents valid for a maximum of six months and may be updated, replaced, or obsoleted by other documents at any time. It is inappropriate to use Internet-Drafts as reference material or to cite them other than as "work in progress." The list of current Internet-Drafts can be accessed at http://www.ietf.org/ietf/1idabstracts.txt. The list of Internet-Draft Shadow Directories can be accessed at http://www.ietf.org/shadow.html. This Internet-Draft will expire on May 2008.
Improved algorithm of C4.5 decision tree on the arithmetic average optimal selection classification attribute

To try to decrease the preference of the attribute values for information gain and information gain ratio, in the paper, the authors puts forward a improved algorithm of C4.5 decision tree on the selection classification attribute. The basic thought of the algorithm is as follows: Firstly, computing the information gain of selection classification attribute, and then get an attribute of the information gain which is higher than the average level; Secondly, computing separately the arithmetic average value of the information gain ratio and information gain of the attribute, and then select the biggest attribute of the average value and set up a branch decision; Finally, to use recursive method to build a decision tree. The experiment shows that this method is applicable and effective.
A forensic insight into Windows 10 Cortana search

Cortana, one of the new features introduced by Microsoft in Windows 10 desktop operating systems, is a voice activated personal digital assistant that can be used for searching stuff on device or web, setting up reminders, tracking users’ upcoming flights, getting news tailored to users’ interests, sending text and emails, and more. Being the platform relatively new, the forensic examination of Cortana has been largely unexplored in literature. This paper seeks to determine the data remnants of Cortana usage in a Windows 10 personal computer (PC). The research contributes in-depth understanding of the location of evidentiary artifacts on hard disk and the type of information recorded in these artifacts as a result of user activities on Cortana. For decoding and exporting data from one of the databases created by Cortana application, four custom python scripts have been developed. Additionally, as a part of this paper, a GUI tool called CortanaDigger is developed for extracting and listing web search strings, as well as timestamp of search made by a user on Cortana box. Several experiments are conducted to track reminders (based on time, place, and person) and detect anti-forensic attempts like evidence modification and evidence destruction carried out on Cortana artifacts. Finally, forensic usefulness of Cortana artifacts is demonstrated in terms of a Cortana web search timeline constructed over a period of time.
CDC: Compressive Data Collection for Wireless Sensor Networks

Data collection is a crucial operation in wireless sensor networks. The design of data collection schemes is challenging due to the limited energy supply and the hot spot problem. Leveraging empirical observations that sensory data possess strong spatiotemporal compressibility, this paper proposes a novel compressive data collection scheme for wireless sensor networks. We adopt a power-law decaying data model verified by real data sets and then propose a random projection-based estimation algorithm for this data model. Our scheme requires fewer compressed measurements, thus greatly reduces the energy consumption. It allows simple routing strategy without much computation and control overheads, which leads to strong robustness in practical applications. Analytically, we prove that it achieves the optimal estimation error bound. Evaluations on real data sets (from the GreenOrbs, IntelLab and NBDC-CTD projects) show that compared with existing approaches, this new scheme prolongs the network lifetime by 1.5X to 2X for estimation error 5-20 percent.
Temporal Relational Ranking for Stock Prediction

Stock prediction aims to predict the future trends of a stock in order to help investors make good investment decisions. Traditional solutions for stock prediction are based on time-series models. With the recent success of deep neural networks in modeling sequential data, deep learning has become a promising choice for stock prediction.
 However, most existing deep learning solutions are not optimized toward the target of investment, i.e., selecting the best stock with the highest expected revenue. Specifically, they typically formulate stock prediction as a classification (to predict stock trends) or a regression problem (to predict stock prices). More importantly, they largely treat the stocks as independent of each other. The valuable signal in the rich relations between stocks (or companies), such as two stocks are in the same sector and two companies have a supplier-customer relation, is not considered.
 In this work, we contribute a new deep learning solution, named Relational Stock Ranking (RSR), for stock prediction. Our RSR method advances existing solutions in two major aspects: (1) tailoring the deep learning models for stock ranking, and (2) capturing the stock relations in a time-sensitive manner. The key novelty of our work is the proposal of a new component in neural network modeling, named Temporal Graph Convolution, which jointly models the temporal evolution and relation network of stocks. To validate our method, we perform back-testing on the historical data of two stock markets, NYSE and NASDAQ. Extensive experiments demonstrate the superiority of our RSR method. It outperforms state-of-the-art stock prediction solutions achieving an average return ratio of 98% and 71% on NYSE and NASDAQ, respectively.
Teaching OO concepts-a case study using CRC-cards and BlueJ

1 Umeå University, Department of Computing Science, SE-901 87 Umeå, Sweden,  The transition to object-oriented programming is more than just a matter of programming language. Traditional syllabi fail to teach students the “big picture” and students have difficulties taking advantage of objectoriented concepts. In this paper we present a holistic approach to a CS1 course in Java favouring general objectoriented concepts over the syntactical details of the language. We present goals for designing such a course and a case study showing interesting results.
Considerations for future automotive radar in the frequency range above 100 GHz

Within the last years automotive radar sensors became more and more important. Started with comfort systems like adaptive cruise control (ACC) and parking aid, the safety aspect increasingly came to the fore. In the near future not only upper class cars will be equipped with various radar applications like pre crash, collision warning and collision avoidance to increase traffic safety. Therefore many high performance radar sensors have to be integrated in the car. This paper describes the demands on future radar sensors. It is discussed how the step to higher operating frequencies could be beneficial particularly for urban situations with high traffic density. Furthermore the paper will provide and discuss design considerations for future mmW-Radar sensors in automotive safety applications.
COMPLEXITY FROM SIMPLICITY

This paper investigates spirolaterals for their beauty of form and the unexpected complexity arising from them. From a very simple generative procedure, spirolaterals can be created having great complexity and variation. Using mathematical and computer-based methods, issues of closure, variation, enumeration, and predictictability are discussed. A historical review is also included. The overriding interest in this research is to develop methods and procedures to investigate geometry for the purpose of inspiration for new architectural and sculptural forms. This particular phase will concern the two dimensional representations of spirolaterals.