nils-herrmann/clean_scirepeval huggingface dataset:
WE-CARE: An Intelligent Mobile Telecardiology System to Enable mHealth Applications

Recently, cardiovascular disease (CVD) has become one of the leading death causes worldwide, and it contributes to 41% of all deaths each year in China. This disease incurs a cost of more than 400 billion US dollars in China on the healthcare expenditures and lost productivity during the past ten years. It has been shown that the CVD can be effectively prevented by an interdisciplinary approach that leverages the technology development in both IT and electrocardiogram (ECG) fields. In this paper, we present WE-CARE , an intelligent telecardiology system using mobile 7-lead ECG devices. Because of its improved mobility result from wearable and mobile ECG devices, the WE-CARE system has a wider variety of applications than existing resting ECG systems that reside in hospitals. Meanwhile, it meets the requirement of dynamic ECG systems for mobile users in terms of the detection accuracy and latency. We carried out clinical trials by deploying the WE-CARE systems at Peking University Hospital. The clinical results clearly showed that our solution achieves a high detection rate of over 95% against common types of anomalies in ECG, while it only incurs a small detection latency around one second, both of which meet the criteria of real-time medical diagnosis. As demonstrated by the clinical results, the WE-CARE system is a useful and efficient mHealth (mobile health) tool for the cardiovascular disease diagnosis and treatment in medical platforms.
An Empirical Comparison of Topics in Twitter and Traditional Media

Twitter as a new form of social media can potentially contain much useful information, but content analysis on Twitter has not been well studied. In particular, it is not clear whether as an information source Twitter can be simply regarded as a faster news feed that covers mostly the same information as traditional news media. In This paper we empirically compare the content of Twitter with a traditional news medium, New York Times, using unsupervised topic modeling. We use a Twitter-LDA model to discover topics from a representative sample of the entire Twitter. We then use text mining techniques to compare these Twitter topics with topics from New York Times, taking into consideration of topic categories and types. We find that although Twitter and New York Times cover similar categories and types of topics, the distributions of topic categories and types are quite different. Furthermore, there are Twitter-specific topics and NYT-specific topics, and they tend to belong to certain topic categories and types. We also study the relation between the proportions of opinionated tweets and retweets and topic categories and types, and find some interesting dependence. To the best of our knowledge, ours is the first comprehensive empirical comparison between Twitter and traditional news media.
Classification of Monte Carlo tree search variants

Many variations of Monte Carlo tree search have been proposed and tested but relatively little comparison of these variants have occurred. In this study an Agent Case Embedding analysis and agglomorative hierarchical clustering was performed using eight variants of Monte Carlo Tree Search as agents and eight games as cases. This allowed us to compare the variant's abilities on each of the games to determine the type of games each are good at handling as well as which variants are similar to others. This comparison of variants exploits the ability of ACEs to compare different types of objects based on their behavior. By looking at the behavior of MCTS variants on a variety of games we obtain a good notion of the degree to which different MCTS variants exhibit different capabilities. A side effect of comparing MCTS variants with agent-case embeddings is that we also are able to compare the games used to test the MCTS variants.
Hierarchical Gaussian Descriptors with Application to Person Re-Identification

Describing the color and textural information of a person image is one of the most crucial aspects of person re-identification (re-id). In this paper, we present novel meta-descriptors based on a hierarchical distribution of pixel features. Although hierarchical covariance descriptors have been successfully applied to image classification, the mean information of pixel features, which is absent from the covariance, tends to be the major discriminative information for person re-id. To solve this problem, we describe a local region in an image via hierarchical Gaussian distribution in which both means and covariances are included in their parameters. More specifically, the region is modeled as a set of multiple Gaussian distributions in which each Gaussian represents the appearance of a local patch. The characteristics of the set of Gaussians are again described by another Gaussian distribution. In both steps, we embed the parameters of the Gaussian into a point of Symmetric Positive Definite (SPD) matrix manifold. By changing the way to handle mean information in this embedding, we develop two hierarchical Gaussian descriptors. Additionally, we develop feature norm normalization methods with the ability to alleviate the biased trends that exist on the descriptors. The experimental results conducted on five public datasets indicate that the proposed descriptors achieve remarkably high performance on person re-id.
Reputation Mechanisms

Reputation mechanisms harness the bi-directional communication capabilities of the Internet in order to engineer large-scale word-of-mouth networks. Best known so far as a technology for building trust and fostering cooperation in online marketplaces, such as eBay, these mechanisms are poised to have a much wider impact on organizations. This paper surveys our progress in understanding the new possibilities and challenges that these mechanisms represent. It discusses some important dimensions in which Internet-based reputation mechanisms differ from traditional word-of-mouth networks and surveys the most important issues related to their design, evaluation, and use. It provides an overview of relevant work in game theory and economics on the topic of reputation. It discusses how this body of work is being extended and combined with insights from computer science, marketing, and psychology in order to take into consideration the special properties of online environments. Finally, it identifies opportunities that this new area presents for information systems research.
Communicating with Cost-based Implicature : a Game-Theoretic Approach to Ambiguity

A game-theoretic approach to linguistic communication predicts that speakers can meaningfully use ambiguous forms in a discourse context in which only one of several available referents has a costly unambiguous form and in which rational interlocutors share knowledge of production costs. If a speaker produces a low-cost ambiguous form to avoid using the high-cost unambiguous form, a rational listener will infer that the high-cost entity was the intended entity, or else the speaker would not have risked ambiguity. We report data from two studies in which pairs of speakers show alignment of their use of ambiguous forms based on this kind of shared knowledge. These results extend the analysis of cost-based pragmatic inferencing beyond that previously associated only with fixed lexical hosts.
Scheduling Precedence Graphs in Systems with Interprocessor Communication Times

The problem of nonpreemptively scheduling a set of rn partially ordered tasks on n identical processors subject to interprocessor communication delays is studied in an effort to minimize the makespan. A new heuristic, called Earliest Task First (ETF), is designed and analyzed. It is shown that the makespan wEx-generated by ETF always satisfies to Eq.. (2 1/n wopti) + C, where Wopti) is the optimal makespan without considering communication delays and C is the communication requirements over some immediate predecessor-immediate successor pairs along one chain. An algorithm is also provided to calculate C. The time complexity of Algorithm ETF is O(nm2).
Mining semantic representation from medical text: A Bayesian approach

Machine learning is a subfield of artificial intelligence that deals with the exploration and construction of systems that can learn from data. Machine learning trains the computers to manage the critical situations via examining, self-training, inference by observation and previous experience. This paper provides an overview of the development of an efficient classifier that represents the semantics in medical data (Medline) using a Machine Learning (ML) perspective. In recent days people are more concerned about their health and explore ways to identify health related information. But the process of identifying the semantic representation for the medical terms is a difficult task. The main goal of our work was to identify the semantic representation for the medical abstracts in the Medline repository using Machine Learning and Natural Language Processing (NLP).
Impact of carboplatin plus paclitaxel combined with endostar against A375 melanoma cells: An in vitro and in vivo analysis.

The aim of the present study was to investigate the efficacy of carboplatin plus paclitaxel (CP) combined with endostar against A375 melanoma cells in vitro and in vivo.



Effects on the cell viability and apoptosis induction were estimated with the Cell counting Kit-8 assay and Annexin V-FITC/Propidium Iodide staining. Fifty female BALB/c-nude mice with subcutaneous injection of A375 cells were randomized to be treated with normal saline, dacarbazine alone, dacarbazine plus endostar, carboplatin plus paclitaxel, and CP plus endostar. Tumor volume of mice was monitored after injection and survival time was adopted for survival analysis.



CP plus endostar significantly decreased the cell survival rate compared with CP (P<0.01). Combination of CP and endostar showed higher cytotoxicity to A375 cells in vitro than endostar plus dacarbazine (P<0.01). The percentage of apoptotic cells in A375 cells treated with CP plus endostar was appreciably higher when compared to CP group (P<0.05). The mean relative tumor size in CP group was definitely larger (p<0.05) than CP plus endostar group. In addition, the mean survival time in CP plus endostar group was notably elevated compared with the CP group (P<0.05).



Our data indicated that treatment with CP plus endostar significantly reduced cell growth and induced a high rate of apoptotic cells in the A375 melanoma cell line. CP and endostar exhibited synergistic anti-tumor activities in A375 melanoma cells in vitro. CP plus endostar suppressed the growth of xenograft tumors and prolonged the survival time of mice with xenograft tumors. Combination of CP and endostar may be a promising treatment for melanoma.
A proposal of LDMOS using Deep Trench poly field plate

In this paper, we developed Deep Trench structure of LDMOS instead of STI in conventional one, with 0.18um 60V BCD process. By forming vertical drift region using Deep Trench, we achieved lower Ron.sp from cell pitch shrink while BVdss does not change. As a result we achieved 76V BVdss and 49.3 mohm*mm2 Ron.sp of low side 60V rated LDNMOS.
Classifier ensembles: Select real-world applications

Broad classes of statistical classification algorithms have been developed and applied successfully to a wide range of real world domains. In general, ensuring that the particular classification algorithm matches the properties of the data is crucial in providing results that meet the needs of the particular application domain. One way in which the impact of this algorithm/application match can be alleviated is by using ensembles of classifiers, where a variety of classifiers (either different types of classifiers or different instantiations of the same classifier) are pooled before a final classification decision is made. Intuitively, classifier ensembles allow the different needs of a difficult problem to be handled by classifiers suited to those particular needs. Mathematically, classifier ensembles provide an extra degree of freedom in the classical bias/variance tradeoff, allowing solutions that would be difficult (if not impossible) to reach with only a single classifier. Because of these advantages, classifier ensembles have been applied to many difficult real world problems. In this paper, we survey select applications of ensemble methods to problems that have historically been most representative of the difficulties in classification. In particular, we survey applications of ensemble methods to remote sensing, person recognition, one vs. all recognition, and medicine.
A Review of Brain-Computer Interface Games and an Opinion Survey from Researchers, Developers and Users

In recent years, research on Brain-Computer Interface (BCI) technology for healthy users has attracted considerable interest, and BCI games are especially popular. This study reviews the current status of, and describes future directions, in the field of BCI games. To this end, we conducted a literature search and found that BCI control paradigms using electroencephalographic signals (motor imagery, P300, steady state visual evoked potential and passive approach reading mental state) have been the primary focus of research. We also conducted a survey of nearly three hundred participants that included researchers, game developers and users around the world. From this survey, we found that all three groups (researchers, developers and users) agreed on the significant influence and applicability of BCI and BCI games, and they all selected prostheses, rehabilitation and games as the most promising BCI applications. User and developer groups tended to give low priority to passive BCI and the whole head sensor array. Developers gave higher priorities to "the easiness of playing" and the "development platform" as important elements for BCI games and the market. Based on our assessment, we discuss the critical point at which BCI games will be able to progress from their current stage to widespread marketing to consumers. In conclusion, we propose three critical elements important for expansion of the BCI game market: standards, gameplay and appropriate integration.
Batteries for electric and hybrid-electric vehicles.

Batteries have powered vehicles for more than a century, but recent advances, especially in lithium-ion (Li-ion) batteries, are bringing a new generation of electric-powered vehicles to the market. Key barriers to progress include system cost and lifetime, and derive from the difficulty of making a high-energy, high-power, and reversible electrochemical system. Indeed, although humans produce many mechanical and electrical systems, the number of reversible electrochemical systems is very limited. System costs may be brought down by using cathode materials less expensive than those presently employed (e.g., sulfur or air), but reversibility will remain a key challenge. Continued improvements in the ability to synthesize and characterize materials at desired length scales, as well as to use computations to predict new structures and their properties, are facilitating the development of a better understanding and improved systems. Battery research is a fascinating area for development as well as a key enabler for future technologies, including advanced transportation systems with minimal environmental impact.
Optimal Government Debt Maturity ∗

This paper develops a model of optimal government debt maturity in which the government cannot issue state-contingent bonds and cannot commit to fiscal policy. If the government can perfectly commit, it fully insulates the economy against government spending shocks by purchasing short-term assets and issuing long-term debt. These positions are quantitatively very large relative to GDP and do not need to be actively managed by the government. Our main result is that these conclusions are not robust to the introduction of lack of commitment. Under lack of commitment, large and tilted positions are very expensive to finance ex-ante since they exacerbate the problem of lack of commitment ex-post. In contrast, a flat maturity structure minimizes the cost of lack of commitment, though it also limits insurance and increases the volatility of fiscal policy distortions. We show that the optimal maturity structure is nearly flat because reducing average borrowing costs is quantitatively more important for welfare than reducing fiscal policy volatility. Thus, under lack of commitment, the government actively manages its debt positions and can approximate optimal policy by confining its debt instruments to consols.
Immersive authoring of tangible augmented reality applications

In this paper, we suggest a new approach for authoring tangible augmented reality applications, called 'immersive authoring.' The approach allows the user to carry out the authoring tasks within the AR application being built, so that the development and testing of the application can be done concurrently throughout the development process. We describe the functionalities and the interaction design for the proposed authoring system that are specifically targeted for intuitive specification of scenes and various object behaviors. Several cases of applications developed using the authoring system are presented. A small pilot user study was conducted to compare the proposed method to a non-immersive approach, and the results have shown that the users generally found it easier and faster to carry out authoring tasks in the immersive environment.
The Levels of Conceptual Interoperability Model

Interoperability of systems is not a cookie-cutter-function. There are various levels of interoperability between two systems ranging from no interoperability to full interoperability. In the technical domain, various models for levels of interoperability already exist and are used successfully to determine the degree of interoperability between information technology systems. However, such models are not yet established in the domain of conceptual modeling. This paper introduces a general model dealing with various levels of conceptual interoperability that goes beyond the technical reference models for interoperable solutions. The model is intended to become a bridge between the conceptual design and the technical design for implementation, integration, or federation. It should also contribute to the standardization of V&V procedures as well as to the documentation of systems that are designed to be federated. It is furthermore a framework to determine in the early stages of the federation development process whether meaningful interoperability between systems is possible. To this end, the scope of the model goes beyond the implementation level of actual standards, which focus on the exchange of data using standardized formats and interfaces. Another practical application of the model is, that it enhances the only recently published DoD Net-Centric Data Strategy for the Global Information Grid (GIG) and is directly applicable to derive necessary metadata to reach the DoD Data Goal to “enable Data to be understandable.”
A Noise‐aware Filter for Real‐time Depth Upsampling

A new generation of active 3D range sensors, such as time-of-flight cameras, enables recording of full-frame depth maps at video frame rate. Unfortunately, the captured data are typically starkly contaminated by noise and the sensors feature only a rather limited image resolution. We therefore present a pipeline to enhance the quality and increase the spatial resolution of range data in real-time by upsampling the range information with the data from a high resolution video camera. Our algorithm is an adaptive multi-lateral upsampling filter that takes into account the inherent noisy nature of real-time depth data. Thus, we can greatly improve reconstruction quality, boost the resolution of the data to that of the video sensor, and prevent unwanted artifacts like texture copy into geometry. Our technique has been crafted to achieve improvement in depth map quality while maintaining high computational efficiency for a real-time application. By implementing our approach on the GPU, the creation of a real-time 3D camera with video camera resolution is feasible.
Invited Paper: Enhanced Architectures, Design Methodologies and CAD Tools for Dynamic Reconfiguration of Xilinx FPGAs

The paper describes architectural enhancements to Xilinx FPGAs that provide better support for the creation of dynamically reconfigurable designs. These are augmented by a new design methodology that uses pre-routed IP cores for communication between static and dynamic modules and permits static designs to route through regions otherwise reserved for dynamic modules. A new CAD tool flow to automate the methodology is also presented. The new tools initially target the Virtex-II, Virtex-II Pro and Virtex-4 families and are derived from Xilinx's commercial CAD tools
The Situated Function - Behaviour - Structure Framework

This paper extends the Function-Behaviour-Structure (FBS) framework, which proposed eight fundamental processes involved in designing. This framework did not explicitly account for the dynamic character of the context in which designing takes place, described by the notion of situatedness. This paper describes this concept as a recursive interrelationship between different environments, which, together with a model of constructive memory, provides the foundation of a situated FBS framework. The eight fundamental processes are then reconstructed within this new framework to represent designing in a dynamic world.
MIMO-SAR: Opportunities and Pitfalls

This paper reviews advanced radar architectures that employ multiple transmit and multiple receive antennas to improve the performance of future synthetic aperture radar (SAR) systems. These advanced architectures have been dubbed multiple-input multiple-output SAR (MIMO-SAR) in analogy to MIMO communication systems. Considerable confusion arose, however, with regard to the selection of suitable waveforms for the simultaneous transmission via multiple channels. It is shown that the mere use of orthogonal waveforms is insufficient for the desired performance improvement in view of most SAR applications. As a solution to this fundamental MIMO-SAR challenge, a new class of short-term shift-orthogonal waveforms is introduced. The short-term shift orthogonality avoids mutual interferences from the radar echoes of closely spaced scatterers, while interferences from more distant scatterers are suppressed by digital beamforming on receive in elevation. Further insights can be gained by considering the data acquisition of a side-looking imaging radar in a 3-D information cube. It becomes evident that the suggested waveforms fill different subspaces that can be individually accessed by a multichannel receiver. For completeness, the new class of short-term shift-orthogonal waveforms is also compared to a recently proposed pair of orthogonal frequency-division multiplexing waveforms. It is shown that both sets of waveforms require essentially the same principle of range time to elevation angle conversion via a multichannel receiver in order to be applicable for MIMO-SAR imaging without interference.
Autostereoscopic 3D displays

Most of the perceptual cues that humans use to visualize the world's 3D structure are available in 2D projections. This is why we can make sense of photographs and images on a television screen, at the cinema, or on a computer monitor. Such cues include occlusion, perspective, familiar size, and atmospheric haze. Four cues are missing from 2D media: stereo parallax - seeing a different image with each eye, movement parallax - seeing different images when we move our heads, accommodation - the eyes' lenses focus on the object of interest, and convergence - both eyes converge on the object of interest. All 3D display technologies (stereoscopic displays) provide at least stereo parallax. Autostereoscopic displays provide the 3D image without the viewer needing to wear any special viewing gear.