nils-herrmann/clean_scirepeval huggingface dataset:
Substrate Integrated Horn Antenna With Uniform Aperture Distribution

A new substrate integrated horn antenna with hard side walls combined with a couple of soft surfaces is introduced. The horn takes advantage of the air medium for propagation inside, while having a thickness of dielectric on the walls to realize hard conditions. The covering layers of the air-filled horn are equipped with strip-via arrays, which act as soft surfaces around the horn aperture to reduce the back radiations. The uniform amplitude distribution of the aperture resulting from the hard conditions and the phase correction combined with the profiled horn walls provided a narrow beamwidth and −13 dB sidelobe levels in the frequency of the hard condition, which is validated by the simulated and measured results.
A ZigBee-Based Location-Aware Fall Detection System for Improving Elderly Telecare

Falls are the primary cause of accidents among the elderly and frequently cause fatal and non-fatal injuries associated with a large amount of medical costs. Fall detection using wearable wireless sensor nodes has the potential of improving elderly telecare. This investigation proposes a ZigBee-based location-aware fall detection system for elderly telecare that provides an unobstructed communication between the elderly and caregivers when falls happen. The system is based on ZigBee-based sensor networks, and the sensor node consists of a motherboard with a tri-axial accelerometer and a ZigBee module. A wireless sensor node worn on the waist continuously detects fall events and starts an indoor positioning engine as soon as a fall happens. In the fall detection scheme, this study proposes a three-phase threshold-based fall detection algorithm to detect critical and normal falls. The fall alarm can be canceled by pressing and holding the emergency fall button only when a normal fall is detected. On the other hand, there are three phases in the indoor positioning engine: path loss survey phase, Received Signal Strength Indicator (RSSI) collection phase and location calculation phase. Finally, the location of the faller will be calculated by a k-nearest neighbor algorithm with weighted RSSI. The experimental results demonstrate that the fall detection algorithm achieves 95.63% sensitivity, 73.5% specificity, 88.62% accuracy and 88.6% precision. Furthermore, the average error distance for indoor positioning is 1.15 ± 0.54 m. The proposed system successfully delivers critical information to remote telecare providers who can then immediately help a fallen person.
Energy aware resource allocation of cloud data center: review and open issues

The demand for cloud computing is increasing dramatically due to the high computational requirements of business, social, web and scientific applications. Nowadays, applications and services are hosted on the cloud in order to reduce the costs of hardware, software and maintenance. To satisfy this high demand, the number of large-scale data centers has increased, which consumes a high volume of electrical power, has a negative impact on the environment, and comes with high operational costs. In this paper, we discuss many ongoing or implemented energy aware resource allocation techniques for cloud environments. We also present a comprehensive review on the different energy aware resource allocation and selection algorithms for virtual machines in the cloud. Finally, we come up with further research issues and challenges for future cloud environments.
Genetic structure of Plasmodium falciparum populations across the Honduras-Nicaragua border

The Caribbean coast of Central America remains an area of malaria transmission caused by Plasmodium falciparum despite the fact that morbidity has been reduced in recent years. Parasite populations in that region show interesting characteristics such as chloroquine susceptibility and low mortality rates. Genetic structure and diversity of P. falciparum populations in the Honduras-Nicaragua border were analysed in this study. Seven neutral microsatellite loci were analysed in 110 P. falciparum isolates from endemic areas of Honduras (n = 77) and Nicaragua (n = 33), mostly from the border region called the Moskitia. Several analyses concerning the genetic diversity, linkage disequilibrium, population structure, molecular variance, and haplotype clustering were conducted. There was a low level of genetic diversity in P. falciparum populations from Honduras and Nicaragua. Expected heterozigosity (H e ) results were similarly low for both populations. A moderate differentiation was revealed by the FST index between both populations, and two putative clusters were defined through a structure analysis. The main cluster grouped most of samples from Honduras and Nicaragua, while the second cluster was smaller and included all the samples from the Siuna community in Nicaragua. This result could partially explain the stronger linkage disequilibrium (LD) in the parasite population from that country. These findings are congruent with the decreasing rates of malaria endemicity in Central America.
Learning Entity Types from Query Logs via Graph-Based Modeling

Entities (e.g., person, movie or place) play an important role in real-world applications and learning entity types has attracted much attention in recent years. Most conventional automatic techniques use large corpora, such as news articles, to learn types of entities. However, such text corpora focus on general knowledge about entities in an objective way. Hence, it is difficult to satisfy those users with specific and personalized needs for an entity. Recent years have witnessed an explosive expansion in the mining of search query logs, which contain billions of entities. The word patterns and click-throughs in search logs are not found in text corpora, thus providing a complemental source for discovering entity types based on user behaviors. In this paper, we study the problem of learning entity types from search query logs and address the following challenges: (1) queries are short texts, and information related to entities is usually very sparse; (2) large amounts of irrelevant information exists in search logs, bringing noise in detecting entity types. In this paper, we first model query logs using a bipartite graph with entities and their auxiliary information, such as contextual words and clicked URLs. Then we propose a graph-based framework called ELP (Ensemble framework based on Lable Propagation) to simultaneously learn the types of both entities and auxiliary signals. In ELP, two separate strategies are designed to fix the problems of sparsity and noise in query logs. Extensive empirical studies are conducted on real search logs to evaluate the effectiveness of the proposed ELP framework.
Designing and Modeling Fuzzy Control Systems

Fuzzy logic provides a formal framework for constructing systems exhibiting both good numeric performance (precision) and linguistic representation (interpretability). Fuzzy modeling—meaning the construction of fuzzy systems—is an arduous task, demanding the identification of many parameters. This paper analyses the fuzzy-modeling problem and different approaches to coping with it, focusing on evolutionary fuzzy modeling— the design of fuzzy inference systems using evolutionary algorithms. The purpose of this paper is twofold. We first provide an overview of the standard approach to constructing a fuzzy control system and then identify a wide variety of relevant system modeling techniques. The later part of the paper deals with discussing Fuzzy modeling problem – curse of dimensionality and techniques to solve the problem. The paper provides an introduction to the use of fuzzy sets and fuzzy logic for the approximation of functions and modeling of static and dynamic systems. The concept of a fuzzy system is first explained. Afterwards, the motivation and practical relevance of fuzzy modeling are highlighted.
Abdominal obesity and metabolic syndrome

Metabolic syndrome is associated with abdominal obesity, blood lipid disorders, inflammation, insulin resistance or full-blown diabetes, and increased risk of developing cardiovascular disease. Proposed criteria for identifying patients with metabolic syndrome have contributed greatly to preventive medicine, but the value of metabolic syndrome as a scientific concept remains controversial. The presence of metabolic syndrome alone cannot predict global cardiovascular disease risk. But abdominal obesity — the most prevalent manifestation of metabolic syndrome — is a marker of 'dysfunctional adipose tissue', and is of central importance in clinical diagnosis. Better risk assessment algorithms are needed to quantify diabetes and cardiovascular disease risk on a global scale.
A Simple Yet Effective Baseline for 3d Human Pose Estimation

Following the success of deep convolutional networks, state-of-the-art methods for 3d human pose estimation have focused on deep end-to-end systems that predict 3d joint locations given raw image pixels. Despite their excellent performance, it is often not easy to understand whether their remaining error stems from a limited 2d pose (visual) understanding, or from a failure to map 2d poses into 3- dimensional positions.,,With the goal of understanding these sources of error, we set out to build a system that given 2d joint locations predicts 3d positions. Much to our surprise, we have found that, with current technology, "lifting" ground truth 2d joint locations to 3d space is a task that can be solved with a remarkably low error rate: a relatively simple deep feedforward network outperforms the best reported result by about 30% on Human3.6M, the largest publicly available 3d pose estimation benchmark. Furthermore, training our system on the output of an off-the-shelf state-of-the-art 2d detector (i.e., using images as input) yields state of the art results – this includes an array of systems that have been trained end-to-end specifically for this task. Our results indicate that a large portion of the error of modern deep 3d pose estimation systems stems from their visual analysis, and suggests directions to further advance the state of the art in 3d human pose estimation.
An 80 GHz radar level measurement system with dielectric lens antenna

In this contribution, a Frequency Modulated Continuous Wave (FMCW) radar system, working in the 75 to 85 GHz frequency range, for level measurement of liquids in tanks is presented. The realized radar antenna has been specially designed to cope with the given harsh environmental conditions (high pressure, high temperature, explosion protection requirements, etc.) in industrial applications. The basic design concept of the antenna is to utilize a dielectric lens as `barrier element' between the tank and the radar electronics module, and especially for high-pressure applications, a cylindrical glass element is included into the lens system. Different configurations of dielectric lens antennas (different biconvex and plano-convex lenses with 45 mm diameter), also in versions including a glass barrier element with 20 mm thickness, have been evaluated by means of electromagnetic field simulations and measurements. A circular waveguide with 2.6 mm inner diameter equipped with a conical horn with 4.8 mm diameter and 3 mm length is used as feeding structure. Results demonstrate a range pulse width (-6 dB) of 30 mm with the full bandwidth of 10 GHz of the system. As expected, the best antenna performance is given with the biconvex lens antenna without the glass barrier element. Nevertheless, a reasonably good performance in the case of equipping the glass barrier with plano-convex dielectric lenses at its front and rear sides can be achieved.
Re-evaluating the role of the mammillary bodies in memory

Although the mammillary bodies were among the first brain regions to be implicated in amnesia, the functional importance of this structure for memory has been questioned over the intervening years. Recent patient studies have, however, re-established the mammillary bodies, and their projections to the anterior thalamus via the mammillothalamic tract, as being crucial for recollective memory. Complementary animal research has also made substantial advances in recent years by determining the electrophysiological, neurochemical, anatomical and functional properties of the mammillary bodies. Mammillary body and mammillothalamic tract lesions in rats impair performance on a number of spatial memory tasks and these deficits are consistent with impoverished spatial encoding. The mammillary bodies have traditionally been considered a hippocampal relay which is consistent with the equivalent deficits seen following lesions of the mammillary bodies or their major efferents, the mammillothalamic tract. However, recent findings suggest that the mammillary bodies may have a role in memory that is independent of their hippocampal formation afferents; instead, the ventral tegmental nucleus of Gudden could be providing critical mammillary body inputs needed to support mnemonic processes. Finally, it is now apparent that the medial and lateral mammillary nuclei should be considered separately and initial research indicates that the medial mammillary nucleus is predominantly responsible for the spatial memory deficits following mammillary body lesions in rats.
Accurate cell segmentation in microscopy images using membrane patterns

Identifying cells in an image (cell segmentation) is essential for quantitative single-cell biology via optical microscopy. Although a plethora of segmentation methods exists, accurate segmentation is challenging and usually requires problem-specific tailoring of algorithms. In addition, most current segmentation algorithms rely on a few basic approaches that use the gradient field of the image to detect cell boundaries. However, many microscopy protocols can generate images with characteristic intensity profiles at the cell membrane. This has not yet been algorithmically exploited to establish more general segmentation methods.



We present an automatic cell segmentation method that decodes the information across the cell membrane and guarantees optimal detection of the cell boundaries on a per-cell basis. Graph cuts account for the information of the cell boundaries through directional cross-correlations, and they automatically incorporate spatial constraints. The method accurately segments images of various cell types grown in dense cultures that are acquired with different microscopy techniques. In quantitative benchmarks and comparisons with established methods on synthetic and real images, we demonstrate significantly improved segmentation performance despite cell-shape irregularity, cell-to-cell variability and image noise. As a proof of concept, we monitor the internalization of green fluorescent protein-tagged plasma membrane transporters in single yeast cells.



Matlab code and examples are available at http://www.csb.ethz.ch/tools/cellSegmPackage.zip.
Online Persuasion for E-Commerce Websites

The persuasive design of e-commerce websites has been shown to support people with online purchases. Therefore, it is important to understand how persuasive applications are used and assimilated into e-commerce website designs. This paper demonstrates how the PSD model’s persuasive features could be used to build a bridge supporting the extraction and evaluation of persuasive features in such e-commerce websites; thus practically explaining how feature implementation can enhance website persuasiveness. To support a deeper understanding of persuasive e-commerce website design, this research, using the Persuasive Systems Design (PSD) model, identifies the distinct persuasive features currently assimilated in ten successful e-commerce websites. The results revealed extensive use of persuasive features; particularly features related to dialogue support, credibility support, and primary task support; thus highlighting weaknesses in the implementation of social support features. In conclusion we suggest possible ways for enhancing persuasive feature implementation via appropriate contextual examples and explanation.
Efficient Path Profiling

A path profile determines how many times each acyclic path in a routine executes. This type of profiling subsumes the more common basic block and edge profiling, which only approximate path frequencies. Path profiles have many potential uses in program performance tuning, profile-directed compilation, and software test coverage. This paper describes a new algorithm for path profiling. This simple, fast algorithm selects and places profile instrumentation to minimize run-time overhead. Instrumented programs run with overhead comparable to the best previous profiling techniques. On the SPEC95 benchmarks, path profiling overhead averaged 31%, as compared to 16% for efficient edge profiling. Path profiling also identifies longer paths than a previous technique, which predicted paths from edge profiles (average of 88, versus 34 instructions). Moreover, profiling shows that the SPEC95 train input datasets covered most of the paths executed in the ref datasets.
Antioxidant and antiproliferative activities of sweet potato (Ipomoea batatas (L.) Lam 'Tainong 57') constituents

The aim of this study is to examine possible antioxidant and antiproliferative activities of the different extracts from sweet potato (Ipomoea batatas [L.] Lam ‘Tainong 57’) organs. DPPH staining, total phenolic compounds and flavonoid content, DPPH radical, reducing power method, FTC method, and cell proliferation were all employed. In the DPPH staining, ethanol extract of vein had the highest radical-scavenging activity when it was diluted to 6.25 mg dry matter/mL. Among all the extracts, the highest amount of total phenolic and flavonoid compounds was found in the ethanol extract of vein. In the DPPH colorimetric method, it was found that ethanol extract of leaf had the highest radical-scavenging activity, followed by water extract of vein. In the reducing power activity assay, it was found that the water extract of leaf had the highest reducing power activity, followed by ethanol extract of vein. Like phenolic compounds, the highest FTC activity was found in the ethanol extract of vein. The antiproliferative activities aof sweet potato were studied in vitro using human lymphoma NB4 cells, and the following results were found: water extract of vein had the highest antiproliferative activity with an EC 50 of 449.6 ± 27.73 μg/mL, followed by water extract of storage root, water extract of leaf, ethanol extract of storage root, and ethanol extract of leaf. Although the ethanol extract of vein showed strong antioxidant activity, it had no antiproliferative activity under the experimental conditions tested.
Planar-adaptive routing: low-cost adaptive networks for multiprocessors

Network throughput can be increased by allowing multipath, adaptive routing. Adaptive routing allows more freedom in the paths taken by messages, spreading load over physical channels more evenly. The flexibility of adaptive routing introduces new possibilities of deadlock. Previous deadlock avoidance schemes in k-ary n-cubes require an exponential number of virtual channels, independent of network size and dimension. Planar adaptive routing algorithms reduce the complexity of deadlock prevention by reducing the number of choices at each routing step. In the fault-free case, planar-adaptive networks are guaranteed to be deadlock-free. In the presence of network faults, the planar-adaptive router can be extended with misrouting to produce a working network which remains provably deadlock free and is provably livelock free. In addition, planar adaptive networks can simultaneously support both in-order and adaptive, out-of-order packet delivery.
Planar-adaptive routing is of practical significance. It provides the simplest known support for deadlock-free adaptive routing in k-ary n-cubes of more than two dimensions (with k > 2). Restricting adaptivity reduces the hardware complexity, improving router speed or allowing additional performance-enhancing network features. The structure of planar-adaptive routers is amenable to efficient implementation.
Investigation of Multi Band Microstrip Line Fed Antenna using DGS Technique for WLAN/WiMAX Applications

A multiband planar monopole antenna fed by microstrip line feed with Defected Ground Structure (DGS) is presented for simultaneously satisfying wireless local area network (WLAN) and worldwide interoperability for microwave access (WiMAX) applications. The proposed antenna consists of a rectangular microstrip patch with rectangular slit, including the circular defect etched on the ground plane forming DGS structure. The soft nature of the DGS facilitates improvement in the performance of microstrip antennas. The simulated -10 dB bandwidth for return loss is from 2. 9-3. 77 GHz, 3. 91-6. 36, covering the WLAN: 5. 15–5. 35 and 5. 725–5. 85 GHz and WiMAX: 3. 3–3. 8 and 5. 25–5. 85 GHz bands. The design and optimization of DGS structures along with the parametric study were carried out using IE3D ZELAND which is based on method of moment.
Thermal Emission Imaging System ( THEMIS ) infrared observations of atmospheric dust and water ice cloud optical depth

The Mars Odyssey spacecraft entered into Martian orbit in October 2001 and after successful aerobraking, began mapping in February 2002. Thermal infrared images taken by the Thermal Emission Imaging System (THEMIS) on board the Odyssey spacecraft allow for the quantitative retrieval of atmospheric dust and water ice aerosol optical depth. Data collected so far cover late northern winter, spring, and summer (Ls = 330 –160 ). During this period, THEMIS observed the decay of a regional dust storm, a number of local dust storms along the edge of the retreating north polar cap, and the growth of the low-latitude aphelion water ice cloud belt. Data from THEMIS complements the concurrent Mars Global Surveyor Thermal Emission Spectrometer (TES) data by sampling a later local time ( 1400 LT for TES versus 1600–1730 LT for THEMIS) and by observing at much higher spatial resolution. Comparison of water ice optical depth in the aphelion cloud belt from THEMIS and TES shows a significantly higher optical depth in the late afternoon (THEMIS) than in the early afternoon (TES).
Hybrid semi-Markov CRF for Neural Sequence Labeling

This paper proposes hybrid semiMarkov conditional random fields (SCRFs) for neural sequence labeling in natural language processing. Based on conventional conditional random fields (CRFs), SCRFs have been designed for the tasks of assigning labels to segments by extracting features from and describing transitions between segments instead of words. In this paper, we improve the existing SCRF methods by employing word-level and segment-level information simultaneously. First, word-level labels are utilized to derive the segment scores in SCRFs. Second, a CRF output layer and an SCRF output layer are integrated into an unified neural network and trained jointly. Experimental results on CoNLL 2003 named entity recognition (NER) shared task show that our model achieves state-of-the-art performance when no external knowledge is used.