nils-herrmann/clean_scirepeval huggingface dataset:
Dynamic Random Access Memory: A Survey

The increasing gap between processor speed and main memory latency has accelerated the development of various innovative architectures from dynamic random access memory(DRAM) vendors. We note that recent DRAM innovations have taken place along three dimensions: reduce access latency, develop new interfaces to achieve higher bandwidth, and add simple logic to the DRAM core for application speciic memories, particularly for graphics. The goal of this report is to provide a comprehensive survey of DRAM operations and the recent DRAM architec-tures from a computer system designer's point of view. We also propose certain mechanisms to exploit the on-chip bandwidth of DRAM array to support certain memory bandwidth-intensive operations that are essential to overall system performance. We show that the current DRAM architectures can be extended to perform those operations by simple modiication to the on-chip self testing logic (Built-In Self-Test) circuit on high-density DRAM chips.
High Isolation Internal Dual-Band Planar Inverted-F Antenna Diversity System with Band-Notched Slots for MIMO Terminals

Internal dual-band planar inverted-F antenna (PIFA) diversity system for portable devices, operating in the 2.4-GHz band (2400-2484MHz) and 5.2-GHz band (5150-5350MHz), is presented. To reduce the mutual coupling and get the high isolation between the two internal dual-band antennas, we proposed the band-notched lambda/4 slots on the ground plane. The optimized band-notched slots for high isolation, high radiation efficiency and high effective diversity gain are analyzed. The band-notched slots can be used to the small MIMO terminals which require high isolation between antennas
Free-living monitoring of Parkinson's disease: Lessons from the field.

Wearable technology comprises miniaturized sensors (eg, accelerometers) worn on the body and/or paired with mobile devices (eg, smart phones) allowing continuous patient monitoring in unsupervised, habitual environments (termed free-living). Wearable technologies are revolutionizing approaches to health care as a result of their utility, accessibility, and affordability. They are positioned to transform Parkinson's disease (PD) management through the provision of individualized, comprehensive, and representative data. This is particularly relevant in PD where symptoms are often triggered by task and free-living environmental challenges that cannot be replicated with sufficient veracity elsewhere. This review concerns use of wearable technology in free-living environments for people with PD. It outlines the potential advantages of wearable technologies and evidence for these to accurately detect and measure clinically relevant features including motor symptoms, falls risk, freezing of gait, gait, functional mobility, and physical activity. Technological limitations and challenges are highlighted, and advances concerning broader aspects are discussed. Recommendations to overcome key challenges are made. To date there is no fully validated system to monitor clinical features or activities in free-living environments. Robust accuracy and validity metrics for some features have been reported, and wearable technology may be used in these cases with a degree of confidence. Utility and acceptability appears reasonable, although testing has largely been informal. Key recommendations include adopting a multidisciplinary approach for standardizing definitions, protocols, and outcomes. Robust validation of developed algorithms and sensor-based metrics is required along with testing of utility. These advances are required before widespread clinical adoption of wearable technology can be realized.
Digital fingerprinting codes: problem statements, constructions, identification of traitors

We consider a general fingerprinting problem of digital data under which coalitions of users can alter or erase some bits in their copies in order to create an illegal copy. Each user is assigned a fingerprint which is a word in a fingerprinting code of size (the total number of users) and length . We present binary fingerprinting codes secure against size-coalitions which enable the distributor (decoder) to recover at least one of the users from the coalition with probability of error exp( ( )) for = exp( ( )). This is an improvement over the best known schemes that provide the error probability no better than exp( ( 1 )) and for this probability support at most exp( ( 1 )) users. The construction complexity of codes is polynomial in . We also present versions of these constructions that afford identification algorithms of complexity ( ) = ( ), improving over the best previously known complexity of ( ). For the case = 2, we construct codes of exponential size with even stronger performance, namely, for which the distributor can either recover both users from the coalition with probability 1 exp( ( )), or identify one traitor with probability 1.
The Pittsburgh Sleep Quality Index: Validity and factor structure in young people.

The Pittsburgh Sleep Quality Index (PSQI) is a widely used measure of sleep quality in adolescents, but information regarding its psychometric strengths and weaknesses in this population is limited. In particular, questions remain regarding whether it measures one or two sleep quality domains. The aims of the present study were to (a) adapt the PSQI for use in adolescents and young adults, and (b) evaluate the psychometric properties of the adapted measure in this population. The PSQI was slightly modified to make it more appropriate for use in youth populations and was translated into Spanish for administration to the sample population available to the study investigators. It was then administered with validity criterion measures to a community-based sample of Spanish adolescents and young adults (AYA) between 14 and 24 years old (N = 216). The results indicated that the questionnaire (AYA-PSQI-S) assesses a single factor. The total score evidenced good convergent and divergent validity and moderate reliability (Cronbach's alpha = .72). The AYA-PSQI-S demonstrates adequate psychometric properties for use in clinical trials involving adolescents and young adults. Additional research to further evaluate the reliability and validity of the measure for use in clinical settings is warranted.
Pseudoangiomatous Stromal Hyperplasia: A Rare Cause of Idiopathic Gigantomastia

Gigantomastia remains a rare clinical diagnosis with significant physical and psychological impacts on patients. We present the case of a 40-year-old woman with idiopathic breast enlargement. Further histological analysis of the breast tissue revealed pseudoangiomatous stromal hyperplasia. This is the first reported case of diffuse breast enlargement resulting from pseudoangiomatous stromal hyperplasia.
Spatially-Adaptive Filter Units for Deep Neural Networks

Classical deep convolutional networks increase receptive field size by either gradual resolution reduction or application of hand-crafted dilated convolutions to prevent increase in the number of parameters. In this paper we propose a novel displaced aggregation unit (DAU) that does not require hand-crafting. In contrast to classical filters with units (pixels) placed on a fixed regular grid, the displacement of the DAUs are learned, which enables filters to spatially-adapt their receptive field to a given problem. We extensively demonstrate the strength of DAUs on a classification and semantic segmentation tasks. Compared to ConvNets with regular filter, ConvNets with DAUs achieve comparable performance at faster convergence and up to 3-times reduction in parameters. Furthermore, DAUs allow us to study deep networks from novel perspectives. We study spatial distributions of DAU filters and analyze the number of parameters allocated for spatial coverage in a filter.
A Faster-RCNN Based Chemical Fiber Paper Tube Defect Detection Method

Chemical fiber paper tubes are the essential spinning equipment on filament high-speed spinning and winding machine of the chemical fiber industry. The precision of its application directly impacts on the formation of the silk, determines the cost of the spinning industry. Due to the accuracy of its application requirements, the paper tubes with defects must be detected and removed. Traditional industrial defect detection methods are usually carried out using the target operator's characteristics, only to obtain surface information, not only the detection efficiency and accuracy is difficult to improve, due to human judgment, it's difficult to give effective algorithm for some targets. And the existing learning algorithms are also difficult to use the deep features, so they can not get good results. Based on the Faster-RCNN method in depth learning, this paper extracts the deep features of the defective target by Convolutional Neural Network (CNN), which effectively solves the internal joint defects that the traditional algorithm can not effectively detect. As to the external joints and damaged flaws that the traditional algorithm can detect, this algorithm has better results, the experimental accuracy rate can be raised up to 98.00%. At the same time, it can be applied to a variety of lighting conditions, reducing the pretreatment steps and improving efficiency. The experimental results show that the method is effective and worthy of further research.
Four Switch Buck-Boost Converter for Telecom DC-DC power supply applications

Converters for telecom DC/DC power supply applications often require an output voltage somewhere within a wide range of input voltages. While the design of traditional converters will come with a heavy penalty in terms of component stresses and losses, and with the restrictions on the output voltage. Besides that, the high efficiency around the nominal input is another restriction for traditional converters. A controlling scheme for the four switch buck-boost converter is proposed to achieve high efficiency within the line range and the highest efficiency around the nominal input. A 48 V(36-75 V) input 12 V@25 A output two-stage prototype composed of the proposed converter and a full bridge converter is built in the lab. The experimental results verified the analysis.
Adaptive Partitioning Spline Neural Networks: Template Matching, Memorization, Inhibitor Connections, Inversion, Semi-Sup, Topology Search

Deep Neural Networks (DNNs) are universal function approximators providing state-ofthe-art solutions on wide range of applications. Common perceptual tasks such as speech recognition, image classification, and object tracking are now commonly tackled via DNNs. Some fundamental problems remain: (1) the lack of a mathematical framework providing an explicit and interpretable input-output formula for any topology, (2) quantification of DNNs stability regarding adversarial examples (i.e. modified inputs fooling DNN predictions whilst undetectable to humans), (3) absence of generalization guarantees and controllable behaviors for ambiguous patterns, (4) leverage unlabeled data to apply DNNs to domains where expert labeling is scarce as in the medical field. Answering those points would provide theoretical perspectives for further developments based on a common ground. Furthermore, DNNs are now deployed in tremendous societal applications, pushing the need to fill this theoretical gap to ensure control, reliability, and interpretability. 1 ar X iv :1 71 0. 09 30 2v 3 [ st at .M L ] 6 N ov 2 01 7
An unusual cause of hydronephroureter.

Intrauterine contraceptive device (IUCD) is a common method of contraception among women because of its low cost and high efficacy. Perforations are possible resulting in multiple complications including urinary complications. Obstructive hydronephrosis and hydroureter is one of the main clinical concerns in genitourinary practice leading to radiological investigations for determination of the cause. Determination of the cause leads to early treatment, hence saving the renal function. In this case report, we describe hydronephrosis and hydroureter secondary to a migrated/displaced IUCD.
Image restoration using Wigner distribution for night vision system

Night vision systems have become an important research area in recent years. Due to variations in weather conditions such as snow, fog, and rain, night images captured by camera may contain high level of noise. These conditions, in real life situations, may vary from no noise to extreme amount of noise corrupting images. Thus, ideal image restoration systems at night must consider various levels of noise and should have a technique to deal with wide range of noisy situations. In this paper, we have presented a new method that works well with different signal to noise ratios ranging from -1.58 dB to 20 dB. For moderate noise, Wigner distribution based algorithm gives good results, whereas for extreme amount of noise 2nd order Wigner distribution is used. The performance of our restoration technique is evaluated using MSE criteria. The results show that our method is capable of dealing with the wide range of Gaussian noise and gives consistent performance throughout.
Hyperspherical Prototype Networks

This paper introduces hyperspherical prototype networks, which unify regression and classification by prototypes on hyperspherical output spaces. Rather than defining prototypes as the mean output vector over training examples per class, we propose hyperspheres as output spaces to define class prototypes a priori with large margin separation. By doing so, we do not require any prototype updating, we can handle any training size, and the output dimensionality is no longer constrained to the number of classes. Furthermore, hyperspherical prototype networks generalize to regression, by optimizing outputs as an interpolation between two prototypes on the hypersphere. Since both tasks are now defined by the same loss function, they can be jointly optimized for multi-task problems. Experimental evaluation shows the benefits of hyperspherical prototype networks for classification, regression, and their combination.
An Online Auction Mechanism for Dynamic Virtual Cluster Provisioning in Geo-Distributed Clouds

It is common for cloud users to require clusters of inter-connected virtual machines (VMs) in a geo-distributed IaaS cloud, to run their services. Compared to isolated VMs, key challenges on dynamic virtual cluster (VC) provisioning (computation + communication resources) lie in two folds: (1) optimal placement of VCs and inter-VM traffic routing involve NP-hard problems, which are non-trivial to solve offline, not to mention if an online efficient algorithm is sought; (2) an efficient pricing mechanism is missing, which charges a market-driven price for each VC as a whole upon request, while maximizing system efficiency or provider revenue over the entire span. This paper proposes efficient online auction mechanisms to address the above challenges. We first design SWMOA, a novel online algorithm for dynamic VC provisioning and pricing, achieving truthfulness, individual rationality, computation efficiency, and <inline-formula><tex-math notation="LaTeX">$(1+2\log \mu)$</tex-math><alternatives> <inline-graphic xlink:href="wu-ieq1-2601905.gif"/></alternatives></inline-formula>-competitiveness in social welfare, where <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math><alternatives> <inline-graphic xlink:href="wu-ieq2-2601905.gif"/></alternatives></inline-formula> is related to the problem size. Next, applying a randomized reduction technique, we convert the social welfare maximizing auction into a revenue maximizing online auction, PRMOA, achieving <inline-formula><tex-math notation="LaTeX">$O(\log \mu)$ </tex-math><alternatives><inline-graphic xlink:href="wu-ieq3-2601905.gif"/></alternatives></inline-formula> -competitiveness in provider revenue, as well as truthfulness, individual rationality and computation efficiency. We investigate auction design in different cases of resource cost functions in the system. We validate the efficacy of the mechanisms through solid theoretical analysis and trace-driven simulations.
Combined effects of low-intensity blood flow restriction training and high-intensity resistance training on muscle strength and size

We investigated the combined effect of low-intensity blood flow restriction and high-intensity resistance training on muscle adaptation. Forty young men (aged 22–32 years) were randomly divided into four groups of ten subjects each: high-intensity resistance training (HI-RT, 75% of one repetition maximum [1-RM]), low-intensity resistance training with blood flow restriction (LI-BFR, 30% 1-RM), combined HI-RT and LI-BFR (CB-RT, twice-weekly LI-BFR and once-weekly HI-RT), and nontraining control (CON). Three training groups performed bench press exercises 3 days/week for 6 weeks. During LI-BFR training sessions, subjects wore pressure cuffs on both arms that were inflated to 100–160 mmHg. Increases in 1-RM were similar in the HI-RT (19.9%) and CB-RT (15.3%) groups and lower in the LI-BFR group (8.7%, p < 0.05). Maximal isometric elbow extension (MVC) increased in the HI-RT (11.3%) and CB-RT (6.6%) groups; there was no change in the LI-BFR group (−0.2%). The cross-sectional area (CSA) of the triceps brachii (TB) increased (p < 0.05) in the HI-RT (8.6%), CB-RT (7.2%), and LI-BFR (4.4%) groups. The change in relative isometric strength (MVC divided by TB CSA) was greater (p < 0.05) in the HI-RT group (3.3%) than in the LI-BFR (−3.5%) and CON (−0.1%) groups. Following training, relative dynamic strength (1-RM divided by TB CSA) was increased (p < 0.05) by 10.5% in the HI-RT group and 6.7% in the CB-RT group. None of the variables in the CON group changed. Our results show that low-intensity resistance training with BFR-induced functional muscle adaptations is improved by combining it with HI-RT.
Analysis and design methodology for Planar Transformer with low self-capacitance used in high voltage flyback charging circuit

In high voltage (HV) flyback charging circuits, the importance of transformer parasitics holds a significant part in the overall system parasitics. The HV transformers have a larger number of turns on the secondary side that leads to higher self-capacitance which is inevitable. The conventional wire-wound transformer (CWT) has limitation over the design with larger self-capacitance including increased size and volume. For capacitive load in flyback charging circuit these self-capacitances on the secondary side gets added with device capacitances and dominates the load. For such applications the requirement is to have a transformer with minimum self-capacitances and low profile. In order to achieve the above requirements Planar Transformer (PT) design can be implemented with windings as tracks in Printed Circuit Boards (PCB) each layer is insulated by the FR4 material which aids better insulation. Finite Element Model (FEM) has been developed to obtain the self-capacitance in between the layers for larger turns on the secondary side. The modelled hardware prototype of the Planar Transformer has been characterised for open circuit and short circuit test using Frequency Response Analyser (FRA). The results obtained from FEM and FRA are compared and presented.
Refugee stress and folk belief: Hmong sudden deaths.

Since the first reported death in 1977, scores of seemingly healthy Hmong refugees have died mysteriously and without warning from what has come to be known as Sudden Unexpected Nocturnal Death Syndrome (SUNDS). To date medical research has provided no adequate explanation for these sudden deaths. This study is an investigation into the changing impact of traditional beliefs as they manifest during the stress of traumatic relocation. In Stockton, California, 118 Hmong men and women were interviewed regarding their awareness of and personal experience with a traditional nocturnal spirit encounter. An analysis of this data reveals that the supranormal attack acts as a trigger for Hmong SUNDS.
Examining the influence of modularity and knowledge management (KM) on dynamic capabilities: Insights from a call center

Modularity in organizations can facilitate the creation and development of dynamic capabilities. Paradoxically, however, modular management can also stifle the strategic potential of such capabilities by conflicting with the horizontal integration of units. We address these issues through an examination of how modular management of information technology (IT), project teams and front-line personnel in concert with knowledge management (KM) interventions influence the creation and development of dynamic capabilities at a large Asia-based call center. Our findings suggest that a full capitalization of the efficiencies created by modularity may be closely linked to the strategic sense making abilities of senior managers to assess the long-term business value of the dominant designs available in the market. Drawing on our analysis we build a modular management-KM-dynamic capabilities model, which highlights the evolution of three different levels of dynamic capabilities and also suggests an inherent complementarity between modular and integrated approaches.
Code-switching as a marker of linguistic competence in bilingual children ∗

Code-switching is a common phenomenon that bilinguals engage in, including bilingual children. While many researchers have analyzed code-switching behaviors to better understand more about the language processes in bilingual children, few have examined how code-switching behavior affects a child’s linguistic competence. This study thus sought to examine the relationship between code-switching and linguistic competency in bilingual children. Fifty-five English–Mandarin bilingual children aged 5 to 6 years were observed during classroom activities over five days (three hours each day). A number of different word roots and mean length of utterance for both languages, and a number of code-switched utterances for each child, were computed. English receptive vocabulary scores were also obtained. Additionally, teachers rated children’s English and Mandarin language competencies approximately six months later. Correlational and hierarchical regression analyses support the argument that code-switching does not indicate linguistic incompetence. Instead, bilingual children’s code-switching strongly suggests that it is a marker of linguistic competence.
A compact low-profile ultra wideband antenna for biomedical applications

This paper presents a compact planar ultra-wideband (UWB) microstrip antenna for microwave medical applications. The proposed antenna has a low profile structure, consisting of a modified radiating patch with stair steps and open slots, microstrip feed line, and T-like shape slots at the ground plane. The optimized antenna is capable of being operated in frequency range of 3.06–11.4 GHz band having good omnidirectional radiation pattern and high gain, which satisfies the requirements of UWB (3.1–10.6 GHz) applications. The antenna system has a compact size of 18×30×0.8mm3. These features make the proposed UWB antenna a good candidate for microwave medical imaging applications.
Small-Signal Stability Analysis of a Grid Connected Doubly-Fed Induction Generator Under Decoupled P-Q Control

Small signal stability analysis is conducted considering grid connected doubly-fed induction generator (DFIG) type. The modeling of a grid connected DFIG system is first set up and the whole model is formulated by a set of differential algebraic equations (DAE). Then, the mathematical model of rotor-side converter is built with decoupled P-Q control techniques to implement stator active and reactive powers control. Based on the abovementioned researches, the small signal stability analysis is carried out to explore and compared the differences between the whole system with the decoupled P-Q controller or not by eigenvalues and participation factors. Finally, numerical results demonstrate the system are stable, especially some conclusions and comments of interest are made. DFIG model; decoupled P-Q control; DAE; small signal analysis;