LLukas22/scidocs huggingface dataset:
W-band GaN power amplifier MMICs

An advanced high power, high frequency GaN semiconductor process has made possible the design and fabrication of W-band power amplifier MMICs with unprecedented performance. The key enabling semiconductor technology is a 150 nm T-gate GaN HEMT with an output power exceeding 300mW and a peak PAE (power added efficiency) of 37%. With this process, W-band power amplifier MMICs have been designed and fabricated that demonstrate output powers of 1.7 watts, power added efficiencies greater than 20%, and small signal gains of 21 dB. In addition, the compactness of these MMIC designs have allowed for MMIC power densities (MMIC output power relative to MMIC area) exceeding 1/2 watt/mm2.
Family presence preference when patients are receiving resuscitation in an accident and emergency department.

AIM
This paper is a report of a study to illuminate the experience of family members whose relatives survived the resuscitation in an accident and emergency department, and their preferences with regard to being present.


BACKGROUND
Family presence during resuscitation can offer benefits to both patient and family members, and large healthcare organizations support and recommend offering the option for their presence. However, many staff believe that this is too distressing or traumatic for families and that they would interfere with the resuscitation process.


METHODS
An interpretive phenomenological approach was used to collect data in 2007-2008 with 18 family members of patients who survived life-sustaining interventions in an accident and emergency department in Hong Kong. Audio-recorded interviews were transcribed verbatim for thematic analysis and verified with the participants in second interviews.


FINDINGS
None of the family members was present in resuscitation room during the life-sustaining interventions, and five entered the room after the patients' condition was stable. The majority indicated a strong preference to be present if given the option. Three interrelated themes emerged: (i) emotional connectedness, (ii) knowing the patient, and (iii) perceived (in)appropriateness, with 10 subthemes representing affective, rational and contextual determinants of family presence preferences. The interplay of these determinants and how they contributed to strong or weak preference for family presence was analysed.


CONCLUSION
Variations among the contributing determinants to each family member's preference to be present were revealed. Appropriate nursing interventions, policy and guidelines should be developed to meet individualized needs during such critical and life-threatening moments in accident and emergency departments.
Effect of acute kidney injury on mortality and hospital stay in patient with severe acute pancreatitis.

AIM
Severe acute pancreatitis (SAP) is believed to be a major risk factor leading to acute kidney injury (AKI) among critically ill patients, but little is known about SAP-induced AKI. We study the incidence of AKI defined by the Acute Kidney Injury Network (AKIN) criteria and the risk factors associated with outcomes among SAP-induced AKI patients.


METHOD
We conducted a multicenter retrospective study of critically ill SAP-induced AKI patients during the period August 2009 to June 2013. Data on enrolled patients were retrieved from electronic records. Univariate and multiple regression analyses were performed.


RESULTS
Among a total of 414 SAP patients admitted to intensive care units(ICU), 287 (69.3%) developed AKI during their ICU stay, with 16.7%, 18.4%, and 34.3% classified as AKI stage I,II, and III, respectively. SAP-induced AKI patients experienced a significantly higher ICU mortality than those without AKI. The risk factors associated with ICU mortality among SAP-induced AKI patients included ACS (odds ratio (OR) 10.58), RRT (OR 3.31), sepsis (OR 2.46), CTSI (OR 3.01), APACHE II score (OR 1.82), AKI III (OR 1.38), ICU-length-of-stay (OR 1.04), and multi-organ failure.


CONCLUSIONS
The paper represents the first attempt to investigate the etiology and epidemiology of AKI following SAP under the AKIN criteria among critically ill patients. Several independent risk factors were found to be associated with ICU mortality for AKI patients. The findings may pinpoint crucial therapeutic measures for preventing AKI among a vulnerable population and for more effective management of SAP-induced AKI to improve the quality of intensive care.
Implementation and evaluation of an interprofessional simulation-based education program for undergraduate nursing students in operating room nursing education: a randomized controlled trial

BACKGROUND
The present study was designed to implement an interprofessional simulation-based education program for nursing students and evaluate the influence of this program on nursing students' attitudes toward interprofessional education and knowledge about operating room nursing.


METHODS
Nursing students were randomly assigned to either the interprofessional simulation-based education or traditional course group. A before-and-after study of nursing students' attitudes toward the program was conducted using the Readiness for Interprofessional Learning Scale. Responses to an open-ended question were categorized using thematic content analysis. Nursing students' knowledge about operating room nursing was measured.


RESULTS
Nursing students from the interprofessional simulation-based education group showed statistically different responses to four of the nineteen questions in the Readiness for Interprofessional Learning Scale, reflecting a more positive attitude toward interprofessional learning. This was also supported by thematic content analysis of the open-ended responses. Furthermore, nursing students in the simulation-based education group had a significant improvement in knowledge about operating room nursing.


CONCLUSIONS
The integrated course with interprofessional education and simulation provided a positive impact on undergraduate nursing students' perceptions toward interprofessional learning and knowledge about operating room nursing. Our study demonstrated that this course may be a valuable elective option for undergraduate nursing students in operating room nursing education.
Regulation of feed intake, growth, nutrient and energy utilisation in European sea bass (Dicentrarchus labrax) fed high fat diets

Three practical isoproteic (54% protein) diets were formulated to contain graded levels of crude fat (diet L: 10%, diet M: 20% and diet H: 30%). Each diet was assigned unrestrictedly to three and restrictedly to two replicate groups of fish (IBW 243 g). In unrestricted groups, increasing the dietary lipid level led to a significant decrease in voluntary feed intake without affecting growth rate. In the feed-restricted groups, daily growth rates increased with increasing dietary fat levels. There was a significant and inverse effect of the dietary fat content on whole body moisture and fat levels, with highest lipid (ca. 20%) and lowest moisture (ca. 58%) contents in sea bass fed diet containing the highest lipid level; muscle lipid concentration was however not affected. Nitrogen retention was significantly increased by an increase in lipid concentration in the diets, with better efficiencies observed in unrestricted (ca. 31%) than in restricted groups (ca. 27%). Nitrogen loss was significantly affected by both the feeding level and the diet composition, with lowest values (ca. 60 g kg) in groups fed diet H unrestrictedly and highest values (ca. 90 g kg) in groups fed diet L at a restricted level. Soluble phosphorus excretion in H groups was less than half that in L groups, regardless of the feeding level.
Genetic analysis and molecular characterization of Chinese sesame (Sesamum indicum L.) cultivars using Insertion-Deletion (InDel) and Simple Sequence Repeat (SSR) markers

Sesame is an important and ancient oil crop in tropical and subtropical areas. China is one of the most important sesame producing countries with many germplasm accessions and excellent cultivars. Domestication and modern plant breeding have presumably narrowed the genetic basis of cultivated sesame. Several modern sesame cultivars were bred with a limited number of landrace cultivars in their pedigree. The genetic variation was subsequently reduced by genetic drift and selection. Characterization of genetic diversity of these cultivars by molecular markers is of great value to assist parental line selection and breeding strategy design. Three hundred and forty nine simple sequence repeat (SSR) and 79 insertion-deletion (InDel) markers were developed from cDNA library and reduced-representation sequencing of a sesame cultivar Zhongzhi 14, respectively. Combined with previously published SSR markers, 88 polymorphic markers were used to assess the genetic diversity, phylogenetic relationships, population structure, and allele distribution among 130 Chinese sesame accessions including 82 cultivars, 44 landraces and 4 wild germplasm accessions. A total of 325 alleles were detected, with the average gene diversity of 0.432. Model-based structure analysis revealed the presence of five subgroups belonging to two main groups, which were consistent with the results from principal coordinate analysis (PCA), phylogenetic clustering and analysis of molecular variance (AMOVA). Several missing or unique alleles were identified from particular types, subgroups or families, even though they share one or both parental/progenitor lines. This report presented a by far most comprehensive characterization of the molecular and genetic diversity of sesame cultivars in China. InDels are more polymorphic than SSRs, but their ability for deciphering genetic diversity compared to the later. Improved sesame cultivars have narrower genetic basis than landraces, reflecting the effect of genetic drift or selection during breeding processes. Comparative analysis of allele distribution revealed genetic divergence between improved cultivars and landraces, as well as between cultivars released in different years. These results will be useful for assessing cultivars and for marker-assisted breeding in sesame.
Risk-Based Asset Allocation : A New Answer to an Old Question ?

WAI LEE is the director of research and CIO of the Quantitative Investment Group at Neuberger Berman in New York, NY. wai.lee@nb.com The global financial crisis in 2008 caused investors to question what went wrong with many of their portfolios, which were believed to be diversified. Mean-variance optimization (MVO), 60/40, modern portfolio theory (MPT), and others seem to have been put on trial by practitioners and critics alike for their apparent underdiversif ication and accused failure to provide risk control. A list of “new paradigms” or “next generation solutions” has been declared to displace MPT. A growing amount of literature on portfolio construction approaches focused on risks and diversification rather than on estimating expected returns, collectively called risk-based asset allocation in this study, has been documented. On the topic of strategic asset allocation, we have been seeing more writings on the various versions of risk-based approaches applied to a global universe of assets, especially in cases of pension and endowment management. Allen [2010] and Foresti and Rush [2010] provide good examples. A common finding among these studies is the superior risk-adjusted return of a portfolio that is constructed in such a way that assets are expected to contribute equal risk to the whole portfolio—an approach commonly labeled risk parity. In a risk parity approach, only risk forecasts are used as inputs, and no forecasts of returns of any assets are required. In recent years, we have also witnessed a growing literature documenting, in particular, that some equity portfolios—naively diversified portfolios (e.g., equally weighted portfolios), portfolios that are constructed to achieve the minimum volatility possible given the universe of risky assets (e.g., stocks), in order to achieve “maximum diversification” subject to the definition of diversif ication, or portfolios that are dubbed as risk parity— outperformed on a risk-adjusted basis both the market capitalization–weighted portfolio and portfolios that ex ante are constructed to be mean-variance optimal as derived by application of the Markowitz optimization. In some studies, these portfolios even outperformed the market portfolio on an absolute return basis. Examples include Clarke, de Silva, and Thorley [2006]; DeMiguel, Garlappi, and Uppal [2009]; Behr, Güttler, and Miebs [2008]; Martellini [2008]; and Choueifaty and Coignard [2008]. We have also seen the parallel development in the industry of the growth of product offerings and client interest in investment vehicles built upon these findings, especially in equities ( Johnson [2008]). One common characteristic across all of these portfolios is that the only input required to determine the portfolio compositions is a model of risk, which is typically measured by the covariance matrix, while explicit modeling of expected returns is not required. Some of these studies argue
CNN architectures for large-scale audio classification

Convolutional Neural Networks (CNNs) have proven very effective in image classification and show promise for audio. We use various CNN architectures to classify the soundtracks of a dataset of 70M training videos (5.24 million hours) with 30,871 video-level labels. We examine fully connected Deep Neural Networks (DNNs), AlexNet [1], VGG [2], Inception [3], and ResNet [4]. We investigate varying the size of both training set and label vocabulary, finding that analogs of the CNNs used in image classification do well on our audio classification task, and larger training and label sets help up to a point. A model using embeddings from these classifiers does much better than raw features on the Audio Set [5] Acoustic Event Detection (AED) classification task.
A complete network-on-chip emulation framework

Current Systems-On-Chip (SoC) execute applications that demand extensive parallel processing. Networks-On-Chip (NoC) provide a structured way of realizing interconnections on silicon, and obviate the limitations of bus-based solution. NoCs can have regular or ad hoc topologies, and functional validation is essential to assess their correctness and performance. In this paper, we present a flexible emulation environment implemented on an FPGA that is suitable to explore, evaluate and compare a wide range of NoC solutions with a very limited effort. Our experimental results show a speed-up of four orders of magnitude with respect to cycle-accurate HDL simulation, while retaining cycle accuracy. With our emulation framework, designers can explore and optimize a various range of solutions, as well as characterize quickly performance figures.
A Partial Enhanced-Scan Approach to Robust Delay-Fault Test Generation for Sequential Circuits

The increasing demand for quality in VLSI designs and fabrication processes is motivating test engineers to test circuits in more comprehensive fault models such as transistor stuckopen faults and delay faults. T h e detection of delay faults requires the application of a vector pair, and the circuit has to be clocked at speed after the application of each vector. Arbitrary vector pairs cannot always be applied a t speed to a sequential circuit implemented with standard scan design. In order to apply an arbitrary vector pair, the circuit may have to be implemented with enhanced-scan flip-flops that can store two bits of state. Using these flip-flops can result in a significant area overhead. In this paper we address the problem of robust path-delayfault test generation for sequential circuits by using a partial1 enhanced-scan/standard-scan approach and by using the notion of scan shifting. Vector pairs that can be obtained by single-bit shifts can be applied a t speed under standard scan. We first give an algorithm to determine an eficient ordering of the flip-flops in the scan chain using the information derived f rom running a delay test generator on the circuit. Given an ordering of the flip-flops, we give an optimization algorithm that attempts to minimize the number of flip-flops to be made enhanced-scan so as to obtain the required level of delay-fault coverage. We show how the test vector sets derived using our test generator can be compacted by solving a clique covering problem. We present test generation, flip-flop ordering, flip-flop selection and test set compaction results on large benchmark circuits.
Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks

Recurrent Neural Networks can be trained to produce sequences of tokens given some input, as exemplified by recent results in machine translation and image captioning. The current approach to training them consists in maximizing the likelihood of each token in the sequence given the current (recurrent) state and the previous token. At inference, the unknown previous token is then replaced by a token generated by the model itself. This discrepancy between training and inference can yield errors that can accumulate quickly along the generated sequence. We propose a curriculum learning strategy to gently change the training process from a fully guided scheme using the true previous token, towards a less guided scheme which mostly uses the generated token instead. Experiments on several sequence prediction tasks show that this approach yields significant improvements.
Solution structure of the dimerization domain of the eukaryotic stalk P1/P2 complex reveals the structural organization of eukaryotic stalk complex

The lateral ribosomal stalk is responsible for the kingdom-specific binding of translation factors and activation of GTP hydrolysis during protein synthesis. The eukaryotic stalk is composed of three acidic ribosomal proteins P0, P1 and P2. P0 binds two copies of P1/P2 hetero-dimers to form a pentameric P-complex. The structure of the eukaryotic stalk is currently not known. To provide a better understanding on the structural organization of eukaryotic stalk, we have determined the solution structure of the N-terminal dimerization domain (NTD) of P1/P2 hetero-dimer. Helix-1, -2 and -4 from each of the NTD-P1 and NTD-P2 form the dimeric interface that buries 2200 A(2) of solvent accessible surface area. In contrast to the symmetric P2 homo-dimer, P1/P2 hetero-dimer is asymmetric. Three conserved hydrophobic residues on the surface of NTD-P1 are replaced by charged residues in NTD-P2. Moreover, NTD-P1 has an extra turn in helix-1, which forms extensive intermolecular interactions with helix-1 and -4 of NTD-P2. Truncation of this extra turn of P1 abolished the formation of P1/P2 hetero-dimer. Systematic truncation studies suggest that P0 contains two spine-helices that each binds one copy of P1/P2 hetero-dimer. Modeling studies suggest that a large hydrophobic cavity, which can accommodate the loop between the spine-helices of P0, can be found on NTD-P1 but not on NTD-P2 when the helix-4 adopts an 'open' conformation. Based on the asymmetric properties of NTD-P1/NTD-P2, a structural model of the eukaryotic P-complex with P2/P1:P1/P2 topology is proposed.
Teacher efficacy : capturing an elusive construct

Teacher efficacy has proved to be powerfully related to many meaningful educational outcomes such as teachers’ persistence, enthusiasm, commitment and instructional behavior, as well as student outcomes such as achievement, motivation, and self-efficacy beliefs. However, persistent measurement problems have plagued those who have sought to study teacher efficacy. We review many of the major measures that have been used to capture the construct, noting problems that have arisen with each. We then propose a promising new measure of teacher efficacy along with validity and reliability data from three separate studies. Finally, new directions for research made possible by this instrument are explored. r 2001 Elsevier Science Ltd. All rights reserved.
Population differentiation within and among Asian elephant (Elephas maximus) populations in southern India

Southern India, one of the last strongholds of the endangered Asian elephant (Elephas maximus), harbours about one-fifth of the global population. We present here the first population genetic study of free-ranging Asian elephants, examining within- and among-population differentiation by analysing mitochondrial DNA (mtDNA) and nuclear microsatellite DNA differentiation across the Nilgiris-Eastern Ghats, Anamalai, and Periyar elephant reserves of southern India. Low mtDNA diversity and ‘normal’ microsatellite diversity were observed. Surprisingly, the Nilgiri population, which is the world's single largest Asian elephant population, had only one mtDNA haplotype and lower microsatellite diversity than the two other smaller populations examined. There was almost no mtDNA or microsatellite differentiation among localities within the Nilgiris, an area of about 15 000 km2. This suggests extensive gene flow in the past, which is compatible with the home ranges of several hundred square kilometres of elephants in southern India. Conversely, the Nilgiri population is genetically distinct at both mitochondrial and microsatellite markers from the two more southerly populations, Anamalai and Periyar, which in turn are not genetically differentiated from each other. The more southerly populations are separated from the Nilgiris by only a 40-km-wide stretch across a gap in the Western Ghats mountain range. These results variably indicate the importance of population bottlenecks, social organization, and biogeographic barriers in shaping the distribution of genetic variation among Asian elephant populations in southern India.
Clustering and Unsupervised Anomaly Detection with l2 Normalized Deep Auto-Encoder Representations

Clustering is essential to many tasks in pattern recognition and computer vision. With the advent of deep learning, there is an increasing interest in learning deep unsupervised representations for clustering analysis. Many works on this domain rely on variants of auto-encoders and use the encoder outputs as representations/features for clustering. In this paper, we show that an l2 normalization constraint on these representations during auto-encoder training, makes the representations more separable and compact in the Euclidean space after training. This greatly improves the clustering accuracy when k-means clustering is employed on the representations. We also propose a clustering based unsupervised anomaly detection method using l2 normalized deep auto-encoder representations. We show the effect of l2 normalization on anomaly detection accuracy. We further show that the proposed anomaly detection method greatly improves accuracy compared to previously proposed deep methods such as reconstruction error based anomaly detection.
Spectral–Spatial Residual Network for Hyperspectral Image Classification: A 3-D Deep Learning Framework

In this paper, we designed an end-to-end spectral–spatial residual network (SSRN) that takes raw 3-D cubes as input data without feature engineering for hyperspectral image classification. In this network, the spectral and spatial residual blocks consecutively learn discriminative features from abundant spectral signatures and spatial contexts in hyperspectral imagery (HSI). The proposed SSRN is a supervised deep learning framework that alleviates the declining-accuracy phenomenon of other deep learning models. Specifically, the residual blocks connect every other 3-D convolutional layer through identity mapping, which facilitates the backpropagation of gradients. Furthermore, we impose batch normalization on every convolutional layer to regularize the learning process and improve the classification performance of trained models. Quantitative and qualitative results demonstrate that the SSRN achieved the state-of-the-art HSI classification accuracy in agricultural, rural–urban, and urban data sets: Indian Pines, Kennedy Space Center, and University of Pavia.