Miracle-dz/UniFAR-FTUs huggingface dataset:
What Skills Really Improve After a Flipped Educational Intervention to Train Medical Students and Residents to Break Bad News?

Breaking bad news (BBN) is necessary in medical practice and requires training. The purpose of this study is to evaluate the efficacy and mainly explore the components involved in medical students’ and residents’ performance after a flipped educational intervention to train them to break bad news. A randomized controlled before-after study was conducted with 43 medical students and residents in the intervention group and 41 in the control group. The intervention combined an online multimedia program (DocCom) with a two-hour workshop. BBN performance was assessed at two clinical stations using Objective Structured Clinical Examination and analyzed using a mixed between-within subject analysis of variance. A factor analysis was conducted to analyze the performance by checklist components. The intervention group improved its overall performance in BBN over time (p = 0.000; Eta2 = 0.38) and when compared to the control group (p = 0.01; Eta2 = 0.12). The factor analysis revealed two main components: Factor 1—“giving bad news and responding with empathy”—and Factor 2—“using general communication skills”. Performance analysis by these components revealed that the improvement occurred mainly in Factor 1 (over time, p = 0.000; Eta2 = 0.48, group x time, p = 0.000; Eta2 = 0.38). The intervention combining DocCom Module 33 and a workshop had a moderate effect on the improvement of medical students’ and medical residents’ BBN overall performance in standardized encounters. This improvement was mainly related to communication skills for giving bad news and responding with empathy, in which the intervention effect was large over time and between groups.
A DC to 22GHz, 2W high power distributed amplifier using stacked FET topology with gate periphery tapering

This paper describes a high power (2W) distributed amplifier (DA) MMIC. DA MMIC was fabricated using an Lg=0. 25μm GaAs PHEMT process. DA MMIC contains an impedance transformer and heavily tapered gate periphery design for constant output power performance over 0.1 to 22GHz operational frequency. To obtain high voltage operation, the DA MMIC employed a three stacked FET topology. A 7-section DA demonstrated 2 W saturated output power and 12 dB small signal gain from 0.1 GHz to 22 GHz with peak output power of 3.5 W with power added efficiency (PAE) of 27%. Those test results exceeded recently reported GaN based power DA performance [4] with large margins.
Capacity of known interference channel with Gaussian modulations

In this paper, we investigate the capacity of known interference channel, where the receiver knows the interference data but not the channel gain of the interference data, assuming the use of Gaussian modulations. We first present a tight upper bound for the capacity of this known-interference channel. After that, we obtain the achievable rate of the channel with a blind known interference cancellation (BKIC) scheme in closed form. We prove that this achievable rate can approach the aforementioned upper bound in the high SNR regime. Moreover, it is much larger than the achievable rate of the traditional interference cancellation scheme. In particular, the achievable rate of BKIC continues to increase with SNR in the high SNR regime, while that of the traditional scheme approaches a fixed bound that does not improve with SNR.
A New Image Registration Method Based on Point Pattern

Data registration is the most important segment in the data fusion process. The image registration problem was firstly analyzed in this paper. Then it uses the interest point detection operator to extract the point pattern, use centroids of these features to define initial matching pairs. By constructing point pattern triangles and uses its similarity to define support degree and match degree, a new six-element point pattern matching algorithm based on initial matching pairs was proposed. Under the condition of scale and rotation, the algorithm achieved the matching correctly.
The Omnipresent Computing Menace to Information Society

Computers have evolved from mere number crunchers to systems demonstrating an astonishing degree of sophistication, decision-making ability, and autonomy. Silicon is no longer the only substrate facilitating information processing. Despite these progresses, machine intelligence is still far from rivaling human intelligence. Nonetheless, we might be all too ready to rely on inferior agents for decision making, to give away sensitive information without fully understanding the consequences involved, or to tinker with genetic code to program carbon-based machines without fully appreciating the risks. This article explores the potentials and risks that information societies may face in the wake of current and emerging intelligent computing paradigms.
A true stochastic gradient adaptive algorithm for applications using nonlinear actuators

This work considers the practical situation where adaptive systems are subject to a saturation nonlinearity at the output of the adaptive filter. Such is the case in active control of noise and vibration. A new adaptive algorithm is proposed which implements the true stochastic gradient approach to the nonlinear problem. Deterministic nonlinear recursions are derived which model the mean weight and mean square error behaviors. The steady-state behavior is also studied. The practical aspects of nonlinearity estimation and hardware implementation are addressed. It is shown that the new algorithm outperforms the LMS algorithm even for considerable errors in estimating the nonlinearity parameters.
Dynamic Cache Partitioning via Columnization

This paper introduces column caching, a exible mechanism that allows software to dynamically customize cache behavior through ne-grain control of its placement policy. For a set-associative cache, speci c data can be restricted to a subset of the usual target cache set during replacement. Through this simple enhancement, column caching enables the cache to be partitioned. When done properly, this improves cache utilization through both constructive and destructive interference, leading to overall better system performance. Column caching provides a basic mechanism which can emulate many di erent hard-wired specializations, such as dedicated SRAM and separate temporal and spatial cache, and further o ers the advantage of dynamical repartitioning under software control. This paper introduces column caching, describes possible implementations and presents a few example usages including preliminary performance numbers.
Carcinoid Heart Disease: Successful Repair of the Valvular Lesions under Cardiopulmonary Bypass

A patient with carcinoid syndrome with both tricuspid and pulmonic valvular lesions has been described. Successful repair of the valvular lesions was performed under cardiopulmonary bypass. The operation consisted of pulmonic valvotomy with the excision of a single pulmonic valve cusp, and replacement of the tricuspid valve by a Starr-Edwards prosthesis. Postoperatively, the patient noted marked improvement in symptomatology, and there was a concomitant improvement in serial hemodynamic studies.The development of the signs of tricuspid regurgitation and pulmonic stenosis in patients with metastatic carcinoid tumor should alert the physician to the possible presence of carcinoid heart disease. If hemodynamic and angiocardiographic studies confirm the presence of significant valvular lesions, corrective cardiac surgery should be performed in selected patients.
Maternal weight, gestational weight gain and preschool wheezing: the Generation R Study

We studied the associations of maternal pre-pregnancy body mass index and gestational weight gain with risks of preschool wheezing in offspring and explored the role of growth, infectious and atopic mechanisms. This substudy of 4656 children was embedded in a population-based birth cohort. Information about maternal pre-pregnancy weight, gestational weight gain and wheezing at the ages of 1–4 years was obtained by either physical measurements or questionnaires. Among mothers with a history of asthma or atopy, maternal pre-pregnancy obesity was associated with an overall increased risk of preschool wheezing (odds ratio 1.47, 95% confidence interval 1.12–1.95). Also, each standard deviation increase of gestational weight gain was associated with an increased overall risk of preschool wheezing (1.09, 1.04–1.14), was independent of pre-pregnancy body mass index and was not different between mothers with and without a history of asthma or atopy. Child’s growth, respiratory tract infections or eczema did not alter the results. Mothers with pre-pregnancy obesity and a history of asthma or atopy, and mothers with higher gestational weight gain showed higher risks of wheezing in their offspring. These associations could not be explained by growth, infectious or atopic mechanisms. Further research is needed to identify underlying mechanisms and long-term consequences. Pre-pregnancy obesity associated with increased risk of wheezing in offspring, if mothers have history of asthma or atopy http://ow.ly/nDG1u
Swiss-Chocolate: Combining Flipout Regularization and Random Forests with Artificially Built Subsystems to Boost Text-Classification for Sentiment

We describe a classifier for predicting message-level sentiment of English microblog messages from Twitter. This paper describes our submission to the SemEval2015 competition (Task 10). Our approach is to combine several variants of our previous year’s SVM system into one meta-classifier, which was then trained using a random forest. The main idea is that the meta-classifier allows the combination of the strengths and overcome some of the weaknesses of the artificially-built individual classifiers, and adds additional non-linearity. We were also able to improve the linear classifiers by using a new regularization technique we call flipout.
Development of a new humanoid robot WABIAN-2

A new humanoid robot-WABIAN-2- that can be used as a human motion simulator is proposed in this paper. Its trunk is designed in order to permit rotation, and forward, backward, and sideway movement. Further, its arms are designed to support its complete weight when pushing a walk-assist machine. Moreover, it can lean on a walk-assist machine by forearm control using trunk motion. Basic walking experiments with WABIAN-2 are conducted with and without a walk-assist machine, thereby confirming its effectiveness
The Quaternary evolutionary history, potential distribution dynamics, and conservation implications for a Qinghai–Tibet Plateau endemic herbaceous perennial, Anisodus tanguticus (Solanaceae)

Abstract Various hypotheses have been proposed about the Quaternary evolutionary history of plant species on the Qinghai–Tibet Plateau (QTP), yet only a handful of studies have considered both population genetics and ecological niche context. In this study, we proposed and compared climate refugia hypotheses based on the phylogeographic pattern of Anisodus tanguticus (three plastid DNA fragments and nuclear internal transcribed spacer regions from 32 populations) and present and past species distribution models (SDMs). We detected six plastid haplotypes in two well‐differentiated lineages. Although all haplotypes could be found in its western (sampling) area, only haplotypes from one lineage occurred in its eastern area. Meanwhile, most genetic variations existed between populations (FST = 0.822). The SDMs during the last glacial maximum and last interglacial periods showed range fragmentation in the western area and significant range contraction in the eastern area, respectively, in comparison with current potential distribution. This species may have undergone intraspecific divergence during the early Quaternary, which may have been caused by survival in different refugia during the earliest known glacial in the QTP, rather than geological isolation due to orogenesis events. Subsequently, climate oscillations during the Quaternary resulted in a dynamic distribution range for this species as well as the distribution pattern of its plastid haplotypes and nuclear genotypes. The interglacial periods may have had a greater effect on A. tanguticus than the glacial periods. Most importantly, neither genetic data nor SDM alone can fully reveal the climate refugia history of this species. We also discuss the conservation implications for this important Tibetan folk medicine plant in light of these findings and SDMs under future climate models. Together, our results underline the necessity to combine phylogeographic and SDM approaches in future investigations of the Quaternary evolutionary history of species in topographically complex areas, such as the QTP.
Bounding data fusion for ubiquitous-applications within wireless sensor networks

We have formulated the data collection process within wireless sensor networks as a scheduling problem to alleviate workload on the data collection centers. A sensor device has three tasks, which are mainly sensing data, processing and transmitting the collected data. We modeled the sensorpsilas three tasks as a task-flow graph (TFG), and then we combined all TFGs for all sensors within WSN as a super task-flow graph (STFG). We have utilized two scheduling algorithms, as soon as possible (ASAP) and as late as possible (ALAP), to order all tasks within STFG subject to the data collection centerspsila limitations. Our experimental results provide excellent bounds on how early and how late to schedule all sensorspsila tasks with the variation of the number of data collection centers.
The CEPS Plan for the Balkans

In 1999, CEPS initiated the Europa South-East Policy Forum, a group of leading independent policy institutes from every country of South-East Europe, the network of Open Society Institutes. The objective was to contribute to the full integration of the whole of the region into the European Union. This report advocates accelerated political and economic reforms in these countries in the aftermath of the war, on the assumption that the European Union itself would make radical moves in its policies to support the process.
Manufacturing Matters…But It's the Jobs That Count

This paper asks, first, whether today’s developing economies can achieve high-income status without first building large manufacturing sectors. We find that practically every economy that enjoys a high income today experienced a manufacturing employment share in excess of 18%–20% sometime since the 1970s. Manufacturing output share thresholds are much poorer predictors of rich-country status than their employment counterparts. This motivates us to ask whether it is becoming more difficult to sustain high levels of manufacturing activity. We find that the maximum expected employment share for a typical developing economy has fallen to around 13%–15%, and that deindustrialization in employment sets in at much lower income per capita levels of $8,000–$9,000, than it once did. Neither manufacturing output shares, nor the level of income at which they decline have fallen as obviously. These results are consistent with the idea that industrialization in employment terms has been more important for eventual prosperity than has industrialization in output terms; and that high manufacturing employment shares are becoming more difficult to sustain as incomes rise. This suggests that the path to prosperity through industrialization may have become more difficult.
Multi-Scale Locality-Constrained Spatiotemporal Coding for Local Feature Based Human Action Recognition

We propose a Multiscale Locality-Constrained Spatiotemporal Coding (MLSC) method to improve the traditional bag of features (BoF) algorithm which ignores the spatiotemporal relationship of local features for human action recognition in video. To model this spatiotemporal relationship, MLSC involves the spatiotemporal position of local feature into feature coding processing. It projects local features into a sub space-time-volume (sub-STV) and encodes them with a locality-constrained linear coding. A group of sub-STV features obtained from one video with MLSC and max-pooling are used to classify this video. In classification stage, the Locality-Constrained Group Sparse Representation (LGSR) is adopted to utilize the intrinsic group information of these sub-STV features. The experimental results on KTH, Weizmann, and UCF sports datasets show that our method achieves better performance than the competing local spatiotemporal feature-based human action recognition methods.
Thermal emission of neutron stars with internal heaters

Using 1D and 2D cooling codes, we study thermal emission from neutron stars with steady state internal heaters of various intensities and geometries (blobs or spherical layers) located at different depths in the crust. The generated heat tends to propagate radially, from the heater down to the stellar core and up to the surface; it is also emitted by neutrinos. In local regions near the heater, the results are well described with the 1D code. The heater’s region projects on to the stellar surface forming a hotspot. There are two heat propagation regimes. In the first, conduction outflow regime (realized at heat rates H0 10 20 erg cm −3 s −1 or temperatures Th 10 9 K in the heater), the thermal surface emission of the star depends on the heater’s power and neutrino emission in the stellar core. In the second,neutrino outflowregime (H0 10 20 ergcm −3 s −1 or Th 10 9 K), the surface thermal emission becomes independent ofheater’spowerandthephysicsofthecore.Thelargest(afewpercent)fractionofheatpower is carried to the surface if the heater is in the outer crust and the heat regime is intermediate. The results can be used for modelling young cooling neutron stars (prior to the end of internal thermal relaxation), neutron stars in X-ray transients, magnetars and high-B pulsars, as well as merging neutron stars.
Tempered Markov Chain Monte Carlo for training of Restricted Boltzmann Machines

Alternating Gibbs sampling is the most common scheme used for sampling from Restricted Boltzmann Machines (RBM), a crucial component in deep architectures such as Deep Belief Networks. However, we find that it often does a very poor job of rendering the diversity of modes captured by the trained model. We suspect that this hinders the advantage that could in principle be brought by training algorithms relying on Gibbs sampling for uncovering spurious modes, such as the Persistent Contrastive Divergence algorithm. To alleviate this problem, we explore the use of tempered Markov Chain Monte-Carlo for sampling in RBMs. We find both through visualization of samples and measures of likelihood that it helps both sampling and learning.
Characterization of NBTI induced temporal performance degradation in nano-scale SRAM array using IDDQ

One of the major reliability concerns in nano-scale VLSI design is the time dependent negative bias temperature instability (NBTI) degradation. Due to the higher operating temperature and increasing vertical oxide field, threshold voltage (Vt) of PMOS transistors can increase with time under NBTI. In this paper, we examine the impact of NBTI degradation in memory elements of digital circuits, focusing on the conventional 6T SRAM array topology. Using an empirical NBTI model based on the reaction diffusion (RD) framework, we first examine the impact of NBTI degradation in critical performance parameters of SRAM array. These parameters include (1) static noise margin (SNM), (2) statistical read & write stability, and (3) standby leakage current (IDDQ). We show that due to NBTI, read stability of SRAM cell degrades, while write stability and standby leakage improve with time. Furthermore, using specific time trend of IDDQ degradation, we proposed efficient characterization technique to predict the lifetime behavior of SRAM array under NBTI.
DEMAND FOR DAIRY PRODUCTS IN NIGERIA: EVIDENCE FROM THE NIGERIAN

Based on the data from the recent Nigerian National Living Standard Survey (NLSS), income, demographic and environmental factors as well as exposure to western culture influence the consumption of dairy products in the country. From the results of the descriptive and quantitative analyses, it is safe to infer that demand will grow and therefore government will need to design polices to promote the production and marketing of dairy products to enhance supply in the local market on a sustainable basis.
Extracting Terms and Terminological Collocations from the ELAN Slovene–English Parallel Corpus

In many scientific, technological or political fields terminology and the production of up-to-date reference works is lagging behind, which causes problems to translators and results in inconsistent translations. Experience gained in various projects involving parallel corpora show that automatic extraction of terms and terminological collocations is an achievable goal, however methods and techniques may vary according to the language pair selected. The paper describes how a methodology for multi-word term extraction and bilingual conceptual mapping was developed for a Slovene-English corpus. We used word-to-word alignment to extract a bilingual glossary of single-word terms, and for multi-word terms two methods were tested and compared. The statistical method is broadly applicable but gives results of very limited use, while the method of syntactic patterns extracts highly useful terminological phrases, however only from a tagged corpus. A vision of further development is given and how these methods might be incorporated into existing translation tools.
Feature Extraction for the Prediction of Multichannel Spatial Audio Fidelity

This paper seeks to present an algorithm for the prediction of frontal spatial fidelity and surround spatial fidelity of multichannel audio, which are two attributes of the subjective parameter called basic audio quality. A number of features chosen to represent spectral and spatial changes were extracted from a set of recordings and used in a regression model as independent variables for the prediction of spatial fidelities. The calibration of the model was done by ridge regression using a database of scores obtained from a series of formal listening tests. The statistically significant features based on interaural cross correlation and spectral features found from an initial model were employed to build a simplified model and these selected features were validated. The results obtained from the validation experiment were highly correlated with the listening test scores and had a low standard error comparable to that encountered in typical listening tests. The applicability of the developed algorithm is limited to predicting the basic audio quality of low-pass filtered and down-mixed recordings (as obtained in listening tests based on a multistimulus test paradigm with reference and two anchors: a 3.5-kHz low-pass filtered signal and a mono signal)
8 A Review on Face and Gait Recognition: System, Data and Algorithms

This chapter reviews two important biometric recognition technologies that have received significant attention recently: face and gait recognition, where individuals are recognized by their faces and the way they walk, respectively. These two technologies are mainly motivated from security-related applications. We first describe typical face and gait recognition systems and three common recognition scenarios. Then, we present the two general approaches for solving the problem, the model-based approach and the appearance-based approach, as well as the fusion of face and gait, followed by discussions on commonly used face and gait databases. Next, a survey of important recognition algorithms are provided, including linear, nonlinear and multilinear subspace learning algorithms. Finally, the current state and future directions are summarized.