Miracle-dz/UniFAR-FTUs huggingface dataset:
Posters: Practice Research

In October 2001 the Royal Pharmaceutical Society of Great Britain (RPSGB) decided to make continuing professional development (CPD) mandatory. A voluntary CPD scheme was introduced in 2002 with the aim of a seamless transition to a mandatory system. Appropriate legislation is not expected before late 2004. CPD is “the process by which pharmacists continuously enhance their knowledge, skills and personal qualities throughout their professional careers”, CPD is not the same as continuing education (CE) nor is it a new concept. This study identified community pharmacists’ attitudes and approaches to the RPSGB CPD initiatives and the current uptake and non-participation of CE and CPD.
Estado de salud de la población mayor de 60 años y su relación con factores sociodemográficos (proyecto ANCO)

OBJECTIVE: To describe the health status of a population over 60 years and to study their relationship with several socio-demographic variables. DESIGN: A cross-sectional study, population based. SETTINGS: A community. PARTICIPANTS: A randomized sample of 1,103 non institutionalized people over 60 years living in the city of Cordoba (Spain). MEASUREMENTS AND MAIN RESULTS: By mean of a personal interview at home we used the OARS-MFAQ-VE questionnaire. Low self-rated health was associated with the age, to be female sex, a low cultural background, and a low income. Only 5.2% of the study people do not suffered any illness and 56% state that their health problems are major problems for doing their current activities. 4.9% declared to have some degree of physical incapacity. 3.7% of elderly population has an important cognitive deficit. CONCLUSIONS: The majority of elderly people has good health. Age is related with a poor health. Women have more health problems than men.
How to Negotiate Difficult Colonoscopy to Optimize Cecal Intubation Rate

Colonoscopy is the current standard method for evaluating colon. Cecal intubation rate is an important indicator of colonoscopy quality. In up to 10–20% colonoscopies, cecal intubation may be considered difficult. There are several consequences of low cecal intubation rate: it limits the efficacy of colonoscopy, increasing risk of complications and cost, and missing in detecting adenoma colorectal or other abnormal mucosa lesion. Failure to intubate the cecal can be a result of: (1) patients factors (female, older, diverticular disease, history of abdominal surgery, low body mass index, history of constipation, laxative use); (2) endoscopist factors (prior experience, the specific techniques and instrument used; (3) or some combination thereof. In an effort to solve these problems endoscopist should increase their technical manoeuvres (minimizing inflation and looping, using water–aided method, appropriate use of positional changes and abdominal pressure) and use various accessories methods (inserting a biopsy forceps through the biopsy channel, pediatric colonoscopy, variable stiffness colonoscopy). Keywords: cecal intubation rate, difficult colonoscopy, colonoscopy quality, water–aided colonoscopy
THE INHIBITION TEST OF RED BETEL LEAVES (Piper crocatum) TOWARDS Staphylococcus aureus AND Salmonella typhi

Abstract The red betel leaves (Piper crocatum) has many benefits such as antioksidant, antidiabetic, antineoplastic, antiseptic, and antiinflammation. This research aims to investigate the antibacteial activity on red betel leaves againsts Staphylococcus aureus and Salmonella typhi with determine the red betel leaves extract minimum resist concentrate which capable to inhibit the growth of bacteria. This research was held on October 2014 at Microbiological Laboratory of Lampung University Medical Faculty. The red betel leaves extract obtained from Organic Chemical Laboratory of Lampung University with maceration technique. The sirih merah leaves activity performed with in vitro particularly with Kirby bauer method which is done by making the well. The results of this research presented that all concentration of red betel leaves such 3,125%, 6,25%, 12,5%, 25%, 50% and 100%, has antibacterial activity towards Staphylococcus aureus and Salmonella typhi bactery, with the highest inhibitory concentration at 100% that is 16,3 mm on Staphylococcus aureus and 12 mm on Salmonella typhi. Conclusion is red betel leaves inhibit the growth of positive gram bacteria stronger than the negative gram bacteria. Kata Kunci: Piper crocatum, Staphylococcus aureus, Salmonella typhi
Model-Agnostic Meta-Learning using Runge-Kutta Methods

Meta-learning has emerged as an important framework for learning new tasks from just a few examples. The success of any meta-learning model depends on (i) its fast adaptation to new tasks, as well as (ii) having a shared representation across similar tasks. Here we extend the model-agnostic meta-learning (MAML) framework introduced by Finn et al. (2017) to achieve improved performance by analyzing the temporal dynamics of the optimization procedure via the Runge-Kutta method. This method enables us to gain fine-grained control over the optimization and helps us achieve both the adaptation and representation goals across tasks. By leveraging this refined control, we demonstrate that there are multiple principled ways to update MAML and show that the classic MAML optimization is simply a special case of second-order Runge-Kutta method that mainly focuses on fast-adaptation. Experiments on benchmark classification, regression and reinforcement learning tasks show that this refined control helps attain improved results.
MLQA: Evaluating Cross-lingual Extractive Question Answering

Question answering (QA) models have shown rapid progress enabled by the availability of large, high-quality benchmark datasets. Such annotated datasets are difficult and costly to collect, and rarely exist in languages other than English, making building QA systems that work well in other languages challenging. In order to develop such systems, it is crucial to invest in high quality multilingual evaluation benchmarks to measure progress. We present MLQA, a multi-way aligned extractive QA evaluation benchmark intended to spur research in this area. MLQA contains QA instances in 7 languages, English, Arabic, German, Spanish, Hindi, Vietnamese and Simplified Chinese. MLQA has over 12K instances in English and 5K in each other language, with each instance parallel between 4 languages on average. We evaluate state-of-the-art cross-lingual models and machine-translation-based baselines on MLQA. In all cases, transfer results are shown to be significantly behind training-language performance.
Aerial Images Processing for Car Detection using Convolutional Neural Networks: Comparison between Faster R-CNN and YoloV3

This paper addresses the problem of car detection from aerial images using Convolutional Neural Networks (CNNs). This problem presents additional challenges as compared to car (or any object) detection from ground images because the features of vehicles from aerial images are more difficult to discern. To investigate this issue, we assess the performance of three state-of-the-art CNN algorithms, namely Faster R-CNN, which is the most popular region-based algorithm, as well as YOLOv3 and YOLOv4, which are known to be the fastest detection algorithms. We analyze two datasets with different characteristics to check the impact of various factors, such as the UAV’s (unmanned aerial vehicle) altitude, camera resolution, and object size. A total of 52 training experiments were conducted to account for the effect of different hyperparameter values. The objective of this work is to conduct the most robust and exhaustive comparison between these three cutting-edge algorithms on the specific domain of aerial images. By using a variety of metrics, we show that the difference between YOLOv4 and YOLOv3 on the two datasets is statistically insignificant in terms of Average Precision (AP) (contrary to what was obtained on the COCO dataset). However, both of them yield markedly better performance than Faster R-CNN in most configurations. The only exception is that both of them exhibit a lower recall when object sizes and scales in the testing dataset differ largely from those in the training dataset.
Identifying Network Ties from Panel Data: Theory and an Application to Tax Competition

Social interactions determine many economic behaviors, but information on social ties does not exist in most publicly available and widely used datasets. We present results on the identification of social networks from observational panel data that contains no information on social ties between agents. In the context of a canonical social interactions model, we provide sufficient conditions under which the social interactions matrix, endogenous and exogenous social effect parameters are all globally identified. While this result is relevant across different estimation strategies, we then describe how high-dimensional estimation techniques can be used to estimate the interactions model based on the Adaptive Elastic Net GMM method. We employ the method to study tax competition across US states. We find the identified social interactions matrix implies tax competition differs markedly from the common assumption of competition between geographically neighboring states, providing further insights for the long-standing debate on the relative roles of factor mobility and yardstick competition in driving tax setting behavior across states. Most broadly, our identification and application show the analysis of social interactions can be extended to economic realms where no network data exists.
Nonlocal gauge equivalence: Hirota versus extended continuous Heisenberg and Landau–Lifschitz equation

We exploit the gauge equivalence between the Hirota equation and the extended continuous Heisenberg equation to investigate how nonlocality properties of one system are inherited by the other. We provide closed generic expressions for nonlocal multi-soliton solutions for both systems. By demonstrating that a specific auto-gauge transformation for the extended continuous Heisenberg equation becomes equivalent to a Darboux transformation, we use the latter to construct the nonlocal multi-soliton solutions from which the corresponding nonlocal solutions to the Hirota equation can be computed directly. We discuss properties and solutions of a nonlocal version of the nonlocal extended Landau–Lifschitz equation obtained from the nonlocal extended continuous Heisenberg equation or directly from the nonlocal solutions of the Hirota equation.
Stochastic orderings of multivariate elliptical distributions

Abstract For two n-dimensional elliptical random vectors X and Y, we establish an identity for $\mathbb{E}[f({\bf Y})]- \mathbb{E}[f({\bf X})]$, where $f\,{:}\, \mathbb{R}^n \rightarrow \mathbb{R}$ satisfies some regularity conditions. Using this identity we provide a unified method to derive sufficient and necessary conditions for classifying multivariate elliptical random vectors according to several main integral stochastic orders. As a consequence we obtain new inequalities by applying the method to multivariate elliptical distributions. The results generalize the corresponding ones for multivariate normal random vectors in the literature.
Gestational Diabetes Mellitus - A Perspective

The prevalence of diabetes is increasing globally and the total number of people with this condition is projected to rise from 171 million in 2000 to 366 million in 2030 (Wild et al, 2004). India is no exception, with projected rates of 79.4 million in 2030—a 151% increase from 31.7 million in 2000 (Wild et al, 2004). The increased prevalence is attributed to the aging population structure, urbanization, the obesity epidemic, and physical inactivity (Hunt & Schuller, 2007). While all these factors contribute to the epidemic of diabetes, intrauterine exposures are emerging as potential risk factors (Barker, 1995). The “fetal origin of adult disease” hypothesis proposes that gestational programming may critically influence adult health and disease (Barker, 1995). Gestational programming is a process whereby stimuli or stresses occurring at critical or sensitive periods of fetal development, permanently change structure, physiology, and metabolism, which predisposes individuals to disease in adult life (Lucas, 1991). If the stimulus happens to be glucose intolerance in pregnancy, it predisposes the offspring to an increased risk of developing glucose intolerance in the future. This vicious cycle is likely to influence and perpetuate the incidence and prevalence of glucose intolerance in any population (Seshiah et al., 2004). Therefore, preventive measures against type 2 diabetes should start during the intrauterine period and continue from early childhood throughout life (Tuomilehto, 2005). In this respect, detection of gestational diabetes mellitus (GDM), defined as carbohydrate intolerance of variable severity with onset or first recognition during the present pregnancy (Metzger, 1991), becomes an important public health issue. The etiopathogenesis of glucose intolerance that develops in women with GDM could be the result of their inability to increase insulin secretion enough to overcome insulin resistance that occurs even in non diabetic pregnancy (Kuhl et al., 1985). The present concept is that GDM represents, detection of chronic β cell dysfunction, rather than development of relative insulin deficiency as insulin resistance increases during pregnancy (Buchanan et al., 2007).
Using Whole Document Context in Neural Machine Translation

In Machine Translation, considering the document as a whole can help to resolve ambiguities and inconsistencies. In this paper, we propose a simple yet promising approach to add contextual information in Neural Machine Translation. We present a method to add source context that capture the whole document with accurate boundaries, taking every word into account. We provide this additional information to a Transformer model and study the impact of our method on three language pairs. The proposed approach obtains promising results in the English-German, English-French and French-English document-level translation tasks. We observe interesting cross-sentential behaviors where the model learns to use document-level information to improve translation coherence.
High-z Dusty Star-forming Galaxies: A Top-heavy Initial Mass Function?

Recent estimates point to abundances of z > 4 submillimeter galaxies far above model predictions. The matter is still debated. According to some analyses the excess may be substantially lower than initially thought and perhaps accounted for by flux boosting and source blending. However, there is no general agreement on this conclusion. An excess of z > 6 dusty galaxies has also been reported albeit with poor statistics. On the other hand, evidence of a top-heavy initial mass function (IMF) in high-z starburst galaxies has been reported in the past decades. This would translate into a higher submillimeter luminosity of dusty galaxies at fixed star formation rate, i.e., into a higher abundance of bright high-z submillimeter galaxies than expected for a universal Chabrier IMF. Exploiting our physical model for high-z protospheroidal galaxies, we find that part of the excess can be understood in terms of an IMF somewhat top-heavier than Chabrier. Such an IMF is consistent with that recently proposed to account for the low 13C/18O abundance ratio in four dusty starburst galaxies at z = 2–3. However, extreme top-heavy IMFs are inconsistent with the submillimeter counts at z > 4.
Teacher algorithms for curriculum learning of Deep RL in continuously parameterized environments

We consider the problem of how a teacher algorithm can enable an unknown Deep Reinforcement Learning (DRL) student to become good at a skill over a wide range of diverse environments. To do so, we study how a teacher algorithm can learn to generate a learning curriculum, whereby it sequentially samples parameters controlling a stochastic procedural generation of environments. Because it does not initially know the capacities of its student, a key challenge for the teacher is to discover which environments are easy, difficult or unlearnable, and in what order to propose them to maximize the efficiency of learning over the learnable ones. To achieve this, this problem is transformed into a surrogate continuous bandit problem where the teacher samples environments in order to maximize absolute learning progress of its student. We present a new algorithm modeling absolute learning progress with Gaussian mixture models (ALP-GMM). We also adapt existing algorithms and provide a complete study in the context of DRL. Using parameterized variants of the BipedalWalker environment, we study their efficiency to personalize a learning curriculum for different learners (embodiments), their robustness to the ratio of learnable/unlearnable environments, and their scalability to non-linear and high-dimensional parameter spaces. Videos and code are available at this https URL.
Performance Analysis on Visible Light Communications With Multi-Eavesdroppers and Practical Amplitude Constraint

In this letter the secure performance for the visible light communication (VLC) system with multiple eavesdroppers is studied. By considering the practical amplitude constraint instead of an average power constraint in the VLC system, the closed-form expressions for the upper and the lower bounds of the secrecy outage probability and the average secrecy capacity are derived. Since the locations for both legitimate receiver and eavesdroppers are unknown as well as random, the stochastic geometry method is introduced for problem formulation. Moreover, accurate expressions with a low computational complexity are obtained with the rigorous derivation. Simulation results verify the correctness of our theoretical analysis.
Photoenhanced excitonic correlations in a Mott insulator with nonlocal interactions

We investigate the effect of nonlocal interactions on the photo-doped Mott insulating state of the two-dimensional Hubbard model using a nonequilibrium generalization of the dynamical cluster approximation. In particular, we compare the situation where the excitonic states are lying within the continuum of doublon-holon excitations to a set-up where the excitons appear within the Mott gap. In the first case, the creation of nearest-neighbor doublon-holon pairs by excitations across the Mott gap results in enhanced excitonic correlations, but these excitons quickly decay into uncorrelated doublons and holons. In the second case, photo-excitation results in long-lived excitonic states. While in a low-temperature equilibrium state, excitonic features are usually not evident in single-particle observables such as the photoemission spectrum, we show that the photo-excited nonequilibrium system can exhibit in-gap states associated with the excitons. The comparison with exact-diagonalization results for small clusters allows us to identify the signatures of the excitons in the photo-emission spectrum.
Accessing the axion via compact object binaries

Black holes in binaries with other compact objects can provide natural venues for indirect detection of axions or other ultralight fields. The superradiant instability associated with a rapidly spinning black hole leads to the creation of an axion cloud which carries energy and angular momentum from the black hole. This cloud will then decay via gravitational wave emission. We show that the energy lost as a result of this process tends toward an outspiraling of the binary orbit. A given binary system is sensitive to a narrow range of axion masses, determined by the mass of the black hole. This proposal provides a complementary alternative to other approaches for detecting or constraining light particles created by superradiance, such as directly measuring the black hole spin or detecting the resulting gravitational wave signal. Pulsar-black hole binaries, once detected in the electromagnetic band, will allow high-precision measurements of black hole mass loss via timing measurements of the companion pulsar. This avenue of investigation is particularly promising in light of the recent preliminary announcements of two candidate black hole-neutron star mergers by LIGO/Virgo (#S190814bv and #S190426c). We demonstrate that for such a binary system with a typical millisecond pulsar and a 3M⊙ black hole, axions with masses between 2.7 × 10−12 eV and 3.2 × 10−12 eV are detectable. Recent gravitational wave observations by LIGO/Virgo of binary black hole mergers imply that, for these binaries, gravitational radiation from the rotating quadrupole moment is a dominant effect, causing an inspiraling orbit. With some reasonable assumptions about the period of the binary when it formed and the spins of the black holes, these observations rule out possible axion masses between 3 × 10−13 eV and 6 × 10−13 eV . Future binary black hole observations, for example by LISA, are expected to provide more robust bounds. In some circumstances, neutron stars may also undergo superradiant instabilities, and binary pulsars could be used to exclude axions with certain masses and matter couplings.
FlowNorm: A Learning-based Method for Increasing Convergence Range of Direct Alignment

Many approaches have been proposed to estimate camera poses by directly minimizing photometric error. However, due to the non-convex property of direct alignment, proper initialization is still required for these methods. Many robust norms (e.g. Huber norm) have been proposed to deal with the outlier terms caused by incorrect initializations. These robust norms are solely defined on the magnitude of each error term. In this paper, we propose a novel robust norm, named FlowNorm, that exploits the information from both the local error term and the global image registration information. While the local information is defined on patch alignments, the global information is estimated using a learning-based network. Using both the local and global information, we achieve a large convergence range in which images can be aligned given large view angle changes or small overlaps. We further demonstrate the usability of the proposed robust norm by integrating it into the direct methods DSO and BA-Net, and generate more robust and accurate results in real-time.
Solving Rubik's Cube with a Robot Hand

We demonstrate that models trained only in simulation can be used to solve a manipulation problem of unprecedented complexity on a real robot. This is made possible by two key components: a novel algorithm, which we call automatic domain randomization (ADR) and a robot platform built for machine learning. ADR automatically generates a distribution over randomized environments of ever-increasing difficulty. Control policies and vision state estimators trained with ADR exhibit vastly improved sim2real transfer. For control policies, memory-augmented models trained on an ADR-generated distribution of environments show clear signs of emergent meta-learning at test time. The combination of ADR with our custom robot platform allows us to solve a Rubik's cube with a humanoid robot hand, which involves both control and state estimation problems. Videos summarizing our results are available: this https URL
Why can't memory networks read effectively?

Memory networks have been a popular choice among neural architectures for machine reading comprehension and question answering. While recent work revealed that memory networks can't truly perform multi-hop reasoning, we show in the present paper that vanilla memory networks are ineffective even in single-hop reading comprehension. We analyze the reasons for this on two cloze-style datasets, one from the medical domain and another including children's fiction. We find that the output classification layer with entity-specific weights, and the aggregation of passage information with relatively flat attention distributions are the most important contributors to poor results. We propose network adaptations that can serve as simple remedies. We also find that the presence of unseen answers at test time can dramatically affect the reported results, so we suggest controlling for this factor during evaluation.