bluuebunny/crossref_metadata_2025 huggingface dataset:
Cowpea and beet intercropping agro-economic dynamics under spatial arrangement and cowpea population density
Authors: Chaves Aridênia P , Bezerra Neto Francisco , Lima Jailma SS , Silva Josimar N , Nunes Renato LC , Barros Júnior Aurélio P , Lima Grace KL , Santos Elizangela C
Published: June 2020

ABSTRACT The objective of this work was to evaluate the effect of planting spatial arrangements between the cowpea and beet crops in intercropping and of cowpea population densities on the crop’s productivity and in the agro-economic efficiency of the association at two growing seasons. The experimental design was of randomised blocks with the treatments arranged in a 4 x 3 factorial scheme, in four replications. The first factor was constituted by three spatial arrangements between the component cultures (2:2, 3:3 and 4:4), formed by beet rows alternated with cowpea rows, while the second factor was constituted by the population densities of cowpea: 40, 60, 80 and 100% of the recommended population in single crop (RDSC). The production and its components were evaluated in the beet and cowpea. Apart from these characteristics, the following agro-economic indicators were also determined for each treatment: system productivity index (SPI), the land equivalent coefficient (LEC), and the monetary equivalent ratio (MER). The maximum production of commercial beet roots was obtained with 15.60 t ha-1 productivity at density of 61.82% of RDSC within the 4:4 arrangement, while the maximum yield of cowpea green grains of 2.23 t ha-1 was reached at density of 79.57% of RDSC within 2:2 spatial arrangement. The productivities of beet roots of extra A, extra AA and great types decreased with increasing cowpea population density, without any influence of the tested spatial arrangements. The maximum mean productivity of cowpea green pods of 1.74 t ha-1 was obtained at density of 87.47% of RDSC, reached within 2:2 spatial arrangement. The maximum economic efficiency of the cowpea-beet intercropping was obtained with MER of 1.11 at cowpea density of 100% of RDSC, irrespective of the spatial arrangement and growing season.
Efficiency of the gravity table in the processing of coriander seeds
Authors: Gadotti Gizele Ingrid , Hornke Nander F , Cavalcante Jerffeson A , Silva Joseano G da , Gonçalves Vanessa P , Capilheira André F
Published: June 2020

ABSTRACT This study aimed at assessing the quality of the physiology and health of the coriander seeds processed in a gravity table. This was investigated at two settings namely, I with angle X 3.5° and angle Y 3.1° and II with 3.9° and 2.7° angles, with five gravity table discharges (high, high intermediate, intermediate, low intermediate and low) besides feeding. The experiment adopted the 2x6 factorial scheme, in a completely randomized design, with four replications. Two coriander seed batches of cultivar Verdão were used. The gravity table was adjusted to have 2.9 m s-1 air speed on the platform. The seed fractions discharged in the lower portion of the gravity table revealed poorer physiological performance than that of the others. Regulation I (angle X 3.5° and Y 3.1°) was sufficient for the coriander seeds to be processed in batch 1 and regulation II (angle X 3.9° Y and 2.7°), was adequate for batch 2. The processing of batch 1 in regulation II was effective in separating seeds of higher sanitary quality by rejecting the contaminated ones.
Sources and splitting of the organic fertilization in top dressing in cabbage production
Authors: Cardoso Antonio Ismael Inácio , Oliveira Jéssica B , Lanna Natália BL , Candian Joara S , Castro Jean Lucas M
Published: June 2020

ABSTRACT Although there exist researches about fertilization in top dressing in the production of cabbage, these are usually made with inorganic fertilizers, being rare the researches with organic fertilization in top dressing in the production of most vegetables. The objective was to evaluate the effect of different splitting of two organic fertilizers in top dressing in the production of cabbage. Six treatments were evaluated, with four replications in randomized block design. The six treatments were the result of the factorial 3x2, with three types of splitting (1/3+1/3+1/3; 4/6+1/6+1/6; 1/6+4/6+1/6 of the total dose in each application) and two organic fertilizers (castor bean cake and organic compost). The total dose of each fertilizer was calculated to apply 120 kg ha-1 N. The evaluated characteristics were number of external leaves, fresh weight of external leaves (g), fresh and dry head weight (g), head diameter (cm) and length (cm), yield and crop cycle (days after transplanting). Fertilization with castor bean cake resulted in greater yield, producing heads with higher weight, diameter and length in relation to the organic compost, besides reducing the cycle. The 4/6+1/6+1/6 splitting was better than the 1/6+4/6+1/6 splitting using castor bean cake.
Política Nacional de Práticas Integrativas e Complementares: trajetória e desafios em 30 anos do SUS
Authors: Silva Gisléa Kândida Ferreira da , Sousa Islândia Maria Carvalho de , Cabral Maria Eduarda Guerra da Silva , Bezerra Adriana Falangola Benjamin , Guimarães Maria Beatriz Lisboa
Published: June 2020

Resumo Este artigo analisa a construção e desenvolvimento da Política Nacional de Práticas Integrativas e Complementares (PNPIC) no SUS e seus antecedentes. O estudo envolveu revisão de literatura, análise documental e entrevistas com atores-chave no processo de construção e implementação da política. Apoiou-se no modelo de múltiplos fluxos proposto por Kingdon no que tange à formulação da agenda política. Os resultados apontam momentos importantes na trajetória da política: influência de parâmetros internacionais; papel das conferências nacionais de saúde na construção da demanda social; experiências iniciais de oferta na rede pública; protagonismo no âmbito do ensino e pesquisa; entrada na agenda política; condução federal e desafios para a institucionalização. A formulação da PNPIC ocorre pela pressão de atores estratégicos que empreenderam ações em condições institucionais favoráveis no âmbito do Ministério da Saúde após mudança política na esfera do Governo Federal em 2003, criando uma janela de oportunidade política. Tal processo foi marcado por resistências oriundas da racionalidade biomédica e disputas de interesses. Apesar do avanço relativo à disseminação e visibilidade da oferta de serviços, as ações no âmbito da condução federal não foram acompanhadas por mudanças significativas na formação dos profissionais e nas práticas hegemônicas de saúde.
Proteção à vida e à saúde da gravidez na adolescência sob o olhar da Bioética
Authors: Rosaneli Caroline Filla , Costa Natalia Bertani , Sutile Viviane Maria
Published: June 2020

Resumo Este estudo foi realizado com o objetivo de analisar o perfil de adolescentes gestantes e de crianças nascidas de mães adolescentes no Estado do Paraná, identificando a proteção do direito à vida e à saúde sob o olhar da Bioética. Foi realizado um estudo epidemiológico quantitativo, utilizando-se indicadores da dimensão da saúde a partir de dados da plataforma digital de domínio público Cadê Paraná, em março e abril de 2016. Os dados foram investigados por meio de análise descritiva e da Bioética para um ensaio reflexivo. Detectaram-se 19.528 mães adolescentes e 21.580 filhos, 91,56% encontravam-se na faixa entre 15 a 17 anos, 58,52% brancas e 82,67% residentes em domicílio urbano. A gravidez na adolescência é vista como situação de vulnerabilidade e risco social. Os números apresentados confrontaram os compromissos éticos e sociais. A intersetorialidade entre políticas sociais é fundamental para melhorar os indicadores. Esforços devem voltar-se para o contexto social e disparidades, a fim de garantir os direitos fundamentais da população estudada. Criar um ambiente favorável à igualdade de gênero, à saúde e aos direitos sexuais e reprodutivos dos adolescentes se faz prioridade entre ações e políticas públicas.
Na contramão dos objetivos do desenvolvimento sustentável: avaliação da pobreza hídrica na região estuarina do Rio Macaé, Macaé/RJ
Authors: Guimarães Édson , Ferreira Maria Inês
Published: June 2020

Resumo O rápido crescimento populacional impulsionado pela indústria do petróleo no município de Macaé/RJ, a fiscalização ineficiente e a especulação imobiliária motivaram ocupações ilegais de espaços territoriais especialmente protegidos, como os manguezais estuarinos do Rio Macaé. Objetiva-se comparar as condições de vulnerabilidade socioeconômica e ambiental que caracterizam essas ocupações à luz dos Objetivos de Desenvolvimento Sustentável (ODS) 1 e 6 da Agenda 2030 da Organização das Nações Unidas: erradicação da pobreza; e acesso à água potável e ao saneamento, respectivamente. Dada a necessidade de construção de indicadores, para acompanhar o cumprimento das metas associadas aos ODS, propõe-se a aplicação do Índice de Pobreza Hídrica, adaptado às comunidades Ilha Colônia Leocádia e Ilha da Caieira, visando comparar o estado socioambiental dessas ocupações em termos de aderência a condições promotoras de sustentabilidade para a região. Um questionário semiestruturado foi aplicado aos moradores das localidades e os resultados permitiram estimar o IPH, confirmando a situação de grave pobreza hídrica e de vulnerabilidade socioeconômica dos moradores da Ilha Colônia Leocádia em relação aos da Ilha da Caieira. Isto indica uma realidade que caminha na contramão da Agenda 2030 e necessita de políticas públicas promotoras do bem-estar dos estuarinos, focadas no acesso à água, na proteção dos recursos hídricos e nas soluções baseadas na natureza.
El uso de softwares para identificar plagio en textos académicos y educacionales
Authors: Pezuk Julia Alejandra , Diniz Susana Nogueira , Pereira Regina Mara , Gonçalves Ivair Donizete , Costa Nielce Meneguelo Lobo da , Dias Marlene Alves
Published: June 2020

Resumen Este estudio corresponde a un proyecto de investigación de un núcleo docente que analizó los trabajos textuales autorales de once alumnos. Estos trabajos se han desarrollado a lo largo de una disciplina del área Ciencias Biológicas del curso de posgrado stricto sensu de una universidad particular del estado de São Paulo, Brasil. El objetivo principal de esta investigación fue evaluar la existencia de plagio, para responder a la pregunta de investigación: ¿alumnos graduados cometen plagio cuando realizan cursos de posgrado? Para eso fueron analizados y comparados los resultados de la verificación de plagio con diversos softwares disponibles online en versiones pagas o gratuitas. Durante este estudio observamos que la mayoría de los trabajos académicos de los alumnos de posgrado presentaba algún rasgo de plagio y comprobamos que la cantidad de plagio apuntada es distinta dependiendo de las características de análisis del programa usado. A pesar de las limitaciones de los softwares, su uso regular para verificación autoral es fundamental para identificar esta práctica y poder tomar medidas educativas que orienten a los alumnos sobre el proceso de elaboración de textos autorales y sobre el impacto que tiene el plagio en el aprendizaje. Así concluimos que es necesario desarrollar y aplicar nuevas estrategias pedagógicas para desestimular el plagio y en consecuencia disminuir el empobrecimiento de la educación. Paralelamente mostramos que la elección del software para verificación de plagio no debe ser liviana, sino realizada de manera consciente y responsable una vez que puede implicar en resultados diferentes.
La acogida de la vulnerabilidad de la infancia: la responsabilidad ética-pedagógica en la escuela
Authors: Ramos Daniel Gómez
Published: June 2020

Resumen Este artículo parte de la inquietud por colocar en el centro del trabajo educativo el cuidado y la responsabilidad ante la vida de la infancia, ya que estos saberes favorecen los procesos de aprendizaje y el crecimiento personal de los estudiantes. Retomando diferentes estudios pedagógicos y filosóficos, se entiende la acogida del otro como el fundamento ético que sustenta toda relación educativa. Desde un enfoque inspirado en la investigación de la experiencia y la fenomenología-hermenéutica, se realizan conversaciones reflexivas con la directora y maestra de una escuela ubicada en contextos de dificultad. Los materiales empíricos o textos de campo creados junto a la directora/maestra han permitido pensar con la experiencia vivida y producir sentidos pedagógicos acerca de los propósitos de origen. Esencialmente, la preocupación que emerge es cómo responder a la vulnerabilidad de la infancia en la escuela, y, además, cómo hacerlo en mitad del flujo de actividades educativas y problemáticas sociales. A través de la indagación, se ha obtenido como resultado tres hilos de sentido o tramas conceptuales que muestran y despliegan una serie de comprensiones, disposiciones y orientaciones que pueden ayudarnos a poner en ejercicio los gestos del acogimiento en el oficio de educar. Además, el estudio profundiza en algunas de las múltiples dimensiones y cualidades de la responsabilidad pedagógica, revelando claves prácticas para establecer relaciones de cuidado con nuestros alumnos y provocar efectos positivos en sus procesos de formación.
Pay Your Trip for Traffic Congestion: Dynamic Pricing in Traffic-Aware Road Networks
Authors: Chen Lisi , Shang Shuo , Yao Bin , Li Jing
Published: June 2020

Pricing is essential in optimizing transportation resource allocation. Congestion pricing is widely used to reduce urban traffic congestion. We propose and investigate a novel Dynamic Pricing Strategy (DPS) to price travelers' trips in intelligent transportation platforms (e.g., DiDi, Lyft, Uber). The trips are charged according to their “congestion contributions” to global urban traffic systems. The dynamic pricing strategy retrieves a matching between n travelers' trips and the potential travel routes (each trip has k potential routes) to minimize the global traffic congestion. We believe that DPS holds the potential to benefit society and the environment, such as reducing traffic congestion and enabling smarter and greener transportation. The DPS problem is challenging due to its high computation complexity (there exist kn matching possibilities). We develop an efficient and effective approximate matching algorithm based on local search, as well as pruning techniques to further enhance the matching efficiency. The accuracy and efficiency of the dynamic pricing strategy are verified by extensive experiments on real datasets.
Adaptive Greedy versus Non-Adaptive Greedy for Influence Maximization
Authors: Chen Wei , Peng Binghui , Schoenebeck Grant , Tao Biaoshuai
Published: June 2020

We consider the adaptive influence maximization problem: given a network and a budget k, iteratively select k seeds in the network to maximize the expected number of adopters. In the full-adoption feedback model, after selecting each seed, the seed-picker observes all the resulting adoptions. In the myopic feedback model, the seed-picker only observes whether each neighbor of the chosen seed adopts. Motivated by the extreme success of greedy-based algorithms/heuristics for influence maximization, we propose the concept of greedy adaptivity gap, which compares the performance of the adaptive greedy algorithm to its non-adaptive counterpart. Our first result shows that, for submodular influence maximization, the adaptive greedy algorithm can perform up to a (1-1/e)-fraction worse than the non-adaptive greedy algorithm, and that this ratio is tight. More specifically, on one side we provide examples where the performance of the adaptive greedy algorithm is only a (1-1/e) fraction of the performance of the non-adaptive greedy algorithm in four settings: for both feedback models and both the independent cascade model and the linear threshold model. On the other side, we prove that in any submodular cascade, the adaptive greedy algorithm always outputs a (1-1/e)-approximation to the expected number of adoptions in the optimal non-adaptive seed choice. Our second result shows that, for the general submodular cascade model with full-adoption feedback, the adaptive greedy algorithm can outperform the non-adaptive greedy algorithm by an unbounded factor. Finally, we propose a risk-free variant of the adaptive greedy algorithm that always performs no worse than the non-adaptive greedy algorithm.
Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer
Authors: Choi Edward , Xu Zhen , Li Yujia , Dusenberry Michael , Flores Gerardo , Xue Emily , Dai Andrew
Published: June 2020

Effective modeling of electronic health records (EHR) is rapidly becoming an important topic in both academia and industry. A recent study showed that using the graphical structure underlying EHR data (e.g. relationship between diagnoses and treatments) improves the performance of prediction tasks such as heart failure prediction. However, EHR data do not always contain complete structure information. Moreover, when it comes to claims data, structure information is completely unavailable to begin with. Under such circumstances, can we still do better than just treating EHR data as a flat-structured bag-of-features? In this paper, we study the possibility of jointly learning the hidden structure of EHR while performing supervised prediction tasks on EHR data. Specifically, we discuss that Transformer is a suitable basis model to learn the hidden EHR structure, and propose Graph Convolutional Transformer, which uses data statistics to guide the structure learning process. The proposed model consistently outperformed previous approaches empirically, on both synthetic data and publicly available EHR data, for various prediction tasks such as graph reconstruction and readmission prediction, indicating that it can serve as an effective general-purpose representation learning algorithm for EHR data.
Predictive Student Modeling in Educational Games with Multi-Task Learning
Authors: Geden Michael , Emerson Andrew , Rowe Jonathan , Azevedo Roger , Lester James
Published: June 2020

Modeling student knowledge is critical in adaptive learning environments. Predictive student modeling enables formative assessment of student knowledge and skills, and it drives personalized support to create learning experiences that are both effective and engaging. Traditional approaches to predictive student modeling utilize features extracted from students’ interaction trace data to predict student test performance, aggregating student test performance as a single output label. We reformulate predictive student modeling as a multi-task learning problem, modeling questions from student test data as distinct “tasks.” We demonstrate the effectiveness of this approach by utilizing student data from a series of laboratory-based and classroom-based studies conducted with a game-based learning environment for microbiology education, Crystal Island. Using sequential representations of student gameplay, results show that multi-task stacked LSTMs with residual connections significantly outperform baseline models that do not use the multi-task formulation. Additionally, the accuracy of predictive student models is improved as the number of tasks increases. These findings have significant implications for the design and development of predictive student models in adaptive learning environments.
Enhancing Personalized Trip Recommendation with Attractive Routes
Authors: Gu Jiqing , Song Chao , Jiang Wenjun , Wang Xiaomin , Liu Ming
Published: June 2020

Personalized trip recommendation tries to recommend a sequence of point of interests (POIs) for a user. Most of existing studies search POIs only according to the popularity of POIs themselves. In fact, the routes among the POIs also have attractions to visitors, and some of these routes have high popularity. We term this kind of route as Attractive Route (AR), which brings extra user experience. In this paper, we study the attractive routes to improve personalized trip recommendation. To deal with the challenges of discovery and evaluation of ARs, we propose a personalized Trip Recommender with POIs and Attractive Route (TRAR). It discovers the attractive routes based on the popularity and the Gini coefficient of POIs, then it utilizes a gravity model in a category space to estimate the rating scores and preferences of the attractive routes. Based on that, TRAR recommends a trip with ARs to maximize user experience and leverage the tradeoff between the time cost and the user experience. The experimental results show the superiority of TRAR compared with other state-of-the-art methods.
Region Focus Network for Joint Optic Disc and Cup Segmentation
Authors: Li Ge , Li Changsheng , Zeng Chan , Gao Peng , Xie Guotong
Published: June 2020

Glaucoma is one of the three leading causes of blindness in the world and is predicted to affect around 80 million people by 2020. The optic cup (OC) to optic disc (OD) ratio (CDR) in fundus images plays a pivotal role in the screening and diagnosis of glaucoma. Existing methods usually crop the optic disc region first, and subsequently perform segmentation in this region. However, these approaches come up with high complexities due to the separate operations. To remedy this issue, we propose a Region Focus Network (RF-Net) that innovatively integrates detection and multi-class segmentation into a unified architecture for end-to-end joint optic disc and cup segmentation with global optimization. The key idea of our method is designing a novel multi-class mask branch which generates a high-quality segmentation in the detected region for both disc and cup. To bridge the connection between the backbone and multi-class mask branch, a Fusion Feature Pooling (FFP) structure is presented to extract features from each level of the pyramid network and fuse them into a final feature representation for segmentation. Extensive experimental results on the REFUGE-2018 challenge dataset and the Drishti-GS dataset show that the proposed method achieves the best performance, compared with competitive approaches reported in the literature and the official leaderboard. Our code will be released soon.
Pose-Assisted Multi-Camera Collaboration for Active Object Tracking
Authors: Li Jing , Xu Jing , Zhong Fangwei , Kong Xiangyu , Qiao Yu , Wang Yizhou
Published: June 2020

Active Object Tracking (AOT) is crucial to many vision-based applications, e.g., mobile robot, intelligent surveillance. However, there are a number of challenges when deploying active tracking in complex scenarios, e.g., target is frequently occluded by obstacles. In this paper, we extend the single-camera AOT to a multi-camera setting, where cameras tracking a target in a collaborative fashion. To achieve effective collaboration among cameras, we propose a novel Pose-Assisted Multi-Camera Collaboration System, which enables a camera to cooperate with the others by sharing camera poses for active object tracking. In the system, each camera is equipped with two controllers and a switcher: The vision-based controller tracks targets based on observed images. The pose-based controller moves the camera in accordance to the poses of the other cameras. At each step, the switcher decides which action to take from the two controllers according to the visibility of the target. The experimental results demonstrate that our system outperforms all the baselines and is capable of generalizing to unseen environments. The code and demo videos are available on our website https://sites.google.com/view/pose-assisted-collaboration.
Towards Cross-Modality Medical Image Segmentation with Online Mutual Knowledge Distillation
Authors: Li Kang , Yu Lequan , Wang Shujun , Heng Pheng-Ann
Published: June 2020

The success of deep convolutional neural networks is partially attributed to the massive amount of annotated training data. However, in practice, medical data annotations are usually expensive and time-consuming to be obtained. Considering multi-modality data with the same anatomic structures are widely available in clinic routine, in this paper, we aim to exploit the prior knowledge (e.g., shape priors) learned from one modality (aka., assistant modality) to improve the segmentation performance on another modality (aka., target modality) to make up annotation scarcity. To alleviate the learning difficulties caused by modality-specific appearance discrepancy, we first present an Image Alignment Module (IAM) to narrow the appearance gap between assistant and target modality data. We then propose a novel Mutual Knowledge Distillation (MKD) scheme to thoroughly exploit the modality-shared knowledge to facilitate the target-modality segmentation. To be specific, we formulate our framework as an integration of two individual segmentors. Each segmentor not only explicitly extracts one modality knowledge from corresponding annotations, but also implicitly explores another modality knowledge from its counterpart in mutual-guided manner. The ensemble of two segmentors would further integrate the knowledge from both modalities and generate reliable segmentation results on target modality. Experimental results on the public multi-class cardiac segmentation data, i.e., MM-WHS 2017, show that our method achieves large improvements on CT segmentation by utilizing additional MRI data and outperforms other state-of-the-art multi-modality learning methods.
Bursting the Filter Bubble: Fairness-Aware Network Link Prediction
Authors: Masrour Farzan , Wilson Tyler , Yan Heng , Tan Pang-Ning , Esfahanian Abdol
Published: June 2020

Link prediction is an important task in online social networking as it can be used to infer new or previously unknown relationships of a network. However, due to the homophily principle, current algorithms are susceptible to promoting links that may lead to increase segregation of the network—an effect known as filter bubble. In this study, we examine the filter bubble problem from the perspective of algorithm fairness and introduce a dyadic-level fairness criterion based on network modularity measure. We show how the criterion can be utilized as a postprocessing step to generate more heterogeneous links in order to overcome the filter bubble problem. In addition, we also present a novel framework that combines adversarial network representation learning with supervised link prediction to alleviate the filter bubble problem. Experimental results conducted on several real-world datasets showed the effectiveness of the proposed methods compared to other baseline approaches, which include conventional link prediction and fairness-aware methods for i.i.d data.
Finding Needles in a Moving Haystack: Prioritizing Alerts with Adversarial Reinforcement Learning
Authors: Tong Liang , Laszka Aron , Yan Chao , Zhang Ning , Vorobeychik Yevgeniy
Published: June 2020

Detection of malicious behavior is a fundamental problem in security. One of the major challenges in using detection systems in practice is in dealing with an overwhelming number of alerts that are triggered by normal behavior (the so-called false positives), obscuring alerts resulting from actual malicious activities. We introduce a novel approach for computing a policy for prioritizing alerts using adversarial reinforcement learning. Our approach assumes that the attacker knows the full state of the detection system and the defender's alert prioritization policy, and will dynamically choose an optimal attack. The first step of our approach is to capture the interaction between the defender and attacker in a game theoretic model. To tackle the computational complexity of solving this game to obtain a dynamic stochastic alert prioritization policy, we propose an adversarial reinforcement learning framework. In this framework, we use neural reinforcement learning to compute best response policies for both the defender and the adversary to an arbitrary stochastic policy of the other. We then use these in a double-oracle framework to obtain an approximate equilibrium of the game, which in turn yields a robust stochastic policy for the defender. We use case studies in network intrusion and fraud detection to demonstrate that our approach is effective in creating robust alert prioritization policies.1
Graph-Driven Generative Models for Heterogeneous Multi-Task Learning
Authors: Wang Wenlin , Xu Hongteng , Gan Zhe , Li Bai , Wang Guoyin , Chen Liqun , Yang Qian , Wang Wenqi , Carin Lawrence
Published: June 2020

We propose a novel graph-driven generative model, that unifies multiple heterogeneous learning tasks into the same framework. The proposed model is based on the fact that heterogeneous learning tasks, which correspond to different generative processes, often rely on data with a shared graph structure. Accordingly, our model combines a graph convolutional network (GCN) with multiple variational autoencoders, thus embedding the nodes of the graph (i.e., samples for the tasks) in a uniform manner, while specializing their organization and usage to different tasks. With a focus on healthcare applications (tasks), including clinical topic modeling, procedure recommendation and admission-type prediction, we demonstrate that our method successfully leverages information across different tasks, boosting performance in all tasks and outperforming existing state-of-the-art approaches.
Topic Enhanced Sentiment Spreading Model in Social Networks Considering User Interest
Authors: Wang Xiaobao , Jin Di , Musial Katarzyna , Dang Jianwu
Published: June 2020

Emotion is a complex emotional state, which can affect our physiology and psychology and lead to behavior changes. The spreading process of emotions in the text-based social networks is referred to as sentiment spreading. In this paper, we study an interesting problem of sentiment spreading in social networks. In particular, by employing a text-based social network (Twitter) , we try to unveil the correlation between users' sentimental statuses and topic distributions embedded in the tweets, then to automatically learn the influence strength between linked users. Furthermore, we introduce user interest to refine the influence strength. We develop a unified probabilistic framework to formalize the problem into a topic-enhanced sentiment spreading model. The model can predict users' sentimental statuses based on their historical emotional status, topic distributions in tweets and social structures. Experiments on the Twitter dataset show that the proposed model significantly outperforms several alternative methods in predicting users' sentimental status. We also discover an intriguing phenomenon that positive and negative sentiment is more relevant to user interest than neutral ones. Our method offers a new opportunity to understand the underlying mechanism of sentimental spreading in online social networks.
Actor Critic Deep Reinforcement Learning for Neural Malware Control
Authors: Wang Yu , Stokes Jack , Marinescu Mady
Published: June 2020

In addition to using signatures, antimalware products also detect malicious attacks by evaluating unknown files in an emulated environment, i.e. sandbox, prior to execution on a computer's native operating system. During emulation, a file cannot be scanned indefinitely, and antimalware engines often set the number of instructions to be executed based on a set of heuristics. These heuristics only make the decision of when to halt emulation using partial information leading to the execution of the file for either too many or too few instructions. Also this method is vulnerable if the attackers learn this set of heuristics.

Recent research uses a deep reinforcement learning (DRL) model employing a Deep Q-Network (DQN) to learn when to halt the emulation of a file. In this paper, we propose a new DRL-based system which instead employs a modified actor critic (AC) framework for the emulation halting task. This AC model dynamically predicts the best time to halt the file's execution based on a sequence of system API calls. Compared to the earlier models, the new model is capable of handling adversarial attacks by simulating their behaviors using the critic model. The new AC model demonstrates much better performance than both the DQN model and antimalware engine's heuristics. In terms of execution speed (evaluated by the halting decision), the new model halts the execution of unknown files by up to 2.5% earlier than the DQN model and 93.6% earlier than the heuristics. For the task of detecting malicious files, the proposed AC model increases the true positive rate by 9.9% from 69.5% to 76.4% at a false positive rate of 1% compared to the DQN model, and by 83.4% from 41.2% to 76.4% at a false positive rate of 1% compared to a recently proposed LSTM model.
Generative Adversarial Regularized Mutual Information Policy Gradient Framework for Automatic Diagnosis
Authors: Xia Yuan , Zhou Jingbo , Shi Zhenhui , Lu Chao , Huang Haifeng
Published: June 2020

Automatic diagnosis systems have attracted increasing attention in recent years. The reinforcement learning (RL) is an attractive technique for building an automatic diagnosis system due to its advantages for handling sequential decision making problem. However, the RL method still cannot achieve good enough prediction accuracy. In this paper, we propose a Generative Adversarial regularized Mutual information Policy gradient framework (GAMP) for automatic diagnosis which aims to make a diagnosis rapidly and accurately. We first propose a new policy gradient framework based on the Generative Adversarial Network (GAN) to optimize the RL model for automatic diagnosis. In our framework, we take the generator of GAN as a policy network, and also use the discriminator of GAN as a part of the reward function. This generative adversarial regularized policy gradient framework can try to avoid generating randomized trials of symptom inquires deviated from the common diagnosis paradigm. In addition, we add mutual information to enhance the reward function to encourage the model to select the most discriminative symptoms to make a diagnosis. Experiment evaluations on two public datasets show that our method beats the state-of-art methods, not only can achieve higher diagnosis accuracy, but also can use a smaller number of inquires to make diagnosis decision.
Generate (Non-Software) Bugs to Fool Classifiers
Authors: Yakura Hiromu , Akimoto Youhei , Sakuma Jun
Published: June 2020

In adversarial attacks intended to confound deep learning models, most studies have focused on limiting the magnitude of the modification so that humans do not notice the attack. On the other hand, during an attack against autonomous cars, for example, most drivers would not find it strange if a small insect image were placed on a stop sign, or they may overlook it. In this paper, we present a systematic approach to generate natural adversarial examples against classification models by employing such natural-appearing perturbations that imitate a certain object or signal. We first show the feasibility of this approach in an attack against an image classifier by employing generative adversarial networks that produce image patches that have the appearance of a natural object to fool the target model. We also introduce an algorithm to optimize placement of the perturbation in accordance with the input image, which makes the generation of adversarial examples fast and likely to succeed. Moreover, we experimentally show that the proposed approach can be extended to the audio domain, for example, to generate perturbations that sound like the chirping of birds to fool a speech classifier.
Beyond Digital Domain: Fooling Deep Learning Based Recognition System in Physical World
Authors: Yang Kaichen , Tsai Tzungyu , Yu Honggang , Ho Tsung-Yi , Jin Yier
Published: June 2020

Adversarial examples that can fool deep neural network (DNN) models in computer vision present a growing threat. The current methods of launching adversarial attacks concentrate on attacking image classifiers by adding noise to digital inputs. The problem of attacking object detection models and adversarial attacks in physical world are rarely touched. Some prior works are proposed to launch physical adversarial attack against object detection models, but limited by certain aspects. In this paper, we propose a novel physical adversarial attack targeting object detection models. Instead of simply printing images, we manufacture real metal objects that could achieve the adversarial effect. In both indoor and outdoor experiments we show our physical adversarial objects can fool widely applied object detection models including SSD, YOLO and Faster R-CNN in various environments. We also test our attack in a variety of commercial platforms for object detection and demonstrate that our attack is still valid on these platforms. Consider the potential defense mechanisms our adversarial objects may encounter, we conduct a series of experiments to evaluate the effect of existing defense methods on our physical attack.
Reinforcement-Learning Based Portfolio Management with Augmented Asset Movement Prediction States
Authors: Ye Yunan , Pei Hengzhi , Wang Boxin , Chen Pin-Yu , Zhu Yada , Xiao Ju , Li Bo
Published: June 2020

Portfolio management (PM) is a fundamental financial planning task that aims to achieve investment goals such as maximal profits or minimal risks. Its decision process involves continuous derivation of valuable information from various data sources and sequential decision optimization, which is a prospective research direction for reinforcement learning (RL). In this paper, we propose SARL, a novel State-Augmented RL framework for PM. Our framework aims to address two unique challenges in financial PM: (1) data heterogeneity – the collected information for each asset is usually diverse, noisy and imbalanced (e.g., news articles); and (2) environment uncertainty – the financial market is versatile and non-stationary. To incorporate heterogeneous data and enhance robustness against environment uncertainty, our SARL augments the asset information with their price movement prediction as additional states, where the prediction can be solely based on financial data (e.g., asset prices) or derived from alternative sources such as news. Experiments on two real-world datasets, (i) Bitcoin market and (ii) HighTech stock market with 7-year Reuters news articles, validate the effectiveness of SARL over existing PM approaches, both in terms of accumulated profits and risk-adjusted profits. Moreover, extensive simulations are conducted to demonstrate the importance of our proposed state augmentation, providing new insights and boosting performance significantly over standard RL-based PM method and other baselines.
Geometry-Constrained Car Recognition Using a 3D Perspective Network
Authors: Rui Zeng , Zongyuan Ge , Simon Denman , Sridha Sridharan , Clinton Fookes
Published: June 2020

We present a novel learning framework for vehicle recognition from a single RGB image. Unlike existing methods which only use attention mechanisms to locate 2D discriminative information, our work learns a novel 3D perspective feature representation of a vehicle, which is then fused with 2D appearance feature to predict the category. The framework is composed of a global network (GN), a 3D perspective network (3DPN), and a fusion network. The GN is used to locate the region of interest (RoI) and generate the 2D global feature. With the assistance of the RoI, the 3DPN estimates the 3D bounding box under the guidance of the proposed vanishing point loss, which provides a perspective geometry constraint. Then the proposed 3D representation is generated by eliminating the viewpoint variance of the 3D bounding box using perspective transformation. Finally, the 3D and 2D feature are fused to predict the category of the vehicle. We present qualitative and quantitative results on the vehicle classification and verification tasks in the BoxCars dataset. The results demonstrate that, by learning such a concise 3D representation, we can achieve superior performance to methods that only use 2D information while retain 3D meaningful information without the challenge of requiring a 3D CAD model.
Real-World Treatment Patterns and Progression-Free Survival Associated with Anaplastic Lymphoma Kinase (ALK) Tyrosine Kinase Inhibitor Therapies for ALK+ Non-Small Cell Lung Cancer
Authors: Jahanzeb Mohammad , Lin Huamao M. , Pan Xiaoyun , Yin Yu , Wu Yanyu , Nordstrom Beth , Socinski Mark A.
Published: June 2020

Background

Little is known about real-world treatment and outcomes of patients with anaplastic lymphoma kinase-positive (ALK+) advanced non-small cell lung cancer (NSCLC).

Patients and Methods

This retrospective study of the Flatiron Health EHR-derived deidentified database included patients with a lung cancer diagnosis and confirmed advanced NSCLC who received ALK tyrosine kinase inhibitor (TKI) therapy (January 1, 2011, through June 30, 2018). Patient characteristics and treatment patterns were characterized. Real-world progression-free survival (rwPFS) and time to discontinuation were calculated using the Kaplan-Meier method.

Results

First-line ALK TKI therapy was administered to 581 patients (27.5% had brain metastasis on or prior to initiation) and second-line ALK TKI therapy to 254 patients post crizotinib (45.7% had brain metastasis on or prior to second-line ALK TKI initiation). Crizotinib (84.6%; n = 492) was the most commonly administered first-line ALK TKI therapy. For second-line ALK TKI post crizotinib (n = 254), 49.6% received ceritinib, 41.7% received alectinib, 5.9% received crizotinib retreatment, and 2.8% received brigatinib. Median (95% confidence interval [CI]) rwPFS was 7.47 (6.48–8.32) months for first-line and 7.30 (5.72–8.42) months for second-line ALK TKI. Median (95% CI) rwPFS was significantly longer among first-line ALK TKI patients without than with brain metastasis (8.52 [7.57–10.59] vs. 4.97 [3.75–5.99] months; p < .0001) and patients with brain metastasis on or prior to first-line ALK TKI therapy had a significantly increased risk of progression (hazard ratio ± SE, 1.976 ± 0.112; p < .0001).

Conclusion

Median rwPFS in patients with advanced ALK+ NSCLC was < 8 months for first- and second-line ALK TKI therapy and was even shorter in patients with brain metastasis, highlighting the need for more effective treatments in this patient population.
LAMOTRİGİNE BAĞLI OLASI DRESS SENDROMU: OLGU SUNUMU
Authors: UYGUR KÜLCÜ Nihan , ERDEDE Özlem , DOĞRU Mahmut , ERASLAN Gökçe Ezgi , SARI Erdal , YILDIZ Feyza
Published: June 2020

DRESS Sendromu (Drug Rush with Eosinophilia and Systemic Symptoms) ilaç ilişkili döküntü,  hematolojik anormallikler (eozinofili, atipik lenfositoz), lenfadenopati, iç organ tutulumları (karaciğer, böbrek, akciğer) ile seyreden nadir, hayatı tehdit edebilen hipersensitivite reaksiyonudur. Antiepileptik lamotrigin tedavisi başlandıktan on dört gün sonra döküntüsü ve ateşi gelişen DRESS Sendromu olduğu düşünülen ergen bir kız hastayı sunduk.
Post Patch Clamp Slice Fixation v5
Authors: Institute for Brain Science Allen
Published: June 2020

This protocol describes the process to fix recorded/filled cells from mouse and human brain slices with 4% PFA/2.5% Glutaraldehyde in PBS for future histochemical processing. Note: Research reported in this publication was supported by the National Institute Of Mental Health of the National Institutes of Health under Award Number U19MH114830. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Mouse Cardiac Perfusion Fixation and Brain Collection v5
Authors: Institute for Brain Science Allen
Published: June 2020

This protocol describes the procedures for intracardiac perfusion fixation of postnatal mice, including anesthesia, exsanguination, fixation, brain removal and post-fixation storage. Note: Research reported in this publication was supported by the National Institute Of Mental Health of the National Institutes of Health under Award Number U19MH114830. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
Hubungan Power Tungkai dengan Hasil Tendangan Mawashi Geri dan Ushiro Mawashi-Geri pada Cabang Olahraga Karate
Authors: Kusmirantini Sari Yurika , Imanudin Iman
Published: June 2020

Dalam olahraga karate teknik tendangan yang biasa dilakukan oleh atlet adalah teknik tendangan mawashi geri dan ushiro mawashi-geri, namun teknik tersebut masih jarang menghasilkan poin karena kurangnya power seorang atlet saat melakukan tendangan tersebut. Tujuan dari penelitian ini adalah untuk mengetahui hubungan antara power tungkai dengan hasil tendangan mawashi geri dan ushiro mawashi-geri pada cabang olahraga karate. Metode penelitian yang digunakan adalah metode kuantitatif dengan studi korelasional. Sampel yang digunakan dalam penelitian ini adalah mahasiswa UKM Karate UPI sebanyak 10 orang yang telah mengikuti berbagai event Nasional.Instrumen yang digunakan dalam penelitian ini adalah digital vertical jump dan nuboya meter dengan validitas sebesar 0,70 dan reabilitas 0,72.. Hasil analisis data dari penelitian menunjukan bahwa terdapat hubungan antara power tungkai dengan hasil tendangan mawashi geri dan ushiro mawashi-geri pada cabang olahraga karate. Hal ini terlihat dari nilai hasil tendangan yaitu 0,815 dengan signifikansi 0,004 0,05 dan 0,782 dengan signifikansi 0,008 0,05. hal ini menggambarkan bahwa power tungkai merupakan faktor penting untuk menghasilkan tendangan mawashi geri dan ushiro mawashi-geri. Dengan kata lain semakin besar power tungkai maka semakin baik upaya menendang dari sisi kekuatan dan kecepatan.                                              Kata kunci: karate, tendangan mawashi geri dan ushiro mawashi-geri, dan power tungkai.
Evaluasi Program Pembinaan Pelatihan Daerah (PELATDA) Cabang Olahraga Shorinji Provinsi NTT Tahun 2016
Authors: Gelu Anna Yunita
Published: June 2020

Penelitian ini bertujuan untuk mengetahui atau memperoleh gambaran mengenai pelaksanaan program pembinaan Pelatihan Daerah (PELATDA) Cabang Olahraga Shorinji Kempo Provinsi NTT dalam menghadapi Pekan Olahraga Nasional ke XIX tahun 2016 di Jawa Barat. Penelitian ini menggunakan model evaluasi CIPP dari Stufflebeam, dengan menggunakan pendekatan kualitatif dan pendekatan kuantitatif. Tehnik analisa data yang digunakan adalah statistik deskriptif. Subyek dalam penelitian adalah Kabid Binpres PERKEMI NTT, Pelatih dan Atlet. Instrumen yang digunakan dalam pengumpulan data adalah wawancara, angket, studi dokumentasi. Hasil penelitian menunjukkan bahwa tahapan context pelaksanaan program pembinaan PELATDA mempunyai dasar hukum yang jelas dengan tujuan meraih prestasi di PON, pada tahapan input memperoleh persentase 78,85%, tahapan process memperoleh presentase 87,83% dan tahapan product memperoleh presentase sebesar 87,67%.Dengan hasil penelitian ini diharapkan cabang olahraga Shorinji Kempo NTT untuk terus melakukan perbaikan-perbaikan guna pencapaian prestasi yang tertinggi baik nasional maupun internasional.